Guidelines for preparing
economic
AN ALYS E S
December 17, 2010
(updated May 2014)
National Center for Environmental Economics
Office of Policy
U.S. Environmental Protection Agency
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Table of Contents
Acronyms and Abbreviations viii
Glossary xi
11ntroduction 1-1
1.1 Background to the Guidelines for Performing Economic Analyses 1-1
1.2 Hie Scope of the Guidelines 1-2
1.3 Economic Framework and Definition of Terms 1-3
1.4 Organization of the Guidelines 1-5
2 Statutory and Executive Order Requirements for Conducting Economic Analyses 2-1
2.1 Executive Orders 2-2
2.1.1 Executive Order 12866, "Regulatory Planning and Review" 2-2
2.1.2 Executive Order 12898, "Federal Actions to Address Environmental Justice
in Minority Populations and Low-Income Populations" 2-3
2.1.3 Executive Order 13045, "Protection of Children from
Environmental Health Risks and Safety Risks" 2-3
2.1.4 Executive Order 13132, "Federalism" 2-3
2.1.5 Executive Order 13175, "Consultation and Coordination with
Indian Tribal Governments" 2-3
2.1.6 Executive Order 13211, "Actions Concerning Regulations that
Significantly Affect Energy Supply, Distribution, or Use" 2-4
2.2 Statutes 2-4
2.2.1 The Regulatory Flexibility Act of 1980 (RFA) as amended by The Small Business
Regulatory Enforcement Fairness Act of 1996 (SBREFA), (5 U.S.C. 601-612) 2-4
2.2.2 The Unfunded Mandates Reform Act of 1995 (UMRA) (P.L. 104-4) 2-4
2.2.3 The Paperwork Reduction Act of 1995 (PRA) (44 U.S.C. 3501) 2-5
3 Statement of Need for Policy Action 3-1
3.1 Problem Definition 3-1
3.2 Reasons for Market or Institutional Failure 3-1
3.3 Need for Federal Action 3-2
4 Regulatory and Non-Regulatory Approaches to Pollution Control 4-1
4.1 Evaluating Environmental Policy 4-1
4.1.1 Efficiency 4-1
4.1.2 Cost-Effectiveness 4-2
4.2 Traditional Command-and-Control or Prescriptive Regulation 4-2
4.2.1 Technology or Design Standards 4-3
4.2.2 Performance Standards 4-4
4.3 Market-Oriented Approaches 4-5
4.3.1 Marketable Permit Systems 4-5
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Table of Contents
4.3.2 Emission Taxes 4-9
4.3.3 Environmental Subsidies 4-10
4.3.4 Tax-Subsidy Combinations 4-11
4.4 Other Market-Oriented or Hybrid Approaches 4-12
4.4.1 Combining Standards and Pricing Approaches 4-12
4.4.2 Information Disclosure 4-13
4.4.3 Liability Rules 4-14
4.5 Selecting the Appropriate Market-Based Incentive or Hybrid Approach 4-15
4.5.1 The Type of Market Failure 4-15
4.5.2 The Nature of the Environmental Problem 4-15
4.5.3 The Type of Pollutant Information that is Available and Observable 4-16
4.5.4 Uncertainty in Abatement Costs or Damages 4-16
4.5.5 Market Competitiveness 4-17
4.5.6 Monitoring and Enforcement Issues 4-17
4.5.7 Potential for Economy-Wide Distortions 4-17
4.5.8 The Goals of the Policy Maker 4-18
4.6 Non-Regulatory Approaches 4-18
4.6.1 How Voluntary Approaches "Work 4-19
4.6.2 Economic Evaluation of Voluntary Approaches 4-19
4.7 Measuring the Effectiveness of Regulatory or Non-Regulatory Approaches 4-21
5 Baseline 5-1
5.1 Baseline Definition 5-1
5.2 Guiding Principles of Baseline Specification 5-2
5.3 Changes in Basic Variables 5-6
5.3.1 Demographic Change 5-6
5.3.2 Future Economic Activity 5-6
5.3.3 Changes in Consumer Behavior 5-6
5.3.4 Technological Change 5-7
5.4 Compliance Rates 5-8
5.4.1 Full Compliance 5-9
5.4.2 Under-Compliance 5-9
5.4.3 Over-Compliance 5-10
5.5 Multiple Rules 5-11
5.5.1 Linked Rules 5-11
5.5.2 Unlinked Rules 5-11
5.5.3 Indirectly Related Policies and Programs 5-12
5.6 Partial Benefits to a Threshold 5-12
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5.7 Behavioral Responses 5-14
5.7.1 Potential for Cost Savings 5-14
5.7.2 Voluntary Actions 5-15
5.8 Conclusion 5-16
6 Discounting Future Benefits and Costs 6-1
6.1 Hie Mechanics of Summarizing Present and Future Costs and Benefits 6-2
6.1.1 Net Present Value 6-2
6.1.2 Annualized Values 6-3
6.1.3 Net Future Value 6-3
6.1.4 Comparing the Methods 6-4
6.1.5 Sensitivity of Present Value Estimates to the Discount Rate 6-4
6.1.6 Some Issues in Application 6-4
6.2 Background and Rationales for Social Discounting 6-6
6.2.1 Consumption Rates of Interest and Private Rates of Return 6-6
6.2.2 Social Rate of Time Preference 6-7
6.2.3 Social Opportunity Cost of Capital 6-8
6.2.4 Shadow Price of Capital Approach 6-8
6.2.5 Evaluating the Alternatives 6-10
6.3 Intergenerational Social Discounting 6-11
6.3.1 The Ramsey Framework 6-12
6.3.2 Key Considerations 6-14
6.3.3 Evaluating Alternatives 6-16
6.4 Recommendations and Guidance 6-18
7 Analyzing Benefits 7-1
7.1 The Benefits Analysis Process 7-1
7.2 Economic value and types of benefits 7-6
7.2.1 Human Health Improvements 7-8
7.2.2 Ecological Benefits 7-15
7.2.3 Other Benefits 7-20
7.3 Economic Valuation Methods for Benefits Analysis 7-21
7.3.1 Revealed Preference Methods 7-21
7.3.2 Stated Preference 7-35
7.3.3 Combining Revealed and Stated Preference Data 7-44
7.4 Benefit Transfer 7-44
7.5 Accommodating Non-monetized Benefits 7-49
7.5.1 Qualitative Discussions 7-49
7.5.2 Alternative Analyses 7-50
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Table of Contents
8 Analyzing Costs 8-1
8.1 Hie Economics of Social Cost 8-1
8.1.1 Partial Equilibrium Analysis 8-2
8.1.2 General Equilibrium Analysis 8-4
8.1.3 Dynamics 8-6
8.1.4 Social Cost and Employment Effects 8-6
8.2 A Typology of Costs 8-7
8.2.1 Alternative Concepts of Cost 8-7
8.2.2 Additional Cost Terminology 8-8
8.2.3 Transitional and Distributional Costs 8-9
8.3 Measurement Issues in Estimating Social Cost 8-9
8.3.1 Evaluating Costs Over Time 8-9
8.3.2 Difficulties in Valuing Social Cost 8-12
8.3.3 Use of Externally-Produced Cost Estimates 8-13
8.4 Models Used in Estimating the Costs of Environmental Regulation 8-13
8.4.1 Compliance Cost Models 8-14
8.4.2 Partial Equilibrium Models 8-15
8.4.3 Linear Programming Models 8-16
8.4.4 Input-Output Models 8-17
8.4.5 Input-Output Econometric Models 8-18
8.4.6 Computable General Equilibrium Models 8-19
9 Economic Impact Analyses 9-1
9.1 Statutes and Policies 9-1
9.2 Conducting an Economic Impact Analysis 9-2
9.2.1 Screening for Potentially Significant Impacts 9-3
9.2.2 Profile of Affected Entities 9-3
9.2.3 Detailing Impacts on Industry 9-5
9.2.4 Detailing Impacts on Governments and Not-for-Profit Organizations 9-11
9.2.5 Detailing Impacts on Small Entities 9-14
9.3 Approaches to Modeling in an Economic Impact Analysis 9-15
9.3.1 Direct Compliance Costs 9-15
9.3.2 Partial Equilibrium Models 9-17
9.3.3 Computable General Equilibrium Models 9-18
10 Environmental Justice, Children's Environmental Health
and Other Distributional Considerations 10-1
10.1 Executive Orders, Directives, and Policies 10-1
10.2 Environmental Justice 10-4
10.2.1 Background Literature 10-4
10.2.2 Analyzing Distributional Impacts in the Context of Regulatory Analysis 10-6
10.2.3 Relevant Populations 10-9
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10.2.4 Data Sources 10-12
10.2.5 Scope and Geographic Considerations 10-12
10.2.6 Defining the Baseline 10-14
10.2.7 Comparison groups 10-14
10.2.8 Measuring and estimating impacts 10-15
10.3 Children's Environmental Health 10-20
10.3.1 Childhood as aLifestage 10-20
10.3.2 Analytical Considerations 10-21
10.3.3 Intersection Between Environmental Justice and Children's Health 10-22
10.4 Other Distributional Considerations 10-22
10.4.1 Elderly 10-22
10.4.2 Intergenerational Impacts 10-22
10.5 Conclusion 10-23
11 Presentation of Analysis and Results 11-1
11.1 Presenting Results of Economic Analyses 11-1
11.1.1 Presenting the Results of Benefit-Cost Analyses 11-2
11.1.2 Presenting the Results of Cost-Effectiveness Analyses 11-4
11.1.3 Presenting the Results of Economic Impact Analyses and Distributional Analyses 11-9
11.1.4 Reporting the Effects of Uncertainty on Results of Economic Analyses 11-9
11.2 Communicating Data, Model Choices and Assumptions, and Related Uncertainty 11-9
11.2.1 Data 11-10
11.2.2 Model Choices and Assumptions 11-10
11.2.3 Addressing Uncertainty Driven by Assumptions and Model Choice 11-11
11.3 Use of Economic Analyses 11-11
Appendix A Economic Theory
A.l Market Economy A-l
A.2 Reasons for Market or Institutional Failure A-4
A.3 Benefit-Cost Analysis A-6
A.4 Measuring Economic Impacts A-7
A.4.1 Elasticities A-7
A.4.2 Measuring the "Welfare Effect of a Change in Environmental Goods A-10
A.4.3 Single Market, Multi-Market, and General Equilibrium Analysis A-12
A.5 Optimal Level of Regulation A-14
A.6 Conclusion A-16
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Table of Contents
Appendix B Mortality Risk Valuation Estimates B-1
B.l Central Estimate of Value of Statistical Life B-1
B.2 Other Value of Statistical Life Information B-3
B.3 Benefit Transfer Considerations B-3
B.4 Adjusted Associated with Risk Characteristics B-3
B.5 Effects on Willingness to Pay Associated with Demographic Characteristics B-4
B.6 Conclusion B-6
Appendix C Accounting for Unemployed Labor in Benefit-Cost Analysis C-1
References D-1
Author Index E-1
Subject Index F-1
List of Figures
Figure 6.1 Distribution of Net Benefits over Time 6-3
Figure 7.1 Benefits of an Environmental Improvement 7-7
Figure 8.1 Competitive Market Before Regulation 8-3
Figure 8.2 Competitive Market After Regulation 8-3
Figure 8.3 Labor Market with Pre-Existing Distortions 8-5
Figure 9.1 Pollution Abatement Costs as a Percentage of Total Revenues for
Industries with Highest Pollution Abatement Costs in 2005 9-7
Figure 9.2 Pollution Abatement Costs are a very Small Percentage of Total Manufacturing Costs 9-7
Figure A. 1 Marginal and Total Willingness to Pay A-l
Figure A.2 Marginal and Total Cost A-2
Figure A.3 Market Equilibrium A-3
Figure A.4 Utility Possibility Frontier A-4
Figure A.5 Negative Externality A-5
Figure A.6 Demand Curve for Tuna A-8
Figure A.7 Indifference Curve A-11
Figure A.8 Change in Optimal Consumption Bundle A-l 1
Figure A.9 Benefits and Costs of Abatement A-l 3
Figure A.10 Maximized Net Benefits A-14
Figure A.l 1 Efficient Level of Pollution A-l 5
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Table of Contents
List of Tables
Table 7.1 Types of Benefits Associated With Environmental Policies:
Categories, Examples, and Commonly Used Valuation Methods 7-9
Table 8.2 Major Attributes of Models Used in the Estimation of Costs 8-13
Table 8.3 Input-Output Table for the United States 8-17
Table 9.1 Potentially Relevant Dimensions to Economic Impact Analyses 9-2
Table 9.2 Commonly Used Profile Sources for Quantitative Data 9-6
Table 9.3 Indicators of Economic and Financial Well-Being of Government Entities 9-13
Table 11.1 Template for Regulatory Benefits Checklist 11-4
Table 11.2 Template for Quantified Regulatory Benefits 11-5
Table 11.3 Template for Dollar-Valued Regulatory Benefits 11-6
Table 11.4 Template for Summary of Benefits and Costs 11-7
Table B.l Value of Statistical Life Estimates B-2
List of Text Boxes
Text Box 4.1 Coase Solution 4-4
Text Box 4.2 U.S. Acid Rain Trading Program for Sulfur Dioxide 4-6
Text Box 4.3 Water Quality Trading of Nonpoint Sources 4-20
Text Box 5.1 Technological Change, Induced Innovation, and the Porter Hypothesis 5-8
Text Box 5.2 Sequencing Unlinked Rules 5-13
Text Box 6.1 Potential Effects of Discounting 6-5
Text Box 6.2 Social Rate and Consumption Rates of Interest 6-6
Text Box 6.3 Estimating and Applying the Shadow Price of Capital 6-9
Text Box 6.4 Alternative Social Discounting Perspectives 6-11
Text Box 6.5 Applying these Approaches to the Ramsey Equation 6-14
TextBox6.6 What's the Big Deal with The Stern Review"'. 6-18
Text Box 7.1 Estimating Benefits From Reducing Carbon Dioxide Emissions:
The Social Cost of Carbon 7-2
Text Box 7.2 Integrating Economics and Risk Assessment 7-5
Text Box 7.3 Non-Willingness to Pay Measures 7-13
Text Box 7.4 Spatial Correlation 7-31
TextBox7.5 Value of Time 7-35
Text Box 7.6 The Benefits and Costs of the Clean Air Act 1990 to 2010:
Reduced Acidification in Freshwater Adirondack Lakes 7-47
Text Box 7.7 Benefits Transfer: Water Quality Benefits in the
Combined Animal Feeding Operations Rule 7-48
Text Box 7.8 Structural Benefit Transfer with an Application to Visibility 7-49
Text Box 8.1 The Sulfur Dioxide Cap-and-Trade Program — A Case Study 8-11
Text Box 8.2 The Pollution Abatement Costs and Expenditures Survey 8-20
Text Box 10.1 Social Welfare Functions and Inequality Indices 10-16
Guidelines for Preparing Economic Analyses I May 2014 vil
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Acronyms and Abbreviations
Acronyms and Abbreviations
ABC
Air Benefit and Cost (Group)
AC
annualized costs
ACN
AirControlNET
ADP
Action Development Process
BAT
best available technology
BCA
benefit-cost analysis
BLS
Bureau of Labor Statistics
BMP
Best Management Practice
BPT
best practicable technology
CA
conjoint analysis
CAA
Clean Air Act
CAFO
Combined Animal Feeding Operations
CAAA
Clean Air Act Amendments
CAIR
Clean Air Interstate Rule
CAMR
Clean Air Mercury Rule
CBO
Congressional Budget Office
CE
certainty equivalent
CEA
cost-effectiveness analysis
CEM
continual emissions monitoring
CEQ
Council on Environmental Quality
CERCLA
Comprehensive Environmental Response, Compensation and Liability Act
CFC
chlorofluorocarbons
CFOI
Census of Fatal Occupational Injuries
CFR
Code of Federal Regulations
CGE
computable general equilibrium
CO
carbon monoxide
co2
carbon dioxide
COI
cost of illness
CPI
Consumer Price Index
CR
contingent ranking
CS
compensating surplus
CV
contingent valuation
CV
compensating variation
DALY
disability-adjusted life year
DICE
Dynamic Integrated model of Climate and the Economy
DOE
Department of Energy
DOT
Department of Transportation
DWL
deadweight loss
EA
economic analysis
EBIT
earnings before interest and taxes
EEAC
Environmental Economics Advisory Committee
EIA
economic impact analysis
ELG
Effluent Limitation Guidelines
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Acronyms and Abbreviations
EO
EPA
ES
EV
EVRI
FINDS
FR
FTE
GDP
GHG
GIS
HCFC
Hg
IAM
ICR
IEc
IMPLAN
IPCC
IPM
LP
MAC
MD
MR
MPC
MSC
MSD
NAAQS
NAICS
NB
NEI
NEPA
NESHAP
NFV
nh3
NIOSH
NOAA
NO
x
NPDES
NPV
NWPCAM
OAQPS
OCC
OECD
OGC
OIRA
OLS
Executive Order
Environmental Protection Agency
equivalent surplus
equivalent variation
Environmental Valuation Reference Inventory
Facility Index Data System
Federal Register
full-time equivalent employment
gross domestic product
greenhouse gases
Geographic Information System
hydrochlorofluorocarbon
mercury
integrated assessment model
Information Collection Request
Industrial Economics, Inc.
Impact Analysis for Planning
Intergovernmental Panel on Climate Change
Integrated Planning Model
linear programming
marginal abatement cost curve
marginal external damage curve
marginal revenue
marginal private costs
marginal social costs
marginal social damages
National Ambient Air Quality Standards
North American Industrial Classification System
net benefits
National Emissions Inventory
National Environmental Policy Act
National Emission Standard for Hazardous Air Pollutant
net future value
ammonia
National Institute of Occupational Safety and Health
National Oceanic and Atmospheric Administration
nitrogen oxide
National Pollutant Discharge Elimination System
net present value
National Water Pollution Control Assessment Model
Office of Air Quality Planning and Standards
opportunity cost of capital
Organization for Economic Cooperation and Development
Office of General Counsel
Office of Information & Regulatory Affairs
ordinary least squares
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Acronyms and Abbreviations
OMB
Office of Management and Budget
OSHA
Occupational Safety and Health Administration
OTEA
Office of Trade and Economic Analysis
PACE
Pollution Abatement Costs and Expenditures
PAOC
pollution abatement operating cost
PM,5
particulate matter, 2.5 microns in diameter or less
PM10
particulate matter, 10 microns in diameter or less
POTW
publicly-owned (wastewater) treatment work
PRA
Paperwork Reduction Act
PVC
present value of costs
QA
quality assurance
QALY
quality-adjusted life year
R&D
research and development
RAPIDS
Rule and Policy Information Development System
RACT
Reasonably Available Control Technology
RCRA
Resource Conservation and Recovery Act
RDD
random digit dialing
REMI
Regional Economic Models, Inc.
RFA
Regulatory Flexibility Act
RIA
regulatory impact analysis
RUM
random utility maximization
S&P
Standard & Poor's
SAB
Science Advisory Board
SAM
social accounting matrix
SBA
Small Business Administration
SBREFA
Small Business Regulatory Enforcement Fairness Act
see
social cost of carbon
SIC
Standard Industrial Classification
SISNOSE
significant economic impact on a substantial number of small
so2
sulfur dioxide
swe
Survey on "Working Conditions
TAMM
Timber Assessment Market Model
TMDL
Total Maximum Daily Loadings
TRI
Toxics Release Inventory
TSLS
two-stage least squares
UMRA
Unfunded Mandates Reform Act
UPF
utility possibility frontier
use
United States Code
voc
volatile organic compounds
VSL
value of statistical life
VSLY
value of a statistical life-year
WTA
willingness to accept
WTP
willingness to pay
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Glossary
Glossary
Annualized value
An annualized value is a constant stream of benefits
or costs. Hie annualized cost is the amount that a
party would have to pay at the end of each period t
to add up to the same cost in present value terms as
the stream of costs being annualized. Similarly, the
annualized benefit is the amount that a party would
accrue at the end of each period t to add up to the
same benefit in present value terms as the stream of
benefits being annualized.
Baseline
A baseline describes an initial, status quo scenario that
is used for comparison with one or more alternative
scenarios. In typical economic analyses the baseline is
defined as the best assessment of the world absent the
proposed regulation or policy action.
Benefit-cost analysis (BCA)
A BCA evaluates the favorable effects of policy
actions and the associated opportunity costs of
those actions. It answers the question of whether the
benefits are sufficient for the gainers to potentially
compensate the losers, leaving everyone at least as well
off as before the policy. The calculation of net benefits
helps ascertain the economic efficiency of a regulation.
Benefits
Benefits are the favorable effects society gains due
to a policy or action. Economists define benefits
by focusing on changes in individual well-being,
referred to as welfare or utility. Willingness to pay
(WTP) is the preferred measure of these changes as it
theoretically provides a full accounting of individual
preferences across trade-offs between income and the
favorable effects.
Benefit-cost ratio
A benefit-cost ratio is the ratio of the net present
value (NPV) of benefits associated with a project
or proposal, relative to the NPV of the costs of the
project or proposal. The ratio indicates the benefits
expected for each dollar of costs. Note that this
ratio is not an indicator of the magnitude of net
benefits. Two projects with the same benefit-cost
ratio can have vastly different estimates of benefits
and costs.
Cessation lag
Cessation lag is the time interval between the
cessation of exposure and the reduction in risk.
See latency for a definition of a related but distinct
concept.
Command-and-control regulation
Command-and-control regulation requires polluters
to meet specific emission-reduction targets defining
acceptable levels of pollution. This type of regulation
often requires the installation and use of specific
types of equipment to reduce emissions. Command-
and-control regulations usually impose the same
requirements on all sources, although new and
existing sources, taken as groups, are frequently
subject to different standards.
Compliance cost
A compliance cost is the expenditure of time
or money needed to conform to government
requirements such as legislation or regulation.
In the case of environmental regulation, these
direct costs are associated with: (1) purchasing,
installing, and operating new pollution control
equipment; (2) changing a production process
by using different inputs or different mixtures
of inputs; and (3) capturing waste products and
selling or reusing them.
Consumption rate of interest
Consumption rate of interest is the rate at which
individuals are willing to exchange consumption
over time. Simplifying assumptions, such as the
absence of taxes on investment returns, imply that
the consumption rate of interest equals the market
interest rate, which also equals the rate of return on
private sector investments.
Cost-effectiveness analysis (CEA)
CEA examines the costs associated with obtaining
an additional unit of an environmental outcome.
It is designed to identify the least expensive way of
achieving a given environmental quality target, or
the way of achieving the greatest improvement in
some environmental target for a given expenditure of
resources.
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Glossary
Costs
Costs are the dollar values of resources needed to
produce a good or service; once allocated, these
resources are not available for use elsewhere. Private
costs are the costs that the buyer of a good or service
pays the seller. Social costs, also called externalities, are
the costs that people other than the buyers are forced
to pay, often through non-pecuniary means, as a
result of a transaction. The bearers of social costs can
be either particular individuals or society at large.
Distributional analysis
Distributional analysis assesses changes in social
welfare by examining the effects of a regulation across
different subpopulations and entities. Two types
of distributional analyses are the economic impact
analysis (EIA) and the equity assessment.
Economic efficiency
Economic efficiency refers to the optimal production
and consumption of goods and services. This
generally occurs when prices of products and services
reflect their marginal costs, or when marginal benefits
equal marginal costs.
Economic impact analysis (EIA)
An EIA examines the distribution of monetized
effects of a policy, such as changes in industry
profitability or in government revenues, as well
as non-monetized effects, such as increases in
unemployment rates or numbers of plant closures.
Elasticity of demand
Elasticity of demand measures the relationship
between changes in quantity demanded of a good and
changes in its price. It is calculated as the percentage
change in quantity demanded that occurs in response
to a percentage change in price. As the price of a
good rises, consumers will usually demand a lower
quantity of that good. The greater the extent to which
quantity demanded falls as price rises, the greater
is the price elasticity of demand. Some goods for
which consumers cannot easily find substitutes, such
as gasoline, are considered price inelastic. Note that
elasticity can differ between the short term and the
long term. For example, if the price of gasoline rises,
consumers will eventually find ways to conserve their
use of the resource. Some of these ways, like finding
a more fuel-efficient car, take time. Hence gasoline
would be price inelastic in the short term and more
price elastic in the long term.
Elasticity of supply
Elasticity of supply measures the relationship between
changes in quantity supplied of a good and changes
in its price. It is measured as the percentage change
in quantity supplied that occurs in response to a
percentage change in price. For many goods the
quantity supplied can be increased over time by
locating alternative sources, investing in an expansion
of production capacity, or developing competitive
products that can substitute. One might therefore
expect that the price elasticity of supply will be
greater in the long term than the short term for such
a good, that is, that supply can adjust to price changes
to a greater degree over a longer period of time.
Emissions tax
An emissions tax is a charge levied on each unit of
pollution emitted.
Environmental justice
Environmental justice is the fair treatment and
meaningful involvement of all people regardless of
race, color, national origin, or income with respect to
the development, implementation, and enforcement
of environmental laws, regulations, and policies. Fair
treatment means that no group of people, including
racial, ethnic, or socioeconomic groups should bear a
disproportionate share of the negative environmental
consequences resulting from industrial, municipal,
and commercial operations or the execution of
federal, state, local, and tribal programs and policies.
Meaningful involvement means that: (1) people have
an opportunity to participate in decisions about
activities that can affect their environment and/or
health; (2) the publics contribution can influence the
regulatory agency's decision; (3) their concerns will
be considered in the decision-making process; and
(4) the decision makers seek out and facilitate the
involvement of those potentially affected.1
1 Definition taken from http://www.epa.gov/compliance/environmentaljustice/
index.html (accessed December 22,2010).
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Glossary
Equity assessment
An equity assessment examines the distribution
of benefits and costs associated with a regulation
across specific sub-populations. Disadvantaged or
vulnerable sub-populations, for example low-income
households, may be of particular concern.
Expert elicitation
Expert elicitation is a formal, highly-structured and
well-documented process for obtaining the judgments
of multiple experts. Typically, an elicitation is
conducted to evaluate uncertainty. This uncertainty
could be associated with: the value of a parameter
to be used in a model; the likelihood and frequency
of various future events; or the relative merits of
alternative models.
Externalities
An externality is a cost or benefit resulting from an
action that is borne or received by parties not directly
participating in the action.
Flow pollutant
A flow pollutant is a pollutant for which the
environment has some absorptive capacity. It does not
accumulate in the environment as long as its emission
rate does not exceed the absorptive capacity of the
environment. Animal and human wastes are examples
of flow pollutants.
Hotspot
A hotspot is a geographic area with a high level of
pollution/contamination within a larger geographic
area of low or "normal" environmental quality.
Kaldor-Hicks criterion
The Kaldor-Hicks criterion is really a combination
of two criteria: the Kaldor criterion and the Hicks
criterion. The Kaldor criterion states that an activity
will contribute to Pareto optimality if the maximum
amount the gainers are prepared to pay is greater
than the minimum amount that the losers are
prepared to accept. Under the Hicks criterion, an
activity will contribute to Pareto optimality if the
maximum amount the losers are prepared to offer to
the gainers in order to prevent the change is less than
the minimum amount the gainers are prepared to
accept as a bribe to forgo the change. In other words,
the Hicks compensation test is conducted from the
losers' point of view, while the Kaldor compensation
test is conducted from the gainers' point of view. The
Kaldor-Hicks criterion is widely applied in welfare
economics and managerial economics. It forms an
underlying rationale for BCA.
Latency
Latency is the time interval from the first exposure
of a pollutant until the increase in health risk. See
cessation lag tor a definition of a related but distinct
concept.
Leakages
A leakage is the displacement of pollution from one
location to another as a result of the imposition of
tighter pollution controls. Under tradable permit
systems, leakages occur when pollution is displaced to
an area not affected by a cap on allowed emissions.
Marginal benefit
The marginal benefit is the benefit received from an
incremental increase in the consumption of a good or
service. It is calculated as the increase in total benefit
divided by the increase in consumption.
Marginal cost
The marginal cost is the change in total cost that
results from a unit increase in output. It is calculated
as the increase in total cost divided by the increase in
output.
Marginal social benefit
The marginal social benefit is the marginal benefit
received by the consumer of a good (marginal private
benefit) plus the marginal benefit received by other
members of society (external benefit).
Marginal social cost
The marginal social cost is the marginal cost incurred
by the producer of a good (marginal private cost)
plus the marginal cost imposed on other members of
society (external cost).
Market failure
Market failure is a condition where the allocation of
goods and services by a market is not efficient. Causes
of market failure include: externalities, concentration
of market power, information asymmetry,
transactions costs, and the nature of the good (e.g.,
Guidelines for Preparing Economic Analyses I May 2014 liii
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Glossary
public goods). For environmental conditions,
externalities are the most likely causes of the failure
of private and public sector institutions to correct
pollution damages.
Market permit systems
A market permit system is a system under which
emissions sources are required to have emissions
permits matching their actual emissions. Each permit
specifies how much the source is allowed to emit and
is transferable among firms.
Market-based incentives
Market-based incentives include a wide variety of
methods for environmental protection. Instruments
such as taxes, fees, charges, and subsidies generally
"price" pollution and leave decisions about the level of
emissions to each source. Another example is the market
permit system, which sets the total quantity of emissions
and then allows trading of permits among firms.
Meta-analysis
Meta-analysis is a statistical method of pooling data
and/or results from a set of comparable studies of
a problem. Pooling in this way provides a larger
sample size for evaluation and allows for a stronger
conclusion than can be provided by any single study.
Meta-analysis yields a quantitative summary of the
combined results.
Net benefits
Net benefits are calculated by subtracting total costs
from total benefits.
Net future value
Net future value is similar to NPV, however, instead
of discounting all future values back to the present,
values are accumulated forward to some future time
period — for example, to the end of the last year of a
policy's effects.
Net present value (NPV)
The NPV is calculated as the present value of a stream
of current and future benefits minus the present value
of a stream of current and future costs.
Non-use value
Non-use value is the value that an individual may
derive from a good or resource without consuming
it, as opposed to the value obtained from use of the
resource. Non-use values can include bequest value,
where an individual places a value on the availability
of a resource to future generations; existence value,
where an individual values the mere knowledge of
the existence of a good or resource; and. paternalistic
altruism, where an individual places a value on others'
enjoyment of the resource.
Opportunity cost
Opportunity cost is the value of the next best
alternative to a particular activity or resource.
Opportunity cost need not be assessed in monetary
terms. It can be assessed in terms of anything that is of
value to the person or persons doing the assessing. For
example a grove of trees used to produce paper may
have a next-best-alternative use as habitat for spotted
owls. Assessing opportunity costs is fundamental to
assessing the true cost of any course of action. In the
case where there is no explicit accounting or monetary
cost (price) attached to a course of action, ignoring
opportunity costs could produce the illusion that
the action's benefits cost nothing at all. The unseen
opportunity costs then become the implicit hidden
costs of that course of action.
Quality-adjusted life year (QALY)
QALY is a composite measure used to convert
different types of health effects into a common,
integrated unit, incorporating both the quality and
quantity of life lived in different health states. This
metric is commonly used in medical arenas to make
decisions about medical interventions.
Shadow price of capital
The shadow price of capital takes into account the
social value of displacing private capital investments.
For example, when a public project displaces private
sector investments, the correct method for measuring
the social costs and benefits requires an adjustment
of the estimated costs (and perhaps benefits as well)
prior to discounting using the consumption rate of
interest. This adjustment factor is referred to as the
"shadow price of capital."
Social cost
From a regulatory standpoint, social cost represents the
total burden a regulation will impose on the economy.
It can be defined as the sum of all opportunity
lif Guidelines for Preparing Economic Analyses I May 2014
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Glossary
costs incurred as a result of the regulation. These
opportunity costs consist of the value lost to society
of all the goods and services that will not be produced
and consumed if firms comply with the regulation and
reallocate resources away from production activities
and towards pollution abatement. To be complete,
an estimate of social cost should include both the
opportunity costs of current consumption that will
be foregone as a result of the regulation, and also the
losses that may result if the regulation reduces capital
investment and thus future consumption.
Social welfare function
A social welfare function establishes criteria
under which efficiency and equity outcomes are
transformed into a single metric, making them
directly comparable. A potential output of such a
function is a ranking of policy outcomes that have
different aggregate levels and distributions of net
benefits. A social welfare function can provide
empirical evidence that a policy alternative yielding
higher net benefits, but a less equitable distribution
of wealth, ranks better or worse than a less efficient
alternative with more egalitarian distributional
consequences.
Stock pollutants
A stock pollutant is a pollutant for which the
environment has little or no absorptive capacity, such
as non-biodegradable plastic, heavy metals such as
mercury, and radioactive waste. A stock pollutant
accumulates through time.
Subsidies
A subsidy is a kind of financial assistance, such as a
grant, tax break, or trade barrier, that is implemented
in order to encourage certain behavior. For example,
the government may directly pay polluters to reduce
their pollution emissions.
Tax-subsidy
A tax-subsidy is any form of subsidy where the
recipients receive the benefit through the tax
system, usually through the income tax, profit tax,
or consumption tax systems. Examples include
tax deductions for workers in certain industries,
accelerated depreciation for certain industries or
types of equipment, or exemption from consumption
tax (sales tax or value added tax).
Total cost
Total cost is defined as the sum of all costs associated
with a given activity.
Use value
Use value is an economic value based on the tangible
human use of some environmental or natural
resource.
Value of statistical life (VSL)
VSL is a summary measure for the dollar value of
small changes in mortality risk experienced by a
large number of people. VSL estimates are derived
from aggregated estimates of individual values for
small changes in mortality risks. For example, if
10,000 individuals are each willing to pay $500 for a
reduction in risk of 1/10,000, then the value of saving
one statistical life equals $500 times 10,000 — or $5
million. Note that this does not mean that any single
identifiable life is valued at this amount. Rather,
the aggregate value of reducing a collection of small
individual risks is, in this case, worth $5 million.
Value of statistical life year (VSLY)
The VSLY is the estimated dollar value for a year of
statistical life. In practice this metric is often derived
by dividing the VSL by remaining life expectancy.
This approach is controversial in that it assumes that
each year of life over the life cycle has the same value,
and it assumes that the value of a statistical life equals
the present discounted value of these annual amounts.
Willingness to accept (WTA)
WTA is the amount of compensation an individual is
willing to take in exchange for giving up some good or
service. In the case of an environmental policy, WTA
is the least amount of money that an individual would
accept to forego an environmental improvement (or
endure an environmental decrement).
Willingness to pay (WTP)
WTP is the largest amount of money that an
individual or group would pay to receive the benefits
(or avoid the damages) resulting from a policy
change, without being made worse off. In the case
of an environmental policy, WTP is the maximum
amount of money an individual would pay to obtain
an improvement (or avoid a decrement) in an
environmental effect of concern.
Guidelines for Preparing Economic Analyses I May 2014
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Hi Guidelines for Preparing Economic Analyses I May 2014
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Chapter 1
Introduction
The Guidelinesfor Preparing Economic Analyses are part of a continuing effort by
U.S. Environmental Protection Agency (EPA) to develop improved guidance
on the preparation and use of sound science in support of the decision-making
process. This document builds on previous work first issued in December
of 1983 as the Guidelinesfor Performing Regulatory Impact Analysis (U.S.
EPA 1983) and later revised in the late 1990s. In September of 2000, the EPA issued its
Guidelinesfor Preparing Economic Analyses (Guidelines) (U.S. EPA 2000b), revised to reflect
the evolution of environmental policy making and economic analysis that had accrued
over the decade and a half since the original guidelines were released. At the time of release,
EPA committed to periodically revise the Guidelines to account for further growth and
development of economic tools and practices.
In an effort to fulfill that commitment, this document incorporates new literature published
since the last revision of the Guidelines. It describes new Executive Orders (EOs) and recent
guidance documents that impose new requirements on analysts, and fills information gaps by
providing more expansive information on selected topics. Furthermore, a loose-leaf format
has been adopted to facilitate the incorporation of new information in the future. This new,
more flexible format, in addition to the electronic release of the document, will allow future
updates and additions without requiring a wholesale revision of the document.
Agency.1 However, new guidance documents and
handbooks on how to comply with a number of
EOs and statutes have been issued both within and
ckground
While economic analysis can provide valuable
insights into the setting of Agency priorities and plans
for meeting them, the focus of this document is on
the conduct of economic analysis to support policy
decisions and meeting the requirements described
by related statutes, EOs, and recommendations
in guidance materials. With a few exceptions, the
collection of EOs and statutes that govern the
conduct of economic analysis and distributional
analysis has remained largely unchanged since 2000.
EO 12866, directing federal agencies to perform
a benefit-cost analysis (BCA) for economically
significant rules (those with an economic impact of
$100 million or more), still provides the primary
impetus for much of the formal BCA within the
outside the Agency in the last several years. The
Office of Management and Budget (OMB), for
instance, released its Circular A-4 in 2003 to replace
both its "Best Practices" document (OMB 1996) and
its "OMB Guidelines" (OMB 2000). Circular A-4
provides recommendations to federal agencies on
the development of economic analyses supporting
regulatory actions. As such, it greatly influences the
conduct of economic analysis and the development
of new analytic tools and approaches within the
Agency The OMB recommendations, as well as other
1 E0 13422, a 2007 amendment to E0 12866, contributed to the formal
benefit-cost framework by requiring agencies to "identify in writing
the specific market failure (such as externalities, market power, lack of
information) or other specific problem that [the regulation] intends to
address... as well as assess the significance of that problem." However,
E0 13422 was revoked in January 2009 through E0 13497.
Guidelines for Preparing Economic Analyses I December 2010 1-1
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Chapter 1 Introduction
guidance documents, are referenced in the revised
Guidelines where appropriate.
As a result of these modifications and updates
the new, revised Guidelines will ensure that
EPA's economic analyses are prepared to inform
the policy-making processes and satisfy OMB's
requirements for regulatory review The new
Guidelines also seeks to establish an interactive
policy development process between analysts
and decision makers through an expanded set
of cost, benefit, economic impacts, and equity
effects assessments; an up-to-date encapsulation of
environmental economics theory and practice; and
an enhanced emphasis on practical applications.
Underlying these efforts is the recognition that
a thorough and careful economic analysis is
an important component in informing sound
environmental policies. Preparing high-quality
economic analysis can greatly enhance the
effectiveness of environmental policy decisions
by providing policy makers with the ability to
systematically assess the consequences of various
actions. An economic analysis can describe the
implications of policy alternatives not just in terms
of economic efficiency, but also in terms of the
magnitude and distribution of an array of impacts.
Economic analysis also serves as a mechanism for
organizing information carefully. Thus, even when
data are insufficient to support particular types of
economic analysis, the conceptual scoping exercise
can provide useful insights.
It is important to note that economic analysis is
but one component in the decision-making process
and under some statutes it cannot be used in
setting standards. Other factors that may influence
decision makers include enforceability, technical
feasibility, affordability, political concerns, and
ethics, to name but a few. Nevertheless, economic
analysis provides a means to organize information
and to comprehensively assess alternative actions
and their consequences. Provided early in the
regulatory design phase, economic analysis can
help guide the selection of options. Ultimately,
good economic analysis based on sound science
should lead to better, more defensible rules.
1,2 The Scope of the
Guidelines
The scope of the Guidelines is on economic analysis
typically conducted for environmental policies
using regulatory or non-regulatory management
strategies. Separate guidance documents exist
for related analyses, some of which are inputs to
economic assessments. No attempt is made here
to summarize these other guidance materials.
Instead, their existence and content are noted in
the appropriate sections.
As with the 2000 Guidelines, the presentation
of economic concepts and applications in
this document assumes the reader has some
background in microeconomics as applied to
environmental and natural resource policies. To
fully understand and apply the approaches and
recommendations presented in the Guidelines,
readers should be familiar with basic applied
microeconomic analysis, the concepts and
measurement of consumer and producer surplus,
and the economic foundations of benefit-cost
evaluation. Appendix A provides the reader with
a brief review of economic foundations and the
Glossary defines selected key terms.
These Guidelines are designed to provide
assistance to analysts in the economic analysis
of environmental policies, but they do not
provide a rigid blueprint or a "cookbook" for
all policy assessments. The most productive and
illuminating approaches for particular situations
will depend on a variety of case-specific factors
and will require professional judgment. The
Guidelines should be viewed as a summary of
analytical methodologies, empirical techniques,
and data sources that can assist in performing
economic analysis of environmental policies.
When drawing upon these various resources,
there is no substitute for reviewing the original
source materials.
In all cases, the Guidelines recommends adhering
to the following general principles as stated by
OMB (1996):
'"Analysis of the risks, benefits, and costs
associated with regulation must be guided
1-2
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 1 Introduction
by the principles of full disclosure and
transparency. Data, models, inferences, and
assumptions should be identified and evaluated
explicidy, together with adequate justifications
of choices made, and assessments of the effects
of these choices on the analysis. The existence
of plausible alternative models or assumptions,
and their implications, should be identified.
In the absence of adequate valid data, properly
identified assumptions are necessary for
conducting an assessment."
"Analysis of the risks, benefits, and costs
associated with regulation inevitably also
involves uncertainties and requires informed
professional judgments. There should be
balance between thoroughness of analysis
and practical limits to the agency's capacity
to carry out analysis. The amount of analysis
(whether scientific, statistical, or economic)
that a particular issue requires depends on the
need for more thorough analysis because of the
importance and complexity of the issue, the
need for expedition, the nature of the statutory
language and the extent of statutory discretion,
and the sensitivity of net benefits to the choice
of regulatory alternatives.'"
Economic analyses should always strive to be
transparent by acknowledging and characterizing
important uncertainties that arise. In addition,
economic analyses should clearly state the
judgments and decisions associated with
these uncertainties and should identify the
implications of these choices. When assumptions
are necessary in order to carry out the analysis,
the reasons for those assumptions must be stated
explicitly and clearly. Analysts must take care
to avoid double counting of benefits and costs
when there are overlapping regulatory initiatives.
Further, economic analyses of environmental
policies should be flexible enough to be tailored
to the specific circumstances of a particular
policy, and to incorporate new information
and advances in the theory and practice of
environmental policy analysis.
i ^ t onom: br amework and
Definition of Terms
The conceptually appropriate framework for
assessing all the impacts of an environmental
regulation is an economic model of general
equilibrium. The starting point of such a model
is to define the allocation of resources and
interrelationships for an entire economy with
all its diverse components (households, firms,
government).
One of the first methodological questions an
analyst must answer when conducting economic
analysis is: who has "standing?" The most inclusive
answer allows all persons who may be affected by
the policy to have standing, regardless of where
(or when) they live. For domestic policy making,
however, the norm is to limit standing to the
national level. This decision is based on the fact
that authority to regulate only extends to a nation's
own residents who have consented to adhere to
the same set of rules and values for collective
decision making, as well as the assumption that
most domestic policies will have negligible effects
on other countries (Kopp et al. 1997, Whittington
etal. 1986).
OMB's Circular A-4 gives the following guidance
to agencies with regard to conducting economic
analyses in support of rulemakings: "Analysis
should focus on benefits and costs that accrue
to citizens and residents of the United States.
"Where you choose to evaluate a regulation that
is likely to have effects beyond the borders of the
United States, these effects should be reported
separately" (OMB 2003, p. 15). Potential
regulatory alternatives are then modeled as
economic changes that move the economy from
a state of equilibrium absent the regulation (the
baseline) to a new state of equilibrium with the
regulation in effect. The differences between
the old and new states are measured as changes
in prices, quantities produced and consumed,
income and other economic quantities. These
measurements can be used to characterize the net
welfare changes for each affected group identified
in the model. Analysts can rely on different
outputs and conclusions from the general
equilibrium framework to assess issues of both
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 1 Introduction
efficiency and distribution. These issues often take
the form of three distinct questions:
1. Is it theoretically possible for the "gainers"
from the policy to fully compensate the
"losers" and still remain better off ?
2. Who are the gainers and losers from the
policy and associated economic changes?
3. How did a particular group, especially a
group considered to be disadvantaged, fare as
a result of the policy change ?
The first question is directed at the measurement
of efficiency, and is based on the Potential
Pareto criterion. This criterion is the foundation
of BCA, requiring that a policy's net benefits
to society be positive. Measuring net benefits
by summing all of the welfare changes for all
affected groups provides an answer to this
question. Net benefits are derived by summing
all of the benefits that accrue as a result of a
policy change (including spillover effects)
less costs imposed by the policy on society
(including externalities). Since spillovers and
externalities by definition are not captured in
market transactions, counting private costs and
private benefits accruing to market participants
is not sufficient for estimating social benefits and
costs. The policy that maximizes net benefits is
considered the most efficient.2
The last two questions are related to the
distributional consequences of the policy. Because
a general equilibrium framework provides for the
ability to estimate welfare changes for particular
groups, these questions can be pursued using the
same approach taken to answer the efficiency
question, provided that the general equilibrium
model is developed at an appropriate level of
disaggregation.
Although a general equilibrium framework can,
in principle, provide the information needed to
address all three questions, in practice analysts have
limited access to the tools and resources needed
2 Appendix A gives a conceptual overview of this discussion. See in
particular Section A.3 on BCA.
to adopt a general equilibrium approach.3 More
often, EPA must resort to assembling a set of
different models to address issues of efficiency and
distribution separately. However, the limitations
on employing general equilibrium models have
greatly diminished in recent years with advances
in the theory, tools and data needed to use
the approach. Chapter 8 contains additional
information on general equilibrium models.
Analysts should weigh the need for additional
precision against the cost of employing general
equilibrium models over other methods. In doing
so analysts should consider the size, impact, and
complexity of the question at hand. In general, the
more detailed methods are justified by questions
with larger and more complex impacts. This
question is considered in each of the chapters on
specific models.
The Guidelines follows more traditional practices
and adopts conventional labels to distinguish
models or approaches used to answer questions on
the efficiency and distribution of environmental
regulations. For purposes of this document,
the presentation separates the concepts and
approaches into the following three general
categories:
* the examination of net social benefits using a
benefit-cost analysis (BCA);
* the examination of impacts on industry,
governments, and non-profit organizations
using an economic impacts analysis (EIA); and
* the examination of effects on various sub-
populations, particularly low-income,
minority, and children, using distributional
analyses.
This division is necessary not only because of data
and resource limitations, but because analysts often
lack models that are sufficiently comprehensive
to address all of these dimensions concurrently.
Within a BCA, for example, EPA is generally
unable to measure benefits with the same models
3 The general equilibrium framework will at least capture all "market"
benefits and costs, but may not include non-market benefits, such as
those associated with existence value. In practice, models of general
equilibrium may be unable to analyze relatively small sectors of the
economy. For more on general equilibrium analysis see Chapter 8,
Section 4.6.
1-4
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 1 Introduction
used for estimating costs, necessitating separate
treatment of costs and benefits. Further, when
estimating social costs there are cases in which
some direct expenditures can be identified, but
data and models are unavailable to track the
"ripple" effects of these expenditures through
the economy. For most practical applications,
therefore, a complete economic analysis is
comprised of a BCA, an EIA, and an equity
assessment.
BCA evaluates the favorable effects of policy
actions and the associated opportunity costs of
those actions. The favorable effects are defined
as benefits. Opportunities foregone define
economic costs. While conceptually symmetric,
benefits and costs are often evaluated separately
for "traditional" environmental problems (e.g.,
emissions of pollutants from point sources into
air and water) due to practical considerations.
Analysts may organize the analysis of benefits
differently from the analysis of costs, but they
should be aware of the conceptual relationship
between the two. Assessing the effects of
environmental policy is inherently a complex
process in which results from various disciplines
are integrated to predict environmental outcomes
and their economic consequences. As EPA
addresses increasingly complex environmental
problems (e.g., climate change), so in turn
will be the models needed to track the various
processes to describe and capture policy effects.
Computable general equilibrium (CGE) models
for these types of policies will become increasingly
important.
Once the change in pollution levels resulting from
a policy is predicted, this change is translated into
health outcomes or other outcomes of interest
using information provided by risk assessors.
Benefits analyses then apply a variety of economic
methodologies to estimate the value of these
anticipated health improvements and other sources
of environmental benefits. Social cost analyses
attempt to estimate the total welfare costs, net of
any transfers, imposed by environmental policies.
In most instances, these costs are measured by
higher costs of consumption goods for consumers
and lower earnings for producers and other
factors of production. Some of the findings of a
social cost analysis are inputs for benefits analyses,
such as predicted changes in the outputs of
goods associated with a pollution problem. More
information on analyzing benefits can be found in
Chapter 7 while details on estimating social costs
can be found in Chapter 8.
The assumptions and modeling framework
developed for the BCA can describe gains and
losses to assess efficiency. However the BCA
framework often limits detailed examination
of the gainers and losers and the impacts on
disadvantaged sub-populations. To estimate these
two categories of impacts analysts rely upon EIA
and equity assessments, which use a multiplicity of
estimation techniques. Chapters 9 and 10 provide
information on how these analyses relate to BCA
and detail estimation techniques.
Note that none of these three types of analyses
(BCA, EIA, and equity assessment) address
the cost-effectiveness of a policy option.
Cost-effectiveness analyses (CEA) report the
estimated costs needed to achieve a specific
goal or an additional unit of environmental
improvement. Costs-per-life-saved and costs-
per-ton-of-pollution-reduction are examples of
cost-effectiveness measures. When comparisons
are made across policies, CEA can be used to help
identify the least costly approach to achieving a
specific goal.4
t ,4 ;.',n Nation ^ the
Guidelines
The remainder of this document is organized into
ten main chapters as follows:
• Chapter 2: Statutory and Executive Order
Requirements for Conducting Economic
Analyses reviews the major statutes and
other directives mandating certain economic
assessments of the consequences of policy
actions;
4 Note that CEA is not covered extensively in this document. Additional
sources for details on CEA include I0M (2006) and Boardman etal.
(2006).
Guidelines for Preparing Economic Analyses I December 2010
1-5
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Chapter 1 Introduction
• Chapter 3: Statement of Need for the
Proposal provides guidance on procedures
and analyses for clearly identifying the
environmental problem to be addressed, and
for justifying federal intervention to correct
the problem;
• Chapter 4: Regulatory and Non-Regulatory
Approaches to Consider discusses the variety
of regulatory and non-regulatory approaches
analysts and policy makers ought to consider
in developing strategies for environmental
improvement;
• Chapter 5: Baselines provides a definition
of baseline and discusses how analysts should
approach conducting a baseline analysis;
• Chapter 6: Analysis of Social Discounting
presents a review of discounting procedures
and provides guidance on social discounting
in conventional contexts and over very long
time horizons;
• Chapter 7: Analyzing Benefits provides
guidance for assessing the benefits of
environmental policies including various
techniques of valuing risk-reduction and other
benefits;
• Chapter 8: Analyzing Costs presents the
basic theoretical approach for assessing the
costs of environmental policies and describes
how this can be applied in practice;
• Chapter 9: Economic Impact Analyses and
Equity Assessment provides guidance for
performing a variety of different assessments
of the economic impacts of environmental
policies;
• Chapter 10: Environmental Justice,
Children's Environmental Health and
Other Distributional Considerations
discusses key analytical issues and
considerations to keep in mind when
performing distributional analyses; and
• Chapter 11: Presentation of Analysis and
Results concludes the main body of the
Guidelines with suggestions for presenting
the quantified and unquantified results of the
various economic analyses to policy makers.
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 2
Statutory and Executive Order
Requirements for Conducting
Economic Analyses
Agencies are subject to a number of statutes and executive orders (EOs) that
direct the conduct of specific types of economic analyses.1 Many of these
directives are potentially relevant for all of EPA's programs while others
target individual programs. This chapter highlights directives that may apply
to all of EPA's programs.2
The scope of requirements for economic analysis can vary substantially. In some cases, a
statute or EO may contain language that limits its applicability to only those regulatory
actions, or rules, that fall above a specified threshold in significance or impact. Economic
analysis may be necessary to determine if a regulatory action exceeds a significance or
impact threshold, and thus falls in the class of regulatory actions targeted by the statute
or EO. If a regulatory action must comply with the requirements of a given statute or
EO, additional economic analysis (e.g., analysis of benefits and costs as required by EO
12866), procedural steps (e.g., consultation with affected state and local governments
as required by EO 13132), or a combination of economic analysis and procedural steps
may be required. This chapter describes the general requirements for economic analysis
contained in selected statutes and EOs, identifies thresholds beyond which a regulatory
action must follow additional economic analysis requirements, and provides further
direction for analysts seeking guidance on compliance with the statute or EO.3 For
each EO or statute highlighted in this chapter, references to applicable OMB and EPA
guidelines are provided. Another resource for determining the type and scope of economic
analysis required for a rule is a program's Office of General Counsel (OGC) attorney.4
Requirements of the statutes and EOs that do not necessitate economic analysis are not
covered in this chapter.
1 For the text statutes and EOs appearing in this chapter, and guidance specific to them, or for more information on their implications for EPA rule development,
visit the Action Development Process (ADP) Library on EPA's intranet http://intranet.epa.gov/adplibrary (accessed April 28,2004, internal EPA document).
Many of the citations for other applicable guidelines included in this section can be found at that site. Alternatively, information on statutes and EOs can easily
be found using http://usasearch.gov/.
2 Statutory provisions that require economic analysis but apply only to specific EPA programs are not described here. However, analysts should carefully
consider the relevant program-specific statutory requirements when designing and conducting economic analyses, recognizing that these requirements may
mandate specific economic analyses.
3 Note that for some statutes and EOs, requirements ioi proposed regulatory actions may vary slightly from the requirements for final regulatory actions.
4 See U.S. EPA (2005b) for more information.
Guidelines for Preparing Economic Analyses I December 2010 2-1
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Chapter 2 Statutory and Executive Order Requirements for Conducting Economic Analyses
m Orders
2.1.1 Executive Order 12866,
"Regulate inning and
Review"
Threshold: Significant regulatory actions. A
"significant regulatory action" is defined by
Section 3(f)(l)-(4) as one that is likely to result in
a rule that may:
• Have an annual effect on the economy of
$100 million or more or adversely affect in
a material way the economy, a sector of the
economy, productivity, competition, jobs,
the environment, public health or safety,
or State, local, or tribal governments or
communities;
• Create a serious inconsistency or otherwise
interfere with an action taken or planned by
another agency;
• Materially alter the budgetary impact of
entitlements, grants, user fees, or loan programs
or the rights and obligations of recipients
thereof; or
• Raise novel legal or policy issues arising out of
legal mandates, the President's priorities, or
the principles setforth in this Executive order.
Any one of the four criteria listed above can
trigger a regulatory action to be defined as
"significant;" a regulatory action that meets the
first criteria is generally defined as "economically
significant." While the determination of economic
significance is multi-faceted, it is most often
triggered by the $100 million threshold. This
threshold is interpreted as being based on the
annual costs or benefits ofthe proposed orfinalized
option. If one rule option poses costs or benefits
in excess of $100 million, but the rule option to
be proposed or finalized has costs and benefits
that fall below the $100 million range, the rule
is not considered economically significant.
The same definition applies whether the rule is
regulatory or deregulatory in nature. In the case
of a deregulatory rule with cost savings, transfers
should not be netted out. For example, if there are
additional costs in one market and cost savings in
another, they should not be combined to get "net"
cost savings. If one company loses $100 million in
business to another company, that is sufficient for
an economic significance determination, even if
the net effect is zero. The EO is silent on whether
the threshold should be adjusted for inflation. As
such, nominal values have been used in practice,
implying that as inflation increases the threshold
becomes more stringent.
Requirements contingent on threshold: A
statement of the need for the proposed action and
an assessment of social benefits and costs (Section
6(a)(3)(B) are required. The requirements for
BCA increase in complexity and detail for
economically significant rules (i.e., those that fall
under the definition in the first bullet above).
For these rules, the EO requires that agencies
conduct an assessment of benefits and costs of the
action, that benefits and costs be quantified to the
extent feasible, and that the benefits and costs of
alternative approaches also be assessed (Section
6(a)(3)(C)).5
Guidance: Chapters 3 through 8 of this document
provide guidance for meeting these requirements.
OMB's Circular A-4 (2003) provides guidance to
federal agencies on the development of regulatory
analysis of economically significant rules as required
by EO 12866. More specifically, Circular A-4 is
intended to define good regulatory analysis and
standardize the way benefits and costs of federal
regulatory actions are measured and reported.
Chapter 9 of this document describes methods for
analyzing and assessing distributional effects of a
rule through EIA. Chapter 10 addresses how to
assess environmental justice implications.6
5 E0 13422 and amended E0 12866 formerly required analysts to
"identify in writing the specific market failure (such as externalities,
market power, lack of information) or other specific problem" and
extended the BCA requirement to "significant" guidance documents.
Although E0 13497, issued in January 2009, revoked E0 13422
together with any "orders, rules, regulations, guidelines, or policies"
enforcing it, a subsequent memo issued by then Director of 0MB Peter
R. Orszag offering guidance on the implementation of the new E0
indicated that "significant policy and guidance documents.. .remain
subject to OIRA's review."
6 In its Statement of Regulatory Philosophy, E012866 states that
agencies should consider the distributional and equity effects of a rule
(Section 1(a)).
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Chapter 2 Statutory and Executive Order Requirements for Conducting Economic Analyses
2.1=2 Executive Order 12898,
"Federal Actions to Address
Environmental Justice in Minority
Populatio d Low-Income
Populations"
Threshold: No specific threshold; Agencies
are required to "...identify and address
disproportionately high and adverse human health
or environmental effects of its programs, policies,
and activities on minority populations and low-
income populations..."
Requirements contingent on threshold: No
specific analytical requirements.
Guidance: EPA issued interim guidance for
considering environmental justice in the Action
Development Process (U.S. EPA 2010); EPA
and the Council on Environmental Quality
(CEQ) have prepared guidance for addressing
environmental justice concerns in the context
of National Environmental Policy Act (NEPA)
requirements [U.S. EPA 1998a and CEQ (1997)].
These materials provide guidance on key terms in
the EO. Chapter 10 of this document addresses
environmental justice analysis.
2.1.3 Executive Order 13045,
"Protection of Children from
Environmental Health Risks and
Safe slcs"
Threshold: Economically significant regulatory
actions as described by EO 12866 that
involve environmental health risk or safety
risk that an agency has reason to believe may
disproportionately affect children.
Requirements contingent on threshold: An
evaluation of the health or safety effects of the
planned regulation on children, as well as an
explanation of why the planned regulation is
preferable to other potentially effective and
reasonably feasible alternatives the agency is
considering.
Guidance: EPA has prepared guidance for rule
writers on compliance with EO 13045 (U.S.
EPA 1998b). EPA's Children's Health Valuation
Handbook (U.S. EPA 2003b) discusses special
issues related to estimation of the value of health
risk reductions to children. Guidance in Chapter
10 of this document addresses equity analyses
focused on children.
1 r, I Exeeutp -O? !a m, 2,
"Federalism"
Threshold: Rules that have "federalism
implications" due to either substantial compliance
costs or preemption of state or local law. Rules
with federalism implications are defined as those
rules "that have substantial direct effects on the
States [includinglocal governments], on the
relationship between the national government
and the States, or on the distribution of power
and responsibilities among the various levels of
government." Rules maybe considered to impose
substantial compliance costs on state or local
governments unless the costs are expressly required
by statute or there are federal funds available to
cover them.
Requirements contingent on threshold:
Submission to OMB of a Federalism Summary
Impact Statement and consultation with elected
officials of affected state and local governments.
Guidance: Specific guidance on EO 13132 can
be found in the internal EPA document Guidance
on Executive Order 13132: Federalism (U.S. EPA
2008c).7
2.1.5 Executive Order 13175,
"Consultation and Coordination
w I dian Hrbal Governments"
Threshold: Rules and policy statements that
have tribal implications; that is, those that
have "substantial direct effects on one or more
Indian tribes, on the relationship between the
Federal Government and Indian tribes, or on the
distribution of power and responsibilities between
the Federal Government and Indian tribes."
7 This document is located at http://intranet.epa.gov/adpiibrary/
documents/federaiismguidel 1 -00-08.pdf (accessed March 4,2010,
internal EPA document).
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 2 Statutory and Executive Order Requirements for Conducting Economic Analyses
Requirements contingent on threshold: To
the extent practicable and permitted by law,
if a regulatory action with tribal implications
is proposed and imposes substantial direct
compliance costs on Indian tribal governments,
and is not required by statute, then the agency
must either provide the funds necessary to pay the
tribal governments' direct compliance costs, or
consult with tribal officials early in the process of
regulatory development and provide to OMB a
Tribal Summary Impact Statement.
Guidance: A tribal guidance document is
currently under development by EPA's Regulatory
Management Division.8 Guidance in Chapter 9 of
this document addresses equity analyses focusing
on minority populations.
2.1.6 Executive Order 13211,
"Actioi ncerning Regulations
that Significantly Affect Energy
Supply, Distribution, or Use"
Threshold: Rules that are a significant regulatory
action under EO 12866 and that are likely to
have significant adverse effects on the supply,
distribution, or use of energy.
Requirements contingent on threshold:
Submission of a Statement of Energy Effects to
OMB. The Statement of Energy Effects addresses
the magnitude of expected adverse effects,
describes reasonable alternatives to the action, and
describes the expected effects of such alternatives
on energy supply, distribution, and use.
Guidance: EPA has prepared guidance on
what effects might be considered significant in
Memorandum on Energy Executive Order 13211
— Preliminary Guidance (2008d). OMB has
guidance for implementing EO 13211 as well.9
8 Please check the ADP Library on EP/& intranet, http://intranet.epa.
gov/adpiibrary (accessed April 8,2010, internal EPA document) for the
status of this guidance.
9 U.S. EPA 2008d, Memorandum on Energy Executive Order 13211 —
Preliminary Guidance, located at http://intranet.epa.gov/adplibrary/
statutes.htmlenergy under the heading "Preamble Template"
(accessed July 8,2008, internal EPA document). OMB's guidance for
implementing E013211 is located at http://www.whitehouse.gov/omb/
memoranda/m01_27.html (accessed July 8,2008).
5 ites
,-i I f s [ I- rigulata. f lexibility
Act of 1980 (RFA), as Amended by
Fht->mall Busine ho gulatory
Enforcement Fairness Act of 1938
(SBREFA) (5 U.S.C. 601-612)
Threshold: Regulations that have a significant
economic impact on a substantial number of small
entities, including small businesses, governments
and non-profit organizations.
Requirements contingent on threshold:
Preparation of a regulatory flexibility analysis,
and compliance with a number of procedural
requirements to solicit and consider flexible
regulatory options that minimize adverse
economic impacts on small entities.
Guidance: EPA has issued specific guidance for
complying with RFA/SBREFA requirements
in the internal document EPA Final Guidance
for EPA Rulewriters: Regulatory Flexibility Act
as amended by the Small Business Regulatory
Enforcement Fairness Act (2006c).10
2.2, tided Mandates
Ref< . 'IRA)
{P.L. iiuJ-n
Threshold one (Sections 202 and 205 of UMRA):
Regulatory actions that include federal mandates
"that may result in the expenditure by State, local,
and tribal governments, in the aggregate, or by the
private sector, of $100 million or more (adjusted
annually for inflation) in any one year."11
Requirements contingent on threshold one:
Section 202 of UMRA requires preparation
of a written statement that includes the legal
authority for the action; a BCA; a distributional
analysis; estimates of macroeconomic impacts;
and a description of an agency's consultation with
elected representatives of the affected state, local,
or tribal governments. Section 205 of UMRA
10 U.S. EPA 2006c, available at http://intranet.epa.gov/adplibrary
(accessed May 1,2008, internal EPA document).
11 Note that the threshold in this case is "adjusted annually for inflation"
as opposed to the threshold under E0 12866.
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Chapter 2 Statutory and Executive Order Requirements for Conducting Economic Analyses
requires an agency to consider a reasonable number
of regulatory alternatives and select the least
costly, most cost-effective, or least burdensome
alternative, or to publish with the final rule an
explanation of why such alternative was not
chosen.
Threshold two (Section 203 of UMRA):
Regulatory requirements that might "significantly"
or "uniquely" affect small governments.
Requirements contingent on threshold
two: Agencies must solicit involvement from,
and conduct outreach to, potentially affected
small governments during development and
implementation.
Guidance: EPA has issued Interim Guidance
on the Unfunded Mandates Reform Act of1995,
(1995b), and OMB provides general guidance on
complying with requirements contingent on each
of the two thresholds under UMRA.12
2.2.cv Tlh- P perwo? ~ - duction
Act of 19$
Threshold: Actions (both regulatory and non-
regulatory) that include record-keeping, reporting,
or disclosure requirements or other information
collection activities calling for answers to identical
questions imposed upon or posed to ten or more
persons, other than federal agency employees.
Requirements contingent on threshold: 'Ihe
agency must submit an information collection
request (ICR) to OMB for review and approval
and meet other procedural requirements including
public notice. Note that 1320.3 (c) (4) (ii) states
that "any collection of information addressed
to all or a substantial majority of an industry
is presumed to involve ten or more persons."
However, OMB guidance on this issue indicates
that if agencies have evidence showing that this
presumption is incorrect in a specific situation
(i.e., fewer than 10 persons would be surveyed),
the agency may proceed with the collection
without seeking OMB approval. Agencies must
12 See U.S. EPA 1995b available at http://intranet.epa.gov/adplibrary/ 13 See http://intranet.epa.gov/icrintra/(accessed April 14,2004, internal
statutes/umra.htm (accessed December 21,2010). EPA document).
be prepared to provide this evidence to OMB on
request and abide by OMB's determination as to
whether the collection of information ultimately
requires OMB approval.
Guidance: Both guidance and templates for
completing an ICR and associated Federal Register
(FR) notices can be found on EPA's intranet site,
"ICR Center."13
Guidelines for Preparing Economic Analyses I December 2010
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Guidelines for Preparing Economic Analyses I December 2010
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Chapter
Statement of Need for Policy Action
A clear statement of needfor policy action is an essential component in economic analyses
of environmental policy prepared for economically significant rules.1 This chapter
discusses the key elements that comprise this statement:
• Problem Definition: Section 3.1 provides components to include in a definition of the
environmental problem to be addressed;
• Reasons for Market or Institutional Failure: Section 3.2 identifies factors relevant to
an analysis of the reasons existing legal and other institutions have failed to correct the
problem; and
• Need for Federal Action: Section 3.3 describes items to consider in preparing a
justification of the need for federal intervention instead of other alternatives.
The statement of need for policy action should also describe any statutory or judicial
requirements that mandate the promulgation of particular policies or the evaluation
of specific effects pertaining to the action. In some instances, statutes prohibit the use
of certain types of analysis in policy making. In these cases, the guidance presented in
Guidelines should be applied in a manner consistent with such mandates.
efinition
Hie problem definition discussion should briefly
review the nature of the environmental problem to
be addressed. The following considerations are often
relevant:
• The primary pollutants causing the problem and
their concentration;
• The media through which exposures or damages
take place;
• Private and public sector sources responsible for
creating the problem;
• Human exposures involved and the health effects
due to those exposures;
1 E0 12866 states that "Federal agencies should promulgate only such
regulations as are required by law, are necessary to interpret the law,
or are made necessary by compelling need, such as material failures of
private markets to protect or improve the health and safety of the public,
the environment, or the well-being of the American people..." (emphasis
added). E0 13422 extended the requirements in E0 12866 to guidance
documents, but has since been revoked.
• Non-human resources affected and the
resulting outcome;
• Expected evolution of the environmental
problem over the time horizon of the analysis;
• Current control and mitigation techniques;
• The amount or proportion (or both) of the
environmental problem likely to be corrected by
federal action.
3.2 Reasons for Market or
Institutional
After defining the problem, the statement of need
should examine the reasons why the market and
other public and private sector institutions have
failed to correct it. This identification is an important
component of policy development because the
underlying failure itself often suggests the most
appropriate remedy for the problem.
Guidelines for Preparing Economic Analyses I December 2010
3-1
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Chapter 3 Statement of Need for Policy Action
OMB's Circular A-4 discusses three categories
of market failure, including externalities,
market power, and inadequate or asymmetric
information.2 Circular A-4 also points out that
there may be other social purposes for regulation
beyond correcting market failures, such as
improving government function, removing
distributional unfairness, or promoting privacy and
personal freedom. Externalities are the most likely
cause of the failure of private and public sector
institutions to completely correct environmental
damages. However, information asymmetries and
pre-existing government-induced distortions can
also be responsible for these problems.
Externalities occur when the market does not
compensate for the effect of one party's activities
on another party's well-being. Externalities
can occur for many reasons, for example, high
transaction costs can make it difficult for injured
parties to ensure that polluters internalize the cost
of damage through bargaining, legal, or other
means. Externalities can also result when activities
that pose environmental risks are difficult to
link to the resulting damages, such as those that
occur over long periods of time or those that are
transferred from one location to another.
3.3 Ne r Federal Action
The final component of the statement of need for
policy action is an analysis of why a federal remedy
is preferable to actions by private and other public
sector entities, such as the judicial system or state
and local governments.3 Federal involvement is
often required for environmental problems that
cross jurisdictional boundaries (for instance,
international environmental problems). In some
cases, federal involvement is mandated by statute
or EO as described in Chapter 2. This analysis
should justify the basis for federal involvement by
comparing it to the performance of a variety of
realistic alternatives that rely on other institutional
arrangements. This component of the statement
of need for policy action should verify that the
proposed action is within the jurisdiction of the
relevant statutory authorities, and that the results
of the policy will be preferable to no action.
Finally, the statement of need should identify any
aspects of the regulations being proposed that are
necessitated by statutory requirements rather than
being discretionary, as this may have an influence
on the development of the economic analysis and
presentation of the results.
Consistent with EO 12866, the statement of need
should assess the significance of the problem.
Economic analyses should explore, for example,
why transaction costs are high or what information
asymmetries exist. Similar analyses are appropriate
for situations where other factors are responsible
for the failure of the market or public and private
sector institutions to adequately address an
environmental problem.
3-2
3 See E013132 on "Federalism" for introductory statements regarding
principles of federalism, and a section describing the special
requirements for preemption.
2 For further discussion of market failure, see Perman et al. (2003),
Hanleyetal. (2001), and Nicholson (1995).
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 4
Regulatory and Non-Regulatory
Approaches to Pollution Control
This chapter briefly describes several regulatory and non-regulatory approaches
used in environmental policy making. The goals of this chapter are to introduce
several important analytic terms, concepts, and approaches; to describe the
conceptual foundations of each approach; and to provide additional references
for those interested in a more in-depth discussion.1 Specifically, this chapter
discusses the following four general approaches to environmental policy making: (1)
command-and-control regulation; (2) market-based incentives; (3) hybrid approaches; and
(4) voluntary initiatives. While command-and-control regulations have been a commonly
used method of environmental regulation in the United States, EPA also employs the
three other approaches. Market-based incentives and hybrid approaches offer the regulated
community an opportunity to meet standards with increased flexibility and lower costs
compared to many command-and-control regulations, while voluntary initiatives may allow
environmental improvements in areas not traditionally regulated by EPA. The chapter also
includes a discussion of criteria used to evaluate the effectiveness of regulatory and non-
regulatory approaches to pollution control.
aluating
Environmenta cy
Once federal action is deemed necessary to address an
environmental problem, policy makers have a number
of options at their disposal to influence pollution
levels. In deciding which approach to implement,
policy makers must be cognizant of constraints and
limitations of each approach in addressing specific
environmental problems. It is important to account
for how political and information constraints,
imperfect competition, or pre-existing market
distortions interact with various policy options. Even
when a particular approach is appealing from a social
welfare perspective, it may not be consistent with
statutory requirements, or may generate additional
concerns when considered along with other
1 Baumol and Oates (1988), particularly Chapters 10-14; Kahn (1998);
Kolstad (2000); Sterner (2003); and Field and Field (2005) are useful
references on the economic foundations of many of the approaches
presented here.
existing regulations. "While any policy option under
consideration must balance cost considerations with
other important policy goals (including benefits),
economic efficiency and cost-effectiveness are two
economic concepts useful for framing the discussion
and comparison of the regulatory options presented
in the remaining sections of this chapter.
4.1.1 Economic Efficiency
Economic efficiency can be defined as the
maximization of social welfare. An efficient market is
one that allows society to maximize the net present
value (NPV) of benefits: the difference between
a stream of social benefits and social costs over
time. The efficient level of production is referred
to as Pareto optimal because there is no way to
rearrange production or reallocate goods in such
a way that someone is better off without making
someone else worse off in the process. The efficient
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
level of production occurs without government
intervention in a market characterized by no
market failures or externalities (see Appendix A
for a more detailed discussion of efficiency and for
a graphical representation of the efficient point
of production). Government intervention may
be justified, however, when a market failure or
externality exists (see Appendix A), in which case
the government may attempt to determine the
socially optimal point of production once such
externalities have been internalized. Said differently,
government analysts may evaluate which of the
various policy approaches under consideration
maximizes the benefits of reducing environmental
damages, net the resulting abatement costs.
Conceptually, the socially optimal level is
determined by reducing emissions until the benefit
of abating one more unit of pollution (i.e., the
marginal abatement benefit) — measured as a
reduction in damages — is equal to the cost of
abating one additional unit (i.e., the marginal
abatement cost).2 In the simplest case, when
each polluter chooses the level at which to emit
according to this decision rule (i.e., produce at a
level at which the marginal abatement benefit is
equal to the marginal abatement cost), an efficient
aggregate level of emissions is achieved when the
cost of abating one more unit of pollution is equal
across all polluters. Any other level of emissions
would result in a reduction in net benefits.
This definition of efficiency describes the simplest
possible world where a pollutant is a uniformly
mixed flow pollutant — the pollutant does
not accumulate or vary over time — and the
marginal damages that result are independent
of location. When pollution levels and damages
vary by location, the efficient level of pollution is
achieved when marginal abatement costs adjusted
by individual transfer coefficients are equal across
all polluters. Temporal variability also implies an
2 The idea that a given level of abatement is efficient — as opposed to
abating until pollution is equal to zero — is based on the economic
concept of diminishing returns. For each additional unit of abatement,
marginal social benefits decrease while marginal social costs of that
abatement increase. Thus, it only makes sense to continue to increase
abatement until the point where marginal benefits and marginal costs
are just equal. Any abatement beyond that point will incur more
additional costs than benefits.
adjustment to this equilibrium condition. In the
case of a stock pollutant, marginal abatement costs
are equal across the discounted sum of damages
from todays emissions in all future time periods.
In the case of a flow pollutant, this condition
should be adjusted to reflect seasonal or daily
variations. Under uncertainty, it is useful to think
of the efficient level of pollution as a distribution
instead of as a single point estimate.
The reality of environmental decision making is
that Agency analysts are rarely in the position
to select the economically efficient point of
production when designing policy. This is partly
because the level of abatement required to reduce
a particular environmental problem is often
determined legislatively, while the implementation
of the policy to achieve such a goal is left to the
Agency. In cases where the Agency has some
say in the stringency of a policy, its degree of
flexibility in determining the approach taken varies
by statute. This may limit its ability to consider
particular approaches or to use particular policy
instruments. It is also important to keep in mind
analytic constraints. In cases where it is particularly
difficult to quantify benefits, cost-effectiveness
maybe the most defensible analytic framework.
4.1.2 Cost-Effectiveness
The efficiency of a policy option differs from
its cost-effectiveness. A policy is cost-effective
if it meets a given goal at least cost, but cost-
effectiveness does not encompass an evaluation
of whether that goal has been set appropriately
to maximize social welfare. Ail efficient policies
are cost-effective, but it is not necessarily true
that all cost-effective policies are efficient. A
policy is considered cost-effective when marginal
abatement costs are equal across all polluters. In
other words, for any level of total abatement, each
polluter has the same cost for their last unit abated.
4,2 Traditional Cpm dw rind-
and-Control or Prescriptive
lulation
Many environmental regulations in the United
States are prescriptive in nature (and are often
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
referred to as command-and-control regulations).3
A prescriptive regulation can be defined as a
policy that prescribes how much pollution an
individual source or plant is allowed to emit and/
or what types of control equipment it must use to
meet such requirements. Such a standard is often
defined in terms of a source-level emissions rate.
Despite the introduction of potentially more cost-
effective methods for regulating emissions, this
type of regulation is still commonly used and is
sometimes statutorily required. It is almost always
available as a "backstop" if other approaches do
not achieve desired pollution limits.
Because a prescriptive standard is commonly
defined in terms of an emissions rate, it does not
directly control the aggregate emission level. In
such cases, aggregate emissions will depend on
the number of polluters and the output of each
polluter. As either production or market size
increase, so will aggregate emissions. Even when
the standard is defined in terms of an emission
level per polluting source, aggregate emissions will
still be a function of the total number of polluters.
When abatement costs are similar across
regulated sources, a source-level standard may
be reasonably cost-effective. However when
abatement costs vary substantially across
polluters, reallocating abatement activities so
that some polluters have stricter standards than
others could lead to substantial cost savings. If
reallocation were possible (e.g., through a non-
prescriptive approach), a polluter facing relatively
high abatement costs would continue to emit at
its current level but would pay for the damages
incurred (e.g., by paying a tax or purchasing
permits), while a polluter with relatively low
abatement costs would reduce its emissions.
Note that regulators can at least partially
account for some variability in costs by allowing
3 Goulder and Parry (2008) refer to these as "direct regulatory
instruments" because they feel that "command-and-control" has a
"somewhat negative connotation." Ellerman (2003) refers to them
as prescriptive regulations. We follow that convention here. Notable
exceptions to this type of regulation in the U.S. experience include
the phase-down in lead content in gasoline, which allowed trading of
credits among refineries and offset programs applied in non-attainment
areas. For more information on early applications of market incentives,
see U.S. EPA (2001b).
prescriptive standards to vary according to size
of the polluting entity, production processes,
geographic location, or other factors. Beyond
this, however, a prescriptive standard usually does
not allow for reallocation of abatement activities
to take place — each entity is still expected to
achieve a specified emissions standard. Thus, while
pollution maybe reduced to the desired level,
it is often accomplished at a higher cost under a
prescriptive approach.4
It is common to "grandfather," or exempt, older
polluters from new prescriptive regulations,
thereby subjecting them to a less stringent standard
than newer polluters. Grandfathering creates
a bias against constructing new facilities and
investing in new pollution control technology or
production processes.5 As a result, grandfathered
older facilities with higher emission rates tend to
remain active longer than they would if the same
emissions standard applied to all polluters.
The most stringent form of prescriptive regulation
is one in which the standard specifies zero
allowable source-level emissions. For instance, EPA
has completely banned or phased out the use or
production of chlorofluorocarbons (CFCs) and
certain pesticides. This approach to regulation
is potentially useful in cases where the level of
pollution that maximizes social welfare is at or
near zero.6
Two types of prescriptive regulations exist:
technology or design standards; and performance-
based standards.
4,2.1 Technology or
ign Standards
A technology or design standard, mandates the
specific control technologies or production
4 See Tietenberg (2004) for a discussion of empirical studies that
examine the cost-effectiveness of prescriptive air pollution regulations.
Of the ten studies included, eight found that prescriptive regulations
cost at least 78 percent more than the most cost-effective strategy.
5 For a discussion of grandfathering, see Helfand (1991).
6 For cases where the optimal level of pollution is at or near zero, the
literature also indicates that market-based incentives can sometimes
be useful as a transition instrument for the phasing-out of a particular
chemical or pollutant. See Sterner (2003) and Kahn (1998).
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
Text Box 4.1 - Cease Solution
Government intervention for the control of environmental externalities is only necessary when parties cannot work
out an agreement between themselves. Coase (1960) outlined conditions under which a private agreement between
affected parties might result in the attainment of a social welfare maximizing level of pollution without government
intervention. First, property rights must be clearly defined. In situations where the resource in question is not
"owned" by anyone, there are no incentives to negotiate, and the offending party can "free ride," or continue to
pollute, without facing the costs of its behavior.
When property rights have been allocated, a social welfare maximizing solution can be reached regardless of which
party is assigned the property rights, although the equity of the assignment may vary. Take for example a farm
whose pesticide application to its crops contributes pollution to the well water of nearby homeowners. If property
rights of the watershed are assigned to the homeowners, then the farm may negotiate with the homeowners to allow
it to continue to use the pesticide. The payment need not be in the form of cash but could be payments in kind. If
property rights of the watershed are given to the farm, then the homeowners would have to pay the farm to stop
applying the pesticide.
In each case, the effectiveness of the agreement is contingent on meeting additional conditions: bargaining must
be possible, and transaction costs must be low. These conditions are more likely to be met when there are only a
small number of individuals involved. If either party is unwilling to negotiate or faces high transaction costs, then no
private agreement will be reached. Asymmetric information can also hinder the ability of one or more party to come
to an agreement. Going back to the example, consider a case where there are many farms in the watershed using the
pesticide on their crops. Clearly homeowners would have more difficulty in negotiating an agreement with every farm
than they would when negotiating with one farm.
processes that an individual pollution source must
use to meet the emissions standard. This type of
standard constrains plant behavior by mandating
how a source must meet the standard, regardless
of whether such an action is cost-effective.
Technology standards may be particularly useful
in cases where the costs of emissions monitoring
are high but determining whether a particular
technology or production process has been put in
place to meet a standard is relatively easy. However,
since these types of standards specify the abatement
technology required to reduce emissions, sources
do not have an incentive to invest in more cost-
effective methods of abatement or to explore new
and innovative abatement strategies or production
processes that are not permitted by regulation.
4,2.2 Performar ised
Standards
Kperformance-based standard also requires
that polluters meet a source-level emissions
standard, but allows a polluter to choose among
available methods to comply with the standard.
At times, the available methods are constrained
by additional criteria specified in a regulation.
Performance-based standards that are technology-
based do not specify a particular technology, but
rather consider what is possible for available and
affordable technology to achieve when establishing
a limit on emissions.7
In the case of a performance-based standard,
the level of flexibility a source has in meeting
the standard depends on whether the standard
specifies an emission level or an emission rate (i.e.,
emissions per unit of output or input). A standard
that specifies an emission level allows a source to
7 As an example, Reasonably Available Control Technology (RACT)
specifies that the technology used to meet the standard should
achieve "the lowest emission limit that a particular source or source
category is capable of meeting by application of control technology
that is reasonably available considering technological and economic
feasibility." RACT defines the standard on a case-by-case basis,
taking into account a variety of facility-specific costs and impacts on
air quality. EPA has been restrictive in its definition of technologies
meeting this requirement and eliminates those that are not
commercially available (see Swift 2000).
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
choose to implement an appropriate technology,
change its input mix, or reduce output to meet
the standard. An emission rate, on the other hand,
may be more restrictive depending on how it is
defined. If the emissions rate is defined per unit
of output, then it does not allow a source to meet
the standard through a reduction in output. If the
standard is defined as an average emissions rate
over a number of days, then the source may still
reduce output to meet the standard.
The flexibility of performance-based standards
encourages firms to innovate to the extent
that they allow firms to explore cheaper ways
to meet the standard; however, they generally
do not provide incentives for firms to reduce
pollution beyond what is required to reach
compliance.8 For emissions that fall below the
amount allowed under the standard, the firm
faces a zero marginal abatement cost since the
firm is already in compliance. Also, because
permitting authority is often delegated to the
States, approval of a technology in one state
does not ensure its use is allowed in another.
Firm investment in research to develop new, less
expensive, and potentially superior technologies
is therefore discouraged.9
4,3 Market-Oriented
Approaches
Market-oriented approaches (or market-based
approaches) create an incentive for the private
sector to incorporate pollution abatement into
production or consumption decisions and to
innovate in such a way as to continually search
for the least costly method of abatement.10
Market-oriented approaches can differ from
more traditional regulatory methods in terms
of economic efficiency (or cost-effectiveness)
and the distribution of benefits and costs. In
particular, many market-based approaches
8 For a theoretical analysis of incentives for technological change, see
Jung etal. (1996) and Montero (2002). Empirical analyses can be
found in Jaffe and Stavins (1995), and Kerr and Newell (2003).
9 See Swift (2000) and U.S. EPA (1991) for a detailed discussion of how
emission rate-based standards hinder technological innovation.
10 The incentive to innovate means that the marginal abatement cost
curve shifts downward over time as cheaper abatement options are
introduced.
minimize polluters' abatement costs, an objective
that often is not achieved under command-and-
control based approaches. Because market-based
approaches do not mandate that each polluter
meet a given emissions standard, they typically
allow firms more flexibility than more traditional
regulations and capitalize on the heterogeneity
of abatement costs across polluters to reduce
aggregate pollution efficiently. Environmental
economists generally favor market-based policies
because they tend to be least costly, they place
lower information burden on the regulator, and
they provide incentives for technological advances.
Four classic market-based approaches are discussed
in this section:
• Marketable permit systems;
• Emission taxes;
• Environmental subsidies; and
• Tax-subsidy combinations.11
While operationally different (e.g., taxes and
subsidies are price-based while marketable
permits are quantity-based), these market-
based instruments are more or less functionally
equivalent in terms of the incentives they put in
place. This is particularly true of emission taxes
and cap-and-trade systems, which can be designed
to achieve the same goal at equivalent cost. The
sections that follow discuss each of these market-
based approaches in turn.
4.3.1 Marketab! Systems
Several forms of emissions trading exist, including
cap-and-trade systems, project-based trading
11 The literature on applied market-based approaches for environmental
protection should be consulted, along with the references they contain,
for information concerning the design, operation, and performance of
these approaches. Anderson and Lohof (1997) and Stavins (1998a,
2000b) compile information on both the theory and empirical use of
economic incentives. Newell and Stavins (2003) generate rules-of-
thumb designed to make it easy for policy makers to determine when
market-based incentives may result in cost savings over command-
and-control regulations. Harrington et al. (2004) compare the costs
and outcomes of command-and-control and incentives-based
regulatory approaches to the same environmental problem in the
United States and Europe. Additional sources include Sterner (2003),
Stavins (2003), Tietenberg (1999,2002), U.S. EPA (2004a, 2001a),
OECD (1994a, 1994b), and proceedings published under the "Project
88" forum, Stavins (1988,1991).
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
In 1995, Title IV of the 1990 Clean Air Act Amendments established a cap-and-trade system for SO emissions to
address the problem of acid rain. Two hundred and sixty three of the highest emitting SO. units of 110 electricity-
generating plants were selected to participate in the first phase of the trading program. Emissions of SO in 1995
were initially limited to 8.7 million tons for those facilities. Of the plants that participated, most were coal-fired units
located east of the Mississippi River. Under this system, allowances were allocated to units on a historical basis, after
which they could use the allowances, sell them to other units, or "bank" the allowances for use in subsequent years.
Continual emission monitoring (CEM) systems have allowed the government to easily monitor and enforce emission
restrictions in accordance with the allowances. The second phase of the program, initiated in 2000, imposed a
national SO emissions cap of 10 million tons and brought almost all SO generating units into the system.
Initial evaluations of the first phase of implementation suggest that the SO trading system has significantly reduced
emissions at a relatively low cost. In fact, allowance prices have been considerably lower than predicted, reflecting
lower than expected marginal costs. A significant level of trading has occurred and has resulted in savings of over $1
billion per year as compared to command-and-control alternatives. Emissions in 1995 were almost 40 percent below
the 10 million ton limit. The evaluations demonstrated that one reason for such large reductions in SO emissions
below the allowable limit is the ability to bank allowances for future use. The success of the program has continued
into the second phase, with recent estimates of the full U.S. Acid Rain Program's benefits [including SO trading and
direct nitrogen oxide (NO.) controls] reaching upwards of $120 billion annually in 2010 with annual costs around
$3 billion (in 2000$); a benefit to cost ratio of about 40 to 1. Trends over the life of the program show that while
electricity generation has grown steadily and SO. and NO emissions have fallen substantially, electricity retail prices,
until very recently, have declined in real terms.
o
50% -
40% -
30% -
20% -
10% -
0% -
-10% -
-30% -
-40% -
-50% -
-60%
¦ Electricity Retail Price —¦— NO^ Emissions
S0;, Emissions Electricity Generation
Source: U.S. EPA 2007a
For more information, see Burtraw and Bolii (1997). Schmalensee et al. (1998). Stavins (19981). 2003). Carlson et al.
(2000). Chestnut and Mills (2005). and U.S. EPA (2007a).
systems and emissions rate trading systems. Hie
common element across these programs is that
sources are able to trade credits or allowances so
that those with opportunities to reduce emissions
at lower costs have an incentive to do so. Each of
these systems is discussed in turn below.12
12 For a more detailed discussion of the various systems and how to
design them, see U.S. EPA (2003c).
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
4.3,1,1 Gap-and-Trade Systems
In a cap-and-trade system the government sets the
level of aggregate emissions, emission allowances
are distributed to polluters, and a market is
established in which allowances may be bought or
sold. The price of emission allowances is allowed
to vary Because different polluters incur different
private abatement costs to control emissions,
they are willing to pay different amounts for
allowances. Therefore, a cap-and-trade system
allows polluters who face high marginal abatement
costs to purchase allowances from polluters with
low marginal abatement costs, instead of installing
expensive pollution control equipment or using
more costly inputs. Cap-and-trade systems also
differ from command-and-control regulations in
that they aim to limit the aggregate emission level
over a compliance period rather than establish an
emissions rate.
If the cap is set appropriately, then the equilibrium
price of allowances, in theory, adjusts so that
it equals the marginal external damages from
a unit of pollution. This equivalency implies
that any externality associated with emissions is
completely internalized by the firm. For polluters
with marginal abatement costs greater than the
allowance price, the cheapest option is to purchase
additional units and continue to emit. For polluters
with marginal abatement costs less than the
allowance price, the cheapest option is to reduce
emissions and sell their permits. As long as the
price of allowances differs from individual firms'
marginal abatement costs, firms will continue to
buy or sell them. Trading will occur until marginal
abatement costs equalize across all firms.13
Generally, allowances initially sold at auction
represent income transfers from the purchasers to
the government in the amount of the price paid for
the allowances. The collection of revenue through
this method of allowance allocation gives the
government the opportunity to reduce pre-existing
13 The U.S. Acid Rain Program established under Title IV of the 1990
Clean Air Act Amendments is a good example of a marketable permit
program. For economic analyses of this program see Joskow et al.
(1998), Stavins (1998b), Ellerman etal. (2000), and Chestnut and
Mills (2005). For more information on the program itself see Text
box 4.2 and EPA's (2008a) Acid Rain Website at http://www.epa.gov/
acidrain (accessed April 5,2004).
market inefficiencies, to reduce distributional
consequences of the policy, or to invest in other
social priorities. Allowances may also be allocated
to polluters according to a specified rule. This
represents a transfer from the government to
polluting firms, some of which may find that the
value of allowances received exceeds the firm's
aggregate abatement costs.
The distribution of rents under cap-and-trade
systems should be considered when comparing
these systems with more traditional regulatory
approaches. If the allowances are auctioned or
otherwise sold to polluters, the distributional
consequences will be similar to those experienced
when regulating using taxes. If allowances
are distributed for free to polluters, however,
distributional consequences will depend on the
allocation mechanism (e.g., historical output
or inputs), on who receives the allowances,
and on the ability of the recipients to pass
their opportunity costs on to their customers.
If new entrants must obtain allowances from
existing polluters, then the policy maker should
also consider potential barrier-to-entry effects.
Differing treatment applied to new versus existing
polluters can affect the eventual distribution of
revenues, expenses, and rents within the economy.
Additional considerations in designing an effective
cap-and-trade system include "thin" markets,
transaction costs, banking, effective monitoring,
and predictable consequences for noncompliance.
The United States' experience suggests that a
market characterized by low transaction costs and
being "thick" with buyers and sellers is critical if
pollution is to be reduced at the lowest cost. This
is because small numbers of potential traders in a
market make competitive behavior unlikely, and
fewer trading opportunities result in lower cost
savings. Likewise, the number of trades that occur
could be significantly hindered by burdensome
requirements that increase the transaction costs
associated with each trade.14
14 This is also often the case for bubbles and offsets. See O'Neil (1983)
for an evaluation of an early example of a permit-trading program in
the United States and the main reasons for its failure.
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
Cap-and-trade systems should also be sensitive to
concerns about potential temporal or spatial spikes
(i.e., hotspots — areas in which the pollution
level has the potential to increase as a result of
allowance trading). This may happen, for example,
in an area in which two facilities emit the same
amount of pollution, but due to differences
in exact location and site characteristics, one
facility's impact on environmental quality differs
substantially from that of the other polluter.
While one potential solution to this problem is
to adjust trading ratios to equalize the impact
of particular polluters on overall environmental
quality, determining the appropriate adjustments
to these ratios can be costly and difficult. Other
possible solutions include zone-based trading and
establishing pollution "floors."
Two recent reviews of the literature (Burtraw et
al. 2005 and Harrington et al. 2004) find little
evidence of spatial or temporal spikes in pollution
resulting from the use of market-based approaches.
In fact, market-based approaches have led to
smoothing of emissions across space in some cases.
These results come primarily from studies of the
S02 and NOx trading programs and if the market-
based policy is not carefully designed, the results
may not transfer to other pollutants that have
more localized effects.
Banking introduces increased flexibility into
a trading system by allowing polluters to bank
unused permits for future use. A firm may reduce
emissions below the allowance level now, and
bank (or save) remaining allowances to cover
excess emissions or sell to another polluter at a
later time. In this way, polluters that face greater
uncertainty regarding future emissions, or that
expect increased regulatory stringency, can bank
allowances to offset potentially higher future
marginal abatement costs.
For a cap-and-trade system to be effective, reliable
measurement and monitoring of emissions
must occur with predictable consequences for
noncompliance. At the end of the compliance
period, emissions at each source are compared
to the allowances held by that source. If a source
is found to have fewer allowances than the
monitored emission levels, it is in noncompliance
and the source must provide allowances to cover its
environmental obligation. In addition, the source
must pay a penalty automatically levied per each
ton of excess emissions.15
4.3.1.2 Project-Based Trading Systems
Offsets and bubbles (sometimes known
as "project-based" trading systems) allow
restricted forms of emissions trading across
or within sources to allow sources greater
flexibility in complying with command-and-
control regulations such as emission limits or
facility-level permits. An offset allows a new
polluter to negotiate with an existing source to
secure a reduction in the latter's emissions. A
bubble allows a facility to consider all sources
of emissions of a particular pollutant within
the facility to achieve an overall target level of
emissions or environmental improvement. While
offsets and bubbles are mosdy used to control air
pollution in non-attainment areas, they have been
historically hindered by high administrative and
transaction costs because they require case-by-case
negotiation to convert a technology or emission
rate limit into tradable emissions per unit of
time, to establish a baseline, and to determine
the amount of credits generated or required (U.S.
EPA 2001a).
4.3.1.3 Rate-Based Trading Systems
Rather than establish an emissions cap, the
regulatory authority under a rate-based trading
program, establishes a performance standard or
emissions rate. Sources with emission rates below
the performance standard can earn credits and
sell them to sources with emission rates above
the standard. As with the other trading systems,
sources able to improve their emissions rate at
low cost have an incentive to do so since they can
sell the resulting credits to those sources facing
higher costs of abatement. However, emissions
may increase under these programs if sources
increase their utilization or if new sources enter
the market. Therefore, the regulating authority
15 Notably, the U.S. Acid Rain Trading Program has nearly 100 percent
compliance and requires only about 50 EPA staff to administer.
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
may need to periodically impose new rate
standards to achieve and maintain the desired
emission target, which in turn may lead to
uncertainty in the long term for the regulated
sources. Rate-based trading programs have been
used in the United States to phase out lead in
gasoline (1985) and to control mobile source
emissions (U.S. EPA 2003c).
4,3.2 Emissions Tax
Emissions taxes are exacted per unit of pollution
emitted and induce a polluter to take into account
the external cost of its emissions. Under an
emissions tax, the polluter will abate emissions up
to the point where the additional cost of abating
one more unit of pollution is equal to the tax, and
the tax will result in an efficient outcome if it is set
equal to the additional external damage caused by
the last unit of pollution emitted.
As an example of how an emissions tax works,
suppose that emissions of a toxic substance are
subject to an environmental charge based on
the damages the emissions cause. To avoid the
emissions tax, polluters find the cheapest way to
reduce pollution. This may involve a reduction
in output, a change in inputs to production, the
installation of pollution control equipment, or
a process change that prevents the creation of
pollution. Polluters decide individually how
much to control their emissions, based on the
costs of control and the magnitude of the tax.
The polluting firm reduces emissions to the
point where the cost of reducing one more unit
of emissions is just equal to the tax per unit of
emissions. For any remaining emissions, the
polluter prefers to pay the tax rather than to
abate further. In addition, the government earns
revenue that it may use to reduce other pollution
or reduce other taxes, or may redistribute to
finance other public services.16 While difficult to
implement in cases where there is temporal and/
or spatial variation in emissions, policy makers can
more closely approximate the ambient impact of
emissions by incorporating adjustment factors for
16 For more information on how the government can use revenues from
taxes to offset distortions created by other taxes, see Goulder (1995)
and Goulder etal. (1997).
seasonal or daily fluctuations or individual transfer
coefficients in the tax.
Despite the apparent usefulness of such a tax,
true emissions taxes — those set equal or close to
marginal external damages — are relatively rare in
the United States.17 This is because taxing emissions
directly may not be feasible when emissions are
difficult to measure or accurately estimate, when it
is difficult to define and monetarily value marginal
damages from a unit of emissions (which is needed
to properly set the tax), or when taxes are applied
to emissions that are difficult to monitor and/or
enforce. In addition, attempts to measure and tax
emissions may lead to illegal dumping.18 Other
considerations when contemplating the use of
emission taxes include the potential imposition of
substantially different cost burdens on polluters
as compared with other regulatory approaches,
political incentives to set the tax too low, and
the collection of revenues and distribution of
economic rents that result from such programs.
User or product charges are a variation on
emission taxes that are occasionally utilized in
the United States. These charges may be imposed
directly upon users of publicly operated facilities
or upon intermediate or final products whose
use or disposal harms the environment. User or
product charges may be effective approximations
of an emissions tax for those cases in which the
product taxed is closely related to emissions.
User charges have been imposed on firms
that discharge waste to municipal wastewater
treatment facilities and on non-hazardous solid
wastes disposed of in publicly-operated landfills.
Product charges have been imposed on products
that release CFCs into the atmosphere, that
utilize more gasoline (such as cars), or require
more fertilizer. In practice, both user and product
charges are usually set at a level only sufficient to
recover tht private costs of operating the public
system, rather than being set at a level selected to
create proper incentives for reducing pollution to
the socially optimal level.
17 These taxes are called "Pigovian" after the economist, Arthur Pigou,
who first formalized them. See Pigou (1932).
18 See Fullerton (1996) for a discussion of the advantages and
disadvantages of emission taxes.
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
Taxes and charges facilitate environmental
improvements similar to those that result from
marketable permit systems. Rather than specifying
the total quantity of emissions, however, taxes,
fees, and charges specify the effective "price" of
emitting pollutants.
4.3.3 Environmental Subsidies
Subsidies paid by the government to firms or
consumers for per unit reductions in pollution
create the same abatement incentives as emission
taxes or charges. If the government subsidizes the
use of a cleaner fuel or the purchase of a particular
control technology, firms will switch from the
dirtier fuel or install the control technology to
reduce emissions up to the point where the private
costs of control are equal to the subsidy. It is
important to keep in mind that an environmental
subsidy is designed to correct for an externality
not already taken into account by firms when
making production decisions. This type of subsidy
is fundamentally different from the many subsidies
already in existence in industries such as oil and
gas, forestry, and agriculture, which exist for other
reasons apart from environmental quality, and
therefore can exacerbate existing environmental
externalities.
Unlike an emissions tax, a subsidy lowers a firm's
total and average costs of production, encouraging
both the continued operation of existing polluters
that would otherwise exit the market, and the
entry into the market by new firms that would
otherwise face a barrier to entry. Given the
potential entrance of new firms under a subsidy,
the net result may be a decrease in pollution
emissions from individual polluters but an increase
in the overall amount.19 For this reason, subsidies
and taxes may not have the same aggregate social
costs, or result in the same degree of pollution
control. A subsidy also differs from a tax because it
requires government expenditure. Analysts should
always consider the opportunity costs associated
with using public funds.
19 See Sterner (2003) for a more in-depth discussion of how subsidies
work and for numerous examples of subsidy programs in the United
States and other countries.
It is possible to minimize the entry and exit
of firms resulting from subsidies by redefining
the subsidy as a partial repayment of verified
abatement costs, instead of defining it as a per
unit payment for emissions reductions relative to
a baseline. Under this definition, the subsidy now
only relates to abatement costs incurred and does
not shift the total or average cost curves, thereby
leaving the entry and exit decisions of firms
unaffected. Defining the subsidy in this way also
minimizes strategic behavior because no baseline
must be specified.20
Instead of pursuing a per unit emissions subsidy,
the government may choose to lower the
private costs of particular actions to the firm or
consumer through cost sharing. For example, if
the government wishes to encourage investment
in particular pollution control technologies, the
subsidy may take the form of reduced interest
rates, accelerated depreciation, direct capital
grants, and loan assistance or guarantees for
investments. Cost-sharing policies alone may
not induce broader changes in private behavior.
In particular, such subsidies may encourage
investment in pollution control equipment, rather
than encouraging other changes in operating
practices such as recycling and reuse, which
may not require such costly capital investments.
However, in conjunction with direct controls,
pollution taxes, or other regulatory mechanisms,
cost sharing may influence the nature of private
responses and the distribution of the cost burden.
As is the case with emissions taxes, subsidy rates
also can be adjusted to account for both spatial and
temporal variability.
A government "buy-back" constitutes another type
of subsidy. Under this system, the government
either direcdy pays a fee for the return of a
product or subsidizes firms that purchase recycled
materials. For instance, consumers maybe offered
20 Strategic behavior is a problem common to any instrument or
regulation that measures emissions relative to a baseline. In cases
where a firm or consumer may potentially receive funds from the
government, they may attempt to make the current state look worse
than it actually is, in order to receive credit for large improvements. If
firms or consumers are responsible for paying for certain emissions
above a given level, they may try to influence the establishment of that
level upward in order to pay less in fines or taxes.
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
a cash rebate on the purchase of a new electric or
push mower when they scrap their old one. The
rebate is earned when the old gasoline mower is
turned in and a sales receipt for the new device
is provided.21 Buy-back programs also exist to
promote the scrapping of old, high-emission
vehicles.
Environmental subsidies in the United States have
been used to encourage proper waste management
and recycling by local governments and businesses;
the use of alternative fuel vehicles by public bus
companies, consumers, and businesses; and land
conservation by property owners using cost-
sharing measures. While most of these subsidies
are not defined per unit of emissions abated, they
can be effective when the behavioral changes they
encourage are closely related to the use of products
with reduced emissions.
4.3.4 Tax-Subsi imbinations
Emission taxes and environmental subsidies can
also be combined to achieve the same level of
abatement as achieved when the tax and subsidy
instruments are used separately. One example of
this type of instrument is referred to as a deposit-
refund system in which the deposit operates as a
tax and the refund serves as a partially offsetting
subsidy. As with the other market instruments
already discussed, a deposit-refund system creates
economic incentives to return a product for reuse
or proper disposal, or to use a particular input in
production, provided that the deposit exceeds the
private cost of returning the product or switching
inputs.
Under the deposit-refund system, the deposit is
applied to either output or consumption, under
the presumption that all production processes of
the firm pollute or that all consumption goods
become waste. A refund is then provided to the
extent that the firm or consumer provides proof
of the use of a cleaner form of production or
of proper disposal. In the case where a deposit-
refund is used to encourage firms to use a cleaner
input, the deposit on output induces the firm to
21 For more information on the Office of Air's Small Engine Buy-back
Program see U.S. EPA (2006c).
reduce its use of all inputs, both clean and dirty.
The refund, however, provides the firm with an
incentive to switch a specific input or set of inputs
that result in a refund, such as a cleaner fuel or a
particular pollution control technology.
A tax and offsetting subsidy combination
functions best when it is possible to discern
a direct relationship between an input, or
output, and emissions. For instance, a tax on the
production or use of hydrochlorofluorocarbons
(HCFCs) combined with a refund for HCFC
recycled or collected in a closed system is a
good proxy for a direct emissions tax on ozone
depletion.22
The most common type of tax-subsidy
combination is the deposit-refund system, which
is generally designed to encourage consumers to
reduce litter and increase the recycling of certain
components of municipal solid waste.23 The most
prominent examples are deposit-refunds for items
such as plastic and glass bottles, lead acid batteries,
toner cartridges and motor oil. Other countries
have implemented deposit-refund systems on
a wider range of products and behaviors that
contribute to pollution, including the sulfur
content of fuels (Sweden), product packaging
(Germany), and deforestation (Indonesia). Tax-
subsidy combinations have also been discussed in
the literature as a means of controlling nonpoint
source water pollution, cadmium, mercury, and the
removal of carbon from the atmosphere.24
The main advantage of a combined tax and subsidy
is that both parts apply to a market transaction.
Because the taxed and subsidized items are easily
observable in the market, this type of economic
instrument may be particularly appealing when
it is difficult to measure emissions or to control
illegal dumping. In addition, polluters have
an incentive to reveal accurate information on
abatement activity to qualify for the subsidy.
22 See Sterner (2003) for a more detailed description of this and other
examples of tax-subsidy combinations.
23 For example, Arnold (1995) analyzes the merits of a deposit-refund
system in a case study focusing on enhancing used-oil recycling.
Sigman (1995) reviews policy options to address lead recycling.
24 See U.S. EPA (2004a), Fisher etal. (1995), and O'Connor (1994).
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
Because firms have access to better information
than the government does, they can measure and
report emissions with greater precision and at a
potentially lower cost.
Disadvantages of the combined tax-subsidy system
may include potentially high implementation and
administrative costs, and the political incentive
to set the tax too low to induce proper behavior
(a danger with any tax). Policy makers may
adjust an emissions tax to account for temporal
variation in marginal environmental damages,
but a tax on output sold in the market cannot
be matched temporally or spatially to emissions
during production. In addition, to the extent
that emissions (e.g., S02 from power plants)
are easily and accurately monitored, other
market incentives maybe more appropriate. If a
production process has many different inputs with
different contributions to environmental damages,
then it is necessary to tax the inputs at different
rates to achieve efficiency. Likewise, if firms are
heterogeneous and select a different set of clean
inputs or abatement options based on firm-specific
cost considerations, then the subsidy should
be adjusted for differences in these production
functions.25 A uniform subsidy combined with
an output tax may be a good proxy, however,
when there is limited heterogeneity across inputs'
contribution to emissions and across firms.
Conceptually similar to the tax-subsidy
combination is the requirement that firms post
performance bonds that are forfeited in the
event of damages, or that firms contribute up-
front funds to a pool. Such funds may be used
to compensate victims in the event that proper
environmental management of a site for natural
resource extraction does not occur. To the extent
that the company demonstrates it has fulfilled
certain environmental management or reclamation
obligations, the deposited funds are usually
refunded. Financial assurance requirements have
been used to manage closure and post-closure
care for hazardous waste treatment, storage, and
disposal facilities. Performance bonds have also
25 The main advantages and disadvantages of deposit-refund systems are
discussed in U.S. GAO (1990); Palmer, Sigman, and Walls (1997); and
Fullerton and Wolverton (2001,2005).
been required in extraction industries such as
mining, timber, coal, and oil.26
her Market-Oriented or
Hybrid Approaches
In addition to the four classic market-based
instruments discussed above, several other market-
oriented approaches are often discussed in the
literature and are increasingly used in practice.
Often, these approaches combine aspects of
command-and-control and market-based incentive
policies. As such, they do not always present the
most economically efficient approach. Either the
level of abatement or the cost of the policy is likely
to be greater than what would be achieved through
the use of a pure market-based incentive approach.
Nevertheless, such approaches are appealing to
policy makers because they often combine the
certainty associated with a given emissions standard
with the flexibility of allowing firms to pursue
the least costly abatement method. This section
discusses the following market-oriented approaches:
• Combining standards and pricing approaches;
• Liability rules; and
• Information as regulation.
4.4.1 Combining Standards and
Pricing Approaches
Pollution standards set specific emissions limits,
thereby reducing the probability of excessively
high damages to health or the environment. Such
standards may impose large costs on polluters.
Emissions taxes restrict costs by allowing polluters
to pay a tax on the amount they emit rather than
undertake excessively expensive abatement. Taxes,
however, do not set a limit on emissions, and
leave open the possibility that pollution may be
excessively high. Some researchers suggest a policy
that limits both costs and pollution, referred to
as a "safety-valve" approach to regulation, which
combines standards with pricing mechanisms.27
In the case of a standard and tax combination,
the same emissions standard is imposed on all
26 For more information on the use of financial assurance or performance
bonds, see Boyd (2002).
27 See Roberts and Spence (1976) and Spence and Weitzman (1978).
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polluters and all polluters are subject to a unit tax
for emissions in excess of the standard.
While a standard and pricing approach does not
necessarily ensure the maximization of social
welfare, it can lead to the most cost-effective
method of pollution abatement. This policy
combination has other attractive features. First, if
the standard is set properly, the desired protection
of health and the environment will be assured. This
feature of the policy maintains the great advantage
of a standards approach: protection against
excessively damaging pollution levels. Combining
approaches allows for more certainty in the
expected environmental and health effects of the
policy than would occur with a market-based
approach alone. Second, high abatement cost
polluters can defray costs by paying the emissions
fee instead of cleaning up. This feature preserves
the flexibility of emissions taxes: overall abatement
costs are lower because polluters with low
abatement costs reduce pollution while polluters
with high abatement costs pay taxes.
4.4,2 Information Disclosure
Requiring disclosure of environmental information
has been increasingly used as a method of
environmental regulation. Disclosure strategies are
most likely to work when there is a link between
the polluting firm and affected parties such as
consumers and workers.28 Disclosure requirements
attempt to minimize inefficiencies in regulation
associated with asymmetric information, such as
when a firm has more and better information on
what and how much it pollutes than is available to
the government or the public. By collecting and
making such information publicly available, firms,
government agencies, and consumers can become
better informed about the environmental and
human health consequences of their production
and consumption decisions. In some cases, the
availability of this information may also encourage
more environmentally benign activities and
discourage environmentally detrimental ones. For
example, warning labels on hazardous substances
28 See 0MB (201 Ob) for guidance issued to regulatory agencies on the
use of information disclosure and simplification in the regulatory
process.
that describe safe-handling procedures or the risks
posed by the product may encourage hazardous
substance handlers to take greater precautions,
and/or may encourage consumers to switch to
less damaging substitutes for some or all uses
of the substance. Similarly, a community with
information on a nearby firm's pollution activity
may exert pressure on the firm to reduce emissions,
even if formal regulations or monitoring and
enforcement are weak or nonexistent.29
Requirements for information disclosure need
not be tied explicitly to an emissions standard;
however, such requirements are consistent
with a standard-based approach because the
information provided allows a community to easily
understand the level of emissions and the polluters'
level of compliance with existing standards or
expectations. As is the case with market-based
instruments, polluters still have the flexibility
to respond to community pressure by reducing
emissions in the cheapest way possible.
The use of information disclosure or labeling rules
has other advantages. When expensive emissions
monitoring is required to collect such information,
reporting requirements that switch the burden
of proof for monitoring and reporting from the
government to the firm might result in lower
costs, because firms are often in a better position
to monitor their own emissions. If accompanied
by spot checks to ensure that monitoring
equipment functions properly and that firms
report results accurately, information disclosure
can be an effective form of regulation. Without
the appropriate monitoring, however, information
disclosure might not result in an efficient outcome.
While information disclosure has its advantages,
it is important to keep three caveats in mind
when considering this method for environmental
regulation. First, the use of information as
regulation is not costless: U.S. firms report
spending approximately $346 million per year
29 For more information on how information disclosure may help to
resolve market failures, see Pargal and Wheeler (1996), Tietenberg
(1998), Tietenberg and Wheeler (2001), and Brouhle and Khanna
(2007).
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
to monitor and report releases.30 Any required
investments in pollution control are in addition
to this amount. Second, the amount of pressure
a community exerts on an emitting plant may
be related to socioeconomic status. Poorer, less-
educated populations tend to exert far less pressure
than communities with richer, well-educated
populations.31 Third, information disclosure may
not result in a socially efficient level of pollution
when consumers either consider only the effect of
emissions on them as individuals, ignoring possible
ecological or aggregate societal effects, or when
they do not understand how to properly interpret
the released information in terms of the health risks
associated with exposure to particular pollutants.
EPA-led information disclosure efforts include
the Toxics Release Inventory (TRI) and the
mandatory reporting of greenhouse gases (GHG).
Both the TRI and the GHG reporting rule require
firms to provide the government and public with
information on pollution at each plant, on an
annual basis, if emissions exceed a threshold. There
are also consumer-based information programs
targeting the risks of particular toxic substances,
the level of contamination in drinking water,
the dangers of pesticides, and air quality index
forecasts for more than 300 cities. There is some
evidence in the literature regarding the impact of
TRI reporting on firm value: the most polluting
firms experience small declines in stock prices on
the day TRI emission reports are released to the
public. Hamilton (1995) finds a stock price return
of -0.03 percent due to TRI report release. Firms
that experienced the largest drop in their stock
prices also reduced their reported emissions by the
greatest quantity in subsequent years.32
4.4.3 Liability Rules
Liability rules are legal tools of environmental
policy that can be used by victims (or the
30 See O'Connor (1996) for information on the costs of monitoring and
reporting environmental information. See World Bank (2000) for a
discussion of the main advantages and disadvantages of information
disclosure as a policy tool.
31 See Hamilton (1993), and Aroraand Cason (1999).
32 Hamilton (1995); Konar and Cohen (1997); and Khanna, Quimio, and
Bojilova (1998) are empirical studies that have investigated how the
TRI has affected firm behavior and stock market valuation.
government) to force polluters to pay for
environmental damages after they occur. These
instruments serve two main purposes: (1)
to create an economic incentive for firms to
incorporate careful environmental management
and the potential cost of environmental damages
into their decision-making processes; and (2) to
compensate victims when careful planning does
not occur. These rules are used to guide courts in
compensation decisions when the court rules in
favor of the victim. Liability rules can serve as an
incentive to polluters. To the extent that polluters
are aware that they will be held liable before
the polluting event occurs, they may minimize
or prevent involvement in activities that inflict
damages on others. In designing a liability rule it
is important to evaluate whether damages depend
only on the amount of care taken on the part of
the polluter or also on the level of output; and
whether damages are only determined by polluter
actions or are also dependent on the behavior
of victims. For instance, if victims do not
demonstrate some standard of care in an attempt
to avoid damages, the polluter may not be held
liable for the full amount. If damages depend
on these other factors in addition to polluter
actions, then the liability rule should be designed
to provide adequate incentives to address these
other factors.
While a liability rule can be constructed to mimic
an efficient market solution in certain cases, there
are reasons to expect that this efficiency may not
be achieved. First, uncertainty exists as to the
magnitude of payment. The amount that polluters
are required to pay after damages have occurred is
dependent on the legal system and may be limited
by an inability to prove the full extent of damages
or by the ability of the firm to pay. Second, liability
rules can generate relatively large costs, both in
terms of assessing the environmental damage
caused, and the damages paid.33 Thus, liability rules
are most useful in cases where damages requiring
compensation are expected to be stochastic
(e.g., accidental releases), and where monitoring
firm compliance with regulatory procedures is
33 See Segerson (1995), and Alberini and Austin (2001) for discussions
of the types of liability rules, the efficiency properties of each type of
rule, and an extensive bibliography.
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
difficult. Depending on the likely effectiveness
of liability rules to provide incentives to firms to
avoid damages, they can be thought of as either
an alternative to or as a complement to other
regulatory approaches.
Strict liability and negligence are two types of
liability rules relevant to polluters. Under strict
liability, polluters are held responsible for all
health and environmental damage caused by their
pollution, regardless of actions taken to prevent
the damages. Under negligence, polluters are
liable only if they do not exhibit "due standard
of care." Regulations that impose strict liability
on polluters may reduce the transactions costs of
legal actions brought by affected parties. This may
induce polluters to alter their behavior and expend
resources to reduce their probability of being
required to reimburse other parties for pollution
damages. For example, they may reduce pollution,
dispose of waste products more safely, install
pollution control devices, reduce output, or invest
in added legal counsel.
Liability rules have been used in the remediation
of contaminated sites under the Comprehensive
Environmental Response, Compensation
and Liability Act (CERCLA), also known as
Superfund, and under the Corrective Action
provisions of the Resource Conservation and
Recovery Act (RCRA). These rules have also
been used in the redevelopment of potentially
contaminated industrial sites, known as
brownfields.
lecting the Appropriate
Market-Bas sentive or
Hybrid Approach
Selection of the most appropriate market-based
incentive or hybrid regulatory approach depends
on a wide variety of factors, including:34
• The type of market failure being addressed;
• The specific nature of the environmental
problem;
34 Helpful references that discuss aspects to consider when comparing
among different approaches include Hahn and Stavins (1992), OECD
(1994a, 1994b), Portney and Stavins (2000), and Sterner (2003).
• The type of pollutant information that is
available and observable;
• The degree of uncertainty surrounding costs
and benefits;
• Concerns regarding market competitiveness;
• Monitoring and enforcement issues;
• Potential for exacerbating economy-wide
distortions; and
• The ultimate goals of policy makers.
of Market Failure
There are two main types of market failure that are
commonly addressed through the use of market-
based or hybrid instruments. The first, externality,
occurs when firms or consumers fail to integrate
into their decision making the impact of their own
production or consumption decisions on entities
external to themselves. The second type of market
failure, asymmetric information, occurs when firms
or consumers are unable to make optimal decisions
due to lack of information on available abatement
technologies, emission levels, or associated
risks. Market-based or hybrid instruments that
incorporate the marginal external damages of a
unit of pollution into a firm or consumers cost
function address the first type of market failure.
Information disclosure or labeling are often
suggested when the second type of market failures
occurs. As discussed in Section 4.4.2, policy makers
believe that private- and public-sector decision
makers will act to address an environmental
problem once information has been disseminated.
4,5.2 The Nature of the
Environmental Probl
The use of a particular market-oriented approach
is often direcdy associated with the nature of the
environmental problem. Do emissions derive from
a point source or a nonpoint source ? Do emissions
stem from a stock or flow pollutant? Are emissions
uniformly mixed or do they vary by location? Does
pollution originate from stationary or mobile
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
sources ?35 Point sources, which emit at identifiable
and specific locations, are much easier to control
than diffuse and often numerous nonpoint sources,
and therefore are often responsive to a wide
variety of market instruments. Although nonpoint
sources are not regulated under EPA, the pollution
emitted from a nonpoint source is. Clearly, this
makes the monitoring and control of nonpoint
source emissions challenging. In instances where
both point and nonpoint sources contribute to a
pollution problem, a good case can be made for a
tax-subsidy combination or a marketable permit
system. Under these alternatives, emissions from
point sources might be taxed while nonpoint
source controls are subsidized.
Flow pollutants tend to dissipate quickly, and it
is possible to rely on a wide variety of market and
hybrid instruments for emissions control. But
stock pollutants persist in the environment and
tend to accumulate over time. Controlling stock
pollutants may require strict limits to prevent
bioaccumulation or detrimental health effects at
small doses, making direct regulation a potentially
more appealing approach. If these limits are not
close to zero, then potentially practical instrument
options include a standard-and-pricing approach
or a marketable permit approach that defines
particular trading ratios to ensure that emission
standards are not violated at any given source
are. These same instruments are appealing when
pollutants are not uniformly mixed across space.
In the case of non-uniformly mixed emissions, it
is important to account for differences in baseline
pollution levels, and differences in emissions across
more and less polluted areas.
Stationary sources of pollution are easier to
identify and control through a variety of market
instruments than are mobile sources. Highly
mobile sources are usually numerous, each
emitting a small amount of pollution. Emissions
therefore vary by location and damages can vary
by time of day or season. For example, health
impacts associated with vehicle traffic are primarily
35 For a detailed discussion of how the nature of the environmental
problem affects instrument choice, see Kahn (1998), Goulder etal.
(1999), Parry and Williams (1999), Harris (2002), Tietenberg (2002),
and Sterner (2003).
a problem at rush hour when roads are congested
and cars spend time idling or in stop-and-go
traffic. Differential pricing of resources used by
these mobile sources (such as higher tolls on roads
or greater subsidies to public transportation during
rush hour) is a potentially useful tool.
of Pollutant
InformatU it is Available
a iservabie
The selection of market-oriented approach
may depend on the available data. Is the level
of pollutant actually observable or measurable ?
Or will the level need to be imputed based on
inputs and technology used? Are the sources
heterogeneous ? Does the pollutant vary across time
and space ? Are information technologies available
to the analyst to improve data collection? When the
pollutant concentration can be directly and easily
measured then it is possible to directly regulate the
level of the pollutant. But if monitoring costs are
high, it may be easier to target a particular input
or require a specific technology known to reduce
pollutants by a certain amount. The pollutant levels
can be imputed based on regulation placed on the
input or the technology used.
The link between pollution and heterogeneous
sources is often difficult and costly to determine,
and costs may increase if the pollutant levels
vary over time. Uniform policies are often used
for the sake of simplicity. However, information
technologies such as continuous emissions
monitoring equipment (CEMs) or geographical
information systems (GIS) can be used to link
sources to pollutant levels. In these cases, policies
that make use of this new information may be
used and often can reduce costs. As technology
improves or more data become available, analysts
should consider reassessing the regulation design.36
4,5.4 Uncertainty in Abatement
Costs or Damages
The choice between price-based instruments
(e.g., taxes or charges) and quantity-based
36 For more information see Xabadia, Goetz, and Zilberman (2008)
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
instruments (e.g., marketable permits) has been
shown theoretically to rest on the uncertainty
surrounding estimated benefits and costs of
pollution control, as well as on how marginal
benefits and costs change with the stringency
of the pollution control target. If uncertainty
associated with the cost of abatement exists but
damages do not change much with additional
pollution, then policy makers can effectively
limit costs by using a price instrument without
having much impact on the benefits of the policy.
If, on the other hand, there is more uncertainty
associated with the benefits of controlling
pollution and policy makers wish to guard
against high environmental damages, a quantity
instrument is likely preferable.37 In this way,
the policy maker can avoid potentially costly or
damaging mistakes. The policy maker should also
be aware of any discontinuities or threshold values
above which sudden large changes in damages or
costs could occur in response to a small increase in
the required abatement level.
4.5,5 Market Competitiveness
Market power is a type of market failure in and of
itself, as it may result in output that is too low and
prices that are too high compared to what would
occur in a competitive market. Instruments that
cause firms to further restrict output may create
additional inefficiencies in sectors where firms
have some degree of market power. A combination
of market-based instruments may work more
effectively than a single instrument in this instance.
To the extent that cost burdens are differentiated,
the use of certain market-based instruments may
cause a change in market structure that favors
existing firms by creating barriers of entry and
allowing existing firms a certain amount of control
over price. Permit systems that set aside a certain
number of permits for new firms, for instance, may
guard against such barriers.
4.5.6 Monitoring a
Enforcement Issues
Market-oriented instruments differ in the degree
of effort required to monitor and enforce the
desired emissions level. For example, subsidies,
deposit-refund systems, and information
disclosure shift the burden of proof to demonstrate
compliance from government to the regulated
entities. Because firms are generally in a better
position than government to monitor and report
their own emissions, they likely can do so at a
potentially lower cost. This feature makes such
approaches attractive when monitoring is difficult
or emissions must be estimated (e.g., when there
are nonpoint sources or large numbers of small
polluters). In these cases, attempts to prohibit or
tax the actions of polluters are likely to fail due to
the risk of widespread noncompliance (e.g., illegal
dumping to avoid the tax) and costly enforcement.
4.5.7 Potential for
Economy-Wide Distortions
Analysts should consider the potential
distortionary effects of any policy option
considered. Even if a policy is deemed relatively
efficient on its own, it may interact with
pre-existing environmental, economic, or
agricultural policies (e.g., product standards,
non-environmental subsidies, trade barriers) in
non-intuitive ways that can exacerbate distortions
in the economy and result in unintended
environmental consequences. Instruments that
include a revenue-raising component, such
as auctioned permits or taxes, may allow for
opportunities to direct collected resources to
reduce other taxes and fees and the associated
inefficiencies.38 See Chapter 8 and Appendix A
for a more detailed discussion of economy-wide
distortions.
37 See Weitzman (1974) for the classic paper on the ways in which
uncertainty (also referred to as lack of information) affects instrument
choice. See Chapter 10 of these Guidelines for more information on the
treatment of uncertainty in analyses.
38 For useful references on the issues concerning the uses of revenues
from pollution charges (e.g., applying environmental tax revenues so
as to reduce other taxes and fees in the economy) and ways to analyze
these policies, see Bovenberg and de Moojii (1994), Goulder (1995),
Bovenberg and Goulder (1996), Goulder etal. (1997), and Jorgenson
(1998a, 1998b).
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
4.5,1' fix .aIs rf if
Policy Maker
Finally, the goals of policy makers may influence
the instrument selected to regulate pollution.
Each considered instrument may have different
distributional and equity implications for both
costs and benefits; these implications should be
accounted for when deciding among instruments.
For example, policy makers may wish to ensure
clean-up of future pollution by firms. Policy
makers may consider using insurance and financial
assurance mechanisms to supplement existing
standards and rules when there is a significant
risk that sources of future pollution might be
incapable of financing the required pollution
control or damage mitigation method. In addition,
the degree to which policy makers want to allow
the market to determine exact outcomes may
influence the choice of instrument. The quantity
of marketable permits issued, for example, sets the
total level of pollution control, but the market
determines which polluters reduce emissions. On
the other hand, taxes let the market determine
both the extent of control by individual polluters
and the total level of control.
4,6 Non-Regulatory
Approaches
EPA has pursued a number of non-regulatory
approaches that rely on voluntary initiatives
to achieve emissions reductions and improve
management of environmental hazards. These
programs are usually not intended as substitutes
for formal regulation, but instead act as important
complements to existing regulation. Many of EPA's
voluntary programs encourage polluting entities to
go beyond what is mandated by existing regulation.
Other voluntary programs have been developed to
improve environmental quality in areas that policy
makers expect may be regulated in the future
but are currently not regulated, such as GHG
emissions and nonpoint source water pollution.39
39 While this chapter only discusses government-led voluntary initiatives
at the federal level at EPA, other government agencies, industry,
non-profits, and international organizations have also initiated
and organized voluntary initiatives designed to address particular
environmental issues. These initiatives are beyond the scope of this
chapter, which limits itself to a brief description of policy options
available to EPA.
Much of the technical foundation for these
voluntary initiatives rests on the concepts
underlying a "pollution prevention" approach
to environmental management choices. In the
Pollution Prevention Act of 1990, Congress
established a national policy that:
• Pollution should be prevented or reduced at
the source whenever feasible;
• Pollution that cannot be prevented should be
recycled in an environmentally safe manner
whenever feasible;
• Pollution that cannot be prevented or recycled
should be treated in an environmentally safe
manner whenever feasible; and
• Disposal or other release into the
environmental should be employed as a
last resort and should be conducted in an
environmentally safe manner.
EPA typically designs its voluntary programs
through regular consultation (but little direct
negotiation) with affected industries or
consumers.40 In many cases, voluntary programs
facilitate problem solving between EPA and
industry because information on procedures or
practices that reduce or eliminate the generation
of pollutants and waste at the source are shared
through the consultative process.
In slightly more than a decade, voluntary programs
at EPA have increased from two programs to
approximately 40 programs involving more than
13,000 organizations. Partner organizations
include small and large businesses, citizen groups,
state and local governments, universities, and
trade associations.41 Voluntary programs in which
these groups participate tend to have either broad
environmental objectives targeting a variety of
firms from different industries, or focus on more
specific environmental problems relevant to a
single industrial sector. In the United States, nearly
40 Because these programs are voluntary there is no need for formal
public comment. However, industry often is consulted during the
design phase.
41 For information on EPA's voluntary programs, see the Partners for
the Environment List of Programs at http://www.epa.gov/partners/
programs/index.htm (accessed November 03,2010) (U.S. EPA 2008e).
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
one third of all multi-sector federal voluntary
programs focus on energy efficiency and climate
change issues. General pollution prevention efforts
represent the next most popular type of voluntary
program. Single-sector federal voluntary programs
tend to target environmental problems associated
with transportation-related issues and energy
producing sectors such as coal mining and power
generation. These programs strive to provide
participating firms with targeted and effective
technological expertise and assistance.42
4.6,1 How Voluntary
Approaches Work
Voluntary programs can use the following four
general methods to achieve environmental
improvements: (1) require firms or facilities to set
specific environmental goals; (2) promote firm
environmental awareness and encourage process
change; (3) publicly recognize firm participation;
and (4) use labeling to identify environmentally
responsible products. These methods are not
mutually exclusive, and most U.S. voluntary
programs use a combination of methods.
Goal setting is a very common method used in the
design of voluntary programs. Implementation-
based goals are typically EPA-specified, program-
wide targets designed to provide a consistent
objective across firms. Target-based goals are
usually qualitative and process-oriented so that
firms may individually set a unique target. EPA's
WasteWise and Climate Challenge programs are
examples of programs with target-based goals.
EPA's 33/50 program, which set a goal of a 33
percent reduction of toxic emissions by firms in
the chemical industry by 1992, and a 50 percent
reduction by 1995 (relative to a 1988 baseline),
is an example of a voluntary program with an
implementation-based goal.
Programs designed to promote environmental
awareness and to encourage process change within
firms often involve implementing a system to
42 See Khanna (2001); OECD (1999, 2003); U.S. EPA (2002a); and
Brouhle, Griffiths, and Woiverton (2005) for discussions of how
voluntary programs work and how they are used in U.S. environmental
policy making.
evaluate firms' ongoing operations and to provide
information on newly available technologies.
Examples of this type of approach include the
SmartWay program, which encourages firms to
adopt energy efficient changes that also yield
fuel savings for freight trucking companies, and
the Green Suppliers Network program, which
provides partner firms with technical reviews
and suggestions on how to eliminate waste from
production processes.
Voluntary programs that publicly recognize
firm participation are designed to provide
green consumers and investors with new
information that may alter their consumption
and investment patterns in favor of cleaner
firms. Firms may also use their environmental
achievements to differentiate their products
from competitors' products.43 These
information and firm differentiation effects are
the intent of the Green Power Partnership and
the WasteWise program.
Finally, product labeling can be applied to either
intermediate inputs in a production process or
to a final good. Labels on intermediate goods
encourage firms to purchase environmentally
responsible inputs. Labels on final goods allow
consumers to identify goods produced using a
relatively clean production process. For example,
products deemed by EPA to be energy efficient
may be eligible for the Energy Star or Design for
the Environment labels.
4.S.2 Economic Evaluation of
Voluntary Approaches
A formal economic analysis is not required for the
selection and implementation of a non-regulatory
or voluntary approach to pollution reduction.
Several factors contribute to the difficulty of
evaluating voluntary approaches. Many programs
target general environmental objectives and thus
43 See Aroraand Cason (1995); Arora and Gangopadhyay (1995); Konar
and Cohen (1997,2001); Videras and Alberini (2000); Brouhle,
Griffiths, and Woiverton (2005); and Morgenstern and Pizer (2007) for
more information on the main arguments for why firms participate in
voluntary programs.
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Text Box 4,3 - Water Quality- Trading of Nonpoirit Sources
In 2003, EPA issued a "Water Quality Trading Policy" (U.S. EPA 2003d) that encourages states and tribes to develop
and implement voluntary water-quality trading to control nutrients and sediments in areas where it is possible to
achieve these reductions at lower costs. Under the Clean Water Act, EPA is required to establish Total Maximum
Daily Loadings (TMDL) of pollutants for impaired water bodies. The TMDL does not establish an aggregate cap
on discharges to the watershed, but it does provide a method for allocating pollutant discharges among point and
nonpoint sources. Point sources are regulated by EPA and, as such, are required to hold National Pollutant Discharge
Elimination System (NPDES) permits that limit discharges. However, many water bodies are still threatened by
pollution from unregulated, nonpoint sources. Nutrients and sediment from urban and agricultural runoff have led
to water quality problems that limit recreational uses of rivers, lakes, and streams; that create hypoxia in the Gulf of
Mexico; and that decrease fish populations in the Chesapeake Bay. The impetus for allowing effluent trading between
point and nonpoint sources is to lower nutrient and sediment loadings and to improve or preserve water quality.
To ensure that the reduction resulting from the trade has the same effect on the water quality as the reduction that
would be required without the trade, trading ratios are often applied. These ratios attempt to control for the differential
effects resulting from a variety of factors, which may include:
¦ location of the sources in the watershed relative to the downstream area of concern;
¦¦ distance between the permit buyer and seller;
-¦ uncertainty about nonpoint source reductions;
¦ equivalency of different forms of the same pollutant discharged by the trading partners; and
¦- additional water quality improvements above and beyond those required by regulation.
The idea behind trading is to allow point sources to meet the discharge limit at a lower cost. This allows continued
growth and expansion of production, while giving nonpoint sources an incentive to reduce pollution through
participation in the market. To the extent that it is cheaper for a nonpoint source to reduce pollution than to forgo
revenues earned from the sale of any unused credits to point sources, the nonpoint source is predicted to choose to
emit less pollution.
As of March 2007, 98 NPDES permits, covering 363 dischargers, included provisions for trading. However, only
about a third of the dischargers had carried out one or more trades under these permits (U.S. EPA 2007f). Trading
has been limited for several reasons. First, there is no aggregate "cap" on discharges that applies to both point
and nonpoint sources within a watershed. Reductions by nonpoint sources are essentially voluntary. Point-source
dischargers often explore trading as a way to expand production while meeting the requirements of their individual
permits, but there is no general signal in the market to do so. Second, these are often thin markets. The way in which
the market is designed or trading ratios are established can make it difficult or expensive for an entity to identify and
complete a trade. Third, while Best Management Practices (BMPs) are typically used to define a pollution reduction
credit from a nonpoint source, uncertain or changing climatic conditions, river flow, and stream conditions make
it difficult to measure the effect of a BMP on water quality. Such uncertainty also makes measuring and enforcing a
pollution reduction from a nonpoint source difficult. Fourth, encouraging nonpoint source involvement in trading,
given the agriculture industry's distrust of regulators, is challenging. Finally, it is difficult to define appropriate trading
ratios between point and nonpoint sources.
lack a measurable environmental outcome. Even
if a measurable output exists, there may be a lack
of data on a firm's or industry's environmental
outputs. In order to perform an evaluation,
a reasonable baseline from which to make a
comparison must be established. This requires
an extensive analysis comparing the actions of
participants to non-participants in the program;
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
such data is likely difficult and costly to obtain.44
Any economic evaluation of voluntary programs
should net out pollution abatement activities
that would have occurred even if the voluntary
program were not in place. Some of these
evaluation obstacles can be overcome if voluntary
approaches use more defined and detailed goal
setting and require more complete data collection
and reporting from the outset.45
The economic literature evaluating the efficacy
of voluntary programs is decidedly mixed. The
vast majority of existing empirical studies focus
on a few large, multi-sector voluntary programs
such as 33/50, Green Lights, and Energy Star.
For these programs, there is some evidence
of success in reducing participant emissions.
However, studies generally fail to account for
non-program factors such as the ability to count
reductions that occurred prior to the start of
the program; to compare reductions relative to
a baseline counterfactual may overstate these
reductions. Researchers have been less successful
in demonstrating that voluntary programs have
led to greater emission reductions than would
have occurred without the program in place. One
thread of literature points to the positive impact
of a regulatory threat on voluntary program
effectiveness. When the threat of regulation is
weak, abatement levels are likely to be lower.
However, when the threat of regulation is strong,
levels achieved are closer to those under optimal
regulatory action.
4,7 Measuring the
t r .1 verier. . f f -Matory or
Nor i .V "'V r< a proaches
Several policy criteria should be considered
when evaluating the success of regulatory or non-
regulatory approaches. These include environmental
effectiveness; economic efficiency; savings in
administrative, monitoring and enforcement
costs; inducement of innovation; and increased
44 See Chapter 5 for a discussion of baselines and specifically Section
5.7 for a discussion of behavioral responses.
45 SeeSegerson and Miceli (1998); Khannaand Damon (1999); National
Research Council (2002); Segerson and Wu (2006); Morgenstern and
Pizer (2007); and Brouhle, Griffiths, and Wolverton (2009).
environmental awareness. In many cases, analysis
of these factors will make evident the particular
advantages of one or more market-based incentive
approaches over command-and-control regulation.
While a formal analysis may not be required when
considering the implementation of a non-regulatory
approach, these factors are still important to
consider. According to recent reviews (Harrington
et al. 2004, and Goulder and Parry 2008) it is
unlikely that any one policy will dominate on all of
these factors. However, in many areas an incentive
policy, if available, can be more cost-effective than a
competing command-and-control policy.
In determining the effectiveness of a policy
approach, policy makers should consider the
following factors and questions:
• Environmental Effectiveness: Does the
policy instrument accomplish a measurable
environmental goal? Does the policy
instrument result in general environmental
improvements or emission reductions? Does
the approach induce firms to reduce emissions
by greater amounts than they would have in
the absence of the policy?
• Economic Efficiency: How closely does
the approach approximate the most efficient
outcome ? Does the policy instrument reach
the environmental goal at the lowest possible
cost to firms and consumers ?
• Reductions in Administrative, Monitoring,
and Enforcement Costs: Does the
government benefit from reductions in costs ?
How large are these cost savings compared to
those afforded by other forms of regulation?
• Environmental Awareness and Attitudinal
Changes: In the course of meeting particular
goals, are firms educating themselves on the
nature of the environmental problem and
ways in which it can be mitigated? Does
the promotion of firm participation or
compliance affect consumers' environmental
awareness or priorities and result in a demand
for greater emissions reductions ?
• Inducement of Innovation: Does the
policy instrument lead to innovation in
Guidelines for Preparing Economic Analyses I December 2010 4-2'
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control
abatement techniques that decrease the cost
of compliance with environmental regulations
over time ?
To address a number of these key evaluation
criteria, Guidelines Chapters 8 and 9 offer
instruction on how to measure social costs and
how to address equity issues, respectively
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Chapter 5
Baseline
The baseline of an economic analysis is a reference point that reflects the world
without the proposed regulation. It is the starting point for conducting an
economic analysis of the potential benefits and costs of a proposed regulation.
Because an economic analysis considers the impact of a policy or regulation
in relation to this baseline, its specification can have a profound influence on
the outcome of the economic analysis. A careful and correct baseline specification assures the
accuracy of benefit and cost estimates. The baseline specification can vary in terms of sources
analyzed (e.g., facilities, industries, sectors of the economy), geographic resolution (e.g.,
census blocks, GIS grid cells, counties, state, regions), environmental objectives (e.g., effluents
and emissions versus pollutant concentrations), and years covered. Because the level of detail
presented in the baseline specification is an important determinant of the kinds of analysis
that can be conducted on proposed regulatory options, careful thought in specifying the
baseline is crucial.
The drive for a thorough, rigorous baseline analysis should be balanced against other
competing objectives such as judicial and statutory deadlines, and legal requirements. The
analyst is responsible for raising questions about baseline definitions early in the regulatory
development process to ensure that the analysis is as comprehensive as possible. Doing so will
facilitate analysis of regulatory changes to the baseline regulation.
seline Definition
A baseline is defined as the best assessment of the
world absent the proposed regulation or policy
action.1 This "no action" baseline is modeled
assuming no change in the regulatory program
under consideration. This does not necessarily
mean that no change in current conditions will
take place, since the economy will change even in
the absence of regulation. A proper baseline should
incorporate assumptions about exogenous changes
in the economy that may affect relevant benefits
and costs (e.g., changes in demographics, economic
activity, consumer preferences, and technology),
industry compliance rates, other regulations
promulgated by EPA or other government entities,
1 A policy action includes both regulations and the issuance of Best
Management Practices (BMPs) or guidance documents, which do not carry
the same force as a regulation, but do affect the decisions of firms and
consumers.
and behavioral responses to the proposed rule by
firms and the public.
On occasion a regulatory program may be set to
expire or dramatically change, even in the absence
of the proposed action. In this case, the baseline
specification might consider a state of the world
different from current conditions. This situation,
however, is less common.
The baseline serves as a primary point of comparison
for an analysis of a proposed policy action. An
economic analysis of a policy or regulation
compares the current state of the world, the baseline
scenario, to the expected state of the world with the
proposed policy or regulation in effect, tht policy
scenario. Economic and other impacts of policies or
regulations are measured as the differences between
these two scenarios.
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Chapter 5 Baseline
In most cases, a single, well-defined description
of the world in the absence of the regulation is
generally all that is needed as a baseline. A single
baseline produces a clear point of comparison with
the policy scenario and allows for an unequivocal
measure of the benefits, costs, and other
consequences of the rule. There are a few cases in
which more than one baseline maybe necessary.
Multiple baseline scenarios are needed, for
example, when it is impossible to make a
reasonable unique description of the world in
the absence of the proposed regulation. For
instance, if the current level of compliance with
existing regulations is not known, then it may
be necessary to compare the policy scenario to
both a full compliance baseline and a partial
compliance baseline. Further, if the impact
of other rules currently under consideration
fundamentally affects the economic analysis of
the rule being analyzed, then multiple scenarios,
with and without these rules in the baseline, may
be necessary.
The decision to include multiple baselines
should not be taken lightly as a complex set of
modeling choices and analytic findings may result.
These must be interpreted and communicated
to decision makers, increasing the possibility
of erroneous comparisons of costs and benefits
across different baselines. "When more than one
baseline is required, analysts should endeavor to
construct scenarios that can provide benchmarks
for policy analysis. The number of baselines
should be limited to as few as possible that cover
the key dimensions of the economic analysis and
any phenomena in the baseline about which there
is uncertainty.
In some cases, probabilistic analysis can be used
to avoid the need for multiple baselines and still
provide an appropriate benchmark for policy
analysis. A probabilistic analysis is a form of
uncertainty analysis in which a single modeling
framework is generally specified, but statistical
distributions are assigned to the uncertain input
parameters. The policy scenario is then compared
to a continuum of baselines, with a probability for
any given outcome, rather than being compared to
a single baseline. The benefit-cost analysis (BCA)
would then report the probability that a policy
intervention produces net benefits rather than
reporting the net benefits compared to one (or
more) deterministic baseline(s).
Analysts are advised to seek clear direction from
management about baseline definitions early on in
the development of a rule. Each baseline-to-policy
comparison should be internally consistent in its
definition and use of baseline assumptions.
iding Principles of
Baseli ification
In specifying the baseline, analysts should employ
the following guiding principles each of which is
discussed more fully below:
1. Clearly specify the current and future
state of relevant economic variables, the
environmental problem that the regulation
addresses and the regulatory approach being
considered;
2. Identify all required parameters for the
analysis;
3. Determine the appropriate level of effort for
baseline specification;
4. Clearly identify all assumptions made in
specifying the baseline conditions;
5. Specify the "starting point" of the baseline
and policy scenario;
6. Specify the "ending point" of the baseline
and policy scenario;
7. Detail all aspects of the baseline specification
that are uncertain; and
8. Use the baseline assumptions consistently for
all analyses for this regulation.
Though these principles exhibit a general
common-sense approach to baseline specification,
the analyst is advised to provide her own explicit
statements on each point. Failure to do so may
result in a confusing presentation, inefficient use
of time and resources, and misinterpretation of the
economic results.
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Chapter 5 Baseline
Clearly specify the current and future state of
relevant economic variables, the environmental
problem that the regulation addresses and the
regulatory approach being considered. A clear
written statement about the current state of the
relevant economic variables (see Chapter 8 in
particular to determine what variables are relevant)
and environment will help decision makers and
the general public to understand both the positive
and negative consequences of a regulation. The
statement should include a description of: (1) the
pollution problem being addressed; (2) the current
regulatory environment; (3) the method by which
the problem will be addressed; and (4) the affected
parties. There should also be a discussion of why a
particular approach [e.g., best available technology
(BAT), performance measures, market incentives,
or non-regulatory approaches] was chosen.
In general, the most appropriate baseline will
be the "no change" or "reality in the absence
of the regulation" scenario; but in some cases,
a baseline of some other regulatory approach
may be considered. For example, if an industry
is certain to be regulated (e.g., by court order
or congressional mandate) but that regulation
has not yet been implemented, then a baseline
including this regulation should be used. To ensure
that provisions contained in statutes or policies
preceding the regulatory action in question
are appropriately addressed and measured, it is
common practice to assume full compliance with
regulatory requirements, although sensitivity
analyses assuming less-than-full compliance may
be considered. However, analysts should consult
with their management and the Office of General
Counsel (OGC) before doing so.
Identify all required parameters for the
analysis. To ensure that the baseline scenario
can be compared to the policy scenario, there
should be a clear understanding of the path from
environmental damage to adverse impact on
humans. The models and parameters required for
the baseline analysis should be chosen so that the
baseline assumptions can feed into all subsequent
analyses. Measured differences between the
baseline and policy scenario can include changes in
usage or production of toxic substances, changes in
pollutant emissions and ambient concentrations,
and incidence rates for adverse health effects
associated with exposure to pollutants. This
does not mean that the analyst must identify all
parameters that could possibly change, but the
analyst should recognize all relevant parameters
needed to compare the baseline scenario to the
policy scenario. As a general rule of thumb, at
a minimum, the analyst should identify the
parameters that are expected to vary by option, the
parameters that are expected to have the largest
impact on cost and benefit differences, and the
parameters that are anticipated to come under
close public scrutiny.
Determine the appropriate level of effort
for baseline specification. The analyst
should concentrate analytic efforts on those
components (e.g., assumptions, data, models)
of the baseline that are most important to the
analysis, taking into consideration factors such
as the time given to complete the analysis, the
person-hours available, the cost of the analysis,
and the available models and data. If several
components of the baseline are uncertain, the
analyst should concentrate limited resources on
refining the estimates of those components that
have the greatest effect on the interpretation of
the results. Analysts should pay special attention
to the components that will be used to calculate
costs and benefits and those that are important
determinants of the policy option selected.
Clearly identify all assumptions made in
specifying the baseline conditions. Whether
variables are modeled or set by fixed assumptions,
the analyst should explain the assumptions and
uncertainties about the parameters in detail.
Assumptions should include changes in behavior
and business trends, and how these trends may
be affected by regulatory management options.
Analysts may observe trends in economic activity
or pollution control technologies that occur
for reasons other than direct environmental
regulations. For example, as the purchasing
power of consumer income increases over time,
demand for different commodities may change.
Demand for some commodities may grow at rates
faster than the rate of change in income, while
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Chapter 5 Baseline
demand for other goods may decrease. Where
these trends are highly uncertain or are expected
to have significant influence on the evaluation of
regulatory alternatives (including a "no-regulatory
control" alternative), the analyst should clearly
explain and identify the assumptions used in
the analysis, with the goal of laying out the
assumptions clearly enough so that other analysts
(with access to the appropriate models) would be
able to replicate the baseline specification.
Specify the "starting point" of the baseline and
policy scenario. A starting point of an analysis
is the point in time at which the comparison
between the baseline and policy scenarios begins.
This is conceptually the point in time at which the
two scenarios diverge. For example, one approach
is to organize the analysis assuming that the policy
scenario conditions diverge from those in the
baseline at the time an enforceable requirement
becomes effective. Another convenient approach
is to set the starting point as the promulgation
of the final rule. These dates may be appropriate
to use because they are clearly defined under
administrative procedures or because they
represent specific deadlines.
However, where behavioral changes are motivated
by the expected outcome of the regulatory process,
the actual timing of the formal issuance of an
enforceable requirement may not be the most
appropriate starting point to define differences
between the baseline and policy scenarios. Earlier
starting points, such as the date when authorizing
legislation was signed into law, the date the
rule was first published in a Notice of Proposed
Rulemaking, or other regulatory development
process milestones, may be justified when
divergence from the baseline occurs due to the
anticipation of promulgation.
Specify the "ending point" of the baseline and
policy scenario. The ending point of an analysis
is the point in time at which the comparison
between the baseline and policy scenarios ends.
Generally, the duration of important effects of
a policy determines the period chosen for the
analysis and baseline. However, other analytical
considerations, such as the relative uncertainty
in projecting out-year conditions, may also need
to be weighed. To compare the benefits and costs
of a proposed policy, the analyst should estimate
the present discounted values of the total costs
and benefits attributable to the policy over the
period of the study. How one defines the ending
point of the baseline is particularly important
in situations where the accrual of costs and/or
benefits do not coincide due to lagged effects,
or where they occur over an extended period of
time. For example, the human health benefits
of a policy that reduces leachate from landfills
may not manifest themselves for many years if
groundwater contamination occurs decades after
closure of a landfill. In theory, the longer the time
frame, the more likely the analysis will capture
all of the major benefits and costs of the policy.
Naturally, the forecasts of economic, demographic,
and technological trends that are necessary for
baseline specification should also span the entire
period of the analysis. However, because forecasts
of the distant future are less reliable than forecasts
of the near future, the analyst should balance the
advantages of structuring the analysis to include a
longer time span against the disadvantages of the
decreasing reliability of the forecasts for the future.
In some cases, the benefits of a policy are expected
to increase over time. "When this occurs, analysts
should extend the analysis far enough into the
future to ensure that benefits are not substantially
underestimated. For example, suppose a proposed
policy would greatly reduce greenhouse gas
(GHG) emissions. In the baseline scenario, the
level of GHG in the atmosphere would steadily
increase over time, with a corresponding increase
in expected impacts on human health and welfare
and ecological outcomes. A BCA limited to the
first decade after policy initiation would likely
distort the relationship of benefits and costs
associated with the policy. In this case, the conflict
between the need to consider a long time frame
and the decreasing reliability of forecasting far
into the future may be substantial. In most cases,
primary considerations in determining the time
horizon of the analysis will be the time span of the
physical effects that drive the benefits estimates
and capital investment cycles associated with
environmental expenditures.
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Chapter 5 Baseline
In some circumstances, it may make sense to model
the annual flow of benefits and costs rather than
model them over time. For example, if the benefits
and costs remain constant (in real terms) over
time, then an estimate for a single year is all that is
necessary The duration of the policy will not affect
whether there are net benefits nor will it affect the
choice of the most economically efficient option,
although it will obviously still affect the magnitude
of net benefits. In this case, an "ending point" may
not be needed and a present discounted value
of the net benefits may be unnecessary as well.
However, the absence of these values should be
explicit in the analysis. An alternative to providing
no present discounted value is to conduct a single
year estimate of costs and benefits, but calculate a
present discounted value of net benefits assuming
an infinite time period.
Detail all aspects of the baseline specification
that are uncertain. Because the analyst does
not have perfect foresight, the appropriate
baseline conditions cannot be characterized with
certainty. Future values always have some level
of uncertainty associated with them, and current
values often do as well. To the extent possible,
estimates of current values should be based
on actual data, and estimates of future values
should be based on clearly specified models and
assumptions. "Where reliable projections of future
economic activity and demographics are available,
this information should be adequately referenced.
In general, uncertainties underlying the baseline
conditions should be treated in the same way as
other types of uncertainties in the analysis. All
assumptions should be clearly stated and, where
possible, all models should be independently
reproducible.
It is important to detail information that was
not included in the analysis due to scientific
uncertainty. For example, a health or ecological
effect may be related to the regulated pollutant,
but the science behind this connection may be too
uncertain to include the effect in the quantitative
analysis. In this case, the effect should not be
included in the baseline, but a discussion of why the
effect was excluded should be added — especially
if the magnitude is such that it could significandy
affect the net benefit calculation. A similar
recommendation can be made for model choice
or even the choice of parameter values; known
aspects of the analysis, which are not included in
the baseline due to scientific uncertainty, should be
included in the uncertainty section.
Large uncertainty in significant variables may
require the construction of alternative baselines
or policy scenarios. This leads to numerous
complications in policy analysis, especially in cost-
effectiveness analysis (CEA) and the calculation of
net benefits. "While sensitivity analysis is usually a
better choice, multiple scenarios maybe beneficial
in selecting policy options, especially if there is a
significant probability of irreversible consequences
or catastrophic events.
Use the baseline assumptions consistently for
all analyses for this regulation. The models,
assumptions, and estimated parameters used in
the baseline should be carried through for all
components of the analysis. For example, the
calculation of both costs and benefits should
draw upon estimates derived using the same
underlying assumptions of current and future
economic conditions. If the benefits and costs are
derived from two different models, then the initial
baseline conditions of costs and benefits should be
compared to ensure that they are making identical
assumptions. Likewise, when comparing and
ranking alternative regulatory options, comparison
to the same baseline should be used for all options
under consideration.2
In some cases, an analysis may not have been
anticipated during the baseline specification.
For example, a sector might be singled out for
more detailed analysis, or a follow-on analysis
might be needed to assess impacts on a particular
low-income or minority group. In this case, a
complete baseline specification that would make
this secondary analysis fully consistent with the
primary analyses may not be available. Even in
2 In the less common case in which more than one baseline scenario
is modeled, the analyst must avoid the mistake of combining analytic
results obtained from different baseline scenarios. To limit confusion
on this point, if multiple baseline scenarios are included in an analysis,
the presentation of economic information should clearly describe and
refer to the specific baseline scenario being used.
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Chapter 5 Baseline
this case, however, some type of baseline will have
to be produced in order to conduct the analysis.
While it may not be identical to the baseline
used to analyze the benefits and costs, the analyst
should endeavor to make it as similar as possible.
The analyst should explicitly state the differences
between the two baselines or any uncertainty
associated with the secondary baseline.
an rn £ • .?ic Variables
Certain variables are very important for modeling
both the baseline scenario and the policy scenario.
Some of these variables, such as population and
economic activity, are commonly modeled by other
government agencies and are available for use in
economic analyses. The values of these variables
will change over the period of study and, as a result
of the policy, may differ significantly between the
two scenarios. Even when they are the same across
scenarios, these values can have a substantial impact
on the overall benefits and costs and should be
explicitly reported over time. Other variables, such
as consumer spending patterns and technological
growth in an industry, are also important for
modeling, but are more difficult to estimate. In
these cases, the analyst should specify the variable
levels and report whether these variables changed
during the period of the study. When they are
assumed to change, both over time and between
scenarios, the analyst should explicitly state the
assumptions of how and why they change.
5.3.1 Demographic Change
Changes in the size and distribution of the
population can affect the impact of EPA programs
and, as a consequence, can be important in
economic analyses. For example, risk assessments
of air toxics standards require assumptions about
the number of individuals exposed. Therefore,
assumptions about future population distributions
are important for measuring potential future
incidence reductions and for estimating the
maximum individual risk or exposures. Another
example is when population growth affects the
level of vehicle emissions due to an increased
number of cars and greater highway congestion.
For most analyses, U.S. Census Bureau projections
of future population growth and distribution
can be used. In some cases, however, behavioral
models may be required if the population growth
or distribution changes as a consequence of the
regulation. For example, demographic trends
in an area may change as a result of cleaning up
hazardous waste sites. EPA analyses should reflect
the consequences of population growth and
migration, especially if these factors influence the
regulatory costs and benefits.
5.3.2 Futu onomic Activity
Future economic activity can have a significant
effect on regulatory costs and benefits because it
is correlated with emissions and, in some cases,
can influence the feasibility or cost-effectiveness
of particular control strategies. Even small changes
in the rate of economic growth may, over time,
result in considerable differences in emissions
and control costs. Assuming no change in the
economic activity of the regulated sector, or in
the nation as a whole, will likely lead to incorrect
results. For example, if the regulated industry is in
significant decline, or is rapidly moving overseas,
this information should be accounted for in the
baseline. In such a case, incremental costs to the
regulated community (and corresponding benefits
from the regulation) are likely to be less than if the
targeted industry were growing.
Official government estimates of future economic
growth are the most appropriate values to use. In
many cases, however, the future economic activity
of the particular sectors under regulation will
have to be modeled. In both cases, the models and
assumptions used should be made as explicit as
possible. When economic growth is a significant
determinant of the relative merits of regulatory
alternatives or when there are significant
differences between official and private growth
estimates, then sensitivity analyses using alternative
growth estimates should be included.
5.3.3 Changes in
Consumer Behavior
The bundle of economic goods purchased by
consumers can affect the benefits and costs of a
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Chapter 5 Baseline
rule. An increase in the price and decrease in the
quantity of goods from the regulated sector should
be included as part of the cost of the regulation.
Likewise, a reduction in the number of goods (e.g.,
bottled water) that were previously purchased
to reduce health effects caused by the regulated
pollutant will result in economic benefits to
the public. Thus, changes in consumer behavior
are important in the overall economic analysis.
Changes in consumer purchasing behavior should
be supported by estimates of demand, cross-
price, and income elasticities allowing changes in
consumer behavior to be estimated over time and
for the baseline and policy scenarios.3
One controversial extension involves the income
elasticity for environmental protection. There is
some evidence that the demand for environmental
quality rises with income (Baumol and Oates
1988). However, this does not necessarily
justify adjusting the benefit of environmental
improvements upward as income rises. This
is because the willingness to pay (WTP) for a
marginal improvement in the environmental
amenity, the appropriate measure of the benefits
of environmental protection, may not necessarily
have a positive income elasticity (Flores and
Carson 1997). It is appropriate to account for
income growth over time where there are empirical
estimates of income elasticity for a particular
commodity associated with environmental
improvements (e.g., for reduced mortality risk).
In the absence of specific estimates, it would
be appropriate to acknowledge and explain the
potential increase in demand for environmental
amenities, as incomes rise.
5.3=4 Technological Change
Future changes in production techniques
or pollution control may influence both the
baseline and the costs and benefits of regulatory
alternatives. Estimating the future technological
3 Demand elasticities show how the quantity of a product purchased
changes as its prices changes, all else equal. Cross-price elasticities
show how a change in the price of one good can result in a change in
the price of another good (either a substitute or a complement), thereby
altering the quantity purchased. Income elasticity allows a modeler
to forecast how much more of a good consumers will buy when their
income increases. See Appendix A for more information on elasticity.
change is quite difficult and often controversial.
Technological change can be thought of as having
at least two components: true technological
innovation, such as a new pollution control
method; and learning effects, in which experience
leads to cost savings through improvements in
operations, experience, or similar factors. It is not
advisable to assume a constant, generic rate of
technological progress, even if the rate is small,
simply because the continuous compounding of
this rate over time can lead to implausible rates of
technological innovation. However, in some cases
learning effects may be included in analyses.
Undiscovered technological innovation is often
considered to be one reason why regulatory costs
are overstated (Harrington et al. 1999). Because
of the difficulty and controversy associated with
estimating technological change in an economic
analysis, analysts should be careful to avoid
the perception of bias when introducing it. If
technological change is introduced in the cost
analysis, then it should be introduced in the
benefits analysis as well. While technological
innovation in the regulated sector can reduce
the cost of compliance, technological innovation
in other sectors can reduce the benefits of the
regulation. For example, the cost of controlling
CFCs has declined over time due to technological
improvements. However, innovation in mitigating
factors, such as improvements in skin cancer
treatments and efficacy of sunscreen lotions — both
of which decrease the benefits of the regulation
— have also occurred. Further, the analysis
should include the costs associated with research
and development (R&D) for the innovations
to correctly value cost-reducing technological
innovation, but only if the costs are policy-induced
and do not arise from planned R&D budgets. This
distinction is sometimes difficult to make.
If technological innovation is included in the
policy scenario, then it should be included in
the baseline as well (see Text Box 5.1). While
accepting that innovation will occur in the baseline
and policy scenarios, rates across scenarios may
differ because regulation may cause firms to
innovate more to reduce the cost of compliance.
In cases where small changes in technology could
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 5 Baseline
Tcvi E)r. » i f I iinlnnlral Phanna lr»rli i/>orl Innnuatinn anrl tho
There are many proposed mechanisms by which environmental regulation could cause technological change. One
mechanism is by induced innovation: the induced innovation hypothesis states that as the relative prices of factors
of production change, the relative rate of innovation for the more expensive factor will also increase. This idea is well
accepted; for example, Newell etal. (1999) found that a considerable amount of the increase in energy efficiency over
the last few decades has been caused by the increase in the relative price of energy over that time.
A similar idea has also been described (somewhat less formally) as the "Porter Hypothesis" (Porter and van der Linde
1995, and Heyes and Liston-Heyes 1999). Jaffe and Palmer (1997) delineate three versions of the hypothesis: weak,
narrow, and strong.
The weak version of the hypothesis assumes that an environmental regulation will stimulate innovation but it does
not predict the magnitude of these innovations or the resulting cost savings. This version of the hypothesis is very
similar to the induced innovation hypothesis. The narrow version of the hypothesis predicts that flexible regulation
(e.g., incentive-based) will induce more innovation than inflexible regulation and vice versa. There is empirical
evidence that this is the case (Kerr and Newell 2003, and Popp 2003). Analysts may be able to estimate the rate of
change of innovation under the weak or narrow version of the hypothesis, or under induced innovation. However, this
innovation may crowd out other forms of innovation.
The strong version predicts cost savings from environmental regulation under the assumption that firms do not
maximize cost saving without pressure to do so. While anecdotal evidence of this phenomenon may exist, the
available economic literature has found no statistical evidence supporting it as a general claim (Jaffe et al. 1995:
Palmer, Gates, and Portney 1995; Jaffe and Palmer 1997; and Brannlund and Lundgren 2009). The strong version
of the Porter Hypothesis may be true in some cases, but it requires special assumptions and an environmental
regulation combined with other market imperfections (such as bounded rationality) that are difficult to generalize.
Analysts should not assume cost savings from a regulation based on the strong version of the Porter Hypothesis.
dramatically affect the costs and benefits, or where
technological change is reasonably anticipated, the
analyst should consider exploring these effects in a
sensitivity analysis. This might include probabilities
associated with specific technological changes or
adoption rates of a new technology, or it may be
an analysis of the rate required to alter the policy
decision. Such an analysis should show the policy
significance of emerging technologies that have
already been accepted, or are, at a minimum, in
development or reasonably anticipated.
In some cases it maybe possible to make the case that
learning effects will lead to lower costs over time.4
Estimated rates of learning effects often indicate
that costs decline by approximately 5 percent
to 10 percent for every doubling of cumulative
4 See U.S. EPA (1997b, 2007b).
production. If learning effects are to be included in
an analysis, the analyst should carefully examine the
existing data for relevance to the problem at hand.
Estimated learning effects can vary according to
many factors, including across industries and by the
length of the time period considered. Also, because
estimates of learning effects are based on doubling of
cumulative production, inclusion of learning effects
will have a greater influence on rules with longer
time periods and may have litde effect on rules with
short time periods.
ince Rates
One aspect of baseline specification that is
particularly complex, and for which assumptions
are typically necessary, is the setting of compliance
rates. The treatment of compliance in the baseline
scenario can significantly affect the results of the
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Chapter 5 Baseline
analysis. It is important to separate the changes
associated with a new regulation from actions
taken to meet existing requirements. If a proposed
regulation is expected to increase compliance with
a previous rule, the correct measure of the costs
and benefits generally excludes impacts associated
with the increased compliance.5 This is because
the costs and benefits of the previous rule were
presumably estimated in the economic analysis
for that rule, and should not be counted again for
the proposed rule. This is of particular importance
if compliance and enforcement actions taken to
meet existing requirements are coincident with,
but not caused by, changes introduced by the new
regulation.
Assumptions about compliance behavior for
current and new requirements should be clearly
presented in the description of the analytic
approach used for the analysis. When comparing
regulatory options on the basis of their social costs
and benefits, the effect of compliance assumptions
on the estimated economic impacts should be
described, along with the sensitivity of the results
to these assumptions.
In most cases, a full compliance scenario should
be analyzed. If a baseline is used that assumes a
scenario other than full compliance, the analyst
should take care to explain the compliance
assumption for the current regulation under
consideration. The Agency is unlikely to propose
a rule that it believes will not be followed, but if
there is widespread non-compliance with previous
rules then this suggests a persistent problem.
ull Compliance
As a general rule, when preparing analyses of
regulations analysts should develop baseline and
policy scenarios that assume full compliance
with existing and newly enacted (but not
yet implemented) regulations. Assuming full
compliance with existing regulations enables the
analysis to focus on the incremental economic
effects of the new rule or policy without double
5 An exception would be if the proposed regulation were designed to
correct the under-compliance from the previous rule. This is discussed
in Section 5.4.2.
counting benefits and costs captured by analyses
performed for other rules.
Assuming full compliance with all previous
regulations when current observed or reported
economic behavior indicate otherwise may pose
some challenges to the analyst. For example,
it is possible to observe over-compliance by
regulated entities with enforceable standards.
One can find industries whose current effluent
discharge concentrations for regulated
pollutants are measured below concentrations
legally required by existing effluent guideline
regulations. On the other hand, evidence for
under-compliance is apparent in the convictions
of violators and negotiated settlements
conducted by EPA.
As a practical matter, before rejecting full
compliance assumptions for existing policies, the
emissions from noncompliant firms should be
known, estimable, and occurring at a rate that can
affect the evaluation of policy options. In some
cases, two baselines may have to be assumed: one
assuming full compliance with existing regulation
and a separate "current practice" baseline. In the
case of a deregulatory rule, which is designed to
address potential changes in or clarify definitions
of regulatory performance that frees entities from
enforceable requirements contained in an existing
rule, it may make sense to perform the analysis
using both baselines. A full-compliance scenario in
this instance introduces some added complications
to the analysis, but it may be important to report
on the economic effects of failing to take the
deregulatory action.
5.4.2 Under-Compliance
When compliance issues are important and there is
sufficient monitoring data to support the analysis,
a "current practice" baseline can be used. A
"current practice" baseline is established using the
actual degree of compliance rather than assumed
full compliance. Current practice baselines are
useful for actions intended to address or "fix-up"
compliance problems associated with existing
policies. In these cases, assuming a full-compliance
baseline that disregards under-compliant behavior
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Chapter 5 Baseline
could obscure the value of investigating additional
or alternative regulatory actions. This was the
case in a review of the banning of lead from
gasoline, which was precipitated, in part, by the
noncompliance of consumers who put leaded
gasoline in vehicles that required non-leaded fuel
to protect their catalytic converters, resulting in
increased vehicle emissions (U.S. EPA 1985).
If under-compliance is assumed in the baseline,
then the nature of that non-compliance becomes
important. For example, in a case where under-
compliance occurs uniformly (or at random)
across an industry, then changing the compliance
rate assumption will not affect the benefit-cost
ratio nor the sign of net benefits, assuming
the effect on ambient concentrations is also
uniform (or random), although it will affect
the magnitude of net benefits. In other words, a
proposed regulation that can be justified from a
net benefit perspective under full compliance can
also be justified under any baseline compliance
rate. However, if non-compliance with previous
regulation occurs selectively when compliance
costs are high, then the benefit-cost ratio will
decline as higher rates of compliance are assumed,
and net benefits could potentially switch from
positive to negative for a proposed regulation. This
occurs because the cost per unit of benefit will
continue to increase as full compliance is reached.
Analysts may elect to incorporate predicted
differences in compliance rates within policy
options in cases where compliance behavior is
known to vary systematically.
While a baseline assuming under-compliance
can be useful in some cases, it should be executed
carefully or the issue should be examined with a
sensitivity analysis. A partial compliance baseline
has the potential for double counting both benefits
and costs. A sequence of emissions tightening rules
could be justified by repeatedly factoring under-
compliance into the baseline, while assuming that
entities will fully comply with the new rule under
consideration. Summing the benefits from the
total sequence of rules would overstate benefits
because each rule claims part of the same benefits
each time. Additionally, while the benefits flowing
from previous regulations may not have been
realized due to lack of compliance, the full costs of
their implementation may not have been realized
either. The additional costs associated with coming
into compliance should also be included to avoid
producing inflated net benefits. In the case where
an under-compliance baseline (or sensitivity
analysis) is justified, care should be taken to
explain these potential biases.
5.4,3 Over-Compliance
Over-compliance may occur due to risk aversion,
technological lumpiness, uncertainty in pollution
levels, or other behavioral responses. Here the benefits
(and potentially the costs) of the previous regulation
have been understated rather than overstated. In
this case, as with under-compliance, true societal net
benefits of a regulation will not be calculated correcdy
under an assumption of full compliance.
In cases of over-compliance with existing policies,
current practices can be used to define baseline
conditions unless these practices are expected to
change. For example, over-compliance may be
the result of choices made in anticipation of more
stringent regulations. If these stringent regulations
are not implemented, the analyst will need to
establish whether over-compliance will be reduced
to meet the relatively less stringent requirements.
If the regulated entities are expected to continue
to over-comply despite the absence of the more
stringent regulation, then the costs and benefits
attributable to this behavior are not related to the
policy under consideration. In this case, it would
be appropriate to account for the over-compliance
in the baseline scenario that describes the "world
without the regulation." However, if the regulated
entities are expected to relax their pollution
control practices to meet relatively less stringent
requirements, then the costs and benefits of the
over-compliance behavior should be attributed
to the new policy scenario, and over-compliance
should not be included in the baseline. In these
situations, it maybe useful to consider performing
a sensitivity analysis to demonstrate the potential
economic consequences of different assumptions
associated with the expected changes in behavior.
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5,5! ale Rules
Although regulations that have been finalized
clearly belong in the baseline of a proposed rule,
the baseline specification maybe complicated if
other regulations in addition to the one being
implemented are under consideration or nearing
completion. In this case it becomes difficult to
determine which regulations are responsible for
the environmental improvements and can "take
credit" for reductions in risks. It is also necessary
to determine how these other regulations affect
market conditions that directly influence the costs
or the benefits associated with the policy of interest.
This is true not only for multiple rules promulgated
by EPA, but also for rules passed by other federal,
state, and local agencies. In addition to agencies that
regulate environmental behavior, other agencies
that regulate consumer and industrial behavior [e.g.,
Occupational Safety and Health Administration
(OSHA), Department of Transportation (DOT),
and Department of Energy (DOE)] develop rules
that may overlap with upcoming EPA regulations.
Even tht potential implementation of another such
rule may affect the benefits and costs of an EPA
regulation being analyzed, due to the strategic
behavior of regulated entities. Therefore, it is
important to consider the impact of other rules
when establishing a baseline. If another federal,
state, or local agency is legally required to impose
a regulation but is still in the process of finalizing
that regulation, then a baseline which includes this
impending regulation should be considered. The
intent of the baseline is always to characterize the
world in the absence of regulation being analyzed.
5.5.1 Link lies
In some cases it is possible to consider multiple
rules together as a set. For example, some regulatory
actions have linked together rules that affect the
same industrial category. This was true of the
pulp and paper effluent guidelines and National
Emissions Standards for Hazardous Air Pollutants
(NESHAP) rules (U.S. EPA 1997c). In other
cases, multiple rules may not necessarily be a set of
similar policies associated with the same industry,
but rather are a set of different policies that are all
necessary to achieve a policy objective. For example,
EPA may issue effluent limitation guidelines
(ELG) to provide technical requirements for a
type of pollution discharge, and may then issue
a complementary National Pollution Discharge
Elimination System (NPDES) rule, providing
details of the permitting system. Since ELG and
NPDES work together to achieve one objective it
would not make sense to analyze them separately.
The optimal solution in both of the cases described
above is to include all of the rules in the same
economic analysis. In this case, the multiple rules
are analyzed as if they were one rule and the
baseline specification simplifies to one with none
of the rules included. While statutory requirements
and judicial deadlines can inhibit promulgating
multiple rules as one, coordination between
rulemaking groups is still possible. The sharing
of data, models, and joint decisions on analytic
approaches may make a unified baseline possible so
that the total costs and benefits resulting from the
package of policies can be assessed.
5.5.2 Unlinked Rules
In some cases, it is simply not feasible to analyze a
collection of overlapping rules together in a single
economic analysis with a single baseline. This may
be true for rules originating from different program
offices or different regulatory agencies, or when
the timing of the various rules is not clear. In this
case, each rule should be analyzed separately with
its own baseline, but the order in which the rules
are analyzed may have a substantial effect on the
outcome of a BCA. For example, in 2005, EPA
promulgated both the Clean Air Interstate Rule
(CAIR) and the Clean Air Mercury Rule (CAMR)
to reduce pollution from coal fired power plants.
While the primary purpose of CAIR was to reduce
sulfur dioxide (SO ) and nitrogen oxides (NO ),
the control technologies necessary to achieve
this also reduced mercury emissions. Because the
CAMR analysis assumed that CAIR had been
implemented and was, therefore, in the baseline,
the estimated incremental reduction in mercury
from CAMR was much smaller than if CAIR
had not been included in the baseline. In a similar
fashion, if some of the costs of fully complying
with the second rule are incurred in the process of
complying with the first rule, then these costs are
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Chapter 5 Baseline
part of the baseline and are not considered as costs
of the second rule. In general, only the incremental
benefits and costs of the second rule should be
included if the first rule is in the baseline.
The practical assumption commonly made when
rules cannot be linked together is to consider the
actual or statutory timing of the promulgation
and/or implementation of the policies, and use
this to establish a sequence with which to analyze
related rules. However, this may not always be
possible. For example, a rule maybe phased in over
time, complicating the analysis of a new rule going
into effect during that same period. In that case, the
baseline for the new rule should include the timing
of each stage of the phased rule and its resulting
environmental, health, and economic changes.
In the absence of some orderly sequence of events
that allows the attribution of changes in behavior
to a unique regulatory source, there is no non-
arbitrary way to allocate the costs and benefits of a
package of overlapping policies to each individual
policy. That is, there is no theoretically correct
order for conducting a sequential analysis of
multiple overlapping policies that are promulgated
simultaneously. The only solution in this case
is to make a reasonable assumption and clearly
explain it, detailing which rules are included in the
baseline (see Text Box 5.2). If the costs and benefits
from these rules are small, then this may be all
that is necessary. It may not be worth additional
time and resources to reconcile the overlapping
rules. On the other hand, for major rules or if
the number of overlapping rules is small, then
a sensitivity analyses can be included to test for
the implications of including or omitting other
regulations. Under this sensitivity analysis, it may
also be possible to use the overlapping nature of the
regulations to allow for some regulatory flexibility
in compliance dates and regulatory requirements.
5.5,3 Indired lated Policies
a ©grams
In some instances, less direcdy related
environmental policies or programs can influence
the baseline. For example, potential changes in
farm subsidy programs may significantly influence
future patterns of pesticide use. In an ideal analysis,
all of the potential direct and indirect influences on
baseline conditions (and on the costs and benefits
of regulatory alternatives) would be examined and
estimated. In other words, this situation can be
handled in the same way as unlinked overlapping
rules described above. Practically speaking, however,
it is up to the analyst to determine if these indirect
influences are important enough to incorporate
into the regulatory analysis. If indirect influences
are known but are not considered to be significant
enough to be included in the quantitative analysis,
they can be discussed qualitatively.
5,6 Partial Benefits .hold
Some benefits only occur after a threshold has
been reached. For example, the benefits associated
with improving a stream to allow for recreational
swimming are realized only when all of the pollutants
have been reduced enough to allow for primary
contact and an enjoyable swimming experience.
Likewise, valued species populations may only
recover when multiple limiting factors are addressed.
However, a particular benefits threshold may not
be met with a single rule. In such cases, associating
the benefits only with the rule that actually passes
the threshold could make it impossible to justify
the incremental progress (via previous rules). It is
generally reasonable to account for the benefits of
making progress toward a goal, even if the threshold
is not met in the rule under consideration.
For example, EPA's Office of Water has calculated
the benefits associated with improving river miles
for various designated uses (e.g., swimming,
fishing, and boating) in a number of rules. In
each case, some river miles were improved for the
designated use, while other miles were improved,
but not enough to change their designated use.
Earlier rules claimed benefits only if a river mile
actually changed its designation, implicitly giving
a value of zero to partially improved river miles.
More recent regulation claims partial benefit for
incremental improvements toward the threshold.
Neither approach is necessarily correct, but
accounting for the benefits of partial gains provides
better information to decision makers and the
public and allows the Agency to justify incremental
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Chapter 5 Baseline
Text Bo* 5.2 - Sequencing Unlinked Rules
It is impossible to identify all of the possible scenarios one might need to consider when determining which rules to
include in a baseline, but a few illustrative cases are provided below.
Including final rules that have not yet taken effect: '.v j. ¦.
promulgated prior to the rule under consideration should be included in the baseline. The costs and benefits of
the regulation under consideration must be evaluated against a baseline that assumes firms will comply with these
promulgated rules. For example, on March 15,2005, EPA issued the Clean Air Mercury Rule (CAMR) to reduce
mercury emissions from coal-fired power plants. Five days earlier, on March 10,2005, EPA finalized the Clean Air
Interstate Rule (CAIR) to reduce sulfur dioxide (SO ) and nitrogen oxides (NO ) emissions from coal-fired power
plants. Because the control technology assumed under CAIR included some mercury reductions, the baseline used
for CAMR included the actions that firms would need to take to comply with CAIR.
Including rules anticipated to occur after a regulation is promulgated but before it takes effect:
This is a more difficult case and only applies to regulations that have a long lag between the date on which they are
issued and the date when they take effect. The longer the difference between these two dates, the more important it is
to include rules that can be expected in the interim. For example, National Ambient Air Quality Standards (NAAQS)
can have a number of years between the date on which a standard is announced and the date on which designations
of attainment or nonattainment are made. In this case, if another rule is imminent and will take effect prior to the
effective date of the new NAAQS, then it should be included in the baseline for the NAAQS. It is important, however,
that the analyst not simply speculate that another rule will be implemented. Any other rule included in the baseline,
other than those already promulgated, should be imminent or reasonably anticipated with a high degree of certainty.
In addition, the analyst should be clear as to what assumptions have been made.
Including state rules that are legally required but not yet implemented: : ¦
case. Actions by state (and even local) governments can affect the costs and benefits of federal rules, particularly if they
are regulating the same sector or pollutant. As with the case above, any state regulation that has been finalized should be
included in the baseline. The more difficult case occurs when the state has a legal obligation to implement a regulation
but either has not done so or is in the process of doing so. In this case, the analyst must use professional judgment to
determine what would happen in the absence of EPA action. If the state would implement the regulation in the absence of
EPA action, then a reasonable case can be made that this state regulation should be included in the baseline.
Two of the most important things to remember when sequencing multiple unlinked rules are transparency and
objective reasoning. Transparency requires that the analyst clearly state all assumptions. Objective reasoning requires
that the analyst not engage in speculation. If there is uncertainty about the anticipated rules, then two baselines, one
with anticipated rules and one without, should be considered. If resources are constrained and only one baseline can
be considered, then it should be constructed using only final rules and those that are reasonably expected with a high
dearee of certainty in the absence of EPA action.
progress to a threshold.6 Note that once partial
gains to a threshold have been claimed, there is a
6 Sometimes calculating partial benefits to a threshold may not be a
satisfactory solution, either because the progress to a threshold is uncertain
due to multiple limiting factors (e.g., in some ecological improvements) or
because it does not comport with the economic values (e.g., the value of
avoiding the extinction of a species). In this case, a rulemaking incremental
progress to the threshold might have to be justified on something other than
a benefit-cost test. This, however, does not affect the choice of a baseline.
danger of double counting when evaluating the
potential benefits of future rules. If partial gains
have been valued in one rule, then subsequent rules
cannot claim full credit for crossing the threshold.
In effect, some of the benefits have already been
used to justify the previous incremental rules and
therefore claiming full credit in future rules would
double count those benefits.
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Chapter 5 Baseline
"While the actual valuation of incremental
progress is a benefits issue, the specification
of that portion of the benefits that have been
claimed in previous rules is a baseline issue. If
previous rules have claimed partial benefits, the
benefits available for the current rule should be
clearly identified in the baseline specification.
In the simplest case, this means calculating
benefits in the same way as previous rules.
However, this approach is not always possible,
or even reasonable. New valuation studies or
new models of ambient pollution may make
the previous benefits estimates obsolete. In this
more complicated case, the baseline specification
should be developed so that the current benefits
estimates can be compared with the previous
estimates while avoiding double counting.
havioral Responses
To measure a policy's costs and benefits, it is
important to clearly characterize the behavior of
firms and individuals in both the baseline and
the policy scenarios. Behavior is contrasted with
the baseline and is often anticipated to change in
response to the policy options. Some policies are
prescriptive in specifying what actions are required
— for example, mandating the use of a specific type
of pollution control equipment. Responses to less-
direct performance standards, such as bans on the
production or use of certain products or processes
or market-based incentive programs are somewhat
more difficult to predict and commonly require
some underlying model of economic behavior.
Estimating responses is often difficult for pollution
prevention policies because these options are more
site- and process-specific when compared to end-of-
pipe control technologies. Predicting the costs and
environmental effects of these rules may require
detailed information on industrial processes.
Parties anticipating the outcome of a regulatory
initiative may change their economic behavior,
including spending resources to meet expected
emission or hazard reductions prior to the
compliance deadline set by enforceable
requirements. The same issues arise in the
treatment of non-regulatory programs, in which
voluntary or negotiated environmental goals may
be established, leading parties to take steps to
achieve these goals at rates different from those
expected in the absence of the program. In these
cases, it may be appropriate to include the costs
and benefits of changed behavior in the analysis
of the policy action, and not subsume them into
the baseline scenario. Nevertheless, the dynamic
aspects of market and consumer behavior, and the
many motivations leading to change, can make it
difficult to attribute economic costs and benefits
to specific regulatory actions. Where behavioral
changes are uncertain, an uncertainty analysis using
various behavioral assumptions can provide insight
into how important these assumptions may be.
Behavioral responses are usually characterized as
reactions to proposed policy options. However,
the behavioral assumptions used in the baseline,
when no regulatory action is taken, are also very
important. Individuals may attempt to mitigate
the affect of pollution (e.g., by buying bottled
water, using masks, or purchasing medication), or
prevent their exposure altogether through some
type of averting behavior (e.g., keeping windows
closed or relocating). Careful consideration of
this behavior is important to correctly measure
the costs and benefits of regulation. Analysts
should make explicit all assumptions about firm
and individual behavioral in both the baseline
and policy scenarios so that a proper comparison
between the two can be made.
5,7,1 Potential for Cost Savings
Predicting firm-level responses begins with a
comprehensive list of possible response options. In
addition to the possible compliance technologies
(if the technology is not specified by the policy
itself) or waste management methods, less obvious
firm-level responses should be considered. These
include changes in operations (e.g., input mixtures,
re-use or recycling, and developing new markets
for waste products) to avoid or reduce the need
for new controls or the use of restricted materials,
shutting down a production line or plant to avoid
the investments required to achieve compliance,
relocation of the firm, or even exiting the industry.
The possibility of noncompliance should also be
explored, including the use of lawsuits to delay the
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Chapter 5 Baseline
required investment. In general, affected parties
are assumed to choose the option that minimizes
their costs.
In some cases, compliance implies a reduction in
costs from the baseline. In other words, choosing
the least costly regulatory solution would
provide cost savings to the firms. In this case, it is
important to provide an analysis of why these cost-
saving measures are not undertaken in the baseline.
It is not always obvious why firms would actively
choose to not undertake a change that results in
cost savings. If firms will eventually voluntarily
undertake these changes without the regulation,
then the regulatory intervention cannot be
credited with the cost savings.
One possibility is that firms may not adopt cost-
saving measures because of market failures (e.g.,
informational asymmetries or transactions costs)
and other circumstances. In these cases, regulation
can motivate economically beneficial actions, but
there should be a reasonable description of the
market failure or circumstances that the regulation
is correcting. A second possibility is that firms are
actively choosing a higher cost option in order to
reduce legal liabilities or to achieve compliance
with other implemented or proposed rules. In this
latter case, firms will continue to choose the higher
cost solution in both the baseline and the policy
scenario and the costs savings can only be achieved
by relaxing the legal liability or eliminating the
other rule. In other words, the additional costs of
compliance in excess of a least-cost strategy would
be attributed to these other causes, but the rule
itself will not achieve the cost savings.
* oluntr* t tions
Occasionally, polluting industries adopt voluntary
measures to reduce emissions. This can be
implemented through a formal, government-
sponsored voluntary program or a firm or
sector may independently adopt measures. Such
voluntary measures are adopted for a variety of
reasons, including public relations considerations,
to avoid regulatory controls, or to gain access
to incentives associated with joining a formal
program. "When this is the case, it is important to
account for these voluntary actions in the baseline
and to be explicit about the assumptions of firms'
future actions.
Typically, the economic baseline should reflect
current circumstances, which means that voluntary
reductions in emissions should be included in the
baseline assumptions. This is not always possible,
however, as voluntary actions are often difficult to
measure (Brouhle, Griffiths, and Wolverton 2005).
In the case of data or resource limitations, analysts
may be compelled to adopt a "current regulations"
baseline, which effectively ignores these emission
reductions.
For the policy scenario, analysts should generally
not assume that the current trends in voluntary
reductions will persist. If firms are required to
reduce emissions below their current level, then
it should be assumed that the firms would meet
the new standard without over-complying. While
firms that go beyond compliance are often "good
actors" who will continue to make reductions
beyond the regulatory threshold, there is no a
priori reason to expect this without a formal model
explaining the firms' motivation. If the regulatory
threshold is set above the emissions of these "good
actions" then it is important to hypothesize why
the voluntary actions were taken in the first place.
If firms were making voluntary reductions in
anticipation of the regulation or to dissuade the
Agency from passing the regulation, then the firm
can probably be expected to increase emissions
to the regulatory level. On the other hand, if
firms were making the reduction for some other
incentive that continues to be present after the
regulation is passed, then the voluntary emissions
level may remain unchanged.
In some cases, it may be appropriate to
demonstrate the significance of voluntary actions
in a sensitivity analysis. This might involve
analyzing competing assumptions of voluntary
behavior. In all cases, the potential impact of
the regulation on formal voluntary programs
should be discussed. If participation in voluntary
programs was motivated by the threat of the
proposed regulation, then that voluntary program
will likely be affected. In the extreme case, the
Guidelines for Preparing Economic Analyses I December 2010 5-15
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Chapter 5 Baseline
voluntary program maybe curtailed or eliminated
as a consequence of the regulation. These potential
implications should be included in the economic
analysis.
lusion
Developing a baseline plays a critical role in
analyzing policy scenarios, because it is the
basis for BCA and option selection. However,
developing a baseline is not a straightforward
process, and analysts must make many decisions on
the basis of professional judgment.
As stated in this chapter, a well-specified baseline
should address exogenous changes in the economy,
industry compliance rates, other concurrent
regulations, and behavioral responses. The
assumptions used in the baseline will be derived
from models, published literature, or government
agencies and should be clearly referenced. In
cases where the data are uncertain, or not easily
quantified, but may have a significant influence
on the results, the analyst should describe the
weaknesses in the data and assumptions, and
include some type of sensitivity analysis. In some
cases, multiple baselines or alternative scenarios
maybe required.
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Chapter 6
Discounting Future Benefits
and Costs
Discounting renders benefits and costs that occur in different time periods
comparable by expressing their values in present terms. In practice, it is
accomplished by multiplying the changes in future consumption (broadly
defined, including market and non-market goods and services) caused
by a policy by a discount factor. At a summary level, discounting reflects
that people prefer consumption today to future consumption, and that invested capital is
productive and provides greater consumption in the future. Properly applied, discounting can
tell us how much future benefits and costs are worth today.
Social discounting, the type of discounting discussed in this chapter, is discounting from
the broad society-as-a-whole point of view that is embodied in benefit-cost analysis (BCA).
Private discounting, on the other hand, is discounting from the specific, limited perspective
of private individuals or firms. Implementing this distinction can be complex but it is an
important distinction to maintain because using a given private discount rate instead of a
social discount rate can bias results as part of a BCA.
This chapter addresses discounting over the relatively short term, what has become known
as intragenerational discounting, as well as discounting over much longer time horizons, or
intergenerational discounting. Intragenerational, or conventional, discounting applies to
contexts that may have decades-long time frames, but do not explicitly confront impacts on
unborn generations that may be beyond the private planning horizon of the current ones.
Intergenerational discounting, by contrast, addresses extremely long time horizons and the
impacts and preferences of generations to come. To some extent this distinction is a convenience
as there is no discrete point at which one moves from one context to another. However, the
relative importance of various issues can change as the time horizon lengthens.
Several sensitive issues surround the choice of discount rate. This chapter attempts to address
those most important for applied policy analysis. In addition to the sensitivity of the discount
rate to the choice of discounting approach, a topic discussed throughout this chapter,
these issues include: the distinction and potential confounding of efficiency and equity
considerations (Section 6.3.2.1); the difference between consumption and utility discount
rates (Sections 6.2.2.2 and 6.3.1); "prescriptive" vs. "descriptive" approaches to discount
rate selection (Section 6.3.1); and uncertainty about future economic growth and other
conditions (Sections 6.3.2.1 and 6.3.2.2).
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 6 Discounting Future Benefits and Costs
Mechanics of
Summarizing Present and
Rffw\- and Benefits
Discounting reflects: (1) the amount of time
between the present and the point at which these
changes occur; (2) the rate at which consumption
is expected to change over time in the absence
of the policy; (3) the rate at which the marginal
value of consumption diminishes with increased
consumption; and (4) the rate at which the future
utility from consumption is discounted with time.
Changes in these components or uncertainty
about them can lead to a discount rate that
changes over time, but for many analyses it may
be sufficient to apply a fixed discount rate or rates
without explicit consideration of the constituent
components or uncertainty.1
There are several methods for discounting future
values to the present, the most common of
which involve estimating net present values and
annualized values. An alternative is to estimate a
netfuture value.
6.1.1 Net Present Value (NPV)
The NPV of a projected stream of current and
future benefits and costs relative to the analytic
baseline is estimated by multiplying the benefits
and costs in each year by a time-dependent weight,
or discount factor, d, and adding all of the weighted
values as shown in the following equation:
NPV= NB0 + d1NB1 + d2NB2 +
- + dn_xNBn_x + dnNBn (1)
where NB is the net difference between benefits
and costs (Bt - C) that accrue at the end of period
t. The discounting weights, d, are given by:
d' = (T+7)f (2)
where r is the discount rate. The final period of the
policy's future effects is designated as time n.
1 Note that accounting for changes in these components through
discounting is distinct from accounting for inflation, although observed
market rates reflect expected inflation. Both values (i.e., benefits and
costs) and the discount rate should be adjusted for inflation; therefore
most of the discussion in this chapter focuses on real discount rates
and values.
The NPV can be estimated using real or nominal
benefits, costs, and discount rates. The analyst can
estimate the present value of costs and benefits
separately and then compare them to arrive at net
present value.
It is important that the same discount rate be used
for both benefits and costs because nearly any
policy can be justified by choosing a sufficiently
low discount rate for benefits, by choosing
sufficiently high discount rates for costs, or by
choosing a sufficiendy long time horizon. Likewise,
making sufficiently extreme opposite choices could
result in any policy being rejected.
When estimating the NPV, it is also important to
explicitly state how time periods are designated
and when, within each time period, costs and
benefits accrue. Typically time periods are years,
but alternative time periods can be justified if
costs or benefits accrue at irregular or non-annual
intervals. The preceding formula assumes that
t=0 designates the beginning of the first period.
Therefore, the net benefits at time zero (i\lBg)
include a Cg term that captures startup or one-time
costs such as capital costs that occur immediately
upon implementation of the policy. The formula
further assumes that no additional costs are
incurred until the end of the first year of regulatory
compliance.2 Any benefits also accrue at the end of
each time period.
Figure 6.1 illustrates how net benefits (measured
in dollars) are distributed over time. NB is the
sum of benefits and costs that may have been
spread evenly across the four quarters of the first
year (NB through NB ) as shown in the bottom
part of the figure. There may be a loss of precision
by "rounding" a policy's effects in a given year to
the end or beginning of that year, but this is almost
always extremely small in the scope of an entire
economic analysis.
2 See U.S. EPA (1995c) for an example in which operating and monitoring
costs are assumed to be spread out evenly throughout each year of
compliance. While the exponential function in equation (2) is the most
accurate way of modeling the relationship between the present value
and a continuous stream of benefits and costs, simple adjustments to
the equations above can sometimes adapt them for use under alternative
assumptions about the distribution of monetary flows over time.
6-2
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Chapter 6 Discounting Future Benefits and Costs
Figure 6,1 - Distribution of Net Benefits
TIME ¦
Year t 0 1
S NB. NB.
2
NB.,
3
NB..
4
NB,
n
NB.
TIME -
Year t 0
S NB...
NB..
NB
NB...
PVC = present value of costs (estimated as in
equation 1, above);
r = the discount rate per period; and
n = the duration of the policy.
Annualizing costs when there is initial cost at t=0
is estimated using the following slightly different
equation:
AC=PVC*
r *
(1 + r)n
(1 +r)(" + 1)- 1
(4)
6.1=2 Annualized Values
An annualized value is the amount one would have
to pay at the end of each time period t so that the
sum of all payments in present value terms equals
the original stream of values. Producing annualized
values of costs and benefits is useful because it
converts the time varying stream of values to a
constant stream. Comparing annualized costs to
annualized benefits is equivalent to comparing
the present values of costs and benefits. Costs and
benefits each may be annualized separately by
using a two-step procedure. While the formulas
below illustrate the estimation of annualized costs,
the formulas are identical for benefits.3
To annualize costs, the present value of costs
is calculated using the above formula for net
benefits, except the stream of costs alone, not the
net benefits, is used in the calculation. The exact
equation for annualizing depends on whether or
not there are any costs at time zero (i.e., at t= 0).
Annualizing costs when there is no initial cost at t=0
is estimated using the following equation:
Note that the numerator is the same in both
equations. The only difference is the "n+1" term in
the denominator.
Annualization of costs is also useful when
evaluating non-monetized benefits, such as
reductions in emissions or reductions in health
risks, when benefits are constant over time.
The average cost-effectiveness of a policy or
policy option can be calculated by dividing the
annualized cost by the annual benefit to produce
measures of program effectiveness, such as the cost
per ton of emissions avoided.
As mentioned above, the same formulas would
apply to estimating annualized benefits.
Future Value
Instead of discounting all future values to the
present, it is possible to estimate value in some
future time period, for example, at the end of the
last year of the policy's effects, n. The net future
value is estimated using the following equation:
AC=PVC*
r*
(1 + r)n
(1 + r)n - I
(3)
where
AC = annualized cost accrued at the end of
each of n periods;
3 Variants of these formulas may be common in specific contexts. See,
for example, the Equivalent Uniform Annual Cost approach in EPA's
Air Pollution Control Cost Manual (U.S. EPA 2002b).
NFV= d.JS[BQ + dlNBl + d2NB2
+ - + dn_xNBn_x + NBn
NB is the net difference between benefits
and costs (B - C) that accrue in year t and the
accumulation weights, d, are given by
(5)
d = (1 + r)
(n-t)
(6)
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Chapter 6 Discounting Future Benefits and Costs
where r is the discount rate. It should be noted
that the net present value and net future value can
be expressed relative to one another:
NPV= (177)- (?>
6.1.4 Comparing the Methods
Each of the methods described above uses a
discount factor to translate values across time, so
the methods are not different ways to determine
the benefits and costs of a policy, but rather are
different ways to express and compare these
costs and benefits in a consistent manner. NPV
represents the present value of all costs and
benefits, annualization represents the value
as spread smoothly through time, and NFV
represents their future value. For a given stream of
net benefits, the NPV will be lower with higher
discount rates, the NFV will be higher with
higher discount rates, and the annualized value
may be higher or lower depending on the length
of time over which the values are annualized.
Still, rankings among regulatory alternatives are
unchanged across the methods.
Depending on the circumstances, one method
might have certain advantages over the others.
Discounting to the present to get a NPV is likely to
be the most informative procedure when analyzing
a policy that requires an immediate investment and
offers a stream of highly variable future benefits.
However, annualizing the costs of two machines
with different service lives might reveal that the
one with the higher total cost actually has a lower
annual cost because of its longer lifetime.
Annualized values are sensitive to the
annualization period; for any given present value
the annualized value will be lower the longer the
annualization period. Analysts should be careful
when comparing annualized values from one
analysis to those from another.
The analysis, discussion, and conclusions presented
in this chapter apply to all methods of translating
costs, benefits, and effects through time, even
though the focus is mostly on NPV estimates.
6.1.5 Sensitivity of Present Value
Estimates to the Discount Rate
The impact of discounting streams of benefits and
costs depends on the nature and timing of benefits
and costs. The discount rate is not likely to affect
the present value of the benefits and costs for those
cases in which:
• All effects occur in the same period
(discounting may be unnecessary or
superfluous because net benefits are positive or
negative regardless of the discount rate used);
• Costs and benefits are largely constant over
the relevant time frame (discounting costs and
benefits will produce the same conclusion as
comparing a single year's costs and benefits);
and/or
• Costs and benefits of a policy occur
simultaneously and their relative values do
not change over time (whether the NPV is
positive does not depend on the discount
rate, although the discount rate can affect the
relative present value if a policy is compared
to another policy).
Discounting can, however, substantially affect
the NPV of costs and benefits when there is a
significant difference in the timing of costs and
benefits, such as with policies that require large
initial outlays or that have long delays before
benefits are realized. Many of EPA's policies fit
these profiles. Text Box 6.1 illustrates a case in
which discounting and the choice of the discount
rate have a significant impact on a policy's NPV.
6.1.6 Some Issues in Application
There are several important analytic components
that need to be considered when discounting:
risk and valuation, placing effects in time, and the
length of the analysis.
6,1,6,1 Risk and Valuation
There are two concepts that are often
confounded when implementing social
discounting, but should be treated separately.
The first is the future value of environmental
effects, which depends on many factors,
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Chapter 6 Discounting Future Benefits and Costs
Text Bo* 6.1 - Potential Effects of Discounting
Suppose the benefits of a given program occur 30 years in the future and are valued (in real terms) at $5 billion
at that time. The rate at which the $5 billion future benefits is discounted can dramatically alter the economic
assessment of the policy: $5 billion 30 years in the future discounted at 1 percent is $3.71 billion, at 3 percent it
is worth $2.06 billion, at 7 percent it is worth $657 million, and at 10 percent it is worth only $287 million. In this
case, the range of discount rates generates over an order of magnitude of difference in the present value of benefits.
Longer time horizons will produce even more dramatic effects on a policy's NPV (see Section 6.3 on intergenerational
discounting). For a given present value of costs, particularly the case where costs are incurred in the present and
therefore not affected by the discount rate, it is easy to see that the choice of the discount rate can determine whether
this policy is considered, on economic efficiency grounds, to offer society positive or negative net benefits.
including the availability of substitutes and the
level of wealth in the future. The second is the
role of risk in valuing benefits and costs. For both
of these components, the process of determining
their values and then translating the values into
present terms are two conceptually distinct
procedures. Incorporating the riskiness of
future benefits and costs into the social discount
rate not only imposes specific and generally
unwarranted assumptions, but it can also hide
important information from decision makers.
8.1.8.2 Placing Effects in Time
Placing effects properly in time is essential for
NPV calculations to characterize efficiency
outcomes. Analyses should account for
implementation schedules and the resulting
changes in emissions or environmental quality,
including possible changes in behavior between
the announcement of policy and compliance.
Additionally, there may be a lag time between
changes in environmental quality and a
corresponding change in welfare. It is the change
in welfare that defines economic value, and
not the change in environmental quality itself.
Enumerating the time path of welfare changes is
essential for proper valuation and BCA.
8.1.8.3 Length of the Analysis
While there is little theoretical guidance on the time
horizon of economic analyses, a guiding principle
is that the time span should be sufficient to capture
major welfare effects from policy alternatives.
This principle is consistent with the underlying
requirement that BCA reflect the welfare outcomes
of those affected by the policy. Another way to view
this is to consider that the time horizon, T, of an
analysis should be chosen such that:
^£ZT(Bt-cy
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Chapter 6 Discounting Future Benefits and Costs
Hie choice should be explained and well-
documented. In no case should the time horizon
be arbitrary, and the analysis should highlight
the extent to which the sign of net benefits or the
relative rankings of policy alternatives are sensitive
to the choice of time horizon.
ckground and Rationales
for Social Discounting
The analytical and ethical foundation of the social
discounting literature rests on the traditional test
of a "potential" Pareto improvement in social
welfare; that is, the trade-off between the gains
to those who benefit and the losses to those
who bear the costs. This framework casts the
consequences of government policies in terms of
individuals contemplating changes in their own
consumption (broadly defined) over time. Trade-
offs (benefits and costs) in this context reflect the
preferences of those affected by the policy, and the
time dimension of those trade-offs should reflect
the intertemporal preferences of those affected.
Thus, social discounting should seek to mimic the
discounting practices of the affected individuals.
The literature on discounting often uses a variety of
terms and frameworks to describe identical or very
similar key concepts. General themes throughout
this literature are the relationship between
consumption rates of interest and the rate of
return on private capital, the need for a social rate
of time preference for BCA, and the importance
of considering the opportunity cost of foregone
capital investments.
6.2.1 Consumption Rates of
Interest ai ivate Rates
of Return
In a perfect capital market with no distortions, the
return to savings (the consumption rate of interest)
equals the return on private sector investments.
Therefore, if the government seeks to value costs
and benefits in present day terms in the same way
as the affected individuals, it should also discount
using this single market rate of interest. In this
kind of "first best" world, the market interest rate
would be an unambiguous choice for the social
discount rate.
Real-world complications, however, make the
issue much more complex. Among other things,
private sector returns are taxed (often at multiple
levels), capital markets are not perfect, and capital
investments often involve risks reflected in market
interest rates. These factors drive a wedge between
the social rate at which consumption can be traded
through time (the pre-tax rate of return to private
investments) and the rate at which individuals
can trade consumption over time (the post-tax
consumption rate of interest). Text Box 6.2
illustrates how these rates can differ.
A large body of economic literature analyzes the
implications for social discounting of divergences
between the social rate of return on private sector
investment and the consumption rate of interest.
Most of this literature is based on the evaluation of
public projects, but many of the insights still apply
to regulatory BCA. The dominant approaches
in this literature are briefly oudined here. More
complete recent reviews can be found in Spackman
(2004) and Moore et al. (2004).
Text Bo* 6.:- ¦¦ ocial Rate anc ¦'¦¦..¦is " n Rates of Interest
Suppose that the market rate of interest, net of inflation, is 5 percent, and that the taxes on capital income amount to
40 percent of the net return. In this case, private investments will yield 5 percent, of which 2 percent is paid in taxes
to the government, with individuals receiving the remaining 3 percent. From asocial perspective, consumption can
be traded from the present to the future at a rate of 5 percent. But individuals effectively trade consumption through
time at a rate of 3 percent because they owe taxes on investment earnings. As a result, the consumption rate of
interest is 3 percent, which is substantially less than the 5 percent social rate of return on private sector investments
(also known as the social opportunity cost of private capital).
6-6
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Chapter 6 Discounting Future Benefits and Costs
6.2,2 Social Rate of
Time Preference
Hie goal of social discounting is to compare
benefits and costs that occur at different times
based on the rate at which society is willing to
make such trade-offs. If costs and benefits can be
represented as changes in consumption profiles
over time, then discounting should be based on
the rate at which society is willing to postpone
consumption today for consumption in the
future. Thus, the rate at which society is willing
to trade current for future consumption, or the
social rate of time preference, is the appropriate
discounting concept.
Generally a distinction is made between individual
rates of time preference and that of society as
a whole, which should inform public policy
decisions. The individual rate of time preference
includes factors such as the probability of death,
whereas society can be presumed to have a longer
planning horizon. Additionally, individuals
routinely are observed to have several different
types of savings, each possibly yielding different
returns, while simultaneously borrowing at
different rates of interest. For these and other
reasons, the social rate of time preference is
not directly observable and may not equal any
particular market rate.
8.2,2,1 Estimating a Social Rate of Time
Preference Usi k-Free Assets
One common approach to estimating the social
rate of time preference is to approximate it from
the market rate of interest from long-term,
risk-free assets such as government bonds. The
rationale behind this approach is that this market
rate reflects how individuals discount future
consumption, and government should value
policy-related consumption changes as individuals
do. In other words, the social rate of discount
should equal the consumption rate of interest (i.e.,
an individual's marginal rate of time preference).
In principle, estimates of the consumption rate of
interest could be based on either after-tax lending
or borrowing rates. Because individuals may be in
different marginal tax brackets, may have different
levels of assets, and may have different opportunities
to borrow and invest, the type of interest rate that
best reflects marginal time preference will differ
among individuals. However, the fact that, on net,
individuals generally accumulate assets over their
working lives suggests that the after-tax returns
on savings instruments generally available to the
public will provide a reasonable estimate of the
consumption rate of interest.
The historical rate of return, post-tax and
after inflation, is a useful measure because it is
relatively risk-free, and BCA should address risk
elsewhere in the analysis rather than through the
interest rate. Also, because these are longer-term
instruments, they provide more information on
how individuals value future benefits over these
kinds of time frames.
8.2,2,2 Estimating a Social Rate
of Time Preference Using the
"Ramsey' Framework
A second option is to construct the social rate
of time preference in a framework originally
developed by Ramsey (1928) to reflect: (1) the
value of additional consumption as income
changes; and (2) a "pure rate of time preference"
that weighs utility in one period directly against
utility in a later period. These factors are combined
in the equation:
r=Vg + P (9)
where (r) is the market interest rate, the first term
is the elasticity of marginal utility (77) times the
consumption growth rate {g), and the second term
is pure rate of time preference (p). Estimating a
social rate of time preference in this framework
requires information on each of these arguments,
and while the first two of these factors can be
derived from data, p is unobservable and must be
determined.4 A more detailed discussion of the
Ramsey equation can be found in Section 6.3:
Intergenerational Social Discounting.
4 The Science Advisory Board (SAB) Council defines discounting based
on a Ramsey equation as the "demand-side" approach, noting that the
value judgments required for the pure social rate of time preference
make it an inherently subjective concept (U.S. EPA 2004c).
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Chapter 6 Discounting Future Benefits and Costs
6.2.3 Social Opportunity
Cos apital
Hie social opportunity cost of capital approach
recognizes that funds for government projects, or
those required to meet government regulations, have
an opportunity cost in terms of foregone investments
and therefore future consumption. When a regulation
displaces private investments society loses the total
pre-tax returns from those foregone investments. In
these cases, ignoring such capital displacements and
discounting costs and benefits using a consumption
rate of interest (the post-tax rate of interest) does not
capture the fact that society loses the higher, social
(pre-tax) rate of return on foregone investments.
Private capital investments might be displaced
if, for example, public projects are financed
with government debt or regulated firms cannot
pass through capital expenses, and the supply of
investment capital is relatively fixed. The resulting
demand pressure in the investment market will
tend to raise interest rates and squeeze out private
investments that would otherwise have been
made.5 Applicability of the social opportunity
cost of capital depends upon full crowding out of
private investments by environmental policies.
The social opportunity cost of capital can be
estimated by the pre-tax marginal rate of return on
private investments observed in the marketplace.
There is some debate as to whether it is best to
use only corporate debt, only equity (e.g., returns
to stocks) or some combination of the two. In
practice, average returns that are likely to be higher
than the marginal return, are typically observed,
given that firms will make the most profitable
investments first; it is not clear how to estimate
marginal returns. These rates also reflect risks faced
in the private sector, which may not be relevant for
public sector evaluation.
5 Another justification for using the social opportunity cost of capital
argues that the government should not invest (or compel investment
through its policies) in any project that offers a rate of return less than
the social rate of return on private investments. While it is true that
social welfare will be improved if the government invests in projects
that have higher values rather than lower ones, it does not follow that
rates of return offered by these alternative projects define the level of
the social discount rate. If individuals discount future benefits using
the consumption rate of interest, the correct way to describe a project
with a rate of return greater than the consumption rate is to say that it
offers substantial present value net benefits.
6.2.4 Shadow Price of
Capital Approach
Under the shadow price of capital approach costs
are adjusted to reflect the social costs of altered
private investments, but discounting for time
itself is accomplished using the social rate of
time preference that represents how society
trades and values consumption over time.6 The
adjustment factor is referred to as the "shadow
price of capital."7 Many sources recognize this
method as the preferred analytic approach to social
discounting for public projects and policies.8
The shadow price, or social value, of private capital
is intended to capture the fact that a unit of
private capital produces a stream of social returns
at a rate greater than that at which individuals
discount them. If the social rate of discount is the
consumption rate of interest, then the social value
of a $1 private sector investment will be greater
than $1. The investment produces a rate of return
for its owners equal to the post-tax consumption
rate of interest, plus a stream of tax revenues
(generally considered to be consumption) for the
government. Text Box 6.3 illustrates this idea of
the shadow price of capital.
If compliance with environmental policies
displaces private investments, the shadow price
of capital approach suggests first adjusting the
project or policy cost upward by the shadow
price of capital, and then discounting all costs
and benefits using a social rate of discount equal
to the social rate of time preference. The most
complete frameworks for the shadow price of
capital also note that while the costs of regulation
might displace private capital, the benefits could
encourage additional private sector investments.
In principle, a full analysis of shadow price of
6 Because the consumption rate of interest is often used as a proxy for
the social rate of time preference, this method is sometimes known as
the "consumption rate of interest - shadow price of capital" approach.
However, as Lind (1982b) notes, what is really needed is the social rate
of time preference, so more general terminology is used. Discounting
based on the shadow price of capital is referred to as a "supply side"
approach by EPA's SAB Council (U.S. EPA 2004c).
7 A "shadow price" can be viewed as a good's opportunity cost, which
may not equal the market price. Lind (1982a) remains the seminal
source for this approach in the social discounting literature.
8 See 0MB CircularA-4(2003), Freeman (2003), and the report of EPA's
Advisory Council on Clean Air Compliance Analysis (U.S. EPA 2004c).
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Chapter 6 Discounting Future Benefits and Costs
Text Bo* 6.3 - Estimating and Applying the Shadow Price of Capital
To estimate the shadow price of capital, suppose that the consumption rate of interest is 3 percent, the pre-tax rate of
return on private investments is 5 percent, the net-of-tax earnings from these investments are consumed in each period,
and the investment exists in perpetuity (amortization payments from the gross returns of the investment are devoted to
preserving the value of the capital intact). A $1 private investment under these conditions will produce a stream of private
consumption of $.03 per year, and tax revenues of $.02 per year. Discounting the private post-tax stream of consumption
at the 3 percent consumption rate of interest yields a present value of $1. Discounting the stream of tax revenues at
the same rate yields a present value of about $.67. The social value of this $1 private investment - the shadow price of
capital - is thus $1.67, which is substantially greater than the $1 private value that individuals place on it.
To apply this shadow price of capital estimate, we need additional information about debt and tax financing as well as about
how investment and consumption are affected. Assume that increases in government debt displace private investments
dollar-for-dollar, and that increased taxes reduce individuals' current consumption also on a one-for-one basis. Finally,
assume that the $1 current cost of a public project is financed 75 percent with government debt and 25 percent with current
taxes, and that this project produces a benefit 40 years from now that is estimated to be worth $5 in the future.
Using the shadow price of capital approach, first multiply 75 percent of the $1 current cost (which is the amount of
displaced private investment) by the shadow price of capital (assume this is the $1.67 figure from above). This yields
$1.2525; add to this the $.25 amount by which the project's costs displace current consumption. The total social cost
is therefore $1.5025. This results in a net social present value of about $.03, which is the present value of the future
$5 benefit discounted at the 3 percent consumption rate of interest ($1.5328) minus the $1.5025 social cost.
capital adjustments would treat costs and benefits
symmetrically in this sense.
Hie first step in applying this approach is
determining whether private investment flows
will be altered by a policy. Next, all of the altered
private investment flows (positive and negative)
are multiplied by the shadow price of capital
to convert them into consumption-equivalent
units. All flows of consumption and consumption
equivalents are then discounted using the social
rate of time preference. A simple illustration of this
method applied to the costs of a public project and
using the consumption rate of interest is shown in
Text Box 6.3.9
9 An alternative approach for addressing the divergence between
the higher social rate of return on private investments and lower
consumption rate of interest is to set the social discount rate equal to a
weighted average of the two. The weights would equal the proportions
of project financing that displace private investment and consumption
respectively. This approach has enjoyed considerable popularity over
the years, but it is technically incorrect and can produce NPV results
substantially different from the shadow price of capital approach. (For
an example of these potential differences see Spackman 2004.)
8.2.4.1 Estimating the Shadow
Price of Capital
The shadow price of capital approach is data
intensive. It requires, among other things,
estimates of the social rate of time preference, the
social opportunity cost of capital, and estimates
of the extent to which regulatory costs displace
private investment and benefits stimulate it. "While
the first two components can be estimated as
described earlier, information on regulatory effects
on capital formation is more difficult. As a result
empirical evidence for the shadow price of capital
is less concrete, making the approach difficult to
implement.10
Whether or not this adjustment is necessary
appears to depend largely on whether the economy
in question is assumed to be open or closed, and
on the magnitude of the intervention or program
10 Depending on the magnitudes of the various factors, shadow prices
from about 1 to infinity can result (Lyon 1990). Lyon (1990) and Moore
etal. (2004) contain excellent reviews of how to calculate the shadow
price of capital and possible settings for the various parameters that
determine its magnitude.
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Chapter 6 Discounting Future Benefits and Costs
considered relative to the flow of investment
capital from abroad.11
Some argue that early analyses implicitly assumed
that capital flows into the nation were either
nonexistent or very insensitive to interest rates,
known as the "closed economy" assumption.12
Some empirical evidence suggests, however, that
international capital flows are quite large and are
sensitive to interest rate changes. In this case, the
supply of investment funds to the U.S. equity and
debt markets may be highly elastic (the "open
economy" assumption), thus private capital
displacement would be much less important than
previously thought.
Under this alternative view, it would be
inappropriate to assume that financing a public
project through borrowing would result in dollar-
for-dollar crowding out of private investment. If
there is no crowding out of private investment,
then no adjustments using the shadow price of
capital are necessary; benefits and costs should
be discounted using the social rate of time
preference alone. However, the literature to date
is not conclusive on the degree of crowding out.
There is little detailed empirical evidence as to
the relationship between the nature and size of
projects and capital displacement. While the
approach is often recognized as being technically
superior to simpler methods, it is difficult to
implement in practice.
8.2.5 Evaluating the Alternatives
The empirical literature for choosing a social
discount rate focuses largely on estimating the
consumption rate of interest at which individuals
translate consumption through time with
reasonable certainty. Some researchers have
explored other approaches that, while not detailed
here, are described briefly in Text Box 6.4.
11 Studies suggesting that increased U.S. Government borrowing does
not crowd out U.S. private investment generally examine the impact
of changes in the level of government borrowing on interest rates. The
lack of a significant positive correlation of government borrowing and
interest rates is the foundation of this conclusion.
12 See Lind (1990) for this revision of the shadow price of capital
approach.
To estimate a consumption rate of interest that
includes low risk, historical rates of return on "safe"
assets (post-tax and after inflation), such as U.S.
Treasury securities, are normally used. Some may
use the rate of return to private savings. Recent
studies and reports have generally found government
borrowing rates in the range of around 2 percent to 4
percent.13 Some studies have expanded this portfolio
to include other bonds, stocks, and even housing.
This generally raises the range of rates slightly. It
should be noted that these rates are realized rates
of return, not anticipated, and they are somewhat
sensitive to the choice of time period and the class of
assets considered.14 Studies of the social discount rate
for the United Kingdom place the consumption rate
of interest at approximately 2 percent to 4 percent,
with the balance of the evidence pointing toward the
lower end of the range.15
Others have constructed a social rate of
time preference by estimating the individual
arguments in the Ramsey equation. These
estimates necessarily require judgments about
the pure rate of time preference. Moore et al.
(2004) and Boardman et al. (2006) estimate the
intragenerational rate to be 3.5 percent. Other
studies base the pure rate of time preference on
individual mortality risks in order to arrive at a
discount rate estimate. As noted earlier, this may
be useful for an individual, but is not generally
appropriate from a societal standpoint. The
Ramsey equation has been used more frequently
in the context of intergenerational discounting,
which is addressed in the next section.
13 0MB (2003) cites evidence of a 3.1 percent pre-tax rate for ten-year
U.S. Treasury notes. According to the U.S. Congressional Budget
Office (CB0) (2005), funds continuously reinvested in 10-year U.S.
Treasury bonds from 1789 to the present would have earned an
average inflation-adjusted return of slightly more than 3 percent a
year. Boardman etal. (2006) suggest 3.71 percent as the real rate
of return on ten-year U.S. Treasury notes. Newell and Pizer (2003)
find rates slightly less than 4 percent for thirty-year U.S. Treasury
securities. Nordhaus (2008) reports a real rate of return of 2.7 percent
for twenty-year U.S. Treasury securities. The CBO estimates the cost
of government borrowing to be 2 percent, a value used as the social
discount rate in their analyses (U.S. CB0 1998).
14 Ibbotson and Sinquefield (1984 and annual updates) provide historical
rates of return for various assets and for different holding periods.
15 Lind (1982b) offers some empirical estimates of the consumption
rate of interest. Pearce and Ulph (1994) provide estimates of the
consumption rate of interest for the United Kingdom. Lyon (1994)
provides estimates of the shadow price of capital under a variety of
assumptions.
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Chapter 6 Discounting Future Benefits and Costs
Text Box 6.4 - Alternative Social Discounting Perspectives
Some of the literature questions basic premises underlying the conventional social discounting analysis. For
example, some studies of individual financial and other decision-making contexts suggest that even a single
individual may appear to value and discount different actions, goods, and wealth components differently. This "mental
accounts" or "self-control" view suggests that individuals may evaluate one type of future consequence differently
from another type of future consequence. The discount rate an individual might apply to a given future benefit or
cost, as a result, may not be observable from market prices, interest rates, or other phenomena. This may be the case
if the future consequences in question are not tradable commodities. Some evidence from experimental economics
indicates that discount rates appear to be lower the larger the magnitude of the underlying effect being valued.
Experimental results have shown higher discount rates for gains than for losses, and show a tendency for discount
rates to decline as the length of time to the event increases. Further, individuals may have preferences about whether
sequences of environmental outcomes are generally improving or declining. Some experimental evidence suggests
that individuals tend to discount hyperbolically rather than exponentially, a structure that raises time-consistency
concerns. Approaches to social discounting based on alternative perspectives and ecological structures have also
been developed, but these have yet to be fully incorporated into the environmental economics literature.
Hie social opportunity cost of capital represents a
situation where investment is crowded out dollar-
for-dollar by the costs of environmental policies.
This is an unlikely outcome, but it can be useful for
sensitivity analysis and special cases. Estimates of
the social opportunity costs of capital are typically
in the 4.5 percent to 7 percent range depending
upon the type of data used.17
The utility of the shadow price of capital approach
hinges on the magnitude of altered capital flows
from the environmental policy. If the policy will
substantially displace private investment then a
shadow price of capital adjustment is necessary
before discounting consumption and consumption
equivalents using the social rate of time preference.
The literature does not provide clear guidance
on the likelihood of this displacement, but it has
been suggested that if a policy is relatively small
16 See Thaler (1990) and Laibson (1998) for more information on
mental accounts; Guyse, Keller, and Eppell (2002) on preferences for
sequences; Gintis (2000) and Karp (2005) on hyperbolic discounting;
and Sumaila and Waters (2005) and Voinov and Farley (2007) for
additional treatments on discounting.
17 OMB (2003) recommends a real, pre-tax opportunity cost of capital
of 7 percent and refers to Circular^-94(1992) as the basis for this
conclusion. Moore et al. (2004) estimate a rate of 4.5 percent based on
AAA corporate bonds. In recent reviews of EPA's plans to estimate the
costs and benefits of the Clean Air Act, the SAB Advisory Council (U.S.
EPA 2004c and U.S. EPA 2007b) recommends using a single central
rate of 5 percent as intermediate between 3 percent and 7 percent rates,
based generally on the consumption rate of interest and the cost of
capital, respectively.
and capital markets fit an "open economy" model,
there is probably little displaced investment.18
Changes in yearly U.S. government borrowing
during the past several decades have been in the
many billions of dollars. It may be reasonable to
conclude that EPA programs and policies costing
a fraction of these amounts are not likely to
result in significant crowding out of U.S. private
investments. Primarily for these reasons, some
argue that for most environmental regulations it
is sufficient to discount using a government bond
rate with some sensitivity analysis.19
6.3 Intergenerational
SOC' f PfSO UFf,Tj|
Policies designed to address long-term
environmental problems such as global climate
change, radioactive waste disposal, groundwater
pollution, or biodiversity will likely involve
significant impacts on future generations. This
section focuses on social discounting in the context
of policies with very long time horizons involving
multiple generations, typically referred to in the
literature as intergenerational discounting.
18 Lind (1990) first suggested this.
19 See in particular Lesser and Zerbe (1994) and Moore et al. (2004).
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Chapter 6 Discounting Future Benefits and Costs
Discounting over very long time horizons is
complicated by at least three factors: (1) the
"investment horizon" is longer than what is
reflected in observed interest rates that are
used to guide private discounting decisions; (2)
future generations without a voice in the current
policy process are affected; and (3) compared to
intragenerational time horizons, intergenerational
investment horizons involve greater uncertainty
Greater uncertainty implies rates lower than
those observed in the marketplace, regardless
of whether the estimated rates are measured in
private capital or consumption terms. Policies with
very long time horizons involve costs imposed
mainly on the current generation to achieve
benefits that will accrue mainly to unborn, future
generations, making it important to consider how
to incorporate these benefits into decision making.
There is little agreement in the literature on the
precise approach for discounting over very long
time horizons.
This section presents a discussion of the
main issues associated with intergenerational
social discounting, starting with the Ramsey
discounting framework that underlies most of the
current literature on the subject. It then discusses
how the "conventional" discounting procedures
described so far in this chapter might need to
be modified when analyzing policies with very
long ("intergenerational") time horizons. The
need for such modifications arises from several
simplifying assumptions behind the conventional
discounting procedures described above. Such
conventional procedures will likely become less
realistic the longer is the relevant time horizon of
the policy. This discussion will focus on the social
discount rate itself. Other issues such as shadow
price of capital adjustments, while still relevant
under certain assumptions, will be only briefly
touched upon.
Clearly, economics alone cannot provide
definitive guidance for selecting the "correct"
social welfare function or social rate of time
preference. In particular, the fundamental
choice of what moral perspective should guide
intergenerational social discounting — e.g., that
of a social planner who weighs the utilities of
present and future generations or those preferences
of the current generations regarding future
generations — cannot be made on economic
grounds alone. Nevertheless, economics can offer
important insights concerning discounting over
very long time horizons, the implications and
consequences of alternative discounting methods,
and the systematic consideration of uncertainty.
Economics can also provide some advice on the
appropriate and consistent use of the social welfare
function approach as a policy evaluation tool in an
intergenerational context.
msey Framework
A common approach to intergenerational
discounting is based upon methods economists
have used for many years in optimal growth
modeling. In this framework, the economy is
assumed to operate as if a "representative agent"
chooses a time path of consumption and savings
that maximizes the NPV of the flow of utility
from consumption over time.20 Note that this
framework can be viewed in normative terms, as
a device to investigate how individuals should
consume and reinvest economic output over
time. Or it can be viewed in positive terms, as a
description (or "first-order approximation") of
how the economy actually works in practice. It is
a first order approximation only from this positive
perspective because the framework typically
excludes numerous real-world departures from the
idealized assumptions of perfect competition and
full information that are required for a competitive
market system to produce a Pareto-optimal
allocation of resources. If the economy worked
exactly as described by optimal growth models —
i.e., there were no taxes, market failures, or other
distortions — the social discount rate as defined in
these models would be equal to the market interest
rate. And the market interest rate, in turn, would
be equal to the social rate of return on private
investments and the consumption rate of interest.
It is worth noting that the optimal growth
literature is only one strand of the substantial
20 Key literature on this topic includes Arrow et al. (1996a), Lind (1994),
Schelling (1995), Solow (1992), Manne (1994), Toth (1994), Sen
(1982), Dasgupta (1982), and Pearce and Ulph (1994).
6-12
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Chapter 6 Discounting Future Benefits and Costs
body of research and writing on intertemporal
social welfare. This literature extends from
the economics and ethics of interpersonal and
intergenerational wealth distribution to the
more specific environment-growth issues raised
in the "sustainability" literature, and even to the
appropriate form of the social welfare function,
e.g., utilitarianism, or Rawls' maxi-min criterion.
As noted earlier, the basic model of optimal
economic growth, due to Ramsey (1928), implies
equivalence between the market interest rate (r),
and the elasticity of marginal utility (rj) times the
consumption growth rate {g) plus the pure rate of
time preference (p):
r=Vg + P (10)
The first term, rjg, reflects the fact that the
marginal utility of consumption will change over
time as the level of consumption changes. The
second term, p, the pure rate of time preference,
measures the rate at which individuals discount
their own utility over time (taking a positive
view of the optimal growth framework) or the
rate at which society should discount utilities
over time (taking a normative view). Note
that if consumption grows over time — as
it has at a fairly steady rate at least since the
industrial revolution (Valdes 1999) — then
future generations will be richer than the
current generation. Due to the diminishing
marginal utility of consumption, increments to
consumption will be valued less in future periods
than they are today. In a growing economy,
changes in future consumption would be given a
lower weight (i.e., discounted at a positive rate)
than changes in present consumption under this
framework, even setting aside discounting due to
the pure rate of time preference (p).
There are two primary approaches typically used in
the literature to specify the individual parameters
of the Ramsey equation: the "descriptive"
approach and the "prescriptive," or more explicitly,
the normative approach. These approaches
are illustrated in Text Box 6.5 for integrated
assessment models of climate change.
The descriptive approach attempts to derive
likely estimates of the underlying parameters
in the Ramsey equation. This approach argues
that economic models should be based on
actual behavior and that models should be able
to predict this behavior. By specifying a given
utility function and modeling the economy over
time one can obtain empirical estimates for the
marginal utility and for the change in growth rate.
While the pure rate of time preference cannot be
estimated directly, the other components of the
Ramsey equation can be estimated, allowing p to
be inferred.
Other economists take the prescriptive approach
and assign parameters to the Ramsey equation to
match what they believe to be ethically correct.21
For instance, there has been a long debate, starting
with Ramsey himself, on whether the pure rate
of time preference should be greater than zero.
The main arguments against the prescriptive
approach are that: (1) people (individually and
societally) do not make decisions that match this
approach; and (2) using this approach would
lead to an over-investment in environmental
protection (e.g., climate change mitigation) at the
expense of investments that would actually make
future generations better off (and would make
intervening generations better off as well). There
is also an argument that the very low discount rate
advocated by some adherents to the prescriptive
approach leads to unethical shortchanging of
current and close generations.
Other analyses have adopted at least aspects of
a prescriptive approach. For example, the Stern
Review (see Text Box 6.6) sets the pure rate of
time preference at a value of 0.1 percent and
the elasticity of marginal utility as 1.0. With an
assumed population growth rate of 1.3 percent,
the social discount rate is 1.4 percent. Guo et al.
(2006) evaluate the effects of uncertainty and
discounting on the social cost of carbon where
the social discount rate is constructed from the
Ramsey equation. A number of different discount
rate schedules are estimated depending on the
adopted parameters.
21 Arrow etal. (1996a)
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Chapter 6 Discounting Future Benefits and Costs
Text Bo* 6.5 - Applying these Approaches to the Ramsey Equation
The Ramsey approach has been most widely debated in the context of climate change. Most climate economists
adopt a descriptive approach to identify long-term real interest rates and likely estimates of the underlying parameters
in the Ramsey equation. William Nordhaus argues that economic models should be based on actual behavior and
that models should be able to predict this behavior. His Dynamic Integrated model of Climate and the Economy
(DICE), for example, uses interest rates, growth rates, etc., to calibrate the model to match actual historic levels
of investment, consumption, and other variables. In the most recent version of the DICE model (Nordhaus 2008),
he specifies the current rate of productivity growth to be 5.5 percent per year, the rate of time preference to be 1.5
percent per year, and the elasticity of marginal utility to be 2. In an earlier version (Nordhaus 1993) he estimates
the initial return on capital (and social discount) to be 6 percent, the rate of time preference to be 2 percent, and the
elasticity of marginal utility to be 3. Because the model predicts that economic and population growth will slow, the
social discount rate will decline.
While use of the Ramsey discounting framework
is quite common and is based on an intuitive
description of the general problem of trading
off current and future consumption, it has some
limitations. In particular, it ignores differences in
income within generations (at least in the basic
single representative agent version of the model).
Arrow (1996a) contains detailed discussion
of descriptive and prescriptive approaches to
discounting over long time horizons, including
examples of rates that emerge under various
assumptions about components of the Ramsey
equation.
6.3.2 nsiderations
There are a number of important ways in which
intergenerational social discounting differs from
intragenerational social discounting, essentially
due to the length of the time horizon. Over a very
long time horizon it is much more difficult, if not
impossible, for analysts to judge whether current
generation preferences also reflect those of future
generations and how per capita consumption will
change over time. This section discusses efficiency
and intergenerational equity concerns, and
uncertainty in this context.
8.3,2,1 Efficiency and
Intergenerational Equity
A principal problem with policies that span long
time horizons is that many of the people affected
are not yet alive. While the preferences of each
affected individual are knowable (if perhaps
unknown in practice) in an intragenerational
context, the preferences of future generations
in an intergenerational context are essentially
unknowable. This is not always a severe problem
for practical policy making, especially when
policies impose relatively modest costs and
benefits, or when the costs and benefits begin
immediately or in the not too distant future. Most
of the time, it suffices to assume future generations
will have preferences much like those of present
generations.
The more serious challenge posed by long time
horizon situations arises primarily when costs
and benefits of an action or inaction are very
large and are distributed asymmetrically over vast
expanses of time. The crux of the problem is that
future generations are not present to participate
in making the relevant social choices. Instead,
these decisions will be made only by existing
generations. In these cases social discounting can
no longer be thought of as a process of consulting
the preferences of all affected parties concerning
todays valuation of effects they will experience in
future time periods.
Moreover, compounding interest over very long
time horizons can have profound impacts on
the intergenerational distribution of welfare. An
extremely large benefit or cost realized far into the
future has essentially a present value of zero, even
when discounted at a low rate. But a modest sum
invested today at the same low interest rate can
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Chapter 6 Discounting Future Benefits and Costs
grow to a staggering amount given enough time.
Therefore, mechanically discounting very large
distant future effects of a policy without thinking
carefully about the implications is not advised.22
For example, in the climate change context, Pearce
et al. (2003) show that decreasing the discount rate
from a constant 6 percent to a constant 4 percent
nearly doubles the estimate of the marginal benefits
from carbon dioxide (CO ) emission reductions.
Weitzman (2001) shows that moving from a
constant 4 percent discount rate to a declining
discount rate approach nearly doubles the estimate
again. Newell and Pizer (2003) show that constant
discounting can substantially undervalue the
future given uncertainty in economic growth and
the overall investment environment. For example,
Newell and Pizer (2003) show that a constant
discount rate could undervalue net present benefits
by 21 percent to 95 percent with an initial rate of
7 percent, and 440 percent to 700 percent with an
initial rate of 4 percent, depending upon the model
of interest rate uncertainty.
Using observed market interest rates for
intergenerational discounting in the representative
agent Ramsey framework essentially substitutes
the pure rate of time preference exhibited by
individuals for the weight placed on the utilities
of future generations relative to the current
generation (see OMB 2003 and Arrow et al.
1996). Many argue that the discount rate should
be below market rates — though not necessarily
zero — to: (1) correct for market distortions and
inefficiencies in intergenerational transfers; and
(2) so that generations are treated equally based on
ethical principles (Arrow et al. 1996, and Portney
andWeyant 1999).23
Intergenerational Transfers
The notion of Pareto compensation attempts to
identify the appropriate social discount rate in an
22 OMB's Circular A-4 (2003) requires the use of constant 3 percent and
7 percent for both intra- and intergenerational discounting for benefit-
cost estimation of economically significant rules but allows for lower,
positive consumption discount rates, perhaps in the 1 percent to 3
percent range, if there are important intergenerational values.
23 Another issue is that there are no market rates for intergenerational
time periods.
intergenerational context by asking whether the
distribution of wealth across generations could
be adjusted to compensate the losers under an
environmental policy and still leave the winners
better off than they would have been absent the
policy. "Whether winners could compensate losers
across generations hinges on the rate of interest
at which society (the United States presumably,
or perhaps the entire world) can transfer wealth
across hundreds of years. Some argue that in the
U.S. context, a good candidate for this rate is the
federal governments borrowing rate. Some authors
also consider the infeasibility of intergenerational
transfers to be a fundamental problem for
discounting across generations.24
Equal Treatment Across Generations
Environmental policies that affect distant future
generations can be considered to be altruistic
acts.25 As such, some argue that they should be
valued by current generations in exacdy the same
way as other acts of altruism are valued. Under this
logic, the relevant discount rate is not based on
an individual's own consumption, but instead on
an individual's valuation of the consumption (or
welfare) of someone else. These altruistic values
can be estimated through either revealed or stated
preference methods.
At least some altruism is apparent from
international aid programs, private charitable
giving, and bequests within overlapping
generations of families. But the evidence suggests
that the importance of other people's welfare to
an individual appears to grow weaker as temporal,
cultural, geographic, and other measures of
"distance" increase. The implied discount rates
survey respondents appear to apply in trading off
present and future lives also is relevant under this
approach. One such survey (Cropper, Aydede, and
Portney 1994) suggests that these rates are positive
on average, which is consistent with the rates
at which people discount monetary outcomes.
The rates decline as the time horizon involved
lengthens.
24 See Lind (1990) and a summary by Freeman (2003).
25 Schelling (1995), and Birdsall and Steer (1993) are good references for
these arguments.
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Chapter 6 Discounting Future Benefits and Costs
8.3,2,2 Uncertainty
A longer time horizon in an intergenerational
policy context also implies greater uncertainty
about the investment environment and economic
growth over time, and a greater potential for
environmental feedbacks to economic growth
(and consumption and welfare), which in turn
further increases uncertainty when attempting to
estimate the social discount rate.
This additional uncertainty has been shown to
imply effective discount rates lower than those
based on the observed average market interest
rates, regardless of whether or not the estimated
investment effects are predominantly measured as
private capital or consumption terms (Weitzman
1998, 2001; Newell and Pizer 2003; Groom et al.
2005; and Groom et al. 2007).26 The rationale for
this conclusion is that consideration of uncertainty
in the discount rate should be based on the average
of discount factors (i.e., 1/(1 +r)*) rather than the
standard discount rate (i.e., r). From the expected
discount factor over any period of time a constant,
certainty-equivalent discount rate that yields the
discount factor (for any given distribution of r)
can be inferred. Several methods for accounting
for uncertainty into intergenerational discounting
are discussed in more detail in the next section.
6.3.3 Evaluating Alternatives
There is a wide range of options available to
the analyst for discounting intergenerational
costs and benefits. Several of these are described
below, ordered from simplest to most analytically
complex. Which option is utilized in the analysis
is left to expert judgment, but should be based on
the likely consequences of undertaking a more
complex analysis for the bottom-line estimate of
expected net benefits. This will be a function of the
proportion of the costs and benefits occurring far
out on the time horizon and the separation of costs
and benefits over the planning horizon. When
it is unclear which method should be utilized,
the analyst is encouraged to explore a variety of
approaches.
26 Gollier and Zeckhauser (2005) reach a similar result using a model
with decreasing absolute risk aversion.
6, start Discount Rate
One possible approach is to simply make no
distinction between intergenerational and
intragenerational social discounting. For example,
models of infinitely-lived individuals suggest the
consumption rate of interest as the social discount
rate. Of course, individuals actually do not live long
enough to experience distant future consequences
of a policy and cannot report today the present
values they place on those effects. However, it
is equally sufficient to view this assumption as
a proxy for family lineages in which the current
generation treats the welfare of all its future
generations identically with the current generation.
It is not so much that the individual lives forever
as that the family spans many generations (forever)
and that the current generation discounts
consumption of future generations at the same rate
as its own future consumption.
Models based on constant discount rates over
multiple generations essentially ignore potential
differences in economic growth and income and/
or preferences for distant future generations. Since
economic growth is unlikely to be constant over
long time horizons, the assumption of a constant
discount rate is unrealistic. Interest rates are a
function of economic growth; thus, increasing
(declining) economic growth implies an increasing
(decreasing) discount rate.
A constant discount rate assumption also does not
adequately account for uncertainty. Uncertainty
regarding economic growth increases as one goes
further out in time, which implies increasing
uncertainty in the interest rate and a declining
certainty equivalent rate of return to capital
(Hansen 2006).
8.3,3,2 Step Functions
Some modelers and government analysts have
experimented with varying the discount rate with
the time horizon to reflect non-constant economic
growth, intergeneration equity concerns, and/or
heterogeneity in future preferences. For instance,
in the United Kingdom the Treasury recommends
the use of a 3.5 percent discount rate for the first
30 years followed by a declining rate over future
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Chapter 6 Discounting Future Benefits and Costs
time periods until it reaches 1 percent for 301 years
and beyond.27 This method acknowledges that a
constant discount rate does not adequately reflect
the reality of fluctuating and uncertain growth
rates over long time horizons. However, application
of this method also raises several potential analytic
complications. First, there is no empirical evidence
to suggest the point(s) at which the discount rate
declines, so any year selected for a change in the
discount rate will be necessarily ad-hoc. Second,
this method can suffer from a time inconsistency
problem. Time inconsistency means that an
optimal policy today may look sub-optimal in the
future when using a different discount rate and vice
versa. Some have argued that time inconsistency
is a relatively minor problem relative to other
conditions imposed (Heal 1998, Henderson and
Bateman 1995, and Spackman 2004).
6.3.3.3 Declining or N istant
Discount Rate
Using a constant discount rate in BCA is
technically correct only if the rate of economic
growth will remain fixed over the time horizon of
the analysis. If economic growth is changing over
time, then the discount rate, too, will fluctuate.
In particular, one may assume that the growth
rate is declining systematically over time (perhaps
to reflect some physical resource limits), which
will lead to a declining discount rate. This is
the approach taken in some models of climate
change.28 In principle, any set of known changes
to income growth, the elasticity of marginal
utility of consumption, or the pure rate of time
preference will lead to a discount rate that changes
accordingly.
6.3.3.4 Uncertainty-Adjusted
Discounting
If there is uncertainty about the future growth rate,
then the correct procedure for discounting must
27 The guidance also requires a lower schedule of rates, starting with 3
percent for zero to 30 years, where the pure rate of time preference
in the Ramsey framework (the parameter p in our formulation) is
set to zero. For details see HM Treasury (2008) Iritergenerational
wealth transfers and social discounting: Supplementary Green Book
Guidance.
28 See, for example, Nordhaus (2008).
account for this uncertainty in the calculation of
the expected NPV of the policy. Over the long
time horizon, both investment uncertainty and
risk will naturally increase, which results in a
decline in the imputed discount rate. If the time
horizon of the policy is very long, then eventually a
low discount rate will dominate the expected NPV
calculations for benefits and costs far in the future
(Weitzman 1998).
Newell and Pizer (2003) expand on this
observation, using historical data on U.S.
interest rates and assumptions regarding their
future path to characterize uncertainty and
compute a certainty equivalent rate. In this
case, uncertainty in the individual components
of the Ramsey equation is not being modeled
explicitly. Their results illustrate that a constant
discount rate could substantially undervalue
net present benefits when compared to one
that accounts for uncertainty. For instance,
a constant discount rate of 7 percent could
undervalue net present benefits by between 21
percent and 95 percent depending on the way in
which uncertainty is modeled.
A key advantage of this treatment of the discount
rate over the step function and simple declining
rate discounting approaches is that the analyst is
not required to arbitrarily designate the discount
rate transitions over time, nor required to ignore
the effects of uncertainty in economic growth over
time. Thus, this approach is not subject to the time
inconsistency problems of some other approaches.
Another issue that has emerged about the use
of discount rates that decline over time due to
uncertainty is that they could generate inconsistent
policy rankings NPV versus NF V.29 Because the
choice between NPV and NF V is arbitrary, such
an outcome would be problematic for applied
policy analysis. More recent work, however,
appears to resolve this seeming inconsistency,
confirming the original findings and providing
sound conceptual rationale for the approach.30
29 See Gollier (2004) for a technical characterization of this concern, and
Hepburn and Groom (2007) for additional exploration of the issues.
30 See Gollier and Weitzman (2009) provide a concise and clear
treatment. Freeman (2009) and Gollier (2009) also propose solutions.
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Chapter 6 Discounting Future Benefits and Costs
Text Bo* 6.6 - What's the Big Deal with The Stern Review?
In autumn 2006, the U.K. government released a detailed report titled The Economics of Climate Change: The Stem
Review, headed by Sir Nicholas Stern (2006). The report drew mainly on published studies and estimated that
damages from climate change could result in a 5 percent to 20 percent decline in global output by 2100. The report
found that costs to mitigate these impacts were significantly less (about 1 percent of GDP). Stern's findings led him to
say that "climate change is the greatest and widest-ranging market failure ever seen," and that "the benefits of strong
early action considerably outweigh the cost." The Stem Review recommended that policies aimed towards sharp
reduction in GHG emissions should be enacted immediately.
While generally lauded for its thoroughness and use of current climate science, The Stem Reviewdiew significant
criticism and discussion of how future benefits were calculated, namely targeting Stern's assumptions about the
discount rate (Tol and Yohe 2006 and Nordhaus 2008). The Stem Review used the Ramsey discounting equation
(see Section 6.3.1), applying rates of 0.1 percent for the annual pure rate of time preference, 1.3 percent for the
annual growth rate, and a elasticity of marginal utility of consumption equal to 1. Combining these parameter values
reveals an estimated equilibrium real interest rate of 1.4 percent, a rate arguably lower than most returns to standard
investments, but not outside the range of values suggested in these Guidelines for intergenerational discount rates.
So why is the issue on the value of the discount rate so contentious? Perhaps the biggest concern is that climate
change is expected to cause significantly greater damages in the far future than it is today, and thus benefits are
sensitive to discounting assumptions. A low social discount rate means The Stem Review places a much larger
weight on the benefits of reducing climate change damages in 2050 or 2100 relative to the standard 3 percent or 7
percent commonly observed in market rates. Furthermore, Stern's relatively low values of ^ and ij imply that the
current generation should operate at a higher savings rate than what is observed, thus implying that society should
save more today to compensate losses incurred by future generations.
Why did Stern use these particular parameter values? First, he argues that the current generation has an ethical
obligation to place similar weights on the pure rate of time for future generations. Second, a marginal elasticity of
consumption of unity implies a relatively low inequality aversion, which reduces the transfer of benefits between the
rich and the poor relative to a higher elasticity. Finally, there are significant risks and uncertainties associated with
climate change, which could imply using a lower-than-market rate. Stern's (2006) concluding remarks for using a
relatively low discount rate are clear, "However unpleasant the damages from climate change are likely to appear in the
future, any disregard for the future, simply because it is in the future, will suppress action to address climate change."
idations
and Guidance
As summed up by Freeman (2003 p. 206),
"economists have not yet reached a consensus
on the appropriate answers" to all of the issues
surrounding intergenerational discounting. And
while there may be more agreement on matters
of principle for discounting in the context of
intragenerational policies, there is still some
disagreement on the magnitude of capital
displacement and therefore the importance of
accounting for the opportunity costs of capital
in practice.31 The recommendations provided
here are intended as practical and plausible
default assumptions rather than comprehensive
and precise estimates of social discount rates
that must be applied without adjustment in all
situations. That is, these recommendations should
be used as a starting point for BCA, but if the
31 This chapter summarizes some key aspects from the core literature
on social discounting; it is not a detailed review of the vast and
varied social discounting literature. Excellent sources for additional
information are: Lind (1982a, b; 1990; 1994), Lyon (1990,1994), Kolb
and Scheraga (1990), Scheraga (1990), Arrow etal. (1996), Pearce and
Turner (1990), Pearce and Ulph (1994), Groom etal. (2005), Cairns
(2006), Frederick et al. (2002), Moore et al. (2004), Spackman (2004),
and Portneyand Weyant (1999).
Mi
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Chapter 6 Discounting Future Benefits and Costs
analysts can develop a more realistic model and
bring to bear more accurate empirical estimates
of the various factors that are most relevant to
the specific policy scenario under consideration,
then they should do so and provide the rationale
in the description of their methods. With this
caveat in mind, our default recommendations for
discounting are below.
• Display the time paths of benefits and
costs as they are projected to occur over
the time horizon of the policy, i.e., without
discounting.
• The shadow price of capital approach
is the analytically preferred method for
discounting, but there is some disagreement
on the extent to which private capital is
displaced by EPA regulatory requirements.
EPA will undertake additional research and
analysis to investigate important aspects of
this issue, including the elasticity of capital
supply, and will update guidance accordingly.
In the interim analysts should conduct a
bounding exercise as follows:
° Calculate the NPV using the consumption
rate of interest. This is appropriate for
situations where all costs and benefits
occur as changes in consumption flows
rather than changes in capital stocks, i.e.,
capital displacement effects are negligible.
As of the date of this publication, current
estimates of the consumption rate of
interest, based on recent returns to
Government-backed securities, are close to
3 percent.
O Also calculate the NPV using the rate of
return to private capital. This is appropriate
for situations where all costs and benefits
occur as changes in capital stocks rather
than consumption flows. The OMB
estimates a rate of 7 percent for the
opportunity cost of private capital.
OEPA intends to periodically review the
empirical basis for the consumption
discount rate and the rate of return to
private capital.
In most cases the results of applying the more
detailed "shadow price of capital" approach
will lie somewhere between the NPV
estimates ignoring the opportunity costs of
capital displacements and discounting all
costs and benefits using these two alternative
discount rates.
* If the policy has a long time horizon (more
than 50 years or so) where net benefits vary
substantially over time (e.g., most benefits
accrue to one generation and most costs
accrue to another) then the analysis should
use the consumption rate of interest as well
as additional approaches. These approaches
include calculating the expected present
value of net benefits using an estimated time-
declining schedule of discount factors (Newell
and Pizer 2003, Groom et al. 2007, and
Hepburn et al. 2009). This approach accounts
for discount rate uncertainty and variability,
which are known to have potentially large
effects on NPV estimates for policies with
long time horizons. If a time-declining
approach cannot be implemented, it is
possible to capture part of its empirical effect
by discounting at a constant rate somewhat
lower than those used in the conventional
case. For example, the current Interagency
guidance for valuing C02 emission reductions
includes treatment with certainty-equivalent
constant discount rates of 2.5 percent, 3
percent, and 5 percent. (See Text Box 7.1 for
more discussion of the Interagency guidance.)
Other more detailed alternatives, such as
constructing discounts rate from estimates
of the individual parameters in the Ramsey
equation, may merit inclusion in the analysis.
In any case, all alternatives should be fully
described, supported, and justified.
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Chapter 6 Discounting Future Benefits and Costs
When implementing any discounting approach
the following principles should be kept in mind:
• In all cases social benefits and costs should
be discounted in the same manner, although
private discount rates may be used to predict
behavior and to evaluate economic impacts.
• The discount rate should reflect marginal
rates of substitution between consumption
in different time periods and should not be
confounded with factors such as uncertainty
in benefits and costs or the value of
environmental goods or other commodities
in the future (i.e., the "current price" in
future years).
• The lag time between a change in regulation
and the resulting welfare impacts should
be accounted for in the economic analysis.
The monetary benefits from the expected
future impacts should be discounted at the
same rate as other benefits and costs in the
analysis. This includes changes in human
health, environmental conditions, ecosystem
services, etc.
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Chapter 7
Analyzing 13^ €!5 m I3»tff I !!>?§
he aim of an economic benefits analysis is to estimate the benefits, in monetary
terms, of proposed policy changes in order to inform decision making.
Estimating benefits in monetary terms allows the comparison of different types
of benefits in the same units, and it allows the calculation of net benefits — the
sum of all monetized benefits minus the sum of all monetized costs — so that
proposed policy changes can be compared to each other and to the baseline scenario.
The discussion in this chapter focuses on methods and approaches available to monetize
benefits in the context of a "typical" EPA policy, program, or regulation that reduces
emissions or discharges of contaminants. This is not to say that those benefits that cannot
be monetized due to lack of available values or quantification methods are not important.
Chapter 11 on the "Presentation of Analysis and Results" discusses how to carry forward
information on non-monetized benefits to help inform the policy-making process. In
addition, this chapter includes a discussion of several alternatives to monetization that
may add some context to this category of benefits. The general monetization methods and
principles discussed here should apply to other types of EPA polices as well, such as those that
provide regulatory relief, encourage reuse of remediated land, or provide information to the
public to help people avoid environmental risks.1
iefits
Analyi ess
Ideally, benefits analyses would consist of
comprehensive assessments of all environmental
effects attributable to the rule in question.
However, it is seldom possible to analyze all effects
simultaneously in an integrated fashion. In most
cases, analysts will need to address each effect
individually, and then aggregate the individual
values to generate an estimate of the total benefits
of a policy A constant challenge in employing an
effect-by-effect approach is to balance potential
trade-offs between inclusion and redundancy
1 Other methods, such as cost-effectiveness analysis (CEA), can also be
used to evaluate policies. CEA does not require monetization of benefits but
rather divides the costs of a policy by a particular effect (e.g., number of
lives saved). CEA can be used to compare proposed policy changes on an
effect-by-effect basis, but, unlike BCA, cannot be used to calculate a single,
comprehensive measure of the net effects of a policy, nor can it compare
proposed policy changes to the status quo. Other methods for evaluating
policies (e.g., distributional analyses) are covered in Chapter 10.
Ideally, each effect will be measured once and only
once. Techniques intended to bring additional
effects into the analysis may run the risk of double
counting effects already measured. For example,
stated preference methods may be the only way to
measure non-use values, but they may double count
use values already reflected in hedonic or travel cost
analyses. Therefore, the analyst should be careful in
interpreting and combining the results of different
methods.
There are of course exceptions to this "effect-by-
effect" approach to benefits analysis (e.g., efforts
to estimate the social benefits of reducing C02
emissions — see Text Box 7.1), but the remainder of
the discussion below is framed with this approach
in mind.
A second challenge analysts often face is the difficulty
of conducting original valuation research in support
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Chapter 7 Analyzing Benefits
Text Boj mating Benefits from Reducing Carbon Dioxide
Emissions; The Social Cost of Carbon
Monetized estimates of the damages associated carbon dioxide (CO ) emissions allows the social benefits
of regulatory actions that are expected to reduce these emissions to be incorporated into BCA. One way to
accomplish this is through the estimation of the "social cost of carbon" (SCO). The SCO is the present value
of the stream of future economic damages associated with an incremental increase (by convention, one metric
ton) in CO emissions in a particular year. It is intended to be a comprehensive measure and includes economic
losses due to changes in agricultural productivity human health risks, property damages from increased flood
frequencies, the loss of ecosystem services, etc. The SCC is a marginal value so it may not be accurate for valuing
large changes in emissions. However, many U.S. government regulations will lead to relatively small reductions in
cumulative global emissions, so for these regulations the SCC is the appropriate shadow value for estimating the
economic benefits of CO reductions.
Most published estimates of the SCC have been derived from "integrated assessment models" (lAMs) that combine
climate processes, economic growth, and feedbacks between the two in a single modeling framework. These models
include a reduced form representation of the potential economic damages from climate change. Therefore lAMs
used to estimate the SCC are necessarily highly simplified and limited by the current state of the climate economics
literature, which continues to expand rapidly. Despite the inherent uncertainties in models such as these, they are the
best tools currently available for estimating the SCC.
The Interagency SCC Workgroup. ¦.r. :
agencies and various White House offices was convened to improve the accuracy and consistency in how agencies
value reductions in CO emissions in regulatory impact analyses. The resulting range of values is based on estimates
from three integrated assessment models applied to five socioeconomic and emissions scenarios, all given equal
weight. To reflect differing expert opinions about discounting, the present value of the time path of global damages
in each model-scenario combination was calculated using discount rates of 5 percent, 3 percent, and 2.5 percent.
Finally, in a step toward more formal uncertainty analysis, all model runs employed a probabilistic representation of
climate sensitivity (in addition to other parameters in two of the models).
The workgroup selected four SCC estimates from the model runs to reflect the global damages caused by CO
emissions: $5, $21, $35, and $65 for 2010 emission reductions (in 2007 U.S. dollars). The first three estimates
are based on the average SCC across the three models and five socioeconomic and emissions scenarios for the
5 percent, 3 percent, and 2.5 percent discount rates, respectively. The fourth value, the 95 percentile of the SCC
distribution at a 3 percent discount rate, was chosen to represent potential higher-than-expected impacts from
temperature change. The SCC estimates grow over time at rates endogenously determined by the models. For
instance, with a discount rate of 3 percent, the mean SCC estimate increases to $24 per ton of CO in 2015 and $26
per ton of CO in 2020.
Going Forward. ¦ ¦>. ¦ >. >; >. •
the many uncertainties involved and the final report outlined a number of limitations to the analysis. The Interagency
SCC Workgroup is committed to re-visiting these estimates on a regular basis and revising them as needed to reflect
the growing body of scientific knowledge regarding climate change impacts and the potential economic damages
from those impacts.
Further Reading: r. >. r: ^ j-: ¦
Regulatory Impact Analysis Under Executive Order 12866, www.epa.gov/otaq/climate/regulations/scc-tsd.pdf.
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Chapter 7 Analyzing Benefits
of specific policy actions. Because budgetary and
time constraints often make performing original
research infeasible, analysts regularly need to draw
upon existing value estimates for use in benefits
analysis. Hie process of applying values estimated
in previous studies to new policy cases is called
benefit transfer. Hie benefit transfer method is
discussed in detail in Section 7.4, but much of this
chapter is written with benefit transfer in mind. In
particular, the descriptions of revealed and stated
preference valuation methods in Sections 7.3.1 and
7.3.2 include recommendations for evaluating the
quality and suitability of published studies for use
in benefit transfer.
A genera! "effect-by-effect" approach to
benefits analysis
This approach consists of separately evaluating the
major effects of a given policy, and then summing
these individual estimates to arrive at an overall
estimate of total benefits. The effect-by-effect
approach for benefits analysis requires three
fundamental steps:
1. Identify benefit categories potentially
affected by the policies under consideration;
2. Quantify significant endpoints to the extent
possible by working with managers, risk
assessors, ecologists, physical scientists, and
other experts; and
3. Estimate the values of these effects using
appropriate valuation methods for new
studies or existing value estimates from
previous studies that focus on the same or
sufficiently similar endpoints.
Each step in this approach is discussed in more
detail below. Analysts also should consider
whether this general framework is appropriate
for assessing a specific policy or whether a more
integrated approach that incorporates all of the
relevant effects simultaneously can be applied.
When applying the effect-by-effect approach it is
important to avoid double counting benefits across
effects as much as possible. Collaboration with
appropriate experts will be necessary to execute
these steps meaningfully.
Steph Identify potentially affected
benefit categories
The first step in a benefits analysis is to determine
the types of benefits associated with the policy
options under consideration. More information on
benefits categories can be found in Section 7.2. To
identify benefit categories, analysts should, to the
extent feasible:
Develop an initial understanding of policy
options of interest by working with other analysts
and policy makers. Initially, the range of options
considered may be very broad. Resources should
be focused on benefit categories that are likely to
influence policy decisions. Collaboration between
all parties involved in the policy analysis can help
ensure that all potential effects are recognized and
that the necessary and appropriate information
and endpoints are collected and evaluated at each
step in the process. Analysts should take care to
think through potential secondary or indirect
effects of the policy options as well, as these may
prove to be important.
Research the physical effects of the pollutants on
human health and the environment by reviewing
the literature and consulting with other experts.
This step requires considering the transport of the
pollutants through the environment along a variety
of pathways, including movement through the air,
surface water, and groundwater, deposition in soils,
and ingestion or uptake by plants and animals
(including humans). Along these pathways, the
pollutants can have detrimental effects on natural
resources, such as affecting oxygen availability in
surface water or reducing crop yields. Pollutants
can also have direct or indirect effects on human
health, for example affecting cancer incidence
through direct inhalation or through ingestion of
contaminated food.
Consider the potential change in these effects
as a result of each policy option. If policy options
differ only in their level of stringency then each
option may have an impact on all identified
physical effects. In other cases, however, some
effects maybe reduced while others are increased
or remain unchanged. Evaluating how physical
effects change under each policy option requires
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 7 Analyzing Benefits
evaluation of how the pathways differ in the "post-
policy" world.
Determine which benefit categories to include
in the overall benefits analysis using at least the
following three criteria:
1. Which benefit categories are likely to
differ across policy options, including the
baseline option? Analysts should conduct an
assessment of how the physical effects of each
policy option will differ and how each physical
effect will impact each benefit category
2. Which benefit categories are likely to
account for the bulk of the total benefits of
the policy? The cutoff point here should be
based on an assessment of the magnitude
and precision of the estimates of each benefit
category, the total social costs of each policy
option, and the costs of gathering further
information on each benefit category A
benefit category should not be included
if the cost of gathering the information
necessary to include it is greater than the
expected increase in the value of the policy
owing to its inclusion. The analyst should
make these preliminary assessments using
the best quantitative information that is
readily available, but as a practical matter
these decisions may often have to be based on
professional judgments.
3. Which benefit categories are especially
salient to particular stakeholders ? Monetized
benefits in this category are not necessarily
large and so may not be captured by the first
two criteria.2
The outcome of this initial step in the benefits
analysis can be summarized in a list or matrix that
describes the physical effects of the pollutant(s),
identifies the benefit categories associated with
these effects, and identifies the effects that warrant
further investigation.
The list of physical effects under each benefit
category may be lengthy at first, encompassing all
of those that reasonably can be associated with
2 This third criterion relates to distributional considerations detailed in
Chapter 10.
the policy options under consideration. Analysts
should preserve and refine this list of physical
effects as the analysis proceeds. Maintaining the
full list of potential effects — even though the
quantitative analysis will (at least initially) focus
on a sub-set of them — will allow easy revision of
the analysis plan if new information warrants it.
EPA has developed extensive guidance on the
assessment of human health and ecological risks,
and analysts should refer to those documents
and the offices responsible for their production
and implementation for further information
(U.S. EPA 2009a). No specific guidance exists for
assessing changes in amenities or material damages.
Analysts should consult relevant experts and
existing literature to determine the "best practices"
appropriate for these categories of benefits.
Step 2: Quantify significant endpoints
The second step is to quantify the physical
endpoints related to each category, focusing on
changes attributable to each policy option relative
to the baseline. Data are usually needed on the
extent, timing, and severity of the endpoints. For
example, if the risk of lung cancer is an endpoint of
concern, required information will usually include
the change in risk associated with each option, the
timing of the risk changes, the age distribution of
affected populations, and fatality rates. If visibility
is the attribute of concern, required information
will usually include the geographical areas affected
and the change in visibility resulting from each
policy option.
Analysts should keep the following issues in mind
while quantifying significant physical effects.
Work closely with analysts in other fields.
Estimating physical effects is largely, but not
completely, the domain of other experts, including
human health and ecological risk assessors and
other natural scientists. These experts generally
are responsible for evaluating the likely transport
of the pollutant through the environment and its
potential effects on humans, ecological systems,
and manufactured materials.
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Chapter 7 Analyzing Benefits
Text Box 7,2 - Integral! Momics arid Risk Assessment
Historically, health and ecological risk assessments have been designed not to support benefits analyses per se but
rather to support the setting of standards or to rank the severity of different hazards. Traditional measures of risk can
be difficult or impossible to incorporate into benefits analyses. For example, traditional measures of risk are often
based on endpoints not directly related to health outcomes or ecological services that can be valued using economic
methods. These measures are often based on outcomes near the tails of the risk distribution for highly sensitive
endpoints, which would lead to biased benefits estimates if extrapolated to the general population.
Because economists rely on risk assessment outcomes as key inputs into benefits analysis, it is important that risk
assessments and economic valuation studies be undertaken together. Economists can contribute information and
insights into how behavioral changes may affect realized risk changes. For example, if health outcomes in a particular
risk assessment are such that early medical intervention could reduce the chances of illness, economists may be able
to estimate changes in the probability that individuals will seek preventative care. Even in cases where the economists'
contribution to the risk characterization is not direct, economists and risk assessors should communicate frequently
to ensure that economic analyses are complete. Specifically risk assessors and economists should:
¦ Identify a set of human health and ecological endpoints that are economically meaningful. The endpoints
should be linked to human well-being and monetized using economic valuation methods. This may require risk
assessors to model more or different outcomes than they would if they were attempting to capture only the most
sensitive endpoint. This also may require risk assessors and economists to convert specific human health or
ecological endpoints measured in laboratory or epidemiological studies to other effects that can be valued in
the economic analysis.
Estimate changes in the probabilities of human health or ecological outcomes rather than ¦'safety assessment"
measures such as reference doses and reference concentrations.
¦ Work to produce expected or central estimates of risk, rather than bounding estimates as in safety assessments.
At a minimum, any expected bias in the risk estimates should be clearly described.
¦ Attempt to estimate the "cessation lag" associated with reductions in exposure. That is, the analysis should
characterize the time profile of changes in exposures and risks.
Attempt to characterize the full uncertainty distribution associated with risk estimates. Not only does this
contribute to a better understanding of potential regulatory outcomes, it also enables economists to incorporate
risk assessment uncertainty into a broader analysis of uncertainty. Formal probabilistic assessment is required
for some regulations by CircularA-4 (OMB 2003). Also refer to EPA's guidance and reference documents
on Monte Carlo methods and probabilistic risk assessment, including EPA's Policy for Use of Probabilistic
Analysis in Risk Assessments (U.S. EPA 1997e), and the 1997 Guiding Principles for Monte Carlo Analysis
(U.S. EPA 1997d).
Hie principal role of the economist at this stage is
to ensure that the information provided is useful
for the subsequent economic valuation models
that may be used later in the benefits analysis. The
analyst should give special care to ensuring that
the endpoints evaluated are appropriate for use
in benefits estimation. Effects that are described
too broadly or that cannot be linked to human
well-being limit the ability of the analysis to
capture the full range of a policy's benefits. Text
Box 7.2 provides examples and a more detailed
discussion.
Another important role for economists at this
stage is to provide insights, information, and
analysis on behavioral changes that can affect the
results of the risk assessment as needed. Changes
in behavior due to changes in environmental
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Chapter 7 Analyzing Benefits
quality (e.g., staying indoors to avoid detrimental
effects of air pollution) can be significant and care
should be taken to account for such responses in
risk assessments and benefit estimations.
Step 3: Estimate the values of the effects
The next step is to estimate willingness to
pay (WTP) of all affected individuals for the
quantified benefits in each benefit category, and
then to aggregate these to estimate the total
social benefits of each policy option. Typically,
a representative agent approach is used when
deriving estimates of benefits. The analyst
calculates WTP for an "average" individual in a
sample of people from the relevant population and
then multiply that average value by the number
of individuals in the exposed population to derive
an estimate of total benefits. As discussed earlier,
markets do not exist for many of the types of
benefits expected to result from environmental
regulations. Details on the economic valuation
methods suitable for this step and examples of how
they can be applied can be found in Section 7.3. In
applying these methods, analysts should:
Consider using multiple valuation methods
when possible. Different methods often address
different subsets of total benefits and the use
of multiple methods allows for comparison of
alternative measures of value when applied to the
same category of benefits. Double counting is a
significant concern when applying more than one
method. Any potential overlap should be noted
when presenting the results. The discussion of
benefit transfer in Section 7.4 describes many of
the issues involved in applying value estimates
from previous studies to new policy cases,
including various meta-analysis techniques for
combining estimates from multiple studies.
Describe the source of estimates and confidence
in those sources. Valuation estimates always
contain a degree of uncertainty. Using them
in a context other than the one in which they
were initially estimated can only increase that
uncertainty. If many high-quality studies of the
same effect have produced comparable values,
analysts can have more confidence in using these
estimates in their benefits calculations. In other
cases, analysts may have only a single study, or even
no directly comparable study, to draw from. In all
cases, the benefits analysis should clearly describe
the sources of the value estimates used and provide
a qualitative discussion of the reliability of those
sources. The analyst should include a quantitative
uncertainty assessment when possible. Guiding
principles for presenting uncertainty are addressed
in Chapter 11.
Mb onomic Value ar ;
of Benefits
Economic valuation is based on the traditional
economic theory of human behavior and
preferences, which centers on the concept of
"utility" (or "satisfaction" or "welfare") that people
realize from goods and services, both market and
non-market. Different levels and combinations
of goods and services afford different levels of
utility for any one person. Because different people
have different preferences, different sets of goods
and services will appeal more or less to different
people. Utility is inherently subjective and cannot
be measured directly. Therefore, in order to give
"value" an operational definition it must be
expressed in a quantifiable metric. Money generally
is used as the metric, but this choice for the unit
of account has no special theoretical significance.
One could use "apples," "bananas," or anything else
that is widely valued and consumed by individuals.
The crucial assumption is that a person could
be compensated for the loss of some additional
quantity of any good by some quantity of another
good that is selected as the metric. Table 7.1
summarizes the types of benefits associated with
environmental protection policies and provides
examples of each of the benefits types, as well as
valuation methods commonly used to monetize
the benefits for each type.
When goods and services are bought and
sold in competitive markets, the ratio of the
marginal utility (the utility afforded by the last
unit purchased) of any two goods that a person
consumes must be equal to the ratio of the prices
of those goods. If it were otherwise, that person
could reallocate her budget to buy a little more
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 7 Analyzing Benefits
Figure 7,1 - Benefits of an Environmental
$
MAC MD
Emissions
of one good and a little less of the other good to
achieve a higher level of utility. Thus, market prices
can be used to measure the value of market goods
and services directly. A practical rationale for using
money as the metric for non-market valuation is
that money is the principal medium of exchange
for the wide variety of market goods and services
among which people choose on a daily basis.
The benefits of an environmental improvement
are shown graphically in Figure 7.1. Reducing
emissions from eg to e produces benefits equal to
the shaded area under the marginal damages (MD)
curve. Many environmental goods and services,
such as air quality and biological diversity, are
not traded in markets. The challenge of valuing
non-market goods that do not have prices is to
relate them to one or more market goods that do.
This can be done either by determining how the
non-market good contributes to the production of
one or more market goods (often in combination
with other market good inputs), or by observing
the trade-offs people make between non-market
goods and market goods. One way or another, this
is what each of the revealed and stated preference
valuation methods discussed in Section 7.3 is
designed to do. Of course, some methods will be
more suitable than others in any particular case for
a variety of reasons, and some will be better able
to capture certain types of benefits than others.
In principle, though, they are all different ways
of measuring the same thing, which is the total
amount of money required to make all individuals
indifferent between the baseline and policy
scenarios.
The economic valuation of an environmental
improvement is the dollar value of the private
goods and services that individuals would
be willing to trade for the improvement at
prevailing market prices. The willingness to
trade compensation for goods or services can
be measured either as willingness to pay (WTP)
or willingness to accept (WTA). WTP is the
maximum amount of money an individual would
voluntarily pay to obtain an improvement. WTA
is the least amount of money an individual would
accept to forego the improvement.3 The key
theoretical distinction between WTP and WTA
is their respective reference utility levels. For
environmental improvements, WTP uses the level
of utility without the improvement as the reference
point while WTA uses the level of utility with
the improvement as the reference point. Because
of their different reference points, one relevant
factor to consider when deciding whether WTP or
WTA is the appropriate value measure to use in a
BCA is the property rights for the environmental
resource (s) in question. WTP is consistent with
individuals or firms having rights to pollute
and the affected parties needing to pay them to
desist. WTA is consistent with individuals being
entitled to a clean environment and needing to be
compensated for any infringements of that right
(Freeman 2003).
Economists generally expect that the difference
between WTP and WTA will be small, provided
the amounts in question are a relatively small
proportion of income and the goods in question
are not without substitutes, either market or non-
market. However, there may be instances in which
income and substitution effects are important.4 To
simplify the presentation, the term WTP is used
throughout the remainder of this chapter to refer
3 For simplicity, the discussion in this section is restricted to the case
of environmental improvements, but similar definitions hold for
environmental damages. For a more detailed treatment of WTP and
WTA and the closely related concepts of compensating variation,
equivalent variation, and Hicksian and Marshallian consumer surplus,
see Hanley and Spash (1993), Freeman (2003), Just etal. (2005), and
Appendix A of these Guidelines.
4 For more information see Appendix A and Flanemann (1991).
Guidelines for Preparing Economic Analyses I December 2010
7-7
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Chapter 7 Analyzing Benefits
to the underlying economic principles behind
both WTA and WTP, but the analyst should
keep the potential differences between the two
measures in mind.
Based on the connection to individual welfare just
described, estimates of WTP are needed for the
Kaldor and Hicks potential compensation tests that
form the basis of BCA (Boadway and Bruce 1984,
Just et al. 1982, and Freeman 2003). To carry out these
tests, sum the WTP for all affected individuals and
compare the summed WTP value to the estimated
costs of the proposed policy. Because environmental
policy typically deals with improvements rather than
deliberate degradation of the environment, WTP is
generally the relevant measure.5
The types of benefits that may arise from
environmental policies can be classified in multiple
ways (Freeman 2003). As shown in Table 7.1,
these Guidelines categorize benefits as human
health improvements, ecological improvements,
and other types of benefits, including aesthetic
improvements and reduced materials damages,
and list commonly used valuation methods for
reference. The list is not meant to be exhaustive,
but rather to provide examples and commonly
used methods for estimating values.6 The sections
below provide a more detailed discussion of each
of the benefit categories listed in Table 7.1.
7.2,1 Human Health Improvements
Human health improvements from environmental
policies include effects such as reduced mortality
rates, decreased incidence of non-fatal cancers,
chronic conditions and other illnesses, and
reduced adverse reproductive or developmental
effects. While the most appropriate approach to
valuation would consider mortality and morbidity
together, in practice these effects are typically
valued separately, and are therefore discussed
separately in these Guidelines.
7.2,1,1 Mortality
Some EPA policies will lead to decreases in human
mortality risks due to potentially fatal health
conditions such as cancers. In considering the
impact of environmental policy on mortality, it
is important to remember that environmental
policies do not assure that particular individuals
will not die of environmental causes. Rather, they
lead to small changes in the probability of death
for many people.
EPA currently recommends a default central
"value of statistical life" (VSL) of $7.9 million
(in 2008 dollars) to value reduced mortality
for all programs and policies.7 This value is
based on a distribution fitted to 26 published
VSL estimates. The distribution itself can be used
in uncertainty analysis. The underlying studies,
the distribution parameters, and other useful
information are available in Appendix B.
As a general matter, the impact of risk and
population characteristics should be addressed
qualitatively. In some cases, the analysis may
include a quantitative sensitivity analysis. Analysts
should account for latency and cessation lag
when valuing reduced mortality risks, and should
discount appropriately.
Valuing mortality risk changes in children is
particularly challenging. EPA's Handbookfor
Valuing Children's Health Risks (2003b) provides
some information on this topic, including key
benefit transfer issues when using adult-based
studies. Circular A-4 also recognizes this subject,
specifically advising: "For rules where health gains
are expected among both children and adults and
you decide to perform a BCA, the monetary values
for children should be at least as large as the values
for adults (for the same probabilities and outcomes)
unless there is specific and compelling evidence to
suggest otherwise" (OMB 2003). OMB guidance
applies to risk of mortality and of morbidity.
5 See Section A.3 of Appendix A for further explanation of Kaldor-Hicks
conditions.
6 In very rare cases with employment implications for the structurally
unemployed, analysts may need to include job creation as a benefits
category. See Appendix C for more detail.
7 This value is adjusted from the base value reported in U.S. EPA 2000d
($4.8 million in 1990 dollars) using the Consumer Price Index (CPI).
The value is not adjusted for income growth over time.
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 7 Analyzing Benefits
labif sociated With Environmental Policies;
Categories, Examples, and Commonly Used Valuation Methods
Human Health Improvements
Mortality risk reductions
Reduced risk of:
Cancer fatality
Acute fatality
Averting behaviors
Hedonics
Stated preference
Morbidity risk reductions
Ecological Improvements
Reduced risk of:
Cancer
Asthma
Nausea
Averting behaviors
Cost of illness
Hedonics
Stated preference
MarkPt Products
Harvests or extraction of:
Food
Fuel
Fiber
Timber
Fur and Leather
Production function
Recreation activities and aesthetics
Wildlife viewing
Fishing
Boating
Swimming
Hiking
Scenic views
Production function
Averting behaviors
Hedonics
Recreation demand
Stated preference
Valued ecosystem functions
Climate moderation
Flood moderation
Groundwater recharge
Sediment trapping
Soil retention
Nutrient cycling
Pollination by wild species
Biodiversity genetic library
Water filtration
Soil fertilization
Pest control
Production function
Averting behaviors
Stated preference
Non-use values
Other Benefits
Relevant species populations,
communities, or ecosystems
Stated preference
Aesthetic improvements
Visibility
Taste
Odor
Averting behaviors
Hedonics
Stated preference
Reduced materials damages
Reduced soiling
Reduced corrosion
Averting behaviors
Production/cost functions
Note: "Stated preference" refers to all valuation studies based on hypothetical choices, as distinguished from
"revealed preference," which refers to valuation studies based on observations of actual choices.
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 7 Analyzing Benefits
Methods for valuing mortality risk changes
Because individuals appear to make risk-income
trade-offs in a variety of ways, the value of
mortality risk changes are estimated using three
primary methods. The most commonly used
method is the hedonic wage, or wage-risk, method
in which value is inferred from the income-risk
trade-offs made by workers for on-the-job risks.
Averting behavior studies value risk changes by
examining purchases of goods that can affect
mortality risk (e.g., bicycle helmets). Finally,
stated preference studies are increasingly used to
estimate WTP for reduced mortality risks. Key
considerations in all of these studies include the
extent to which individuals know and understand
the risks involved, and the ability of the study to
control for aspects of the actual or hypothetical
transaction that are not risk-related. Because the
value of risk reduction may depend on the risk
context (e.g., work-related vs. environmental),
results from any single study may not be direcdy
applicable to a typical environmental policy case.
There are additional methods that can be used to
derive information on risk trade-offs. Van Houtven
et al. (2008) use a risk-risk trade-off model to
examine preferences for avoiding fatal cancers.
Carthy et al. (1999) examine trade-offs between
fatal and non-fatal risks to indirectly estimate a
WTP. This approach may make the task more
manageable for the respondent, but the analyst
should consider and evaluate the complexity of
the additional steps and the indirect nature of the
resulting estimates.
At one time, reduced mortality risk was valued
under a human capital approach that equated the
value of a statistical life with foregone earnings.
This has largely been rejected as an inappropriate
measure of the value of reducing mortality risks
because it is not based on WTP for small risk
reductions and as such does not capture the value
associated with avoided pain and suffering, dread,
and other risk factors that are thought to affect
value (Viscusi 1993).
Previous studies
While there are many unresolved issues in
valuing mortality risks, the field is relatively rich
in empirical estimates and several substantial
reviews of the literature are available. A general
overview of common approaches and issues in
mortality risk valuation can be found in Hammitt
(2003). Viscusi (1993) and Viscusi and Aldy
(2003) provide detailed reviews of the hedonic
wage literature. Black, Galdo, and Liu (2003)
provide a technical review of the statistical issues
associated with hedonic wage studies. Blomquist
(2004) provides a review of the averting behavior
literature. Some key issues related to stated
preference studies are included in Alberini (2004).
Recently, some researchers have begun to use meta-
analysis to combine study results and examine the
impact of study design. Recent examples include
Viscusi and Aldy (2003), Mrozek and Taylor
(2002), and Kochi et al. (2006). EPA applications
of VSL are numerous, and include the Clean Air
Interstate Rule (CAIR), the Non-Road Diesel
Rule, and the Stage 2 Disinfection By-Products
Rule (DBP).8
important considerations
The analyst should keep three important
considerations in mind when estimating mortality
benefits:
• Characterizing and measuring mortality
effects;
• Heterogeneity in risk and population
characteristics; and
• The timing of health risk changes.
Characterizing and measuring
mortality effects
Reduced mortality risks are typically measured
in terms of "statistical lives." This measure is the
8 The economic analyses for these three rules are available electronically
as follows (accessed May 23, 2008):
CAIR (http://www.epa.gov/air/interstateairquality/pdfs/finaltech08.pdf);
Non-Road Diesel (http://www.epa.goV/nonroad-diesel/2004fr.htm#ria); and
Stage 2 DBP (http://www.epa.gov/safewater/disinfection/stage2/pdfs/
anaylsis_stage2_ecconomic_main.pdf).
Ml
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Chapter 7 Analyzing Benefits
aggregation of many small risks over an exposed
population. Suppose, for example, that a policy
affects 100,000 people and reduces the risk
of premature mortality by one in 10,000 for
each individual. Summing these individual risk
reductions across the entire affected population
shows that the policy leads to 10 premature
fatalities averted, or 10 statistical lives "saved."
Alternative measures attempt to capture the
remaining life expectancy associated with the risk
reductions. This is sometimes referred to as the
"quantity of life" saved (Moore and Viscusi 1988)
and is typically expressed as "statistical life years."
Looking again at the policy described above,
suppose the risks were spread over a population
where each individual had 20 years of remaining
life expectancy. The policy would then save 200
statistical life years (10 statistical lives times 20 life
years each). In practice, estimating statistical life
years saved requires risk information for specific
subpopulations (e.g., age groups or health status).
It is typical to use statistical life years saved in
CEA, but valuing a statistical life year remains
a subject of debate in the economics literature.
Theoretical models show that the relationship
between WTP and factors such as age, baseline
risk, and the presence of co-morbidities is
ambiguous and empirical findings are generally
mixed (U.S. EPA 2006e).
Heterogeneity in risk and
population characteristics
The value of mortality risks can vary both by
risk characteristics and by the characteristics of
the affected population. Key risk characteristics
include voluntariness (i.e., whether risks are
voluntarily assumed), timing (immediate or
delayed), risk source (e.g., natural vs. man-
made), and the causative event (e.g., cancer vs.
accidents). Population characteristics include
those generally expected to influence WTP for
any good (e.g., income and education) as well
as those more closely related to mortality risks
such as baseline risk or remaining lifespan, health
status, risk aversion, and familiarity with the type
of risk. The empirical and theoretical literature
on many of these characteristics is incomplete or
ambiguous. For example, some studies suggest that
older populations are willing to pay less for risk
reductions (Jones-Lee et al. 1993), but others find
this effect to be small if it exists at all (Alberini et
al. 2004). Still others suggest older populations
have higher WTP (Kniesner, Viscusi, and Ziliak
2006). Smith et al. (2004) and Viscusi and Aldy
(2007a) discuss the relationship between age
and VSL in the context of hedonic wage studies.
Appendix B contains a more complete discussion
of risk and population characteristics and how
they may affect WTP.
Timing of health risk changes
Environmental contamination can cause
immediate or delayed health effects. If individuals
typically prefer health improvements earlier in
time rather than later, all else equal, then the
WTP for reductions in exposure to environmental
pollutants will depend on when the resulting
health risk changes will occur. The description here
focuses on mortality risk, but the same principles
apply to non-fatal health risks.
The effects of timing on the present or annualized
value of reduced mortality risk can be considered
in the context of a lifecycle consumption model
with uncertain lifetime (Cropper and Sussman
1990, Cropper and Portney 1990, and U.S. EPA
2007g). In this framework reductions in mortality
risk are represented as a shift in the survival curve
— the probability an individual will survive to
all future ages — which leads to a corresponding
change in life expectancy and future utility.
If the basis for benefit transfer is a marginal
WTP for contemporaneous risk reductions, then
calculating the benefits of a policy with delayed
risk reductions requires three steps: (1) estimating
the time path of future mortality risk reductions;
(2) estimating the annual WTP in all future
years; and (3) calculating the present value of
these annual WTP amounts. The first step should
account for all the factors that ultimately relate
changes in exposure to changes in mortality risk as
defined by shifts in the survival curve.
Guidelines for Preparing Economic Analyses I December 2010 7-11
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Chapter 7 Analyzing Benefits
7.2,1,2 Morbidity
Morbidity benefits consist of reductions in the
risk of non-fatal health effects ranging from mild
illnesses, such as headaches and nausea, to very
serious illnesses such as cancer (see Table 7.1).
Non-fatal health effects also include conditions
such as birth defects or low birth weight. Non-
fatal health effects differ with respect to the
availability of existing value estimates. Values for
reducing the risks of some of these health effects
have been estimated multiple times using a variety
of different methods, while others have been the
subject of only a few or no valuation studies.
WTP to reduce the risk of experiencing an illness
is the preferred measure of value for morbidity
effects. As described in Freeman (2003), this
measure consists of four components:
• "Averting costs" to reduce the risk of illness;
• "Mitigating costs" for treatments such as
medical care and medication;
• Indirect costs such as lost time from paid
work, maintaining a home, and pursuing
leisure activities; and
• Less easily measured but equally real costs of
discomfort, anxiety, pain, and suffering.
Methods used to estimate WTP vary in the extent
to which they capture these components. For
example, cost-of-illness (COI) estimates generally
only capture mitigating and indirect costs,
and omit averting expenditures and lost utility
associated with pain and suffering.9
Methods for valuing morbidity
Researchers have developed a variety of methods
to value changes in morbidity risks. Some
methods measure the theoretically preferred
value of individual WTP to avoid a health effect.
Others can provide useful data, but that data
must be interpreted carefully if it is to inform
9 This is why COI estimates generally understate WTP to reduce the
same risk or avoid a given health effect. Some studies have estimated
that total WTP can be two to four times as large as COI even for minor
acute respiratory illnesses (Alberini and Krupnick 2000). Still, there
is not any broadly applicable "scaling factor" that relates COI to WTP
generally.
economically meaningful measures. Methods
also differ in the perspective from which values
are measured (e.g., before or after the incidence
of morbidity), whether they control for the
opportunity to mitigate the illness (e.g., before or
after taking medication) and the degree to which
they account for all of the components of total
WTP. The three primary methods most often used
to value morbidity in an environmental context
are stated preference (Section 7.3.2), averting
behavior (Section 7.3.1.4), and COI (Section
7.3.1.5). Hedonic methods (Section 7.3.1.3)
are used less frequently to value morbidity from
environmental causes.
Many other approaches do not estimate WTP
and their ability to inform benefits analyses
consequently varies. Risk-risk trade-offs, for
example, do not directly estimate dollar values
for risk reductions, but rather provide rankings of
relative risks based on consumer preferences. Risk-
risk trade-offs can be linked to WTP estimates for
related risks.10
Other methods suffer from certain
methodological limitations and are therefore
generally less useful for policy analysis. For
example, health-state indices, composite metrics
that combine information on quality and
quantity of life lived under various scenarios, are
often used for cost-effectiveness or cost-utility
analyses. These methods cannot be directly
related to WTP estimates as the indices were
developed using very different paradigms than
those for WTP values. As such, they should not
be used for deriving monetary estimates for use in
BCA [Hammitt 2003, and Institute of Medicine
(IOM) 2006], although there is evidence that
components of these indices may still be useful
in a benefit-transfer context (Van Houtven et al.
2006). Another commonly suggested alternative
is jury awards, but these generally should not be
used in benefits analysis, for reasons explained in
Text Box 7.3.
10 EPA analyses have used risk-risk trade-offs for non-fatal cancers in
conjunction with VSL estimates as one method to assess the benefits
of reduced carcinogens in drinking water (U.S. EPA 2005a).
7-12
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Chapter 7 Analyzing Benefits
Text Bo* 7.3 - Non-Willingness to Pay Measures
Economic measures of value calculate willingness to pay (WTP) for environmental changes. WTP is defined as that
amount of money that, if taken away from income, would make an individual exactly indifferent between experiencing
an environmental improvement and not experiencing either the improvement or any change in income. (An analogous
measure can also be constructed for "not experiencing degradation" rather than "experiencing an improvement").
WTP is a valid measure of "economic value" because it is directly useful for applying the potential compensation
tests of Kaldor and Hicks.
Some measures of economic value are not valid, as they do not measure WTP, and cannot be related to changes in
utility. Others should be used only in a limited set of circumstances. Some examples are provided below.
Replacement cost. . -i : -i :r^
analysts have suggested that the economic value of the damage is the cost of replacing the asset. This will only be
true if: (1) damage to the asset is the only cost of the environmental deterioration; and (2) the least expensive way to
achieve the level of satisfaction realized before the deterioration would be to replace the asset. If the first condition is
not met, consideration of replacement costs alone might underestimate the economic consequences of environmental
degradation. If the second condition is not met, replacement costs might overestimate the consequences. Suppose
that water pollution kills fish in a pond. Replacing those fish with healthy, edible ones might prove extremely
expensive: the pond might need to be dredged and restocked. However, people who are no longer able to catch
fish in the pond might be compensated simply by giving them enough money to purchase substitutes at their local
supermarket.
Proxy costs. ¦ -i : : : -i
widely cited work, ecologist H.T. Odum (1996) calculated the number of barrels of petroleum that would be required
to provide the energy to replace the services of wetland ecosystems. However, this number is economically irrelevant.
There is no reason to suppose that people would choose to replace services of damaged wetlands with those of
purchased oil. A similar argument can be made against the interpretation of "ecological footprints" as an estimate
of economic consequences (Wackernagel and Rees 1996). Dasgupta (2002) interprets these approaches as single-
factor theories of value (Karl Marx's labor theory of value is the best known example), fallacies that were disproved in
general by Samuelson's (1951) "non-substitution theorem."
Cost-of-illness (COI). J. ¦ r . ^ j.
treatment and time lost due to illness. Although COI is discussed in greater detail in Section 7.3.1.5, note here that:
(1) COI does not record other expenses incurred in efforts to avoid illness; (2) health insurance may drive a wedge
between the costs incurred to treat illness and WTP to avoid it; and (3) COI ignores factors such as discomfort and
dread that patients would also be willing to pay to avoid.
Jury awards. ' -i -i- -i . ^ -i: : . : v.v :
made by juries. Using such awards may also prove problematic for at least two reasons. First, cases only go to trial
if both sides prefer the risk of an adverse outcome to the certainty of a pre-trial settlement. Cases that go to juries
are "atypical" by definition. Second, since adjudication does not always occur and can never be infallible, jury
awards often do, and arguably should (Shavell 1979), embody "punitive" as well as "compensatory" elements. Juries
make examples of guilty defendants in an attempt to deter others from committing similar offenses. For this reason,
jury awards may overstate typical damages. Finally, jury awards reflect a certain outcome and not the probability
of experiencing an adverse event and therefore include the influence of characteristics typically not included in
statistical analysis, such as pain, suffering, and likeability. These estimates are not appropriate for application to ex
ante evaluation of the value associated with a statistical probability.
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Chapter 7 Analyzing Benefits
Previous studies
A comprehensive summary of existing studies
of morbidity values is beyond the scope of these
Guidelines. Below is a short list of references that
can serve as a starting point for reviewing available
morbidity value estimates for benefit transfer or
for designing a new study Some recent estimates
for particular health effects include Hammitt
and Haninger (2007), who examine food-related
illnesses, and Chestnut et al. (2006), who examine
respiratory and cardiovascular effects. Tolley et al.
(1994) andjohanneson (1995) are useful general
references for valuing non-fatal health effects.
EPA's Handbook for Non-Cancer Valuation (U.S.
EPA 2000c) provides published estimates for many
illnesses and reproductive and developmental
effects. Desvousges et al. (1998) assess a number
of existing studies in the context of performing a
benefit transfer for a benefits analysis of improved
air quality. EPA's Cost of Illness Handbook (U.S.
EPA 2007c) includes estimates for many cancers,
developmental illnesses, disabilities, and other
conditions. EPA analyses of regulations and
policies, including EPA's two comprehensive
studies of the benefits and costs of the Clean Air
Act (U.S. EPA 1997a and U.S. EPA 1999) draw
upon a number of existing studies to obtain values
for reductions of a variety of health effects. These
sources describe how the central estimates were
derived, and attempt to quantify the uncertainty
associated with using the estimates.
At least two meta-analyses have attempted to
examine how the value of non-fatal risk reductions
varies with characteristics of the condition,
the affected population, and the approach to
valuation. Vassanadumrongdee et al. (2004) focus
on air pollution-related morbidity risks and posit
a meta-regression based benefit transfer function.
Van Houtven et al. (2006) evaluate more than 230
WTP estimates from 17 stated preference studies,
finding evidence that illness severity, measured
systematically, is a significant factor explaining
variation in WTP. The authors also illustrate
how a meta-regression-based function can
facilitate benefit transfer based on duration and
severity of acute illnesses, along with population
characteristics. While the specific benefit-transfer
functions in these articles might not be suitable for
application in any particular context, the estimates
contained in them can be helpful. Other studies
are available through the Environmental Valuation
Reference Inventory (EVRI). EVRI is maintained
by Environment Canada and contains more than
1,100 studies that can be referenced according to
medium, resource, stressor, method, and country.11
Important considerations
The analyst should keep two important
considerations in mind when estimating
morbidity benefits:
• Characterizing and measuring morbidity
effects; and
• Incomplete estimates of WTP.
Characterizing and measuring
morbidity effects
The key characteristics that will influence the
values of morbidity effects are their severity,
frequency, duration, and symptoms. Severity
defines the degree of impairment associated with
the illness. Examples of how researchers have
measured severity include "restricted activity
days," "bed disability days," and "lost work
days."12 Severity can also be described in terms
of health state indices that combine multiple
health dimensions into a single measure.13 For
duration, the primary distinction is between
acute effects and chronic effects. Acute effects are
discrete episodes usually lasting only a few days,
while chronic effects last much longer and are
generally associated with long-term illnesses. The
11 See www.evri.ca for more information.
12 As Cropper and Freeman (1991) note, these descriptions are
essentially characterizations of a behavioral response to the illness.
Lost workdays, for example, in some cases require a decision on an
individual's part not to go to work due to illness. Such a response may
depend upon various socioeconomic factors as well as the physical
effect of the illness.
13 The difference in the indices is intended to reflect the relative difference
in disutility associated with symptoms or illnesses. There are serious
questions about the theoretic and empirical consistency between
these "health-related quality of life" index values and WTP measures
for improved health outcomes (Hammitt 2002). Still the inclusion of
some aspects of these indices may prove useful in valuation studies
(Van Houtven etal. 2006). Examples of economic analyses that have
employed some form of health state index include Desvousges et al.
(1998) and Magatetal. (1996).
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Chapter 7 Analyzing Benefits
frequency of effects also can vary widely across
illnesses. Some effects are one-time events that are
unlikely to recur, such as a gastrointestinal illness.
Other effects, such as asthma, do recur or can be
aggravated regularly, causing disruptions in work,
school, or recreational activities.
For chronic conditions or more serious
outcomes, morbidity effects are usually measured
in terms of the number of expected cases of a
particular illness. Given the risks faced by each
individual and the number of people exposed
to this risk, an estimate of "statistical cases" can
be defined analogously to "statistical lives." In
contrast, morbidity effects that are considered
acute or mild in nature can be estimated as
the expected number of times a particular
symptom associated with an illness occurs. These
estimates of "symptom days" may be used in
benefits analysis when appropriate estimates of
economic value are available, although a richer
characterization of combinations of symptoms,
severity, duration, and episode frequency would
be an improvement over much of the existing
literature. Some studies have attempted to deal
with these complexities in a more systematic
manner, but the results have not yet been widely
applied and interpreted for policy analysis
(Cameron and DeShazo 2008). (Refer to Section
7.3.1.5 and Text Box 7.3 on the use of COI
versus WTP measures of value.)
Incomplete estimates of WTP
The widespread availability of health insurance
and paid sick leave shift some of the costs of illness
from individuals to others. While this cost-shifting
can be addressed explicitly in COI studies, it
may lead to problems in estimating total WTP.
If the researcher does not adequately address
these concerns, individuals may understate their
WTP, assuming that some related costs would be
borne by others. However, to the extent that these
costs represent diversions from other uses in the
economy, they represent real costs to society and
should be accounted for in the analysis.
More information on these and other issues to
consider when conducting or evaluating morbidity
value studies is provided in EPA's Handbookfor
Non-Cancer Health Effects Valuation (U.S. EPA
2000c).
7.2.2 Ecological Benefits
In addition to human health benefits, many
EPA policies will produce ecological benefits by
increasing the delivery of "ecosystem services,"
which are the end products of ecological functions
that are valued by people (Daily 1997, National
Research Council 2005, and Millennium
Ecosystem Assessment 2005). There is a large and
growing literature on the valuation of ecosystem
services. Fisher et al. (2009) document an
exponentially increasing number of published
articles on ecosystem services, growing from
essentially none in the early 1980s to around 250
in 2007. Much of this literature focuses on the
impacts of habitat loss and other land use changes
on ecosystem service flows. Because EPA has only
limited authority over private and public land
use decisions, analysts may find that only a subset
of the results in these studies will be directly
transferable to traditional EPA regulations.
Nevertheless, this growing literature can provide
a useful conceptual framework and potentially
transferable methods for analyzing a wide range of
EPA policies that may affect ecological services.
In principle, once the pollutants (or other
environmental stressors) whose emissions will be
altered by the regulation have been identified, the
same general approach used to estimate human
health benefits can be used to estimate ecological
benefits: identify the endpoints that are affected by
those pollutants and that are valuable to society;
estimate dose-response relationships between
stressors and endpoints; and estimate peoples
WTP for changes in the endpoints using revealed
or stated preference valuation methods. In the
case of ecological benefits estimation, the relevant
endpoints will include measures of ecosystem
health rather than human health, and the methods
and data required to estimate the dose-response
functions and WTP will differ accordingly. As
in the human health case, the estimation of dose-
response relationships between pollutants and
endpoints will fall mainly to natural scientists,
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Chapter 7 Analyzing Benefits
although collaboration between scientists and
economists often is needed to help focus the
analysis on the most important endpoints. [The
Agency's Ecological Benefits Assessment Strategic
Plan describes an interdisciplinary approach
for conducting ecological benefits assessments,
as well as research priorities for improving such
assessments (U.S. EPA 2006a)]. Even though the
basic approach for valuing ecological benefits is
similar to that used to value human health benefits,
an entirely different set of complications may arise
when estimating ecological benefits (Freeman
2003 pp. 457-460). Some of these complications
are explored below.
A hypothetical policy
To illustrate some of the complications that can
arise when assessing ecological benefits, consider
a hypothetical policy that would control the
emissions of an industrial chemical that are
believed to decrease survival and reproductive
rates in one or more fish species. First, compared
to the commonly accepted individual-level
mortality and morbidity endpoints used in
human health benefit assessments, it may be
more difficult to identify or define the relevant
endpoints in an ecological benefits assessment
(de Groot et al. 2002, Boyd and Banzhaf 2007,
Wallace 2007, and Fisher and Turner 2008).
Identifying endpoints for estimating use values
maybe relatively straightforward. For example,
endpoints for this hypothetical policy would
include the abundances and distributions of
species that are directly or indirectly affected
by the chemical and are harvested or targeted
for wildlife viewing or other non-consumptive
outdoor activities. Identifying relevant endpoints
for non-use values, on the other hand, can be more
complicated. Even for this simplified hypothetical
policy, it may not be clear which among the wide
variety of measureable ecosystem attributes —
beyond those previously identified as relevant for
use values — would provide an adequate basis
for eliciting non-use values in a stated preference
survey. Evans et al. (2008) discuss some of the
challenges they faced in defining endpoints for
a stated preference survey to value reductions in
acid rain in the Adirondacks. Boyd and Krupnick
(2009) discuss problems of identifying ecological
endpoints more generally.
After relevant endpoints are identified, there
may be additional complications in modeling
the effects of the chemical on those endpoints.
For example, the emissions-transport-exposure
pathway(s) — i.e., the "ecological production
function" (U.S. EPA 2009b) — may involve
complex food web linkages that are less direct
or have more convoluted feedbacks than in the
human health context. Furthermore, some of
the important feedbacks may involve human
responses to the changed ecological conditions.
For example, if some of the fish species in our
hypothetical policy scenario are harvested by
recreational or commercial fishers, then the
nature of the management regime in the fisheries
may influence the response of the fish stocks to
the policy. In an extreme case, if the commercial
fisheries are completely unregulated and subject
to open access conditions, then any increases in
the stock sizes from the policy may be completely
offset in the long run by new entrants to the
fishery (Freeman 1991, Barbier et al. 2002,
Smith 2007, and Newbold and Iovanna 2007).
Therefore, an integrated bio-economic modeling
approach maybe needed to accurately project the
bio-physical effects of the policy. Some examples
of such an approach include Smith and Crowder
(2006), Massey et al. (2006), and Finnoff and
Tschirhart (2008).
After the ecological effects of the policy are
characterized, there maybe further complications
in valuing those effects. For this hypothetical
policy, the main requirement for revealed
preference valuation methods might be data on
commercial and recreational fishing activities
in the affected water bodies. Other recreational
activities also might be affected, and water-related
amenities might influence property values. As with
human health benefits, care must be taken to avoid
double counting when using multiple datasets
and methods that could include overlapping
values (McConnell 1990, and Phaneuf et al.
2008). Furthermore, if a significant portion of
the benefits for ecological changes are thought to
consist of non-use values rather than use values,
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analysts may need to rely more heavily on stated
preference methods when estimating ecological
benefits. Considering the challenges in conducting
reliable stated preference valuation studies even for
well-defined and familiar commodities (described
in detail in Section 7.3.2), this compounds the
extra complications already discussed. This also
points to a larger potential role for non-monetized
and non-quantified benefits in the overall analysis
(U.S. EPA 2006a, and U.S. EPA 2009b).
Application of economic valuation methods
to ecological changes
Extensive treatments of the valuation of ecosystem
services can be found in recent reports from the
National Academy of Science (NAS) (2005)
and EPA's SAB Committee on the Valuation
of Ecological Systems and Services (U.S. EPA
2009b). Analysts are referred to these reports
for more detailed discussions on the application
of economic valuation methods to ecological
benefits than are provided in these Guidelines.
In this section are examples of studies that apply
traditional valuation methods (discussed more
generally in the following sections of this chapter)
to ecosystem goods and services. Some of the
special complications that can arise in such studies
are highlighted.
Production functions
A number of recent contributions to the literature
on valuing of ecosystem services emphasize the
importance of understanding the production
functions relating natural systems to the provision
of products that are valuable to people (Polasky
et al. 2008 a, 2008b; Boyd and Banzhaf 2007;
and U.S. EPA 2009b). Some simple examples
have long been known: commercially valuable
species "produce" themselves. Early work such as
Faustmann's 1848 analysis of optimal rotations
in forestry (see also Samuelson 1976), Clark's
(1990) work in fisheries, and Hammack and
Browns (1974) work on wetlands and waterfowl
have provided templates for later studies. It may
be possible to value the effects of pollution on
the exploitation of renewable resources when
biological production possibilities are affected by
environmental conditions — for example, when
fish stocks are affected by water quality, or when
waterfowl populations are affected by the extent
and configuration ofwedands (Bell 1997, Ellis
and Fischer 1987, and Massey et al. 2006). As
discussed above, analysts should keep in mind
that institutional features such as open access to
renewable resources may dissipate values that
might otherwise be realized from environmental
improvement.
Ecological resources also can contribute to the
production of other useful goods and services,
such as crop yields, groundwater quality, and
surface water flow characteristics. Hence the
degradation of supporting ecological resources
should be reflected in diminished outputs of these
commodities. Direct application of production
function approaches often is hampered by data
and methodological limitations. Specifically, it
can be difficult to measure the flow of non-market
ecosystem services that a particular production
process receives, as well as to statistically control
for the effects of unobserved characteristics of
climate and topography. One approach is to
design observational studies to mimic controlled
experiments as closely as possible. Ricketts et
al. (2004) use this approach in a study of the
value of pollination services to coffee crops. In
some cases production functions might plausibly
be derived from first principles. For example,
Weitzman (1992), Simpson et al. (1996), Rausser
and Small (2000), and Costello and Ward (2006)
use simple probability models to examine the
role of biodiversity in the development of new
pharmaceutical products. Further examples of
studies relating ecological conditions to economic
outputs through production processes include
Acharya and Barbier (2002), who examine ground
water recharge as a function of surrounding land
cover, and Pattanayak and Kramer (2001), who
examine stream flow as a function of land cover.
Hedonic models
Econometricians generally have favored estimating
cost or profit functions to estimating production
functions. This is because the prices that are the
arguments of the former will be uncorrelated with
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Chapter 7 Analyzing Benefits
unobserved factors, whereas input choices will not
(see Varian 1992). "While a cost or profit function
approach could be adopted in the estimation of
ecosystem service values, a more common, and
theoretically equivalent, approach is to estimate
a hedonic price function. In theory, the rental
price of land is equal to the earnings that could be
derived from its use, while the purchase price is
equal to the net discounted value of the stream of
such earnings. A number of authors have estimated
hedonic models relating the value of residential
properties to the proximity and attributes of
nearby forests (Anderson and Cordell 1988,
Tyrvainen and Miettinen 2000, and Willis and
Garrod 1991), wetlands (Lupi et al. 1991, Mahan
et al. 2000, "Woodward and "Wui 2001, Bin and
Polasky 2005, and Costanza et al. 2008), or other
varieties of "open space" (Geoghegan et al. 1997,
Benson et al. 1998, Irwin and Bockstael 2002,
Irwin 2002, and Thorsnes 2002).
il cost models
A large number of studies use travel cost models
to value ecological endpoints. The predominant
activity in the recreational use value literature
has been fishing; where the ecological endpoint
is expected fish catch (or one or more proxy
measures thereof) at one or more recreation
sites. For example, 122 of 325 studies in the
recreational use value database assembled by
Rosenberger and Stanley (2007) focused on either
freshwater or saltwater recreational fishing. The
remaining studies in the database focus on one
of 25 other categories of activities, including bird
watching (Hay and McConnell 1979), wildlife
hunting (Creel and Loomis 1990, Coyne and
Adamowicz 1992, Boxall 1995, Peters et al. 1995,
and Adamowicz et al. 1997), beach use (Bockstael
et al. 1987a, and Parsons and Massey 2003),
backcountry recreation (Boxall et al. 1996), rock-
climbing (Shaw and Jakus 1996), and kayaking
(Phaneuf and Siderelis 2003).
Stated preference methods
Revealed preference methods cannot capture
non-use values, such as those associated with the
existence of biological diversity. This is because it
is not possible to use data on market transactions
or any other observed choices to estimate the value
of goods that leave no "behavioral trail" (Larson
1993) in their enjoyment. In such cases only stated
preference methods can provide estimates of
"WTP orWTA (Freeman 2003). More generally,
stated preference methods maybe employed when
researchers want to identify the widest possible
spectrum of values, both use and non-use (Loomis
et al. 2000).
Stated preference studies have been used to value
a number of ecosystem services. Examples include
the protection of endangered species (Brown and
Shogren 1998), the ecological consequences of
water quality improvements in Europe (Hanley
et al. 2006), improved ecological conditions
resulting from reduced air pollution in the United
States (Banzhaf et al. 2006), and restoration
of the Florida Everglades (Milon and Scrogin
2006). In some instances researchers may want
to combine results of stated preference valuation
studies of particular ecological endpoints with
other data on the effects of pollution, land use,
or other factors on the production of ecosystem
services. See Boyd and Krupnick (2009) for an
extended discussion.
Complications that may apply to
all methods
"When using these valuation methods or when
transferring the results of previous valuation
studies to assess ecological benefits for new policy
cases, analysts should be prepared to confront
several complications. For example:
For new studies, it may be difficult to identify
and/or measure the ecological endpoints that are
most relevant for the policy case. "Without a set
of observable measures of ecological conditions
(or measures that can be linked to ecological
conditions through supplemental bio-physical
modeling) thought to be relevant for outdoor
recreation behavior, housing decisions, etc., it will
not be possible to use revealed preference methods
to value ecological effects. For example, users may
care mainly about water clarity for a certain type of
recreational activity, while the most readily available
MS
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Chapter 7 Analyzing Benefits
data might measure nutrient loading in the water
bodies that would be affected by a policy change.
Under such circumstances it may be difficult to
relate revealed preferences regarding housing
decisions, recreational behavior, etc., to the available
nutrient loading data, as those data are imperfect
proxies for water clarity. There are well-known
statistical pitfalls associated both with specifying the
wrong "right-hand side" variables in an econometric
relationship, as well as with "data mining" by
including right-hand side variables in the absence
of theoretical justification. The best, if not always
practicable, advice that can be given is to think as
carefully as possible about which variables should
motivate choices before running any regressions.
For benefit transfers, it maybe difficult to find
existing studies that value ecological endpoints that
are the same as, or sufficiently similar to, those of
interest in the policy case. This problem is likely to
be more common for ecological benefits than for
human health benefits because the latter has a larger
set of studies to draw from and a smaller set of
common endpoints that have been used in multiple
studies. The less similar are the commodities valued
in the existing ecological benefit studies, the more
difficult it will be to synthesize those studies in a
meta-analysis or preference calibration exercise, and
the less valid will be the transfer of the resulting
value estimate or function.
Estimation difficulties are likely to arise in many
cases of interest. In particular, explanatory variables
may not meet the exogeneity requirement for
estimating their associated coefficients. For
example, in performing hedonic regressions
of property prices on, among other things, the
development status of nearby properties, it is likely
that both the price of the property in question
and the use made of nearby properties would be
determined by factors that cannot be observed by
the econometrician (Irwin and Bockstael 2002,
and Irwin 2002). Similarly, in estimating recreation
demand models in which a recreationist's decision
to visit a particular site depends on, among
other things, congestion (i.e., how many others
decide to visit the site at the same time), it is
likely that all recreationists' site visit choices will
be influenced by the same unobserved factors
(Timmins and Murdock 2007). Similar difficulties
arise in other areas of economics; for example
Durlauf s (2004) survey of empirical approaches to
"neighborhood effects" in urban economics. The
solution in each instance is to identify appropriate
instrumental variables, but this can be difficult in
many cases. One way around such problems may
be to identify "natural experiments." Thorsnes
(2002), for example, identifies instances in which
historical accidents influenced land use patterns
independently of the later realization of adjacent
land value in order to conduct a hedonic study of
the effects of open space.
For resources subject to consumptive use, such
as harvested fish or wildlife species, expected
harvest levels are endogenous variables, which
can lead to biases similar to that introduced by
congestion effects. If the policy of interest leads
to spatially heterogeneous environmental quality
improvements, then it may lead to a re-sorting not
only of recreators but also of the target species
among the recreation sites. Ignoring this spatial
re-sorting effect can give biased welfare estimates
(Newbold and Massey 2010). This can complicate
both the estimation of preference parameters and
the transfer of the estimated preference function to
the policy case.
A basic goal of any benefits assessment is to count all
categories of benefits, but to count each only once.
This may be particularly important for ecological
benefits assessments since stated preference studies
employed to estimate intangible values, such as
existence values of biodiversity, might also capture
use values that are already covered by revealed
preference studies such as recreation demand or
hedonic studies. When combining values estimated
using multiple methods, the analyst should take care
to avoid double counting.
It is important to identify and discuss any
omitted benefit categories that are thought to
be important but that cannot be monetized,
or possibly even quantified. There may be
circumstances in which provision of some
additional information may be helpful, even if
does not rise to the level of presenting an explicit
comparison of benefits with costs. For example,
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Chapter 7 Analyzing Benefits
analysts may be able to identify the most cost-
effective approach among different alternatives,
or to present natural science information that
can convey the biophysical impact of a policy
even if it does not quantify the WTP or WTA
for such a policy. It is better to acknowledge gaps
in information by discussing them qualitatively
or by reporting physical measures (if available)
than to employ conceptually flawed methods of
monetization. In particular, analysts should avoid
the use of replacement cost, embodied energy-
based evaluation methods, or ecological footprint
analysis to derive estimates of WTP or WTA.
7.2.3 Other Benefits
Other types of potential benefits from
environmental policies include aesthetic
improvements and reduced material damages.
Aesthetic improvements include effects such as
improved taste and odor of tap water resulting
from water treatment requirements and enhanced
visibility resulting from reduced air pollution.
EPA typically considers two types of benefits
from increased visibility due to improvements
in air quality: residential visibility benefits and
recreational visibility benefits. Improvements in
residential visibility are typically assumed to only
benefit residents living in the areas in which the
improvements are occurring, while all households
in the United States are usually assumed to derive
some benefit from improvements in visibility in
areas such as National Parks. The benefits received,
however, are assumed to decrease with the
distance from the recreational area in which the
improvements occur.
Reduced materials damages include welfare
impacts that arise from changes in the provision
of service flows from human-made capital assets
such as buildings, roads, and bridges. Materials
damages can include changes in both the quantity
and quality of such assets. Benefits from reduced
material damages typically involve cost savings
from reduced maintenance or restoration of soiled
or corroded buildings, machinery, or monuments.
Methods and previous studies
Changes in the stock and quality of human-made
capital assets are assessed in a manner similar to
their "natural capital" counterparts. Analytically,
the valuation of reduced materials damages
parallels the methods for valuing the tangible
end products from managed ecosystems such as
agriculture or forestry. Effects from changes in
air quality on the provision of the service flows
from physical resources are handled in a similar
fashion to the effects from changes in air quality
on crops or commercial timber stocks. The
most common empirical applications involve air
pollution damages and the soiling of structures
and other property.
Linking changes in environmental quality with
the provision of service flows from materials
can be difficult because of the limited scientific
understanding of the physical effects, the timing
of the effects, and the behavioral responses
of producers and consumers. An analysis of
reduced materials damages typically begins with
an environmental fate and transport model to
determine the direct effects of the policy on the
stocks and flows of pollutants in the environment.
Then stressor-response functions are used to relate
local concentrations of pollutants to corrosion,
soiling, or other physical damages that affect the
production (inputs) or consumption (output) of
the material service flows. The market response to
these impacts serves as the basis for the final stage
of the assessment, in which some type of structural
or reduced-form economic model that relates
averting or mitigating expenditures to pollution
levels is used to value the physical impacts. The
degree to which behavioral adjustments are
considered when measuring the market response
is important, and models that incorporate
behavioral responses are preferred to those that
do not. Adams and Crocker (1991) provide a
detailed discussion of this and other features of
materials damages benefits assessment. Also see
EPA's benefits analysis of household soiling for an
example that employs a reduced-form economic
model relating defensive expenditures to ambient
pollution (U.S. EPA 1997f).
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Chapter 7 Analyzing Benefits
b,;;ciOiii'b ^ luation
Methods for Benefi alysis
For goods bought and sold in undistorted markets,
the market price indicates the marginal social value
of an extra unit of the good. There are virtually
no markets for environmental goods. While
some natural products are sold in private markets,
such as trees and fish, these are "products of the
environment" and not the types of "environmental
goods and services" analysts typically need to value.
Hie analysts concern is typically with non-market
inputs, which are, by definition, not traded in
markets.14 To overcome this lack of market data,
economists have developed a number of methods to
value environmental quality changes. Most of these
methods can be broadly categorized as either revealed
preference or stated preference methods.
In cases where markets for environmental goods do
not exist, WTP can often be inferred from choices
people make in related markets. Specifically, because
environmental quality is often a characteristic
or component of a private good or service, it is
sometimes possible to disentangle the value a
consumer places on environmental quality from
the overall value of a good. Methods that employ
this general approach are referred to as revealed
preference methods because values are estimated using
data gathered from observed choices that reveal
the preferences of individuals. Revealed preference
methods include production or cost functions, travel
cost models, hedonic pricing models, and averting
behavior models. This section also discusses COI
methods, which are sometimes used to value human
health effects when estimates of WTP are unavailable.
In situations where no markets for environmental
or related goods exist to infer WTP, economists
sometimes rely on survey techniques to gather
choice data from hypothetical markets. The
methods that use this type of data are referred
to as stated preference methods because they rely
on choice data that are stated in response to
hypothetical situations, rather than on choice
14 There are examples in which environmental goods have been traded in
markets. The Clean Air Act Amendments of 1990, for example, initiated
a market in sulfur dioxide (S02). However prices in such markets are
determined by regulation-induced scarcity, and not by considerations
of marginal utilities or marginal products.
behavior observed in actual markets. Stated
preference methods include contingent valuation,
conjoint analysis, and contingent ranking.
Each of these revealed and stated preference
methods is discussed in detail below. Included
are an overview of each method, a description of
its general application to environmental benefits
analysis, and a discussion of issues involved in
interpreting and understanding valuation studies.
The discussion concludes with a separate overview
of benefit-transfer methods. It is important to
keep in mind that research on all of these methods
is ongoing. The limitations and qualifications
described here are meant to characterize the
state of the science at the time these Guidelines
were written. Analysts should consult additional
resources as they become available.
7,3.1 Revealed
Preference Methods
A variety of revealed preference methods for
valuing environmental changes have been
developed and are widely used by economists. The
following common types of revealed preference
methods are discussed in this section:
• Production or cost functions;
• Travel cost models;
• Hedonic models;
• Averting behavior models; and
• Cost of Illness (COI).15
7,3,1,1 Production and Cost Functions
Discrete changes in environmental circumstances
generally cause both consumer and producer
effects, and it is common practice to separate
the welfare effects brought about by changes
in environmental circumstances into consumer
surplus and producer surplus.16 Marginal changes
can be evaluated by considering the production
side of the market alone.
15 Although not a revealed preference method (as it does not measure
WTP) COI methods are discussed in this section since estimates are
based on observable data.
16 See Appendix A for more detail.
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Chapter 7 Analyzing Benefits
Economic foundations of production and
cost functions
Inputs to production contribute to welfare
indirectly. Hie marginal contribution of a
productive input is calculated by multiplying the
marginal product of the input by the marginal
utility obtained from the consumption good,
in whose production the input is employed.
The marginal utility of a consumption good is
recorded in its price. While marginal products
are rarely observed, the need to observe them is
obviated when both inputs and outputs are sold
in private markets becaust prices can be observed.
Environmental goods and services are typically not
traded in private markets, and therefore the values of
environmental inputs must be estimated indirectly.
Production possibilities can be represented in
three equivalent ways:
• As a production function relating output to
inputs;
• As a cost function relating production
expenses to output and to input prices; and
• As a profit function relating earnings to the
prices of both output and inputs (see Varian
1992, for an explication of the relationships
among these functions).
The value of a marginal change in some
environmental condition can be represented as
a marginal change in the value of production,
as a marginal change in the cost of production,
or as a marginal change in the profitability of
production.17 It should be noted, however, that
problems of data availability and reliability often
arise. Such problems may motivate the choice
among these conceptually equivalent approaches,
or in favor of another approach.
Note that derivation of values on the margin
does not require any detailed understanding
of consumer demand conditions. To evaluate
marginal effects via the production function
approach, the analyst needs to know the price
of output and the marginal product of the
environmental input. To derive the equivalent
17 For a good review of statistical procedures used for estimating
production, cost, and profit functions see Berndt (1991).
measure using a cost function approach, the
analyst needs to know the derivative of the cost
function with respect to the environmental input.
In the profit function approach, the analyst needs
to know the derivative of the profit function with
respect to the environmental input.18
In the statements note the emphasis that marginal
effects are being estimated. Estimating the net
benefits of larger, non-marginal, changes represent
a greater challenge to the analyst. In general this
requires consideration of changes in both producer
and consumer surplus. The latter necessitates
application of techniques such as travel cost,
hedonics, and stated preference, which are
discussed elsewhere in this chapter.
Before moving on to those topics, note a fourth
equivalent way to estimate environmental effects
on production possibilities. Such effects are
reflected in the profitability of enterprises engaged
in production. That profitability also can be
related to the return on fixed assets such as land.
The value of a parcel of land is related to the stream
of earnings that can be achieved by employing
it in its "highest and best use." Its rental value is
equal to the profits that can be earned from it over
the period of rental (the terms "rent" and "profit"
are often used synonymously in economics). The
purchase price of the land parcel is equal to the
expected discounted present value of the stream
of earnings that can be realized from its use over
time. Therefore, the production, cost, and profit
function approaches described above are also
equivalent to inferences drawn from the effects
of environmental conditions on asset values. This
fourth approach is known as "hedonic pricing,"
and will be discussed in detail in Section 7.3.1.3.
18 Derivation of marginal values often involves an application of the
"envelope theorem" that states that effects from variables that are
already optimized are negligible. In determining the effect of an
improvement in a particular environmental input on welfare arising
from the consumption of a particular product using the cost function
approach, the analyst would determine how$p(q)dq - C(Q, e)
varies with e, the environmental variable. The integral is consumer
surplus, i.e., the area under the demand curve, and the second term
is the cost of producing quantity Q_ given environmental conditions,
e. Differentiating with respect to e yields [p(Q') - dC/dQ) dQjde -
ddde = - dC/de, where the last equality results because competitive
firms set price equal to marginal cost, \.e.,p(Q) = dC/dQ. This is the
basis for the general proposition that marginal values can be estimated
by looking solely at the production side of the market.
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Chapter 7 Analyzing Benefits
It is introduced now to show that production,
cost, or profit function approaches are generally
equivalent to hedonic approaches.
"Production" as a term is broad in meaning and
application, especially with regard to hedonic
pricing. While businesses produce goods and
services in their industrial facilities, one might also
say that developers "produce" housing services
when they build residences. Therefore, hedonic
pricing approaches can measure the value of the
environment in "production," whether they are
focusing on commercial or residential properties.
Similarly, households may "produce" their health
status by combining inputs such as air and water
filtration systems and medical services along with
whatever environmental circumstances they face.
Or they "produce" recreational opportunities by
combining "travel services" from private vehicles,
their own time, recreational equipment purchases,
and the attributes of their destination. Much
of what is discussed elsewhere in this section is
associated with this "production" analysis. This
is not to say that estimation of production, cost,
or profit functions is necessarily the best way to
approach such problems, but rather, that all of
these approaches are conceptually consistent.
General application of production and
cost functions
Empirical applications of production and cost
function approaches are diverse. Among other
topics, the empirical literature has addressed the
effects of air quality changes on agriculture and
commercial timber industries. It also has assessed
the effects of water quality changes on water
supply treatment costs and on the production
costs of industry processors, irrigation operations,
and commercial fisheries.19 Production, cost,
or profit functions have found interesting
applications to the estimation of some ecological
benefits.20 Probabilistic models of new product
discovery from among diverse collections of
natural organisms can also be regarded as a type of
19 Refer to Adams etal. (1986), Kopp and Krupnick (1987), Ellis and
Fisher (1987), Taylor (1993), and U.S. EPA (1997a) for examples.
20 See, for example, Acharya and Barbier (2002) on groundwater
recharge, and Pattanayak and Kramer (2001) on water supply.
"production."21 Finally, work in ecology points to
"productive" relationships among natural systems
that may yield insights to economists as well.22
Considerations in evaluating
and understanding production
and cost functions
The analyst should consider the following factors
when estimating the values of environmental
inputs into production:
Data requirements and implications. Estimating
production, cost, or profit functions requires data
on all inputs and/or their prices. Omitted variable
bias is likely to arise absent such information, and
may motivate the choice of one form over another.
Econometricians have typically preferred to
estimate cost or, better yet, profit functions. Data
on prices are often more complete than are data
on quantities and prices are typically uncorrelated
to unobserved conditions of production, whereas
input quantities are not.
The model for estimation. Standard practice
involves the estimation of "flexible functional
forms," i.e., functions that can be regarded as
second-order approximations to any production
technology. The translog and generalized Leontief
specifications are examples. Estimation often will be
more efficient if a system of equations is estimated
(e.g., simultaneous estimation of a cost function
and its associated factor demand equations),
although data limitations may impose constraints.
Market imperfections. Most analyses assume
perfecdy competitive behavior on the part of
producers and input suppliers, and assume
an absence of other distortions. When these
assumptions do not hold, the interpretation of
welfare results becomes more problematic. While
there is an extensive literature on the regulation
of externalities under imperfect competition,
originating with Buchanan (1969), analysts should
exercise caution and restraint in attempting to
correct for departures from competitive behavior.
21 For example, see Weitzman (1992), Simpson et al. (1996), and Rausser
and Small (2000).
22 For example, see Tilman, Lehman, and Polasky 2005.
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Chapter 7 Analyzing Benefits
Hie issues can become quite complex and, as is
the case with environmental externalities, there is
typically no direct evidence of the magnitude of
departures from perfecdy competitive behavior.
Moreover, in many circumstances it might
reasonably be argued that departures from perfect
competition are not of much practical concern
(Oates and Strassman 1984). Perhaps a more pressing
concern in many instances will be the wedge between
private and social welfare consequences that arise
with taxation. An increase in the value of production
occasioned by environmental improvement typically
will be split between private producers and the
general public through tax collection. Hie issues
here also can become quite complex (see Parry et
al. 1997), with interactions among taxes leading
to sometimes surprising implications. While it is
difficult to give general advice, analysts may wish to
alert policy makers to the possibility that the benefits
of environmental improvements in production may
accrue to different constituencies.
, , w t, J't r:-v-[ i ts
Recreational values constitute a potentially large
class of environmental use benefits. However,
measuring these values is complicated by the fact
that the full benefits of access to recreation activities
are rarely reflected in admission prices. Travel
cost models address this problem by inferring the
value of changes in environmental quality through
observing the trade-offs recreators make between
environmental quality and travel costs. A common
situation recreators may face is choosing between
visiting a nearby lake with low water quality and
a more distant lake with high water quality. Hie
outcome of the decision of whether to incur the
additional travel cost to visit the lake with higher
water quality reveals information about the
recreators value for water quality. Travel cost models
are often referred to as recreation demand models
because they are most often used to value the
availability or quality of recreational opportunities.
Economic foundation of travel cost models
Travel cost models of recreation demand focus on
the choice of the number of trips to a given site or
set of sites that a traveler makes for recreational
purposes. Because there is no explicit market
or price for recreation trips, travel cost models
are frequently based on the assumption that the
"price" of a recreational trip is equal to the cost of
traveling to and from the site. These costs include
both participants' monetary cost and opportunity
cost of time. Monetary costs include all travel
expenses. For example, when modeling day trips
taken primarily in private automobiles, travel
expenses would include roundtrip travel distance
in miles multiplied by an estimate of the average
cost per mile of operating a vehicle, plus any tolls,
parking, and admission fees.
A participant's opportunity cost of time for a
recreational day trip is the value of the participant s
time spent traveling to and from the recreation
site plus the time spent recreating.23 A variety
of approaches have been used in the literature
to define the opportunity cost of time. Most
commonly, researchers have used a fixed fraction
ranging from one third to one whole of a persons
hourly wage as an estimate of participants' hourly
opportunity cost of time. In most cases, the
fraction used depends on how freely individuals are
assumed to be able to substitute labor and leisure.
If a person can freely choose their work hours then
their opportunity cost of time will be equal to their
full wage rate. However, if a person cannot freely
substitute labor for leisure (for example if they have
a set 40 hour work week), then the opportunity
cost of the time they have available for recreation
is unobservable and may be less or more than the
full wage rate. Many other factors can influence
recreators' opportunity cost of time, including
the utility received from traveling, non-wage
income, and other non-work time constraints. A
number of researchers have developed methods
for estimating recreators' endogenous opportunity
cost of time although no one method has yet been
fully embraced in the literature. For examples,
see McConnell and Strand (1981); Smith,
23 If the amount of time spent recreating or doing something else (not
including the time spent traveling to and from the sites) is assumed
to be the same across all alternatives then it will not be identifiable
in estimation and therefore it is not necessary to include it in the
estimation of the participant's opportunity cost of time. See Smith,
Desvousges, and McGivney (1983); and McConnell (1992) for
discussions of the implication of and the methods for allowing time
onsite to vary across trip and alternatives.
7-24
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Chapter 7 Analyzing Benefits
Desvousges, and McGivney (1983); Bockstael
et al. (1987b); McConnell (1992); and Feather
and Shaw (1999). Hourly opportunity costs are
multiplied by round trip travel time and time on-
site to calculate a persons full opportunity cost of
time. Total travel costs are the sum of monetary
travel costs and full opportunity costs. Following
the law of demand, as the cost of a trip increases
the quantity of trips demanded generally falls, all
else equal. This means that participants are more
likely to visit a closer site than a site farther away.
While travel costs are the driving force of the
model, they do not completely determine
a participants choice of sites to visit. Site
characteristics, such as parking, restrooms, or
boat ramps; participant characteristics, such as
age, income, experience, and work status; and
environmental quality also can affect demand for
sites. The identification and specification of the
appropriate site and participant characteristics are
generally determined by a combination of data
availability, statistical tests, and the researchers
best judgment. Ultimately, every recreation
demand study strikes a compromise in defining
sites and choice sets, balancing data needs and
availability, costs, and time.24
General application by type of
cost mocfcI
Travel cost models can logically be divided into
two groups: single-site models and multiple-site
models. Apart from the number of sites they
address, the two types of models differ in several
ways. The basic features of both model types are
discussed below.
of substitute sites, act as demand curve shifters.
For example, sites with good water quality are
likely to be visited more often than sites with
poor water quality, all else equal. Most current
single-site travel cost models are estimated using
count data models because the dependent variable
(number of trips taken to a site) is a non-negative
integer. See Haab and McConnell (2003) and
Parsons (2003a) for detailed discussions and
examples of recreation demand count data models.
Single-site models are most commonly used to
estimate the value of a change in access to a site,
particularly site closures (e.g., the closure of a lake
due to unhealthy water quality). The lost access
value due to a site closure is the difference between
the participant s WTP for the option of visiting
the site, which is given by the area between the
site's estimated demand curve and the implicit
"price" paid to visit it. Estimating the value of a
change in the cost of a site visit, for example the
addition or increase of an admission fee, is another
common application of the model.
A weakness of the single-site model is its inability
to deal with large numbers of substitute sites. If, as
is often the case, a policy affects several recreation
sites in a region, then traditional single-site
models are required for each site. In cases with
large numbers of sites, defining the appropriate
substitute sites for each participant and estimating
individual models for each site can impose
overwhelming data collection and computational
costs. Because of these difficulties, most researchers
have opted to refrain from using single-site models
when examining situations with large numbers of
substitute sites.25
Single-site models. Single-site travel cost models
examine recreators' choice of how many trips to
make to a specific site over a fixed period oftime
(generally a season or year). It is expected that the
number of trips taken will increase as the cost of
visiting the site decreases and/or as the benefits
realized from visiting increase. Site, participant,
and environmental attributes, as well as the prices
24 For a comprehensive treatment of the theoretical and econometric
properties of recreation demand models see Phaneuf and Smith
(2005).
Multiple-site models. Multiple-site models
examine a recreator s choice of which site to visit
from a set of available site (known as the choice set)
on a given choice occasion and in some cases can also
examine how many trips to make to each specific site
25 Researchers have developed methods to extend the single-site
travel cost model to multiple sites. These variations usually involve
estimating a system of demand equations. One example is the Kuhn-
Tucker (KT) model discussed in the following multiple-site model
section. See Bockstael, McConnell, and Strand (1991) and Shonkwiler
(1999) for more discussion and other examples of extensions of the
single-site model.
Guidelines for Preparing Economic Analyses I December 2010 7-25
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Chapter 7 Analyzing Benefits
over a fixed period of time. Compared to the single-
site model, the strength of multiple-site models
lies in their ability to account for the availability
and characteristics of substitute sites. By examining
how recreators trade the differing levels of each
site characteristic and travel costs when choosing
among sites it is possible to place a per trip (or
choice occasion) dollar value on site attributes or
on site availability for single sites or multiple sites
simultaneously
The two most common multiple-site models are
the random utility maximization (RUM) travel
cost model and Kuhn-Tucker (KT) system of
demand models. Both models maybe described
by a similar utility theoretic foundation, but they
differ in important ways. In particular, the RUM
model is a choice occasion model while the KT
model is a model of seasonal demand.
Random utility maximization models. In a
RUM model each alternative in the recreators
choice set is assumed to provide the recreator with
a given level of utility, and on any given choice
occasion the recreator is assumed to choose the
alternative that provides the highest level of utility
on that choice occasion.26 The attributes of each
of the available alternatives, such as the amenities
available, environmental quality, and the travel
costs, are assumed to affect the utility of choosing
each alternative. Because people generally do
not choose to recreate at every opportunity, a
non-participation option is often included as
a potential alternative.27 From the researchers
perspective, the observable components of utility
enter the recreator s assumed utility function. The
26 While the standard logit recreation demand model treats each choice
occasion as an independent event, the model can also be generalized
to account for repeated choices by an individual.
27 In a standard nested logit RUM model, recreators are commonly
assumed to first decide whether or not to take a trip, and then
conditional on taking a trip, to next choose which site to visit. By not
including a non-participation option, the researcher in effect assumes
that the recreator has already decided to take a trip, or in other
words, that the utility of taking atrip is higher than the utility of doing
something else for that choice occasion. Another way to think of it is
that models lacking a participation decision only estimate the recreation
values of the segment of the population that participates in recreation
activities (i.e., recreators), while models that allow for non-participation
incorporate the recreation values of the whole population (i.e.,
recreators and non-recreators combined). Because of this, recreation
demand models without participation decisions tend to predict larger
per person welfare changes than models allowing non-participation.
unobservable portions of utility are captured by an
error term whose assumed distribution gives rise
to different model structures. Assuming that error
terms have type 1 extreme values distribution leads
to the closed form logit probability expression
and allows for maximum likelihood estimation of
utility function parameters. Using these estimated
parameters it is then possible to estimate WTP for
a given change in sites quality or availability.
However, because the RUM model examines
recreation decisions on a choice occasion level,
it is less suited for predicting the number of
trips over a time period and measuring seasonal
welfare changes. A number of approaches have
been used to link the RUM model's estimates of
values per choice occasion to estimates of seasonal
participation rates. See Parsons, Jakus, and Tomasi
(1999) for a detailed discussion of methods of
incorporating seasonal participation estimates into
the RUM framework.
The nested logit and mixed logit models are
extensions of the basic logit. The nested logit
model groups similar alternatives into nests where
alternatives within a nest are more similar with
each other than they are with alternatives outside
of the nest. In very general terms, recreators are first
assumed to choose a nest and then, conditional on
the choice of nest, they then choose an alternative
within that nest. Nesting similar alternatives
allows for more realistic substitution patterns
among sites than is possible with a basic logit. The
mixed logit is a random parameter logit model
that allows for even more flexible substitution
patterns by estimating the variation in preferences
(or correlation in errors) across the sample. If
preferences do not vary across the sample then the
mixed logit collapses to a basic logit.28
The Kuhn-Tucker (KT) model. The KT model is
a seasonal demand model that estimates recreators'
choice of which sites to visit (like a multiple-site
model) and how often to visit them over a season
(like a single-site model). The model is built on the
theory that people maximize their seasonal utility
subject to their budget constraint by purchasing
28 See Train (1998) and Train (2003) for detailed descriptions of the
nested and mixed logit models.
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Chapter 7 Analyzing Benefits
the quantities of recreation and other goods that
give them the greatest overall utility. Similar
to the RUM model, the researcher begins by
specifying the recreator's utility function. Taking
the derivative of this utility function with respect
to the number of trips taken, subject to a budget
and non-negative trip constraint, yields the "Kuhn-
Tucker" conditions. The KT conditions show
that trips will be purchased up to the point that
the marginal rate of substitution between trips
and other spending is equal to the ratio of their
prices. In cases where the price of a good exceeds
its marginal value none will be purchased. Given
assumptions on the form of the utility function
and the distribution of the error term, probability
expressions can be derived and parameter estimates
may then be recovered. While recent applications
have shown that the KT model is capable of
accommodating a large number of substitute sites
(von Haefen, Phaneuf, and Parsons 2004) the
model is computationally intensive compared
to traditional models. For a basic application of
the KT model see Phaneuf and Siderelis (2003).
For more advanced treatments of the models see
Phaneuf, Kling, and Herriges (2000), and von
Haefen and Phaneuf (2005).
Considerations in evaluating
and understanding recreation
demand studies
Definition of a site and the choice set. The
definition of what constitutes a unique site
has been shown to have a significant effect on
estimation results. Ideally, one could estimate
a recreation demand model in which sites are
defined as specific points such as exact fishing
location, campsites, etc. The more exact the site
definition, the more exact the measure of travel
costs and site attributes, and therefore WTP,
that can be calculated. However, in situations
with a large number of potential alternatives, the
large data requirements may be cost and time
prohibitive, estimation maybe problematic, and
aggregation maybe required. The method of
aggregation has been shown to have a significant
effect on estimated values. The direction of the
effect will depend on the situation being evaluated
and the method of aggregation chosen (Parsons
andNeedleman 1992; Feather 1994; Kaoru,
Smith, and Liu 1995; and Parsons, Plantinga, and
Boyle 2000).
In addition to the definition of what constitutes a
site, the number of sites included in a recreator's
choice set can have a significant effect on
estimated values. When defining choice sets,
the most common practice in the literature has
been to include all possible alternatives available
to the recreator. In many cases availability has
been defined by location with a given distance
or travel time.29 This strategy has been criticized
on the grounds that people may not know about
all possible sites, or even if they do know they
exist they may not seriously consider them as
alternatives. In response to this, a number of
researchers have suggested methods that either
restrict choice sets to include only those sites that
the recreators seriously consider visiting (Peters
et al. 1995, and Haab and Hicks 1997) or that
weight seriously-considered alternatives more
heavily than less-seriously-considered alternatives
(Parsons, Massey, and Tomasi 2000).
Multiple-site or multipurpose trips. Recreation
demand models assume that the particular
recreation activity being studied is the sole
purpose for a given trip. If a trip has more than
one purpose, it almost certainly violates the travel
cost model's central assumption that the "price"
of a visit is equal to the travel cost. The common
strategy for dealing with multipurpose trips is
simply to exclude them from the data used in
estimation.30 See Mendelsohn et al. (1992) and
Parsons (2003b) for further discussion.
Day trips versus multi-day trips. The recreation
demand literature has focused almost exclusively
on single-day trip recreation choices. One main
reason researchers have focused mostly on day trips
is that adding the option to stay longer than one
day adds another choice variable in estimation,
29 Parsons and Hauber (1998) explore the implication of this strategy by
expanding the choice set geographically and find that beyond some
threshold the effect of additional sites is negligible.
30 Excluding any type or class of trip (like multiple-site or multipurpose)
will produce an underestimate of the population's total use value of a
site. The amount bywhich benefits will be underestimated will depend
on the number and type of trips excluded.
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Chapter 7 Analyzing Benefits
thereby greatly increasing estimation difficulty
A second reason is that as trip length increases
multipurpose trips become increasingly more
likely, again casting doubt on the assumption that
trip's travel costs represent the "price" of one single
activity (see previous paragraph). A few researchers
have estimated models that allow for varying trip
length. The most common strategy has been to
estimate a nested logit model in which each choice
nest represents a different trip length option. See
Kaoru (1995) and Shaw and Ozog (1999) for
examples. The few multi-day trip models in the
literature find that the per-day value of multi-day
trips is generally less than the value of a single-day
trip, which suggests that estimating the value of
multi-day trips by multiplying a value estimated
for single-day trips value by the number of days of
will overestimate the multi-day trip value.
7.3,1,3 Hedonics
Hedonic pricing models use statistical methods to
measure the contribution of a good's characteristics
to its price. Cars differ in size, shape, power,
passenger capacity, and other features. Houses
differ in size, layout, and location. Even labor
hours can be thought of as "goods" differing in
attributes like risk levels, and supervisory nature,
that should be reflected in wages. Hedonic pricing
models use variations in property prices or wages
and are commonly used to value the characteristics
of properties or jobs. The models are based on
the assumption that heterogeneous goods and
services (e.g., houses or labor) consist of "bundles"
of attributes (e.g., size, location, environmental
quality, or risk) that are differentiated from
each other by the quantity and quality of
these attributes. Environmental conditions are
among the many attributes that differ across
neighborhoods and job locations.
Economic foundations of
hedonic models
Hedonic pricing studies estimate economic
benefits by weighing the advantages against the
costs of different choices. A standard assumption
underlying hedonic pricing models is that markets
are in equilibrium, which means that no individual
can improve her welfare by choosing a different
home or job. For example, if an individual changed
location she might move to a larger house, or one
in the midst of a cleaner environment. However, to
receive such amenities, the individual must pay for
a more expensive house and incur transaction costs
to move. The more the individual spends on her
house, the less she has to spend on food, clothing,
transportation, and all the other things she wants
or needs. Thus, individuals are assumed to choose
a better available option such that the benefits
derived from it are exactly offset by the increased
cost. So, if the difference in prices paid to live in
a cleaner neighborhood is observable, then that
price difference can be interpreted as the WTP for
a better environment.
One key requirement in conducting a hedonic
pricing study is that the available options differ
in measurable ways. To see why, suppose that all
locations in a city's housing market were polluted
to the same degree, or all jobs in a particular
labor market expose workers to the same risks.
Homeowners and workers would, of course, be
worse off due to their exposure to pollution and job
risks, but their losses could not be measured unless
a comparison could be made to purchasers of more
expensive houses in less polluted neighborhoods,
or wages in lower-paying but safer jobs. However,
there is also a practical limit on the heterogeneity
of the sample. "Workers in different countries
earn very different wages and face very different
job risks, but this does not mean it is possible to
value the difference in job risks by reference to
international differences in wages. This is because:
(1) there are many other factors that differ between
widely separated markets; and (2) people simply
are not mobile between very disparate sites. For
these reasons it is important to exercise care in
defining the market in which choices are made.31
Another aspect of the heterogeneity in locations
required to make hedonic pricing studies work is
that people must be able to perceive the differences
among their options. If homeowners are unable
to recognize differences in health outcomes,
visibility, and other consequences of differences
31 Michaels and Smith (1990) offer guidance for defining the extent of the
market.
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Chapter 7 Analyzing Benefits
in air quality at different locations, or if workers
are unaware of differences in risks at different jobs,
then a hedonic pricing study would not be suitable
for estimating the values for those attributes.
Hedonic pricing studies can be used in different
ways in environmental economics. Some are
intended to provide direct evidence of the value of
environmental improvements. Hedonic housing
price studies are good examples. House prices are
related to environmental conditions. The most
frequent example is probably air quality (see
Smith and Huang 1995 for a meta-analysis of
many studies), although water quality (Leggett
and Bockstael 2000), natural amenities (Thorsnes
2002), land contamination (Messer et al. 2006)
and other examples have been studied. Other
hedonic studies evaluate endpoints other than
environmental conditions. A good example would
be hedonic wage studies that are used in the
computation of the VSL. (See Viscusi 2004 for a
recent example.)
General application by type of hedonic
pricing study
Hedonic wage studies, also known as wage-risk
or compensating wage studies, are based on the
premise that individuals make trade-offs between
wages and occupational risks of death or injury.
Most analysts assume that workers understand
on-the-job risks, but others argue that workers
generally underestimate them (Viscusi 1993).
Some studies attempt to account for workers'
perceived risks, but the results of these studies are
not markedly different from those that do not
(Gerking, de Haan, and Schulze 1988). Two of the
most frequently used data sources for hedonic wage
studies are the National Institute of Occupational
Safety and Health (NIOSH) and Bureau of Labor
Statistics (BLS) Survey on "Working Conditions
(SWC) data. The NIOSH data are state-level data
of fatalities by occupation or industry, while the
SWC data provide a finer resolution of occupation
or industry fatalities, but do not vary by location.
Black and Kneiser (2003), however, question
the ability of hedonic wage studies using these
data sources to measure job risks accurately due
to severe measurement error. They find that the
measurement error in the fatality rates reported
from these sources is correlated with covariates
commonly used in the wage equations, making the
consistent estimation of the coefficient on risk in
the standard hedonic wage equation a challenge.
More recent hedonic wage studies have used the
BLS Census of Fatal Occupational Injuries (CFOI)
as the source for workplace risk information
(Viscusi 2004; Viscusi and Aldy 2007b; Aldy and
Viscusi 2008; Kniesner, Viscusi, and Ziliak 2006;
Leeth and Ruser 2003; Viscusi 2003; and Scotton
and Taylor 2009). These data are considered the
most comprehensive data on workplace fatalities
available (Viscusi 2004), compiling detailed
information since 1992 from all states and the
District of Columbia. Not only are the counts
of fatal events reported by 3-digit occupation
and 4-digit industry classifications, but the
circumstances of the fatal events, as well as worker
characteristics like age, gender and race, are also
captured.32 To ensure the veracity and completeness
of the reported data, multiple sources, including
death certificates, workers' compensation reports
and federal and state administration reports are
consulted and cross-referenced.
Although questions still persist about the
applicability of hedonic wage study results to
environmental benefits assessment, hedonic wage
studies have been used most frequendy in benefits
assessments to estimate the value of fatal risk
reductions.33 When a benefits assessment requires
a VSL estimate, hedonic wage estimates are a good
source of information. Historically, EPA has used a
VSL estimate primarily derived from hedonic wage
studies. For more information on the Agency's
VSL estimate, see Section 7.1.1 and Appendix C.34
The VSL determined by a hedonic wage study, for
example, typically relates WTA higher wages in
exchange for the increased likelihood of accidental
death during a person's working years. However,
32 More information on the CFOI data is available at: http://www.bls.gov/
iif/oshfat1 .htm.
33 For example, EPA's SAB has recognized the limitations of these
estimates for use in estimating the benefits of reduced cancer incidence
from environmental exposure. Despite these limitations, however, the
SAB concluded that these estimates were the best available at the time
(U.S. EPA 2000d).
34 As part of the revision of this document, EPA is revisiting the VSL
estimate used in policy analysis; further guidance will be forthcoming.
Guidelines for Preparing Economic Analyses I December 2010 7-28
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Chapter 7 Analyzing Benefits
analysts should take care when applying results
from one hedonic study to a new policy case, for
example, if there are differences in the age groups
facing mortality risks from longer-term conditions.
Hedonic property value studies measure the
different contributions of various characteristics
to the value of property These studies are typically
conducted using residential housing data, but
they have also been applied to commercial and
industrial property, agricultural land, and vacant
land.35 Bartik (1988) and Palmquist (1988,1991)
provide detailed discussions of benefits assessment
using hedonic methods. Property value studies
require large amounts of disaggregated data. To
avoid aggregation problems, market transaction
prices on individual parcels or housing units
are preferred to aggregate data such as census
tract information on average housing units.
Problems can arise from errors in measuring
prices (aggregated data) and errors in measuring
product characteristics (particularly those related
to the neighborhood and the environment).
There are numerous statistical issues associated
with applying hedonic methods to property value
studies. These include the choice of functional
form, the definition of the extent of the market,
identification, endogeneity, and spatial correlation.
Refer to Palmquist (1991) for a thorough
treatment of the main econometric issues.
Recently, advances have been made in modeling
spatial correlation in hedonic models (see Text Box
7.4 on spatial correlation for more information).
Other hedonic studies. Applicability of the
hedonic pricing method is not limited to the
property and labor markets. For example, hedonic
pricing methods can be combined with travel
cost methods to examine the implicit price
of recreation site characteristics (Brown and
Mendelsohn 1984). Results from other studies
can be used to infer the value of reductions in
mortality, cancer, or injury risks. For example,
Dreyfus and Viscusi (1995) use ahedonic analysis
35 See Xu, Mittlehammer, and Barkley (1993), and Palmquist and
Danielson (1989) for hedonic values of agricultural land; Ihlanfeldt and
Taylor (2004) for commercial property; Dale, Murdoch, Thayer, and
Waddell (1999), and McCluskey and Rausser (2003) for residential
property; and Clapp (1990), and Thorsnes (2002) for vacant land.
to determine the trade-offs between automobile
price and safety features to infer the VSL.
Considerations in evaluating and
understanding hedonic pricing studies
Unobservable factors. A concern common to
hedonic pricing studies is that it is impossible to
observe all factors that go into a decision. People
will choose among different jobs or houses not
only because they can trade off differences in
amenities and risks against differences in prices
or wages, but also because they have different
preferences for risks. Idiosyncratic personal tastes
that cannot be observed may be responsible for
a substantial portion of differences in observed
choices. For example, mountain climbers have
been known to pay tens of thousands of dollars to
undertake expeditions that substantially increase
their likelihood of early death.
Source of risks. Similarly, analysts need to
be careful in distinguishing the source of the
risks used to estimate risk premia. Consider an
individual who both works a dangerous job and
lives in unhealthy circumstances. Such a person
may be at greater risk of premature death than
someone who works a different job or lives
elsewhere. Analysts risk underestimating the wage
premium demanded on the job if they fail to
distinguish between causes of death — for example
between on-the-job accidents and environmentally
induced conditions acquired at home — when
relating the wage premium paid on dangerous
jobs to the statistics on premature mortality.
Conversely, if the same job poses multiple risks —
say the risk of both accidental death and serious,
but nonfatal injury were higher on a particular
job — the wage premium the job offers would
overstate WTP for reductions in mortality risks if
the injury risks were not properly controlled for in
the analysis. See Eeckhoudt and Hammitt (2001),
and Evans and Smith (2006) for more discussion
of competing versus specific risks.
Marginal changes. As with many results in
economics, hedonic pricing models are best suited
to the valuation of small, or marginal, changes in
attributes. Under such circumstances, the slope
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Text Box 7.4 - Spatial Correlation
Real property, such as buildings and land, and their associated characteristics are spatially distributed over the
landscape. As such, the characteristics of some of the properties may be spatially correlated. If some of these
characteristics are unobserved or for any other reason are not incorporated into the econometric model, there may
be dependence across the error terms of the model. Spatial econometrics is a subfield of econometrics that has
gained more attention as the capability for assessing such locational relationships within hedonic property data has
improved. Such improvements are primarily due to the increasing use of geographic information systems (GIS)
technology and geographically referenced data sets.
The nature of the correlation in the data can manifest itself so that there is either spatial heterogeneity across
observations, or more importantly, so that the characteristic values (e.g., price of homes) are correlated with those
of nearby observations. Standard econometric techniques can readily deal with the former, but are not well equipped
to handle the latter case. The econometric techniques allow for testing for the presence of spatial correlation, and
specifically modeling and correcting the correlation between observations and correcting for the biasing effect it can
have on parameter estimates. In practice, a relationship is defined between every variable at a given location and the
same variable at other, usually nearby, locations in the data set. In most cases this relationship is based on common
boundaries or is some specified function based on the distances between observations. This relationship between
observations is then accounted for in the econometric model in order to correct the error terms and obtain unbiased
model estimates. For more details on the fundamentals of spatial statistics see Anselin (1988).
of the hedonic price function can be interpreted
as WTP for a small change in the attribute.
Public policy, however, is sometimes geared to
larger, discrete changes in attributes. When this
is the case, calculation of benefits can become
significantly more complicated. Hedonic price
functions typically reflect equilibria between
consumer demands and producer supplies for
fixed levels of the attributes being evaluated. The
demand and supply functions are tangent to the
hedonic price function only in the immediate
neighborhood of an equilibrium point. Palmquist
(1991) describes conditions under which exact
welfare measures can be calculated for discrete
changes. See Freeman (2003) and Ekeland,
Heckman, and Nesheim (2004) for recent
treatments.
ling Behaviors
The averting behavior method infers values for
environmental quality from observations of actions
people take to avoid or mitigate the increased
health risks or other undesirable consequences
of reductions in ambient environmental quality
conditions. Examples of such defensive actions
can include the purchase and use of air filters,
boiling water prior to drinking it, and the purchase
of preventative medical care or treatment. By
analyzing the expenditures associated with these
averting behaviors economists can attempt to
estimate the value individuals place on small
changes in risk (Shogren and Crocker 1991, and
Quiggin 1992).
Economic foundations of averting
behavior methods
Averting behavior methods can be best understood
from the perspective of a household production
framework. Households can be thought of as
producing health outcomes by combining an
exogenous level of environmental quality with
inputs such as purchases of goods that involve
protection against health and safety risks (Freeman
2003). To the extent that averting behaviors are
available, the model assumes that a person will
continue to take protective action as long as the
expected benefit exceeds the cost of doing so.
If there is a continuous relationship between
defensive actions and reductions in health risks,
then the individual will continue to avert until the
marginal cost just equals her marginal WTP for
these reductions. Thus, the value of a small change
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Chapter 7 Analyzing Benefits
in health risks can be estimated from two primary
pieces of information:
• The cost of the averting behavior or good; and
• Its effectiveness, as perceived by the individual,
in offsetting the loss in environmental quality
Blomquist (2004) provides a detailed description
of the basic household production model of
averting behavior. More detail on the difficulties
inherent in applying the averting behavior model
can be found in Cropper and Freeman (1991).
One approach to estimation is to use observable
expenditures on averting and mitigating activities
to generate values that may be interpreted as a
lower bound on WTP. Harrington and Portney
(1987) demonstrate this by showing that WTP
for small changes in environmental quality can
be expressed as the sum of the values of four
components: changes in averting expenditures,
changes in mitigating expenditures, lost time, and
the loss of utility from pain and suffering. The
first three terms of this expression are observable,
in principle, and can be approximated by
calculating changes in these costs after a change in
environmental quality. The resulting estimate can
be interpreted as a lower bound on WTP that may
be used in benefits analysis (Shogren and Crocker
1991, and Quiggin 1992).
General application of averting
behavior method
Although the first applications of the method
were directed toward values for benefits of
reduced soiling of materials from environmental
quality changes (Harford 1984), recent research
has primarily focused on health risk changes.
Conceptually, the averting behavior method
can provide WTP estimates for a variety of
other environmental benefits such as damages to
ecological systems and materials.
Some averting behavior studies focus on behaviors
that prevent or mitigate the impact of particular
symptoms (e.g., shortness of breath or headaches),
while others have examined averting expenditures
in response to specific episodes of contamination
(e.g., groundwater contamination). The difference
in these endpoints is important. Because many
contaminants can produce similar symptoms,
studies that estimate values for symptoms may be
more amenable to benefit transfer than those that
are episode-specific. The latter could potentially be
more useful, however, for assessing the benefits of
a regulation expected to reduce the probability of
similar contamination episodes.
Considerations in evaluating
and understanding averting
behavior studies
Perceived versus actual risks. Analysts should
remember that consumers base their actions on
perceived benefits from defensive behaviors. Many
averting behavior studies explicitly acknowledge
that their estimates rest on consistency between
the consumer s perception of risk reduction and
actual risk reduction. While there is some evidence
that consumers are rational with regard to risk
— for example, consumer expenditures to reduce
risk vary positively with risk increases — there is
also evidence that there are predictable differences
between consumers' perceptions and actual risks.
Thus, averting behavior studies can produce biased
WTP estimates for a given change in objective
risk. Surveys may be necessary to determine the
benefits individuals perceive they are receiving
when engaging in defensive activities. These
perceived benefits can then be used as the object
of the valuation estimates. For example, if surveys
reveal that perceived risks are lower than expert
risk estimates, then WTP can be estimated with
the lower, perceived risk (Blomquist 2004).
Data requirements and implications. Data
needed for averting behavior studies include
information detailing the severity, frequency, and
duration of symptoms; exposure to environmental
contaminants; actions taken to avert or mitigate
damages; the costs of those behaviors and
activities; and other variables that affect health
outcomes, like age, health status, or chronic
conditions.
Separability of joint benefits. Analysts should
exercise caution in interpreting the results of
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studies that focus on goods in which there may be
significant joint benefits (or costs). Many defensive
behaviors not only avert or mitigate environmental
damages, but also provide other benefits. For
example, air conditioners obviously provide
cooling in addition to air filtering, and bottled
water may not only reduce health risks, but may
also taste better. Conversely, it also is possible that
the averting behavior may have negative effects on
utility. For example, wearing helmets when riding
bicycles or motorcycles maybe uncomfortable.
Failure to account for these "joint" benefits and
costs associated with averting behaviors will result
in biased estimates of WTP.
Modeling assumptions. Restrictive assumptions
are sometimes needed to make averting behavior
models tractable. Analysts drawing upon averting
behavior studies will need to review and assess the
implications of these assumptions for the valuation
estimates.
Economic foundations of COI studies
Two conditions must be met for the COI method
to approximate a market value of reduced health
risk. First, the direct costs of morbidity must
reflect the economic value of goods and services
used to treat illness. Second, a persons earnings
must reflect the economic value of lost work
time, productivity, and leisure time. Because of
distortions in medical and labor markets, these
assumptions do not routinely hold. Further, COI
estimates are not necessarily equal to WTP. The
method generally does not attempt to measure
the loss in utility due to pain and suffering, and
does not account for the costs of any averting
behaviors that individuals have taken to avoid an
illness. When estimates of WTP are not available,
the potential bias inherent in relying on COI
estimates should be acknowledged and discussed.
A second shortcoming of the COI method is that
by focusing on ex post costs, it does not capture
the risk attitudes associated with ex ante measures
of reduced health risk.
t of Illness
A frequently encountered alternative to WTP
estimates is the avoided cost of illness (COI). The
COI method estimates the financial burden of
an illness based on the combined value of direct
and indirect costs associated with the illness.
Direct costs represent the expenditures associated
with diagnosis, treatment, rehabilitation, and
accommodation. Indirect costs represent the
value of illness-related lost income, productivity,
and leisure time. COI is better suited as a WTP
proxy when the missing components (e.g., pain
and suffering) are relatively small as they usually
are in cases of in minor, acute illnesses. However,
there are usually better medical treatment and lost
productivity estimates for more severe illnesses.
The COI method is straightforward to implement
and explain to policy makers, and has a number
of other advantages. The method has been used
for many years and is well developed. Collecting
data to implement it often is less expensive than
for other methods, improving the feasibility of
developing original COI estimates in support of a
specific policy.
Although COI estimates do not adequately
capture several components of WTP, COI does
not necessarily serve as a lower bound estimate
of WTP. This is because, for some illnesses, the
cost of behaviors that allow one to avoid an illness
might be far lower than the cost of the illness itself.
Depending on the design of the research question,
WTP could reflect the lower avoidance costs while
COI would reflect the higher costs of treating the
illness once it has been contracted. In addition,
COI estimates capture medical expenses passed on
to third parties such as health insurance companies
and hospitals, whereas WTP estimates generally
do not. Finally, COI estimates capture the value of
lost productivity (see Text Box 7.4 above), whereas
these costs may be overlooked in WTP estimates
— especially when derived from consumers or
employees covered by sick leave.
Available comparisons of COI and total WTP
estimates suggest that the difference can be large
(Rowe et al. 1995). This difference varies greatly
across health effects and across individuals.
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Chapter 7 Analyzing Benefits
General application by type of
COI study
Prevalence-based estimates. Prevalence-based
COI estimates are derived from the costs faced by
all individuals who have a sickness in a specified
time period. For example, an estimate of the
total number of individuals who currently have
asthma, as diagnosed by a physician, reflects the
current prevalence of physician-diagnosed asthma.
Prevalence-based COI estimates for asthma
include all direct and indirect costs associated with
asthma within a given time period, such as a year.
Prevalence-based COI estimates are a measure of
the full financial burden of a disease, but generally
will be lower bound estimates of the total WTP
for avoiding the disease altogether. They are useful
for evaluating the financial burden of policies
aimed at improving the effectiveness of treatment
or at reducing the morbidity and mortality
associated with a disease.
Incidence-based estimates. By contrast,
incidence-based COI estimates reflect expected
costs for new individuals who develop a disease
in a given time period. For example, the number
of individuals who receive a new diagnosis of
asthma from a physician in a year reflects the
annual incidence of physician-diagnosed asthma.
Incidence-based COI estimates reflect the expected
value of direct medical expenditures and lost
income and productivity associated with a disease
from the time of diagnosis until recovery or death.
Because these expenses can occur over an extended
time period, incidence-based estimates are usually
discounted to the year the illness is diagnosed
and expressed in present value terms. Incidence-
based COI estimates are useful for evaluating
the financial burden of policies that are aimed at
reducing the incidence of new cases of disease.
Most existing COI studies estimate indirect
costs based on the typical hours lost from a work
schedule or home production, evaluated at an
average hourly wage. The direct medical costs
of illness are generally derived in one of two
ways. The empirical approach estimates the total
medical costs of the disease by using a database
of actual costs incurred for patients with the
illness. The "expert elicitation" approach uses a
panel of physicians to develop a generic treatment
profile for the illness. Illness costs are estimated by
multiplying the probability of a patient receiving
a treatment by the cost of the treatment. For any
particular application, the preferred approach will
depend on availability of reliable actual cost data as
well as characteristics of the illness under study.
COI estimates for many illnesses are readily
available from existing studies and span a wide
range of health effects. EPA's Cost of Illness
Handbook (U.S. EPA 2007c) provides estimates
for many cancers, developmental illnesses and
disabilities, and other illnesses.
Considerations in evaluating and
understanding COI studies
Technological change. Medical treatment
technologies and methods are constantly
changing, and this could push the true cost
estimate for a given illness either higher or lower.
When using previous COI studies, the analyst
should be sure to research whether and how the
generally accepted treatment has changed from the
time of the study.
Measuring the value of lost productivity. Simply
valuing the actual lost work time due to an illness
may not capture the full loss of an individual's
productivity in the case of a long-term chronic
illness. Chronic illness may force an individual to
work less than a full-time schedule, take a job at a
lower pay rate than she would otherwise qualify
for as a healthy person, or drop out of the labor
force altogether. A second issue is the choice of
wage rate. Even if the direct medical costs are
estimated using individual actual cost data, it
is highly unlikely that the individual data will
include wages. Therefore, the wage rate chosen
should reflect the demographic distribution of
the illness under study. Furthermore, the value of
lost time should include the productivity of those
persons not involved in paid jobs. Homemakers'
household upkeep and childcare services, retired
persons' volunteering efforts, and students' time in
school all directly or indirectly contribute to the
productivity of society. Finally, the value of lost
leisure time to an individual and her family is not
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Text Bo* 7.5 - Value of Time
Estimating the cost of an illness by examining only medical costs clearly understates the true costs experienced by
an individual with ill health. Not only does the individual incur medical expenditures, they also miss production and
consumption opportunities. In particular they miss opportunities to work for wages, produce household goods and
services (e.g., laundry, home-cooked meals), and enjoy leisure activities. These latter two categories are jointly referred
to as non-work time. The value of these lost opportunities has typically been estimated by examining the value of time.
EPA has developed an approach for valuing time losses based on the opportunity cost of time. For paid work, the
approach is relatively straightforward. It rests on the assumption that total compensation (wages and employment
benefits) is equal to the employers' valuation of the worker's output. Therefore, if a worker is absent due to illness, society
loses the value of the foregone output, which can be estimated by examining the worker's wages and employment benefit
values. To value time spent on non-market work and leisure activities, the assumption is made that an individual will
engage in such unpaid activities only if, at the margin, the value of these activities is greater than the wages that could be
earned in paid employment. Hence after-tax wages provide a lower bound estimate of the value of non-work time.
The loss of work time and leisure activities due to illness need not be complete. When an illness reduces but does
not eliminate productivity at work or enjoyment of leisure time, estimates of the value of the diminishments in these
opportunities are legitimate components of the cost of the illness.
Valuing time lost due to illness experienced by children and other subpopulations that do not earn wages is more
difficult. Examples of such subpopulations include the elderly, unemployed, or individuals who are out of the work
force. Analysts could surmise the post-tax wage if such individuals were employed; however, the situation involves
less certainty. For example, the time loss of children who suffer illness is sometimes estimated by considering the
effect of the illness, if any, on future earnings. For this case, however, Circular A-4 (0MB 2003) currently suggests
that, in the absence of better data, monetary values for children should be at least be as large as the values for adults
(for the same risk probabilities and health outcomes).
Accounting for time losses in COI estimates comes closer to a full accounting of the losses borne by individuals
suffering illness than simply assessing medical costs. However, a third cost category remains neglected —the value
of pain and suffering. When an individual is sick, she not only misses opportunities to produce or relax, she also
would be willing to pay some amount to avoid the pain or discomfort of the illness. In most economic models, these
costs are represented as declines in utility and as such are inherently difficult to estimate. To date, there are no good
estimates, or methods for obtaining good estimates, of the value of avoiding pain.
included in most COI studies. (See Text Box 7.5
for a discussion of the value of time.)
7.3.2 Stall iference
Hie distinguishing feature of stated preference
methods compared to revealed preference methods
is that stated preference methods rely on data
drawn from peoples responses to hypothetical
questions while revealed preference methods rely
on observations of actual choices. Stated preference
methods use surveys that ask respondents to
consider one or a series of hypothetical scenarios
that describe a potential change in a non-market
good. The advantages of stated preference methods
include their ability to estimate non-use values and
to incorporate hypothetical scenarios that closely
correspond to a policy case. The main disadvantage
of stated preference methods is that they may be
subject to systematic biases that are difficult to test
for and correct.
National Oceanic and Atmospheric
Administrations (NOAA) The Report of the
NOAA Panel on Contingent Valuation is often
cited as a primary source of information on
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Chapter 7 Analyzing Benefits
stated preference techniques. Often referred to
as the "NOAA Blue Ribbon Panel," this panel,
comprised of five distinguished economists
including two Nobel Laureates, deliberated on
the usefulness of stated preference studies for
policy analysis (Arrow et al. 1993). While their
findings generally mirror the recommendations
offered below, since the release of their report a
number of changes in the survey administration
"landscape" have occurred including the advent of
internet surveys, the decline in representativeness
of telephone surveys, and the growth in popularity
of stated choice experiments.
7.3,2,1 Economic Foundation of Stated
Preference Methods
The responses elicited from stated preference
surveys, if truthful, are either direct expressions
of WTP or can be used to estimate WTP for
the good in question. However, the "if truthful"
caveat is paramount. "While many environmental
economists believe that respondents can provide
truthful answers to hypothetical questions and
therefore view stated preference methods as useful
and reliable if conducted properly, a non-trivial
fraction of economists are more skeptical of the
results elicited from stated preference surveys.
Due to this skepticism, it is important to employ
validity and reliability tests of stated preference
results when applying them to policy decisions.
If the analyst decides to conduct a stated preference
survey or use stated preference results in a benefit
transfer exercise, then a number of survey design
issues should be considered. Stated preference
researchers have attempted to develop methods
to make individuals' choices in stated preference
studies as consistent as possible with market
transactions. Reasonable consistency with the
framework of market transactions is a guiding
criterion for ensuring the validity of stated
preference value estimates. Three components of
market transactions need to be constructed in stated
preference surveys: the commodity, the payment,
and the scenario (Fischoff and Furby 1988).
Stated preference studies need to carefully
define the commodity to be valued, including
characteristics of the commodity such as the
timing of provision, certainty of provision, and
availability of substitutes and complements. The
definition of the commodity generally involves
identifying and characterizing attributes of the
commodity that are relevant to respondents.
Commodity definition also includes defining
or explaining baseline or current conditions,
property rights in the baseline, and the policy
scenarios, as well as the source of the change in the
environmental commodity.36
Respondents also must be informed about the
transaction context, including the method, timing,
and duration of payment. The transaction must
not be coerced and the individual should be aware
of her budget constraint. The payment vehicle
should be described as a credible and binding
commitment should the respondent decide to
purchase the good. The timing and duration
of a payment involves individuals implicitly
discounting payments and calculating expected
utility for future events. The transaction context
and the commodity definition should describe and
account for these temporal issues.
The hypothetical scenario(s) should be described
so as to minimize potential strategic behavior such
as "free riding" or "overpledging." In the case of
free riding, respondents will underbid their true
WTP for a good if they feel they will actually be
made to pay for it but believe the good will be
provided nevertheless. In the case of overpledging,
respondents pledge amounts greater than their
true WTP with the expectation that they will not
be made to pay for the good, but believing that
their response could influence whether or not
the goodwill be provided. Incentive-compatible
choice scenarios and attribute-based response
formats have been shown to mitigate strategic
responses. Both are discussed below.
It is recognized in both the experimental
economics literature and the survey methodology
36 Depending on the scenario, the description of the commodity may
produce strong reactions in respondents and could introduce bias.
In these cases, the detail with which the commodity of the change is
specified needs to be balanced against the ultimate goals of the survey.
Regardless, the commodity needs to be specified with enough detail to
make the scenario credible.
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Chapter 7 Analyzing Benefits
literature that different survey formats can elicit
different responses. Changing the wording
or order of questions also can influence the
responses. Therefore, the researcher should
provide a justification for her choice of
survey format and include a discussion of the
ramifications of that choice.
7.3,2,2 General Application I
Stated Preference Study
Two main types of stated preference survey format
are currendy used: direct WTP questions and
stated choice questions. Stated choice questions can
be either dichotomous choice questions or multi-
attribute choice questions. Following a general
discussion of survey format, each of the stated
preference survey formats is described in detail below.
Goals that should guide selection of the survey
format include the minimization of survey costs, of
non-responsiveness, of unexplained variance, and
of complications associated with WTP estimation.
For example, open-ended questions require smaller
sample sizes and are simpler to analyze than
other methods of asking the valuation question.
These advantages could lead to significant cost
reductions. However, these advantages maybe
mitigated by higher non-response rates and large
unexplained variance in the responses. Moreover,
there remains a great deal of uncertainty over the
effect of the choice mechanism (i.e., open-ended,
dichotomous choice, etc.) on the ability and
willingness of respondents to provide accurate and
well-considered responses.
Because survey formats are still evolving and
many different approaches have been used in the
literature, no definitive recommendations are
offered here regarding selection of the survey
format. Rather, the following sections describe
some of the most commonly used formats and
discuss some of their known and suspected
strengths and weaknesses. Researchers should
select a format that suits their topic, and should
strive to use focus groups, pretests, and statistical
validity tests to address known and suspected
weaknesses in the selected approach.
Direct/open-ended' msiions
Direct/open-ended WTP questions ask
respondents to indicate their maximum WTP
for the specific quantity or quality changes of a
good or service that has been described to them.
An important advantage of open-ended stated
preference questions is that the answers provide
direct, individual-specific estimates of WTP.
Although this is the measure that economists
want to estimate, early stated preference studies
found that some respondents had difficulty
answering open-ended WTP questions and non-
response rates to such questions were high. Such
problems are more common when the respondent
is not familiar with the good or with the idea of
exchanging a direct dollar payment for the good.
An example of a stated preference study using
open-ended questions is Brown et al. (1996).
Various modifications of the direct/open-ended
WTP question format have been developed in an
effort to help respondents arrive at their maximum
WTP estimate. In iterative bidding respondents are
asked if they would pay some initial amount, and
then the amount is changed up or down depending
on whether the respondent says "yes" or "no" to the
first amount. This continues until a maximum WTP
is determined for that respondent. Iterative bidding
has been shown to suffer from "starting point bias,"
wherein respondents' maximum WTP estimates are
systematically related to the dollar starting point in
the iterative bidding process (Rowe and Chestnut
1983, Boyle et al. 1988, and Whitehead 2002). A
payment card is a list of dollar amounts from which
respondents can choose, allowing respondents an
opportunity to look over a range of dollar amounts
while they consider their maximum WTP. Mitchell
and Carson (1989) and Rowe et al. (1996) discuss
concerns that the range and intervals of the dollar
amounts used in payment card methods may
influence respondents' WTP answers.
Stated choice questions
While direct/open-ended WTP questions are
efficient in principle, researchers have generally
turned to other stated preference techniques in
recent years. This is largely due to the difficulties
respondents face in answering direct WTP
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Chapter 7 Analyzing Benefits
questions and the lack of easily implemented
procedures to mitigate these difficulties. Researchers
also have noted that direct WTP questions with
various forms of follow-up bidding may not be
"incentive compatible." That is, the respondents'
best strategy in answering these questions is not
necessarily to be truthful (Freeman 2003).
In contrast to direct/open-ended WTP questions,
stated choice questions ask respondents to choose
a single preferred option or to rank options from
two or more choices. When analyzing the data the
dependent variable will be continuous for open-
ended WTP formats and discrete for stated choice
formats.37 In principle, stated choice questions can
be distinguished along three dimensions:
• The number of alternatives each respondent can
choose from in each choice scenario — surveys
may offer only two alternatives (e.g., yes/no,
or "live in area A or area B); two alternatives
with an additional option to choose "don't
know" or "don't care;" or multiple alternatives
(e.g., "choose option A, B, or C").
• The number of attributes varied across alternatives
in each choice question (other than price) —
alternatives maybe distinguished by variation in
only a single attribute (e.g., mortality risk) or by
variation in multiple attributes (e.g., price, water
quality, air quality, etc.).
• The number of choice scenarios an individual is
asked to evaluate through the survey.
Any particular stated choice survey design could
combine these dimensions in any given way.
For example, a survey may offer two options to
choose from in each choice scenario, vary several
attributes across the two options, and present each
respondent with multiple choice scenarios through
the course of the survey. Using the taxonomy
presented in these Guidelines, a complete (though
cumbersome) description of this format would
be a dichotomous choice/multi-attribute/
37 Some researchers use the term "contingent valuation" to refer to
direct WTP and dichotomous choice/referendum formats and "stated
preference" to refer to other stated choice formats. In these Guidelines
the term "stated preference" is used to refer to all valuation studies
based on hypothetical choices (including open-ended WTP and stated
choice formats), as distinguished from "revealed preference."
multi-scenario survey. The statistical strategy for
estimating WTP is largely determined by the
survey format adopted, as described below.
The earliest stated choice questions were simple
yes/no questions. These were often called
referendum questions because they were often
posed as, "Would you vote for..., if the cost
to you were $X?" However, these questions are
not always posed as a vote decision and are now
commonly called dichotomous choice questions.
In recent years, stated preference researchers
have been adapting a choice question approach
used in the marketing literature called conjoint
analysis. These are more complex choice questions
in which the respondent is asked repeatedly to
pick her preferred option from a list of two or
more options. Each option represents a package
of product attributes. By incorporating a dollar
price or cost in each option, stated preference
researchers are able to extract WTP estimates
for incremental changes in the attributes of the
good, based on the preferences expressed by the
respondents. Holmes and Adamowicz (2003) refer
to this as attribute-based stated choice.
Dichotomous choice WTP questions.
Dichotomous choice questions present
respondents with a specified environmental change
costing a specific dollar amount and then ask
whether or not they would be willing to pay that
amount for the change. The primary advantage of
dichotomous choice WTP questions is that they
are easier to answer than direct WTP questions,
because the respondent is not required to
determine her exact WTP, only whether it is above
or below the stated amount. Sample mean and
median WTP values can be derived from analysis
of the frequencies of the yes/no responses to each
dollar amount. Bishop and Heberlein (1979),
Hanemann (1984), and Cameron and James
(1987) describe the necessary statistical procedures
for analyzing dichotomous choice responses using
logit orprobit models. Dichotomous choice
responses will reveal an interval containing WTP
and in the case of a 'yes' response this interval
will be unbounded from above. As a result,
significantly larger sample sizes are needed for
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Chapter 7 Analyzing Benefits
dichotomous choice questions to obtain the same
degree of statistical efficiency in the sample means
as direct/open-ended responses that reveal point-
values for WTP (Cameron and James 1987).
To increase the estimation efficiency of
dichotomous choice questions, recent applications
have commonly used what is called a double-
bounded approach. In double-bounded questions
the respondent is asked whether she would be
willing to pay a second amount, higher if she
said yes to the first amount, and lower if she said
no to the first amount.38 Sometimes multiple
follow-up questions are used to try to narrow the
interval around WTP even further. These begin
to resemble iterative bidding style questions if
many follow-up questions are asked. Similar to
starting point bias in iterative bidding questions,
the analyses of double-bounded dichotomous
choice question results suggest that the second
responses may not be independent of the first
responses (Cameron and Quiggin 1994,1998; and
Kanninen 1995).
Multi-attribute choice questions. In multi-
attribute choice questions, respondents are
presented with alternative choices that are
characterized by different combinations of
goods and services attributes and prices. Multi-
attribute choice questions ask respondents to
choose the most preferred alternative (a partial
ranking) from multiple alternative goods (i.e.,
a choice set), in which the alternatives within a
choice set are differentiated by their attributes
including price (Johnson et al. 1995 and Roe
et al. 1996). The analysis takes advantage of the
differences in the attribute levels across the choice
options to determine how respondents value
marginal changes in each of the attributes. To
measure WTP, a price (often a tax or a measure
of travel costs), is included in multi-attribute
choice questions as one of the attributes of
each alternative. This price and the mechanism
by which the price would be paid need to be
38 Alberini (1995) illustrated an analysis approach for deriving WTP
estimates from such responses and demonstrates the increased
efficiency of double-bounded questions. The same study showed that
the most efficient range of dollar amounts in a dichotomous choice
study design was one that covered the mid-range of the distribution
and did not extend very far into the tails at either end.
explained clearly and plausibly, as with any
payment mechanism in a stated preference study.
Boyle and Ozdemir (2009) examine the impact
of question design choices, such as the ordering
of attributes and the number of alternatives in a
single question, on the mean WTP estimate.
There are many desirable aspects of multi-
attribute choice questions, including the nature
of the choice being made. To choose the most
preferred alternative from some set of alternatives
is a common decision experience in posted-price
markets, especially when one of the attributes of
the alternatives is a price. One can argue that such
a decision encourages respondents to concentrate
on the trade-offs between attributes rather than
taking a position for or against an initiative or
policy. This type of repeated decision process may
also diffuse the strong emotions often associated
with environmental goods, thereby reducing
the likelihood of yea-saying or of rejecting the
premise of having to pay for an environmental
improvement.39 Presenting repeated choices also
gives the respondent some practice with the
question format, which may improve the overall
accuracy of her responses, and gives her repeated
opportunities to express support for a program
without always selecting the highest price option.
Some applications of multi-attribute survey
formats include Opaluch et al. (1993), Adamowicz
etal. (1994),Viscusietal. (1991), Adamowicz
etal. (1997), Adamowicz etal. (1998a), Layton
and Brown (2000), Johnson and Desvousges
(1997), Boyle et al. (2001), andMorey et al.
(2002). Studies that investigate the effects of
multi-attribute choice question design parameters
include Johnson et al. (2000) and Adamowicz et
al. (1997).
7,3,2,3 Considerations in Evaluating
Stated Preference Results
Survey mode. The mode used to administer
a survey is an important component of survey
research design because it is the mechanism by
39 Yea-saying refers to the behavior of respondents when they overstate
their true WTP in order to show support for a situation described in
survey questions.
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Chapter 7 Analyzing Benefits
which information is conveyed to respondents,
and likewise determines the way in which
individuals can provide responses for analysis.
Until recently there were three primary survey
modes: telephone, in-person, and mail. Telephone
surveys are primarily conducted with a trained
interviewer using random digit dialing (RDD)
to contact households. In-person surveys are
conducted in a variety of ways, including door-
to-door, intercepts at public locations, and via
telephone recruiting to a central facility. Mail
surveys are conducted by providing written survey
materials for respondents to self-administer. As
technology and society has changed, so has the
preference for one mode over the other. With
the influx of market research and telemarketing,
the telephone has become a less convenient way
to administer surveys. Many people refuse to
answer the phone, or to answer questions over
the phone. The same can be said of mail surveys.
People are quick to ignore unsolicited mail. In
recent years the Internet has emerged as a possible
mode for conducting surveys. Internet access and
email accounts are more prevalent and computer
literacy is high in the United States and other
developed countries. As with all of the survey
modes mentioned, there are inherent biases. These
biases are generally classified as social desirability
bias, sample frame bias, avidity bias, and non-
response bias. See Maguire (2009), Loomis and
King (1994), Mannesto and Loomis (1991),
Lindberget al. (1997), andEthieret al. (2000) for
a discussion of different biases in survey mode.
Framing issues. An important issue regarding
survey formats is whether information provided in
the questions influences the respondents' answers
in one way or another. For example, Cameron
andHuppert (1991) and Cooper and Loomis
(1992) find that mean WTP estimates based on
dichotomous choice questions may be sensitive
to the ranges and intervals of dollar amounts
included in the WTP questions. Kanninen and
Kristrom (1993) show that the sensitivity of
mean WTP to bid values can be caused by model
misspecification, failure to include bid values that
cover the middle of the distribution, or inclusion
of bids from the extreme tails of the distribution.
Selection of payment vehicle. The payment
vehicle in a stated preference study refers to the
method by which individuals or households
would pay for the good described in a particular
survey instrument. Examples include increases
in electricity prices, changes in cost of living, a
one-time tax, or a donation to a special fund. It is
imperative that the payment vehicle is incentive
compatible and does not introduce any strategic or
other bias. Incentive compatibility means that the
individual is motivated to respond truthfully and
does not use their responses to try to influence a
particular outcome (e.g., state a WTP value that is
higher than their true WTP to try to make sure a
particular outcome succeeds).
Strategic behavior. Adamowicz et al. (1998a) also
suggests that respondents may be less likely to behave
strategically when responding to multi-attribute
choice experiments. Repeatedly choosing from several
options gives the respondent some practice with
the question format that may improve the overall
accuracy of her responses, and gives her repeated
opportunities to express support for a program
without always selecting the highest price option.
Yea-saying. As mentioned above, yea-saying refers
to the behavior of respondents when they overstate
their true WTP in order to show support for
situation described in survey questions. For example,
Kanninen (1995) finds some evidence of yea-saying
in dichotomous choice responses through testing
in follow-up questions. The extent of this potential
problem is not well established, but it may provide
an explanation for the fact that mean WTP values
based on dichotomous choice responses tend to be
equal to or higher than values from direct WTP
questions for the same good (Cummings et al. 1986,
Boyle et al. 1993, Brown et al. 1996, Ready et al.
1996, and Balistreri et al. 2001). It has not been
determined whether yea-saying can be reduced
by double-bounded dichotomous choice because
in this case the respondent has more than one
opportunity to say yes.
Treatment of "don't know" or neutral responses.
Based on recommendations from the NOAA Blue
Ribbon panel (Arrow et al. 1993), many surveys
now include "don't know" or "no preference"
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Chapter 7 Analyzing Benefits
options for respondents to choose from. There
have been questions about how such responses
should enter the empirical analysis. Examining
referendum-style dichotomous choice questions,
Carson et al. (1998) found that when those who
chose not to vote were coded as "no" responses,
the mean WTP values were the same as when the
"would not vote" option was not offered. Offering
the "would not vote" option did not change the
percentage of respondents saying "yes". Thus, they
recommend that if a "would not vote" option
is included, it should be coded as a "no" vote,
a practice that has become widespread. Stated
preference studies should always be explicit about
how they treat "don't know," "would not vote," or
other neutral responses.
Reliability, in general terms, means consistency or
repeatability. If a method is used numerous times
to measure the same commodity, then the method
is considered more reliable the lower the variability
in the results.
• Test-retest approach. Possibly the most
widely applied approach for assessing
reliability in stated preference studies has been
the test-retest approach. Test-retest assesses
the variability of a measure between different
time periods. Loomis (1989), Teisl et al.
(1995), McConnell et al. (1998), and Hoban
and "Whitehead (1999) all provide examples
of the test-retest method for reliability.
• Meta-analysis of stated preference survey
results for the same good also may provide
evidence of reliability. Meta-analysis
evaluates multiple studies as though each
was constructed to measure the same
phenomenon. Meta-analysis attempts to sort
out the effects of differences in the valuation
approach used in different surveys, along with
other factors influencing the elicited value.
For example Boyle et al. (1994) use meta-
analysis to evaluate eight studies conducted to
measure values for groundwater protection.
(Also see Section 7.4.)
Validity tests seek to assess whether WTP
estimates from stated preference methods behave
as a theoretically correct WTP should. Three types
of validity discussed below are: content validity,
criterion validity, and convergent validity.
• Content validity. Content validity refers to
the extent to which the estimate captures the
concept being evaluated. Content validity is
largely a subjective evaluation of whether a
study has been designed and executed in a way
that incorporates the essential characteristics
of the WTP concept. In a sense, it is akin to
asking, "On the face of it, does the estimate
capture the concept of WTP?" (This approach
is sometimes referred to as "face validity.")
To evaluate a survey instrument, analysts
look for features that researchers should have
incorporated into the survey scenario. First,
the environmental change being valued should
be clearly defined. A careful exposition of
the conditions in the baseline case and how
these would be expected to change over time
if no action were taken should be included.
Next, the action or policy change should
be described, including an illustration of
how and when it would affect aspects of the
environment that people might care about.
Boyd and Banzahf (2007), and Boyd and
Krupnick (2009) put a finer point on this
concept and advocate developing the valuation
scenario based on "ecological endpoints"
rather than intermediate goods that are less
clearly associated with outcomes of interest.
For example, if respondents ultimately care
about the survival of a certain species, it
is more sensible to structure questions to
ask about WTP for the species' survival
than to ask about degradation of habitat,
as respondents are unlikely to know the
relationship between habitat attributes and
species survival. Respondent attitudes about
the provider and the implied property rights
of the survey scenario can be used to evaluate
the appropriateness of features related to
the payment mechanism (Fischhoff and
Furby 1988). Survey questions that probe for
respondent comprehension and acceptance of
the commodity scenario can offer important
indications about the validity of the results
(Bishop et al. 1997).
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Chapter 7 Analyzing Benefits
• Criterion validity. Criterion validity assesses
whether stated preference results relate to
other measures that are considered to be
closer to the concept being assessed (WTP).
Ideally, one would compare results from a
stated preference study (the measure) with
those from actual market data (the criterion).
This is because market data can be used to
estimate WTP more reliably than a stated
preference survey Another approach would
be to estimate a sample of individuals' WTP
for a commodity using a stated preference
survey and then later give the same sample of
individuals or a different random sample of
individuals drawn from the same population
a real opportunity to buy the good. (See
Mitchell and Carson 1989, Carson et al.
1987a, Kealy et al. 1990, Brown et al. 1996,
and Champ et al. 1997 for examples.)
When unable to conduct such comparisons,
sensitivity to scope and income has been
used to assess criterion validity. "Scope tests"
are concerned with how WTP responds to
changes in the amount of the referenced good
provided in the valuation scenario (Smith
and Osborne 1996, Rollins andLyke 1998,
and Heberlien et al. 2005). If the referenced
good is indeed a "normal good" utility theory
implies that WTP should increase with the
provision of the good. For the same reason
one would expect WTP to exhibit positive
income elasticity (McFadden 1994, and
Schlapfer 2006). Neither test is necessary
or sufficient to establish criterion validity
(Heberlein et al. 2005) but can serve as useful
proxies when an alternate measure of WTP
for the same good is unavailable. Diamond
(1996) suggests that stronger scope tests can
be conducted by comparing departures from
strict "adding up" of WTP for partial changes
and relating them to the income elasticity of
WTP. Other researchers, however, argue that
the Diamond test may not be practicable or
even necessarily correct (Carson et al. 2001).
• Convergent validity. Convergent validity
examines the relationship between different
measures of a concept.40 This differs from
criterion validity in that one of the measures
is not taken as a criterion upon which to judge
the other measure. The measure of interest
and the other measure are judged together to
assess consistency with one another. If they
differ in a systematic way (e.g., one is usually
larger than another for the same good), it is
not clear which one is more correct. However,
if stated preference results are found to be
larger than revealed preference results for
the same good, it is often presumed that
the difference is the result of hypothetical
bias because revealed preference results are
based on actual behavior. There can be many
other sources of bias and error in both stated
preference and revealed preference results that
cause them to differ from one another and
from "true" WTP.
Empirical convergent validity tests use
comparisons of stated preference results with
revealed preference or experimental results
that are thought to be free of hypothetical
bias.41 In some circumstances, convergent
validity tests may be incorporated as part of
the study design. Such a test might compare
results of an actual market exercise with the
results of a hypothetical market exercise in
which the exercises are otherwise identical.
In this case there might be evidence of an
upward or downward bias in the hypothetical
results as compared to the simulated market
results. See Section 7.3.3 for a discussion on
combining revealed preference and stated
preference data.
Hypothetical bias occurs when the responses
to hypothetical stated preference questions are
40 Mitchell and Carson (1989) define convergent validity and theoretical
validity as two types of construct validity. Construct validity examines
the degree to which the measure is related to other measures as
predicted by theory.
41 Some analysts include the comparisons of stated preference results
to actual markets under convergent validity rather than criterion
validity as discussed in the previous section, because there is no
actual observable measure of the theoretical construct WTP. Here,
a distinction is made between simulated markets, as in a laboratory
experiment in which values may be "induced" by giving subject cash
at the end based on their choices, and actual markets in which subjects
must pay with their own money.
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Chapter 7 Analyzing Benefits
systematically different than what individuals
would pay if the transactions were to actually
occur. Widely cited as one of the most common
problems with the stated preference method (List
and Gallet 2001, and Murphy and Allen 2005),
and researchers have made advances in techniques
to minimize such bias. These techniques include
the use of "cheap talk" methods to directly tell
respondents about the potential for hypothetical
bias (Cummings and Taylor 1999, and List
2001); calibrating hypothetical values (List and
Shogren 1998, and Blomquist et al. 2009); and
allowing respondents to express uncertainty in
their responses and restricting the set of positive
responses to those about which the respondent
was most certain (Vossler et al. 2003). Several
studies have shown that attribute-based choice
experiments reduce hypothetical bias in the bid
amounts and the marginal value of attributes
relative to other elicitation methods (Carlsson and
Martinsson 2001, Murphy and Allen 2005, and
List et al. 2006).
Tests for hypothetical bias often involve a
comparison of actual payments and responses
to hypothetical scenarios that use the same
solicitation approach. The actual payments
typically occur in one of three scenarios. Market
transactions are the most common (Cummings et
al. 1995, and List and Shogren 1998) but generally
involve payments for private goods while most
stated preference applications are concerned with
public or quasi-public goods. Simulated markets
can be used to solicit actual donations for public
good provision (Champ et al. 1997). However,
donation solicitations are subject to free riding,
so while it may be possible to test for hypothetical
bias using this approach, both the actual and
hypothetical payment scenarios lack incentive
compatibility and may not represent total
WTP. In rare instances comparisons have been
made between actual referenda for public good
provision and hypothetical responses to the same
scenario but the conditions for a valid comparison
of this sort are exceedingly difficult to satisfy
(Johnston 2006).
Non-response bias is introduced when non-
respondents would have answered questions
systematically differently than those who did
answer. Non-response bias can take two forms:
item non-response and survey non-response.
• Item non-response bias occurs when
respondents who agreed to take the survey
do not answer all of the choice questions
in the survey. Information available about
respondents from other questions they
answered can support an assessment of
potential item non-response bias for the WTP
questions that were unanswered. The key issue
is whether there were systematic differences
in potential WTP-related characteristics of
those who answered the WTP questions
and those who did not. Characteristics of
interest include income, gender, age, expressed
attitudes and opinions about the good or
service, and information reported on current
use or familiarity with the good or service.
Statistically significant differences may
indicate the potential for item non-response
bias, while finding no such differences
suggests that the chance of significant non-
response bias is lower. However, the results of
this comparison are only suggestive because
respondents and non-respondents may only
differ in their preference for the good in
question (McClelland et al. 1991).
• Survey non-response bias is created when
those who refuse to take the survey have
preferences that are systematically different
from the preferences of those who do respond.
Although it is generally thought that surveys
with high response rates are less likely to
suffer from survey non-response bias, it is not
a guarantee.42 For survey non-respondents,
there maybe no available data to determine
how they might systematically differ from
those who responded to the survey. The
42 NotethatOMB's Guidance on Agency Survey and Statistical
Collections (0MB 2006) has fairly strict requirements for response
rates and their calculation for Agency-sponsored surveys,
recommending that "ICRs for surveys with expected response rates
of 80 percent or higher need complete descriptions of the basis of the
estimated response rate...ICRs for surveys with expected response
rates lower than 80 percent need complete descriptions of how the
expected response rate was determined, a detailed description of steps
that will betaken to maximize the response rate...and a description of
plans to evaluate non-response bias" (pp. 60-70).
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Chapter 7 Analyzing Benefits
most common approach is to examine the
relevant measurable characteristics of the
respondent group, such as income, resource
use, gender, age, etc., and to compare them to
the characteristics of the study population.
Similarity in mean characteristics across the
two groups suggests that the respondents
are representative of the study population
and that non-response bias is expected to be
minimal.
A second way to evaluate potential survey
non-response bias is to conduct a short
follow-up survey with non-respondents. This
can sometimes be accomplished through
interviews conducted during the recruiting
phase. Such follow-ups typically ask a few
questions about attitudes and opinions on
the topic of the study as well as collecting
basic socioeconomic information. Questions
need to match those in the full survey closely
enough to compare non-respondents to
respondents. The follow-up must be very brief
or response rates will be low (OMB 2006).
>r i C v ibining Reveal-o <^>d
Stat 11 :efererv. Kita
Instead of looking at revealed preference and
stated preference data as two separate methods
for estimating environmental benefits, an
increasing number of researchers are using them
in combination. The practice has been in use
much longer in the marketing and transportation
literature and many of the lessons learned by
those researchers are now being employed in
environmental economics. In theory, the strengths
of each data type should help overcome some
of the weaknesses of the other. As described by
Whitehead et al. (2008) in a recent assessment
of the state of the science, the advantages of
combining revealed preference and stated
preference data include:
• Helping to ground the hypothetical stated
preference data with real world behavior
potentially decreasing any hypothetical bias;
• Providing the ability to test the validity of
both data sources;43
• Increasing the range of historical stated
preference data to include conditions not
observed in the past and thereby reducing the
need to make predictions outside of the sample;
• Increasing the sample size;
• Extending the size of the market or
population to include larger segments than
captured by either method alone; and
• Exploiting the flexibility of stated preference
experimental design to overcome revealed
preference data's potential multicollinearity
and endogeneity problems (von Haefen and
Phaneuf 2008).
The different strategies for combining revealed
preference and stated preference data can be
roughly grouped into three main methods. The
first two methods rely on joint estimation. If the
revealed preference and stated preference data have
similar dependent and independent variables and
the same assumed error structures, then they can
simply be pooled together and treated as additional
observations (Adamowicz et al. 1994; Boxall, Englin,
and Adamowicz 2003; and Morgan, Massey, and
Huth 2009). If the revealed preference and stated
preference data sources cannot be pooled, it is
sometimes possible to use them in a jointly estimated
mixed model that relies on a utility theoretic
specification of the underlying WTP function
(Huang, Haab, and Whitehead 1997; Kling 1997;
and Eom and Larson 2006). If the data cannot be
combined in estimation, it can still be useful to
estimate results separately and then use them to test
for convergent validity between the two data sources
(Carson et al. 1996, and Schlapfer et al. 2004).
7.4 Benei fer
Benefit transfer refers to the use of estimated non-
market values of environmental quality changes
from one study in the evaluation of a different
policy that is of interest to the analyst (Freeman
2003, p. 453). The case under consideration for a
43 Herriges, Kling, and Phaneuf (2004) point out that revealed preference
may not always be valid for estimating WTP for quality changes when
weak complementarity cannot be assured.
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Chapter 7 Analyzing Benefits
new policy is referred to as the "policy case." Cases
from which estimates are obtained are referred to
as "study cases." A benefit transfer study identifies
stated preference or revealed preference study cases
that sufficiendy relate to the policy context and
"transfers" their results to the policy case.
Benefit transfer is necessary when it is infeasible to
conduct an original study focused directly on the
policy case. Original studies are time consuming
and expensive; benefit transfer can reduce both the
time and financial resources required to develop
estimates of a proposed policy's benefits. While
benefit transfer should only be used as a last resort
and a clear justification for using this approach over
conducting original valuation studies should be
provided (OMB 2003), the reality is that benefit
transfer is one of the most common approaches
for completing a BCA at EPA. However, the
advantages of benefit transfer in terms of time
and cost savings must be weighed against the
disadvantages in terms of potential reduced
reliability of the final benefit estimates. The
transfer of benefits estimates from any single study
case is unlikely to be as accurate as a primary study
tailored specifically to the policy case, although it is
difficult to characterize the uncertainty associated
with transferred benefits estimates.
The number and quality of relevant studies
available for application to the policy case can
limit the use of benefit-transfer methods.44 Even
when a study case is qualitatively similar to the
policy case, the environmental change associated
with the policy case may be of a different scope
or nature than the changes considered in the
study cases. In addition, methodological advances
and changes in demographic, economic, and
environmental conditions over time may make
otherwise suitable studies obsolete.45
44 One possible reason that a relatively limited number of value estimates
exist in peer-reviewed literature is that researchers and editors of
scholarly journals may be more interested in new theoretical or
methodological advances than in studies that apply established
valuation methods to confirm earlier findings.
45 A 2006 special issue of Ecological Economics (volume 60) focused
exclusively on benefit transfer for environmental policy covering
diverse topics such as publication bias, theoretical motivation and
emerging issues. Florax et al. (2002), and Navrud and Ready (2007)
are two general references for benefit transfer studies.
Steps for conducting benefit transfer
While there is no universally accepted single
approach for conducting benefit transfer there are
some generalized steps involved in the process.
These steps are described below.
1. Describe the policy case. The first step in
a benefit-transfer study is to clearly describe
the policy case so that its characteristics and
consequences are well understood. Are human
health risks reduced by the policy intervention?
Are ecological benefits expected (e.g., increases
in populations of species of concern) ? It is also
important to identify to the extent possible
the beneficiaries of the proposed policy and to
describe their demographic and socioeconomic
characteristics (e.g., users of a particular set of
recreation sites, children living in urban areas, or
older adults across the United States). Information
on the affected population is generally required to
translate per person (or per household) values to an
aggregate benefits estimate.
2. Select study cases. A benefit-transfer study
is only as good as the study cases from which it
is derived, and it is therefore crucial that studies
be carefully selected. First, the analyst should
identify potentially relevant studies by conducting
a comprehensive literature search. Because peer-
reviewed academic journals may be more likely to
publish work using novel approaches compared
to established techniques, some studies of interest
may be found in government reports, working
papers, dissertations, unpublished research, and
other "gray literature."46 Including studies from the
gray literature may also help mitigate "publication
bias" that results from researchers being more
likely to present and/or editors being more likely
to publish studies that demonstrate statistically
significant results, or results that are of an expected
sign or magnitude.47 Online searchable databases
46 Peer review of benefit-transfer studies using gray literature is highly
advisable.
47 There is some evidence of publication bias towards studies showing
statistically significant results. For example, in a meta-analysis of studies
in labor economics, Card and Krueger (1995) argue that just-significant
results are reported more frequently than would be predicted by chance.
Similar practices may prevail in other areas of economic research.
Combining results from a group of studies that suffer from publication
bias may lead to inaccurate conclusions. See Stanley (2005,2008) for a
discussion of methods to correct for and identify publication bias.
Guidelines for Preparing Economic Analyses I December 2010 7-45
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Chapter 7 Analyzing Benefits
summarizing valuation research may be especially
helpful at this stage.48
Next, the analyst should develop an explicit set of
selection criteria to evaluate each of the potentially
relevant studies for quality and applicability to
the policy case. The quality of the value estimates
in the study cases will in large part determine the
quality of the benefit transfer. As a first step, the
analyst should review studies according to the
criteria listed for each methodology in the previous
sections in this chapter. Results from study cases
must be valid as well as relevant. Concerns about
the quality of the studies, as opposed to their
relevance, will generally hinge on the methods
used. Valuation approaches commonly used in the
past may now be regarded as unacceptable for use
in benefits analysis. Studies based on inappropriate
methods or reporting obsolete results should be
removed from consideration.
It is unlikely that any single study will match
perfectly with the policy case; however each
potential study case should inform at least
some aspect of the policy decision. Study cases
potentially suitable for use in benefit transfer
should be similar to the policy case in their: (1)
definition of the environmental commodity
being valued (include scale and presence
of substitutes); (2) baseline and extent of
environmental changes; and (3) characteristics
of affected populations. Analysts should avoid
using benefit transfer in cases where the policy
or study case is focused on a "good" with unique
attributes or where the magnitude of the change
or improvement across the two cases differs
substantially (OMB 2003).49
48 For example, the EVRI is maintained by Environment Canada and
managed by a working group that includes the U.S. EPA and members
of the European Union. EVRI contains over 1,100 studies that can
be referenced according to medium, resource, stressor, method,
and country. EVRI also provides a bibliography on benefit transfer.
See www.evri.ca for more information. Envalue, developed by the
New South Wales EPA in 1995, is similar: Studies can be identified
according to medium, stressor, method, country, and author.
49 In some cases the transfer method itself may inform the choice of study
cases to include. For example, meta-analysis approaches (discussed
below) can facilitate some forms of statistical validity testing (Hunter
and Schmidt 1990, and Stanley 2001), so some otherwise suitable
studies may be rejected as "outliers."
The analyst should determine whether adjustments
should and can be made for important differences
between each study and policy case. For example,
some case studies will report Marshallian demand
while others may report Hicksian demand.50 The
ability of the analyst to make these adjustments
will depend, in part, on both the number of value
estimates for suitably similar study sites and the
method used to combine these estimates. These
methods are now discussed in turn.
3. Transfer values. There are several approaches
for transferring values from study cases to the
policy case. These include unit value transfers,
value function transfers, and non-structural or
structural meta-analysis. Each of these approaches
is typically used to develop per person or per
household value estimates that are then aggregated
over the affected population to compute a total
benefits estimate. As a general rule, the more
related case study estimates involved in a benefit
transfer, the more reliable the estimate.
Unit value transfers are the simplest of the benefit-
transfer approaches. They take a point estimate
of WTP for a unit change in the environmental
resource from a study case or cases and apply it
directly to the policy case. The point estimate
is commonly a single estimated value from a
single case study, but it can also be the (otherwise
unadjusted) average of a small number of estimates
from a few case studies. For example, a study may
have found a WTP of $20 per household for a
one-unit increase on some water quality scale. A
unit value transfer would estimate total benefits for
the policy case by multiplying $20 by the number
of units by which the policy is expected to increase
water quality and by the number of households
who will benefit from the change. This approach
can be useful for developing preliminary, order-
of-magnitude estimates of benefits, but it should
be possible to base final benefit estimates on more
50 See Desvousges etal. (1992), Brouwer (2000), Florax etal. (2002),
Bergstrom and Taylor (2006), and Navrud and Ready (2007) for
additional information on criteria used to determine quality and
applicability. For more information on applicability as related to
specific benefit categories, see Desvousges et al. (1998), the draft
Handbook for Non-Cancer Valuation (U.S. EPA 2000c), and the
Children's Health Valuation Handbook (U.S. EPA 2003b). It may also
be useful for the analyst to discuss her interpretation and intended use
of the study case with the original authors.
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 7 Analyzing Benefits
T\ r! tutf T SftH Eltf%m&«r ? ! #% «ss»% 1%®^ 8k 4 ftl0*!#"! ##%
One component of the total benefits of the Clean Air Act (CAA) was determined to be improved recreational fishing due
to reduced acidification in freshwater Adirondack lakes. To value this benefit, EPA relied on the results of Montgomery
and Needleman's (1997) New York State Adirondack region recreational fishing study. EPA first developed estimates of
the percentage Adirondack of lakes affected by acidification pre and post CAA. Then, using a probit model, the likelihood
that each individual lake would become acidified was estimated (the model relates acidity to lake characteristics such
as elevation, surface area, watershed, and others) and the lakes were ranked from highest to lowest probability of being
acidified. The acidification status of individual lakes in the choice set was then assigned, starting with the highest probability
lake and proceeding down until the appropriate number of lakes affected under each scenario (i.e., the estimated percentage
of lakes affected) was achieved. Using these lake designations and the Montgomery and Needleman model's estimated
coefficients, welfare was calculated for the pre and post CAA levels of lake acidification. The difference between the two
welfare estimates was assumed to be the value of improved Adirondack freshwater recreational fishing under the CAA.
information than a single point estimate from a
single study. Point estimates reported in study cases
are typically functions of several variables, and
simply transferring a summary estimate without
controlling for differences among these variables
can yield inaccurate results. It is important to
recognize that unit value transfer assumes that
the original good, as well as the characteristics
and tastes of the population of beneficiaries, are
the same as the policy good. Unit values transfers
should only be used if the case and policy studies
are evaluating the same environmental good, the
same change in environmental levels, and same
affected populations.
Function transfers also rely on a single study,
but they use information on other factors that
influence WTP to adjust the unit value for
quantifiable differences between the study case
and the policy case. This is accomplished by
transferring the estimated function upon which
the value estimate in the study case is based to the
policy case. This approach implicitly assumes that
the population of beneficiaries to which the values
are being transferred has potentially different
characteristics, but similar tastes, as the original
one and allows the analyst to adjust for these
different characteristics. Generally, benefit function
transfers are preferable to unit value transfers as
they incorporate information relevant to the policy
scenario (OMB 2003). For example, suppose that
in the hypothetical example above the $20 unit
value was the result of averaging the results of an
estimated WTP function over all individuals in
the study case sample, where the WTP function
included income, the baseline water quality level,
and the change in the water quality level for each
household. A function transfer would estimate
total benefits for the policy case by:
1. Applying the WTP function to a random
sample of households affected in the policy
case using each households observed levels
of income, baseline water quality, and water
quality change;
2. Averaging the resulting WTP estimates; and
3. Multiplying this average WTP by the total
number of households affected in the policy case.
See Text Boxes 7.6 and 7.7 for examples of value
and function transfers.
If the WTP function is nonlinear and statistics
on average income, baseline water quality, and
water quality changes are used in the transfer
instead of household level values, then bias would
result. Feather and Hellerstein (1997) provide
an example of a function transfer that attempts
to correct for such bias. Although unit transfers
can adjust and compensate for small differences
between the case and policy study populations,
they are subject to the same basic usage rules
governing unit value transfers. Function transfers
should only be used if the case and policy studies
are evaluating very similar environmental goods,
change in environmental levels, and affected
populations.
Guidelines for Preparing Economic Analyses I December 2010 7-47
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Chapter 7 Analyzing Benefits
Text Bo snefits Transfer; Water Quality Benefits in the Combined
Animal Feedii srations Rule
There are two prominent water quality benefit-transfer applications in the 2002 Combined Animal Feeding Operations
(CAFO) rule. The first looks at the recreational value of water quality improvements in fresh water lakes and streams (see
Section 4 of U.S. EPA 2002c). Field pollutant loadings were modeled by the National Water Pollution Control Assessment
Model (NWPCAM) to produce pre and post regulation water quality estimates. Predicted changes in water quality were
then valued using the results of Carson and Mitchell's (1993) national water quality contingent valuation survey. First,
benefits were calculated based on estimates of willingness to pay (WTP) for water quality improvements resulting in
discrete movements to higher "rungs" of the water quality ladder (boatable, fishable, swimmable, drinkable). Very
simply described, Carson and Mitchell's "in-state" WTP estimates for discrete movements up the water quality ladder
were multiplied by the number of affected residents in every state and "out-of-state," non-use values were multiplied
times the remaining population. State totals were then summed up to a national total (see Appendix A-4 of U.S. EPA
2002c for more details). Benefits were also estimated a second way based on a continuous (1 to 100) water quality index
constructed from six water quality parameters measured in the NWPCAM model. The minimum thresholds between
rungs on the water quality ladder were then translated into points along the continuous water quality index (i.e., boatable
= 25, fishable = 50, swimmable = 70). Carson and Mitchell's WTP function was then used to value changes in water
quality as measured by the water quality index (see Appendix B-4 of U.S. EPA 2002c for more details). Benefits estimated
by the water quality index method are larger by roughly a factor of two (Exhibits 4-12 and 4-13 of U.S. EPA 2002c).
The second major benefit-transfer application in the CAFO rule involves the valuation of reduced eutrophication in
estuaries (Section 9 of U.S. EPA 2002c). EPA used a case study of Albemarle and Pamlico sounds to demonstrate
the potential importance and value of reduced eutrophication on recreational fishing in affected estuaries. Again,
NWPCAM was used to estimate pre and post regulation water quality levels. In this case, the benefit transfer made
use of three studies (Kaoru 1995; Kaoru, Smith, and Liu 1995; and Smith and Palmquist 1988), all of which were
based in part on the same dataset. All "reasonable" estimates of WTP for reduced phosphorus or nitrogen from the
studies were retained and translated into their corresponding dollar per trip per ton reduction in pollutant per year
value. A range of total benefits was then calculated by multiplying each $/trip/ton/year estimate by the number of trips
taken and the change in loadings (in tons) for each pollutant (see Exhibit 9-3 of U.S. EPA 2002c).
Meta-analysis uses results from multiple valuation
studies to estimate a new transfer function. Meta-
analysis is an umbrella term for a suite of techniques
that synthesize the summary results of empirical
research. This could include a simple ranking of
results to a complex regression. The advantage of
these methods is that they are generally easier to
estimate while controlling for a relatively large
number of confounding variables. This approach
has been widely used in environmental economics
(Poe et al. 2001, Shrestha andLoomis 2003a
and 2003b, Rosenberger and Loomis 2000, and
Bateman and Jones 2003).
There are a number of guidelines for meta-analyses
that oudine protocols that should be followed in
conducting or evaluating a study. See Begg et al.
(1996), Moher et al. (1999), and U.S. EPA (2006e)
for more information.51 More recendy Bergstrom
and Taylor (2006) discuss the theory and practice
underlying meta-analysis for benefit transfer,
discussing three major necessary steps: theory, data
collection, and analysis. In general, when reporting
meta-analysis results, researchers should provide
information on the background of the problem, the
strategy for selecting studies, analytic methods, results,
discussion, and conclusions. See U.S. EPA (2006e)
for a detailed discussion of meta-analysis as applied
to VSL estimates. U.S. EPA (2006e) specifically
recommends carefully specifying the search process,
selection criteria, and analytical methods.
Structural benefit transfer is a relatively new
approach to benefit transfer. The advantages of
51 The last reference contains a detailed discussion of the protocols for
conduction a meta-analsysis.
7-48
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Chapter 7 Analyzing Benefits
Text Bo* 7.8 - Structural Benefit Transfer with an Application to Visibility
U.S. EPA (2006b) employs a structural benefit transfer to derive values for visibility improvements associated with
the Particulate Matter (PM) National Ambient Air Quality Standards (NAAQS). It specified a constant elasticity of
substitution utility function for visibility in residential and Class I (national park and similar) areas. This function
assumes that the value for Class I visibility differs in and out of region but that residential visibility is valued the same
everywhere. EPA also assumed that in-region visibility was valued more highly than out-of-region visibility. The
function further specified utility as a function of: (1) consumption of all goods; (2) visibility in a person's residential
area; (3) recreational visibility in a person's residential region; and (4) recreational visibility outside of a person's
residential region. Given the utility function and a budget constraint, it was then possible to define households'
WTP for changes in visibility as a function of income and visibility measures. The regional preference parameters of
the function were calibrated using existing WTP estimates for visibility in Class I areas (Chestnut and Rowe 1990,
and Chestnut 1997) if estimates existed for a given region. If not, estimates were adjusted by visitation rate. The
preference parameter for residential visibility was assumed to be the same in all counties and was solved for based
on a WTP estimate presented in McClelland et al. (1991). With estimates of visibility (pre and post regulation),
county-level income, and the required preferences parameters, nationwide estimates of the value of increased
visibility were then computed for each of the six regions of the country.
structural transfer functions are that they can
accommodate different types of economic value
measures (e.g., WTP, WTA, or consumer surplus)
and can be constructed in such a way that certain
theoretical consistency conditions (e.g., WTP
bounded by income) can be satisfied. This could
be applied to value transfer, function transfer, or
meta-analysis; although applications to function
transfer are the most common. Structural transfer
functions that have been estimated have specified
a theoretically consistent preference model that is
calibrated according to existing benefit estimates
from the literature (see Smith and Pattanayak
2002; and Smith, Pattanayak, and van Houtven
2006 for descriptions on the method). See Text
Box 7.8 for an application to of structural benefit
transfer to visibility benefits.
4. Report the results. In addition to reporting
the final benefit estimates from the transfer
exercise, the analyst should clearly describe all key
judgments and assumptions, including the criteria
used to select study cases and the choice of the
transfer approach. The uncertainty in the final
benefit estimate should be quantified and reported
when possible. (See Chapter 11 on Presentation of
Analysis and Results.)
commodating
Non-Monetized Benefits
It often will not be possible to quantify all of the
significant physical impacts for all policy options.
For example, animal studies may suggest that a
contaminant causes severe illnesses in humans,
but the available data may not be adequate to
determine the number of expected cases associated
with different human exposure levels. Likewise,
it often is not possible to quantify the various
ecosystem changes that may result from an
environmental policy. While Chapter 11 discusses
how to present these benefits so as to provide
a fuller accounting of all effects, this section
discusses what analysts can do to incorporate these
endpoints more fully into the analysis.
ualrtative Discussions
When there are potentially important effects that
cannot be quantified, the analyst should include
a qualitative discussion of benefits results. The
discussion should explain why a quantitative
analysis was not possible and the reasons for
believing that these non-quantified effects may
be important for decision making. Chapter 11
discusses how to describe benefit categories that
are quantified in physical terms but not monetized.
Guidelines for Preparing Economic Analyses I December 2010 7-48
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Chapter 7 Analyzing Benefits
7.5,2 Alternative Analyses
Alternative analyses exist that can support benefits
valuation when robust value estimates and/or risk
estimates are lacking. These analyses, including
break-even analysis and bounding analysis,
can provide decision makers with some useful
information. However analysts should remember
that because these alternatives do not estimate
the net benefits of a policy or regulation, they
fall short of BCA in their ability to identify an
economically efficient policy. This and other short-
comings should be discussed when presenting
results from these analyses to decision makers.
7.5,2,1 Break-Even Analysis
Break-even analysis is one alternative that can be used
when either risk data or valuation data are lacking.52
Analysts who have per unit estimates of economic value
but lack risk estimates cannot quantify net benefits.
They can, however, estimate the number of cases (each
valued at the per unit value estimate) at which overall
net benefits become positive, or where the policy action
will break even.53 Consider a proposed policy that is
expected to reduce the number of cases of endpoint
X with an associated cost estimate of $1 million.
Further, suppose that the analyst estimates that WTP
to avoid a case of endpoint X is $200, but that because
of limitations in risk data, it is not possible to generate
an estimate of the number of cases of this endpoint
reduced by the policy. In this case, the proposed
policy would need to reduce the number of cases by
5,000 in order to "break even." This estimate then
can be assessed for plausibility either quantitatively or
qualitatively. Policy makers will need to determine if the
break-even value is acceptable or reasonable.
The same sort of analysis can be performed when
analysts lack valuation estimates, producing a
break-even value that should again be assessed for
credibility and plausibility. Continuing with the
example above, suppose the analyst estimates that
the proposed policy would reduce the number of
cases of endpoint X by 5,000 but does not have an
52 Boardman et al. (1996) describes determining break-even points
under the general subject of sensitivity analysis and includes empirical
examples.
53 Circular A-4 (0MB 2003) refers to these values as "switch points" in
its discussion of sensitivity analysis.
estimate of WTP to avoid a case of this endpoint.
In this case, the policy can be considered to break
even if WTP is at least $200.
One way to assess the credibility of economic break-
even values is to compare them to risk values for
effects that are more or less severe than the endpoint
being evaluated. For the break-even value to be
plausible, it should fall between the estimates for
these more and less severe effects. For the example
above, if the estimate of WTP to avoid a case of a
more serious effect was only $100, the above break-
even point may not be considered plausible.
Break-even analysis is most effective when there is
only one missing value in the analysis. For example,
if an analyst is missing risk estimates for two
different endpoints (but has valuation estimates for
both), then they will need to consider a "break-even
frontier" that allows the number of both effects
to vary. It is possible to construct such a frontier,
but it is difficult to determine which points on the
frontier are relevant for policy analysis.
7.5,2,2 Bounding Analysis
Bounding analysis can help when analysts lack
value estimates for a particular endpoint. As
suggested above, reducing the risk of health effects
that are more severe and of longer duration should
be valued more highly than those that are less
severe and of shorter duration, all else equal. If
robust valuation estimates are available for effects
that are unambiguously "worse" and others that are
unambiguously "not as bad," then one can use these
estimates as the upper and lower bounds on the
value of the effect of concern. Presenting alternative
benefit estimates based on each of these bounds can
provide valuable information to policy makers. If
the sign of the net benefit estimate is positive across
this range then analysts can have some confidence
that the program is welfare enhancing. Analysts
should carefully describe judgments or assumptions
made in selecting appropriate bounding values.
7-50
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 8
Analyzing Costs
The previous chapter discussed the process of estimating the benefits of
environmental regulations and policies. This chapter discusses the estimation
of costs, with a primary focus on estimating costs for use in benefit-cost
analyses (BCA). While often portrayed as being relatively straightforward —
particularly compared to the estimation of benefits — the estimation of costs
presents a number of challenges in its own right.
The first challenge is to identify an appropriate measure of cost for a particular application.
A number of concepts of cost exist, with some overlap of ideas. In conducting a BCA,
the correct measure to use is the social cost. Social cost represents the total burden that a
regulation will impose on the economy. It is defined as the sum of all opportunity costs
incurred as a result of a regulation where an opportunity cost is the value lost to society of any
goods and services that will not be produced and consumed as a result of a regulation.
A second challenge involves choosing an economic framework for the analysis. Depending
on the scope of the regulation or policy, either a partial or general equilibrium framework is
employed. Partial equilibrium analysis is usually appropriate when the scope of a regulation is
limited to a single sector, or to a small number of sectors. General equilibrium analysis may be
more appropriate if the analyst expects a large number of sectors to be impacted and that the
effects will be spread more broadly throughout the economy.
The third challenge is choosing one or more models to use in an analysis. Factors to consider
in selecting a model include the types of costs being investigated, the geographic and sectoral
scope of the likely impacts, and the expected magnitude of the impacts. For some analyses, it
may be necessary to use more than one model.
This chapter discusses social cost and its underlying economic theory as well as several
alternative concepts of cost. In addition, the chapter discusses several additional issues in cost
estimation and presents a number of the models that can be employed in the estimation and
analysis of costs.
v1 i Ihfc ^ *nomi nv
Social Cost
Hie most comprehensive measure of the costs of
a regulation — and thus the appropriate measure
to use in a BCA — is "social cost." Social cost
represents the total burden a regulation will impose
on the economy; it can be defined as the sum of
all opportunity costs incurred as a result of the
regulation. These opportunity costs consist of the
value lost to society of all the goods and services that
will not be produced and consumed if firms comply
with the regulation and reallocate resources away
Guidelines for Preparing Economic Analyses I December 2010
8-1
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Chapter 8 Analyzing Costs
from production activities and towards pollution
abatement. To be complete, an estimate of social
cost should include both the opportunity costs
of current consumption that will be foregone as
a result of the regulation, and the losses that may
result if the regulation reduces capital investment
and thus future consumption.1
The purpose of estimating social cost is to have
a reference point for comparing the costs of a
regulation with the estimated benefits. Social cost
is not a particularly meaningful concept unless it
is used as part of a net social welfare calculation, or
perhaps compared to other (less comprehensive)
cost measures.2 Conceptually, it should be noted
that the social cost of a regulation is generally not
the same as a change in gross domestic product
(GDP), or another broad measure of economic
activity, that may result from its imposition.
Expenditures on inputs into pollution abatement,
such as equipment, materials, and labor, are
counted as part of social cost. All or part of their
consumption will at the same time be included
positively in the calculation of GDP. Thus, if a
regulation has the effect of lowering GDP, this
decline will in general be less than the social cost of
the regulation.
Two broad analytical paradigms are used in
the analysis of social cost: partial equilibrium
and general equilibrium. A partial equilibrium
approach is appropriate when it is assumed
that the effects of a regulation will primarily be
confined to a single or small number of closely
related markets. If this is not the case, and the
regulation is expected to cause significant impacts
across the economy, it is more appropriate to use
general equilibrium analysis to estimate social
1 This section discusses the prospective estimation of social cost for
regulations that have not yet been implemented. However, the same
principles apply to estimating costs retrospectively for regulations
already in place. Likewise, while the text refers to the social cost of "a
regulation" the same principles apply to the estimation of the social
cost for each alternative in a set of regulatory alternatives. For a more
rigorous and detailed treatment of the material in this section, see Pizer
and Kopp (2005).
2 For example, comparing the social cost of different regulations
may provide some sense of the relative burden they impose on the
economy, but this exercise alone would not indicate which, if any, of
the regulations may be worthwhile from a public policy standpoint.
However, the accurate measurement of social cost would be an
essential component in attempting to make such a determination.
cost. The use of these two analytical paradigms is
explored in the following sections.
8.1.1 Partial Equilibri ia lysis
When the analyst expects that the effects of a
regulation will be confined primarily to a single
market or a small number of markets, partial
equilibrium analysis is the preferred approach
for estimation of social cost. The use of partial
equilibrium analysis assumes that the effects
of the regulation on all other markets will be
minimal and can either be ignored or estimated
without employing a model of the entire economy.
This section presents some simple diagrams to
show how social cost can be defined in a partial
equilibrium framework.
Figure 8.1 shows a competitive market before the
imposition of an environmental regulation. The
intersection of the supply (S ) and demand (D)
curves determines the equilibrium price (P ) and
quantity (Q). The shaded area below the demand
curve and above the equilibrium price line is the
consumer surplus. The area above the supply curve
and below the price line is producer surplus. The
sum of these two areas defines the total welfare
generated in this market: the net benefits to
society from producing and consuming the good
or service represented in this market.3
In this market, the imposition of a new
environmental regulation raises firms' production
costs. Each unit of output is now more costly
to produce because of expenditures incurred to
comply with the regulation. As a result, firms will
respond by reducing their level of output. For the
industry, this will appear as an upward shift in
the supply curve. This is shown in Figure 8.2 as a
movement from S to S . The effect on the market
of the shift in the supply curve is to increase
the equilibrium price to P and to decrease the
equilibrium output to Q, holding all else constant.
3 It should be noted that total welfare as depicted ignores the negative
pollution externality arising in this market, which the environmental
regulation is designed to correct. Appendix A presents a graphical
description of how to account for this externality. Reduction of this
negative externality would be quantified in the benefits portion of an
analysis. The supply curve in Figure 8.1 corresponds to the marginal
private cost (MPC) curve described in Figure A.5 of Appendix A.
8-2
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 8 Analyzing Costs
¦ i/11! 8»«i
Q "I „ 4?"%hits**i#-»iSHI"s%
~ Consumer Surplus
1=1 Producer Surplus
Q„
Figure 8.2 - Competitive Market After
Q
/ 1=1 Consumer Surplus
^ 1=1 Producer Surplus
Compliance Costs
™ Deadweight Loss
Q, Q,
As seen by comparing Figures 8.1 and 8.2, the
overall effect on welfare is a decline in both
producer and consumer surplus.4
Compliance costs in this market are equal to
the area between the old and new supply curves,
bounded by the new equilibrium output, Q^?
Noting this, a number of useful insights about the
total costs of the regulation can be derived from
Figures 8.1 and 8.2. First, when consumers are
price sensitive — as reflected in the downward
sloping demand curve — a higher price causes
them to reduce consumption of the good. If
costs are estimated ex ante and this price sensitive
behavior is not taken into account (i.e., the
estimate is based on the original level of output
(Qj compliance costs will be overstated. Extending
the vertical dotted line in Figure 8.2 from the
original equilibrium to the new supply curve (S )
illustrates this point.6
A second insight derived from Figures 8.1 and 8.2
is that compliance costs are usually only part of
the total costs of a regulation. The "deadweight
loss" (DWL) shown in Figure 8.2 is an additional,
real cost arising from the regulation. It reflects
the foregone net benefit due to the reduction
in output.7 Moreover, unlike many one-time
compliance costs, DWL will be a component of
social cost in future periods.
Under the assumption that impacts outside
this market are not significant, then the social
cost of the regulation is equal to the sum of
the compliance costs and the deadweight loss
(shown in Figure 8.2). This is exactly equal
to the reduction in producer and consumer
surplus from the pre-regulation equilibrium
(shown in Figure 8.1). This estimate of social
cost would be the appropriate measure to use
in a BCA of the regulation. As noted above, if
some of the compliance costs are spent on other
goods and services or on hiring additional
labor, any fall in GDP attributable to the
imposition of the regulation will be less than
the social cost.
The preceding discussion describes the use of
partial equilibrium analysis when the regulated
The figure depicts an equal distribution of welfare between consumers
and producers, in both the old and new equilibria. Depending on
the elasticities of supply and demand, this may not be the case. The
elasticities will determine the magnitude of the price and quantity
changes induced by the cost increase, as well as the distribution of costs.
Here distinctions between the fixed and variable costs of abatement are
abstracted and it is assumed that all of the costs are represented in the
movement of the supply curve. See Tietenberg (2002).
In the extreme, if the regulation raised production costs so much that
firms decided to halt production altogether, or if an outright ban on the
product was issued, a strict compliance cost analysis would yield zero
cost as no direct expenditures on abatement would be made. Clearly
this would constitute an underestimate of the loss in consumer welfare.
Typically, in a market already distorted with pollution externalities,
the DWL triangle shown in Figure 8.2 will serve to offset (at least in
part) the existing DWL in the market that results when the real costs of
production (including the pollution damages) are not considered in the
production decision. Of course, if the regulatory action is too stringent
and "over controls" the pollution problem, the optimal outcome will not
be achieved and additional DWL will be created. Figure 8.2 is silent on
where the optimal solution is achieved. See Appendix A for more detail.
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Chapter 8 Analyzing Costs
market is perfectly competitive. In many cases,
however, some form of imperfect competition,
such as monopolistic competition, oligopoly, or
monopoly, may better characterize the regulated
market. Firms in imperfectly competitive markets
will adjust differently to the imposition of a new
regulation and this can alter the estimate of social
cost.8 If the regulated market is imperfectly
competitive, the market structure can and should
be reflected in the analysis.
In certain situations, when the effects of a
regulation are expected to impact a limited
number of markets beyond the regulated sector,
it still may be possible to use a partial equilibrium
framework to estimate social cost. Multi-
market analysis extends a single-market, partial
equilibrium analysis of the directly regulated
sector to include closely related markets. These
may include the upstream suppliers of major inputs
to the regulated sector, downstream producers
who use the regulated sectors output as an input,
and producers of substitute or complimentary
products. Vertically or horizontally related markets
will be affected by changes in the equilibrium
price and quantity in the regulated sector. As a
consequence, they will experience equilibrium
adjustments of their own that can be analyzed in a
similar fashion.9
8.1.2 General Equilibrium
Analysis
In some cases, the imposition of an
environmental regulation will have significant
effects in markets beyond those that are directly
subject to the regulation. As the number of
affected markets grows, it becomes less and
8 The opportunity costs of lost production from the regulation will
be less for a monopoly than a perfectly competitive industry even
if they face the same market demand curve. This result may seem
counterintuitive, but the monopolist operates on a more elastic, or
price sensitive, portion of the demand curve. As a result, it will have
lower profits if it tries to increase price (and lower output) by as much
as the competitive industry.
9 In theory, impacts in undistorted related markets are "pecuniary" and
do not need to be included if the social costs have been correctly
measured in the primary market, but pecuniary effects are important in
inefficient related markets and should be considered (Boardman et al.
2006). Just et al. (2005) provide a detailed treatment of multi-market
analysis. Kokoski and Smith (1987) demonstrate, however, that one
must use caution when using these methods.
less likely that partial equilibrium analysis can
provide an accurate estimate of social cost.
Similarly, it may not be possible to accurately
model a large change in a single regulated market
using partial equilibrium analysis. In such cases,
a general equilibrium framework, which captures
linkages between markets across the entire
economy, may be a more appropriate choice for
the analysis.
For example, the imposition of an environmental
regulation on emissions from the electric utility
sector may cause the price of electricity to rise.
As electricity is an important intermediate
input in the production of most goods, the
prices of these products will most likely also rise.
Households will be affected as both consumers
of these goods and as consumers of electricity.
The increase in prices may cause them to alter
their relative consumption of a variety of
goods and services. The increase in the price of
electricity may also cause feedback effects that
result in a reduction in the total consumption of
electricity.
General equilibrium analysis is built around
the assumption that for some discrete period
of time, an economy can be characterized
by a set of equilibrium conditions in which
supply equals demand in all markets. "When the
imposition of a regulation alters conditions in
one market, a general equilibrium model will
determine a new set of prices for all markets
that will return the economy to equilibrium.
These prices in turn determine the outputs
and consumption of goods and services in the
new equilibrium. In addition, the model will
determine a new set of prices and demands
for the factors of production (labor, capital,
and land), the returns to which compose the
income of businesses and households. Changes
in aggregate economic activity, such as GDP,
household consumption, and other variables,
also can be calculated in the model.
The previous section shows how the social
cost of a regulation can be estimated in a single
market using partial equilibrium analysis. The
example demonstrates how a regulation causes
8-4
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Chapter 8 Analyzing Costs
a DWL in that market, reflecting a decline in
economic welfare as measured by consumer
and producer surplus. In reality, DWL already
exists in many, if not most, markets as a result
of taxes, regulations, and other distortions.
When the imposition of a regulation causes a
new distortion in one market, it may interact
with pre-existing distortions in other markets
and this may cause additional impacts on
welfare.
An important example of how a regulation can
interact with pre-existing distortions can be found
in the labor market, depicted in Figure 8.3. Here,
a pre-existing tax on wages causes the net, after-
tax wage {Wf) to be lower than the gross, pre-tax
wage (W*) by the amount of the tax. With this
tax distortion, the quantity of labor supplied is
L0 and there is a DWL. When a new regulation
is imposed in another market, raising production
costs, one of the indirect effects may be an increase
in the price level as those costs are passed through
the economy. This increase in the price level will
reduce the real wage and, given an upward sloping
labor supply curve, the amount of labor supplied.10
This is shown in Figure 8.3 as a decrease in the net
wage to W" and a decrease in the amount of labor
supplied to L .
The interaction between new and pre-existing
distortions is especially pronounced in the labor
market because pre-existing distortions there
are large. As shown in Figure 8.3, even a small
reduction in the amount of labor supplied will
result in a large increase in DWL.11 Similar
interactions are likely to occur in other markets
with pre-existing distortions. In cases where they
are likely to have a significant impact, analysts
10 In general equilibrium analysis, all prices and wages are real, i.e., they
are measured relative to a numeraire, a specific single price or weighted
average of prices, such as the GDP deflator. Here, the consumer price
level rises relative to the numeraire. The result is a fall in the real wage
— the nominal wage divided by the consumer price level.
11 The labor tax distortion affects individual labor supply decisions at the
margin. Thus, a full-time worker may not change (or be able to change)
her hours worked in response to a fall in the real wage. However,
part-time workers, workers in households with more than one full-time
worker, or potential retirees, may be more likely to adjust the number
of hours they work or whether they work at all. A discussion of the
theoretical and empirical basis for this depiction of the labor market
can be found in Parry (2003).
Wage
i=i Pre-Existing
Deadweight Loss
i=i Additional
\ Deadweight Loss
tax
W"
Hours
should incorporate these distortions into models
used to estimate social cost.12
In a general equilibrium analysis, the social cost
of a regulation is estimated using a computable
general equilibrium (CGE) model. CGE models
simulate the workings of a market economy and
can include representations of the distortions
caused by taxes and regulations. As described
above, they are used to calculate a set of price and
quantity variables that will return the simulated
economy to equilibrium after the imposition of
a regulation. The social cost of the regulation
can then be estimated by comparing the value
of variables in the pre-regulation, "baseline"
equilibrium with those in the post-regulation,
simulated equilibrium.13
12 Economists have long recognized these interaction effects (Ballard
and Fullerton 1992). A more recent body of work has focused on
them in the context of environmental regulation. In this literature,
these interactions are known as the "tax-interaction effect." If an
environmental regulation raises revenue through a tax on pollution or
other revenue raising provision, and the revenue is used to reduce pre-
existing distortions such as taxes on wages, the tax-interaction effect
may be offset. This is known as the "revenue recycling effect." The
offset may be partial, complete, or in some cases, the overall efficiency
of the tax system may actually be improved. The net result is an
empirical matter, depending on the nature of the full set of interactions
across the economy and how the revenue is raised. Some of the early
papers in this literature include Bovenberg and de Moojii (1994), Parry
(1995), and Bovenberg and Goulder (1996). Goulder (2000) provides
an accessible summary of the early literature. More recent papers
include Parry and Bento (2000); Murray, Keeler, and Thurman (2005);
and Bento and Jacobsen (2007).
13 CGE models are discussed in more detail in the modeling section of
this chapter. Applications of CGE models to the estimation of the social
cost of environmental regulation include Hazilla and Kopp (1990)
and Jorgenson and Wilcoxen (1990). A version of the Jorgenson and
Wilcoxen model was used as part of EPA's retrospective study of the
benefits and costs of the Clean Air Act for the period 1970 to 1990
(U.S. EPA 1997a).
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Chapter 8 Analyzing Costs
Even in a general equilibrium analysis, analysts
must take care in selecting an appropriate
measure of social cost. Calculating social cost
by adding together estimates of the costs in
individual sectors can lead to double counting.
For example, counting both the increased
costs of production to firms resulting from a
regulation and the attendant increases in prices
paid by consumers for affected goods would
mean counting the same costs twice, leading to
an overestimate of social cost. Instead, focusing
on measures of changes in final demand, so that
intermediate goods are not counted, can avoid
the double-counting problem.14
While it is theoretically possible to estimate social
cost by adding up the net change in consumer
and producer surplus in all affected markets,
the measures most commonly used in practice
are consumers equivalent variation (EV) and
compensating variation (CV). Both are monetary
measures of the change in utility brought about
by changes in prices and incomes resulting from
the imposition of a regulation. As households
are the ultimate beneficiaries of government and
investment expenditures, the EV and CV measures
focus on changes in consumer welfare, rather than
on changes in total final demand.
8.1.3 Dynamics
In most cases, a regulation will continue to have
economic impacts for a number of years after its
initial implementation. If these intertemporal
impacts are likely to be significant, they should
be included in the estimation of social cost. For
example, if a regulation requires firms in the
electric utility sector to invest in pollution control
equipment, they may not invest as much in electric
generation capacity as they would have in the
absence of the regulation. This may result in slower
growth in electricity output and reduce the overall
growth rate of the economy. In some cases, the
effect of a regulation on long-term growth may
be much more significant than the effect on the
regulated sector alone.
14 Final demand consists of household purchases, investment,
government spending, and net exports (exports minus imports).
When conducting a BCA in which the analyst
expects intertemporal effects of a regulation to
be confined to the regulated sector, it may be
appropriate to simply apply partial equilibrium
analysis to multiple periods. Relevant conditions,
like expected changes in market demand and
supply over time, should be taken into account
in the analysis. The costs in individual years can
then be discounted back to the initial year for
consistency.
If the intertemporal effects of a regulation
on non-regulated sectors are expected to be
significant, analysts can estimate social cost
using a dynamic CGE model. Dynamic CGE
models can capture the effects of a regulation
on affected sectors throughout the economy.
They can also address the long-term impacts
of changes in labor supply, savings, factor
accumulation, and factor productivity on the
process of economic growth.15 In a dynamic
CGE model social cost is estimated by
comparing values in the simulated baseline
(i.e., in the simulated trajectory of the economy
without the regulation) with values from a
simulation with the regulation in place.
8.1.4 Social Cost and
Employment Effects
At times of recession, questions arise about
whether jobs lost as a result of a regulation
should be counted as an additional cost of the
regulation. However, counting the number of
jobs lost (or gained) as a result of a regulation
generally has no meaning in the context
of BCA as these are typically categorized
as transitional job losses.16 BCA requires
monetized values of both the social benefits and
costs associated with the regulation. The social
cost of a regulation already includes the value
15 In addition to affecting the growth of the capital stock, an
environmental regulation may also negatively affect the supply of labor
through the interaction effects discussed above, thus increasing social
cost. However, there may also be a positive effect on labor supply if
improved environmental quality confers health benefits that make the
work force more productive.
16 In very rare cases in which a regulation contributes additional job
losses to a sector exhibiting structural unemployment, analysts
should consider including job losses as a separate cost category. See
Appendix C for more detail.
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Chapter 8 Analyzing Costs
of lost output associated with the reallocation
of resources (including labor) away from
production of output and towards pollution
abatement. This does not mean, of course, that
specific individual workers are not harmed by a
policy if they lose their jobs. EPA estimates the
magnitude of such losses as part of an Economic
Impact Analysis (EIA). See Chapter 9 for more
details on this topic.
' - ^ I }\: « is
The previous section defined social cost as the
sum of the opportunity costs incurred as the
result of the imposition of a regulation, and
introduced the basic economic theory used in its
estimation. Conceptually, social cost is the most
comprehensive measure of cost, and is thus the
appropriate measure to use in BCA. In addition
to social cost, a number of other concepts of cost
exist and are often used to describe the effects
of a regulation. This section discusses these
alternative concepts and introduces a number
of additional terms. This section also provides
a discussion of measures that define temporary
costs or define how costs are distributed across
different entities.
8.2:1 Alternative Concepts
of Cost
Three alternative concepts of cost, each of which
is composed of two components, are: explicit and
implicit costs, direct and indirect costs, and private
sector and public sector costs. Like social cost, all
of these concepts are comprehensive in nature.
An important distinction is that while social cost
is a measure derived from economic theory, these
three alternative concepts are in general only
descriptive.17
Consideration of these alternative concepts can
provide insights into the full range of the costs of a
regulation. They may also be useful in determining
the appropriate framework and modeling
methodology for an analysis. Several executive
and legislative mandates require that a number of
17 In certain cases, a single component, such as direct cost, may provide
a reasonable estimate of social cost.
different types of costs be included in a regulatory
impact analysis (RIA).18
8.2.1.1 Explicit and Implicit Costs
The total costs of a regulation can include both
explicit and implicit costs.19 Explicit costs are those
costs for which an explicit monetary payment is
made, or for which it is straightforward to infer a
value. For firms, the explicit costs of environmental
regulation normally include the costs of purchase
and operation of pollution control equipment.
This includes payments for inputs (such as
electricity) and wages for time spent on pollution
control activities. For households, explicit costs
may include the costs of periodic inspections of
pollution control equipment on vehicles. For
government regulatory agencies, wages paid to
employees for developing a regulation and then
for administration, monitoring, and enforcement
are included in explicit costs. Implicit costs are
costs for which monetary values do not readily
exist and are thus likely more difficult to quantify.
Implicit costs may include the value of current
output lost because inputs are shifted to pollution
control activities from other uses, as well as lost
future output due to shifts in the composition of
capital investment. Implicit costs may also include
the lost value of product variety as a result of
bans on certain goods, time costs of searching for
substitutes, and reduced flexibility of response to
changes in market conditions.
8.2.1.2 Direct and Indirect Costs
Direct costs are those costs that fall directly on
regulated entities as the result of the imposition
of a regulation. These entities may include firms,
households, and government agencies. Indirect
costs are the costs incurred in related markets or
experienced by consumers or government agencies
18 E0 12866 specifies that an assessment of the costs of a regulation
should include "any adverse effects on the efficient functioning of the
economy and private sector (including productivity, employment, and
competitiveness)" in addition to compliance costs. The UMRA of 1995
requires that cost estimates take into account both indirect and implicit
costs on state and local governments.
19 The term "total cost" is used here when discussing alternative concepts
of cost in order to reinforce the distinction between these concepts and
social cost.
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Chapter 8 Analyzing Costs
not under the direct scope of the regulation. These
indirect costs are usually transmitted through
changes in the prices of the goods or services
produced in the regulated sector. Changes in these
prices then ripple through the rest of the economy,
causing prices in other sectors to rise or fall and
ultimately affecting the incomes of consumers.
Government entities can also incur indirect costs.
For example, if the tax base changes due to the exit
of firms from an industry, revenues from taxes or
fees may decline. In some cases, the indirect costs
of a regulation may be considerably greater than
the direct costs.
8,2,1,3 Private Sector and Public
Sector Costs
The total costs of a regulation can also be divided
between private sector and public sector costs.
Private sector costs include all of the costs of a
regulation borne by households and firms. Public
sector costs consist of the costs borne by various
government entities.
8.2,2 Additional Cost Terminology
In addition to the conceptual categories and
their components discussed above, a variety
of other terms are often used in describing the
costs of environmental regulation. A number of
these terms are defined here. It should be noted
that there are numerous overlaps between these
concepts, and analysts must take care to avoid
double counting.20
8.2,2,1 Incremental Costs
Incremental costs are the additional costs associated
with a new environmental regulation or policy.
Incremental costs are determined by subtracting
the total costs of environmental regulations and
policies already in place from the total costs after a
new regulation or policy has been imposed.
20 References that provide definitions of cost terminology include U.S.
CBO (1988), and Callan and Thomas (1999).
8.2.2.2 Compliance Costs
Compliance costs (also known as abatement costs)
are the costs firms incur to reduce or prevent
pollution to comply with a regulation. They are
usually composed of two main components:
capital costs and operating costs. Compliance costs
can be further defined to include any or all of the
following:
• Treatment/Capture — The cost of any
method, technique, or process designed to
remove pollutants, after their generation in
the production process, from air emissions,
water discharges, or solid waste.
• Recycling — The cost of postproduction
on-site or off-site processing of waste for an
alternative use.
• Disposal — The cost involving the final
placement, destruction, or disposition of
waste after pollution treatment/capture and/
or recycling has occurred.
• Prevention — The cost of any method,
technique, or process that reduces the amount
of pollution generated during the production
process.
8.2.2.3 Capital Costs
Capital costs include expenditures on installation
or retrofit of structures or equipment with the
primary purpose of treating, capturing, recycling,
disposing, and/or preventing pollutants.
These expenditures are sometimes referred to
as "one-time costs" and include expenditures
for equipment installation and startup. Once
equipment is installed, capital costs generally do
not change with the level of abatement and are
thus functionally equivalent to "fixed costs." In
BCA, capital costs are usually "annualized" over
the period of the useful life of the equipment.
8.2.2.4 Operating and
Maintenance Costs
Operating and maintenance costs are annual
expenditures on salaries and wages, energy inputs,
materials and supplies, purchased services, and
maintenance of equipment associated with
8-8
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Chapter 8 Analyzing Costs
pollution abatement. In general, they are directly
related to the level of abatement. Operating costs
are functionally equivalent to "variable costs."
8.2.2.5 Indu asts
Industry costs are the costs of a regulation to
an industry, including the effects of actual or
expected market reactions. They often differ from
compliance costs because compliance costs do not
normally account for market reactions. Market
reactions may include plant closures, reduced
industry output, or the passing on of some costs
directly to consumers.
8.2.2 sactions Costs
Transactions costs are those costs that are incurred
in making an economic exchange beyond the
cost of production of a good or service. They may
include the costs of searching out a buyer or seller,
bargaining, and enforcing contracts. Transactions
costs may be important when setting up a new
market, such as those markets designed to be used
for market-based regulations.
8,2,2,7 Government Regulatory Costs
Government regulatory costs are those borne
by various government entities in the course of
researching, enacting, and enforcing a policy or
regulation.21
8.2.» sitiona! and
Distributional Costs
In addition to the concepts and terms defined
above, several other types of cost exist. Two
qualitatively different types of cost from those
above are transitional and distributional costs.
8.2,3,1 Transitional Costs
At some point in time after the imposition of a
new environmental regulation, the economy can
be expected to adjust to a new equilibrium. While
21 Government entities may themselves be polluters and therefore subject
to regulation. Compliance costs under this scenario would be captured
as such.
many costs are likely to be permanent additions
to the costs of production, others will be short
term in nature, being incurred only during the
adjustment to the new equilibrium. These are
known as transitional costs. Transitional costs may
include the costs of training workers in the use of
new pollution control equipment. After workers
receive their initial training, the time they spend
on pollution control activities would be counted as
operating costs.
8,2,3,2 Distributional Costs
Distributional costs are those costs that relate
to how certain entities or societal groups are
impacted by the imposition of a policy or
regulation. While BCA is by definition concerned
only with the net benefits, it is likely that most
policies or regulations will result in winners and
losers. In some cases, the models described later in
this chapter can be used for distributional analysis
as well as BCA. Distributional costs are covered in
detail in Chapter 10.
8.3 Measurement Issues in
Estimatir cial Cost
A number of issues may arise when estimating the
expected social cost of a proposed regulation, or
when measuring costs incurred as a result of an
existing regulation. These issues can be divided
into two broad categories: (1) those that arise
when estimating costs over time; and (2) those
associated with difficulties in developing numeric
values for estimating social cost. This section
discusses both these issues in turn. It concludes
with a short analysis of how estimates of Title
IV of the Clean Air Act's costs evolved over
time, illustrating the importance of accurately
accounting for these issues when estimating the
costs of a regulation.
8,3.1 Evaluating Costs Over Time
Most regulations cause permanent changes in
production and consumption activities, leading
to permanent (ongoing) social costs. As a result,
regulations are often phased in gradually over
time in an effort to limit any disruptions created
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8-9
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Chapter 8 Analyzing Costs
by their imposition. When measuring costs over
time, assumptions related to the time horizon of
the analysis, the use of a static versus a dynamic
framework, discounting, and technical change are
extremely important. These assumptions are each
discussed in more detail in the paragraphs that follow.
8.3.1.1 Time Horizon
Irrespective of the method used for the estimation
of social cost, the time horizon for calculating
producer and consumer adjustments to a new
regulation should be considered carefully. Ideally,
the analyst estimates the value of all future costs
of a regulation discounted to its present value. If
the analyst is only able to estimate a regulations
costs for one or a few representative future years,
she must take great care to ensure that the year(s)
selected are truly representative, that no important
transitional costs are effectively dismissed by
assumption, and that no one-time costs are
assumed to be on-going.
In the short term, at least some factors of
production are fixed. If costs are evaluated over
a short period of time, then contractual or
technological constraints prevent firms from
responding quickly to increased compliance costs
by adjusting their input mix or output decisions. In
the long term, by contrast, all factors of production
are variable. Firms can adjust any of their factors
of production in response to changes in costs due
to a new regulation. A longer time horizon affords
greater opportunities for affected entities to
change their production processes (for instance, to
innovate). It is important to select a time horizon
that captures any flexibility the regulation provides
firms in the way they choose to comply.
8.3.1.2 Choosing Betwee itic and
Dynamic Framework
In many cases, costs are evaluated in a static
framework. That is, costs are estimated at a given
point in time or for a selection of distinct points
in time. Such estimates provide snapshots of costs
faced by firms, government, and households but
do not allow for behavioral changes from one time
period to affect responses in another time period.
In addition to the capital-induced growth effects
discussed in Section 8.2.3, the evaluation of costs
in a dynamic framework may be important when a
proposed regulation is expected to affect product
quality, productivity, innovation, and changes in
markets indirectly affected by the environmental
policy.22 These may have impacts on net levels of
measured consumer and producer surplus over time.
8.3,1,3 Discounting
Social discounting procedures for economic
analyses are reviewed in considerable detail in
Chapter 6. Benefits and costs that occur over time
must be properly and consistently discounted
if any comparisons between them are to be
legitimate.23
There is one application of discounting that is
unique to cost analysis. When calculating firms'
private costs (e.g., the internal cost of capital used
for pollution abatement), the analyst should use
a discount rate that reflects the industry's cost of
capital, just as a firm would. The social cost of the
regulation, on the other hand, would be calculated
using the social discount rate, the same discount
rate used for the benefits of the regulation.
8. mical Change and Learning
Estimating the costs of a given environmental
regulation frequently entails estimating future
technical change. Despite its importance as a
determinant of economic welfare, the process of
technical change is not well understood. Different
approaches to environmental regulation present
widely differing incentives for technological
innovation. As a result, the same environmental
end may be achieved at significantly different
costs, depending on the pace and direction of
technical change. Recent empirical work supports
this hypothesis. Most notably, the realized costs of
Title IV of the 1990 Clean Air Act Amendment's
S02 Allowance Trading program are considerably
lower than initial predictions, in part due to
unanticipated technical change (see Text Box 8.1).
22 See Section 8.1.3 for a discussion of dynamics.
23 In a CEA, it is equally important to properly discount cost estimates of
different regulatory approaches to facilitate valid comparisons.
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Chapter 8 Analyzing Costs
Text Box 8.1 - The Sulfur Dioxide Cap-and-Trade Program - ^ ¦. idy24
Under Title IV of the 1990 Clean Air Act Amendments (CAAA), coal fired power plants are required to hold one sulfur dioxide
(SO) allowance for each ton of SO they emit during the year. Utilities are allowed to buy sell and bank unused allowances to
cover future SO. emissions (see Chapter 4 for additional detail). Title IV was subject to intensive ex ante and ex post analysis.
The evolution of these analyses illustrates the importance of complete and thorough estimation of social costs and highlights
the difference some of the issues discussed above (e.g., discounting or uncertainties) can make to actual cost estimates.
Estimates of Title IV's compliance costs have declined over time, particularly so once the program was launched and
researchers were able to observe the behavior of electric utilities. Title IV proved less costly than originally estimated
due to behavior responses, indirect effects, technological improvements, market structure, and prices that changed
over time. Table 8.1 provides a comparison of some of the program's cost estimates over time. Rows that report ex
ante estimates are shaded gray.
Study
Annual Costs
(Billions)
Marginal Costs per ton S02
Average costs per ton of S02
Carlson et al. (2000)
$1.1
$291
$174
Ellerman et al. (2000)
1.4
350
137
Burtraw etal. (1998)
0.9
n/a
239
Goulderet al. (1997)
1.09
n/a
n/a
White (1997)
n/a
436
n/a
ICF (1995)
2.3
532
252
White et al. (1995)
1.4-c.a
543
coo-ao4
GAO (1994)
2,2-3.3
n/a
230-374
Van Horn Consulting et
al. (1993)
2.4-3.3
520
314-405
ICF (1990)
2.3-5.9
579-760
348-499
"Based on Table 2-1. Burtraw and Palmer (2004): n/a — not reported.
Most of the early estimates of Title IV's compliance costs were based on engineering models, which do not fully
capture the concepts of consumer and producer surplus. In addition, many of these studies relied on the data and
methodologies used to evaluate traditional command-and-controI environmental policies, adjusted to estimate
the efficiency gains of a permit trading system. Later studies that included more extensive examinations of both
the regulatory impacts as well as outside economic pressures on the industry came up with significantly smaller
compliance cost estimates for the regulation.
Several developments occurred around the time of Title IV that helped reduce the program's ex post cost estimates. For
example, reductions in the price of low-sulfur coal, along with technological improvements that lowered the cost of fuel
switching, allowed utilities in the East to reduce compliance costs by using low-sulfur coal from the Powder River Basin in
Wyoming (Carlson etal. 2000, and Burtraw and Palmer 2004). Furthermore Popp (2003) concluded that Title IV-induced
R&D led to technological innovations that improved the efficiency of scrubbers, thereby leading to lower operating costs.
The varying cost estimates also show the importance of accounting for changing implementation costs and
uncertainty over time. The ability of facilities to "bank" SO allowances allowed flexibility in implementation and thus
reduced compliance costs. Cost estimates by Carlson et al. (2000) and Ellerman et al. (2000) factor in the discounted
savings from banking. According to the latter study, costs savings are a relatively minor source of overall savings, but
are important in developing a picture of the program's total effectiveness. This is because firms were able to "avoid the
much larger losses associated with meeting fixed targets in an uncertain world" (Ellerman et al. 2000, p. 285).
24 This example is taken from Burtraw and Palmer (2004).
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Chapter 8 Analyzing Costs
Organizations are able to learn with experience,
which permits them to produce a given good or
service at lower cost as their cumulative experience
increases. While there are many different
explanations for this phenomenon (e.g., labor
forces learn from mistakes and learn shortcuts; ad
hoc processes become standardized) its existence
has been borne out by experiences in many sectors.
Indeed, OMB now requires cost analyses to
consider possible learning effects among the cost-
saving innovations.25 Recent EPA Advisory Council
guidance recommends that default learning effects
be applied even when sector- or process-specific
empirical data are not available (U.S. EPA 2007b).
The decrease in unit cost as the number of units
produced increases is referred to as an experience
or learning curve. A useful description of the
calculations used to identify a learning curve can
be found in van der Zwaan and Rabl (2004).
Learning rates for 26 energy technologies are
described in McDonald and Schrattenholzer
(2001). Dutton and Thomas (1984) summarize
more than 100 studies, including some dealing
with the energy and manufacturing sectors. Note
that the empirical estimates in the literature
represent a biased sample, since they only represent
technology that has been successfully deployed
(Sagar and van der Zwaan 2006).26
8.3.2 Other Issues timating
Social Cost
Difficulties in measuring social cost generally fall
into two categories: (1) difficulties in developing a
numeric value for some social cost categories; and
(2) for social cost categories where numeric values
have been successfully developed, accounting for
uncertainty in these values.
25 OMB's CircularA-4asserts that a cost analysis should incorporate
credible changes in technology over time, stating that "...retrospective
studies may provide evidence that learning' will likely reduce the
cost of regulation in future years" (OMB 2003). Other cost-saving
innovations to consider include those resulting from a shift to
regulatory performance standards and incentive-based policies.
26 Note that cost decreases associated with technological change
and learning may not always be free but may have additional costs
associated with them such as training costs. See Section 8.2.3.1 for a
discussion of transitional costs.
8.3.2.1 Difficulties in Developing
Numeric Values
Some consequences of environmental policies are
difficult to represent in the definitive, quantitative
terms of conventional social cost analysis.
Irreversible environmental impacts, substantial
changes in economic opportunities for certain
segments of the population, social costs that span
very long time horizons, socioeconomic effects
on populations, and poorly-understood effects on
large-scale ecosystems are difficult to capture in a
quantitative BCA. Some alternative techniques for
measuring and presenting these effects to policy
makers are reviewed in Section 7.6.3. The relative
significance of social cost categories that are not
quantified — or are quantified but not valued —
should be described in the social cost analysis.
8.3.2.2 Uncertainty
The values of various costs in the social cost
analysis can be estimated, but cannot be known
with certainty. In fact, some data and models will
likely introduce substantial uncertainties into
these estimates. Numerous assumptions are made
regarding the baseline, predictions of responses to
policy, and the number of affected markets. The
conclusions drawn in the social cost analysis are
sensitive to the degree of uncertainty regarding
these assumptions. The uncertainty associated
with the data and methods, the assumptions made,
and how the uncertainty and assumptions affect
the results are all-important components of the
presentation of social cost, and should be carefully
reported.
8.3.2.3 Estimating Costs Under Different
Statutory Criteria
Some statutes require EPA to choose a regulatory
option that is demonstrably affordable. One way
for a decision maker to ensure that a regulatory
option is affordable is to estimate an upper bound
of the compliance cost associated with the chosen
option and then to show that it is affordable.
However, this approach is inconsistent with the
practice of producing the best central estimate of
the cost of a regulation for the RIA and will cause
the net benefits of the regulation to be biased
8-12
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Chapter 8 Analyzing Costs
Table 8.2 - Major Attributes of Models Used in t' - t. iimation of Costs
Compliance Partial Linear Input-Output
Cost Equilibrium Programming Input-Output Econometric CGE
Can be used to measure • •
direct compliance costs
Can be used to measure • • • • •
transitional costs
Can be used to measure • • • • •
distributional impacts
Can capture indirect effects • • • E
Can capture feedback and
interaction effects •
downward. Furthermore, using solely an upper
bound estimate of the cost of a regulation could
result in artificially low levels of regulation in
situations where EPA must determine whether or
not the benefits of the regulation justify the costs.
It is thus very important that analysts rely on the
best central estimate of the cost of a regulation for
the RIA.
8.3 J U ternally-Produced
Cost Estimates
At various times EPA depends on externally (e.g.,
contractor, industry association, or advocacy
group) generated cost estimates for use in its
internal analyses. Any cost estimate produced by
an external source and used by EPA in its internal
analysis should be vetted by EPA to ensure that:
(1) the information is relevant for its intended
use; (2) the scientific and technical procedures,
measures, methods and/or models employed to
generate the information are reasonable for, and
consistent with, the intended application; and (3)
the data, assumptions, methods, quality assurance,
sponsoring organizations, and analyses employed
to generate the information are well documented.
8.4 Models Us^. m Estimating
the lental
Regulation
A number of different types of models have been
used in the estimation the costs of environmental
regulation. They range from models that estimate
costs in a single industry (or part of an industry),
to models that estimate costs for the entire
economy. In practice, implementation of some of
the models can be simple enough to be calculated
in a spreadsheet. Others may be complex systems
of thousands of equations that require highly
specialized software.27
Table 8.2 summarizes some of the major attributes
of the models discussed in this section. Each has
strengths and weaknesses in analyzing different
types of economic costs. When estimating social
cost, there will be some cases where a single model
is enough to provide a reasonable approximation.
In other cases the use of more than one model
is required. For example, a compliance cost
model can be used to estimate the direct costs of
a regulation in the affected sector. These direct
cost estimates could then be used in a partial
equilibrium model to estimate social cost. While
most of the models discussed in this section can be
used in some form in the estimation of social cost,
many of them also have particular strengths in the
estimation of transitional and/or distributional
costs, as may be required as part of an RIA.
Selecting the most appropriate model (or models)
to use in an analysis can be difficult. Below are a
number of factors that may be helpful in making a
choice.28
27 Data requirements for these models vary. Refer to Chapter 9 for a
discussion of the process of conducting an industry profile and details
on a range of public and private data sources that can be used for cost
estimation.
28 This list of factors is derived from Industrial Economics, Inc. (2005).
Proprietary models discussed in this section are examples only and no
endorsement by EPA is given or implied.
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Chapter 8 Analyzing Costs
• Types of impacts being investigated. Model
selection should take into account the types
of impacts that are important in the analysis
being performed because models differ in
their abilities to estimate different types of
costs.
• Geographic scope of expected impacts.
While some models may be well suited for the
analysis of impacts on a national scale, it may
not be possible to narrow their resolution to
focus on regional or local impacts. Similarly
models that are well suited for examining
regional or local impacts may not capture the
full range of impacts at the national level.
• Sectoral scope of expected impacts. Some
models are highly aggregated, and while
proficient at capturing major impacts and
interactions between sectors, are not well
suited for focusing on a single or small
number of specialized sectors. Likewise,
models that are highly specialized for
capturing impacts in a particular sector
will usually be inappropriate for examining
impacts on a broader set of sectors.
• Expected magnitude of impacts. A model
that is well suited for capturing the impacts
of a regulation that is expected to have large
effects may have difficulty estimating the
impacts of a regulation with relatively smaller
expected effects, and vice versa.
• Expected importance of indirect effects.
For a regulation that is expected to have
substantial indirect effects beyond the
regulated sector it is important to choose a
model that can capture those effects.
Usually, some combination of the above factors
will determine the most appropriate model for
a particular application. Finally, it should be
noted that advances in computing power, data
availability, and more user-friendly software
packages continually reduce the barriers to
sophisticated model-based analysis.
8.4.1 Compliani st Models
Compliance cost models are used to estimate
an industry's direct costs of compliance with
a regulation. Estimates by engineers and other
experts are used to produce algorithms that
characterize the changes in costs resulting from
the adoption of various compliance options. The
particular parameters are usually determined for a
number of individual plants with varying baseline
characteristics. To estimate the control costs of a
regulation for an entire industry, disaggregated
data that reflects the industry's heterogeneity
is input into the model. The disaggregated cost
estimates are then aggregated to the industry level.
Compliance cost models may include capital costs,
operating and maintenance expenditures, and costs
of administration. Some compliance cost models
are designed to allow the integrated estimation of
control costs for multiple pollutants and multiple
regulations. Some models are able to account for
cost changes over time, including technical change
and learning. Compliance cost models often are
implemented in a spreadsheet; in general, they are
relatively easy to modify and interpret.
While precise estimates of compliance costs are
an important component of any analysis, it is only
in cases where the regulation is not expected to
significantly impact the behavior of producers
and consumers that compliance costs can be
considered a reasonable approximation of social
cost. As discussed in Section 8.2.1, estimating
social cost often requires knowledge of both supply
and demand conditions. Compliance cost models
focus on the supply side, and in circumstances
where producer and consumer behavior is
appreciably affected, these models are not able to
provide estimates of changes in industry prices
and output resulting from the imposition of a
regulation. However, in these cases, estimates from
compliance cost models can be used as inputs to
other models that estimate social cost.
One example of a compliance cost model or tool is
AirControlNET (ACN). ACN is a database tool for
conducting pollutant emissions control strategy and
costing analysis. It overlays a detailed control measure
database of EPA emissions inventories to compute
source- and pollutant-specific emission reductions
and associated costs at various geographic levels
(national, regional, local) and for many industries.
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Chapter 8 Analyzing Costs
ACN contains a database of control measures and
cost information that can be used to assess the
impact of strategies to reduce criteria pollutants [e.g.,
NOx, S02, volatile organic compounds (VOCs),
PM10, PM25, or Ammonia (NH3)] as well as carbon
monoxide (CO) and mercury (Hg) from point
(utility and non-utility), area, nonroad, and mobile
sources as provided in EPA's National Emission
Inventory (NEI). ACN is stricdy a compliance cost
model, because it does not account for changes in the
behavior of consumers and producers.
Advantages:
• Compliance cost models often contain
significant industry detail and provide
relatively precise estimates of the direct costs
of a regulation. This is particularly true for
regulations with minor cost impacts.
• Once constructed, compliance cost models
require a minimum of resources to implement
and are relatively straightforward to use and
easy to interpret.
limitations:
• As they are focused exclusively on the supply
side, compliance cost models can only provide
estimates of social cost in certain limited cases.
• Compliance cost models are usually limited to
estimating costs for a single industry.
8.4.2 Partial Equilibrium Models
While compliance cost models may provide
reasonable estimates of the compliance costs of
a regulation, they do not incorporate the likely
behavioral responses of producers and consumers.
As shown in Section 8.2.1, if these responses are
not taken into account, estimates of social cost are
likely to be inaccurate. In cases where the effects
of a regulation are confined to a single market,
partial equilibrium models, which incorporate the
behavioral responses of producers and consumers,
can be used to estimate social cost.
Inputs into an analysis employing a partial
equilibrium model may include regulatory costs
estimated using a compliance cost model and the
supply and demand elasticities for the affected
market. Hie model then can be used to estimate
the change in market price and output. Changes
in producer and consumer surplus reflect the
social cost of the regulation. Hie relative changes
between producer and consumer surplus provide
an estimate of the distribution of regulatory costs
between producers and consumers.
In a partial equilibrium model, the magnitude
of the impacts of a regulation on the price and
quantity in the affected market depends on the
shapes of the supply and demand curves. Hie
shapes of these curves reflect the underlying
elasticities of supply and demand. These elasticities
can be either estimated from industry and
consumer data or taken from previous studies.29
If the elasticities used in an analysis are drawn from
previous studies, they should be consistent with
the following conditions:
• They should reflect a similar market structure
and level of aggregation;
• There should be sensitivity to potential
differences in regional elasticity estimates;
• They should reflect current economic
conditions; and
• They should be for the appropriate time
horizon (i.e., short or long run).
In some cases, if the effects of a regulation are
expected to spill over into adjoining markets
(e.g., suppliers of major inputs or consumers of
major outputs), partial equilibrium analysis can
be extended into these additional markets as well.
These "multi-market models" have been used in the
analysis of a number of EPA regulations.30
29 Because of the widespread use of elasticity estimates, the Air Benefit
and Cost (ABC) Group in EPA's Office of Air and Radiation maintains
an elasticity database. This Elasticity Databank serves as a searchable
database of elasticity parameters across economic sectors/product
markets and a variety of types including demand and supply elasticities,
substitution elasticities, income elasticities, and trade elasticities.
An online submittal form allows users to provide elasticity estimates
for consideration as part of this databank. The Elasticity Databank is
available online at http://www.epa.gov/ttn/ecas/Elasticity.htm (U.S. EPA
2007d).
30 See, for example, U.S. EPA (1989) Regulatory Impact Analysis of
Controls on Asbestos and Asbestos Products: Final Report.
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Chapter 8 Analyzing Costs
Advantages;
• Because they usually simulate only a single
market, partial equilibrium models generally
have fairly limited data requirements and are
relatively simple to construct.
• Partial equilibrium models are comparatively
easy to use and interpret.
Limitations:
• Partial equilibrium models are limited to
cost estimation in a single or small number
of markets and do not capture indirect or
feedback effects.
• Because partial equilibrium models are
generally data driven and specific to a
particular application, they are usually not
available "off-the-shelf" for use in a variety of
analyses.
8.4.3 Linear Programming Models
Although linear programming models can be
employed in a variety of applications, their use
in the analysis of EPA regulations occurs most
frequently in the estimation of compliance
costs.31 Linear programming models minimize (or
maximize) an objective function by choosing a set
of decision variables, subject to a set of constraints.
In EPA's regulatory context, the objective function
is usually direct compliance costs, which are
minimized. The decision variables represent the
choices available to the regulated entities. The
constraints may include available technologies,
productive capacities, fuel supplies, and regulations
on emissions.
Although linear programming models can be
constructed to examine multiple sectors or
economy-wide effects, they are more commonly
focused on a single sector. For the regulated sector,
a linear programming model can incorporate a
large number of technologies and compliance
options, such as end-of-pipe controls, fuel
31 An introduction to linear programming is provided in Chiang (1984).
The "linear" in the name refers to the linear specification of the
objective function and constraint equations. Similar, eponymous model
types include non-linear, integer, and mixed integer programming
models.
switching, and changes in plant operations.
Similarly, the model's constraints can include
multiple regulations that require simultaneous
compliance. The objective function usually
includes the fixed and variable costs of each
compliance option. The program then chooses a
set of decision variables that minimize the total
costs of compliance. In addition to compliance
costs, the outputs from the model may include
other related variables, such as projected fuel use,
output and input prices, emissions, and demand
for new capacity in the regulated industry.
An example of a linear programming model used
by EPA is the Integrated Planning Model (IPM).
The IPM is a model of the electric power sector
in the 48 contiguous states and the District of
Columbia. It can provide long-term (10-20 year)
estimates of the control costs of complying with
proposed regulations, while meeting the projected
demand for electricity. In the model, nearly 13,000
existing and planned electrical generating units
are mapped to approximately 1,700 representative
plants. Results are differentiated into 40 distinct
demand and supply regions. IPM can be used to
estimate the impacts on costs for policies to limit
emissions of S02, NOx, C02, and Hg.
Advantages:
• Compared to compliance cost models,
linear programming models are better able
to incorporate and systematically analyze
a wide range of technologies and multiple
compliance options.
• Linear programming models allow for a
considerable amount of flexibility in the
specification of constraints. This permits
an existing model to be used in a range of
applications.
Limitations:
• Linear programming models normally do
not estimate costs beyond a single sector
and are thus unable to estimate indirect or
distributional costs.
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Chapter 8 Analyzing Costs
Table 8.3 - input-Output Table for the United States til, $)
1 Agriculture
2 Manufacturing
3 Services
Total Intermediate Inputs
Value Added
Total Inputs
1
Agriculture
2
Manufacturing
3
Services
i Total
: Intermediate
Outputs
Final
Demand
Total
Outputs
70
150
30
i 250
30
280
50
1,930
840
; 2,820
2,470
5,290
60
1,070
2,810
3,940
!
6,780
10,720
180
100
3,150
2,140
3,680
7,040
i 7,010
!
9,280
16,290
, 9.280 r
280
5,290
10,720
16290 I
Source: Adapted from Bureau of Economic Analysis (BEA) 10-sector table.
• A linear programming model designed for
estimating sectoral compliance costs will
likely be quite complex and have heavy input
requirements. If an existing model is not
available, the time and effort to construct one
may be prohibitive.
• Linear programming models minimize
aggregate control costs for the entire industry
simultaneously, whereas the regulated entities
actually do so individually. This may result in
an underestimation of total compliance costs.
8,4.4 Input-Output Models
While input-output models have been used in
many environmental applications, their primary
use in a regulatory context is for estimating the
distributional and short-term transitional impacts
that may result from the implementation of a
policy. For example, an input-output model
could be used to estimate the regional economic
effects of a regulation that would ban a particular
pesticide. In this case, an input-output model
could provide estimates of the effects on output
and employment in the affected region. A key
feature of input-output models is their ability to
capture both the effects on sectors directly affected
by a regulation and the indirect effects that occur
through spillovers onto other sectors.32
An input-output model is based on an input-
output table. The input-output table assembles
data in a tabular format that describes the
32 Miller and Blair (1985) is a standard reference on input-output
analysis.
interrelated flows of goods and factors of
production over the course of a year. An input-
output table may consist of hundreds of sectors
or may be aggregated into as few as two or three
sectors. Table 8.3 is an example of a highly
aggregated input-output table for the United
States for the year 1999. The columns for the
individual sectors denote how much of each
commodity is used in the production of that
sectors output. These intermediate inputs are
combined with factors of production — labor,
capital, and land — whose payments as wages,
profits, and rents, compose sectoral value added.
For the agricultural sector, total inputs consist of
$70 billion of agricultural inputs, $50 billion of
manufactured inputs, $60 billion of service inputs,
and $100 billion of value added, for a total of $280
billion in inputs. The row for each sector shows
how that sectors output is consumed. In the case
of the agricultural sector, $250 billion is consumed
as intermediate inputs, while the remainder, $30
billion, is consumed as final demand, which is
composed of household consumption, government
purchases, and investment.
An input-output table can be turned into a simple
linear model through a series of matrix operations.
The model relates changes in final demand to
changes in the total amount of goods and services,
including intermediate inputs, required to meet
that demand. The model can also relate the change
in final demand to changes in employment of
factors of production, such as the demand for
labor. In the case of the banned pesticide, if a
separate analysis determines that there will be a
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Chapter 8 Analyzing Costs
decline in the output of cotton, the input-output
model could be used to determine the effect on
those sectors that supply inputs to the cotton
sector, as well as on industries that are users of
cotton, such as the producers of textiles and
clothing. Declines in the output of these industries
will have further effects on the demand for other
intermediate inputs, like electricity, which are also
estimated by the model.
Input-output models are relatively simple to use
and interpret and are often the most accessible
tool for analyzing the short-term impacts of
a regulation on regional output and income.33
However, they embody a number of assumptions
that make them inappropriate for long-term
analysis or the analysis of social cost. Although
their specifications can sometimes be partially
relaxed, input-output models embody the
assumptions of fixed prices and technology, which
do not allow for the substitution that normally
occurs when goods become more or less scarce.
Similarly, input-output models are demand driven
and not constrained by limits on supply, which
would normally be transmitted through increases
in prices. While the rigidities in the models may
be reasonable assumptions in the short run or
for regional analysis, they limit the applicability
of input-output models for long-run or national
issues. Because input-output models do not
include flexible supply-demand relationships or
the ability to estimate changes in producer and
consumer surpluses, they are not appropriate for
estimating social cost.
Advantages:
• Particularly in a regional context, input-
output models are often well suited for
estimating distributional and short-term
transitional impacts.
• Input-output models are relatively transparent
and easy to interpret.
33 An off-the-shelf input-output model often used in the analysis of the
impacts of environmental regulation is Impact Analysis for Planning
(IMPLAN). IMPLAN is based on data for the United States that covers
more than 500 sectors and can be disaggregated down to the county level.
• Some input-output models have a great deal
of sectoral and regional disaggregation and
can be readily applied to issues that require a
high degree of resolution.
Limitations:
• Input-output models are not appropriate for
estimating social cost.
• Because of their lack of endogenous
substitution possibilities in production, input-
output models are not appropriate for dealing
with long-run issues.
• Because of their fixed prices and lack of
realistic behavioral reactions by producers and
consumers, input-output models are not well
suited for dealing with issues that are likely to
have large effects on prices.
8.4.5 Input-Output
Econometric Models
Input-output econometric models are economy-
wide models that integrate the structural detail
of conventional input-output models with
the forecasting properties of econometrically
estimated macroeconomic models. Input-output
econometric models are often constructed with a
considerable amount of regional detail, including
the disaggregation of regional economies at the
state and county level. At EPA, input-output
econometric models, like conventional input-
output models, are often used to examine the
regional impacts of policies and regulations.
However, unlike conventional input-output
models, input-output econometric models are also
able to estimate long-run impacts.
When used for policy simulations, a major
limitation of conventional input-output models
is that the policy under consideration must
be translated into changes in final demand.
Furthermore, because they do not include
resource constraints, the resulting solution
may not be consistent with the actual supply-
demand conditions in the economy. Input-output
econometric models, in contrast, are driven
by econometrically estimated macroeconomic
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Chapter 8 Analyzing Costs
relationships that more accurately account for
these conditions. However, unlike standard
macro-econometric models, input-output
econometric models integrate input-output data
and structure into the specification of production.
This allows them to estimate changes in the
demand for and the production of intermediate
goods. The macroeconomic component enables
the models to be used for long-run forecasting,
including accounting for business cycles and
involuntary unemployment. This makes input-
output econometric models particularly useful
for estimating transitional costs arising from the
implementation of a regulation.
An example of an input-output econometric
model that has been used for policy analysis at
EPA is the Regional Economic Models, Inc.
(REMI) Policy Insight. The standard REMI
model includes 70 production sectors and 25
final demand sectors and can provide output on
changes in income and consumption for more than
800 separate demographic groups. The model is
both national in scope and can be specially tailored
to individual regions. The REMI model has been
applied to a wide range of regional environmental
policy issues, including extensive analysis of air
quality regulation in the greater Los Angeles area.
Advantages:
• Input-output econometric models can be
used to estimate both long- and short-run
transitional costs.
• Input-output econometric models can be used
to estimate distributional costs.
Limitations:
• Because input-output econometric models
combine elements of both macro and micro
theory, it may not be easy to disentangle the
mechanisms actually driving model results.
• Compared to standard input-output models,
input-output econometric models may not
have the sectoral resolution necessary to
analyze the impact of a policy expected to
have limited impacts.
8,4.6 Computet neral
Equilibrium Models
CGE models have been used in a number of
applications in the analysis of environmental
regulation. Examples include estimation of the
costs of the Clean Air Act (CAA), the impacts
of domestic and international policies for GHG
abatement, and the potential for market-based
mechanisms to reduce the costs of regulation.
CGE models simulate the workings of the price
system in a market economy. Markets exist for
commodities and can also be specified for the
factors of production: labor, capital, and land. In
each market, a price adjusts to equilibrate supply
and demand. A CGE model may contain several
hundred sectors or only a few, and may include
a single "representative" consumer or multiple
household types. It may focus on a single economy
with a simple representation of foreign trade, or
contain multiple countries and regions linked
through an elaborate specification of global trade
and investment. The behavioral equations that
govern the model allow producers to substitute
among inputs and consumers to substitute among
final goods as the prices of commodities and
factors shift. The behavioral parameters can be
econometrically estimated, calibrated, or drawn
from the literature. In some models, agents may
be able to make intertemporal trade-offs in their
consumption and investment choices.
Simulating the effects of a policy change involves
"shocking" the model, by, for example, introducing
a regulation, such as a tax on emissions. Prices
in affected markets will then move up or down
until a new equilibrium is established. Prices
and quantities in this new equilibrium can be
compared to those in the initial equilibrium.
A static CGE model will be able to describe
changes in economic welfare measures due to a
reallocation of resources across economic sectors
following a policy shock. In a policy simulation
using a dynamic CGE model, a time path of
new prices and quantities is generated. This
time path can be compared to a baseline path of
prices and quantities that is estimated by running
the model without the policy shock. As some
policies can be expected to have impacts over a
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Chapter 8 Analyzing Costs
Text Bo* 8.2 - The Pollution Abatement Costs arid Expenditures Survey
The Pollution Abatement Costs and Expenditures (PACE) survey is the primary source of information on pollution
abatement-related operating costs and capital expenditures for the U.S. manufacturing sector (U.S. Bureau of the
Census, various years). The PACE survey collects data on costs of pollution treatment (i.e., end-of-pipe controls),
pollution prevention (i.e., production process enhancements to prevent pollution from being produced), disposal,
and recycling. The survey is sent to approximately 20,000 establishments (who are required by law to respond to it)
and was conducted annually by the U.S. Census Bureau from 1973 to 1994 (except in 1987) and then again in 1999.
EPA funded the 1999 PACE survey. However, this survey was substantially different from its predecessors, making
direct longitudinal analysis difficult (see Becker and Shadbegian 2005 for a comprehensive description of the
conceptual differences between the 1994 and 1999 PACE surveys). More recently, with the guidance and financial
support of EPA, a completely revised version of the PACE survey was administered by the Census Bureau to collect
2005 data. The 2005 PACE survey was the result of a multi-year effort to evaluate the quality of the survey instrument
and the accuracy and reliability of the responses to the survey. The 2005 PACE data, which was released in April
2008, is longitudinally consistent with previous PACE surveys, with the exception of the 1999 iteration. EPA has no
current plan to collect PACE data beyond 2005, but hopes to reinstate the survey in the future to once again collect
data on an annual basis. The annual collection of pollution abatement costs would provide EPA with information
required for its RIAs, and would better enable researchers to answer questions of interest, particularly those that
require longitudinal data.
The PACE survey contains operating costs and capital expenditures disaggregated by media: air, water, and solid
waste; and by abatement activity: pollution treatment, recycling, disposal, and pollution prevention. Total operating
costs are further disaggregated into: salary and wages, energy costs, materials and supplies, contract work, and
depreciation.
The PACE survey data, both aggregate and establishment-level, have been used to analyze a wide range of policy
questions. These include assessing the impact of pollution abatement expenditures on productivity growth,
investment, labor demand, environmental performance, plant location decisions, and international competitiveness.
longer time horizon, dynamic models are used to
capture, in addition to static impacts, the welfare
consequences of reallocating resources over time,
such as the impact that changes in savings may
have on capital accumulation. Forward-looking
models can also capture the effects that future
policies may have on current decisions.
An example of the use of a CGE model at EPA
is the retrospective BCA of the CAA, which
used a dynamic CGE model to compute the
costs of CAA compliance over the period
1970 to 1990 (U.S. EPA 1997a). Estimates of
pollution abatement expenditures for the U.S.
manufacturing sector were first calculated using
Pollution Abatement Costs and Expenditures
(PACE) survey data (see Text Box 8.2). As the
analysis was retrospective, the relevant policy
simulations involved removing the long-term
capital and operating costs from the industries that
incurred them. The retrospective BCA compared
the simulated path of the economy without these
abatement expenditures and the actual path of the
economy, which included them. EPA computed
changes in both long-run GDP and equivalent
variation, as well as impacts on investment,
household consumption, and sectoral prices,
output, and employment.
CGE models have also been used extensively in
estimating the costs of GHG mitigation. Here,
the analyses have been prospective, such as efforts
to estimate the costs of complying with the Kyoto
Protocol and more recently, proposed climate
change legislation. Some studies have focused
on the control of C02 emissions by introducing
8-20
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 8 Analyzing Costs
carbon taxes or emissions trading. Other studies
have expanded the analysis by examining
other GHGs and incorporating the effects of
changes in land use patterns and carbon sinks.
Of particular concern has been the problem
of "leakage," in which a fall in emissions in
participating countries is offset by an increase in
emissions in non-participating countries, induced
by the fall in demand, and thus the world price,
of energy inputs.
CGE models can be useful tools for examining
the medium- to long-term impacts of policies
that are expected to have relatively large,
economy-wide effects. A growing use of
these models has been to quantify previously
unrecognized welfare costs that can occur when
environmental policies interact with pre-existing
distortions in the economy. An expanding
body of work has begun to include non-market
goods into CGE models (Smith et al. 2004, and
Carbone and Smith 2008).
Given the large number of parameters in a
typical CGE model, analysts should take great
care in ensuring the accuracy of a model's data
and specifications. Sensitivity analysis should be
performed on critical parameters. One strategy,
currently used in EPA's analyses of climate
legislation, is to use two CGE models concurrently
to analyze the same policy scenarios.
Limitations:
• Because of their equilibrium assumptions,
CGE models are generally not appropriate
for analyzing short-run transitional costs.
However, when appropriate specifications are
included in a model, they may be used in this
type of analysis.
• CGE models are generally not well suited
for estimating the effects of policies that
will affect only small sectors or will impact
a limited geographic area. Although the
costs have been reduced in recent years, the
effort and data required to construct a new
CGE model or revise an existing one may be
prohibitive for some analyses.
Advantages:
• CGE models are best suited for estimating the
cost of policies that will have large economy-
wide impacts, especially when indirect
and interaction effects are expected to be
significant.
• CGE models are generally most appropriate
for analyzing the medium- or long-term
effects of policies or regulations.
• With the appropriate specifications
incorporated, CGE models can be used to
estimate the distributional impacts of policy
shocks on household groups or industrial
sectors.
Guidelines for Preparing Economic Analyses I December 2010 8-21
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Guidelines for Preparing Economic Analyses I December 2010
-------
Employment Impacts Update
EPA is currently revising its guidance for assessing the employment impacts of
environmental regulation. Section 9.2.3.3 "Impacts on employment" will be replaced
with a discussion based on more recent literature and feedback from the Economy
Wide Modeling Science Advisory Board Panel.1 The new section will summarize the
theory and methods for assessing employment impacts. Please note that subsequent
to publication of the current Section 9.2.3.3, researchers attempted to replicate and
extend the empirical estimates in Morgenstern, et al. (2002)." However, as Belova, et
al. (2013) note, "the original datasets and data management code used by MPS
[Morgenstern, et al. (2002)] in the Census Research Data Center were not available to
us because of the failure of the backup drive at the Census on which they had been
archived." In light of this loss, replication attempts were not successful (Belova et al.
2013, 2015).111 In preparing economic analyses, analysts should not rely on the
empirical estimates from Morgenstern, et al. (2002). Likewise, analysts should not rely
on the estimates from Belova et al. (2013, 2015) as the authors "recommend that
EPA refrain from using these results until the underlying cause(s) for the implausibly
large estimates in the employment effects found in Belova et al. (2013a) are
uncovered and resolved."lv
While EPA is awaiting the Science Advisory Board Panel report and continuing to
explore recent areas of the literature, analysts are encouraged to look at recent EPA
Regulatory Impact Analyses (RIAs) for best available methods and approaches for
conducting employment impact analyses. Recent RIAs include those for the final
Clean Power Plan published in August 2015,v the Residential Wood Heater New
Source Performance Standard in February 2015,V1 and the final Tier 3 Vehicle
Emission and Fuel Standards Program in March 2014.™ These employment impact
analyses contain an updated description of theoretic models and empirical methods
that are more reflective of what will be incorporated into the employment impacts
update to the Guidelines. Please contact EPA's National Center for Environmental
Economics with any questions.
National Center for Environmental Economics
US Environmental Protection Agency
Mail Code 1809T
EPA West Building
1200 Pennsylvania Avenue, N.W.
Washington, DC 20460
Phone: 202-566-2244
Fax: 202-566-2363
email: ncee@epa.gov
-------
' For more information please see
http://yosemite.epa.goV/sab/sabproduct.nsf//LookupWebProjectsCurrentBOARD/07E67CF77B54734285257BB0004F
87ED?OpenDocument
" Morgenstern, R.D., W.A. Pizer, and J. Shih. 2002. Jobs Versus the Environment: An Industry Level Perspective.
Journal of Environmental Economics and Management 43:412-436.
Belova, A., W.B. Gray, J. Linn, and R.D. Morgenstern. 2013. Environmental Regulation and Industry Employment: A
Reassessment. Discussion Papers, U.S. Census Bureau, Center for Economic Studies 2K132B, 4600 Silver Hill
Road, Washington, DC 20233.
Belova, A., W.B. Gray, J. Linn, R.D. Morgenstern, and W. Pizer. 2015. Estimating the Job Impacts of Environmental
Regulation. Journal of Benefit-Cost Analysis, 6(2), pp 325 - 340.
iv Quote is from Belova et al. (2015). Note that Belova et al. (2013a) in the quote is identical with Belova et al. (2013)
cited above.
v See Chapter 6 of the RIA (EPA-HQ-OAR-2013-0602 at https://www.epa.gov/cleanpowerplan/clean-power-plan-final-
rule-reaulatorv-impact-analvsis).
vl See Chapter 5, Section 5.7 of the RIA (EPA-452/R-15-001 at https://www.epa.gov/sites/production/files/2015-
02/documents/20150204-residential-wood-heaters-ria.pdf).
vii See Chapter 9 of the RIA (EPA-420-R-14-005 at https://www3.epa.gov/otaq/documents/tier3/420r14005.pdf).
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Chapter i
Economic Impact Analysis
The detailed study of regulatory consequences allows policy makers to fully
understand a regulation's impacts, and to make an informed decision on its
appropriateness. Economic information is necessary for the evaluation of
at least two types of consequences of a regulatory policy: the regulations
efficiency, and its distributional effects. In principle, both could be estimated
simultaneously using a general equilibrium model. In practice however, they are usually
estimated separately.
The distributional effects of environmental regulations can be examined through an
economic impact analysis (EIA). A related analysis, called an equity assessment, addresses
the distribution of impacts across individuals and households, with particular attention to
economically or historically disadvantaged or vulnerable groups (e.g., low-income households,
racial or ethnic minorities, and young children). Equity assessments are sometimes referred to
as environmental justice (EJ) analyses and are the subject of Chapter 10.
An EIA identifies the specific entities that benefit from or are harmed by a policy, and
then estimates the magnitude of their gains and losses including changes in profitability,
employment, prices, government revenues or expenditures, and trade balances. These
estimates are derived from a study of the economic changes that occur across broadly-defined
economic sectors of society, including industry, government, and not-for-profit organizations,
but may also include more narrowly defined sectors within these broad categories, such as the
solid waste industry or even an individual solid waste company. EIAs can measure a broad
variety of impacts, such as direct impacts on individual plants, whole firms, and industrial
sectors, as well as indirect impacts on consumers and suppliers.
1 f i cf itutes arid Policies
Hie following major statutes and EOs, all described in
Chapter 2, directly address impact analyses:1
• Regulatory Flexibility Act of 1980 (RFA), as
amended by the Small Business Regulatory
Enforcement Fairness Act of 1996 (SBREFA);
• Unfunded Mandates Reform Act of 1995 (UMRA);
1 EPA's Regulatory Management Division's Action Development Process
(ADP) Library (http://intranet.epa.gov/adplibrary) is a resource for thosf
who wish to access relevant statutes, EOs, or Agency policy and guidan
documents in their entirety.
• EO 13132, "Federalism";
• EO 13175, "Consultation and Coordination
with Indian Tribal Governments;" and
• EO 13211, "Actions Concerning Regulations
That Significantly Affect Energy Supply,
Distribution, or Use."
e
ice
Together with OMB's Circular A-4, they raise
important dimensions relevant for economic impact
analyses as summarized in Table 9.1.
Guidelines for Preparing Economic Analyses I December 2010 8-1
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Chapter 9 Economic Impact Analyses
Table 9,1 - Potentially Relevant Dimensions to Economic impact Analyses2
Dimension
Statute, Order, or
Directive
Entity
Subpopuiation
Sector
UMRA; EO 13132; 0MB
Circular A-4
Industry or government
Industries or state, local, or tribal
governments
Entity size
RFA; UMRA; 0MB Circular
A-4
Businesses, governments, or
not-for-profit organizations
Small businesses, small governmental
jurisdictions, or small not-for-profit
organizations
Time
0MB Circular A-4
individuals or households
Current or future generations
Geography
0MB Circular A-4 UMRA
Region
Regions, states, counties, or
non-attainment areas
Energy
E0 13211
Entities that use, distribute, or
generate energy
Energy sector
Hie term "affected" is used throughout this
chapter as a general term. Analysts should be aware
that the authorizing statute for the rule, as well
as other applicable statutes and administrative
orders noted in this chapter, may make more
specific use of this term. For example, the
Regulatory Flexibility Act includes the clause
"subject to the requirements of the rule" when
quantifying economic impacts, meaning that
the analysis considers only those entities that are
directly regulated by the rule. On the other hand,
provisions in the UMRA and EO 12866 address
both direct and indirect impacts, and therefore
define the affected population more broadly. Care
should be taken to avoid double counting when
estimating direct and indirect impacts.
1,1 i. i inducting an K anomic
iysis
There are three important distinctions between
BCA and EIA to keep in mind when conducting
an EIA.3 First, total social benefits and total social
costs are not of primary importance in an EIA, as
they are in a BCA. Rather, the main focus is on
the components and distribution of the total social
benefits and costs.
2 Some environmental statutes may also identify subpopulations that
merit additional consideration. This document is limited to those
statutes with broad coverage.
3 Traditionally ElAs focus on the costs of a particular rule or regulation.
However, it is also possible to focus on the distribution of benefits or to
calculate the net benefits for particular entities.
Second, transfers of economic welfare from one
group to another are no longer assumed to cancel
each other out, as they do in a BCA. Taxpayers,
consumers, producers, governments, and the many
sub-categories of these groups are all considered
separately. While a BCA relies on estimates of
the social benefits and costs of a regulation, an
EIA focuses on the private benefits and costs
associated with compliance responses. The EIA
should use the same "starting point" as the BCA
(i.e., same engineering or direct compliance costs,
same benefit categories, etc.) for developing
private benefit and cost estimates. In addition,
some adjustments to these costs may be needed,
as discussed below. For example, the tax status of
a required piece of equipment is considered in
private costs, but not in social costs.
Finally, there is a greater need for disaggregation in
EIAs than in BCAs. Results may be presented for
specific counties or other geographic units or types
of entities, as appropriate, placing heavy demands
on the modeling framework.
For any regulation, it is essential to ensure
consistency between the EIA and the benefit-
cost analysis (BCA). If a BCA is conducted, the
corresponding EIA must be conducted within the
same set of analytical assumptions. To the extent
possible, adjustments to these assumptions or to
the overall modeling framework used for the BCA
should only be made when absolutely necessary,
and then should be noted clearly in the text of the
analysis.
9-2
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 9 Economic Impact Analyses
9.2:1 Screening for Potentially
Significant Impacts
A comprehensive analysis of all aspects of all
economic impacts associated with a rule can
require significant time and resources, and its
accuracy and thoroughness depend on the quality
and quantity of available data. Thus, screening
analyses are often employed to determine data
availability, the severity of a rule's anticipated
impacts, and the potential consequences of
further analysis if undertaking it would require
a delay in the regulatory schedule. A screening
analysis can be thought of as a "mini-EIA"
consisting of a rough examination of the data
to identify sectors that may warrant further
analysis.4 Screening is effective for identifying
the magnitude of the overall level of impacts on
the regulated industry, but may fail to identify
potentially large impacts on a single sector, region,
or facility.
There are no established definitions for what
constitutes a large or a small impact. However,
a screening analysis is a tiered approach that
initially captures most of the possible impacts
(i.e., allows for many false positives) followed by
a more detailed analysis that can help eliminate
unfounded impacts. In this way, the screening
analysis will eventually balance the risk of
identifying "false positives" and "false negatives."
3.2,2 Profile of Affect itities
Analysts should consider changes imposed by
the rule in the regulated industry, as well as how
related industries maybe affected. Some industries
may benefit from the regulation, while others may
be subject to significant costs. If the regulation
causes a firm to use different inputs or new
technologies, then the producers of the new inputs
will gain, while the producers of the old inputs
will suffer. Developing a detailed industry profile
will identify those industries that may be affected
positively and negatively by the regulation.
4 The screening analysis discussed in this section is distinct from the
screening analysis required to comply with the Regulatory Flexibility
Act (as referred to in Section 9.3).
3,2,2 ipiling an Industry Profile
and Projected Baseline
To determine the impacts of a particular regulation
the analyst must understand the underlying
structure of the affected industry and its various
linkages throughout the economy.5 This includes
an understanding of the condition of the industry
in terms of its finances and structure in the absence
of the rule —the baseline of the EIA. A rule may
impose different requirements and costs on new
versus existing entities. Such rules may affect
industry competition, growth, and innovation
by raising barriers to new entry or encouraging
continued use of outdated technology. Thus,
a substantial portion of an EIA involves
characterizing the state of the affected firms and
industries in the absence of the rule as a basis for
evaluating economic impacts.
The following are important inputs to defining an
industry profile:
• North American Industrial Classification
System (NAICS) industry codes. NAICS
has replaced the U.S. Standard Industrial
Classification (SIC) system in the U.S.
Department of Commerce (DOC) Economic
Census and other official U.S. Government
statistics. NAICS was developed to provide
comparable statistics about business activity
across North America. It identifies hundreds
of new, emerging, and advanced technology
industries and reorganizes existing industries
into more meaningful sectors, particularly in
the service sector.6
• Industry summary statistics. Summary
statistics of total employment, revenue,
number of establishments, number of firms,
and size of firms are available from U.S. DOC
Economic Census or the Small Business
Administration.7
5 Generally analysts should initially assume a perfectly competitive
market structure. One of the primary purposes of developing an
industry profile is to confirm this assumption or discover evidence to
the contrary.
6 For more information seewww.census.gov/epcd/www/naics.html,
which includes a NAICS/SIC correspondence (accessed on January
21,2011).
7 See www.sba.gov/advocacy/849 for more information (accessed on
January 21,2011).
Guidelines for Preparing Economic Analyses I December 2010
8-3
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Chapter 9 Economic Impact Analyses
• Baseline industry structure. Industry-level
impacts depend on the competitive structure
and organization of the industry and the
industry's relationship to other economic
entities. The number and size distribution
of firms/facilities and the degree of vertical
integration within the industry are important
aspects of industry structure that affect the
economic impact of regulations.
• Baseline industry growth and financial
condition. Industries and firms that are
relatively profitable in the baseline will be
better able to absorb new compliance costs or
take advantage of potential benefits without
experiencing financial distress. Industries that
are enjoying strong growth may be better
able to recover increased costs through price
increases than they would if there were no
demand growth. Section 9.3.3.3 provides
suggestions for using financial ratios to assess
the significance of economic impacts on a
firm's financial condition.
• Characteristics of supply and demand.
Assessing the likelihood of changes in
production and prices requires information
on the characteristics of supply and demand
in the affected industries. The relevant
characteristics are reflected in price elasticities
of supply and demand, which, if available,
allow direct quantitative analysis of changes
in prices and production. Often, reliable
estimates of elasticities are not available and
the analysis of industry-level adjustments
must rely on simplifying assumptions and
qualitative assessments. See Appendix A for a
discussion of elasticities.
3,2,2.2 Profile of Government Entities
and Not-for-Profit Organizations
Analysts should carefully consider whether a
particular rule will directly affect government
entities, not-for-profit organizations, or
households.8 For example, air pollution regulations
8 Government entities that may be affected include states, cities,
counties, townships, water authorities, villages, Indian Tribes, special
districts, and military bases. Not-for-profit entities that may be affected
include not-for-profit hospitals, colleges, universities, and research
institutions.
that apply to power plants may affect government
entities such as municipally-owned electric
companies. Air regulations that apply to vehicles
may affect municipal buses, police cars, and public
works vehicles. Effluent guidelines for machinery
repair activities may affect municipal garages.
The profile of these affected entities should
include a brief description of relevant factors or
characteristics.
Relevant factors for government entities
may include:
• Number of people living in the community;
• Property values;
• Household income levels (e.g, median,
income range);
• Number of children;
• Number of elderly residents;
• Unemployment rate;
• Revenue amounts by source; and
• Credit or bond rating of the community.
If property taxes are the major revenue source,
then the assessed value of property in the
community and the percentage of this assessed
value represented by residential versus commercial
and industrial property should be determined. If
a government entity serves multiple communities,
such as a regional water or sewer authority, then
relevant information should be collected for all the
communities covered by the government entity.
Socioeconomic factors influence demands on state
or local government resources; for example a high
proportion of children means more educational
resources.
Data on community size, income, number of
children and elderly, and unemployment levels
are available from the U.S. Census Bureau.
Data on property values, amount of revenue
collected from each revenue source, and credit
rating may be available from the community
or state finance agencies. Most county websites
provide information on property values. Private
companies, such as Standard and Poor's (S&P),
or Fitch's, provide community credit ratings.
9-4
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 9 Economic Impact Analyses
Depending on the number of communities
affected and the level of detail warranted, the
analysis may rely on generally available aggregate
data only. In other cases, a survey of affected
communities maybe necessary.9
Relevant characteristics of not-for-profit
entities include:
• Entity size and size of community served;
• Goods or services provided;
• Operating costs; and
• Amount and sources of revenue.
If the entity is raising its revenues through user
fees or charging a price for its goods or services
(such as university tuition), then the income levels
of its clientele are relevant. If the entity relies on
contributions, then it would be helpful to know
the financial and demographic characteristics of
its contributors and beneficiaries. If it relies on
government funding (such as Medicaid) then
possible future changes in these programs should
be identified.
3.2.2.3 Profile of Small Entities
Small entities include small businesses,
small governments and small not-for-profit
institutions. "While these entities may require
special considerations, as detailed below, the
profiling of them should follow the same steps as
discussed above.
9.2.2.4 Data Sources for Profiles
Profiles generally rely on information from
the following sources: websites for affected
communities, industry trade publications, and
the U.S. Census Bureau.10 Relevant literature can
be useful in characterizing industry activities and
markets as well as regulations that already affect
the industry. Relevant literature can usually be
efficiently identified through a computerized
9 In cases where a survey is needed, care should be taken to comply with
the requirements of the Paperwork Reduction Act (PRA) (44 U.S.C.
3501).
10 Academic literature may or may not contain quantitative data.
search using on-line services such as Dialog, BRS/
Search Services, Dow Jones News/Retrieval, or
EconLit. These on-line services contain more
than 800 databases covering business, economic,
and scientific topic areas. Table 9.2 describes
some commonly used data sources for retrieving
quantitative data.11
The industry profile may also identify situations
where insufficient data are available from standard
sources. This situation could potentially arise
when the affected industry has many product
lines or activities affected by the rule. In addition,
for some rules it may be difficult to identify the
appropriate NAICS industry for all the firms or
facilities affected by the rule if the industry can be
categorized in multiple ways. In these cases, and
particularly if facility-level data are required to
estimate economic impacts, a survey of affected
facilities may be required to provide sufficient data
for analysis.
9.2.3 Detailing Impacts
on industry
This section explains how to determine the impact
on individual plants or businesses so as to identify
whether a particular plant or industry is likely to
bear a disproportionate portion of the costs or
benefits of a regulation.
3.2,3,1 Impacts on Prices
Predicted impacts on prices form the basis
for determining how compliance costs are
distributed between the directly-affected firms,
their customers, and other related parties in
a typical market. At one extreme, regulated
firms may not be able to raise prices at all, and
would consequently bear the entire burden of
the added costs in the form of reduced profits.
Reduced profits may result from reduced
earnings on continuing production, lost profits
on products or services that are no longer
produced, or some combination of the two.
11 The Thomas Registry (www.thomasnet.com) is a source of qual itative
information on manufacturing companies in the United States
(accessed on January 21,2011). In addition, Lavin (1992) provides
sources of business information.
Guidelines for Preparing Economic Analyses I December 2010
9-5
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Chapter 9 Economic Impact Analyses
Table 9,2 - Common! v t [j,:ecl Profile Sources for Quantitative Data
Source
Data
Trade Publications and Associations
Market and technological trends, sales, location, regulatory
events, ownership changes
U.S. Department of Commerce, Economic Census
(www.census.gov)
Sales, receipts, value of shipments, payroll, number of
employees, number of establishments, value added, cost
of materials, capital expenditures by sector, household and
community characteristics
U.S. Department of Commerce, U.S. Industry & Trade
Outlook
(http://www.ita.doc.gov/td/industry/OTEA/outiook/or
http://outiook.gov/)
Description of industry, trends, international
competitiveness, regulatory events
U.S. Department of Commerce, Pollution Abatement Costs
and Expenditures Survey
(www.census.gov/mcd)
Pollution abatement costs for manufacturing facilities by
industry, state, and region
U.S. Department of Commerce, Census of Governments
(www.census.gov/govs/index.htmi)
Revenue, expenditures debt, employment, payroll, assets
for counties, cities, townships, school districts
United Nations, International Trade Statistics Yearbook
Foreign trade volumes for selected commodities, major
trading partners
Risk Management Association, Annual Statement Studies
(www.rmahg.org/ann_studies/asstudies.html)
Income statement and balance sheet summaries,
profitability, debt burden and other financial ratios, all
expressed in quartiles and available for recent years (based
on loan applicants only)
Dun & Bradstreet Information Services
(www.dnb.com/us/)
Type of establishment, NAICS code, address, facility and
parent firm revenues and employment
Standard & Poors
(www.standardandpoors.com)
Publicly-held firms, prices, dividends, and earnings,
line-of-business and geographic segment information,
S&P ratings, quarterly history (10 years), income
statement, ratio, cash flow and balance sheet analyses and
trends
Securities and Exchange Commission Filings and Forms
(EDGAR System Database)
(www.sec.gov/edgar.shtml)
Income statement and balance sheet, working capital, cost
of capital, employment, outlook, regulatory history, foreign
competition, lines of business, ownership and subsidiaries,
mergers and acquisitions
Value Line Industry Reports
Industry overviews, company descriptions and outlook,
performance measures
Suppliers to the directly-affected firms might
bear part of the burden in lost earnings if the
regulation results in a decline in demand for
particular products.12 At the other extreme,
firms may be able to raise prices enough to
recover costs fully. In this case, there is no
impact on the profitability of the directly-
affected firms but their customers bear the
burden of increased prices. Assuming perfect
competition, the amount of price pass-through
depends on the relative elasticity of supply and
demand. Another economic impact to consider
is the potential backward shifting of regulatory
costs (e.g., lowering wages of workers).
12 For example, regulations limiting SQ emissions may result in reduced
demand for high-sulfur coal, which results in a fall in the price of such
coal and lost profits for its producers. While there is no clear rule for
how far down the chain of effects one needs to consider, it is important
to address effects that are likely to be substantial.
In general, the likelihood that price increases will
occur can be evaluated by considering whether
competitive conditions allow the affected
facilities to pass their costs on to consumers.
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Guidelines for Preparing Economic Analyses I December 2010
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Chapter 9 Economic Impact Analyses
Hie methods used to conduct the analysis of
the directly-affected markets depend on the
availability of appropriate estimates of supply
and demand elasticities.13 As noted above, in
cases where reliable estimates of elasticities are
not available, the analyst must rely on a more
basic investigation of the characteristics of supply
and demand in the affected market to reach a
conclusion about the likelihood of full or partial
pass-through of costs via price increases. An
examination of the number of firms, quantity of a
product produced, and industry size will provide
basic information about supply and demand.
If an industry is highly concentrated with few
producers then firms maybe able to easily pass
costs on to households and a 100 percent pass-
through assumption may be justifiable. Of course,
an industry with many producers would mean the
opposite assumption.
3,2.3,2 Impacts on Production
Abatement costs tend to be only a small fraction
of total manufacturing revenues. As such, even
small changes in wage rates, materials costs, or
capital costs are likely to have a much larger effect
on manufacturing industries than any changes
in environmental regulation. The U.S. Census
Bureau collects data on pollution abatement
capital expenditures and operating costs incurred
to comply with local, state, and federal regulations
and on voluntary or market-driven pollution
abatement activities.14 According to the 2005
PACE Survey, the U.S. manufacturing sector
spent approximately $20.7 billion dollars on
pollution abatement operating costs. This figure
represents less than 1 percent of the sectors
total revenue, which is similar to the historical
average. Moreover, every manufacturing industry,
including the most highly regulated ones,
spend less than 1.2 percent of their revenues on
pollution abatement. Figure 9.1 presents data
for the five industries with the highest pollution
abatement operating costs (PAOC) as a percent of
total revenues.
Figure 3,1 - Pollution Abatement Costs as a
Percentage of Total Revenues for Industries with
Highest Pollution Abatement Costs in 2005
1.20
1.00'
0.80'
0.60 ¦
0.40:
0.20.
0.00.
**
¦¦¦AverageHII »l«li
Chemicals Petroleum Primary Metal Paper Leather
Industry
Source: Pollution Abatement Costs and Expenditures: 2005
Figure 9,2 - Pollution Abatement Costs are a very
Small Percentage of Total Manufacturing Costs
Pollution
Abatement
Operating Costs , .
12 2% Energy
' 2.0%
Profits, Taxes,
Interests on Debt,
and Other
31.3%
0.4%
\
Depreciation
2.4%
Materials
51.7%
13 See Appendix A for a more complete discussion of elasticity.
14 More detail on the PACE Survey is available at http://yosemite.epa.gov/
ee/epa/eed.nsf/pages/pace2005.html (accessed March 13,2011).
Source: U.S. Census Bureau, Pollution Abatement Costs and
Expenditures: 2005
U.S. Census Bureau, Annual Survey of Manufacturers: 2005
Considering the historical data, it is unlikely
that the typical pollution control regulation will
sufficiently increase the cost of doing business
so as to make a meaningful part of production
unprofitable, or will significantly reduce the
quantity of output demanded as producers raise
their prices to maintain profitability. Figure 9.2
shows the relative magnitude of each cost category
for the manufacturing sector. Based on these
relative magnitudes, reducing abatement costs by
10 percent will only reduce the total costs faced
by industry by less than 1 tenth of 1 percent.
Conversely, lowering material costs by 10 percent
will reduce total costs by just over 5 percent as
Guidelines for Preparing Economic Analyses I December 2010
9-7
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Chapter 9 Economic Impact Analyses
material costs were roughly 50 percent of revenues
in 2005. Exceptions maybe regulations banning
the sale or manufacture of a specific product (e.g.,
a chemical ban) or when a production process
is made obsolete. In these situations, the analyst
should assess whether the existing plants have
other profitable uses.
3,2,3,3 Impacts on employment
The chapters on benefits (Chapter 7) and costs
(Chapter 8) point out that regulatory-induced
employment impacts are not, in general, relevant
for a BCA. For most situations, employment
impacts should not be included in the formal
BCA.15 However, if desired the analyst can assess
the employment impacts of a regulation as part
of an EIA. If this task is undertaken, the analyst
needs to quantify all of the employment impacts,
positive and negative, to present a complete
picture of the effects. This section identifies
pitfalls often encountered when performing an
EIA and discusses the preferred approaches for
conducting one.
Many analyses only present the employment
effect on the regulated industry as a result of
higher regulatory compliance costs. In doing so,
these analyses make simplifying assumptions that
employment in a given industry is proportional to
output, i.e., if production goes down by 1 percent,
employment goes down by 1 percent. These
limited assessments on employment impacts from
regulation examine how higher manufacturing
costs lead to fewer sales and therefore lower
employment in that sector. However, empirical
and theoretical modeling suggests that these
simplified relationships are faulty and should not
be used.
In fact, it is not even clear that employment in
the regulated industry goes down as a result of
environmental regulation. Morgenstern et al.
(2002) decompose the labor consequences in an
industry facing increased abatement costs. They
identify three separate components:
15 Appendix C discusses long-term, structural employment changes
brought on by land clean up and reuse or other policies that may have
a benefit component to them.
• Demand effect: Higher production costs
raise market prices. Higher prices reduce
consumption (and production) reducing
demand for labor within the regulated
industry;
• Cost effect: As production costs increase,
plants use more of all inputs including labor
to produce the same level of output. For
example, pollution abatement activities
require additional labor services to produce
the same level of output; and
• Factor-shift effect: Post-regulation
production technologies maybe more or
less labor intensive (i.e., more/less labor is
required per dollar of output).
Morgenstern et al. empirically estimate this model
for four highly polluting/regulated industries
to examine the effect of higher abatement costs
from regulation on employment. They conclude
that increased abatement expenditures generally
do not cause a significant change in employment.
Specifically, their results show that, on average
across the industries they consider, each additional
$1 million of spending on pollution abatement
results in a {statistically insignificant) net increase
of 1.5 jobs. However, they find that for two
of their four industries (pulp and paper, and
steel) additional abatement spending leads to a
statistically significant, yet quite small, net increase
in jobs due to the substitution of labor for other
inputs and relatively inelastic estimated demand
for their output.16
Finally, one effect that Morgenstern et al. do
not consider is the effect regulation has on
employment in industries that make substitute
products, often cleaner products. Demand for
these products increases as consumers respond
to changes in costs. For example, more expensive
virgin paper will cause a shift to more recycled
paper. The recycled paper industry will employ
more workers as sales increase. Similarly,
employment in industries that are complements
16 These results are similar to Berman and Bui (2001) who find that while
sharply increased air quality regulation in Los Angeles to reduce NOx
emissions resulted in large abatement costs they did not result in
substantially reduced employment.
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Chapter 9 Economic Impact Analyses
may decrease. Hie analyst should also take these
effects into consideration when analyzing the
effect of regulations on employment.
In addition to the changes in the regulated
industry as modeled by Morgenstern et al., the
analyst should assess the increased employment
in the environmental protection industry. The
engineering analysis may provide some data on
the labor required to design, build, install (and
in some cases operate) the pollution control
equipment. For example, a recent study by
Industrial Economics Inc. shows that a $19 million
order for a new scrubber will immediately fund
77 to 91 new jobs for a year constructing and
installing the new equipment. It will also create
16 permanent jobs to operate the new equipment
(Price et al. 2010).
9.2.3.4 Impacts on Profitability and
Plant Closures
In other cases, analysts may assess the impacts
of rules on the profitability of specific firms or
industry segments and identify potential plant
closures based on a financial analysis. If partial
or full plant closures are projected, then it is
important to consider whether the production
lost at the affected facilities will be shifted to other
existing plants or to new sources, or simply vanish.
If excess industry capacity exists in the baseline
and facilities are able to operate profitably while
complying with the rule, then these facilities may
expand production to meet the demand created by
the loss of plants that are no longer able to operate
profitably. Some surviving plants could experience
increases in production, capacity utilization,
and profits even though they are subjected to
regulatory requirements, if their competitors face
even greater cost increases.
9.2.3.5 Impacts on Related Industries
The economic and financial impacts of regulatory
actions spread to industries and communities
that are linked to the regulated industries and to
the pollution abatement industries, resulting in
indirect business impacts. To build scrubbers, the
environmental protection industry will order more
steel. If a plant produces less, it will order fewer
raw materials. These indirect impacts may include
employment and income gains and losses.
Although in principle every economic entity
can be thought of as having a connection with
every other entity, practical considerations
usually require an analysis of indirect impacts for
a manageable subset of economic entities that
are most strongly linked to the regulated entity.
In addition to considering major customers and
specialized suppliers of the affected industry, it is
important to consider less obvious but potentially
significant links, such as basic suppliers like
electricity generators.
For these reasons, the analysis of linkages should
use a framework that thoroughly measures
indirect as well as direct linkages. Whatever the
approach, the goal of the analysis is to measure
how employment, competitiveness, and income are
likely to change for related entities and households
given a certain amount of employment,
competitiveness, and income in a regulated
market.
9.2,3,6 Impacts on Economic Growth
a hnicai Inefficiency
While regulatory interventions can theoretically
lead to macroeconomic impacts, such as growth
and technical efficiency, such impacts maybe
impossible to observe or predict. In some cases,
however, it may be feasible to use macroeconomic
models to evaluate the regulatory impact on
GDP, factor payments, inflation, and aggregate
employment. For regulations that are expected to
have significant impacts in a particular region, use
of regional models, either general equilibrium or
other regionally-based models, may be valuable.17
Typically in regulatory impact analyses some
macroeconomic regulatory effects go unquantified
due to analytic constraints. For example, price
changes induced by a regulation can lead to
technical inefficiency because firms are not
choosing the production techniques that minimize
17 Chapter 8 discusses the use of regional modeling.
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Chapter 9 Economic Impact Analyses
the use of labor and other resources in the long
run. However, measuring these effects can be
difficult due to data or other analytical limitations.
8,2,3,7 Impacts on Industry
Competitiveness
Regulatory actions that substantially change
the structure or conduct of firms can produce
indirect impacts by changing the competitiveness
of the regulated industry, as well as that of
linked industries.18 An analysis of impacts on
competitiveness begins by examining barriers to
entry and market concentration, and by answering
the following two key questions:
• Does the regulation erect entry barriers that
might reduce innovation by impeding new
entrants into the market ? High sunk costs
associated with capital costs of compliance or
compliance determination and familiarization
would be an entry barrier attributable to the
regulation. Sunk costs are fixed costs that
cannot be recovered in liquidation; they can be
calculated by subtracting the liquidation value
of assets from the acquisition cost of assets
facing a new entrant, on an after-tax basis.19
Lack of access to debt or equity markets to
finance fixed costs of entering the market can
also present entry barriers, even if none of the
fixed costs are sunk costs. However, if financing
is available and fixed costs are recoverable in
liquidation, the magnitude of fixed costs alone
may not be sufficient to be a barrier to entry.
• Does the regulation tend to create or
enhance market power and reduce the
economic efficiency of the market ?
Important measures of competitiveness of an
industry are degrees of horizontal and vertical
integration (i.e., concentration) between
both buyers and sellers in the baseline
compared to post-compliance. If an industry
becomes more concentrated as a result of the
regulation then there are fewer firms within
the industry. In this case, market power will
be concentrated in the hands of a few entities,
18 See Jaffe et al. (1995) for an overview.
19 Sunk costs are sometimes referred to as exit barriers.
which may result in a less efficient market
than before the regulation. Closely related to
concentration, product differentiation may
occasionally either increase or decrease due to
a regulatory action. A regulation may result in
less product differentiation due to restrictions
on production. This could mean that market
power is more concentrated among the firms
that manufacture the product.
9.2,3,8 Impacts on Energy Supply,
Distribution, or Use
EO 13211 requires agencies to prepare a
Statement of Energy for "significant energy
actions," which are defined as significant regulatory
actions (under EO 12866) that also are "likely
to have a significant adverse effect on the supply,
distribution, or use of energy."20 These significant
adverse effects are defined as:
• Reductions in crude oil supply in excess of
10,000 barrels per day;
• Reductions in fuel production in excess of
4,000 barrels per day;
• Reductions in coal production in excess of 5
million tons per year;
• Reductions in natural gas production in excess
of 25 million mcf per year;
• Reductions in electricity production in excess
of 1 billion kilowatt-hours per year or in
excess of 500 megawatts of installed capacity;
• Increases in energy use required by the
regulatory action that exceed any of the
thresholds above;
• Increases in the cost of energy production in
excess of 1 percent;
• Increases in the cost of energy distribution in
excess of 1 percent; or
• Other similarly adverse outcomes.
For actions that may be significant under EO
12866, particularly for those that impose
requirements on the energy sector, analysts must be
prepared to examine the energy effects listed above.
20 See Section 2.1.6 for EPA and OMB's guidance on E013211
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Chapter 9 Economic Impact Analyses
9.2.4 Detailing Impacts on
Governments and Not-for-Profit
Organizations
Section 9.3.5 discusses how to measure the impact
of regulations and requirements on private entities,
such as firms and manufacturing facilities. When
dealing with private entities, an important focus
is on measures that assess changes in profits (or
proxy measures of profit). This section describes
impact measures for situations where profits and
profitability are not the focus of the analysis. Rather,
the ultimate measure of impacts is the ability of
the organization or its residents to pay for the
requirements. Many of the same questions apply:
• Which entities are affected and what are their
characteristics ?
• To what extent does the regulation increase
operating costs ?
• To what extent does the regulation impact
operating procedures ?
• Does the regulation change the amount and/
or quality of the goods and services provided?
• Can the entity raise the necessary capital to
comply with the regulation?
• Does the regulation change the entity's ability
to raise capital for other projects?
EPA regulations can affect governments and not-
for-profit organizations in at least three significant
ways. First, a regulation may directly impose
requirements on the entity, such as imposing
water pollution requirements for publicly-owned
wastewater treatment works, or initiating air
pollution restrictions that affect municipal bus
systems or power plants. Second, a regulation may
impose implementation and enforcement costs
on government agencies. Finally, a regulation
may impose indirect costs. For example increased
unemployment due to reduced production (or
even plant closure) could result in less tax revenues
in a community.
3,2,4.1 Direct Impacts on Government
and Not-for-Profit Entities
Direct impact measures can fall into two
categories:
• Those that measure the impact itself in terms
of the relative size of the costs and the burden
it places on residents; and
• Those that measure the economic and
financial conditions of the entity that affect its
ability to pay for the requirements.
For each category, there are several types of
measures that can be used either as alternatives or
joindy to illuminate aspects of the direct impacts.
Measuring the relative cost and burden of
the regulations
There are three commonly used approaches to
measuring the direct burden of a rule; all involve
calculating the annualized costs of complying with
the regulation. For government entities the three
approaches are:
• Annualized compliance costs as a
percentage of annual costs for the affected
service. This measure defines the impact as
narrowly as possible and measures impacts
according to the increase in costs to the entity.
In practice, EPA has often defined compliance
costs that are less than 1 percent of the current
annual costs of the activity as placing a small
burden on the entity.
• Annualized compliance costs as a
percentage of annual revenues of the
governmental unit. The second measure
corresponds to the commonly used private-
sector measure of annualized compliance
costs as a percentage of sales. Referred to as
the "Revenue Test," it is one of the measures
suggested in the RFA Guidance (U.S. EPA
2006b).
• Per household (or per capita) annualized
compliance costs as a percentage of median
household (or per capita) income. The third
measure compares the annualized costs to the
ability of residents to pay for the cost increase.
The ability of residents to pay for the costs
affects government entities because fees and
taxes on residents fund these entities. To the
extent that residents can (or cannot) pay for
the cost increases, government entities will
Guidelines for Preparing Economic Analyses I December 2010 9-11
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Chapter 9 Economic Impact Analyses
be impacted. Commonly referred to as the
"Income Test," this measure is described in
the RFA Guidance (U.S. EPA 2006b) and
the EPA Office of Water Interim Economic
Guidancefor Water Quality Standards:
Workbook (U.S. EPA 1995a).21 Costs can be
compared to either median household or
median per capita income. In calculating the
per household or per capita costs, the actual
allocation of costs needs to be considered. If
the costs are paid entirely through property
taxes, and the community is predominately
residential, then an average per household
cost is probably appropriate. If some or all of
the costs are allocated to users (e.g., fares paid
by bus riders or fees paid by users for sewer,
water, or electricity supplied by municipal
utilities), then a more narrow measure may
be appropriate. If some of the costs are borne
by local firms, then that portion of the costs
should be analyzed separately.
There are two commonly used impact measures for
not-for-profit entities: (1) annualized compliance
costs as a percentage of annual operating costs; and
(2) annualized compliance costs as a percentage
of total assets. The first is equivalent to the first
of the impact measures described for government
entities, measuring the percentage increase in
costs that would result from the regulation
being analyzed. The second is a more severe test,
measuring the impacts if the annualized costs are
paid out of the institutions assets.
Measuring the economic and
financial health of the community
or government entity
The second category of direct impact measures
examines the economic and financial health of the
community involved, since this affects its ability
to finance or pay for expenditures required by a
program or rule. A given cost may place a much
heavier burden on a poor community than on a
21 For example, in the water guidance and other EPA Office of Water
analyses compliance costs are considered to have little impact if
they are less than 1 percent of household income. Compliance costs
greater than 2 percent are categorized as a large impact, and a range
from 1 to 2 percent fall into a gray area and are considered to have an
indeterminate impact.
wealthy one of the same size. As with the impact
measures described above, there are three categories
of economic and financial condition measures:
• Indicators of the community's debt
situation. Debt indicators are important
because they measure both the ability of the
community to absorb additional debt (to
pay for any capital requirements of the rule)
and the general financial condition of the
community. While several debt indicators
have been developed and used, this section
describes two common indicators. One
measure is the government entity's bond
rating. Awarded by companies such as
Moody's and Standard & Poor's, bond ratings
evaluate a community's credit capacity and
thus reflect the current financial conditions of
the government body.22 A second frequently
used measure is the ratio of overall net debt
to the full market value of taxable property
in the community, i.e., debt to be repaid
by property taxes. Overall net debt should
include the debt of overlapping districts. For
example, a household may be part of a town,
regional school district, and county sewer
and water district, all of which have debt that
the household is helping to pay.23 See Table
9.3 for interpretations of the values for these
measures. Debt measures are not always
appropriate. Some communities, especially
small ones, may not have a bond rating. This
does not necessarily mean that they are not
creditworthy; it may only mean that they
have not had an occasion recently to borrow
money in the bond market. If the government
entity does not rely on property taxes, as
may be the case for a state government or
an enterprise district, then the ratio of debt
22 The indicators and benchmark values in Table 9.3 are drawn from
Combined Sewer Overflows — Guidance for Financial Capability
Assessment and Schedule Development, which discusses how to
assess the feasibility of systems being able to comply with rules (U.S.
EPA 1997b). These are general benchmarks that may prove useful in
assessing financial stability in an EIA.
23 An alternative to the net debt as percent of full market value of taxable
property is the net debt per capita. Commonly used benchmarks for
this measure are: net debt per capita less than $1,000 indicates a
strong financial condition, between $1,000 and $3,000 indicates a
mid-range or gray area, and greater than $3,000 indicates a weak
financial condition.
9-12
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Chapter 9 Economic Impact Analyses
Table 9.3 - indicators of Economic and Financial Well-Being
Government Entities
of
Indicator
Weak
Mid-Range
Strong
Bond rating
Below BBB (S&P)
Below Baa (Moody's)
BBB (S&P)
Baa (Moody's)
Above BBB (S&P)
Above Baa (Moody's)
Overall net debt as percent of full
market value of taxable property
Above 5%
2% - 5%
Below 2%
Unemployment rate
More than 1 percentage
point above national
average
Within 1 percentage point
of national average
More than 1 percentage
point below national
average
Median household income
More than 10% below the
state median
Within 10% of the state
median
More than 10% above
the state median
Property tax revenue as percent
of full market value of taxable
property
Above 4%
2% - 4%
Below 2%
Property tax collection rate
Less than 94%
94% - 98%
More than 98%
Source: U.S. EPA 1997b
to full market value of taxable property
is not relevant. Information on debt and
assessed property values are available from
the financial statement of each community.
The state auditors office is likely to maintain
this information for all communities within a
state.
• Indicators of the economic/financial
condition of the households in the
community. There are a wide variety
of household economic and financial
indicators. Commonly used measures are
the unemployment rate, median household
income, and foreclosure rates. Unemployment
rates are available from the Bureau of Labor
Statistics. Median household income is
available from the U.S. Census Bureau.
Benchmark values for these and other
measures are presented in Table 9.3.
• Financial management indicators. This
category consists of indicators that gauge the
general financial health of the community, as
opposed to the general financial health of the
residents. Because most local communities rely on
property taxes as their major source of revenues,
there are two ratios that provide an indicator of
financial strength. First, property tax revenue as
a percentage of the full market value of taxable
property indicates the burden that property taxes
place on the community.24 Second, the property
tax collection rate gauges the efficiency with
which the community's finances are managed,
and indirecdy whether the tax burden may
already be excessive. As the property tax burden
on taxpayers increases, they are more likely to
avoid paying their taxes or to pay them late.
Measuring the financial strength of not-for-profit
entities includes assessing:
• The size of the entity's reserves;
• How much debt the entity already has and
how its annual debt service compares to its
annual revenues; and
• How the entity's fees or user charges compare
with the fees and user charges of similar
institutions.
As with government entities, this analysis is meant
to judge whether the entity is in a strong or weak
financial position to absorb additional costs.
3.2,4,2 Administrative, Enforcement,
and Monitoring Burdens on
Governments
Many EPA programs require effort on the part of
24 If the state caps local property taxes (e.g., Proposition 13 in California
or Proposition 2V4 in Massachusetts) then it may be relevant to
examine the ratio of property tax to the allowed level of the taxes.
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Chapter 9 Economic Impact Analyses
different levels of government for administration,
enforcement, and monitoring. These costs must be
included when estimating impacts of a regulation
to comply with UMRA and to calculate the full
social costs of a program or rule. See Chapter 8 for
more information on government regulatory costs.
9,2.4.3 Induced Impacts on
Government Entities
The induced impacts on government entities
should also be considered. For example, a
manufacturing facility may reduce or suspend
production in response to a regulation, thus
reducing the income levels of its employees. In
turn, these reductions will spread through the
economy by means of changes in household
expenditures. These induced impacts include
the multiplier effect, in which loss of income in
one household results in less spending by that
household and therefore less income in households
and firms associated with goods previously
purchased by the first household.
Decreased household and business income can affect
the government sector by reducing tax revenues and
increasing expenditures on income security programs
(the automatic stabilizer effect), employment
training, food and housing subsidies, and other
fiscal line items. Due to wide variation in these
programs and in tax structures, estimating public
sector impacts for a large number of government
jurisdictions can be prohibitively difficult.
On the other hand, compliance expenditures
increase income for businesses and employees that
provide compliance-related goods and services.
These income gains also have a multiplier effect,
offsetting some of the induced losses in tax
revenue and increases in government expenditures
identified above. As some linkages may be more
localized than others, it is important to clearly
identify where the gains and losses occur.
9.2.5 Detailing Impacts on
Small Entities
The Regulatory Flexibility Act, as amended by the
Small Business Regulatory Fairness Act of 1996
(RFA), and Section 203 of the Unfunded Mandates
Reform Act of 1995 (UMRA) require agencies to
consider a proposed regulations economic effects
on small entities, specifically, small businesses, small
governmental jurisdictions, or small not-for-profit
organizations. The definition of "small" for each
of these entities is described below. For guidance
on when it is necessary to examine the economic
effects of a regulation under the RFA or UMRA,
analysts should consult EPA guidelines on these
administrative laws (U.S. EPA 2006b and U.S.
EPA 1995b, respectively). In general, the Agency
must fulfill certain procedural and/or analytical
obligations when a rule has a "significant impact on
a substantial number of small entities" (abbreviated
as SISNOSE) under the RFA or when a rule
might "significantly" or "uniquely" affect small
governments under Section 203 of UMRA.
3.2,5,1 Small Businesses
The RFA requires agencies to begin with the
definition of small business that is contained in
the Small Business Administrations (SBA) small
business size standard regulations.25 The RFA
also authorizes any agency to adopt and apply an
alternative definition of small business "where
appropriate to the activities of the Agency" after
consulting with the Chief Counsel for Advocacy
of the SBA and after opportunity for public
comment. The agency must also publish any
alternative definition in the Federal Register (U.S.
EPA 2006b).
The analytical tasks associated with complying
with the RFA include a screening analysis for
SISNOSE. If the screening analysis reveals that a
rule cannot be certified as having no SISNOSE,
then the RFA requires a regulatory flexibility
analysis be conducted for the rule, which includes
a description of the economic impacts on small
entities. Impacts on small businesses are generally
assessed by estimating the direct compliance costs
and comparing them to sales or revenues. Because
an estimate of direct compliance costs tends to
be a conservatively low estimate of a regulations
impact, further analysis examining the impacts
25 The current version of SBA's size standards can be found at
http://www.sba.gov/size (accessed March 13,2011).
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Chapter 9 Economic Impact Analyses
discussed in Section 9.3.3 (specifically in relation
to small businesses) may provide additional
information for decision makers.26
3.2,5,2 Small Governmental
Jurisdictions
Hie RFA defines a small governmental jurisdiction
as the government of a city, county, town, school
district, or special district with a population of
less than 50,000. Similar to the definition of small
business, the RFA authorizes agencies to establish
alternative definitions of small government after
opportunity for public comment and publication
in the Federal Register. Any alternative definition
must be "appropriate to the activity of the Agency"
and "based on such factors as location in rural or
sparsely populated areas or limited revenues due
to the population of such jurisdiction" (U.S. EPA
2006b). Under the RFA, economic impacts on
small governments are included in the SISNOSE
screening analysis, and any required regulatory
flexibility analysis for a rule.
UMRA uses the same definition of small
government as the RFA with the addition of tribal
governments. Section 203 of UMRA requires
the Agency to develop a "Small Government
Agency Plan" for any regulatory requirement
that might "significantly" or "uniquely" affect
small governments. In general, "impacts that may
significantly affect small governments include —
but are not limited to — those that may result in
the expenditure by them of $100 million [adjusted
annually for inflation] or more in any one year."
Other indicators that small governments are
uniquely affected may include whether they would
incur the higher per-capita costs due to economies
of scale, a need to hire professional staff or
consultants for implementation, or requirements
to purchase and operate expensive or sophisticated
equipment.27 See Section 9.3.4 for information on
measures of impacts to governments in general.
26 See Agency guidance (U.S. EPA 2006c) for details on complying with
the RFA.
27 Guidance on complying with Section 203 of UMRA, "Interim Small
Government Agency Plan," is available on EPA's intranet site, ADP
Library at http://intranet.epa.gov/adplibrary/statutes/umra.htm
(accessed March 21,2011, internal EPA document)
3,2,5,3 Small Not-for-Profit
Organizations
The RFA defines a small not-for-profit
organization as an "enterprise which is
independendy owned and operated and is not
dominant in its field." Examples may include
private hospitals or educational institutions.
Here again, agencies are authorized to establish
alternative definitions "appropriate to the activities
of the Agency" after providing an opportunity for
public comment and publication in the Federal
Register. Under the RFA, economic impacts on
small not-for-profit organizations are included in
the SISNOSE screening analysis, and if required,
the regulatory flexibility analysis for a rule. See
Section 9.3.4 for more information on measuring
impacts on not-for-profit organizations in general.
^ proaches to Mo n i
an I „ononf .. fm pact Analysis
This section returns to the methods for estimating
social costs covered in Chapter 8, adding
more insight on their application to EIA. The
reader should refer to Chapter 8 for a more in-
depth discussion. As noted above, the analytic
assumptions used for the EIA of a particular
regulation should be consistent with those used for
the corresponding BCA.
9,3.1 Direct Compliar ssts
The simplest approach to measuring the economic
impacts is to estimate and verify the private
costs of compliance. This is necessary regardless
of whether the entities affected are for-profit,
governmental, communities, or not-for-profit.
Direct compliance costs are considered the most
conservative estimate of private costs and include
annual costs (e.g., operation and maintenance of
pollution control equipment), as well as any capital
costs. Direct compliance costs do not include
implicit costs.
Verifying the compliance cost estimates entails
two steps. First, the full range of responses to the
rule needs to be identified, including pollution
prevention alternatives and any differences in
response across sub-sectors and/or geographic
Guidelines for Preparing Economic Analyses I December 2010 9-15
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Chapter 9 Economic Impact Analyses
regions. Second, the costs for each response need
to be examined to determine if all elements are
included and if the costs are consistent within a
given base year. To ensure consistency across years,
either a general inflation factor, such as the GDP
implicit price deflator, or various cost indices
specific to the type of project should be used.28 The
base year and indexing procedure should be stated
clearly.
Implicit costs that do not represent direct outlays
maybe important. The cost estimates should
include such elements as production lost during
installation, training of operators, and education of
users and citizens on programs involving recycling
of household wastes. The cost of acquiring a
permit includes the permit fee as well as the
lost opportunities during the approval process.
Likewise, the cost of having a car's emissions
inspected is not so much the fee as it is the value of
a registrants time.
In addition, it is important to recognize that
these expenditures may have other benefits
and costs. For example, they may confer tax
breaks (complying with regulations maybe a tax
deductible expense) and the new capital may
be more productive than the old capital. These
"offsets" should be considered, particularly when
they may be substantial.
There are several issues analysts should consider
when estimating the direct compliance costs of
environmental polices for an EIA. These include:
• Before- versus after-tax costs. For businesses,
the cost of complying with regulations is
generally deductible as an expense for income
tax purposes. Therefore, the effective burden
is reduced for taxable entities because they
can reduce their taxable income by the
amount of the compliance costs. The effect of
a regulation on profits is therefore measured
by after-tax compliance costs. Operating costs
28 The GDP implicit price deflator is reported by the U.S. DOC, BEA in
its Survey of Current Business (http://www.bea.gov/scb/index.htm).
The annual Economic Report of the President, Executive Office of the
President, is another convenient source for the GDP deflator, available
at www.gpoaccess.gov/eop/(accessed March 13,2011).
are generally fully deductible as expenses
in the year incurred. Capital investments
associated with compliance must generally
be depreciated.29 In most cases, communities,
not-for-profits, and governments do not
benefit from reduced income taxes that
can offset compliance costs. Therefore,
adjustments to cost estimates, annualization
formulas, and cost of capital calculations
required to calculate after-tax costs should
not be used in analyses of impacts on
governments, not-for-profits, and households.
• Transfers. Some types of compliance
costs incurred by the regulated parties may
represent transfers among parties. Transfers,
such as payments for insurance or payments
for marketable permits, do not reflect use
of economic resources. However, individual
private cost estimates used in the EIA include
such transfers.30
• Discounting. Compliance costs often vary
over time, perhaps requiring initial capital
investments and then continued operating
costs. To estimate impacts, the stream of costs
is generally discounted to provide a present
value of costs that reflects the time value
of money.31 In contrast to social costs and
benefits, which are discounted using a social
discount rate, private costs are discounted
using a rate that reflects the regulated entity's
cost of capital.32 The private discount rate used
will generally exceed the social discount rate
by an amount that reflects the risk associated
with the regulated entity in question.
For firms, the cost of capital may also be
determined by their ability to deduct debt
from their tax liability.
29 Current federal and state income tax rates can be obtained from the
Federation of Tax Administrators, State Tax Rates & Structure, available
at http://www.taxadmin.org/fta/rate/default.html (accessed January 31,
2011).
30 These transfers cancel out in a BCA. In an EIA the distribution of
results is important, therefore the transfers are included.
31 The present value of costs can then be annualized to provide an annual
equivalent of the uneven compliance cost stream. Annualized costs are
also discussed in Chapter 6.
32 While the discount rate differs, the formula used to discount private
costs is the same as used for social costs. See Chapter 6 for details.
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 9 Economic Impact Analyses
• Annualized costs. Annualizing costs involves
calculating the annualized equivalent of
the stream of cash flows associated with
compliance over the period of analysis. This
provides a single annual cost number that
reflects the various components of compliance
costs incurred over this period. The annual
value is the amount that, if incurred each year
over the selected time period, would have
the same present value as the actual stream of
compliance expenditures. Annualized costs
are therefore a convenient compliance cost
metric that can be compared with annual
revenues and profits. It is important to
remember that using annualized costs masks
the timing of actual compliance outlays.
For some purposes, using the underlying
compliance costs maybe more appropriate.
For example, when assessing the availability
of financing for capital investments, it is
important to consider the actual timing of
capital outlays.
• Fixed versus variable costs. Some types of
compliance costs vary with the size of the
regulated enterprise, such as quantity of
production. Other components of cost may
be fixed with respect to production or other
size measures, such as the costs involved
in reading and understanding regulatory
requirements. Requirements that impose
high fixed costs will impose a higher cost per
unit of production on smaller firms than on
larger firms. It is important that the effects
of any economies of scale are reflected in the
compliance costs used to analyze economic
impacts.33 Using the same average annualized
cost per unit of production for all firms may
mask the importance of such fixed costs and
understate impacts on small entities.
9,3.2 Partial Equilibrium Models
A partial equilibrium framework is an alternative
way to examine distributional effects when impacts
are limited to a few directly and indirectly affected
output markets only. For example, a regulation
may increase the costs of producing a particular
33 Economies of scale characterize costs that decline on a per unit basis
as the scale of the operation increases.
chemical. Partial equilibrium models can be used
to examine the distribution of these changes across
directly affected industries, and a small number
of indirecdy affected entities (e.g., upstream
and downstream). Partial equilibrium models
can range in size from an analysis that estimates
compliance costs for the affected industry only
(i.e., direct compliance costs) to multi-market
models encompassing several directly and
indirectly affected sectors.
If a single-market partial equilibrium model is the
only information source available for an analysis
of impacts, then it may be possible to adopt
further assumptions and acquire additional data
to approximate impacts on other areas of concern.
This may include deriving ratios to aggregate
changes in order to assign these changes to
specific regions or sectors. These new assumptions
should be consistent with those used for the
corresponding BCA.
Multi-market models consider the interactions
between a regulated market and other important
related markets (outputs and inputs), requiring
estimates of elasticities of demand and supply for
these markets as well as cross-price-elasticities
(also found in CGE models). These models are
best used when potential impacts on related
markets might be considerable, but more complete
modeling using a CGE framework may not be
available or practical. Partial equilibrium models
may also be more appropriate for regionally based
or resource specific regulations that are too specific
for more aggregated CGE models.34 Care should
be taken, however, to avoid double counting,
particularly when both upstream and downstream
entities are affected and included in the partial
equilibrium analysis. If cost increases due to a
regulation are passed on from the upstream to the
downstream businesses then analysts should take
care not to include impacts on both sets of entities
to avoid double counting results.
34 See the discussion of multi-market modeling in Chapter 8 and Just et
al. (1982).
Guidelines for Preparing Economic Analyses I December 2010 9-17
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Chapter 9 Economic Impact Analyses
9.3.3 Computable General
Equilibrium Models
CGE models are particularly effective in
assessing resource allocation and welfare effects.
These effects include the allocation of resources
across sectors (e.g., employment by sector), the
distribution of output by sector, the distribution
of income among factors, and the distribution of
welfare across different consumer groups, regions,
and countries. As noted in Chapter 8, for example,
regulations in the electric utility sector are likely
to cause electricity prices to increase. The price
increase will affect all industries that use electricity
as an input to production (i.e., most industries),
as well as households. A CGE model can assess
the distribution of the changes in production
and consumption that result. By design, the basic
capacity to describe and evaluate these sorts of
impacts exists to some extent within every CGE
model. More detailed impacts (e.g., affects on a
particular facility) or impacts of a particular kind
(e.g., affects on drinking water) will require a more
complex and/or tailored model formulation and
the data to support it.
As effective as CGE models can be for looking
at long-term resource allocation issues, they
have limitations for the kinds of impact analyses
described above. CGE models assume that
markets clear in every period and often do not
consider short-term adjustment costs, such as
lingering unemployment. The analyst should be
careful to select a model that does not assume
away the underlying issue addressed by the
distribution analysis. Moreover, a CGE model
may not be feasible or practical to use when data
and resources are limited or when the scope of
expected significant market interactions is limited
to a subset of economic sectors. In such instances
a partial equilibrium model can be adopted as a
more appropriate alternative to a CGE model.35
Finally, it is worth noting that while CGE
modeling is complex, the effort may be worthwhile
when data are available and the distributional
impacts are likely to be widespread.
The simplest CGE models generally include a
single representative consumer, a few production
sectors, and a government sector, all within a
single-country, static framework. Additional
complexities can be specified for the model in
a variety of ways. Consumers may be divided
into different groups by income, occupation, or
other socioeconomic criteria. Producers can be
disaggregated into dozens or even hundreds of
sectors, each producing a unique commodity. The
government, in addition to implementing a variety
of taxes and other policy instruments, may provide
a public good or run a deficit. CGE models can
be international in scope, consisting of many
countries or regions linked by international flows
of goods and capital. The behavioral equations
that characterize economic decisions may take
on simple or complex functional forms. The
model can be solved dynamically over a long time
horizon, incorporating intertemporal decision
making on the part of consumers or firms. These
choices have implications for the treatment of
savings, investment, and the long-term profile of
consumption and capital accumulation.
35 For a discussion of CGE analysis see Chapter 8 and Dixon et al.
(1992).
Guidelines for Preparing Economic Analyses I December 2010
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Chapter
Environmental Justice, Children's
Environmental Health and Other
Distributional Considerations
Evaluating a regulation's distributional effects is an important complement to
benefit-cost analysis. Rather than focusing on quantifying and monetizing total
benefits and costs, economic impact and distributional analyses examine how
a regulation allocates benefits, costs and other outcomes across populations or
groups of interest. See Chapter 9 of these Guidelines for more information on
analyzing economic impacts. This chapter considers the distribution of environmental quality
and human health risks across several populations: those that have traditionally been the
focus of environmental justice (EJ) (i.e., minority, low-income, or indigenous populations);
children; and the elderly. Consideration of costs or other potential impacts may also be
addressed in a distributional analysis using approaches discussed in this chapter. The chapter
also briefly discusses inter-generational impacts.
This chapter suggests approaches that EPA program
offices can use for characterizing distributional
effects of policy choices associated with rulemaking
activities. Based on academic literature and EPA
documents and policies, the chapter provides a
variety of methodological approaches that may be
suitable across various regulatory scenarios. A clear
consensus does not exist, however, regarding the
most appropriate methods. Instead, this chapter
provides a broad overview of options for analyzing
distributional effects in regulatory analysis.
Information in the chapter is intended to provide
flexibility to programs that face dissimilar data,
resources and other constraints while introducing
greater consistency in the way EJ is addressed in
rulemaking activities.1
The purpose of analyzing distributional effects in
regulatory analysis is to examine how benefits (e.g.,
risk reductions or environmental quality) and, when
1 The guidance in this chapter complements, and does not supersede, any
subsequent EJ-reiated guidance released by EPA. In addition, the Office of
Environmental Justice website (http://www.epa.gov/environmentaljustice/
resources/policy/index.htm I) provides resources on Plan EJ2014 and other
implementation guidelines related to EJ (accessed on January 24,2012).
relevant and feasible, costs are distributed across
population groups and lifestages of interest.2 "While
the chapter is focused on EJ, children, and the elderly,
the methods discussed could be applied to any
population of concern.
The chapter begins with an overview of Executive
Orders (EOs) and policies related to distributional
analyses. It then discusses the analysis of
distributional impacts in the context of EJ and
children's health. The chapter concludes with a brief
discussion of other distributional considerations,
including the elderly and inter-generational impacts
that may arise in select rules.
iw I t' i :ut. ^ v'>'ers,
Directive.', -cmi Pericles
Consideration of distributional effects arises from a
variety of executive orders, directives, and other
2 This chapter recommends examining the distribution of benefits prior to
monetization for reasons discussed in Section 10.1.
Guidelines for Preparing Economic Analyses I May 2014 11-1
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
documents with broad coverage, including:3
• EO 12898, "Federal Actions to Address
Environmental Justice in Minority
Populations and Low-Income Populations"
(1994);
• EO 13045, "Protection of Children From
Environmental Health Risks and Safety Risks"
(1997);
• EO 13166, "Improving Access to Services for
Persons With Limited English Proficiency"
(2000); and the subsequent EPA Order
No. 1000.32, "Compliance with Executive
Order 13166: Improving Access to
Services for Persons with Limited English
Proficiency" (2011);
• EO 13175, "Consultation and Coordination
with Indian Tribal Governments" (2000);
• EO 12866, "Regulatory Planning and
Review" (1993);
• Circular A-4, Regulatory Analysis
(OMB 2003);
• National Environmental Policy Act (NEPA)
Guidance (U.S. EPA 1998a);
• EPA's Interim Guidance on Considering
EnvironmentalJustice During the Development
of an Action (U.S. EPA 2010a); and
. EPA's FY2011-2015 Strategic Plan (U.S. EPA
2010b).
Each of these is described below. Some
environmental statutes may also identify
population groups that merit additional
consideration.4
EO 12898, "Federal Actions to Address
Environmental Justice in Minority Populations
and Low-Income Populations"5 (1994), calls on
3 EPA's Regulatory Management Division's Action Development Process
Library (http://intranet.epa.gov/adplibrary) is a resource for accessing
relevant statutes, executive orders, and EPA policy and guidance
documents in their entirety (accessed on December 1,2011).
4 See Plan EJ2014 Legal Tools (U.S. EPA 2011a) for a review of legal
authorities under the environmental and administrative statutes
administered by EPA that may contribute to the effort to advance
environmental justice.
5 This chapter addresses analytical components of E0 1 2898, and does
not cover other components such as ensuring proper outreach and
meaningful involvement.
each Federal agency to make achieving EJ part of
its mission. It directs Federal agencies, "[t]o the
greatest extent practicable and permitted by law,"
to "identify[...] and address[...], as appropriate,
disproportionately high and adverse human health
or environmental effects" of agency programs,
policies, and actions on minority populations
and low-income populations. Issued by President
Clinton in 1994, it requires that EJ be considered in
all Agency activities, including rulemaking activities.
The President issued a memorandum to
accompany EO 12898 directing Federal agencies
to analyze environmental effects, including human
health, economic, and social effects, of Federal
actions when such analysis is required under the
National Environmental Policy Act (NEPA).
The Presidential memorandum also states that
existing civil rights statutes provide opportunities
to address environmental hazards in minority
communities and low-income communities.6
EO 13045, "Protection of Children From
Environmental Health Risks and Safety Risks"
(1997), states that each Federal agency: (1) shall
make it a high priority to identify and assess
environmental health risks and safety risks that
may disproportionately affect children; and (2)
shall ensure that its policies, programs, activities,
and standards address disproportionate risks to
children that result from environmental health
risks or safety risks. The EO also states that each
"covered regulatory action" submitted to the
Office of Management and Budget (OMB), unless
prohibited bylaw, should be accompanied by "...
an evaluation of the environmental health or safety
effects of the planned regulation on children."7
6 "In accordance with Title VI ofthe Civil Rights Act of 1964,
each Federal agency shall ensure that all programs or activities
receiving Federal financial assistance that affect human health or
the environment do not directly, or through contractual or other
arrangements, use criteria, methods, or practices that discriminate on
the basis of race, color, or national origin." See Memorandum for the
Heads of All Departments and Agencies: Executive Order on Federal
Actions to Address Environmental Justice In Minority Populations and
Low-Income Populations (White House 1994).
7 A "covered regulatory action" is any substantive action in a rulemaking
that may be economically significant (i.e., have an annual effect
on the economy of $100 million or more or would adversely affect
in a material way the economy, a sector of the economy, or the
environment) and concern an environmental health risk that an agency
has reason to believe may disproportionately affect children.
11-2 Guidelines for Preparing Economic Analyses I May 2014
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
EO 13166, "Improving Access to Services for
Persons With Limited English Proficiency"
(2000), requires Federal agencies to examine the
services they provide, identify any need for services
to those with limited English proficiency (LEP),
and develop and implement a system to provide
those services so LEP persons can have meaningful
access to them. The EO also requires Federal
agencies work to ensure that recipients of Federal
financial assistance provide meaningful access
to their LEP applicants and beneficiaries. EPA's
Order 1000.32 "Compliance with Executive Order
13166: Improving Access to Services for Persons
with Limited English Proficiency"8 requires
that EPA ensure its programs and activities are
meaningfully accessible to LEP persons.
EO 13175, "Consultation and Coordination
with Indian Tribal Governments" (2000), calls on
Federal agencies to have "an accountable process
to ensure meaningful and timely input by tribal
officials in the development of regulatory policies
that have tribal implications." To the extent
practicable and permitted by law, if a regulatory
action with tribal implications is proposed and
imposes substantial direct compliance costs on
Indian tribal governments, and is not required by
statute, then the agency must either provide funds
necessary to pay direct compliance costs of tribal
governments or consult with tribal officials early in
the process of regulatory development and provide
OMB a tribal summary impact statement.
EO 12866, "Regulatory Planning and Review"
(1993), allows agencies to consider "distributive
impacts" and "equity" when choosing among
alternative regulatory approaches, unless
prohibited by statute. EO 13563, issued in January
2011, supplements and reaffirms the provisions of
EO 12866.
OMB's Circular A-4 states that regulatory
analyses "should provide a separate description
of distributional effects (i.e., how both benefits
and costs are distributed among populations of
particular concern) so that decision makers can
properly consider them along with the effects
8 EPA Order 1000.32 is available at http://www.epa.gov/civilrights/docs/
lep_order_1000_32.pdf (accessed on May 28,2013).
of economic efficiency." It specifically calls for a
description of "the magnitude, likelihood, and
severity of impacts on particular groups" if the
distributional effects are expected to be important
(OMB 2003).
The President s memorandum to heads of
departments and agencies that accompanied
EO 12898 specifically raised the importance
of procedures under NEPA for identifying and
addressing environmental justice concerns (White
House 1994). The memorandum states that "each
Federal agency shall analyze the environmental
effects, including human health, economic and
social effects, of Federal actions, including effects
on minority communities and low-income
communities when such analysis is required
by [NEPA]." The Council on Environmental
Quality (CEQ) issued EJ guidance for NEPA
in 1997 (CEQ 1997). EPA issued guidance
in 1998 for incorporating EJ goals into EPA's
preparation of environmental impact statements
and environmental assessments under NEPA (U.S.
EPA 1998a).
In July 2010, EPA published its Interim Guidance
on Considering EnvironmentalJustice During the
Development of an Action (U.S. EPA 2010a). This
guide is designed to help EPA staff incorporate
EJ into the rulemaking process, from inception
through promulgation and implementation. The
guide also provides information on how to screen
for EJ effects and directs rulewriters to respond to
three basic questions throughout the rulemaking
process:
1. How did your public participation process
provide transparency and meaningful
participation for minority, low-income,
indigenous populations, and tribes ?
2. How did you identify and address existing and
new disproportionate environmental and public
health impacts on minority, low-income, and
indigenous populations during the rulemaking
process?
3. How did actions taken under # 1 and #2 impact
the outcome or final decision?
Guidelines for Preparing Economic Analyses I May 2014 11-3
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
Finally, in September 2010 EPA released its
FY2011-2015 Strategic Plan outlining how EPA
would achieve its mission to protect human health
and the environment over the next five years (U.S.
EPA 2010b). Included in the plan is a cross-cutting
fundamental strategy to focus on "working for
environmental justice and children's health." To
implement this strategy, EPA released Plan EJ
2014 in September 2011 that provides a roadmap
for the Agency to incorporate environmental
justice into policies, programs and activities. One
of five cross-agency focus areas identified in Plan
EJ 2014 is "Incorporating Environmental Justice
into Rulemaking."9
Together these documents provide a solid
foundation for considering distributional
effects for population groups of concern in the
rulemaking process.
ital Justice
EPA defines environmental justice as "the fair
treatment and meaningful involvement of all
people regardless of race, color, national origin,
or income with respect to the development,
implementation, and enforcement of
environmental laws, regulations, and policies"
(EPA 2010a). EO 12898 specifically states
that Federal agencies should "...identify and
address...disproportionately high and adverse
human health or other environmental effects...
on minority populations and low-income
populations..." (EPA 2010a).
For policies that strengthen an environmental
standard, EPA regulatory analyses have often
relied on a default assumption that these policies
have no EJ concerns because they reduce overall
environmental burdens. However, it is incorrect
to conclude that tighter standards necessarily
improve environmental quality for everyone. The
nuances of a rule could result in negative effects,
such as higher emissions in some areas, even
though net environmental quality improves. It is
also possible that older, more polluting facilities
9 Plan EJ 2014 is available at http://www.epa.gov/compliance/ej/
resources/policy/plan-ej-2014/plan-ej-2011 -09.pdf (accessed on May
9,2012).
close as a result of a rule and new facilities open
in different locations, changing the distribution
of emissions across communities.10 Hence, when
data are available, a basic analysis can support
conclusions regarding potential distributional
effects. In addition, while there maybe no adverse
environmental impacts, other economic impacts,
like costs, could affect population groups of
concern disproportionately and may warrant
examination.11
Distributional analysis also improves transparency
of rulemaking and provides decision makers
and the public with more complete information
about a given policy's potential effects. Such
documentation helps EPA and the public track
and measure progress in addressing EJ concerns.
Analysts play a role in ensuring meaningful
involvement by explaining distributional
analysis in plain language, including key
assumptions, methods, and results, and by asking
for information from the public (e.g., asking
for comment in the proposed rulemaking) on
exposure pathways, end points of concern, and
data sources that may improve the distributional
analysis.12 Further guidance on ensuring
meaningful engagement of environmental justice
stakeholders in the rulemaking process can be
found in U.S. EPA (2010a).
.1 Background Literature
The study of economic efficiency (the focus of
benefit-cost analysis) of regulatory approaches
has a long history in the economics literature,
including an established theoretical foundation
and generally accepted empirical methodology.
But an assessment of distributional consequences
10 U.S. EPA (2010a) provides additional information on how an EJ
concern may arise in the context of a rule.
11 See U.S. EPA (2008a) for an example where changes in costs are
addressed in an analysis of distributional impacts in the context of EJ.
12 Meaningful involvement is defined by EPA to mean that "1) potentially
affected community members have an appropriate opportunity to
participate in decisions about a proposed activity that will affect their
environment and/or health; 2) the public's contribution can influence
the regulatory agency's decision; 3) the concerns of all participants
involved will be considered in the decision-making process; and 4)
the decision makers seek out and facilitate the involvement of those
potentially affected" (U. S. EPA 2010a, U.S. EPA 2012a).
Guidelines for Preparing Economic Analyses I May 2014
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
has received relatively less attention.13 Media and
government interest in potential environmental
inequity arising from landfill siting decisions in
the mid-1980s led to an increased focus in the
economics literature on distributional issues in the
context of race, poverty, and income.14 This section
provides a brief overview of key studies from
the economics and health literature. For a more
comprehensive discussion see Ringquist (2005),
Banzhaf (2012a), and Banzhaf (2012b).
Studies of EJ can vary by specific pollutant, the
proxy used for risk or exposure, geographic area,
and time period, making it difficult to directly
apply general findings to a particular rulemaking.
The literature illustrates, however, that EJ is a
potential concern with regard to plant emission
decisions and is therefore worthy of analysis in a
regulatory context (see, for example, Wolverton
2009). It is important to note that the economics
literature typically focuses on addressing the
question of whether certain population groups are
exposed to greater amounts of pollution. There
is also the possibility that some populations are
more susceptible to pollution for a given level of
exposure and that socioeconomic factors may play
a role. "While literature addressing this issue is not
discussed here, Section 10.2.8.5 of this chapter
discusses various risk considerations including
susceptibility. In addition, both the EJ literature
and this chapter tend to focus on the distribution
of physical aspects of environmental outcomes.15
Evidence exists of potential disproportionate
impacts from environmental stressors on various
population groups using a wide variety of proxies
13 For a discussion of the possible distributional effects of environmental
policies with regard to income, see Fullerton (2009).
14 The rise in concern over environmental justice is often traced to
demonstrations in Warren County, North Carolina in 1982 over the
siting of a polychlorinated biphenyl (PCB) landfill in a poor and
minority community.
15 Differences in exposures or health effects alone may not be
representative of differences in total benefits and costs. As discussed
in Serret and Johnstone (2006) and Fullerton (2011), for example,
the full distribution of environmental policy could include differences
in product prices, wage rates, employment effects, economic rents,
etc. It is likely, however, that the methods used to analyze the full
distributional effects (e.g., computable general equilibrium models) are
beyond the scope of a typical regulatory analysis and the policy tools
to address any resultant distributional concerns (e.g., tax policy and
redistribution programs) are beyond the scope of environmental policy.
for exposure. Many studies are proximity-based:
distance to a polluting facility is a surrogate for
exposure. These studies often find evidence that
locally-unwanted land-uses such as landfills or
facilities that treat, store, or dispose of hazardous
waste are more likely to be concentrated
in predominantly minority or low-income
neighborhoods (for example, Bullard 1983; GAO
1983; UCC 1987; Boer et al. 1997; and Mohai et
al. 2009).16
Other studies attempt to better approximate
exposure by examining whether existing
emission patterns are related to socio-economic
characteristics. These studies often focus on a
particular type of pollution and geographic area.
They also often differ in how they define the
relevant neighborhood and comparison group. As
such, results with regard to race and income vary
across studies. For example, after controlling for
other factors, Hamilton (1993,1995) finds that
expansion decisions for waste sites are unrelated to
race and finds mixed evidence for income, while
Aurora and Cason (1998) find both race and
poverty are positively related to toxicity-weighted
Toxic Release Inventory (TRI) emissions,
although the significance of these relationships
varies by region. Gray and Shadbegian (2004) find
poor communities are exposed to more air and
water pollution from pulp and paper mills, but
find the opposite for minority communities.
Finally, other studies attempt to account for
health risks. For example, Rosenbaum et al. (2011)
combine information on ambient concentrations
of diesel particulate matter in marine harbor
areas throughout the United States with exposure
and carcinogenic risk factors broken out by race,
ethnicity, and income. They find that the most
important factor in predicting higher particulate
16 Others note the strength of this contemporaneous relationship but find
that the direction and magnitude of the relationship between location
and race or income at time of siting is less clear (see Been 1994;
Been and Gupta 1997; and Wolverton 2009). See Shadbegian and
Wolverton (2010) for a summary of the literature on firm location and
environmental justice, including a discussion of whether plant location
precedes changes in socioeconomic composition that result in higher
percentages of non-white and poor households nearby or vice versa.
Most of these studies examine partial correlations between pollution
and household characteristics, using statistical techniques that control
for other factors.
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
matter intake fractions (i.e., mass of a pollutant
inhaled or ingested divided by mass emitted)
is population density and that low-income and
minority individuals are over-represented in marine
harbor areas that exceed risk thresholds. Likewise,
Morello-Frosch et al. (2001) combine estimates of
hazardous air pollutant concentrations in southern
California with information on lifetime cancer
risks by socioeconomic status and race and find
that even though lifetime cancer risks are high
for all individuals in the study, race and ethnicity
are positively related to lifetime cancer risk after
controlling for economic and land use variables.
Ringquist (2005) conducts a meta-analysis of both
facility location and emissions across 49 studies
published prior to 2002 and finds evidence that
plant location and higher emissions are more likely
to occur in communities with a higher percent
non-white population. He finds little evidence,
however, that this is the case in communities
with lower income or higher poverty rates. The
finding for race holds across a wide variety of
environmental risks (e.g., hazardous waste sites and
air pollution concentrations), levels of aggregation
(e.g., zip codes, census tracts, and concentric
circles around a facility), and controls (e.g., land
value, population density, and percent employed
in manufacturing). The finding for race appears
sensitive, however, to comparison groups (e.g., all
communities versus a subset of communities).
A potential unintended consequence of improving
environmental quality in some communities
more than others is that rents may increase in the
improved neighborhoods, making them potentially
unaffordable for poorer households. For example,
Grainger (2012) shows that about half of the
increases in home prices due to the Clean Air
Act Amendments are passed through to renters.
Thus, the net health effect of improvements in
environmental quality for renters depends on
whether or not they move. Those who do not
move experience higher rents, but also improved
neighborhoods. For those who do move the net
effect depends on the quality of the neighborhood
to which they relocate. If these households receive
far less of the health benefit predicted from a static
model and also face transaction costs from moving,
they could be worse off. The literature refers to this
phenomenon as "environmental gentrification"
(see also Banzhaf and McCormick 2012).
Sieg et al. (2004) find that even with no moving
costs, local households could be worse off
because other households move into the clean
neighborhood and bid up the rents.17 Earlier
work by Banzhaf and Walsh (2008) shows that
neighborhood income increases following cleanup,
but more recent analysis (Banzhaf et al. 2012)
shows racial characteristics in the neighborhood
may not change. The authors postulate that richer
minorities may move back into neighborhoods
following cleanup.
.2 Analyzing Distributional
Impacts in ritext of
Regulatory Analysis
In the context of regulatory analysis, examining
distributional effects of health and environmental
outcomes or costs can be accomplished, when data
are available, by comparing effects in the baseline
to post-regulatory scenarios for minority, low-
income, or indigenous populations.18
When evaluating health and environmental
outcomes, the following fundamental questions
can guide the process of considering potential
analytical methods for assessing EJ.19
* What is the baseline distribution of health
and environmental outcomes across
population groups of concern for pollutants
affected by the rulemaking?20
17 The market dynamics associated with the relationship between
household location decisions and pollution was first examined in a
rigorous context in Been and Gupta (2007), and further explored by
Banzhaf and Walsh (2008).
18 0MB (2003) defines the baseline as "the best assessment of the
way the world would look absent the proposed action." Section
10.2.6 describes the concept of baseline briefly. For a more detailed
discussion on properly defining a baseline to measure the incremental
effects of regulation, see Chapter 5 of these Guidelines.
19 See Maguire and Sheriff (2011) for more detail.
20 The term "outcome" is used to indicate that these questions should
be interpreted more broadly than just applying to health effects. EPA
Program Offices have the flexibility to adapt the wording of these
questions to reflect the realities of the particular endpoints under
consideration for a rulemaking.
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
• What is the distribution of health and
environmental outcomes for the options
under consideration for the rulemaking effort?
• Under the options being considered, how
do the health and environmental outcomes
change for population groups of concern?21
Note that these analytic questions recommend the
analyst provide information on the distribution
of outcomes, but do not ask for a determination
of whether differences across population groups
constitute disproportionate impacts.22 The term
disproportionate is neither defined in EO 12898,
nor does the academic literature provide clear
guidance on what constitutes a disproportionate
impact. The determination of whether an impact is
disproportionate is ultimately a policy judgment.
This chapter presents a suite of methods for
analyzing distributional effects across a variety of
regulatory contexts. Because the data, time, and
resource constraints will differ across programs
and rules, these guidelines are intended to provide
flexibility to the analyst while introducing greater
rigor and transparency in how EJ is considered in a
regulatory context.
10,2. iluating Changes in
the Distribution of Health and
Environmental Outcomes
The analysis of EJ should ideally consider how a
policy affects the distribution of relevant health
and environmental outcomes (e.g., mortality
risk from a regulated pollutant). If the outcome
data are unavailable, distribution of ambient
21 It would be useful to quantify the degree to which disparities change
from baseline, so that one could rank in order of preference the
relative merits of various options. Any ranking metric, however,
would require adoption of an implicit social welfare function. Such
approaches are analytically meaningful, but still under development
and recommendation of a specific social welfare function is beyond the
scope of this chapter. Text Box 10.1 provides additional discussion on
this topic.
22 The EJ guidance for NEPA (CEQ 1997) provides some guidance on the
use of the term. A population group may be disproportionately affected
if health effects are significant or "above generally accepted norms,"
the risk or rate of exposure is significant or "appreciably exceeds or is
likely to appreciably exceed the risk or rate to the general population or
other appropriate comparison group," or is subject to "cumulative or
multiple adverse exposures from environmental hazards."
environmental quality indicators (e.g., pollutant
concentrations) can be a useful proxy. Such
indicators are less informative than the outcomes
themselves if population groups of concern vary
in vulnerability to the pollutant, for example.23
If projecting ambient environmental quality
is not feasible, then the analysis may examine
the distribution of pollutants from regulated
sources. Distribution of pollutants is less desirable
than distributions in ambient environmental
quality or health and environmental outcomes
due to uncertainty regarding how a reduction
in emissions from a given source translates into
environmental quality and how that, in turn,
translates into the human impacts that are the
ultimate objective of the analysis.
It is important to consider changes in distributions
of health and environmental outcomes between
baseline and various policy options, rather than
just the distribution of changes since an unequal
distribution of environmental improvements may
actually help alleviate existing disparities (Maguire
and Sheriff 2011). For example, suppose a policy
is expected to reduce a pollutant, causing a greater
reduction in particular adverse health outcomes
for non-minorities than for minorities. One might
conclude that this change in the distribution of
outcomes could pose an EJ concern. If, however,
the non-minority population suffered greater
ill effects from the pollutant at baseline than
the minority population, such a change in the
distribution of outcomes may reduce, rather than
increase, a pre-existing disparity in outcomes.
The difference between these two measures
— the distribution of change in health and
environmental outcomes and the change in
the distribution of health and environmental
outcomes — has implications for the suitability
of data for analysis. In particular, analyzing the
distribution of monetized benefits from a benefit-
cost analysis can be problematic. Benefit-cost
analyses do not estimate each affected individual's
monetized welfare at baseline and policy
levels of environmental quality. Instead, they
23 A large epidemiological literature explores differences in health effects
across various demographic groups. See, for example, Schwartz et al.
(2011b).
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
estimate society's willingness to pay for a change
in environmental quality. Thus, although the
distribution of this change in welfare across groups
may be of interest in its own right, in isolation
it does not inform the question of whether the
policy increases or reduces pre-existing disparities.
To address the question of how a policy
affects disparities it is necessary to evaluate
the distribution of environmental and health
outcomes in the baseline and for each policy
option. As an alternative to the change in
willingness to pay one could examine the
distribution of physical indicators. Such an
evaluation is fairly straightforward if there is only
one outcome to consider. Analysis of multiple
outcomes (e.g., asthma risk and fatal heart attack
risk) raises the problem of whether and how to
aggregate these outcomes into a single measure.
Combining several outcomes into a single
aggregate measure maybe desirable, but entails
normative value judgments regarding the weight
to be given to each component. For example, how
much asthma risk is equivalent to a given risk
of a fatal heart attack? One possible weighting
scheme would be to use quality-adjusted life
years (QALYs) or similar measures, but these are
generally not consistent with willingness-to-pay
measures and benefit-cost analysis (IoM 2006).
Another alternative is to use the willingness-to-pay
values from the benefit-cost analysis as weights (see
Chapter 7 of these Guidelines for a discussion of
willingness to pay).
A standard benefit-cost analysis aggregates
multiple outcomes by multiplying the number of
cases of each outcome by its respective marginal
willingness-to-pay. In principle one could
use this weighting scheme in a distributional
analysis. There is a theoretical issue, however. The
empirical techniques used to monetize health and
environmental benefits estimate an individual's
marginal willingness to pay for a change in the
outcome. That is, they reflect the amount of
money an individual would give up for a very
small improvement in the outcome variable,
evaluated at a particular level. The problem is
that economic theory suggests that even if all
individuals had identical preferences, the marginal
willingness to pay to avoid a bad outcome should
increase with the level of the outcome (e.g., an
individual would be willing to pay more to reduce
her probability of death from a particular disease
from 99 percent to 98 percent, than she would
to reduce it from 2 percent to 1 percent). As a
practical matter, however, marginal willingness-
to-pay measures typically used in benefit-cost
analysis are constant values. The approximation
implicit in this approach is defensible when the
changes considered are not too large. However, it
is not necessarily reasonable to multiply, say, the
baseline mortality risk by the value of a statistical
life in order to get the dollar value of eliminating
the entire baseline risk. Yet this type of calculation
would be necessary in order to evaluate how
policy options would change the distribution
of monetized environmental outcomes across
population groups of concern. Consequently,
if analysts use monetized values to aggregate
across outcomes, the exposition should include
appropriate caveats and be presented alongside
outcome-by-outcome levels for the baseline and
each policy option.
10,2.2.2 Evaluating the Distribution
of Costs
Activities to address environmental justice often
focus on reducing disproportionate environmental
and health outcomes in communities. However,
certain directives (e.g., EO 13175 and OMB
Circular A-4) specifically identify distribution of
economic costs as an important consideration. The
economic literature also typically considers both
costs and benefits when evaluating distributional
consequences of an environmental policy in order
to understand their net effects on welfare. For
instance, Fullerton (2011) discusses six possible
types of distributional effects that may result
from an environmental policy: higher product
prices, changes in the relative returns to factors of
production, how scarcity rents are distributed, the
distribution of environmental benefits, transitional
effects of the policy, and the capitalization of
environmental improvements into asset prices
(e.g., land or housing values). Policy decisions
involve trade-offs, and these may differ across
affected groups. While health or environmental
10-8
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
improvements may accrue to certain population
groups of concern, costs may be borne by others.
As a result, some groups may experience net
costs even if everyone is expected to receive gross
environmental benefits.
This chapter frames the discussion in terms of
environmental and health outcomes (referred
to as benefits, when monetized), but many
of the methods can be applied to costs and
other impacts as well. Whether or not costs are
included in an evaluation of EJ issues associated
with a regulation should be evaluated on a
case-by-case basis. If regulatory costs are spread
fairly evenly across many households (e.g., in
the form of higher prices) and expected to be
small on a per-household basis, further analysis
is likely not warranted or feasible. However,
there may be cases where the analysis of the
distribution of costs is warranted.24 Such cases
may include situations where costs to consumers
maybe concentrated among particular types
of households (e.g., renters); identifiable plant
closures or facility relocations that could adversely
affect certain communities; or when households
may change their behavior in response to the
imposition of costs.
In many cases, detailed analyses of costs may be
challenging due to data or modeling constraints.
For example, EPA may expect air pollution control
costs to be passed on to electricity consumers. The
Agency might not have information, however,
on how costs are passed through as rate increases,
how these increases may be broken down between
residential and commercial customers, what
assistance is available for low-income consumers,
and how consumption patterns differ by race and
income. Likewise, if air quality improvements
associated with a regulation are unevenly
distributed, demand for housing in particular
neighborhoods may affect rental prices. While
hedonic approaches (discussed in Chapter 7)
may be useful for demonstrating how changes
in environmental quality factor into housing
prices, predicting the effect of such price changes
24 EPA's Lead Renovation, Remodeling, and Painting Final Rule (U.S. EPA
2008c) provides the best example to date of consideration of costs in
the context of a rulemaking.
on household migration by race or income may
be infeasible.25 Absent such data, it might not
be possible to predict the total impact of the
rule on different populations. In these instances,
those issues that cannot be quantified can be
qualitatively discussed.
.3 Relevant Populations
EO 12898 identifies a number of relevant
population groups of concern: minority
populations, low-income populations, Native
American populations and tribes, and "populations
who principally rely on fish and/or wildlife for
subsistence."26 It may be useful to analyze these
categories in combination — for example, low-
income minority populations — or to include
additional population groups of concern, but
such analysis is not a substitute for examining
populations explicitly mentioned in the Executive
Order. In this section, we discuss existing Federal
definitions for population groups of concern in
the context of EJ. We also discuss credible options
for defining these populations in the absence of a
Federal definition.
10,2.3,1 Minority and Native
American Populations
OMB (1997) specifies minimum standards for
"maintaining, collecting, and presenting data
on race and ethnicity for all Federal reporting
purposes.... The standards have been developed
to provide a common language for uniformity
and comparability in the collection and use of
data on race and ethnicity by Federal agencies." In
particular, it defines the following minimum race
and ethnic categories:
• American Indian or Alaska Native
• Asian
• Black or African American
25 See Section 8.2.5.1 of the Handbook on the Benefits, Costs and
Impacts of Land Cleanup and Reuse (U.S. EPA 2011c) for a more
detailed discussion of EJ in the context of the potential effects of
environmental policy on land values and household location decisions.
26 E0 12898 clarifies in Section 6 that the EO applies to Native Americans
and also Indian Tribes, as specified in 6-606, as well as populations
who principally rely on fish and/or wildlife for subsistence as specified
in 4-401.
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
• Native Hawaiian or Other Pacific Islander
. White
• Hispanic or Latino
Statistical data collected by the Federal
government, such as the U.S. Census Bureau, use
this classification system.27 Beginning with the
2000 Census, individuals were given the option
of selecting more than one race, resulting in
63 different categories. OMB (2000) provides
guidance on how to aggregate these data in a
way that retains the original minimum race
categories (i.e., the first five categories listed
above) and four double race categories that are
most frequently reported by respondents.28 In
addition, the U.S. Census Bureau collects data
useful for identifying minority populations
not completely captured by either the race or
ethnicity categories, such as households that
speak a language other than English at home or
foreign-born populations.
CEQ's NEPA Guidance for EJ (CEQ1997)
provides useful direction for defining minority
and minority population based on these Federal
classifications. Minority is defined as "individual(s)
who are members of the following population
groups: American Indian or Alaskan Native;
Asian or Pacific Islander; Black, not of Hispanic
origin; or Hispanic." A population is identified
as minority if "either (a) the minority population
of the affected area exceeds 50 percent or (b) the
minority population percentage of the affected
area is meaningfully greater than the minority
population percentage in the general population or
other appropriate unit of geographic analysis." The
term meaningfully greater is not defined, although
the guidance notes that a minority population
exists "if there is more than one minority
group present and the minority percentage, as
calculated by aggregating all minority persons,
meets one of the above-stated thresholds."
Finally, the CEQ Guidance states that analysts
27 Analysts should refer to the OMB Federal Register notice for
the specific definitions: http://www.whitehouse.gov/omb/
fedreg_1997standards/ (accessed on December 20,2012).
28 See OMB (2000) for specific guidance on how to conduct this
aggregation.
"may consider as a community either a group of
individuals living in geographic proximity to one
another, or a geographically dispersed/transient
set of individuals (such as migrant workers or
Native Americans), where either type of group
experiences common conditions of environmental
exposure or effect."
10,2.3,2 Low-Income Populations
OMB has designated the U.S. Census Bureau's
annual poverty measure, produced since 1964,
as the official metric for program planning
and analytic work by all Executive branch
agencies in Statistical Policy Directive No. 14
(Federal Register 1978), although it does not
preclude the use of other measures. Many
Federal programs use variants of this poverty
measure for analytic or policy purposes, and the
U.S. Census Bureau publishes data tables with
several options.
The U.S. Census Bureau measures poverty by using
a set of money income thresholds that vary by
family size and composition to determine which
households live in poverty. If a family's total income
is less than the threshold, then that family and every
individual in it is considered in poverty. The official
poverty thresholds do not vary geographically, but
they are updated for inflation using the national
Consumer Price Index for All Urban Consumers
(CPI-U). The official poverty definition uses money
income before taxes and does not include capital
gains or noncash benefits (such as public housing,
Medicaid, and food stamps).29 This measure of
poverty has remained essentially unchanged —
apart from relatively minor alterations in 1969 and
1981— since its inception.30
There is considerable debate regarding this
poverty measure's ability to capture differences in
29 See "How the Census Bureau Measures Poverty" available at
http://www.census.gov/hhes/www/poverty/about/overview/measure.
html (accessed on November 30,2011).
30 The U.S. Census Bureau produces single-year estimates of median
household income and poverty by state and county, and poverty by
school district as part of its Small Area Income and Poverty Estimates.
It also provides estimates of health insurance coverage by state and
county as part of its Small Area Health Insurance Estimates. These data
are broken down by race at the state level and by income categories at
the county level.
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
economic well-being. In particular, the National
Research Council (NRC) recommended that
the official measure be revised because "it no
longer provides an accurate picture of the
differences in the extent of economic poverty
among population groups or geographic areas
of the country, nor an accurate picture of trends
overtime" (Citro and Michael 1995). OMB
convened an interagency group in 2009 to define
a supplemental poverty measure based on NRC
recommendations. The U.S. Census Bureau
released the Supplemental Poverty Measure
(SPM) in November 2011 (Short 2011). This
measure uses different measurement units to
account for "co-resident unrelated children (such
as foster children) and any co-habitors and their
children," a different poverty threshold, and
modified resource measures (to account for in-
kind benefits and medical expenses, for example).
It also adjusts for differences in housing prices by
metropolitan statistical area, as well as family size
and composition.
The NRC recognized that annual income is not
necessarily the most reliable measure of relative
poverty as it does not account for differences in
accumulated assets across households. Neither the
SPM nor the official U.S. poverty thresholds take
into account differences in wealth across families.
However, the SPM examines whether a household
is likely to fall below a particular poverty threshold
as a function of inflows of income and outflows of
expenses. The U.S. Census Bureau asserts that this
measure is therefore more likely to capture short-
term poverty since many assets are not as easily
convertible to cash in the short run (Short 2012).
The U.S. Census Bureau also includes several
additional measures that may prove useful in
characterizing low-income families. Unlike
poverty, there is no official or standard
definition of what constitutes "low-income,"
though it is expected to vary similarly by
region due to differences in cost-of-living as
well as with family composition. It is therefore
appropriate to examine several different low-
income categories, including families that make
some fixed amount above the poverty threshold
(e.g., two times the poverty threshold) but still
below the average household income for the
United States or for a region.
Educational attainment or health insurance
coverage may also be useful for characterizing
low-income families relative to other populations,
although we caution analysts that some measures
may be hard to interpret and use in a regulatory
context. It is also possible to examine the percent
of people who are chronically poor versus those
that experience poverty on a more episodic
basis using the Survey of Income and Program
Participation which provides information on
labor force participation, income, and health
insurance for a representative panel of households
on a monthly basis over several years (see Iceland
2003). Finally, cross-tabulations often are available
between many of these poverty measures and
other socioeconomic characteristics of interest
such as race, ethnicity, age, sex, education, and
work experience.
10,2,3,3 Populations that Principally
Subsist on Fish and Wildlife
EO 12898 directs agencies to analyze populations
that principally subsist on fish and wildlife. CEQ's
NEPA Guidance for EJ (CEQ1997) defines
subsistence on fish and wildlife as "dependence by
a minority population, low-income population,
Indian tribe or subgroup of such populations on
indigenous fish, vegetation and/or wildlife, as the
principal portion of their diet." It also states that
differential patterns of subsistence consumption
are defined as "differences in rates and/or
patterns of subsistence consumption by minority
populations, low-income populations, and
Indian tribes as compared to rates and patterns of
consumption of the general population."
Neither the U.S. Census Bureau nor other Federal
statistical agencies collect nationally representative
information on household consumption of fish
and/or wildlife. However, EPA has conducted
consumption surveys in specific geographic areas.
If fish and wildlife consumption is a substantial
concern for a particular rulemaking, EPA's
guidance can provide useful information for
collecting these data (see U.S. EPA 1998b). There
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
may also be surveys conducted by state or local
governments. It is important to verify that any
survey used in an analysis of distributional impacts
in the context of EJ adheres to the parameters and
methodology set out in U.S. EPA (1998b).
.4 Data Sources
Many data sources can be used for conducting
analyses of EJ issues. The U.S. Census Bureaus
"Quick Facts" website contains frequently
requested Census data for all states, counties, and
urban areas with more than 25,000 people.31 Data
include population, percent of population by race
and ethnicity, and income (median household
income, per-capita income, and percent below
poverty line).
In 2010 the U.S. Census Bureau began to
administer the decennial Census using a short
form to collect basic socioeconomic information.
More detailed socioeconomic information is now
collected annually by the American Community
Survey (ACS), which is sent to a smaller
percentage of households than the decennial
Census.32 The ACS provides annual estimates
of socioeconomic information for geographic
areas with more than 65,000 people, three-year
estimates for areas with 20,000 or more people,
and five-year estimates for all areas.33 The five-year
estimates, which are based on the largest sample,
are the most reliable and are available at the census
tract and block group levels. Some of the Quick
Facts data include estimates from the ACS.
The U.S. Census Bureaus American Housing
Survey (AHS), is a housing unit survey that
provides data on a wide range of housing
and demographic characteristics, including
31 Quick Facts is available at: http://quickfacts.census.gov/qfd/index.html.
The year associated with data from Quick Facts is important to note.
Data are updated as new information becomes available. Therefore, not
all data elements represent the same year.
32 The ACS is available at: http://www.census.gov/acs/www/index.html.
(accessed December 1,2011.)
33 Because ACS variables change over time, caution should be used
when comparing ACS estimates across samples and years. Guidance
for comparing ACS data can be found at: http://www.census.gov/acs/
www/guidance_for_data_users/comparing_data/ (accessed on April
27,2011).
information on renters.34 Unlike the ACS, which
selects a random sample every year, the AHS
returns to the same 50,000 to 60,000 housing units
every two years.
s' *r Scope an 1 > r jraphic
Considerations
Most EPA rules are national in scope. Therefore,
the entire country is typically considered within
the scope of analysis. However, there maybe
reasons to consider a rule's distributional effects at
a sub-national level. For example, for a regulation
of hazardous waste sites it maybe appropriate
to conduct separate state-level analyses due to
differences in implementation of state-level
regulations. A rule may also affect a limited part of
the country. The 2011 Cross-State Air Pollution
Rule (U.S. EPA 201 lb), for example affects mainly
eastern states.35 In such cases the analyst may
wish to evaluate the effects of the regulation at a
regional level. Finally, for some regulations, such
as those governing the use of a household chemical
or as a product ingredient, geography may not
be as relevant for determining how health and
environmental outcomes vary across population
groups of concern. Two main issues to consider
when comparing impacts of a rulemaking on
minority, low-income, or indigenous populations
across geographic areas are:
• Unit of analysis (e.g., facilities or aggregate
emissions to which a population group is
exposed within a designated geographic
area); and
• Geographic area of analysis used to
characterize impacts (e.g., county or
census tract).36
The unit of analysis refers to how the
environmental harm is characterized. For
instance, in a proximity-based analysis the unit
of analysis could be an individual facility or the
34 Information on owner-occupied homes versus renters may be useful
when exploring issues of gentrification, where renters could be worse
off due to rising housing costs.
35 See http://www.epa.gov/airtransport/for details, (accessed December
1,2011.)
36 This is often referred to in the literature as geographic scale.
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
total number of facilities within a particular
geographic area (e.g., a county or census tract).
In an exposure-based analysis the unit of analysis
could be emissions aggregated within a particular
geographic area to which the population is
exposed. The unit of analysis is often identical
to the geographic scale used to aggregate and
compare effects on minority, low-income, or
indigenous populations in one area to another
(see Section 10.2.7 regarding how to select an
appropriate comparison group).37 The choice will
vary depending on the nature of the pollutant
(e.g., point sources may use a facility as the
unit of analysis, while area sources may use a
geographic unit). In considering various units,
an important consideration is whether the data
are sufficiently disaggregated to pick up potential
variation in impacts across socioeconomic
characteristics. More aggregated units of analysis
(e.g., metropolitan statistical area (MSA) or
county) may mask variation in impacts across
socioeconomic groups compared to more
disaggregated levels (e.g., facility or census tract).
The geographic area of analysis is the area used
to characterize impacts (e.g., distance around a
facility). Outcomes are aggregated by population
groups within geographic areas to compare
across groups. As with unit of analysis, choice
of options for defining the geographic area will
vary depending on pollutant and rule. Some air
pollutants, for example, may travel hundreds of
miles away from the source, making it appropriate
to choose a large area for measuring impacts. In
contrast, water pollutants or waste facilities may
affect smaller areas, making it appropriate to
consider a smaller area for analysis. Likewise, an
assessment of outcomes from specific industrial
point sources may require more spatially resolved
air quality, demographic and health data than one
that affects regional air quality, where coarser air
quality, demographic and health data may suffice.
Using more than one geographic area of analysis to
compare effects across population groups may also
be useful since outcomes are unlikely to be neady
contained within geographic boundaries. The
literature has demonstrated that results are sensitive
37 In Fowlieetal. (2012), for example, the scale of the analysis varies
between 0.5,1 and 2 miles of the facility (which is the unit of analysis).
to the choice of the geographic area of analysis
(Mohai and Bryant 1992; Baden et al. 2007).
Commonly used geographic areas of
analysis include:
Counties: The United States has more than
3,000 counties according to the 2007 Census of
Governments. Although counties are well-defined
units of local government and provide complete
coverage of the United States, they vary in size from
a few to thousands of square miles and population
density ranges from less than one person per
square mile in some Alaskan counties to over
66,000 in New York County. In addition, spatial
considerations associated with using counties
present concerns for an analysis of distributional
impacts in the context of EJ. A facility located in
one corner of a county may have greater effects
on neighboring counties than on residents of the
county where the plant is located.38'39
Metropolitan and Micropolitan Statistical
Areas: The U.S. Census Bureau publishes data on
metropolitan and micropolitan statistical areas,
as defined by OMB (OMB 2009). Metropolitan
statistical areas include an urban core and adjacent
counties that are highly integrated with the urban
core. A micropolitan statistical area corresponds
to the concept of a metropolitan statistical area
but on a smaller scale. Metropolitan statistical
areas have an urban core of at least 50,000 persons;
micropolitan statistical areas have an urban core
population between 10,000 and 50,000 persons.
Rural areas of the United States are not covered by
these statistical designations, though according to
the U.S. Census Bureau, almost 94 percent of the
U.S. population lived in a metro- or micropolitan
statistical area in 2010.
Zip codes: Zip codes are defined by the U.S.
Post Office for purposes of mail delivery and
may change over time. They also may cross state,
county, and other more disaggregated Census
38 These same advantages and disadvantages can apply to other units of
government.
39 For criteria pollutants, baseline health data may be available at the
county level (e.g., baseline death rates, hospital admissions, and
emergency department visits).
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
statistical area definitions, making them difficult
to use for analysis. Zip code tabulation areas
are statistical designations first developed by
the U.S. Census Bureau in 2000 to approximate
the zip code using available census block level
data on population and housing characteristics.
Data are readily available for the approximately
33,000 U.S. zip code tabulation areas. While
smaller than counties, they also vary greatly in
size and population. As a result, they may often
be less preferable than other geographic areas for
analyzing distributional effects across population
groups of concern.
Census tracts/block groups/blocks: Census
tracts are small statistical subdivisions of a
county, typically containing from 1,500 to 8,000
persons. The area encompassed within a census
tract may vary widely, depending on population
density. Census tracts in denser areas cover
smaller geographic areas, while those in less dense
areas cover larger geographic areas. Census tract
boundaries were intended to remain relatively
fixed. However, they are divided or aggregated
to reflect changes in population growth within
an area over time. Although they were initially
designed to be homogeneous with respect to
population characteristics, economic status, and
living conditions, they may have become less so
over time as demographics have changed.
Analysts may also choose to use census blocks or
block groups. A census block is a subdivision of
a census tract and the smallest geographic unit
for which the U.S. Census Bureau tabulates data,
containing from 0 to 600 persons. Many blocks
correspond to individual city blocks bounded
by streets, but may include many square miles,
especially in rural areas. And census blocks may have
boundaries that are not streets, such as railroads,
mountains or water bodies. The U.S. Census Bureau
established blocks covering the entire nation for
the first time in 1990. Census block groups are a
combination of blocks that are within — and a
subdivision of — a given census tract. Block groups
typically contain 600 to 3,000 persons.40
40 Other Census statistical area definitions (e.g., public use microdata
areas or PUMAs) are also available.
CIS methods: Because Census-based definitions
often reflect topographical features such as rivers,
highways, and railroads, they may exclude affected
populations that, although separated by some
physical feature, receive a large portion of the
adverse impacts being evaluated. Since Census-
based definitions vary in geographic size due to
differences in population density, Geographic
Information System (CIS) software and methods
may enable the use of spatial buffers around an
emissions source that are more uniform in size and
easier to customize to reflect the appropriate scale
and characteristics of emissions being analyzed for
a given rulemaking.
Analysts should be aware that there are a number
of challenges typical of working with geospatial
data. In some cases, statistical techniques rely on
assumptions that often are violated by these types
of data (Chakraborty and Maantay 2011). For
instance, spatial autocorrelation — when locations
in closer proximity are more highly correlated than
those further away from each other — violates the
assumption that error terms are independently
distributed (an assumption that underlies ordinary
least squares).
i * LVfiniNj WfF f aseline
Proper definition of the baseline is crucial for
evaluating a rule's distributional effects. OMB
(2003) defines the baseline as "the best assessment
of the way the world would look absent the
proposed action." The baseline allows one to
determine how a rule's effects are distributed
across population groups of concern and to assess
whether some groups may be disproportionately
affected. Baseline assumptions used in a
distributional analysis should be consistent with
those used in the benefit-cost analysis. See Chapter
5 for a more detailed discussion of baseline issues.
.7 Comparison groups
The choice of a relevant comparison group is
important for evaluating changes in health, risk,
or exposure effects across population groups of
concern relative to a baseline. Within-group
comparisons involve comparing effects on the
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
same demographic group across different areas
in the state, region or nation, while across-
group comparisons examine effects for different
socioeconomic groups within an affected area.
From the perspective of EO 12898, across-group
comparisons may be most relevant. The literature
suggests using more than one comparison group
to analyze whether a finding of disproportionate
impacts is sensitive to how it is defined. Bowen
(2001) also argues that restricting the comparison
group to alternative locations within the same
metropolitan area may be more defensible than
a national level comparison in some instances,
given heterogeneity across geographic regions in
industrial development and economic growth over
time and inherent differences in socioeconomic
composition (e.g., relatively more Hispanics reside
in the Southwest). Ringquist (2005), however,
notes that placing restrictions on comparison
groups in this way may "reduce the power of
statistical tests by reducing sample sizes" or bias
results against a finding of disproportionate
impacts because such restrictions reduce variation
in socioeconomic variables of interest.
!,8 Measuring and
estimating impacts
This section presents a range of potentially
useful approaches for describing distributions
in regulatory analysis. To the extent feasible,
basic summary statistics of a regulations impacts
on relevant endpoints by race and income
are recommended for distributional analyses.
Summary statistics may be straightforward to
calculate when data are available, and providing
such information promotes consistency across
EPA analytical efforts. A related document, the
Interim Guidance on Considering Environmental
Justice During the Development of an Action (U. S.
EPA 2010a), suggests conducting a screening
process for determining when an action may
require evaluation. For economically significant
actions, it is recommended that the results of
the screening be demonstrated through the use
of summary statistics. Summary statistics can be
supplemented with other approaches described
below when a screening analysis indicates that a
more careful evaluation is needed.
The health effects of exposure to pollution
may vary across populations (likewise, with
costs). One way to capture these effects is to
use information regarding variation in risk
and incidence by groups, when available, to
characterize the baseline and projected response
to a change in exposure (for example, see Fann et
al. 2011). However, available scientific literature
and data (which also often requires some level
of spatial resolution) may not allow for a full
characterization. In these cases, it is recommended
that the analyst qualitatively discuss conditions
that are not adequately accounted for in the
risk and exposure characterization used to
assess health effects for minority populations
or low-income populations and the key sources
of uncertainty highlighted in the literature
(U.S. EPA 2010a). When data are available
to approximate risk or exposure, for instance
location of emitting facilities, some level of
quantitative analysis maybe possible.
Text Box 10.1 discusses the potential usefulness
of social welfare functions and inequality indices
for ranking distributions. While these methods
are useful for combining efficiency and equity
considerations into one measure, these tools
are not sufficiently developed for application to
regulatory analysis. For a more detailed discussion
of the advantages and disadvantages of methods
commonly used to rank environmental outcomes
see Maguire and Sheriff (2011).
10,2,8,1 Simple Summary Statistics
Simple summary measures can characterize
potential differences in baseline and regulatory
options within and across populations of concern
relative to appropriate comparison groups. Such
statistics can be calculated, if data are available, to
address the three questions outlined in Section
10.2.2. It is important to note, however, that
summary statistics alone do not necessarily provide
a complete description of differences across groups.
Omitted variables are one important limitation of
examining single statistics. In addition, summary
statistics (e.g., means) can mask important details
about the tails of the distribution which can be
important for identifying potential EJ concerns
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
Text Bo* 10.1 - Social Welfare Functions and Inequality indices
The costs, benefits, and distributional effects of a regulation can be evaluated by a single social welfare function
(SWF). A SWF provides a way to aggregate welfare or utility across individuals into a single value, thus allowing
simple, direct comparisons in ranking alternative allocations. Such comparisons are potentially useful in evaluating
whether a change from the baseline to a regulatory option makes society better off. Likewise, they can also facilitate
comparisons between possible regulatory options (see Adler 2008,2012 for a discussion). Sen (1970), Arrow
(1977), and Just et al. (2004) provide theoretical discussions of SWFs, and Norland and Ninassi (1998) provide an
example of an application to energy markets. Adler (2012) addresses practical issues of incorporating both health
and income effects in a SWF.
Any ranking of alternative outcomes uses an implicit set of normative criteria; a SWF makes the criteria explicit
regarding how society prefers to distribute resources across individuals. Since there is no consensus regarding those
preferences, a universally-accepted SWF does not exist. For example, suppose an increase in exposure to a particular
pollutant results in an average loss of 0.1 IQ points across a population of 1,000 children (100 IQ points total). It is
not obvious how society should rank alternative distributions of this loss. Is it worse to have 250 individuals suffer a
loss of 0.1 each, 250 suffer a 0.3 loss, and 500 suffer no loss? Or 500 individuals suffer a loss of 0.01 and 500 suffer
a loss of 0.19? Many sensible SWFs could be specified; some may prefer the first outcome, some may prefer the
second, and some may be indifferent between the two.
An inequality index is a related concept used to assign a numerical value to distributions of a single "good" or "bad"
(e.g., income or pollution), independent of the total amount produced. A distribution with a higher index value is
less "equal" than one with a lower number. Commonly used indices are based on simple SWFs and are subject to
the same limitations (Blackorby and Donaldson 1978,1980). However, unlike a SWF, an index number value has
cardinal significance, i.e., the magnitudes, not just the rankings, contain information about how much society would
be willing to give up in exchange for the rest to be equally distributed.
Inequality indices were originally developed for ranking "goods," like income. In general, it is inappropriate simply
to use positive values of a bad outcome (e.g., pollution exposure) in the formula for an index, since doing so would
imply that the underlying SWF is increasing in pollution, i.e., it would rank scenarios with higher overall pollution
as more desirable. Since indices cannot accommodate negative values, some commonly used income inequality
measures, such as the Gini coefficient, and Atkinson index, are inappropriate for evaluating distributions of adverse
outcomes. The Kolm index (Kolm 1976a, 1976b), in contrast, does not suffer from this problem (see Maguire and
Sheriff 2011). Given that the peer-reviewed literature does not yet contain environmental applications of the Kolm
Index, and the Atkinson Index is undefined for "bads," we do not recommend inequality indices be used in regulatory
analysis of distributional impacts in the context of EJ at this time.
(see Gochfeld and Burger 2011). Nonetheless,
such information can provide useful information
on potential differences.
After reviewing the available data and feasible
methods for developing information on potential
differences, the analyst should present information
in a transparent and accessible manner such that
the decision maker can consider:
• Population groups of concern for the
regulatory action,
• Geographic scale and unit of analysis,
when relevant,
• Primary conclusions (e.g., statistical differences),
• Sources of uncertainty across alternative
results (e.g., comparison groups and
geographic scale), and
• Data quality and limitations of the results.
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
A variety of measures can be used to characterize
an actions distributional effects for population
groups of concern.
Heans and quantiles
Reporting mean outcomes by group at the
baseline and for each regulatory option is a
straightforward way to display information. Tests
for statistical significance across means provide
additional information about differences (see
Been and Gupta 1997 and Wolverton 2009).
However, mean estimates can mask what might
be important information in the tails of the
distribution. For example, the baseline outcomes
could be uniformly distributed across the
population but concentrated around the mean
for the regulatory scenario. Examining differences
around the central tendency only would not reveal
this information. Presenting data using different
quantiles can provide additional information
illuminating these effects.
Ratios
A simple ratio can be calculated to determine
whether certain groups are relatively more
exposed to an environmental hazard. For instance,
the probability that an individual is minority
conditional on being exposed can be divided by
the probability that an individual is not minority
conditional on being exposed. Alternatively, one
can also create a ratio of the probability that an
individual is exposed to an environmental risk
conditional on being minority divided by the
probability that an individual is not exposed
conditional on being in the same demographic
group. Because ratios may mask absolute
differences, ratios should be used in conjunction
with other statistics. For example, a ratio may
show a 100-fold difference between two groups'
exposure to an environmental hazard but the
absolute difference could be small. Ratios may
exaggerate the importance of differences.
Tests for Differences
Statistical tests can determine whether a
significant disparity exists across demographic
groups. One of the simplest is a /"-test of the
difference in means. However, a /-test assumes a
normal distribution so it would be inappropriate
for non-normal distributions. For non-normal
distributions, nonparametric methods may
be used. In cases where comparisons are made
based on the difference in probabilities between
two groups, tests such as the Kendall test and
the Fisher Exact test (for small samples) may be
used. These tests compare standard errors of two
separate and independent statistics to determine
how likely it is that the calculated distribution is
the actual one. More sophisticated tests are needed
when making comparisons across more than two
groups or a more formal examination of the full
distribution is desired.
Correlation coefficients
Simple pair-wise correlations between impacts
and relevant demographic groups may be useful
information for characterizing distributional
effects (e.g., Brajer and Hall 2005). It is important
to note, however, that the value of a Pearson
correlation coefficient, for example, is a measure
of how closely the distribution of the relationship
between two variables (e.g., percent minority
population and ambient pollution concentrations)
can be represented by a straight line. It does
not provide information regarding the slope of
the line, apart from being positive or negative.
Similarly, a Spearman rank correlation coefficient
measures how closely the relationship can be
captured by a generic monotonically increasing
or decreasing function. Determination of what
constitutes a "strong" or "weak" correlation
is somewhat arbitrary, and caution should be
used when comparing coefficients across socio-
economic variables of interest.
Counts
A count of geographic areas (e.g., counties) where
the incidence of an environmental outcome
affected by a rule, disaggregated by race/ethnicity
and income, exceeds the overall average is a useful
measure. For comparison, this count should be
accompanied by a count of geographic areas where
the incidence does not exceed the overall average.
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
These counts do not account for magnitude of
differences, but can help identify the need for
more detailed analysis.
10.2.8.2 Visual Displays
Maps, charts, graphs, and other visual displays are
commonly used in EJ analyses (see Shadbegian et
al. 2007, for example). With increased access to
GIS software and built-in graphical functions in
spreadsheet or statistical software, it is relatively
easy to produce a variety of visual displays of
EJ-related information. Visual displays can be
helpful in displaying baseline levels of pollutants or
locations of certain facilities, and the distribution,
demographic profile and baseline health status of
population groups of concern.
There are several challenges with GIS analysis
of distributional information. These include
spatial and data deficiencies as well as geographic
considerations that can lead to misleading
or inaccurate results.41 It may be difficult to
discern differences that arise between baseline
and regulatory options, unless such differences
are stark. While the use of visual displays in an
analysis of distributional impacts in the context
of EJ may be useful for helping to communicate
the geographic distribution of impacts, this
information may be more effective if it is
accompanied by other analytical information.
10.2.8.3 Proximity-Based Analysis
Proximity- or distance-based analysis is an
approach commonly used in the EJ literature as
a surrogate for more direct measures of risk or
exposure when such information is not easily
available. This approach examines demographic
and socioeconomic characteristics in proximity to a
particular location, typically a waste site, permitted
facility, or some other polluting source (for
instance, see Baden and Coursey 2002, Cameron et
al. 2012, and Wolverton 2009). While a simplistic
approach is to examine the population within a
Census-defined geographic boundary of a location,
it is also possible to use GIS methods to draw a
41 See Chakraborty and Maantay (2011) for further discussion of the
limitations of using GIS for EJ analyses.
concentric buffer around an emission source, such
as a one mile radius around a site to approximate
the distance that a particular pollutant may
travel. In some cases, it may also be possible to
use dispersion models to select a buffer that
approximates the effect of atmospheric conditions
(for instance, wind direction and weather patterns)
on exposure, though these types of models are data-
intensive (Chakraborty and Maantay 2011).
Several analytical considerations are important
for conducting a proximity-based analysis.42 First,
accurate information is needed for the location of
polluting sources. Addresses or latitude/longitude
coordinates must reflect physical locations of
polluting facilities, and not the location of a
headquarters building, for example. Second, a
decision must be made regarding the appropriate
distance from the facility to examine community
characteristics. A solid waste facility with strict
monitoring and safety controls is likely to have a
limited geographic impact, whereas a permitted air
pollution source may have the potential for a more
widespread geographic impact. In general, Census-
defined geographic boundaries (e.g., county, MSA)
are unlikely to provide an accurate portrayal of
the relevant affected population because emission
sources are often not found in the center of the
area (i.e., they are sometimes along a boundary
and thus mostly affect a neighboring jurisdiction)
and pollutant exposures do not conform to these
boundaries.43 In addition, Census-defined areas
often vary widely in size, implying that they may
differ in how well they proxy for actual exposure.
Defining proximity or distance using buffer-based
approaches (e.g., through GIS or fate and transport
modeling) around an emissions source has the
potential to more closely approximate actual
risk and exposure, but the appropriate distance
measure can vary by situation. The literature has
demonstrated that results in proximity-based
analyses can vary substantially with the choice
42 For an overview of proximity analysis, including a discussion
of various spatial analysis techniques used in the literature see
Chakraborty and Maantay (2011) and Mohai and Saha (2007).
43 Mohai and Saha (2007) refer to this as the "unit-hazard coincidence"
approach because the analyst uses the available geographic units and
determines whether they are coincident with an environmental hazard
instead of first identifying the exact location of the hazard and then
examining effects within a particular distance.
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
of the geographic area of analysis (see Rinquist
2005; Mohai and Saha 2007). For this reason,
it is recommended that the analyst explore the
potential value of defining and applying more than
one specification for distance or proximity.44
When this approach is used, it is important to be
aware of biases and limitations introduced when
proximity or distance is used as a substitute for risk
and exposure modeling and that these limitations
be clearly discussed (see Chakraborty and Maantay
2011). In particular, it may only be possible to
make limited observations with regard to the
possibility of disproportionate impacts based on
proximity-based analysis alone.
10,2,8,4 E.sposni - » sessment
Spatial patterns associated with environmental
burdens across individuals or communities are
difficult to analyze when pollution is diffuse. Air
and water pollution, for example, are typically
dispersed widely and subject to atmospheric
or geologic features. As such, identifying the
"proximity" to the hazards via some type of GIS
analysis, as described above, is less useful. However,
monitoring and/or modeling data may generate
distributional effects at a disaggregated level.
Criteria air pollutants (i.e., carbon monoxide, lead,
nitrogen dioxide, ozone, particulate matter and sulfur
dioxide) are monitored nationally. EPA's National
Air Toxics Assessment (NATA) data provide an
assessment of hazardous air pollutants across the
U.S. at the census tract level.45 Data from these
monitoring networks may potentially be combined
with demographic data and dispersion models to
generate baseline and regulatory distributions of
pollutants by population groups of concern.46
44 The analysis of distributional impacts in the context of EJ completed
for EPA's proposed Definition of Solid Waste is an example of this type
of analysis in a rule-making context. See EPA's Draft Environmental
Justice Methodology for the Definition of Solid Waste Final Rule,
January 13,2009, available at: http://www.epa.gov/epawaste/hazard/
dsw/ej-meth.pdf (accessed on December 1,2011).
45 See Apelberg etal. (2005) for an application to Maryland and Morello-
Frosch et al. (2002) for an application to southern California.
46 See, for example, U.S. EPA (2011b), Fann etal. (2011), and Post etal.
(2011).
While this approach is promising due to spatial
detail associated with monitoring data, it is
currendy only available for certain air pollutants.
In addition, it is important to note that monitoring
data measure emissions, not individual exposures
or health effects associated with the pollutant
under consideration. As such, these data are a
proxy for actual effects associated with a particular
regulation. Further, all individuals within a grid cell
are assigned the same emissions (or concentrations
based on air quality modeling). Actual exposures
or health effects may differ across individuals for a
variety of reasons discussed throughout this chapter.
10,2,8,5 Risk Considerations
Certain factors make some populations more
susceptible (i.e., experience a greater biological
response to a specific exposure) to a particular
environmental stressor (see Adler and Rehkopf
2008, Sacks et al. 2011 and Schwartz et al.
201 la).47'48 These factors can be genetic or
physiological (such as sex and age). They may also
be acquired due to variation in factors such as
health-care access, nutrition, fitness, stress, housing
quality, other pollutant exposures, or drug and
alcohol use.49 For instance, many populations face
exposures from multiple pollutants or exposures
that have accumulated in ways that may affect
their susceptibility to a particular pollutant
and introduce complex considerations when
attempting to address EJ concerns.50
47 Aspecial issue ofthe American Journal of Public Health (Volume 101,
Issue S1, December 2011) provides a set of papers exploring these
and other issues.
48 EPA's Integrated Risk Information System (IRIS) defines susceptibility
as "increased likelihood of an adverse effect, often discussed in
terms of relationship to a factor that can be used to describe a human
subpopulation (e.g., life stage, demographic feature, or genetic
characteristic)." See http://www.epa.g0v/iris/help_gl0ss.htm#s
(accessed on December 1,2011).
49 Sexton (1997) suggests that low-income families may be more
susceptible to environmental stressors due to differences in quality
of life and lifestyle. Centers for Disease Control data show higher
incidences of asthma-related emergency room visits and asthma-
related deaths among African-American populations. See http://
minorityhealth.hhs.gov/templates/content.aspx?ID=6170 (accessed
December 1,2011).
50 EPA's Framework for Cumulative Risk Assessment may serve as a
useful reference when assessing how prior exposures may affect the
impacts of emission changes from the rule being analyzed, available at
http://oaspub.epa. gov/eims/eimscomm.getfile?p_download_id=36941
(accessed November 2,2010).
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
In addition, activities linked to a specific
cultural background or socioeconomic status
could expose populations to higher levels of
pollution. For example, some indigenous peoples
and immigrant populations rely on subsistence
fishing which could result in higher mercury
levels from consumption of fish or expose these
populations to other forms of pollution if fishing
occurs in contaminated waters (see Donatuto
and Harper 2008).51
lental
Health
Distributional analysis may shed light on
differential effects of regulation on children,
a lifestage-defined group characterized by a
multitude of unique behavioral, physiological,
and anatomical attributes. There are two sets
of important differences between children and
adults regarding health benefits. First, there are
differences in exposure to pollutants and in the
nature and magnitude of health effects resulting
from the exposure. Children may be more
vulnerable to environmental exposures than adults
because their bodily systems are still developing;
they eat, drink, and breathe more in proportion
to their body size; their metabolism may be
significantly different — especially shortly after
birth; and their behavior can expose them more
to chemicals and organisms (e.g., crawling leads
to greater contact with contaminated surfaces
while hand-to-mouth and object-to-mouth
contact is much greater for toddler age children).
Second, individuals may systematically place a
different economic value on reducing health risks
to children than on reducing such risks to adults
(U.S. EPA 2003).
EO 13045 requires that each federal agency
address disproportionate health risks to children.
In addition, EPA's Children's Health Policy
requires the Agency "consider the risks to infants
and children consistently and explicitly as a part
51 It is also worth considering conditions that reduce a community's
ability to participate fully in the decision-making process such as time
and resource constraints, lack of trust, lack of information, language
barriers, and difficulty in accessing and understanding complex
scientific, technical, and legal resources (see Dietz and Stern 2008).
of risk assessments generated during its decision
making process, including the setting of standards
to protect public health and the environment."52
Generally, many approaches described earlier in
this chapter to characterize the distribution of
impacts may be adapted to evaluate children's
environmental health risks.53 For example,
when proximity-based analysis is appropriate
for evaluating environmental justice impacts, it
might also be used to examine whether children
are disproportionately located near facilities
of concern. In such a case, the considerations
described earlier about geography, defining the
baseline and comparison groups, and use of
summary statistics would all apply.
'iii'J.'i Childhood?,; ** I ifestage
Evaluating distributional impacts of regulatory
actions on children differs in an important way
from evaluating the same impacts on population
groups of concern for EJ. When EPA evaluates
disproportionate health risk impacts from
environmental contaminants, it views childhood as
a sequence of lifestages from conception through
fetal development, infancy, and adolescence, rather
than a distinct "subpopulation."
Use of the term "subpopulation" is ingrained in
both EPA's past practices as well as various laws
that EPA administers such as the Safe Drinking
Water Act Amendments. Prior to publication
of revised risk assessment guidelines in 2005,54
EPA described all groups of individuals as
"subpopulations." In the 2005 guidelines,
the Agency recognizes the importance of
distinguishing between groups that form a
relatively fixed portion of the population, such as
those described Section 3 of this document, and
52 See http://yosemite.epa.gov/ochp/ochpweb.nsf/content/
policy-eval_risks_children.htm (accessed on December 1,2011).
53 In principle there is a potential distinction in distributional analysis
to be made between factors that are fixed, such as race and sex, and
those defined by lifestages. The latter raises the possibility, at least,
of examining distribution concerns through the lens of differences in
lifetime utility or well-being rather than focusing on a single lifestage.
See Adler (2008) for one proposal consistent with this approach.
54 See http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=55907
(accessed on December 1,2011).
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
lifestages or age groups that are dynamic groups
drawing from the entire population.
The term "lifestage" refers to a distinguishable
time frame in an individual's life characterized
by unique and relatively stable behavioral and/
or physiological characteristics associated with
development and growth. Thus, since 2005 EPA
characterizes childhood as a sequence of lifestages.55
sensitive subpopulations and/or lifestages
such as childhood. The Cancer Guidelines
were augmented by Supplemental Guidancefor
Assessing Susceptibility from Early-Life Exposure
to Carcinogens.56 Recommendations from this
supplement include calculating risks utilizing
lifestage-specific potency adjustments in addition
to lifestage-specific exposure values which should
be considered for all risk assessments.
2 Analytical Considerations
Assessing distributional consequences of policies
that affect children's health requires considerations
that span risk assessment, action development,
and economic analysis. In each case there are
existing Agency documents that can assist in the
evaluation.
10,3, ;k Assessment
Effects of pollution can differ depending
upon age of childhood exposure. Analysis of
disproportionate impacts to children or from
childhood lifestages begins with health risk
assessment, but also includes exposure assessment.
Many risk guidance and related documents address
how to consider children and childhood lifestages
in risk assessment.
A general approach to considering children
and childhood lifestages in risk assessment is
found in A Framework for Assessing Health Risks
of Environmental Exposures to Children (U.S.
EPA 2006a). The framework identifies existing
guidance, guidelines and policy papers that relate
to children's health risk assessment. It emphasizes
the importance of an iterative approach between
hazard, dose response, and exposure analyses. In
addition, it includes a discussion of principles for
weight of evidence consideration across life stages.
EPA's 2005 Cancer Guidelines (U.S. EPA 2005a)
explicitly call for consideration of possible
55 The 2005 Risk Assessment Guidelines "view childhood as a sequence
of lifestages rather than viewing children as a subpopulation, the
distinction being that a subpopulation refers to a portion of the
population, whereas a lifestage is inclusive of the entire population."
(U.S. EPA 2005, p 1-15).
EPA's Child-Specific Exposures Handbook (U.S.
EPA 2008b)57 and. Highlights of the Child-
Specific Exposure Factors Handbook (U.S.
EPA 2009a)58 help risk assessors understand
children's exposure to pollution. The handbook
provides important information for answering
questions about lifestage specific exposure
through drinking, breathing, and eating. EPA's
guidance to scientists on selecting age groups to
consider when assessing childhood exposure and
potential dose to environmental contaminants is
identified in Guidance on Selecting Age Groupsfor
Monitoring and Assessing Childhood Exposures to
Environmental Contaminants (U.S. EPA 2005c).
10,3,2,2 Action Development
Disproportionate impacts during fetal
development and childhood are considered
in EPA guidance on action development,
particularly the Guide to Considering Children's
Health When Developing EPA Actions:
Implementing Executive Order 13045 and EPA's
Policy on Evaluating Health Risks to Children
(U.S. EPA 2006b). The guide helps determine
whether EO 13045 and/or EPA's Children's
Health Policy applies to an EPA action and, if so,
how to implement the Executive Order and/or
EPA's Policy. The guide clearly integrates EPA's
Policy on Children's Health with the Action
Development Process and provides an updated
listing of additional guidance documents.
56 Available at http://www.epa.gov/cancerguidelines/guidelines-
carcinogen-supplement.htm (accessed on December 1,2011).
57 Available at http://cfpub.epa.gov/ncea/cfm/recordisplay.
cfm?deid=199243 (accessed on December 1,2011).
58 Available at http://cfpub.epa.gov/ncea/cfm/recordisplay.
cfm?deid=200445 (accessed on December 1,2011).
Guidelines for Preparing Economic Analyses I May 2014 10-21
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
10,3,2.3 Economic Analysis
While these Economic Guidelines provide general
information on benefit-cost analyses of policies
and programs, many issues concerning valuation
of health benefits accruing to children are not
covered. Information provided in the Children's
Health Valuation Handbook (U.S. EPA 2003),
when used in conjunction with the Guidelines,
allows analysts to characterize benefits and impacts
of Agency policies and programs that affect
children.
The Handbook is a reference tool for analysts
conducting economic analyses of EPA policies
when those policies are expected to affect risks
to children's health. A major emphasis of the
Handbook is ensuring that a regulation or policy's
economic impacts on children are fully considered
in supporting analyses. This analysis includes
incorporating children's health considerations
in an assessment of efficiency, as well as in any
distributional analysis focused on children.
Decision makers may also find it useful to have
information on a policy's specific impact on
children's health, regardless of whether the impact
heavily influences overall benefit-cost analysis.
Economic factors may also play a role in other
analyses that evaluate children's environmental
health impacts. For example, if a higher
proportion of children live in poverty, the ability
of households with children to undertake averting
behaviors might be compromised. This type of
information could inform the exposure assessment.
.3 Intersection Between
Environmental Justice and
Children's Health
The burden of health problems and environmental
exposures is often borne disproportionately by
children from low-income communities and
minority communities (e.g., Israel et al. 2005;
Lanphear et al. 1996; Mielke et al. 1999; Pastor et
al. 2006).
The challenge for EPA is to integrate both
environmental justice and lifestage susceptibility
considerations for children where appropriate
when conducting distributional analysis. This
is especially true when short-term exposure to
environmental contaminants such as lead or
mercury early in life can lead to life-long health
consequences.
jr Distributional
isiderations
lerly
Another important lifestage to consider
is that of the elderly.59 "While there are no
standard procedures for including the elderly
in a distributional analysis, EPA stresses the
importance of addressing environmental issues
that may adversely impact them. Most of the
Agency's work in this area has been related to risk
and exposure assessment.
Older adults may be more susceptible to adverse
effects of environmental contaminants due to
differential exposures arising from physiological
and behavioral changes with age, disease status,
drug interactions, as well as the body's decreased
capacity to defend against toxic stressors. These
considerations are highlighted in EPA's Exposure
Factors Handbook (U.S. EPA 201 Id) and have led
EPA's Office of Research and Development to
consider an exposure factors handbook specifically
for the aging (see U.S. EPA 2007). Additionally,
the toxicokinetic and toxicodynamic impacts of
environmental agents in older adults have been
considered in EPA's document entitled Aging and
Toxic Response: Issues Relevant to Risk Assessment
(U.S. EPA 2005b).60
Intergenerationa! Impacts
Concern for intergenerational impacts arises
when those affected by a policy are not yet alive
when the policy is developed. If a policy's benefits,
costs, and impacts primarily fall upon the current
59 There is a lack of broad agreement about the beginning of the "elderly"
lifestage. The U.S. and other countries typically define this lifestage to
begin at the traditional retirement age of 65, but, for example, the U.N.
defines "elderly" to begin at age 60 (U.S. EPA 2005b).
60 Available at http://cfpub.epa.gov/ncea/cfm/recordisplay.
cfm?deid=156648 (accessed on December 1,2011).
11-22 Guidelines for Preparing Economic Analyses I May 2014
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
generation, or if policy decisions are reversible
within this time frame, there is little need for
explicit consideration of intergenerational impacts.
However, in other cases, benefits and/or costs of
the policy will be borne by future generations,
and it is important to consider impacts on these
generations. One such case would be policies to
reduce greenhouse gases, which are expected to
result in benefits related to reduced changes in
climate for future generations. Other examples
may relate to toxic chemical exposures. Exposures
to parents prior to their child's conception can
result in adverse health effects in the child,
including effects that may not become apparent
until the child reaches adulthood.61
Assessing intergenerational impacts can be
related to the social welfare function approach,
described in Text Box 10.1 of this chapter, and
to social discounting. In both cases, normative
judgments need to be made about which there
is no consensus. Under the Ramsey approach
to intergenerational discounting, this judgment
is reflected in a "pure rate of time preference"
parameter that weighs the welfare of current and
future generations. See Section 6.3.1 for more
information on intergenerational discounting
and debate about the value of this parameter.
One way to clarify distributional consequences
if intergenerational impacts are important is to
display time paths of benefits and costs without
discounting, as recommended in Chapter 6 of
these Guidelines.
elusion
This chapter provides a variety of tools, analytical
considerations and guidance for conducting
distributional analyses for environmental justice,
children's environmental health and other factors.
Tools and methods are intended to be flexible
enough to accommodate various data and other
constraints associated with particular scenarios,
while introducing consistency and rigor in the way
regulatory analyses consider distributional effects.
Methods for analyzing distributional impacts in
the context of EJ, in particular, are continually
being discussed, debated, and improved. For
instance, EPA is in the process of developing more
specific guidance on considering environmental
justice concerns when planning human health risk
assessments (U.S. EPA 2012b). Updates to this
chapter about strengths and limitations of various
analytical options, as well as new approaches, will
be added when appropriate.
61 See U.S. EPA (2006a) and WHO (2007). The latter is available at
http://www.who.int/ipcs/features/ehc/en/index.html (accessed on
January 11,2013).
Guidelines for Preparing Economic Analyses I May 2014 10-23
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Chapter 10 Environmental Justice, Children's Environmental Health and Other Distributional Considerations
11-24 Guidelines for Preparing Economic Analyses I May 2014
-------
Chapter
Presentation of Analysis and Results
This chapter provides some general guidance for presenting analytical results
to policy makers and others interested in environmental policy development.
Economic analyses play an important role throughout the policy development
process. From the initial, preliminary evaluation of potential options through
the preparation of a final economic analysis document, economic analysts
participate in an interactive process with policy makers. The fundamental goal of this process
is to collect, analyze, and present information useful for policy makers.
Economic analysis is often motivated by a desire to find an optimal outcome, such as a
degree of stringency in a regulation, or a level of provision of a public good that yields the
largest possible net benefits. Environmental statutes sometimes mandate criteria other than
economic efficiency, such as best available control technology or lowest achievable emission
rate. Policy makers rely on quantitative analysis to promulgate these approaches. In particular
they rely on analyses that delineate the costs, benefits, or other impacts of a wide range of
control options.
This guidance for presenting inputs, analyses, and results applies at all stages of this process,
not only for the final document embodying the completed economic analysis. Conveying
uncertainty effectively and reporting critical assumptions and key unquantified effects to
decision makers is critical at all points in the policy-making process.
This chapter begins by providing general guidance on how to present the results of
economic analyses, with a particular emphasis on presenting benefits and costs, including
those that cannot be quantified and/or put into dollar terms. The chapter then discusses
the components, or inputs, of an economic analysis, and how their effect on the economic
analysis can best be communicated.
I i ,1 h K'mn\n'i\ Fh .suits of
nomic Analyses
Hie presentation of the results of an economic
analysis should be thorough and transparent. The
reader should be able to understand:
• What the primary conclusions of the economic
analysis are;
• How the benefits and costs were estimated;
• What the important non-quantified or non-
monetized effects are;
• What key assumptions were made for the
analysis;
• What the primary sources of uncertainty are in
the analysis; and
• How those sources of uncertainty affect the
results.
Guidelines for Preparing Economic Analyses I December 2010 11-1
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Chapter 11 Presentation of Analysis and Results
An economic analysis of regulatory or policy
options should present all identifiable costs and
benefits that are incremental to the regulation or
policy under consideration. These should include
directly intended effects and associated costs, as well
as ancillary (or co-) benefits and costs.
Benefits and costs should be reported in monetary
terms whenever possible. In reality, however, there
are often effects that cannot be monetized, and the
analysis needs to communicate the full richness of
benefit and cost information beyond what can be
put in dollar terms. Benefits and costs that cannot
be monetized should, if possible, be quantified (e.g.,
expected number of adverse health effects avoided).
Benefits and costs that cannot be quantified should
be presented qualitatively (e.g., directional impacts
on relevant variables). Section 11.1.2 contains more
detailed guidance on presenting this information in
EPA's economic analyses.
Agencies are also required to provide OMB with
an accounting statement reporting benefit and cost
estimates when sending over each economically
significant rule. Analysts should rely upon these
Guidelines and Circular A-4 for developing these
estimates. Circular A-4 describes the accounting
statement on pages 44-46 and contains a suggested
format for this accounting statement.1
In addition to requirements under Circular
A-4, the 2010 OMR Annual Report to Congress
on the Costs and Benefits of Federal Regulations
asks agencies to provide a "simple, clear table
of aggregated costs and benefits" of each
economically significant rule in the regulatory
Preamble of the Federal Register Notice and in the
Executive Summary of the RIA (OMB 2010a, p.
51). EPA's guidance for satisfying these criteria is
described more fully in Section 11.1.2 as part of
the Agency's general guidance on reporting the
results of benefit-cost analysis (BCA).
The results of economic analyses of environmental
policies should generally be presented in three
sections.
1 The accounting statement is on page 47 of Circular A-4, available at
www.whitehouse.gov/sites/default/files/omb/assets/omb/circulars/
a004/a-4.pdf (accessed on January 21,2011).
• Results from BCA. Estimates of the net
social benefits should be presented based on
the benefits and costs expressed in monetary
terms. Non-monetized and unquantifiable
benefits and costs should also be included and
described in the presentation.
• Results from cost-effectiveness analysis
(CEA). Under OMB Circular A-4, CEA
should generally be performed for rules in
which the primary effect is human health or
safety. Results of these analyses should also be
presented when they are conducted.2
• Results from economic impact analysis
(EIA) and distributional assessments.
Results of the EIA should be reported,
including predicted effects on prices, profits,
plant closures, employment, and any other
effects. Distributional impacts for particular
groups of concern, including small entities,
governments, and environmental justice
populations should also be presented.
The relative importance of these three sections will
depend on the policy and statutory context of the
analysis.
n,"i, e Pit ntir t; i'hf hesults of
Ben lalyses
When presenting the results of a BCA, the
expected benefits and costs of the preferred
regulatory option should be reported, together
with the expected benefits and costs of alternative
approaches. OMB's Circular A-4 requires that
at least one alternative be more stringent and
one less stringent than the preferred option,
and the incremental costs and benefits would be
reported for each increasingly stringent option.
Separate time streams of benefits and costs should
be reported, in constant (inflation-adjusted),
undiscounted dollars. Per the discussion in
2 The Institute of Medicine (I0M) (2006) recently issued
recommendations to regulatory agencies on how to perform health-
based CEA. Recent examples of CEA can be found in appendices of
several recent RIAs including those for PM NAAQS [see Appendix G
listed at http://www.epa.gov/ttn/ecas/ria.html (accessed March 13,
2011)] and the Ground Water Rule [see Appendix H listed at
http://www.epa.gov/safewater/disinfection/gwr/regulation.html
(accessed March 13,2011)].
11-2
Guidelines for Preparing Economic Analyses I December 2010
-------
Chapter 11 Presentation of Analysis and Results
Chapter 6, appropriately discounted benefits and
costs should be reported as well.
Ideally, all benefits and costs of a regulation
would be expressed in monetary terms, but this
is almost never possible because of data gaps,
unquantifiable uncertainties, and other challenges.
It is important not to exclude an important benefit
or cost category from BCA even if it cannot be
placed in dollar terms. Instead, such benefits
and costs should be expressed quantitatively if
possible (e.g., avoided adverse health impacts).
If important benefit or cost categories cannot
be expressed quantitatively, they should be
discussed qualitatively (e.g., a regulations effect on
technological innovation).
Quantifiable benefits and costs, properly
discounted, should be compared to determine
a regulations net benefits, even if important
benefits or costs cannot be monetized. However,
an economic analysis should assess the likelihood
that non-monetized benefits and costs would
materially alter the net benefit calculation for a
given regulation.
Incremental benefits, costs, and net benefits of
moving from less to more stringent regulatory
alternatives should also be presented. If a
regulation has particularly significant impacts
on certain groups or sub-populations, the
various options' incremental impacts on these
subpopulations or source categories should be
reported. This should include a discussion of
incremental changes in quantified and qualitatively
described benefits and costs.
Given the number of potential models presented
in Chapters 7 and 8, the analyst should take care
to clearly indicate the correspondence between
the benefit and cost estimates. For example, the
cost analysis may include results from a general
equilibrium model but the benefit analysis may
only include partial equilibrium effects.3 In this
case, the cost side of the equation includes general
equilibrium feedback effects while the benefit
3 While there have been some attempts to include benefit estimates in
general equilibrium models, these efforts are nascent (Sieg etal. 2004,
Yang et al. 2004, and Jena et al. 2008).
side does not. This difference should be clearly
presented and explained.
The tables at the end of this chapter contain
templates for presenting information on regulatory
benefits and costs, including those benefits that
cannot be quantified or put into dollar terms.
The analyst's primary goal, using these tables, is to
communicate the full richness of benefit and cost
information instead of focusing narrowly on what
can be put in dollar terms. Some guiding principles
for constructing these tables follow.
• All meaningful benefit and costs are included
in all of the tables even if they cannot be
quantified or monetized. Not only does this
provide consistency for the reader, but it
also maintains important information on
the context of the quantified and monetized
benefits.
• The types of benefits and costs are described
briefly in plain terms to make them clearer to
the public and to decision makers, and they
should be well-defined and mutually exclusive,
to the extent possible. Benefits should be
grouped a manner consistent with the
categories in Table 7.1 of Chapter 7, although
the order and specific characterization can be
expected to vary by rule as needed.
• The benefits are expressedfirst in natural
or physical units (i.e., number) to provide
a more complete picture of what the rule
accomplishes. These units are not discounted
as they would be in a CEA because the goal
here is to describe what might be termed the
"physical scope" of the rule's benefits. It may
be the case that physical or natural units are
not relevant for presenting costs.
• Explanatory notes accompany each benefit and
cost entry and can be used to describe whatever
the most salient or important points are about
scientific uncertainty, the type of benefit or
cost, how it is estimated, or the presentation.
The benefit categories in these templates (e.g.,
improved human health, improved environment,
and other benefits,) will need to be revised to
reflect the benefits categories for the rule under
Guidelines for Preparing Economic Analyses I December 2010 I
-------
Chapter 11 Presentation of Analysis and Results
Tabk i f i Template for Regulatory Benefits Checklist
BUI
Benefits
Improved Human Health
Reduced incidence of adult premature
mortality from exposure to PM25
Effect can be
Quantified?
(put in numeric
terms)
V
Effect can be
Monetized?
(put in dollar
terms)
V
More Information
(e.g., reference to
section of the economic
analysis)
v'.y., Ov'v' jii j.£ ji
the economic analysis
• Reduced incidence of fetal loss from reduced
exposure to disinfection byproducts
V
--
Notes and reference to
section of the economic
analysis
• Unquantified human health benefit with a brief
Improved Environment
--
--
Notes and reference
Fewer fish killed from reduced nutrient loadings
into waterways
V
V
Notes and reference
• Improved timber harvest from lower tropospheric
ozone concentrations
V
V
Notes and reference
Other environmental benefit with a brief description
Other Benefits
—
—
Notes and reference
~ Fuel savings from improved efficiency in
automobiles and light trucks
V
V
Notes and reference
• Other benefit with a brief description
"
"
Notes and reference
consideration. Simpler analyses may need only
the overview (Table 11.1) and the final summary
(Table 11.4).
Table 11.1 is a quick-glance summary of regulatory
benefits and costs, the extent to which they could
be quantified and monetized, and a reference
to where they are more fully characterized or
estimated in the economic analysis. Some benefits
may be described only qualitatively.
Table 11.2 reports benefits in non-monetary terms
along with the units and additional explanatory
notes. The goal of this table is to communicate the
physical scope of the regulations benefits and costs
rather than the dollar equivalent. Benefits here do
not need to be discounted to present value, but the
time associated with the quantities should be made
clear (e.g., "annual" or "more than ten years").
Table 11.3 reports benefits in monetary terms
along with a total for dollar-valued benefits. Here
it is important to specify the reference year for the
dollars (i.e., real terms), the discount rate(s) used,
and the unit value and/or source.
Table 11.4 contains a template for bringing all this
information together in summary that includes
the type of benefit or cost, how it is measured,
its quantity, and dollar benefits. When multiple
regulatory options are included in this table, it
is appropriate for including in the regulatory
preamble as requested by OMB.
Consistent with recommendations in these
Guidelines for communicating uncertainty,
quantitative entries should generally include a
central or best estimate in addition to a range
or confidence interval. The ability to do this, of
course, may be limited by data availability.
, k Pit ntii t; i'hf hesults of
jt-Effectiveness Analyses
When BCA is not possible, CEA may be the best
available option. The cost-effectiveness of a policy
Guidelines for Preparing Economic Analyses I December 2010
-------
Chapter 11 Presentation of Analysis and Results
Tabl irnpiate for Quantified Regulatory Benefits
Quantified Benefits
Quantified
More Information
Benefits
Benefits
(confidence
interval or range)
Units
(w/possible reference to
section of the economic
analysis)
Improved Human Health
Reduced incidence of adult premature
estimate
expected avoided
expected
e.g., range represents
mortality from exposure to PM25
(range)
premature deaths
per year
confidence interval
• Reduced incidence of fetal loss from reduced
estimate
expected avoided
fetal losses per
year
e.g., confidence interval
cannot be estimated.
exposure to disinfection byproducts
(range)
Range based on
alternative studies
• Unquantified human health benefit with a
*
*
e.g., data do not allow
brief description
for quantification
Improved Environment
• Fewer fish killed from reduced nutrient loadings
estimate
thousands of fish
Notes
into waterways
(range)
per year
(reference)
• Improved timber harvest from lower
tropospheric ozone concentrations
estimate
(range)
thousands of
board feet per
year
Notes
(reference)
• Other environmental benefit with a brief
*
*
Notes
f rpfpmnpp)
Other Benefits
(l UlUl Ul luU/
millions of
• Fuel savings from improved efficiency in
estimate
gallons of
Notes
automobiles and light trucks
(range)
gasoline reduced
per year
(reference)
• Other benefit with a brief description
*
*
Notes
(reference)
Note: * indicates the benefit cannot be quantified with available information
option is calculated by dividing the annualized cost
of the option by non-monetary benefit measures.
Options for such measures range from quantities of
pollutant emissions reduced, measured in physical
terms, to a specific improvement in human health
or the environment, measured in reductions in
illnesses or changes in ecological services rendered.
In the context of RIA, or other analyses of
specific regulatory or policy options, CEA is
most informative when several different options
are analyzed. Hie analysis should include at least
one option that is less stringent and at least one
option that is more stringent than the preferred
option. Hie incremental costs and non-monetary
benefit yield of each option, in order of increasing
stringency, should be reported.
Hie non-monetary measure of benefits used in a
CEA must be chosen with great care to facilitate
valid comparisons across options. Hie closer the
chosen measure is to the variable that directly
impacts social welfare, the more robust a CEA
will be. Consider the following steps that a typical
environmental economic assessment follows:
• Changes in emissions are estimated (e.g., tons
of emissions); then
Guidelines for Preparing Economic Analyses I December 2010 i
-------
Chapter 11 Presentation of Analysis and Results
Table 11.3 - Template for Dollar-Valued Regulatory Benefits
DuMai-Valued Benef
Benefit
Improved Human Health
Dollar Benefits
(millions per year)
Basis of Value
More Information
(w/possible reference)
Reduced incidence of adult premature
mortality from exposure to PM25
$ estimate
($ range)
e.g., $X based on
Agency guidance
Notes
(reference)
• Reduced incidence of fetal loss from
reduced exposure to disinfection byproducts
*
Not available
Notes
(reference)
• Unquantified human health benefit
with a brief description
Improved Environment
* J *
e.g., data insufficient
to quantify
(reference)
e.g., range reflects
two different valuation
approaches
(reference)
• Fewer fish killed from reduced nutrient
loadings into waterways
$ estimate
($ range)
e.g., $X based on
WTP for recreational
fishing
• Improved timber harvest from lower
tropospheric ozone concentrations
$ estimate
($ range)
e.g., change in
consumer and
producer surplus
e.g., estimated from
market model across
several species
(reference)
• Other environmental benefit with a brief
description
Other Benefits
*
*
Notes
(reference)
e.g., there is debate on
how well fuel savings
represent consumer
benefits
(reference)
• Fuel savings from improved efficiency in
automobiles and light trucks
$ estimate
($ range)
e.g., $X, based on
net-of-tax average
per gallon price
• Other benefit with a brief description
*
Not available
Notes
(reference)
TOTAL Benefits that can be monetized
(Smillions per year)
$ estimate
($ range)
Note: * indicates the benefit cannot be quantified with available information.
• Changes in environmental quality (e.g.,
changes in ambient concentrations of a given
air pollutant) are estimated; then
• Changes in human health or welfare (e.g.,
changes in illness or visibility) are estimated.
Each successive step in this sequence yields a better
measure for CEA.
To illustrate, consider a typical air pollution
scenario. Depending on where and when air
pollutants are released into the atmosphere, a
given ton of a particular pollutant can have widely
divergent impacts on ambient air quality. Similarly,
depending on when and where air quality
changes, widely different levels of human health
impacts may result. Particularly when different
regulatory approaches are under consideration
(e.g., regulation of different source categories in
different locations), failing to standardize the
analyses on the benefit measure that directly affects
human health or welfare will significantly reduce
Guidelines for Preparing Economic Analyses I December 2010
-------
Chapter 11 Presentation of Analysis and Results
labif srnplate for Summary of Benefits and Costs
Benefits
Notes: e.g., "annual average numbers; 2006 dollars annualized at 3% discount rate"
Best estimate, with range
Option 1
Proposed Option
Option 3
Source,
limitations, or
other key notes
Improved Human Health
Number \$ Millions
• Reduced incidence
of adult premature
mortality from
exposure to PM„
estimate
(range)
$ estimate
(range)
estimate
(range)
$ estimate
(range)
estimate
(range)
$ estimate
(range)
highlight most
important points,
as needed
• Reduced incidence
of fetal loss from
reduced exposure to
disinfection byproducts
estimate
(range)
*
estimate
(range)
*
estimate
(range)
*
e.g., no valuation
data exist. Effects
are sensitive to
dose-response
model.
• Unquantified human
health benefit with a
brief description
Improved Environment
Fewer fish killed
loadings into waterways
*
pstimatp
(range)
*
p^timatp
(range)
*
pstimafp
(range)
*
P^tjmatp
(range)
*
Pstimatp
(range)
*
P^tjmatp
(range)
e.g., risk data
insufficient for
quantification
• Improved timber
harvest from lower
tropospheric ozone
concentrations
estimate
(range)
$ estimate
(range)
estimate
(range)
$ estimate
(range)
estimate
(range)
$ estimate
(range)
Notes
• Other environmental
benefit with a brief
Other Benefits
*
*
*
*
*
*
Notes
• Fuel savings from
improved efficiency in
automobiles and light
trucks
estimate
(range)
$ estimate
(range)
estimate
(range)
$ estimate
(range)
estimate
(range)
$ estimate
(range)
Notes
• Other benefit with a
brief description
*
*
*
*
*
*
Notes
TOTAL Benefits that
can be monetized
(annualized, millions
$2006)
$ estimate
(range)
$ estimate
(range)
: e.g., total range
! may be overstated
$ estimate I becaus<;.of
I aggregation
( 9 ) | (See Section
| 8.1 of economic
analysis)
Note: * indicates the benefit cannot be quantified with available information.
Guidelines for Preparing Economic Analyses I December 2010 '
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Chapter 11 Presentation of Analysis and Results
Table 11,4 - Template for Summary of Benefits and Costs (continued)
Costs
2006 dollars annualized at 3% discount rate
Best estimate, with range
Option 1
Proposed
Option
Option 3
Source,
limitations, or
other key notes
$ Millions
$ Millions
$ Millions
• Initial capital costs with any brief
description and units.
$ estimate
(range)
$ estimate
(range)
$ estimate
(range)
e.g., estimated
from engineering
cost models
* Type of cost with a brief description and
units. (This could include non-monetized
costs.)
$ estimate
(range)
$ estimate
(range)
$ estimate
(range)
Notes
Type of cost with a brief description and
units. (This could include non-monetized
costs.)
$ estimate
(range)
$ estimate
(range)
$ estimate
(range)
Notes
TOTAL Costs that can be monetized
$ estimate
$ estimate
$ estimate
(annualized, millions $2006)
(range)
(range)
(range)
TOTAL Net Benefits that can be
monetized
(annualized, millions $2006)
$ estimate
$ estimate
$ estimate
(range)
(range)
(range)
the value of the analysis to decision makers (and
the public).
When presenting the results of a CEA, the
rationale for the selection of the non-monetary
benefit measure must be described in detail.
The presentation of results should also include
a discussion of the limitations of the analysis,
especially if an inferior measure, such as cost per
ton of pollutant, must be used.
CEA is most useful when the policy or regulation
in question affects a single endpoint. When
multiple endpoints are affected (e.g., cancer and
kidney failures), combining endpoints into a
single effectiveness measure is impossible unless
appropriate weighting factors exist for the multiple
endpoints. The theoretically correct weights to
apply are the dollar values associated with each
endpoint, but generally it is the absence of these
values that necessitates CEA. Therefore, it is not
possible to compare a policy or regulation that
reduces relatively more expected cancers, but
fewer expected cases of kidney failure, with one
that has the opposite relative effects. When this
occurs, the effects of each option for each endpoint
should be reported. A single endpoint may be
selected for calculating cost-effectiveness, while
other endpoints can be listed as ancillary benefits
(or, if possible, their monetary value should
be subtracted from the options cost prior to
calculating its cost-effectiveness) (OMB 2003).
The most cost-effective option — i.e., the
option with the lowest cost per unit of benefit
— is not necessarily the most economically
efficient. Moreover, other criteria, such as
statutory requirements, enforcement problems,
technological feasibility, or quantity and location
of total emissions abated may preclude selecting
the least-cost solution in a regulatory decision.
However, where not prohibited by statute, CEA
can indicate which control measures or policies are
inferior options.
11-8
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 11 Presentation of Analysis and Results
n, r - Pfi f i ntifiM thr. I-Tesuls of
EIA and Distributional Analyses
EIA and distributional outcomes focus on
disaggregating effects to show impacts separately
for the groups and sectors of interest. If costs and/
or benefits vary significantly among the sectors
affected by the policy, then both costs and benefits
should be shown separately for the different
sectors. Presenting results in disaggregated form
will provide important information to policy
makers that may help them tailor the rule to
improve its efficiency and distributional outcomes.
The results of the EIA should also be reported for
important sectors within the affected population
— identifying specific segments of industries,
regions of the country, or types of firms that may
experience significant impacts or plant closures
and losses in employment.
Reporting the results in distributional assessments
may include the expected allocation of benefits,
costs, or both for specific subpopulations including
those highlighted in the various mandates. These
include minorities, low-income populations,
small businesses, governments, not-for-profit
organizations, and sensitive and vulnerable
populations (including children). Where these
mandates specify requirements that depend on the
outcomes of the distributional analyses, such as the
Regulatory Flexibility Act, the presentation of the
results should conform to the criteria specified by
the mandate.
Vi:i A RepoMiM tht Lnects
of Uncertainty < suits of
Economic Analyses
Estimates of costs, benefits and other economic
impacts should be accompanied by indications
of the most important sources of uncertainty
embodied in the estimates, and, if possible, a
quantitative assessment of their importance.
OMB requires formal quantitative analysis of
uncertainties for rules with annual economic
effects of $1 billion or more.
In economic analysis, uncertainty encompasses
two different concepts:
* Statistical variability of key parameters; and
• Incomplete understanding of important
relationships.
Economic analyses of environmental policies
and regulatory options will frequently have to
accommodate both concepts. The importance
of statistical variability is commonly assessed
using Monte Carlo analyses (see U.S. EPA 1997).
Delphic panels, or expert elicitation techniques,
can help close knowledge gaps surrounding key
relationships (see IEc 2004).
Ideally, an economic analysis would present results
in the form of probability distributions that reflect
the cumulative impact of all underlying sources of
uncertainty. When this is impossible, due to time
or resource constraints, results should be qualified
with descriptions of major sources of uncertainty.
If at all possible, information about the underlying
probability distribution should be conveyed. (A
forthcoming section of these Guidelines will more
fully address uncertainty issues.)
As recommended in Chapter 6, many EPA
analyses will employ more than one discount
rate to reflect different underlying approaches to
discounting. When the choice of discount rate
affects the outcome of the analysis, analysts should
take extra care to convey the underlying theory and
assumptions to decision makers. See Chapter 6 for
more information.
t f - - ?• ^ ^eating
Data, Model Choices and
Assumptions, an iated
Uncertainty
An economic analysis of an environmental
regulation should carefully describe the data
used in the analysis, the models it relies on,
major assumptions that were made in running
the models, and all major areas of uncertainty in
each of these elements. Presentations of economic
analyses should strive for clarity and transparency.
An analysis whose conclusions can withstand close
scrutiny is more likely to provide policy makers
with the information they need to develop robust
environmental policies.
Guidelines for Preparing Economic Analyses I December 2010 I
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Chapter 11 Presentation of Analysis and Results
.1 Data
An economic analysis should clearly describe
all important data sources and references
used. Unless the data are confidential business
information or some other form of private data,
they should be available to policy makers, other
researchers, policy analysts and the public.
Providing documentation and access to the data
used in an analysis is crucial to the credibility and
reproducibility of the analysis.
EPA Order 5360.1 A2 (U.S. EPA 2000a) and
the applicable federal regulations established a
mandatory quality system for EPA. As required by
the quality system, all EPA offices have developed
quality management plans to ensure the quality of
their data and information products.
Until recently, federal quality assurance (QA)
requirements only applied to measurement and
collection ofprimary environmental data. This
meant that QA requirements often did not apply
to economic analyses, which usually rely on the use
of secondary data. However, this changed with the
introduction of QA requirements regarding use of
secondary data. In 2002 the Agency released QA
guidelines regarding use of secondary data, and
released Agency guidance, Guidancefor Quality
Assurance Project Plans, that includes procedures
for documenting secondary data (U.S. EPA
2002f).
In any economic analysis, there should be a
clear presentation of how data are used and a
concise explanation of why the data are suitable
for the selected purpose. The data's accuracy,
precision, representativeness, completeness,
and comparability should be discussed when
applicable. "When data are available from more
than one source, a rationale for choosing the
source of the data should be provided.
.2 Model Choices and
Assumptions
An economic analysis of an environmental
regulation should carefully describe the models it
relies on, the major assumptions made in running
the models (to be discussed more fully below), and
any areas of outstanding uncertainty. The analyst
should take particular care to explain any results
that might be viewed as counter-intuitive. In
particular, analysts should be careful not to accept
model output blindly. Any model that is used
without proper thought given to both its input
and output may become a "black box" insofar as
nonsensical results may result from a misspecified
scenario, a coding error, or any of a number of
other causes.
In the process of conducting an economic analysis,
it is sometimes necessary to bridge an information
gap by making an assumption. Analysts should
not simply note the information gap, but should
also justify the chosen assumption and provide a
rationale for choosing one assumption over other
plausible options. The analyst should take care not
to overlook information gaps that are filled with
a piece of information that is only slightly related
to the desired information. Analysts are advised to
keep a running list of assumptions. This will make
it easier to identify "key assumptions" for the final
report. The likely impact of errors in assumptions
should be characterized both in terms of direction
and magnitude of effect when feasible.
Maintaining a list of assumptions can benefit the
analysis in several ways. In the short run, a list
can serve to focus analysts' attention on those
assumptions with the greatest potential to affect
net benefits, possibly leading to new approaches
to bridging an information gap. In the long run,
highlighting information gaps may encourage
EPA or others to devote attention and resources to
generating that information.
Whenever the likely errors in a particular
assumption can be characterized numerically
or statistically, the factor is a good candidate
for sensitivity analysis or uncertainty analysis,
respectively. In many cases, only a narrative
description of the impact of errors in assumptions
is possible. The analyst should include a table that
clearly lays out all of the key assumptions and the
potential magnitude and direction of likely errors
in assumptions in the summary of results.
1 Guidelines for Preparing Economic Analyses I December 2010
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Chapter 11 Presentation of Analysis and Results
"r .3 Addressing Uncertarntv
Driv isumptions and
Model Choice
Every analysis should address uncertainties
resulting from the choices the analyst has made.
For example, many economic analyses performed
at EPA include assessments of economic impacts
expected to occur decades into the future.
Estimates of the future costs and benefits of a
regulation will be sensitive to assumptions about
growth rates for populations, source categories,
economic activity, and technological change, as
well as many other factors. Sensitivity analyses
on key variables in the baseline scenario should
be performed and reported when possible. This
allows the reader to assess the importance of the
assumptions made for the central case. Some of
these variables may be affected by a regulation,
particularly the assumed rate of technological
innovation. (Please see Chapter 5 for additional
guidance on specifying baselines.)
The impact of using alternative assumptions or
alternative models can be assessed quantitatively in
many cases.
• Alternative analysis. An analysis of
alternative assumptions or "alternative
analysis" is the substitution of one of the key
assumptions with another. In presenting the
results, the alternative analysis is presented
with equal weight as the primary analysis and
is presented alongside of the primary analysis,
even if the probability of the alternative
assumption differs from that of the primary
analysis. Because performing an alternative
analysis on all the assumptions in an analysis
is prohibitively resource intensive, the analyst
should focus on the assumptions that have
the largest impact on the final results of the
particular analysis. Thus, keeping a running
list of the "key assumptions" in an analysis is
recommended.
• Sensitivity analysis. A sensitivity analysis
is used to assess how the final results or
other aspects of the analysis change as input
parameters change, particularly when only
point estimates of parameters are available.
A regulatory impact analysis benefits from
knowing how the cost-effectiveness of a
particular technology changes as fuel prices
change, or how the net benefits of aBCA
change as one of the model coefficients change.
Typically, a sensitivity analysis measures how
the model's output changes as one of the input
parameters change. Joint sensitivity analysis
(varying more than one parameter at a time) is
sometimes useful as well.
• Model uncertainty. In addition to explaining
the uncertainty in a model's parameters,
analysts should discuss the uncertainty
generated by the choice of model. Multiple
models are often available to the analyst, and
choosing among them is similar to making an
assumption. Implicit in the choice of a model
are many factors. For example, one model
may take long-run effects into account while
another model does not. When possible,
presenting results of an alternate model can
inform the reader. When resource limitations
prevent the use of an alternative model, it is
still often possible to predict the direction
and likely magnitude of the use of an alternate
model, and the analyst should present this
information to the reader.
) r, Usf ;;ii E.rsnomk ; lalyses
The primary purpose of conducting economic
analysis is to provide policy makers and others
with detailed information on a wide variety
of consequences of environmental policies.
One important element these analyses have
traditionally provided to the policy-making
process is estimates of social benefits and costs —
the economic efficiency of a policy. For this reason,
these Guidelines reflect updated information
associated with procedures for calculating benefits
and costs, monetizing benefits estimates, and
selecting particular inputs and assumptions.
Determining which regulatory options are
best even on the restrictive terms of economic
efficiency is often made difficult by uncertainties in
data and by the presence of benefits and costs that
can be quantified but not monetized, or that can
only be qualitatively assessed. Even if the criterion
of economic efficiency were the sole guide to
Guidelines for Preparing Economic Analyses I December 2010
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Chapter 11 Presentation of Analysis and Results
policy decisions, social benefit and costs estimates
alone would not be sufficient to define the best
policies.
A large number of social goals and statutory
and judicial mandates motivate and shape
environmental policy For this and other reasons,
these Guidelines contain information concerning
procedures for conducting analyses of other
consequences of environmental policies, such
as economic impacts and equity effects. This is
consistent with the fact that economic efficiency is
not the sole criterion for developing good public
policies.
Even the most comprehensive economic analyses
are but part of a larger policy development
process, one in which no individual analytical
feature or empirical finding dominates. The role
of economic analysis is to organize information
and comprehensively assess the economic
consequences of alternative actions — benefits,
costs, economic impacts, and equity effects
— and the trade-offs among them. Ultimately
statutory requirements dictate if and how the
analytic results are used in standard setting. In
any case, these results, along with other analyses
and considerations, serve as important inputs for
the broader policy-making process and serve as
important resources for the public.
Guidelines for Preparing Economic Analyses I December 2010
-------
Appei
Economic Theory
his appendix provides a brief overview of the fundamental theory underlying
I the approaches to economic analysis discussed in Chapters 3 through 9. The
first section summarizes the basic concepts of the forces governing a market
economy in the absence of government intervention. Section A.2 describes
why markets may behave inefficiently. If the preconditions for market efficiency
are not met, government intervention can be justified.1 The usefulness of benefit-cost analysis
(BCA) as a tool to help policy makers determine the appropriate policy response is discussed
in Section A.3. Sections A.4 and A.5 explain how economists measure the economic impacts
of a policy and set the optimal level of regulation. Section A.6 concludes and provides a list of
additional references.
A.1 Mark onomy
Hie economic concept of a market is used to describe
any situation where exchange takes place between
consumers and producers. Economists assume that
consumers purchase the combination of goods
that maximizes their well-being, or "utility," given
market prices and subject to their household budget
constraint. Economists also assume that producers
(firms) act to maximize their profits. Economic
theory posits that consumers and producers are
rational agents who make decisions taking into
account all of the costs — the full opportunity
costs — of their choices, given their own resource
constraints.2 The purpose of economic analysis is to
understand how the agents interact and how their
interactions add up to determine the allocation
of society's resources: what is produced, how it is
produced, for whom it is produced, and how these
decisions are made. The simplest tool economists use
to illustrate consumers' and producers' behavior is a
market diagram with supply and demand curves.
1 EPA's mandates frequently rely on criteria other than economic efficiency
so policies that are not justified due to a lack of efficiency are sometimes
adopted.
2 Opportunity cost is the next best alternative use of a resource. The ful I
opportunity cost of producing (consuming) a good or service consists
of the maximum value of other goods and services that could have been
produced (consumed) had one not used the limited resources to produce
(purchase) the good or service in question. For example, the full cost of
driving to the store includes not only the price of gas but also the value of
the time required to make the trip.
The demand curve for a single individual shows the
quantity of a good or service that the individual will
purchase at any given price. This quantity demanded
assumes the condition of holding all else constant,
i.e., assuming the budget constraint, information
about the good, expected future prices, prices
of other goods, etc. remain constant. The height
of the demand curve in Figure A.l indicates the
maximum price, P, an individual with units of a
good or service would be willing to pay to acquire
an additional unit of a good or service. This amount
reflects the satisfaction (or utility) the individual
receives from an additional unit, known as the
marginal benefit of consuming the good. Economists
generally assume that the marginal benefit of an
additional unit is slighdy less than that realized by
Figure A.1 - Marginal and Total WTP
Price
Demand
Curve
. Quantity
Q Q
Guidelines for Preparing Economic Analyses I December 2010 1-1
-------
Appendix A Economic Theory
the previous unit. Hie amount an individual is
willing to pay for one more unit of a good is less
than the amount she paid for the last unit; hence,
the individual demand curve slopes downward.
A market demand curve shows the total quantity
that consumers are willing to purchase at different
price levels, i.e., their collective willingness to pay
(WTP) for the good or service. In other words,
the market demand curve is the horizontal sum of
all of the individual demand curves.
The concept of an individual s WTP is one of the
fundamental concepts used in economic analyses,
and it is important to distinguish between total
and marginal WTP. Marginal WTP is the
additional amount the individual would pay for
one additional unit of the good. The total WTP is
the aggregate amount the individual is willing to
pay for the total quantity demanded (Qj)- Figure
A.l illustrates the difference between the marginal
and total WTP. The height of the demand curve
at a quantity j gives the marginal WTP for
the yh unit. The height of the demand curve
at a quantity gives the marginal WTP for the
QJ1* unit. Note that the marginal WTP is greater
for the ^ unit. The total WTP is equal to the
sum of the marginal WTP for each unit up to Q^.
The shaded area under the demand curve from the
origin up to shows total WTP.
Price
Supply
Curve
Quantity
Q Q
An individual producers supply curve shows the
quantity of a good or service that an individual
or firm is willing to sell (Qj at a given price. As
a profit-maximizing agent, a producer will only
be willing to sell another unit of the good if the
market price is greater than or equal to the cost
of producing that unit. The cost of producing
the additional unit is known as the marginal cost.
Therefore, the individual supply curve traces out
the marginal cost of production and is also the
marginal cost curve. Economists generally assume
that the cost of producing one additional unit is
greater than the cost of producing the previous
unit because resources are scarce. Therefore the
supply curve is assumed to slope upward. In Figure
A.2, the marginal cost of producing the unit
of the good is given by the height of the supply
curve at The marginal cost of producing the
Qs+jth unit of the good is given by the height of
the supply curve at Qs+j> which greater than the
cost of producing the unit, and greater than
the price, P. The total cost of producing units
is equal to the shaded area under the supply curve
from the origin to the quantity Q^. The market
supply curve is simply the horizontal summation
of the individual producers' marginal cost curves
for the good or service in question.
In a competitive market economy, the intersection
of the market demand and market supply curves
determines the equilibrium price and quantity
of a good or service sold. The demand curve
reflects the marginal benefit consumers receive
from purchasing an extra unit of the good (i.e., it
reflects their marginal WTP for an extra unit).
The supply curve reflects the marginal cost to the
firm of producing an extra unit. Therefore, at the
competitive equilibrium, the price is where the
marginal benefit equals the marginal cost. This is
illustrated in Figure A.3, where the supply curve
intersects the demand curve at equilibrium price
P and equilibrium quantity Q .
m 1 1 J
A counter-example illustrates why the equilibrium
price and quantity occur at the intersection of
the market demand and supply curves. In Figure
A.3, consider some price greater than Pm where
is greater than there is excess supply). As
producers discover that they cannot sell off their
inventories, some will reduce prices slighdy, hoping
to attract more customers. At lower prices consumers
will purchase more of the good increases)
although firms will be willing to sell less
Guidelines for Preparing Economic Analyses I December 2010
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Appendix A Economic Theory
Price
Supply =
Consurr
Surplus
Producer
Surplus
Marginal
Cost
Demand = Mar9inal
Benefit
Quantity
Q (= Q .= Q )
decreases). This adjustment continues until equals
Qj Hie reverse situation occurs if the price becomes
lower thanP . In that case, Q ,will exceedQ (i.e.,
m a
there is excess demand) and consumers who cannot
purchase as much as they would like are willing to
pay higher prices. Therefore, firms will begin to
increase prices, causing some reduction in the but
also increasing^. Prices will continue to rise until
equals At this point no purchaser or supplier will
have an incentive to change the price or quantity;
hence, the market is said to be in equilibrium.
Economists measure a consumers net benefit from
consuming a good or service as the excess amount
that she is willing to spend on the good or service
over and above the market price. The net benefit of
all consumers is the sum of individual consumers net
benefits — i.e., what consumers are willing to spend
on a good or service over and above that required
by the market. This is called the consumer surplus.
In Figure A.3, the market demands price P for the
purchase of quantity Q^. However, the demand
curve shows that there are consumers willing to
pay more than price P for all units prior to Q .
i y 1 m i -^-rn
Therefore, the consumer surplus is the area under
the market demand (marginal benefit) curve but
above the market price. Policies that affect market
conditions in ways that decrease prices by decreasing
costs of production (i.e., that shift the marginal cost
curve to the right) will generally increase consumer
surplus. This increase can be used to measure the
benefits that consumers receive from the policy.3
On the supply side, a producer can be thought
to receive a benefit if he can sell a good or service
for more than the cost of producing an additional
unit — i.e., its marginal cost. Figure A.3 shows
that there are producers willing to sell up to
units of the good for less then the market price P .
Hence, the net benefit to producers in this market,
known asproducer surplus, can be measured as the
area above the market supply (marginal cost) curve
but below the market price. Policies that increase
prices by increasing market demand for a good
(i.e., that shift the marginal benefit curve to the
right) will generally increase producer surplus. This
increase can be used to measure the benefits that
producers receive from the policy.
Economic efficiency is defined as the maximization
of social welfare. In other words, the efficient
level of production is one that allows society to
derive the largest possible net benefit from the
market. This condition occurs where the (positive)
difference between the total WTP and total
costs is the largest. In the absence of externalities
and other market failures (explained below), this
occurs precisely at the intersection of the market
demand and supply curves where the marginal
benefit equals the marginal cost. This is also the
point where total surplus (consumer surplus plus
producer surplus) is maximized. There is no way
to rearrange production or reallocate goods so
that someone is made better off without making
someone else worse off — a condition known as
Pareto optimality. Notice that economic efficiency
requires only that net benefits be maximized,
irrespective of to whom those net benefits accrue.
It does not guarantee an "equitable" or "fair"
distribution of these surpluses among consumers
and producers, or between sub-groups of
consumers or producers.
Economists maintain that if the economic conditions
are such that there are no market imperfections
(as discussed in Section A.2), then this
condition of Pareto-optimal economic efficiency
3 Section A.4.2 provides a more technical discussion of how consumer
surplus serves as a measure of benefits.
Guidelines for Preparing Economic Analyses I December 2010
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Appendix A Economic Theory
occurs automatically.4 That is, no government
intervention is necessary to maximize the sum
of consumer surplus and producer surplus. This
theory is summarized in the two Fundamental
Theorems of Welfare Economics, which originate
with Pareto (1906) andBarone (1908):
1. First Fundamental Welfare Theorem. Every
competitive equilibrium is Pareto-optimal.
2. Second Fundamental Welfare Theorem. Every
Pareto-optimal allocation can be achieved
as a competitive equilibrium after a suitable
redistribution of initial endowments.
One graphical representation of these results is
given in Figure A.4, which shows utility (welfare)
levels in a two-person economy.5 The curve
shown is the utility possibility frontier (UPF)
curve; the area within it represents the set of all
possible welfare outcomes. Each point on the
negatively sloped UPF curve is Pareto optimal
since it is not possible to increase the utility of
Figure A.4 - Utility Possibility Frontier
V
c
VB
H
A
\ UPF
\D
u
4 Technically, there are two types of efficiency. Allocative efficiency
means that resources are used for the production of goods and
services most wanted by society. Productive efficiency implies that
the least costly production techniques are used to produce any mix
of goods and services. Allocative efficiency requires that there be
productive efficiency but productive efficiency can occur without
allocative efficiency. Goods can be produced at the least-costly method
without being most wanted by society. Perfectly competitive markets
in the long run will achieve both of these conditions, producing the
"right" goods (allocative efficiency) in the "right" way (productive
efficiency). These two conditions imply Pareto-optimal economic
efficiency. (See Varian 1992 or any basic econom ics text for a more
detailed discussion.)
5 Another, perhaps more commonly used, graphical tool to explain the
First and Second Welfare Theorems is an Edgeworth box. See Varian
(1992) or other basic economic textbook for a detailed discussion.
one person without decreasing the utility of the
other. If the initial allocation is at point A, then
the set of Pareto-superior (welfare-enhancing)
outcomes include all points in the shaded area,
bordered byH, V, and the UPF curve.6 If trading is
permitted, the First "Welfare Theorem applies and
the market will move the economy to a superior,
more efficient point such as B. Then the Second
"Welfare Theorem simply says that for any chosen
point along the UPF curve, given a set of lump
sum taxes and transfers, an initial allocation can be
determined inside the UPF from which the market
will achieve the desired outcome.7
A oris for Market or
Institutional
If the market supply and demand curves reflect
society's true marginal social cost andWTP,
then a laissez-faire market (i.e., one governed
by individual decisions and not government
authority) will produce a socially efficient result.
However, when markets do not fully represent
social values, the private market will not achieve the
efficient outcome (see Mankiw 2004, or any basic
economics text); this is known as a marketfailure.
Market failure is primarily the result of externalities,
market power, and inadequate or asymmetric
information. Externalities are the most likely
cause of the failure of private and public sector
institutions to account for environmental damages.
Externalities occur when markets do not account
for the effect of one individual's decisions on
another individual's well-being.8 In a free market
producers make their decisions about what and
how much to produce, taking into account the
cost of the required inputs — labor, raw materials,
6 Note that efficiency could be obtained by moving along the vertical
line 1/ which keeps utility of person 1 (U) constant while increasing
utility of person 2 (U), or by moving along the horizontal line H, which
only shows improvements in utility for person 1. Moving to point B
improves the utility for both individuals.
7 Note that outcomes on the frontier such asC and D, although efficient,
may not be desired on equity, or fairness, grounds.
8 More formally, an externality occurs when the production or
consumption decision of one party has an unintended negative
(positive) impact on the profit or utility of a third party. Even if one
party compensates the other party, an externality still exists (Perman
etal. 2003). See Baumol and Oates (1988) or any basic economics
textbook for similar definitions and more detailed discussion.
A-4
Guidelines for Preparing Economic Analyses I December 2010
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Appendix A Economic Theory
machinery, energy. Consumers purchase goods
and services taking into account their income and
their own tastes and preferences. This means that
decisions are based on the private costs and private
benefits to market participants. If the consumption
or production of these goods and services poses an
external cost or benefit on those not participating
in the market, however, then the market demand
and supply curves no longer reflect the true
marginal social benefit and marginal social cost.
Hence, the market equilibrium will no longer be
the socially (Pareto) efficient outcome.
Externalities can arise for many reasons.
Transactions costs or poorly defined property
rights can make it difficult for injured parties to
bargain or use legal means to ensure that the costs
of the damages caused by polluters are internalized
into their decision making.9 Activities that pose
environmental risks may also be difficult to link to
the resulting damages and often occur over long
periods of time. Externalities involve goods that
people care about but are not sold in markets.10
Air pollution causes ill health, ecological damage,
and visibility impacts over a long time period,
and the damage is often far from the source (s)
of the pollution. The additional social costs of
air pollution are not included in firms' profit
maximization decisions and so are not considered
when firms decide how much pollution to emit. The
lack of a market for clean air causes problems and
provides the impetus for government intervention
in markets involving polluting industries.
9 A property right can be defined as a bundle of characteristics that
confer certain powers to the owner of the right: the exclusive right to
the choice of use of a resource, the exclusive right to the services of a
resource, and the right to exchange the resource at mutually agreeable
terms. Externalities typically arise from the violation of one or more
of the characteristics of well-defined property rights. This implies
that the distortions resulting from an externality can be eliminated by
appropriately establishing these rights. This insight is summarized by
the famous "Coase theorem" which states that if property rights over an
environmental asset are clearly defined, and bargaining among owners
and prospective users of the asset is allowed, then externality problems
can be corrected and the efficient outcome will result regardless of who
was initially given the property right. The seminal paper is Coase (1960).
10 Often these are goods that exhibit public good characteristics. Pure
public goods are those that are non-rivalrous in consumption and
non-excludable. [See Perman et al. (2003) for a detailed discussion of
these, as well as congestible and open access resources — i.e., goods
that are neither pure public nor pure private goods.] Because exclusive
property rights cannot be defined for these types of goods, pure private
markets cannot provide for them efficiently.
Figure A.5 illustrates a negative externality
associated with the production of a good. For
example, a firm producing some product might
also be generating pollution as a by-product. The
pollution may impose significant costs — in the
form of adverse health effects, for example — on
households living downwind or downstream of
the firm. Because those costs are not borne by the
firm, the firm typically does not consider them
in its production decisions. Society considers
the pollution a cost of production, but the firm
typically will not. In this figure:
Figure A.5 - Negative Externality
Price
MSG
D (=MB)
Quantity
• D is the market demand (marginal benefit)
curve for the product;
• MPC is the firm's marginal private real-
resource cost of production, excluding the
cost of the firm's pollution on households;
• MSD is the marginal social damage of
pollution (or the marginal external cost) that
the firm is not considering; and
• MSC is society's marginal social cost
associated with production, including the cost
of pollution (MSC = MPC + MSD).
In an incomplete market, producers pay no
attention to external costs, and production occurs
where market demand (D) and the marginal
private real-resource cost {MPC) curves intersect
— at a price P and a quantity Q . In this case,
i m i J
net social welfare (total WTP minus total social
costs) is equal to the area of the triangle P0)PjXless
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Appendix A Economic Theory
the area of triangle XYZ.11 If the full social cost
of production, including the cost of pollution, is
taken into consideration, then the marginal cost
curve should be increased by the amount of the
marginal social damage (MSD) of pollution.12
Production will now occur where the demand
and marginal social cost (MSC) curves intersect
— at a price P* and a quantity (£. At this point
net social welfare (now equal to the area of the
triangle, PgPX, alone) is maximized, and therefore
the market is at the socially efficient point of
production. This example shows that when there
is a negative externality such as pollution, and the
social damage (external cost) of that pollution
is not taken into consideration, the producer
will oversupply the polluting good.13 The shaded
triangle (XYZ), referred to as the deadweight loss
(DWL), represents the amount that society loses
by producing too much of the good.
P«ne '• -¦ -< > lalysis
If a negative externality such as pollution exists,
an unregulated market will not account for its
cost to society, and the result will be an inefficient
outcome. In this case, there may be a need for
government intervention to correct the market
failure. A correction may take the form of dictating
the allowable level of pollution or introducing a
market mechanism to induce the optimal level of
pollution.14 Figure A.5 neatly summarizes this in a
single market diagram. To estimate the total costs
and benefits to society of an activity or program,
the costs and benefits in each affected market, as
well as any non-market costs or benefits, are added
up. This is done through BCA.
11 Recall from Section A.1 that total WTP is equal to the area under the
demand curve from the origin to the point of production (OP/QJ.
Total costs (to society) are equal to the area under the MSC curve from
the origin to the point of production (OP^QJ.
12 When conducting BCA related to resource stocks, the MSD or marginal
external cost is the present value of future net benefits that are lost to
due to the use of the resource at present. That is, exhaustible resources
used today will not be available for future use. These foregone future
benefits are called user costs in natural resource economics (see Scott
1953,1955). The marginal user cost is the user cost of one additional
unit consumed in the present, and is added together with the marginal
extraction cost to determine the MSC of resource use.
13 Similarly, the private market will undersupply goods for which there are
positive externalities, such as parks and open space.
14 Chapter 4 discusses the various regulatory techniques and some non-
regulatory means of achieving pollution control.
BCA can be thought of as an accounting
framework of the overall social welfare of a
program, which illuminates the trade-offs involved
in making different social investments (Arrow et
al. 1996). It is used to evaluate the favorable effects
of a policy action and the associated opportunity
costs. The favorable effects of a regulation are the
benefits, and the foregone opportunities or losses
in utility are the costs. Subtracting the total costs
from the total monetized benefits provides an
estimate of the regulations net benefits to society.
An efficient regulation is one that yields the
maximum net benefit, assuming that the benefits
can be measured in monetary terms.
BCA can also be seen as a type of market test
for environmental protection. In the private
market, a commodity is supplied if the benefits
that society gains from its provision, measured
by what consumers are willing to pay, outweigh
the private costs of producing the commodity.
Economic efficiency is measured in a private
market as the difference between what consumers
are willing to pay for a good and what it costs to
produce it. Since clean air and clean water are
public goods, private suppliers cannot capture
their value and sell it. The government determines
their provision through environmental protection
regulation. BCA quantifies the benefits and costs
of producing this environmental protection in the
same way as the private market, by quantifying the
WTP for the environmental commodity. As with
private markets, the efficient outcome is the option
that maximizes net benefits.
The key to performing BCA lies in the ability
to measure both benefits and costs in monetary
terms so that they are comparable. Consumers
and producers in regulated industries and
the governmental agencies responsible for
implementing and enforcing the regulation (and
by extension, taxpayers in general) typically
pay the costs. The total cost of the regulation is
found by summing the costs to these individual
sectors. (An example of this, excluding the costs
to the government, is given in Section A.4.3.)
Since environmental regulation usually addresses
some externality, the benefits of a regulation
often occur outside of markets. For example, the
1-6
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Appendix A Economic Theory
primary benefits of drinking water regulations
are improvements in human health. Once the
expected reduction in illness and premature
mortality associated with the regulation is
calculated, economists use a number of techniques
to estimate the value that society places on these
health improvements.15 These monetized benefits
can then be summed to obtain the total benefits
from the regulation.
Note that in BCA gains and losses are weighted
equally regardless of to whom they accrue.
Evaluation of the fairness, or the equity, of the
net gains cannot be made without specifying
a social welfare function. However there is no
generally agreed-upon social welfare function, and
assigning relative weights to the utility of different
individuals is an ethical matter that economists
strive to avoid. Given this dilemma, economists
have tried to develop criteria for comparing
alternative allocations where there are winners
and losers without involving explicit reference
to a social welfare function. According to the
Kaldor-Hicks compensation test, named after
its originators Nicholas Kaldor and J.R. Hicks, a
reallocation is a welfare-enhancing improvement
to society if:
1. The winners could theoretically compensate the
losers and still be better off; and
2. The losers could not, in turn, pay the winners to
not have this reallocation and still be as well off
as they would have been if it did occur (Perman
et al. 2003).
While these conditions sound complex, they are
met in practice by assessing the net benefits of a
regulation through BCA. The policy that yields
the highest positive net benefit is considered
welfare enhancing according to the Kaldor-
Hicks criterion. Note that the compensation
test is stated in terms of potential compensation
and does not solve the problem of evaluating
the fairness of the distribution of well-being in
society. "Whether and how the beneficiaries of a
regulation should compensate the losers involves
15 Chapter 7 discusses a variety of methods economists use to value
environmental improvements.
a value judgment and is a separate decision for
government to make.
Finally, BCA may not provide the only
criterion used to decide if a regulation is in
society's best interest. There are often other,
overriding considerations for promulgating
regulation. Statutory instructions, political
concerns, institutional and technical feasibility,
enforceability, and sustainability are all important
considerations in environmental regulation. In
some cases a policy may be considered desirable
even if the benefits to society do not outweigh
its costs, particularly if there are ethical or equity
concerns.16 There are also practical limitations
to BCA. Most importantly, this type of analysis
requires assigning monetized values to non-
market benefits and costs. In practice it can be
very difficult or even impossible to quantify gains
and losses in monetary terms (e.g., the loss of a
species, intangible effects).17 In general, however,
economists believe that BCA provides a systematic
framework for comparing the social costs and
benefits of proposed regulations, and that it
contributes useful information to the decision-
making process about how scarce resources can be
put to the best social use.
A.4 Meas
F-'t:>nmor?iEv !'; y- ,ts
A.4.1 Elasticities
The net change in social welfare brought about
by a new environmental regulation is the sum
of the negative effects (i.e., loss of producer and
consumer surplus) and the positive effects (or
social benefits) of the improved environmental
quality. This is shown graphically for a single
market in Figure A.5 above. The use of demand
and supply curves highlights the importance of
assessing how individuals will respond to changes
in market conditions. The net benefits of a policy
will depend on how responsively producers and
consumers react to a change in price. Economists
16 Chapter 9 addresses equity assessment and describes the methods
available for examining the distributional effects of a regulation.
17 Kelman (1981) argues that it is even unethical to try to assign
quantitative values to non-marketed benefits.
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Appendix A Economic Theory
measure this responsiveness by the supply and
demand elasticities.
The term "elasticity" refers to the sensitivity of
one variable to changes in another variable. The
price elasticity of demand (or supply) for a good
or service is equal to the percentage change in
the quantity demanded (or supplied) that would
result from a 1 percent increase in the price of that
good or service. For example, a price elasticity of
demand for tuna equal to -1 means that a 1 percent
increase in the price of tuna results in a 1 percent
decrease in the quantity demanded. Changes
are measured assuming all other things, such as
incomes and tastes, remain constant. Demand and
supply elasticities are rarely constant and often
change depending on the quantity of the good
consumed or produced. For example, according to
the demand curve for tuna shown in Figure A.6,
at a price of $1 per pound, a 10 percent increase
in price would reduce quantity demanded by 2.5
percent (from 8 lbs to 7.8 lbs). At a price of $4 per
pound, a 10 percent increase in price would result
in a 40 percent decrease in quantity demanded
(from 2 to 1.2 lbs). This implies that the price
elasticity of demand is -0.25 when tuna costs $1/
lb but -4 when the price is $4/lb. When calculating
elasticities it is important realize where one is
on the supply or demand curve, and the price
or quantity should be stated when reporting an
elasticity estimate.
Elasticities are important in measuring economic
impacts because they determine how much of a
Figure A.6 - Demand Curve for Tuna
S/lb
Tuna (lbs)
price increase will be passed on to the consumer.
For example if a pollution control policy leads to
an increase in the price of a good, multiplying the
price increase by current quantity sold generally
will not provide an accurate measure of impact of
the policy. Some of the impact will take the form
of higher prices for the consumer, but some of
the impact will be a decrease in the quantity sold.
The amount of the price increase that is passed
on to consumers is determined by the elasticity
of demand relative to supply (as well as existing
price controls). "Elastic" demand (or supply)
indicates that a small percentage increase in price
results in a larger percentage decrease (increase)
in quantity demanded (supplied).18 Ail else equal,
an industry facing a relatively elastic demand is
less likely to pass on costs to the consumer because
increasing prices will result in reduced revenues.
In determining the economic impacts of a rule,
supply characteristics in the industries affected
by a regulation can be as important as demand
characteristics. For highly elastic supply curves
relative to the demand curves, it is likely that
cost increases or decreases will be passed on to
consumers.
The many variables that affect the elasticity of
demand include:
• The cost and availability of close substitutes;
• The percentage of income a consumer spends
on the good;
• How necessary the good is for the consumer;
• The amount of time available to the consumer
to locate substitutes;
• The expected future price of the good; and
• The level of aggregation used in the study to
estimate the elasticity.
The availability of close substitutes is one of the
most important factors that determine demand
elasticity. A product with close substitutes at
similar prices tends to have an elastic demand,
18 Demand (or supply) is said to be "elastic" if the absolute value of the
price elasticity of demand (supply) is greater than one and "inelastic"
if the absolute value of the elasticity is less than one. If a percentage
change in price leads to an equal percentage change in quantity
demanded (supplied) (i.e., if the absolute value of elasticity equals
one), demand (supply) is "unit elastic."
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Appendix A Economic Theory
because consumers can readily switch to
substitutes rather than paying a higher price.
Therefore, a company is less likely to be able to pass
through costs if there are many close substitutes
for its product. Narrowly defined markets (e.g.,
salmon) will have more elastic demands than
broadly defined markets (e.g., food) since there are
more substitutes for narrow goods.
Another factor that affects demand elasticities
is whether the affected product represents a
substantial or necessary portion of customers' costs
or budgets. Goods that account for a substantial
portion of consumers' budgets or disposable
income tend to be relatively price elastic. This
is because consumers are more aware of small
changes in the price of expensive goods compared
to small changes in the price of inexpensive
goods, and therefore may be more likely to seek
alternatives. A similar issue concerns the type of
final good involved. Reductions in demand may
be more likely to occur when prices increase for
"luxuries" or optional purchases. If the good is a
necessity item, the quantity demanded is unlikely
to change drastically for a given change in price.
Demand will be relatively inelastic.
Elasticities tend to increase over time, as firms and
customers have more time to respond to changes in
prices. Although a company may face an inelastic
demand curve in the short run, it could experience
greater losses in sales from a price increase in
the long run. Over time customers begin to find
substitutes or new substitutes are developed.
However, temporary price changes may affect
consumers' decisions differently than permanent
ones. The response of quantity demanded during
a one-day sale, for example, will be much greater
than the response of quantity demanded when
prices are expected to decrease permanendy.
Finally, it is important to keep in mind that
elasticities differ at the firm versus the industry
level. It is not appropriate to use an industry-level
elasticity to estimate the ability of only one firm to
pass on compliance costs when its competitors are
not subject to the same cost.
Characteristics of supply in the industries affected
by a regulation can be as important as demand
characteristics in determining the economic
impacts of a rule. For relatively elastic supply
curves, it is likely that cost increases or decreases
will be passed on to consumers. The elasticity of
supply depends, in part, on how quickly per unit
costs rise as firms increase their output. Among the
many variables that influence this rise in cost are:
• The cost and availability of close input
substitutes;
• The amount of time available to adjust
production to changing conditions;
• The degree of market concentration among
producers;
• The expected future price of the product;
• The price of related inputs and related
outputs; and
• The speed of technological advances in
production that can lower costs.
Similar to the determinants of demand elasticity,
the factors influencing the price elasticity of supply
all relate to a firm's degree of flexibility in adjusting
production decisions in response to changing
market conditions. The more easily a firm can
adjust production levels, find input substitutes,
or adopt new production technologies, the more
elastic is supply. Supply elasticities tend to increase
over time as firms have more opportunities to
renegotiate contracts and change production
technologies. When production takes time, the
quantity supplied may be more responsive to
expected future price changes than to current price
changes.
Demand and supply elasticities are available for the
aggregate output of final goods in most industries.
They are usually published in journal articles
on research pertaining to a particular industry.19
19 Another useful source of elasticity estimates is the recently developed
EPA Elasticity Databank (U.S. EPA 2007d). In the absence of an
encyclopedic "Book of Elasticities" the Elasticity Databank serves as a
searchable database of elasticity parameters across a variety of types
(i.e., demand and supply elasticities, substitution elasticities, income
elasticities, and trade elasticities) and economic sectors/product
markets. The database is populated with EPA-generated estimates used
in Environmental Impact Assessment studies conducted by the Agency
since 1990, as well as estimates found in the economics literature. It can
be accessed from the Technology Transfer Network Economics and Cost
Analysis Support website: http://www.epa.gov/ttnecas1/Elasticity.htm.
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Appendix A Economic Theory
When such information is unavailable, as is
often the case for intermediate goods, elasticities
may be quantitatively or qualitatively assessed.20
Econometric tools are frequently used to estimate
supply and demand equations (thereby the
elasticities) and the factors that influence them.
A.4.2 Measuring the Welfare
EnV hange in
Environmental Goods
As introduced in Section A. 1 changes in consumer
surplus are measured by the trapezoidal region
below the ordinary, or Marshallian, demand curve
as price changes. This region reflects the benefit a
consumer receives by being able to consume more
of a good at a lower price. If the price of a good
decreases, some of the consumers satisfaction
comes from being able to consume more of a
commodity when its price falls, but some of it
comes from the fact that the lower price means that
the consumer has more income to spend. However,
the change in (Marshallian) consumer surplus only
serves as a monetary measure of the welfare gain or
loss experienced by the consumer under the strict
assumption that the marginal utility of income is
constant.21 This assumption is almost never true in
reality. Luckily, there are alternative, less demanding
monetary measures of consumer welfare that prove
useful in treatments of BCA. Intuitively, these
measures determine the size of payment that would
be necessary to compensate the consumer for the
price change. In other words, they estimate the
consumers WTP for a price change.
As mentioned above, a price decline results in two
effects on consumption. The change in relative
prices will increase consumption of the cheaper
good (the substitution effect), and consumption
will be affected by the change in overall purchasing
power (the income effect). A Marshallian demand
curve reflects both substitution and income
effects. Movements along it show how the quantity
20 Final goods are those that are available for direct use by consumers
and are not utilized as inputs by firms in the process of production.
Goods that contribute to the production of a final good are called
intermediate goods. It is of course possible for a good to be final from
one perspective and intermediate from another (Pearce 1992).
21 See Perman etal. (2003), Just etal. (2005) or any graduate level text
for a more thorough exposition of this issue.
demanded changes as price changes (holding all
other prices and income constant), so it reflects
both the substitution and the income effects. The
Hicksian (or "compensated") demand curve, on
the other hand, shows the relationship between
quantity demanded of a commodity and its price,
holding all other prices and utility (rather than
income) constant. This is the correct measure of a
consumers WTP for a price change. The Hicksian
demand curve is constructed by adjusting income
as the price changes so as to keep the consumers
utility the same at each point on the curve. In
this way, the income effect of a price change is
eliminated and the substitution effect can be
considered alone. Movements along the Hicksian
demand function can be used to determine the
monetary change that would compensate the
consumer for the price change.
Hicks (1941) developed two correct monetary
measures of utility change associated with a price
change: compensating variation and equivalent
variation. Compensating variation (CV) assesses
how much money must be taken away from
consumers after a price decrease occurred to return
them to the original utility level. It is equal to
the amount of money that would 'compensate'
the consumer for the price decrease. Equivalent
variation (EV) measures how much money would
need to be given to the consumer to bring her to
the higher utility level instead of introducing the
price change. In other words, it is the monetary
change that would be 'equivalent' to the proposed
price change.
Before examining the implications of these
measures for valuing environmental changes, it
is useful to understand CV and EV in the case of
a reduction in the price of some normal, private
good, C22 This is shown with indifference curves
and a budget line, as seen in Figure A.7.
Assume that the consumer is considering the
trade-off between C and all other goods, denoted
by a composite good, C. The indifference curve,
U0, depicts the different combinations of the two
goods that yield the same level of utility. Because of
22 The notation and discussion in this section follow Chapter 12 of
Perman etal. (2003).
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Appendix A Economic Theory
Figure A.8 - Change in Optima!
c.
Y,
C,
Y.
Y,
Y
C,
C
diminishing marginal utility, the curve is concave,
where increasing amounts of C must be offered
for each unit of C2 given up to keep the consumer
indifferent. Hie budget line on the graph reflects
what the consumer is able to purchase given her
income, Y , and the prices of the two goods —
P 'andP ', respectively.23 A utility-maximizing
consumer will choose quantities C 'and C ', the
point where the indifference curve is tangent to
the budget constraint.24
Figure A.8 shows the change in the optimal
consumption bundle resulting from a reduction in
the price of C. If the price of C falls, the budget
line shifts out on the C axis because more C can
be purchased for a given amount of money. Hie
consumer now chooses C "and C2"at point b and
moves to a new, higher utility curve, U. CV then
measures how much money must be taken away at
the new prices to return the consumer to the old
utility level. That is, starting at point b and keeping
the slope of the budget line fixed at the new level,
by how much must it be shifted downward to
make it tangent to the initial indifference curve,
£7 ? It is, therefore, the maximum amount the
consumer would be willing to pay to have the price
fall occur — i.e., the precise monetary measure of
23 In Figure A.7, is considered the numeraire good (i.e., prices are
adjusted so that P/ is equal to 1).
24 For a review of the utility maximizing behavior of consumers, see any
general microeconomics textbook.
the welfare change.25 In Figure A.8, CV is simply
given by the amount Y( - Y. EV, on the other
hand, measures how much income must be given
to the individual at the old price set to maintain
the same level of well-being as if the price change
did occur. That is, keeping the slope of the budget
line fixed at the old level, by how much must
it be shifted upwards to make it tangent to £7 ?
EV is, then, the minimum amount of money the
consumer would accept in lieu of the price fall.
This too is a proper monetary measure of the
utility change resulting from the price decrease. In
Figure A.8 then EV is the amount Y2 - Y , leaving
the individual at pointf.
CV and EV are simply measures of the distance
between the two indifference curves. However,
the amount of money associated with CV, EV, and
Marshallian consumer surplus (MCS) is generally
not the same. For a price fall, it can be shown
that CV < MCS < EV, and for a price increase,
CV > MCS > EV.26 Notice that in the case of a
price decrease, the CV measures the consumers
willingness to pay (WTP) to receive the price
reduction and EV measures the consumers
25 In Figure A.8, this would result in a shift fromC,"to C*. This is known
as the income effect of the price change. The shift from C/to C* is
considered the substitution effect.
26 This can be seen by redrawing Figure A.8 using a graph of Marshallian
and Hicksian demand curves. See Perman et al. (2003) for a detailed
explanation.
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Appendix A Economic Theory
willingness to accept (WTA) to forgo the lower
price. If the price of C were to increase, then the
relationships between WTP/WTA and CV/
EV would be reversed. CV would measure the
consumers WTA to suffer the price increase and
EV would be the individual's WTP to avoid the
increase in price.
In order to examine the implications of these
measures for valuing changes in environmental
conditions, one can think of C in the above
discussion as an environmental commodity,
henceforth denoted by E. Then an improvement
in environmental quality (or an increase in an
environmental public good) resulting from some
policy is reflected by an increase in the amount of
E. Holding all else constant, such an increase is
equivalent to a decrease in the price of E and can
be depicted as a shifting outward of the budget line
along the E axis.
"Welfare changes due to an increase in E follow
along the lines of the previous discussion.
However, because E is generally non-exclusive
and non-divisible, the consumer consumption
level cannot be adjusted. Therefore, the
associated monetary measures of the welfare
change are not technically CV and EV, but are
referred to as compensating surplus (CS) and
equivalent surplus (ES). In practice, however, the
process is the same; a Hicksian demand curve is
estimated for the unpriced environmental good.
Analogous to the preceding discussion, if there
is an environmental improvement, then CS
measures the amount of money the consumer
would be willing to pay for the improvement
that would result in the pre-improvement level
of utility. For the purposes of environmental
valuation, this is the primary measure of
concern when considering environmental
improvements. ES measures how much society
would have to pay the consumer to give him
the same utility as if the improvement had
occurred. In other words, this is how much he
would be willing to accept to not experience
the gain in environmental quality. If valuing an
environmental degradation, then CS measures
the WTA and ES measures WTP.
Whereas statements can be made about the
relative size of CV, EV, and MCS for price changes
of normal goods, Bockstael and McConnell
(1993) find that it is not possible to make
similar statements about CS, ES, and MCS for
a change in environmental quality.27 Given that
environmental quality is generally an unpriced
public good, ordinary Marshallian demand
functions cannot be estimated, so it may seem
irrelevant that one cannot say anything about
how MCS approximates the proper measure.
However, Bockstael and McConnell's results are
important in relation to indirect methods for
environmental valuation. However, most indirect
valuation studies are based on Marshallian demand
functions in practice, in the hope of keeping the
associated error small.
A.4,3 Single Market, Multi-Market,
a ineral Equilibrium Analysis
Both supply and demand elasticities are affected
by the availability of close complements and
substitutes. This highlights the fact that
regulating one industry can have an impact
on other, non-regulated markets. However,
this does not necessarily imply that all of these
other markets must be modeled. Changes due
to government regulation can be captured
using only the equilibrium supply and demand
curves for the affected market, assuming: (1)
there are small, competitive adjustments in all
other markets; and (2) there are no distortions
in other markets. This is referred to as partial
equilibrium analysis.
For example, suppose a new environmental
regulation increases per unit production costs.
The benefits and costs of abatement in a partial
equilibrium setting are illustrated in Figure
A.9 where the market produces the quantity
0 in equilibrium without intervention. The
external costs of production are shown by the
marginal external costs (MEC) curve without
27 Willig (1976) shows that ordinary, or Marshallian, demand curves can
provide an approximate measure of welfare changes resulting from
a price change. In most cases the error associated with using MCS,
with respect to CV or EV, will be less than 5 percent (see Perman et al.
2003).
A-12
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Appendix A Economic Theory
Figure A.9 - Benefits and Costs of Abatement
any abatement. Total external costs are given by
the area under the MEC curve up to the market
output, Q or the area of triangle Q EO.
With required abatement production, costs
are the total of supply plus marginal abatement
costs (MAC), shown as the new, higher supply
curve in the figure. These higher costs result
in a new market equilibrium quantity shown
as <2*. The social cost of the requirement is
the resulting change in consumer and supplier
surplus, shown here as the total observed
abatement costs (parallelogram PgPjAC) plus
the area of triangle ABC, which can be described
as deadweight loss.
Abatement also produces benefits by shifting
the MEC curve downward, reflecting the fact
that each unit of production now results in less
pollution and social costs. Additionally, the
reduced quantity of the output good results in
reduced external costs. The reduced external
costs, i.e., the benefits, are given by the difference
between triangle Q^EO and triangle Q^DO,
represented by the shaded area in the figure.
The net benefits of abatement are the benefits (the
reduced external costs) minus the costs (the loss
in consumer and producer surplus). In the figure
this would equal the shaded area (the benefits)
minus total abatement costs and deadweight loss
as described above.
While the single market analysis is theoretically
possible, it is generally impractical for rulemaking.
As mentioned in Section A.3, this is often because
the gains occur outside of markets and cannot
be linked directly to the output of the regulated
market. Therefore BCA is frequently done as two
separate analyses: a benefits analysis and a cost
analysis.
When a regulation is expected to have a large
impact outside of the regulated market, then the
analysis should be extended beyond that market.
If the effects are significant but not anticipated to
be widespread, one potential improvement is to
use multi-market modeling in which vertically or
horizontally integrated markets are incorporated
into the analysis. The analysis begins with the
relationship of input markets to output markets.
A multi-market analysis extends the partial
equilibrium analysis to measuring the losses in
other related markets.28
In some cases, a regulation can have such a
significant impact on the economy that a general
equilibrium modeling framework is required.29
This maybe because regulation in one industry has
broad indirect effects on other sectors, households
may alter their consumption patterns when they
encounter increases in the price of a regulated
good, or there maybe interaction effects between
the new regulation and pre-existing distortions,
such as taxes on labor. In these cases, partial
equilibrium analyses are likely to result in an
inaccurate estimation of total social costs. Using
a general equilibrium framework accounts for
linkages between all sectors of the economy and
all feedback effects, and can measure total costs
comprehensively.30
28 An example of the use of multi-market model for environmental policy
analysis is contained in a report prepared for EPA on the regulatory
impact of control on asbestos and asbestos products (U.S. EPA 1989).
29 General equilibrium analysis is built around the assumption that, for
some discrete period of time, an economy can be characterized by a
set of equilibrium conditions in which supply equals demand in all
markets. When this equilibrium is "shocked" through a change in
policy or a change in some exogenous variable, prices and quantities
adjust until a new equilibrium is reached. The prices and quantities
from the post-shock equilibrium can then be compared with their pre-
shock values to determine the expected impacts of the policy or change
in exogenous variables.
30 Chapter 8 provides a more detailed discussion of partial equilibrium,
multi-market, and general equilibrium analysis.
Guidelines for Preparing Economic Analyses I December 2010 1-13
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Appendix A Economic Theory
A.5 Optimal Level ulation
Following from the definition in Section A. 1, the
most economically efficient policy is the one that
allows for society to derive the largest possible social
benefit at the lowest social cost. This occurs when
the net benefits to society (i.e., total benefits minus
total costs) are maximized. In Figure A. 10, this is at
the point where the distance between the benefits
curve and the costs curve is the largest and positive.
$ Costs
Benefits
Pollution
Abatement
Note that this is not necessarily the point at which:
• Benefits are maximized;
• Costs are minimized;
• Total benefits = total costs (i.e., benefit-cost
ratio =1);
• Benefit-cost ratio is the largest; or
• The policy is most cost-effective.
If the regulation were designed to maximize
benefits, then any policy, no matter how expensive,
would be justified if it produced any benefit, no
matter how small. Similarly, minimizing costs
would, in most cases, simply justify no action at all.
A benefit-cost ratio equal to one is equivalent to
saying that the benefits to society would be exactly
offset by the cost of implementing the policy.
This implies that society is indifferent between
no regulation and being regulated; hence, there
would be no net benefit from adopting the policy.
Maximizing the benefit-cost ratio is not optimal
either. Two policy options could yield equivalent
benefit-cost ratios but have vastly different net
benefits. For example, a policy that cost $100
million per year but produced $200 million in
benefits has the same benefit-cost ratio as a policy
that cost $100,000 but produced $200,000 in
benefits, even though the first policy produces
substantially more net benefit for society.31 Finally,
finding the most cost-effective policy has similar
problems because the cost-effectiveness ratio can
be seen as the inverse of the benefit-cost ratio. A
policy is cost effective if it meets a given goal at
least cost — i.e., minimizes the cost per unit of
benefit achieved. Cost-effectiveness analysis (CEA)
can provide useful information to supplement
existing BCA and may be appropriate to rank
policy options when the benefits are fixed and
cannot be monetized, but it provides no guidance
in setting an environmental standard or goal.
Conceptually, net social benefits will be maximized
if regulation is set such that emissions are reduced up
to the point where the benefit of abating one more
unit of pollution (i.e., marginal social benefit)32
is equal to the cost of abating an additional unit
(i.e., marginal abatement cost).33 If the marginal
31 Benefit-cost ratios are useful when choosing one or more policy options
subject to a budget constraint. For example, consider a case where five
options are available and the budget is $1,000. The first option will cost
$1,000 and will deliver benefits of $2,000. Each of the other four will
cost $250 and deliver benefits of $750. If options are selected according
to the net benefits criterion, the first option will be selected, because
its net benefits are $1,000 while the net benefits of each of the other
options are $500. However if options are selected by the benefit-cost
ratio criterion, the other four options will be selected, as each of their
benefit-cost ratios equal 3, versus a benefit-cost ratio of 2 for the first
option. In this case, choosing options by the net benefits criterion will
yield $1,000 in total net benefits, while choosing options by the benefit-
cost ratio criterion will yield $500 in total net benefits. In most cases,
choosing options in decreasing order of benefit-cost ratios will yield
the largest possible net benefits given a fixed budget. This method will
guarantee the optimal solution if the benefits and costs of each option
are independent, and if each option can be infinitely subdivided: simply
select the options in decreasing order of their benefit-cost ratios and
once the budget is exceeded subdivide the last option selected such
that the budget constraint is met exactly (see Dantzig 1957). Also note
that this strategy does not require measuring benefits and costs in the
same units, which means that it is directly useful for CEA (Hyman and
Leibowitz 2000), while the net-benefit criterion is not.
32 The benefits of pollution reduction are the reduced damages from
being exposed to pollution. Therefore, the marginal social benefit of
abatement is measured as the additional reduction in damages from
abating one more unit of pollution.
33 The idea that a given level of abatement is efficient — as opposed to
abating until pollution is equal to zero — is based on the economic
concept of diminishing returns. For each additional unit of abatement,
marginal social benefits decrease while marginal social costs of that
abatement increase. Thus, it only makes sense to continue to increase
abatement until the point where marginal abatement benefits and
marginal costs are just equal. Any abatement beyond that point will incur
more additional costs than benefits. (Alternatively, one can understand
the efficient level of abatement as the amount of regulation that achieves
the efficient level of pollution. If one considers a market for pollution, the
socially-efficient outcome would be the point where the marginal WTP
for pollution equals the marginal social cost of polluting.)
A-14
Guidelines for Preparing Economic Analyses I December 2010
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Appendix A Economic Theory
benefits are greater than the marginal costs, then
additional reductions in pollution will offer greater
benefits than costs, and society will be better off. If
the marginal benefits are less than marginal costs,
then additional reductions in pollution will cost
society more than they provide in benefits, and will
make society worse off. When the marginal cost
of abatement is equal to society's marginal benefit,
no gains can be made from changing the level of
pollution reduction, and an efficient aggregate level
of emissions is achieved. In other words, a pollution
reduction policy is at its optimal, most economically
efficient point when the marginal benefits equal the
marginal costs of the rule?A
The condition that marginal benefits must
equal marginal costs assumes that the initial
pollution reduction produces the largest
benefits for the lowest costs. As pollution
reduction is increased (i.e., regulatory
stringency is increased), the additional benefits
decline and the additional costs rise. "While
it is not always true, a case can be made that
the benefits of pollution reduction follow this
behavior. The behavior of total abatement
costs, however, will depend on how the
pollution reduction is distributed among
the polluters since firms may differ in their
ability to reduce emissions. The aggregate
marginal abatement cost function shows the
least costly way of achieving reductions in
emissions. It is equal to the horizontal sum
of the marginal abatement cost curves for
the individual polluters. Although each firm
faces increasing costs of abatement, marginal
cost functions still vary across sources. Some
firms may abate pollution relatively cheaply,
while others require great expense. To achieve
economic efficiency, the lowest marginal cost of
abatement must be achieved first, and then the
next lowest. Pollution reduction is achieved at
lowest cost only if firms are required to make
equiproportionate cutbacks in emissions. That
is, at the optimal level of regulation, the cost
34 It is important to reemphasize the word "marginal" in this statement.
Marginal, in economic parlance, means the extra or next unit of the
item being measured. If regulatory options could be ranked in order of
regulatory stringency, then marginal benefits equal to marginal costs
means that the additional benefits of increasing the regulation to the
next degree of stringency is equal to the additional cost of that change.
of abating one more unit of pollution is equal
across all polluters.35
Figure A.l 1 illustrates why the level of pollution
that sets the marginal benefits and marginal costs
of abatement equal to each other is efficient.36
Emissions are drawn on the horizontal axis and
increase from left to right. The damages from
emissions are represented by the marginal damage
(MD) curve. Damages may include the costs
of worsened human health, reduced visibility,
lower property values, and loss of crop yields
or biodiversity. As emissions rise, the marginal
damages increase. E represents the amount of
emissions in the absence of regulation on firms.
The costs of controlling emissions are represented
by the marginal abatement cost curve (MAC). As
emissions are reduced belowE , the marginal cost
of abatement rises.
Figure A.11 - Efficient Level of Pollution
Costs, Damages
MD
MAC
Emmissions
E,
The total damages associated with emissions
level E* are represented by the area of the triangle
AE/JE*, while the total abatement costs are
represented by area AEfi*. The total burden on
35 Thus a regulation that requires all firms to achieve the same level
of reduction will probably result in different marginal costs for each
firm and not be efficient. (See Field and Field 2005 or any other
environmental economics text for a detailed explanation and example.)
36 Figure A.11 illustrates the simplest possible case, where the pollutant
is a flow (i.e., it does not accumulate over time) and marginal damages
are independent of location. When pollution levels and damages
vary by location, then the efficient level of pollution is reached when
marginal abatement costs adjusted by individual transfer coefficients
are equal across all polluters. Temporal variability also implies
an adjustmentto this equilibrium condition. In the case of a stock
pollutant, marginal abatement costs are equal across the discounted
sum of damages from today's emissions in all future time periods. In
the case of a flow pollutant, this condition should be adjusted to reflect
seasonal or daily variations (see Sterner 2003).
Guidelines for Preparing Economic Analyses I December 2010 1-15
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Appendix A Economic Theory
society of this level is equal to the total abatement
costs of reducing emissions from E to I;* plus the
total damages of the remaining emissions, E*. That
is, the total burden is the darkly shaded triangle,
E^Er
Now assume that emissions are something other
than E*. For example, suppose emissions were
Er which is greater than E*. Total damages for
this level of emissions are equal to the area of the
triangle, while total costs of abatement
to this level is equal to the area CE E . The total
burden on society of this level is the sum of the
areas of the darkly shaded and the lightly shaded
triangles. This means that the excess social cost
of choosing emissions E rather than E* is equal
to the area of the lightly shaded triangle, ABC.
A similar analysis could be done if emissions
levels were below level, E*. Here, the additional
abatement costs would be greater than the decrease
in damages, resulting in excess social costs. The
policy that sets the emissions level at E* — at
the point where marginal benefits of pollution
reduction (represented by the MD curve) and the
MAC curve intersect — is economically efficient
because it imposes the least net cost on, and yields
the highest net benefits for, society. That is, the
triangle EgAE1 is the smallest shaded region that
can be obtained.
fusion
The purpose of this appendix is to present a
brief explanation of some of the fundamental
economics relevant to Chapters 3 through 9.
It is not intended to provide a comprehensive
discussion of all microeconomic theory and its
application to environmental issues. The interested
reader can turn to undergraduate or graduate level
textbooks for a more thorough exposition of the
topics covered here. At the undergraduate level,
Field and Field (2005) provide an introduction to
the basic principles of environmental economics.
Tietenberg's (2002) and Perman et al.'s (2003)
presentations are more technical but still used
primarily for undergraduate courses. Freeman
(2003) is the standard text for graduate courses
in environmental economics and deals with
the methodology of non-market valuation.
Supplemental texts that provide a good handle
on environmental economics with less technical
detail include Stavins (2000a), and Portney and
Stavins (2000). Finally, general microeconomics
textbooks (Mankiw 2004, and Varian 2005 at the
undergraduate level; and Mas-Colell et al. 1995,
Kreps 1990, and Varian 2005 at the graduate
level), and applied welfare economics textbooks
(Just et al. 2005) are useful references as well.
This section has focused on first-best optimal
regulation when there are no pre-existing market
distortions. However, it is important to note that
realizable policy outcomes will often be "second
best" due to information constraints, political
constraints, imperfect competition, and market
distortions created by tax and other government
interventions. For example, many of the emissions-
based policies emphasized in these Guidelines
may be less feasible for addressing nonpoint
source pollution, such as agriculture, which is less
observable and more stochastic than emissions
from point sources. Agriculture is also subject to
multiple non-environmental policy distortions
that must be considered in the measurement of the
social benefits and costs of regulating agriculture.
Guidelines for Preparing Economic Analyses I December 2010
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Api
Mortality Risk
Va i u at 10 Estimates
ome EPA policies are designed to reduce the risk of contracting a potentially
® fatal health effect such as cancer. Reducing these risks of premature death
provides welfare increases to those individuals affected by the policy. These
policies generally provide marginal changes in relatively small risks. That
is, these policies do not provide assurance that an individual will not die
prematurely from environmental exposures; rather, they marginally reduce the probability
of such an event. For BCA, analysts generally aggregate these small risks over the affected
population to derive the number of statistical lives saved (or the number of statistical
deaths avoided) and then use a "value of statistical life" (VSL) to express these benefits in
monetary terms.
The risk reductions themselves can generally be classified according to the characteristics
of the risk in question (e.g., voluntariness or controllability) and the characteristics of the
affected population (e.g., age and health status). These dimensions may affect the value of
reducing mortality risks. Ideally the VSL would account for all possible risk and demographic
characteristics that matter. It would be derived from the preferences of the population
affected by the policy, based on the type of risk that the policy is expected to reduce. For
example, if a policy were designed to remove carcinogens at a suburban hazardous waste site,
the ideal measure would represent the preferences for reduced cancer risks for the exposed
population in the area and would reflect the changes in life expectancy that would result.
Unfortunately, time and resource constraints make it difficult if not impossible to obtain such
unique valuation estimates for each EPA policy. Instead, analysts need to draw from existing
VSL estimates obtained using well-established methods (see Chapter 7).
This appendix describes the default VSL estimate currently used by the Agency and its
derivation, as well as how analysts should characterize and assess benefit transfer issues
that may arise in its application. Benefit transfer considerations that are common to all
valuation applications, including the effect of most demographic characteristics of the
study and policy populations, are described in Chapter 7 Section 7.3 and will not be
repeated here.
B i > ynt! -1 Esl'U-n *U; »t \ ;l VSL estimate. Fitting a Weibull distribution to these
Table B.l contains the VSL estimates that currently estimates yields a central estimate (mean) of $7.4
form the basis of the Agency's recommended central million ($2006) with a standard deviation of $4.7
Guidelines for Preparing Economic Analyses I December 2010
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Appendix B Mortality Risk Valuation Estimates
Table D,, f ¦ v'clue of Statistical Life Estimates (mean values in millions
of 2006 dollars)
Study
Method
Value of Statistical Life
Kniesner and Leeth (1991 - US)
Labor Market j $0.85
Smith and Gilbert (1984)
Labor Market
$0.97
Dillingham (1985)
Labor Market
$1.34
Butler (1983)
Labor Market
$1.58
Miller and Guria (1991)
Contingent Valuation
$1.82
Moore and Viscusi (1988)
Labor Market
$3.64
Viscusi, Magat, and Huber (1991)
Contingent Valuation
$4.01
Marin and Psacharopoulos (1982)
Labor Market
$4.13
Gegaxet al. (1985)
Contingent Valuation
$4.86
Kniesner and Leeth (1991 - Australia)
Labor Market
CO
CO
"vl-
-69-
Gerking, de Haan, and Schulze (1988)
Contingent Valuation
$4.98
Cousineau, Lecroix, and Girard (1988)
Labor Market
$5.34
Jones-Lee (1989)
Contingent Valuation
$5.59
Dillingham (1985)
Labor Market
$5.71
Viscusi (1978)
Labor Market
$6.07
R.S.Smith (1976)
Labor Market
$6.80
V.K.Smith (1983)
Labor Market
$6.92
Olson (1981)
Labor Market
$7.65
Viscusi (1981)
Labor Market
$9.60
R.S.Smith (1974)
Labor Market
$10.57
Moore and Viscusi (1988)
Labor Market
$10.69
Kniesner and Leeth (1991 - Japan)
Labor Market
$11.18
Herzog and Schlottman (1987)
Labor Market
$13.36
Leigh and Folsom (1984)
Labor Market
$14.21
Leigh (1987)
Labor Market
$15.31
Garen (1988)
Labor Market
$19.80
Derived from U.S. EPA (1997a) and Viscusi (1992). Updated to 2006$ with GDP deflator.
million.1'2 EPA recommends that the central
estimate, updated to the base year of the analysis,
be used in all benefits analyses that seek to quantify
mortality risk reduction benefits.
This approach was vetted and endorsed by the
Agency when the 2000 Guidelines for Preparing
1 The VSL was updated from the $4.8 million ($1990) estimate
referenced in the 2000 Guidelines by adjusting the individual study
estimates for inflation using a GDP deflator and then fitting a Weibull
distribution to the estimates. The updated Weibull parameters are:
location = 0, scale = 7.75, shape = 1.51 (updated from location = 0;
scale = 5.32; shape = 1.51). The Weibull distribution was determined
to provide the best fit for this set of estimates. See U.S. EPA 1997a for
more details.
2 This VSL estimate was produced using the GDP deflator inflation
index. Some economists prefer using the Consumer Price Index (CPI)
in some applications. The key issue for EPA analysts is to ensure that
the chosen index is used consistently throughout the analysis.
Economic Analyses were drafted.3 It remains EPA's
default guidance for valuing mortality risk changes
although the Agency has considered and presented
alternatives.4
3 The studies listed in Table B.1 were published between 1974 and
1991, and most are hedonic wage estimates that may be subject to
considerable measurement error (Black et al. 2003, and Black and
Kniesner 2003). Although these were the best available data at the time,
they are sufficiently dated and may rely on obsolete preferences for risk
and income. The Agency is currently considering more recent studies
as it evaluates approaches to revise its guidance.
4 EPA is in the process of revisiting this guidance and has recently engaged
the SAB-EEAC on several issues including the use of meta-analysis as a
means of combining estimates and approaches for assessing mortality
benefits when changes in longevity may vary widely (U.S. EPA 2006d).
The Agency is committed to using the best available science in its analyses
and will revise this guidance in response to SAB recommendations (see
U.S. EPA 2007g for recent SAB recommendations).
Guidelines for Preparing Economic Analyses I December 2010
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Appendix B Mortality Risk Valuation Estimates
B.2 Other \ 'rH h mation
For most of mortality risk reductions EPA
uniformly applies the VSL estimate discussed
above. For a period of time (2004-2008), the Office
of Air and Radiation (OAR) valued mortality
risk reductions using a VSL estimate derived
from a limited analysis of some of the available
studies. OAR arrived at a VSL using a range of $ 1
million to $10 million (2000$) consistent with
two meta-analyses of the wage-risk literature. The
$1 million value represented the lower end of the
interquartile range from the Mrozek and Taylor
(2002) meta-analysis of 33 studies. The $10 million
value represented the upper end of the interquartile
range from the Viscusi and Aldy (2003) meta-
analysis of 43 studies. The mean estimate of $5.5
million (2000$) was also consistent with the mean
VSL of $5.4 million estimated in the Kochi et al.
(2006) meta-analysis. However, the Agency neither
changed its official guidance on the use of VSL in
rulemakings nor subjected the interim estimate to
a scientific peer-review process through the Science
Advisory Board (SAB) or other peer-review group.
During this time, the Agency continued work
to update its guidance on valuing mortality risk
reductions. EPA commissioned a report from
meta-analytic experts to evaluate methodological
questions raised by EPA and the SAB on
combining estimates from the various data
sources. In addition, the Agency consulted several
times with the SAB Environmental Economics
Advisory Committee (SAB-EEAC) on the issue.
With input from the meta-analytic experts, the
SAB-EEAC advised the Agency to update its
guidance using specific, appropriate meta-analytic
techniques to combine estimates from unique data
sources and different studies, including those using
different methodologies such as wage-risk and
stated preference (U.S. EPA 2007g).
Until updated guidance is available, the Agency
determined that a single, peer-reviewed estimate
applied consistently best reflects the SAB-EEAC
advice received to date. Therefore, the VSL
described above that was vetted and endorsed by
the SAB should be applied in relevant analyses
while the Agency continues its efforts to update its
guidance on this issue.
B.3 Bene msfer
isiderations
Policy analysts valuing mortality risk reductions
should account for differences in risk and
population characteristics between the policy and
study scenarios and their potential effect on the
overall results. The ultimate objective of the benefit
transfer exercise is to account for all of the factors
that significantly affect the value of mortality risk
reduction in the context of the policy. Analysts
should carefully consider the implications of
correcting for some relevant factors, but not for
others, recognizing that it may not be feasible to
account for all factors.
stments Associated
with Risk Characteristics
Risk characteristics appear to affect the value that
people place on risk reduction. A large body of
work identifies eight dimensions of risk that affect
human risk perception:5
• voluntary/involuntary
• ordinary/catastrophic
• delayed/immediate
• natural/man-made
• old/new
• controllable/uncontrollable
• necessary/unnecessary
• occasional/continuous
Transferring VSL estimates among these categories
may introduce bias. There have been some recent
efforts attempting to quantitatively assess these
sources of bias.6 These studies generally conclude
that voluntariness, control and responsibility
affect individual values for safety, although there
is no consensus on the direction and magnitude of
these effects.
5 A review of issues in risk perception is found in Lichtenstein and
Slovic (2006). Other informative sources include Siovic (1987), Rowe
(1977), Otway (1977), and Fischoff etai. (1978).
6 Examples include Hammittand Liu (2004), Sunstein (1997), Mendeloff
and Kaplan (1990), McDaniels et al. (1992), Savage (1993), Jones-Lee
and Loomes (1994,1995,1996), and Covey etal. (1995).
Guidelines for Preparing Economic Analyses I December 2010
-3
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Appendix B Mortality Risk Valuation Estimates
In addition, environmental risks may differ from
those that form the basis of VSL estimates in many
of these dimensions. Occupational risks, for example,
are generally considered to be more voluntary in
nature than are environmental risks, and may be
more controllable. As part of the Agency's review
of our mortality risk guidance we are evaluating the
literature from which the studies are drawn.
Support for quantitative adjustments in the
empirical literature is lacking for most of these
factors. Hie SAB reviewed an Agency summary
of the available empirical literature on the effects
of risk and population characteristics on WTP
for mortality risk reductions (U.S. EPA 2000d).
Hie SAB review concludes that among the
demographic and risk factors that might affect
VSL estimates, the current literature can only
support empirical adjustments related to the
timing of the risk. Hie review supports making
the following adjustments to primary benefits
estimates: (1) adjusting WTP estimates to account
for higher future income levels, though not for
cross-sectional differences in income; and (2)
discounting risk reductions that are brought about
in the future by current policy initiatives (that is,
after a cessation lag), using the same rates used
to discount other future benefits and costs. Ail
other adjustments, if made, should be relegated to
sensitivity analyses.
Increases in income over time. The economics
literature shows that the income elasticity of WTP
to reduce mortality risk is positive, based on cross-
sectional data. As a result, benefits estimates of
reduced mortality risk accruing in future years may
be adjusted to reflect anticipated income growth,
using the range of income elasticities (0.08, 0.40
and 1.0) employed in The Benefits and Costs of the
Clean Air Act, 1990-201 ft7 Recent EPA analyses
have assumed a triangular distribution from
these values and used the results in a probabilistic
assessment of benefits.8 At the time of this
writing, EPA is engaged in a consultation with the
SAB-EEAC on the appropriate range of income
elasticities and will update this guidance as needed.
7 For details see Kleckner and Neuman (2000).
8 See, for example, pp. 6-84 of the Final Economic Analysis for the Stage
2 DBPR (U.S. EPA 2005a).
Timing of reduced exposure and reduced risk.
Many environmental policies are targeted at
reducing the risk of effects such as cancer, where
there may be an extended period of time between
the reduced exposure and the reduction in the risk
of death from the disease.9 This delay between the
change in exposure and realization of the reduced
risk may affect the value of that risk reduction.
Most existing VSL estimates are based on risks
of relatively immediate fatalities making them
an imperfect fit for a benefits analysis of many
environmental policies. Economic theory suggests
that reducing the risk of a delayed health effect
will be valued less than reducing the risk of a more
immediate one, when controlling for other factors.
WTP
Associated wi mographic
Characteristics
Two population characteristics are particularly
noteworthy for their potential effect on mortality
risk valuation estimates: age and health status of
the exposed population. In September 2006, the
Agency requested an additional advisory from the
SAB-EEAC on issues related to valuing changes
in life expectancy for which age and baseline
health status are close correlates.10 Because the
outcome of this review is not yet available, we
focus here on previous advice received from the
SAB on related questions.
Age. It has sometimes been posited that older
individuals should have a lower WTP for changes in
mortality risk given the fewer years of life expectancy
remaining compared to younger individuals. This
hypothesis may be confounded, however, by the
finding that older persons reveal a greater demand
for reducing mortality risks and hence have a greater
implicit value of a life year (Ehrlich and Chuma
1990). Several authors have attempted to explore
9 Although latency is defined here as the time between exposure and
fatality from illness, alternative definitions may be used in other
contexts. For example "latency" may refer to the time between exposure
and the onset of symptoms. These symptoms may be experienced for
an extended period of time before ultimately resulting in fatality.
10 U.S. EPA (2006d) summarizes much of the literature related to the
effects of age and health status on WTP for changes in mortality risk
and includes the charge questions put to the SAB-EEAC on these
issues.
Guidelines for Preparing Economic Analyses I December 2010
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Appendix B Mortality Risk Valuation Estimates
potential differences in mortality risk valuation
estimates associated with differences in the average
age of the affected population using theoretical
models oflife-cycle consumption.11 In general
this literature has shown that the relationship
between age and WTP for mortality risk changes
is ambiguous, requiring strong assumptions to
even sign the relationship.12 Empirical evidence is
also mixed. A number of empirical studies (mostly
hedonic wage studies) suggest that the VSL follows
a consistent "inverted-U" life-cycle, peaking in
the region of mean age.13 Others find no such
statistically significant relationship and still others
show WTP increasing with age.14 Stated preference
results are also mixed, with some studies showing
declining WTP for older age groups and others
finding no statistically significant relationship
between age and WTP.15
In spite of the ambiguous relationship between
age and WTP, two alternative adjustment
techniques have been derived from this literature.
The first technique, value of statistical life-years
(VSLY), is derived by dividing the estimated VSL
by expected remaining life expectancy. This is by
far the most common approach and presumes
that: (1) the VSL equals the sum of discounted
values for each life year; and (2) each life year
has the same value. This method was applied as
an alternative case in an effort to evaluate the
sensitivity of the benefits estimates prepared for
EPA's retrospective and prospective studies of the
costs and benefits of the Clean Air Act (U.S. EPA
1997a, and U.S. EPA 1999).
11 See, for example, Shepard and Zeckhauser (1982), Rosen (1988),
Cropper and Sussman (1988,1990), and Johannson (2002).
12 See Evans and Smith (2006) for a recent summary.
13 See Jones-Lee et al. (1985), Aldy and Viscusi (2008), Viscusi and Aldy
(2007a and b), and Kniesner et al. (2006).
14 Viscusi and Aldy (2003) review more than 60 studies of mortality risk
estimates from 10 countries and discuss eight hedonic wage studies
that explicitly examine the age-WTP relationship. Only five of the eight
studies found a statistically significant, negative relationship between
age and the return to risk. Smith et al. (2004) and Kniesner et al.
(2006) find that WTP increases with age.
15 Krupnicketal. (2002) report that WTP for mortality risk reductions
changes significantly with age after age 70. Alberini et al. (2004) find
no difference in the WTP for younger age groups and find a 20 percent
reduction for those aged 70 and older. However this difference was not
statistically significant.
A second technique is to apply a distinct value
or suite of values for mortality risk reduction
depending on the age of incidence. However, there
is relatively little available literature upon which to
base such adjustments.16
Neither approach enjoys general acceptance
in the literature as they both require large
assumptions to be made, some of which have
been contradicted in empirical studies. Since
published support is lacking, neither approach is
recommended at this time.
Analysts are advised to note the age distribution
of the affected population when possible,
especially when children are found to be a
significant portion of the affected population.17
Although the literature on the valuation of
children's health risks is growing, there is still
not enough information currently to derive age-
specific valuation estimates.
Health status. Individual health status may also
affect WTP for mortality risk reduction. This
is an especially relevant factor for valuation of
environmental risks because individuals with
impaired health are often the most vulnerable
to mortality risks from environmental causes.
For example, particulate air pollution appears
to disproportionately affect individuals in an
already impaired state of health. Health status
is distinct from age (a "quality versus quantity"
distinction) but the two factors are clearly
correlated and therefore must be addressed
jointly when considering the need for an
adjustment. Again, both the theoretical and
empirical literatures on this point are mixed
with some studies showing a declining WTP
for increased longevity with a declining baseline
health state (Desvousges et al. 1996) and other
16 This second approach was illustrated in one EPA study (U.S. EPA,
2002d) for valuation of air pollution mortality risks, drawing upon
adjustments measured in Jones-Lee et al. (1985).
17 See U.S. EPA (2003a) for more information on the valuation of
children's health risks. OMB's CircularA-4advises agencies to use
estimates of mortality risk valuation for children that are at least as
large as those used for adult populations (0MB 2003).
Guidelines for Preparing Economic Analyses I December 2010 B-5
-------
Appendix B Mortality Risk Valuation Estimates
studies showing no statistically significant effects
(Krupnick et al. 2002).18
Application of existing VSLY approaches
implicitly assumes a linear relationship in which
each discounted life year is valued equally As
OMB (1996) notes "current research does not
provide a definitive way of developing estimates of
VSLY that are sensitive to such factors as current
age, latency of effect, life years remaining, and
social valuation of different risk reductions." The
second alternative, applying a suite of values for
these risks, lacks broad empirical support in the
economics literature. However, the potential
importance of this benefit transfer factor suggests
that analysts consider sensitivity analysis when
risk data — essentially risk estimates for specific
age groups — are available. An emerging literature
on the value of life expectancy extensions, based
primarily on stated preference techniques, is
beginning to help establish a basis for valuation in
cases where the mortality risk reduction involves
relatively short extensions of life.19
inclusion
Due to current limitations in the existing
economic literature, these Guidelines conclude
that, for the present time, the appropriate default
approach for valuing these benefits is provided
by the central VSL estimate described earlier.
However, analysts should carefully present the
limitations of this estimate. Economic analyses
should also fully characterize the nature of the
risk and populations affected by the policy action,
and should confirm that these parameters are
18 The fields of health economics and public health often account for
health status through the use of quality-adjusted life years (QALYs)
or disability adjusted life years (DALYs). These measures have their
place in evaluating the cost-effectiveness of medical interventions and
other policy contexts, but have not been fully integrated into the welfare
economic literature on risk valuation. More information on QALYs can
be found in Gold etal. (1996) and additional information on DALYs can
be found in Murray (1994).
19 It should be noted that many observers have expressed reservations
over adjusting the value of mortality risk reduction on the basis of
population characteristics such as age. One of the ethical bases
for these reservations is a concern that adjustments for population
characteristics imply support for variation in protection from
environmental risks. Another consideration is that existing economic
methods may not capture social WTP to reduce health risks. Chapter 9
details how some these considerations may be informed by a separate
assessment of equity.
within the scope of the situations considered in
these Guidelines. While a qualitative discussion
of these issues is generally warranted in EPA
economic analyses, analysts should also consider
a variety of quantitative sensitivity analyses on a
case-by-case basis as data allow. The analytical goal
is to characterize the impact of key attributes that
differ between the policy and study cases. These
attributes, and the degree to which they affect the
value of risk reduction, may vary with each benefit
transfer exercise, but analysts should consider the
characteristics described above (e.g., age, health
status, voluntariness of risk, and latency) and
values arising from altruism.
As the economic literature in this area
evolves, WTP estimates for mortality risk
reductions that more closely resemble those
from environmental hazards may support
more precise benefit transfers. Literature on
the specific methods available to account for
individual benefit-transfer considerations will
also continue to develop. In addition, EPA will
continue to conduct periodic reviews of the
risk valuation literature and will reconsider and
revise the recommendations in these Guidelines
accordingly. EPA will seek advice from the SAB
as guidance recommendations are revised.
-6
Guidelines for Preparing Economic Analyses I December 2010
-------
Appei
Accounting for Unemployed
Labor i 3enefit-Cost Analysis
In very rare cases, the implementation of a rule or policy may result in the job implications
for the structurally unemployed. This appendix (under development) will review the
literature on estimating the value unemployed individuals place on their time and will
describe what estimates of the costs of labor are most appropriate for use in regulatory
impact analysis (RIA) under this scenario.
Guidelines for Preparing Economic Analyses I December 2010
-------
Guidelines for Preparing Economic Analyses I January 2011
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P-44 Guidelines for Preparing Economic Analyses I May 2014
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Author Index
Acharya, G.,7-17,7-23
Adamowicz, W. 7-18,7-38,7-39,7-40,7-44
Adams, R., 7-20,7-23
Adler, N., 10-16,10-19,10-20
Alberini, A., 4-14,4-19,7-10,7-11,7-12,
7-39, B-5
Aldy,J., 7-10,7-11,7-29, B-3, B-5
Allen, P., 7-43
Anderson, L., 7-18
Anderson, R., 4-5
Anselin, L., 7-31
ApelbergB., 10-19
Arnold, F.,4-11
Arora, S„ 4-14,4-19,10-5
Arrow, K„ 6-1,6-12,6-13,6-14,6-15,6-18,7-36,
7-40,10-16, A-6
Austin, D., 4-14
Aydede, S., 6-15
Baden, B„ 10-13,10-18
Balistreri, E., 7-40
Ballard, C., 8-5
Banzhaf, H., 7-18,10-5,10-6
Banzhaf, S., 7-16,7-17,7-41
Barbier, E„ 7-16,7-17,7-23, A-4
Bartik, T„ 7-30
Bateman, I., 6-17,7-48
Baumol, W. 4-1, 5-7, A-4
Becker, R„ 8-20
Been, V., 10-5,10-6,10-17
Begg, C., 7-48
Bellinger, D., 10-7
Bell, E, 7-17
Benson, E., 7-18
Bento, A., 8-5
Bergstrom, J., 7-46,7-48
Berman, E., 9-8
Berndt, E., 7-22
Bin, O., 7-18
Birdsall, N., 6-15
Bishop, R., 7-38,7-41
Black, D., 7-10,7-29, B-2
Blackorby, C., 10-16
Blair, P., 8-17
Blomquist, G., 7-10,7-32,7-43
Boadway, R., 7-8
Boardman, A., 1-5,6-10,7-50, 8-4
Bockstael, N., 7-18,7-19,7-25,7-29, A-12
Boer, T„ 10-5
Bohi, D., 4-6
Guidelines for Preparing Economic Analyses I May 2014
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Author Index
Bovenberg, A., 4-17, 8-5
Bowen, W., 10-15
Boxall, P., 7-18,7-44
Boyd, J., 4-12,7-16,7-17,7-18,7-41
Boyle, K„ 7-37,7-39,7-40,7-41
Brajer, V., 10-17
Brannlund, R., 5-8
Brouhle, K„ 4-13,4-19,4-21, 5-15
Brouwer, R., 7-46
Brown, G., 7-30,7-39
Brown, Jr., G., 7-17,7-18
Brown, T„ 7-37,7-40,7-42
Bruce, N., 7-8
Bryant, B„ 10-13
Buchanan, Jr., J., 7-23
Bui, L„ 9-8
Bullard, R., 10-5
Burger, J., 10-16
Burtraw, D., 4-6,4-8, 8-11
Butler, R., B-2
Cairns, J., 6-18
Callan, S., 8-8
Cameron, T„ 7-15,7-38,7-39,7-40,10-18
Carbone, J., 8-21
Card, D., 7-45
Carlson, C., 4-6, 8-11
Carlsson, F., 7-43
Carson, R„ 5-7,7-37,7-41,7-42,7-44,7-48
Carthy, T., 7-10
Cason, T., 4-14,4-19,10-5
ChakrabortyJ., 10-14,10-18,10-19
Champ, P., 7-42,7-43
Chestnut, L„ 4-6,4-7,7-14,7-37,7-49
Chiang, A., 8-16
Chuma, H., B-4
Citro, C.F., 10-11
Clapp, J., 7-30
Clark, C., 7-17
Coase, R., 4-4, A-5
Cohen, M.,4-14,4-19
Cooper, J., 7-40
Cordell, H., 7-18
Costanza, R., 7-18
Costello, C., 7-17
Coursey, D., 10-18
Cousineau,J., B-2
Covey, J., B-3
Coyne, A., 7-18
Creel, M.,7-18
Crocker, T„ 7-20,7-31,7-32
Cropper, M„ 6-15,7-11,7-14,7-32, B-5
Crowder, L., 7-16
2
Guidelines for Preparing Economic Analyses I May 2014
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Cummings, R., 7-40,7-43
Daily, G., 7-15
Dale, L„ 7-30
Damon, L., 4-21
Danielson, L„ 7-30
Dantzig, G., A-14
Dasgupta, P., 6-12,7-13
de Groot, R., 7-16
de Haan, M„ 7-29, B-2
de Moojii, R., 4-17, 8-5
DeShazo, J., 7-15
Desvousges, W., 7-14,7-24,7-39,7-46, B-5
Diamond, P., 7-42
Dietz, T„ 10-20
Dillingham, A., B-2
Dixon, P., 9-18
Donaldson, D., 10-16
Donatuto,J., 10-20
Dreyfus, M„ 7-30
Durlauf, S., 7-19
Dutton,J., 8-12
Eeckhoudt, L„ 7-30
Ehrlich, I., B-4
Ekeland, I., 7-31
Ellerman, A., 4-7, 8-11
Ellerman, D., 4-3
Author Index
Ellis, G., 7-17,7-23
Englin,J., 7-44
Eom, Y„ 7-44
Ethier, R., 7-40
Evans, D., 7-16
Evans, M„ 7-30, B-5
Fann, N„ 10-15,10-19
Farley, J., 6-11
Faustmann, M„ 7-17
Feather, P., 7-25,7-27,7-47
Field, B„ 4-1, A-15, A-16
Field, M„ 4-1, A-15, A-16
Finnoff, D., 7-16
Fischhoff, B., 7-36,7-41
Fischoff, B., B-3
Fisher, A., 7-17,7-23
Fisher, B„ 4-11,7-15,7-16
Florax, R. ,7-45,7-46
Flores, N., 5-7,7-41
Folsom, R., B-2
Fowlie, M„ 10-13
Frederick, S., 6-18
Freeman, A., 6-8,6-15,6-18,7-7,7-8,7-12,7-14,
7-16,7-18,7-31,7-32,7-38 7-44, A-16
Freeman, M., 6-17
Fullerton, D„ 4-9,4-12, 8-5,10-5,10-8
Furby, L„ 7-36,7-41
Guidelines for Preparing Economic Analyses I May 2014 E-3
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Author Index
GaldoJ., 7-10, B-2
Gallet, C., 7-43
Gangopadhyay, S., 4-19
Garen, J., B-2
Garrod, G., 7-18
Gegax, D. , B-2
Geoghegan, J., 7-18
Gerking, S., 7-29, B-2
Gilbert, C., B-2
Gintis, H., 6-11
Glass, T„ 10-7
Gochfeld, M., 10-16
Goetz, R., 4-16
Gold, M„ B-6
Gollier C., 6-16,6-17
Goulder, L„ 4-3,4-9,4-16,4-17,4-21, 8-5, 8-12
Grainger, C., 10-6
Gray, W., 10-5
Griffiths, C., 4-19,4-21,5-15
Groom, B. 6-16, 6-17,6-18,6-19
Guo,J., 6-13
Gupta, F„ 10-5,10-6,10-17
Guria,J., B-2
Guyse,J., 6-11
Haab, T„ 7-25,7-27,7-44
Hahn, R.,4-15
Hall, J. 10-17
Hamilton, J., 4-14,10-5
Hammack, J., 7-17
Hammitt, J., 7-10,7-12,7-14,7-30, B-3
Hanemann, W., 7-7,7-38
Haninger, K., 7-14
Hanley, N., 3-2,7-7,7-18
Hansen, A., 6-16
Harford, J., 7-32
Harper, B., 10-20
Harrington, W., 4-5,4-8,4-21, 5-7,7-32
Harris, J., 4-15
Hauber, A., 7-27
Hay, M„ 7-18
Hazilla, M„ 8-5
Heal, G., 6-17
Heberlein, T„ 7-38,7-42
Heckman, J., 7-31
Helfand, G., 4-3
Hellerstein, D., 7-47
Henderson, N., 6-17
Hepburn, C., 6-17, 6-19
Herriges, J., 7-44
Herzog, H„ B-2
Heyes, A., 5-8
Hicks, J., A-10
4
Guidelines for Preparing Economic Analyses I May 2014
-------
Hicks, R„ 7-27
HobanJ., 7-41
Holland, S., 10-13
Holmes, T„ 7-38
Huang, J., 7-29,7-44
Hunter, J., 7-46
Huppert, D., 7-40
Hyman,J., A-14
Ibbotson, R., 6-10
Iceland, J., 10-11
Ihlanfeldt, K„ 7-30
Iovanna, R., 7-16
Irwin, E„ 7-18,7-19
Israel, B„ 10-22
Jacobsen, M„ 8-5
Jaffe, A., 4-5,5-8,9-10
Jakus, P., 7-18,7-26
James, M„ 7-38,7-39
Jena, A., 11-3
Johanneson, P., 7-14
Johannson, P., B-5
Johnson, F„ 7-39
Johnston, R., 7-43
Johnstone, N., 10-5
Jones-Lee, M„ 7-11, B-2, B-3, B-5
Jones, A., 7-48
Author Index
Jorgenson, D., 4-17, 8-5
Joskow, P., 4-7
Jung, C., 4-5
Just, R., 7-7,7-8, 8-4,9-17,10-16, A-10, A-16
Kahn,J„ 4-1,4-3,4-16
Kanninen, B., 7-39,7-40
Kaoru, Y„ 7-27,7-28,7-48
Kaplan, R„ B-3
Karp, L„ 6-11
Kealy, M„ 7-42
Keeler, A., 8-5
Keller, L., 6-11
Kelman, S., A-7
Kerr, S., 4-5, 5-8
Kerry Smith, V., 7-28
Khanna, M„ 4-13,4-14,4-19,4-21
King, M., 7-40
Kleckner, N., B-4
Kling, C., 7-27,7-44
Kniesner, T„ 7-11,7-29, B-2, B-5
Kochi, I., 7-10, B-3
Kokoski, M„ 8-4
Kolb,J., 6-18
Kolm, S., 10-16
Kolstad, C., 4-1
Konar, S., 4-14,4-19
Guidelines for Preparing Economic Analyses I May 2014
Esf
-5
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Author Index
Kopp, R., 7-23, 8-2, 8-5
Kramer, R., 7-17,7-23
Kreps, D.M., A-16
Kristrom, B., 7-40
Krueger, A., 7-45
Krupnick, A., 1-3,7-12,7-16,7-18,7-23,7-41,
B-5, B-6
Laibson, D., 6-11
Israel, B„ 10-22
Larson, D., 7-18,7-44
Lavin, M., 9-5
Layton, D., 7-39
Lecroix, R., B-2
Leeth, J., 7-29
Leggett, C., 7-29
Leibowitz, S., A-14
Lesser, J., 6-11, B-2
Lichtenstein, S., B-3
Lind, R., 6-8,6-10,6-11,6-12,6-15,6-18
Lindberg, K., 7-40
List, J., 7-43
Liston-Heyes, C., 5-8
Liu, J., B-3
Lohof, A., 4-5
Loomes, G., B-3
Loomis, J., 7-18,7-40,7-41,7-48
Lundgren, T., 5-8
E-l Guidelines for Preparing Economic Analyses I May 2014
Lupi, Jr., F., 7-18
Lyke, A., 7-42
Lyon, R„ 6-9,6-10, 6-18
Maantay.J., 10-14,10-18,10-19
Magat, A, 7-14
Maguire, K„ 7-40,10-6,10-7,10-15,10-16
Mahan, B.,7-18
Mankiw, N., A-4, A-16
Manne, A., 6-12
Mannesto, G., 7-40
Mansur, E., 10-13
Marin, A., B-2
Martinsson, R, 7-43
Mas-Colell, A., A-16
Massey, D., 7-16,7-17,7-18,7-19,7-27,7-44
McClelland, G., 7-43,7-49
McCluskey, J., 7-30
McConnell, K„ 7-16,7-18,7-24,7-25,7-41, A-l 2
McCormick, E., 10-6
McDaniels, T., B-3
McDonald, A., 8-12
McFadden, D., 7-42
Mendeloff,J., B-3
Mendelsohn, R., 7-30,7-27
Messer, K., 7-29
Miceli, T„ 4-21
-------
Michael, R„ 10-11
Michaels, R., 7-28
Mielke, H„ 10-22
Miettinen, A., 7-18
Miller, R„ 8-17
Miller, T„ B-2
Mills, D„ 4-6,4-7
Milon,J„ 7-18
Mitchell, R., 7-37,7-42,7-48
Mittlehammer, R., 7-30
Mohai, P., 10-5,10-13,10-18,10-19
Moher, D„ 7-48
Montero, J., 4-5
Montgomery, M„ 7-47
Moore, M„ 6-6, 6-9,6-10,6-11, 6-18,7-11, B-2
Morello-Frosch, R., 10-6,10-19
Morey, E., 7-39
Morgan, O., 7-44
Morgenstern, R., 4-19,4-21,9-8, 9-9
Mrozek, J., 7-10, B -3
Murdoch, J., 7-30
Murdock, J., 7-19
Murphy, J., 7-43
Murray, B., 8-5
Murray, C., B-6
Navrud, S., 7-45,7-46
Author Index
Needelman, M„ 7-27,7-47
Neuman, J., B-4
Newbold, S., 7-16,7-19
Newell, R., 4-5,5-8,6-10,6-15,6-16,6-17,6-19
Nicholson, W., 3-2
Ninassi, K„ 10-16
Nordhaus, W., 6-10,6-14, 6-17, 6-18
Norland, D., 10-16
O'Connor, D., 4-11,4-14
O'Neil, W., 4-7
Oates, W., 4-1, 5-7, 5-8,7-24, A-4
Odum, H.T., 7-13
Olson, C.A., B2
Opaluch,J., 7-39
Osborne, L„ 7-42
Otway, H., B-3
Ozdemir, S., 7-39
Ozog, M„ 7-28
Palmer, K„ 4-12,5-8,8-11
Palmquist, R„ 7-30,7-31,7-48
Pareto, V., A-4
Pargal, S., 4-13
Parry, I., 4-3,4-16,4-21,7-24, 8-5
Parsons, G., 7-18,7-25,7-26,7-27
Pastor, Jr., M„ 10-22
Pattanayak, S„ 7-17,7-23,4-49
Guidelines for Preparing Economic Analyses I May 2014
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Author Index
Pearce, D., 6-10, 6-12,6-15,6-18, A-10
Perman, R„ 3-2, A-4, A-5, A-7, A-10, A-l 1, A-12,
A-16
Peters, T„ 7-18,7-27
Phaneuf, D., 7-16,7-18,7-25,7-27,7-44
Pigou, A., 4-9
Pizer, W., 4-19,4-21,6-10,6-15,6-16,6-17,6-19,
8-2
Plantinga, A., 7-27
Poe, G., 7-48
Polasky, S., 7-17,7-18,7-23
Popp.D., 5-8, 8-11
Porter, M„ 5-8
Post, E., 10-19
Portney, P., 4-15, 6-15, 6-18,7-11,7-32, A-16
Price, J., 9-9
Psacharopoulos, G., B-2
QuigginJ., 7-31,7-38,7-39
Quimio, W., 4-14
Rabl, A., 8-12
Ramsey, F„ 6-7, 6-13
Rausser, G., 7-17,7-23,7-30
Ready, R., 7-40,7-45,7-46
Rees, W.,7-13
Rehkopf, D., 10-19
Ricketts, T„ 7-17
Ringquist, A., 10-5,10-6,10-15,10-19
E-i Guidelines for Preparing Economic Analyses I May 2014
Roberts, M„ 4-12
Roe, B„ 7-39
Rollins, K„ 7-42
Rosen, S., B-5
Rosenbaum, A., 10-5
Rosenberger, R., 7-18,7-48
Rowe, R., 7-33,7-37,7-49
Rowe, W.,B-3
Ruser, J., 7-29
Sacks J., 10-19
Sagar, A., 8-12
Saha, R., 10-18,10-19
Samuelson, P., 7-13,7-17
Savage, I., B-3
Schelling, T., 6-12, 6-15
Scheraga,J., 6-18
Schlapfer, F„ 7-42,7-44
Schlottman, A., B-2
Schmalensee, R., 4-6
Schmidt, F„ 7-46
Schrattenholzer, L., 8-12
Schwartz J., 10-7,10-19
Scott, A., A-6
Scotton, C., 7-29
Scrogin, D., 7-18
Segerson, K„ 4-14,4-21
-------
Sieg, H.,10-6
Sen, A., 6-12,10-16
Serret, Y„ 10-5
Sexton, K„ 10-19
Shadbegian, R., 8-20,10-5,10-18
Shavell, S., 7-13
Shaw, D., 7-18
Shaw, W., 7-25,7-28
Shepard, D., B-5
Sheriff, G., 10-6,10-7,10-15,10-16
Shogren, J., 7-18,7-31,7-32,7-43
Shonkwiler, J., 7-25
Short, K„ 10-11
Shrestha, R., 7-48
Siderelis, C., 7-18,7-27
Sidon,J., 10-6
Sieg, H.,11-3
Sigman, H., 4-11,4-12
Simpson, R., 7-17,7-23
Sinquefield, R., 6-10
Slovic, P., B-3
Small, A., 7-17,7-23
Smith, M., 7-16
Smith, R., B-2
Smith, V., 7-11,7-24,7-25,7-27,7-29,7-30,7-42,
7-48,7-49,8-4, 8-21, B-2, B-5
Solow, R., 6-12
Author Index
Spackman, M., 6-6,6-9, 6-17,6-18
Spash, C., 7-7
Spence, A., 4-12
Stanley, T„ 7-18,7-45,7-46
Stavins, R., 4-5,4-6,4-7,4-15, A-16
Steer, A., 6-15
Stern, N., 6-18
Stern, P., 10-20
Sterner, T„ 4-1,4-3,4-5,4-10,4-11,4-15,4-16,
A-15
Strand, I., 7-24,7-25
Strassman, D., 7-24
Sumaila, U., 6-11
Sunstein, C., B-3
Sussman, F., 7-11, B-5
Swift, B., 4-4,4-5
Taylor, C., 7-23
Taylor, L„ 7-10,7-29,7-30,7-43,7-46,7-48, B-3
Teisl, M., 7-41
Thaler, R., 6-11
Thayer, M., 7-14
Thomas, A., 8-12
Thomas, J., 8-8
Thorsnes, P., 7-18,7-19,7-29,7-30
Tietenberg, T., 4-3,4-5,4-13,4-16, 8-3 A-16
Tilman, D., 7-23
Timmins, C., 7-19
Guidelines for Preparing Economic Analyses I May 2014 E-9
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Author Index
Tol, R., 6-18
Tolley, G., 7-14
Toth, F., 6-12
Train, K., 7-26
Tschirhart, J., 7-16
Turner, R., 6-18,7-16
Tyrvainen, L., 7-18
Ulph, D., 6-10,6-12,6-18
Valdes, B., 6-13
van der Linde, C., 5-8
Van der Zwaan, B., 8-12
Van Houtven, G., 7-12,7-14
Van Houtven, G., 7-10
Varian, H„ 7-18,7-22, A-4, A-16
Vassanadumrongdee, S., 7-14
ViderasJ., 4-19
Viscusi, W., 7-10,7-11,7-29,7-30,7-39, B-2, B-3,
B-5
Voinov, A., 6-11
von Haefen, R.,7-27,7-44
von Haefen, R., 7-27
Vossler, C., 7-43
Wackernagel, M., 7-13
Wallace, K„ 7-16
Walsh, R„ 10-6
Walters, C., 6-11
Ward, M., 7-17
Guidelines for Preparing Economic Analyses I May 2014
Weitzman, M„ 4-12,4-17, 6-15,6-16,6-17,7-17,
7-23
Weyant,J., 6-15, 6-18
Wheeler, D., 4-13
White, K., 8-11
Whitehead, J., 7-37,7-44
Whitehead, T„ 7-41
Whittington, D., 1-3
Wilcoxen, P., 8-5
Williams, R., 4-16
Willig, R., A-12
Willis, K., 7-18
Wolverton, A., 4-12,10-5,10-17,10-18
Woodward, R. 7-18
Wu,J., 4-21
Wui, Y., 7-18
Xabadia, A.,4-16
Xu, F„ 7-30
Yang, T., 11-3
Yohe, G., 6-18
Zeckhauser, R., 6-16, B-5
Zerbe, R., 6-11
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Subject Indi
Acid Rain Program, 4-6,4-7, D-6, D-9
aesthetic improvements, 7-8,7-20
allocative efficiency, A-4
annualized costs, viii, 6-3,9-11, 9-12, 9-17
annualized value, ix, 6-2, 6-4,7-11
baseline, ix, 1-3,1-6,4-8,4-10,4-16,4-19,4-20,4-21,
5-1,5-2, 5-3,5-4, 5-5,5-6, 5-7,5-8, 5-9,5-10,
5-11, 5-12,5-13,5-14, 5-15, 5-16,6-2,7-1,7-4,
7-7,7-11,7-36,7-41,7-46,7-47, 8-5, 8-6, 8-12,
8-14, 8-19,9-3, 9-4,9-9, 9-10,10-6,10-7,10-8,
10-14,10-15,10-16,10-17,10-18,10-19,10-20,
11-11, B-4.B-5
behavioral responses, 4-21, 5-1, 5-10, 5-16,7-20, 8-15
benefit-cost analysis, viii, xiii, 1-1,1-4,1-5,2-2,2-4,
5-2,5-4, 5-11, 5-16,6-1,6-5,6-6,6-7,6-17,6-18,
7-1,7-2,7-3,7-7,7-8,7-9,7-12,7-45,7-49, 8-1,
8-3, 8-6, 8-7, 8-8, 8-9, 8-12, 8-20, 9-2,9-8, 9-15,
9-16, 9-17,10-1,10-4,10-8,10-14,10-22,11-2,
11 -3,11 -4,11 -11, A-1, A-6, A-7, A-10, A-13,
A-14, B-l
benefits, ix, x, xi, xii, xiii, xiv, xv, 1-2,1-3, 1-4, 1-5,1-6,
2-1,2-2,4-1,4-2,4-5,4-6,4-15,4-17,4-18, 5-1,
5-2,5-3, 5-4,5-5, 5-6,5-7, 5-8,5-9, 5-10,5-11,
5-12, 5-13,5-14,6-1,6-2,6-3,6-4,6-5,6-6,6-7,
6-8,6-9,6-10,6-11,6-12,6-14,6-15,6-16,6-17,
6-18, 6-19,6-20,7-1,7-2,7-3,7-4,7-5,7-6,7-7,
7-8,7-10,7-11,7-12,7-13,7-14,7-15,7-16,7-17,
7-18,7-19,7-20,7-21,7-22,7-23,7-24,7-25,
7-27,7-28,7-29,7-30,7-31,7-32,7-33,7-44,
7-45,7-46,7-47,7-48,7-49,7-50, 8-1, 8-2, 8-5,
8-6, 8-9, 8-10, 8-12, 8-13,9-2, 9-4, 9-5,9-8, 9-16,
10-1,10-3,10-7,10-8,10-9,10-20,10-22,10-23,
11-1,11-2,11-3,11-4,11-5,11-6,11-8,11-9,11-
10,11-11,11-12, A-3, A-5, A-6, A-7, A-13, A-14,
A-15, A-16, B-l, B-2, B-4, B-5, B-6, D-2, D-6,
D-8, D-17, D-20
benefit-cost ratio, xi, 5-10, A-14
cessation lag, xiii, 7-5,7-8, B-4
CGE models, 8-5, 8-6, 8-19, 8-20, 8-21, 9-17,9-18
childhood, 10-20,10-21
children, 1-4, 2-3,7-8,7-35,7-45,9-1, 9-4,10-1,
10-2,10-4,10-11,10-20,10-21,10-22,10-23,11-
9, B-5
Circular 1-1,1-3, 2-2,3-2,6-8, 6-15,7-5,7-8,7-35,
7-50, 8-12,9-1, 9-2,11-2, B-5, D-23
closures, xii, 7-25,8-9, 9-9,11-2,11-9
Coase, 4-4, A-5, D-7
Coase theorem, A-5
command-and-control regulation, 4-1,4-3,4-5,4-7,
4-8,4-21
performance-based standards, 4-3,4-5
technology standard, 4-3
compensating surplus, viii, A-12
compensating variation, viii, 7-7, 8-6, A-10, A-l 1,
A-12
competitiveness, 4-15, 8-7, 8-20,9-6, 9-9, 9-10
compliance cost, xi, 8-3, 8-11, 8-12, 8-13, 9-15, 9-16,
9-17
compliance cost models, 8-14, 8-15, 8-16
compliance costs, 2-4, 5-10, 8-3, 8-7, 8-9, 8-10, 8-11,
8-13, 8-14, 8-15, 8-16, 8-17, 9-2,9-4, 9-5, 9-8,
9-11, 9-12,9-14,9-15, 9-16, 9-17, A-9,10-3
compliance rate, 5-1, 5-8, 5-10, 5-16
Guidelines for Preparing Economic Analyses I May 2014
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Subject Index
computable general equilibrium (CGE) models,
viii, 1-5,8-5,8-19, 9-18
consumer preferences, 5-1,7-12
consumer surplus, 7-21,7-22,7-49, 8-2, 8-3, 8-15,
8-18, A-3, A-4, A-7, A-10
consumption rate of interest, xi, xiv, 6-6, 6-7, 6-8,
6-9,6-10,6-11,6-12,6-16,6-19
cost-effectiveness analysis, viii, xi, 1-5, 5-5,7-1,
7-11, 8-10,11-2,11-3,11-4,11-5,11-6,11-8,
11-14, A-14
cost-effectiveness, 1-5,4-1,4-2,4-3,4-5, 5-6,6-3,
7-12,11-4,11-8,11-11, A-14,B-6.D-12
costs, xi, xii, xiii, xiv, xv, 1-2, 1-3,1-4, 1-5,1-6, 2-1,
2-2,2-3, 3-2,4-1,4-2,4-3,4-4,4-5,4-6,4-8,
4-9,4-10,4-12,4-13,4-14,4-15,4-16,4-17,
4-18,4-21,4-22,5-1, 5-2,5-3, 5-4, 5-5,5-6, 5-7,
5-8,5-9, 5-10, 5-11,5-12,5-14, 5-15,6-1,6-2,
6-3,6-4,6-5,6-6,6-7,6-8,6-9,6-10,6-11,6-12,
6-14,6-16,6-18,6-19,6-20,7-1,7-4,7-8,7-12,
7-13,7-14,7-15,7-19,7-23,7-24,7-25,7-26,
7-27,7-28,7-32,7-33,7-34,7-37,7-39, 8-1,
8-2, 8-3, 8-4, 8-5, 8-6, 8-7, 8-8, 8-9, 8-10, 8-11,
8-12, 8-13, 8-14, 8-15, 8-16, 8-18, 8-19, 8-20,
8-21, 9-2,9-3, 9-4,9-5, 9-6, 9-7,9-8, 9-10, 9-11,
9-12, 9-13,9-14,9-15, 9-16, 9-17,10-1,10-3,
10-4,10-6,10-8,10-9,10-15,10-16,10-22,10-
23,11-1,11-2,11-3,11-4,11-5,11-8,11-11,
11 -12, A-l, A-3, A-5, A-6, A-7, A-8, A-9, A-l 2,
A-13, A-14, A-l 5, A-16, B-4, B-5, C-l, D-6
cost savings, 2-2,4-3,4-5,4-7,4-21, 5-7, 5-8, 5-15,
7-20,7-45
deadweight loss, viii, 8-3, A-6, A-13
demand curve, 7-22,7-24,7-25, 8-2, 8-3, 8-4,
8-15, A-l, A-2, A-3, A-4, A-6, A-8, A-9, A-12
demand elasticities, 8-15, 9-7, A-8, A-9, A-12
direct costs, xi, 7-33, 8-7, 8-13, 8-14, 8-15
discounting, xiv, 1-6,6-1, 6-2,6-3, 6-4,6-6, 6-7,
6-8,6-11,6-12,6-13,6-14,6-15,6-16,6-17,
6-18, 6-19,6-20,7-2,7-36, 8-10, 8-11,10-23,
11-9, B-4, D-14,D-16,D-29
distributional analysis, 1-1, 2-4, 8-9,10-1,10-4,
10-8,10-14,10-20,10-22
distributional costs, 8-9, 8-13, 8-16, 8-19
distributional impacts, 8-21, 8-13,9-18,10-1,10-
6,10-12,10-13,10-16,10-18,10-20,10-23
economic efficiency, xi, 1-2,4-1,4-5,4-21, 6-5,
9-10,10-3,10-4,11-1,11-11,11-12, A-l, A-3,
A-4, A-15
economic impact analysis (EIA), viii, xii, 1-4,1-5,
2-2, 8-7, 9-1,9-2, 9-3,9-8, 9-12,9-15,9-16,
11-2,11-9
Edge worth box, A-4
effect-by-effect approach, 7-1,7-3
elasticity, xii, 5-7, 6-7,6-13, 6-14, 6-17,6-18, 6-19,
7-41,7-42,7-49, 8-15, 9-6, A-8, A-9, B-4,
elderly, 10-1,10-22
emission taxes, 4-5,4-9,4-10
product charges, 4-9
employment, ix, 7-8,7-35, 8-7, 8-17, 8-20,9-1,
9-3,9-6, 9-8,9-9, 9-14, 9-18,11-2,11-9
environmental justice, 2-2,2-3, 9-1,10-1,10-3,
10-4,10-20,10-22,10-23,11-2
equity assessment, xii, xiii, 1-5, 9-1, A-7
equivalent surplus, ix, A-12
equivalent variation, ix, 7-7, 8-6, 8-20, A-10
ethnicity, 10-5,10-6,10-9,10-11,10-12,10-17
excess demand, A-3
F-2 Guidelines for Preparing Economic Analyses I May 2014
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Subject Index
excess supply, A-2
Executive Order 12866, 2-2,7-2, D-9, D-10, D-35
Executive Order 12898, 2-3, D-9,10-2,10-4,10-7,
10-9,10-11,10-15
Executive Order 13045, 2-3, D-9, D-32,10-2,
10-21
expert elicitation, 7-34,11-9
exposure, xi, xiii, 3-1,4-14, 5-3, 5-6, 5-14,7-5,
7-11,7-16,7-28,7-29,7-32,7-49,11-4,11-5,
11-6,11-7, B-l.B-4
externalities, xii, xiii, xiv, 1-1,1-4, 2-2, 3-2,4-1,
4-2,4-10,7-23,7-24, 8-3, A-3, A-4, A-6
fixed costs, 8-8, 9-10,9-17
flow pollutants, xiii
full compliance, 5-2, 5-3, 5-9, 5-10
future economic activity, 5-5, 5-6
general equilibrium, 1-3,1-4, 8-1, 8-2, 8-4, 8-5,
8-6,9-1,9-9,11-3, A-13
general equilibrium analysis,1-4, 8-2, 8-5, 8-6,
A-13
health risks, 10-1,10-2,10-11,10-20
Hicksian demand curve, A-10, A-l 1, A-12
households, xiii, 1-3,7-20,7-23,7-39,7-40,7-46,
7-47,7-49, 8-4, 8-5, 8-6, 8-7, 8-8, 8-10,9-1, 9-2,
9-4,9-7, 9-9,9-13,9-14, 9-16, 9-18,10-6,10-9,
10-10,10-11,10-12,10-22, A-5, A-13
income effect, A-10, A-l 1
input-output models, 8-17, 8-18, 8-19
Kaldor-Hicks, 7-8, A-7
Kaldor-Hicks criterion/compensation test, xiii,
A-7
labeling rules, 4-13
latency, xi, 7-8, B-4, B-6
liability rules, 4-14,4-15
lifestages, 10-1,10-20,10-21,10-22
linear programming models, 8-16
linked rules, 5-13
lost productivity, 7-33,7-34
low-income, 10-1,10-2,10-3,10-4,10-5,10-6,
10-10,10-11,10-12,10-15,10-22
marginal abatement cost curve, ix, 4-5, A-13,
A-15, A-16
marginal benefit, xii, xiii, 4-2,4-17, 6-15, A-l, A-2
A-3, A-5, A-14, A-15, A-16
marginal cost, xii, xiii, 4-2,4-6,7-22,7-31, A-2,
A-3, A-6, A14, A-15
marginal social benefit, xiii, 4-2, A-5, A-14
marginal social cost, ix, xiii, 4-2, A-4, A-5, A-6,
A-14
marginal willingness to pay, 7-11,7-31,10-8, A-l,
A-2, A-14
indigenous, 10-1,10-3,10-12,10-20
information disclosure, 4-13,4-14,4-17
input-output econometric models, 8-18, 8-19
Guidelines for Preparing Economic Analyses I May 2014
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Subject Index
market, ix, xi, xiii, 1-4,2-2, 3-1,3-2,4-1,4-2,4-3,
4-5,4-6,4-7,4-8,4-10,4-11,4-12,4-13,4-14,
4-15,4-16,4-17,4-18,4-21,5-3,5-11,5-14,
6-1,6-2,6-6,6-7,6-8,6-10,6-11,6-12,6-13,
6-15,6-16,6-18,7-6,7-7,7-18,7-20,7-21,
7-22,7-24,7-28,7-30,7-33,7-35,7-36,7-39,
7-40,7-42,7-43,7-44, 8-2, 8-3, 8-4, 8-5, 8-6,
8-7, 8-9, 8-10, 8-11, 8-12, 8-15, 8-16, 8-19,
8-21, 9-3,9-6, 9-7,9-8, 9-9, 9-10, 9-12,9-13,
9-17, 9-18, A-l, A-2, A-3, A-4, A-5, A-6, A-7,
A-9.A-12, A-13, A-14, A-16, D-4, D-17, D-19,
D-36, D-37
market economy, 8-5, 8-19, A-l, A-2
market failure, xiii, 1-1,2-2, 3-2,4-1,4-2,4-13,
4-15,4-17,5 -15,6-12,6-18, A-3, A-4, A-6
market permit system, xiv
bubbles, 4-7,4-8
grandfathering, 4-3
offsets, 4-7,4-8,9-16
market power, xiii, 1-1, 2-2,3-2,4-17, 9-10, A-4
market-based incentives, 4-1,4-3,4-5
Marshallian consumer surplus, 7-7, A-l 1, A-12
Marshallian demand curve, A-10
material damage, 7-4,7-20
meta-analysis, 7-6,7-10,7-19,7-29,7-41,7-45,
7-46,7-48,7-49,10-6, B-2, B-3
minority, 10-1,10-2,10-3,10-4,10-5,10-6,10-7,
10-9,10-10,10-12,10-15,10-17,10-22
mobile sources, 4-16, 8-15
monitoring and enforcement, 4-13,4-21
multiple baselines, 5-2, 5-16
multiple rules, 5-11
need for policy action, 3-1, 3-2
net benefits, ix, xi, xv, 1-3,1-4,4-2, 5-2, 5-5, 5-10,
6-2,6-3,6-4,6-5,6-6,6-8,6-16,6-19,7-1,7-22,
7-50, 8-2, 8-9, 8-12,9-2,11-1,11-3,11-10,11-
11, A-3, A-6, A-7, A-13, A-14, A-16
net future value, ix, 6-2, 6-3,6-4, 6-17
net present value, ix, xi, xiv, 4-1, 6-2,6-4, 6-5,6-9,
6-12, 6-17,6-19
North American Industrial Classification System
(NAICS), ix, 9-3,9-5, 9-6
open economy, 6-10, 6-11
opportunity cost, ix, xi, xiv, xv, 1-5,4-7,4-10,6-6,
6-8,6-9,6-11,6-18,6-19,7-24,7-25,7-35,8-1,
8-2, 8-4, 8-7, A-l, A-6
optimal growth, 6-12, 6-13
over-compliance, 5-9, 5-10
PACE Survey, 9-7
partial equilibrium, 8-2, 8-3, 8-4, 9-17,11-3, A-13
partial equilibrium analysis, 8-2, 8-3, 8-4, 8-6,
8-15, 9-17, A-12, A-13
partial equilibrium models, 8-15, 8-16
point sources, 1-5,4-16,4-17,4-20, A-16
Pollution Abatement Costs and Expenditures
(PACE) Survey, x, 8-20, 9-6,9-7, D-2
poor, 7-25, 6-18,9-12,10-11
price elasticity of supply/demand, xii, A-8, A-9
private costs, 1-4,4-9,4-10, 8-10,9-2, 9-15,9-16,
A-5, A-6
producer surplus, 1-2,7-21, 8-2, 8-5, 8-6, 8-10,
8-11,11-6, A-3, A-4, A-13
productive efficiency, A-4
F-4
Guidelines for Preparing Economic Analyses I May 2014
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Subject Index
profit, xv, 7-17,7-18,7-22,7-23, 8-4, 8-17, 9-5,
9-6,9-9, 9-11, 9-16,9-17,11-2, A-l, A-4, A-5
property rights, 4-4, 7-7,7-36,7-41, A-5
race, 10-4,10-5,10-6,10-9,10-10,10-11,10-12,
10-15,10-17
Regulatory Flexibility Act, x, 2-4,9-1, 9-2, 9-3,
9-11, 9-12,9-14,9-15,11-9, D-l, D-34
risk, xi, xiii, xv, 1-2,1-3,1-5,1-6, 2-3,3-2,4-13,
4-14,4-15,4-17,4-18, 5-6,5-7, 5-10, 5-11,6-3,
6-4,6-5,6-6,6-7,6-8,6-10,6-16,6-17,6-18,
7-1,7-2,7-3,7-4,7-5,7-6,7-8,7-10,7-11,7-12,
7-13,7-14,7-15,7-28,7-29,7-30,7-31,7-32,
7-33,7-38,7-45,7-50, 9-3,9-16, A-5, B-l, B-2,
B-3, B-4, B-5, B-6, D-17, D-19, D-27, D-32
risk assessment, 10-21,10-23
shadow price of capital, xiv, 6-8, 6-9,6-10,6-11,
6-12, 6-19
small entities, 2-4,9-14, 9-17,11-2
social cost, xii, xiii, xiv, xv, 1-5,4-1,4-10,4-22,
5-9,6-8, 6-13,7-2,7-4, 8-1, 8-2, 8-3, 8-4, 8-5,
8-6, 8-7, 8-9, 8-10, 8-11, 8-12, 8-13, 8-14, 8-15,
8-18,9-2,9-14, 9-15, 9-16, A-5, A-6, A-7, A-13,
A-14, A-16
social welfare, xii, 4-1,4-2,4-3,4-4,4-12, 6-6,6-8,
6-12,7-23, 8-1,11-5, A-3, A-5, A-6, A-7
social welfare function, xv, 6-12,6-13,10-15,10-
16,10-23, A-7
standard and pricing approach, 4-13
statistical life years, 7-11
stock pollutants, 4-16
sub-populations, xiii, 1-4,1-5,10-20,10-21,11-3
subsidies, xii, 4-5,4-10,4-11,4-16,4-17,9-14
buy-back, 4-10
substitution effect, 7-7, A-10, A-l 1
supply curve, 8-2, 8-3, 8-5, A-2, A-3, A-5, A-7,
A-8, A-9, A-13
supply elasticities, 8-15, A-8, A-9
taxes, xi, xiv, 4-5,4-7,4-9,4-10,4-11,4-12,4-13,
4-16,4-17,4-18,6-6,6-9,6-12,7-24, 8-4, 8-5,
8-8, 8-21,9-4, 9-12,9-13,9-16, 9-18, A-4, A-13
tax-subsidy, xv, 4-11,4-12,4-16
dep osit-refund, 4-11,4-12,4-17
technological change, 4-5, 5-7, 5-8, 6-5, 8-12,
11-11
total cost, xiii, xiv, xv, 5-4, 5-11,6-4,7-13, 8-2, 8-3,
8-7, 8-8, 8-16, 9-7, A-2, A-3, A-6, A-13, A-14,
A-16
transfers, 1-5, 2-2,4-7, 6-15,6-17,7-19,7-45,
7-46,7-47,9-2,9-16, A-4, B-6, D-14
transitional costs, 8-9, 8-10, 8-12, 8-13, 8-19, 8-21
tribal, 10-3
uncertainty, xiii, 4-2,4-8,4-9,4-14,4-15,4-17,
4-20, 5-2,5-4, 5-5,5-6, 5-10,5-13, 5-14,6-1,
6-2,6-12,6-13,6-14,6-15,6-16,6-17,6-19,
6-20,7-2,7-5,7-6,7-8,7-14,7-37,7-43,7-45,
7-49, 8-11,8-12,11-1,11-3,11-4,11-9,11-10,
11-11
under-compliance, 5-9, 5-10
Unfunded Mandates Reform Act, x, 2-3,2-4, 8-7,
9-1,9-2, 9-14, 9-15, D-l, D-31
use value, 7-1,7-16,7-18,7-19,7-27
utility, x, 6-1,6-2,6-7,6-11,6-12,6-13,6-14,6-17,
6-18,7-6,7-7,7-11,7-12,7-13,7-22,7-24,
7-25,7-26,7-31,7-32,7-33,7-35,7-36,7-42,
7-44,7-49, 8-4, 8-5, 8-6, 8-15, 9-18, A-l, A-4,
A-6, A-7, A-10, A-ll, A-12
utility possibility frontier, x, A-4
Guidelines for Preparing Economic Analyses I May 2014 F-5
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validity, 7-36,7-37,7-41,7-42,7-44,7-46, D-13,
D-16, D-37
variable costs, 8-3, 8-9, 8-16
voluntary actions, 5-15
voluntary approaches, 4-19,4-21
willingness to accept, x, xv, 7-7,7-8,7-18,7-20,
7-29,7-49, A-12
willingness to pay, x, xi, xv, 5-7,7-3,7-6,7-7,7-8,
7-10,7-11,7-12,7-13,7-14,7-15,7-18,7-20,
7-21,7-25,7-26,7-27,7-28,7-30,7-31,7-32,
7-33,7-34,7-35,7-36,7-37,7-38,7-39,7-40,
7-41,7-42,7-43,7-44,7-46,7-47,7-48,7-49,
7-50,10-8, A-l, A-2, A-3, A-4, A-6, A-10,
A-l 1, A-12, A-14, B-4, B-5, B-6, D-2, D-12,
D-17, D-35
Guidelines for Preparing Economic Analyses I May 2014
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