FINAL REPORT MAY 15,1971
AN ECONOMIC MODEL SYSTEM FOR
THE ASSESSMENT OF EFFECTS
OF AIR POLLUTION ABATEMENT
DEVELOPMENT AND DEMONSTRATION PHASE
Prepared for:
OFFICE OF AIR PROGRAMS
ENVIRONMENTAL PROTECTION AGENCY
by
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FINAL REPORT
AN ECONOMIC MODEL SYSTEM
FOR THE ASSESSMENT OF EFFECTS
OF AIR POLLUTION ABATEMENT
Volume I: The OAP Economic Model System Development
and Demonstration
Prepared for:
Office of Air Programs
Environmental Protection Agency
Air Pollution Control Office
Prepared by:
CONSAD Research Corporation
121 N. Highland Avenue
Pittsburgh, Pennsylvania 15206
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PREFACE
To economists, optimal resource allocation decisions require
knowledge of the costs, effects and benefits of alternatives. Because
the real world is complex, models or simulations are used in economic
analysis to test the outcome of alternatives strategies.
This study by the Office of Air Programs and CONSAD Research
Corporation represents the first systematic effort to determine the in-
direct economic effects of air pollution control costs and to test the
possible results and implications of some alternative approaches to the
sharing of costs of control among producers, consumers and the public
generally.
Because it is a new modeling effort and the data inputs somewhat
old, the results must not yet be considered anything but illustrative of
the potential capability of economic modeling on such a wide scale.
The present exercise utilizes engineering estimates of costs of control
and makes specific assumptions regarding the level of benefits attain-
able from air pollution control and the manner in which those benefits
are introduced to and experienced by the system.
It will quickly be obvious to the careful reader that better control
cost estimates are needed together with a more flexible method of intro-
ducing benefits if the model is to come closer to describing reality.
The model obviously requires new information on the facts of economic
life and of the control requirements, technology and costs implicit in
the Clean Air Act Amendments of 1970.
It will be for the users and future developers of the model to sup-
ply the necessary improvements needed in order to make it a usable
contributor to policy analysis and formulation. Despite its limits, how-
ever, we believe that this economic modeling effort represents a most
important first step and that it should be viewed at in that light.
Paul H. Gerhardt
Chief Economist
Office of Program Development
Office of Air Programs
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ACKNOWLEDGEMENTS
The research reported here has been carried out by CONSAD Research Corporation
for the Office of Air Programs* Environmental Protection Agency (EPA), under subcon-
tract to TRW S.ystems, Inc. The CONSAD team that developed and demonstrated in a
preliminary manner the Regional Economic Model System include:
Robert J. Anderson Consultant
Robert F. Byrne
Penny Globus
T. R. Lakshmanan Project Director
Fu-Chen Lo Project Manager
Kathryn C. Mason
Arthur Silvers
Nat Simons, Jr.
John M. Thompson, Jr.
Venkaiah Yedla
CONSAD could not have prepared the analytic and conceptual tasks without the
encouragement, guidance, and review of the staff of APCO and TRW, in particular:
APCO Staff TRW Staff
Allen Basala Michael Frankel
Larry Barrett Donald Lewis
Ronald Campbell
Paul H. Gerhardt (Project Officer)
Henry Kahn
Ken Woodcock
Any opinions expressed in this report are those of CONSAD and do not neces-
sarily reflect the views of the individuals cited above.
*Formerly Air Pollution Control Office
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TABLE OF CONTENTS
Page
EXECUTIVE SUMMARY ix
1. 0 AIR POLLUTION AND THE
ECONOMY: AN OVERVIEW 1
1. 1 The Context 1
1. 2 The APCO Economic Model System 10
2. 0 THE RAP A PROGRAM AND THE
REGIONAL ECONOMIC MODEL SYSTEM 13
2. 1 Purpose of the RAPA Program 13
2. 2 The Role of the Economic Model in RAPA 16
2. 3 The APCO Economic Model System (Phase III) 20
3. 0 MODEL DEVELOPMENT
AND FORMULATION 27
3. 1 The Model in General 27
3.2 The APCO Economic Model System 30
3.2. 1 The Regional Model 30
3.2.2 I-O Model and Interregional Feedback 36
3.3 Empirical Estimation 40
4. 0 PRELIMINARY STUDY OF A MODEL
FOR NATIONAL ECONOMIC EFFECTS
ASSESSMENT 43
4. 1 The Transition from Regional to
National Policy Analysis: Background 43
4. 2 The OBE Quarterly Model 46
4. 3 Desired Attributes in a National Model 52
4. 4 Data Availability and Reliability 56
4. S Alternative Approaches to
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TABLE OF CONTENTS (continued)
5. 0 SIMULATING THE IMPACT OF AIR
POLLUTION CONTROL STRATEGIES:
A SIMULATION OF THREE STRATEGIES 61
5. 1 Introduction 61
5. 2 The Scope and Nature of Simulation
of the Regional Model 62
5. 2. 1 Nature of Simulation in General 62
5.2.2 APCO Policies and the Regional Model 64
5. 2. 3 Regional Model System and Control Inputs 66
5.3 Simulating Three Strategies:
The Definition of Strategies 70
5.4 Preparing Inputs for Simulation 82
6. 0 INTERPRETATION OF THE EFFECTS
OF THREE TRIAL STRATEGIES 93
6. 1 The Three Strategies 93
6. 2 An Approach to Assessment of
Effects of the Three Strategies 95
6. 3 Changes in Unemployment Rates
Under the Three Strategies 97
6.4 Changes in Profits and Personal Income 106
6. 5 Total Net Effects of Three Alternative
Strategies for 91 AQCRs 117
6. 6 Concluding Comments 122
7. 0 AN ASSESSMENT OF THE APCO
REGIONAL ECONOMIC MODEL SYSTEM:
PROMISES AND PITFALLS 125
8. 0 REFINEMENT AND UTILIZATION OF
THE APCO ECONOMIC MODEL SYSTEM:
RECOMMENDATIONS 135
APPENDIX A: A MODEL TO ASSESS THE ECONOMIC
EFFECTS OF AIR POLLUTION ABATE-
MENT IN ST. LOUIS AQCR (PHASE I) A. 1
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TABLE OF CONTENTS (continued)
Page
APPENDIX B: 31 AQCR MODEL (PHASE II) B. 1
APPENDIX C: THE APCO REGIONAL ECONOMIC
MODEL SYSTEM (PHASE III) C. 1
APPENDIX D: INPUT-OUTPUT MODEL SYSTEM
AND INTERREGIONAL FEEDBACK D. 1
APPENDIX E: DATA USED IN THE MODEL E. 1
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LIST OF FIGURES
Figure Page
1. 1 Short-Term Effects of Standards 3
1.2 Long-Term Effects of Standards 6
2. 1 Regional Air Pollution Analysis Process 14
2.2 The APCO Economic Model System:
Development and Use 22
3. 1 Economic Modelling for Policy Simulations 28
3. 2 Major Components of the Model 32
3. 3 Notation of the Regional Model Variables 33
3.4 The Regional Model Formulation 34
3. 5 Regional Model -- A More Detailed Look 37
4. 1 Effect of Tax Credit Policy Upon
Implementation Plan 48
5. 1 APC Policies and Model Inputs 67
5.2 Regional Model and Control Inputs 68
5.3 Regional Model and Control Policies:
A More Detailed Look 71
5.4 Supply Schedule 73
5. 5(a) Shift Due to Increase in Disposable Income 74
5. 5(b) Shift Due to Increase in Production Costs 74
5. 6 Air Pollution Control Cost Per Unit Output 75
5. 7 Price Freeze Demand Schedule 78
5. 8 Demand and Supply Relations Under Strategy 1 79
5. 9 Demand and Supply Relations Under Strategy 3 81
6. 1 The Three Strategies at a Glance 94
6. 2 Geographic Distribution of Economic Effects
Under Strategy 1 (Measured by Change of
Unemployment Rate) 102
6. 3 Geographic Distribution of Economic Effects
Under Strategy 2 (Measured by Change of
Unemployment Rate) 104
6.4 Geographic Distribution of Economic Effects
Under Strategy 3 (Measured by Change of
Unemployment Rate) 105
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LIST OF TABLES
Table Page
5. 1 Incidence of Control Costs Under the
Three Strategies 90
6. 1 Sample Output of an AQCR: A Summary Table 96
6. 2 Change of Unemployment Rate from Simulation
of Three APC Strategies 98
6. 3 Changes in Unemployment Rate, Profits and
Personal Income in the AQCRs under Strategy 1 108
6.4 Changes in Unemployment Rate, Profits and
Personal Income in the AQCRs under Strategy 2 111
6. 5 Changes in Unemployment Rate, Profits and
Personal Income in the AQCRs under Strategy 3 114
6.6 Median Values of the Distributions of the Changes
in Unemployment Rate, Percentage Changes in
Profits and Personal Income in the Three Strategies 117
6. 7 Net Effects of Three Alternative Strategies
on 91 AQCRs 119
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EXECUTIVE SUMMARY
1. INTRODUCTION
In sustaining rapid population growth, high levels of consumption,
technological innovation, and a quickening pace in the use of resources,
Americans have been hard on their environment -- the vegetation, land,
air, and water that sustain the biotic and industrial processes. As a
result, clean air and clean water have become scarce resources.
Pure or relatively unpolluted air is no longer a free good in our
society; money outlays must be made to go where air is relatively
cleaner or to trap pollutants before they escape into the air. In the
past, those who were responsible for pollution did not bear the external
costs they generated to the entire society.
With the recent enactment of the Clean Air Act of 1967 and Amend-
ments of 1970, business and industry arfe now being required to control
the amount of pollutants that they discharge into the air. To industry,
this requirement means that the production costs for the same amount
of output produced prior to the legislation will be increased in propor-
tion to the required investment in air pollution control equipment.
Thus, certain industries and regions that have in the past enjoyed the
economic advantage of low-cost production may face some degree of
economic decline due to the requirements of pollution abatement.
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There would be, however, offsetting economic effects from air
quality control strategies in such regions in at least two ways. First,
there would be increased demand for the products of the industries that
produce pollution control equipment and low pollutant fuels leading to
increased output, employment, and income in those sectors. Second,
a variety of general economic benefits result from pollution abatement.
Some of those gains are increased labor productivity, and property
values, reduced health expenditures, reduced outlays on physical main-
tenance of homes and plants, and savings in agricultural production
activities. These gains would lead to increased consumption, output,
employment and income, and would begin to offset the economic reduc-
tions resulting from the pollution control costs.
Consequently, air pollution abatement leads to changes in eco-
nomic output, labor markets, the availability of capital, as well as re-
distributions within the entire economy. Further, the implementation
of air quality programs would have a variety of other effects, such as
tax base impacts on communities or variations in land-use and indus-
trialization in various regions.
The Air Pollution Control Office (APCO) of the Environmental
Protection Agency (EPA) commissioned CONSAD Research Corporation,
under subcontract to TRW Systems, Inc. , for the development and
demonstration of such an economic model as an operational analytical
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tool for abatement policy assessment. This economic model initiated
in 1968 as part of the Regional Air Pollution Analysis (RAPA) program
focused on the regional economies of the various Air Quality Control
Regions (AQCRs). As such, it will be useful for assessment of control
strategies at the regional level, where most of the implementation plans
are prepared. However, there is an increasing interest in air pollution
control policy decisions in the last three years at the Federal govern-
ment level. Therefore, there has been correspondingly an increased
focus in the model system described here toward the assessment of in-
terregional and national economic effects in addition to regional effects.
However, the model system developed by CONSAD is still a regional
economic system with capabilities to measure, in a limited manner,
the interregional and national effects. It is called the APCO Regional
Economic Model System and has been demonstrated to some degree as
an operational tool for control strategy assessment.
2. THE APCO REGIONAL, MODEL
SYSTEM AND THE RAPA PROGRAM
The APCO Regional Model System was conceived as part of the
RAPA program in July 1968. To accomplish the task of developing
timely abatement strategies requires an extensive examination of the
factors involved in the air pollution system, such as meteorology, air
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pollution control technology, air pollution growth trends, source emis-
sion inventories, existing regional air quality conditions, and regional
economic impact. Figure 1 outlines the RAPA process and the role of
the APCO Economic Model System there.
In the first year (Phase I), CONSAD developed a Regional Econ-
ometric Model of the St. Louis region where the RAPA was explored
first in depth. This model describes the growth patterns of key eco-
nomic sectors (both high emission and other industries) and estimates
the regional product, employment, capital stock and investment change,
and value-added by industry, tax receipts and regional unemployment.
These estimates are sensitive to a variety of air pollution control strat-
egies. In fact, the economic effects of five hypothetical air quality con-
trol strategies were simulated and interpreted, using the model for the
St. Louis region.
The second year (Phase II) witnessed an extension of the model to
31 large metropolitan areas. These large urban areas have a varied
industrial structure highly representative of the national industrial com-
position and comprise a significant segment of national output. The
structure and outputs of this model are very similar to those of the
St. Louis model.
In the current year (Phase III), this model system has been ex-
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FIGURE I
REGIONAL AIR POLLUTION ANALYSIS PROCESS
Goals (Air
Quality or
Emission
Standards)
Abatement
Strategy
Controls
Sources
Diffusion
Receptors
Control Cost
and Benefit
(Damage)
Implemen-
tation Plan
Economic
Effect
*| (APCO Eco-
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embrace 100 AQCRs, at the same time providing a broader range of
economic effects than has been possible so far. Second, the Phase III
model provides the capabilities of capturing the economic effects of
abatement strategies in one region on all other regions and the struc-
ture changes taking place in the national economy.
The Regional Model System as currently developed in Phase III
thus covers a significant portion of the AQCRs in the nation and treats
them all as a set of interrelated regions. Ib is a large, complex model
which is an operational tool. Its workability has been demonstrated as
shown in Figure 2. Three strategies specified by the APCO staff and
relating to the incidence of control costs (on the high emission indus-
tries, consumer or government), associated with the air quality stand-
ards of the 1970 Report to the Congress under Section 305(a) of the
Clean Air Act of 1967, were fed into the economic model and the simu-
lation program run to develop the economic effects.
The rest of this report will provide a brief summary of the devel-
opment, demonstration, and assessment of the regional model system.
3. THE MODEL SYSTEM: AN OUTLINE
The Regional Economic Model System has been developed as de-
scribed above in three phases. At the end of Phase II, the regional
model was structured as though AQCRs were economically independent
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Inputs
Model Development
Model
Outputs
Assessment
Regional and
National
Economic
Activity
Regional
Economic
Model
System
Policy Assessment
1
Three
Strategies
(APCO
Specified)
Computer
Simulation
Program
Outputs
Economic
Change s
Interpretation
of Model
Output
FIGURE 2
THE APCO REGIONAL ECONOMIC MODEL SYSTEM:
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of one another. There was no allowance for interregional effects. If,
for example, Region A instituted air pollution control and in order to
do so imported air pollution equipment from Region B, the model sim-
ulated the economic impact of Region A's program on Region A alone,
and not on Region B. Thus, the regional model gave no indication of
the increase in employment in Region B, resulting from the increased
production of air pollution control equipment for export to Region A.
There was, however, a source of pessimistic bias in the statement of
economic effects of air pollution abatement embedded in the very struc-
ture of the model.
Second, the models at the end of the second year focused only on
the economic impacts of control expenditures and accounted in no way
for any benefits which might result from air quality improvement. This
again tended to cause unjustifiably pessimistic conclusions about the
economic effects of air pollution control.
During the current year, CONSAD approached the problem of
eliminating these biases and making some preliminary assessment of
the national impact of air pollution control in two ways. First, the
cross-section Regional Model was restructured to eliminate, insofar
as possible, the pessimistic bias induced by structural exclusion of
interregional feedback effects and benefits and to permit preliminary
estimates, a national Input-Output (I-O) model has been introduced to
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capture (a) a preliminary estimate of structural changes in the national
economy, and (b) an interregional feedback scheme to the AQCRs.
Second, the feasibility and desirability of an expressly national
model to be interconnected with the current regional model was inves-
tigated in considerable detail.
These developments are both described next.
The APCO Economic Model System consists of two major com-
ponents, namely, a 100 AQCR regional model and interregional feed-
back from a national I-O model, as shown in Figure 3. The modules
comprising both these major components are also indicated in Figure 3.
A generalized description of the model system appears in the text.
The concept of "export-base" or economic base theory has tra-
ditionally been the central guiding concept in the description of urban
economies. In line with this concept, this study treats manufacturing
industries as export-oriented industries. The growth of the manufac-
turing industries leads to the growth of the regional economy. This
model structure is consistent with the familiar Keynsian-type trade
multiplier in an open economic system. Consequently, in Figure 3,
the manufacturing industry module feeds into the regional economy,
and growth of manufacturing industry and regional economy determine
the regional employment in the regional labor market. By the same
token, production and consumption activities are related to the demand
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FIGURE 3
MAJOR COMPONENTS OF THE MODEL
I-O Model and Inter-
regional Feedback
Regional Model
1
1
1
1
1
1
1
I
1
1
1
1
1
1
1
1
1
1
1
1
1
1
National
I-O Model
1
J
f
Regional
Market
Share
Matrix
1
1
i
I
\
1
i
1
1
1
1
*
1
1
1
1
l
l
i
i
i
i
Manufac-
turing
Industries
1
Regional
Economy
Income
Consumption
Government
Regional
Labor
Market
_ - __.._ |
I
i
1
1
I
1
\ 1
\ i
\
| 1
Electricity ; |
I and Fuel i
[Demand '
V i
/ 1
/ i
/ \
r \
1
1
1
t
1
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for electric power and fuels. All these relationships have been formu-
lated into a mathematical model.
In the manufacturing module, the production relations of the man-
ufacturing industry are presented. Output is related to the production
factors: labor and capital. Gross profit is given as residual between
value-added and wage bill. Investment behavior is related to the profit
and capital stock from the previous period. With new investment ex-
penditures and adjustment of depreciation, capital stock of present
capacity is determined. Finally, employment in manufacturing sectors
is derived from the level of production and wage level.
In the regional economy module, regional income is determined
by the level of manufacturing production, regional consumption expen-
ditures and local government expenditures. On the other hand, regional
consumption is related to the regional income, and government expen-
ditures are related to the government revenues.
In the regional labor market module, employment by industry,
other than manufacturing industry, is derived from the income gener-
ated from non-manufacturing sectors. Regional employment, which is
the summation of employment from manufacturing and non-manufactur-
ing industries, and unemployment rate, determines the regional labor
force in each AQCR.
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Finally, electricity and fuel demand modules provide the demand
of electricity and fuel derived from production and consumption activity
of the region. A substitutional relationship between different types of
fuel is also included.
A national I-O system is introduced to serve the role of external
market for the regional economy described in the regional model, and
also, hopefully, to measure the structural change of the national eco-
nomy attendant upon air pollution control in the 100 AQCRs.
A more detailed look at the regional model which comprises 162
equations is provided in the text.
As a part of Phase III of the RAPA project, CONSAD also inves-
tigated the possibilities and potentials of adding another model to the
Regional Economic Model System for the express purpose of assessing
the national economic effects of air pollution control. CONSAD planned
the use of the Office of Business Economics (OBE) Quarterly Econo-
metric Model to make preliminary estimates of national economic
effects. An intensive study of the structure of the OBE model, how-
ever, revealed a number of structural properties which rendered it not
an entirely desirable tool for national effects assessment.
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4. TRIAL SIMULATION OF THE
MODEL SYSTEM
A computer simulation program of the APCO Economic Model
System was developed and a user's guide was provided in Volume II of
this report. The Regional Model System, utilizing this simulation pro-
gram, can trace the effects of a variety of policy tools such as stand-
ards or incentives available to APCO, provided the latter are converted
into inputs constant with the model logic.
APCO specified three alternative strategies to be tested with the
APCO Economic Model System as their basis the control costs envis-
ioned in the 1970 Report to the Congress as required by Section 305(a)
of the Clean Air Act of 1967. In this report, cost estimates were made
of controlling the emissions of selected pollutants within 100 AQCRs
during the period fiscal years 1970 through 1975. The costs reflect
the emission reductions of particulates, sulfur oxides, hydrocarbons
and carbon monoxides in these 100 AQCRs by 1975. *
*Fogel, M. E. , et^ aL , Comprehensive Economic Cost Study of
Air Pollution Control Costs for Selected Industries and Selected Re-
gions, Research Triangle Institute, February, 1970, Chapter 4.
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Emission
Pollutant Source Category Decrease (%)
Particulates Solid waste 77. 8
Stationary combustion 91.7
Industrial process 86. 1
Sulfur oxides Stationary combustion 52.2
Industrial process 36. 2
Hydrocarbon Solid waste 69.4
Industrial process 57. 8
Carbon monoxide Solid waste 84. 7
Industrial process 90. 2
The simulation runs interpreted in this report reflect differing control
strategies with respect to the incidence of control cost expenditures --
that is, with respect to who finally pays the cost of controlling air pol-
lution. In Strategy 1, high emission industries pay. In Strategy 2,
they are able to pass part of the cost on to consumers as a price in-
crease. In Strategy 3, the government subsidizes 50 percent of the
burden the industry picks up in Strategy 2. In all three strategies,
electric utilities are assumed to be able to pass on the control costs
in the form of price increase to the users
Table 1 presents the sample summary table for one AQCR under
the "Industry Pays" strategy. First, a key economic indicator, change
in unemployment rate, is analyzed in various AQCRs. This shows that
there is a considerable difference between the three alternatives in
terms of incidence of adverse effects. The geographic distribution of
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TABLE 1
SAMPLE OUTPUT OF AN AQCR:
A SUMMARY TABLE
MANUFACTURING INDUSTRIES
Profit (millions)
Investment (millions)
Value Added (millions)
Capital Stock (millions)
OTHER INDUSTRIES
Employment (1000 S)
Regional Consumption (millions)
Total Personal Income for the Region (millions)
Total Regional Employment (1000 S)
Regional Unemployment (percent)
Total Labor Force (1000 S)
Government Expenditure for the Region (millions)
Government Revenue from the Region (millions)
ELECTRIC POWER DEMAND
Without
Control
5738.90Z
452.000
13836.699
10884.758
3979. 71
Net
Change
33. 051
20. 394
5.273
19.132
- 2.866
Percent
Change
-0.57591
-4.51190
-0.09929
-0.56227
-0.07202
32445.000
53712.000
5123.711
4.000
5337. 199
5944. 000
6416.000
-30. 164
-41.488
- 9.293
0.1741
- 9. 660
- 3.929
- 4. 165
-0.09297
-0.07724
-0. 18137
4.35349
-0. 18100
-0.06610
-0.06491
Total Electric Consumption for the Region (1000 KWS) 4878.000
Electricity Used by Manufacturing Industries (1000 KWH) 518.000
Electricity Used by Other Industries (1000 KWH) 3205.000
Residential Consumption in the Region (1000 KWH) 1155.000
6. 364
2.736
2. 205
1.423
-0.13047
0.52817
0.06881
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all 91 AQCRs show that most of the AQCRs seriously (adversely)
affected are located in the highly industrialized north-central (Michigan,
Ohio, Indiana, Illinois) and central-east (Pennsylvania, West Virginia)
states. AQCRs located in the west and south, in general, do not seem
to'be affected by air pollution control and some are even better off.
The following table shows that adversely affected regions improve from
Strategies 1 to 3.
Categories of Change of AQCRs Included (%)
Unemployment Rate (%)
in AQCRs Strategy 1 Strategy 2 Strategy 3
1. Better off (increase in
employment) 5.5% 7.7% 9.9%
2. Negligible (0.01% to
1.00%) 67.0% 69.2% 83.5%
3. Moderate (1. 01% to
2.0%) 19.8% 19.8% 3.3%
4. Serious (2. 01% and
over) 7.7% 3.3% 3.3%
Total 100.0% 100.0% 100.0%
When two other economic indicators -- profits of manufacturing
industry and regional personal income -- are also analyzed. The pat-
terns of economic change among AQCRs are similar to that of unem-
ployment. However, if one observes the total effects of 91 AQCRs
under the three strategies, the pattern is entirely different.
The view of cost sharing which emerges when results for individ-
ual regions are considered is thus quite different from that which
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emerges from solely aggregative considerations. While cost sharing
results in overall worsened economic performance, the number of re-
gions severely affected adversely by air pollution control is reduced.
There appears to be a policy trade-off to be made. The policy-maker
must decide whether he wishes to affect a few regions adversely to a
considerable degree while maintaining good overall economic perform-
ance, or slightly reduce overall performance so that a few regions may
be spared severe hardship. It should be remembered that the results
are preliminary and presuppose a particular cost sharing scheme. It
is not the only one which could be undertaken.
These results on differential regional impact are suggestive in
many veins, but one in particular deserves comment. Some regions
evidently have an economic capacity to meet more rigorous emission
and air quality standards than do others.
Some caveats on the structure, data inputs, and use of the model
system are in order at this stage. Structurally, the APCO Model Sys-
tem is a cross-sectional regional model; consequently, its strength lies
in its assessment of geographical patterns of change in the AQCRs. Its
estimates of aggregate changes are less reliable. Second, the 91
AQCRs used in the model covers the greater portion of the economic
activity in the nation, but not all. Finally, the model system does not
capture the dynamic effects and macro-effects that only a national
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model can handle. Consequently, the interpretation presented below
must be viewed with a dose of caution.
The data inputs used in demonstrating the model have limitations
that must be considered. First, the two billion dollar estimate of total
annual benefits from control was selected primarily because it was just
large enough to offset the direct costs of control and not as an accurate
reflection of the actual value of damages reduced. In this sense, the
value of benefits may be considered quite conservative. Second, the
control cost estimates for each AQCR were drawn from the 1970 Cost
of Clean Air Report to the Congress. As such, the control cost esti-
mates related to implementation of previous legislation and involved a
number of assumptions regarding availability and price of various con-
trol technologies. Furthermore, the price elasticity of demand for the
products of high emission industries that were applied in testing strategy
alternatives 2 and 3 were based heavily on qualitative information as
distinguished from detailed product demand studies.
In conclusion, it must be observed that the trial simulation of
three strategies and the interpretation of results has demonstrated to
a limited degree the operational nature and strategy assessment poten-
tial of the model system. Surely, what has been done here is but
scratching the surface of the utilization potential of the model system.
What has been done, however, clearly points to the view of the regional
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economy and environmental system (quality levels) as interrelated sys-
tems, and that control activities affect profoundly, in many dimensions,
economic activity. However, these economy-environment relationships
are more complex than evident in this demonstration. This suggests
the need for further efforts in model utilization, sensitivity testing,
and model system refinement as appropriate. In such an effort of
further utilization of the model system, it is hoped that the possible
extension of the model to other media -- water and solid waste -- will
receive consideration.
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1. 0 AIR POLLUTION AND
THE ECONOMY:
AN OVERVIEW
1. 1 The Context
The recent upsurge of public concern over environmental ques-
tions reflects a recognition that man has been too cavalier in his rela-
tions with nature. This concern has manifested itself in a spate of
legislation in the last few years on abatement of air, water, noise, and
other kinds of pollution.
In the field of air pollution in the last few years, two parallel
trends are discernible. On the one hand, the Federal administration
has proposed and Congress has passed a Clean Air Act of 1967 and
Amendments to Clean Air Act of 1970. This legislation and adminis-
trative activity is evidence of the increasing public pressures for
cleaner air. On the other hand, concerns about dire consequences of
such actions are repeatedly and increasing frequently expressed by
business and community.
The fears of industry in terms of adverse economic consequences,
though expressed often extravagantly, are not baseless. Cleaner air
is not free and high emission industries or regions incur costs that
affect their output and profits and the regions in which they are located
-------
In this regard, it may be useful to explore the complex chain of
interrelationships between economic activity and various abatement
policies such as setting emission standards or achieving the standards
through incentives and government expenditures on research and devel-
opment.
The sshort-term (0-5 years) chain of events following the promul-
gation of emission standards is fairly clear in form if not in magnitude.
Imposition of standards will result in investment in control equipment,
in process changes, and in outlays for non-capital inputs (e.g. , low
sulfur fuel, maintenance) used in air pollution control. As a result of
these outlays, emission and ambient air concentrations will be reduced.
Finally, production and consumption activities sensitive to ambient air
concentration of pollutants will change. This chain of interdependent
events is represented in Figure 1. 1.
Figure 1. 1 highlights the interdependence between the environ-
ment and the economy. Control outlays directly influence production
of air pollution control inputs. In addition, implementation plans may
directly change production and consumption patterns, as for example,
do local plans prohibiting the sale of non-returnable containers or
phosphate detergents. Indirectly, control outlays influence production
and consumption in two ways: (1) by shifting aggregate supply schedule
-------
FIGURE 1. 1
SHORT-TERM EFFECTS OF STANDARDS
APCO
Designated
Standards
Implementation
Production
and
Consumption
Activities
Ambient
Air
Quality
Control Outlays
Emission
-------
changing air quality which will shift both aggregate supply and aggre-
gate demand schedules. Moreover, these changes in production and
consumption will feed back on control outlays and on emissions since
level of production and consumption influence both control cost needed
to meet any given standard and also to influence the level of emissions.
The system is thus almost completely interdependent. The medium by
which the Air Pollution Control Office (APCO) actuates a chain of events
within the system is by establishing standards and by supervising their
implementation. Not only will the level at which standards are met be
important but also the rate at which they are applied.
The longer-term effects (5 years +) of control standards are even
more pervasive. Spurred on by standards, there will be an effort to
look for cheaper ways to meet emission control requirements. An
accelerated private research act development effort should, over the
longer haul, result in significant changes in production functions, which
changes will influence both demand and production, control cost outlays,
and environment quality.
One greatly important longer-term effect of National Standards
which deserves preliminary discussion in this report is the effect of
differential treatment of technologies of different vintages in the setting
of performance standards. In Section 112 of the Clean Air Amendments
-------
sources that "contribute significantly to air pollution which causes or
contributes to the endangerment of public health or welfare. " APCO
is not limited in establishing such standards to pollutants fortwhich
criteria and control technology documents have been published. It may
thus happen that older sources of some pollutants will go uncontrolled,
while their newer counterparts will be subject to control standards. It
may also happen that controls required of new sources will be costlier
than those required of existing sources by state and local governments.
The net result will be to make new plants and equipment more expensive
relative to existing plants and equipment, and, therefore, to retard in-
vestment in new plants and equipment, feince new investment in the
manner in which new, more productive, technology frequently comes
in line, national economic growth could be slowed.
Long-term effects of National Standards Policies are schemati-
cally presented in Figure 1.2.
In the longer term, virtually every facet of modern life may be
influenced, to a greater or lesser extent, by the character of standards
adopted.
The use of Incentive Policies to implement standards differ from
other implementation methods (coercion and moral evasion) in that it
attempts to use the economic mechanism itself to solve the pollution
-------
FIGURE 1.2
LONG-TERM EFFECTS OF STANDARDS
APCO
Standa rds
Rate of
Investment
in New
Technology
Research
and
Development
Economic and
-------
economic policies since Incentives will be effected either through tax-
ation or expenditures. In either event, there will be a marked effect
on government finance.
As a part of stepped-up air pollution control activity, all levels
of government will be increasing their expenditures for air pollution
control. Some of this increased expenditure will be directly for con-
trol of emissions from governmental facilities. The majority, how-
ever, will be expended in administering air pollution control programs.
What effect such expenditures will have on the economy depends pri-
marily on how they are financed. If air pollution control agency expen-
ditures are financed at the expense of other governmental expenditures,
the net effect is likely to be small or non-existent. * If, however, air
pollution control agency expenditures are additional expenditures, over
and above what would otherwise be undertaken, they would have a mul-
tiplier effect on Gross National Product (GNP).
In summary, it appears that there are wide and far-reaching
economic consequences of abatement strategies expressed in whatever
form -- standards, incentives, research and development, and so forth.
*No reason to think multiplier associated with APC expenditures is
-------
Indeed, there are signs in the Clean Air Amendments of 1970
itself and in the companion water pollution control legislation that the
President and the Congress are already beginning to worry about ad-
verse economic consequences. Particularly with regard to the pace
at which implementation of the various standards established under
the Amendments is to proceed, the Environmental Protection Agency
Administrator is directed to consider practicability, a major dimen-
sion of which is, undoubtedly, the implementation costs and the result-
ing economic effects.
Thus, it appears that as pressures for cleaning up the
-------
To get on with the business of cleaning up in an economically
humane fashion, a way must be found to separate the well-founded from
baseless fears, and to tailor policies to mitigate insofar as possible,
the undesirable consequences of air pollution control policies.
Virtually all of the economic policy questions concerning air pol-
lution control which have been raised over the last four years, and
which are continuing as a central concern, are questions of fact. Vir-
tually everyone agrees that pollution control is, by itself, a good thing.
Virtually everyone agrees that unemployment, high and rising prices,
falling profits, and a falling growth rate are by themselves bad things.
The disagreement arises when the extent to which good things, "air
pollution control strategies, " contribute to bad things, "unemployment,
high prices, etc. , " is considered.
Because these disagreements concern observable facts, it is in
concept possible to think of an economic-environmental model that
would identify and estimate these relationships. One could then manip-
ulate air pollution control strategies in the model, observe the result-
ing employment, price, output, etc. , changes holding all else constant,
and infer something about the direction and magnitude of the economic
impacts of air pollution control strategies.
The development and demonstration of such an Economic Model
for assessment of abatement strategies for Air Quality Control Regions
-------
1.2 The APCO Economic
Model System
An economic model is a simplified abstract version of the real
world built on concepts of economic theory. A model is more than a
hypothesis; it is a set of functional relations between the various ele-
ments of which an economy is composed. The crucial economic ele-
ments -- wages, capital, investment, income, consumption, employ-
ment and so forth -- are identified and relationships between them are
postulated. For the purposes of any particular policy assessment,
certa.'.n factors are assumed to be constant and the consequences of
postulated relationships between policy and key economic indicators
worked out for a variety of assumptions. The notion behind this pro-
cedure is that the many facets of economic life constitute a system and
intervention such as by air pollution abatement strategies causes a
variety of measurable economic effects.
APCO commissioned CONSAD Research Corporation, under sub-
contract to TRW Systems, Inc. , for the development and demonstration
of such an economic model as an operational analytical tool for abate-
ment strategy assessment. This economic model initiated in 1968 as
part of the Regional Air Pollution Analysis (RAPA) program focused on
the regional economies of the various AQCRs. As such, it will be use-
ful for assessment of control strategies at the regional level, where
-------
most of the implementation plans are prepared. However, there is an
increasing interest in air pollution control policy decisions in the last
three years at the Federal government level. Therefore, there has
been correspondingly an increased focus in the model system described
here toward the assessment of interregional and national economic
effects in addition to regional effects. However, the model system
developed by CONSAD is still a regional economic system with capabil-
ities to measure, in a limited manner, the interregional and national
effects. It is called the APCO Regional Economic Model System and
has been demonstrated to some degree as an operational tool for con-
trol strategy assessment.
The rest of the report is devoted to a description of the context,
scope, and operation in the use of this model system.
-------
-------
2. 0 THE RAPA PROGRAM AND THE
REGIONAL ECONOMIC MODEL SYSTEM
2. 1 Purpose of the RAPA Program
In July of 1968, the APCO initiated a systems analysis of regional
air pollution control. It was clear, at the outset, that this study's con-
tribution to the pollution problem's solution would be in the integration
of contemporary air pollution control developments into a workable
analytical tool, rather than in fundamental research areas. With this
in mind, a tool was developed -- Regional Air Pollution Analysis
(RAPA) --to demonstrate the usefulness of looking at the many facets
of the air pollution problems. With this tool, more precise estimates
of the problem can be made, and more importantly, estimates can be
made of specific approaches for the solution of the problems.
The RAPA program is a system of mathematical models arranged
in a modular fashion and relating both engineering and economic effects
of the analysis. Relations between the major components of the system
are described in Figure 2, I.
Information on the effects of air pollution is reported in terms of
air pollutant criteria, which are a compendium of today's knowledge of
scientific findings on the range of adverse effects of specific air pollu-
tants and combinations of pollutants on man and his environment. The
-------
FIGURE 2. 1
REGIONAL AIR POLLUTION ANALYSIS PROCESS
Goals (Air '
Quality or
Emission
Standards)
Abatement
Strategy
1
Controls
Implemen-
tation Plan
,
f
Sources
Diffusion
Receptors
Control Cost
and Benefit
(Damage) 1
Economic
Effect
(APCO Eco-
nomic Mode])
-------
evidence in the criteria documents, with respect to human health,
animal health, plant damage, material damage and visibility, are not
necessarily the lowest levels of exposure below which there are no
adverse effects; however, they do provide quantitative guidance in the
setting of regional air quality standards.
Air quality standards that are developed with the guidance of
these air quality criteria are goals established for the protection of
public health and welfare. They provide a basis for controlling exist-
ing sources of pollution emission and preventing future regional growth
from adding to the pollution problem. Regional goals may reflect more
than one air quality standard, insuring minimum air quality levels, as
well as higher levels of air quality, to preclude any significant deteri-
oration of existing high air quality levels.
On the other hand, engineering and economic information on con-
trol of air pollution is reported by the Federal government in terms of
handbooks on available control devices such as filters and precipitators,
and non-device control measures such as the use of low-sulfur content
fuels or alterations to basic industrial processes that inherently cause
less pollution.
The government's role starts with setting air quality standards
which reflect goals for clean air within a specified time period. After
the air quality standards are set, an effort is made to establish
-------
implementation plans which may set forth regulatory procedures, such
as pollutant source emission standards to achieve air quality standards.
Limiting pollutant emissions through source emission standards, along
with other types of regulatory procedures such as zoning regulations or
fuel restrictions, forms an abatement strategy designed to achieve re-
gional air quality within a specified time period.
To accomplish the task of developing abatement strategies will
require an extensive examination of the factors involved in the air pol-
lution system, such as meteorology, air pollution control technology,
air pollution growth trends, source emission inventories, existing re-
gional air quality conditions, and regional economic impact.
The Regional Economic Model was expressly developed as a way
to respond to these requirements of information on economic impacts
of abatement strategies.
2. 2 The Role of the Economic Model in RAPA
The recently enacted Federal air pollution abatement legislation
requires business and industry to control the amount of pollutants that
they discharge into the air. To industry, this requirement means that
the production costs for the same amount of output produced prior to
the legislation will be increased in proportion to the required invest-
ments to air pollution control equipment. Thus, certain industries
-------
and regions that have in the pa,st enjoyed the economic advantage of low-
cost production may face some degree of economic decline due to the
requirements of pollution abatement.
There would be offsetting economic effects from air quality con-
trol strategies in such regions in at least two ways. First, there would
be increased demand for the products of the industries that produce pol-
lution control equipment and low pollutant fuels leading to increased
output, employment, and income in those sectors. Second, a variety
of general economic benefits result from pollution abatement. Some
of those gains are increased labor productivity, reduced health expend-
itures, reduced outlays on physical maintenance of homes and plants,
and savings in agricultural production activities. These gains would
lead to increased consumption, output, employment and income, and
would begin to offset the economic reductions resulting from the pollu-
tion control costs (as discussed above).
Consequently, air pollution abatement leads to changes in eco-
nomic output, labor markets, the availability of capital, as well as re-
distributions within the entire economy. Further, the implementation
of air quality programs would have a variety of other effects, such as
tax base impacts on communities or variations in land-use and indus-
trialization in various regions.
-------
CONSAD Research Corporation has developed a regional economic
model that will provide pollution abatement policy-makers capabilities
to assess the effects of various pollution control strategies. The
CONSAD model is intended to provide the following types of information
for public-policy analysis:
. Regional economic changes (e.g., output, in-
vestment, employment, income, and consump-
tion) expected to result from enforcement of
varying abatement standards upon high-emis-
sion industries.
. Regional economic effects expected to result
from reduction of industrial damage and growth
in air pollution equipment industries.
Fiscal effects of regional implementation of
air quality control programs, including tax
base impacts of economic change and the
effects of tax credits upon economic change
and the rate of achievement of emission
standards in terms of the implementation plan.
With alternative abatement strategies at hand, not only associ-
ated direct control costs of each strategy, but also the measurement
of the corresponding economic impacts of chain reaction throughout
the region, will also provide useful information to the different levels
of decision-making for the implementation plan. The changes in major
economic indicators are the focal point in the analysis. For example,
given that control costs directly increase production costs, then as
profits decline, new investments may be reduced. Therefore, demands
-------
of the products from other firms and industries, and demands for labor
are affected, resulting in changes in regional income and unemployment
levels. On the other hand, those industries (and individuals) that orig-
inally suffer damages and pay additional costs due to the polluted air
may start to enjoy some economic gains from the air pollution control.
Meanwhile, fuel substitutions might occur in some firms, especially
those in the electric power industry. The demands for pollution control
equipment will also result in a new series of product demands within
and outside the area. Basically, there is a shift in economic structure
of the region.
Abatement strategies not only cost more to the high emission in-
dustries, but also result in an initial decline in regional employment
and income. Decision-making is required in the public sector to weigh
these adverse consequences. The trade-off between the total costs and
benefits to the entire regional community may depend upon the informa-
tion from these measured economic impacts of alternative control
strategies. Tax policies and techniques for financing and distributing
air pollution control costs can be determined through a full examina-
tion of the total impacts to the community.
During the first year of the RAPA program, CONS AD developed
a model of the economic effects of air pollution control (Regional
Econometric Model) of the St. Louis metropolitan region based on
-------
time series data. * During this second year of the program, the aim
was to focus on the development of a model which can be applied to the
major cities in this country. This resulted in estimation of a cross-
sectional economic model for the 31 largest Standard Metropolitan
Statistical Areas (SMSAs) across the country. The roles of Keynsian
theory and regional export base theory will be central as they were in
the St. Louis time series model. However, the entire equation system
has been reformulated in order to integrate the cross-sectional struc-
ture into the model.
In the current year (Phase III), the 31 AQCR Model System has
been extended further and utilized for policy assessment. The nature
and scope of this extension and utilization of the Regional Model is de-
scribed in the next section.
2. 3 The APCO Economic Model System (Phase III)
In the current year (Phase III), two types of activities have been
carried out:
*CONSAD Research Corporation, Structure Requirements of an
Econometric Model of St. Louis SMSA, prepared for TRW Systems,
January, 1969, and Final Version of the CONSAD Regional Econometric
Model as Applied to the St. Louis SMSA for Air Pollution Control Analy-
sis, May, 1969. (A summary of these reports appears in Appendix A. )
-------
. Further model development, and
Preliminary demonstration of model use
and assessment of three trial strategies.
These activities are outlined in Figure 2,2.
Model Development
The APCO Regional Economic Model developed in the third year
(Phase III) by CONSAD is a logical extension of the concepts and em-
pirical work of the first two years into an operational tool amenable to
assessment of various control strategies. In particular, it builds on
and extends the Phase II 31 AQCR Model in three important dimensions.
First, the Phase III model is extended to cover 100 AQCRs.
These 100 AQCRs account for over 65 percent of the national output.
Thus, the APCO model will cover a major portion of the economic
i
activity in the nation.
Second, the Phase III model overcomes a major drawback of the
31 AQCR model, which treats each AQCR as an isolated region. In
reality, economic effects attendant on pollution abatement are not
localized. Price increases in certain industries, the growth of con-
trol equipment industries or demand spurred by reduced expenditures
on health, etc. , in one region will have feedback effects on the econ-
omies of other regions. The Phase III APCO Economic Model System
-------
t\»
Inputs
Model Development
* Model »
Regional Economic Model System
Outputs
Assessment
Regional Economic
Activity
National Economic
Activity
Regional
Model
Interregional
Feedback
I-O Model and
National Effects
Assessment
Policy Assessment
APCO Policy Variables
Standards
Incentives
Subsidies
Fuels
Research and
development
Government
expenditures
Computerized Simula-
tion Program
Program RMS
. Program FEE
. Program IOA
Outputs: Changes in:
Manufacturing
activity
. Power and fuel
consumption
. Income
. Employment
. Government
expenditures
. Qthe r
Interpretation
of Model
Outputs
Model. System
Assessments
FIGURE 2.2
THE APCO ECONOMIC MODEL SYSTEM: DEVELOPMENT AND USE
Recommenda-
tions on
Further Refine-
ment and Utili-
zation of Model
-------
share matrix to capture these interregional feedback effects and en-
compasses the 100 AQCRs as an interrelated system of regions.
Third, the Phase III model incorporates a regional fuel demand
submodel as a component of the Regional Economic Model System. As
air pollution control policy is implemented, sulfur content in coal and
fuel oils will greatly affect their price in view of increased demand for
low sulfur fuels and their limited supply. Prices of natural gas and
electricity will also tend to change. Industries will choose an optimal
combination of fuels and electricity which minimizes the total cost of
energy, to the degree substitution is possible. The fuel demand model
will describe these relationships.
Figure 2.2 shows the three modules -- Regional Model, Inter-
regional Feedback, and the National I-O Model --of the Regional Model
System that is described in detail in the next chapter. It should be
noted a link is needed to a national macro model to capture the over-
time and structure change of national effects. Such a link to the Office
of Business Economics (OBE) Quarterly Model was assessed for feasi-
bility in this regard. This effort forms the theme of Chapter 4.
Policy Assessment
A simulation program consisting of three modules corresponding
to the three modules of the Regional Model System was prepared. This
program is a flexible, efficient tool to estimate changes in the regional
-------
and national economies when alternative abatement strategies are spe-
cified. The control strategies have to be translated into variables con-
sistent with the model structure before the simulation can be initiated.
Such specification and use of the three control strategies specified by
APCO for input into the model is described in Chapter 5.
Chapter 6 identifies a wide range of model outputs resulting from
the three strategies. It also develops an approach to organize in a pre-
liminary way these outputs for analysis. Finally, a tentative interpre-
tation of the complex economic effects of the three strategies is pro-
vided.
An assessment of the model structure, data and simulation pro-
gram forms the theme of Chapter 7.
Chapter 8 explores the utilization potential of the APCO Regional
Economic Model System and advances recommendations for further
-------
-------
Inputs
Mode 1 Development
aT E c oja^S
- i t y x' x ' -
,Xa t io j\a 1 JE c on'Qjaft ic
.Activity ' - x '
-Model «.
:gional Economic Model System
Outputs
XModcl
^
1
^ ,
^ nt eXr e g i o na 1
Feedback
___J.__^_.^
I-Q Mode>xa,rid
National" Effects
Assessment
Policy Assessment
APCO Policy Variables
Standards
Incentives
Fuels
Research and
development
Government
expenditures
Computerized Simula-
tion Program
Program RMS
. Program FBE
. Program IOA
Outputs: Changes in:
. Manufacturing
activity
. Power and fuel
consumption
Income
Employment
Government
expenditures
_.. Other
Interpretation
of Model
Outputs
Model System
Assessments
Recommenda-
tions on
Further Refine-
ment and Utili-
zation of Model
-------
3. 0 MODEL DEVELOPMENT
AND FORMULATION
3. 1 The Model in General
A model is essentially a representation of a real world phenom-
enon devoid of those elements deemed irrelevant or unimportant for
the purpose at hand. A model consists then of variables embedded in
mathematical formulae (structural relationships), numerical constants
(parameters), solution methods (algorithms), and processes used for
establishing the values of parameters (calibrating procedures). A
variable is a measurable quantity in which one has some interest and
which varies from observation to observation. Variables are to be
distinguished from a parameter which essentially describes the struc-
ture. Parameters change slowly over time (in response to a structural
change or break) and in this sense are "less variable variables. "
An econometric model generally consists of a system of equations
embodying a prior reasoning of economic theory. This reasoning is
given empirical content by estimating and testing the model through
statistical methods.
In brief, an econometric model for policy simulation purposes
includes the following logical steps as shown in Figure 3. 1.
-------
Econor.ijc
Theory
Test of the
ReEulv.s
Policy
Simulation
Decinion
Making
FIGURE 3. 1
ECONOMIC MODELLING FOR
POLICY SIMULATIONS
Step 1: Hypothesis: Abstract
relations between key variables
Step 2; Formulation: Mathemat-
ical formulation of equation sys-
tem includes:
endogeneous (output)
exogeneous (input)
variables
Step 3: Estimation: Data collec-
tion, statistical methods, and
computation
Step 4: Evaluation: Test of eco-
nomic theory by statistical
methods, acceptable or not
Step 5: Simulation: Impact sim-
ulation of different strategies
Step 6: Use: Information from
results of policy simulations used
in decisions
-------
From the complexity of real economic space, economic theory
develops by simplification of the relationships between key variables
under investigation. For example, the relation between income and
consumption of society is isolated from other numerous factors and
presented under a set of assumptions.
The second step includes a mathematical formulation of eco-
nomic theory into a model which consists of a set of equations. In this
mathematical model, endogenous variables will be determined by the
model using a set of exogenous variables which are given outside of the
model. In other words, exogenous variables provide model input and
endogenous variables the output of the model. A policy-oriented model
usually integrates the policy questions or variables of the structure of
the model as well.
In the case of a mathematically formulated model, empirical data
are corrected and estimation of the model parameters by statistical
methods takes place as in Step 3.
In Step 4, the results of statistical estimation are assessed by
evaluation criteria from probability theory to determine whether the
results are acceptable. If not, theoretical assumptions in Step 1 will
be rejected and reformulation is in order.
After an acceptable model is "empirically" obtained, the model
is ready for policy simulation. Policy, by itself, is an exogenous
-------
"shock" to the model to observe the change of endogenous variables in
the model. It can be in the form of a change in exogenous variables or
an exogenous information of change in the parameters of the model.
Outputs from the simulation provide the information of the endo-
genous variables before and after the policy simulation for evaluation
of policy impact to the economic system under study.
3.2 The APCO Economic Model System
The APCO Economic Model System consists of two major com-
ponents, namely, a 100 AQCR regional model and interregional feed-
back from a national I-O model, as shown in Figure 2.2. The modules
comprising both these major components are also indicated in Figure
3.2. A generalized description of the model system appears in this
section, and a more detailed development and estimation of the model
is provided in Appendix A through Appendix D.
3. 2. 1 The Regional Model
There is a fundamental difference between the economy of an
AQCR and the national economy. The former is an open economy
where growth and development are closely related to its capability to
carry on external trade with other regions. The latter is rather more
self-contained by its nature. The concept of "export-base" or eco-
nomic base theory has traditionally been the central guiding concept
-------
in the description of urban economies. In line with this concept, this
study treats manufacturing industries as export-oriented industries.
The growth of the manufacturing industries leads to the growth of the
regional economy. This model structure is consistent with the
familiar Keynsian-type trade multiplier in an open economic system.
Consequently, in Figure 3, 2, the manufacturing industry module feeds
into the regional economy, and growth of manufacturing industry and
regional economy determine the regional employment in the regional
labor market. By the same token, production and consumption activ-
ities are related to the demand for electric power and fuels. All these
relationships have been formulated into a mathematical model as
shown in Figure 3.4, using the notations defined in Figure 3.3.
In the manufacturing module, the production relations of the
manufacturing industry are presented. Output is related to the pro-
duction factors: labor and capital. Gross profit is given as residual
between value-added and wage bill. Investment behavior is related to
the profit and capital stock from the previous period. With new in-
vestment expenditures and adjustment of depreciation, capital stock
of present capacity is determined. Finally, employment in manufac-
turing sectors is derived from the level of production and wage level.
In the regional economy module, regional income is determined
by the level of manufacturing production, regional consumption
-------
FIGURE 3.2
MAJOR COMPONENTS OF THE MODEL
I-O Model and Inter-
regional Feedback
Regional Model
National
I-O Model
Regional
Market
Share
Matrix
1
1
1
1
1
1
1
1
1
1
1
1
»
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Manufac-
turing
Industries
!
Regional
Economy
Income
Consumptior
Government
Regional
Labor
Market
i
i
1
1
r !
\ i
\ , i
' i
Electricity |
and Fuel j I
Demand j '
/i
i
i
' !
!
!
i
!
1
-------
FIGURE 3, 3
NOTATION OF THE REGIONAL MODEL VARIABLES
V value-added by industry j of ith, AQCR
K.. capital stock by industry j of ith. AQCR
N.. employment by industry j of ith AQCR
! investment expenditure by industry j of ith AQCR
n.. capital share or gross profit by industry j of ith AQCR
W.. average wage by industry j of ith AQCR
Y- regional personal income of ith AQCR
C. regional consumption expenditure of ith AQCR
G. local government expenditure of ith AQCR
T. local government revenue of ith AQCR
N. employment by industries other than manufacturing
industries of ith AQCR
N. .-. (*> j\i.. -( N. ) total regional employmcnl of ith AQCR
j -1
L. regional labor force of ith AQCR
Q. total regional consumption of electric power in ith AQCR
Q.. electric power consumed by industry j of jrh AQCR
QCJ electricity consumed by residents of ith AQCR
Q. electricity consumed by industries other thnn
manufacturing industries of ith AQCR
ch- price of fuel type r of ith AQCR, type of fuel including
coal, coke, fuel oil and natural gas
^'r- clcmr-nd of fuel, type r by jth industry by ith AQCR
i 1, . . . , 10(1 1 L'O AQCR'r,
j r; 1 > I1-* \vhichis two-digit manufacturing industries
-------
1* 'J
(ID u.(t) = Li(t? - Ni(t)
1 Li(t)
(!Z) L.ft) -: f(N. (t), u-ff) )
i "i l
FIGURE 3.4
THE REGIONAL MODEL FORMULATION
I. Manufacturing Industry
a j i - a .
(!) V..(l) - A.. K. (t-) Ktj J(t)
(2) 1T..(1) - (] -.o.) V..(t)
IJ .1 IJ
(3) I..(t) --. .f (ii (i.), K (I:-!)) j=l, . . . , 19
''"' ij ij
(4) K (t) = K.. (l-l) +I..(t) - d K. (t) j=l,. . . , 19
i.i i.l ij j ij
(5) AV..(l) - JC-.V.. (t-1), u.(tl) j=l., . . , 19
1.1 !J 1
II. Regional Economy
(6 ) Y . (t ) = f (C. (1: ) s v Vj j (1 ) , G. (1. ) )
,i
Co c.(i.j -.. c(V.(0, C.(i-D)
3. " ' J-
(P) c-^t) J'(T.(D)
III. Regional Labor Market
_ !
(9) N.(t)= f (Y.(t) -
J
_ 19
N i) - N.(t> +
-------
FIGURE 3. 4 (continued)
IV. Electricity and Fuel Demand
(13) Q..(t) = f (V..(t))
1J XJ
(H) Q .(t) - ;f(C.(iM
r*i >
(15) Q (t) :: J (Y.(t) - Z V..(l) )
1 1 . lj
J
(16) Q.(t) - ŁQ..(t) +Q .(t) +Q.(1:)
1 j 1J Cl 1
(17) Er.. ..qp. , .v..
(18) Z..(t) - B (t)
-------
expenditures and local government expenditures. On the other hand,
regional consumption is related to the regional income, and govern-
ment expenditures is related to the government revenues.
In the regional labor market module, employment by industry,
other than manufacturing industry, is derived from the income gen-
erated from non-manufacturing sectors. Regional employment, which
is the summation of employment from manufacturing and non-manu-
facturing industries, and unemployment rate, determines the regional
labor force in each AQCR.
Finally, electricity and fuel demand modules provide the de-
mand of electricity and fuel derived from production and consumption
activity of the region. A substitutional relationship between different
types of fuels is also included.
A more detailed look at the regional model which comprises 162
equations is provided in Figure 3. 5.
3.2.2 I-O Model and Interregional Feedback
As emphasized earlier in the model formulation, a region's
growth is closely dependent upon its capability to carry on external
trade with other regions. A national input-output system is introduced
to serve the role of external market for the regional economy described
in the regional model, and also, hopefully, to measure the structural
change of the national economy upon air pollution control to the 100
AQCRs.
-------
FIGURE 3.5
REGIONAL MODEL -- A MORE DETAILED LOOK
Capital
Stock
Output
3ZE
Employ-
ment
Electric
Power
Demand
Fuel:
Coal, oil
gas
Regional
Income
Govern -
ment Ex-
penditures
Other
Industry
Regional
Employ-
ment
Regional
Unemploy-
ment
Investment
Profit
Wage
Deter-
mination
Price of
Electrici-
ty
Price of
Fuels
Regional
Consump-
tion
Govern-
ment
Revenue
Employ-
ment in
other sec.
Regional
Labor
Force
Manufacturing
Industries
(By 2-digit
SIC)
Electricity
and Fuel
Demand
Regional Economy:
. Income
. Consumption
. Government
Regional
Labor
Market
-------
The relation between a national I-O system and a cross-sectional
regional model of Keynsian-type formulation can be better explained
from policy questions to be answered from this model system. The re-
gional model developed in Phase II is appropriate for an isolated region
without interregional feedback scheme, although the export activity is
explicitly treated. When an air pollution control policy is implemented
across the nation, costs increase. The production will result in an up-
ward shift of the supply curve. Whether an industry or an individual
firm may be able to pass on the increased cost per unit of output is de-
pendent upon the elasticity of demand and supply of the corresponding
products. If price changes are obtained from exogenous information, *
the equilibrium supply of the products will be known. **
However, price increases in high emission industries under air
pollution control will not only reduce the demand of their products but
also affect the demand of those products which are the intermediate
products in the production of the high emission industries. For exam-
ple, a higher price for steel products will not only reduce the demand
*A study of price markup which air pollution control is instituted
has been reported in D. A. LeSourd, e^ aL , Comprehensive Study of
Specified Air Pollution Sources to Assess the Economic Effects of Air
Quality Standards. Research Triangle Institute, December, 1970.
**See Appendix C for detailed illustration.
-------
for steel but also affect sales in transportation, coal products, and
other materials and services related to the steel industry. Further,
such effects originating in a given region (or AQCR) in the nation will
not be limited to the region but would affect the economic activity in
other regions (or AQCRs) and in all likelihood, create a feedback to
the region. *
For a limited number of regions, the formulation of an interre-
gional I-O system is perhaps feasible. However, a 20-sector regional
I-O system with 100 regions, the size of the matrix will be 2000 x 2000
(with such detailed information largely non-existent).
An alternative formulation was consequently necessary. A na-
tional input-output system linked to a regional market share matrix
was used to capture the regional feedback. It is argued that the re-
gional share of the national market by industry (termed as the "loca-
tion quotient"**) is relatively stable. For example, if steel production
in the Pittsburgh AQCR is 12 percent of the nation's steel product, then
12 percent of a change in the national steel market will have an affect
*These interregional feedback phenomenon were observed in a
pioneer study by Ronald E. Miller, "Interregional Feedback Effects in
Input-Output Models: Some Preliminary Results, " Papers, Regional
Science Association, Vol. 17, 1966.
**A location quotient is the ratio of the regional activity level in
an industry to that in the nation in that industry.
-------
on the Pittsburgh AQCR. This concept is particularly useful for a
cross 'sectional model which deals with the geographic distribution of
economic activity at a given period of time.
In brief, using exogenous information on price changes in high
emission industries occasioned by air pollution control, the high emis-
sion industries in each AQCR will fall in output to the point correspond-
ing to an upward shifting supply curve and the new price after control.
An aggregation of changes in regional production for each high emis-
sion industry will give the change of demand in the nation. By use of
the national I-O system, the impact of changes in high emission indus-
tries on other industries can be measured. Finally, through the use
of regional market share matrix, the national impact can be distributed
to each AQCR as the net interregional feedback from the other regions
under study (99 AQCRs) and the rest of the nation.
3. 3 Empirical Estimation
A detailed account of the data used in the model and methods of
statistical estimation is provided in Appendices C, D and E. It is
worth noting here that the economic data used were for 1967.
In general, the equation in each of the modules shown in the re-
gional model component in Figure 3.2 is solved simultaneously. How-
ever, the modules themselves are related recursively. The model
-------
system appears to perform well on criteria of plausibility, explanatory
power, and reliability.
The I-O model is a 42-sector model obtained by collapsing the
1963 national I-O table. The details of the development of this model
and the interregional market share matrix are described in Appendix D.
-------
Inputs
Model Development
Model **
Regional Economic Model System
Outputs
Assessment
Regional Economic
Activity
National Economic
Activity
Regional
Model
Interregional
Feedback
Policy Assessment
APCG Policy Variables
Standards
Incentives
Fuels
Research and
development
Government
expenditures
Computerized Simula-
tion Program
. Program RMS
. Program FEE
. Program IOA
Outputs: Changes in:
Manufacturing
activity
Power and fuel
consumption
Income
Employment
. Government
expenditures
_.. Other
Interpretation
of Model
Outputs
Model System
Assessments
Recommenda-
tions on
Further Refine-
ment and Utili-
zation of Model
-------
4. 0 PRELIMINARY STUDY OF A MODEL
FOR NATIONAL ECONOMIC EFFECTS
ASSESSMENT
4. 1 The Transition from Regional to
National Policy Analysis: Background
Over the past three years, there is an increasing interest in air
pollution control policy decisions at the Federal government level. This
transfer is institutionalized under the Clean Air Amendments of 1970,
which delegates significant new authorities and responsibilities for
cleaning up the nation's air resource to APCO of EPA. Directly or in-
directly, APCO decisions will result in billions of dollars of emission
control expenditures each year. As air quality is changed due to these
control expenditures, additional billions of dollars worth of changes in
production and consumption patterns can be expected to ensue. Taking
the longer view, changes in technology fostered by Federal government-
sponsored and private research and development can be expected to
make revolutionary changes in modern living patterns (e. g. , transpor-
tation and energy).
Paralleling this trend, there has been a shift of emphasis in
CONSAD's efforts to develop a model for assessment of the economic
effects of air pollution control. During the first year of the project,
-------
a time series econometric model of a single region -- the St. Louis
metropolitan area -- was constructed and the economic impacts of
alternative control strategies for that region were simulated. Over
the second year of the project, a model like the St. Louis model was
built for 31 AQCRs using cross-section data.
Simulations of these regional models, in the context of over-
increasing emphasis on a Federally-coordinated national program of
air pollution control, highlighted the need for two types of improve-
ments in the scope and structure of these models.
First, the regional model was structured as though AQCRs were
economically independent of one another. There was no allowance for
interregional effects. If, for example, Region A instituted air pollu-
tion control and in order to do so imported air pollution equipment
from Region B, the model simulated the economic impact of Region
A's program on Region A alone, and not on Region B. Thus, the re-
gional model gave no indication of the increase in employment in
Region B resulting from the increased production of air pollution con-
trol equipment for export to Region A. There was, therefore, a
source of pessimistic bias in the statement of economic effects of air
pollution abatement embedded in the very structure of the model.
Second, the models at the end of the second year focused only on
the economic impacts of control expenditures and accounted in no way
-------
for any benefits which might result from air quality improvement.
This again tended to cause unjustifiably pessimistic conclusions about
the economic effects of air pollution control.
During the current year, CONSAD approached the problem of
eliminating these biases and making some preliminary assessment of
the national impact of air pollution control in two ways. First, the
cross-section Regional Model was restructured to eliminate, insofar
as possible, the pessimistic bias induced by structural exclusion of
interregional feedback effects and benefits and to permit preliminary
estimates, a national I-O model has been introduced to capture: (1) a
preliminary estimate of structural changes in the national economy,
and (2) an interregional feedback scheme to the AQCRs.
Second, the feasibility and desirability of an expressly national
model to be interconnected with the current regional model was inves-
tigated in considerable detail.
It is the primary purpose of this chapter to present the findings
of this evaluation study. It opens with a brief discussion of CONSAD's
attempt to use an existing national econometric model for making pre-
liminary estimates of national effects of alternative national control
strategies.
-------
Given the nature and difficulties of access of existent models,
the chapter next explores the feasibility of the development of a national
model specifically structured for air pollution policy assessment.
4. 2 The QBE Quarterly Model
As a part of Phase III of the RAPA project, CONSAD investigated
the possibilities and potentials of adding another model to the Regional
Economic Model System for the express purpose of assessing the na-
tional economic effects of air pollution control. CONSAD planned the
use of the OBE Quarterly Econometric Model to make preliminary esti-
mates of national economic effects.
A brief review of the nature of concern with a national macro
model will be a useful prelude to the discussion of the OBE model uti-
lization.
The national economic system and the environment conditions
form two interrelated dynamic systems. Pollution is a by-product of
economic activity. In this broad sense, growth of GNP and hence the
industrial component are closely related to the environment conditions
in the future. On the other hand, implementation of a given emission
(an air quality) standard, possibly involves an over time policy planning
and stimulates a dynamic economic impact to the nation. Even if the
control technology is available, an overnight switch of entire production
-------
line, for example, a highly efficient automobile engine, may be eco-
nomically impractical. The question of tax policy, choice of evalua-
tion criteria, or optional time table of a given standard are all key
questions involved in the trade-off between the growth of GNP and a
better or worse off environmental condition of the nation.
For example, given a national standard for a certain type of
emission, there are a set of "implementation phase. " Each imple-
mentation phase may be thought of as depicting a schedule for achiev-
ing air quality standards over time (see Figure 4. 1).
One factor in determining the rate of implementation of standards
will be the estimated rate at which industry can absorb the cost of air
quality control equipment installations. A time series national model
may be designed to provide estimates of such rates for each industrial
sector. In this manner, the model may provide data for facilitating
the current process of implementation planning.
The future process of implementation planning may be enhanced
by providing APCO with additional control measures for mounting an
implementation strategy. One such measure mentioned in the past at
the Federal level is the provision of tax credits to industries that in-
vest in costly pollution control equipment. One effect of the proposed
measure would be to accelerate the rate at which industry can absorb
-------
FIGURE 4. 1
EFFECT OF TAX CREDIT STRATEGIES
UPON IMPLEMENTATION PLAN
Emission
Rate
Present
esent.
Level
Gradual Implementation, No Tax Credits
.Low Tax Credit Policy
High Tax Credit Policy
Desired
Standard
Time
-------
pollution control costs, and this, in turn, would increase the rate at
which desired standards can be implemented.
Although acceleration of the feasible implementation schedule
will occur under a tax-credit strategy, the effectiveness of this mea-
sure will depend upon the growth rate of industrial composition of the
nation. The impacts upon the feasible rate of implementation that may
be expected of a proposed tax credit strategy may be estimated with a
national model.
On the other hand, the regional model developed in the last chap-
ter is a cross-sectional model, although some lagged variables are
included. It is argued that economic impact of air pollution control
strategies can be described in two facets, namely, spatial impact of
resource allocation and over time impact related to the growth of the
economy. The regional model is particularly useful as demonstrated
in Chapter 6 when a region-by-region differential of control impacts
are of concern. It will be shown that while the aggregate net change
on all 91 AQCRs under study is an increase of unemployment rate of
0. 5 percent (3. 5 to 4 percent), the unemployment rates in some of the
AQCRs with concentrations of high emission industries will be 2 per-
cent or more. Some other AQCRs are shown to be even "better off"
in terms of unemployment effects under the same air quality standards.
-------
But, the limitations of the regional model is also clear; a cross -
sectional model needs different sets of economic data over time to pro-
vide a preliminary picture of dynamic impact of the air pollution con-
trol. Thus, it requires projections of large sets of data, and restrict-
ing thereby the accuracy of the model.
Thus, it appears that a cross-sectional regional model linked
with a time series national model will be an ideal system for the study
of economic effects.
It was with this objective that CONSAD began the exploration of
the link-up and use of an extant national model to the Regional Model.
The candidate for this experiment was the OBE Quarterly Model. This
model was to provide a "quick look" at the national effects over time.
The nation model will cover the rest of the nation other than the AQCR
and hopefully a preliminary over time economic impact of air pollution
control will emerge.
An intensive study of the structure of the OBE model, however,
revealed a number of structural properties which rendered it not an
entirely desirable tool for national effects assessment. In particular,
the model is structured primarily for the purpose of making short-
term (roughly eight quarters) forecasts of the effect of conventional
government fiscal and monetary strategies. Both the temporal and
-------
industrial aggregation of the model are inappropriate to simulations of
the national economic effects of air pollution control.
In contrast to the high degree of time disaggregation of the OBE
mode.., it exhibits a high degree of industrial aggregation. No differ-
entiation is made, for example, of investment by industrial sector,
GNP by sector or employment by sector. Since a principal concern of
APCO must be the effect of its policies on particular industries, the
OBE model must be judged less than satisfactory for APCO strategy
analysis purposes.
Meeting with OBE personnel generally confirmed CONSAD's find-
ings. Indeed, OBE professionals had such serious reservations about
the appropriateness of the model for air pollution control strategy sim-
ulations that access to the model was effectively refused. It would
seem, therefore, that if an effective vehicle is to be sought for national
economic effects assessment, APCO must look to some other national
model, or perhaps consider building one expressly for its purposes.
Given the results of the OBE model applicability to air pollution
control strategy assessment, it appears appropriate to inquire whether
other models are to be explored or whether a special model needs to
be developed for APCO use. Before this query is addressed, it will
be useful to identify the characteristics of what CONSAD believes to
be a desirable national model.
-------
4. 3 Desired Attributes in a
National Model
Ideally, a national model and the current Regional Model would
be moulded into one large interdependent system of models. In rough
terms, the system of models would be composed of three submodels,
each of which would pertain to a particular geographic area:
. the nation,
. specific AQCRs, and
. the rest of the United States not in AQCRs
specifically included in the model.
CONSAD stresses the importance of retaining specific regions within
the model since the economic effects of air pollution control have been
found to be strikingly different from region to region. A solely national
picture would thus be misleading in important ways.
Submodels (1) and (2) of the ideal model structure would have
stochastic equations. Submodel (3) would be made up wholly of identi-
ties. Each of the three submodels would be sectored conformably to
facilitate interconnection. The submodels would then be interconnected
into one large model as indicated in the following simple Keynesian
model.
Submodel 1
YN = CN + IN
-------
where: N stands for nation, and
Y, C and I are income consumption and investment,
respectively.
Submodel 2
GI = ai + bj YI + ej
where: "1" stands for region i.
Submodel 3
Yo = YN - Yj
C0 = CN - Cj
where: "0" stands for the nation that is outside the AQCRs.
Submodel (2) would consist of the CONSAD's regional model for
all AQCRs currently has or will develop, further refined to incorpor-
ate interregional flows of air pollution control factors. Submodel (1)
will be estimated according to the sectoring and specification neces-
sary for air pollution control study.
The output of this integrated model would be a comprehensive
and logically consistent forecast of the economic and gross environ-
mental effects of air pollution control programs for each of the specif-
ically included AQCRs, for the nation, and for all other areas of the
*
United States not in the specifically included AQCRs.
Submodels (1) and (2) should be sectored at least into three broad
categories, namely, sectors affected by pollution, sectors affecting
-------
pollution, and environment sectors which provide gross environmental
indicators related to economic variables. Consideration should be
given to the sectors affected by the level of pollutants. For example,
reduction of agriculture productivity, health and productivity losses,
extra household maintenance, cleaning services are highly related to
the level of pollutants in ambient air. These effects can be reflected
in the production function of agriculture.
For sectors affecting pollution, emission of pollutants from a
given source depends upon the level of output. Air pollution abatement
will lead to cost increases in the high emission industries and be re-
flected in price increases and in changes in interindustrial structure.
Such high emission industries include steam-electric power, iron and
steel mills, petroleum refining, sulfate pulp mills, hydraulic cement
manufacturers, gray-iron foundries, etc. For these industries, de-
mands for low sulfur fuel and control devices will depend both on out-
put level and emission standards. Environmental sectors, since pollu-
tion and economic activity are interrelated, should also contain a set of
equations predicting emission from production and level of control activ-
ity. These sectors would also include supply and demand relations of
control device and low sulfur fuels.
Besides the sector specification, environmental variables should
be explicitly integrated into the related equations. The following exam-
ples will provide some ideas on this approach.
-------
Sectors Affected by Pollution
The production function of the agriculture sector can be specified
as follows:
X = f (K, L, P)
where X, K and L are output, capital and labor, respectively, and P is
measurement of pollution concentration such that:
< 0
Reduction of emission increases output level, such treatment is quite
similar to the technical change introduced in production function.
In the consumption functions, benefits from air pollution control
(B) can be introduced as follows:
C = f (Y, B)
B = f (P)
Reductions in air pollution levels (P) will save expenditures on health,
household maintenance, cleaning service, etc. Types of benefits and
consumption expenditures can be further divided if such data are avail-
able.
Sectors Affecting Pollution
Production functions of the high emission industries can explicitly
include low sulfur fuel (S) and control devices (D) as additional factors
-------
of production:
X = f (K, L, S, D)
From these production functions can be derived projections of emis-
sions and projections of the demand for inputs for air pollution control.
In sum, the ideal model is a comprehensive model, fairly com-
plete not only in its coverage of national economic phenomena, but also
regional economic phenomena and gross environmental phenomena.
4. 4 Data Availability and Reliability
Apart from the theoretical and structural advantages of a national
model in dealing with the dynamic nature of the economic impacts of
air pollution control, a major profit of the national modelling is the
ability to capitalize on the rich and reliable economic data and the con-
trol input data available at the national level.
It often happens that data on a two-digit SIC industry in a given
AQCR is not disclosed in order to protect the financial conditions of
firms when less than four operate in that AQCR in that industry.
Therefore, there are limits to specifying industrial detail in the re-
gional model. At the national level, all economic data are available
at four-digit SIC detail for any given industry. In other words, a much
more detailed industry study is possible for any specific industry of
interest, if a national model was linked to the APCO Economic Model
System.
-------
The same situation is true for the availability of the control input
data as well. The regional control cost data available in the 305(a)
Report of 1970 is the only source of control cost by region for 100
AQCRs and even that is a set of aggregate control costs by region.
Only for a limited number of AQCRs control costs at the two-digit de-
tail are available. However, data on control inputs are available for
the nation in greater specificity. Utilization of such information will
strengthen the understanding of the economic impact of air pollution
control strategies.
4. 5 Alternative Approaches to
National Effects Assessment
If it is granted that evaluation of the economics of alternative
national strategies is desirable and that an economic model is a useful
tool for such evaluation, it remains to consider the different kinds of
models which might fulfill this need. CONSAD has identified three
broad modelling alternatives to capture in varying degrees the alter-
natives described in the previous section.
1. One alternative would be to expand the geographic
scope of the current Regional Model System to in-
clude the remaining AQCRs in the nation. In the
process, the model structure may be refined as
appropriate.
2. An existing national economic model (e. g. , Klein-
Goldberger) may be modified and adopted for pur-
-------
3. A riew model could be built expressly for the
purpose of assessing national effects of Con-
gressionally mandated responsibilities of
APCO.
For reasons which are best left to the last chapter, CONSAD
believes an effort which takes, in part, all three approaches is advis
able.
CONSAD also believes such a national model is feasible on the
basis of preliminary work on formulation of such a model.
-------
-------
MiuK-1
Outputs
Assessment
Model Development
Regional Economic Model System
Regional Economic
I Activity
National Economic
Activity
Rc-gLonal
Model
Interregional
Feedback
I-O Model and
National Effects
Assessment
Policy Assessment
/ IhcenfeiyXs
F^erls
. Re sea-re h an
/developme
x-expendirfure'S
.,, R-fqgrarn. IOA
Outputs: Changes in:
. Manufacturing
activity
. Power and fuel
consumption
. Income
Employment
Government
expenditures
. Other
Interpretation
of Model
Outputs
Model System
Assessments
Recommenda-
tions on
Further Refine-
ment and Utili-
zation of Model
-------
5. 0 SIMULATING THE IMPACT OF AIR
POLLUTION CONTROL STRATEGIES:
A SIMULATION OF THREE STRATEGIES
5. 1 Introduction
The raison d'etre of any economic model is to facilitate accurate
and comprehensive prediction of the consequences of changes in partic-
ular variables and parameters under the control of policy-makers.
The adequacy of predictions so made depends not only on the adequacy
of the model itself, but also on the proficiency of the model-user in
translating the changes in policy variables he wishes to investigate in-
to appropriate changes in variables and parameters of the model. The
computerized economic simulation model does not supplant the expert,
but rather, follows through difficult but routine complex logic and cal-
culations, thus freeing the expert for the more creative and demanding
task of using the model intelligently.
This chapter of the report is intended to explore the simulation
potential of the Regional Economic Model System in the following order.
. First, it describes the range of air pollution control
policies for which the model is developed and how
these policies need to be translated into appropriate
variables of the model before they are introduced in-
to the simulation. An outline of the way the Regional
Economic Model System transforms control policy
inputs into various model outputs of interest to policy
assessment is also provided.
-------
Second, it describes the implications of the three
strategies prescribed by APCO staff for the excer-
cise of the model.
Third, the chapter outlines the manner in which
the three strategies are prepared as inputs for the
simulation run and the nature of the outputs of the
simulation.
5. 2 The Scope and Nature of Simulation
of the Regional Model
5.2.1 Nature of Simulation in General
An economic model purports to describe the relationships be-
tween a set of variables of interest in some context, most though not
all of which are economic variables. Variables are typically classi-
fied dichotomously as either exogenous or endogenous, paralleling the
engineer's classification of variables as either input variables or out-
put variables. Exogenous variables are variables not determined by
the model, but variables whose values influence the values taken by
variables determined by the model. The latter type of variable is
called an endogenous variable. For instance, in a model of the wheat
market, the average annual price of wheat and annual output would be
endogenous variables, while weather indicators, since the weather is
not significantly (if at all) influenced by wheat prices and output, are
exogenous variables.
-------
Model variables are related to one another in a system of simul-
taneous equations which system actually comprises the model. The
parameters (or coefficients) which appear in these equations are called
the structural parameters of the model. They are hopefully stable con-
stants which characterize relatively invariant relationships among
model variables. The model is either linear or non-linear depending
upon whether all its equations are linear or not.
These concepts may perhaps be clarified by consideration of an
actual representation of a model. Ley Y be an nxl vector, the com-
ponents of which are values of variables endogenous to some model and
let Z be an rxl vector of values of variables exogenous or lagged endo-
genous to some model. Then the notion that Y and Z are linearly re-
lated can be represented as:
(1) A Y + B Z = 0
where A and B are respectively nxn and nxr matrices of structural
parameters.
A simulation is really nothing more nor less than solving of the
model for the endogenous variables given values of the structural
parameters and the exogenous variables. The process is particularly
simple where the model is linear, as is that outlined above in equation
(1). In this common case, the model solution may be obtained explicitly
as:
-------
(2) Y = -A'1 B Z
Where the model is non-linear, more complicated techniques for solv-
ing systems of non-linear equations (e.g., modified Newton) must be
invoked.
Simulation of the effect of a .policy involves (1) solving the model
for no change in policy, (2) translation of the proposed policy into
changes in exogenous variables and/or structural parameters, and
(3) solving the model for these changed values. Comparison of the
solution values with and without the policy demonstrates the effect of
the policy. Steps 1 and 3 are computerized and are thus trivially sim-
ple. Step 2, however, demands thorough familiarity with the model,
patience, and sometimes considerable ingenuity. Because Step 2 is
essentially an endeavor of skillful and critical decision in the absence
of all but the most general guidelines, it is best mastered by example
and practice. And that is the purpose of the following sections of this
chapter.
5.2.2 APCO Strategies and the Regional Model
The purpose of this section is to review the range of policies at
the disposal of APCO and how they should be transferred for simula-
tion through the model system.
There is a variety of tools available to APCO to meet its Con-
gressionally mandated responsibilities to improve air quality. Under
-------
current and foreseeable law, APCO has, as indicated earlier, policy
or advisory responsibilities in the following areas:
1. Setting of standards (air quality, emission)
a. For stationary sources
b. For mobile sources
2. Incentive policies
a. Environmental subsidies
b. Environmental degradation taxes
c. Accelerated depreciation
d. Low interest loans
3. Government expenditures
4. Research and development
5. Fuel regulation
The Regional Economic Model System can trace a significant por-
tion of these effects, say when standards set by APCO are used in a
simulation of the model. The standards assumed in the 305(a) Report
to the Congress are an illustration of such standards.
However, before the 305(a) standards can be used in the simula-
tion, they need to be translated into model variables or model inputs.
In other words, the 305(a) standards must be translated into model in-
puts reflecting these standards, suitable to the model logic, before
their effects can be simulated. The 305(a) standards are also express -
able in terms of the control costs incident to the high emission indus-
tries in the AQCR and are entered into the model as control costs.
-------
These control costs are distinguishable into annualized investment
costs and operating costs and are fed into the appropriate component
of the model (see Figure 5. 1).
Figure 5. 1 provides an illustrative translation of the various
APC policies into appropriate inputs into the model. The model inputs
into which APC policies are translatable are of two kinds:
Changes in exogenous variables of the model,
e.g., control costs, damage functions, etc.
These are represented by the rectangles in
Figure 5. 1.
. Changes in structural-parameter (changes
in behavior patterns in the economy), e. g. ,
modified consumption patterns, modified tax
structure and changes in the production pro-
cess; represented by the diamonds in Figure
5. 1
The first step, therefore, in simulation is to identify the model inputs
in which form the control policies can be expressed.
5. 2. 3 Regional Model System and Control Inputs
The scope of this section is to illustrate how the various control
inputs into the model relate to various components of the model. Such
an understanding is vital to effective use of the model.
Figure 5.2 identifies the four major modules of the Regional
Model and identifies their relationships to five types of control policy
model inputs.
-------
FIGURE 5. 1
APC POLICIES AND MODEL, INPUTS
APC Policies
Model Inputs
Standa rds
Air Quality Standards
Emission standards
a. stationary sources
b. vehicles
Control Costs
Damage
Estimates
of consump
tion patterns
Incentives
subs idies
taxes
accelerated
depreciation
Emission
Reductions
Control Costs
Damage
Estimates
Government
Expenditures
od-
ification of
tax struc-
ture
Research and
Development
Control Costs
re-
duction
process
change
Fuel Regulation
Control Costs
Damage
Estimates
Input Changes
Parameter Changes
-------
FIGURE 5.2
REGIONAL MODEL AND CONTROL INPUTS
Regional Model
Manufacturing
Industry
Power and
Fuel Demand
Regional Income
Determination
Regional Labor
Market
Control Policy
(control input)
Control
cost in high
emission in-
dustry
Interregional
feedback effect
Fuel price
change
Property
value bene-
fit in AQCR
Local tax
policy
-------
The control cost to the high emission industries associated with
a given emission or air quality standard will lead to a cost increase in
those industries and power industries in particular. These cost in-
creases will enter into the manufacturing industry module (in the in-
vestment and profit components) and the fuel demand module (price of
electricity equations) and will cause changes in the output of manufac-
turing industries. Changes in the production will affect the fuel de-
mand of the industries and affect the regional income and regional
labor market.
Interregional feedback effect, from the benefits to be realized
from cleaner air is brought to the regional model from a national I-O
model. This feedback effect can also feed in price changes of products,
increased demand for control equipment, etc. , through the link it pro-
vides between the regional model and the national I-O system. Fuel
price changes will affect the fuel substitution in the regional model and
relate to the other parts of the model. If property value benefits by
AQCR can be estimated, net increase of disposable income, will aug-
ment additional local consumption expenditures. An increase of the
productivity attendant on pollution abatement can lead to an increase
in the profit. Finally, if any local taxes are introduced to aid the im-
plementation of air pollution abatement, its impacts on the regional
economy can be traced.
-------
Figure 5. 3 provides a more detailed look at the way control in-
puts course through the model.
A simulation program consisting of the following three major
modules has been prepared:
. RMS (Regional Model),
. IOA (Input-Output Model), and
. FEE (Interregional Feedback).
A detailed description of the simulation program appears in Volume
II. It will be used in the next section to simulate the effects of three
strategies specified by APCO staff.
5. 3 Simulating Three Strategies:
The Definition of Strategies
APCO specified three strategies to be simulated by the model.
All three of the simulations to be discussed here have as their basis
the control costs envisioned in the 1970 Report to Congress as required
by Section 305(a) of the Clean Air Act of 1967. The simulations re-
flect differing policies and assumptions with respect to the incidence
of control cost expenditures -- that is, with respect to who finally pays
the cost of controlling air pollution. Before beginning a detailed de-
scription and analysis of the strategies studied here, some general
i
comments on incidence and its determinants are in order.
-------
FIGURE 5. 3
REGIONAL MODEL AND CONTROL POLICIES:
MORE DETAILED LOOK
Capital
Stock
Investment
ontrol
cost in high
emission industries
operation cost
investment cost
subsidies
price increase
Wage
Determina-
tion
In te r -
regional feed-
back:
. change in value
added
Electric
Power
Demand
Price of
Electricity
Price of
Fuels
coal, gas,
Fuel Policy:
. price changes
Regional
Income
Regional
Consump
tion
increase in
disposable
income
Govern-
ment
Expenditure
Govern-
ment
Revenue
increase in
roductivity
Employ-
ment in
other sec
Other
Industry
subsidies
Regional
Employment
Regional
Labor
Force
Regional
Unemploy-
ment
REGIONAL MODEL
CONTROL INPUT
-------
The diagram presented in Figure 5.4 is a supply schedule (a
schedule relating market price to quantities which suppliers will wish
to produce and sell per unit time) and a demand schedule (a schedule
relating market price to quantities which consumers will wish to pur-
chase per unit time). That price at which the quantity consumers wish
to purchase just equals the quantities suppliers wish to produce and
sell is called the equilibrium price, and the corresponding quantity is
called the equilibrium quantity. The effect of shifts in either schedule,
i.e. , shifts in willingness to sell or buy, causes equilibrium price and
quantity to change. For example, in Figure 5. 5(a) below, consumers'
disposable income increases, causing the demand schedule to shift out-
ward to the right. In Figure 5. 5(b), the supply schedule is shifted up-
ward reflecting an increase in costs of production.
In simple graphical terms, the effect of imposing air pollution
control on an industry is rather like Figure 5. 5(b). Air pollution con-
trol adds to the cost per unit of output, causing the supply curve to
shift upward by the amount of the additional cost of air pollution con-
trol. This is depicted in Figure 5.6. Equilibrium price is then in-
creased from po to pi and equilibrium decreased from qo to qj. The
total air pollution control expenditure is (pj - a) times qj. Of this
total amount (pj - po) times qj is paid by purchasers in the form of
higher prices for each unit purchased. The amount paid by producers
-------
FIGURE 5.4
SUPPLY SCHEDULE
Supply
Schedule
Demand
Schedule
Units Purchased
Per Unit Time
-------
FIGURE 5. 5
Price
P'
P
(a)
Shift due to increase in
disposable income
q'
Quantity
Price
p1
p
(b)
Shift due to increase in
production costs
Quantity
-------
FIGURE 5.6
AIR POLLUTION CONTROL COST PER UNIT OUTPUT
Price
Additional Air
Pollution Control
Cost/Unit Output
Paid by
purchaser
qi
D
Quality/
Unit Time
-------
in the form of lower revenues net of control costs is given by (p - a)
times qj. The difference in prices paid by consumers, (pi - po) is
called the incidence of control costs on consumers, and the comple-
mentary price difference (po ~ a) is called the incidence of control
costs on producers. The sum of the incidences thus equals the total
per unit cost of control.
Incidence for a single industry is not, of course, the whole story
of the economic impact of air pollution control. Not only is one inter-
ested in changes in prices and output, but also changes in wages, prof-
its, employment, consumption, investment, use of raw materials and
other artifacts of production. And this interest extends to those quan-
tities not primarily for the case where control is applied to a single
industry, but where most, if not all, industries are controlled in
greater or lesser degree. There are, therefore, important interin-
dustrial effects. This is where the APCO Economic Model System
makes its contribution. Using the results of incidence calculations
and other input information, the APCO Economic Model System fore-
casts the consequences of alternative control policies for a wide range
of economic variables, taking into account interindustrial effects.
The three strategies selected by APCO for simulation and analysis
in this report differ from one another in the kind and degree of govern-
mental intervention to shift the incidence of air pollution control costs.
-------
A general description of the three strategies follow.
Strategy 1: Industry Pays
There are some who contend that industry should bear the full
cost of controlling air pollution; that is, that the incidence of control
costs on consumers or government should be zero. The argument
buttressing this policy position is a moral one (as are those which lie
behind other policy positions) and is thus beyond the purview of scien-
tific enquiry. The concern here is solely with discovery of the prob-
able effects of such a policy if it were pursued.
The most plausible specific policy which the government could
use to insure that industry alone bears the incidence of air pollution
control costs is to freeze prices at their level prior to instituting air
pollution control. This effectively produces a kinked demand curve,
perfectly elastic, at quantities less than that which was obtained before
air pollution control. This is diagrammed in Figure 5. 7. Since the
effect air pollution control is to shift the supply curve upward, the new
equilibrium price and quantity will fall along the perfectly elastic por-
tion of the demand curve. Remembering that where demand is per-
fectly elastic, the full burden of air pollution control costs falls on the
supplier, it is demonstrated that price freezes coupled with air pollu-
tion control will achieve the distribution of incidence desired under
Strategy 1. This approach is diagrammed in Figure 5. 8.
-------
FIGURE 5.7
PRICE FREEZE DEMAND SCHEDULE
Pre -abatement
Equilibrium
Post-Price Freeze
Demand Schedule
Pm
-------
FIGURE 5.8
DEMAND AND SUPPLY RELATIONS UNDER
STRATEGY 1
Paid by
producer
S1 (post-abatement)
S (pre-abatement)
D (post-price freeze)
-------
It is assumed that Strategy 1 differentiates between public util-
ities and other industries, allowing the former to pass along in toto
the additional cost of control so as not to jeopardize the already sup-
posedly minimal rate of return in this sector.
Strategy 2: Hands Off
Under Strategy 2, the incidence of control costs falls where it
may, with no attempt by the government to alter the distribution
achieved by the market. In other words, Strategy 2 is simply an air
pollution control policy with no accompanying economic policy. In
diagrammatic terms, the consequences of this policy for incidence
and changes in price and output may be seen by referring to Figure
5.6.
Strategy 3: Cost Sharing
It is frequently argued that if government shares some of the
cost of control which would normally fall on industry, undesirable eco-
nomic effects of control would be much attenuated. In particular, the
net result of cost sharing, it is argued, would be to reduce the amount
by which prices will rise and output will fall. The reasoning behind
this argument is presented diagrammatically in Figure 5. 9. As shown
earlier, the immediate effect of air pollution control is to shift the
supply curve upward by the amount of the additional cost of air pollu-
tion control. Producers are willing to produce and sell only if they
-------
FIGURE 5.9
DEMAND AND SUPPLY RELATIONS UNDER STRATEGY 3
P '
Pi
Po
xSj(abatement without
cost sharing)
^(abatement
with cost
sharing)
Cost borne by .,$ (pre-
governmentx^ abatement)
incidence on buyers
Incidence on sellers
-------
are able to cover all costs of so doing. Hence, without cost sharing,
the supply curve shifts from S to S1. If the government were then to
undertake to reimburse industry for a portion of the control cost, the
supply curve would shift downward to some position intermediate be-
tween S and S', say S". The effect of cost sharing is simply to reduce
the additional cost of control as viewed by industry. It is clear, that
as argued by proponents of this policy, price increases and output falls
are attenuated.
Because elasticities of demand, at least in the short run, are
typically low for the services of public utilities, these industries
should have little difficulty in passing along cost increases with little
reduction in output. It has, therefore, been supposed here that no
cost sharing is to be done with utilities.
For purposes of the simulation, it is assumed that government
is to bear 50 percent of all industrial process and combustion control
costs, which would otherwise fall on industry, financed by an increase
in Federal personal income taxes, and no portion of other control costs
as indicated above.
5.4 Preparing Inputs for Simulation
The difficult task remains of translating each of these three
strategies into values of exogenous variables and parameters in the
-------
APCO Economic Model System so that simulation can be executed.
The process by which this is done is described in the text and figures
of the following pages.
The raw data on which simulations with the APCO Economic
Model System rest are (1) control costs by AQCR (and by 2-digit SIC
if available) for industrial processes and combustion, (2) control costs
for steam electric power generation, and (3) benefits which will occur
because of air pollution control outlays. Data on items (1) and (2),
for the simulations reported here, were taken from the 1970 Report
to the Congress required by Section 305(a) of the Air Quality Act of
1967. For purposes of benefit determination as required under item
(3), the conservative estimate is that air pollution causes $10 billion
worth of damage annually. These data, however, form only the basis
for simulation. The model user must still exercise considerable judg-
ment in deciding precisely how they shall be used in the simulation.
To fully understand the simulation process for this model, how-
ever, one must delve yet deeper into its inner workings. Appendices C,
D and Volume II provides such a detailed review of these details.
With this rather detailed background in mind, it is time to dis-
cuss conversion of raw data into model inputs. Benefits are discussed
first since the treatment of benefits is precisely the same under the
-------
three strategies. A conservative total damage figure of $10 billion*
is assumed, and it is assumed that roughly a 20 percent reduction in
annual damages would occur if the control costs programmed in the
1970 Report to the Congress under Section 305(a) of the Air Quality
Act of 1967 were incurred. An annual damage reduction of $2 billion
is thus implied. This annual damage reduction is treated as an in-
crease in consumers' disposable income, which treatment implies an
increase in consumption, assuming a marginal propensity to consume
0. 8, of $1. 6 billion. This increase in consumption was treated as an
increase in final demand in the Interregional Feedback Submodel, dis-
tributed among the various sectors in that model according to the his-
torical distribution of consumption in each of the feedback models'
sectors.
All the raw cost of control information was taken from the 1970
305(a) Report. These costs reflect the following emission reductions
of particulates, sulfur oxides, hydro carbons and carbon monoxides in
the 100 AQCRs:
*U. S. Department of Health, Education and Welfare, National
Air Pollution Congrol Administration, Costs and Economic Impacts of
Air Pollution Control, Fiscal Years 1970-1974. January, 1969, p. iv.
-------
Emission
Pollutant Source Category Decrease (%)
Particulates Solid waste 77. 8
Stationary combustion 91.7
Industrial process 86. 1
Sulfur oxides Stationary combustion 52.2
Industrial process 36.2
Hydrocarbon Solid waste 69.4
Industrial process 57. 8
Carbon monoxide Solid waste 84. 7
Industrial process 90.2
The only difference between the three strategies is in the treat-
ment of control cost, which treatment reflects, as noted above, differ-
ing policies with respect to who shall bear the cost of control. Under
Strategy 1 (Industry Pays), the full burden of industrial control costs
and increased costs of electric power used by the manufacturing and
non-manufacturing sector falls upon industry. Consumers pay only
the increased cost of the electric power they use.
For each AQCR, control costs can be identified in three cate-
gories, namely, investment expenditures on control equipment, annual
operation cost,, and control cost of electric power industry. * By
'''Aggregation of the five-year average control cost over 91 AQCRs
under the present study is $1, 333 million. Unfortunately, control costs
by AQCR are not available on 2-digit SIC breakdown, although the pres-
ent model can use such data. A separate simulation run, based on 2-
digit SIC detail control costs for St. Louis, Cincinnati, and Washington,
D. C. AQCR, has been conducted.
-------
notation:
A Ci = A Cn + A Ci2 i=l ..... 91
where A C^ is the total control cost of manufacturing industry
in ith AQCR,
A Cji is the investment expenditure of control in i"1 AQCR,
A C^2 is the annual operational cost of control in i*n AQCR.
It has been assumed that electric power industry will pass on the entire
control cost to the users by a price increase of:
A - - °i
qi Qi
where Aqi price increase of electricity in i^1 AQCR,
Oi control cost of electric power industry in ith AQCR,
Qi total regional consumption of electricity in i"1 AQCR.
Under Strategy 1 (Industry Pays), annual operation cost A C^ has
been reduced from the gross profit (Ł flj;) of the manufacturing indus-
try, and the investment expenditure AC^j from the gross investment
(Ł I^j) in each AQCR. On the other hand, control cost to the electric
power industry becomes a price increase of electricity and passed on
to three types of users:
Control cost passed to
manufacturing industry Acii'? Q{\
j J
Control cost passed to
the AQCR residents Aqi'Qci
Control cost passed to _
the other industries Aqi' Qj
-------
where QJJ is electricity used by j'k industry in i"1 AQCR. Therefore,
Aqi ? Qij becomes further reduction to the gross profit from manufac-
turing industry in each AQCR. Qc^ is the electricity consumed by res-
idence of i"1 AQCR, hence, Acjj Qc^ is a reduction on the regional dis-
posable income (Y-). Finally, Aqi Qj is passed to the other industries.
The benefits from control (i.e. , $1. 6 billion additional consump-
tion as the total benefit in the nation) was distributed to corresponding
sectors of the nation based on the average propensity to consumers by
sectors. * By use of the national I-O model (Program IOA) direct and
indirect increase of production (value of shipment) by sector were es-
timated and then, by use of regional market share matrix (Program
FEE), the impact of benefit at the national level is distributed to each
AQCR.
Under Strategy 2 (Hands Off), a part of the control cost is passed
on as price increase. A prior estimation of price increase by industry
was reported by the 305(a) Report of 1971. In this study, a weighted
average price increase of 0.4 percent for manufacturing industries was
*See Appendix D for percentage distributions among consumption
items and regional market share matrix.
-------
derived.* Therefore, under Strategy 2, the control cost to the manu-
facturing industries becomes:
AC* =
= r jj
j J
r is percentage price increase and Ł Vjj is total production (value-
added) of manufacturing industries. Hence, APj is the part of control
cost passed on to the consumer by price increase which reduces ACj
and AC2 proportionally, although the price increase in electricity re-
mains the same as before. On the other hand, the price increase of
the manufacturing product in the nation will result in an inflationary
effect on disposable income, which will be reduced by the same amount.
It is estimated that $624 million out of $1,333 million control cost has
been passed on the consumer by the price increase. This reduction of
the disposable income in the nation is distributed to each AQCR in the
same manner as benefit.
*In a recent study of the price increase among high emission in-
dustries, it is shown that the percentage price increase among differ-
ent products depends upon the price elasticity of the corresponding
product. An 0.4 percent "average" price increase for manufacturing
industries was derived from the weighted average of the percentage
price increase by two- to three-digit SIC industries weighted by the
corresponding volume of sales by each industry. Data used in this
calculation is obtained from LeSourd, D. A. , et al. , Comprehensive
Study of Specified Air Pollution Sources to Assess the Economic
Effects of Air Quality Standards, Research Triangle Institute,
December, 1970.
-------
Under Strategy 3 (Cost Sharing), it has been assumed that the
Federal government will subsidize 50 percent of the remaining control
cost which industry cannot pass on by price increase. Therefore, the
control cost of the manufacturing industries become:
**
ACi = AC^ - APj - AFi
AFL = J(ACi - APi)
The government subsidy of A Fi is 50 percent of the remaining
control cost to the manufacturing industries. This again is propor-
tionally reduced from ACji and AC^- On the other hand, it is
assumed that the Federal government will raise the same amount of
tax from the public. An estimated amount of $355 million reduction
of disposable income in the nation was assumed. The impact on each
AQCR is also treated through the I-O model and regional market share
matrix as before (see Table 5. 1).
The results of the simulation of the three strategies are pre-
sented and interpreted in the next chapter.
-------
TABLE 5. 1
INCIDENCE OF CONTROL COSTS UNDER THE
THREE STRATEGIES (in millions)*
Strategy
1
2
3
Cost Incurred
Manufactur-
ing Industry
$1333
$ 709
$ 354
Consumers
$ 624
$ 629
By
Government
$ 355
Total
$1333
$1333
$1333
*In Strategy 1, high emission industries are assumed to absorb the
entire cost of control. In Strategy 2, high emission industries will
pass on part of the control costs as a 0.4 percent price increase to the
consumer. In Strategy 3, it is assumed that the Federal government
will raise, by a special tax to subsidize the high emission industries
by an amount equal to 50 percent of the control costs not covered by
the price increase in Strategy 2. In all three strategies, it is further
assumed that electric utilities will pass on their control costs to the
consumers as a price increase. For a detailed description of the three
strategies, see page 94 or Chapter 5.
-------
-------
Inputs
Model Development
» Model ^
Regional Economic Model System
Outputs
Assessment
Regional Economic
Activity
National Economic
Activity
Regional
Model
Interregional
Feedback
I-O Model and
National Effects
Assessment
vO
tsJ
Policy Assessment
APCO Policy Variables
Standards
Incentives
Fuels
Research and
development
Government
expenditures
Computerized Simula-
tion Program.
. Program RMS
. Program FEE
. Program IOA
Model System
Assessments
Recommenda-
tions on
Further Refine-
ment and Utili-
zation of Model
-------
6. 0 INTERPRETATION OF THE EFFECTS
OF THREE TRIAL STRATEGIES
This chapter is intended to provide a brief interpretation of the
preliminary results of the simulation of the three strategies specified
by APCO. It opens with a brief recapitulation of the assumptions un-
derlying the three strategies. It proceeds to a brief discussion of the
framework of analysis of the model output through a series of questions.
Next, it identifies the geographic patterns of incidence of a key eco-
nomic indicator unemployment rate under the three alternatives.
Finally, it explores the patterns of change of two other key indicators --
profits and personal income -- under the three strategies on the 91
AQCRs, and assesses the potentials of the interpretations attempted
here.
6. 1 The Three Strategies
As described in the previous chapter, the three alternative strat-
egies tested with the APCO Economic Model System have as their basis
the control costs envisioned in the 1970 Report to Congress as required
by Section 305(a) of the Clean Air Act of 1967. * These three strate-
gies are summarized in Figure 6. 1.
*Hereafter, it will be referred to as the 305(a) Report.
-------
FIGURE 6. 1
THE THREE STRATEGIES AT A GLANCE
APC Strategy 1
1. High emission industries will absorb the entire control cost as
costs increase in the production without price increase.
2. Electric utilities (SIC 4911) will pass over the control cost to the
users as price increases to industries and consumers.
3. The benefits from air pollution control will result in a $1.6 billion
increase of consumption in the nation based on average propensity
for consumption of goods and services, generating an increase in
production of corresponding industries in each AQCR.
APC Strategy 2
1. High emission industries will increase the price of products as part
of control cost increase. It is estimated that $624 million out of a
total control cost of $1, 333 million will be passed on to the con-
sumer as the result of an average 0.4 percent increase of the man-
ufacturing product price.
2. Because of the price increase in the products of the high emission
industries, consumers in the nation will reduce consumption to
$623 million.
APC Strategy 3
1. Besides price increases assumed in the APC Strategy 2, high emis-
sion industries will receive 50 percent of Federal government sub-
sidies for the part of control cost which is not passed on by price
increases of the products.
2. Federal government will raise an amount of tax equal to the subsidy
to the high emission industries; therefore, the national disposable
income will be reduced by $355 million, in addition to the price in-
crease on the products of high emission industries.
-------
6. 2 An Approach to Assessment of
Efi'ects of the Three Strategies
For each strategy simulated, the APCO Economic Model System
gives a varied and detailed output describing the economic situation in
the region with and without the control strategy. For example, Table
6. 1 presents the summary table for the New York AQCR under the
"Industry Pays" strategy. Evidently, the model produces so great an
output that one needs a framework to analyze these outputs and compare
the three strategies, otherwise it may be difficult to "see the forest for
the trees. "
The approach taken here is to pose questions on which the model
impinges. The questions to be considered are the following:
. What effect does air pollution control have on the
nation's regions?
How is this effect modified by government strate-
gies for modifying the incidence of control expen-
ditures ?
. How are geographical effects modified by govern-
ment strategies for modifying the incidence of
control expenditures?
. Are there any geographical patterns of economic
effects of air pollution control?
. If so, what explanations can be advanced for these
patterns ?
. What changes in the structure of economic activity
occur within regions?
-------
TOTAL NET EFFECT OF ALL CONTROL STRATEGIES PURSUED IN "HIS RUN
AOCR 1 NEK YORK, N.Y.
MANUFACTURING INDUSTRIES
PROFIT (MILLIONS)
INVESTMENT (MILLIONS)
VALUE ADDED (MILLIONS)
CAPITAL STOCK (MILLIONS)
EMPLOYMENT ( 1000 S)
OTHER INDUSTRIES
EMPLOYMENT (1000 S)
REGIONAL CONSUMPTION (MILLIONS)
TOTAL PERSONAL INCOME ?OR THE. REGION (MILLIONS)
TOTAL REGIONAL EMPLOYMENT (1000 S)
REGIONAL UNEMPLOYMENT (PERCENT)
TOTAL LABOR FORCE (1000 S)
GOVERNMENT EXPENDITURE FOR THE REGION (MILLIONS)
GOVERNMENT REVENUE FROM THE REGION (MILLIONS)
NO
ELECTRIC POWER DEMAND
TOTAL ELECTRIC CONSUMPTION FOR THE REGION (1000 KWS)
ELECTRICITY USED BY MANUFACTURING INDUSTRIES (1000 KWH)
ELECTRICITY USED BY OTHER INDUSTRIES (1000 KWH)
RESIDENTIAL CONSUMPTION IN THE REGION (1000 KWH/
WITHOUT
CONTROL
5738.002
452.000
13836.699
10884.758
1144.000
3979.71
NET
CHANGE
-33.051
-?0.394
5.273
-19.132
-6.432
-2.866
PERCENT
CHANGE
0.57591
4.51190
-0.09929
-0.17577
0.56227
-0.07202
32445.000
537 12.000
5123.711
4.000
5337.199
5944.000
6416.000
-30.164
-41.4PB
-9.293
0.1741
-9.660
-3.929
-4.165
-0.09297
-0.07724
-0.18137
4.35349
-0. 1 a 100
-0.06610
-0.06491
4878.000
518.00
3205.00
1155.000
-6.364
-2.736
-2.205
-1.423
-0.13047
-0.52817
-0.068S1
-0.12322
TABLE 6.. 1
-------
How are these changes moderated by incidence
strategies ?
The issues raised by these questions are addressed in several
steps. First, a key economic indicator -- change in unemployment
rate --is analyzed in the various AQCRs and patterns of change under
the alternative strategies are interpreted. Then the consistency of two
other key indicators -- profits in manufacturing industries, regional
personal income -- with the patterns evidenced in unemployment rate
changes is explored. Next, an interpretation of the net effects aggre-
gated over 91 AQCRs as evidenced by these three indicators is
attempted. Finally, the potentials of the interpretation of results
displayed here are assessed.
6. 3 Changes in Unemployment Rates
Under the Three Strategies
A key economic indicator, change in regional unemployment rate
attendant on the three alternative air pollution control strategies, is
analyzed here.
Table 6. 2 shows the change of unemployment rate in each AQCR
from the simulation outputs of the three alternative APC strategies.
If one compares the change of unemployment rate among differ-
ent AQCRs, it is obvious that the air quality standard to be imple-
mented in the 305(a) Report will have considerably different economic
-------
TABLE 6.2
CHANGE OF UNEMPLOYMENT RATE FROM SIMULATION OF THREE
APC STRATEGIES
Change of Unemployment Rate (%)
AQCR Strategy Strategy Strategy
Code AQCR I 2 3
1 New York, N. Y. 0.17 0.13 0.07
2 Chicago, 111. 1.38 1.15 0.69
3 Los Angeles, Calif. -0.003 0..02 0.03
4 Philadelphia, Pa. 0.58 0.46 0.29
5 Detroit, Mich. 1.21 1.07 0.62
6 San Francisco, Calif. -0.03 0.07 0.13
7 Boston, Mass. 0.28 0.24 0.26
8 Pittsburgh, Pa. 2.06 1.57 1.03
9 St. Louis, Mo. 1.74 1.53 0.91
10 Washington, D. C. 0.22 0.13 0.06
11 Cleveland, Ohio 0.98 0.60 0.40
12 Baltimore, Md. 1.00 0.81 0.47
14 Minneapolis-St. Paul, Minn. 0.53 0.47 0.15
15 Houston, Texas 0.49 0.58 0.39
16 Buffalo, N. Y. 1.40 1.08 0.69
17 Milwaukee, Wis. .1.39 1.20 0.86
18 Cincinnati, Ohio 1.54 1.14 0.58
19 Louisville, Ky. 1.58 1.39 0.96
20 Dallas, Texas 0.13 0.08 0.02
21 Seattle-Everett, Wash. 0.09 0.03 0.06
22 Kansas City, Mo. 0.25 0.22 0.16
23 San Diego, Calif. -0.03 -0.02 -0.01
24 Atlanta, Ga. 0. 08 0. 04 0. 06
25 Indianapolis, Ind. 0.78 0.67 0.31
26 Miami, Fla. 0.03 -0.01 0.0
27 Denver, Colo. 0.51 0.38 0.20
28 New Orleans, La. 0. 52 0. 12 0. 08
29 Portland, Ore. 0.40 0.15 0.08
30 Providence-Pawtucket, R.I. 0.23 0.18 0.14
31 Phoenix, Ariz. 0.29 0.17 0.09
32 Tampa, Fla. 0.47 0.29 0.15
33 Columbus, Ohio 0.07 0.04 0.02
34 San Antonio, Texas 0. 12 0. 04 0. 02
35 Dayton, Ohio 0.78 0.63 0.45
36 Birmingham, Ala. 1.95 1.51 1.10
37 Toledo, Ohio 0.91 0.78 0.44
38 Steubenville-Weirton,
Ohio/W. Va. 8.89 7.37 3.92
39 Chattanooga, Tenn. 0.43 0.45 0.13
-------
TABLE 6. 2 (continued)
Change .of Unemployment Rate (%)
AQCR
Code AQCR
40 Memphis, Tenn.
41 Salt Lake City, Utah
42 Oklahoma City, Okla.
43 Omaha, Neb.
44 Honolulu, Hawaii
45 Beaumont-Port Arthur-
Orange, Texas
46 Charlotte, N. C.
47 Portland, Maine
48 Albuquerque, N. M.
49 Lawrence-Haverhill/
Lowell, Mass.
50 El Paso, Texas
51 Las Vegas, Nev.
52 Fargo-Moorhead, N.D. ,
Minn.
53 Boise, Idaho
54 Billings, Montana
55 Sioux City, Iowa
61 Allentown-Bethelehem-
Easton, Pa. , N. J.
63 Bakersfield, Calif.
64 Davenport-Rock Island-
Moline, Iowa, 111.
66 Grand Rapids/Muskegon-
Muskegon Hts. , Mich.
67 Greensboro, N. C.
68 Harrisburg, Pa.
69 Jacksonville, Fla.
70 Knoxville, Tenn.
71 Nashville, Tenn.
72 Peoria, 111.
73 Richmond, Va.
74 Rochester, N. Y.
75 Saginaw/Bay City, Mich.
76 Scranton-Wilkes Barre-
Hazelton, Pa.
77 Syracuse, N. Y.
78 Tulsa, Okla.
Strategy
1
-0.03
0. 72
0.31
0.69
0. 18
0. 77
0.50
0.07
0.02
0.87
0.35
0. 16
0. 57
0.29
0.30
0.02
2.03
0.22
1.22
1.61
0.08
1. 14
0.25
1.04
0.08
1.80
0.45
-0.63
5.23
5.27
0.33
0.61
Strategy
2
-0.04
0.38
0. 18
0.51
0. 12
0. 18
0.35
-0. 74
0. 08
0. 79
0. 11
0. 13
0.25
0. 11
0.05
-0.01
1.56
0.05
0.95
1.45
0.05
0.69
0. 10
0.50
0.09
1.54
0.35
0.22
4.67
4.49
0.30
0.33
Strategy
3
-0. 02
0. 19
0. 12
0.22
-0.05
0.09
0. 16
0.20
-0.02
0.39
0.09
0. 07
0. 12
0.07
0.03
-0. 06
1.24
0.02
0.61
0.69
0. 02
0.31
0.02
0.20
0.05
0.81
0.20
-0. 17
2.35
2.25
0.23
0. 17
-------
TABLE 6.2 (continued)
Change of Unemployment Rate (%)
AQCR Strategy Strategy Strategy
Code AQCR 1 2 3
80 Youngstown-Warren, O. 1.69 1.13 0.61
81 Albany-Schenectady-
Troy, N. Y. 0.28 0.22 0. 15
82 Binghamton, N. Y. 0.84 0.65 0.32
83 Charleston, S. C. 0.51 0.24 0.12
84 Charleston, W. Va. 1.73 1.48 0.74
85 Des Moines, Iowa 0.79 0.59 0.30
86 Fresno, Calif. 0.07 0.01 0.01
87 Fort Wayne, Ind. 0.91 0.73 0.36
88 Jackson, Miss. 0.01 -0.28 0.05
89 Johnstown, Pa. 2.07 1.25 0.62
90 Lancaster, Pa. 0.77 0.62 0.34
91 Mobile, Ala. 0.64 0.43 0.20
92 Norfolk-Portsmouth/
Newport News-Hampton, Va. 0.42 0.21 0.11
93 Raleigh/Durham, N. C. 0.77 0.6l 0.30
94 heading, Pa. 1.52 1.23 . 0.75
95 Rockford, 111. 1.47 1.34 0.70
96 Sacramento, Calif. 0.02 0.01 0.0
97 South Bend, Ind. 2.05 1.66 0.86
98 Utica-Rome, N. Y. 0.42 0.35 0.25
99 Wichita, Kan. 0.01 -0.07 -0.03
100 York, Pa. 1.08 0.92 0.48
-------
effects on each AQCR. Some AQCRs, for example, Steubenville-
Weirton, Pittsburgh, etc. , will possibly expect a 2 percent or more
increase in the regional unemployment. However, for others, the im-
pacts are negligible; some AQCRs are even better off with a decrease
in employment rate as a result of air pollution control. This could
mean that higher air quality standards for those AQCRs with negligible
effects may be "practical. " The 91 AQCRs under this study can be
classified into four major categories, namjely, better off (decrease in
unemployment rate), negligible (increases under one percent), mod-
erate (increases between one to two percent), and serious (increases
more than two percent).
The geographic distribution of all 91 AQCRs under this classifi-
cation is shown in Figure 6.2. Under APC Strategy 1 (Industry Pays),
most of the AQCRs seriously adversely affected are located in the
heavily industrial north-central (Michigan, Ohio, Indiana, Illinois) and
central-east (Pennsylvania, West Virginia) states. AQCRs located in
the west and south, in general, do not seem to be affected by air pollu-
tion control and some are even better off. Therefore, air pollution
control under Strategy 1 may conceivably lead to a locational redistri-
bution of the economic activity of the nation, by increased growth in
the new metropolitan areas and under the greater economic pressure
on the older heavy industrial areas.
-------
FIGURE 6. I
GEOGRAPHIC DISTRIBUTION OF ECONOMIC EFFECTS UNDER
APC STRATEGY 1 (MEASURED BY CHANGE OF UNEMPLOYMENT RATE)
O Better Off
Negligible
3 Moderate
-------
The geographic distribution of the outputs from Strategy 2 and
Strategy 3 is provided in Figures 6.3 and 6.4, respectively. By com-
paring the outputs for alternative strategies, the economic effects of
different strategies seem to be clear.
Under Strategy 2, the high emission industries, to some degree,
would be able to pass on a portion of control costs to the public as price
increase in the products. Therefore, the AQCRs with heavy concentra-
tions of high emission industries will be in a better condition than under
Strategy 1.
Under Strategy 3, the cost pressures on the high emission indus-
tries are further released by 50 percent of government subsidies.
Therefore, those AQCRs with such industries are improved further
than in Strategy 2. However, price increases and additional taxes (for
pollution subsidies) will have an adverse economic effect on those
AQCRs with large populations (and, hence, regional income and con-
sumption).
The number of AQCRs in each category changes considerably
under different strategies. The percentage distribution between the
four categories is provided below:
-------
FIGURE 6. 3
GEOGRAPHIC DISTRIBUTION OF ECONOMIC EFFECTS UNDER
APC STRATEGY 2 (MEASURED BY CHANGE OF UNEMPLOYMENT RATE)
O Better Off
© Negligible
3 Moderate
-------
FIGURE 6.4
GEOGRAPHIC DISTRIBUTION OF ECONOMIC EFFECTS UNDER
APC STRATEGY 3 MEASURED BY CHANGE OF UNEMPLOYMENT RATE)
O Better Off
ź Negligible
3 Moderate
-------
Categories of Change "of AQCRs Included (%)
Unemployment Rate (%)
in AQCRs Strategy 1 Strategy 2 Strategy 3
1. Better off (increase
in employment) 5. 5% 7. 7% 9. 9%
2. Negligible (0.01% to
1.00%) 67.0% 69.2% 83.5%
3. Moderate (1. 01% to
2.0%) 19.8% 19.8% 3.3%
4. Serious (2. 01% and
over) 7.7% 3.3% 3.3%
Total 100.0% 100.0% 100.0%
Under Strategy 1, 27. 5 percent of AQCRs will have a moderate or
serious economic effect with an increase of more than one percent of
unemployment rate. This figure is reduced to 23 percent under Strate-
gy 2 and further down to 6. 6 percent under Strategy 3. In other words,
regional difference of economic impacts among AQCRs will be much
greater in Strategy 1 and Strategy 2 than Strategy 3, although all three
alternative strategies were under "same" emission standard specified
in the 305(a) Report of 1970.
6.4 Changes in Profits and Personal Income
Unemployment rate, though important, is but one indicator. Con-
sequently, this section explores the patterns of change in two other key
indicators. The first is the decline in profits of manufacturing indus-
tries in the AQCRs --a key indicator of the high emission industries.
The second indicator is the decline in regional personal income.
-------
The changes by region, in the unemployment rate, and percent-
age changes in personal income and profits under each policy, are pre-
sented in Tables 6. 3 through 6. 5. For each column, the median and
quartiles have been identified. The figure beside each number indi-
cates the quartile in which the region falls where (1) indicates least
negatively affected quartile, and (4) indicates most negatively affected
quartile. In addition, a rank column has been placed by each result
column ranking the AQCRs from least negatively affected to most neg-
atively affected.
These tables, singularly and severally, tell an interesting story.
First, for any given strategy, there appears to be rough consistency as
between the quartile rankings of any given AQCR for each of the three
indicators of economic activity selected for analysis. That is, if un-
employment increases a relatively great amount in some region, per-
sonal income and profits decrease by a relatively great amount. There
also appears to be a rough consistency between the ranking of an AQCR
for the changes arrayed for any given policy. It would, indeed, be in-
teresting and perhaps informative exercise to look at the rank correla-
tion coefficients between these various measures.
For all indicators studied here, in fact, median values tend to
decrease as incidence becomes less geographically localized. The
-------
TABLE 6. 3
CHANGES IN UNEMPLOYMENT RATE, PROFITS AND PERSONAL INCOME
IN THE AQCRs UNDER STRATEGY 1
o
00
AQCR
1
2
3
4
5
6
7
8
9
10
11
12
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Increase in
Unemploy-
ment Rate
0. 1741
1.3850
-0. 0034
0. 5851
1.2074
-0. 0380
0.2775
2.0567
1.7359
0.2174
0.9812
1. 0027
0.5293
0.4904
1.4004
1.3947
1.5448
1.5802
0. 1312
0. 0946
0.2510
-0. 0303
0. 0763
0. 7753
0. 0291
0. 5057
0. 5179
0.3992
0.2309
0.2901
Rank
22
72
7
50
70
2
29
86
82
24
65
66
48
43
74
73
77
78
20
18
28
3
15
58
11
45
47
37
26
31
Q
1
4
1
3
4 '
1
2
4
4
2
3
3
3
2
4
4
4
4
1
1
2
1
1
3
1
2
3
2
2
2
% Change
in Profit
-0.5759
-1.8681
-0.0177
-0.7452
-1.5538
-0.0297
-0.3103
-4.3805
-2. 1337
-2.9775
-1.8266
-1.2875
-0.6567
-0.2687
-2. 1073
-1. 1307
-1.4598
-1.0973
-0. 1315
-0.2603
-0.3093
0.0887
-0.4437
-0.7649
-0.2047
-1.4104
-0.8355
-0.7502
-0.3380
-0.2044
Rank
43
80
10
50
77
11
28
89
83
86
79
70
48
25
82
68
75
67
16
24
27
6
33
53
20
73
55
51
29
19
Q
2
4
1
3
4
1
2
4
4
4
4
4
3
2
4
3
4
3
1
2
2
1
2
3
1
4
3
3
2
1
% Change
in Personal
Income
-0.0772
-0.8325
0. 0303
-0.3880
-0.6638
0. 0847
-0. 1329
-1..8569
-1.0087
-0. 1485
-0.8609
-0.7273
-0.2745
-0.3593
-1.3009
-0.8505
-0.8754
-0.9246
-0. 1060
-0.0631
-0. 1560
0.0301
0.0080
-0.4213
-0.0478
-0.3106
-0.6643
-0.4447
-0. 1783
-0.2474
Rank
17
67
5
47
61
2
22
85
74
23
69
65
32
41
82
68
70
71
20
16
24
6
7
49
15
35
62
50
25
30
Q
1
3
1
3
3
1
1
4
4
1
3
3
2
2
4
3
4
4
1
1
2
1
1
3
1
2
3
3
2
-------
TABLE 6.3 (continued)
CHANGES IN UNEMPLOYMENT RATS, PROFITS AND PERSONAL INCOME
IN THE AQCRs UNDER STRATEGY 1
AQCR
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
61
63
64
66
67
68
Increase in
Unemploy-
ment Rate
0.4742
0.0681
0.1195
0.7827
1.9490
0.9080
8.8929
0.4265
-0.0284
0.7163
0.3130
0.6940
. 0. 1757
0.7718
0.4986
0.0726
0.0199
0.8728
0.3488
0. 1643
0.5717
0.2932
0.3017
0.0194
2. 0333
0.2176
1.2150
1.6144
0.0766
1. 1409
Rank
42
12
19
59
84
64
91
40
4
54
34
53
23
57
44
14
10
62
36
21
49
32
33
9
85
25
71
79
16
69
Q
2
1
1
3
4
3
4
2
1
3
2
3
1
3
2
1
1
3
2
1
3
2
2
1
4
2
4
4
1
3
% Change
in Profit
-1.4185
-0.0554
-0.5954
-0. 7164
-3.9978
-1. 0426
-6.7709
-0.2110
0,0983
-0. 8865
-1. 0664
-0.4881
-0.5232
-0.0497
-0.5148
-0.4591
-0.3007
-1.0052
-0. 7613
-0.5383
0.4038
-0.5470
0.0691
0. 1336
-2.2695
-0.5119
-0.5209
-1.5296
-0. 0064
-1. 7794
R.ank
74
14
44
49
88
63
91
21
4
57
65
35
39
13
37
34
26
61
52
41
1
42
7
3
84
36
38
76
9
78
Q
4
1
2
3
4
3
4
1
1
3
3
2
2
1
2
2
2
3
3
2
1
2
1
1
4
2
2
4
1
4
% Change
in Personal
Income
-0.3768
-0.0438
-0. 1233
-0.5055
-2. 1093
-0.5222
-12.0438
-0,3667
0.0673
-0.6629
-0.3216
-0.3869
-0. 1013
-0.9856
-0.3625
-0.3381
-0.0248
-1. 1710
-0.4044
-0. 1807
-0.5441
-0.2882
-0.3472
0. 0679
-1.6683
-0. 1870
-1.0585
-0.9517
-0. 0281
-1. 0803
Rank
44
14
21
52
88
53
91
43
4
60
36
46
19
73
42
37
10
79
48
26
54
33
38
3
84
27
75
72
12
77
Q
2
1
1
3
4
3
4
2
1
3
2
2
1
4
2
2
1
4
3
2
3
2
2
1
4
2
4
4
1
-------
TABLE 6.3 (continued)
CHANGES IN UNEMPLOYMENT RATE, PROFITS AND PERSONAL INCOME
IN THE AQCRs UNDER STRATEGY 1
AQCR
69
70
71
72
73
74
75
76
77
78
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
Increase in
Unemploy-
ment Rate
0.2502
1. 0365
0.0766
1. 8022
0.4490
-0.5312
5.2258
5.2672
0.3257
0.6129
1.6879
0.2815
0. 8404
0.5143
1.7346
0.7975
0.0681
0.9059
-0.0058
2.0708
0.7706
0.6360
0.4220
0.7677
1.5157
1.4734
0. 0178
2.0593
0.4194
-0.0138
1.0832
Rank
27
67
17
83
41
1
89
90
35
51
80
30
61
46
81
60
13
63
6
88
56
52
39
55
76
75
8
87
38
5
68
Q
2
3
1
4
2
1
4
4
2
3
4
2
3
2
4
3
1
3
1
4
3
3
2
3
4
4
1
4
2
1
3
% Change
in Profit
-0.6084
-0.9269
0.0971
-0.9415
-0.2514
0.0042
-3.3248
-4.6192
-0. 1973
-1.0661
-2.0096
-0.3918
-0.5369
-0.9075
-1.3058
-1.079-6'
-0. 1798
-0.8417
-0. 1189
-2.9529
-0.6337
-0.8280
-1. 1985
-0.4098
-1.0319
-0.3757
-0.0404
-1.3659
-0.2200
0. 1466
-0.6439
Rank
45
59
5
60
23
8
87
90
18
64
81
31
40
58
71
66
17
56
15
85
46
54
69
32
62
30
12
72
22
2
47
Q
2
3
1
3
1
1
4
4
1
3
4
2
2
3
4
3
1
3
1
4
2
3
3
2
3
2
1
4
1
1
3
% Change
in Personal
Income
-0.2712
-1. 1647
-0.0430
-1.0764
-0. 3089
0.3802
-2.6546
-3.6113
-0.2157
-0.5510
-1.9618
-0.2080
-0. 5480
-0.5832
-1.2865
-0.6645
-0.0894
-0.6359
-0. 0016
-2.0432
-0.6940
-0.4740
-0.3776
-0.3589
-1. 1798
-0. 7762
-0. 0227
-1.3341
-0.3563
-0.0251
-0.6172
Rank
31
78
13
76
34
1
89
90
29
56
86
28
55
57
81
63
18
59
8
87
64
51
45
40
80
66
9
83
39
11
58
Q
2
4
1
4
2
1
4
4
2
3
4
2
3
3
4
3
1
3
1
4
3
3
2
2
4
3
1
4
2
1
-------
TABLE 6.4
CHANGES IN UNEMPLOYMENT RATE, PROFITS AND PERSONAL INCOME
IN THE AQCRs UNDER STRATEGY 2
AQCR
1
2
3
4
5
6
7
8
9
10
11
12
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Increase in
Unemploy-
ment Rate
o. 1290
1. 1541
0.0233
0.4594
1.0678
0. 0749
0.2380
1.5702
1.5329
0. 1257
0.5983
0.8060
0.4729
0.5811
1.0833
1. 1975
1.1433
1.3923
0. 0804
0.0279
0.2169
-0.0219
0. 0360
0.6666
-0.0148
0.3834
0. 1200
0. 1466
0. 1848
0. 1698
Rank
28
75
10
51
71
18
39
87
84
27
57
68
52
55
72
76
74
80
20
11
36
5
13
62
6
48
26
30
33
3]
Q
2
4
1
3
4 '
1
2
4
4
2
3
3
3
3
4
4
4
4
1
1
2
1
1
3
1
3
2
2
2
2
% Change
in Profit
-0.2086
-0.9241
0.0055
-0.3901
-0.9805
-0.0153
-0. 1558
-2.2961
-1.2837
-0.9358
-0.8366
-0. 7327
-0.3125
-0. 1317
-1.2047
-0.7096
-0.6951
-0.5938
-0.0512
-0.0218
-0. 1541
0.0669
-0. 1453
-0.4332
-0.0160
-0.5753
-0. 1794
-0. 1833
-0. 1732
-0. 1138
Rank
38
77
8
53
79
12
30
88
84
78
74
70
49
25
83
69
68
62
19
16
29
5
28
57
13
61
34
35
32
22
Q
2
4
1
3
4
1
2
4
4
4
4
4
3
2
4
3
3
3
1
1
2
1
2
3
1
3
2
2
2
1
% Change
in Personal
Income
-0.0298
-0.5672
-0.0014
-0.2394
-0.5200
-0.0245
-0.0910
-1.2675
-0.7640
-0.0559
-0.4117
-0.4924
-0.2003
-0.4418
-0.8931
-0.6022
-0.4295
-0.6888
-0.0455
0.0003
-0. 1130
0.0217
0. 0611
-0.2929
-0. 0019
-0. 1643
-0. 1502
-0. 1404
-0. 1192
-0. 1029
Rank
17
71
11
53
70
16
28
87
78
23
65
69
48
67
82
72
66
74
22
10
30
8
7
55
12
41
38
37
31
29
Q
1
4
1
3
4
1
2
4
4
1
3
3
3
3
4
4
3
4
1
1
2
1
1
3
1
2
2
2
2
-------
TABLE 6.4 (continued)
CHANGES IN UNEMPLOYMENT RATE, PROFITS AND PERSONAL INCOME
IN THE AQCRs UNDER STRATEGY 2
AQCR
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
61
63
64
66
67
68
Increase in
Unemploy-
ment Rate
0.2898
0.0360
0. 0446
0.6327
1. 5072
0. 7756
7.3687
0.4463
-0.0363
0.3819
0. 1753
0. 5088
0. 1153
0. 7802
0.3502
-0. 7406
0.0776
0. 7851
0. 1112
0. 1301
0.2502
0. 1138
0. 0492
-0.0132
1. 5642
0.0539
0.9514
1.4548
0. 0497
0.6891
Rank
41
12
14
60
83
65
91
50
4
47
32
54
25
66
45
1
19
67
23
29
40
24
15
7
86
17
70
81
16
63
Q
2
1
1
3
4
3
4
3
1
3
2
3
2
3
2
1
1
3
1
2
2
2
1
1
4
1
4
4
1
3
% Change
in Profit
-0.6123
-0.0198
-0.2052
-0.4063
-2. 1350
-0.5692
-3.0258
-0.0829
0.0735
-0.2759
-0.6231
-0.2200
-0. 1168
-0.0113
-0.2317
-0.2198
-0.0204
-0.8491
-0. 1367
-0. 1122
-0.3681
-0. 1389
0. 1058
0.2249
-1.4259
-0. 1168
-0.3859
-1. 1449
0.0026
-1.0432
Rank
63
14
37
56
87
60
90
20
4
44
64
41
23
11
43
40
15
75
26
21
50
27
3
1
85
24
52
82
9
80
Q
3
1
2
3
4
3
4
1
1
2
3
2
1
1
2
2
1
4
2
1
3
2
1
1
4
2
3
4
1
4
% Change
in Personal
Income
-0. 1557
-0.0020
-0.0346
-0.3308
-1.4813
-0.3728
-8.0880
-0.3922
0.0720
-0.2312
-0. 1271
-0. 1661
-0.0407
-0.2292
-0. 1631
0. 5802
-0.0898
-0.9060
-0. 1265
-0. 1276
-0. 1697
-0.0795
-0.0420
0.0985
-1.0557
-0.0455
-0.7013
-0. 7463
0.0078
0.4522
Rank
39
13
18
57
88
58
91
62
6
52
34
42
19
51
40
1
27
83
33
36
43
26
20
4
85
21
75
76
9
2
Q
2
1
1
3
4
3
4
3
1
3
2
2
1
3
2
1
2
4
2
2
2
2
1
1
4
1
4
4
1
-------
TABLE 6.4 (continued)
CHANGES IN UNEMPLOYMENT RATE, PROFITS AND PERSONAL INCOME
IN THE AQCRs UNDER STRATEGY 2
uo
AQCR
69
70
71
72
73
74
75
76
77
78
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
Increase in
Unemploy-
ment Rate
0.0958
0. 5045
0. 0934
1.5374
0.3534
0.2158
4.6720
4.4876
0.3003
0.3339
1. 1264
0.2170
0.6460
0.2376
1.4824
0.5910
0.0135
0. 7348
-0.2814
1.2532
0.6178
0.4294
0.2096
0.6087
1.2275
1.3387
0. 0070
1.6596
0. 3480
-0. 0697
0.9217
Rank
22
53
21
85
46
35
90
89
42
43
73
37
61
38
82
56
9
64
2
78
59
49
34
58
77
79
8
88
44
3
69
Q
1
3
1
4
2
2
4
4
2
2
4
2 .
3
2
4
3
1
3
1
4
3
3
2
3
4
4
1
4
2
1
3
% Change
in Profit
-0.2110
-0.3738
0. 0431
-0. 5421
-0. 1750
0. 0434
-2.6109
-4. 0735
-0. 1684
-0.6621
-1.0612
-0.2268
-0.3116
-0.3905
-0.8828
-0.8235
-0.0410
-0.6403
-0.0271
-1. 7245
-0.5345
-0.2828
-0.6494
-0.3062
-0.8172
-0.2946
-0.0092
-0.7454
-0. 1939
0. 1070
-0.4029
Rank
39
51
7
59
33
6
89
91
31
67
81
42
48
54
76
73
18
65
17
86
58
45
66
47
72
46
10
71
36
2
55
Q
2
3
1
3
2
1
4
4
2
3
4
2
3
3
4
4
1
3
1
4
3
2
3
3
4
2
1
4
2
1
3
% Change
in Personal
Income
-0.0780
-0.3851
-0.0676
-0. 7500
-0. 1866
-0.3886
-2.0691
-2.4319
-0. 1837
-0.2035
-1.2202
-0. 1276
-0.2961
-0. 1877
-0.9238
-0.3730
-0.0179
-0.4115
0.4219
-0.8606
-0.4570
-0. 1936
-0. 1251
-0.2161
-0. 7900
-0.6081
-0. 0083
-0. 8302
-0.2638
0. 0949
-0.4024
Rank
25
60
24
77
45
61
89
90
44
49
86
35
56
46
84
59
15
64
3
81
68
47
32
50
79
73
14
80
54
5
63
Q
2
3
2
4
2
3
4
4
2
3
4
2
3
2
4
3
1
3
1
4
3
3
2
3
4
4
1
4
3
1
-------
TABLE 6. 5
CHANGES IN UNEMPLOYMENT RATLJ, PROFITS AND PERSONAL INCOME
IN THE AQCRs UNDER STRATEGY 3
AQCR
1
Z
3
4
5
6
7
8
9
10
11
12
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Increase in
Unemploy-
ment Rate
0.0714
0.6910
0.0349
0.2918
0.6206
0. 1270
0.2562
1.0321
0.9080
0.0629
0.3981
0.4717
0. 1463
0.3858
0.6931
0.8577
0.5819
0.9592
0.0176
0.0557
0. 1579
-0.0069
0. 0623
0.3100
0. 0022
0.2009
0. 0796
0. 1430
0. 1381
0.0889
Rank
25
75
18
55
73
35
54
86
84
23
65
68
39
63
76
82
70
85
12
21
42
7
22
59
9
47
27
38
37
29
Q
2
4
1
3
4
2
3
4
4
1
3
3
2
3
4
4
4
4
1
1
2
1
1
3
1
3
2
2
2
2
% Change
in Profit
-0. 1128
-0.5141
-0.0199
-0.2475
-0.5788
-0.0568
-0.0969
-1.4975
-0.7246
-0.4698
-0. 5404
-0.4738
-0. 1349
-0. 1054
-0. 7274
-0.4764
-0.3673
-0.3142
-0.0409
-0.0295
-0. 1054
0.0350
-0. 1046
-0.2271
-0.0245
-0.2936
-0. 1221
-0. 1386
-0. 1347
-0.0850
Rank
34
76
14
54
81
21
27
89
82
73
79
74
39
32
83
75
67
60
19
18
33
4
30
53 -
16
57
35
41
38
25
Q
2
4
1
3
4
1
2
4
4
4
4
4
2
2
4
4
3
3
1
1
2
1
2
3
1
3
2
2
2
2
% Change
in Personal
Income
-0.0225
-0.3900
-0. 0219
-0. 1871
-0.3500
-0.0891
-0. 1632
-0.9354
-0.4859
-0.0273
-0.2978
-0.3930
-0. 0578
-0.3050
-0.6356
-0.4053
-0.2069
-0.5045
0. 0022
-0.0216
-0. 1144
0.0075
0.0188
-0. 1429
-0.0063
-0. 0911
-0. 1010
-0. 1374
-0. 1201
-0.0714
Rank
22
72
21
57
69
37
54
86
80
23
66
73
27
67
84
74
61
81
12
19-
45
10
8
51
13
38
43
50
48
30
Q
1
4
1
3
3
2
3
4
4
1
3
4
2
3
4
4
3
4
1
1
2
1
1
3
1
2
2
3
3
-------
TABLE 6. 5 (continued)
CHANGES IN UNEMPLOYMENT RATE, PROFITS AND PERSONAL INCOME
IN THE AQCRs UNDER STRATEGY 3
AQCR
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
61
63
64
66
67
68
Increase in
Unemploy-
ment Rate
0. 1466
0. 0234
0.0176
0.4475
1. 1020
0.4393
3.9276
0. 1342
-0.0157
0. 1946
0. 1174
0.2215
-0.0503
0.0913
0. 1678
0.2042
-0.0202
0.3947
0.0863
0.0724
0. 1155
0.0661
0.0251
-0. 0596
1.2386
0. 0235
0.6115
0.6943
0. 0177
0.3068
Rank
40
15
11
67
87
66
91
36
6
46
34
51
3
30
43
50
5
64
28
26
32
24
17
2
88
16
72
77
13
58
Q
2
1
I
3
4
3
4
2
1
2
2
3 .
1
2
2
3
1
3
2
2
2
2
1
1
4
1
4
4
1
3
% Change
in Profit
-0. 3040
-0.0272
-0. 1048
-0.2188
-1.4771
-0.3317
-1.6558
-0.0411
0.0339
-0. 1391
-0.3409
-0.0991
0.0312
-0.0057
-0. 1294
-0.3884
0.0197
-0.4246
-0.0651
-0. 1334
-0.3159
-0.0733
0. 0445
0. 1714
-0.9607
-0.0584
-0.2913
-0.5651
0.0026
-0. 5159
Rank
58
17
31
52
88
64
90
20
5
42
66
28
6
11
36
68
8
71
23
37
61
24
3
1
86
22
56
80
9
77
Q
3
1
2
3
4
3
4
1
1
2
3
2
1
1
2
3
1
4
1
2
3
2
1
1
4
1
3
4
1
4
% Change
in Personal
Income
-0.0799
-0.0137
-0.0093
-0.2365
-1.2348
-0.2453
-4.5999
-0..1512
0.0281
-0. 1182
-0.0803
-0.0592
0.0194
-0.1159
-0.0836
0.2040
0.0201
-0.4589
-0. 0979
-0. 0302
-0.0776
-0.0464
-0. 0218
0.0824
-0.9283
-0.0204
-0.5241
. -0.3578
0.0085
-0. 1893
Rank
32
16
15
64
90
65
91
52
5
47
34
28
7
46
35
2
6
79
42
24
31
25
20
3
85
17
82
70
9
59
Q
2
1
1
3
4
3
4
3
1
3
2
2
1
2
2
1
1
4
2
2
2
2
1
1
4
1
4
4
1
-------
TABLE 6.5 (continued)
CHANGES IN UNEMPLOYMENT RATE, PROFITS AND PERSONAL INCOME
IN THE AQCRs UNDER STRATEGY 3
AQCR
69
70
71
72
73
74
75
76
77
78
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
Increase in
Unemploy-
ment Rate
0.0180
0.2036
0. 0473
0.8108
0. 1897
-0. 1650
2.3466
2.2496
0.2262
0. 1709
0.6056
0. 1548
0.3215
0.1169
0. 7427
0.2981
0.0051
0.3630
0.0526
0.6218
0.3399
0.2039
0. 1078
0.2955
0.7491
0.6990
-0. 0035
0.8587
0.2542
-0.0287
0.4812
Rank
14
48
19
81
45
1
90
89
52
44
71
41
60
33
79
57
10
62
20
74
61
49
31
56
80
78
8
83
53
4
69
Q
1
3
1
4
2
1
4
4
3
2
4
2
3
2
4
3
1
3
1
4
3
3
2
3
4
4
1
4
3
1
3
% Change
in Profit
-0.0925
-0. 1702
0. 0246
-0.3190
-0. 1001
-0.0088
-1.3073
-2.0409
-0. 1377
-0.3387
-0.7389
-0. 1544
-0.1590
-0.1931
-0.4437
-0.4128
-0.0205
-0.3195
-0.0136
-0.8602
-0.2903
-0. 1421
-0.3080
-0.1542
-0.5197
-0. 1678
-0.0046
-0.3905
-0. 1595
0.0511
-0.2178
Rank
26
49
7
62
29
12
87
91
40
65
84
45
46
50
72
70
15
63
13
85
55
43
59
44
78
48
10
69
47
2
51
Q
2
3
1
3
2
1
4
4
2
3
4
2
2
3
4
4
1
3
1
4
3
2
3
2
4
3
1
3
3
1
3
% Change
in Personal
Income
-0.0206
-0. 1742
-0.0530
-0.4343
-0. 1212
0.2035
-1.0372
-1.2092
-0. 1780
-0.0963
-0.9520
-0. 1144
-0. 1584
-0.0947
-0.4543
-0. 1877
-0.0069
-0.2023
-0.0800
-0.4271
-0.3757
-0.0852
-0.0700
-0.0977
-0.5721
-0.3477
0. 0034
-0.4403
-0.2361
0.0396
-0.2279
Rank
18
55
26
76
49
1
88
89
56
40
87
44
53
39
78
58
14
60
33
75
71
36
29
41
83
68
11
77
63
4
62
Q
1
3
2
4
3
1
4
4
3
2
4
2
3
2
4
3
1
3
2
4
4
2
2
2
4
3
1
4
3
1
-------
median for each indicator under each of the three policies is presented
below in Table 6.6.
TABLE 6.6
Median Values of the Distributions of the Changes
in Unemployment Rate, Percentage Changes in
Profits and Personal Income in the Three Strategies*
Increase in Unemployment Rate
Percentage Decrease in Profits
of Manufacturing Industries
Percentage Decrease in
Personal Income
Strategy 1
(Industry
Pays)
0.5455
0.6667
0.4286
Strategy 2
(Hands Off)
0.3654
0.2885
0.2035
Strategy 3
(Cost
Sharing)
0.2102
0.2000
0. 1557
*In Strategy 1, high emission industries are assumed to absorb the
entire cost of control. In Strategy 2, high emission industries will
pass on part of the control costs as a 0. 4 percent price increase to the
consumer. In Strategy 3, it is assumed that the Federal government
will raise, by a special tax to subsidize the high emission industries
by an amount equal to 50 percent of the control costs not covered by
the price increase in Strategy 2. In all three strategies, it is further
assumed that electric utilities will pass on their control costs to the
consumers as a price increase.
The trend in the three indicators presented in this table tend to
be consistent with the analysis of changes in unemployment rate pre-
sented in the previous section. The profits of manufacturing industries
show the highest sensitivity among the three variables.
-------
A variety of other economic indicators that reflect the impact of
alternative strategies are printed out in the simulation as shown in
Table 6. 1.
6. 5 Total Net Effects of Three Alternative
Strategies for 91 AQCRs
This section presents some evidence on the effects of the three
air pollution control strategies totalled for 91 AQCRs. A number of
caveats are in order.
First, the APCO Model System, as it now stands, is a cross-
sectional regional model; consequently, its strength lies in its assess-
ment of geographical patterns of change in the AQCRs. Its estimates
of aggregate changes are less reliable. Second, the 91 AQCRs used in
the model cover the greater portion of the economic activity in the
nation, but not all. Finally, the model system does not capture the
dynamic effects and macro effects that only a national model can handle.
Consequently, the interpretation presented below must be viewed with
a dose of caution.
Some evidence on the overall impact of air pollution control and
the effect of incidence policy thereon is presented in Table 6. 7, which
-------
TABLE 6.7
NET EFFECTS OF THREE ALTERNATIVE
STRATEGIES ON 91 AQCRS*
Changes
in Key
Indicators
Profits in
Manufacturing
Industries
Regional
Personal
Income
Employment**
Strategy 1
No. %
-$ 345 -0.47
(million)
-$ 841 -0.21
(million)
-146,000 -0.43
Strategy 2
No. %
-$ 396-0.54
(million)
-$ 1, 123 -0. 28
(million)
-171, 000-0.48
Strategy 3
No. l/0
-$ 429 -0.59
(million)
-$ 1,279 -0. 32
(million)
-185,000 -0.50
*For a detailed description of the three strategies, see page 94
or Chapter 5. : .
**This indicates an increment of base unemployment rate of 3. 45
to 3. 88, 3. 94 and 3. 95, respectively.
-------
presents totals for the 91 AQCRs in the simulation. * The table reflects
a decline in economic activity regardless of the incidence policy pursued.
Under the "Hands Off" strategy, which is in many ways the most plaus-
ible Federal posture, the unemployment rate for the 91 regions in-
creases from 3.45 percent to 3.93 percent, personal income declines
by 0. 28 percent and profits fall by 0. 54 percent.
Somewhat more surprising at first blush are the results as one
moves from a strategy which localizes the incidence of control (Strate-
gy 1 -- Industry Pays) costs to one which spreads the incidence over the
whole of the nation (Strategy 3 -- Cost Sharing). From an aggregative
point of view, Table 6. 7 shows that the total effect of control is mini-
mized if steps are taken to see that the sources incurring air pollution
control costs bear their full incidence. This can be seen by comparing
the percentage changes in these three tables. Note that those in the
declines for Strategy 1 are uniformly smaller than those for Strategy 2,
which are themselves uniformly smaller than those for Strategy 3. One
might tentatively conclude, therefore, that from a purely aggregative
standpoint, policies which spread the incidence of control costs tend to
*The 100 AQCRs for which data was corrected in this study, only
91 AQCRs have been included in the model estimation and simulation.
For the remaining nine AQCRs, some of the economic data was not
available. These AQCRs are: Hartford, Connecticut; Cheyenne,
Wyoming; Anchorage, Alaska; Burlington, Vermont; San Juan, Puerto
Rico; Virgin Islands; Anderson, Indiana; Flint, Michigan; and
Worcester, Massachusetts.
-------
increase the aggregative negative economic effects of air pollution con-
trol. If one were willing to extrapolate these findings to the nation,
what this implies is that cost sharing financed by increases in the per-
sonal income tax, could actually make unemployment worse rather than
better. This result is not unreasonable since an increase in personal
income taxes for the purpose of financing subsidies (which are not gov-
ernment purchases of goods and services) reduces final demand by the
amount of the marginal propensity to consume times the tax increase.
The view of cost sharing which emerges when results for individ-
ual regions are considered is thus quite different from that which
emerges from solely aggregative considerations. While cost sharing
results in overall worsened economic performance, the number of re-
gions severely affected adversely by air pollution control is reduced.
There appears to be a policy trade-off to be made. The policy-maker
must decide whether he wishes to have a few regions experience con-
siderable adverse effects while maintaining good overall economic per-
formance, or slightly reduce overall performance so that a few regions
may be spared severe hardship. It should be remembered that the re-
sults that support this finding presuppose a particular cost sharing
scheme. It is not the only one which could be undertaken.
These results on differential regional impact are suggestive in
many veins, but one in particular deserves comment. Some regions
-------
evidently have an economic capacity to meet more rigorous emission
and air quality standards than do others.
6. 6 Concluding Comments
In conclusion, it must be observed that the trial simulation of
three strategies and the interpretation of results has demonstrated to
a limited degree the operational nature and strategy assessment poten-
tial of the model system. Surely, what has been done here is but
scratching the surface of the utilization potential of the model system.
What has been done, however, clearly points to the view of the regional
economy and environmental system (quality levels) as interrelated sys-
tems and that control activities affect profoundly in many dimensions
economic activity. However, these economy-environment relationships
are more complex than evident in this demonstration. This suggests
the need for further efforts in model utilization, sensitivity testing,
model system refinement as appropriate, and more up to date informa-
tion.
The fundamental question of incidence of economic burden of con-
trol policies and the caveats expressed in this interpretation do cer-
tainly warrant further exploration of the model utilization.
-------
-------
CsJ
Inputs
Model Development
Model JB»
Regional Economic Model System
Outputs
Assessment
Regional Economic
Activity
National Economic
Activity
Regional
Model
Interregional
Feedback
I-O Model and
National Effects
Assessment
Policy Assessment
APCO Policy Variables
Standards
Incentives
Fuels
Research and
development
Government
expenditures
Computerized Simula-
tion Program
Program RMS
Program FEE
. Program IOA
Outputs: Changes in:
. Manufacturing
activity
Power and fuel
consumption
Income
Employment
Government
expenditures
. Other
Interpretation
of Model
Outputs
/
Recommenda-
tions on
Further Refine-
ment and Utili-
zation of Model
-------
7.0 AN ASSESSMENT OF THE APCO
REGIONAL ECONOMIC MODEL SYSTEM:
PROMISES AND PITFALLS
The objective of the effort described in previous chapters was to
develop and demonstrate the use of an operational analytical tool for
assessing the complex and far-reaching economic effects of air pollu-
tion abatement strategies.
First in St. Louis, then in 31 AQCRs and now in 100 AQCRs,
such an operational model amenable to some policy assessment was
developed. In other words, the elements of the economic system
were defined; the air pollution control tools identified in this context;
the equations set up; the relations established which determine the
values of the elements; that is, the economic system described; effects
of certain alternative strategies estimated, and the properties of this
"solution" investigated and assessed. The logical question at this
stage is: How good or useful is the Regional Economic Model System?
An unambiguous answer to this'question is not possible at this
stage. It is true that the use of the Regional Model System as a tool
to assess these strategies has been demonstrated; this is no mean
feat considering the complexity of the economic system modelled.
Further, the simulations showed considerable sensitivity of the model
system and the complex and profound interrelationships between
-------
control policies and various facets of the regional economies. How-
ever, this demonstration has explored only a limited portion of the
utilization potential of the model systems. Judgments on the utiliza-
tion potential of the model system have to be necessarily tentative.
The model system, however, can be assessed from a set of
morŁ general criteria. The three parts of the Regional Model Sys-
tem -- Model Structure, Data and Simulation -- can be evaluated by
the following five criteria.
1. Model Scope: What sorts of control policies
are covered by the model structure? Which
are excluded? How does this policy coverage
of the model affect its relevance as an ongoing
policy assessment tool?
2. Model Structure: Given the policy scope, how
efficiently is the model structured? How
comprehensible is the model structure? Is
the model system a "black box" or a "white
box"?* How extravagant is the model in
terms of data requirements?
3. Model Sensitivity and Accuracy: How readily
manipulable is the model? How sensitive is
it to policy inputs? How accurately is the
model system representing economic reality?
4. Data Limitations: What are the quality and
currency of the data used in the model? What
additional data would improve model scope and
performance?
*A black box model is one whose structure is not clear but de-
fined in terms of inputs or outputs and can be contrasted with "white
box" models whose structure is "transparent. "
-------
5. Efficiency of Simulation Program: How efficiently
is the simulation program assembled? Is the
program extravagant of processing time? How
user oriented is the program?
Model Scope
The range of policies to which the Regional Model System is
designed for was described in Chapter 5 and is quite large. However,
there are two major limitations in scope. First, the Regional Model
System in its present stage of development does not fully incorporate
the interdependency between the economic system and environmental
quality. Since air pollution is a by-product of economic activity, the
level of total emissions is directly related to the level of economic
growth (say GNP), geographic distribution of the growth and the indus-
trial composition of the economy. The present model system provides
only an estimate of economic effects, with given air pollution levels
(before and after the control is instituted).
A feasible approach for addressing this limitation would be to
build into the model system relationships between unit levels of eco-
nomic activity by industrial sector and emission levels and control
costs. In this manner, as economic activity increases, emission
levels and control costs will also increase and the effects of such
emission and cost increases on economic activity can be computed.
Thus, a more interdependent economic environmental regional model
system can be developed.
-------
The second limitation of the model system is its cross-sectional
and static structure. In its current stage, the model system traces
the cross-sectional reallocation of economic activity among the ACQRs
attendant on control strategies pursued in the 305(a) Report. Thus, it
is possible to identify which AQCRs are affected adversely by the con-
trol policies. But the overall effects of control on different industrial
regions and their varying growth rates in the future are not captured
now. A dynamic model, if developed, would assess, for instance, a
"practical timetable" for control of stationary emissions, the minimal
economic dislocations, demand for control devices, etc. , over time
and be a realistic aid to important issues likely to confront policy-
makers in the next several years.
Model Structure
The Regional Economic Model System must be viewed as a well
structured regional model with elaborate sectoring of high emission
industries and careful integration of regional economic concepts. The
sectoring is elaborate enough to be sensitive to a wide range of high
emission industries and electric power industry --no mean achieve-
ment. Given its complexity, the operationability of the model is
worthy of note. Furthermore, the equations all conform to a priori
expectations of theory.
-------
As is to be expected, this model's structure provides room for
improvement in many different ways.
First, the treatment of interregional effects in the model leaves
something to be desired. The use of the locational quotient or regional
market share matrix assumes a fixed regional distribution of economic
activity -- over time an unrealistic assumption.
Second, the nonavailability of a national macro model limits the
estimation of national effects to the structural changes obtainable from
the I-O model. The latter represents only a partial statement of
national effects. This drawback can be addressed only by one of the
alternatives proposed at the end of Chapter 4.
Third, there is one important kind of variable not explicitly
treated in the model -- price variables. The model itself thus makes
no forecasts of changes in prices occasioned by air pollution control,
but rather requires such information as input information. Since one
of the chief concerns about air pollution control is that it will raise
prices, it would be desirable to modify the model so that price effects
of air pollution control are predicted as well.
While the model gives generally good coverage to important vari-
ables of regional economic activity, it is deficient in geographical
scope for national economic effects assessment. Although the model
is large and covers all major urban AQCRs, only about 65 percent of
-------
GNP is accounted for by output from these regions. There is thus a
large portion of economic activity and impacts of air pollution thereon
left unaccounted for by the model.
Fourth^ the method by which the model treats air pollution con-
trol costs and benefits is restrictive. Such information now is treated
as input information into the model. That is, the model user must
translate his policies into control costs and control benefits, feed
these values into the economic model system, and the computer does
the rest. Or to put it more technically, control costs and benefits are
treated as exogenous variables in the system (see Chapter 5). In fact,
control costs and benefits ought to be endogenous in the system since
control costs depend in major measure on the level of economic activ-
ity, which indicators are themselves the key endogenous variables of
the model. In like fashion, damages reductions (or benefits) depend
upon changes in the level of economic activity and control activity.
Treating these variables as exogenous is thus likely to result in faulty
predictions of the net effect of air pollution control of as yet unknown
extent.
The remedy for this particular defect of structure is the same
as proposed earlier to incorporate interdependencies between the eco-
nomy and the environment.
-------
Model Sensitivity and Accuracy
The model system appears to be sensitive to the range of strat-
egies tested. The differences in key economic indicators among the
various AQCRs are encouragingly significant. The same assertion,
however, cannot be made about effects totalled over the AQCRs. In
any event, further sensitivity testing is warranted before any assess-
ment in this regard can be made.
As yet, no thorough test of model accuracy has been conducted
since the data on which model estimation was based are the only data
CONSAD has assembled. For a true model test, it is necessary to
see whether the model accurately predicts data that has not been used
in estimating the model. Insofar as accuracy of the model in explain-
ing sample data itself is concerned, however, the following points
should be made:
By themselves, individual equations of the model
appear to satisfactorily explain model data. The
percentage of variation accounted for in the equa-
tions are, on the .average, about 90 percent.
Comparisons of actual with predicted values for
a few AQCRs suggest that the model does reason-
ably well in predicting values for industrial cities,
but not so well for non-industrial AQCRs like
Washington, D. C.
More testing will be needed, however, to adequately assess the model
system1 s accuracy and to pinpoint the source of any inaccuracies for
elimination.
-------
Data Limitations
This model system uses a large mass of data for 1967 (see
Appendix E). The parameters of the model reflect 1967 situation and
the continued use of the model calls for an updating of these data or the
use of forecast data from other sources such as the OBE data for 50
AQCRs.
Five year average data on control costs have been used from the
305(a) Report of 1970. Year by year data for 1971-75 would have been
more realistic.
Benefit data used in the model is very much in the nature of
"informed judgment". Alternative levels of the benefit estimates or
better estimates would improve the outputs from the model.
Simulation
As yet, little experience has been had with the use of the model
system. It can be said with some confidence that the model simulation
program works, which, for a model of the size and complexity of this
one, is no small achievement. Also, it is efficient in terms of com-
puter processing time. Furthermore, the model and its simulation
program appear to be sufficiently flexible to permit investigation of a
wide range of policy alternatives.
There are, however, two drawbacks in the manner in which the
simulation program is assembled. First, the model, including its
-------
simulation program, is very large. As a consequence, it is difficult
to understand and, therefore, less than simple to manipulate by the
general user for policy evaluation purposes other than those expressly
provided for in the simulation program.
Second, there is a non-linear component for which there is no
"exact solution. " Therefore, an interative algorithm --an extended
Newton's method*-- has been applied. The accuracy of this method is
dependent upon the number of iterations involved in the computer runs
and process of convergence of the system. This aspect calls for a
further testing of this algorithm for errors due to aborted convergence,
Third, at the present stage, only a limited number of strategies
has been tested. It is more plausible that additional strategies and
hence nature of inputs, be applied to improve the use potential of this
simulation program.
*See M. K. Evans and L. R. Klein, The Wharton Econometric
Forecasting Model, University of Pennsylvania, 1967, Chapter IV.
-------
Inputs
Modol
Outputs
Assessment
Model Development
Regional Economic Model System
Regional Economic
Activity-
National Economic
Activity
Regional
Model
Interregional
Feedback
I-O Model and
National Effects
Assessment
Policy Assessment
APCO Policy Variables
Standards
Incentives
Fur-Is
Research and
development
Government
expenditures
Computerized Simula-
tion Program
. Program RMS
. Program FEE
. Program IOA
Outputs: Changes in:
Manufacturing
activity
Power and fuel
consumption
Income
Employment
Government
expenditures
. Other
Interpretation
of Model
Outputs
Model System
Assessments
j/' /
irVcie r Re ij^ie
S ' "^ S
rfient and .LTtili-
.,zatiari of Model
-------
8. 0 REFINEMENT AND UTILIZATION OF
THE APCO ECONOMIC MODEL SYSTEM:
RECOMMENDATIONS
This report covers a great deal of material in fairly minute
detail. Much of the material concerns what has been done over the
past three years. It was clear at the outset that CONSAD's contribu-
tion to the Regional Air Pollution Analysis (RAPA) would be to develop
and demonstrate the use of a workable analytical tool, to assess the
economic effects of various abatement strategies in the AQCRs.
The model system has been developed and demonstrated in a
limited manner for three strategies.
In describing the model system and the uses to which it has been
put to date, CONSAD has attempted to alert the potential user to defi-
ciencies in the model system, as well as to indicate its achievements.
This chapter is different in character since it deals not with what is
past, but rather the potential of the model system for future assess-
ment of economic effects of air pollution control strategies. In a
nutshell, the key question which this chapter addresses itself is
"Where, if anywhere, should APCO go from here?"
In the process of describing the model system as it stands and
in evaluating the potential of a program of model refinement and utili-
zation, a number of ideas have been expressed. It has been suggested,
-------
for example, that (1) the model be employed, if possible, for national
economic effects assessment, (2) that the model be applied to assess-
ing alternative timing of implementation policies, (3) that the model be
used to predict the effects of different strategies for setting secondary
ambient air quality standards and performance standards for new
sources, (4) that the model be applied to the problem of forecasting
pollution emissions, and (5) that the effect of ambient air quality im-
provements on the productivity of labor and capital be investigated.
There is evidently no dearth of interesting and important questions
which could be posed for the model system, some of which the model
is now fully capable of evaluating and others of which the model could
be refined to handle. Indeed, what is needed is an orderly plan of
attack.
Based upon its own evaluation of the issues and possibilities
raised in this report, CONS AD has identified the following four recom-
mendations for maximum utilization of the economic model system.
Recommendation 1: Further Utilization of the
APCO Economic Model System
CONSAD recommends that an in-depth utilization of the APCO
Economic Model System developed in the current year be carried out.
The further utilization of the model system will serve three purposes.
First, it will follow-up on the preliminary analyses of the three
-------
strategies and their variations to assess in-depth the economic effects
of various strategies. Second, it will incorporate data that has become
available since the 1967 economic data used in the model development.
In this manner, the model system will be more honed in. Third, the
utilization of the model system with new data will identify potential
areas for model refinement, sensitivity analysis, and further applica-
tions of the model. Among other possible applications could be poten-
tial opportunity to extend the model to assess the effects of water qual-
ity and solid waste control strategies.
Recommendation 2: Sensitivity Analysis and Refine-
ment of the Model System
With the experience gained from the in-depth utilization of the
model system, CONSAD recommends a sensitivity analysis of the
model system. The model system should be refined and modified
accordingly, to remove any deficiencies insofar as possible. As these
modifications are carried out, the effort should be directed to develop
a broad adaptable operational tool. Therefore, a generalized computer
simulation program should be developed for the refined model system.
Recommendation 3: Expanded National Model Study
The model system, in its current form, is largely "comparative
static" and "regional. " CONSAD recommends that "dynamic" and
"national" aspects of the economic impacts of air pollution control
-------
neeids to be built in. Ideally, this can be accomplished by links between
the cross-sectional regional model and a time-series national model,
with interaction between economic and environment systems to be inte-
grated in the model system.
There are a number of ways, however, in which one might go
about attaining the ideal. CONSAD has identified three alternatives
which it believes to be worthy of further study:
One might try to make do with the Economic Model
System as it is currently structured (or as refined
if Recommendation 2 is pursued), since the model
encompasses approximately 65 percent of the na-
tion's economic activity. This percentage could
be increased by increasing the number of regions
included in the model.
. An existing national economic model (e. g. , Klein-
Goldberger, Michigan) might be modified and
adopted for purposes of strategy assessment.
A new model could be built expressly for the pur-
pose of assessing national effects of national air
pollution control strategies.
-------
APPENDIX A
A MODEL TO ASSESS THE ECONOMIC EFFECTS OF
AIR POLLUTION ABATEMENT IN ST. LOUIS AQCR
-------
Appendix A: A Model to Assess the Economic
Effects of Air Pollution Abatement
in the St. Louis AQCR (Phase I)
The purpose of this appendix is to describe an econometric model
which simulates the basic economic structure of an Air Quality Control
Region (AQCR) for testing the impacts of air pollution control policy in
that AQCR. The model will be specified for the St. Louis Standard
Metropolitan Statistical Area (SMSA). The model will be presented in
its abstract form in order to illustrate the theoretical background and
implications of the entire equation system.
A comparison of the development of regional models with several
advanced models at the national level* reveals that the existing econo-
metric models at a regional level are still few -- their scope much
more limited and the in results less sophisticated. The relative back-
wardness of the regional model stem from three reasons.
First, at the national level, good time series data have been
available for all major economic variables and other variables, such
*For example, Klein, L. R. , "A Postwar Quarterly Model, "
Models of Income Determination, Studies in Income and Wealth, Vol.
28, Princeton, New Jersey, Princeton University Press, 1964; Klein,
L. R. , and Goldberger, A. S. , An Econometric Model of the United
States. 1929-1952, Amsterdam, North-Holland, 1955; Suits, Daniel B. ,
"Forecasting and Analysis with an Econometric Model, " American
Economic Review, Vol. 52, March, 1962, pp. 104-132; and Dusenberry,
J. S. , Fromm, G. , Klein, L. R. , and Kay, E. (eds. ), The Brookings
Quarterly Econometric Model of the United States, 1965.
-------
as capital stock are even published as the result of theoretical inquiry.
But at the regional level -- either state or SMSA -- data are unrelia-
ble and unavailable, especially in a continuous time series form.
Second, econometricians began the development of their models
at the national level. From time to time, different equations and theo-
ries have been tested with the same base of data in a well-defined econ-
omy, say the United States. By repetition of tests and the accumula-
tion of experience, models calibrated to good theoretical structures
are now in sight. On the other hand, regional models always deal with
different geographic units. Beyond the uniqueness of data, suggested
by location theory, geographic and cultural environment and socio-
economic structures may differ from region to region.
Third, theoretically speaking, macro-economic models are usu-
ally based on well-established economic theory in their formulation.
However, at the regional level, economic base theory, location theory,
gravity concepts, migration theory, etc. (especially the concept of
"distance" emphasized by regional scientists for some time), are much
harder to integrate into an overall hierarchical system.
-------
In spite of such shortcomings, some notable regional models
have been recently estimated. For example, Bell estimated a Mass-
achusetts model. An Alaskan model by Tuck, a Puerto Rico model
by Lakshmanan and Lo, a Northeast Corridor model by Crow, a
Michigan model by Suits, a Philadelphia SMSA model by Glickman,
and a Hyogen-ken model in Japan by Kaneko can be indicated. * Klein**
recently suggested a general strategy in building regional econometric
models to link with national models.
*See Frederick W. Bell, "An Econometric Forecasting Model
for a Region, " Journal of Regional Science. Vol. 7, No. 2, 1967;
B. H. Tuck, An Aggregate Income Model of a Semi-Autonomous
Alaskan Economy, Anchorage, Federal Field Development Committee
for Development Planning, 1967; T. R. Lakshmanan and Fu-chen Lo,
A Regional Growth Model of Puerto Rico: An Analysis of Municipio
Growth Patterns and Public Investments. Pittsburgh, Pa. , CONSAD
Research Corporation, September, 1970; Robert Crow, "Econometric
Model of the Northeast Corridor, " (mimeograph) MATHEMATICA,
October, 1967; Daniel B. Suits, "Econometric Model of Michigan, "
(mimeograph), Research Seminar in Quantitative Economics of the
University of Michigan; Norman J. Glickman, "An Annual Econometric
Model of the Philadelphia SMSA, 1949-1966, " (mimeograph), Ph. D.
Dissertation, Department of Economics, University of Pennsylvania,
November, 1968; Yukio Kaneka, "An Econometric Approach to Annual
Forecast on Regional Economy by Local Government, " Paper and Pro-
ceedings, The Second Far East Conference of Regional Science Associ-
ation, 1965, University of Tokyo Press, Tokyo, 1967; pp. 119-144;
**Klein, L. R. , "Econometric Analysis of the Tax Cut of 1964, ''
(mimeograph), Department of Economics, University of Pennsylvania,
1967.
-------
For the present study, the following characteristics in this model
may be different from national models:
national influence upon local economy,
. migration patterns,
interregional commodity flows,
. interindustrial structure, and
. function of local government.
The details are presented formally below.
-------
Notation
*X = gross regional output
*C = regional consumption
*I = regional investment
*E = regional export
*M = regional import
*G = local government expenditures
F
G = federal government expenditures
p^. = local consumption price index
O
*Y = regional capital stock
r = national interest rate
II ~ total rcgioucu profit or property income
T = local direct taxes
*T = local indirect taxes
Łj
F
*T = taxes paid to federal government by the region
*T = transfer payments
D = depreciation on regional capital stock
Q. = regional gross output by industry i (i = 1, . . . , n)
N
Q. = national gross output by industry i
p. = regional price of output i
N
p. - national price of output i
-------
11. = profit level of regional industry i
*E. = exports by regional industry i
GNP = gross national product
O. = domestic final demand for regional output i
a.. = input-output coefficient of local economy
W = wage bill
P = regional population
U = regional unemployment
*N = regional total employment
*N. = employment by regional industry i
w. = wage rate of regional industry i
*L = regional labor force
M
*P = regional net migration
p
N = regional female employment
N
w = national wage rate
t = time period
-------
The Model Framework
The gross regional product can be first identified as an identity:
(1) X = C+I + E-M + GL + GF
Three of the variables on the right-hand side of this identity -- con-
sumption, investment and export -- always have been emphasized in
other regional growth models. These three variables determine the
regional multiplier effect on regional growth through the Keynesian
type multiplier-acceleration principle or regional export base theory.
The government sector plays an important role in the regional econ-
omy and is, therefore, included in the regional model. It is classified
into local and Federal components.
The consumption and investment function can be formulated as:
(2) C- = _
(3) I = f3 (X, Kt_lf r, n)
where the consumption function has been presented in real term as func-
tion of disposable income.
The regional disposable income identity is:
(4) Y = X - T^ - T^ - TF + TR - D,
T T -r-»
that is, gross regional product after tax (Tj , To , T ) and depreciation
(D) of existing capital stock plus transfer payments (T ). The invest-
ment function may be disaggregated into manufactures and non-
-------
manufactures or even according to the sectoral structure. However,
the general form of equation (3) should do.
It has been indicated that for a regional model, the export sector
usually plays an important role, especially for a relatively small re-
gional unit such as the St. Louis SMSA. Trade with the "rest-of-the-
world" is the key to economic growth. Regional export may be strongly
related with metropolitan's interindustrial structure. Thomas sen, *
Bell, Glickman, and Klein** link exports also with Gross National
Product (GNP). However, it is important that the export sector be
disaggregated, and that sectoral exports should be taken as a function
of national sectoral output and of comparative sectoral prices between
the region and the "rest-of-the-world. " The reason is that the inter-
industrial structure of a large metropolitan area may be quite different
from that at the national level. As suggested by location theory, re-
gional resource endowments and other socio-economic characteristics
determine the industrial structure of a local economic unit. Therefore,
the use of a GNP trend is a less desirable indicator of external market
demand than national outputs by industry. In general, regional sectoral
output (Qi) is a function of local interindustrial demand (Qi, j=l, . . . , n),
*See H. Thomassen, "A Growth Model for a State, " Southern
Economic Journal, No. 24, 1957, pp. 123-139-
**Frederick W. Bell, Norman J. Glickman, and L. R. Klein,
op. cit.
-------
national sectoral output (Q ), comparative price ( ), profit (17;), and
pN
regional output (x).
(5) Qt =f5 (Qif...,Qn, Q^, ~, Hi, X) i = 1 n
PiN
However, such a formulation may face a serious multicollinearity
problem upon estimation. A possible solution to this problem is to
separate (5) into two equations, namely, export demand and domestic
demand. Noting that export data is unavailable, residual exports of
the i industry (Ej) can be defined as:
A n
(6) Ei = QJ - 2 ajj QJ - Oi i = 1, . . . , n
where O^ is domestic final demand of industry i. Equation (6) may be
obtained from an output-input table, a technique that has been applied
in the recent Brookings' model.* For these export-oriented industries,
the residual export function will be:
<7> *i = /7 (Q* -V n.) 1= i, . . . , k, k+i, . . . m
pi
where Ihe first k industries arc high emission sources.
However, since there is some industry (i - k+1, . . . , n) which
has a small role in export, we can aggregate into one equation:
*Dusenberry, J. S. , e_t aL , op. cit.
-------
n ~
(8) Z Ei = ER
(9) ER = fg (GNP,
Imports may be divided into consumption goods (M^) and indus
*
trial demand (Mj).
(10) M = Mc + Mj
(11) MC = fn (Y, )
PC
(12) MI = f 12 (x, ^)
Equation (12) can be disaggregated according to export sectors,
or equations (10), (11) and (12) can be aggregated into one equation:
(10') M = f (X, ~).
The ability to estimate the import function is highly dependent upon
availability of data and relations with other equations.
The government sector plays an important role in the regional
economy, and must therefore be included in the regional model.
In most of the national models, government expenditures have
been treated as exogenous. * Klein has strongly suggested to make
*The Brookings Model has allowed for endogeneous treatment.
See Dusenberry, J. S. , eŁ aL , op. cit. ; and Ando, A. , Brown, E.G. ,
and Adams, Jr., E. W. , "Government Revenue and Expenditures. "
-------
them endogeneous at the regional level.* After a small modification of
his proposal, we have:
(13) T^ = fn (W, H)
(14) T2 = f14 (x)
(15) TF = i
315 (w, n)
(16) TR - f (U, P)
(17) GL = f (T^+ T^ , P)
*l J. d
where direct local taxation (Tj ) and Federal tax (T^) are functions of
wage bill and profit; indirect local tax (Tjf) is a function of regional
output, while transfer payments (T ) is a function of unemployment
and population. Finally, local government expenditures (GL|) is a func-
tion of local taxation and population.
Through the multiplier effect, the mix of expenditures from pri-
vate and public sectors (effective demand) determines gross regional
product; by the same token, level of gross regional product determines
"Klein, L. R. , op. cit^
-------
the regional employment levels. From our last experience in the Eco-
nomic Development Administration (EDA) labor force model, * the
labor market behavior can be given by the following equations:
\A TT
L = f!9 (ZNi' P» P * . . . .)
(20) PM= -f fpM W , N
J*>/\L *^ i """ f " '- 1 i
(21) U = L - 2N.
i 1
Employment is a function of output, wage rate and capi.tal stock;
and labor force is determined by employment, population (P), migra-
tion (p) ancj female worker. While regional migrations are deter-
mined by past figures (P. ,), wage difference (iTTivf) and employment-
t"" 1 VV
N
labor force (j^t-l' Finally, the differences between labor force and
employment determines regional unemployment.
*CONSAD Research Corporation, A Study of the Effects of Public
Investment. Pittsburgh, Pa. , 1968.
-------
The St. Louis Model
Except for the income-determination block, the model is recur-
sive in nature. GNP is expressed in terms of national income by sec-
tors (two-digit SIC in manufacturing industries) and, as external mar-
ket demand, determines the level of exports from the St. Louis SMSA.
The exports then determine the output levels of St. Louis manufactur-
ing sectors and, hence, their import demands. By the production
functions, once output level is given, capital share (profit) can be
obtained (since the Cobb-Douglas production function is assumed).
Investments are a function of profit and previous capital stocks. After
adjustment for depreciation, capital stock is given by the following
accounting relationship:
Kfc = Kt_1 + It - dKt
where I and K are investment and capital, respectively, and ci is depre-
ciation rate. Given output level and capital stock, the employment level
also can be obtained through the production function.
On the other hand, given exports, imports, investment, and gov-
ernment expenditures (the latter is a function of tax revenue), regional
income and consumption expenditures are determined simultaneously.
As to income determination, two types of theories are emphasized in
the Keynesian type of model -- the multiplier-acceleration principle
which is based on the propensity to consume, and the economic-base
-------
Finally, non-manufacturing employment level is a function of
non-manufacturing income. Labor force is then determined by total
employment, national unemployment rate, and migration. The differ-
ence between labor force and total employment determines local un-
employment. Because the electric-power industry has been consid-
ered a major pollutant in the St. Louis SMSA, electric-power demand
functions have also been estimated.
The disaggregation of manufacturing industries into two-digit
SIC levels requires further explanation. There were three major rea-
sons for disaggregation: first, inter industrial structur at the regional
level may be quite different from that at the national level. Second,
the present model is designed to measure the economic impacts of air
pollution control, and it is more realistic to observe the impact for
each major manufacturing industry at the two-digit SIC level than to
do so for the manufacturing industry as a whole. Third, measurement
of exports, as emphasized by economic-base theory, requires sectoral
disaggregation.
However, in the St. Louis economy, because certain two-digit
manufacturing industries either are too small in scale or lack suffi-
cient data, only 11 two-digit SIC industries were so isolated; and the
manufacturing industries other than these 11 have been aggregated as
"other manufacturing industry.." The final 12 groups are as follows:
-------
. SIC 20 - Food and kindred products,
. SIC 26 - Paper and allied products,
SIC 27 - Printing and publishing,
SIC 28 - Chemicals and alliec products,
. SIC 29 - Petroleum and coal products,
. SIC 32 - Stone, clay and glass products,
. SIC 33 - Primary metal industries,
SIC 34 - Fabricated metal products,
SIC 35 - Machinery, except electrical
. SIC 36 - Electrical machinery,
. SIC 37 - Transportation equipment, and
. Other manufacturing - Including tobacco products
(SIC 21), textile mill products (SIC 22), apparel
and related products (SIC 23), lumber and wood
products (SIC 24), furniture and fixtures (SIC
25), rubber and plastics (SIC 30), leather
products (SIC 31), instruments and related
products (SIC 38), and miscellaneous .-manufac-
turing (SIC 39).
The Variables of the St. Louis Model
Y = national income by sector i ($ million) at time t
E , = export by industry i ($ million) at time t
V = value added by industry i ($ million) at time t
-------
M = import by industry i ($ million) at time t
it
N = employment by industry i (1000 employees) at time t
K. = cap:.tal stock of industry i (million dollars) at time t
I. = investment by industry i ($ million) at time t
n. = capital share of value added or profit by industry i ($ million) »t time t
Q = electric power demand by industry i (1000 kilowatt hours) at time t
For the above variables:
i = 20, 26, 27, 28, 29, 32, 33, 34, 35, 36, 37, and other manufactures.
(These SIC numbers identify the 12 manufacturing sectors of the model)
P = industrial commodity price index (1957-59 = 1.00)
T = local tax revenue ($ million) at time t
G = local government expenditure ($ million) at time t
C = consumption ($ million) at time t
Y* (= C + 21. + Z E. - SM. + G) = regional expenditure ($ million) at time t
Y = regional income ($ million) at time t
N = employment by industries other than manufacturing industries
(100 employees) at time t
Nfc (= Z N. + ~N) = total employment
L = total labor force
N
u = national unemployment rate
U = unemployment
-------
u. = local unemployment rate
Q = electric power demand for residential use at time t (1000 kilowatt hrs)
Q = electric power demand for non-manufacturing industries at time t
* (1000 kilowatt hrs. )
t = time period (1954 = 0, 1955 = 1, . . . , 1966 = 12)
w. = wage per man-year by industry i (1000 dollars)
-------
Estimation of the Model
The
(1)
general form
Ei = aj + bj
of the export
YNit
function will be:
The results of estimation are:
Equation
Number
(1.1)
(1.2)
(1.3)
(1.4)
(1.5)
(1.6)
(1.7)
(1.8)
(1.9)
(1.10)
Industry
Code
SIC 20
SIC 26
SIC 27
SIC 28
SIC 29
SIC 32
SIC 33
SIC 34
SIC 35
SIC 36
a.
i
244. 585
(45. 371)
27.688
(4. 163)
10. 998
(7. 128)
- 4.462
(22. 090)
-344.071
(86. 110)
21. 522
(10. 528)
-144. 539
(83.865)
55.472
(12.206)
93. 853
(7.792)
141. 997
(18.689)
b. R2
1
. 0284 . 832
(.000366)
.00663 .830
(.000861)
.0152 .947
(.00104)
.0664 .986
(. 00230)
.124 .789
(.0183)
.0151 .791
(. 00222)
.0435 .750
(.00716)
.0155 .915
(. 00136)
.0105 .965
(. 000574)
.00539 .435
(.00169)
Durbin-Watson
Statistic
1. 713
1. 697
. 534
2. 295
2.55
.772
2.218
2. 064
2. 105
1.079
-------
(continued)
Equation Industry
Number Code
(1. 11) SIC 37
(1.12) Other
Manu.
All Manufacturing Ind.
a.
i
-468.752
(88. 511)
230. 314
(19. 383)
-176. 337
(181. 595)
2 Durbin-Watson
i Statistic
.13Q .940
(.00945)
0.05181 .747
(.00053)
29.527 . 975
(1.351)
1. 812
2. 517
1.287
Manufacturing (or base) industries export supply function:
(2)
V4 =
This is a set of identities expressing the proportional relationships of
exports to value-added in corresponding manufacturing industries; aj's
have been obtained from the St. Louis input-output table. *
Equation
Number
(2.1)
(2.2)
(2.3)
(2.4)
(2.5)
(2.6)
#Liu, Ben-chieh,
Output Study of the St.
Industry
Code
SIC 20
SIC 26
SIC 27
SIC 28
SIC 29
SIC 32
Interindustrial Structure
a.
i
.639
1. 143
1.143
. 553
. 553
1. 320
Analysis: An Input-
Louis Region, 1967, St. Louis Regional Indus -
trial Development Corporation, December, 1968.
-------
(continued)
Equation
Number
(2.7)
(2.8)
(2.9)
(2.10)
(2.11)
(2.12)
Industry
Code
SIC 33
SIC 34
SIC 35
SIC 36
SIC 37
Other
Manu.
a
i
.688
.972
.781
.708
. 809
.949
When the model is to be used to simulate the impact of air pollu-
tion control costs upon the St. Louis economy, value-added can be de-
rived in terms of both (a) export demands and (b) the ratio of the level
of capital stock in the presence of air pollution control costs to the
level of capital stock that would have occurred without such control
costs:
Import demand function:
(3) Mit - aj Vit
The levels of manufacturing industry output determine the imports of
raw materials and intermediate goods from other regions. Import co-
efficients (a^) also were obtained from the St. Louis input-output table
as was done in the previous formulae.
-------
Equation
Number
(3.1)
(3.2)
(3.3)
(3.4)
(3,5)
(3.6)
(3.7)
(3.8)
(3.9)
(3.10)
(3.11)
(3.12)
Industry
Code
SIC 20
SIC 26
SIC 27
SIC 28
SIC 29
SIC 32
SIC 33
SIC 34
SIC 35
SIC 36
SIC 37
Other
Manu.
a.
i
.661
.285
.285
,907
. 907
.329
.605
.391
. 342
. 548
1.567
.501
Production function with price index adjustment factor:
= A.
where P. has been calculated as the geometric mean of the annual
St. Louis labor share.
-------
Equation
Number
(4.1)
(4.2)
(4.3)
(4.4)
(4.5)
(4.6)
(4.7)
(4.8)
(4.9)
(4.10)
(4.11)
(4.12)
Industrial
Code
SIC 20
SIC 26
SIC 27
SIC 28
SIC 29
SIC 32
SIC 33
SIC 34
SIC 35
SIC 36
SIC 37
Other
Manu.
All Manufacturing
Industry
A.
i
3.257
(. 02073)
2.766
(.0177)
4.637
(. 0282)
2. 044
(. 0174)
1.439
(.121)
2. 605
(.0188)
3. 090
(.0658)
4.473
(.0278)
4.259
(.0278)
2.234
(. 0422)
4.241
(. 0469)
4.269
(.0368)
3. 554
(.0217)
\
. 0285
(. 00293)
.0225
(. 00250)
. 0274
(.00399)
.0467
(.00247)
.0309
(.01790)
. 0251
(. 00266)
. 0247
(.009305)
. 0284
(.00274)
.0213
(. 00393)
-.0156
(. 00596)
. 04904
(. 00663)
. 0186
(. 00623)
.0360
(. 00366)
\
.450
. 581
.617
.319
.316
.440
.580
. 558
. 577
. 586
. 501
.848
.528
(1-P.)
.550
. 419
. 383
.681
. 684
. 560
.420
.412
.423
.414
.499
. 152
.472
R2
.711
. 821
. 914
. 974
. 159
. 908
. 841
. 906
.856
. 328
. 957
. 560
.969
D-W
Stat.
1.629
.955
.847
2. 120
. 777
1.0818
1.735
1. 178
.829
1.228
2. 338
2.478
.942
-------
Capital share (profit) relation:
(5) nit = (i -TI) vit
Given the Cobb-Douglas production function, capital share or profit in
the broad sense can be derived and the coefficients can be obtained as
in the preceding table for the production function. Altogether, there
are 12 equations (5. 1) through (5. 12), comparable to those in 4. 1.
through 4. 12.
In addition, to consider the effect of air pollution control costs
(C) upon industry investment, a modification of the equation(s) is re-
quired:
(5') n-t = nt - ci
Investment function:
The investment functions presented here differ more from indus-
try to industry than do the other equations. This is so, first, because
investment functions differ from model to model because there are dif-
ferent theories behind each model (usually involving a lag structure*)
and, second, because investment behavior differs from industry to in-
dustry and tends to be non-linear. Therefore, the results have been
presented individually.
*For example, see Shirley Almon, "The Distributed Lag Between
Capital Appropriations and Expenditures, " Econometrica, 1965, pp.
178-196.
-------
(6.1)
(6.2)
where
(6.3)
where
(6.4)
(6.5)
(6.6)
(6.7)
(6.8)
(6.9)
(6.10)
where
(6.11)
(6.12)
I- = 73. 865 + . 261FI- - . 421 K?
"
(2.031) (3.27) (2.38)
I_, = 1.250 + 3. 861 6 + . 0648IT+ . 321 t
Zbt t t
(2.768) (.811) (.104) (.0763)
6=1, when t = 7; otherwise 6 = 0.
I2?t = 11.672 + .0983II27t - .235K2 + 7.9276
(14.910) (.321) (.562) (3,911)
6 = 1, when t = 8, 9, 10; otherwise 6 = Q
I2gt = 10.222 + .114n-.0394K28t_1
(.0849) (1.328) (.086)
I29 = 51.014 .29in29t-.236K29t_1
(1.04) (1.78) (1.24)
I32t = 174. 52+ .0486 II - 1.424 K32t_j
(2.268) (.417) (2.348)
I33t= 146.13 + .347n33t-.76733t_1 + 9.788t
(1.22) (2.84) (1.33) (1.755)
I34t = -3.950 + .l64II34t
(2.794) (.0359)
I = 29.697 + . 0829FI - . 369K ,, + 5. 301 6
.3 3 1 J C* C .3 3 v
(26.549) (.132) (.236) (2.165)
+ 1. 317t where 6=1, when t = 4, 5,
(. 889) 6, 7; otherwise 6 = 0
I = -.553 + .0750FI + 4. 497 6 + . 169T
3ot t t
(1.838) (.0311) (1.0527) (.0790)
6 = 1, when t = 8; otherwise 6=0
I,_ = 66. 549 + . I66n__ - . 4tf6
j ( t J 1 1 J ( C i
(2.05) (3.40) (2.01)
I = -. 0919 + . 0635R . + .685t
other other
(11.111) (.256) (.342)
S I. = 618. 82 - .424 ZK. ,+.307Sn.
R =
D-W =
R2 ,
D-W =
R2 .
"
R2 .
D-W =
R2 .
D-W =
R2 =
D-W =
R2 ,
D-W =
R2 =
D-W =
2
R =
R2 =
D-W =
2
R =
D-W =
R2 ,
D-W =
R2 =
.443
2. 131
. 844
2. 131
.637
'
.440
2. 013
.239
1.62
. 353
2.615
.685
2. 425
.622
1. 854
. 709
2? An
. i*\j\j
. 709
1. 783
. 589
1. 911
. 500
2. 89
. 906
i - i = 2 89
(457. 33) (.275) (.007)
-------
Capital stock identity:
(7) Kit^K^t.j +Iit-diKit
where dj is depreciation rate.
Equation
Number
(7-1)
(7.2)
(7.3)
(7.4)
(7.5)
(7.6)
(7.7)
(7.8)
(7.9)
(7.10)
(7.H)
(7.12)
Industry
Code
SIC 20
SIC 26
SIC 27
SIC 28
SIC 29
SIC 32
SIC 33
SIC 34
SIC 35
SIC 36
SIC 37
Other
Manu.
Depreciation
rate d.
i
.0674
. 0534
. 0686
.06)69
.0614
. 0646
. 0560
. 0700
.0722
.0738
.0784
.0751
Government expenditure equation:
(8) Gt = 27. 517 + 0.951 Tfc
(15.978) (0.0529)
Regional expenditures:
(9) YŁc = Ct + 2 Iit + 2 Eit - S Mit + G
i i i
IT = 0.973
D-W d = 2. 173
-------
Consumption function:
(10) Ct = 287.524 + . 592YŁ R2 = . 990
(467.215) (.0797) D-W d = . 882
(10) has been estimated by two-stage least squared method.
Relation between regional expenditure Y* and regional incon-e Y:
(11) Yt = -3667.206 + 1.565^* R2 = . 989
(310.85) (.053) D-W d = 2. 06
Employment by industries other than manufacturing industries:
(12) l*t= 384.798 + . 0821 (Yt - S V.^ R2 = . 793
(37.88) (.0146) { l D-W d = 2. 19
Total employment:
(13) N = S N.+ N.
t . it t
Labor force function:
= 68. 01 + . . .
(63.74) (.0539) (3. 427) m D-W d= 1.18
(14) L = 68. 01 + .9384 N+ 5.366 u R2 = .995
Regional unemployment:
(15) US L-N
Regional unemployment rate:
t
(16) u, - Uf X 100
Electric power demand functions for manufacturing industries:
(17) Q. = a. + b. V. + c. t
it i i it i
-------
Equation
Number
(17.1)
(17.2)
(17.3)
(17.4)
(17.5)
(17.6)
(17.7)
(17.8)
(17.9)
(17.10)
(17.11)
(17.12)
Industry
Code
SIC 20
SIC 26
SIC 27
SIC 28
SIC 29
SIC 32
SIC 33
SIC 34
SIC 35
SIC 36
SIC 37
Other
Manu.
All Manufacturing
Industry
a. b. c.
i i i
-39. 302
(71.82)
. 5970
(.2054)
8. 171
(1.974)
-6. 706 + 44. 12d + . 2978V_,i.
tot
(12.46) (1.95) .204)
+ 5.758t
(.449)
where d = 1 if t < 6; d = 0
if t > 6
-11. 361
(1.586)
79. 910
(.919)
120. 806
(10.103)
160. 527
(.837)
-65.049
(.873)
-58.124
(1. 814)
24.652
(2.955)
19.996
(.692)
24.206
(1.10)
-33. 100
(287.8)
-633. 620
(193.74)
.4063
(.7791)
.3252
(.0495)
.2672
(.1935)
1.0533
(.0558)
1. 3238
(.3553)
.8176
(.4963)
. 1070
(.2430)
.7681
(.4305)
.4236
(.3925)
.2178
(. 0966)
1. 1356
(.861)
38.992
(2.781)
21. 031
(15.450)
7.424
(1. 036)
2. 505
(.296)
-1.450
(1.750)
8.095
(1.180)
1.665
(8. 516)
13.410
(13.693
R2
.937
. 984
. 857
. 914
. 975
. 450
.787
.703
.329
. 623
.972
. 555
.993
f .
D-W
d
1. 875
2.41
1.259
. 906
1. 944
.806
1. 823
.521
1. 475
2.296
1.925
1.443
-------
Electric power demand function for residential users:
-1548.94 + . 9485
(201.75) (.538)
(18) C- = -1548. 94 + .9485 C R2 = .972
ct t
D-Wd = . 566
Electric power demand function of the industries other than man-
ufacturing industries:
(19) a = 479. 98 + .4552 (Yt - SV.J R2 = .895
t t . iv
i
(129.36) (.0516) D-W d = .1.81
Wage function: Collective bargaining power should also be in-
cluded in the model for wage determination;
(20) w.^ = w. a.
it 10 i
where w. is wage (per man-year) at 1954 by industry i.
Equation
Number
(20.1)
(20.2)
(20.3)
(20. 4)
(20. 5)
(20.6)
(20.7)
(20.8)
(20.9)
Industry
Code
SIC 20
SIC 26
SIC 27
SIC 28
SIC 29
SIC 32
SIC 33
SIC 34
SIC 35
w.
4. 505
3.918
4.744
4.641
5. 181
4.062
4.509
4.211
4.410
a.
i
1.0413
1.0407
1.0343
1.0422
1.0409
1.0461
1.0474
1.0430
1.0419
R2
.996
.991
.990
.998
.993
.986
. 988
.995
.998
-------
(continued)
Equation Industry w.
Number Code ($10$)) i R
(20.10) SIC 36 4.305 1.0323 .994
(20.11) SIC 37 4.662 1.0519 .995
(20.12) Other 3.283 1.0562 .986
Manu.
-------
FIGURE A. 1
ACTUAL VERSUS PREDICTED REGIONAL
EXPORTS BY MAJOR TWO-DIGIT MANUFACTURING
INDUSTRIES IN ST. LOUIS SMS A
-------
Figure A. 1
ST. IjOUIS SMSA
Export by Industry 20
ST. LOUIS SMSA
Export by Industry 28
1954 '55 'it, '57 '5'8 '59 '60 '6'l '62 '63 '64 '65 '66
1 -( 1 ( 1 1 r 1 I 1 1 1 1
1954 '55 '56 '57 '58 '59 '60 '61 '61 '63 '64 '65 '66
ST. LOUIS SMSA
Export by Industry 26
Predicted/
(million)
400 T
ST. LOUIS SMSA
Export for Industry 29
1954 '55 '56 '57 '58 '59 '60 '61 '62 '63 '64 '65 '66
1954 '55 '56 '57 '58 '59 '60 '61 '62 '63 '64 '65 '66
ST. LOUIS SMSA
Export for Industry 27
1954 '55 '56 '^7 '58 '59 '60 '61 '62 '63 '64 '65 '66
ST. LOUIS SMSA
Export for Industry 32
Predicted
T 1 1 1 1 1 1 1
1954 '55 '56 '57 '58 '59 '60 '61 '62 '61 '*>4 'dS '66
-------
Figure A. 1 (continued)
ST. LOUIS SMSA
Export by Industry 36
1954 '55 '56 '51
61 '62 '63 '64 '65 '66
ST. LOUIS SMSA
Export by Industry 34
(milUi
1300-
1200
1100-
1000-
900-
800-
700-
600-
500 -
400-
300-
ST. LOUIS SMSA
Export by Industry 37
Actual
1954 '55 '56 '57 '58 '59
'64 '65 '66
1 1 1 1 1 1 1 1 1 1 1 ' 1
1954 '55 '56 '57 '58 '59 '60 '61 '62 '63 '64 '65 '66
ST. LOUIS SMSA
Export by Industry 35
(million)
425 '
ST. LOUIS SMSA
Export by Other Industry
1954 '55 '56 '57 '5« '59 'bO '41
-------
APPENDIX B
31 AQCR MODEL
-------
Appendix B: SlAQCRModel
Introduction
In the first year of model development, CONSAD developed a
Regional Econometric Model of the St. Louis metropolitan region,
based on the time series data. In the second year program plan,
the aim has been to focus on the development of a cross-sectional re-
gional econometric model which includes 31 AQCRs across the country.
In this model, the roles of Keynsian theory and regional export base
theory will remain as the basic framework as before. However, the
entire equation system has been reformulated in order to integrate the
cross-sectional structure into the model.
At the regional level, cross-sectional models are not uncommon
for the single equation model (as are some partial equilibrium-type
models), partially due to the fact that time series data are usually un-
available in continuous time series form. However, an economy-wide
cross-section model, at this stage, is more a conceptual constrict
than a well-developed operational model. * This is partially due to the
*Some Keynsian type cross-sectional models have been sug-
gested in Carl F. Christ, Econometric Models and Methods, New York,
1966, and a cross-sectional income model has been estimated in
J. M. Mattila and W. R. Thompson, "Toward an Econometric Model
of Urban Economic Development, " (Appendix), Issues in Urban Eco-
nomics, edited by H. S. Perloff and L. Wingo, Jr. , Resources for
the Future, 1968.
-------
fact that growth models at the national level always deal with time
series data, for a country, unless international comparisons of the
growth patterns are the focus.
A Cross-Section Regional Econometric Model
There is a fundamental difference between the economy of metro-
politan regions and the national economy. The former is based on an
open economy where growth and development is closely related to its
capability to carry on external trade with other regions. The latter is
rather more self-contained by its ;aature. In spite of the Keynsian
macro-economic theory, the concept of "export-base" or economic
base theory, has been the core of the analytical frameworks of urban
economies since its first appearance in 1928. * However, the measure-
ment of the economic base multipliers originally based on calculation
of the ratio between export-oriented (or basic) employment and local-
oriented (or service) employment has been changed by using the con-
cept of value-added instead of employment. **
*Haig, Robert M. , Major Economic Factors in Metropolitan
Growth and Arrangement, Vol. I, Regional Survey of New York and
Environs, New York, 1928. See also Thompson, Wilbur R. , A Pref-
ace to Urban Economics, Resources for the Future, 1965.
--'As an early example, see Leven, Charles L. , "Measuring the
Economic Base, " Papers and Proceedings, Vol. 2, The Regional
Science Association, 1956.
-------
This later development now takes into account different produc-
tion structures among local industries, so that factor intensity of cap-
ital and labor may contribute different weight to the multiplier. Since
manufacturing industries are more capital-intensive, the role of man-
ufacturing industries in regional growth becomes decisive in that they
usually dominate the value of exports greater than 80 percent of the
total value-added. * It is therefore quite safe to treat manufacturing
industry as an export-oriented industry in the regional growth model
that in some of the recent successful regional econometric models ex-
plicitly or implicitly embodied a causal relationship of manufacturing
industry leads the overall regional growth. **
The change in the measurement from employment to value-added
in economic base theory not only has improved the applicability of the
regional multiplier in recent regional growth analysis, but it also
seems to be consistent with the familiar Keynsian-type trade multiplier
in the open economic system. However, economic base theory is only
a partial phenomenon in that it explains the demand side of the urban
:;Tn a case study of five midwest metropolitan areas, Charles C.
Leven reported that manufacturing industry dominates 80 to 96 percent
of export measured by value-added, while it only counts 45 to 71 per-
cent if it were measured by employment.
**A typical and successful model of this nature is Frederick W.
Bell, op. cit.
-------
growth without proper inclusion of the supply side. * The relations
between labor and capital markets, export-oriented industry and other
local service industry, are also important. Details are given in the
formulation of the entire model.
Since the main interest in this model is focused on air pollution
control, the sectoral structure must be based on criteria that reflect
the major air pollution industries being considered in the present
study. In total, 13 manufacturing industries have been chosen as
shown in Table B. 1. **
The variables of the model developed in this section can be spe-
cified and defined as follows:
YI = regional income of i* SMSA,
Ci = regional consumption expenditure of i"1 SMSA,
Ij; = investment expenditure by industry j of i^ SMSA,
Fiji = capital share or gross profit by industry j of
ith SMSA,
K^ = capital stock by industry j of i^1 SMSA,
'"Thompson, W. R. , op. cit. , pp. 27-60.
#*See CONS AD Research Corporation, Progress Report on Sec-
toral Structure and Data Collection, prepared for TRW Systems, Inc. ,
September 20, 1969. This classification has been based on the infor-
mation available in R. L.. Duprey, Compilation of Air Pollution Emis-
sion Factors. Washington, D. C. , Department of Health, Education and
Welfare, Public Health Service, 1968.
-------
TABLE B..1
CLASSES OF MANUFACTURING INDUSTRIES
Industry Title
Canning and grain mill products
Fabrics, yarn and thread mills
Paper and allied products
Chemicals and selected chemical
products
Petroleum refining and related
industries
Rubber and plastics products
Glass and glass products
Stone and clay products
Primary iron and steel, copper
and aluminum manufacturing
Other non-ferrous metals
manufacturing
Motor vehicles and equipment
Aircraft and other transportation
equipment
Other manufacturing industries
Related SIC Codes
203,204
221, 222, 223, 224, 226, 228
26
281,286,287,289
29
30
321-323
324-329
331,332,3391,3399,3331,
3351,3362,2819,3334,3352,
3361
3332, 3333, 3339,3341,3356,
3357,3369, 3392
371
372-379
20-39, except included in
above classification
-------
Xjj = value-added by industry j of it SMSA,
Nij = employment by industry j of i**1 SMSA,
Wij = average wage by industry j of i*h SMSA,
Ui = unemployment rate of i^ SMSA,
Ui = unemployment of i*n SMSA,
Ni = employment by industries other than manufacturing
industries of i*h SMSA,
NT = (S Ntj + Nj) total regional employment of ith SMSA,
Li = regional labor force of i*h SMSA,
Pi = population of ith SMSA,
Qji = electric power demand by industry of i*h SMSA,
Mt = migration of ith SMSA,
Gj = government expenditure of i*^1 SMSA,
Tj = government revenue of i"1 SMSA.
Integration of the Keynesian system and economic base theory
can be best explained by the income determination block of this model:
(1) Yi = f (Ci, S Xjj, Gi)
(2) ^ = f (Yi, Ci (t-1))
(3) Gi = f(Ti)
This model is a cross-sectional regional growth model. Thus, the
national influence upon a local economy can be measured by the re-
gional market share of the output in each manufacturing industry.
-------
Because manufacturing is also regarded as export-oriented, the value-
added, S Xi4, also reflects the role of economic base theory of local
j J
demand, namely, local consumption and government expenditures. In
this model, therefore, government expenditure is a simple function of
government revenue.
The sectoral structure of the export-oriented industries has been
based on 13 sectors.
(4) Xij (t) = f (Yi (t), Yi (t-1), Xij (t-1)) j = l 13
(5) lij (t) = f (Ilij (t), Kij (t-1)) j = l 13
(6) Kij (t) = KIJ (t-1) +Iij (t) - dj KIJ (t) j=l 13
(7) Xij (t) = AIJ Nij (t) K^"* (t) j = l 13
(8) Wij(t) = f (14, M^ Wij (t-1)) j = l 13
(9) Qij (t) = f (Xij (t)) j = l, 13
Equation (4) is the demand function for the products in terms of the
value-added by industry j in region i, where Yi (t) and Yi (t-1) includes
both local and external market demand of present and previous years,
and Xij (t-1) measures market share from the previous period. Equa-
tion (5) is a typical investment function and equations (6) and (7) are
familiar production structures developed in the St. Louis Model. Since
output level (value-added) and capital stock have been determined by
the three previous equations ((4), (5), and (6)), equation (7) becomes
an implicit demand function for labor requirements by industry j (Njj).
-------
Wages (equation 8) are a function of the local unemployment rate (Ui),
migration (Mj) and lagged wages (Wij (t-1)). This relationship not only
includes labor productivity but it also shows, in part, the union bar-
gaining power based on the last period wage level. Finally, equation
(9), electric power demand by industry, is expressed in terms of a
single function, current industrial output levels.
The equations for total industrial employment, other than for
manufacturing industries, and therefore a functional description for
the labor market in the regional economic model, is as follows:
(10) Ni = f (Yi - S Xij)
N? = ? Nii + Ni
J J
(12) Li = f(ui, Pi, 2 Wy Nij/2 Nij)
(13) Ui = ^ - N?
Lj - N?
(14) u- = ; x 100
1 Lt
Equation (10) is a labor demand function for the industries other than
manufacturing industry. Equation (11) is derived from equations (7)
and (10). Equation (12) represents the supply side of the labor market.
The labor force (Li) is expressed as a function of regional unemploy-
ment (ui), the weighted average wage rate (S W^ Nii/Z N:;) and size
J J J J J
of the population (Pi). Unemployment in equation (13) is the result of
the difference between the size of the labor force and total regional
-------
employment. The amount of unemployment is expressed as a rate of
unemployment in equation (14).
AQCRs Included in the Model
The main interest of the Phase II Regional Econometric Model
was on the ability of the model to be applied to the major cities across
the country. In total, the 31 largest SMS As have been chosen for the
present model. The size of the city, in general, is reported to be con-
sistent with the degree of the severity of the air pollution problem. *
In the following Table B. 2, both rank in size of population and in sev-
erity of air pollution have been included for the cities under study.
Model Estimation
The model developed in Section 2. 2. 2 has been estimated on the
data from 31 largest SMS As:
Regional income equation:
13
(1) Yi = 64. 78 + 0. 702 Ct + 0.481 2 X^ + 3.02 Gi
J = 1 R2 = 0.994
(194.4) (0. 137) (0. 100) (0.591)
Regional consumption function:
(2) CA = 15. 11 + 0.012 Yi + 1.061 Ci,..! R2 = 0. 999
(40.9) (0.030) (0.048)
*Middleton, John T. , "Air Pollution: How 65 Metropolitan Areas
Rank in Severity, " Nation's Cities. Vol. 5, No. 8, August, 1967, pp.
8-11/
-------
TABLE B.2
Rank of Rank of
Code SMSA City* Severity**
1 New York, New York 1 1
2 Chicago, Illinois 22
3 Los Angeles/Long Beach, California 3 4
4 Philadelphia, Pa. - New Jersey 4 3
5 Detroit, Michigan 5 9
6 San Francisco/Oakland, California 6 35
7 Pittsburgh, Pennsylvania 8 6
8 St. Louis, Missouri - Illinois 9 10
9 Washington, D. C. - Maryland - Va. 10 18
10 Cleveland, Ohio 11 5
11 Baltimore, Maryland 12 13
12 Newark, New Jersey 13 8
13 Minneapolis/St. Paul, Minnesota 14 32
14 Houston, Texas 15 43
15 Buffalo, New York 16 30
16 Milwaukee, Wisconsin 17 20
17 Cincinnati, Ohio - Indiana - Kentucky 18 19
18 ' Paterson/Clifton/Passaic, New Jersey 19 21
?9 Pall?.?. Tex?.? 20 57
20 Seattle/Everett, Washington 21 36
21 Kansas City, Kansas 22 25
22 San Diego, California 23 61
23 Atlanta, Georgia 24 51
24 Indianapolis, Indiana 25 14
25 Miami, Florida 26 63
26 Denver, Colorado 27 27
27 New Orleans, Louisiana 28 59
28 Portland, Oregon - Washington 29 49
29 Columbus, Ohio 33 46
30 Rochester, New York 34 53
31 Dayton, Ohio 35 26
*Rank of city is based on the size of the population reported in
Current Population Reports: Technical Studies, Series P-23, No. 23,
October, 1967. Boston SMSA ranked 7th, Providence-Pawtucket-
Warwick SMSA ranked 30th, San Bernadino-Riverside-Ontario SMSA
ranked 31st and Tampa-St. Petersburg SMSA have been excluded from
present study because of changes in definition of SMSA or lack in
certain type of data.
**See John T. Middleton, ibid.
-------
Demand function of export-oriented industry:
(3) Xijt= aj + bj Yit + Cj Y^.j +dj X^^ j = l,2 13
The results of estimation are:
Equation Industry aj b;
Number Code
(3.1) 1
(3.2) 2
(3.3) 3
(3.4) 4
(3.5) 5
(3.6) 6
(3.7) 7
(3.8) 8
(3.9) 9
(3.10) 10
(3.11) 11
-441.9
(532.0)
224. 3
(242.3)
-529.3
(1138.7)
-97.52
(927.9)
-449. 9
(927.8)
-695.9
(946.7)
-57.36
(117.7)
-1745.2
(1089.7)
-6063. 1
(5835. 1)
-412.4
(710.5)
4680. 7
(6835.9)
. 103
(.65)
.116
(.218)
.672
(1.38)
.252
(1.11)
.313
(1.08)
.635
(1.20)
1.899
(.717)
.738
(1.248)
3. 783
(6.07)
9.380
(2.59)
1.309
(. 814)
Cj
-. 155
(.698)
-. 160
(.232)
-.942
(1.47)
-.220
(1.18)
-.234
(1.15)
-.864
(1.29)
-2.028
(.767)
-.890
(1.34)
-3.580
(7.04)
10. 127
(2.77)
dj
1. 135
(.009)
1.085
(.006)
1. 122
(.013)
1.090
(.005)
1.097
(.006)
1. 135
(.012)
1. 061
(.003)
1. 131
(.013)
1.095
(.008)
1. 121
(.011)
1.047
(.006)
*2
.998
.999
.998
.999
.999
.998
.999
.997
.998
.999
.999
-------
(continued)
Equation
Number
(3.12)
(3.13)
Industry
Code
12
13
AJ bj
-3165.2 60.785
(3635.3) (23.32)
23296.6 7.337
(29916.3) (41.2)
Production functions:
(4) Xij = Aj N"J KJp
Equation
Number
(4.1)
(4.2)
(4.3)
(4.4)
(4.5)
(4.6)
(4.7)
(4.8)
(4.9)
Industry
Code
1
2
3
4
5
6
7
8
9
AJ
4.505
(1.002)
2.6375
(.659)
3.1312
(.418)
4. 708
(.919)
2.924
(.811)
4. 000
(.366)
3.548
(.531)
3.579
(.525)
3.296
(.423)
CJ dj
-66.56 1.159
(24.89) (.008)
-26.331 1.154
(42.7) (.030)
j X-J
5217 .4783
5208 .4792
5315 .4685
5269 .4731
5293 .4707
5215 .4785
5085 .4915
5269 .4731
5301 .4699
R2
.999
.997
, 13
R2
.974
.993
.993
.973
.983
.997
.986
.995
.998
-------
(continued)
Equation Industry
Number Code
(4. 10) 10
(4.11) 11
(4. 12) 12
(4.13) 13
J J J
3.559 .5292 .4708
(.525)
4.517 .52152 .4748
(1.101)
6.204 .5231 .4769
(.358)
4.656 .5269 .4731
(.358)
*2
.997
.998
.996
.998
Capital share (or gross profit) relation:
Given the Cobb-Douglas production function, capital share or
gross profit can be derived as follows:
(5) ny = (1 -«j) Xjj j = l,...,13
Capital shore coefficients, cj, can be obtained as in the preceding
table for the production function. Therefore, there are 13 equations
(5. 1) through (5. 13).
Investment function:
(6)
= a
.
13
-------
Equation
Number
(o. 1)
(6.2)
(6.3)
(6.4)
(6.5)
(6.6)
(6.7)
(6.8)
(6.9)
(6. 10)
(6.11)
(6.12)
(6.13)
Industry
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
aj
-420. 6
(326.1)
-40.24
(183.7)
-903.6
(949.3)
-123. 1
(824. 1)
-624.5
(1668.6)
-782.2
(504.4)
87. 10
(108.2)
-2046.9
(1078.2)
-2923.3
(2378)
-62.79
(414. 1)
5832.6
(2637.3)
1459.8
(997.9)
(11708.3)
(11516)
bJ
. 168
(.067)
.4707
(.116)
.6033
(.099)
-.067
(. 047)
-.4419
(.133)
.4573
(.0942)
.0686
(. 147)
.0686
(.126)
. 6747
(.0595)
.6177
(.118)
.2157
(.0398)
.2174
(. 044)
.3586
(.048)
Cj
.0788
(.0516)
-.0809
(.0526)
-0.0852
(.0411)
.2032
(. 0304)
.2792
(. 034)
-. 1403
(.0682)
. 1004
(.079)
. 1004
(.0589)
-.0979
(.023)
-. 1568
(.062)
.0034
(.025)
-. 1571
(.078)
-. 1348
(.0412)
R2
.963
.986
.963
.971
.974
.967
.989
.989
.992
.982
.986
.979
.964
-------
Capital stock identity:
(7)
where
Equation
Number
(7.1)
(7.2)
(7.3)
(7.-4)
(7.5)
(7.6)
(7.7)
(7.8)
(7.9)
(7.10)
(7. 11)
(7. 12)
(7.13)
Kijt = Kijt-l + Iijt = dj Kijt
d; is depreciation rate in industry j.
Industry
Code
1
2
3
4
5
6
7
8
9
10
11
12
13
j = l 13
Depreciation Rate
-------
Equation
Numbe r
(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)
Industry
Co3e
1
2
3
4
5
6
7
8
9
10
11
12
13
aj
.:388
(.402)
-.658
(.458)
-2. 103
(.482)
.6105
(.432)
.0244
(.454)
.4938
(.399)
.0817
(.171)
. 1974
(.571)
.2051
(.522)
.2925
(.120)
-.2676
(1.34)
.2202
(.416)
. 8552
(.90)
bJ
.985
(.031)
1. 009
(.063)
1.2906
(.066)
.9961
(.032)
.9782
(.023)
.9593
(.059)
1.0179
(.018)
1.048
(.055)
1.0629
(.085)
.8941
(.018)
.9721
(. 064)
.9936
(.029)
1. 0008
(.078)
CJ
-.0516
(.0969)
.2109
(. 106)
. 1455 .
(.112)
-. 1556
(.146)
.0098
(.135)
-.0909
(. 076)
-.0214
(. 044)
-.1399
(.127)
-. 1701
(.175)
. 0419
(.025)
.2279
(.490)
.0906
(.157)
-.2850
(.278)
dJ
-.0183
(.022)
-.0069
(.0195)
.0384
(. 0263)
-.0135
(.0298)
.0473
(. 028)
.0025
(.018)
.0062
(.010)
-.0275
(.032)
-.0233
(.039)
.0206
(.006)
.0057
(. 064)
-.0050
(.0195)
-.0443
(.035)
R2
.981
.949
.944
.979
.987
.911
.996
.934
.917
.998
.938
.984
.847
-------
Employment by industries other than manufacturing industries:
1 1
(9) Ni = 121. 73 + 0. 1262 (Yi - S X^) R2 = 0.968
(31.96) (0.004)
Total employment:
_~ _ 13
(10) N^ = Ni + 2 N^
j = l
Labor force supply function:
13 13
(11) 1^ = 312. 735 + 0.4662 Pi - 39.749 (S W^ Nij/2 Nij)
(149.9) (0.005) (18.62)
- 31.319 ut R2 = 0.995
(17. 1)
Regional employment
(12) ^ = Lj_ - NiT
Regional unemployment rate:
(13)
j.
Li
Electric power
(14)
Equation
Number
(14. 1)
(14.2)
(14.3)'
Qijt = »j
Industry
Code
1
2
3
x 100
demand function by
+ bj Vijt
aj
4.6152
(2.81028)
29. 0536
(11.39258)
2.49808
(. 00002362)
industry:
bJ
. 79804
(.042681)
.205259
(.27310)
-.0049799
(.0031956)
R2
.933
.799
.999
-------
Equation
Number
(14.4)
(14.5)
(14.6)
(14.7)
(14.8)
(14.9)
(14. 10)
(14. 11)
(14. 12)
(14.13)
Industry
Code
4
5
6
7
8
9
10
11
12
13
aj
-22. 75882
(32. 82634)
-.0051536
(.0052080)
-0.0054016
(.0017908)
-5.84172
(4. 16915)
0. 028437
(3.85147)
2. 01174
(253.2238)
-5.53004
(2. 19407)
-11.33485
(10.35392)
-0.005127
(.007177)
-.004861
(.0020049)
bJ
11.98117
(.20547)
3. 76464
(.00003890)
1.50925
(.00001555)
1.83096
(. 14389)
2.38909
(.033961)
5.15529
(.39807)
1. 76572
(. 05378)
. 75855
(.010675)
. 75402
(.00001392)
. 10026
(.00000058)
R2
.991
.999
.999
.915
.994
.879
.994
.996
.999
.999
Government expenditure function:
(15) G^ = 55.2434 + 0.92961T
(10. 1457) (0. 007116)
R2 = 0.998
-------
APPENDIX C
THE APCO REGIONAL ECONOMIC
-------
The APCO Regional Economic Model developed in three years
(Phase HI) by CONSAD is a logical extension of the concepts and em-
pirical work of the first two years into an operational tool amenable
to assessment of various control strategies. In particular, it builds
on and extends in Phase II 31 AQCR model (Appendix B) in three im-
portant dimensions.
First, the Phase III model is extended to cover 100 AQCR's.
These 100 AQCR's account for over 65% of the nation. Thus, the APCO
model will cover a major portion of the economic activity in the nation.
Second, the Phase in model overcomes a major drawvack of the
31 AQCR model, which treats each AQCR as an isolated region. In
reality, economic effects are attendant on pollution abatement. Stra-
tegies such as price increases in certain industries, the growth of
control equipment industries or demand spurred by reduced expendi-
tures on health, etc. , in one region will have feedback affects on the
economies of other regions. The Phase m APCO Economic Model
system uses a national input-output system linked to a regional market
share matrix to capture these interregional feedback effects and en-
compasses the 100 AQCR's as an interrelated system of regions.
Third, the Phase III model incorporates a regional fuel demand
submodel as a component of the Regional Economic Model system. As
-------
air pollution control policy is implemented, sulfur content in coal and
fuel oils will greatly affect their price in view of increased demand for
low sulfur fuels and their limited supply. Prices of natural gas and
electricity will also tend to change. Industries will choose an optimal
combination of fuels and electricity which minimizes the total cost of
energy to the degree substitution is possible. The fuel demand model
will describe these relationships.
This appendix is addressed in particular to the description of
the incorporation of the interregional feedbacks and regional fuel de-
mand model into the APCO Regional Economic Model system. The
rest of the model system represents an extension of the 31 AQCR
model to the 100 AQCR's.
-------
1. I-O System and
Interregional Feedback
As emphasized earlier in the model formulation, a region's
growth is closely dependent upon its capability to carry on external
trade with other regions. A national input-output system is introduced
to serve the role of external market for the regional economy described
in the regional model, and also, hopefully, to measure the structural
change of the national economy upon air pollution control to the 100
AQCR's.
The input-output system has been one of the most popular ap-
proaches used in economic structure analysis in the past several
decades. With sufficient knowledge in input-output coefficients and
details of the final demand, levels of interindustrial demand can be
derived by its inverse matrix. However, one major weakness of the
input-output system is that its operational feasibility is solely depen-
dent upon exogeneous information regarding final demand. Therefore,
a combined structure of the Keynsian macro-economic system and in-
put-output table has been introduced in some recent econometric model
development.* A modification of Klein's formulation will be sum-
* For example, Dusenberry, J. S. , Fromm, G. , Klein, L. R. ,
and Kuh, E. (eds. ), The Brookings Quarterly Econometric Model of
the United States, 1965, Chapter 17.
-------
marized as follows. * The input-output system will be
(I - A) X = Y
where:
X is a vector of industry outputs
Y is a vector of final demand by sector
A is a matrix of technical coefficient
I is a unit matrix
In this model, aggregation of sectoral demand (the elements of
Y) is equal to GNP.
n
Y. = GNP
This approach can be viewed as a model system when those sec-
tor demands are explained in terms of Engel curves or its analogues
are related to the Keynsian type macro-economic system.
The relation between a national input-output system and a cross -
sectional regional model of Keynsian type formulation can be better ex-
plained from policy questions to be answered from this model system.
The regional model developed in Phase II is appropriate for an isolated
** Klein, Lawrence R. , "What Kind of Macro-Econometric
Model for Developing Economies?", Econometric Annual of the Indian
Economic Journal, Volume 13, No. 3, 1965, also reprinted in A.
Zellner (ed. ), Readings in Economic Statistics and Econometrics, 1968.
-------
region without interregional feedback scheme, although the export
activity is explicitly treated. When an air pollution control policy is
implemented across the nation, costs of production increases. This
will result in an upward shift of the supply curve. Whether an indus-
try or an individual firm may be able to pass on the increased cost per
unit of output is dependent upon the elasticity of demand and supply of
the corresponding products. If price changes are obtained from exo-
geneous information, * the equilibrium supply of the products will be
known as shown in the following figure.
However, price increases in high emission industries under
air pollution control will not only reduce the demand of their products
but also effect the demand of those products which are the intermediate
products in the production of the high emission industries. For ex-
ample, a higher price for steel products will not only reduce the de-
mand for steel but also affect sales in transportation, coal products,
and other materials and services related to the steel industry.
Further, such effects originating in a given region (or AQCR) in the
nation will not be limited to the region but would affect the economic
* A study of price markup which air pollution control is insti-
tuted has been reported in LeSourd, D. A. etc. , Comprehensive Study
of Specified Air Pollution Sources to Assess the Economic Effects of
Air Quality Standards, Research Triangle Institute, December, 1970.
-------
Price
(with abatement) S,
S (pre-abatement)
Quantity
-------
activity in other regions (AQCR's) and in all likelihood, create a
feedback to the region. *
For a limited number of regions, the formulation of an inter-
regional I-O system is perhaps feasible. However, a 20 sector
regional I-O system with 100 regions, the size of the matrix will be
2000 x 2000 (with such detailed information largely nonexistent).
An alternative formulation was consequently necessary. A
national input-output system linked to a regional market share matrix
/
was used to capture the regional feedback. It is argued that the re-
gional share of the national market by industry (termed as the "loca-
tion quotient") is relatively stable. For example, if steel production
in Pittsburgh AQCR is 12% of the nation's steel product, a change in
the national steel market will have a 12% effect on the Pittsburgh
AQCR. This concept is particularly useful for a cross-sectional model
which deals with the geographic distribution of economic activity at
a given period of time.
In brief, using exogeneous information on price changes in high
emission industries occasioned by air pollution control, the high
emission industries in each air quality control region (AQCR) will fall
* These inte rregional feedback phenomenon were observed in a
pioneer study by Ronald E. Miller, "Interregional Feedback Effects in
Input-Output Models: Some Preliminary Results, " Paper; Regional
Science Association, Vol. 17, 1966.
-------
in output to the point corresponding to an upward shifting supply curve
and the new price after control. An aggregation of changes in regional
production for each high emission industry will give the change of
demand in the nation. By use of the national input-output system,
the impact of changes in high emission industries on other industries
can be measured. Finally, through the use of regional market share
matrix, the national impact can be distributed to each region (AQCR)
as the net interregional feedback from the other regions under study
(99 AQCR's) and the rest of the nation.
Mathematically, high emission industry in each region will have
an exogeneous determined price equation and supply curve as follows.
P. = P. (exogeneous price determination
^ ^ of industry j)
Q.. = f(P., factor prices, etc.) (regional supply
curve of high emis-
sion industry j
in region i)
Price increase after air pollution control, AP.,will determine change
of production A Q.. with other factors of the regional supply curve of
J
industry j in region i.
Aggregation of AQ.., over regions (say 100 AQCR's) under con-
trol will give the "national" change of the demand (supply = demand
at equilibrium in each region).
m
AY. = AQ.. ' j = 1, . . . n
J
-------
Change of final demand by high emission sectors, AY., will affect
the structure change of the national economy since
(i - A) AX = AY
then
AX = (I - A)"1 AY
A X is a vector of change in gross product by sector in the nation as
the results of air pollution control are implemented, say in 100 AQCRs.
A X is then distributed to each region with the regional market share
matrix B. B is an nxm matrix.
B = [b.. ] i=j, . . . m
j = 1, . . . n
By definition,
where
b.. is the regional market share of jth industry product
in the ith region
X is the output of the jth industry in the ith region
U
X. is the output of jth industry in the nation
Appendix D provides the empirical development of the input-
output model system and the interregional feedback formulated here
-------
2 Regional Fuel Demand Model
It has been observed that the burning of coal, fuel oil and natural
gas to produce power and heat is onS of the most important sources of
particulates, sulfur oxides, and nitrogen emission to the air. Coal,
coke, fuel oil, natural gas and electricity are also the most important
energy sources available to the manufacturing industries in the nation.
Demand for energy increases as the manufacturing output increases.
However, each type of manufacturing industry differs from the other
in the production process; therefore, the type of fuels and combination
01 different type of fuel and electric power also differ from industry
to industry.
On the other hand, it is true that there are substitutional rela-
tions among the different types of fuel and/or electricity to produce
the energy (power and heat) necessary for any given level of product
of an industry. Hence, industry may choose an optimal combination
of fuels and electricity which minimizes the total cost of energy. This
is to say, the prices of fuels and electricity also affect the demand
for each type of fuel or amount of electricity in the production process
of each type of manufacturing industry. Therefore, if the price of
electricity or any type of fuel changes, then the demand for the fuels
and electricity changes according to a new optimal combination which
minimizes the total cost of the energy.
-------
As the air pollution control policy is implemented, sulfur con-
tent in coal and fuel oils will greatly affect the price because of in-
creased demand for low sulfur fuels and their limited supply. Prices
of natural gas and electricity (partly by the increase in production cost)
tend to change because of changes in demand and supply relations.
Demand for energy, and hence fuels, like the demand for labor
or capital is an induced demand from moderation. Therefore, an
appropriate way to incorporate an energy demand model into the regional
model would be to reformulate the production functions in the regional
model.
A production function describes the maximum output obtainable
from every possible combination of inputs. Some of the inputs are
substitutable for one another while others are non-substitutable and
are proportional to the output. A general production relation can be
conceived of various type of inputs, with substitutional relations among
a group of inputs categoried into a number of sub-groups. Between
any pair of sub-groups of inputs there is no substitutional relation.
Further assume that inputs for a given industry have been classified into
3 groups of inputs. Since there is no substitutional relations among
those 3 inputs, the productions function can be given as
X = min (- X,, -X_, -X,)
a1 1 a2 2 a^ 3'
Each group input is proportionate to the output. However, within each
-------
group of inputs there are substitutional relations
X. = f(xu, x.2 x.n) 1.1. 2, 3
f has all properties of a new-classical production function. Therefore,
X = min i fl(Xl. . . x ). ± f-2 (x .... x ),
L 1 1 Z 1 ^
In the Phase II model, the production functions treated the rela-
tion between value added as output, and labor and capital as inputs in
the form of a Cobb-Douglas type function. Consequently, interme-
diari products included in the production were omitted as explicit fac-
tors of production.
Assume that X is gross product (or value of shipments) of a given
industry, V is its value-added, Z is energy requirement in this pro-
duction, M is another intermediate good. *
X = min
lin - V, - Z, - M
Lai a2 a3 J
Thus we can derive the relation between value-added and total energy as
*2
Z = V
al
i.e. , energy demand is proportionate to the value-added.
* Although M can be an aggregation of other intermediate goods,
-------
Suppose there are five types of fuels E. (i = l, . . . , 5), the tech-
nical relations between fuels as inputs and total energy produced can be
described as an energy production function which is similar to the
relation between labor and capital to the value-added.
Z = f(Er .... E5)
This relation provides the substitutional relation between each pair of
E. with a given level of energy (Z).
As shown in Figure 1, with a given level of energy, there are
various combinations of fuel which produce the same level of energy.
For example, in Figure 1 combination of E, and E^ gives the same
it "
energy as combination of E and E^ .
Given the price of each type of fuel i(i=l, . . . , 5), total cost of
energy, C, is the sum of the cost of all types of fuel which is product
of price and quantity of fuel
C = C, + C +C + C + C
12345
=
-------
FIGURE 1
FUEL SUBSTITUTION: ISO-ENERGY LINE
(fuel type 1)
M
E
1
iso-energy line Z
E (fuel type 2)
-------
Given the cost function C and energy production function E, the
optional behavior for an industry will be:
5
Minimize cost C = S q. E*
i= 1
Subject to energy production function Z = -f (Ej, . . . , Eg)
The solution gives the optional condition.
M
9E
1L [- (i * j), i, j = 1 5
= !Ł
'qj 8E.
That is to say, price ratio of the types of fuel equals to the ratio of
marginal (or additional unit of) productivity of energy. If price of fuel
q.and total energy Z(which is proportion to the output) are given then
as optimal combination of fuels E. (i=l . . . , 5) can be divided. This
relation can better be explained in the following figure
-------
FIGURE 2
DETERMINATION OF OPTIMUM FUEL COMBINATIONS
-------
3. The Regional Model
With interregional relations and fuel demand submodel being
formulated as indicated above, the rest of the Phase III model is quite
similar to that of Phase n 3 1 AQCR regional model.
Notation:
V value-added by industry j of ith AQCR
ij
K. . capital stock by industry j of ith AQCR
N. . employment by industry j of ith AQCR
L investment expenditure by industry j of ith AQCR
n.. capital share or gross profit by industry j of ith AQCR
W. . average wage by industry j of ith AQCR
Y- regional personal income of ith AQCR
C. regional consumption expenditure of ith AQCR
G. local government expenditure of ith AQCR
T. local government revenue of ith AQCR
N. employment by industries other than manufacturing
industries of ith AQCR
N
_
i' + N- ^ total regional employment of ith AQCR
^
L. regional labor force of ith AQCR
Q. total regional consumption of electric power in ith AQCR
-------
Q.. electric power consumed by industry j of ith AQCR
QCJ electricity consumed by residents of ith AQCR
Q. electricity consumed by industries other than
manufacturing industries of ith AQCR
qr price of fuel type r of ith AQCR, type of fuel including
coal, coke, fuel oil and natural gas
E .. demand of fuel, type r by jth industry by ith AQCR
i = 1, . . . , 100 100 AQCR's
j = 1, .... 19 which is two-digit manufacturing industries
The sector structure of the manufacturing industries included
two digit SIC industries as follows.
MANUFACTURING INDUSTRY CODE
SIC Industry
20 Food and kindred products
22 Textile mill products
23 Apparel and related products
24 Lumber and wood products
25 Furniture and fixtures
26 Paper and allied products
27 Printing and publication
28 Chemicals and allied products
29 Petroleum and coal products
30 Rubber and plastics products
31 Leather and leather products
32 Stone, clay, and glass products
33 Primary metal industries
34 Fabricated metal products
35 Machinery, except electrical
36 Electrical machinery
37 Transport equipment
38 Instruments and related products
39 Miscellaneous products
-------
For manufacturing industry in each AQCR, the following equations
provide a submodel of manufacturing sectors.
(1) V (t) = A.. N..Q(t) Ky QJ(t) j-1 19
(2) n..(t) = (1 -a.) V..(t) j=l 19
*-J J J
(3) I..(t) = f(FI (t), K (t-1)) j=l 19
1J ij ij
(4) K (t) = K.. (t-D +I..(t) - d K..(t) j=l 19
ij iJ iJ j ij
(5) W..(t) = f(W.. (t-1), u.(t))
Equation (1) is a typical Cobb-Douglas production function. Equa-
tion (2) states the profit as capital share from value-added. Equa-
tion (3) relates investment to the current profit and existing capital
stock. Equation (4) is capital identity which defines capital stock
of time t as capital stock of previous period plus new investment
M>>nus depreciation, dj is the depreciation rate by industry (j = 1,
. , 19).
-------
Integration of Keynsian system and economic base theory can be
treated in a regional income determination as before (Phase II).
(6) Y.(t) = f (C.(t). rVjjCt), Gj(t))
j
(7) C.(t) = f(Y.(t), C.(t-l))
(8) G.(t) = f(T.(t))
In Equation (6), manufacturing industries are treated as export activity
while regional consumption C. and local government expenditure also
are included in income determination. Equation (7) is a typical con-
sumption function, and Equation (8) merely relates local government
expenditure to local government revenue.
Industries other than manufacturing industries have been treated
as residual of regional economic activity. Therefore, the employment
by industries other than manufacturing industries is related to the non-
manufacturing income in Equation (9).
_ 19
(9) N.(t)= f (Y.(t)- Ł>..(t)
The labor market in each AQCR can be given by
__ 19
(10) N (t) = N(t) + y)N..(t) )
i ! *rr ij
(ID u.(t, . Li(t> - Ni(t)
1 L.(t)
(12) L.(t) =f(N.(t), ujft))
-------
Electric power demands are vital to the air pollution control
problem, it gives
(13) Q..(t) - f(V..(t)) j = l, . . . , 19
(14) Q .(t) = f(C.(t))
!
Cl
(15) Q.(t) = f (Y.(t) - Z V.,.(t))
(16) Q.(t) = ZQ..(t) +Q .(t) +Q.(t)
1 j IJ Cl 1
Electricity consumed by each economic sector is related to the
level of production of manufacturing industries, consumption expendi-
ture of residents, and income of other industrial sectors respectively.
Equation (16) gives the total regional demand of electricity.
Fuel demand model developed in the previous section can be
given as
(18) Z..(t) = B (t) E^1-", Ef2j, . . . E^.5.J j=l ..... 19
ij ij lij 2tj 5i3
Equation (17) gives the relation that the cost of fuel in the production
is proportionate to the value-added by industry in each AQCR. And
Equation (19) is an energy production function for a given industry.
-------
4. Statistical Estimations
This section provides the results of statistical estimation of 162
equations in the region component of the Model System. *
(1) Production
V.. = A.
ij J
Equation
Number SIC
(1. 1) 20
(1.2) 22
(1.3) 23
(1.4) 24
(1.5) 25
(1.6) 26
(1.7) 27
(1.8) 28
(1.9) 29
functions
Ni°iKij"
*i
3. 6408
(.2714)
3. 5518
(.5138)
7.0735
(1.0456)
3. 8879
(.6094)
5.4057
(.3146)
3.3285
(. 0830)
5. 6188
(.4513)
2.4928
(. 1923)
1. 2408
(. 1455)
j = l.. . . , 19
j '-«,
.3846 .6154 .997
(1.3488)
.5614 .4386 .995
(1.1609)
.5696 .4304 .998
(1. 1444)
.5780 .4220 .988
(1. 1726)
.5569 .4431 .997
(1.3215)
.5153 .4847 .994
(1. 1829)
. 5306 .4694 . 995
(1. 1687)
.2890 .7110 .994
(1.5465)
.2499 .7501 .994
(1.6557)
* This count of equations does not include the I-O Model and
the market share matrix of the Regional Model System.
-------
(1.
(1.
(1.
(1.
(1.
(1.
(1.
(1.
(1.
(1.
10)
11)
12)
13)
14)
15)
16)
17)
18)
19)
30
31
32
33
34
35
36
37
38
39
4. 1808
(.4282)
6. 2163
(.7523)
3.4098
(. 3146)
3. 5046
(.3079)
4. 9152
(. 3368)
5. 2706
(.4248)
5. 9796
(.5209)
6. 2431
(.6891)
5. 7923
(.6361)
5.098
(.4871)
. 5030
(1.1932)
. 5423
(1. 1617)
.4649
(1. 2411)
. 5156
(1. 1858)
. 5192
(1. 1801)
. 5257
(1. 1753)
. 5356
(1. 1665)
. 5726
(1. 1365)
. 5013
(1. 1999)
. 5271
(1. 1718)
.4970
.4577
. 5351
.4844
.4808
.4743
.4644
.4274
.4987
.4729
. 995
. 995
. 988
. 998
. 998
. 995
. 998
. 998
. 998
. 994
Given the Cobb-Douglas production function, capital share
or gross profit can be derived as follows.
(2) n.. = (1 -a) V.. j = l,. ... 19
ij J iJ
Capital share coefficients, (1 -a.), can be obtained as in the
preceding table for the production function. Therefore, there are 19
equations (2. 1) through (2. 19).
-------
(3) Investment function
I.. = a + b n
*J J J ij
=l.. . . , 19
Equation
Number SIC
(3.1)
(3.2)
(3.3)
(3.4)
(3.5)
(3.6)
(3.7)
(3.8)
(3.9)
(3. 10)
(3. 11)
20
22
23
24
25
26
27
28
29
30
31
a
J
2.
(1
0.
(0
0.
(0
0.
(0
0.
(0
2.
(1
2.
(0
1.
(2
-1.
(2
3.
(3
0.
(0
1030
.047)
1677
. 1721)
8537
.6129)
4099
.4073)
6597
.4689)
8315
.7716)
6523
.6559)
6595
.0899)
1015
. 1012)
6158
.6618)
0728
.0671)
b.
J
0.
(0
0.
(0
0.
(0
0.
(0
0.
(0
0.
(0
0.
(0
0.
(0
0.
(0
0.
(0
0.
(0
084
.0050)
1068
. 0044)
0265
.0035)
1111
.0382)
0725
. 0174)
1502
. 0348)
0585
. 0027)
1042
. 0093)
2979
. 0205)
2204
. 0784)
0342
.0028)
R2
0.799
0.934
0.478
0. 119
0.222
0.229
0.869
0. 670
0.853
0. 117
0.778
-------
(3. 12)
(3. 13)
(3. 14)
(3. 15)
(3. 16)
(3. 17)
(3. 18)
(3, 19)
(4) Capital
32 1.0897
(0.8574)
33 -0. 5863
(1.6544)
34 -0.0969
(0.4430)
35 5. 1995
(6.5116)
36 1,8848
(1. 1608)
37 1. 1828
(1.2960)
38 -0.0229
(0. 1575)
39 0. 1637
(0.3164)
Stock Identity
K = K. . , i I
ijt ijt-1 ijt
where d. is depreciation rate in
Equation
Numbe r
(4. 1)
(4.2)
(4,3)
(4.4)
SIC
20
22
23
24
0.1564 0.506
(0.0190)
0.2715 0.933
(0.0095)
0.1260 0.967
(0.0029)
0.1343 0.216
(0. 0320)
0. 1116 0.844
(0.0063)
0.1122 0.922
(0. 0043)
0.1076 0.992
(0. 0012)
0.0715 0.917
(0. 0028)
<*. K j=l,
J ijt
industry j.
Depreciation Rate
d.
J
.06738
.05248
.09060
. 09149
. , 19
-------
(4.5)
(4.6)
(4.7)
(4.8)
(4.9)
(4. 10)
(4. 11)
(4. 12)
(4. 13)
(4. 14)
(4. 15)
(4. 16)
(4. 17)
(4. 18)
(4. 19)
(5) Regional
Y
i
(6) Regional
C
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
Income Equation
13,
= .42836 7 V.. + . 94812
Ł1 1J
(.0564) (.0661
Consumption Function
= 137.99 + .6133 Y. + .012
.07366
.05343
.06859
.06690
.06139
.06182
.07549
. 06457
.05598
.06995
.07216
.07379
. 07840
.06714
.07321
C. + 2.8304 G. R2 = . 997
i i
1) (.30932)
14 C , R2 = .992
(50.09) (.0057) (.0012)
-------
(7) Local Government Expenditure
G. = 23.4227 + . 94215T R2 = . 998
(4. 108) (.0048)
(8) Employment by industries other than
manufacturing industries
- 19
N. = 56. 3560 + . 10323 (Y. - J^ V..) R = . 978
- V* V )
1 FI ij
(9.523) (.0016)
(9) Total employment
N = N + N
i i Z-f ij
(10) Labor force supply function
L. = -13.958 + 1.0392 N. + 361.374 U. R2 = . 999
i 11
(2.080) (.0009) (55.965)
(11) Regional Unemployment Rate
L. -N .
U. = - - x 100
L.
i
(12) Electric power demand
function by industry
Q . = a + b V.. j = l, .... 19
-------
Equation
Number
(12.
(12.
(12.
(12.
(12.
(12.
(12.
(12.
(12.
(12.
(12.
(12.
(12.
1)
2)
3)
4)
5)
6)
7)
8)
9)
10)
11)
13)
14)
SIC
20
22
23
24
25
26
27
28
29
30
31
32
33
a
J
1.4987
(.6969)
1.4681
(.7354)
-.01340
(.0931)
. 025681
(. 1584)
.05072
(.07771)
1.9802
(1.6883)
1.0508
(. 3265)
12.0593
(5.492)
2. 00296
(2. 5082)
-. 3307
(.3239)
.07498
(. 0530)
3. 1259
(1.3261)
8. 8908
(6. 1280)
b.
J
. 006517
(.00024)
. 009954
(.00109)
.002932
(.000030)
. 009854
(.00065)
.004665
(.00015)
.01484
(.002:.2)
. 002547
(.00010)
.0120
(.0022)
. 0325
(.01533)
.01533
(.00045)
.00308
(.00012)
. 01581
(.0018)
. 02730
(.0021)
R2
.918
. 667
. 994
. 809
. 943
.457
.913
.329
. 7971
.958
. 939
. 549
. 749
-------
(12.
(12.
(12.
(12.
(12.
(12.
14)
15)
16)
17)
18)
19)
34
35
36
37
38
39
. 2557
(. 5316)
. 6298
(. 6848)
2.08744
(1. 1652)
-.2536
(1.3973)
. 6778
(.3588)
.7320
(.7793)
.0074
(.00025)
. 005378
(.00023)
. 00547
(.00038)
.007513
(.00026)
. 002845
(.00024)
.006066
(.00075)
. 938
. 902
. 796
. 939
. 728
. 540
Electricity demand by residentials
in each AQCR
(13) Q . = .04718C
C1 (.00216) R2 = .880
Electricity demand by other industries in each AQCR
(14) Q~: = . 07947 (YŁ = Z VH) R2 = . 846
(.00423) j J
Regional demand of electricity
19
(15) Qi = Z Qij + Qci + Qi
-------
Energy demand function
(16)
= aJ
j = 1,..., 19
Equation
Number
(16.1)
(16.2)
(16.3)
(16.4)
(16.5)
(16.6)
(16.7)
(16.8)
(16.9)
(16. 10)
(16. 11)
(16. 12)
(16. 13)
(16. 14)
(16. 15)
(16. 16)
(16. 17)
(16. 18)
(16. 19)
Industry
SIC
20
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
aj
2. 13525
3. 04152
0. 74246
2. 57780
1.33572
3. 97124
0.61895
2. 79554
9.43424
2. 83225
1. 15811
6. 14368
10. 68980
2. 10111
1. 34915
1. 19740
1. 35564
1. 00903
1.44677
-------
Energy production function
M71 7 R TT71J F72J F"1^
(17) Z.j - Bj Enj E2ij E3ij 4i
j = 1 19
Equation SIC B- "^ "^ y3 y4
Number Code
(17.
(17.
(17.
(17.
(17.
(17.
(17.
(17.
(17.
(17.
(17.
(17.
1) 20
2) 22
3) 23
4) 24
5) 25
6) 26
7) 27
8) 28
9) 29
10) 30
11) 31
12) 32
17.304
19.563
(5.246)
21.914
(7.570)
19.061
(3.693)
23.206
(4. 134)
20.941
(4.732)
19.616
(1.963)
12. 312
(4. 514)
3. 063
(. 700)
14. 935
(1.617)
17.965
(12.313)
9. 586
(2.476)
>0993
(.0597)
. 1133
(.0523)
.0
.0
. 0882
(.0237)
.2150
(.1233)
.0
. 1564
(.0798)
. 0
. 0
. 1191
(.0318)
. 1382
(. 0785)
. 0
.0
.0
.0
.0
. 0
.0
. 0
. 0
.0
. 0
. 0
. 1107
(.0687)
.1544
(.0786)
. 0934
(.0572)
. 1707
(.0337)
.0873
(.0282)
. 1318
(.0961)
. 0670
(.0206)
.0587
(.0618)
.0620
(.0594)
. 1030
(.0307)
. 1535
(.0387)
. 0818
(. 0455)
.2250
(. 0908)
. 1070
(.0429)
.0822
(.0359)
. 1110
(. 0323)
. 1243
(. 0280)
. 1378
(.0995)
. 1325
(. 0352)
.2067
(. 1109)
. 5371
(. 1387)
. 1236
(.0347)
. 1105
(.0199)
.3780
(.1195)
ys
. 5651
(. 0820)
.6254
(. 1204)
. 8244
(.0817)
. 7183
(.0493)
. 7002
(. 0618)
. 5154
(. 1390)
. 8006
(. 0503)
. 5782
(. 1352)
.4009
(. 1308)
. 7734
(.0436)
.6169
(. 0601)
.4020
(.0857)
R2
.984
.976
.998
.970
.995
.930
.982
. 920
. 959
.989
.941
. 857
-------
(17.13) 33
(17.14) 34
(17.15) 35
(17. 16) 36
(17.17) 37
(17.18) 38
(17.19) 39
19.482
(4.450)
17. 059
(1.719)
19.981
(2. 734)
16.827
(2.912)
17. 572
(5.738)
18.982
(8.577)
20. 846
(4.008)
.0465
(.0195)
.0352
(.0173)
.0688
(.0363)
.0493
(.0142)
.0905
(.0321)
. 1141
(.0591)
. 0608
(.0182)
. 1838 .1062
(.0852) (.0546)
. 0
.0
.0
.0
. 1108
(.0588)
. 1087
(.0663)
.0800
(.0391)
.0717
(.0406)
. 1159
(.0226)
. 1289
(.0436)
.2293
(.0701)
.2338
(. 0762)
. 1656
(.0597)
. 1567
(.0436)
. 1345
(.0394)
. 1199
(.0197)
. 1437
(.0661)
.4342
(.0982)
.6202
(.0691)
.6569
(.0687)
. 7139
(.0556)
. 7032
(.0536)
.6500
(.0593)
.6666
(.0980)
.983
.993
.987
.955
.990
.969
.985
-------
APPENDIX D
INPUT-OUTPUT MODEL SYSTEM
-------
1. I-O Model
An input-output table of a nation Shows the total output and the
interindustry transactions in the national economy. The national eco-
nomy is broken down into a large number of industries or sectors (up
to more than 380 sectors in 1963 OBE Input-Output table) and repre-
sented in a matrix form. Each row provides the distribution of domes-
tic outputs of a given industry to all other industries as intermediate
products, and to the final demand for consumption. Each column repre-
sents the purchase of all inputs including intermediate products from all
other industries and contribution of labor capital. The transactional
patterns provided in an input-output table are assumed to be relatively
stable regardless of level of output in each sector. The percentage
distribution (or ratios) of inputs in a given column to one unit of out-
put is called input-output coefficients which describe the present stage
of production technology in a given industry. In this manner, the input-
output table displays the pattern of the present stage of production
technology of the nation. The input-output table is particularly useful
when addressing the following type of policy. "If the final demand for
goods and services changes, what would be the new gross domestic pro-
duction in each industry?" Such information can be obtained through
the use of so-called "inverse coefficient" matrix. The inverse
-------
matrix describes the direct and indirect input requirements of all
sectors to deliver one unit of final demand.
As national air pollution control policies are implemented, the
resulting cost increases in high emission industries would induce a
price increase in the products of those industries. This increase in
price may result in a reduction in the demands for the products of
those industries. On the other hand, air pollution abatement would
bring about cleaner air in the nation as a whole, leading in turn to a
reduction in health expenditures, increase in property values, etc.
These benefits can be viewed as additional money available to the
United States residents for consumer expenditures which would create
additional demand for a variety of goods and services.
Thus, the implementation of air pollution abatement policies
in the nation will cause reduced demand for certain industries but the
resulting benefits of cleaner air may stimulate the demand for products
of the same or other industries. Such changes in demand and resulting
outputs of various industries attendant upon an air pollution control
policy can be ideally captured by the use of the national I-O model
when it is properly sectored for the purpose at hand. Such a use of
the model is explained in detail in the following section.
-------
2. Use of the I-O Model
In order to provide an analytical tool for the study of national air
pollution policies on the U. S. economic structure, CONSAD has de-
veloped a computer program entitled Program IOA (Input-Output Analy-
sis). This program accepts as input a hundred sector input-output table
and aggregates to any desired number of sectors. It also calculates the
corresponding input-output coefficient table and inverse matrix. This
program also takes changes of final demand by sectors and costs and
benefits of national air pollution control policies to estimate the changes
in production of each industry in the national economy.
To illustrate the use of this model, a 42-sector analysis has
been conducted in this appendix. First, the total control costs by in-
dustry were transformed into the changes in the final demand in the
nation.*
Second, the effects of benefit are estimated to be $l.-i> billion of
additional consumption expenditures to the national economy. This
additional consumption expenditure is then distributed among 42-sectors
* In this example, we use total control cost by industry provided
in Comprehensive Economic Cost Study of Air Pollution Control Cost
for Selected Industries and Selected Regions (February, 1970) by
Research Triangle Institute to APCO. Control costs by sector are
then transformed into changes of demands by the use of coefficients
estimated in the regional model.
-------
to the consumption pattern of the nation; in other words, proportionally
increase the sector consumption as before (this is average propensity
to consume by sector). Sectoring of the I-O model, coefficient and
inverse matrices are given in the following tables.
-------
Input-Output Coefficient Matrix and Inverse Matrix
Sectoring
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
SECTOR
of the Input- Output Model
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
AGRICULTURAL
IRON MINES
NON-FERROUS MINES
COAL MINES
CRUDE PETRO-NATURAL GAS
STONE AND CLAY MINING
CHEMICAL MINING
CONSTRUCTION
ORDINANCE (SIC 19)
FOOD PRODUCTS (SIC 20)
TOBACCO PRODUCTS (SIC 21)
TEXTILE PRODUCTS (SIC 22, 23)
LUMBER-WOODPRODUCTS (SIC 24)
FURNITURE-FIXTURES (SIC 25)
PAPER PRODUCTS (SIC 26)
PRINTING- PUBLISHING (SIC 27)
CHEMICAL PRODUCTS (SIC 28)
PETROLEUM REFINING (SIC 29)
RUBBER-PLASTIC PROD. (SIC 30)
LEATHER PRODUCTS (SIC 31)
STONE, CLAY.AND GLASS (SIC 32)
IRON AND STEEL MANU. (SIC 33)
COPPER MANUFACTURING (SIC 33)
ALUMINUM MANUFACTURE (SIC 33)
OTHER NON-FERROUS MET (SIC 33)
FABRICATED METAL PROD (SIC 34)
MACHINERY, EX. ELECTRIC (SIC 35)
ELECTIRCAL MACHINERY (SIC 36)
MOTOR VEHICLES (SIC 371)
OTHER TRANSPORTATION (SIC 37)
INSTRUMENTS (SIC 38)
MISCELLANEUS MANUFAC. (SIC 39)
TRANSPORTATION- WAREHOUSING
OTHER SERVICES
MEDICAL, EDUC. & NON-PROFIT
ELECTRIC UTILITIES (SIC 4911
GAS UTILITIES (SIC 492)
-------
Input-Output Coefficient Matrix and Inverse Matrix
Sectoring of the Input-Output Model (continued)
SECTOR 38 WHOLESALE & RETAIL
SECTOR 39 FINANCE & SERVICES
SECTOR 40 IMPORT OF GOODS AND SERVICES
SECTOR 41 GOVERNMENT ENTERPRISES
SECTOR 42 MISCELLANEOUS BUSINESS
-------
Input/Output Coefficient Matrix:
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
SOW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
SOW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
1
2
3
4
5
6
7
8
9
10
11
12
13
r*r-
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
'o.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
COL I
3063585
0
0
0001381
0
0012640
0005380
0123653
0
0669180
0
0023912
0019046
0
0008181
0002341
0225669
0174438
0032423
0000921
0005985
0
0
0
0000381
0024070
0042577
0005380
0011825
0004538
0
0000802
0163981
0641467
0030594
0027280
0003420
0337471
0096294
0203269
0001973
0009497
COL 2
0.0
0.0665909
0.0311749
0.0044598
0.0
0.0
0.0
0.0005684
0.0
0.0
0.0
0.0001312
0.0058590
0.0
0.0000437
0.0000437
0.0127235
0.0093131
0.0005247
0.0
0.0007433
0.0170959
0.0000874
0.0000874
0.0017489
0.0021425
0.0247038
0.0021862
0.0005247
0.0022299
0.0002186
0.0
0.0997771
0.0764724
0.0007433
0.0139915
0.0008745
0.0194570
0.0053343
0.2990252
0.0007433
0.0044598
COL 3
0.0
0.0088175
0.1529400
0.0009241
0.0
0.0000385
0.0008086
0.0008086
0.0
0.0
0.0
0.0013092
0.0010011
0.0
0.0003080
0.0002310
0.0294945
0.0056217
0.0023873
0.0
0.0047746
0.0360403
0.0001540
0.0001540
0.0047746
0.0016942
0.0250279
0.0036194
C. 0007316
0.0
0.0003465
0.0
0.0316507
0.0338070
0.0007316
0.0144777
0.0054676
0.0244H9
C. 0114743
0.1587927
0.0009241
0.0038505
COL 4
0.0
0.0
0.0
0.1821136
0.0
0.0004641
0.0000273
0.0008463
0.0
0.0
0.0
0.0006825
0.0074254
0.0
0.0026753
0.0002457
0.0174170
0.0108651
0.0078076
0.0
0.0023477
0.0084082
0.0004095
0.0000819
0.0064426
0.0118752
0.0501215
0.0032213
0.0021566
0.0042041
0.0000546
0.0012285
0.0073435
0.0278999
0.0009555
0.0227403
0.0000546
0.0328684
0.0096640
0.0010101
0.0012012
0.0040949
COL 5
0.0
0.0
0.0
0.0000179
0.0263655
0.0
0.0
0.0003882
0.0
0.0
0.0
0.0002090
0.0005793
0.0
0.0004658
0.0000717
0.0047476
0.0046998
0.0032069
0.0000060
0.0004121
0.0002747
0.0000119
0.0000119
0.0007465
0.0061330
0.0144219
0.0038100
0.0008779
0.0
0.0000597
0.0000358
0.0254876
0.1597037
0.0008480
0.0047416
0.0016482
0.0124870
0.0097878
0.0822316
0.0004419
0.0063958
COL 6
0.0
0.0002724
0.0005837
0.0017123
0.0
0.0070050
0.0003892
0.0012064
0.0
0.0
0.0
0.0000389
0.0000389
0.0
0.0096124
0.0002724
0.0118306
0.0272416
0.0187189
0.0
0.0649907
0.0141267
0.0000778
0.0000389
0.0011286
0.0007783
0.0706336
0.0019458
0.0039306
0.0001557
0.0002335
0.0003113
0.0158780
0.0365816
0.0008951
0.0215598
0.0017902
0.0603597
0.0106242
0.0592700
0.0015956
0.0049424
COL 7
0.0
0.0006233
0.0005194
0.0008311
0.0018699
0.0151673
0.0639934
0.0005194
0.0
0.0001039
0.0
0.0003117
0.0004155
0.0
0.0055059
0.0
0.0312695
0.0088303
0.0042593
0.0
0.0005194
0.0177644
0.0002078
0.0002078
0.0031166
0.0017661
0.0348016
0.0043632
0.0021816
0.0003117
0.0002078
0.0002078
0.0597342
0.0196343
0.0008311
0.0208810
0.0203615
0.0243286
0.0058176
0.0891337
0.0010389
0.0082070
COL 8
0.0028276
0.0
0.0
0.0000010
0.0000010
0.0100778
0.0
0.0001299
0.0000459
0.0001563
0.0
0.0000840
0.0507102
0.0064024
0.0052489
0.0001319
0.0215386
0.0181455
0.0054081
0.0000088
0.0707602
0.0333012
0.0052821
0.0003741
0.0171297
0.1041405
0.0193126
0.0252892
0.0000225
0.0000332
0.0031978
0.0018919
0.0313585
0.0506789
0.0009396
0.0020706
0.0000088
0.0879836
0.0081888
0.0
0.0002393
0.0038629
COL 9
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0015735
0.0184039
0.0
0.0
0.0007553
0.0011959
0.0000126
0.0047206
0.0017875
0.0034491
0.0021526
0.0236154
0.0000378
0.0036631
0.0134819
0.0073641
0.0142372
0.0287765
0.0190962
0.1096930
0.0693732
0.0041667
0.2121102
0.0238419
0.0025176
0.0104985
0.0201662
0.0009567
0.0029204
0.0006294
0.0288772
0.0063067
0.0018882
0.0009693
-------
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
COL 10
.3086993
.0
.0
.0006669
.0
.0000480
.0001243
.0033984
.0
.1710971
.0000075
.0025205
.0013931
.0
.0186234
.0018951
.0063447
.0038100
.0019045
.0000057
.0084461
.0000245
.0000207
.0004380
.0000876
.0271165
.0002618
.0004728
.0
.0
.0000019
.0004606
.0382386
.0599993
.0009306
.0034389
.0013328
.0348505
.0050128
.0352716
.0009155
.0054838
COL 11
0.1955482
0.0
0.0
0.0002436
0.0
0.0
0.0
0.0000530
0.0
0.0065455
0.1907821
0.0002012
0.0016523
0.0
0.0240106
0.0019594
0.0205896
0.0004448
0.0015146
0.0000424
0.0000106
0.0
0.0000212
0.0011650
0.0000212
0.0030609
0.0001165
0.0001059
0.0
0.0
0.0
0.0011333
0.0121801
0.0469408
0.0009850
0.0006355
0.0001589
0.0141818
0.0020547
0.0033998
0.0021500
0.0018535
COL 12
0.0435766
0.0
0.0
0.0006306
0.0
0.0000020
0.0000315
0.0004927
0.0
0.0009242
0.0
0.4189151
0.0000532
0.0006602
0.0095339
0.0009045
0.0549413
0.0012415
0.0052872
0.0019687
0.0010503
0.0002168
0.0000138
0.0000296
C. 0000769
0.0012829
0.0025126
0.0001360
0.0000315
0.0000512
0.0003015
0.0103143
0.0158340
0.0214346
0.0010168
0.0053010
0.0004178
0.0380115
0.0060695
0.0197497
0.0014287
0.0057405
COL 13
0.1142030
0.0
0.0000227
0.0001744
0.0
0.0000152
0.0000076
0.0015015
0.0
0.0000076
0.0
0.0014863
0.2677022
0.0024418
0.0089254
0.0028892
0.0158337
0.0075225
0.0049215
0.0000607
0.0042163
0.0019868
0.0000152
0.0007962
0.0001896
0.0100326
0.0034883
0.0011754
0.0000455
0.0007432
0.0000152
0.0013043
0.0435503
0.0215514
0.0008797
0.0048381
0.0002806
0.0393720
0.0054144
0.0624325
0.0009782
0.0065670
COL 14
0.0
0.0
0.0
0.0004359
0.0
0.0
0.0
0.0004480
0.0
0.0046615
0.0
0.0508838
0. 1031579
0.0308868
0.0210554
0.0005085
0.0189728
0.0019857
0.0295187
0.0016830
0.0232953
0.0466269
0.0000363
0.0087297
0.0008233
0.0635898
0.0091777
0.0028332
0.0007991
0.0005933
0.0019978
0.0074099
0.0196388
0.0331994
0.0010170
0.0048310
0.0003753
0.0553566
0.0050368
0.0001332
0.0008839
0.0096014
COL 15
0.0
0.0
0.0
0.0057325
0.0
0.0017786
0.0011649
0.0038579
0.0000919
0.0049017
0.0000084
0.0078911
0.0500813
0.0001169
0.2826012
0.0081040
0.0340194
0.0095278
0.0128095
0.0001253
0.0044424
0.0002129
0.0000084
0.0007766
0.0003006
0.0111394
0.0051229
0.0013945
0.0
0.0
0.0003298
0.0007933
0.0362698
0.0236816
0.0009645
0.0087261
0.0039664
0.0378856
0.0060331
0.0646193
0.0020208
0.0063003
COL 16
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0034071
0.0004181
0.0
0.0
0.0012735
0.0000481
0.0003844
0.1801769
0.1155335
0.0137917
0.0008458
0.0010716
0.0000481
0.0000144
0.0
0.0000144
0.0009995
0.0002547
0.0021144
0.0036810
0.0009323
0.0001538
0.0013359
0.0026190
0.0025854
0.0145510
0.0935868
0.0010668
0.0040270
0.0
0.0249357
0.0094428
0.0024700
0.0069631
0.0249212
COL 17
0.0016479
0.0023061
0.0018100
0.0041014
0.0010806
0.0009578
0.0158431
0.0015472
0.0000049
0.0176605
0.0
0.0022300
0.0019082
0.0000049
0.0300801
0.0025222
0.2598557
0.0323150
0.0096272
0.0000147
0.0100987
0.0033523
0.0004224
0.0020556
0.0047719
0.0185078
0.0078049
0.0009382
0.0000123
0.0000393
0.0017633
0.0010241
0.0302422
0.0706838
0.0009603
0.0085368
0.0057616
0.0281178
0.0088217
0.0174149
0.0011902
0.0118547
COL 18
0.0
0.0002255
0.0
0.0006063
0.5085889
0.0032976
0.0000665
0.0014824
0.0
0.0006913
0.0
0.0002736
0.0001664
0.0
0.0051867
0.0000370
0.0343695
0.0720884
0.0003734
0.0000037
0.0022662
0.000022?
0.0000333
0.0000333
0.0000739
0.0184398
0.0002773
0.0004695
0.0000148
0.0
0.0000776
0.0003586
0.0531975
0.0327836
0.000^464
0.0048946
0.0101145
0.0107023
0.0066395
0.0317337
0.0022625
-------
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
"OW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
COL 19
0
0
0
0016051
0
0005813
0012234
0009631
0
0001996
0
0870933
0017353
0002256
0114788
0028892
1869843
0024207
0325971
0019695
0108281
0021951
0000087
0001822
0022385
0153658
0054488
0036961
0000174
0027851
0016051
0040258
0222375
0402409
0010498
0088586
0010238
0327967
0058912
0372823
0011540
0095180
COL 20
0.0003601
0.0
0.0
0.0006220
0.0
0.0
0.0002128
0.0001146
0.0000164
0.0007038
0.0
0.0362211
0.0091494
0.0002619
0.0182006
0.0044683
0.0208194
0.0011785
0.0528668
0.2661997
0.0042883
0.0
0.0000491
0.0000491
0.0003437
0.0077909
0.0006220
0.0014240
0.0000491
0.0000818
0.0019805
0.0030771
0.0153526
0.0389708
0.0011948
0.0037645
0.0002619
0.0337496
0.0075454
0.0135686
0.0031589
0.0073326
COL 21
0.0003353
0.0007803
0.0008656
0.0071993
0.0
0.0457013
0.0022677
0.0004450
0.0
0.0007010
0.0
0.0022921
0.0074005
0.0005303
0.0413610
0.0016825
0.0383313
0.0090525
0.0079735
0.0000975
0.1080201
0.0022921
0.0001219
0.0010790
0.0010302
0.0127527
0.0034869
0.0045232
0.0001463
0.0000975
0.0006218
0.0018715
0.0504988
0.0295592
0.0010180
0.0137951
0.0162213
0.0329973
0.0095889
0.0141182
0.0023957
0.0096865
COL 22
0.0
0.0524958
0.0002456
0.0218467
0.0
0.0025895
0.0003335
0.0059491
0.0000515
0.0003548
0.0
0.0009127
0.0011704
0.0000728
0.0030837
0.0012098
0.0115070
0.0067951
0.0027259
0.0000030
0.0147242
0.2052591
0.0016161
0.0019285
0.0112524
0.0253610
0.0207096
0.0042208
0.0016738
0.0007944
0.0002820
0.0004730
0.0477566
0.0185174
0.0009248
0.0117890
0.0091207
0.0330657
0.0067951
0.0473745
0.0014130
0.0043390
COL 23
0.0
0.0
0.1268308
0.0012342
0.0
0.0010696
0.0
0.0003497
0.0
0.0002674
0.0
0.0005348
0.0001234
0.0
0.0027563
0.0006788
0.0062531
0.0040727
0.0013781
0.0
0.0032088
0.0080015
0.2751155
0.0185330
0.0908755
0.0182039
0.0123828
0.0030648
0.0007611
0.0
O.OOOC617
0.0006994
0.0215361
0.0151185
0.0007611
0.0074667
0.0049778
0.0389996
0.0060268
0.1343592
0.0007611
0.0045047
COL 24
0.0
0.0
0.0339620
0.0016575
0.0
0.0002706
0.0000507
0.0005243
0.0
0.0
0.0
0.0012008
0.0003383
0.0
0.0024186
0.0007949
0.0160338
0.0054123
0.0013362
0.0
0.0042960
0.0070698
0.0211924
0.2833830
0.0931586
0.0173531
0.0144778
0.0096068
0.0035180
0.0000677
0.0003214
0.0005412
0.0182833
0.0187907
0.0011839
0.0259112
0.0111797
0.0248288
0.0072727
0.0691248
0.0008457
0.0054968
COL 25
0.0
0.0065186
0.1005513
0.0008878
0.0
0.0000851
0.0003527
0.0001824
0.0003040
0.0
0.0
0.0043538
0.0021161
0.0000122
0.0042565
0.0007419
0.0294552
0.0024201
0.0015080
0.0
0.0053997
0.0165275
0.0985325
0.0393303
0.1307119
0.0216475
0.0156275
0.0232285
0.0
0.0011918
0.0006446
0.0018607
0.0200300
0.0135722
0.0006324
0.0079171
0.0033323
0.0458853
0.0061902
0.1633776
0.0006081
0.0040741
COL 26
0.0
0.0
0.0001762
0.0002715
0.0
0.0001155
0.0000116
0.0006614
0.0002224
0.0000116
0.0
0.0019465
0.0054324
0.0017993
0.0091580
0.0013632
0.0102642
0.0048346
0.0050310
0.0002310
0.0077949
0.2379037
0.0149803
0.0350379
0.0137096
0.0694693
0.0403952
0.0134525
0.0076389
0.0044967
0.0052794
0.0021025
0.0166179
0.0233066
0.0009444
0.0052851
0.0012765
0.0350523
0.0071624
0.0055335
0.001 1726
0.0093631
COL 27
0.0001125
0.0000792
0.0
0.0003084
0.0
0.0003667
0.0
0.0010918
0.0005000
0.0000458
0.0
0.0012522
0.0024315
0.0003438
0.0037837
0.0006938
0.0036003
0.0040045
0.0099384
0.0004417
0.0064173
0.0916253
0.0073757
0.0097905
0.0122574
0.0416623
0.1472430
0.0447334
0.0147222
0.0085987
0.0032399
0.0020981
0.0112573
0.0304029
0.0008813
0.0043004
0.0004084
0.0399621
0.0064756
0.0103281
0.0011793
-------
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
0.
COL 28
0
0005931
0003239
0002691
0
0000100
0
0010142
0106875
0
0
0016272
0022925
0098877
0128754
0009195
0149386
0018614
0155267
0003763
0164263
0406296
0076325
0102415
0353718
0445717
0285715
1659720
0032693
0049364
0115921
0017194
0131246
0397724
0010466
0046398
0004136
0398247
0041514
0061499
0026488
0187686
COL 29
0.0
0.0
0.0
0.0006382
0.0
0.0000041
0.0
0.0028133
0.0002442
0.0000021
0.0
0.0109125
0.0005520
0.0002319
0.0043584
0.0004412
0.0069973
0.0018776
0.0250671
0.0002873
0.0122258
0.0836145
0.0032298
0.0052121
0.0025014
0.0626678
0.0296923
0.0222128
0.2960653
0.0011676
0.0043194
0.0007244
0.0184228
0.0343195
0.0009131
0.0033283
0.0006320
0.0293537
0.0037141
0.0329221
0.0017073
0.0041717
COL 30
0.0
0.0
0.0
0.0003402
0.0
0.0000187
0.0
0.0013671
0.0389611
0.0
0.0
0.0019507
0.0085425
0.0043602
0.0012984
0.0006430
0.0070101
0.0028714
0.0067292
0.0001405
0.0063234
0.0572197
0.0021286
0.0142979
0.0102248
0.0429187
0.0539175
0.0394886
0.0087673
0.1609098
0.0131774
0.0018227
0.0114389
0.0159240
0.0009239
0.0043009
0.0006710
0.0272132
0.0035456
0.0057741
0.0010549
0.0064233
COL 31
0.0007061
0.0
0.0006012
0.0004962
0.0
0.0000191
0.0
0.0002004
0.0192092
0.0019658
0.0
0.0091322
0.0007920
0.0028246
0.0202779
0.0004008
0.0218715
0.0016318
0.0092754
0.0013550
0.0133023
0.0148291
0.0064031
0.0093517
0.0221101
0.0306220
0.0310801
0.0561197
0.0094376
0.0119568
0.0617689
0.0040365
0.0116228
0.0420826
0.0009256
0.0030918
0.0002863
0.0470639
0.0051625
0.0232170
0.0014696
0.0189038
COL 32
0.0020368
0.0
0.0
0.0001419
0.0
0.0001013
0.0
0.0027968
0.0000912
0.0013984
0.0001115
0.0260324
0.0203071
0.0011755
0.0578609
0.0046106
0.0379693
0.0027866
0.0320718
0.0131834
0.0076607
0.0273801
0.0078533
0.0105487
0.0343315
0.0307139
0.0076709
0.0142372
0.0010741
0.0026752
0.0012971
0.0578103
0.0158788
0.0448904
0.0010133
0.0045802
0.0
0.0657243
0.0083599
0.0528450
0.0019659
0.0132948
COL 33
0.0010322
0.0
0.0
0.0008328
0.0
0.0000369
0.0000240
0.0358942
0.0
0.0029398
0.0
0.0013055
0.0007867
0.0
0.0012206
0.0019722
0.0025280
0.0429113
0.0068620
0.0001163
0.0002844
0.0011320
0.0000979
0.0002604
0.0012446
0.0018411
0.0044078
0.0042693
0.0027311
0.0136390
0.0008107
0.0014090
0.0628583
0.0762683
0.0009048
0.0032500
0.0003305
0.0289916
0.0201520
0.0319166
0.0252726
0.0054087
COL 34
0.0177620
0.0000429
0.0000386
0.0004623
0.0008692
0.0000530
0.0000111
0.0500873
0.0014637
0.0006002
0.0000159
0.0044165
0.0002512
0.0008437
0.0467155
0,0015721
0.0052424
0.0069461
0.0009150
0.0001919
0.0017428
0.0001644
0.0000265
0.0000371
0.0004835
0.0016594
0.0098879
0.0067870
0.0094641
0.0026149
0.0052284
0.0049811
0.0056411
0.1005907
0.0013629
0.0055176
0.0010274
0.0224413
0.0232382
0.0015774
0.0199816
0.0080969
COL 35
0.0004350
0.0
0.0
0.0000099
0.0
0.0
0.0
0.0305973
0.0
0.0079045
0.0000074
0.0048149
0.0001681
0.0038756
0.0141060
0.0002101
0.0249791
0.0027090
0.0
0.0001285
0.0003164
0.0
0.0
0.0
0.0
0.0009244
0.0001409
0.0009615
0.0000840
0.0009442
0.0140566
0.0012902
0.0054254
0.1178879
0.0133126
0.0125711
0.0045158
0.0189753
0.0118864
0.0002175
0.0013273
0.0237877
COL 36
0.0
0.0
0.0002641
0.0286448
0.0000393
0.0000225
0.0000056
0.0334714
0.0000225
0.0000393
0.0000112
0.0001854
0.0002697
0.0000056
0.0012080
0.0002641
0.0011743
0.0162778
0.0004551
0.0000056
0.0013710
0.0020677
0.0002023
0.0005675
0.0000225
0.0078046
0.0006686
0.0008653
0.0000730
0.0001011
0.0000056
0.0003034
0.0337636
0.0202503
0.0007810
0.0606216
0.0277907
0.0152102
0.0056183
0.0012530
0.1388021
-------
ROW 1
ROW 2
KOW 3
ROW 4
ROW 5
ROW 6
ROW 7
ROW 8
ROW 9
ROW 10
ROW 11
ROW 12
ROW 13
ROW 14
ROW 15
ROW 16
ROW 17
ROW 18
ROW 19
ROW 20
ROW 21
ROW 22
ROW 23
ROW 24
ROW 25
ROW 26
ROW 27
ROW 28
ROW 29
ROW 30
ROW 31
ROW 32
ROW 33
ROW 34
ROW 35
ROW 36
ROW 37
ROW 38
ROW 39
ROW 40
ROW 41
ROW 42
COL 37
0.0
0.0
0.0
0.0005441
0.1902156
0.0
0.0
0.0233889
0.0
0.0
0.0
0.0
0.0
0.0
0.0001360
0.0001280
0.0000160
0.0048650
0.0001280
0.0000080
0.0004161
0.0004401
0.0
0.0
0.0
0.0028326
0.0001680
0.0002721
0.0
0.0000160
0.0
0.0001440
0.0010722
0.0105782
0.0002721
0.0000160
0.3569863
0.0044329
0.0020004
0.0027926
0.0170996
0.0013363
COL 38
0.0015527
0.0
0.0
0.0000498
0.0000027
0.0000375
0.0
0.0081364
0.0000942
0.0062929
0.0000321
0.0013842
0.0016749
0.0002716
0.0083254
0.0022516
0.0025915
0.0077787
0.0023684
0.0002321
0.0025185
0.0000792
0.0000212
0.0000416
0.0001003
0.0022701
0.0025977
0.0019097
0.0023547
0.0008416
0.0006580
0.0012449
0.0041873
0.1273597
0.0010013
0.0150497
0.0025499
0.0162250
0.0165696
0.0003044
0.0144081
0.0189940
COL 39
0.0
0.0
0.0
0.0002397
0.0
0.0
0.0
0.0044963
0.0
0.0
0.0
0.0020231
0.0
0.0
0.0047865
0.0134762
0.0004332
0.0034910
0.0018486
0.0000421
0.0
0.0
0.0
0.0
0.0
0.0
0.0001746
0.0000736
0.0
0.0003680
0.0
C. 0008517
0.0088811
0.1348231
0.0052765
0.0032261
0.0007928
C.C096529
0.2022780
0.0028097
0.0151229
0.0188242
COL 40 COL 41
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0111706
0.0000336
0.0
0.0025558
0.0004505
0.0000069
0.0
0.0223111
0.0
0.0053317
0.0
0.0001239
0.0000012
0.0005246
0.0009705
0.0004192
0.0008882
0.0001123
0.0
0.0000151
0.0002652
0.0000440
0.0
0.0
0.0
0.0004922
0.0000359
0.0000359
0.0004354
0.0
0.0
0.0000058
0.0151381
0.0061342
0.0000046
0.0067075
0.0012739
0.0019536
0.0009276
0.0033074
0.0002223
0.0018622
COL 42
0.0125010
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0005118
0.2218103
0.0161653
0.0007438
0.0002047
0.0425865
0.0874385
0.0011600
0.0035551
0.0013647
0.0
0.0028455
0.0005391
0.0002934
0.0
0.0
0.0
0.0009144
0.0007506
0.0068783
0.0
0.0
0.0023269
0.0289938
0.2994363
0.1188616
0.0041147
0.0
0.0
0.0406213
0.0010167
0.1043680
0.0
-------
THE LEONTIEF INVERSE MATRIX OF THE INPUT/OUTPUT TABLE ( 42 SECTORS )
DETERMINANTS. 0012276925
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
COL 1
.5044756
.0007345
.0007447
.0017042
.0199930
.0028480
.0019132
.0300775
.0004494
.1258757
.0001632
.0105866
.0087251
.0008705
.0220194
.0022061
.0569692
.0364245
.0079898
.0004072
.0070416
.0096064
.0013008
.0017623
.0025341
.0161414
.0138565
.0055938
.0056236
.0025892
.0015779
.0018834
.0450953
.1511621
.0055629
.0092303
.0038912
.0715984
.0287046
.0464898
.0078832
.0077860
COL 2
0.0083713
1.0738344
0.0406267
0.0081650
0.0121228
0.0006251
0.0005629
0.0136298
0.0006007
0.0049675
0.0002013
0.0032771
0.0116229
0.0008535
0.0137504
0.0013887
0.0276031
0.0211718
0.0036557
0.0001981
0.0046322
0.0353789
0.0019402
0.0021781
0.0053378
0.0106710
0.0386597
0.0087203
0.0044029
0.0061959
0.0017166
0.0015978
0.1287200
0.1256626
0.0015820
0.0205304
0.0046768
0.0389340
0.0179324
0.3412825
0.0107569
0.0098421
COL 3
0.0059906
0.0150318
1.1829691
0.0046175
0.0106211
0.0009801
0.0020400
0.0093633
0.0003812
0.0050108
0.0001932
0.0055348
0.0043449
0.0007533
0.0125054
0.0016070
0.0552313
0.0149342
0.0056759
0.0001820
0.0102453
0.0644230
0.0027596
0.0026665
0.0099489
0.0109119
0.0411521
0.0102392
0.0039457
0.0020551
0.0015772
0.0013052
0.0562844
0.0764168
0.0015807
0.0225635
0.0137255
0.0446320
0.0246478
0.2058599
0.0089575
0.0094748
COL 4
0.0076513
0.0021121
0.0025958
1.2253637
0.0114803
0.0012816
0.0007828
0.0081456
0.0006578
0.0050696
0.0002134
0.0056044
0.0154670
0.0009043
0.0162194
0.0016255
0.0392294
0.0203316
0.0129469
0.0002647
0.0071311
0.0332212
0.0043599
0.0040981
0.0132567
0.0258527
0.0782850
0.0122211
0.0072367
0.0081806
0.0014766
0.0030129
0.0251693
0.0690820
0.0018958
0.0337646
0.0038237
0.0569393
0.0219854
0.0160336
0.0099369
0.0104448
COL 5
0.0086419
0.0005819
0.0006685
0.0010188
1.0335855
0.0004306
0.0002795
0.0131649
0.0005377
0.0045486
0.0002203
0.0038056
0.0037002
0.0009132
0.0180575
0.0012825
0.0134146
0.0105010
0.0053227
0.0002117
0.0032348
0.0088774
0.0012484
0.0015353
0.0029012
0.0123561
0.0225795
0.0092167
0.0050045
0.0018622
0.0017222
0.0018303
0. 0380905
0.2021729
0.0015677
0.0081753
0.0042537
0.0259726
0.0215468
0.0925642
0.0076138
0.0107470
COL 6
0.0067015
0.0025293
0.0020880
0.0054629
0.0211999
1.0111170
0.0012801
0.0097217
0.0004459
0.0056718
0.0002431
0.0063580
0.0048789
0.0009509
0.0304192
0.0019549
0.0340308
0.0371648
0.0235299
0.0003107
0.0777666
0.0355744
0.0025022
0.0030334
0.0052027
0.0130378
0.0899910
0.0106030
0.0094971
0.0025507
0.0016956
0.0020862
0.0382642
0.0819654
0.0017892
0.0291223
0.0085313
0.0814304
0.0229149
0.0767699
0.0106914
0.0118742
COL 7
0.0060722
0.0029827
0.0022526
0.0038142
0.0196298
0.0169745
1.0693502
0.0096666
0.0003991
0.0063491
0.0002765
0.0038106
0.0037355
0.0009342
0.0193404
0.0012334
0.0536638
0.0194659
0.0080865
0.0002122
0.0054265
0.0362080
0.0024927
0.0026739
0.0071203
0.0109666
0.0505215
0.0111588
0.0061187
0.0028916
0.0013690
0.0016092
0.0839990
0.0583800
0.0015870
0.0279521
0.0373156
0.0433877
0.0157917
0.1118456
0.0103176
0.0136514
COL 8
0.0252139
0.0056614
0.0067590
0.0046237
0.0181908
0.0148954
0.0012303
1.0118361
0.0010066
0.0089489
0.0002958
0.0069759
0.0767707
0.0084283
0.0324601
0.0026589
0.0520707
0.0318599
0.0119609
0.0004302
0.0878886
0.0914516
0.0155797
0.0110019
0.0293244
0.1277483
0.0402830
0.0399607
0.0053223
0.0036265
0.0060491
0.0045870
0.0662074
0.1158310
0.0023001
0.0120068
0.0070573
0.1233608
0.0250815
0.0346334
0.0093855
0.0143888
COL 9
0.0112770
0.0054838
0.0121006
0.0039565
0.0076194
0.0013408
0.0007501
0.0106925
1.0314569
0.0118704
0.0006780
0.0122457
0.0107600
0.0043829
0.0288629
0.0042943
0.0327976
0.0120020
0.0342861
0.0007956
0.0154592
0.0852984
0.0246626
0.0373659
0.0548241
0.0583279
0.1656926
0.1156542
0.0165225
0.2653965
0.0333077
0.0064593
0.047225?
0.080R800
0.0025415
0.0129247
0.0063227
0.0737397
0.0211921
0.0423067
0.0086183
-------
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
COL 10
0.5678055
0.0012012
0.0010727
0.0030720
0.0145968
0.0021423
0.0013139
0.0245993
0.0005081
1.2583876
0.0002991
0.0123989
0.0106423
0.0012003
0.0531135
0.0048173
0.0405034
0.0252257
0.0078510
0.0003716
0.0171186
0.0183495
0.0021265
0.0040551
0.0034628
0.0464972
0.0115589
0.0063834
0.0047204
0.0028653
0.0013351
0.0028001
0.0786241
0.1643877
0.0038560
0.0116158
0.0062454
0.0832513
0.0264005
0.0714816
0.0104300
0.0140039
COL 11
0.3735555
0.0005210
0.0006638
0.0017312
0.0080619
0.0010433
0.0012373
0.0134555
0.0003217
0.0440034
1.2359066
0.0056276
0.0094225
0.0006501
0.0570177
0.0043915
0.0544195
0.0140859
0.0057880
0.0003080
0.0037438
0.0062321
0.0009704
0.0033061
0.0019489
0.0118828
0.0066916
0.0034211
0.0029032
0.0015276
0.0011683
0.0028552
0.0356039
0.1184679
0.0030104
0.0056048
0.0027196
0.0446639
0.0153997
0.0240891
0.0082021
0.0071643
COL 12
0.1224919
0.0008790
0.0010536
0.0034480
0.0091070
0.0009409
0.0026521
0.0113538
0.0003351
0.0207081
0.0003440
1.7276945
0.0062111
0.0022537
0.0433977
0.0038274
0.1430452
0.0151457
0.0142837
0.0052224
0.0066463
0.0084072
0.0014175
0.0020664
0.0033223
0.0117046
0.0115080
0.0041908
0.0025994
0.0018757
O.C018934
0.0206145
0.0502056
0.0944411
0.0029888
0.0153499
O.OC48199
0.0884924
0.0241046
0.0520657
0.0103108
0.0167666
COL 13
0.2410139
0.0009759
0.0009116
0.0019852
0.0134804
0.0012218
0.0010415
0.0145099
0.0004008
0.0257287
0.0003000
0.0089355
1.3711281
0.0044672
0.0326338
0.0061027
0.0469749
0.0241988
0.0107704
0.0004222
0.0104100
0.0143353
0.0014609
0.0036336
0.0028808
0.0229468
0.0124981
0.0060140
0.0029870
0.0035766
0,0012631
0.0035693
0.0835018
0.0887179
0.0027046
0.0121179
0.0035917
0.0784793
0.0211984
0.1042699
0.0089831
0.0147091
COL 14
0.0426248
0.0054904
0.0028374
0.0050096
0.0086112
0.0022629
0.0012883
0.0108434
0.0005386
0.0172709
0.0003907
0.1005805
0.1524943
1.0337019
0.0530885
0.0033572
0.0598559
0.0139275
0.0375090
0.0031777
0.0340861
0.0926486
0.0043896
0.0188757
0.0083581
0.0840557
0.0242213
0.0105696
0.0050088
0.0034878
O.C040552
0.0115274
0.0566008
0.0925626
0.0024205
0.0141696
0.0060369
0.0931891
0.0197854
0.0364417
0.0090926
0.0191565
COL 15
0.0291225
0.0010015
0.0010899
0.0117860
0.0158130
0.0035623
0.0032330
0.0153606
0.0005023
0.0177633
0.0003342
0.0251526
0.0993707
0.0015036
1.4142389
0.0146730
0.0815269
0.0253792
0.0223761
0.0005949
0.0118492
0.0130837
0.0016914
0.0040315
0.0035545
0.0261674
0.0159329
0.0067524
0.0026097
0.0024007
0.0018288
0.0032480
0.0767000
0.0840295
0.0023467
0.0186365
0.0121719
0.0761216
0.0210801
0.1116329
0.0114692
0.0157212
COL 16
0.0165404
0.0006236
0.0008421
0.0033282
0.0069672
0.0011256
0.0011640
0.0168315
0.0011056
0.0144462
0.0007150
0.0106139
0.0234353
0.0025283
0.3052018
1.1347790
0.0436987
0.0114344
0.0077394
0.0005265
0.0051664
0.0082794
0.0013360
0.0035655
0.0029783
0.0131360
0.0125630
0.0063740
0.0034555
0.0038885
0.0048481
0.0059102
0.0499144
0.1595440
0.0024009
0.0115576
0.0040589
0.0565127
0.0251282
0.0354122
0.0161989
0.0353075
COL 17
0.0284836
0.0048957
0.0054359
0.0093752
0.0347586
0.0032484
0.0234345
0.0157274
0.0005684
0.0392487
0.0004684
0.0119092
0.0117136
0.0015488
0.0781650
0.0061029
1.3715668
0.0567939
0.0175367
0.0004058
0.0202205
0.0233657
0.0039842
0.0074500
0.0114449
0.0375579
0.0225131
0.0077802
0.0039460
0.0026972
0.0042854
0.0039166
0.0715456
0.1546938
0.0024730
0.0189643
0.0172948
0.0630493
0.0274112
0.0522261
0.0142367
0.0230704
COL 18
0.0095524
0.0013018
0.0010204
0.0025527
0.5740113
0.0043219
0.0012074
0.0159502
0.0005239
0.0070663
0.0002327
0.0045483
0.0048168
0.0009781
0.0268452
0.0017449
0.0620990
1.0905399
0.0054260
0.0002378
0.0071430
0.0147172
0.0018366
0.0027409
0.0035089
0.0324068
0.0173410
0.0081464
0.0044692
0.0027070
0.0018878
0.0023175
0.0905425
0.1726167
0.0023639
0.0126992
0.0210899
0.0364505
0.0266943
0.0936819
0.0119678
-------
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
SOW
ROW
ROW
ROM
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
«*OW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
COL 19
.0223425
.0018206
.0021206
.0054482
.0119825
.0022313
.0063147
.0112317
.0005979
.0146502
.0003914
.1602871
.0087157
.0016471
.0461808
.0059069
.2843417
.0193402
.0404663
.0035615
.0192489
.0179386
.0024810
.0039157
.0072796
.0299066
.0165031
.0096333
.0029216
.0054794
.0037044
.0082145
.0540078
.1057435
.0023431
.0176117
.0074336
.0649900
.0206498
.0626673
.0100804
.0192578
COL 20
0.0188650
0.0007525
0.0008882
0.0029621
0.0061575
0.0009407
0.0017779
0.0090769
0.0004377
0.0101055
0.0003425
0.0998992
0.0229461
0.0015841
0.0548501
0.0090156
0.0736083
0.0101131
0.0782984
1.3635445
0.0109548
0.0092054
0.0014145
0.0021095
0.0031438
0.0193668
0.0073412
0.0061564
0.0024434
0.0019849
0.0042721
0.0074706
0.0427476
0.1000878
0.0026144
0.0111067
0.0035549
0.0686827
0.0226032
0.0374972
0.0111618
0.0168159
COL 21
0.0117189
0.0021264
0.0024613
0.0125403
0.0184544
0.0525192
0.0041398
0.0109037
0.0004157
0.0095061
0.0003487
0.0099237
0.0191134
0.0017976
0.0807824
0.0042670
0.0732394
0.0229300
0.0143141
0.0004610
1.1289406
0.0161931
0.0019882
0.0041799
0.0045692
0.0239958
0.0161076
0.0104448
0.0030260
0.0024800
0.0020480
0.0040274
0.0816974
0.0810924
0.0020833
O.C228921
0.0322899
0.0601478
0.0237265
0.0392945
0.0121352
0.0171637
COL 22
0.0069106
0.0724767
0.0072330
0.0363077
0.0144572
0.0049137
0.0011315
0.0170422
0.0005840
0.0060425
0.0002380
0.0056051
0.0070194
0.0011199
0.0187835
0.0031578
0.0325852
0.0193431
0.0072952
0.0002454
0.0255263
1.2825737
0.0078258
0.0082618
0.0216064
0.0445675
0.0427287
0.0133558
0.0063829
0.0043449
0.0018449
0.0022379
0.0882140
0.0708023
0.0020442
0.0225186
0.0217308
0.0634040
0.0207494
0.0998092
0.0107982
0.0116729
COL 23
0.0069436
0.0060835
0.2310888
0.0053929
0.0119682
0.0024824
0.0009755
C. 0095584
0.0005228
0.0067267
0.0002843
0.0062757
0.0045347
0.0010876
C.0196051
0.0027647
0.0370188
0.0159998
0.0059920
0.0002576
0.0115076
0.0477176
1.4039736
0.0482170
0.1565312
0.0407012
0.0382534
0.0163322
0.0050835
0.0025497
0.0016900
0.0029182
0.0607491
0.0736406
0.0021774
0.0220322
0.0178954
0.0855585
0.0240640
0.2679546
0.0099087
0.0139411
COL 24
0.0071526
0.0044265
0.0858477
0.0067497
0.0154365
0.0014815
0.0011487
0.0105084
0.0006529
0.0067172
0.0003045
0.0077217
0.0048972
0.0012509
0.0198830
0.0029242
0.0501109
0.0176709
0.0059872
0.0002655
0.0128257
0.0412353
0.0651284
1.4095707
0.1621291
0.0411164
0.0382958
0.0268053
0.0102499
0.0026423
0.0021979
0.0027863
0.0534888
0.0739221
0.0027077
0.0465884
0.0310906
0.0648118
0.0241994
0.1600282
0.0131533
0.0149775
COL 25
0.0079437
0.0132201
0.1685048
0.0050512
0.0101900
0.0014108
0.0018321
0.0089249
0.0011897
0.0069181
0.0002737
0.0131164
0.0077463
0.0013483
0.0228430
0.0028258
0.0671172
0.0140047
0.0065505
0.0003142
0.0139338
0.0562724
0.1647955
0.0730444
1.1810884
0.0427868
0.0393138
0.0406493
0.0039569
0.0042903
0.0026876
0.0041769
0.0553068
0.0701323
0.0019299
0.0212299
0.0139658
0.0860575
0.0224541
0.2641056
C. 0093544
0.0134038
COL 26
0.0093724
0.0198867
0.0124152
0.0113559
0.0109919
0.0023325
0.0008780
0.0112885
0.0012852
0.0078740
0.0003791
0.0093818
0.0137383
0.0035144
0.0308720
0.0037845
0.0355194
0.0165541
0.0110457
0.0007111
0.0199716
0.3447180
0.0313340
0.0594881
0.0349929
1.0977659
0.0696896
0.0286980
0.0165357
0.0090868
0.0079658
0.0045906
0.0587681
0.0778509
0.0023112
0.0175351
0.0114080
0.0730790
0.0225024
0.0569022
0.0095501
0.0186893
COL 27
0.0091822
0.0099960
0.0079037
0.0063035
0.0081723
0.0019377
0.0005899
0.0101789
0.0022987
0.0085746
0.0004608
0.0089806
0.0088206
0.0026108
0.0233383
0.0026879
0.0240458
0.0130641
0.0172239
0.0011168
0.0165672
0.1687656
0.0197381
0.0241722
0.0289604
0.0698200
1.1897917
0.0711396
0.0284347
0.0151104
0.0066631
0.0048545
0.0434669
0.0827346
0.0021178
0.0132300
0.0063565
0.0748756
0.0199640
0.0451855
0.0084348
-------
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
ROW
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
COL 28
0.0118712
0.0069692
0.0128505
0.0048314
0.0075485
0.0020218
0.0010295
0.0110062
0.0143199
0.0116801
0.0006256
0.0124838
0.0118823
0.0142745
0.0427327
0.0033030
0.0465654
0.0117642
0.0247532
0.0011388
0.0293977
0.0978632
0.0246436
0.0273250
0.0602185
0.0740098
0.0565677
1.2116385
0.0104115
0.0135401
0.0172976
0.0051057
0.0486079
0.0999770
0.0024338
0.0141620
0.0064219
0.0789868
0.0180740
0.0462436
0.0108564
0.0309877
COL 29
0.0103308
0.0114705
0.0054321
0.0081049
0.0086241
0.0022683
0.0009581
0.0148686
0.0015542
0.0071698
0.0003123
0.0368958
0.0071903
0.0021218
0.0287439
0.0028422
0.0413302
0.0132551
0.0424011
0.0010996
0.0289198
0.1979286
0.0135014
0.0199943
0.0164358
0.1141499
0.0679085
0.0488436
1.4262447
0.0059424
0.0092721
0.0038334
0.0593245
0.1030271
0.0025381
0.0142746
0.0079583
0.0755836
0.0195192
0.0799911
0.0106569
0.0153269
COL 30
C. 0096340
0.0078093
0.0073294
0.0053812
0.0075493
0.0014598
0.0006669
0.0094258
0.0493466
0.0071350
0.0003516
0.0106795
0.0194556
0.0074275
0.0188515
0.0026335
0.0297957
0.0118349
0.0154526
0.0006952
0.0169832
0.1312855
0.0131483
0.0343002
0.0295789
0.0762557
0.0980608
0.0734903
C. 0206787
1.2076855
0.0207544
0.0046945
0.0420057
0.0652112
0.0022208
0.0133691
0.0065022
0.0641669
0.0159144
0.0380248
0.0078571
0.0173552
COL 31
0.0139856
0.0036166
0.0095093
0.0037570
0.0066544
0.0016105
0.0010748
0.0093025
0.0230140
0.0139610
0.0005926
0.0240882
0.0085552
0.0057446
0.0509659
0.0025657
0.0516336
0.0105709
0.0169226
0.0025267
0.0230843
0.0540121
0.0187482
0.0228705
0.0395160
0.0531676
0.0561446
0.0834436
0.0190050
0.0237532
1.0693836
0.0074282
0.0416379
0.0968558
0.0021462
0.0109393
0.0050270
0.0802597
0.0180179
0.0558505
0.0086569
0.0293155
COL 32
0.0239006
0.0041050
0.0105488
0.0041996
0.0090076
0.0016340
0.0017762
0.0137326
0.0009319
0.0138532
0.0006252
0.0600355
0.0405369
0.0031494
0.1094448
0.0085861
0.0866556
0.0147706
0.0419946
0.0196524
0.0163883
0.0603122
0.0210783
0.0230793
0.0510627
0.0498473
0.0222847
0.0258373
0.0052907
0.0059933
0.0035927
1.0645638
0.0503054
0.1091086
0.0024083
0.0142195
0.0055749
0.1037990
0.0244275
0.0969812
0.0107275
0.0235706
COL 33
0.0112794
0.0008098
0.0010287
0.0022533
0.0291162
0.0011094
0.0003556
0.0472284
0.0011263
0.0087762
0.0002166
0.0062544
0.0066447
0.0011930
0.0162858
0.0037231
0.0152599
0.0543040
0.0099049
0.0004016
0.0062373
0.0123745
0.0019249
0.0024014
0.0046662
0.0135373
0.0126793
0.0110376
0.0068965
0.0187440
0.0025382
0.0031554
1.0827579
0.1228515
0.0017699
0.0074882
0.0031075
0.0473876
0.0348267
0.0461963
0.0325729
0.0105861
COL 34
0.0374613
0.0009534
0.0011333
0.0023955
0.0096402
0.0014592
0.0005416
0.0611715
0.0022721
0.0092724
0.0003058
0.0125521
0.0116514
0.0023593
0.0827535
0.0039283
0.0213339
0.0148298
0.0050735
0.0006282
0.0096743
0.0138802
0.0021851
0.0024204
0.0045747
0.0159729
0.0198740
0.0149932
0.0166907
0.0052455
0.0074052
0.0072818
0.0248553
1.1472855
0.0023945
0.0104681
0.0042569
0.0456011
0.0386974
0.0176855
0.0269010
0.0139812
COL 35
0.0173555
0.0006186
0.0007785
0.0016430
0.0072554
0.0009418
0.0008351
0.0422655
0.0007911
0.0207360
0.0005976
0.0124764
0.0075534
0.0059718
0.0401551
0.0017999
0.0439604
0.0098973
0.0029095
0.0005314
0.0062436
0.0082014
0.0014085
0.0016641
0.0029014
0.0113198
0.0067151
0.0067137
0.0032155
0.0028140
0.0167602
0.0038264
0.0262571
0.1635222
1.0143499
0.0172519
0.0096433
0.0383261
0.0241195
0.0132968
0.0092014
C. 0291467
COL 36
0.0072243
0.0007666
0.0010585
0.0385862
0.0223180
0.0009753
0.0002068
0.0453490
0.0002534
0=0043872
0.0001677
0.0020366
0.0052479
0.0008938
0.0093450
0.0011494
0.0090272
0.0245145
0.0024688
0.0001303
0.0066329
0.0123091
0.0016563
0.0023064
0.0026761
0.0176733
0.0075332
0.0047587
0.0017974
0.0016680
0.0008163
0.0013030
0.0509868
0.0501166
0.0013505
1.0687637
0.0478263
0.0298846
0.0135649
0.0110276
0.1524147
-------
ROW 1
ROW 2
ROW 3
ROW 4
ROW 5
ROW 6
ROW 7
ROW 8
ROW 9
ROW 10
ROW 11
ROW 12
ROW 13
ROW 14
ROW 1.5
ROW 16
ROW 17
ROW 18
ROW 19
ROW 20
ROW 21
ROW 22
ROW 23
ROW 24
ROW 25
ROW 26
ROW 27
ROW 28
ROW 29
ROW 30
ROW 31
ROW 32
ROW 33
ROW 34
ROW 35
ROW 36
ROW 37
ROW 38
ROW 39
ROW 40
ROW 41
ROW 42
COL 37
0.0053363
0.0005657
0.0005597
0.0017805
0.3112421
0.0008016
0.0001655
0.0429335
0.0002654
0.0029575
0.0001353
0.0018788
0.0044356
0.0007896
0.0095150
0.0009407
0.0074405
0.0132548
0.0025522
0.0001354
0.0053988
0.0090410
0.0011864
0.0012510
0.0023201
0.0140719
0.0095424
0.0053351
0.0021920
0.0009695
0.0009673
0.0012399
0.0185812
0.0878640
0.0011100
0.0037179
1.5571680
0.0214603
0.0126469
0.0351312
0.0302036
0.0066253
COL 38
0.0162305
0.0004118
0.0004600
0.0014736
0.0082723
0.0006817
0.0002678
0.0194045
0.0005706
0.0161312
0.0005065
0.0058003
0.0065064
0.0017274
0.0293644
0.0039113
0.0113272
0.0131940
0.0044131
0.0005820
0.0060291
0.0061440
0.0009020
0.0011526
0.0017821
0.0086291
0.0078348
0.0060056
0.0061928
0.0023090
0.0021095
0.0033114
0.0203835
0.1656057
0.0017799
0.0190590
0.0061704
1.0300436
0.0286646
0.0103655
0.0221482
0.0230862
COL 39
0.0121505
0.0002912
0.0003483
0.0013299
0.0058625
0.0004804
0.0002193
0.0190405
0.0005057
0.0095735
0.0005637
0.0079926
0.0043403
0.0016880
0.0330240
0.0202463
0.0088747
0.0096522
C. 0042070
0.0003717
0.0029604
0.0042250
0.0006665
0.0008062
0.0013981
0.0053038
0.0050987
0.0038771
0.0033364
0.0020476
0.0017204
0.0035271
0.0282086
0.2099456
0.0074163
0.0074697
0.0031252
0.0263918
1.2627697
0.0133718
0.0258499
0.0279068
COL 40
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
1.0000000
0.0
0.0
COL 41
0.0213432
0.0002380
0.0002256
0.0036311
0.0024369
0.0004493
0.0000986
0.0246993
0.0000735
0.0092151
0.0000581
0.0009269
0.0023983
0.0008866
0.0041787
0.0007442
0.0040851
0.0027291
0.0007614
0.0000638
0.0027684
0.0031686
0.0004801
0.0004413
0.0009217
0.0045794
0.0019226
0.0014779
0.0011191
0.0005144
0.0002931
0.0003533
0.0204232
0.0162152
0.0002292
0.0080133
0.0027026
0.0079115
0.0033399
0.0066084
1.0024595
0.0028643
COL 42
0.1648893
0.0012300
0.0014076
0.0032523
0.0169084
0.0015946
0.0010171
0.0309133
0.0015405
0.2889343
0.0202470
0.0166590
0.0232192
0.0454040
0.1595598
0.0060204
0.0364149
0.0300317
0.0110452
0.0049730
0.0112361
0.0185895
0.0026250
0.0042418
0.0056629
0.0263813
0.0141588
0.0174380
0.0062742
0.0079004
0.0053546
0-0344236
0.3585163
0.2379844
0.0064732
0.0100850
0.0048984
0.0969115
0.0279144
0.1528694
0.0184115
1.0126419
-------
Distribution by Average Consumption by Sector
SECTOR- 1
SECTOR- 2
SECTOR- 3
SEC TQ"- 4
SECTOR- 5
SECTOR- 6
SECTOR- 7
SECTOR- 8
SECTOR- 9
SECTQR-IO
SSCTTR-11
SECTOR-12
SECTOR-13
SSCTC;?-14
SEC TOP- 15
SECTOR-16
SECTOR-17
SECTOR-IB
SEC TOR- IQ
SECTOR-20
SECTOR-?!
SECTHR-22
SECTOR-23
SECTQR-24
SECTnR-25
SECTQP-26
SECTOR-27
SECTOR-28
SECTOR-29
SECTOR-30
SECTOR-31
SECTOR-32
SECTOR-33
SECTOR-34
SECTOP.-35
SECTQR-36
SECTOR-37
SECTOR-38
SECTOR-39
SECTOR-40
SECTOR-41
SECTOR-42
PERCENT
0.011847
0.0
0.0
0.000704
0.0
0.000052
0.000004
0.0
C.OOC595
0. 154956
0.014550
0.0479BO
0.000510
O.OOH813
0.002897
0.008361
0,014029
0.0??549
0. 004912
C. 00394 7
0.001244
C. 000059
C.O
C.OOC020
0.000017
0.002557
C. 00 1701
0.016406
0.037152
0.003175
C. 003129
0.009590
0.026651
0.225173
0.080286
0.015634
0.009600
0. 199479
0.049824
0.013115
0.003497
0.000015
SECTOR DEFINITION
AGRICULTURAL
IRON MINES
NON-FERROUS MINES
COAL MINES
CRUDE PETRO.-NATURAL GAS
STONE AND CLAY MINING
CHEMICAL MINING
CONSTRUCTION'
ORDINANCE (SIC IS)
FOOD PRODUCTS (SIC 20)
TOBACCO PRODUCTSISIC 21)
TEXTILE PRODUCTS (SIC 22,?31
LUMBER-VOHD PRODUCTS!SIC 24)
FURNITURE-FIXTURES (SIC 25)
PAPER PRODUCTS (SIC 26)
PRINTING-PUBLISHING (sic. 27)
CHEMICAL PRODUCTS (SIC 28)
PETROLEUM REFINING (SIC 29)
RUBBER-PLASTIC PROD.(SIC 30)
LEATHER PRODUCTS (SIC 31)
STONE,CLAY AND GLASS(SIC 32)
IRON AND STEFL MANU.'SIC 33)
COPPER MANUFACTURING;STC 3?)
ALUMINUM MAM;FACTURE< sic 3?)
OTHER NGN-FERROUS ME"f(SIC33)
FABRICATED METAL PROD'SIC34)
MACHINERY, EX.ELECT*IC(STC35)
ELECTRICAL MACHINERY(SIC 36)
MOTOR VEHICLES (SIC 371)
OTHER TRANSPORTATIONS SIC 37)
INSTRUMENTS (SIC 3B)
MISCELLANEUS MANUFAC.(S1C 39)
TRANSPORTATION-WAREHDUSING
OTHER SERVICES
MEDICAL* EDUC. & NGN-PROFIT
ELECTRIC UTILITIES (SIC 4911
GAS UTILITIES (SIC 49?)
WHOLESALE Ł RETAIL TRADE
FINANCE & SERVICES
IMPORT OF GOODS AND SERVICES
GOVERNMENT ENTERPRISES
MISCELLANEOUS BUSINESS
-------
3. Interregional Feedback
Regional Market Shares
by AQCR by Industry
As described in Appendix C, changes in the final demand of the
nation (by sectors) will have a chain reaction on the national economy.
The next question is how will such structural changes affect
each AQCR. It is argued that the regional share of the national mar-
ket by each industry is stable. * Therefore, any changes of production
at national level will have a proportional effect on each region of its
market shares.
By definition, regional market share by industry by AQCR is:
->.. -
b.. is the regional market share of jth industry product
IJ from ith AQCR
X.. is the output (value-added) of jth industry in ith AQCR
N
X. is the output (value-added) of jth industry in the nation
A regional market share matrix
B = [b..]
1 iJJ
has been estimated for 100 AQCR's and two-digit manufacturing indus
tries (SIC 20 to SIC39).
* Sometimes this is called "locational quotient" of the industry.
-------
CONSAD has developed a computer program, namely, Program
FEE (Feedback Effects), which uses B matrix to distribute the change
obtained from the I-O model.
-------
B = (bŁj) REGIONAL MARKET SHARE COEFFICIENT MATRIX
po
o
AQCR
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
<»1
S 1C
20
.0624
.0832
.0464
.0387
.0158
.0356
.0201
.0 131
.0237
.0060
.0091
.0180
.0043
.0161
.0133
.0121
.0 189
.0205
.0153
.0104
.0083
.0130
.0034
.0093
.0089
.0046
.0108
.0093
.0079
.0037
.0038
.0
.0062
.0
.0044
.0037
.0040
.0009
.0024
.0069
.0033
SIC
22
.0359
.0
.0062
.0259
.0020
.0006
.0053
.0004
.0018
.0
.0028
.0012
.0019
.0008
.0003
.0016
.0012
.0009
.0004
.0004
.0002
-.0001
.0
.0033
.0
.0005
.0001
.0
.0023
.0152
.0
.0
.0
.0
.0009
.0008
.0
.0
.0074
.0008
.0
SIC
22
.17«0
.0161
.02(5
.02C,9
.0070
.0042
.01 17
.OOC9
.0061
.OOU
.0076
. 00 «0
.OOC6
.0026
.OOC8
.0011
.00 14
.0026
.0012
.0061
.0013
.0040
.OOC8
.0062
.OOC4
.0027
. OOC6
.0051
.0020
.0018
.OOC8
.0
.OOC3
.0
.OOC4
.OOC5
.OOC2
.OOC2
.OOC3
.OOC8
.OOC3
SIC
24
.0099
.0091
.0134
.0051
.0040
.0035
.0035
.0015
.0022
.0008
.0012
.0020
.0008
.0054
.0034
.0014
.0014
.0015
.0051
.0012
.0125
.0011
.0006
.0012
.0027
.0009
.0007
.0008
.0119
.0
.0010
.0
.0006
.0
.0012
.0008
.0010
.0
.0009
.0053
.0
SIC
25
.0478
.0468
.0624
.0166
.0070
.0131
.0080
.0026
.0079
.0011
.0115
.0066
.0014
.0041
.0037
.0043
.0031
.0097
.0073
.0057
.0021
.0044
.0015
.0063
.0021
.0056
.0024
.0007
.0049
.0
.0015
.0
.0031
.0
.0024
.0023
.0024
.0
.0007
.0042
.0014
SIC
26
.C440
.C504
.0283
.0464
.0091
.0160
.0171
.0053
.0113
.0022
.0094
.0113
.0029
.0102
.0074
.0143
.0069
.0120
.0027
.0059
.0133
.0076
.0001
.0090
.0050
.0020
.0017
.0036
.0184
.0038
.0009
.0
.0030
.C
.C089
.0012
.C056
.0007
. C022
.0102
.0
SIC
27
.2470
.1135
.0442
.0469
.0205
.0233
.0304
.0089
.0157
.0211
.0195
.0104
.0035
.0187
.0057
.0085
.0109
.0149
.0060
.0097
.0052
.0136
.0036
.0076
.0069
.0049
.0066
.0031
.0039
.0040
.0033
.0
.0062
.0
.0120
.0024
.0022
.0
.0009
.0031
.0022
SIC
28
.1077
.1244
.0606
.1054
.0440
.0288
.0177
.0143
.0510
.0022
.0299
.0328
.0019
.0195
.0811
.0294
.0075
.0486
.0308
.0117
.0023
.0205
.0013
.0090
.0270
.0015
.0053
.0039
.0048
.0046
.0017
.0
.0065
.0
.0064
.0028
.0049
.0
.0147
.0174
.0011
SIC
29
.0109
.0654
.1304
.1396
.0341
.1437
.0100
.0055
.0883
.0042
.0251
.0078
.0
.0251
.2215
.0135
.0032
.0
.0032
.0035
.0032
.0328
.0016
.0011
.0064
.0005
.0080
.0141
.0048
.0
.0006
.0
.0
.0
.0010
.0016
.0388
.0
.0
.0036
.0
SIC
30
.0323
.0631
.0612
.0415
.0286
.0069
.0420
.0050
.0067
.0004
.0137
.0092
.0003
.0
.0042
.0126
.0033
.0122
.0009
.0030
.0008
.0026
.0004
.0014
.0114
.0015
.0148
.0005
.0014
.0205
.0005
.0
.0049
.0
.0271
.0005
.0024
.0
.0013
.0090
-------
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
7C
71
72
73
74
75
76
77
78
7V
80
81
82
83
84
S5
66
67
88
.0
.0112
.0059
.0
.0035
.0
.0010
.0
.0016
.0
.0
.OC05
.0009
.0036
.0
.0
.0005
.0
.0
.3043
.0
.0
.0029
.0
.0
.0016
.0
.0
.0
.0001
.0098
.0028
.0107
.0
.0
.0053
.0
.0
.0015
.0042
.OOOtt
.0
.0
.0
.0
.0
.u
.0
.0
.0
.0
.0044
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0060
.0
.0
.0
.0
.0
.0092
.0
.0
.0
.0014
.0
.0
.0007
.0
.0
.0005
.0
.0
.0
.0041
.0001
.0
.0
.0
.0
.0
.0
.0
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.0
.0
.0011
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.0
.0
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.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0073
.0
.0
.OOC4
.0
.0
.0016
.0
.0
.0
.0017
.OOC1
.0014
.0045
.0
.0
.OOC3
.0
.0
.OOC2
.0027
.OOC2
.0
.0
.0
.0
.0
.c
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.0
.0
.0
.0
.0
.0007
.0
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.0
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.0
.0
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.0
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.0
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.0002
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.0
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.0
.0
.0
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.0007
.0017
.0006
.C
.0
.0006
.C
.0
.0003
.0007
.0005
.C
.0
.C
.0
.0
.0
.0
.0022
.0
.0
.0016
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0009
.0
.0
.0010
.0
.0
.0143
.0
.0
.0
.0031
.0006
.0028
.0027
.0
.0
.0006
.0
.0
.0043
.0006
.0022
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.0
.0
.0
.0
.0
.0
. C009
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.0
.0018
.0
.0
.0
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.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
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.0008
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.0
.0
.0009
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. C056
.0038
.0
.0
.0060
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.0
.0005
.0105
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.0
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.0
.0
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.0
.0
.0027
.0017
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.0025
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.0008
.0
.0008
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.0005
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.0
.0
.0
.0
.0
.0
.0035
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.0
.0
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.0
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.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
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.0
.0
.0152
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.0
.0
.0
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.0019
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.0
.0
.0
.0
.0
.0
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.0
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.0
.0
.0
.0
.0
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.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
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.0
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.0
.0
.0
.0
.0
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.0
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.0
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.0009
.0045
.0
.0
.0
.0
.0
-------
39
90
91
92
93
94
95
96
97
98
99
100
.0
.0026
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.0
.0
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.0028
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.0019
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.0016
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.00 18
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.0007
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.0
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.0087
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.0007
.0142
.0
.0
.C025
.0005
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.C
.C022
.C004
.0066
.0
.0019
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.0
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.0010
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.0016
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.0071
.0
.0
.0026
.0015
.0
.0
.0026
.0015
.0004
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.0
.0
.0
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w
.0
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o
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.0016
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n
. \j
n
. \j
.0005
.0004
-------
AQCR
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
,28
29
30
31
32
33
34
35
36
37
38
39
40
41
S 1C
31
.0616
.0182
.0094
.0085
.0004
.0031
.0418
.0003
.0 139
.0
.0002
.0042
.0002
.0011
.0002
.0
.0 160
.0034
.0
.0005
.0005
.0016
.0002
.0046
.0002
.0026
.0059
.0
.0003
.0039
.0002
.0
.0041
.0
.0
.0
.0002
.0
.0016
.0
.0
SIC
32
.0231
.0403
.0430
.0268
.0263
.0162
.0060
.0306
.0217
.0055
.0095
.0135
.0022
.0201
.0118
.0149
.0051
.0060
.0064
.0081
.0069
.0093
.0022
.0072
.0046
.0040
.0055
.0091
.0045
.0043
.0043
.0
.0091
.0
.0052
.0058
.0276
.0030
.0047
.0020
.0024
SIC
33
.0170
.0655
.0251
.0430
.0671
.0140
.0036
.1185
.0244
.0
.0458
.0448
.0008
.0022
.0118
.0378
.0163
.0043
.0023
.0050
.0022
.0073
.0001
.0019
.0040
.0009
.0010
.0036
.0049
.0095
.0024
.0
.0023
.0
.0028
.0428
.0053
.0112
.0014
.0005
.0
SIC
34
.0466
.0952
.0553
.0411
.0468
.0226
.0230
.02 Ł4
.01 Ł0
.0021
.0329
.01C4
.0043
.00<7
.0123
.OOS8
.0152
.0124
.00*5
.0056
.0051
.0073
.OOC9
.0029
.00t6
.0051
.0039
.0041
.0048
.0062
.0018
.0
.0123
.0
.0027
.OOfO
.00*3
.0022
.0054
.00 18
.0019
SIC
35
.0283
.0828
.0484
.0349
.0732
.0099
.0202
.0148
.0129
.0008
.0376
.0082
.0139
.0269
.0144
.0096
.0241
.0121
.0082
.0079
.0041
.0059
.0021
.0020
.0100
.0007
.0033
.0007
.0040
.0065
.0034
.0
.0069
.0
.0252
.0012
.0056
.0
.0013
.0029
.0020
SIC
36
.0634
.0927
.0638
.0390
.0049
.0127
.0334
.0148
.0085
.0057
.0195
.0174
.0028
.0129
.0018
.0089
.0290
.0088
.0068
.0148
.0016
.0060
.0030
.0019
.0108
.0008
.0017
.0001
.0034
.0043
.0055
.0
.0106
.0
.0167
.0006
.0034
.0
.0005
.0017
.0008
SIC
37
.0466
.0309
. 1118
.0231
.1788
.0112
.0140
.0051
.0474
.0021
.0393
.0208
.0363
.0076
.0012
.0267
.0109
.0252
.0036
.0115
.0636
.0222
.0091
.0290
.0234
.0010
.0143
.0063
.0030
.0014
.0048
.0
.0100
.0
.0064
.0050
.0139
.0
.0005
.0008
.0
SIC
38
.0936
.1268
.0643
.0510
.0085
.0061
.0677
.0123
.0119
.0028
.0148
.0019
.0048
.0335
.0019
.0087
.0123
.0048
.0004
.0028
.0008
.0022
.0008
.0004
.0018
.0010
.0026
.0012
.0035
.0149
.0059
.0
.0060
.0
.0006
.0004
.0020
.0
.0
.0008
.0
SIC
39
.1678
.0678
.0482
.0164
.0104
.0096
.0134
.0059
.0087
.0010
.0102
.0037
.0022
.0087
.0019
.0048
.0057
.0102
.0018
.0023
.0020
.0020
.0013
.0061
.0035
.0024
.0029
.0012
.0023
.0553
.0007
.0
.0032
.0
.0019
.0009
.0017
.0
.0005
.0024
-------
d
IN)
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0023
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0049
.0
.0002
.0007
.0
.0
.0011
.0
.0
.0009
.0
.0151
.0
.0
.0
.0
.0
.0
.0
.0015
.0025
.0
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.0022
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.0033
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.0
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.0022
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.0
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.0
.0
.0
.0
.0
.0031
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0
.0062
.0
.0
.0011
.0
.0
.000$
.0
.0
.0
.0012
.0004
.0010
.0067
.0
.0
.0032
.0
.0
.0003
.0025
.0002
.0
.0
.0
.0
.0
-------
89
90
91
92
93
94
95
96
97
98
99
100
.0
.0053
.0
.0
.0
.0033
.0
.0
.0
.0043
.0
.0069
.0
.0037
.0017
.0
.0
.0015
.0025
.0
.0
.0006
.0007
.0025
.0
.0020
.0
.0
.0
.0070
.0015
.0
.0005
.0050
.0002
.0013
.0
.0051
.0
.0
.0
.0021
.01C9
.0
.00 11
.0018
.0026
.0032
.0
.0037
.0
.0
.c
.0028
.0116
.C
.0050
.0051
.0007
.0050
.0
.0060
.0
.0
.0
.0030
.0002
.0
.0005
.0047
.0
.0014
.0
.0004
.0
.0
.0
.0026
.0029
.0
.C006
.C020
.0183
.0005
.0
.0087
.0
.0
.0
.0036
.0138
.0
.0
.0009
.0013
.0032
.0
.0094
.0
.0
.0
.0015
.0016
.0
.0
.0061
.0005
.0012
to
-------
APPENDIX E
-------
Appendix E
This appendix provides a listing of sources of data, and types of
data specified by input card format of the model.
-------
LIST OF DATA
Sources
Annual Survey of Manufacturers, 1954-1966
Census of Manufacturers, 1963, 1967
County-City Data Book, 1967, 1969
Individual Income Tax Returns, 1968
Current Population Reports
County Business Patterns, 1967, 1969
Employment and Earnings Statistics for States and Areas, 1968, 1969
Manpower Report of the President, 1969
Census of Government, 1962, 1967
Consumer Expenditures and Income, 1960-1961, for SMS A
Sales Management Magazine, 1956, 1969
Survey of Current Business (monthly publication)
Statistical Abstracts of the United States, 1967, 1968, 1969
Costs and Economic Impact of Air Pollution Control, Fiscal Years
1970-1974 (NAPCA, January, 1969)
Comprehensive Economic Cost Study of Air Pollution Control Costs
for Selected Industries and Selected Regions (RTI, February,
1970)
The Fuel of Fifty Cities (Ernst and Ernst, November, 1968)
Manufacturer's Report of Air Pollution Control Equipment Sales,
1966, 1967, 1968 (Industrial Gas Cleaning Institute Co. )
Economic Projections for Air Quality Control Regions (OBE Depart-
ment of Commerce, June, 1970)
-------
LIST OF THE 100 AIR QUALITY
CONTROL REGIONS (AQCRs)
Code AQCR
1 New York, New York
2 Chicago, Illinois
3 Los Angeles, California
4 Philadelphia, Pa.
5 Detroit, Michigan
6 San Francisco, California
7 Boston, Massachusetts
8 Pittsburgh, Pa.
9 St. Louis, Missouri
10 Washington, D. C.
11 Cleveland, Ohio
12 Baltimore, Maryland
13* Hartford-New Haven, Connecticut
14 Minneapolis-St. Paul, Minnesota
15 Houston, Texas
16 Buffalo, New York
17 Milwaukee, Wisconsin
18 Cincinnati, Ohio
19 Louisville, Kentucky
20 Dallas, Texas
21 Seattle-Everett, Washington
22 Kansas City, Missouri
23 San Diego, California
24 Atlanta, Georgia
25 Indianapolis, Indiana
26 Miami, Florida
27 Denver, Colorado
28 New Orleans, Louisiana
29 Portland, Oregon
30 Providence-Pawtucket, Rhode Island
31 Phoenix, Arizona
32 Tampa, Florida
33 Columbus, Ohio
34 San Antonio, Texas
35 Dayton, Ohio
36 Birmingham, Alabama
37 Toledo, Ohio
-------
100 AQCRs (continued)
Code AQCR
38 Steubenville-Weirton, Ohio/Wheeling, West Virginia
39 Chattanooga, Tennessee
40 Memphis, Tennessee
41 Salt Lake City, Utah
42 Oklahoma City, Oklahoma
43 Omaha, Nebraska
44 Honolulu, Hawaii
45 Beaumont-Port Arthur-Orange, Texas
46 Charlotte, North Carolina
47 Portland, Maine
48 Albuquerque, New Mexico
49 Lawrence-Haverhill/Lowell, Massachusetts
50 El Paso, Texas
51 Las Vegas, Nevada
52 Fargo-Moorhead, North Dakota/Minnesota
53 Boise, Idaho
54 Billings, Montana
55 Sioux City, South Dakota
56* Cheyenne, Wyoming
57* Anchorage, Alaska
58* Burlington, Vermont
59* San Juan, Puerto Rico
60* Virgin Islands
61 Allentown-Bethlehem-Easton, Pa., New Jersey
62* Anderson-Muncie, Indiana
63 Bakersfield, California
64 Davenport-Rock Island-Moline, Iowa, Illinois
65* Flint, Michigan
66 Grand Rapids/Muskegon-Muskegon Hts. , Michigan
67 Greensboro, North Carolina
68 Harrisburg, Pa.
69 Jacksonville, Florida
70 Knoxville, Tennessee
71 Nashville, Tennessee
72 Peoria, Illinois
73 Richmond, Virginia
74 Rochester, New York
75 Saginaw/Bay City, Michigan
-------
100 AQCRs (continued)
Code AQCR
76 Scranton/Wilkes Barre-Hazelton, Pa.
77 Syracuse, New York
78 Tulsa, Oklahoma
79* Worcester, Massachusetts
80 Youngstown-Warren, Ohio
81 Albany-Schenectady-Troy, New York
82 Binghamton, New York
83 Charleston, South Carolina
84 Charleston, West Virginia
85 Des Moines, Iowa
86 Fresno, California
87 Fort Wayne, Indiana
88 Jackson, Mississippi
89 Johnstown, Pa.
90 Lancaster, Pa.
91 Mobile, Alabama
92 Norfolk-Portsmouth/Newport News-Hampton, Va.
93 Raleigh/Durham, North Carolina
94 Reading, Pa.
95 Rockford, Illinois
96 Sacramento, California
97 South Bend, Indiana
98 Utica-Rome, New York
99 Wichita, Kansas
100 York, Pa.
*Data not available for this AQCR.
-------
CARD TYPE IDENTIFICATION
AND VARIABLE LIST
The general format for all cards is 7F10.0, II, 12, 13, and 212.
The first seven fields contain specific data depending upon the codes
in columns 71 through 80. These codes are as follows:
Column Contains
71 1 -- Indicates the card contains figures for fuel
and electric energy consumption
Blank -- Anything else
72-73 Last two digits of year of the data
74-76 From 001 to 100 -- Indicates card contains data
for a specific AQCR (Air Quality Control
Region)
Blank -- Indicates card contains totals for the
United States
77-78 00 -- Indicates the card contains all industry
totals
From 20 to 39 -- Indicates the card contains
data for a particular industry (2-digit SIC)
40 -- Indicates card contains Air Quality Control
costs
41 -- Indicates card contains Unemployment
figures and Sulfur Maximum Restriction
costs
42 -- Indicates card contains Local Government
figures and Population figures
79-80 Number of cards within a specific AQCR or SIC
and year for a particular set of data
-------
DESCRIPTION OF CARD TYPES
Card
Type
1
Identifying
Codes Col.
71
77-78
71
77-78
77&7S
77&78
71
72&73
71
7Z&73
74-76
79-80
71
72&73
74-76
79-80
Contains
Blank
00
Blank
20 to 39
77&7S
79&80
77&7S
79&80
40
01
40
02
41
42
1
67
1
62 or 58
000
01
1
62 or 58
000
02
Data Sets Using This Card Type
1963 General Statistics -- All Industries
Total for AQCR's
1967 General Statistics -- All Industries
Total for AQCR's 1954-1967 Time Series
Data -- All Industries Total for AQCR's
1963 General Statistics --by SIC for
AQCR's
1967 General Statistics --by SIC for
AQCR's 1954-1967 Time Series Data by
SIC for AQCR's
1971-1975 Air Quality Control Costs
for AQCR's (Card 1)
1971-1975 Air Quality Control Costs for
AQCR's (Card 2)
1967 Unemployment & Work Force
figures for AQCR's
1967 AQCR's Local Government and
Private Income
1967 Total for U.S. of Fuel and Electric
Energy Consumption by SIC
1962 and 1958 Total for U. S. of Fuel
and Electric Energy Consumption
by SIC (Card 1)
1962 and 1958 Total for U. S. of Fuel
and Electric Energy Consumption
by SIC (Card 2)
-------
DESCRIPTION OF CARD TYPES (continued)
Card
Type
10
11
Identifying
Codes Col.
71
72&73
74-76
79-80
71
7Z&73
74-76
79-80
Contains
1
62
001 to 100
01
1
62
001 to 100
01
Data Sets Using This Card Type
1962 AQCR's Fuel and Electric Energy
Consumption by SIC (Card 1)
1962 AQCR's Fuel and Electric Energy
Consumption by SIC (Card 2)
-------
VARIABLE LIST
General Statistics All Industries Total for AQCR
Card
Type Field Description
1 1 Number of Total Employees for AQCR (in 1, 000)
1 2 All Employees Wages for AQCR (in $100, 000)
1 3 Value added by manufacture for AQCR (in $100, 000)
1 4 Value of shipment for AQCR (in $100, 000)
1 5 New Capital Expenditures for AQCR (in $100, 000)
1 6-7 No Data
General Statistics for SIC in AQCR
Card
Type Field Description
2 1 Number of Total Employees for SIC in AQCR
(in 1, 000)
2 2 All Employees Wages for SIC in AQCR (in $100, 000)
2 3 Value added by manufacture for SIC in AQCR
(in $100, 000)
2 4 Value of shipment for SIC in AQCR (in $100, 000)
2 5 New Capital Expenditures for SIC in AQCR
(in $100, 000
2 6-7 No Data
-------
VARIABLE LIST (continued)
Air Quality Control Costs 1971-1975 (Card 1)
Card
Type Field Description
s
? 1 Investment by Industrial Process in AQCR
(in $100, 000)
3 2 Annual Cost by Industrial Process in AQCR
(in $100, 000)
3 3 Investment by Stationary Combustion in AQCR
(in $100, 000)
3 4 Annual Cost by Stationary Combustion in AQCR
(in $100, 000)
3 5 Investment by Solid Waste in AQCR (in $100, 000)
3 6 Annual Cost by Solid Waste in AQCR (in $100, 000)
3 7 No Data
Air Quality Control Costs 1971-1975 (Card 2)
Card
Type Field Description
4 1 Total Investment - Lower Limit in AQCR (in $100, 000)
4 2 Total Annual Cost - Lower Limit in AQCR (in $100, 000)
4 3 Total Investment - Expected in AQCR (in $100, 000)
4 4 Total Annual Cost - Expected in AQCR (in $100, 000)
4 5 Total Investment - Upper Limit in AQCR (in $100, 000)
4 6 Total Annual Cost - Upper Limit in AQCR (in $100, 000)
4 7 No Data
-------
VARIABLE LIST (continued)
1967 Unemployment and Work Force Figures for AQCR
Card
Type Field Description
5 1 Work Force for March 1967 in AQCR (in 100)
5 2 Unemployment Rate in AQCR (in . 001)
5 3 1.0 Percent Sulfur Maximum Fuel Restriction
Annual Regional Cost to Steam-Electric Power
Generation Combustion Sources: Low for 1974
(in $100, 000)
5 4-5- No Data
6-7
1967 AQCR's Local Government Income and Expenditures
Card
Type Field Description
6 1 Population of AQCR's on July 1, 1968 (in 1, 000)
6 2 Per Capita Personal Income 1968 (in dollars) for
AQCR
6 3 AQCR Local Government Total 1967 General
Revenue (in $1, 000, 000)
6 4 AQCR Local Government Total 1967 Direct
General Expenditures (in $1, 000, 000)
6 5-6-7 No Data
-------
VARIABLE LIST (continued)
Total for U. S. of Fuel and Electric Energy Consumed by SIC's -1967
Card
Type Field Description
7 1 Total Cost of Purchased Fuels and Electric Energy
by SIC (in $1,000,000)
7 2 Total Cost of Purchased Fuels by SIC's (in $1, 000, 000)
7 3 Quantity of Electric Energy Purchased by SIC's
(million kw/hrs. )
7 4 Cost of Electric Energy Purchased by SIC's
(in $1, 000, 000)
7 5-6-7 No Data
Total for U. S. of Fuel and Electric Energy Consumption by SIC's -
1958 and 1962 - Card 1
Card
Type Field Description
8 1 Total cost of purchased fuels and electric energy
by SIC in U. S. ($1, 000)
8 2 Total cost of purchased fuels by SIC in U. S.
($1,000)
8 3 Quantity of bituminous coal, lignite and anthracite
purchased by SIC in U. S. (1, 000 short ton)
8 4 Cost of bituminous coal, lignite and anthracite
purchased by SIC in U. S. ($1,000)
8 5 (Only Industry 33) Quantity of coke and breeze
purchased by SIC in U. S. (1, 000 short ton)
-------
VARIABLE LIST (continued)
Total for U. S. of Fuel and Electric Energy Consumption by SIC's -
1958 and 1962 - Card 1 (continued)
Card
Type Field Description
8 6 (Only Industry 33) Cost of coke and breeze purchased
by SIC in U. S. ($1, 000)
8 7 No Data
Total for U.S. of Fuel and Electric Energy Consumption by SIC's -
1958 and 1962 - Card 2
Card
Type Field Description
9 1 Quantity of Fuel Oil (distillate and residual) pur-
chased by SIC in U. S. (1, 000 barrels of 42 gal. )
9 2 Cost of Fuel Oil purchased by SIC in U. S. ($1,000)
9 3 Quantity of Gas - natural, manufactured, still blast
furnace, and coke oven - purchased by SIC in U. S.
(million cubic foot)
9 4 Cost of Gas purchased by SIC in U. S. ($!, 000)
9 5 Quantity of Electric Energy purchased by SIC in
U. S. (million kw/hrs. )
9 6 Cost of Electric Energy purchased ($1, 000)
9 7 No Data
-------
VARIABLE LIST (continued)
1962 Fuel and Electric Energy Consumption by SIC in AQCR - Card 1
Card
Type Field Description
10 1 Total cost of purchased fuels and electric energy
by SIC in AQCR ($1, 000)
10 2 Total cost of purchased fuels by SIC in AQCR ($1, 000)
10 3 Quantity of bituminous coal, lignite and anthracite
purchased by SIC in AQCR (1, 000 short tons)
10 4 Cost of bituminous coal, lignite and anthracite pur-
chased by SIC in AQCR ($1, 000)
10 5 (Only Industry 33) Quantity of coke and breeze pur-
chased by SIC in AQCR (1, 000 short tons)
10 6 (Only Industry 33) Cost of coke and breeze purchased
by SIC in AQCR ($1, 000)
10 7 No Data
1962 Fuel and Electric Energy Consumption by SIC in AQCR - Card 2
Card
Type Field Description
11 1 Quantity of fuel oil (distillate and residual) purchased
by SIC in AQCR (1, 000 barrels of 42 gal. )
11 2 Cost of fuel oil purchased by SIC in AQCR ($1, 000)
11 3 Quantity of gas (natural, manufactured', still blast
furnace, and coke oven) (million cubic feet)
11 4 Cost of gas purchased by SIC in AQCR ($1, 000)
-------
VARIABLE LIST (continued)
1962 Fuel and Electric Energy Consumption by SIC in AQCR - Card 2
(continued)
Card
Type
11
11
11
Field
6
7
Description
Quantity of electric energy purchased by SIC in
AQCR (million kw/hrs. )
Cost of electric energy purchased by SIC in AQCR
No Data
-------
Source: 1963 Census of Manufacturers, Vol. 1, Summary and Subject
Statistics, Chapter 7, Fuels and Electric Energy Consumption,
Table 6, Total Consumption of Heat and Major Industry Groups
Table 10, Consumption According to SMSAs
Card 1
Column Contains
1-10 Total cost of purchased fuels and electric energy ($1, 000)
11-20 Total cost of purchased fuels ($1, 000)
21-30 Quantity of bituminous coal, lignite and anthracite pur-
chased (1,000 short ton)
31-40 Cost of 11-20 ($1, 000)
41-50 (Only Industry 33) Quantity of coke and breeze purchased
(1, 000 short ton)
51-60 (Only Industry 33) Cost of 41-50 ($1, 000)
71-80 Codes
72-73 Last two digits of year
74-76 AQCR (Air Quality Control Region)
Blank for total of all AQCR's
77-78 Industry code - SIC
79-80 Card number 01 to 02 for this industry
Card 2
Column
1-10
11-20
Contains
Quantity of fuel oil (distillate and residual) purchased
(1, 000 barrels of 42 gal. )
Cost of fuel oil ($1, 000)
-------
Card 2 (continued)
Column Contains
21-30 Quantity of gas (natural, manufactured, still blast furnace,
and coke oven (million cubic feet)
31-40 Cost of gas ($1, 000)
41-50 Quantity electric energy (million kw/hrs. ) purchased
51-60 Cost of electric energy ($1, 000)
71-80 Codes
Same of card 1
-Fuel and Electric Energey Consumed 1962 + 1958 totals for United
States According to SIC.
-Fuel and Electric Energy Consumed 1962 for AQCR's by SIC's.
-------
Base Year Air Quality Control Costs
Annual and Investment Costs by
Metropolitan Area, Fiscal Years 1971-1975
Card
1
1
1
1
1
1
Card
2
2
2
2
2
2
Column
1-10
11-20
21-30
31-40
41-50
51-60
72-73
74-76
77-78
79-80
Column
1-10
11-20
21-30
31-40
41-50
51-60
Contains
Investment by industrial process (in $100, 000)
Annual cost by industrial process (in $100, 000)
Investment by stationary combustion (in $100, 000)
Annual cost by stationary combustion (in $100, 000)
Investment by solid waste (in $100, 000)
Annual cost by solid waste (in $100, 000)
Year - last two digits
Air Quality Control Region
Identifies card as "Air Quality Control Cost"
card (contains 40)
Card numbers 01 or 02 of "Air Quality Control
Costs" cards
Contains
Total investment - lower limit (in $100, 000)
Total annual cost - lower limit ($100, 000)
Total investment - expected (in $100, 000)
Total annual cost - expected (in $100, 000)
Total investment - upper limit (in $100, 000)
Total annual cost - upper limit (in $100, 000)
-------
Base Year Air Quality Control Costs
Annual and Investment Costs by
Metropolitan Area, Fiscal Years 1971-1975 (continued)
Card Column Contains
72-73 Year - last two digits
74-76 Air Quality Control Region
77-78 Identifies card as "Air Quality Control Cost"
card
79-80 Card 01 or 02 of "Air Quality Control Cost"
cards
*Air Quality Control Costs 1971-1975 for AQCR's.
Source: Comprehensive Economic Cost Study of Air Pollution Costs
for Selected Industries and Selected Regions, prepared for
NAPCA, Appendix D.
-------
Area Trends in Employment and Unemployment
June, 1968
U. S. Department of Labor
Manpower Administration
Pages 51-54: Work Force, Unemployment, and Employment in 150
Major Labor Areas
Column Contains
1-10 Work force for March 1967 in hundreds (100)
11-20 Unemployment rate in tenths of one percent (. 001)
21-30 1974 Low Exhibit V. 17 (1.0% Sulfur Maximum Restric
tion: Annual Regional Costs to Steam-Electric Power
Generation Combustion Sources, Fiscal 1970-1974;
Costs and Economic Impacts of Air Pollution Control,
Fiscal 1970-1974, Ernst and Ernst)
71-80 Codes
72-73 Last two digits of the year
74-76 AQCR code
77-78 Card identification (blank - for these cards)
79-80 41
Source: 1967 Unemployment and Work Force figures.
-------
Statistical Abstract of the United States
Section 33: Metropolitan Area Statistics
Table 1: SMSA's with 250, 000 population or more
Table 2: SMSA's with population below 250, 000
AQCR Income and Expenditures for 1967
Column Contains
1-10 Population (1968 July 1) Total (in 1,000)
11-20 Personal income, 1968 (per capita total) (in dollars)
21-30 Local governments, 1967 (total general revenue)
(in million dollars)
31-40 Local governments, 1967 (direct general expenditures,
total) (in million dollars)
71-80 Codes
72-72 Year "67"
74-76 AQCR code
77-78 Identifies the card 42
79-80 Card no. 1 in all cards
*AQCR's Income and Expenditures 1967.
-------
1967 Census of Manufacturers
Summary Series - Fuels and Electric Energy Used
All AQCR Total
Column Contains
1-10 Total cost of purchased fuels and electric energy
(million dollars - $1,000,000)
11-20 Total cost of purchased fuels (million dollars)
21-30 Quantity of electric energy purchased (million kw/hrs. )
31-40 Cost of electric energy (million dollars)
41-50
71-80 Codes
72-73 Last two digits of year
74-76 AQCR (Air Quality Control Region)
Blank - total for all regions
77-78 Industry Code - SIC
79-80 Card number within industry
(always 01 for this data)
-Fuel and Electric Energy Consumed, 1967 -- Totals for the United
States According to SIC.
------- |