Economic Impact Analysis of
Proposed Effluent Limitations
Guidelines and Standards for the
Pesticide Manufacturing Industry
Dr. Lynne G. Tudor, Economist
Economic and Statistical Analysis Branch
Engineering and Analysis Division
Office of Science and Technology
U.S. Environmental Protection Agency
Washington, DC 20460
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ACKNOWLEDGEMENTS
The most credit must be given to Dr. Thomas E. Fielding for his knowledge, experience,
cooperation, and leadership as project officer, and to Maria D. Smith who always provided good
review and a fast turn around. Credit must also be given to Paul Bangser for his good advice, Eric
Strassler for his help with the questionnaire, and the whole pesticide team for their professional
manner conscientious effort, and contributions.
Credit must be given to Abt Associates for their assistance and support in performing the
underlying analysis supporting the conclusions detailed in this report. Their study was performed
under Contracts 68-CO-0080 and 68-03-3548. Particular thanks are given to Randi Currier and
Marianne Beauregard.
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EXECUTIVE SUMMARY
Introduction
The Federal Water Pollution Control Act Amendments of 1972 established a comprehensive program to
"restore and maintain the chemical, physical, and biological integrity of the Nation's waters" (section 101(a)). To
implement the Act, the U.S. Environmental Protection Agency (EPA) is to issue effluent limitations guidelines,
pretreatment standards, and new source performance standards for industrial dischargers. This Economic Impact
Analysis (EIA) documents the assessment of the economic impacts of the guidelines and standards applying
specifically to the pesticide manufacturing industry.
The EIA estimates the probable economic effect of compliance costs in terms of facility closures, product
line closures, profitability impacts, and ability to incur debt. Firm-level impacts, local community impacts,
international trade effects, and the effect on new pesticide manufacturing facilities are also presented. A Regulatory
Flexibility Analysis detailing the small business impacts is also included in the EIA for this industry.
A total of 90 pesticide manufacturing facilities, owned and operated by 64 firms that manufacture one or
more pesticide active ingredients (PAIs), are potentially subject to regulation. The EPA analyzed the impacts of
two possible regulatory options: a Treated Discharge Option (the proposed option) and a Zero Discharge Option
based on on-site and off-site injection or incineration. The economic impacts under each regulatory option were
calculated separately for facilities discharging wastewater directly to surface water (direct dischargers) and facilities
discharging wastewater to a publicly owned treatment works (POTW) (indirect dischargers). Impacts on direct
dischargers were calculated for compliance with a Best Available Technology Economically Achievable (BAT)
regulation; impacts on indirect dischargers were calculated for compliance with Pretreatment Standards for Existing
Sources (PSES) regulation. Each discharge category was further analyzed by two subcategories: Organic Pesticide
Chemicals Manufacturing (Subcategory A) and Metallo-Organic Pesticide Chemicals Manufacturing (Subcategory
B).
The proposed regulation applies to Subcategory A and corresponds to the Treated Discharge Option. Total
BAT investment costs (capital and land) for the proposed regulation are projected to be $14.9 million, with
annualized costs of $14.7 million including depreciation and interest. Total investment costs for PSES for the
proposed regulation are projected to be $9.4 million, with annualized costs of $5.9 million including depreciation
and interest.
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Cost of Implementing BAT and PSES Regulations for Subcategory A
(in millions of 1986 dollars)
BAT
PSES
Capital Costs
Total Annualized Costs
$14.9
$14.7
$9.4
$5.9
The costs, presented in 1986 dollars, are based on the assumption that, whenever possible, facilities will
improve existing treatment rather than build new treatment. Although 90 facilities are potentially subject to the
regulation, EPA analyzed only 88 facilities for economic impacts. Financial data were not obtained for one facility
originally classified as a formulator/packager. The other facility for which economic impacts were not calculated
was a research and development facility with no revenues associated with in-scope PAIs. One of these facilities
is expected to incur no cost under the proposed regulation; the other is expected to incur only monitoring costs.
Methodology
The costs and impacts of implementing the regulatory options are analyzed on an PAI-specific basis for
each facility. Building on the PAI-specific data, the EIA uses three primary impact measures: facility closures,
product line closures, and other significant impacts short of closure. The analysis of significant impacts short of
closure measures the effect of compliance costs on the ability of facilities to incur debt and on facilities' return on
assets. The analysis evaluates these impacts in a hierarchical manner that corresponds to the severity of the
projected impact: if a facility closes, product line closures and other significant impacts are not evaluated; if a
facility sustains a product line closure, other significant impacts are not evaluated. The impacts are estimated for
pesticide manufacturing facilities incurring costs using a combination of data from the Pesticide Manufacturing
Facility Census for 1986 (hereinafter referred to as the Census) and secondary sources, such as Standard and Poor's
Compustat financial data, plus facility-specific compliance cost estimates developed by the EPA. First, pre-
compliance (baseline) estimates of each of the three primary impact measures are calculated for each facility, to
gauge the economic vitality of each facility prior to the proposed regulation. If a facility fails one of the measures
(e.g., a facility closes) in the baseline scenario, the model does not recount this same level of failure in the post-
compliance scenario. The model does allow, however, for progressively severe impacts due to compliance (e.g.,
a baseline product line closure may become a post-compliance facility closure).
The facility-level closure analysis considers the portion of the facility involved in manufacturing, and also
formulating/packaging or performing contract work, for both in-scope pesticides (i.e., those 270 PAIs considered
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for regulation) and out-of-scope pesticides (all others).1 A facility closure is projected to result from the regulation
if the salvage value exceeds the present value of cash flow in the post-compliance, but not the baseline, scenario.
A product line, or cluster, is composed of PAIs that are close substitutes for each other for a specific end-
use. For example, insecticides used on corn is one cluster. Fifty-six clusters were identified as part of the impact
analysis, forty-five of which contain in-scope PAIs produced in 1986. A baseline product line closure is projected
if the unit cost (average variable cost plus average fixed cost per pound of PAI) of the product line exceeds the unit
price (average price per pound of PAI). A post-compliance product line closure is projected if the product line
remained open in the baseline, but showed unit costs exceeding unit price due to the addition of compliance costs.
Short of closure, other significant impacts of compliance with the effluent limitations are calculated based
on the comparison, between each facility and the industry averages, of two key financial ratios: the "interest
coverage ratio"2 (earnings before interest and taxes divided by interest expense) and "return on total assets"3
(earnings before interest and taxes divided by assets). If either ratio for a facility falls in the lowest quartile for the
industry in the post-compliance but not the baseline scenario, it is said to sustain a significant impact short of
closure.
Baseline Results
The baseline economic analysis evaluated each facility's financial operating condition prior to incurring
compliance costs for this regulation. This analysis included the estimated costs associated with two significant EPA
regulations not in place in 1986 (the base year) and whose costs were therefore not reflected in the annual operating
expenses provided by the firm in the Census. Baseline cost additions include (1) RCRA costs for refining surface
impoundments that treat, store, and dispose of hazardous wastes, and (2) compliance with the effluent guidelines
for the Organic Chemicals, Plastics, and Synthetic Fibers (OCPSF) industry. Of the 90 facilities potentially subject
to the proposed effluent guidelines, 15 are projected to close in the baseline analysis after incorporating the costs
of RCRA and OCPSF regulations. In fact, three of these facilities have closed and another two have closed one
or more product lines since 1986. An additional 20 facilities are projected to close pesticide product lines. Of
these, two have closed entirely, five have closed a pesticide product line, and two have changed ownership since
1986.
or m Pf T* C ™•> W™6 deVel°Ped for omy that P°rtion °f the facility engaged in manufacturing one
o more of the m-scope PAIs.) The facility closure analysis uses a net present value approach (which compares
discounted cash flow to salvage value) to project whether pesticide operations would remain open after regulatory
costs are mcurred. The first step in the facility closure analysis was to project baseline costs and revenues over tZ
life of the faculty. Projected regulatory costs were then added to the baseline costs; these post-compliance costs
were used to estimate a post-compliance cash flow. P"«*uws ousts
2Also called "times interest earned."
3Also called "return on investment."
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Effects of Regulatory Compliance on Facilities
The EPA analyzed the impacts of two possible regulatory options for BAT and PSES: a Treated Discharge
Option, and a Zero Discharge Option based on on-site or off-site injection or incineration. The economic impacts
associated with these two options are discussed below, by both discharge type and subcategory.
Treated Discharge Option
Impacts on Direct Dischargers
Organic Pesticides Chemicals Manufacturing (Subcategory A)
For manufacturers included in this subcategory, the incremental capital and annualized total costs (including
capital, operating and maintenance, and monitoring costs) of complying with BAT limitations are expected to be
$14.9 million and $14.7 million, respectively. No facilities are projected to close due to compliance with BAT.
One facility, equal to three percent of the 32 direct discharge facilities covered under this subcategory, is projected
to close a product line as a result of the regulation. (One other facility projected to close a product line, a zero
discharger, incurs only monitoring costs.) No facilities are expected to experience other significant financial impacts
short of facility or product line closure. Job losses totalling 31 full-time equivalents (FIE) are expected to occur
as a result of the product line closures and the decrease in demand resulting from higher prices. This employment
loss represents less than one percent of employment in the pesticide-related portions of all pesticide manufacturing
facilities. One firm, equal to 1.5 percent of the 64 firms in the industry, is expected to experience significant
financial impacts as a result of compliance with BAT. Foreign trade in PAIs is expected to fall by $5.5 million due
to compliance with BAT. In 1986, the United States was a net exporter of PAIs, with a trade balance of $897
million; the decrease in PAI trade is projected to be less than one percent. When compared with U.S. net imports
of $152 billion in merchandise for 1986, compliance with the BAT regulation is seen to cause an increase in net
imports of less than one one-thousandth of one percent.
Metallo-Organic Pesticides Chemicals Manufacturing (Subcategory B)
No new limitations on direct dischargers are proposed by the EPA for Subcategory B. Therefore, there
are no associated costs or economic impacts.
Impacts of PSES Regulations on Indirect Dischargers
Subcategory A
For manufacturers included in this subcategory, the total capital and annualized costs of compliance with
PSES are projected to be $9.4 million and $5.9 million, respectively. No facilities are projected to close due to
compliance with PSES. One facility, or three percent of the 36 facilities in this subcategory, is projected to close
a product line as a result of the regulation. No facilities are estimated to experience other significant financial
impacts short of facility or product line closure. Job losses totalling 97 FTEs are expected to occur as a result of
the product line closures and the decrease in demand resulting from higher prices. This employment loss represents
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less than one percent of employment in the pesticide-related portions of all pesticide manufacturing facilities. Two
firms are expected to sustain significant financial impacts as a result of compliance with PSES. Foreign trade in
PAIs is expected to fall by $16.1 million due to compliance with PSES. This decrease in trade represents about
two percent of 1986 net exports of PAIs and about one-hundredth of one percent of the 1986 net national trade
imports of all goods.
Subcategory B
No new limitations on indirect dischargers are proposed by the EPA for this subcategory. Therefore, there
are no associated costs or economic impacts.
Zero Discharge Option
Impacts of BAT Regulations on Direct Dischargers
Subcategory A
Compliance with limitations based on the Zero Discharge Option is expected to cost manufacturers of
Subcategory A pesticides $1.13 million hi incremental capital costs and $4.81 billion in annualized costs. Total
pesticide-related revenue for all 88 pesticide manufacturing facilities equaled $4.84 billion in 1986: only slightly
greater than the projected annualized Zero Discharge Option compliance costs for direct dischargers in this
subcategory.
Sixteen facilities (50 percent of the 32 direct discharge facilities in this subcategory) are projected to close
due to compliance with this option. Three additional facilities, equal to ten percent of the 32 direct discharge
facilities covered under this guideline, are projected to close a product line. (One of the facilities expected to close
a product line, a zero discharger, would incur only monitoring costs. Because the 32 facilities against which
impacts are compared do not include zero dischargers, the percentage of facilities affected is overstated.) No
facilities are expected to experience other significant financial impacts short of facility or product line closure. Job
losses totalling 7,110 FTEs are expected to occur as a result of the facility closures, product line closures, and the
decrease hi demand resulting from higher prices. This employment loss represents 72 percent of employment in
the pesticide-related portions of all pesticide manufacturing facilities. Seven firms, equal to about eleven percent
of the 64 firms hi the industry, are expected to experience significant financial impacts as a result of compliance
with this option. Foreign trade in PAIs is expected to fall by $2.4 billion, shifting the U.S. PAI balance of trade
from $897 million in exports hi 1986 to $1.5 billion hi imports. The U.S. national net imports of merchandise
would increase by about two percent.
Subcategory B
No new limitations on direct dischargers are proposed for this subcategory. Therefore, there are no
associated costs or economic impacts.
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Impacts of PSES Regulations on Indirect Dischargers
Subcategory A
For manufacturers of organic pesticides, the total capital and annualized costs of compliance with the Zero
Discharge Option are estimated to be $1.1 million and $518.8 million, respectively. Eleven facilities (31 percent
of the 36 facilities with indirect discharges in this subcategory) are projected to close if forced to comply with this
option. Three facilities (8 percent of the 36 facilities with indirect discharges hi this subcategory) are projected to
close a product line. No facilities are expected to experience other significant financial impacts short of facility
or product line closure. Job losses totalling 802 FTEs are expected to occur as a result of the facility closures,
product line closures, and the decrease in demand resulting from higher prices. This employment loss represents
8 percent of employment in the pesticide-related portions of all pesticide manufacturing facilities. Seven firms,
equal to about eleven percent of the firms in the industry, are expected to sustain significant financial impacts as
a result of compliance with this option. Foreign trade in PAIs is expected to fall by $179.6 million due to
compliance with the Zero Discharge Option. This decrease in trade represents 20 percent of 1986 U.S. net PAI
exports and 0.12 percent of 1986 net national imports of all goods.
Subcategory B
No new limitations on indirect dischargers are being proposed for this subcategory. Therefore, there are
no associated costs or economic impacts.
Effects of Regulatory Compliance on New Sources of Pesticide Manufacture
The EPA is also proposing to establish New Source Performance Standards (NSPS) and Pretreatment
Standards for New Sources (PSNS) for the organic pesticide chemicals manufacturing subcategory. These
regulations are proposed to be equal to BAT/PSES limitations for PAIs, modified to reflect a wastewater flow
reduction of 28 percent hi some cases. The NSPS for priority pollutants is being set equal to the BAT limitations.
The projected impact of the proposed regulations on new sources is expected to be less burdensome than the impact
of the BAT/PSES regulations on existing sources; designing a new technology prior to facility construction is
typically less expensive than retro-fitting a facility for a new technology. Because compliance with the Treated
Discharge Option has been found to be economically achievable for existing facilities, it is expected that compliance
with this option will also be achievable for new sources. NSPS/PSNS for metallo-organic pesticide chemicals are
not being proposed at this time. Therefore, there are no associated impacts on new sources.
Regulatory Flexibility Analysis
The Regulatory Flexibility Act (5 U.S.C. 601 et seq., Pub. L. 96-354) calls for the EPA to prepare a
Regulatory Flexibility Analysis (RFA) for proposed regulations having a significant impact on a substantial number
of small entities. The purpose of the Act is to ensure that, while achieving EPA's statutory goals, the EPA's
regulations do not impose disproportionate impacts on small entities.
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Both the Treated Discharge Option and the Zero Discharge Option were evaluated to determine their
impacts on small entities. The analysis concludes that a substantial number of small entities will not be impacted
significantly under the Treated Discharge Option. Although a substantial number of small entities would be expected
to be impacted significantly under the Zero Discharge Option, that impact would not be expected to fall
disproportionately on small entities. Therefore, no alternative regulations for small entities were considered.
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Chapter 1
Chapter 2
Chapter 3
Chapter 4
TABLE OF CONTENTS
Introduction and Overview j j
1.0 Background and Definitions j j
1.1 Structure of the Report j'j
Data Sources 2 i
Pesticide Manufacturers Profile 3 j
3.0 Introduction 3 •,
3.1 Categorization of Data 32
3.2 Sources of Demand for Chemical Pesticides 34
3.2.A Agriculture Market 3 g
3.2.B Industrial/Institutional/Commercial Market (I/I/C) 3.8
3.2.C Home/Lawn/Garden Market 3.10
3.3 Facility Characteristics 3 12
3.3.A Physical Characteristics 3^2
3.3.B Industry Output 3 12
3.3.C Production Characteristics 3 15
3.3.D Production Costs 3 2Q
3.3.E Employment Characteristics 3 24
3.3.F Revenues and Profit 3 28
3.3.G Capital Expenditures 3 32
3.3.H Production Capacity Utilization 3,35
3.4 Firm Characteristics 3 39
3.5 Industry Market Structure 3 42
3.5.A Barriers to Entry 3 42
3.5.B Vertical Integration 3 45
3.5.C Concentration 3 48
3.5.D Demand Elasticity and Product Substitution 3.50
3.6 International Trade 3 53
3.6.A U.S. Pesticide Imports and Exports 3.55
3.6.B U.S. Pesticide Industry in the World Market 3.6!
3.7 Summary 3 g<-
Chapter 3 References 3 66
Facility Impact Analysis 4 j
4.0 Introduction . \
4.1 Economic Model 4*2
4.1. A Generalized Model of the Pesticide Manufacturing Industry ...4.2
4.1.B Applied Model of the Pesticides Manufacturing Industry 4.4
4.2 Facility Closure Analysis 4 19
4.2.A Baseline Facility Closure Analysis 4 20
4.2.B Post-Compliance Facility Closure Analysis 4.26
4.3 Product Line Closure Analysis 4 28
4.4 Other Significant Financial Impacts 4 29
4.5 Facility Impacts 4'32
4.5.A Baseline ••••. ^ ................. ' 432
4.5.B Effects of Compliance with the Regulatory Options 4.32
Chapter 4 References -
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Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Community Impact Analysis 5.1
5.0 Introduction 5.1
5.1 Methodology 5>1
5.1.A Primary Impacts on Employment 5.2
5.1.B Measuring Impact Significance •• 5.3
5.1.C Secondary Impacts on Employment 5.4
5.2 Results 5-5
5.2.A Treated Discharge Option 5.5
5.2.B Zero Discharge Option 5.5
Chapter 5 References 5.9
Foreign Trade Analysis "•!
6.0 Introduction ^.1
6.1 Methodology 6>1
6.1.A Exports 6-2
6.1.B Imports 6-3
6.2 Results 6-4
6.2.A Treated Discharge Option 6-4
6.2.B Zero Discharge Option 6-6
Chapter 6 References • 6-9
7 1
Firm Impact Analysis
7.0 Introduction 7-1
7.1 Analytic Approach 7.1
7.1.A Firm Financial Performance • 7.2
7.1.B Ability to Manage Financial Commitments 7.3
7.2 Analytic Procedure > ' -^
7.3 Results 7-12
7.3.A Baseline Analysis 7.12
7.3.B Post-Compliance Analysis: Treated Discharge Option 7.12
7.3.C Post-Compliance Analysis: Zero Discharge Option 7.13
Chapter 7 References 7-14
Small Business Impacts 8-*
8.0 Introduction 8>1
8.1 Methodology . 8.1
8.2 Results 8-5
8.2.A Treated Discharge Option 8-5
8.2.B Zero Discharge Option 8-5
8.3 Conclusions • • 8<8
Chapter 8 Reference 8-9
Impacts on New Sources "•*
9.0 Introduction ^-i
9.1 New Source Performance Standards 9.1
9.2 Pretreatment Standards for New Sources 9.2
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Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
1986 Pesticide Manufacturer Facility Census
Mapping of Pesticide Active Ingredients into Clusters
Methodology for Estimating the Price Elasticity of Demand for Pesticide Clusters
Sensitivity Analysis of Cost Pass-Through Ability
Details of Analysis of Impacts on Small Businesses
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LIST OF TABLES
Table 3.1
Table 3.2
Table 3.3
Table 3.4
Table 3.5
Table 3.6
Table 3.7
Table 3.8
Table 3.9
Table 3.10
Table 3.11
Table 3.12
Table 3.13
Table 3.14
Table 3.15
Table 3.16
Table 3.17
Table 3.18
Table 3.19
Table 3.20
Table 3.21
Table 4.1
Table 4.2
Table 4.3
Table 4.4
Table 5.1
Table 5.2
Representative Classes of Pesticides and the Pests They Control . 3.3
Pesticide Clusters 35
Pesticide Agricultural Production and Distribution 3.9
Pesticide Manufacturing Facilities by Facility Age, 1986 3.14
Production and Sales of Pesticides, 1980-1988 3.15
Distribution of In-Scope Pesticide Facility Production
and Sales, 1986 3 jg
Total Facility Employment Characteristics by Facility Size, 1986 3.27
Average Facility Employment Characteristics by Facility Size,
1986 3 29
Pesticide Capital Expenditures, 1975-1987, SIC 2879 3.34
U.S. Pesticide Production Capacity Utilization Rates, 1980-1989 3.37
Research and Development Costs as a Percent of Total Facility
Sales, 1986 by Firm Size 3 47
Share of Value of Pesticide Shipments Accounted for by the
4, 8, 20, and 50 Largest Companies, 1972-1982 3.51
Share of Value of In-Scope Pesticide Shipments Accounted for
by the 4, 8, and 20 Largest Firms, 1986 3.52
Summary of Estimates of Elasticity of Demand for Clusters with
Production, 1986 3 54
U.S. Import and Export Values for All Pesticides 3.57
U.S. Import and Export Values for Herbicides 3.53
U.S. Import and Export Values for Insecticides 3.59
U.S. Pesticide Trade Compared to U.S. Pesticide Shipments and
New Supply, 1978-1987 3 60
U.S. Trade as a Percentage of the World Market Economy for
Pesticides, 1978-1987 3 62
Value of Pesticide Exports for Leading Export Nations as a
Percent of the Total World Pesticide Exports, 1979-1987 3.63
Value of Pesticide Imports for Leading Importers to the United
States as a Percent of Total U.S. Imports, 1980-1987 3.54
Costs of the Regulatory Options 4 ^3
Summary of Estimates'of Elasticity of Demand for Clusters with
Production, 1986 4 17
Baseline Closures 4 32
Impacts of the Regulatory Options on Facilities 4.34
Community Impact - Treated Discharge Option ................... 5.6
Community Impact - Zero Discharge Option 5.7
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Table 6.1
Table 6.2
Table 7.1
Table 7.2
Table 8.1
Table 8.2
Foreign Trade Impact - Treated Discharge Option
Foreign Trade Impact - Zero Discharge Option
Determination of Firm-level Financial Viability
Calculation of Firm-Level Financial Measures in Post-Compliance
Analysis
Logistic Regression Analysis Zero Discharge Option:
Direct Dischargers
Logistic Regression Analysis Zero Discharge Option:
Indirect Dischargers
6.5
6.7
7.9
8.7
8.7
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LIST OF FIGURES
Figure 1.1
Figure 3.1
Figure 3.2
Figure 3.3
Figure 3.4
Figure 3.5
Figure 3.6
Figure 3.7
Figure 3.8
Figure 3.9
Figure 3.10
Figure 3.11
Figure 3.12
Figure 3.13
Figure 3.14
Figure 3.15
Figure 3.16
Figure 3.17
Figure 3.18
Figure 3.19
Figure 3.20
Figure 8.1
Economic Impact Analysis of Pesticides Manufacturing Industry
Effluent Limitations Guidelines: Analytic Components 1.3
U.S. Market Demand for All Pesticides 3.7
Production and Distribution Channels for the Industrial/Institutional/
Commercial and Home/Lawn/Garden Markets 3.H
Facilities and In-Scope Pesticide Production by Region, 1986 3.13
Fungicide, Herbicide and Insecticide Production, 1980-1988 3.1?
Pesticide Production and Total Planted Acres, 1977-1987 3.18
Composition of Facility Production Activity, 1986 3.21
Composition of Facility Production Activity by Facility Size, 1986 3.22
Composition of Pesticide-Related Facility Fixed Costs by Facility
Size, 1986 3 23
Composition of Pesticide-Related Facility Variable Costs by
Facility Size, 1986 3 25
Ratio of Pesticide-Related Fixed Costs to Pesticide-Related Total
Costs by Facility Size, 1986 3_26
Employment Trends, 1975-1987 3.30
Composition of Facility Revenue by Facility Size, 1986 ............. 3.31
Pre-Tax In-Scope Pesticide Facility Profit as a Percent of In-Scope
Pesticide Sales, 1986 3 33
Capital Expenditures in 1986 Dollars 3.35
Comparison of All Manufacturing Capacity Utilization and Pesticide
Production Capacity Utilization Rates 3.35
Number of Individual or Classes of In-Scope PAIs Produced by Firms
1986
3.40
Number of U.S. In-Scope Pesticide Manufacturing Facilities Owned
by Firms, 1986 3 41
Composition of Firm Sales, 1986 3 43
Number of Firms That Produce an Individual PAI or Class of PAI
1986
3.44
Number of Facilities Acquired by Firms 3 49
Discontinuous Step Function 84
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Chapter 1: INTRODUCTION AND OVERVIEW
1.0 Background and Definitions
The Federal Water Pollution Control Act Amendments of 1972 established a comprehensive program to
"restore and maintain the chemical, physical, and biological integrity of the Nation's waters" (Section 101(a)).
To implement these amendments, the U.S. Environmental Protection Agency (EPA) issues effluent limitations
guidelines, pretreatment standards, and new source performance standards for categories of industrial
dischargers. Specifically, the regulations that the EPA establishes are:
Best Practicable Control Technology Currently Available (BPT). These rules apply to existing
industrial direct dischargers, and generally cover control of conventional pollutant discharge.1
Best Available Technology Economically Achievable (BAT). These rules apply to existing
industrial direct dischargers and the control of priority and non-conventional pollutant
discharges.
New Source Performance Standards (NSPS). These rules apply to new industrial direct
dischargers and cover all pollutant categories.
Pretreatment Standards for Existing Sources (PSES). These rules apply to existing indirect
dischargers (whose discharges enter Publicly Owned Treatment Works, or POTWs). They
generally cover the control of toxic and non-conventional pollutant discharges that pass through
the POTW or interfere with its operation. They are analagous to the BAT controls.
Pretreatment Standards for New Sources (PSNS). These rules apply to new indirect
dischargers and generally cover the control of toxic and non-conventional pollutant discharges
that pass through the POTW or interfere with its operation.
This Economic Impact Analysis (EIA) documents the assessment of the economic impacts of the proposed BAT,
NSPS, PSES, and PSNS applying specifically to the pesticide manufacturing industry.
1.1 Structure of the Report
Two regulatory options are evaluated: one that would require treatment of process wastewater
pollutants (Treated Discharge Option), and another that would require no discharge of process wastewater
pollutants to POTWs or surface water (Zero Discharge Option).2 The economic impacts are calculated
'Conventional pollutants are defined as biochemical oxygen demand (BOD), total suspected solids (TSS) oil
and grease, and pH. Other pollutants may also be regulated at the BPT level.
'The Zero Discharge Option would limit discharges from the facility site to POTWs or to surface water
only; discharges to other media may remain constant or increase as a result of changes in discharge to surface
water. For example, pesticide manufacturing facilities could, theoretically, achieve compliance with a zero
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separately for the two options and, within each option, for direct and indirect dischargers. Direct dischargers
would be required to comply with a BAT regulation; indirect dischargers would be required to comply with a
PSES regulation.
This EIA describes both the methodology employed to assess impacts of the proposed options and the
results of the analysis. The overall structure of the analysis is summarized in Figure 1.1. There are two main
inputs to the analysis: (1) data on industry baseline financial and operating conditions, and (2) projected costs
of complying with the proposed regulation.
The industry baseline financial and operating data are based principally on the Pesticide Manufacturing
Facility Census for 1986 conducted under Section 308 of the Clean Water Act.3 The Census, which reported
facility-level data, was divided into two parts. Part A contained technical data, and Part B contained economic
and financial data. The projected costs of compliance with the proposed regulation (the second major input to
the analysis) were developed by the EPA. Details on the compliance cost estimates can be found in the
Technical Development Document for the proposed rule.4 Additional information on all data sources is
presented hi Chapter 2.
the EIA:
To fully evaluate the expected impacts of the proposed options, six measures of impact are examined hi
Impacts on facilities that manufacture PAIs covered by the regulation;
Employment losses and associated community effects;
Impacts on U.S. balance of trade;
Impacts on firms that own facilities affected by the regulation;
Impacts on pesticide facilities defined as small businesses; and
Effects on the construction of new facilities and expansion of existing facilities.
discharge effluent guideline by transferring the waste streams previously discharged to surface water to landfills,
incinerators, or deep well injection sites.
3Baseline conditions also include certain costs deemed necessary to comply with particular regulations
imposed under the Resource Conservation and Recovery Act (RCRA), and the effluent guidelines for the
Organic Chemicals, Plastics, and Synthetic Fibers (OCPSF) Industry. Portions of these regulations took effect
after the base year of the Census, and imposed costs on certain pesticide manufacturers. These costs are also
included in the analysis.
"Full title: Technical Development Document for Proposed Effluent Limitations Guidelines, New Source
Performance Standards and Pretreatment Standards for the Pesticide Chemicals Point Source Category.
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Figure 1.1
Economic Impact Analysis of Pesticides Manufacturing
Industry Effluent Limitations Guidelines:
Analytic Components
Facility Level
Analysis
Economic
Models
B Facility
Closure
Analysis
Other
Financial
Impacts
Product
Line
Closure
Analysis
Facility
Impacts
Employment
Impacts
Production
Losses
Firm
Impacts
Community
Impacts
Foreign Trade
Impacts
Small Business
Impacts
New
Source
Impacts
Data Inputs
Analytical Outputs
I I Key Analytical Components
1.3
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The EIA methodology is based upon a facility-level impact analysis. This analysis drives the other
components of the EIA (See Figure 1.1.) The facility-level economic model estimates post-compliance
revenues, costs, and profits. The post-compliance financial data are then used to analyze three potential effects
of the increased costs on facilities: facility closure, product line closure, and other financial impacts short of
closure. The analysis of facility closure is based on comparing the post-compliance facility discounted cash
flow to the facility liquidation value. The product line closure analysis compares prices and costs of products to
predict whether product lines remain in production post-compliance. The analysis of other significant financial
impacts considers changes hi financial indicators of facilities' operating conditions between the baseline (i.e.,
pre-compliance) and post-compliance scenarios.
The impacts of the regulatory options on facilities drive other, secondary impacts, including those on
local communities and foreign trade. The effects on communities are measured by the level of employment loss
expected to correspond to the decreased production of PAIs potentially subject to this regulation. The
significance of the employment loss is evaluated by its impact on the community employment rate. Foreign
trade impacts may result from changes in the domestic production of pesticides, because pesticides are traded in
an international market. Changes hi the balance of trade are calculated based on both the estimated decreases in
exported production and the increases hi pesticide imports that result from meeting regulatory requirements.
The expected changes in exports and Imports are compared with baseline (1986) exports and imports for the
entire pesticide industry, and with total U.S. merchandise trade (1986), to measure the significance of the
change.
The effects of compliance costs are also evaluated at the firm level by considering changes in financial
indicators at the level of the parent company. The firm analysis projects whether a firm is capable of financing
the investment required to comply with the proposed regulation. The analysis is conducted by examining
changes in the financial indicators of a firm's operations conditions between the baseline and post-compliance
scenarios.
Two additional potential impacts of the proposed regulation, using the results of both the facility and
the firm analyses, are impacts on (1) small businesses and (2) new sources of pesticide production. The
evaluation of impacts on small businesses is conducted hi three steps. First, it is determined whether the
proposed regulation is expected to impact a substantial number of small businesses significantly. Impacts are
defined as either a facility closure, a product line closure, or another significant financial facility impact short of
closure. If a substantial number of small businesses are projected to sustain significant impacts, the second
stage of the analysis evaluates whether these impacts are expected to fall disproportionately on those businesses.
Third, if the regulatory burden on small businesses is disproportionate relative to that on larger businesses,
alternative regulatory methods that mitigate or eliminate the economic impacts on small businesses would be
examined.
1.4
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Impacts on the construction of new facilities and the expansion of existing facilities are examined in the
final section of the EIA.
The following chapter presents a description of the data sources consulted for this EIA. Chapter 3
profiles the pesticide industry, examining both the industry segments involved in PAI production and prevailing
market conditions for pesticide products.
Having set the stage for the analysis, each of the remaining chapters describes the data and
methodology used to estimate one type of potential impact and the resulting impact estimates themselves.
Chapter 4 details the methodology used to estimate the facility impacts. As stated above, facility impacts
provide the methodological foundation for this EIA. First, the markets to be analyzed and the basic model of
market structure are defined. Then, baseline and post-compliance costs, prices, and production quantities are
estimated. This chapter also describes the tests used to predict facility closure, product line closure, and other
significant impacts.
Chapter 5 describes the methodology for and results of the community impact analysis, based on the
results of the facility analysis. Methods for estimating international trade effects, and the expected effects
themselves, are described hi Chapter 6. A discussion of the expected impacts of the proposed regulation on
firms owning pesticide manufacturing facilities is presented in Chapter 7. Procedures for assessing the impacts
on small businesses are presented in Chapter 8, along with the projected impacts themselves. Finally, Chapter 9
describes the expected effects of the regulation on new sources of PAI manufacture.
1.5
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Chapter!: DATA SOURCES
This EIA employs data from many sources at differing levels of aggregation. The various sources used
are described below.
The Pesticide Manufacturing Facility Census for 1986, a census of pesticide manufacturing facilities
conducted under Section 308 of the Clean Water Act,1 is the principal source of facility-level data. The Census
includes the 90 facilities that, in 1986, manufactured one or more of the 270 individual or classes of pesticide
active ingredients (PAIs) that are within the scope of the proposed regulation. Part A of the Census
questionnaire requested the data necessary to perform the technical and treatment cost estimation analysis,
including PAI-specific production for 1986. Part B of the Census questionnaire requested detailed economic and
financial data, including balance sheet and income statement information for 1985, 1986, and 1987. Three
years of data were collected so that the EPA could construct a "typical" year upon which to base the impact
analysis. Part B was also designed to obtain information on facility liquidation values and the cost of capital. A
copy of Part B of the Census is included as Appendix A. A copy of Part A of the Census can be found in the
Administrative Record. Throughout the remainder of this document, the term "Census", if not further
specified, will refer to Part B of the Pesticide Manufacturing Facility Census.
Part A of the Census questionnaire was sent in July 1988; Part B was mailed in January 1989. Based
on an initial review of Part A responses, Part B was sent only to those facilities known to manufacture one or
more of the PAIs within the scope of the regulation. Because Part B was sent to a reduced number of facilities,
two facilities that were later determined to be manufacturing one or more of the PAIs subject to regulation were
omitted. One was thought to be exclusively a formulator/packager; the other performs only research and
development.
In the proposed Census questionnaire sent to the Office of Management and Budget (OMB), the EPA
proposed to request PAI-specific unit cost and price data. These data would permit the EPA to incorporate the
different unit costs, prices, and profit margins of PAIs in the impact analysis. The National Agricultural
Chemicals Association (NACA), the trade association representing numerous chemical manufacturing firms and
individuals in the industry, was reluctant to have the industry provide these detailed data and voiced objections
to the OMB. OMB subsequently rejected the proposed questionnaire. As a compromise, the EPA allowed
pesticide manufacturers a choice in the final questionnaire. Manufacturers could provide the PAI-specific data,
or could elect to have their facility's impact analysis done using averages. In this latter method, the EPA would
'Federal Water Pollution Control Act, 33 U.S.C. 1318.
2.1
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assume that all PAIs produced by a single facility have the same unit cost, price, and profitability.2 Twenty of
the 88 facilities responding to Part B chose to provide the PAI-specific cost and price data.
The other major data input to the EIA was the estimated compliance costs of the regulation.3 The
EPA considered compliance costs for the 90 facilities under two potential regulatory options: a Treated
Discharge Option and a Zero Discharge Option. The Treated Discharge Option limitations are based on
hydrolysis, activated carbon, chemical oxidation, resin adsorption, solvent extraction, incineration, and/or
recycle/reuse to control the discharge of PAIs hi wastewater.4 Zero Discharge Option limitations would require
no discharge of pesticide manufacturing process wastewater pollutants to surface water by using on-site or off-
site incineration and/or recycle/reuse.
Three categories of compliance costs associated with pesticide manufacturing were evaluated for both
the Treated and. Zero Discharge Options: capital costs, land costs, and operating and maintenance costs.
Operating and maintenance costs include monitoring costs, required by permit writers to demonstrate
compliance, as well as the costs of sludge disposal. All of the compliance cost estimates are presented in 1986
dollars and are based on the assumption that, whenever possible, facilities will build on existing treatment. For
facilities that both manufacture and formulate/package PAIs, the compliance costs apply only to the
manufacturing operations of the facility.
The Census data base and the compliance cost estimates were required for all components of this EIA:
the industry profile, and the impact analyses for facilities, communities, foreign trade, firms, small businesses,
and new sources. The EPA also used data from secondary sources in each of the chapters. The profile of the
pesticide industry relied on the Annual Survey of Manufactures published by the U.S. Department of Commerce,
Kline and Company's Kline Guide to the U.S. Chemical Industry, and the International Trade Commission's
(ITC) Synthetic Organic Chemicals, which together provided production and aggregate industry data. The
profile also used import and export data from the United Nations' International Trade Statistics Yearbook.
The facility impact analysis used secondary price data from the Annual Market Survey published by
Doane Marketing Research and from Agchemprice published by DPRA, Inc. The facility impact analysis also
employed data from The EPA's Office of Pesticides Programs (OPP). The OPP maintains data on PAI-specific
sales, prices, and usage from a number of proprietary sources. The OPP data were among those used to
2See Part B of the Census, page 26, text preceding question 2-H.
3Full details of the compliance cost estimates can be found in the Technical Development Document.
4For some PAIs the Treated Discharge Option limits discharge to zero.
2.2
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estimate prices, and were also used to calculate the percentage of pesticide production that will not be covered
by this regulation at this time.
Data from the OPP also served as the basis for determining the substitutability among PAIs. In 1980,
the OPP defined pesticide markets to ensure that the EPA reviewed competing products on roughly the same
schedule, so that one pesticide does not have an unfair advantage over another. The pesticide markets were
defined as clusters of PAIs that are substitutes for a specific end-use. This classification was adapted and used
as the basis for defining pesticide markets hi this EIA (see Appendix B). In addition, the facility-level analysis
used the estimates of price elasticity of demand developed hi the document entitled Estimates of the Price
Elasticity of Demand for Pesticide Clusters (EPA, 1991; see Appendix C).
The community impact analysis required the use of regional employment multipliers developed by the
Bureau of Economic Analysis, population data from the Current Population Reports in Statistical Abstract of the
United States (Bureau of the Census), and employment rates from the Bureau of Labor Statistics. The foreign
trade analysis used import data from the OPP and data on the U.S. trade balance from the International Trade
Statistics Yearbook (United Nations) and the Statistical Abstract of the United States. The firm-level analysis
was developed using financial statistics from Standard and Poor's Compustat and from Robert Morris
Associates' Annual Statement Studies, hi addition to Parts A and B of Biof the Census. The Compustat data
provided financial information on domestic firms subject to public reporting requirements, while the information
available through Robert Morris Associates was used for the remaining firms. Finally, the analysis of small
businesses required data from Dun and Bradstreet's Million Dollar Directory to calculate the number of
employees at the firm level.
2.3
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Chapter 3: PESTICIDE MANUFACTURERS PROFILE
3.0 Introduction
The following profile of the chemical pesticide industry describes the products, facilities, and firms
associated with pesticide active ingredient (PAI) manufacturing and sales. It is intended to provide a backdrop
for the EIA by identifying and discussing key variables defining the market structure of the pesticide
manufacturing industry. The prevailing market conditions for pesticide products provide insight into firms'
reactions to increased costs due to regulatory compliance.
The pesticide industry is organized vertically into two major segments: pesticide manufacturing and
pesticide formulating/packaging/repackaging. Pesticide manufacturing involves the production of PAIs. PAIs
are not used directly for pest control, but are instead combined with solid, liquid and/or gaseous diluents before
use. PAIs are marketed in many formulations that may be either liquid or dry, and include a wide variety of
solutions, emulsions, powders, dusts, granules, pellets, and aerosols. Formulating and packaging therefore
involves the combination of active with inert ingredients, such as diluents, inorganic carriers, stabilizers,
emulsifiers, aerosol propellants or wetting agents; and packaging the product in plastic, glass, paperboard, or
metal containers for distribution and sale. The concentration of a PAI in a formulation may be high or low.
Some formulations are ready to use; others must be further diluted before use. Repackaging involves
transferring a single PAI or single formulation from any marketable container to another marketable container
without intentionally mixing any inerts, diluents, solvents, other PAIs, or other materials of any sort. Data
from the Census show that in 1986, 50 of the 90 pesticide manufacturing facilities (56 percent) also engaged in
formulating and packaging, indicating that the majority of pesticide manufacturers are vertically integrated.1
The seven sections in this chapter focus on pesticide manufacturers, but some of the information
presented pertains to both manufacturers of PAIs, and formulators/packagers/repackagers. Section 3.1
categorizes the data used to develop the profile. Section 3.2 describes sources of demand for chemical
pesticides in the United States. Characteristics of pesticide manufacturing facilities, including physical
characteristics, production costs, revenue, profits, employment, labor productivity, and capital expenditures are
described in Section 3.3. Section 3.4 examines the organization of firms in the industry, including firm
ownership and vertical industrial integration. Section 3.5 portrays the market structure of the pesticide industry,
and includes discussions of barriers to market entry, demand elasticity and product substitution, and firm
concentration in the industry. Section 3.6 provides an overview of international trade in pesticides, including a
• t 1988 SUrVey °fthe Pesticide Formulating, Packaging, and Repackaging Industry indicate that
in 1988, 51 of the pesticide manufacturers were engaged in formulating and packaging. Since that time, however
4 of these manufacturers have discontinued production.
3.1
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discussion of the balance of trade for chemical pesticides and the nature of foreign competition. Section 3.7
summarizes the information presented in the profile.
3.1 Categorization of Data
The Federal Insecticide, Fungicide and Rodenticide Act (FIFRA) defines a pesticide as "(1) any
substance or mixture of substances intended for preventing, destroying, repelling or mitigating any pest, and
(2) any substance or mixture of substances intended for use as a plant regulator, defoliant, or desiccant."
Section 2(t) of FIFRA defines a pest as "(1) any bisect, rodent, nematode, fungus, weed, or (2) any other form
of terrestrial or aquatic plant or animal life or virus, bacteria, or other microorganism (except viruses, bacteria,
or other microorganisms on or in living man or other living animals) which the administrator declares to be a
pest under Section 25(c)(l)."
Other data sources used in this profile categorized pesticides in a variety of manners. The Census of
Manufactures (Bureau of the Census, 1986) classifies the pesticide industry primarily into two standard
industrial classifications (SICs). Establishments engaged primarily in the manufacture or formulation of
agricultural chemicals not elsewhere classified, and the formulation and preparation of pesticides, are classified
as SIC 2879. Establishments involved in the manufacture of pesticides, and other organic agricultural chemicals
that are PAIs used to formulate pesticides, are classified as SIC 28694. The Kline Guide to the U.S. Chemical
Industry classifies pesticides by three major types: herbicides, insecticides, and fungicides. The International
Trade Commission's Synthetic Organic Chemicals classifies pesticides into cyclic and acyclic fungicides,
herbicides and plant growth regulators; and insecticides, rodenticides, and related products such as seed
disinfectants, soil conditioners, soil fumigants, and synergists. The U.N. International Trade Statistics Yearbook
classifies pesticides into disinfectants, insecticides, fungicides, and herbicides for retail sale as preparations or as
PAIs. The tables and graphs that present data from these sources refer to all pesticide production, both in-scope
(including 270 individual or classes of PAIs) and out-of-scope (all non in-scope PAIs). As an aid in
understanding these categorizations, brief descriptions of the primary functions of pesticides are listed in
Table 3.1.
The market analysis for this profile relies on another classification of PAIs, based on the cluster groups
established by the EPA's Office of Pesticide Programs (OPP). In 1980, the OPP defined PAI markets to ensure
that the EPA regulated competing PAIs on roughly the same schedule, so that one PAI did not have an unfair
advantage over another. Six hundred PAIs were classified into 48 clusters according to the major use of the
chemicals. For instance, all herbicides used on corn production were classified into the same cluster. Each
cluster therefore contains PAIs that may be roughly substituted for one another on major use sites.
3.2
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Class
Table 3.1
Representative Classes of Pesticides and the Pests They Control
Target Pest
Acaricide
Algicide
Attractant
Avicide
Bactericide
Defoliant
Dessicant
Fungicide
Growth regulator
Herbicide
Industrial Microbiocide
Insecticide
Miticide
Molluscicide
Nematicide
Piscicide
Predacide
Repellents
Rodenticide
Silvicide
Slimicide
Sterliants
Mites, ticks
Algae
Insects, birds, other animals
Birds
Bacteria
Unwanted plant leaves
Unwanted plant tops
Fungi
Insect and plant growth
Weeds
Microorganisms
Insects
Mites
Snails, slugs
Nematodes
Fish
Carnivorous animals
Insects, birds, other animals
Rodents
Trees and woody vegetation
Slime molds
Insects, other animals
Source: Minnesota Department of Agriculture, Rinse and Win Brochure, 1989.
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The EPA's Office of Water used the OPP's cluster segmentation to define individual markets for
groups of pesticides, because economic variables, such as demand elasticity, would not be meaningful for a
market defined as all pesticides. The Office of Water expanded upon the OPP's cluster segmentation in two
ways. First, PAIs registered after 1980 were assigned to one of the 48 clusters. Second, the 48 clusters were
expanded to 56 clusters, based upon differences in the sensitivity of product demand to changes in price (see
Table 3.2).2 In addition, although the OPP's cluster segmentation assigned each PAT to only one cluster, this
analysis allowed for a PAI to be assigned to more than one cluster if it had more than one important use. The
allocation of PAIs to clusters can be found hi Appendix B.
Although the economic impact analysis of the proposed effluent guidelines is built on the individual
facility's production of PAIs that can be classified as belonging to one or more of these clusters, in the
remainder of this profile chapter EPA has aggregated the Census data to prevent disclosure of confidential
business information. Information is generally presented in five categories: fungicides, herbicides, insecticides,
multiple types of pesticides, and other pesticides.
3.2 Sources of Demand for Chemical Pesticides
The major markets for pesticides are agriculture, industrial/institutional/commercial, and home/lawn/
garden.3 Agricultural sales account for approximately 70 percent of domestic pesticide sales.
Industrial/institutional/commercial and home/lawn/garden each constitute about 15 percent of U.S. sales (see
Figure 3.1).
Much of the pesticide application for the three markets is performed by commercial applicators.
Commercial applicators are trained professionals skilled in applying pesticides in an efficient and
environmentally safe manner. The National Pest Control Association estimated that in 1990 the commercial
applicator industry would contain 14,250 firms and have annual billings of $3.5 billion (National Pest Control
Association, 1991). Commercial applicators are contracted by the agricultural industry to apply pesticides to
agricultural crops, as well as to food products during storage and transit. The industrial/institutional/
commercial sectors use the services of commercial applicators to control pests in many settings, including
schools, health care facilities, prisons, food processing establishments, hotels, restaurants, factories, and
Clusters were split when (1) there was a wide variety of price elasticities of demand among PAIs within a
cluster, and (2) the PAIs among which demand elasticity varied had distinctive uses. For example, the cluster that
encompasses herbicides used on fruit trees was split into three clusters: herbicides used on grapes, herbicides used
on oranges, and herbicides used on fruit trees (excluding grapes and oranges).
Additional markets, such as stored grain products (elevators), seed treatment, pest control operations
(termiticides), cattle, golf courses, utility right of ways, etc., also exist. That level of detail, however, is not
necessary in this discussion.
3.4
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Table 3.2
Pesticide Clusters
Pagel
Cluster Primary Application
Herbicides used on;
H-l Broad spectrum of uses
H-2 Corn
H-3 Soybeans, cotton, peanuts, alfalfa
H-4 Sorghum, rice, and small grains
H-5a Oranges
H-5b Grapes
H-5c Fruit trees
H-6 Sugarbeets, beans and peas
H-7 Drainage ditches, rights of way, forestry and ponds
H-8 Turf
H-9a Vegetables
H-9b Tobacco
H-10 Unclassified uses
Insecticides used on/for/as:
I-la Cotton
I-lb Soybeans, peanuts, wheat and tobacco
I-2a Corn and alfalfa
I-2b Sorghum
1-3 Fruit, and nut trees, excluding oranges and grapes
I-4a Oranges
I-4b Grapes
1-5 Vegetables
1-6 Livestock and domestic animals
1-7 Non-agricultural sites (as repellent)
1-8 Domestic bug control and for food processing plants
1-9 As fumigants and nematicides
1-10 Termite control
1-11 Lawns, ornamental's, and forest trees
1-12 Mosquito larva
1-13 Unclassified uses
3.5
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Table 3.2
Pesticide Clusters
Page 2
Cluster Primary Application
Fungicides used ont
F-l Broad spectrum of uses
F-2a Fruits and nuts
F-2b Grapes
F-3 Vegetables
F-4 Oranges
F-5 Seed treatments
F-6 Post-harvest fruit and vegetables
F-7 Grain storage
F-8 Ornamentals
F-9 Turf
F-10 Unclassified uses
Other Pesticides;
R-l Industrial preservatives
R-2 Slimicides used in pulp and paper, cooling towers, and
R-3 Industrial microbiocides
R-4 Sanitizers used in dairies, food processing, restaurants,
R-5 Synergists used as insecticide synergists, sufacants,
cheleating agents and carriers
R-6 Food preservatives
R-7 Wood preservatives, used for industrial, commercial
R-8 Disinfectants
R-9 Water disinfectants
R-10 Plant regulators, defoliants, and desiccants
R-ll Preservatives, disinfectants, slimicides
R-12 Molluscides and misc. vertebrate control agents
R-13 Bird chemosterilants, toxicants, and repellants
R-14 Dog and/or cat repellants
R-15 Rodent toxicants, anticoagulants, predator control
U-l Unclassified uses
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Figure 3.1
U.S. Market Demand for All Pesticides1,1988
(Dollar Percentages)
Home, Lawn
and Garden
15%
Industrial,
Institutional,
Commercial and
Government
16%
U.S. Agriculture
69%
Includes both in-scope and out-of-scope PAIs.
Source: Pesticide Industry Sales and Usage: 1988 Market Estimates, U.S. EPA, Office
of Pesticides and Toxic Substances, February, 1988.
Note: Census data were not used for this figure, because the question in the Census
that refers to markets refers to total facility production, not pesticide production.
3.7
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warehouses. Household consumers use commercial applicators to manage pests that typically inhabit dwellings,
such as termites, cockroaches, and mice, and to rid their lawn and garden of pests. Government entities use the
services of commercial applicators to control mosquitos, and to maintain vegetation around roads, and public
recreational areas. In 1985, residential services comprised about 60 percent of the non-agricultural commercial
applicator Industry, commercial services constituted 25 percent, and services to institutions, industries and the
government represented 7, 6, and 2 percent respectively (Kline & Company, 1986).
3.2.A Agriculture Market
Agriculture forms the largest market for chemical pesticides. The agricultural market is diverse in
terms of the types and amounts of pesticides used and in pesticide management practices, which vary
significantly among regions of the country, states, and sometimes even counties. This diversity is an important
distinction that separates agriculture from the other pesticide markets, which tend to be more homogeneous
nationwide.
Approximately 62 percent of all planted agricultural acres are treated with at least one type of pesticide
product (Pimental et al., 1986). Herbicides are the most commonly used type of pesticide in terms of quantity
of pesticide product applied. In 1987, the herbicides that were most widely used were Alachlor, Atrazine and
2,4-D (U.S. EPA, 1990). These pesticides were used primarily on peanuts, corn, soybeans, cotton, and rice.
Insecticides were the second most commonly used pesticide type. In 1987, the most widely used insecticides
were Carbaryl, Malathion, and Chlorpyrirbs (U.S. EPA, 1990). These pesticides were used primarily on
cotton, fruits, vegetables, nuts, and ornamentals. Fungicides are applied to fewer acres than herbicides or
insecticides, but are generally applied to high-value fruit and vegetables. In 1987, Maneb and Captan were the
most widely used fungicides (U.S. EPA, 1990).
Table 3.3 provides a brief description of the steps taken to move a PAI through process and distribution
channels and then to the end user. As indicated in Table 3.3, end users include farmers, government, and
commercial applicators. Farmers either purchase and apply pesticide products themselves or pay commercial
applicators to apply pesticides to their crops. The government uses agricultural chemicals to control vegetation
around highways, roads, railroads, waterways, pipelines, power lines, government buildings, military
complexes, and parking lots.
3.2.B Industrial/Institutional/Commercial Market (I/I/C)
The I/I/C market includes many products, such as disinfectants, cleaning supplies, and air conditioning
biocides, that are generally not perceived as pesticides by the public. In'addition, products such as paint and
wood preservatives may contain substantial amounts of pesticides. The I/I/C market is estimated to exceed $200
million annually, with about 45 percent involving health care institutions (U.S. EPA, 1991).
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Table 3.3
Pesticide Agricultural Production and Distribution1
Agent
Purpose
Registrant
Registers the pesticide formulation with EPA. Registration
involves a long, expensive R&D process to develop the
pesticide, produce the data required for registration, and
proceed through the registration process.
Manufacturer
Synthesizes the active ingredient from raw materials.
Formulator/Packager
Produces the pesticide formulation by combining the active
ingredient(s) with other substances, including surfactants,
clays, powders and solvents; involves mixing or blending
operations. Formulation may be done in-house, by
independent formulators, or by tollers who formulate the
product under contract to the manufacturer.
Distributor
Acts as the "middle man;" buys pesticide from the
registrant/manufacturer/formulator and sells to the dealer.
Dealer/Co-op/Repackager Sells the pesticide to the user.2
In many cases several steps are performed by one entity. Large companies might
register, manufacture, and formulate their pesticides. Some distributors also
formulate several pesticides. Additionally, a single facility might function as a
distributor, dealer, and commercial applicator.
A user is defined as a farmer, government, commercial ground applicator, commercial
aerial applicator, etc.
Source:
Based on a table in: Pesticide Container Report to Congress, U.S. EPA,
Office of Pesticides and Toxic Substances, Draft, March 8, 1991.
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The I/I/C market differs significantly from the agricultural market in several ways. First, the use of
l/JJC products is generally more uniform across the country. The need for disinfectants in various parts of the
United States is approximately the same. However, the use of pesticides for wood preservation and in cooling
towers varies somewhat according to the climate (U.S. EPA, 1991). Second, I/I/C pesticides are generally used
in smaller quantities than agricultural chemicals. Third, I/I/C products hi general are usually less expensive per
unit volume of product than agricultural pesticides, because they are less concentrated.
Another major difference between I/I/C and agricultural markets is that fewer manufacturers of
pesticides used in the I/I/C market both register and formulate their pesticides; independent
formulators/packagers are more predominant in the I/I/C market. In addition, a greater variety of paths exist
between the formulators and end users. This is evident in Figure 3.2, which illustrates distribution channels
within the I/I/C and home/lawn/garden markets.
The distinction among industrial, institutional, and commercial pesticides is based on the setting in
which the pesticide is used. In some cases, the same formulation is used hi different types of facilities. Typical
industrial end-users include personnel in food processing facilities and breweries. Industrial pesticides, such as
preservatives, slimicides or biocides, are used hi cooling towers, paper and textile mills, oil wells, metalworking
coolants, etc. (U.S. EPA, 1991). Typical institutional end-users include personnel in hospitals, nursing homes,
schools, restaurants, hotels, and contract cleaning businesses that serve stores, apartment houses, office
buildings, and garages (U.S. EPA et al., 1989). Commercial establishments use pesticides to protect
landscaping and to maintain cleanliness and health standards. The federal, state and local governments use I/I/C
chemicals on military bases, and hi hospitals and other government buildings.
Producers of pesticide products used hi institutional settings may sell directly to large users (e.g.,
hospitals), or they may use distributors at janitorial supply houses to sell indirectly to smaller users.
Institutional distributors usually sell general maintenance products (e.g., cleaning supplies and non-pesticide
cleaners, as well as sanitizers and disinfectants). Similarly, producers of industrial and commercial pesticides
may sell directly to the end-user or indirectly through a warehouse (U.S. EPA et al., 1983).
3.2.C Home/Lawn/Garden Market
The home/lawn/garden pesticide market includes pesticide products that are commonly used hi and
around the home. These products include rodenficides, insect repellents, lawn and garden pesticides,
disinfectants and other pesticidal cleaners, insecticides to protect pets and eliminate household pests, herbicides,
fertilizers with herbicides/insecticides, and insect baits and traps. In general, household pesticides are packaged
hi containers that are smaller than those used in the other markets and may also be less concentrated. Some
household pesticides are seasonal (e.g., lawn and garden products), while others meet a demand that remains
fairly constant throughout the year.
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Figure 3.2
Production and Distribution Channels for the Industrial/
Institutional/Commercial and Home/Lawn/Garden Markets
Basic Pesticide Manufacturers
l
life-House Forniolators";, —
Independent
Formulators
Contract
Formulators
"Tollers"
Consumer
Companies
LJ
Formulators/
Distributors
Distributors
±
I
Food Brokers, Etc.
I
Retailers
Industrial,
Institutional &
Commercial Dealers
Home, Lawn
and Garden
Users
Source: Based on a diagram in: Pesticide Container
Report to Congress, U.S. EPA, Office of
Pesticides and Toxic Substances, Draft,
March 8, 1991.
Institutional
Users
Industrial
Users
Commercial
Users
Government
Users
3.11
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The home/lawn/garden pesticide production and distribution chain, similar to the I/I/C chain, is
included in Figure 3.2. The main difference between the household market and the other markets is that the
end user, the household consumer, purchases household pesticides from a wide variety of common retail
establishments. These include grocery, drug, and discount stores, as well as home and garden shops and pet
supply companies. The producer of household pesticide products can sell directly to the retail stores or
indirectly through a distributor warehouse. Consumer companies, another distribution channel from
manufacturers to retail stores, make consumer products, applying their label to the finished good. Like
formulators, consumer companies can sell directly to retail establishments or indirectly through food brokers
who distribute products to retail stores.
3.3 Facility Characteristics
3.3.A Physical Characteristics
Figure 3.3, drawn from Census data, shows the geographic distribution of the PAI manufacturing
facilities and provides the percentage of in-scope PAI production hi each region. Although pesticide facilities
are located in all regions of the country, the southeast/south central region of the country has the heaviest
facility concentration (35 percent).4 The northwest/southwest region has the second heaviest concentration (33
percent).5 Although the southeast/south central region accounts for a larger percentage of facilities, the
northwest/southwest region has the largest share of in-scope pesticide production (52 percent).
The Census also provides information on the age of pesticide facilities. The data indicate that most of
the facilities are relatively old (i.e., constructed prior to 1970). The 1960s was the most active decade for
facility construction, with almost a quarter of the facilities constructed prior to 1970. After 1980 only about 7
percent of existing facilities were constructed. Table 3.4 presents the distribution of facilities by the number of
years in which they have produced pesticides. This distribution is shown for the five categories of pesticide
type-6
3.3.B Industry Output
Several factors have affected the demand for chemical pesticides. These include the decline in
agricultural acreage; the production of new, more highly concentrated pesticide products; more efficient
"The southeast/south central region includes Alabama, Delaware, Florida, Georgia, Kentucky, Maryland,
Mississippi, North Carolina, South Carolina, Tennessee, Virginia, West Virginia,
5The northwest/southwest region includes all states west of the Mississippi River.
*Many of the facilities in the Census did not begin pesticide production until many years after construction.
Approximately 38 percent of the facilities have produced pesticides for more than 30 years, while less than 13
percent of the facilities have produced pesticides for fewer than 10 years.
3.12
-------
Figure 3.3
Facilities and In-Scope Pesticide Production
by Region, 1986
Percent
75%-
70-
65-
60-
55-
50
45 "1
35
30
25-
20-
15-
10-
5-
0-
32%
."^ v^^^NX^ '
J^ -#S.S % %S*^^wS*'-\.
18%
•'!-''X--'X--'!--'.'-
Northeast/
North Central
35%
•N^^^vN"^^^. x>
30%
33%
^xM^^^^x
52%
•'X--'X'X-v\!-
, j
Southeast/ Northwest/
South Central Southwest
Region
1 Percent of Facilities
3 Percent of In-Scope Production
Source: Census.
3.13
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Table 3.4
Pesticide Manufacturing Facilities by Facility Age, 19861
v,, Dumber of
5 to 10 to
<5 < 10 < 20
Pesticide
Type
Fungicides
Herbicides
Insecticides
Other Pesticides*
Multiple Types of
Pesticides**
% f
20 to
< ; 30
' '"", ,'&
Years
^
30 to
< 40 40+ All*
ft. V}.S J -
• i
(Number of Facilities)
0
1
1
0
0
All in-scope Facilities 2
* Refer to Table 3.2 for a description
** Multiple types of pesticides include
the groups outlined above.
*** Excluded from the 88 facilities that
facility age.
1 Facility age is
Source: Census
2
4
3
0
0
9
5
5
3
1
7
21
of other pesticides
manufacturers that
provided financial
the number of years the
3
5
4
2
7
21
produce
data are
1
1
3
4
8
17
pesticides in
two facilities
0 11
4 20
3 17
1 8
8 30
16 86***
more than one of
that did not report
facility has been producing pesticides.
# Faculties
2
Number of Facilities by Facility Age, 1986
9 21 21 17
16
<5 5 to < 10 10to<20 20 to <30
Age (Years)
30 to <40 40+
3.14
-------
application of pesticides; the increase in pesticide resistance; the increase in environmental regulations; and
greater awareness of environmental issues on the part of both the seller and the buyer. Although these factors
have led to a contraction in pesticide production and sales, profitability from pesticide sales in the industry
appears to have been largely unaffected by the decline in output (Kline & Company, 1990). Production
characteristics of the pesticide manufacturing industry are outlined below.
In 1988, total pesticide production was about 1.2 billion pounds. Production declined by an average of
two percent per year from 1980 to 1988 (U.S. Department of Commerce, 1987). The volume of pesticides sold
declined by four percent per year (see Table 3.5) (U.S. Department of Commerce, 1987). Figure 3.4 illustrates
the decline in pesticide production for fungicides, herbicides, and insecticides from 1980 to 1988. The graph
shows that herbicide production reached a trough in 1983, recovered somewhat, and then fell to a new low in
1987. Insecticide production declined to its lowest point in 1983 and recovered somewhat thereafter. Fungicide
production was at its lowest point in 1987.
The most significant factor has been a decline in agricultural acreage. Figure 3.5, which plots total
pesticide production and total U.S. planted crop acres using 1986 as a base year, shows how pesticide
production mirrors planted acres.7 Pesticide production was lowest in 1983, when the United States
Department of Agriculture (USDA) implemented the Payment-In-Kind (PIK) program, taking 48 million acres
out of production. Although the number of planted acres increased after 1983, other USDA programs, such as
the Conservation Reserve Program, continued to reduce agricultural acreage (Ribaudo, 1989).8
Also contributing to the decline in pesticide production was the introduction of new, low-volume
pesticides such as postemergence herbicides. Because these new pesticides are effective in significantly smaller
doses; the overall volume of pesticide production was reduced (Kline & Company, 1990).
3.3.C Production Characteristics
Table 3.6 details the distribution of 1986 in-scope facility production and sales by facility size. The
Census data indicate that, in terms of in-scope PAI production, most facilities (about 68 percent) are small- and
medium-sized, producing fewer than 6 million pounds of in-scope PAIs annually. These facilities, however,
account for only ten percent of total in-scope pesticide production.
divided * 19S6 productl°°
40 " 45 mi"ion — °f
3.15
-------
a
'
S S
to
s" s
»" a
invi
R
cl
s
i
o\
YH
O
OO
a
I
a
oCrf
^
-------
Figure 3.4
Fungicide, Herbicide, and Insecticide Production1
1980-1988
(in 1,000 pounds)
1,000 Pounds
1,000,000-1
900,000-
Herbicide Production
(Including plant growth
regulators)
Insecticide Production
(Including rodenticides, soil
conditioners and fumigants)
Fungicide Production
1980 1981 1982 1983 1984 1985 1986 1987 1988
Years
Production data are reported in terms of manufactured PAIs.
Source: International Trade Commission, Synthetic
Organic Chemicals, 1980-1988.
3.17
-------
Figure 3.5
Pesticide Production and Total Planted Acres,
1977-1987
(1986 Base Year)
Pounds Produced
Indexed to 1986
1.3—1
1.2-
1.1-
1.0-
Planted Acres
Pesticide Production
1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987
Years
Source: International Trade Commission, Synthetic Organic
Chemicals, 1977-1987 and United States Department of
Agriculture, Agricultural Statistics 1984 and 1989.
3.18
-------
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3.19
-------
In terms of in-scope facility sales, the Census data indicate that the majority of facilities (51 percent)
are relatively small, with in-scope sales of less than $10 million (see Table 3.6). Only 22 percent of all
facilities have annual in-scope pesticide sales greater than or equal to $50 million.
For most facilities, large and small, in-scope pesticide production makes up only a part of the facility's
production activity. Figure 3.6, which presents the 1986 composition of production activity for facilities in the
Census, indicates that, on average, about 41 percent of facility production activity is devoted to the
manufacturing and/or formulating and packaging of In-scope pesticides. The manufacture and/or formulating
and packaging of chemicals other than EPA-registered pesticides account for another 41 percent of activity.
The remaining activities include: other (i.e., non-chemical) production activity (12 percent); manufacturing
and/or formulating and packaging out-of-scope EPA-registered pesticides (5 percent); and manufacturing
intermediates (1 percent). All pesticide-related activities (in-scope and out-of-scope), on average, account for 47
percent of production activity.
The extent to which a facility is involved hi pesticide-related activities vs. non-pesticide-related
activities varies slightly, depending upon the size of the facility (see Figure 3.7). Smaller facilities (with total
revenues of less that $20 million) devote approximately 31 percent of their production to non-pesticide related
activities. Large and medium-sized facilities (with revenues greater than or equal to $20 million) are more
diversified, with between 58 and 62 percent of production devoted to non-pesticide related activities. The
composition of facility production activity varies more dramatically among facilities when comparing chemical-
related (including pesticides) production activities to non-chemical-related production activities. Large facilities
(with total revenues greater than or equal to $250 million) are more diversified, with 36 percent of production
devoted to non-chemical-related activities. In contrast, small and medium-size facilities (with total revenues of
less than $250 million) devote between 5 and 10 percent of production to non-chemical-related activities.
3.3.D Production Costs
Production costs can be classified into two categories: fixed and variable. Fixed costs are independent
of the level of production and include depreciation on capital, fixed overhead, costs for product research and
development (R&D), and interest on capital. Figure 3.8 shows the composition of pesticide-related facility
fixed costs by facility size.9 In most cases, fixed overhead is the largest component of fixed costs.
Depreciation is the second largest component of fixed costs for facilities with revenues greater than or equal to
$1 million. While R&D costs constitute the largest component of facility fixed costs for facilities with pesticide
'Facility fixed costs were not broken down by pesticide-related vs. non-pesticide-related fixed costs in the
Census. This is because facilities maintained records of their fixed costs at the facility level. During the pretest,
it was determined that the respondent burden that would have been imposed by requiring facilities to break down
costs were too great. Consequently, the ratio of pesticide-related revenues to total facility revenues was applied to
each of the categories of fixed costs to obtain estimates of pesticide-related fixed costs.
3.20
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Figure 3.6
Composition of Facility Production Activity, 1986
(Averaged Across All Facilities )
Other Production
Activity
12%
Manufacturing
Chemicals Other
Than EPA-Registered
Pesticides
41%
Manufacturing and
Formulating and/or
Packaging In-Scope PAIs
41%
Manufacturing and
Formulating and/or Packaging
Out-of-Scope PAIs
5%
Manufacturing
Intermediates to be Sold
(others included in in-scope PAIs)
1%
Source: Census.
3.21
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Figure 3.7
Composition of Facility Production Activity
by Facility Size1,1986
(Averaged Across Size Categories)
Greater than or
equal to $250 Million
Between $75 and $250 Million
Between $20 and $75 Million
Less than $20 Million
• Manufacturing and Formulating and/or Packaging In-Scope PAIs
Q Other Production Activity
fl Manufacturing and Formulating and/or Packaging Out-of-Scope PAIs
Ejj Manufacturing Chemicals Other Than EPA-Registered Pesticides
D Manufacturing Intermediates to be Sold (others included in in-scope PAIs)
1 Facility size is measured by total facility revenues.
Source: Census.
3.22
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Figure 3.8
Composition of Pesticide-Related Facility Fixed Costs
by Facility Size1,1986
Greater than or equal to $50 Million
Between $25 and $50 Million
Between $1 and $25 Million
Depreciation
Fixed Overhead
Research and Development
Interest
Other Expenses
r^:>:>:>:>:>:>:>:> Less than $1 Million
Facility size is measured by revenues from all pesticide-related activities.
Source: Census.
3.23
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revenues of less than $1 million, R&D expenditures as a percent of total fixed costs (26.1 percent) are only
slightly greater than the percentage of fixed costs attributable to fixed overhead (25.7 percent).
Variable costs depend upon the level of production. These costs include pesticide material and product
costs, labor costs, contract or tolling costs, taxes, and other pesticide manufacturing costs (i.e., all other
pesticide-related operating costs not included in the aforementioned categories).10 Figure 3.9 shows the
composition of pesticide variable costs by facility size. The figure shows that pesticide material and product
costs are the largest component of variable costs across all facility sizes. Labor costs, contract work, and other
pesticide costs are small in comparison.
Figure 3.10 compares fixed and variable costs by facility size, to show the proportion of fixed costs to
total costs by facility size. If fixed costs are a large proportion of total costs, smaller firms may find it difficult
to enter the market. The Census data suggest only minor differences in the ratio of fixed costs to total costs
across facility size, indicating that fixed costs are not likely to be a barrier to entry.u For the category of
smallest facilities (with pesticide revenues of less than $1 million), fixed costs comprise 27 percent of total
costs. For the category of largest facilities (with pesticide revenues greater than or equal to $50 million), fixed
costs comprise 41 percent of total costs. Very large facilities, which often produce a greater variety of pesticide
types (e.g., insecticides, fungicides, and herbicides) and PAIs may be more capital Intensive, thereby facing a
different set of cost constraints than medium and small facilities.
3.3.E Employment Characteristics
According to the Census data, the pesticide manufacturing industry supported a total of 3,432
production workers La 1986 (see Table 3.7). The thirteen largest facilities (all with revenues of greater than or
equal to $250 million) employed 58 percent of the total number of pesticide manufacturing production workers
in the industry. In contrast, the twenty smallest facilities (all with revenues of less than $20 million) employed
5 percent of the total number of pesticide manufacturing production workers in the Industry.
The data presented La Table 3.7 lend further evidence that larger facilities tend to be more diversified
than smaller facilities. As facilities increase In size, the percent of the labor dedicated to non-pesticide-related
production increases from 23 to 44 percent of total facility employment.
'"Facility taxes were not broken down by pesticide-related vs. non-pesticide-related In the Census.
Consequently, the ratio of pesticide-related revenues to total facility revenues was applied to total facility taxes to
obtain estimates of pesticide-related taxes.
"Facilities can recover costs incurred by introducing a new product to the market by adjusting the price once
they have obtained patent protection. The fact that facilities may be willing to operate at a loss hi the short run,
knowing that they will ultimately recover their costs, mitigates the barrier to entry that is associated with large fixed
costs such as R&D.
3.24
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Figure 3.9
Composition of Pesticide-Related Facility Variable
Costs by Facility Size1,1986
Greater than or equal to $50 Million
Between $25 and $50 Million
Between $1 and $25 Million
Less than $1 Million
• Pesticide Material and Product Costs
E3 Labor Costs
fl Contract Costs
E3 Other Pesticide Costs
D Taxes
Facility size is measured by revenues from all pesticide-related activities.
Source: Census.
3.25
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Figure 3.10
Ratio of Pesticide-Related Fixed Costs to Pesticide-
Related Total Costs
by Facility Size1,1986
Greater than or equal to $50 Million
Fixed to total costs 41%
Between $25 and $50 Million
Fixed to total costs 32%
Between $1 and $25 Million
Fixed to total costs 29%
Less than $1 Million
Fixed to total costs 27%
• Fixed Costs
E3 Variable Costs
1 Facility size is measured by revenues from all pesticide related activities.
Source: Census.
3.26
-------
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00
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en
o
-------
Figure 3.9 shows that labor costs make up a relatively small portion of total pesticide variable costs,
suggesting that pesticide production is not a labor-intensive industry. On average, pesticide manufacturing
facilities employed 527 employees (full-time equivalents, or FTEs), with 40 employees devoted to pesticide
manufacturing, 19 to formulating and packaging, 225 to other production, and 250 to non-production (see Table
3.8). On average, production workers (for both pesticide and non-pesticide production) represented 54 percent
of total employment, with similar percentages for individual facility sizes. This ratio is in reasonable agreement
with data from the Census of Manufactures, which reports 1986 production employment to be 59 percent of
total employment for both SIC 2879 and SIC 2869.
Figure 3.11 plots employment trends from 1975 to 1987 for all manufactured goods against
employment in SIC 2879 (agricultural chemicals, not elsewhere classified [n.e.c.], in pesticide preparations and
formulations), SICs 2865 and 2869 (organic chemicals, except gum and wood)12, and SIC 28 (chemicals and
allied products). The figure shows a close correlation between employment trends in all manufacturing
industries, and hi both the agricultural chemical and organic chemical industries, as well as the chemical \
industry as a whole. Between 1980 and 1981, however, employment in the agricultural chemical industry
increased, while the employment in the organic chemical industry, chemical industry, and all manufacturing
decreased. ;
3.3.F Revenues and Profit
Consistent with the review of production data, examination of facility revenues reveals that facilities
derive a large percentage of their revenues from sources other than in-scope pesticide sales (see Figure 3.12).
Facilities with revenues greater than or equal to $250 million derive more than half their revenues
(approximately 58 percent) from sources other than in-scope pesticide sales, while facilities with revenues of
less than $20 million obtain about 42 percent of their revenues from other sources.13 Although the proportion
of revenues derived from sources other than in-scope pesticide sales varies across facility size, the figure
illustrates diversity at the facility level for all facility sizes.
12Industrial organic chemicals include SIC 2865 (cyclic crudes and intermediates), SIC 2869 (industrial organic
chemicals, n.e.c.), and SIC 2861 (gum and wood chemicals). The U.S. Industrial Outlook presents data for organic
chemicals as industrial organic chemicals except gum and wood, i.e., SICs 2865 and 2869. Consequently, for
consistency in presenting data from secondary sources, organic chemicals are classified as SICs 2865 and 2869
throughout this profile. (Note: In 1986, SIC 2861 constituted only 5 percent of the value of shipments for SICs
2861, 2865 and 2869 combined.)
13In-scope revenues are defined as the revenues derived from the sale of in-scope pesticide chemicals. This
definition excludes revenues from contract work or tolling, which may be entirely or partially attributable to in-scope
pesticides. The figures presented may therefore be larger if a facility also obtains revenues from contract work or
tolling.
3.28
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3.29
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Figure 3.11
Employment Trends, 1975-1987
(1975 Base Year)
Number of Employers
Indexed to 1975
1.5-,
SIC 2879
'All Manufacturing
SIC 28
iSIC 2865, 2869
1975
I I I I I I I I I
1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987
Years
SIC 2879 (Agricultural Chemicals, n.e.c., and Formulation & Preparation of Pesticides)
SIC 2865, 2869 (Organic Chemicals, except gum & wood)
SIC 28 (Chemicals and Allied Products)
All Manufacturing
Source: Census of Manufacturers, 1987.
3.30
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Figure 3.12
Composition of Facility Revenue
by Facility Size1,1986
Greater than or
equal to $250 Million
Between $75 and $250 Million
Between $20 and $75 Million
Less than $20 Million
In-Scope Pesticide Chemicals
Other EPA Registered Pesticide Chemicals
Pesticide Contract Work or Tolling 2
Other Revenues
1 Facility size is measured by total facility revenues.
2 Tolling work maybe either in-scope or out-of-scope.
Source: Census.
3.31
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On average, 1986 pre-tax in-scope pesticide facility profits equalled 13 percent of in-scope pesticide
facility sales. Figure 3.13 presents 1986 pre-tax in-scope pesticide facility profits as a percent of in-scope
pesticide sales categorized by pesticide type, revenues of in-scope pesticides, and total facility revenues.14:
When profits were broken down by pesticide type, facilities that produced only fungicides averaged the highest
profit to sales ratio: nearly 0.32. This profit level contrasts with the profit to sales ratio of -0.03 for facilities
that produced only insecticides. Facilities that produce multiple types of pesticides (these also tend to be larger
facilities) have pre-tax profit to sales ratios of about 0.16. When profits are broken down based on facilities'
in-scope pesticide revenues, the data indicate that larger facilities (with revenues greater than or equal to $25
million) were more profitable than smaller facilities (with revenues of less than $25 million) in 1986. This
information may indicate that larger facilities, many of which produce several different types of pesticides, are
more efficient.
Industry experts, however, attribute the high profits in portions of the pesticide industry to the ability of
manufacturers to produce patent-protected pesticides with specific uses.15 Many of the pesticides included in
these profit figures represent patent-protected chemicals produced by only one manufacturer. Although patented
I
products face competition from pesticides with the same end use, many manufacturers appear to have been
successful at differentiating their products. Future profits, experts say, will most likely depend on producers'
ability to develop new patented products (Kline & Company, 1991). Most competition in the industry is among
producers whose products have similar biological activity.
3.3.G Capital Expenditures
Capital expenditures represent funding for additional capacity and/or automating or streamlining
existing facilities. Table 3.9 shows that capital expenditures by the pesticide manufacturing industry varied
significantly from year to year between 1975 and 1987. On average, capital expenditures decreased by 3
percent per year from 1975 to 1987. Most of the decline took place in the late 1970s and early 1980s. Annual
(and, in some cases, biennial) change appears to be cyclical, with downturns followed by upswings. The
contraction in the demand for pesticides may be partially responsible for the decline in capital expenditures in
the industry.
MAlthough revenue information in the Census was broken down by in-scope vs. out-of-scope, facility costs were
not. In-scope-related facility costs were therefore calculated by applying the total cost figure to either the ratio of
in-scope pesticide revenues to total revenues or, where applicable, the ratio of in-scope pesticide revenues to total
pesticide-related revenues. i
15Production data collected in Part A of the Census indicate that most clusters include production from multiple
facilities. In addition, data presented in Section 3.3.F of the profile shows that facilities experience a wide range
of profitability, suggesting that the pesticide market is competitive. Conversely, few facilities produce the same PAI
within clusters, indicating that product differentiation exists within markets. These characteristics indicate that the
pesticide market is competitive with differentiated products.
3.32
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Figure 3.13
Pre-Tax In-Scope Pesticide Facility Profit as a Percent of
In-Scope Pesticide Sales, 1986
Profit as a Percent
of Sales
Profit as a Percent
of Sales
Profit as a Percent
of Sales
35% —|
30 —
25 —
20 —
15 —
10 —
5 —
0-
-5 —
-10%-
31.7%
Pesticide Type
16.5%
10.7%
8.6%
-2.8%
Fungicides Herbicides Insecticides
Other
Pesticides
Multiple
Types of
Pesticides
35% —n
30 —
25 —
20 —
15-
10 —
5 —
0-
Revenues from In-Scope Pesticides
22.6%
11.4%
6.7%
Less than $2 Million
25% —|
20 —
15 —
10 —
5 —
0-
$2-$25 Million
Total Facility Revenues
Greater than or
Equal to $25 Million
20.8%
13.8%
10.1%
Less than $50 Million
$50-$250 Million
Source: Census.
Greater than or
Equal to $250 Million
Note: Revenue categorizations for in-scope revenues and facility revenues are broader than
those that appear elsewhere in the profile, to prevent disclosure of confidential
business information. In addition, the two facilities that changed ownership in 1986
are not included in the information presented in this figure.
3.33
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Table 3.9
Pesticide Capital Expenditures, 1975-1987
SIC 2&791
(in 1986 dollars)
Year
Capital
Expenditures
(million $)
Annual
Percent
Change
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
342.6
301.7
340.9
381.4
280.8
246.4
263.3
295.9
145.0
199.7
192.6
200.6
224.1
73%
-12%
13%
12%
-26%
-12%
7%
12%
-51%
38%
-4%
4%
12%
Average Annual Change
-3%
1 SIC 2879 includes establishments involved in
manufacturing or formulating agricultural
chemicals, n.e.c., and formulating and preparing
pest control chemicals.
Source: Census of Manufactures, Preliminary
Report, Industry Series, 1987
3.34
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In general, capital expenditures tend to follow the business cycle. Figure 3.14 compares capital
expenditures for all manufacturing, as an indicator of the business cycle, to capital expenditures in SIC 2879
(agricultural chemicals, n.e.c., and pesticide formulations and preparations), SICs 2865 and 2869 (organic
chemicals, except gum and wood), and SIC 28 (chemicals and allied products). Agricultural chemicals and
organic chemicals both exhibit a cyclical trend, with an overall decrease in expenditures of approximately 35
percent from 1975 to 1987. While exhibiting similar swings in capital expenditures to those of agricultural and
organic chemicals, the chemicals and allied products industry declined by only 20 percent between 1975 and
1987. Capital expenditures in the manufacturing industry as a whole, like the agricultural chemical industry,
appear to be cyclical. From 1978 to 1981, however, "all manufacturing" maintained a fairly constant level of
capital expenditures, while capital outlays in the agricultural chemical industry declined. In addition, overall
capital expenditures from 1975 to 1987 for "all manufacturing" increased by approximately 20 percent.
In the Census, facilities provided the year of the most recent major expansion of facility or equipment
with respect to pesticide production. Almost 90 percent of the facilities indicated that they had made some sort
of expansion of facility or equipment related to pesticide production since 1960. More than 80 percent of the
facilities invested in an expansion or improvement after 1970, while almost 40 percent of the facilities reported
an expansion or improvement after 1985.
3.3.H Production Capacity Utilization
Table 3.10 shows pesticide production capacity utilization rates from 1980 to 1989. The data indicate
that production capacity utilization for all pesticides varied significantly during the decade, averaging
approximately 68 percent for all pesticides. At times, however, some types of pesticides had much lower
production capacity utilization. During 1983 and 1984, for example, capacity utilization for insecticide
production was particularly low, declining to 29 percent in 1984. Figure 3.15 compares the capacity utilization
rate for pesticide production to that for all manufacturing. The figure shows that the manufacturing capacity
utilization trend runs counter to that for pesticides. Capacity utilization for all manufacturing hit a low in 1982
and rose thereafter. Capacity utilization for pesticide production, on the other hand, peaked in 1982 and hit its
lowest point in 1984.16
The post-1982 decline in pesticide manufacturing capacity utilization may be attributable hi part to the
Payment-in-Kind (PIK) program.17 In addition, pesticide production capacity utilization rates may fluctuate
over time because some pesticides are not produced on an annual basis. Rather, PAIs may be produced for a
limited time period (every second or third year) on what the industry commonly refers to as a campaign basis.
"This is reasonable, since pesticide production is more closely related to agricultural production than to
measures of industrial activity.
17Recall that PIK took 48 million acres out of production in 1983.
3.35
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1986 Dollars Indexed
to 1975
Figure 3.14
Capital Expenditures
in 1986 Dollars
(1975 Base Year)
1.5-
1.4-
1.3-
1.2-
1.1-
0.9-
0.8-
0.7-
0.6-
0.5-
0.4-
0.0
All Manufacturing
SIC 28
SIC 2879
' SIC 2865, 2869
~~l I I 1 1 1 1 1 1 1 1 1
1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987
Years
— SIC 2879 (Agricultural Chemicals, n.e.c., and Formulation & Preparation of Pesticides)
""" SIC 2865, 2869 (Organic Chemicals, except gum & wood)
•—• SIC 28 (Chemicals and Allied Products)
*««;«« All Manufacturing :
Source: Census of Manufacturers, 1987.
3.36
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U.S. Pesticide
Year Herbicides
1980
1981
1982
1983
1984
1985
1986
1987
1988
19893
Average
Capacity
Utilization
77
74
84
66
67
62
64
63
75
72
70.4
Table 3,10
Production Capacity Utilization Rates, 1980-1989
(Percent)
Annual
All Percent Change
Insecticides Fungicides Pesticides All Pesticides1
79
72
68
33
29
56
63
61
76
76
61.3
84
68
70
71
73
66
61
59
59
63
67.4
78
73
80
54
52
61
65
62
75
81
Average
Annual
68.1 Change
n/a2
-6%
10%
-33%
-4%
17%
7%
-5%
21%
8%
4%
1 The rate for all pesticides may be higher than those for herbicides, insecticides, or
fungicides. This difference is due to the inclusion of detailed information on capacity rates
associated with pesticides either classified as rodenticides or unclassified.
2 Not available.
3 Projected.
Source: USDA
1989.
Agricultural
Resources: Situation and Outlook
Report, AR-13, February
3.37
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Figure 3.15
Comparison of AH Manufacturing Capacity Utilization
and Pesticide Production Capacity Utilization Rates
Capacity Utilization
Rate
20
1980 1981 1982 1983
Pesticide
Herbicide
Insecticide
Fungicide
All Manufacturing
All
Manufacturing
Insecticide
Pesticide
Herbicide
Fungicide
1 1 1 1 f
1984 1985 1986 1987 1988
Years
Source: USDA Agricultural Resources: Situation and Outlook Report
AR-13 February, 1989.
Statistical Abstract of the United States , 1989.
3.38
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Although many PAIs are produced annually, it is common industry practice to produce a specific PAI less
frequently. This typically occurs when the pesticide is used on a low-volume specialty crop, or for those
pesticides with high concentrations that allow for reduced volume. During production, materials are fed into a
reactor in order to produce a desired chemical reaction; labor and equipment are used to monitor the process to
make sure that all necessary conditions of production are met.
Although the frequency of production is generally determined by product demand, the quantity
produced is typically a function of the volume required to make the run cost-efficient. Due to start-up costs
such as energy and labor, costs per unit produced increase as quantities are reduced. Total costs associated with
the minimum volume a facility is willing to produce may be only slightly greater than total costs for production
of much smaller amounts of the pesticide.18
3.4 Finn Characteristics
This profile has thus far focused primarily on characteristics of the facility. This section describes the
ownership structure of the industry and the way in which firms are organized.
The Census indicates that most in-scope pesticide facilities are owned or controlled by a parent firm (85
percent). Although a number of smaller, single-facility firms control small portions of total production, overall
production is becoming increasingly concentrated among large producers as a result of mergers and acquisitions.
Only 15 percent of the facilities are single entities not owned or controlled by another firm as of December 31,
1986. Approximately 35 percent of all parent firms are controlled in turn by another company. Large R&D
costs, including registration fees, may be a reason why the majority of pesticide producers tend to be part of a
larger, multi-facility firm.
In 1986, 64 firms produced in-scope pesticides in the United States. These firms owned 90 facilities,
which produced 136 individual or classes of in-scope PAIs. The number of PAIs manufactured by each firm
varies (see Figure 3.16). Approximately 45 percent of the firms owning in-scope facilities in 1986 produced
only one PAI, although one firm manufactured 11 PAIs.
According to the Census data, approximately three-quarters of the firms owned only one in-scope
pesticide manufacturing facility. The remaining firms tended to own two or three in-scope pesticide producing
facilities. Of these firms, 44 percent produced the same pesticide at more than one of their in-scope facilities.
Figure 3.17 presents the number of in-scope facilities owned by firms.
18Per unit costs increase as quantities produced decrease. Producing larger quantities may therefore cost less
on a per unit basis.
3.39
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Figure 3.16
Number of Individual or Classes of In-Scope PAIs
Produced by Firms, 1986
Number of Firms
20-
15-
10-
5-
0
8
9 10 11
Number of In-Scope PAIs Produced
Source: Census.
3.40
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Figure 3.17
Number of U.S. In-Scope Pesticide Manufacturing
Facilities Owned by Firms, 1986
Number of
Firms
0
Number of Manufacturing Facilities Owned by a Single Firm
Source: Census.
3.41
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Figure 3.18 shows the composition of 1986 firm sales activity. At the firm level, pesticides constitute a
small portion of sales. On average, pesticide manufacturing and pesticide formulating/packaging combined
represent five percent of firms' sales. !
3.5 Industry Market Structure 1
Several factors play an important role in determining market structure, including (1) the barriers firms
face in entering and exiting the market, (2) vertical integration, (3) the concentration of production, and (4) the
degree to which products are substitutable in consumption. This section describes how these factors affect the
competitiveness of the industry.
3.5.A Barriers to Entry |
Firms' abilities to enter and exit the market determine, in part, the competitiveness of the industry. If
significant barriers to entry exist, potential entrants may be dissuaded and existing firms may enjoy market
power. If few barriers to entry exist, existing firms are more likely to face competition for market share.
There are several types of entry barriers. The most relevant to the pesticide industry are (1) capital
requirements, (2) economies of scale, and (3) R&D requirements, including registration costs. Although; data
about barriers to entry are limited, the available data reveal that market power exists for many firms in the
industry. ;
A significant number of the PAIs in the Census are produced by only one firm. Given that patent
protection exists for pesticide products, it is possible that there is room for only one producer of each PAI, and
that each producer maintains market power for that PAI. Figure 3.19 exhibits data to support this assumption,
revealing that 106 of the 136 individual or classes of in-scope PAIs manufactured in 1986 were produced by
only one firm. The concentration of individual PAI production among single firms may be countered, however,
by the fact that some pesticide products are substitutable. Consequently, individual firms that do not produce
the same PAIs may produce products that compete in the market place.
Capital Costs
Firms require capital in order to begin, improve, or expand production. The capital required to enter an
industry may be sufficient to impede market entry. There are no readily available data on the amount of capital
required for new construction or expansion of a pesticide chemical facility. There are measures, however, that
provide an indication of capital intensity in the industry.
3.42
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Figure 3.18
Composition of Firm Sales, 1986
(Averaged Across All Firms)
Pesticide
Manufacturing
4% Formulating and/1
or packaging
1%
Activity not related to in-scope or
out-of-scope pesticides
95%
Includes in-scope and out-of-scope production activity.
Source: Census.
3.43
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Figure 3.19
Number of Firms that Produce an Individual PAI or
Class of PAI, 1986
Number of PAIs
150-,
125
100-
136 PAIs Produced
75-
50-
25-
3 PAIs produced by 4 or more firms \
5 PAIs produced by 3 firms ]
22 PAIs produced by 2 firms
106 PAIs produced by only 1 firm ;
Source: Census.
3.44
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The ratio of the value added by manufacturing to gross book value of depreciable assets provides a
measure of the capital intensity of the industry. The data indicate that pesticide manufacturing is capital
intensive, especially when compared to formulating/packaging and to all manufacturing. SIC 2869, which
includes the manufacture of basic pesticides and many other organic chemicals, had a value added: depreciable
assets ratio of 0.51 in 1987; i.e., the value added represents 51 percent of the value of depreciable assets (U.S.
Department of Commerce, 1989a). SIC 2879, industrial organic chemicals, which includes primarily pesticide
formulation, had a much higher ratio of 1.13, indicating less capital intensity (U.S. Department of Commerce,
1989a).19 SICs 20-39, which include all manufacturing, had a ratio of 1.34, demonstrating the relative capital
intensity of pesticide production to manufacturing in general (U.S. Department of Commerce, 1989a).
Existence of Economies of Scale
The relative capital intensity of the pesticide industry is one indication of the extent to which economies
of scale exist. Although technology determines the minimum efficient size of a facility, efficient scales of
production appear to vary widely across PAIs. Comparing facilities that produce the same PAIs suggests that
there is a large difference in the quantities produced. Facilities can range in annual output from a few thousand
pounds to more than 10 million pounds of the same PAL The fact that there are vast differences, in the size of
facilities producing the same product indicates that economies of scale probably are not a major factor within the
pesticide manufacturing industry.20
Research and Development
Large capital outlays for R&D represent another barrier to entry. Research used to develop new,
patented products is considered to be key to chemical producers' success. Patents are important to the pesticide
industry because they give producers a monopoly in the production of that pesticide and allow the producer to
price a product above cost. Pesticide products carry a 17-year patent; firms need this patent protection to price
above costs to recover their R&D expenditures.21 Since different patented products may compete for the same
use, however, pure monopolies do not exist.
Although patented products play an extremely important role in the industry, there are unpatented
products on the market that are profitable. The existence of unpatented products signifies that patents alone do
19A higher ratio of value added by manufacturing to gross book value of depreciable assets may also result from
the use of older equipment.
analysis of economies of scale within the pesticide manufacturing industry is complex. Because multiple
PAIs may be produced on the same line, using the same equipment, comparing production across individual PAIs
may not provide definitive evidence on whether economies of scale exist.
21 After a pesticide product is patented, the manufacturer must register the product for use. Therefore,
manufacturers often have fewer than 17 years to recoup their R&D costs.
3.45
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not protect profits. Nevertheless, patents for most pesticides are instrumental in recovering R&D costs, and are
also a factor in restricting market entry.
Research and development costs are one of the fastest growing components of fixed costs that firms
face. In 1976, the average R&D costs of a single new pesticide were estimated at $10 million (1986 dollars),
while in 1987 the estimated costs to develop a single new pesticide were $40 million (1986 dollars) (U.S.
Department of Commerce, 1987). The increase in costs is partly due to more stringent toxicity tests performed
in compliance with environmental regulations. Specifically, use restriction based on the amount of residue
toxicity left on food products places new pesticide products under greater scrutiny than existing pesticide
products. According to industry experts, it can take 10 years to bring a chemical pesticide from the R&D stage
to registration with the EPA (Rich, 1988). To register a pesticide for a major food use, there is a flat fee of
SISO.OOO22. In order to support R&D and the registration of new products, firms must be able to generate
sufficient pesticide sales. The need for a large sales volume may be one explanation for the number of mergers
and acquisitions in the 1980s.
The Census data indicate that total average R&D costs for all firms represent about 4 percent of total
facility sales.23 Different levels of R&D are sustained, depending upon the size of firms. Table 3.11 breaks
down R&D costs as a percent of total facility sales for three firm sizes.24 According to the Census, firms with
total revenues of between $1 billion and $6 billion have the highest R&D expenses as a percent of sales. High
R&D costs and the uncertainty of product success may make it difficult for new firms to put up the capital and
to absorb the risk from R&D ventures. These costs may bar entry, with the result that the industry becomes
less competitive.
3.5.B Vertical Integration
Vertical integration is the extent to which the different stages of production are organized in a single
firm. According to the Census, both small and large firms tend to be vertically integrated, engaging in the
R&D, manufacturing, and formulating/packaging of pesticides.
Compared to developing and manufacturing PAIs, formulating/packaging is less expensive but often
adds considerable value to the end product. As mentioned previously, data from the Census indicate that; 50 of
22The annual maintenance fee is $425 for each registration up to 50 registrations; and $100 for each additional
registration, with the exception that no fee is charged for more than 200 registered products held by any registrant
(HERA, Section 4).
23The Census collected total facility, not pesticide-specific, R&D costs.
costs were estimated based on firm size rather than facility size, because firm size is generally more
important than facility size in determining the level of R&D.
i
3.46 I
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Table 3.11
Research and Development Costs as a Percent of Total Facility Sales, 1986
by Firm Size1
Percent of
R&D Costs to
No» of Total Facility
Facilities Sales
Firm Size (Annual Revenues)
Revenues less than $1 Billion
46
3.3%
Revenues between $1 Billion and $6 Billion
26
5.5%
Revenues greater than $6 Billion
12
3.7%
All Facilities
842
4.0%
1 Average R&D to sales ratio across all facilities, by firm size.
2 Excluded from the 88 facilities that provided financial data are four facilities
that did not report firm revenues.
Source: Census.
3.47
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the 90 in-scope PAI manufacturing facilities also engaged in formulating/packaging. When evaluated at the firm
level, these data reveal that 39 of the 64 firms represented in the Census have PAI formulating/packaging ,
capabilities at one or more of their in-scope PAI manufacturing facilities. In addition, five of the firms that do
not formulate/package PAIs at their in-scope PAI manufacturing facilities reported that they own other facilities
at which PAIs are formulated/packaged. Of the 64 firms represented hi the Census, therefore, 44 (69 percent)
have both PAI manufacturing and formulating/packaging capabilities. ;
In addition to in-house formulating/packaging capabilities, many firms, both large and small, contract
out some aspects of the production process (tolling), typically the formulating/packaging process. It is estimated
that approximately 80 percent of the formulated pesticide business is controlled by PAI manufacturers, either
directly with in-house capacity or indirectly through contracting (Kline & Company, 1990).
3.5.C Concentration
like many industries, the pesticide industry underwent significant restructuring in the 1980s. According
to the International Trade Commission's Synthetic Organic Chemicals, the number of facilities producing _
pesticides declined by 23 percent from 1979 to 1988. The Census indicates that between 1980 and 1986, 20 in-
scope pesticide facilities had parent firms that were purchased by or merged with other firms. Although the
majority of the facilities did not change ownership status, the number of mergers and acquisitions is significant
in terms of overall production and sales. Some of the industry's largest firms were restructured during this
period, concentrating production further. The number of mergers and acquisitions involving in-scope facilities
is shown in Figure 3.20. Further concentration of the industry has occurred since 1986.
I
Two mam types of restructuring occurred in the United States hi the 1980s. First, foreign firms
acquired U.S. firms either in total or in part;25 second, U.S. firms acquired or merged with other domestic
firms. Some industry experts attribute the foreign component of restructuring to the volatility of the U.S. dollar
from 1980 to 1990. The strong U.S. dollar prior to 1985 strengthened foreign firms' positions hi the world
market, because U.S. products were more expensive relative to foreign counterparts. The increase hi
environmental controls implemented in the United States during the 1980s also contributed to the price increase
of U.S. products. As the dollar weakened after 1985, foreign firms began purchasing production capacity, hi the
United States. As stated above, mergers and acquisitions among U.S. firms may have resulted primarily from
the firms' need to generate large amounts of sales to support the rising costs of both R&D and environmental
compliance (U.S. Department of Commerce, 1989d and Sine, 1990). ,
"Based on parent firm information reported hi the Census, 9 of the 90 facilities (10 percent) were owned by
foreign companies hi 1986. Note: Foreign ownership was not explicitly requested hi the Census, and was determined
based on the parent firm address reported hi the Census hi conjunction with information presented hi Dun and
Bradstreet's Million Dollar Directory.
3.48
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Number of
Facilities
Figure 3.20
Number of Facilities Acquired by Firms
(From Jan. 81 to Dec. 86,
by Method of Acquisition)
Purchase
Merger
Founded
Other Status1
Method of Acquisition
Of the two facilities that reported other, one indicated that the facility
was acquired through the contribution of capital by the parent
company; the other indicated that the facility was newly constructed
Source: Census.
3.49
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In a concentrated industry, the dominant firm or firms are better able to influence market outcomes to
their advantage. Industry concentration is frequently measured by concentration ratios, which are the percentage
of total sales accounted for by a given number of firms. The Bureau of the Census calculates concentration
ratios for the top 4, 8, 20, and 50 producers of basic pesticides. These concentration ratios are displayed in
Table 3.12. In SIC 28694 (pesticides and other synthetic organic agricultural chemicals except preparations),
the top four firms accounted for 54 percent of the value of shipments in 1982. In SIC 2879 (agricultural
chemicals, n.e.c., and pesticide preparations and formulations), the top four firms accounted for 39 percent of
the value of shipments. Examining concentration ratios by pesticide type in Table 3.12 shows the fungicide
preparations market to be the most concentrated and insecticide preparations to be the least concentrated.
Concentration ratios based on sales of in-scope pesticides were calculated using the Census data. These
ratios, shown in Table 3.13, indicate that the four largest firms account for 42 percent of the value of all in-
scope pesticide shipments. Like the Bureau of Census data, examination of concentration ratios by pesticide
type based on the data presented in Table 3.13 shows that the fungicide market is the most heavily concentrated,
while the insecticide market is the least concentrated. The concentration ratios indicate that there may be no
dominant firm in the industry as a whole. The pesticide industry is highly differentiated, however, meaning that
there may be dominant firms in individual pesticide markets.
3.5.D Demand Elasticity and Product Substitution
Single firms dominate the production of specific pesticides. For these firms to enjoy market power,
however, consumers must be unable to find substitutions for their products easily. A common indicator of
substitutability in consumption is the price elasticity of demand, which shows the percentage change in demand
given a percentage change in the price of a pesticide. Price elasticity of demand is calculated by dividing the
percentage change in demand by the percentage change in price. Numeric values associated with price
elasticities of demand are generally expressed relative to a one percent change in price. For example, an
elasticity of -0.5 suggests that a 1 percent increase in price would result in a 0.5 percent decrease hi the quantity
demanded. ;
Price elasticities of demand were estimated for each pesticide cluster in the analysis.26 In order to;
develop the elasticity estimates, the EPA developed a comprehensive approach, including: i
is section is based on detailed analyses of pesticide demand elasticities. See Appendix C for further details.
3.50
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Table 3.12
Share of Value of Pesticide Shipments Accounted for by the
4, 8, 20, and 50 Largest Companies, 1972-1982
Total
Year (Mill. 1986$)
4 largest
companies
8 largest
companies
20 largest
companies
50 largest
companies
Synthetic Organic Pesticides. Not Formulated. SIC 2R6Q4
1832 54 76 93
1982
1977
1972
2285
1424
65
57
80
79
93
97
100
Agricultural Chemicals, n.e.c.. and formulations and preparations, sic 287Q
1982
1977
1972
4919
4191
3438
39
37
34
58
57
51
81
76
73
91
87
Insecticide Preparations. SIC 28795
1982
1977
1972
991
1284
1039
46
45
48
71
67
67
90
87
99
98
98
Herbicide Preparations. SIC 28796
1982
1977
1972
2710
1825
1243
62
65
77
77
84
89
95
96
98
99+
99+
99+
Fungicide Preparations. SIC 28797
1982
1977
358
305
69
70
84
85
98
97
100
99+
Other Pesticide Preparations, STC 2879R
1982
1977
198
186
49
47
65
66
87
91
99+
99+
Household Pestieida! Preparations. SIC 28799
1982
1977
480
353
53
56
70
71
89
89
99
99
Concentration Ratios from the 1987 Census expected to be available April 1992.
Source: Census of Manufactures, Concentration Ratios in Manufacturing, 1982.
3.51
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Table 3.13
Share of Value of Ih-Scope Pesticide Shipments Accounted for by the
4, 8. and 20 Largest Firms, 19S6
j Number of Facilities
All Pesticides
Fungicides
Herbicides
| Insecticides
Concentration Ratio
—
All Pesticides
Fungicides
Herbicides
Insecticides
Total Sales (Million
—
All Pesticides
Fungicides
Herbicides
1 Insecticides
4 largest
firms
11
4
7
9
(Percent of
42
67
61
57
$)
1,640
278
1,510
531
8 largest
firms
18
9
14
13
Sales)
68
90
83
81
2,654
375
2,049
749
20 largest
firms
39
24
31
27
94
1001
99
99
3,634
416
2,448
918
Total
i
90
30 •
39
36 !
100 j
100 :
100 :
100
3,884 i
416 i
2,463 ;
928
1 Remaining six firms constitute less than 1 % of total fungicide sales.
Source: Census.
3.52
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(1) Review of empirical studies of pesticide production and use;
(2) U.S. Department of Agriculture's analysis of the price elasticity of demand for food
commodities (USDA, 1985, 1989);27
(3) Feasibility of employing non-chemical, non-biological pest control methods (Pimental, D., et
al., 1991).28 (The greater the feasibility of substitution, the higher the expected price
elasticity of demand.);
(4) An analysis of pesticides' contribution to the cost of production of a commodity, based on
estimates of the cost of production in the farm sector (USDA, 1989a).29 (The greater the
contribution of pesticides to the cost of production, the higher the expected price elasticity of
demand.);
(5) Analysis of the marginal productivity of pesticides (USDA, 1989, USDA, 1989a);30 and
(6) Expert opinions within the OPP.
The estimated price elasticities of demand vary significantly among the clusters, since each cluster faces
different market forces. Table 3.14 shows that the estimates of elasticity of demand for pesticide clusters with
in-scope products in 1986. Elasticity of demand varies among these clusters from -0.12 to -1.38. Despite the
wide range of demand elasticities among pesticide clusters, 38 of the 45 have inelastic demand, i.e., the
absolute values of the demand elasticities are less than 1. This indicates that demand at a cluster level (although
not necessarily at the PAI level) will not vary significantly with moderate price increases.
3.6 International Trade
The U.S. pesticide industry holds a sizable share of the world export market for pesticides:
approximately 23 percent of the total value of shipments in 1987 (United Nations, 1987, and Department of
Commerce, 1989d). During the last decade, however, the margin between exports and imports has been
declining, although the United States remains a net exporter of pesticides. Both the strong U.S. dollar from
1980 to 1985 and increasing foreign competition contributed to the change in U.S. position. U.S. imports,
although increasing, do not appear to threaten the market power of domestic firms.
^USDA (1985). U.S. Demand for Food: A Complete System of Price and Income Effects., and U.S.D.A.
(1989). Retail to Farm Linkage for a Complete Demand System of Food Commodities.
^Pimentel, D., et al. (1991). Environmental and Economic Impacts of Reducing U.S. Agricultural Pesticide
Use. Pest Management in Agriculture. CRC press.
2?USDA (1989a). Economic Indicators of the Farm Sector: Cost of Production, 1987. February.
^SDA (1989). Retail to Farm Linkage for a Complete Demand System of Food Commodities., USDA (1989a).
Economic Indicators of the Farm Sector: Costs of Production, 1987. February.
3.53
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Summary of Estimates of Elasticy of Demand
for Clusters with deduction,
Cluster
Herbicides on sugar beets, beans, peas
Herbicides on tree fruits (except oranges), sugar cane, nuts
Herbicides on tobacco
Fungicides on fruit and nuts trees (except oranges)
Fungicides for seed treatment
Herbides on vegetables
Fungicides on grain in storage
Insecticides on vegetables
Slimicides
Fumigants and nematicides
Insecticides on termites
Wood preservatives
Insect repellents at non-agricultural sites
Domestic bug control and food processing plants
Mosquito larvacides
Fungicides on turf
Industrial preservatives
Insecticide synergists and surfactants
Plant regulators, defoliants, desiccants
Sanitizers - dairies, food processing, restaurants, air treatment
Insecticides on livestock and domestic animals
Industrial microbicides, cutting oils, oil well additives
Preservatives, disinfectants, and slimicides
Fungicides - ornamentals
Insecticides on lawns, ornamentals and forest trees
Unclassified uses
Elasticity Estimate
-0.12
-0.20
-0.20
-0.23 ;
-0.27
-0.27
-0.31
-0.33
-0.33 ;
-0.33
-0.33 |
-0.33
-0.33 ;
-0.33
-0.33
-0.33
-0.33
-0.33 ;
-0.33
-0.33 •
-0.33:
-0.33;
-0.33
-0.33
-0.33
-0.33
3.54
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Table 3.14
Summary of Estimates of Elasticity of Demand
for Clusters with Production, 1986
Page 2
Cluster
Elasticity Estimate
Fungicides on vegetables
Fungicides - broad spectrum
Herbicides - broad spectrum
Insecticides on soybeans, peanuts, wheat, tobacco
Fungicides - post harvest
Herbicides on rights of way, drainage ditches
Herbicides on turf
Herbicides on soybeans, cotton, peanuts, alfalfa
Herbicides on corn
Insecticides on corn and alfalfa
Insecticides on sorghum
Herbicides on sorghum, rice, small grains
Herbicides on oranges
Insecticides on fruit and nut trees, except oranges and grapes
Insecticides on oranges
Herbicides - other agricultural uses
Insecticides on cotton
Fungicides on grapes
Herbicides on grapes
-0.38
-0.40
-0.48
-0.56
-0.65
-0.66
-0.66
-0.67
-0.69
-0.69
-0.69
-0.69
-1.00
-1.00
-1.00
-1.00
-1.06
-1.38
-1.38
Source: Estimates of the Price Elasticity of Demand for Pesticide Clusters, U.S. EPA and Abt
Associates Inc., May 1991.
3.55
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3.6.A U.S. Pesticide Imports and Exports
Table 3.15 shows U.S. import and export values for pesticides from 1978 through 1987. The table
shows that pesticide imports increased more than exports over this period. On average, the value of pesticide
imports increased by 7 percent, while the value of pesticide exports increased by only 1 percent. Although
imports increased substantially during the period, the United States maintained a positive trade balance. .
Similarly, Tables 3.16 and 3.17 show import and export values for herbicides and insecticides,
respectively.31 Exports of herbicides, which comprise the largest U.S. pesticide export, witnessed a dramatic
decline in the 1980s. In particular, the value of herbicide exports fell by 64 percent in real terms between 1984
and 1985. In the same year, herbicide imports increased by 41 percent to fill the vacuum left by a facility that
closed.32 In 1985, the United States was a net importer of herbicides. Over the ten year period from 1978 to
1987, exports of herbicides decreased by 5 percent per year, while imports increased by 12 percent per year.
Although herbicides have been given the most research funding of all pesticide types, thereby exhibiting the
most technological progress, they have also been the most susceptible to violations of intellectual property rights
due to the lack of patent protection outside the United States. Of the three major groups of pesticides,
herbicides had the least favorable ratio of exports to imports in the 1980s (U.S. Department of Commerce,
I
1989d).
Insecticides comprise the second largest component of U.S. pesticide exports. From 1978 to 1987,
insecticide exports decreased by 4 percent as imports increased by 9 percent. In spite of these trends,
insecticides showed a positive trade balance throughout the period. Part of the decline in insecticide exports
may be attributed to the decline in chlorinated hydrocarbon insecticide production.
Table 3.18 presents U.S. pesticide exports as a percent of the value of total U.S. pesticide shipments,
and U.S. pesticide imports as a percent of new supply for 1978 to 1987. The table shows that pesticide exports
as a percent of the value of shipments have decreased over the period, from 25 percent in 1978 to 21 percent hi
1987, while the value of overall shipments increased over the same period. These data, coupled with data from
Table 3.5 showing a decrease in the quantity of pesticides produced and sold, indicate that U.S. producers have
increased sales to domestic markets. Table 3.18 also shows that imports have maintained approximately the
same share of new supply: 5 percent in 1978 and 6 percent in 1987.
31Similar data is unavailable for fungicides.
32Much of the decline in exports and increase in imports was due to the closing of one facility.
3.56
-------
Table 3.15
U.S. Import and Export Values for All Pesticides
(in thousand 1986 $)
Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Average
Annual
Change
Source:
Value of
Imports % Change
260,098
268,846
317,718
307,553
284,196
271,512
322,874
413,772
402,782
414,800
—
United Nations International
65%
3%
18%
-3%
-8%
-4%
19%
28%
-3%
3%
7%
Trade
Value of
Trade
Exports % Change Balance % Change
1,238,508
1,320,896
1,241,047
1,132,425
1,157,006
1,173,584
1,357,235
1,231,455
1,299,974
1,305,959
—
Statistics Yearbook,
99%
7%
-6%
-9%
2%
1%
16%
-9%
6%
<1%
1%
1978-1987
gj-
978,410
1,052,050
923,329
824,872
872,810
902,071
1,034,361
817,683
897,192
891,159
—
^ ^ -™^_^__ ^_
111%
8%
-12%
-11%
6%
3%
15%
-21%
10%
1%
-1%
3.57
-------
Table 3.iF
U.S* Import and Export Values for Herbicides
(in thousand 1986$)
Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Average
Annual
Change
Source:
i •
Value of
Imports
88,467
146,755
160,924
158,292
166,396
119,767
157,569
221,698
192,526
183,863
—
% Change
NA
66%
10%
-2%
5%
-28%
32%
41%
-13%
-4%
12%
United Nations International Trade
Value of
Trade
Exports % Change Balance
462,023
494,605
495,111
460,619
470,692
526,205
586,791
212,157
197,936
233,650
—
Statistics Yearbook,
==^====
NA
7%
<1%
-7%
2%
12%
12%
-64%
-7%
18%
-5%
1978-1987
—
373,556
347,850
334,187
302,327
304,296
406,438
429,222
(9,541)
5,410
49,787
=====
I
j
% Change
NA
\ -7%
-4%
-10%
\ 1%
34%
> 6%
' -102%
i 157%
; 820%
-10%
=^
3.58
-------
Table 3.17
U.S, Import and Export Values for Insecticides
(in thousand 1 986 $)
Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Average
Annual
Change
Source:
Value of
Imports % Change
60,539
79,350
90,055
90,854
73,625
74,508
65,906
76,508
90,964
111,376
—
United Nations International
NA
31%
13%
1%
-19%
1%
-12%
16%
19%
22%
9%
Trade
Value of Trade
Exports % Change Balance % Change
304,671
358,331
301,474
294,367
289,169
268,194
345,073
239,421
251,425
204,867
—
Statistics Yearbook,
NA
18%
-16%
-2%
-2%
-7%
29%
-31%
5%
-19%
-4%
1978-1987
244,132
278,981
211,418
203,513
215,544
193,686
279,167
162,913
160,461
93,491
—
NA
14%
-24%
-4%
6%
-10%
44%
-42%
-2%
-42%
-7%
3.59
-------
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3.60
-------
3.6.B U.S. Pesticide Industry in the World Market
Table 3.19 shows U.S. trade in pesticides as a percentage of the world market economy for pesticides
from 1978 to 1987. In 1978, U.S. pesticide exports accounted for 26.2 percent of the world export market. In
1981, the U.S pesticides exports percentage peaked, capturing 30.5 percent of the world export market. In
1987, the U.S share of the world pesticide market was 23.4 percent, the lowest percentage of the preceding ten
years.
The shift hi the U.S. pesticide export position is due, in part, to the increased strength of the dollar
relative to other currencies. As mentioned above, the strong U.S. dollar from 1981 to 1985 caused U.S.
products to be more expensive than foreign products, thereby contributing to the decline. Because exports and
imports do not respond immediately to changes in currency exchange rates, it may take months, even years, for
changes in exchange rates to have an impact. The steady reduction in exports, resulting from the price increase
of U.S. products, may not be evident in the trade statistics until after 1984 due to the length of contracts for
pesticide sales.
Foreign competition hi the pesticides industry has increased substantially hi the last decade, causing a
deterioration hi the competitive position of U.S. firms hi recent years. Table 3.20 lists the leading pesticide
exporting countries hi the world economy from 1979 to 1987. Although the United States remains the largest
world exporter of pesticides, its export lead has decreased as other countries' pesticide export markets have
matured.33 In particular, the United Kingdom, Switzerland, Italy, and Brazil have increased their share of
world pesticide exports.
As indicated hi Table 3.19, the U.S. share of world imports for pesticides increased during the 1980s.
Between 1982 and 1984, the most dramatic expansion hi manufacturing facilities took place outside western
Europe and the United States. This expansion took place hi major markets such as Brazil, India and eastern
Europe. Together with the development of pesticides manufactured hi Taiwan and South Korea, this expansion
further increased the competition for products manufactured hi western Europe and the United States (Shenton,
1989).
Table 3.21 shows the value of pesticide imports from leading importers to the United States as a
percentage of total U.S. pesticide imports. As seen hi this table, although imports from western Europe still
33As stated previously, some of the U.S. companies included hi the Census are owned by foreign entities.
3.61
-------
Table 3.19
U.S. Trade as a Percentage of the World Market Economy
1978-1987
Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Source:
U.S. Share
of World
Imports
6.8
6,9
8.7
11.7
7.6
7.5
8.5
10.4
8.6
8.1
% Change in
Share of
Imports
32.3%
.7%
26.6%
34.7%
-34.9%
-1.5%
12.5%
23.5%
-17.4%
-6.4%
United Nations International Trade
U.S. Share of
World
Exports
26.2
26.3
24.7
30.5
27.1
27.4
29.3
26.8
24.9
23.4
Statistics Yearbook,
for Pesticides,
% Change in
Share of
Exports
40.5% '
.5%
-5.9% ;
23.4%
-11.3%
1.0% ;
7.0%
-8.5%
-7.0%
-6.4%
1977-1987
3.62
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comprise the largest share of the U.S. import market, imports from other countries (such as Brazil) realized
substantial increases in exports to the United States.
3.7 Summary
During the 1980s the demand for U.S. pesticide products declined. This decline resulted from various
influences, including a decline in agricultural acreage, the introduction of highly concentrated products, more
effective application techniques, and various environmental influences. Although these factors resulted in a
contraction of pesticide production and sales, the industry as a whole has remained profitable. Continued
profitability within the pesticide manufacturing industry is most likely due to patent protection and producers'
ability to introduce new products with unique uses.
Data collected in Part A of the Census indicate that the majority of PAIs are produced by only one firm.
Although the production data indicate that firms have monopoly power for specific PAIs, this situation lends
itself to market power only if no substitutable products exist. Further analysis of production by cluster reveals
that while most clusters include production from multiple facilities, few facilities produce the same PAI within
clusters. This information indicates that substitutable products exist in the pesticide manufacturing industry, and
suggests that the pesticide market is competitive with differentiated products.
The information presented in the profile provides evidence that although barriers to entry exist in the
pesticide manufacturing industry (e.g., the high R&D costs required to introduce new products), they are
somewhat offset by patent protection. Firms may be willing to incur short-term losses stemming from the
introduction of a new product, knowing that with patent protection they will be able to recover their losses in
the long run. Because firms require patent protection to recover large outlays in R&D, it is likely that
competition within the industry will come in the form of new products, where profits are somewhat protected,
rather than from new producers of existing products.
Although the United States remains a net exporter of pesticides, the value of pesticide exports decreased
while imports increased during the 1980s. Factors such as the strong dollar and the implementation of more
stringent environmental regulations in the United States, which made U.S. products more expensive relative to
foreign products, contributed to the deterioration of the United States's trade position in the mid-1980s.
Although competition from western European countries is still the most predominant influence on the United
States's competitive position in the world pesticide market, there is increasing competition outside western
Europe in countries such as Brazil, Korea, and those in eastern Europe.
3.65
-------
Chapter 3 References
Kline & Company, Inc. (1986). PCO Industry Thrives; Hits $2.5 Billion Mark. Pest Control Technology,
December.
Kline & Company, Inc. (1990). Kline Guide to the U.S. Chemical Industry, Fifth Edition. New Jersey.,
Minnesota Department of Agriculture (1989). Rinse and Win Brochure.
National Pest Control Association, Inc. (1991). Fact Sheet.
Pimentel, D., et al. (1991). Environmental and Economic Impacts of Reducing U.S. Agricultural Pesticide
Use. Pest Management in Agriculture. CRC press.
Pimental, P. and L. Levitan (1986). Pesticide Amount Applied and Amount Reaching Pests. Bioscience, 36,
86. \
Ribaudo, Marc O. (1989). Water Quality Benefits from the Conservation Reserve Program. Agricultural
Economic Report No. 606, February.
Rich, Laurie, A. (1988). Environmental Concerns Force Global Changes in the Market. Chemical Week,
May. ;
Shenton, Tom (1989). Crop Protection: An Agrochemical Company Perspective. Chemistry and Industry,
March.
Sine, Charlotte (1990). A Stronger Ag Chem Industry Emerges From the '80s. Farm Chemicals, January.
United Nations, Statistical Office (1978-1987). International Trade Statistics Yearbook. New York. Annual.
U.S. Department of Agriculture (1984). Agricultural Statistics 1984. Washington, D.C. ;
U.S. Department of Agriculture (1989). Agricultural Statistics 1989. Washington, D.C.
U.S. Department of Agriculture (1989a). Agricultural Resources Situation and Outlook Report, AR-13.
Washington, D.C., February.
U.S. Department of Commerce, Bureau of the Census (1986). 1982 Census of Manufactures, Concentration
Ratios in Manufacturing. Washington, D.C.
U.S. Department of Commerce, International Trade Administration (1987). 1987 U.S. Industrial Outlook.
Washington, D.C., January.
U.S. Department of Commerce, Bureau of the Census (1989). 1987 Census of Manufactures, Preliminary
Report Industry Series: Agricultural Chemicals. Washington, D.C., July.
U.S. Department of Commerce, Bureau of the Census (1989a). 1987 Census of Manufactures, Preliminary
Report Industry Series: Industrial Organic Chemicals. Washington, D.C., July.
U.S. Department of Commerce, Bureau of the Census (1989b). 1987 Census of Manufactures.
Washington, D.C., January.
3.66
-------
U.S. Department of Commerce, Bureau of the Census (1989c). Statistical Abstract of the United States, 1989.
Washington, D.C., January.
U.S. Department of Commerce, International Trade Administration (1989d). 1989 U.S. Industrial Outlook.
Washington, D.C., January.
U.S. EPA, and ICF, Inc. (1980). Economic Profile of the Pesticide Industry. Office of Pesticide Programs,
August.
U.S. EPA, and Mitre Corporation (1983). The Supply and Use Patterns of Disinfectants and Sanitizers at
Selected Sites. January.
U.S. EPA, International Sanitary Supply Association, Research Triangle Institute (1989). Meeting Summary.
Research Triangle Institute, July.
U.S. EPA (1990). Pesticide Industry Sales and Usage: 1988 Market Estimates. Office of Pesticides and Toxic
Substances, February.
U.S. EPA (1991). Pesticide Container Report to Congress, Draft. Office of Pesticides and Toxic Substances,
March.
U.S. EPA, and Abt Associates, Inc. (1991a). Estimates of the Price Elasticity of Demand for Pesticide
Clusters. May.
U.S. International Trade Commission (1977-1988). Synthetic Organic Chemicals, U.S. Production and Sales.
Washington, D.C., Annual.
3.67
-------
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Chapter 4: FACILITY IMPACT ANALYSIS
4.0
Introduction
This chapter presents the methodology for projecting impacts of the proposed effluent limitations
guidelines and standards at the facility level and describes the results of the analysis. As discussed in
Chapter 1, the facility analysis is the principal building block of the entire economic impact assessment. The
facility impact analysis is characterized by the following:
(1) use of economic models to estimate pre- and post-compliance costs, prices, and quantities for
groups of pesticide active ingredients (PAIs) produced by individual facilities;
(2) application of a discounted cash flow analysis to project facility closures;
(3) comparison of unit prices to unit fixed costs plus unit variable costs to project product line
closures; and
(4) use of financial ratios to identify facilities that are expected to sustain significant financial
impacts, short of closure.
The cost, price, and quantity outputs from the first step provide input to the facility closure, product line
closure, and significant financial impact analyses of steps 2, 3, and 4. The analysis evaluates these three
impacts in a hierarchical manner: if a facility closes, product line closures and other significant impacts are not
evaluated; if a facility closes a product line, other significant impacts are not evaluated. This hierarchy
corresponds to the severity of the projected impact; i.e., a facility closure is more severe than a product line
closure, which is more severe than a significant financial impact.
The impacts are estimated for 88 of the 90 pesticide manufacturing facilities producing one or more of
the 270 PAIs or classes of PAIs considered for regulation. As discussed hi Chapter 2, 90 pesticide
manufacturing facilities completed Part A of the Census and 88 pesticide manufacturing facilities completed Part
B of the Census. Only one of the two pesticide manufacturing facilities from which Part B data were not
obtained is predicted to incur costs (for monitoring only) due to the proposed regulation.
This chapter describes the economic models, and then discusses the methodologies for the facility
closure analysis, product line closure analysis, and significant financial impact analysis. Finally, the facility-
level results are discussed.
4.1
-------
4.1 Economic Model
Before presenting the specific model used in the analysis to estimate post-compliance costs, prices, and
quantities, a brief overview of the conceptual problem is provided. !
4.1.A Generalized Model of the Pesticide Manufacturing Industry
The model of the pesticide manufacturing industry focuses on the short run. The focus on the short
run, by definition, limits facilities' and firms' options for responding to increased costs for pollution control and
is therefore conservative (i.e., it tends to overstate impacts). For example, in the short run, firms cannot
register new products or make major modifications to physical plants. They are free, however, to decrease
production, increase production (to the extent that capacity is underutilized), or change the production mix when
faced with new pollution control requirements.
Each facility must decide the quantity of each pesticide to produce, given certain technological
constraints. Some pesticides may have to be produced together if one is a byproduct of the manufacturing
process of another. Pesticides may also be produced as by-products of other organic chemical manufacturing.
Pesticide manufacturing equipment may be flexible enough so that the facility may choose to use it to produce
an alternate product, perhaps with minor modifications. A producer may also elect to use a facility at a higher
level of capacity (perhaps by adding an additional shift), thereby increasing the production of one or more
pesticides.
In addition to incorporating the short run options, the model must capture the nature of regulatory
compliance costs and their effect on production decisions. Ideally, these costs are a function of the production
mix. For example, additional controls may be required if a facility decides to produce pesticide i instead of
pesticide j. A facility may also find that the same controls are required for two different pesticides, so that the
incremental control costs of producing pesticide i may be very small as long as pesticide k is also produced.
Given all these considerations, the profit maximizing problem for facility f can be depicted as:
4.2
-------
= £ W '=
where:
n,
EC,,
profit of facility f;
price of product i, a function of total industry production of product i (Q;), and
industry production of all products competing with product i;
production of product i by facility f (The sum of the Q^'s, f= 1,N equals QJ;
total cost to facility f of producing product i; and
total pollution control costs to facility f required under the proposed option to produce
product i.
Each facility in the industry attempts to maximize profits simultaneously. The equilibrium solution is
represented by the matrix Q (total industry production), whose typical element Q^ represents facility f s
production of product i, that solves the profit maximizing problem for all facilities simultaneously.
Data limitations, however, require that the model be simplified. In particular, the entire production
choice set (of registered products) available to each facility is unknown. Additional engineering studies of each
facility's production process, as well as analysis of firm-level pesticide registrations, would be necessary to
relax this assumption. Given this limitation, it is assumed that a facility may respond to a new effluent
guideline only by decreasing current production of any or all of the pesticides currently manufactured. This
assumption does not allow for the production of new chemicals, i.e., those that were not being manufactured
before the guidelines were introduced. Neither does it allow one U.S. PAI manufacturer to benefit from the
compliance costs and subsequent decrease in PAI production of another manufacturer. Note that this assumption
is extremely conservative, since it severely limits the options available to each facility and thus overstates the
impact of the regulation.
This major simplification allows each market to be modeled separately, because the production
decisions no longer affect one another. If a facility decides to decrease the production of one chemical, it does
not "free up" capacity to produce another chemical. As a result, the supply curve for chemical A does not shift
when the supply of chemical B changes. It now becomes possible to find a new equilibrium in each market
separately and independently. Built on this generalized model, the applied economic model of the pesticide
manufacturing industry is described below.
4.3
-------
4.1.B Applied Model of the Pesticides Manufacturing Industry
The construction of a model of the pesticides manufacturing industry, and the simulation of the effects
of new effluent limitation guidelines and standards, require the following basic steps: i
(1) Define the markets to be analyzed;
(2) Determine the basic model of market structure;
(3) Estimate baseline prices for each PAI cluster at each facility; ;
(4) Estimate baseline costs for each PAI cluster at each facility;
(5) Adjust baseline costs for other government regulations;
(6) Project facility compliance costs;
(7) Estimate post-compliance costs for each PAI cluster at each facility;
(8) Develop a pricing rule to estimate post-compliance prices for each PAI cluster at each facility;
and !
(9) Estimate a price elasticity of demand to solve for post-compliance quantities for each PAI
cluster at each facility.
These steps are explained below.
Markets to be Analyzed
A market is defined by competing products. Not all PAIs, however, compete with each other at the
consumer level. For example, PAIs used as herbicides on corn do not compete with PAIs used as fungicides on
residential gardens. Neither do all PAIs used as herbicides compete with one another. Because PAIs compete
with each other individually or in groups rather than as a whole, separate PAI markets that capture this
competitiveness are defined. ;
The EPA's Office of Pesticides Programs (OPP) has undertaken a similar categorization exercise for its
regulatory purposes. In 1980, the OPP defined pesticide markets to ensure that the EPA regulated competing
products on roughly the same schedule, so that one pesticide does not have an unfair advantage over another.
As described in Chapter 3, the pesticide markets were defined as clusters of PAIs that are substitutes for a
specific end-use. For example, insecticides used on corn is one market or cluster. The OPP assigned each of
the PAIs registered in 1980 to one of 48 separate clusters1. As reported hi Section 3.1, the EPA's Office of
Water made minor adjustments to these pesticide clusters for this analysis. First, PAIs registered after 1980
'In the OPP's classification, each PAI appeared in only a single cluster, since the purpose of the
classification was to develop a regulatory schedule for each PAI.
4.4
-------
were assigned to clusters. In addition, clusters were split when a wide range of price elasticities of demand
were estimated to exist within a single cluster and it was possible to further differentiate corresponding PAI uses
within the cluster (see Appendix C). Four clusters were split, increasing their number from 48 to 56.2
Finally, PAIs were allocated to more than one cluster when the PAI was known to be used in substantial
quantities for different end uses. The adjusted PAI clusters were used as the basis for this EIA. The 270 PAIs,
or classes of PAIs, considered for regulation are mapped into the 56 separate clusters in Appendix B.
Basic Model of Market Structure
Assumptions made about market structure have important implications for empirical modeling. For
example, the standard model of supply and demand (i.e., perfect competition) necessarily predicts at least one
facility closing if production costs increase. (When the supply curve shifts up to reflect the cost increase,
quantity must decrease and the marginal facility must close.) The production data contained in Part A of the
Census indicates that most clusters include production by several different facilities. In addition, Part B of the
Census shows that the pesticide manufacturing facilities experience a range of profitability.
This situation suggests that the pesticide manufacturing markets can be characterized as competitive.
The market does not appear to be perfectly competitive, however, since few firms produce the same PAI;
product differentiation exists within the markets. For example, PAIs within a cluster may be differentially
effective on a regional basis due to climate differences. PAIs may also vary in their effectiveness on different
varieties of pests and on different varieties of crops. The structure of the pesticide markets can therefore
generally be described as competitive with differentiated products (i.e., monopolistic components). In an
industry with these characteristics, different prices may exist for products within a single market. Firms must
compete for customers in terms of both price and the lands of products they sell. Also, new firms may enter
the industry with a new product whose differentiation from its competitors' products may make it profitable.
Baseline Prices for Each Pesticide Cluster at Each Facility
Baseline prices for each PAI cluster at each PAI manufacturing facility served as foundations of the
economic model. To estimate prices at the cluster level for each facility, prices were first estimated at the PAI
level for each facility in one of five ways, as described below.
45 of these clusters had production of one or more of the 270 PAIs or classes of PAIs in 1986.
4.5
-------
PAI-speclfic data provided. Provision of PAI-specific prices in the Census was optional. If
these data were provided, they were used in the analysis. Seventeen of the 88 pesticide
manufacturing facilities (19 percent) chose to provide price data on their technical grade .
products.3
PAI-specific data not reported in the Census and only one in-scope PAI produced. In this
case, reported in-scope revenues were divided by the production quantity of the PAI to obtain
the PAI price. '.
PAI-specific data not reported in the Census, multiple PAIs are produced, and price data for
all the PAIs are available from a secondary source. Secondary data on prices were obtained
from Agchemprice (DPRA, 1990), the Annual Market Survey (Doane Marketing Research^
1987), telephone calls to PAI dealers, and EPA estimates. These secondary prices are
reasonable indicators of the relative prices of the PAIs. If used directly, however, the
secondary prices may overstate the price the manufacturer receives for PAIs, because \
manufacturers may offer volume discounts or sell to a wholesale distributor. Because most
facilities in the Census reported their production of, and revenues from, in-scope PAIs, facility
PAI prices were estimated using these Census data and the relative, rather than the actual, PAI
prices from secondary sources. For example, assume Facility A produces two in-scope PAIs.
From secondary sources, the price of PAIt is found to be twice the price of PAL,. If Facility
A reported producing 200 pounds of PA^ and 500 pounds of PAL,, with total in-scope
revenues of $4,500, the analysis would calculate the price of PAIj as:
200(2/>) +500(p)=$4,500
where p = the price of PAIj.
The solution for "p" is $5. PAIj would therefore be estimated to have a price of $10.
PAI-specific data not reported, multiple PAIs are produced, and price data from a secondary
source is available for only some of the PAIs produced. For those PAIs for which secondary
price data is not available, prices were estimated by first dividing facility in-scope revenue by
facility in-scope production. Using these average prices, the analysis proceeded as described
in the above paragraph.
Seventeen facilities provided PAI-specific data for technical products, nine facilities provided data on
formulated/packaged products, and two facilities provided data on intermediates. A total of twenty facilities
provided PAI-specific data for at least one of these product groups.
4.6 !
-------
• PAI-speciftc data not reported, in-scope revenue not reported, secondary price information is
available for all PAIs produced.* In this situation, the secondary price information was used
directly to estimate price.
Cluster-level prices for each facility were then generated as a weighted average of the PAI prices hi each
cluster. The weightings were based on the production quantities of each PAI at the facility.
Baseline Costs for Each Pesticide Cluster and Facility
Baseline (i.e., pre-compliance) costs were needed for the EIA. Specifically, unit fixed costs and unit
variable costs by cluster were required for each facility. The methods of estimating fixed costs and variable
costs differed, as discussed below.
Fixed costs were reported on a facility-level in the Census, not on a PAI-specific or a pesticide-related
basis. Fixed costs for all in-scope PAIs at a facility were estimated by multiplying 3-year average (1985, 1986,
and 1987) total facility fixed costs by the 3-year average percentage of facility revenues derived from sales of
in-scope pesticides.5 This is represented by the equation:
IF=F x (JRJTR)
where:
IF
F
IR
TR
fixed costs associated with in-scope PAIs;
3-year average fixed costs for the entire facility;
3-year average revenues from in-scope PAIs; and
3-year average total facility revenues.
Cluster-level fixed costs were then allocated based on the revenues for each cluster. Unit fixed costs at the
cluster level were calculated as total cluster fixed costs divided by the in-scope cluster production quantity.
"Prices were estimated in this manner for only one facility projected to incur compliance costs. This
facility's only pesticide-related revenues were for tolling. Due to the construction of the Census, tolling
revenues cannot be separated into sales of in-scope vs. other pesticides. For this reason, the reported revenues
could not be used to estimate prices of in-scope PAIs. This facility incurs only monitoring costs under the
proposed option.
5Three-year averages were used hi an effort to modulate the variability of particular years and to create data
that represents a typical year.
4.7
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Variable costs were estimated in one of two ways, depending upon whether the facility provided PAI-
specific data in the Census.6 For those facilities that provided PAI-specific unit variable costs, these costs were
multiplied by PAI-specific production to obtain total variable costs for each PAL These variable costs were
then summed within clusters. The cluster variable costs were divided by total in-scope production for that
cluster to obtain an average unit variable cost for each cluster.
If no PAI-specific data were provided, estimates of unit variable cost at the cluster level were generated
assuming a constant (average) profit margin across all pesticide products. Facility pesticide-related variable
costs as a percent of facility pesticide sales were multiplied by the unit price of each PAI cluster at the facility
to arrive at that cluster's unit variable costs. Algebraically, unit variable costs for each cluster at each facility
were calculated as:
UVC. = P, x (V/PR)
where:
V
PR
unit variable costs associated with cluster j;
price of cluster j;
3-year average variable (i.e., manufacturing) costs associated with pesticides; and
3-year average revenues from all pesticides.
Unit variable and fixed costs were summed to estimate cluster total unit costs for each facility. !
Baseline Cost Adjustments Due to Other Government Regulations .
Since 1986, the principal year for which much of the Census data were collected, the EPA has ,
promulgated two regulations whose compliance costs to facilities are not reflected in that data. These
regulations are (1) Resource Conservation and Recovery Act (RCRA) land disposal restrictions (40 CFR 268),
and (2) effluent guidelines for the Organic Chemicals, Plastics, and Synthetic Fibers (OCPSF) industry (40 CFR
414).7 The costs associated with these regulations are not reflected in the cost data reported by facilities in the
Census. To accurately represent the costs faced by the pesticide manufacturing industry, the costs associated
with these regulations are added to reported facility fixed costs. The procedure for allocating these costs to PAI
clusters is identical to the allocation of facility reported fixed costs (discussed above). The regulations and their
impacts on pesticide manufacturing costs are discussed below.
6As previously discussed, provision of average unit variable costs by PAI code was optional in the Census.
Respondents who chose not to provide these data were informed by the Census that EPA would use financial
averages to represent all products at a facility. :
Compliance costs for OCPSF include changes to the Economic Impact Analysis through January 21,1992.
4.8
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The 1984 Hazardous and Solid Waste Amendments (HSWA) to RCRA had several new provisions,
some of which went into effect after 1986. In particular, the Land Disposal Restrictions included in HSWA are
likely to have affected PAI manufacturers. These regulations prohibit land disposal of hazardous waste until it
has been treated to the level achieved by the Best Demonstrated Available Technology (BOAT).
Congress directed the EPA to write the rules in three stages. Stage 1 regulated solvents and dioxin and
was promulgated in 1986. Stage 2, signed July 8, 1987, regulated a group of wastes known as the "California
List." For Stage 3, the remaining hazardous wastes were divided into thirds, and signed into regulation on
August 17, 1988; June 23, 1989; and May 8, 1990. Each of these rules became effective immediately upon
promulgation.
Many pesticide manufacturers generate RCRA-listed wastes as a result of pesticide production, and will
therefore have incurred costs of complying with the land disposal restrictions since 1986. For this reason, the
compliance costs estimated for the "California List" and the Stage 3 hazardous wastes were added to the
baseline fixed costs for PAI manufacturers8. The cost estimates were developed from two sources. The 1986
Survey of Hazardous Waste Generators (GENSUR), conducted by the EPA's Office of Solid Waste, was used
to determine the waste streams for pesticide manufacturing facilities. These data were combined with cost data
from the Regulatory Impact Analyses (RIAs) for the land disposal rules. Of the 90 facilities potentially covered
by the pesticide manufacturers effluent guidelines, 45 facilities were included in the GENSUR data base. The
GENSUR data are organized by facility and waste stream. For each facility and waste stream, the following
data were available:
• RCRA waste codes (up to 10 codes per waste stream);
• Quantity of waste generated on-site and quantity disposed off-site;
• On-site waste management train (up to 10 waste management procedures); and
• Off-site disposal train
For purposes of estimating costs associated with the land disposal restriction rules, the data were first scanned to
select only those components dealing with land disposal, e.g., landfill, surface impoundments, and waste piles.
The RIAs for the first and last third of the Stage 3 Land Disposal Restrictions included total gallons of waste to
8Because Stage 1 of the rule became effective in 1986, the costs associated with this rule are assumed to be
reflected in the Census data.
4.9
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be treated and total incremental costs by baseline management practice and RCRA waste code. This allows
calculation of unit (per gallon) costs for each RCRA waste by management practice.9 :
For each pesticide manufacturing facility and waste stream, management and RCRA waste codes were
matched to the corresponding codes in the RIA to obtain unit costs for each facility, waste stream, and
management combination. These unit costs were then multiplied by the appropriate quantities (e.g., gallons of
each waste at each facility managed, using each relevant method) to estimate a total cost for each RCRA rule.
Because the middle third of the Stage 3 rule was not considered to be a major regulation (costs were
less than $100 million), compliance costs were not available in similar detail. The available information
included total quantity of regulated waste generated and total incremental costs by baseline management practice
(i.e., not broken down by RCRA waste code). It was therefore necessary to assume that the wastes covered by
this rule had the same unit costs. Given the small number of wastes in this group, this assumption is not
expected to affect the analysis substantially. j
Costs of complying with restrictions on land disposal of the California List were available in a third
format. The RIA contained a table showing total land-disposed wastes and associated costs by four-digit SIC
codes. SIC 2879 (pesticide and agricultural chemicals, not elsewhere classified) was among the industries
shown. An average unit cost was estimated by dividing total compliance costs by total regulated wastes that
were land disposed. This unit cost was assumed to be constant across all RCRA wastes.
Thirty-four pesticide manufacturing facilities incurred costs due to the RCRA rules described above.
Total annualized RCRA costs for these facilities are estimated to be $1.3 million (1986 dollars). Not all of
these costs may have been borne by the pesticide manufacturers; however, a portion may have been passed
through to customers in the form of higher prices. Because no data on the portion of costs likely to be passed
through to customers are readily available, the analysis assumes that the burden of the cost increase is split
evenly between the facilities and the customers. In other words, the facilities are assumed to bear 50 percent of
the cost increase10. These costs were added to the baseline fixed costs of the affected facilities.
*The RIA for the first third examined two alternatives and two scenarios within the first alternative. The
costs for Alternative A, Scenario I were used because this option was closest to the final rule.
10An alternate assumption, in which all RCRA compliance costs were borne by the manufacturers, would
result in the projection of additional baseline closures in the current analysis. As a result, fewer closures
resulting from the pesticide effluent guideline limitations and standards would be projected. EPA therefore
believes that the assumption of a 50 percent cost pass-through is conservative.
4.10
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The final OCPSF Effluent Guidelines, issued November 1987, established effluent limitations guidelines
and standards for OCPSF process wastewater. The regulations for direct dischargers covered about 60 priority
pollutants; those for indirect dischargers covered 47 priority pollutants. For purposes of the regulation, OCPSF
process wastewater was defined to include establishments, or portions thereof, whose products are classified in
any one of five SIC codes: SIC 2821 (plastics and resin materials), SIC 2823 (cellulosic manmade fibers), SIC
2824 (non-cellulosic synthetic fibers), SIC 2865 (tar crudes, cyclic intermediates, dyes and organic pigments)
and SIC 2869 (industrial organic chemicals, not elsewhere classified). Most facilities were required to comply
with these regulations by November 5, 1990.
Substantial overlap exists between facilities subject to the OCPSF effluent guidelines and those covered
by the proposed pesticide manufacturer effluent guidelines. (Manufacture of organic PAIs is included in SIC
2869.) Of 90 facilities in the Census, 55 also manufacture compounds regulated under the OCPSF rule. Thirty
of these pesticide manufacturers incur costs to comply with the OCPSF effluent guidelines. The estimated costs
to comply with the pesticides effluent guidelines will be incremental to those of meeting the OCPSF rule. For
this reason, OCPSF costs for all facilities affected by both rules are added to the economic baseline. Capital
and annualized OCPSF costs for these 30 facilities total $105 million and $36 million, respectively (1986
dollars). Again, 50 percent pass-through to the customers is assumed. As a result, additional annualized fixed
costs for all pesticide manufacturing facilities due to OCPSF effluent guidelines total $18 million.11
Facility Compliance Costs
Full details of the methods by which the costs of complying with the proposed regulation were
estimated can be found in the Technical Development Document (Chapter 8, Engineering Costs and Non-Water
Quality Aspects). A brief summary of the regulatory options and their associated costs is provided below.
As discussed previously, a total of 90 pesticide manufacturing facilities producing one or more of 270
PAIs, or classes of PAIs, are potentially subject to regulation. The EPA has projected costs for these 90
facilities under two regulatory options: one that would require treatment of process wastewater pollutants
(Treated Discharge Option) and another that would require no discharge of process wastewater pollutants to
POTWs or surface water (Zero Discharge Option). The Treated Discharge Option limitations would be based
on the use of hydrolysis, activated carbon, chemical oxidation, resin adsorption, solvent extraction, incineration
and/or recycle/reuse to control the discharge of PAIs hi wastewater. The Zero Discharge Option is based on
"Estimated costs of compliance may vary substantially from actual costs incurred, since companies
frequently meet regulatory requirements by means other than those the EPA used for estimating compliance
costs.
4.11
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on-site or off-site incineration and/or recycle/reuse.12 For both regulatory options, the economic impacts on
facilities were calculated separately for direct and indirect dischargers.13 Each discharge category was
analyzed further for two subcategories: organic pesticide chemicals manufacturing (Subcategory A) and
metallo-organic pesticide chemicals manufacturing (Subcategory B).
Three categories of compliance costs associated with pesticide manufacturing were evaluated: capital
costs, land costs, and operating and maintenance costs (including compliance self-monitoring and sludge
disposal). The capital and land costs were one-time "lump sum" costs; the operating and maintenance costs
were evaluated on an annual basis. Capital and land costs, annualized using the conservative assumption that
they have a productive life of ten years, were adjusted over the ten-year period using the weighted average cost
of capital.14 These annualized capital and land costs were added to operating and maintenance costs to produce
total annualized costs. For facilities that both manufacture and formulate/package pesticides, the compliance
costs apply only to the manufacturing operations of the facility. All of the compliance cost estimates are |
presented in 1986 dollars and are based on the assumption that, whenever possible, facilities will build on
existing treatment.
The costs and impacts of implementing the regulations were estimated on a PAI-specific basis for each
facility. Table 4.1 presents the capital and land, operation and maintenance, and annualized costs associated
with the two regulatory options for Best Available Technology Economically Achievable (BAT) and i
Pretreatment Standards for Existing Sources (PSES) by subcategory. Under the Treated Discharge Option, it is
expected that 61 pesticide manufacturing facilities will incur compliance costs: 32 direct dischargers and 30
indirect dischargers (one facility is a joint discharger). Under the Zero Discharge Option, 67 facilities are
projected to incur compliance costs: 35 direct dischargers and 33 indirect dischargers (again, one facility is a
joint discharger). Under the Treated Discharge Option, total BAT annualized costs (applying to direct
dischargers) are projected to be $14.7 million for Subcategory A. There are no BAT costs associated with
Subcategory B chemicals. These chemicals are already limited by Best Practicable Control Technology
Currently Available (BPT), which requires no discharge of process wastewater pollutants. Total annualized
iaThe Zero Discharge Option would limit discharges from the facility site to POTWs or to surface water
only; discharges to other media may remain constant or increase as a result of changes in discharge to surface
water. For example, pesticide manufacturing facilities could, theoretically, achieve compliance with a zero
discharge effluent guideline by transferring the waste streams previously discharged to surface water to landfills,
incinerators, or deep well injection sites.
'Impacts of zero discharge requirements are reported with impacts on direct discharge requirements. Zero
dischargers may be subject to monitoring costs if they have any process wastewater. Monitoring costs would be
imposed by the permitting authority (no separate monitoring requirements are contained in the proposed effluent
guidelines for pesticide manufacturers). These monitoring costs are included in the analysis to capture the full
cost to industry of controlling process wastewater pollutants.
MFor details on the weighted average cost of capital, see Section 4.2.A.
4.12
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4.13
-------
costs for PSES (applying to indirect dischargers) under the Treated Discharge Option are projected to $5.9
million and $120,000 for Subcategories A and B, respectively.15
The costs associated with the Zero Discharge Option are substantially higher than those for the Treated
Discharge Option. Total Zero Discharge Option BAT annualized costs are projected to be $4.8 billion for
Subcategory A. Again, there are no costs associated with Subcategory B chemicals under the proposed BAT
guideline. Total annualized costs for PSES under the Zero Discharge Option are estimated at $518.8 million
and $2.8 million for Subcategories A and B, respectively. i
Post-compliance Costs for Each PAI Cluster at Each Facility
As stated above, the compliance costs were estimated on a PAI basis for each facility. To combine
compliance costs with other facility costs, cluster-level compliance costs for each facility were calculated by
summing annualized PAI compliance costs for all PAIs within each cluster for each facility. Dividing total
cluster-level compliance costs for each facility by the cluster production quantity at that facility yielded unit
compliance costs for each market and each facility. These costs were added to baseline unit costs to arrive at
post-compliance unit costs.
Pricing Rule to Estimate Post-compliance Prices16
Changes in PAI prices and product demand are determined Interactively in the market place.
Typically, a producer will raise prices based on the actions expected of competitors and the extent to which
consumers will decrease demand. Consumers will then respond to the increased prices with a drop in demand
based on several factors, including the percent of their production cost contributed by the product and the
availability of substitute products. Producers then examine the impact of the price increase and demand i
decrease on profitability and reevaluate their price. Consumers again react. This iterative process continues
until producers believe they have maximized profit.
This analysis attempts to model an approximate end point of the supply and demand interaction. The
percentages of the compliance costs that are translated to price increases for each cluster depend on (1) the
degree of substitutability of alternative products, and (2) the extent of supplier price competition. Substitution
among PAIs is included by addressing impacts on a cluster basis. Substitution of PAIs with non-chemical
alternatives is discussed in the following section on post-compliance quantities.
15The EPA is not proposing to regulate Subcategory B chemicals at this time.
"An analysis of economic impacts based on zero pass-through of compliance costs to consumers is
presented in Appendix D.
4.14
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A pricing rule was developed to take into account the effect of supplier competition on the percentage
of compliance costs that are passed to the consumer.17 This rule is based upon the assumption that if
production bearing compliance costs makes up a small percentage of total cluster production, then a price
increase due to regulation is unlikely. If all production in a cluster is projected to bear compliance costs, then
all regulatory costs are likely to be reflected in higher prices.
To capture this effect, price increases for each market and each facility were calculated as:
where:
APf!
PCj
T;
change in unit price for facility f, cluster j;
unit compliance costs for facility f, cluster j;
total U.S. production of cluster j that incurs compliance costs; and
total U.S. production of cluster j.
The quantity of PAI production in each cluster incurring costs was calculated from the production data provided
in the Census (Parts A and B) and the estimated compliance costs. Total production of PAIs for each cluster
was calculated from the Census and other proprietary data. Post-compliance unit prices were calculated for
each facility and each cluster as the baseline unit price plus the change in unit price due to the installation of
pollution control equipment.18
Post-compliance Quantities
Having estimated post-compliance costs and prices, the remaining step solved for post-compliance
quantities. An estimate of the price elasticity of demand for each cluster was used to predict changes in
quantities demanded given changes in price. The price elasticity of demand can be defined as the percentage
change in the quantity demanded, divided by the percentage change in price. If consumers cut back their
purchases to such a large extent that any price increase reduces total revenues, then demand is said to be elastic,
i.e., customers are sensitive to price changes. If consumers cut back their purchases only slightly in response to
higher prices, resulting in an increase in revenues, demand is said to be inelastic, i.e., customers are not as
"Theoretically, the effects of supplier competition could be evaluated by modeling a supply curve in the
pre- and post-compliance scenarios. This model was not used for the EIA because production cost data for
pesticides not included in the Census are unavailable. In addition, production cost functions within facilities are
also unknown, allowing only marginal costs of production to be estimated.
18The pricing rule is not meant to be a perfect theoretical simulation of the price response to regulatory cost
increases. Given the uncertainty and limited availability of data on production functions and costs by facility
and PAI, use of the measure provides a reasonable basis for simulating the pricing response by producers.
4.15
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sensitive to price changes. The value of the price elasticity of demand is unbounded and may be positive or
negative. It is expected, however, that price and demand are negatively correlated, i.e., an increase in price
results hi a decrease hi the quantity demanded. The price elasticity of demand is therefore usually negative.
The methodology for generating estimates of the elasticity of demand relied on five sources. First, the
EPA reviewed empirical studies of the price elasticity of demand for pesticides. Few such studies were located,
however, and the existing studies offer conflicting conclusions, most of them controversial. Second, the EPA
reviewed the U.S. Department of Agriculture's (USDA, 1985) analysis of the price elasticity of demand for
food commodities. The elasticity of demand for farm inputs can be derived from the elasticity of demand for
farm commodities because demand for production inputs must ultimately reflect demand for the end product.
For this reason, the USDA estimates of the elasticity of demand for food commodities provided the basis for
estimating the demand elasticity for PAI clusters. Three additional factors were examined as indicators of how
the demand elasticity for PAIs might vary from the demand elasticity for food: (1) the feasibility of employing
non-chemical or non-biological pest control methods, (2) the percent of production cost contributed by the PAIs
of interest, and (3) the productivity of expenditures for PAIs. The elasticity estimates generated from this
process were reviewed by OPP staff, whose comments were incorporated into the methodology. A complete
description of the process by which the elasticity estimates were developed can be found hi Appendix C.
A list of the elasticity estimates by cluster is shown in Table 4.2, in order of increasing elasticity of
demand. As can be seen from the table, the elasticity estimates range from -0.12 (herbicides on sugar beets,
beans, and peas) to -1.38 (fungicides on grapes and herbicides on grapes). The elasticity estimates vary
substantially within the fungicide, herbicide, and insecticide clusters; the type of pesticide is not seen to affect
the elasticity of demand. ;
The demand for pesticides hi all but three of the clusters is expected to have unit elasticity (i.e., -1) or
to be inelastic. Demand is expected to be elastic for fungicides and herbicides applied to grapes and for
insecticides applied to cotton. The main factor driving the high elasticity for the grape clusters is the high
elasticity of demand for grapes at the retail level. Demand for insecticides on cotton is expected to be
somewhat elastic, based on both the literature estimates of the elasticity and the low marginal productivity of
insecticides applied to cotton.
The methodology employed to estimate the elasticity of demand for the PAI clusters yields reasonable
best estimates of elasticities. The estimates are a good indicator of whether demand for a certain cluster of
PAIs is extremely or only moderately elastic or inelastic; the specific numeric values should not be viewed as
definitive. The estimates of elasticity of demand for clusters of PAIs, developed through this analysis, are the
most reliable estimates known at this time.
4.16
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Table 4.2
Summary of Estimates of Elasticity of Demand
.for Clusters with Production, 1986
Page 1
Cluster
Elasticity Estimate
Herbicides on sugar beets, beans, peas
Herbicides on tree fruits (except oranges), sugar cane, nuts
Herbicides on tobacco
Fungicides on fruit and nuts trees (except oranges)
Fungicides for seed treatment
Herbides on vegetables
Fungicides on grain in storage
Insecticides on vegetables
Slimicides
Fumigants and nematicides
Insecticides on termites
Wood preservatives
Insect repellents at non-agricultural sites
Domestic bug control and food processing plants
Mosquito larvacides
Fungicides on turf
Industrial preservatives
Insecticide synergists and surfactants
Plant regulators, defoliants, desiccants
Sanitizers - dairies, food processing, restaurants, air treatment
Insecticides on livestock and domestic animals
Industrial microbicides, cutting oils, oil well additives
Preservatives, disinfectants, and slimicides
Fungicides - ornamentals
Insecticides on lawns, ornamentals and forest trees
Molluscides and misc. vertebrate control agents
-0.12
-0.20
-0.20
-0.23
-0.27
-0.27
-0.31
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
4.17
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Table 4%
Summary of Estimates of Elasticity of Demand
for Clusters with Production, 1986
f f/t
-------
4.2 Facility Closure Analysis
As previously discussed, the results of the economic model described above are used to estimate three
potential impacts of the proposed effluent limitations guidelines at the facility level. The first, and most severe,
potential impact on a facility is facility closure. For purposes of this EIA, a pesticide manufacturing facility is
defined as the portion of the facility involved in manufacturing and formulating/packaging, or performing
contract work for both in-scope and out-of-scope pesticides. A pesticide manufacturing facility, as defined for
this analysis, does not include any non-pesticide related activity occurring at the physical facility. A pesticide
manufacturing facility that is predicted to close may continue with non-pesticide-related operations, such as
production of other organic chemicals. Facility liquidation value, in the case where other products are produced
at the facility, refers to the liquidation value of the pesticide product lines and any related fixed assets, working
capital, and real estate.
A decision to close a facility is typically made at the firm level. The firm holds pesticide registrations
and can consider transferring both pesticide and other products among facilities. In general, a facility owner
(i.e., a firm) faced with pollution control requirements must decide whether to make the additional investment in
pollution control, to change the products produced at the facility (both in-scope and out-of-scope), or to liquidate
the facility. Because data on other products to which a facility may convert are unavailable or limited, this
analysis assumes that either the pollution control investment is made or the facility is liquidated. This
simplification ignores the possibility that the pesticide product lines at some facilities may be used for the
production of other chemicals. The analysis is conservative in that it assumes that facility owners have very
limited options.
The evaluation of whether to close a facility is complex and involves a number of factors including:
• Present and expected profitability of the facility;
• Current market or salvage value of the facility;
• Required capital investment in pollution control technology equipment;
• Expected increase in annual operating costs due to pollution control requirements; and
• Expected product price, production costs, and profitability of the facility after pollution control
equipment is installed and operating.
In the majority of cases, a rational owner would decide to continue operations if the discounted cash
flows are greater than the current liquidation value of the facility. If the expected cash flows are less than the
current liquidation value of the facility, the owner would be better off selling the facility.
4.19
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The calculation used to estimate whether or not a facility will close is intended to model the decision-
making process of the owners of the facility. It compares the value of the facility if it is shut down to its value
if the necessary treatment were installed and operations continued. Specifically, this calculation entails a
comparison of the present value of the cash flow (i.e., discounted cash flow) generated by the facility to the
liquidation value of the facility. That is, it compares the value that the firm would receive from its future
stream of profits if it continued to operate the facility to the value that it would receive if it sold the facility for
its liquidation value. If the liquidation value of the facility is greater than the discounted cash flow, the facility
is considered to be a closure.
The analysis of facility closure was conducted in two stages: baseline and post-compliance with the
proposed effluent limitations guidelines. If, in the baseline analysis, a facility was projected to close regardless
of the imposition of compliance costs, such a facility was not seen as financially viable. If a facility closed in
the baseline analysis, it was not considered in the post-compliance analysis. In other words, no economic
impacts of the proposed regulation on baseline facility closures were predicted.
4.2.A Baseline Facility Closure Analysis
The steps in the construction of the baseline facility closure analysis involved the estimation of four
variables: facility cash flow, cost of capital, discounted cash flow (DCF), and liquidation value. These .
variables are discussed below. ;
Facility Cash Flow
Facility cash flow consists of facility net income plus noncash expenditures. Baseline, or pre- ,
compliance, facility cash flow was estimated based on data from the income statement reported in the Census.
Cash flow was adjusted to account for the costs of complying with the RCRA land disposal restrictions and the
OCPSF effluent limitations guidelines. As discussed above, these rules (or portions thereof) were effective after
1986, the base year for the analysis. The compliance costs associated with the rules were therefore not
f
reflected in the Census data. Specifically, cash flow for each facility was estimated as:
CFO=NI+IW.-CT)+DEP-OC(1-CT)
where:
CFO = Cash flow;
MI = Net income (i.e., after tax profits calculated from the Census);
IT = Interest expenses (taken directly from the Census);
4.20
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CT
DEP =
OC =
Corporate income tax rate (calculated as taxes divided by before-tax profits calculated
from the Census);
Depreciation expenses (taken directly from the Census); and
cost of compliance with other EPA regulations first effective after 1986 (RCRA land
disposal restrictions and OCPSF effluent guidelines).
Cost of. Capital
The cost of capital is the rate at which a firm obtains funds for financing capital investments. The cost
of capital is required for two purposes: (1) to discount future cash flows for the facility closure analysis so that
the present value of cash flows can be compared to the facility liquidation value; and (2) to annualize the capital
costs associated with the proposed rule so that post-compliance changes in cost and price can be projected.19
The cost of capital to a particular firm depends on how the investment is financed. One option, equity
financing, is taken when a firm issues stocks or retains earnings. A second option involves acquiring additional
debt, through bonds, notes, or short-term commercial paper.20 Typically, acquiring debt is the less expensive
option. As a firm expands its debt holdings, however, the cost of debt increases, forcing the firm to reach an
equilibrium between debt and equity financing. It is assumed in this analysis that firms use some combination
of debt and equity to finance compliance costs. The measure of a firm's overall cost of a capital investment,
based on the percentage values of debt and equity used to finance the investment, is termed the weighted
average cost of capital (WACC). Thus, the WACC is the average after-tax cost of all funds used to finance a
capital investment.
The WACC can be presented in either nominal terms (i.e., not adjusted for inflation) or real terms
(i.e., adjusting the nominal WACC for inflation). This analysis uses the real cost of capital to allow for the use
of constant annual cash flows (i.e., cash flows that are not inflated over time). The two inputs to calculating the
real WACC - nominal WACC and the inflation rate - are discussed below.
19The cost of capital is determined by firm, rather than facility, characteristics. As a key variable in the
facility level analyses, however, it is discussed in this section.
^Debt capital is provided as a loan which creates a contractual obligation on the borrower to repay the loan
and contractually specified interest charges. Traditional sources of debt financing include commercial banks,
non-bank lending institutions, and the public capital markets. Except as provided by a security agreement, debt
financing does not provide the creditor any rights of ownership in the assets of the borrower. Equity capital
represents a right of ownership in the assets of the firm seeking to finance a treatment system (e.g., a
corporation or sole proprietorship). Equity capital may be obtained as externally provided funds (through the
sale of new equity) or may be generated internally (from the cash flow provided by the firm's operations).
4.21
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Nominal WACC
The nominal WACC was calculated by weighting the cost of equity and the cost of debt by the
percentage of the investment expected to be financed by these two methods. The equation used was:
WACC=R(EfA)+Y(l -CT)(PIA)
where:
WACC = nominal weighted average cost of capital;
R = after-tax return on equity;
E = amount of investment financed by equity;
A = total amount of the investment;
Y = pre-tax interest rate on debt;
CT = marginal corporate tax rate; and
D = amount of investment financed by debt.
The estimates of the nominal WACC vary by firm. The sources of each of the variables hi the WACC equation
are discussed below.
The percentages of the investment that a firm is assumed to finance through equity (e/a) and debt (d/a)
are assumed to match the firm's historical mix of equity and debt investment. The values of these variables for
each firm are obtained from one of two sources. For each domestic public-reporting firm, the mix of debt and
equity is obtained from Standard and Poor's Compustat service for that firm in 1986. For all firms not included
in the Compustat data base, the mixture of debt and equity financing was assumed to match the 1986 median
mixture of debt and equity financing for the "industrial chemical industry" as calculated from Robert Morris
Associates' Annual Statement Studies.'11 The calculated values taken from the Annual Statement Studies are
40.5 percent equity financing and 59.5 percent debt financing.
The annual return on equity (R) was calculated as:
2lThe "industrial chemical industry" includes SICs 2861, 2865, and 2869.
4.22
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where:
i = the risk-free rate of return = 10.18 percent (calculated from the 1981-1990 average interest
rate on 30-year U.S. Treasury Bonds as reported in Statistical Abstract of the United States,
Bureau of the Census, 1989, 1990);22
(Rm-l)= Typical risk premium, or the rate of return on market portfolio minus the rate of return on risk
free investments = 8.0 percent, a standard value based on the Standard & Poor's 500.
13
A measure of the risk of an individual firm compared with the market. Beta values are based
directly on Value Line Investment Survey, Part I Summaries & Indexes (February 14, 1992) for
publicly traded companies. For private firms, the median beta value calculated for the public
PAI manufacturing firms was used. This value is 1.056, indicating that the average risk of the
public PAI manufacturing companies is close to the market average risk.
The pre-tax interest rate on debt (Y) is assumed to be 10.95 percent. This interest rate equals the
1981-1990 average yield on AA 10-year industrial bonds (U.S. Department of Commerce, 1990 and 1991).23
Finally, the marginal corporate tax rate (CT) is assumed to be 34 percent.24
Real WACC
To allow the use of cash flows that are not adjusted for inflation, the real WACC was needed. The
real WACC was estimated as:
22The variable i represents the risk-free component of the return on equity. Equity has no maturity date;
therefore, i is best calculated as the return on long-term Treasury Bonds.
^Interest rate information reported by individual facilities in the Census was not used for this analysis due
to difficulties of interpreting the reported values. For example, a number of respondents reported that funds for
capital outlays were obtained from a parent firm at zero percent. This reporting reflects internal accounting
conventions but does not accurately represent the interest cost borne by the firm for debt financing. Other firms
indicated that interest costs were tied to the prime rate (e.g., prime rate or "prime rate plus one"). Such
interest terms would generally apply to a working capital credit line or other short-term credit instrument. The
short-term liabilities are usually replaced, however, by longer-term debt to match the expected life of the capital
asset being financed. The interest rate charged on longer-term debt is usually higher than that associated with
short-term credit rates, so short-term rates may understate potential interest costs. The resulting WACC used
for each facility in the EIA is higher than the cost of debt reported in the Census for that facility, thereby
increasing the projected burden of compliance. Use of the WACCs is therefore conservative.
^Because the/I/TO, not the facility, tax rate is needed, use of the facility-level data from the Census was
inappropriate.
4.23
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RWACC=(£i+WACC)l(l
where:
RWACC
G
the real weighted average cost of capital; and
the rate of inflation = 4.74 percent.
The rate of inflation (G) is calculated as the mean annual inflation rate as reported by the unadjusted Consumer
Price Index between 1981 and 1990.
Discounted Cash Flow
The discounted cash flow (DCF) is the present value of a stream of annual cash flows. In this analysis,
the ten-year DCF was compared to the liquidation value of each facility to predict facility closures. The DCF is
calculated as:
10
CF
ti (l+RWACC)'
where:
DCF = facility present value cash flow over 10 years;
CF = facility annual cash flow;
RWACC = the real weighted average cost of capital; and :
i = number of years over which cash flows are discounted.
The time period over which cash flows are discounted, ten years, was chosen as a conservative estimate of the
average life of the pollution control equipment. I
Liquidation Value
Liquidation values for each facility were estimated based on data from the Census. A facility's
liquidation value is defined as the gross value the facility would receive from selling its lines for pesticide
production and formulating/packaging. The liquidation value includes the value of fixed assets, working;capital,
and real estate.25
25The current analysis used gross rather than net liquidation values, thereby overstating the likelihood of
facility closure. The EPA expects that the EIA supporting the final rule will use net liquidation values.
4.24
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For those facilities that reported the liquidation value of their pesticide production and
formulating/packaging lines, this value was used as the facility liquidation value. For facilities that could not
provide this information in the Census, liquidation values were estimated using regression analysis based on the
liquidation values provided by other pesticide manufacturing facilities. Several different regression models were
evaluated and are presented in the Administrative Record.
The model used in the analysis has two independent variables: (1) 1986 local property tax assessment
of facility land, buildings, equipment, and machinery, and (2) 1986 facility inventories. The liquidation value
of a facility is dependent upon the market value of facility-owned land, buildings, and equipment as well as on
the facility's inventories of products. As the valuation of these assets increases, one would expect the
liquidation value to increase, producing a positive coefficient for each of the independent variables. Given this
model specification, the regression equation yielding the strongest results, as measured by goodness-of-fit tests,
was:
LV = -12,906 + (0.417 x TA) + (1.159 x INV)
where:
LV =
TA
INV
facility liquidation value;
1986 local property tax assessment of facility land, buildings, equipment and machinery; and
1986 facility inventories.
The F value for this equation was 2099 with 46 degrees of freedom.26 The adjusted R-squared was 0.99.27
The standard error for the TA coefficient was 0.006 while the standard error for the INV coefficient was
0.391.K This equation was used in the analysis to estimate liquidation value for facilities that did not provide
this data in the Census.
26The F statistic tests the overall significance of the regression. The reported value leads to rejection of the
hypothesis that the coefficients of all of the independent variables are equal to zero, indicating that the variables
are useful in projecting liquidation values.
^Adjusted R2 indicates the proportion of variation explained by the regression model. Values of R2 that are
close to 1 imply that most of the variability in the dependant variable is explained by the regression model.
standard error for a regressor indicates the accuracy with which the coefficient of that regressor is
measured, given the other regressors in the model. The reported standard errors indicate that the contribution
of the regressors is significant.
4.25
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4.2.B Post-Compliance Facility Closure Analysis
Facilities for which baseline DCF was less than the facility liquidation value (i.e., those predicted to
have baseline facility closures) were not considered as potential facility closures in the post-compliance scenario.
For the remaining facilities, however, the post-compliance DCF was compared with the facility liquidation value
to project facilities that would close due to the regulation.
Although the liquidation values of the facilities do not change as a result of the regulation, post-
compliance DCFs must be calculated. Three factors are included when estimating the DCF in the post-
compliance scenario:
• the compliance costs, including capital, land, and operating and maintenance;
• the resulting change hi revenue associated with the new price and quantity; and
• the decrease in variable costs of production due to the reduction in quantity.
Facility changes hi DCF were calculated by summing the present value of compliance costs, the present
value of the change in revenue, and the present value of the change hi variable costs over all clusters produced
at a facility. The post-compliance DCF was then calculated by adding the changes hi cash flow to the baseline
DCF. The corresponding equation is: j
where:
PCDCF =
DCF
CCadji =
the post-compliance facility discounted cash flow;
facility baseline discounted cash flow;
compliance cost adjustment to discounted cash flow for cluster i;
the adjustment hi the discounted cash flow due to the change m revenue for cluster i;
the adjustment hi the discounted cash flow due to the change hi variable costs for cluster i;
The three cluster level adjustments are described below.
Adjustment for compliance costs
The compliance costs have three components: operating and maintenance costs, capital costs, and land
costs. Operating and maintenance costs will be somewhat offset by the corresponding decrease hi taxes the
4.26
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facility will pay due to reduced profit. A present value of operating and maintenance costs is generated by
multiplying this value by a present value factor. The present value factor is based on the WACC, as discussed
in the previous section. The capital and land costs need no adjustments because they are already in the form of
a present value. The equation for the compliance cost adjustment is:
CCadj.=-((PVFx(.OM.x(l -CZ))) +CPT.+LANDJ
where:
CCadj; =
PVF
OM;
CT
CPT; =
LAND, =
compliance cost adjustment to DCF for cluster i;
present value factor (sum from i = 1 to 10 of I/ (1 +WACC)1);
operating and maintenance costs of compliance for cluster i;
corporate tax rate;
capital costs of compliance for cluster i; and
land costs of compliance for cluster i.
Adjustment for change in revenue
The change in revenue contains two components: the increase in revenue resulting from the increase hi
price and the decrease in revenue resulting from the decrease in quantity. Present values of both changes in
streams of revenue are needed to adjust the baseline DCF. The cluster-level adjustment to the baseline DCF for
the change in revenue is shown by the equation:
Radj.=PVF((AP. x
where:
PVF =
AP:
AQj
the adjustment to the DCF due to the change hi revenue for cluster j;
present value factor (sum from i = 1 to 10 of I/ (1 +WACC)');
the change in cluster j price from baseline to post-compliance;
the post-compliance quantity for cluster j;
the baseline price for cluster j; and
the change hi cluster j quantity from baseline to post-compliance.
Adjustment for change in variable cost of production
The final adjustment to the baseline DCF reflects the decrease hi variable costs associated with
decreased production. Variable costs were assumed to decrease hi proportion to the decrease hi quantity of
pesticides produced. The equation is:
4.27
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x FC.)
where:
Cadjj =
PVF =
the adjustment to the DCF due to the change in variable costs for cluster j;
present value factor (sum from i = 1 to 10 of I/ (1 +WACC)1);
the change hi cluster j quantity from baseline to post-compliance;
the baseline quantity of cluster j; and
the unit variable cost for cluster j.
As previously discussed, a facility with a post-compliance DCF less than the facility liquidation value was
predicted to close as a result of the regulation. The projection of closure refers only to the pesticide-related
portion of the facility. Other operations, such as production of OCPSF chemicals or pharmaceutical, may
continue at the location.
4.3 Product Line Closure Analysis
Facilities that did not close hi either the baseline or the post-compliance scenario were analyzed for
possible product line closures. The impact of a product line closure is less severe than that of a facility closure.
A facility that closes a product line may still profit from producing and formulating other pesticide products,
and may continue to operate while new products are registered or changes are made to the physical plant. Like
the facility closures analyzed above, product line closures are evaluated in the baseline scenario first. If a
facility is projected to close a product line hi the baseline, that facility is not re-evaluated for a product line
closure hi the post-compliance scenario.
The evaluation of baseline and post-compliance product line closures is straightforward. A product line
closure is predicted when the unit total (i.e., fixed plus variable) cost of the product line (i.e., cluster) exceeds
the unit price. Note that the comparison of price to total costs is very conservative. A comparison of price to
variable costs only is a reasonable alternative (in the short run), and would result in an equal or lesser number
of product line closures. The calculation of unit prices and costs hi both the baseline and post-complianbe
scenarios was described previously.
Given the methodologies used to calculate facility and product line closures, it is possible that a facility
may be projected to close all pesticide product lines, but the facility itself is not projected to close. In such a
case, the product line closure analysis serves as an alternate and complementary analysis of potential facility
4.28
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closures. Such results would not be contradictory, because the product line closure analysis evaluates closures
based on price and cost while the facility closure analysis also includes asset valuation.
4.4 Other Significant Financial Impacts
Facilities may sustain other significant financial impacts short of facility or product line closure. These
impacts are indicative of other less immediate, but also potentially damaging, effects that may occur as a result
of compliance. For example, a firm may decide to keep a facility in operation for several years, but may cease
reinvestment in the facility's building and equipment, eventually closing it. The impacts measured in this
section are less severe than the closure of a facility or a product line, because the facility remains profitable
with time to register new products, find ways to cut costs, or shift to other pesticide or non-pesticide products.
Other financial impacts were assessed based on financial indicators of operating performance. Two
financial indicators are examined in this analysis: interest coverage ratio (ICR) and return on assets (ROA).29
The ICR and ROA gauge a facility's ability to continue doing business long term, and also indicate a facility's
ability to qualify for a loan or to attract investors. In this way, the ratios are key indicators of a facility's
ability to finance costs associated with the proposed regulation.
The ICR is calculated as earnings before interest and taxes (EDIT) divided by interest expense. This
ratio provides a comprehensive measure of a facility's ability to meet its fixed cost obligations (e.g., short- and
long-term debt) out of operating earnings. Facilities must manage their fixed cost obligations in order to
achieve profitability and raise additional capital. With that in mind, lenders and investors tend to avoid potential
debtors/investments that have a high proportion of debt or other fixed obligations relative to operating earnings.
ROA is calculated as EDIT divided by assets. ROA is a measure of a facility's operating profitability
and asset management capability. This ratio demonstrates the rate of return on the total investment in the
facility.
lance
Other significant financial impacts are reported only for facilities that were not projected to experience
one of the more severe impacts (e.g., a facility or product line closure) in either the baseline or post-compli
scenario. Significant financial impacts were evaluated by comparing each facility's post-compliance financial
ratios to the lowest quartile ratios established for all in-scope pesticide manufacturing facilities. A significant
29The ICR is also known as "times interest earned;" the ROA is also known as the "return on investment.1
Additional information on these ratios can be found in Chapter 7.
4.29
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impact is said to result from the proposed guidelines if a facility shifts into the lowest quartile of either the ICR
or the ROA for all pesticide manufacturing facilities due to the regulation. w ;
The analysis of other significant financial impacts was conducted in three steps: (1) estimate the ICR
and ROA for all pesticide manufacturing facilities, (2) determine the lowest quartile values for the two ratios,
and (3) recalculate the post-compliance ICR and ROA for each facility. These steps are discussed below.
Baseline Ratios
The values marking the lowest quartiles for the ICR and ROA were determined by calculating the ratios
for all pesticide manufacturing facilities. The three components used to calculate these two ratios were EBIT,
interest, and assets. EBIT was calculated as three-year average revenues from pesticides minus three-year
average costs (except interest and taxes) associated with pesticides. Pesticide-related revenues were taken
directly from the Census. Pesticide-related costs are composed of pesticide variable costs and pesticide fixed
costs. Pesticide variable costs were taken directly from the Census. Fixed costs (e.g., depreciation, fixed
overheads, R&D, and other) are not broken down in the Census into those related or unrelated to pesticides, but
are reported for the entire facility. As a result, the percentage of fixed costs generated by pesticide-related
activity was assumed to match the percentage of facility revenues from pesticide-related activity. The equation
for calculating EBIT is therefore:
EBIT=PREV-VC-FC(PREV/TREV)
where:
EBIT =
PREV =
VC
FC
TREV =
earnings before interest and taxes;
pesticide related revenue for a facility;
pesticide related variable cost for a facility;
total fixed costs (minus interest and taxes) for a facility; and
total facility revenues.
Interest related to pesticides was calculated as the interest reported in the Census multiplied by the
percent of facility revenue from pesticides. Likewise, assets related to pesticides were calculated as assets
reported in the Census multiplied by the percent, of facility revenue from pesticides. EBIT divided by interest
provided the ICR; EBIT divided by assets gave the ROA.
30The firm analysis is analogous to the "other significant impact analysis" for the facility level. See Chapter
7 for further details.
4.30
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Lowest Quartile Values
The lowest quartile value for ROA was determined directly from the calculated baseline ROAs for all
pesticide manufacturing facilities. Determination of the lowest quartile value for the interest coverage ratio,
however, required a decision on where to place firms reporting a zero interest payment. A value of zero cannot
be used in the denominator of a ratio, so an assumption must be made regarding these cases for the ICR. The
analysis ranked facilities reporting positive EBIT and zero interest as having interest coverage superior to any
firm reporting a positive interest value. If EBIT was negative and the reported interest expense was zero, the
facility was assigned an EBITrinterest value of zero. In effect, such a facility was seen as being worse off than
a facility with positive EBIT and a positive interest expense, but better off than a facility with negative EBIT
and a positive interest expense. The EBIT: interest ratio marking the lowest quartile for pesticide manufacturing
facilities is 1.13; the lowest quartile ROA value is 0.04.
Post-compliance Ratios
The post-compliance ratios for each facility with compliance costs that was not predicted to have a
facility or product line closure were calculated as follows:
post-compliance EBIT =
baseline EBIT
minus compliance operating and maintenance costs
minus the change in variable production costs
plus the change in revenues
post-compliance interest expense =
baseline interest expense
plus the current interest component of compliance debt?1
post-compliance total assets =
baseline total assets
plus compliance capital and land costs
31Compliance debt is the debt the firm is expected to incur in order to finance projected capital and land
expenses associated with the proposed regulation.
4.31
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4.5 Facility Impacts
t
As discussed previously, a total of 90 pesticide manufacturing facilities produced one or more of the
270 PAIs, or classes of PAIs, potentially subject to regulation. The EPA is regulating 122 of these chemicals
and has projected compliance costs for the pesticide manufacturing facilities under two potential regulatory
options: a Treated Discharge Option and a Zero Discharge Option. The economic impacts of both these
options on the facilities were calculated separately for direct and indirect dischargers. Each discharge category
was further analyzed for two subcategories: organic pesticide chemicals manufacturing (Subcategory A) and
metallo-organic pesticide manufacturing (Subcategory B).
4.5.A Baseline
Fifteen of the 90 pesticide manufacturing facilities are expected to close in the baseline (see Table 4.3).
Three of these 15 facilities have, in fact, closed since 1986, and another 2 of the 15 facilities have closed one or
more product lines since that time. An additional 20 facilities are projected to close particular pesticide product
lines in the baseline. Two of these 20 facilities have closed entirely; 5 of the facilities closed a pesticide
product line, and 2 of the facilities have changed ownership since 1986.
Table 43
Baseline Closures
Plant Closures
Subcategory A/Subcategory B*
Product Line Closures
Subcategory A/Subcategory B*
15
15/0
20
18/3
* Five facilities produce PAIs in both Subcategories A and B. Two of these facilities have costs for both
Subcategories. Therefore, total closures may not equal the sum of Subcategory A and Subcategory B;
closures.
4.5.B Effects of Compliance with the Regulatory Options
The economic impacts of the two regulatory options evaluated by the EPA are discussed below.
Having reviewed the costs, impacts, and pollutant removals associated with the two options, the EPA is
proposing the Treated Discharge Option. The projected results of the Zero Discharge Option are shown for
comparison. Although the EPA is not proposing further regulations for Subcategory B chemicals, the costs and
impacts that would result from regulation of Subcategory B chemicals are shown below.32
32As discussed previously, the analysis of impacts of the regulatory options incorporates the effects of
facilities passing a portion of the compliance costs to their customers. An alternative method of analyzing
impacts would be to assume that pesticide manufacturers bear the entire burden of the cost increase in reduced
4.32
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Treated Discharge Option
Impacts of BAT Regulations on Direct Dischargers
Organic Pesticide Chemicals Manufacturing (Subcategory A)
Thirty-two direct discharging and zero discharging facilities producing Subcategory A chemicals are
expected to incur costs under this regulatory option (see Table 4.4).33 For manufacturers included in this
Subcategory, the incremental capital and annualized costs of complying with BAT limitations are expected to be
$14.9 million and $14.7 million, respectively. No facilities are projected to close due to compliance with BAT.
Two facilities are projected to close a product line as a result of the regulation. No facilities are expected to
experience other significant financial impacts short of facility or product line closure.
Metallo-Organic Pesticide Chemicals Manufacturing (Subcategory B)
Direct dischargers of Subcategory B chemicals are limited to zero discharge of process wastewater
pollutants under BPT. No additional options were considered and no new limitations are proposed for the
metallo-organic pesticide chemicals manufacturing Subcategory. There are therefore no associated costs or
economic impacts.
Impacts of PSES Regulations on Indirect Dischargers
Subcategory A
Twenty-seven indirect discharging facilities producing Subcategory A chemicals are expected to incur
costs under the Treated Discharge Option. For manufacturers included in this Subcategory, the incremental
capital and annualized costs of complying with PSES limitations are expected to be $9.4 million and $5.9
million, respectively. No facilities are projected to close due to compliance with PSES. One facility, or 3
percent of the facilities subject to regulation under this category, is projected to close a product line as a result
of the regulation. No facilities are expected to experience other significant financial impacts short of facility or
product line closure.
profits. EPA conducted a sensitivity analysis using this zero cost pass-through assumption. The results are
reported hi Appendix D. For the main analysis, however, the EPA presents impacts using the assumption of
partial cost pass-through, because the EPA believes that, in reality, pesticide manufacturing facilities will not
bear the entire costs of the regulation. The analysis of zero pass-through (i.e., manufacturers bear all
compliance costs) served. as a theoretical construct to limit the upper range of impacts of the regulation on
facilities.
33Impacts of zero discharge requirements are reported with impacts of direct discharge requirements. Zero
dischargers may be subject to monitoring costs if they have any process wastewater. Monitoring costs would be
imposed by the permitting authority (no separate monitoring requirements are contained in the proposed effluent
guidelines for pesticide manufacturers). These monitoring costs are included in the analysis to capture the full
cost to industry of controlling process wastewater pollutants.
4.33
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4.34
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Subcategory B
Five Subcategory B facilities would be expected to incur costs under PSES if the EPA regulated these
chemicals. The total expected capital costs would be $40,000, while the annualized costs would be $120,000.
No plant closures, product line closures, or other significant financial impacts would be expected to result from
these costs.
Zero Discharge Option
Impacts of BAT Regulations on Direct Dischargers
Subcategory A
Thirty-five direct discharging facilities producing Subcategory A chemicals would be expected to incur
costs under this regulatory option (see Table 4.4). For manufacturers included in this Subcategory, the
incremental capital and annualized costs of complying with BAT limitations is expected to be $1.1 million and
$4.8 billion, respectively. Sixteen facilities would be projected to close due to compliance with BAT. Three
additional facilities would be projected to close a product line as a result of the regulation. No facilities would
be expected to experience other significant financial impacts short of facility or product line closure.
Subcategory B
As discussed under the Treated Discharge Option, Subcategory B direct dischargers are already limited
to zero discharge of process wastewater pollutants under BPT. No additional options were considered and no
new limitations are proposed for the metallo-organic pesticide chemicals manufacturing Subcategory. There are
therefore no associated costs or economic impacts.
Impacts of PSES Regulations on Indirect Dischargers
Subcategory A
Thirty indirect discharging facilities producing Subcategory A chemicals would be expected to incur
costs under the Zero Discharge Option. For manufacturers included in this Subcategory, the incremental capital
and annualized costs of complying with PSES limitations is expected to be $1.1 million and $518.8 million,
respectively. Eleven facilities would be projected to close due to compliance with PSES. Three facilities under
this category would be projected to close a product line as a result of the regulation. No facilities would be
expected to experience other significant financial impacts short of facility or product line closure.
Subcategory B
Five Subcategory B facilities would be expected to incur costs under PSES. The total capital costs
would be projected to be $80,000, while the annualized costs would be $2.8 million. One facility is projected
to close as a result of the regulation/ An additional facility is expected to close a product line. No facilities
would be expected to experience otlier significant financial impacts.
4.35
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Chapter 4 References
Doane Marketing Research (1987). Annual Marketing Survey. St. Ixmis, Missouri.
DPRA, Inc. (1990). Agchemprice; Current U.S.A. Prices of Non-fertilizer Agricultural Chemicals. January.
Manhattan, KS.
U S Department of Agriculture (1985). t/.5. Demand for Food: A Complete System of Price and 7«cc^
Effects. By Kuo S. Huang, National Economics Division, Economic Research Service. Technical Bulletin
No. 1714.
U.S. Department of Commerce (1989, 1990, 1991). Bureau of the Census. Statistical Abstract of the United
States. Washington, D.C. January.
4.36
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Chapter 5: COMMUNITY IMPACT ANALYSIS
5.0
Introduction
This chapter evaluates community impacts resulting from both pesticide facility closures and other
significant reductions in pesticide active ingredient (PAI) production. Community impacts are measured by the
level of employment loss expected to correspond to decreased production resulting from compliance with the
proposed regulation.
The impacts corresponding to both the Treated Discharge Option and the Zero Discharge Option are
presented. For each option, impacts are shown separately for direct dischargers (including zero dischargers)
and indirect dischargers.1 For the Treated Discharge Option, only those impacts associated with Subcategory A
(Organic Pesticide Chemicals Manufacturing) chemicals are shown; no closures or other significant decreases in
production are expected for manufacturers of Subcategory B (Metallo-Organic Pesticides Chemicals
Manufacturing).2 For the Zero Discharge Option, impacts are shown for both Subcategory A and Subcategory
B chemicals.
5.1 Methodology
Community impacts are analyzed in two stages. The first stage analyzes the primary impact of facility
layoffs due to facility closures and other significant production reductions. If the primary employment losses
estimated in the first stage of the analysis are determined to be significant, the analysis is then taken to a second
stage that determines secondary impacts on the community employment level. Secondary impacts arise from
reduced demand for inputs to the affected facility, and reduced consumption due to losses in earnings.
Secondary impacts are assessed through multiplier analysis, which measures the extent to which employment
levels in other industries are affected by employment changes in a given industry. Secondary and primary
employment losses are summed to obtain the total impact on community employment levels resulting from
pesticide facility closures and other decreases in pesticide production.
'Impacts of zero discharge requirements are reported with impacts of direct discharge requirements. Zero
dischargers may be subject to monitoring costs if they have any process wastewater. Monitoring costs would be
imposed by the permitting authority (no separate monitoring requirements are contained in the proposed effluent
guidelines for pesticide manufacturers). These monitoring costs are included in the analysis to capture the full
cost to industry of controlling process wastewater pollutants.
2Direct discharges of Subcategory B chemicals are already limited to zero under the Best Practicable Control
Technology Currently Available (BPT) regulation. Best Available Technlogy Economically Available (BAT)
regulations are therefore not considered for Subcategory B chemicals under either the Treated Discharge Option
or the Zero Discharge Option.
5.1
-------
S.l.A Primary Impacts on Employment i
Primary impacts on employment are considered for facilities predicted to experience either a closure or
a decrease in in-scope PAI production of at least ten percent due to the regulation. All pesticide-related
employment at a facility is assumed to be lost in the case of facility closures. The percentage of employment
lost due to other significant reductions in production is assumed to equal the percentage of revenues lost.
Facility Closures
Employment loss resulting from a facility closure is assumed to equal the total annual pesticide-related
employment hours calculated from that facility's Census data.3 Total pesticide-related hours are calculated as
the sum of both pesticide-related production and non-production hours. Pesticide-related production hours are
obtained directly from the Census by adding pesticide manufacturing hours and pesticide formulating/packaging
hours. Pesticide-related non-production hours are estimated by computing the ratio of total non-production
hours to total production hours and multiplying the pesticide production hours by this ratio.4 These calculations
are shown below algebraically.
Total pesticide production employee hours (TPH) are computed as:
where:
MH = Annual employee hours spent in pesticide chemical manufacturing production; and
FH = Annual employee hours spent in pesticide formulating/packaging. >
Non-production employee hours related to pesticide production (TNH) are estimated as:
where:
N
P
TNH =TPH x —
P
Annual non-production employee hours spent at facility; and
Annual employee hours spent in all production at facility.
Total facility production hours (P), used in the above equation, are computed as:
Employment in the pesticide manufacturing industry tends to be seasonal. Facilities reported employee
hours for the months of January, May and November to account for this seasonally. "Annual hours" are
estimated by multiplying the average hours of the three months by 12.
'The inclusion of pesticide formulating/packaging hours is conservative, because facilities that discontinue
manufacture of certain PAIs could purchase the PAIs and continue to formulate/package them.
5.2
-------
where:
OPH =
Annual estimate of employee hours spent in other production.
Total pesticide-related employee hours lost due to a facility closure, i.e., the sum of pesticide-related
production hours and pesticide-related non-production hours, are converted to full time equivalents (FTE),
assuming that 2000 hours = 1 FTE.5
Other Significant Reductions in Production
Reductions in pesticide production that fall short of facility closure may also affect employment levels
at a facility. In order to capture these impacts, this analysis calculates employment loss for any facility that is
projected to have at least a 10 percent reduction hi revenues from in-scope PAIs due to the proposed regulation.
The percentage of in-scope employment that is lost is assumed to equal the percentage of in-scope revenue that
is lost.
Employee hours dedicated to in-scope pesticide work must be estimated because they are not reported
in the Census. The ratio of in-scope pesticide hours to total facility-wide hours is assumed to equal the ratio of
in-scope pesticide production volume to total facility-wide production volume. Facility-wide employee hours
and the ratio of in-scope pesticide production volume to total facility production volume are reported in the
Census.6 Hours related to production of in-scope pesticides are multiplied by the percentage loss of in-scope
revenues to estimate lost hours. Employee hours lost are again converted to full time equivalents (FTE),
assuming that 2000 hours = 1 FTE.
5.1.B Measuring Impact Significance
The significance of facility employment loss on the community is measured by its impact on the
community's level of employment as a whole. For purposes of this analysis, the community is defined as the
Metropolitan Statistical Area (MSA), in which the facility is located7. The MSA is assumed to represent the
labor market area within which residents could reasonably commute to work. If the facility is located in a
Primary Metropolitan Statistical Area (PMSA) within the MSA, then the PMSA population is used. If a facility
is not located within an MSA, then the community is defined as a county (or township, for eastern states). A
5Computed: (50 weeks/year)(40 hours/week) = 2000 hours/year.
ratio of in-scope pesticide production volume to total facility production volume, although not the
separate numerator and denominator, is reported in the Census.
7MSAs are defined by the U.S. Office of Management and Budget.
5.3
-------
decline in the community employment rate equal to or greater than one percent is considered significant. Data
necessary to determine the community impact from the employment loss include the community's population and
employment rate. The community population information used in this analysis is for 1986, as estimated by the
Bureau of the Census (1986). Due to inconsistencies in MSA and county-level employment data, state |
employment rates are used to represent community employment rates. State employment rates are based on
1986 data from the Bureau of Labor Statistics (1989).
5.1.C Secondary Impacts on Employment
As stated above, if primary employment losses are found to have a significant impact on a community,
then secondary effects on employment levels are assessed by multiplier analysis. Secondary effects arise from
(1) the reduction in demand for inputs by the affected facility, and (2) induced impacts attributable to reductions
hi consumption due to both primary and secondary losses in earnings. Multiplier analysis is used to accotmt for
these secondary effects, and provides a straightforward framework as long as the direct effects are small and a
number of other important limitations (e.g., constant returns to scale, fixed input ratios) hold. '
I
The multiplier used in this analysis is based on input/output tables developed by the Department of
Commerce, Bureau of Economic Analysis (BEA, 1986). The BEA multipliers are estimated via the Regional
Industrial Multiplier System developed by the Regional Economic Analysis Division of the BEA. The
multipliers reflect the total national change in the number of jobs given a change in the number of jobs for a
particular industry.8 In this analysis, the industry directly affected is Chemicals and Selected Chemical
Products.9 The multiplier reported by BEA for this industry is 8.3710. The change in total number of jobs is
computed by:
CTJ = 8.37 x CDCJ
where:
CTJ
CDCJ =
Change in total jobs; and
Change in direct chemical industry jobs (FTEs).
8"Jobs" include both full- and part-time positions.
'Multipliers based on direct employment changes are available at an aggregated industry level only.
lcThe use of this national multiplier may overstate the number of jobs affected within the community
because some of the inputs may be from sources outside the community or even outside the country. No
multipliers that differentiate among the locations of inputs sources are known to exist.
5.4 i
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5.2 Results
5.2.A Treated Discharge Option
Impact of Best Available Control Technology Economically Achievable (BAT) Regulations on
Direct Dischargers11
Under the Treated Discharge Option, no direct discharging facilities are expected to close, while two
facilities are expected to have a decline in in-scope revenues of 10 percent or greater. As shown hi Table 5.1,
total estimated employment loss is 31 FTEs, less than one percent of the total pesticide-related employment
figures reported by all PAI manufacturers (approximately 9,940 FTEs). The employment rates in the two
affected communities are expected to decline by less than one percent. Therefore, the projected employment
loss for direct dischargers under the Treated Discharge Option is considered insignificant.
Impact of Pretreatment Standards for Existing Sources (PSES) Regulations on Indirect
Dischargers
The proposed effluent guidelines under the Treated Discharge Option for direct dischargers are not
projected to result in any facility closures, while one facility is expected to experience a reduction in in-scope
pesticide revenues of at least ten percent. As indicated in Table 5.1, total expected employment loss is about 97
FTEs, approximately one percent of total pesticide-related employment reported in the industry. The
community employment levels are not projected to decline by more than one percent and, consequently, the
estimated reduction hi employment is not considered significant.
5.2.B Zero Discharge Option
Impact of BAT Regulations on Direct Dischargers
Employment losses were considered for 16 direct discharging facilities subject to closure and 3
additional facilities expected to experience a decline hi in-scope revenues of at least 10 percent. Under the Zero
Discharge Option (see Table 5.2), employment losses due to the primary effects of facility closures and reduced
production equal approximately 55 percent (5,461 FTEs) of total reported pesticide-related employment for PAI
manufacturers. Only one community is expected to be significantly impacted by the loss of employment. This
community, located hi the Southeast, is projected to lose 224 jobs within the pesticide industry and 1,649 jobs hi
other industries, representing 11 percent of the community's total employment base. Total expected
employment loss, from both primary and secondary effects, is predicted to be 7,110 FTEs for direct dischargers
under the Zero Discharge Option.
"Impacts of zero discharge requirements are discussed with direct discharge requirements.
5.5
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fable 5.
Community Impact
Employment Loss (FTEs)
Option
^Discharger Type
Direct1
Indirect
Total
Subcategory A2
FTE's Lost Due to Plant Closures
FTE's Lost Due to Reduced Production
FTE's Lost Due to Secondary Effects
Total Subcategory A FTE's Lost
0.0
31.0
0.0
31.0
0.0
96.8
0.0
96.8
0.0
127.8
0.0
127.8
1 Impacts of zero discharge requirements are reported with impacts of direct discharge
requirements. Zero dischargers may be subject to monitoring costs if they have any
process wastewater. Monitoring costs would be imposed by the permitting authority (no
separate monitoring requirements are contained in the proposed effluent guidelines for
pesticide manufacturers). These monitoring costs are included in the analysis to capture
the full cost to industry of controlling process wastewater pollutants.
2 Subcategory B is not shown, since no closures or other significant decreases in production
are projected for this subcategory under the Treated Discharge Option.
5.6
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Table 5,2
Community Impact — Zero Discharge Option
Employment Loss (ETEs)
Discharger Type
Direct1
Total
Subcategory A
FTE's Lost Due to Plant Closures
FTE's Lost Due to Reduced Production
FTE's Lost Due to Secondary Effects
Total Subcategory A FTE's Lost
5,289.2
171.8
1,649.3
7110.3
738.2
64.0
0.0
802.2
6,027.4
235.8
1,649.3
7,912.5
Subcategory B2
FTE's Lost Due to Plant Closures
FTE's Lost Due to Reduced Production
FTE's Lost Due to Secondary Effects
Total Subcategory B FTE's Lost
0.0
0.0
0.0
0.0
0.0
3.9
0.0
3.9
0.0
3.9
0.0
3.9
Subcategories A and B
FTE's Lost Due to Plant Closures
FTE's Lost Due to Reduced Production
FTE's Lost Due to Secondary Effects
Total Employment Loss
5,289.2
171.8
1,649.3
7110.3
738.2
67.9
0.0
806.1
6,027.4
239.7
1,649.3
7,916.4
1 Impacts of zero discharge requirements are reported with impacts of direct discharge
requirements. Zero dischargers may be subject to monitoring costs if they have any
process wastewater. Monitoring costs would be imposed by the permitting authority (no
separate monitoring requirements are contained in the proposed effluent guidelines for
pesticide manufacturers). These monitoring costs are included in the analysis to capture
the full cost to industry of controlling process wastewater pollutants.
2 Subcategory B is already limited to zero direct discharge under BPT.
5.7
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Impact of PSES Regulations on Indirect Dischargers
Organic Pesticide Chemicals Manufacturing (Subcategory A) .
Employment losses were considered for 11 facilities subject to closure and five facilities experiencing a
decline in in-scope revenues of at least 10 percent.12 Total expected employment loss among Subcategory A
indirect dischargers under the Zero Discharge Option is projected to be 802 FTEs, approximately eight percent
of total reported pesticide-related employment by PAI manufacturers (see Table 5.2). Community employment
levels did not show a significant change under the Zero Discharge Option for Subcategory A indirect
dischargers.
Metdtto-Organic Pesticide Chemicals Manufacturing (Subcategory B)
Employment losses for Subcategory B indirect dischargers under the Zero Discharge Option are
expected to total approximately 4 FTEs (less than one percent of the total reported pesticide-related
employment) by PAI manufacturers. This change stems from two facilities that are expected to experience a
decline in in-scope revenues of at least 10 percent. There are no projected closures under the Zero Discharge
Option for Subcategory B indirect dischargers. Given that the decline in the employment rates for both of the
communities affected is less than one percent, the impacts are considered insignificant.
In summary, total expected employment loss due to the Treated Discharge Option is only 128 FTEs.
Total employment losses expected under the Zero Discharge Option are projected to be nearly 62 times the
employment losses under the Treated Discharge Option. Under the Zero Discharge Option, one community is
expected to experience a significant decline in the employment rate (11 percent). This job loss may overstate
the community impact, however, since the use of a national multiplier cannot differentiate between input sources
within and outside this community. '
12One impacted facility did not report any employment data. According to the records of a follow-up call,
facility personnel indicated that employment information was unavailable in the format requested.
5.8
-------
Chapter 5 References
Bureau of the Census (1986), Current Population Reports: Population and Per Capita Income Estimates for
Counties and Incorporated Places, U.S. Department of Commerce.
Bureau of the Census (1988), Statistical Abstract of the United States, U.S. Department of Commerce.
Bureau of Economic Analysis (1986), Regional Multipliers, A User Handbook for the Regional Input-Output
Modelling System (RIMS II), U.S. Department of Commerce, May.
Bureau of Labor Statistics (1989), Handbook of Labor Statistics.
5.9
-------
-------
Chapter 6: FOREIGN TRADE ANALYSIS
6.0
Introduction
Pesticide active ingredients (PAIs) are traded in an international market, with producers and buyers
located worldwide. Changes in domestic PAI production due to the regulation of effluent from PAI
manufacturing facilities may therefore affect the balance of trade. This chapter estimates the extent to which the
two regulatory options for PAI manufacturers would affect the balance of trade. To measure the significance of
the expected changes in exports and imports, these changes are compared with current U.S. exports and imports
for the pesticide industry, and with total U.S. merchandise trade.
The impacts corresponding to both the Treated Discharge Option and the Zero Discharge Option are
presented. For each option, impacts are shown for direct dischargers (including zero dischargers) and indirect
dischargers.1 For the Treated Discharge Option, only those impacts associated with Subcategory A (Organic
Pesticide Chemicals Manufacturing) are shown; no closures or other significant decreases in production are
expected for Subcategory B (Metallo-Organic Pesticide Chemicals Manufacturing). For the Zero Discharge
Option, impacts are shown for both Subcategory A and Subcategory B chemicals.2 The proposed rule,
however corresponds to the Treated Discharge Option and does not include Subcategory B chemicals.
6.1 Methodology
Decreased production resulting from compliance with effluent guideline limitations may result in both
decreased U.S. exports and increased U.S. imports of PAIs.3 Exports may decrease as previously exported
products are no longer manufactured; imports may increase as domestic purchasers seek new sources of PAIs no
longer offered by a particular manufacturer. Changes in exports and imports are considered for facilities
'Impacts of zero discharge requirements are reported with impacts of direct discharge requirements. Zero
dischargers may be subject to monitoring costs if they have any process wastewater. Monitoring costs would be
imposed by the permitting authority (no separate monitoring requirements are contained in the proposed effluent
guidelines for pesticide manufacturers). These monitoring costs are included in the analysis to capture the full cost
to industry of controlling process wastewater pollutants.
2Direct discharges of Subcategory B chemicals are already limited to zero under Best Practicable Control
Technology Currently Available (BPT) regulation. For this reason, Best Available Technology Economically
Achievable (BAT) regulations are not considered for Subcategory B chemicals under either the Treated Discharge
Option or the Zero Discharge Option.
Environmental laws in other countries are changing, often reflecting the changes in U.S. environmental laws.
This analysis conservatively assumes, however, that current foreign environmental laws will remain in effect. As
a result of this assumption, effects of the regulation on foreign trade may be overstated.
6.1
-------
predicted to close under a regulatory option and for facilities predicted to have a decrease in in-scope PAI
production of at least ten percent due to regulation.
6.1. A Exports
Changes in exports are considered only for those facilities expected to incur compliance costs, and who
also indicated in the Census that they exported a portion of their production hi 1986. These changes are
calculated assuming that the foreign response to increased price matches the domestic response, i.e., foreign
demand elasticities equal domestic demand elasticities. The analysis assumes that none of the decreased I
production of exported PAIs is replaced by alternate U.S. products. This "worst case" assumption is very
conservative and is likely to overestimate the reduction in exports. If the impact on foreign trade is not
significant in this worst-case scenario, then more realistic scenarios would also indicate no significant impacts.
The methods of estimating changes hi PAI exports are discussed below for four categories of facilities. :
Separate methods were required, depending on whether the facility was projected to close and whether the
facility chose to provide PAI-specific data hi the Census.
Facility Closures with PAI-Specific Information
If a facility is projected to close and PAI-specific export percentages were reported hi the Census, the
loss hi exports is estimated as the product of the revenue from each PAI and the export percentage for that PAI,
summed over all PAIs produced.4 Algebraically, export revenue losses are computed as: i
AIX =
i=l
where:
ADC = Change hi export revenues for a facility;
AIV; = Facility revenues from PAI i; and
ABCP| = Percentage of PAI i production that is exported by the facility.5
The export data reported are expressed in percentage of volume. Because percentages of revenue are
unavailable, it is assumed that the percentage of revenues generated from exports is equal to the percentage of
volume exported.
sFor facilities projected to close, a full accounting of changes in exports would include changes hi exports of
formulated/packaged pesticides as well as PAIs. The single facility that reported PAI-specific data and is projected
to close, however, did not formulate/package PAIs hi 1986. For this reason, changes hi exports of PAIs alone are
considered hi this section.
6.2
-------
Facility Closures without PAI-Specific Information
Although the provision of PAI-specific export data in the Census was optional, all facilities were
required to provide the percentage of the facility's total 1986 production that was exported. If PAI-specific
information was not provided by the facility, then the percentage of exported PAI sales is assumed to equal the
percentage of exported facility-level production. Revenues from pesticides and pesticide contract work are
added to obtain total pesticide-related sales. The loss in export revenues is estimated by multiplying total
facility pesticide sales by the percentage of total production exported by a facility.6
Facilities with Reduced Demand and PAI-Specific Information
Facilities incurring compliance costs and remaining open may experience a decline in exports due to
decreased demand resulting from price increases. Changes in exports are considered only for those facilities
whose in-scope revenues are expected to decrease by at least ten percent due to the regulation.
The decrease in in-scope revenues for facilities with reduced demand is calculated on a cluster basis.
Production-based weighted averages of the PAI-specific export data are calculated for each cluster at each
affected facility. The decline in exports for each cluster is determined by multiplying the facility's decline in
cluster revenues by the facility's cluster export percentage. If a facility is expected to close a product line, the
percentage change in production for that product line is 100 percent. The total decline in a facility's exports
equals the sum of the decline in exports for all affected clusters in that facility.
Facilities with Reduced Demand and No PAI-Specific Information
As discussed above, if PAI-specific export data are unavailable, the facility-level export percentage is
used. The decline in a facility's exports is estimated by multiplying the decline in the facility's revenues by the
percent of the facility's total 1986 production that was exported.
6.1.B Imports
An analysis of changes in imports is performed for facilities projected to either close or lose at least ten
percent of in-scope pesticide revenues, and that also produce a PAI that was imported to the United States in
1986. Because changes in revenues are evaluated for each facility at the cluster level, the analysis of imports
also focuses on clusters. Production of each cluster of PAIs was classified as replaceable by imports if any PAI
within the cluster was imported in 1986.7 As a worst-case scenario, it is assumed that all lost revenue in
The facility-reported export data may not reflect actual exports for facilities that perform contract work, because
facilities may not know the trade status of such products.
'Import data from several sources were reviewed for this analysis. Sources include the Office of Pesticides
Programs (OPP), the Bureau of the Census, and the International Trade Commission. Data published by the Bureau
of the Census and the International Trade Commission were so highly aggregated that they were not useful for this
6.3
-------
clusters with imported PAIs (with the exception of revenue lost due to reduced exports) is replaced by imports.
This assumption is very conservative and is likely to overestimate the increase in imports. If this worst-case
scenario does not result in a significant impact on foreign trade, then neither would a more realistic scenario.
6.2
Results
6.2.A Treated Discharge Option
Impact of Best Available Technology Economically Achievable (BAT) Regulations on Direct
Dischargers8
Under the Treated Discharge Option, no direct discharging facilities are projected to close, and two
facilities are expected to have a decline hi in-scope revenues of ten percent or greater. Of the two facilities
affected, only one facility reported export data (non-PAI-specific). Using the methods outlined above, it is
estimated that exports from this facility could decline by about $114,000 due to the regulation (see Table 6.1).
The two direct discharging facilities expected to experience a decline hi in-scope revenues of tea
percent or greater under the Treated Discharge Option produce PAIs in five clusters. The PAI production hi
each of these clusters is replaceable by imports. In the worst-case scenario described above, imports are
expected to rise by $5.4 million.
The changes hi exports and imports expected to result from the BAT regulation are more meaningful
when compared to the trade balance of the pesticide industry and the total U.S. merchandise trade balance. In
1986, U.S. exports of pesticides exceeded imports of pesticides by $897 million (United Nations, 1986).
Considering all merchandise trade hi 1986, however, the U.S. had a negative net trade balance of $152 billion
(U.S. Department of Commerce, 1988). The change in pesticide trade due to the BAT regulation under the
Treated Discharge Option is minor (less than one percent) hi comparison to both total U.S. pesticide trade and
total U.S. merchandise trade.
Impacts of Pretreatment Standard for Existing Sources (PSES) Regulations on Indirect
Dischargers
Under the Treated Discharge Option, no indirect discharging facilities are projected to close, and only
one facility is expected to have a decline hi in-scope revenues of ten percent or greater. This facility reported
analysis. Details of the data review are contained hi the Administrative Record.
"Impacts of zero discharge requirements are reported with impacts of direct discharge requirements. Zero
dischargers may be subject to monitoring costs if they have any process wastewater. Monitoring costs would be
imposed by the permitting authority (no separate monitoring requirements are contained in the proposed effluent
guidelines for pesticide manufacturers). These monitoring costs are included hi the analysis to capture the full cost
to industry of controlling process wastewater pollutants.
6.4
-------
Table fcl
Foreign Trade Impact — Treated Discharge Option
(in $ thousands)
Decline in Pesticide Exports
Discharger Type
Direct1
Indirect
Total
Subcategory A
Due to Plant Closures
Due to Reduced Production
Total Subcategory A
0
114
114
0
5,477
5,477
0
5,591
5,591
Increase in Pesticide Imports
Discharger Type
Direct
Indirect
Total
Subcategory A
Due to Plant Closures
Due to Reduced Production
Total Subcategory A
0
5,408
5,408
0
10,632
10,632
0
16,040
16,040
Net Decline in Pesticide Trade Balance
Discharger Type
Direct
Indirect
Total
Subcategory A
Due to Plant Closures
Due to Reduced Production
Total Subcategory A
0
5,522
5,522
0
16,109
16,109
0
21,631
21,631
1 Impacts of zero discharge requirements are reported with impacts of direct discharge requirements.
Zero dischargers may be subject to monitoring costs if they have any process wastewater. Monitoring
costs would be imposed by the permitting authority (no separate monitoring requirements are contained
hi the proposed effluent guidelines for pesticide manufacturers). These monitoring costs are included
in the analysis to capture the full cost to industry of controlling process wastewater pollutants.
2 Subcategory B is not shown, since no closures or other significant decreases hi production are
projected for this Subcategory under the Treated Discharge Option.
6.5
-------
export data (non-PAI-specific). Using the methods outlined above, it is estimated that exports from this facility
could decline by about $5.5 million due to the regulation. :
1
The one indirect discharging facility expected to experience a decline in in-scope pesticide revenues of
ten percent or greater under the Treated Discharge Option produces PAIs in three clusters. The PAI production
in each of these clusters is replaceable by imports. In the worst-case scenario described above, imports are
expected to rise by $10.6 million. Even with the conservative assumptions incorporated in the analysis, PSES
regulations under the Treated Discharge Option are projected to reduce the U.S. pesticide trade balance from
$897 million to $886 million, slightly more than a one percent decline. The PSES regulation would increase the
total U.S. merchandise net imports by about one one-hundredth of one percent.
6.2.B Zero Discharge Option
Impact of BAT Regulations on Direct Dischargers i
Under the Zero Discharge Option, 16 direct discharging facilities are projected to close, and 3 facilities
are expected to have a decline in in-scope revenues of 10 percent or greater. Fourteen of these facilities
reported export data (only one facility reported PAI-specific data). Using the methods outlined above, exports
from these facilities are estimated to decline by about $529 million due to the regulation (see Table 6.2).
The 19 direct discharging facilities impacted under the Zero Discharge Option produce PAIs in 29
clusters. The PAI production in each of these clusters is replaceable by imports. In the worst-case scenario
described above, imports are expected to rise by $1.9 billion.
These dramatic impacts of the BAT regulation under the Zero Discharge Option would shift the U.S.
pesticide industry from a net export position to a net import position. The change in pesticide trade would
increase the total U.S. net merchandise imports by about two percent.
Impacts of PSES Regulations on Indirect Dischargers
Organic Pesticide Chemicals Manufacturing (Subcategory A)
Under the Zero Discharge Option, 11 Subcategory A indirect discharging facilities are projected to
close, and 5 facilities are expected to have a decline in in-scope revenues of 10 percent or greater. Ten of
these facilities reported export data (non-PAI-specific). Using the methods outlined above, exports from these
facilities are estimated to decline by about $59 million due to the proposed regulation. :
The 16 Subcategory A indirect dischargers impacted under the PSES regulations produce PAIs ;in 23
clusters. The PAI production in each of these clusters is replaceable by imports. In the worst-case scenario
described above, imports are expected to rise by $121 million. Based on the conservative assumptions '
incorporated in the analysis, PSES regulations applied to Subcategory A facilities under the Zero Discharge
6.6 !
-------
Table 6.2
Foreign Trade Impact — Zero Discharge Option
(in $ thousands)
Decline in Pesticide Exports
Discharger Time
Direct*
Indirect
Total
Subcategory A
Due to Plant Closures
Due to Reduced Production
Total__Subcategory A
Subcategory B2
Due to Plant Closures
Due to Reduced Production
Total Subcategory B
520,258
8,399
..5.?M5.7.
0
0
0
57,843
871
..58,71.1.
0
59
59
578,101
9,270
587..371
0
59
59
Increase in Pesticide Imports
Discharger Type
Direct
Indirect
Total
Subcategory A
Due to Plant Closures
Due to Reduced Production
Subcategory B2
Due to Plant Closures
Due to Reduced Production
Total Subcategory B
1,705,567
197,884
..1,.903,451
0
0
0
96,963
23,943
1,802,53
221,827
..120,906 2,024,35,
0
1,147
1,147
0
1,147
1,147
Net Decline in Pesticide Trade Balance
Discharger Type
Direct
Indirect
Total
Subcategory A
Due to Plant Closures
Due to Reduced Production
Total Subcategory A
Subcategory B2
Due to Plant Closures
Due to Reduced Production
Total Subcategory B
2,225,825
206,283
.2?432,108
0
0
0
154,806 2,380,63
24,814 231,097
..179,620 2,611,72.
0
1,206
1,206
0
1,206
1,206
Impacts of zero discharge requirements are reported with impacts of direct discharge requirements. Zero
dischargers may be subject to monitoring costs if they have any process wastewater. Monitoring costs
would be imposed by the permitting authority (no separate monitoring requirements are contained hi the
proposed effluent guidelines for pesticide manufacturers). These monitoring costs are included hi the
analysis to capture the full cost to industry of controlling process wastewater pollutants.
2 Subcategory B is already limited to zero direct discharge under BPT.
i.7
-------
Option are projected to reduce the U.S. pesticide trade balance from $897 million to $717 million, a 20 percent
decline. The PSES regulation would increase the total U.S. merchandise net imports by about one-tenth of one
percent. p
Metallo-Organic Pesticide Chemicals Manufacturing (Subcategory B)
Under the Zero Discharge Option, no Subcategory B indirect discharging facilities are projected to
close, and two facilities are expected to have a decline hi in-scope revenues of ten percent or greater. Only
one of these facilities reported export data (non-PAI-specific). Using the methods outlined above, it is estimated
that exports from these facilities will decline by about $59,000 due to the proposed regulation.
The two Subcategory B indirect dischargers impacted under the PSES regulations produce PAIs in four
clusters. The PAI production in each of these clusters is replaceable by imports. In the worst-case scenario
described above, imports are expected to rise by $1.1 million. Based on the conservative assumptions
incorporated in the analysis, PSES regulations applied to Subcategory B facilities under the Treated Discharge
Option are projected to have minimal impact on both the U.S. pesticide trade balance and total U.S. i
merchandise net imports.
In summary, neither BAT nor PSES regulations under the Treated Discharge Option have a substantial
impact on the U.S. pesticide trade balance or the U.S. total merchandise trade balance. Conversely, the impacts
of BAT and PSES regulations under the Zero Discharge Option could result in a $2.6 billion decline in the U.S.
pesticide trade balance, leading to a trade deficit of $1.7 billion within the U.S. pesticide manufacturing •
industry. The impacts under the Zero Discharge Option are less dramatic when compared to the total U.S.
merchandise trade balance. The $2.6 billion increase in net imports would increase the U.S. trade deficit by
approximately 1.7 percent.
6.8
-------
Chapter 6 References
United Nations (1986). Statistical Office. International Trade Statistics Yearbook. New York.
U.S. Department of Commerce (1988). Bureau of the Census. Statistical Abstract of the United States.
Washington, D.C. January.
6.9
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Chapter 7: FIRM IMPACT ANALYSIS
7.0
Introduction
The firm analysis evaluates the impact of regulatory compliance on firms owning facilities subject to a
pesticide active ingredient (PAI) manufacturing effluent guideline. Due to the differences between firms and
facilities, the firm analysis may capture impacts not included in the facility analysis. For example, some firms
may be in too weak a financial condition to undertake the treatment investment required for regulatory
compliance, even though the investment may appear to be financially desirable at the facility level. Such
circumstances may occur if a firm owns more than one pesticide manufacturing facility that would be subject to
regulation; in that case, analysis at the individual facility level will not address the total impact of the financing
requirements on the firm.1 The regulatory action may therefore result in firms deciding to curtail pesticide
manufacturing activities at a facility, or a firm may restructure its finances or sell assets to allow the completion
of treatment investments. Analysis of the economic impact of regulatory options at the firm level is therefore
an important component of the EIA.
The firm impact analysis is organized into three sections. The first section reviews the concepts used
to drive the financial analysis. The second section describes the methodology that employs these concepts. This
section also highlights some analytic difficulties encountered due to data limitations, and the steps required to
overcome them. The third part of the discussion presents the results of the firm analysis.
7.1 Analytic Approach
A firm's ability to comply with regulatory requirements is assessed in two stages:
(1) The baseline analysis identifies firms whose financial condition, independent of regulatory
action, is sufficiently weak to contraindicate the implementation of a treatment program
required by a regulation. Such firms would be at risk of financial failure even without
regulatory costs. For this reason, firms that fail the baseline analysis are excluded from the
post-compliance analysis.
'Conversely, a firm may be able to reduce its cost of compliance by consolidating the manufacturing activities
and, therefore, the treatment investments required of several facilities. This would mitigate the projected impact
predicted by a facility-level analysis. While such cases are plausible, it is beyond the scope of this analysis to
identify them.
7.1
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(2) The post-compliance analysis identifies those firms, otherwise financially sound, whose ,
financial viability may be impaired by regulatory compliance. Such firms would be weakened
by the financing burden and additional operating expenses of a treatment program. These
firms are characterized as likely to be significantly affected by a regulatory option.
The firm financial impact analysis is conducted from the perspective of creditors and equity investors
who would be the sources of capital to finance a firm's purchase of treatment systems.2 To attract the
financing for a treatment program, a firm must demonstrate financial strength both before and, on a projected
basis, after the treatment program (baseline and post-compliance, respectively). The financial analysis presented
in this report simulates that performed by investors and creditors in deciding whether to finance the installation
of a pollution prevention or wastewater treatment system. Two considerations that influence this decision are
(1) the financial performance of the firm (particularly in relation to its competitors) and (2) the expected ability
of the firm to manage its financial commitments without risk of financial failure. These considerations, ;
discussed below, form the basis of this analysis. '
\
7.1.A Firm Financial Performance :
If a firm's performance is weaker than that of its competitors, the firm may not be able to provide the
expected investment return to its creditors and investors. Unless significant improvement in performance is
likely, investors and creditors will generally avoid providing financing to such firms. Alternatively, investors
and creditors may seek higher returns (in the form of higher interest rates or higher required returns on equity)
to compensate for the additional risk associated with the capital they provide. The higher cost of capital may in
turn decrease the likelihood that such firms will invest in the treatment options required for compliance with an
effluent guideline. :
The measure of financial performance used in the firm analysis is pre-tax return on assets (pre-tax
ROA, hereinafter referred to as "ROA"), computed as the ratio of earnings before interest and taxes (EBIT) to
assets:3
^or a further discussion of debt and equity financing, see Section 4.2. A.
3ROA is also known as "return on investment."
7.2
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ROA =
Assets
ROA is a measure of the profitability of a firm's capital assets, independent of the effects of taxes and
financial structure. It is perhaps the single most comprehensive measure of a firm's financial performance.4
ROA provides information about the quality of management, the competitive position of a firm within its
industry, and the economic condition of the industry in which the firm competes. In addition, ROA
incorporates information about a firm's operating margin and asset management capability: the ratio of pre-tax
income to sales (operating margin), multiplied by the ratio of sales to assets (asset turnover), equals ROA. If a
firm cannot sustain a competitive ROA, on both a baseline and post-compliance basis, it will probably have
difficulty financing the pollution control investment. This is true regardless of whether financing is to be
obtained as debt or equity.
Illustrating typical ROA values from 1982 to 1990, the median ROA for the U.S. industrial chemical
industry (as represented by SIC codes 2861, 2865, and 2869) ranged from 10.1 percent to 18.9 percent (Robert
Morris Associates [RMA], 1991).5 At the 75 percent quartile, ROA ranged from 14.5 percent to 23.6 percent
over this same period (i.e., firms at this level were more profitable than 75 percent of those in the industry).
At the 25 percent quartile, which is indicative of weak performance, ROA ranged from 7.2 percent to 13.4
percent. The computation of ROA, and the interpretation of the computed values as the basis for determining
financial viability, are discussed in Section 7.2.
7.1.B Ability To Manage Financial Commitments
The second general area of concern to creditors and investors is the extent to which the firm can be
expected to manage its financial burdens without risk of financial failure. In particular, if a firm's operating
cash flow does not comfortably exceed its contractual payment obligations (e.g., interest and lease obligations),
the firm is seen as vulnerable to a decline in sales or increase in costs.6 Either scenario may: (1) sharply
reduce or eliminate returns to the equity owners of the firm; and/or (2) prevent the firm from meeting its
contractual payment obligations. In the first case, earnings might fall or become negative, with a consequent
reduction or elimination of dividends and/or reinvested earnings. The market value of the firm's equity is also
4For credit analysis in particular, pre-tax ROA is important because interest payments are made from pre-tax
income.
5RMA provides financial statistics based on bank credit reports from public-reporting and non-public-reporting
firms in a variety of industries. The RMA industry group that corresponds best to the pesticides manufacturing
industry is the "industrial chemicals" industry, which includes SIC codes 2861, 2865, and 2869. The ROA values
are calculated from RMA's reported "operating profit/sales" ratio and "sales/asset" ratio.
6For this discussion, a firm's operating cash flow is considered to be revenues minus costs, with the exception
of interest, lease expense and depreciation.
7.3
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likely to fall, causing a capital loss to investors. In the second case, failure to make contractual credit payments
will expose the firm, and its equity owners to the risk of bankruptcy, forced liquidation of assets, and probable
loss of the entire equity value of the firm.
The ability to manage financial commitments is expressed by the ratio of EBIT to interest obligations,
or the interest coverage ratio (ICR):7 ;
EBIT
Interest
Weakness hi these characteristics of firm financial condition and performance, as would be indicated by
a low ICR, indicates vulnerability of the firm to financial failure and difficulty hi obtaining financing for
treatment investments. From 1982 to 1990, the median value of interest coverage for industrial chemicals firms
(as defined by RMA, see footnote 5) ranged from 2.3 to 5.6. Over the same period, the 75th percentile value
ranged from 7.2 to 16.3, and the 25th percentile value ranged from 1.0 to 2.2 (RMA, 1991).
7.2 Analytic Procedure
As described hi the preceding section, the firm analysis is based on two financial measures: ROA and
ICR. Firm-level data required to calculate these financial measures were obtained from public sources for
domestic firms subject to public reporting requirements. In contrast, data for foreign-owned or closely-held
domestic firms were not publicly available.8 The only firm-specific data available for these firms were gross
revenues obtained from the Census. Where firm-level data were not publicly available, industry norms of
financial condition and performance were used as the basis for firm analysis. For example, baseline financial
measures were developed using median values for the industrial chemicals business sector reported by RMA.
As a result of these data limitations, the analysis for foreign-owned and closely-held domestic firms is less
precise than for public-reporting domestic firms.
For the Treated Discharge Option, detailed financial data were available for 20 of the 44 firms \
expected to incur costs; the remaining 24 firms, closely-held or foreign-owned entities, required the use of data
obtained from RMA. For the Zero Discharge Option, detailed financial data were available for 22 of the 48
firms expected to incur costs; analysis for the remaining 26 firms is based on the industry norms obtained from
RMA.
7The ICR is also known as "tunes interest earned." r
8Closely-held firms are owned by only a few individuals. They do not trade securities publicly and are therefore
not subject to public-reporting requirements under the rules of the Securities and Exchange Commission (SEC).
7.4
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As mentioned above, ROA is calculated by dividing EBIT by total assets. Data used to calculate ROA
for public-reporting firms were obtained from income statement compilations in Compustat for 1986.9 For non-
public-reporting firms, firm-level revenues were obtained from the Census. Finn-level values of assets, and
EBIT for non-public-reporting firms, were estimated from firm-specific revenues and RMA data (e.g., median
values for assets and EBIT as a percentage of revenues in 1986).
Dividing EBIT by interest expense yields the ICR. For public-reporting firms, data for this calculation
were obtained from Compustat. For non-public-reporting firms, the data sources and calculation procedures are
the same as those outlined for ROA. That is, firm-specific interest and EBIT were calculated from firm-specific
revenues from the Census and the RMA-reported median values for both interest, and EBIT as a percentage of
revenues.
Baseline EBIT, baseline total assets, and baseline interest expense are the components used to
determine ROA and ICR. The data sources and calculations used in this analysis differ depending on whether
or not the required data are publicly available. The calculation procedure for public-reporting firms and non-
public-reporting firms are therefore presented separately.
Computing Baseline Measures for Public-Reporting Firms
Baseline data for public-reporting firms are taken from Compustat. The three components of the two
financial ratios are described below:
9Compustat, a data base, provides financial information from SEC 10-K filings. The 10-K document is the form
in which public-reporting firms are required to file detailed financial information annually with the SEC. A 10-K
document contains information similar to that contained in an annual report but with additional detail.
7.5
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PUBLIC REPORTING FWMS
Baseline EBIT
equals Operating Income (operating revenues minus all production and operating costs, selling expenses,
and general and administrative expense; but before taxes, interest and depreciation)
minus Depreciation and Amortization (non-cash, cost items recognized as a charge against income and
meant to reflect the consumption of wasting assets)
minus Losses from discontinued operations
plus Nonoperating Income.
Baseline Total Assets
equals Total Current Assets
plus Net facility, property, and equipment
plus "Other" assets.
Baseline Interest Expense
Taken directly from Compustat, which lists interest expense as a single line item.
Computing Baseline Measures for Non-Public-Reporting Firms
Baseline financial measures for non-public-reporting firms required firm-level values to be estimated on
the basis of: (1) firm-specific revenue information obtained in the Census; and (2) industry averages obtained
from RMA's 1991 Annual Statement Studies for the industrial chemicals business sector and Compustat. All
values were for the year 1986. The components of baseline financial ratios for non-public-reporting firms were
estimated in the following manner: ,
7.6
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Baseline EBTT
Estimated EB1T = Firm Revenues x
Operating Profit]
Revenue
EBTT
Operating Profit\COMPUSTAT
Estimated EDIT = Firm Revenues x 0.058 x 1.18 = Firm Revenues x 0.068
Firm revenues were taken from the responses of individual firms to the Census. RMA, which did not
provide an EBIT/revenue ratio directly, gave an industry median operating profit/revenue ratio of 0.058 for
1986. The estimated average EBIT/revenue ratio was determined by increasing the RMA operating
profit/revenue ratio by the percentage amount by which EBIT exceeded operating profit for the public-
reporting pesticides manufacturing firms included in the analysis. Based on Compustat data for the public-
reporting firms in the analysis, EBIT was found to be 18 percent higher on average than operating profit.
For the analysis of non-public-reporting firms, an EBIT/revenue ratio of 0.068 (i.e., 1.18 x 0.058) was
multiplied by firm-level revenue data to calculate firm-level EBIT. To summarize, for each $100 million in
revenues, a non-public-reporting firm was assumed to have EBIT of $6.8 million.
Baseline Total Assets
Calculated using the median RMA revenue/assets ratio of 2.0 to 1. A firm with $100 million in revenues
was therefore assumed to have $50 million in assets.
Baseline Interest Expense
Calculated from the median RMA value of the EBIT/interest ratio, 3.0 to 1. Assuming that the estimated
EBIT/revenue ratio for non-public-reporting pesticides firms is 0.068, an EBIT/interest ratio of 3.0 indicates
that interest expense averages 2.3 percent of revenue for RMA firms (i.e., 0.068/3.0 = 0.0227 or
approximately 2.3 percent). This value was multiplied by firm-level revenue data taken from the Census to
estimate baseline interest expense for all non-public-reporting firms. To summarize, for each $100 million
in firm-level revenues, annual interest expense was estimated at $2.3 million.
Because the baseline ratio values for all of the non-public-reporting firms in the analysis were
calculated using median RMA values, they are the same.10 Specifically, the estimated ROA is 13.6 percent
and the ICR is 2.96. Although these values are the same in the baseline analysis for all non-public-reporting
10If firm-level financial data were available for the non-public-reporting firms, the baseline ratio values could
be estimated more accurately.
7.7
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firms, they differ across firms in the post-compliance analysis. This is due to differences in the cost of !
compliance for facilities, as well as to differences in the numerators and denominators of the baseline ICR and
ROA ratios (although not the ratios themselves) among the firms.
Evaluating Baseline Performance Measures
To evaluate the baseline viability of the firms analyzed, the firm-specific values of baseline financial
performance were compared against the lowest quartile (i.e., 25th percentile) value in 1986 for the financial
performance measures as reported by RMA for the industrial chemicals business sector. The lowest quartile
value for the ICR was 1.1; the lowest quartile for ROA was 8.8. Those firms for which the value of either the
ROA or the ICR was less than the first quartile value from RMA were judged to be "vulnerable" to financial
failure, independent of the application of a pesticides effluent guideline. Because both measures are judged to
be critically important to financial success and the ability to attract capital, failure with regard to either measure
alone was deemed adequate for the finding of "vulnerability" (see Table 7.1). Because the ratio values for non-
public-reporting firms were based on the RMA median values rather than firm-specific data, none of the non-
public-reporting firms could be judged to be vulnerable hi the baseline analysis.
Two points addressing the methodology's limitations and interpretation should be considered:
(1) The 25th percentile value is an arbitrary one for defining poor financial performance and
condition. This approach assumes that the weakest one-fourth of firms in an industry are
automatically in poor financial condition and at risk of financial failure. By definition, such
firms are in poorer condition than 75 percent of their competitors. In spite of this, some and
possibly all firms in the lowest quartile might still be in good financial condition, particularly
during periods of stronger economic performance. Alternatively, during a period of weaker
i
economic performance, more than 25 percent of the firms in an industry might be in poor
condition and at risk of failure. Although the 25th percentile values can provide insight into a
firm's ability (or lack thereof) to manage the financial requirements of regulatory compliance,
such an analytic procedure is imperfect.
(2) Using the 25th percentile values from RMA does not mean that 25 percent of the firms in this
EIA will be judged to be in poor financial condition. The firms in the RMA sample on which
the percentiles were calculated include those in the industrial chemicals business as a whole.
The PAI manufacturing firms analyzed in this study are therefore a subset of the RMA sample.
7.8
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Table 74t Beterminatfon of Firm-level financial Viability
ROA
Highest Quartile
Third Quartile
Second Quartile
Lowest Quartile
Interest Coverage Ratio :
Lowest Quartile Median Highest Quartiles
Vulnerable
Vulnerable
Vulnerable
Vulnerable
Vulnerable
Vulnerable
Vulnerable
Note: Baseline firms in the indicated quadrants are labeled "vulnerable. " In the post-compliance analysis,
firms that move to these quadrants become vulnerable due to compliance costs and are said to sustain a
"significant impact. "
The post-compliance analysis is undertaken only for those firms that were not found to be "vulnerable"
to financial failure in the baseline analysis. La the post-compliance analysis, if either the re-computed ROA or
ICR for a firm was found to fall below the RMA first quartile value, then that firm was judged to be
"vulnerable" to financial failure as the result of regulatory action, and was said to sustain a "significant impact"
(see Table 7.1).
To recalculate ROA and ICR, the three baseline components (i.e., EBIT, total assets, and interest
expense) were adjusted to reflect compliance costs estimated at the facility level. In the facility analysis,
compliance costs were estimated in three categories: capital costs (facility and equipment), land costs, and
annual operating and maintenance costs.11 In the firm analysis, these values were summed over the facilities
owned by each firm and used to adjust the baseline components as shown below (see also Table 7.2 for the
mathematical formulation of the analysis):
"Discharge costs (e.g., the cost of sludge disposal) and monitoring costs are included within the operating and
maintenance cost category.
7.9
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Tabte 7,2: Calculation of UntirLeyel Financial Measures in Post-Compliance Analysis
Firm Financial Performance (ROA)
Baseline ROA =
EBIT
Total Assets
Post-Compliance ROA
EBIT -
A? * —
- °2 '
H [(A^ *
^2) - (Pi
* A#)]
Total Assets •*• c
where:
EBIT = Baseline earnings before interest and taxes
o, = Baseline operating and maintenance expenses
o2 = Compliance operating and maintenance expenses
Aq = Change in production quantity due to elasticity (qt - qj
q, = Baseline production quantity
q2 = Post-compliance production quantity
Ap = Change hi price due to elasticity (pt - p^)
p, = Baseline unit price
p2 = Post-compliance unit price
c = Cost of compliance capital equipment and associated land requirements
Ability to Manage Financial Commitments (ICR)
Baseline ICR =
EBIT
Interest Expense
EBIT -
Post-Compliance ICR =
+ [(A/J * qz) - (PI * A?)]
Interest Expense + i
where:
ICR = Interest Coverage Ratio ;
i = Average interest payment on debt for capital and land, assuming 10-year repayment,
where:
Average Annual
Interest Payment ~
(d * c) * 0.1095
1 - (1 + 0.1095)
-10
10
d = Percent of compliance capital equipment and land assumed to be financed by debt
d * c = Debt financing required for compliance capital equipment and associated land
12For firms with multiple plants, compliance costs and production quantities are summed. In addition, the
average price (baseline and post-compliance) is weighted according to each plant's production quantity. :
7.10
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Post-Compliance EBIT
equals Baseline EBIT
minus Compliance operating and maintenance costs (summed over facilities)
minus the change in variable production costs (assumed to decrease by the same percentage as
production decreases for each facility)
plus Change in revenues (based on price elasticity response and summed over facilities)
Post-Compliance Total Assets
equals Baseline Total Assets
plus Compliance capital and land costs (summed over facilities)
Post-Compliance Interest Expense
equals Baseline interest expense
plus Annual interest expense for the debt component of compliance capital and land requirements
(summed over facilities)
The calculation of these values and the subsequent evaluation of post-compliance firm financial viability
were based on several secondary financial assumptions. These assumptions are outlined below:
• The percentages of the investment that a firm is assumed to finance through equity (e/a) and
debt (d/a) are assumed to match the firm's historical mix of equity and debt investment. The
values of these variables for each firm are obtained from one of two sources. For each
domestic public-reporting firm, the mix of debt and equity is obtained from Standard and
Poor's Compustat service for that firm in 1986. For all firms not included in the Compustat
data base, the mixture of debt and equity financing was assumed to match the 1986 median
mixture of debt and equity financing for the "industrial chemical industry" as calculated from
RMA's Annual Statement Studies. The calculated values taken from the Annual Statement
Studies are 40.5 percent equity financing and 59.5 percent debt financing.
• To be consistent with the facility analysis (in which capital equipment is assumed to have a ten-
year useful life), a ten-year loan period was assumed for the debt used to finance compliance
capital and land outlays. To estimate a "steady state" interest payment burden on the firm, debt
is assumed to be repaid on the basis of a constant annual payment amortization schedule over
the ten-year period. This average annual interest payment is the value used for additional
interest expense, and is used to calculate both post-compliance interest expense and the ICR.
7.11
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The interest charged on compliance-related debt is assumed to equal the average interest rate,
10.95 percent, for AA-rated industrial debt with 10 years to maturity, over the period 1981-
1990, as reported by Salomon Brothers' An Analytical Record of Yields and Yield Spreads
(U.S. Department of Commerce, 1990 and 1991).13 To convert this value to a real (i.e.,'
inflation-free) rate, the rate was discounted on the basis of the average annual growth in the
Consumer Price Index (CPI-U) for the period 1981-1990 (4.74 percent), resulting in a real
interest rate of 5.93 percent (Survey of Current Business, 1991).14
7.3
Results
Analyses of baseline and post-compliance financial viability were undertaken for those firms projected
to incur costs as the result of regulatory action. The findings from this analysis are presented below, first for
the baseline and then for the two regulatory options analyzed: the Treated Discharge Option and Zero
Discharge Option. '.
i
7.3.A Baseline Analysis
Forty-eight firms were projected to incur compliance costs under at least one of the two regulatory
options. In the baseline analysis, only one of these firms had an ROA below the first RMA quartile value.
This firm was also the only one whose ICR fell hi the lowest RMA quartile. Because this firm was found to be
"vulnerable" to financial failure independent of regulatory action, it was excluded from the post-compliance
analysis.
7.3.B Post-Compliance Analysis: Treated Discharge Option
Under the Treated Discharge Option, compliance costs were projected for 44 pesticides manufacturing
firms, one of which was found to be vulnerable to financial failure in the baseline analysis. The post-
compliance analysis was therefore performed for only the remaining 43 firms. Three of these firms had both
"Interest rate information reported by individual facilities in the Census was not used for this analysis due to
difficulties of interpreting the reported values. For example, a number of respondents reported that funds for capital
outlays were obtained from a parent firm at zero percent. This reporting reflects internal accounting conventions
but does not accurately represent the interest cost borne by the firm for debt financing. Other firms indicated that
interest costs were tied to the prime rate (e.g., prime rate or "prime rate plus one"). Such interest terms would
generally apply to a working capital credit line or other short-term credit instrument. However, the short-term
liability would usually be replaced by longer-term debt to match the expected life of the capital asset being financed.
The interest rates on longer-term debt are usually higher than short-term credit rates, so short-term rates may
understate potential interest costs.
"The interest on debt, the inflation rate, and the mix of debt and equity assumed in the firm-level analysis all
match the assumptions in Chapter 4 (the facility-level analysis). An assumption regarding the cost of equity is not
required in the firm-level analysis since it is not an input to the calculation of post-compliance EBIT, interest, or
assets. :
7.12
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ROA and ICR in the lowest RMA quartile in the post-compliance analysis, and were therefore said to incur
significant financial impacts.
7.3.C Post-Compliance Analysis: Zero Discharge Option
Under the Zero Discharge Option, compliance costs were projected for 48 pesticides manufacturing
firms. Again, one firm, found to be vulnerable to financial failure in the baseline analysis, was excluded from
the post-compliance analysis. On a post-compliance basis, fourteen of the remaining 47 firms shifted into the
lowest RMA quartile for both ROA and ICR. The finding of a substantially greater firm impact under the Zero
Discharge Option reflects the much higher level of compliance costs estimated for this option in comparison to
those estimated for the Treated Discharge Option.
7.13
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Chapter 7 References
Robert Morris Associates (1991). Annual Statement Studies. Philadelphia, PA.
U.S. Department of Commerce (1990, 1991). Bureau of the Census, Statistical Abstract of the United States,
An Analytical Record of Yields and Yield Spreads.
U.S. Department of Commerce (1991). Bureau of Economic Analysis, Survey of Current Business.
Washington, D.C.
7.14
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Chapter 8: SMALL BUSINESS IMPACTS
8.0
Introduction
This chapter considers the expected effects of the proposed effluent limitations guidelines and standards
for the pesticide manufacturing industry on small businesses. The Regulatory Flexibility Act (RFA) (Public
Law 96-354) requires the Environmental Protection Agency to determine if a proposed regulation is likely to
have a significant impact on a substantial number of small entities. If such an impact is expected to be
disproportionately large compared to that on larger entities, then alternative regulatory methods to mitigate or
eliminate economic impacts on small businesses are examined.
8.1 Methodology
This analysis proceeded in three stages. The first stage of the analysis considers whether the regulatory
options are likely to have a significant impact on a substantial number of small entities. At the outset, the term
"small entity" was defined. The first stage of this analysis used the threshold for small businesses established
by the Small Business Administration (SBA). The SBA thresholds define small businesses based on revenue
and/or employment at firms (including all affiliates and divisions) for each SIC group. Pesticide manufacturers
are classified in SIC code 28694 (pesticide and other organic agricultural chemicals, composed of active
ingredients used to formulate pesticides). The SBA size threshold for SIC 28694, given in terms of employment
only, is defined as firms employing fewer than 1,000 people. Because firm employment data were not collected
in the Census, these data were taken from Dun and Bradstreet's Million Dollar Directory. Firms meeting the
SBA definition of small entities were then analyzed for the likelihood of sustaining any significant impacts
resulting from regulatory compliance (e.g., facility closure, product line closure, or "other significant impact"
as defined in Chapter 4). If such an impact on a substantial number of small entities is indicated by the results
of the first analytical stage, then the analysis proceeds to the second stage.
The second stage of the analysis examined whether the impacts on small businesses would be
disproportionately large. In contrast to the first stage of the analysis, which defined an entity as equivalent to a
firm, two additional definitions of an entity were considered: the facility (including pesticide- and non-pesticide-
related activities) and the pesticide-related portion of the facility (including activities related to the manufacturing
8.1
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of both in-scope and out-of-scope pesticide active ingredients [PAIs], pesticide
formulating/packaging/repackaging, and pesticide contract work or tolling).1 The second stage of the analysis
therefore considered five measures of entity size:
• firm revenues
• total facility revenues
• total facility employment
• pesticide-related facility revenues
• pesticide-related facility employment ;
To examine whether the economic impacts of the proposed guidelines were expected to fall
disproportionately on small businesses, the relationships between financial impacts, (e.g., facility and product
line closures), and the five measures of entity size were examined using two analytical methods.2 First,
impacts vs. measures of entity size were plotted to provide a visual understanding of the relationship between
the two variables. Second, a series of logistic regressions was performed to test the hypothesis that an entity is
less likely to suffer adverse impacts commensurate with an increase in size.3 For both the plotting and j
regression analyses, impacts were translated into binary variables. Entities expected to be impacted as a result
of the regulation were assigned a value of 0, while entities without impacts were assigned a value of 1. The
relationship between impacts and entity size was examined separately for direct and indirect dischargers.4 ?
'The RFA states that the promulgating agency has the discretion to establish a new definition of a small entity
that it considers more appropriate for conducting a regulatory flexibility analysis if it is determined that the SBA
criteria are not suitable. Although the EPA agrees that the firm level is the appropriate one at which to examine
small business impacts in this industry, this analysis also considers other definitions of a small entity for two
reasons. First, because earlier chapters evaluated the potential impacts of the regulatory options at the pesticide-
related level of the facility, small business impacts were also examined at this level. Second, assessing impacts at
the firm level based solely on employment would draw inconclusive results because firm employment data were not
publicly available for all firms represented in the Census. The entire facility was also examined in this analysis as
a mid-point between the firm and the pesticide-related portion of the facility.
2No "other significant impacts" (as defined in Chapter 4) were projected to occur under either the Treated
Discharge or Zero Discharge Options. "Other significant impacts" are therefore not included hi this analysis.
3In a linear regression, the response values are unbounded. In contrast, response values hi logistic regression
are bounded by 0 and 1. Given that the dependent variables used in the analysis are binary, logistic regression was
used hi the second stage of this analysis.
"Zero dischargers, deep well injectors, and off-site incinerators were included with direct dischargers.;
5The relationship between impacts and entity size was also analyzed combining the direct and indirect
dischargers. The results of this analysis are shown hi Appendix E. I
8.2
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For the plotting analysis, each of the five measures of entity size were plotted against the two measures
of impacts to determine whether a size (threshold) exists above which impacts are stable, i.e., the
facility/product line(s) remain open. If any of the plots resemble a discontinuous step function, as illustrated in
Figure 8.1, this would indicate that small entities would be impacted disproportionately if the regulation was
applied uniformly.
The regressions examined the probability that a facility/product line would remain open as a function of
entity size, using the following specification:
1+e
where:
the probability that an entity's facility/product line(s) will remain open for a given entity size
= 1) =
Yj = decision to close facility/product line(s); l=open, 0=close
e = base of natural logarithms (2.71828)
X£ = measure of entity size; and
Bi = the coefficient estimate for Xj.
Altogether, ten regression analyses were performed, each using one of the five measures of entity size
as the independent variable and one of the two potential impacts (facility closure and product line closure) as the
dependent variable.6 7 Coefficient estimates from the regression models that were positive and statistically
different from zero would indicate that as entity size increases, the probability that a facility/product line will
remain open also increases. This case would support the argument that a disproportionate number of small
facilities would suffer adverse economic impacts if the regulation was applied uniformly.
6A11 of the five independent variables incorporated in the ten different regression models are expected to be
highly correlated because they all measure the same influence: entity size. For this reason, they were not examined
together within the context of one regression model.
7A total of 30 plots resulted from the analysis, since the 10 regressions were examined for 3 discharge
categories: direct dischargers, indirect dischargers, and all dischargers combined.
8.3
-------
Figure 8,1
Discontinuous Step Function
IMPACT
Open 1
Close 0
XX X
XXX XXX X X XXX XX X XXX X
XX X XX XXX XXX X XXX X XXX >
X XX
THRESHOLD SIZE
8.4
-------
8.2 Results
8.2.A Treated Discharge Option
Impact of Best Available Control Technology Economically Achievable (BAT) Regulations on
Direct Dischargers
Under the Treated Discharge Option, no facility closures are projected for direct dischargers and two
facilities are expected to close product lines. Employment data were available for both of the firms owning
facilities expected to close product lines. Both of these firms are considered small based on the SBA size
standard. Because two firms do not constitute a "substantial number of small entities," no further analysis of
direct dischargers under the Treated Discharge Option was required.
Impact of Pretreatment Standards for Existing Sources (PSES) Regulations on Indirect
Dischargers
Under the Treated Discharge Option, no indirect discharging facilities are expected to close and only
one facility is expected to close a product line. The firm owning this facility is not small based on the SBA size
standard; therefore, no further analysis of indirect dischargers under the Treated Discharge Option is required.
8.2.B Zero Discharge Option
Impact of BAT Regulations on Direct Dischargers
Under the Zero Discharge Option, 16 direct discharging facilities are expected to close and 3 facilities
are expected to close a product line. Firm employment date were available for 13 of the 19 facilities projected
to incur significant adverse impacts. Three of the 13 facilities for which firm employment data were available
are small based on the SBA size standard. These three facilities are owned by three different firms. Because
firm employment data are not available for all firms, the results of the first analytical stage are somewhat
inconclusive. To ensure that disproportionate impacts on small entities are fully considered, the impacts on
direct dischargers under the Zero Discharge Option are examined hi the second analytical stage.
In the second stage of analysis, none of the ten plots (see Figures E.ll - E.20 in Appendix E) showing
the relationship between the two impact measures and five measures of entity size for direct dischargers show a
disproportionate impact on small entities.8 In fact, some plots show that larger entities bear a
disproportionately large portion of the impacts (see Figures E.16, E.17 and E.19).
8A11 plots of entity size vs. impact can be found in Appendix E.
8.5
-------
Table 8.1 presents the results of the ten regressions performed for direct discharging facilities under the
Zero Discharge Option. To evaluate the data presented in Table 8.1, it is necessary to examine the coefficient
estimates and their associated p-values.9 If a coefficient is significantly different than 0 at the 90 percent '
confidence level (p <. 10) and the coefficient is positive, then an increase in entity size is expected to increase
the probability that a facility/product line remains open. If the coefficient is not significantly different than
zero, then entity size is not expected to have an impact on whether a facility/product line remains open.
From the data shown hi Table 8.1, it is evident that small entities are not disproportionately subject to
facility/product line closures. Although the estimated coefficients for the size of the entity in eight of the ten
models are significant at the 90 percent confidence level (p< .10), the estimates are all negative. Applying any
of these estimates into the logistic regression equation previously presented shows that larger facilities have a
higher probability than smaller facilities of closing. For example, if model 4 (Zero Discharge Option) is used to
predict a facility closure, a facility with 100 pesticide-related FTEs would have a 0.39 probability of remaining
open, while a facility employing 200 FTEs would have a 0.30 probability of remaining open.
Impact of PSES Regulations on Indirect Dischargers
Under the Zero Discharge Option, 11 indirect discharging facilities are expected to close and 3 facilities
are projected to close a product line. Firm employment data were available for 13 of the 14 facilities projected
to be impacted under the PSES regulation. Seven of the 13 facilities for which employment data were available
are small based on the SBA size standard. These seven facilities are owned by seven different firms. Even
without complete firm employment data, a substantial number of small entities are expected to be impacted
significantly under the Zero Discharge Option. It is therefore necessary to advance to the second stage of the
analysis to further evaluate the, impacts on small entities for indirect dischargers under the Zero Discharge
Option.
In examining the ten plots (see Figures E.21 - E.30 in Appendix E) showing the relationship between
impacts and entity size for indirect dischargers, it does not appear that small indirect discharging facilities would
be impacted disproportionately under the Zero Discharge Option. None of the figures resemble the
discontinuous step function presented hi Figure 8.1.
Table 8.2 shows the results of the ten regressions performed to examine the probability that an entity
would be adversely impacted as a function of entity size for indirect dischargers under the Zero Discharge
Option. The data presented in the table show that none of the coefficients are significant at the 90 percent
*The p-value is the probability of obtaining the value of the coefficient if the true value were equal to zero.
Small values of p are interpreted as an indication that the coefficient is not equal to zero.
8.6
-------
Table 8.1
Logistic Regression Analysis
Zero Discharge Option: Direct Dischargers
Model
#
1
2
3
4
5
6
7
8
9
10
#of
Observations
45
45
44
46
46
20
20
20
21
21
Impact
(y,)
Facility Closure
Facility Closure
Facility Closure
Facility Closure
Facility Closure
Product Line Closure
Product Line Closure
Product Line Closure
Product Line Closure
Product Line Closure
Measure of Entity Size
(*,)
Pesticide Revenues
Facility Revenues
Firm Revenues
Pesticide Employment
Facility Employment
Pesticide Revenues
Facility Revenues
Firm Revenues
Pesticide Employment
Facility Employment
Coefficient
(ft)
-9.7E-9
-6.8E-9
-7.0E-11
-4.3E*
-1.6E-3
-2.0E-*
-LIE"8
-1.9E-10
-1.5E-2
-2.6E-3
Note: At the 95 percent confidence level p< .05 indicates that the coefficient is significant, while p<
indicates significance at the 90 percent confidence level. Coefficients that are in shaded sections are
significant to the 90 percent confidence level.
P
value
.0027
.0094
.1297
.0382
.0496
.0639
.0344
.1528
.0958
.0964
.10
Table 8.2
Logistic Regression Analysis
Zero Discharge Option: Indirect Dischargers
Model
#
1
2
3
4
5
6
7
8
9
10
Note: At
indicates
# of Impact
Observations (y,)
27
27
26
27
27
12
12
11
12
12
the 95 percent
significance at
Facility Closure
Facility Closure
Facility Closure
Facility Closure
Facility Closure
Product Line Closure
Product Line Closure
Product Line Closure
Product Line Closure
Product Line Closure
Measure of Entity Size
(x.)
Pesticide Revenues
Facility Revenues
Firm Revenues
Pesticide Employment
Facility Employment
Pesticide Revenues
Facility Revenues
Firm Revenues
Pesticide Employment
Facility Employment
confidence level p < .05 indicates that the coefficient is
the 90 percent confidence level.
Coefficient p
(0i) value
-2.6E-8
-2.7E-9
-6.2E-"
-1.8E-2
-8.3E^
3.8E-9
2.3E-8
5.3&9
-1.3E'2
7.4E-3
significant,
.3158
.6746
.4532
.1304
.5717
.9141
.4317
.4187
.5867
.4756
while p<. 10
8.7
-------
confidence level (p< .10). This result indicates that among indirect discharging facilities, entity size does not
have a significant impact on whether a facility or product line remains open.
8.3 Conclusions
Under the proposed option, the Treated Discharge Option, it is not necessary to consider an alternative
regulation for small businesses, since the regulation is not expected to have a significant impact on a substantial
number of small .entities. In addition, no alternative regulations for small businesses need to be considered
under the Zero Discharge Option. Although a substantial number of small entities are expected to be impacted
significantly under this option, the impacts are not expected to be disproportionate in comparison to those on
larger businesses.
8.8
-------
Chapter 8 Reference
Dun's Marketing Services, Inc. (1991). Million Dollar Directory. New Jersey.
8.9
-------
-------
Chapter 9: IMPACTS ON NEW SOURCES
9.0
Introduction
In this chapter, two categories of regulation are considered based on the manner in which a new source
of pesticide active ingredients (PAIs) discharges wastewater. Direct dischargers are regulated under New
Source Performance Standards (NSPS); indirect dischargers are regulated under Pretreatment Standards for New
Sources (PSNS). New facilities using either discharge method have the opportunity to incorporate the best
available demonstrated technologies, including process changes, in-plant controls, and end-of-pipe treatment
technologies, and to use facility site selection to ensure adequate treatment system installation. Both NSPS and
PSNS represent the most stringent numerical values attainable through the application of the best available
demonstrated treatment technologies for nonconventional, conventional, and priority pollutants. (Zero discharge
regulations were not considered for new sources due to the unacceptably large economic impacts projected for
existing sources under this option.) The proposed NSPS and PSNS regulations, and the reasonableness of the
associated costs, are discussed below by chemical subcategory.
9.1 New Source Performance Standards
Subcategory A (Organic Pesticide Chemicals Manufacturing)
The Environmental Protection Agency (EPA) is proposing NSPS under Subcategory A for the
conventional pollutants regulated under Best Practicable Control Technology Currently Available (BPT), 122
organic PAIs, and 28 priority pollutants. The EPA proposes NSPS effluent limitations guidelines that equal
Best Available Technology Economically Achievable (BAT) limitations, modified where appropriate to reflect
the wastewater flow reduction capability at new facilities. Based on a comparison of wastewater generation and
discharge practices at recently constructed vs. older pesticide manufacturing facilities, the EPA concluded that
28 percent wastewater flow reduction had been demonstrated at some of the newer facilities where appropriate.
For this reason, the production-based mass limits developed for organic PAIs based on BAT treatment
performance data were modified to reflect the 28 percent reduction in wastewater discharge at new facilities.
For other non-conventional pollutants and conventional pollutants generated by Subcategory A, the proposed
NSPS requires that the BPT limitations for biological oxygen demand (BOD), chemical oxygen demand (COD),
and total suspended solids (TSS) be modified to reflect the 28 percent wastewater flow reduction demonstrated
at new facilities.
The projected impact of the NSPS on new sources is expected to be less burdensome than that of the
BAT regulations on existing sources. Designing a new technology prior to facility construction is typically less
expensive than retrofitting a facility for a new technology. Because the BAT technologies proposed for existing
pesticide manufacturers were found to be economically achievable, with some existing facilities already
9.1
-------
achieving a 28 percent wastewater flow reduction, the proposed NSPS are expected to be economically
achievable. Moreover, given the structure of the pesticide manufacturing industry, it is unlikely that expansions
in the industry will occur through additional manufacture of currently produced PAIs. Instead, it is more likely
that new PAIs will be manufactured at any expanded or new facilities. It is not possible to project NSPS
guidelines for treatment of new PAIs, given the difficulty in predicting the nature of the treatability of new
PAIs.
Subcategory B (Metallo-Organic Pesticide Chemicals Manufacturing) ,
The EPA is proposing to reserve NSPS for subcategory B chemicals because BPT already requires zero
discharge of process wastewater pollutants.
9.2 Pretreatment Standards for New Sources
Subcategory A Chemicals
Proposed PSNS for the organic pesticide chemicals manufacturing subcategory are based on thfe
proposed Pretreatment Standards for Existing Sources (PSES) technologies, modified where appropriate to
reflect the 28 percent flow reduction capability at new facilities. As with Pretreatment Standards for Existing
Sources (PSES), the PAI standards are production-based mass limits, while the priority pollutant standards are
based on achievable concentrations. The EPA is proposing to establish PSNS for the same conventional
pollutants, 122 organic PAIs, and 26 priority pollutants proposed under NSPS.
Similarly to NSPS, PSNS guidelines are expected to be economically achievable because the impact on
new sources should be less than that on existing sources, and the proposed PSES guidelines have been found to
be economically achievable. In addition, 28 percent reductions in wastewater flow have been demonstrated at
some facilities. Also, as discussed above, it is more likely that new PAIs, rather than those currently produced,
will be manufactured at any expanded or new facilities. The EPA does not believe it is possible to project
PSNS guidelines for treatment of new PAIs, owing to the difficulty hi predicting the nature of the treatability of
new PAIs. ;
Subcategory B Chemicals
Under Subcategory B, the EPA is reserving the right to set PSNS at a later date. For this reason,
economic impacts have not been calculated. l
9.2
-------
APPENDIX A
1986 PESTICIDE MANUFACTURER FACILITY CENSUS
-------
-------
Appendix A: 1986 PESTICIDE MANUFACTURER FACILITY CENSUS
This appendix includes Part B of the Pesticide Manufacturer Facility Census for 1986, which served as one
of the main data sources for the EIA. Part B requested detailed economic and financial data from the facilities,
including balance sheet and income statement information for 1985, 1986, and 1987. Part B was also designed to
obtain information on facility liquidation values and the cost of capital.
A.I
-------
-------
Form Approved
OtyBNo.: 2040-0111
Expiration Date: 4/30/90
U.S. ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY
CENSUS FOR 1986
PART B. FINANCIAL AND ECONOMIC INFORMATION
January 17,1989
Public reporting burden for this collection of information is estimated to average 65 hours per response.
The reporting burden includes time for reviewing instructions, gathering data, and completing and
reviewing the questionnaire.
Send comments regarding the burden estimate or any other aspect of this collection of information.
including suggestions for reducing the burden, to:
Chief, Information Policy Branch (PM-223)
U.S. Environmental Protection Agency
401 M Street, SW
Washington, DC 20460
and
Office of Management and Budget
Paperwork Reduction Project
(2040-0111)
Washington, DC 20503
A.2
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
Part 8 Financial and Economic Information
Part B: General Instructions
The Pesticide Manufacturing Facility Census has three parts:
Introduction;
Part A: Technical Information; and
Part B: Financial and Economic Information.
The Introduction and Part A were mailed separately and have been completed by your facility. This
package contains the Part B questionnaire and its instructions. All recipients who completed the
introduction and Part A of the Pesticide Manufacturing Facility Census must complete Part B at this time.
Throughout this questionnaire you will be asked about the Pesticide Active Ingredients listed in Table 1.
pages 4 through 12, of this booklet. It may be helpful to review the list and identify active ingredients
handled at this facility before completing the questionnaire.
Authority
This mandatory census is conducted under the authority of Section 308 of the Clean Water Act (the Federal
Water Pollution Control Act, 33 U.S.C. 1251 et seq., as amended). Late filing or failure otherwise to comply
with these instructions may result in criminal fines, civil penalties and other sanctions as provided by law.
Provisions concerning confidentiality of the data collected are explained below.
Purpose
The Pesticide Manufacturing Facility Census questionnaire is designed to collect data on pesticide
manufacturing activities and waste treatment practices for the calendar year beginning January 1, 1986 and
ending December 31, 1986. Part B requests financial and economic information for the calendar years
1985,1986 and 1987.
Who Must Respond
All recipients who completed the Introduction and Part A of the Census questionnaire must complete
Part B at this time. The entire Pesticide Manufacturing Facility Census questionnaire must be completed by
all manufacturers of the Pesticide Active Ingredients listed in Table 1, pages 4 through 12, of this booklet.
Completing the Census
Although Part B may be completed by different officials, the individual who signed the certification for
Part A should also certify all parts of the questionnaire by completing and signing the Part B Certification
Statement located on page 3 of this questionnaire.
If the space allotted for the answer to any question is not adequate for your complete response, please
continue the response in the Comments space at the end of each section. Reference the comments to the
appropriate question.
A.3
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSU$ FOR 1986
Part B Financial and Economic Information
GENERAL INSTRUCTIONS - Continued
When and How to Return the Part B Questionnaire
"he Pesticide Manufacturing Facility Census Pan B questionnaire must be comoieted ana returned witnm
50 days of receipt to:
Dr. Lynne Tuaor WH586
U.S. Environmental Protection Agency
Analysis and Evaluation Division
401 M Street, SW
Washington, D.C. 20460
Questions on the Part B Questionnaire
Questions pertaining to any item in Part B mav oe directed to:
Dr. Lynne Tudor WH586
U.S. Environmental Protection Agency
Analysis and Evaluation Division
401 M Street, SW
Washington, D.C. 20460
(202) 382 5834
Provisions Regarding Data Confidentiality
Regulations governing the confidentiality of business information are contained in 40 CFR Part 2 Subpart B
and 43 Fed. Reg. 40001 (Sept. 8, 1978). Under these regulations, all records, reports, or information
supplied to the EPA may be made public by the EPA without further notice if not accompanied by a
business confidentiality claim. You may assert a business confidentiality claim covering part or all of the
information you submit, other than effluent data, as described in 40 CFR 2.203(b):
*(b) Method and time of asserting business confidentiality claim. A business which is
submitting information to EPA may assert a business confidentiality claim covering the
information by placing on (or attaching to) the information, at the time it is submitted to
EPA, a cover sheet, stamped or typed legend, or other suitable form of notice employing
language such as 'trade secret,' 'proprietary,' or 'company confidential.' Allegedly
confidential portions of otherwise non-confidential documents should be clearly identified
by the business, and may be submitted separately to facilitate identification and handling
by EPA. If the business desires confidential treatment only until a certain date or until the
occurrence of a certain event, the notice should so state."
Information covered by a claim of confidentiality will be disclosed by EPA only to the extent, and by means
of the procedures, set forth in 40 CFR Part 2 Subpart B. In general, submitted records, reports, or
information protected by a business confidentiality daim may be disclosed to other employees, officers, or
authorized representatives of the United States concerned with carrying out the Clean Water Act, or when
relevant to any proceeding under the Act. Effluent data are not eligible for confidential treatment.
A.4
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSU^ FOR 1986
Part B Financial and Economic Information
:D
INTRODUCTION
Enter the name of this facility.
Enter the EPA Federal Insecticide, Fungicide and Rodertticide Act (FIFRA) Establishment Numoer
for this facility, as reported to the EPA on Form 3540-16 ("Pesticides Report for Pesticide-Producing
Establishments'). Check the box next to "Not Applicable" if this facility does not have an EPA FIFRA
Establishment Number
!_j | '_!_;-!__!_! -!_'_:_! -2A 'Z Not Applicable I2B
Enter the DUNS Number of this facility. Check the box next to "Not Applicable if this facility does not
have a DUNS Number.
j I
I3A
Enter the facility mailing address.
.1.
Not Applicable
I4A
Street or P.O. Box
City or Town
I4B
State
I4C
Zip Code
I4D
Enter the address of the physical location of the facility if different from the mailing address.
! I
ISA
Street or Route Number
l_l_l_l_l_l_l_
City or Town
I5B
Certification Statement
State
ISC'
Zil
I certify that I have personally examined and am familiar with the information submitted in all three
parts of the Census questionnaire and all attached documents, and that based on my inquiry of
those individuals Immediately responsible for obtaining the information, I believe that the submitted
information is true, accurate and complete. I am aware that there are significant penalties for sub-
mitting false information, including the possibility of fine and imprisonment.
Date Survey Completed:
Month
I_J - I_J_I_I_J I6A
Day
Year
Signature of Certifying Official
Name of Certifying Official (please print or type)
I_I_I_I_I_I_I_I_I_I_U_U
Title
I6B
I6D
A.5
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSU^ FOR 1986
Part B Financial and Economic Information
•;ODE
3
7
3
9
:o
:i
12
13
14
I4a
I4b
14C
I4d
15
15a
i5b
15c
15d
16
I6a
i6b
16c
i6d
17
I7a
i7b
17c
18
19
20
21
22
23
24
25
26
INTRODUCTION - Continued
Review the Pesticide Active ingredients listed in Table 1 below and circle all codes that correspond
to active ingredients manufactured, formulated or packaged at this facility.
TABLE 1. PESTICIDE ACTIVE INGREDIENTS
ACTIVE INGREDIENT " ~
1,1 -Bis(chlorophenyl)-2.2,2-trichloroethanoi '•
1,2-Dihydro-3,6-pyridazinedione ;
1,2-Ethylene dibromide
1,3,5-Triethylhexahydro-s-triazine
1,3-Oichloropropene
10,10'-Oxybisphenoxarsine
1 -(3-Chloroa!lyi)-3,5,7-triaza-l -azoniaaaamantane cnloride
1 -{4-Chlorophenoxy)-3,3-dimethyl-i -d H-1,2,4-triazol-l -yl)-2-butanone
2,2'-Methylenebis(3,4,6-trichloropnenoi)
2,2'-Methylenebis(4,6-dichlorophenoi
2.2'-Methyleneois(4-chlorophenoJ)
2,2-Dichlorovinyl dimethyl phosphate
2,3,5-Trimethylphenyfmethyicarbarnate
2,3,6-Trichlorophenylacetic acid or any salt or ester
2,4,5-Trichloropnenoxyacetic acid or any salt or ester
2,4-Oichiorophenoxyacetic acid or any salt or ester
2,4-Dichlorophenoxybutyric acid or any salt or ester
2,4-Dichloro-6-(o-chloroaniiino)-s-tria2ine
2,4-Dintro-6-octylphenylcrotonate, 2.6-Dinitro-4-octylphenylcrotonate, and Nrtrooctylphenois
(The octyl's are a mixture of 1-Methylheptyl, 1 -Ethylhexyi, and 1-Propylpentyl)
2,6-Dichloro-4-nitroaniline
2-Bromo-4-hyd roxyacetophenone
2-Carbomethoxy-1 -methyjvinyl dimethyl phosphate, and related compounds
2-Chloroallyl diethyldithiocarbamate
2-Chloro-1-(2,4-dichlorophenyl)vinyl diethyl phosphate
2-Chloro-4-((l -cyano-1 -methylethyl)amino)-6-ethylamino)-s-triazine
2-Chloro-N-isopropytacetanilide
A.6
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued
ACTIVE INGREDIENT
27
273
27b
27C
27d
28
29
30
30a
30b
30C
30d
31
3la
31b
31C
31d
32
33
34
343
34b
34C
34d
35
36
37
38
39
40
41
42
43
44
45
46
463
46b
46C
46d
47
473
47b
47C
47d
48
49
50
2-Methyl-4-chlorophenoxyacetic acid or any salt or ester
2-n-Octyl-4-isothiazo)in-3-one
2-Pivalyl-1,3-Indandione
2-(2.4-Dichlorophenoxy)propionic acid or any salt or ester
2-(2-Methyi-4-chloropnenoxy)propiomc acid or any salt or ester
2-(4-Thiazolyl)benzimidazole
2-(Methyithio)-4-(ethyiamino)-6-(1.2-dimethylpropyl)amino-s-triazine
2-(m-Chlorophenoxy)propionic acid or any salt or ester
2-(Thiocyanomethylthio)ben20thiazote
2-((Hydroxymethyl)amino) ethanol
2-((p-bhlorophenyt)phenylacetyl)-l ,3-indandione
3,4,5-Trimethylphenyimethyicarbamate
3,5-Dichloro-N-(1,1 -dimethyl-2-propynl)benzamide
3^-Dimethyl-4-{methytthio)phenyldimethylcarbamate
3',4'-Dichloropropionaniltde
3-lodo-2-propynyt butylcarbamate
3-(a-Acetonylfurfuryl)-4-hydroxycoumarin
4,6-Dinitro-o-cresoi
4-Amino-6-(l ,1 -dimethylethyl)-3-(methylthio)-l ,2,4-triazin-5(4H)-one
4-Chlorophenoxyacetic acid or any salt or ester
4-(2-Methyi-4-chlorophenoxy)butyric acid or any salt or ester
4-(Dimethylamino)-m-tolyt methytcarbamate
5-Ethoxy-3-(trichloromethyl)-l,2,4-thiadiazole
6-Etnoxy-1,2-dihydro-2,2.4-trimethyl quinoiine
A.7
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued
ACTIVE INGREDIEOT
8-Quinolinol sulfate
Acephate (0,S-Dimethyl acetytphosDnoramidothloate)
Acifluoren (5-(2-Chloro-4-(trifluoromethyl)phenoxy)-2-nitrobenzoic acid) or any salt or ester
Alachlor(2-Chloro-2',6'-diethyl-N-(metnoxymethyl)acetaniiide)
Aldicarb (2-Methyi-2-(methylthio)prooionaldehyde O-(methylcarbamoyl)oxime)
Alkyt* dimethyl benzyl ammonium cnlonde '(50% C14, 40% C12, 10% C16)
Allethrin (all isomers and allethrin coil) •
Ametryn (2-(Ethyiamino)-4-(isopropytamino)-6-(methylthlo)-s-tria2ine)
Amitraz (N'-2.4-Dimethylphenyl)-N-(((2,4-dimethy(phenyi)imino)methyi)-N-methylmethanimidamiae)
Atrazine (2-Chlorc-4-(ethyiamino)-6-(isopropy1amino)-s-triazlne)
Bendiocarb (2.2-DimethyM ,3-benzoaioxoM-yl methylcarbamate)
Benomyl (Methyl 1-(buty1carDamoyi)-2-benzimidazolecamamate)
Benzene hexachloride
Benzyl benzoate
Beta-Thiocyanoethyl esters of mixed fatty acids containing from 10-18 carbon atoms
Bifenox (Methyl 5-(2,4-dichlorophenoxy)-2-nitrobenzoate)
Biphenyl
Bromacil (5-Bromo-3-sec-Butyl-6-Methyluracil) or any salts or esters
Bromoxynil (3,5-Dibromo-4-hydroxybenzonitrile) or any salt or ester
Butachlor (N-(Butoxymethyl)-2-chloro-2',6'-diethy1acetaniiide)
b-Bromo-b-nrtrostyrene (Note: b » beta)
Cacodylic acid (Dimethylarsenic acid) or any salts or ester
Captafol (cis-N-((1,1,2.2-Tetrachloroethyi)thio)-4-cyclohexene-l ,2-dicarboximide)
Captan (N-Trichloromethylthio-4-cycfohexene-l ,2-dicarboximide)
Carbaryl (1-Naphthylmethylcarbamate)
Carbofuran (2,3-Dinydro-2,2-dirnethyi-7-benzofuranyl methylcarbamate)
Carbosulfan(2,2-Dihydro-2.2-dimethyl-7-benzofuranyl(dibutylamino)thio)rnethylcarbarnate)
Chloramben (3-Amino-2,5-dichlorobenzoic acid) or any salt or ester
Chlordane (Octachloro-4,7-methanotetrahyd roindane)
Chloroneb (1,4-Dichloro-2,5-dimethoxybenzene)
Chloropicrin (Trichloronitromethane)
Chlorothalonil (2,4,5,6-Tetrachioro-1,3-dicyanobenzene)
51
52
33
54
55
56
57
58
59
60
51
52
53
64
55
66
67
68
683
68b
58c
63d
69
59a
69b
59c
69d
70
71
72
723
72b
72C
72d
73
74
75
76
77
78
78a
78b
78c
78d
79
80
81
82
A.8
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued
ACTIVE INGREDIENT
Chloroxuron (3-(4-(4-Chlorophenoxyjphenyl)-l, 1 -dimethylurea)
ChIoro-l-(2,4,5-trichlorophenyl)vinyi dimethyl phosphate
Chlorpyrifos methyl (0,0-Dimethyl 0-(3.5,6-trichloro-2-pyridyl) phosphorothioate)
Chlorpyrifos (O.O-Diethyl O-(3,5.6-trichloro-2-pyridyl) phosphorthioate)
Coordination product of Manganese 16%, Zinc 2% and Ethyienebisdithiocaroamate 62%
Copper 8-quinolinolate
Copper ethyienediaminetetraacetate
Cyano(3-phenoxyphenyl)methyl 4-chioro-a-(l-methy!ethyl)benzeneacetate (9CA)
Cydoheximide(3-(2-(3,5-Dimethy-2-oxocyclohexyl)-2-hydroxyethyl)glutarimide)
Dalapon (2.2-Dichloropropionic acid) or any salt or ester
Decachlorc-bis(2,4-cyclopentadiene-i-yl)
Demeton (O,0-Diethyl O-(and S-) (2-ethylthio)ethyl)phosphorothioate)
Desmedipham (Ethyl m-hydroxycarbanilate carbaniiate)
Diammonium salt of ethylenebisdithiocarbamate
Dibromo-3-chloropropane
Dicamba (3.6-Dichloro-o-anisic acid) or any salt or ester
Dichione (2,3-Dichloro-1,4-naphthoquinone)
Diethyi 4,4'-o-phenytenebis(3-thioallophanate)
Diethyi diphenyl dichloroethane and related compounds
Diethyi dithiobis(thionoformate)
Diethyi O-(2-isopropyl-6-methyl-4-pyrimidinyl) phosphorothioate
Difluben2uron(N-(((4-Chlorophenyl)amino)carbonyl)-2.6-difiuorobenzamide)
Diisobutylphenoxyethoxyethyl dimethyl benzyl ammonium chloride
Dimethoate (O.O-Dimethyl S-((methylcarbamoyl)methyl)phosphorothioate)
Dimethyl O-p-nrtrophenyi phosphorothioate
Dimethyl phosphate ester of 3-hydroxy-N,N-dimethyl
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued
ACTIVE INGREDIENT
Endothall (7-Oxabicyc!o(2 2 1)heDtane-2,3-dicart>oxyiic acid) or any salt or ester
CODE
'23
'233
123b
'23C
:23d
•24
125
126
127
128
129
130
131
132
133
134
135
136
137
138
1383
138b
138c
138d
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
1533
153b
153C
153d
154
155
156
Endrin (Hexachloroepoxyoctahyaro-enao.enao-oimethanonapntnalene)
Ethalfluralin(N-Ethy4-N-(2-rnethyl-2-DroDenyl)-2.6-dinitro-4-(trifluorornethyl)benzeneamine)
Ethion (O.O.O'.O'-Tetraethyl S.S'-metnyiene bisohosphorodithioate)
Ethoprop (0-Ethyl S,S-dipropyl phosonoroaithioate)
Ethyl 3-methyl-4-(methylthio)phenyi 1 -fmethylethyl) phosphoramidate
Ethyl 4,4'-dichlorobenzilate
Ethyl diisobutylthiocarbamate
Famphur(0,0-DimethylO-(p-(dimetriyisulfamoyi)pheny1)phosphorothioate)
Fenarimoi (a-(2-Chlorophenyi)-a-(4-cnloropnenyl)-5-pyrimidinemethanol)
Fenthion (6,0-Dimethyl 0-(4-rnethylthio)-m-tdyi)phosphorothioate)
Ferbam (Ferric dimethyldithiocarbamate)
Ruometuron (1.1 -Dimethyl-3-(a,a.a-tnfiuoro-m-tdyi)urea)
Ruoroacetamide
Fdpet (N-((Trichioromethyl)thio)phthalimide)
Glyphosate (N-(Phosphonomethyl)glycine) or any salt or ester
G!yphosine(N,N-bis(Phosphonomethy!)glycine)
Heptachlor (Heptachlorotetrahydrcnt, 7-methanoindene)
Hexadecyl cydopropanecarboxylate
Hexazinone (3-Cyclohexyi-6-(dimethylamino)-1 -methyl-1,3,5-triazine-2.4(1 H,3H)-dione)
Isofenphos (1 -Methyiethyt 2-{(ethoxy((l -methylethyl)amino)phosphinothioyl)oxy)ben2oate)
Isopropalin (2,6-Dinitro-N,N-dipropylcumidine)
Isopropyl N-phenyi carbamate
Karbutilate (tert-Butylcaroamic acid ester of 3-(m-hydroxyphenyl)-l ,1 -dimethylurea)
LJndane (gamma isomer of benzene hexachloride) 99% pure
Linuron (3-(3,4-Dichlorophenyl)-l -methoxy-1 -methylurea)
Malachite green (Ammonium (4-(p-(dimethy)amino)-alpha-phenyibenzylidine)-2I
5-cydohexadien-l -y(kJene)-dimethy( chloride
Malathion (O,O-Dimethyl dithiophospnate of diethyi mercaptosuccinate)
Maneb (Manganese salt of ethytenebisdithiocarbamate)
Manganese dimethyldithiocarbamate
Mefluidide (N-(2,4-Dimethyl-5-(((trifluoromethyl)sulfonyl)amino)phenyl acetamide) or any salt or ester
Methamidophos (O,S-Dimethyl phospnoramidothioate)
Methidathion (O,O-Dimethyl phosphorodithioate, S-ester of 4-(mercaptomethyl)-2-methoxy-delta
2-1,3,4-thiadiazolin-5-one)
Methomyl (S-Methyl N-((metnylcarbamoyl)oxy)thioacetimidate)
A.10
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part 8 Financial and Economic Information
TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued
ACTIVE INGREDIENT
Methoprene (lsopropy1(E,E)-l l-methoxy-3.7,1 i-trimethyl-2,4-dodecadienoate)
Methoxychlor (2.2-bis(p-Methoxypnenyi)-l .1,1 -trichloroethane)
Methyl benzethonium chloride
Methyl bromide
Methylarsonic acid or any salt or ester
168
169
170
171
172
173
174
175
176
1763
176b
176C
176d
177
178
179
180
181
182
183
184
185
186
187
188
188a
188b
188C
188d
Methyldodecylbenzyi trimethyi ammonium cnloride 80% and methyldoaecyixylytene
bis(trimethylammoriium chloride) 20%
Methyfene bisthiocyanate
Methyl-2.3-quinoxalinedithioi cyclic S.S-dithiocarbonate
Metolachlor (2-Chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-l -methylethyl)acetamide)
Mexacarbate (4-(Dimethy1amino)-3.5-xytyl methylcarbamate)
Mixture of 83 9% Ethylenebis(dithiocarDamato) zinc and 161% Ethylenebisdithiocarbamate. '
bimolecular and trimolecuiar cyclic annydrosulfides and disulfides
MonuronTCA » Monuron trichloroacetate
Monuron (3-(4-Chtorophenyl)-ltl-dimethylurea)
N,N-Diethyl-2-(l-naphatnaienyioxy)propionamide
N.N-Diethyi-meta-toluamide and other isomers
Nabam (Oisodium salt of ethylenebisdithiocarbamate)
Naled (1,2-D!bromo-2,2-dicnloroethyi dimethyl phosphate)
Norea (3-Hexahydro-4,7-methanoindan-5-yl-l ,1 -dimethylurea)
Norflurazon (4-Chloro-5-(methylamino)-2-(a,a,a-trifIuoro-m-tolyl)-3(2H)-pyndazinone) '•
N-1 -Naphthylphtnalamic acid or any salt or ester
N-2-Ethylhexyl bicydoheptene dicarboximide
N-Butyl-N-ethyl-a.a.a-trifluoro-2,6-dinitro-p-toluidine
O.O.O.O-Tetraethyl dithiopyrophosphate
O.O.O.O-Tetrapropyldithiopyrophospnate
O.O-Diethyl O-(3-chloro-4-methyi-2-oxo-2H-l -benzopyran-7-yl) phosphorothioate
O,O-Diethyi O-(p-(methylsulfiny1)phenyt) phosphorothioate
O.O-Oiethyt S-(2-(ethylthio)ethyl) phosphorodithioate
O,O-DimethylO-(4-nitro-m-tolyl)phosphor6thioate
O,O-D!methyiS-(phthalimidomethy1)phosphorodithioate
O.O-Oimethyt S-((4-oxo-1,2,3-benzotriazin-3(4H)-yl)methyl)phosphorodithioate
O.O-Oimethyl S-((ethylsulfinyl)ethyl phosphorothioate
Organo-arsenic pesticides (not otherwise listed)
A.11
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued
ACTIVE INGREDIENT
Organo-cadmium pesticides
191 a
191b
191C
191d
192
192a
192b
192C
•192d
193
194
195
196
197
198
199
200
201
202
203
204
205
206
2063
206b
206C
206d
207
208
209
210
211
212
213
214
Organo-copper pesticides
Organo-mercury pesticides
Organo-tin pesticides
Orthodichlorobenzene
Oryzalin (3,5-Dinitro-N4,N4-clipropyisuifanilamide) (Note: N4 = N superscript 4)
Oxamyl (Methyl N',N'-dimethyl-N-((methylcarbamoyl)oxy)-l-thiooxamidate)
Oxyfluorfen (2-Chlorc-1 -(3-ethoxy-4-nitrophenoxy)-4-(trifluoromethyl)benzene)
0-Ethyl O-(4-(methylthio)phenyl) S-propyi phosphorodithioate
0-Ethyi O-(4-(metnylthio)phenyO S-propyl phosphorothioate (9CA)
0-Ethyl O-(p-Nitrophenyl)phenytphosphonothioate
0-Ethyi S-phenyi ethytphosphonodithioate
0-lsopropoxyphenyt methylcaroamate
Paradichlorobenzene
Parathion (O,O-Diethyl O-(p-nitrophenyl)phosphorothioate)
Petxjimethalin (N-(1 -Ethylpropy1)-3.4^jimethyl-2,6-dinitrobenzenamine)
Pentachloronrtrobenzene
PentachlorophenoJ or any salt or ester
Perfluidone (1,1,1 -Trifluoro-N-(2-metnyl-4-(phenyisulfonyl)phenyl)methanesulfonamide)
Permethrin ((3-Phenoxyphenyl)methyl 3-(2.2-dichloroethenyl)-2.2-climethylcyclopropanecarboxyiate)
Phenmedipham (Methyl m-hydroxycarpanilate m-methyl carbanilate)
Phenothiazine
Phenylphenol
Phorate (O,O-Diethyl S-((ethylthioimethyl)phosphorodithioate)
Phosalone (O,O-Diethyl S-((6-chioro-2-oxobenzoxazolin-3-yl)methyl) phosphorothioate)
Phosphamidon (2-Chioro-N,N-diethyi-3-hydroxycrotonamide ester of dimethyl phosphate)
A.12
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued
ACTIVE INGREDIENT
Pidoram (4-Amino-3.5,6-trichloropicolinic acid) or any salts or esters
Piperonyi butoxide ((Buty1carbity1)(6-propylpiperonyl)ether)
Poly(oxyethyiene(dimethyliminio)ethyiene(dimethyliminio)ethylenedichloride
Potassium dimethyidithiocarbamate
Potassium N-hydroxymethyl-N-methyldithiocarbamate
Potassium N-methyldithiocarbamate
Potassium N-(a-(nitroethyl)benzyi)ethylenediamine
Profenofos (O-(4-Bromo-2-chloroprienyi) 0-ethyl S-propyl phosphorothioate)
Prometon (2I4-bis(lsopropylamino)-6-methoxy-s-triazine)
Prometryn (2,4-bis(lsopropyiamino)-6-(methyfthio)-s-triazine)
Propargite (2-(p-tert-Butyiphenoxy)cyclohexyl 2-propynyl sulfrte)
Propazine (2-Chloro-4,6-bis(isopropylamino)-s-triazine)
Propionic acid
Propyt (3-dimethylamino)propyl carbamate hydrochioride
Pyrethrin coils
Pyrethrin I
Pyrethrin II
Pyrethrum (synthetic pyrethrin)
Resmethrin ((5-Phenyimethyl)-3-furanyi)methyi 2,2-dimethyl-3-
(2-methyM-propenyi)cyclopropanecarboxyiate)
Rohnel (O,O-Dimethyl O-(2,4,5-trichlorophenyl)phosphorothioate)
Rotenone
S.S.S-Tributyl phosphorotrithioate
SkJuron (1 -(2-Methytcydohexyi)-3-phenyiurea
SBvex (2-(2.4.5-Trichiorophenoxypropionic acid)) or any salt or ester
Simazine(2_Ch!oro-4,6-bis(ethylamino)-s-triazine)
Sodium beritazon (3-lsopropyl-lH-2,1,3-benzothiadiazin-4(3H)one 2,2-dioxide)
Sodium dimethyidithiocarbamate
Sodium fluoroacetate
Sodium methyldithiocarbamate
SulfoxkJe (1,2-Methylenedioxy-4-(2-(octylsulftdynyl)propyl) benzene
S-Ethyl cydohexyiethylthiocarbamate
S-Ethyl dipropyithiocarbamate
S-Ethyl hexahydro-lH-azepine-1-carbothioate
S-Propyi butylethytthiocarbamate
S-Propyl dipropyithiocarbamate
S-(2-Hydroxypropyl)thiomethanesulfonate
S-(O,O-Diisopropyl phosphorodithioate ester of N-(2-mercaptoethyl)benzenesulfonamide
Tebuthiuron (N-(5-(1,1 -Dimethylethyl)-1,3,4-thiadiazol-2-yl)-N,N'-dimethy)urea)
Temephos(O,O,O',O'-Tetramethyi-O,0'-thiodi-p-phenyienephosphorothioate)
215
215a
215b
215c
215d
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
238a
238b
238C
238d
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
A.13
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSU§ FOR 1986
Part B Financial and Economic Information
TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued
ACTIVE INGREDIEMT
Terbacil (3-tert-8utyt-5-chloro-6-methyiuracil)
Terbufos (S-(((1,l-Dimethylethyi)thioimethyl) O.O-diethyl phosphorodithioate)
Terbuthylazine (2-(tert-Butylamino)-4-cnloro-6-(ethyiamino)-s-tna2ine
Terbutryn (2-(tert-8utylamino)-4-(ethy!annino)-6-(methylthio)-s-trJa2ine)
Tetrachloropheno) or any salt or ester
CODE
254
255
256
257
258
2583
258b
258C
258d
259
260
261
262
263
264
265
265a
265b
265C
265d
266
267
268
269
270
271
272
Tetrahydro-3.5-dlmethyl-2H-1,3,5-thiaaia2ine-2-thione
Thiophanate-methyl (Dimethyl 4,4'-o-pnenyienebis(3-thioallophanate))
Thiram (Tetramethytthiuram disuifide)
Toxaphene (technical chlorinated camphene (67-69% chlorine))
Tributyl phosphorotrithiorte
Trifluralin (a,a,a-Trifluro-2,6-dinitro-N.N-dipropy(-p-toluidine)
Warfarin (3-(a-Acetonylbenzyl)-4-hyaroxycoumarin) or any salt or ester
Zinc 2-mercaptQbenzotniazolate
ZIneb (Zinc ethylenebisdithiocarbamate)
Ziram (Zinc dimethyidithiocarbamate)
(2,3,3-Trichloroallyl)diisopropylthiocarbamate
(3-Phenoxyphenyl)methyl d-cis and tran" 2.2-dimethyl-3-(2-methylpropenyl)cyclopropanecarboxylate
•(Max d-cis 25% ; Min. trans 75%)
(4-Cydohexene-l ,2-dicarboximido)methyl 2.2-dimethyl-3-
(2-methyipropenyl)cyclopropanecarboxyiate
Isopropyl N-(3-chlorophenyi) carbamate
A.14
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS; FOR 1986
Part B Financial and Economic Information
SECTION 1: FIRM FINANCIAL INFORMATION
1-A. Was this facility owned or controlled by a parent firm or firms on December 31, 1986?
S1A
YES 1 (GO TO BOX 1-A)
NO 2 (SKIP TO SECTION 2, PAGE 18)
30X 1-A
If there is ,more than one parent firm, such as in a joint venture, photocopy Section 1,
pages 13 through 16. and complete all Section 1 questions for each parent firm.
1-B. Report the name, mailing address and DUNS number of the parent firm.
[1] Name of Parent Firm
S1B1
[2] Mailing Address of Headquarters
I ! ! I I I !_!_!_!_!_!_L
S1B2A
__
Street or P.O.Box
Citv nr town 1 State
S1B2B S1B2C
[3] What is the DUNS Number of the parent firm?
• I_J_!-I_I_J_I-I_!_I_I_!
S1B3A
Zip Code
S1B2D
Q Not Applicable
S1B3B
1-C. Report the percentage of the parent firm's total 1986 sales (in dollars) generated by each of the
activities listed beiow. (Enter zero if the activity was not applicable. The sum of all percentages
must be 100%).
[1] Percentage of sales generated by manufacturing pesticides listed
in Table 1. pages 4 through 12 'sIcT """•"•- l_J_l^_i%
[2] Percentage of sales generated by formulating or packaging pesticides
listed in Table 1, pages 4 through 12 ..................................... -sic? ..... "-
[3] Percentage of sales generated by other activities (SPECIFY) .................
Total
1 00%
A.15
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 1: FIRM FINANCIAL INFORMATION
1-D. Did the parent firm acquire this facility after December 31, 1980?
YES 1 (CONTINUE)
31D NO 2 (SKIP TO QUESTION 1 -E)
[1 ] In what year was this facility acauired by the parent firm?
l_L!J.I_i_l
Year S1D1
[2] How was this facility acquired by the parent firm? (CHECK ONE): S1D2
Q Purchase
fj Merger: Please list names of the companies that merged
J_!_:_J_!_i_!_l_J_!_l siD2A
J_I__!_!_!_!_J_I_!_I S1D2B
l_l_l_J_l_l_l_!_l_l_i_l_l_!_J_J S1D2C
Q Takeover
Fj Founded
Q Other (SPECIFY)
1-E. On December 31, 1986, did the parent firm own or control any other U.S. facilities at which any of
the pesticides listed on Table 1, pages 4 through 12, were manufactured or formulated and/or
packaged?
S1E YES 1 (CONTINUE)
NO 2 (SKIP TO QUESTION 1-G)
A.16
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 1: FIRM FINANCIAL INFORMATION
1-F. Report the names and EPA Federal Insecticide. Fungicide and Rodenticide Act (FjFRA'i
Establishment Numbers (as reported to the EPA on Form 3540-16) for all other facilities owned cr
controlled by the parent firm at which any of the pesticides listed on Table 1, pages 4 througn 12.
were manufactured or formulated ana/or packaged. Check the box next to "Not Applicable' if the
facility does not have an EPA FIFRA Establishment Number. Check whether each facility was a
manufacturer or formulator/packager of the pesticides listed on Table 1. If more space is required
to give a complete answer to this question, photocopy this page. :
[1]
SIFIA
_
Name of Facility .
! __'_'_'_!_! •!—'—' •'—
EPA FIFRA Establishment Numoer
Not Applicable
Manufacturer
S1F1D
Formulator/Packager
S1F1E
S1F2B
Name of Facility
___
EPA FIFRA Establishment Numoer
_____ SIFZA
D Not Applicable S1F2C
Manufacturer
S1F2D
^j Formulator/Packager
S1§2E
[3] !_J_i_J_l_l_i_l_!_:_!
Name of Facility
S1F3B I_I_!_J_I__I ' I_J_I - I_J
EPA FIFRA Establishment Number
l_!_!_!_i_!_!_J S1F3A
D Not Applicable S1F3C
Manufacturer
Formulator/Packager
S1F3E
W
Name of Facility
S1F4B l_i_!___-__ -__
EPA FIFRA Establishment Numoer
Not Applicable
S1F4A
S1F4C
Manufacturer
S1F4D
] Formulator/Packager
S1F4E
A.17
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 1: FIRM FINANCIAL INFORMATION
1-F. Report the names and EPA Federal Insecticide, Fungicide and Rodenticide Act (FIFRA)
Establishment Numbers (as reported to the EPA on Form 3540-16) for all other facilities owned or
controlled by the parent firm at which any of the pesticides listed on Table 1, pages 4 through 12.
were manufactured or formulated and/or Dacxaged. Check the box next to 'Not Applicable" if the
facility does not have an EPA FIFRA Estaolishment Number. Check whether each facility was a
manufacturer or formulator/packager of the oesticides listed on Table 1. If more space is required
to give a complete answer to this question, pnotocopy this page.
[1]
Name of Facility
S1F5B l — '-'-J-J — M^LI-^
EPA FIFRA Establishment Numoer
Q Manufacturer ~
S1F5D
J_l_l_!_J S1F5A
Not Applicable S1F5C
rormulator/Packager
S1F5E
[2]
S1F6B
Name of Facility
EPA FIFRA Establishment Number
Q Manufacturer Q Formulator/Packager
S1F6D S1F6E
_i_l_l_l_l S1F6A
Q Not Applicable S1F6C
[3]
I
S1F7B
Name of Facility
EPA RFRA Esta"bTishment"NumbeT
Q Manufacturer [
S1F7D
S1F7A
Not Applicable S1F7C
Formulator/Packager
S1F7E
[4]
S1F8B
Name of Facility
l_!_l_J_!_i-l_l_l-!_
EPA FiFRA Establishment Number
Q Manufacturer [
S1F8D
—.' — ' — > —' S1F8A
Not Applicable S1F8C
Formulator/Packager
S1F8E
A.18
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSU^FOR 1986
Part B Financial and Economic Information
SECTION 1: FIRM FINANCIAL INFORMATION
1 -G. Report the total revenue of the parent firm for 1985,1986, and 1987 in thousands of dollars.
($000)
[1] 1985 Revenue -
[2] 1986 Revenue
[3] 1987 Revenue
1-H. Was the parent firm (listed on question 1 B) itself owned or controlled by another company?
SIH
YES 1 (CONTINUE)
NO 2 (SKIP TO SECTION 2)
1-1. Report the name, mailing address and DUNS number of the controlling firm.
[1] Name
sin
[2] Mailing Address of Headquarters
Street or P.O.Box
SII2A
City or Town
SI
[3] DUNS Number
State
Zip Code
Not Applicable
.
A.19
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 1: FIRM FINANCIAL INFORMATION
Section 1 Comments. Reference entry by question number.
A.20
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILJTY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILJTY FINANCIAL INFORMATION
All of the information requested in Section 2 applies to this facility.
2-A. Report the percent by quantity of total 1986 production volume generated by each of the following
activities at this facility. (Enter zero if the activity was not applicable. The sum of all percentages
must be 100%).
[1 ] Production generated by manufacturing and/or formulating and packaging S2A1
pesticide active ingredients listed in Table 1, pages 4 through 12 ; ;_!%
[2] Production generated by manufacture of intermediates that S2A2
are sold •• _; _' _ %
[3] Production generated by manufacturing and/or formulating and packaging S2A3
EPA registered pesticides not listed in Table 1, pages 4 through 12 | %
[4] Production generated by manufacturing and/or formulating and packaging S2A4
chemicals other than EPA registered pesticides j j |i %
[5] Production generated by other activities (SPECIFY) _! _J _J %
S2A5A(Variable), S2A5B (Description) :
Total 1 0 0 %
2-B. Report the calendar year during which:
eoni
[1 ] Operations began at this facility .._ i _!_!_'_
Year
[2] Manufacturing and/or formulating/packaging
of either pesticide active ingredients or S2B2
pesticide products began at this facility ! ! | |._"
Year
[3] The most recent major expansion of plant and
equipment with respect to pesticides occurred S2B3
at this facility I _ i _ I _ I _
Year
A.21
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-C. Instructions for reporting Balance Sheet information on oaae 21.
Question 2-C on page 21 requests facility Balance Sheet information. Please read the instructions
and definitions below before completing Question 2-C. The number in brackets, for example,
'[1 ] Inventones,* correspond to Balance Sheet entries.
Reporting Period
Amounts for items in the Balance Sheets must be reported as of December 31, of calendar years
1985. 1986 and 1987 or. the last day of the facility fiscal year. If your facility does not operate on a
calendar year, you may substitute fiscal year data.
Reporting Conventions
Report all data for the facility. Report all dollar amounts in thousands.
If, for certain items, you do not have amounts at the facility level, you may use the balance sheets of
the firm that owns and controls your facility to estimate the amounts at the facHity level. Base the
estimate on vour facility's share of sales. If you have estimated an amount for a particular item, then
place an asterisk (*) to the right of the entry.
Balance Sheet Definitions
Current Assets: Report current assets, including cash and other assets that are reasonably
expected to be converted to cash, sold or consumed during the year.
[1] Inventories: Report the total value of all inventories owned by this facility
regardless of where the inventories are held. Inventories consist of finished
products, products in the process of being manufactured, raw materials,
supplies, fuels etc. Report inventories at cost or market value, whichever is
lower.
[2] Other Current Assets: Report all other current assets such as prepaid
expenses like rent, operating supplies, and insurance; also include cash and
accounts receivable.
[3] Total Current Assets: Report the sum of items [1] and [2].
A.22
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSU^FOR 1986
Part 8 Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-C. Instructions for reporting Balance Sheet information on pane 21 - continued
Noncurrent Assets: Report the total dollar value of all noncurrent assets, including physical
items such as property, plant and equipment; long-term investments and intangibles.
Include:
Land: Report the original cost of land.
Buildings/Plant: Report the cost of buildings including expansions and
renovations net of depreciation.
Equipment and Machinery: Report the cost of all equipment and machinery
net of depreciation.
Intangibles: Report intangibles including franchises, patents, trademarks,
copyrights net of accumulated amortization.
Other Noncurrerrt Assets: Report all noncurrent assets, like investments in
capital stocks and bonds. i
[4] Total Noncurrent Assets: Report the total noncurrent assets from each of the
Items listed above that apply.
[5] Total Current and Noncurrerrt Assets: Report the sum of items [3] and [4].
Current Liabilities: Report the total dollar value of all current liabilities that fall due for
payment within the year.
[6] Total Current Liabilities: Report all current liabilities like accounts payable,
accrued expenses and taxes and the current portion of long-term debt.
Noncurrent Liabilities and Equity. Report all noncurrent liabilities that fall due beyond one
year.
[7] Long Term Debt and Other Noncurrerrt Liabilities: Report all long-term debt
such as bonds, debentures, and bank debt, and all other noncurrent liabilities
like deferred income taxes.
[8] Owner Equity: Report the difference between total assets and total liabilities.
The amount obtained should include contributed or paid in capital (preferred
and common stock) and retained earnings.
[9] Total Noncurrerrt Liabilities and Equity: Report the sum of items (7] and [8].
[10] Total Liabilities and Equity: Report the sum of items [6] and [9].
A.23
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ENVIRONME NTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-C. Complete the facility Balance Sheet: Table 2-C below. Enter all information in thousands of dollars
as of December 31 for calendar years 1985, 1986, and 1987. If the facility fiscal year does not
correspond to the calendar year, please enter the months of the facility fiscal year below.
Facility 1986 fiscal year was from S2CA mOnth to S2C3 month.
TA
Current assets
[1] Inventories
[2] Other current assets
[3] Total current assets
Noncurrent assets
i
[4] Total noncurrent assets
[5] Total current and
noncurrent assets
Current liabilities
[6] Total current liabilities
Noncurrent liabilities and equity
[7] Long term debt and
other noncurrent liabilities
[8] Owner equity
[9] Total noncurrent liabilities
and equity
[10] Total liabilities and equity
3LE2-C. BALANCE SHEET
ASSETS
1985
($000)
S2C1A
S2C2A
S2C3A
S2C4A
S2C5A
LIABILITIES AND EQUITY
1985
($000)
S2C6A
S2C7A
S2C8A
S2C9A
S2C10A
1986
($000)
S2C1B
S2C2B
S2C3B
•S2C4B
S2C5B
1986
($000)
S2C6B
S2C7B
S2C8B
S2C9B
S2C10B
1987
($000)
S2C1C
S2C2C
S2C3C
-S2C4C' "-'-•-
S2C5C
1987
($000)
S2C6C
S2C7C
S2C8C
S2C9C
S2C10C
A.24
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-D. Instructions for reporting facility Income Statement Information on pace 24.
Question 2-D on page 24 requests facility Income and exoense information. Please read the
instructions and definitions below before completing Question 2-D. The numbers in brackets, for
example. '[1 ] Sales of Pesticide Chemicals,' correspond to the entries on Table 2-D. :
Reporting Period
Amounts for items in the Income Statements must be reported as of December 31 of calendar years
1985.1986 and 1987 or the last day of the facility fiscal year. If your facility does not operate on a
calendar year basis, you may substitute fiscal year data.
Reporting Conventions
Report all data for the facility. Report all dollar amounts in thousands.
If. for certain items, you do not have amounts at the facility level, you may use the Income
Statements of the firm that owns and controls your facility to estimate the amounts at the facility
level. If you need to estimate any items, estimate them based on your facility's share of sales. If you
have estimated an amount for a particular item, then place an asterisk (*) to the right of the entry.
Income Statement Definitions
Revenues
[1]
[2]
[3]
W
Sale of Pesticide Chemical*: Rpport the total sales value of a!! pssticids chemicals.
This should Include all pesticide active ingredients, intermediates, and finished
pesticide products. In cases where the pesticide chemical is not sold (there is no
known sales price) but is transferred to another facility owned by the company for
further processing and/or formulating/packaging, the fadlity share of sales generated
by the final product should be allocated to the facility. This share shquld be estimated
based on its percent of total production costs. Divide the sale of pesticide chemicals
into the following categories:
[a] Pesticide chemicals listed In Table 1: Report revenues from the manufacture
and/or formulating/packaging of pesticide active ingredients listed in Table 1,
pages 4 through 12 or intermediates produced during the manufacture of active
Ingredients listed in Table 1.
[b] Other Registered Pesticide Chemicals: Report revenues from pesticide
chemicals not reported In [la].
Revenue from Pesticide Contract Work or Toiling: Report the revenue from
pesticide contract work done by this facility for other facilities or firms.
Other Revenue: Report all other revenues like the sales value of products and
services not reported in items [1] and [2].
Total Facility Revenues: Report the sum of items [1 ] through (3J.
A.25
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-D. Instructions for reporting facility Income Statement information on oaoe 24 - continued
Expenses
Manufacturing Costs (Cost of Materials and Services Used): Include all manufacturing
and/or formulating/packaging costs like direct materials, direct labor and indirect costs that
were either put into production, used as operating supplies, or used in repair and
maintenance. Report total delivered cost after discounts and including freight of materials
actually consumed or put into production during the year. Include purchases, cost of
interpiant transfers to the facility, and withdrawal from inventories.
Pesticides
[5]
[6]
[8]
Material and Product Costs: Report the total cost of all raw materials
including packaging materials that were used in the production and/or
formulating/packaging of pesticide chemicals/products. Include cost of
products bought and sold.
Direct Labor Costs: Report the total cost, including fringe benefits, of
all direct labor that can be traced to the production and/or
formulating/packaging of pesticide chemicals/products. •
Cost of Pesticide Contract Work or Tolling: Report the cost of all
contract work done for you by others using materials furnished by your
facility, include the total payments mads during the year for such work.
including freight out and in.
Other Pesticide Costs: Include all other pesticide related expenses.
such as effluent treatment and disposal, and energy used directly in
producing the product, not included in [5] through [7].
Non Pesticides
[9] Nonpesticidt Costs: Report all other manufacturing costs not included
in items [5] through (8]. Include manufacturing costs associated with
nonpesticide chemicals or products. Report the types of cost for items
[5] through [8] for nonpesticide products and services.
Report the expenses listed below for the whole facility, not Just pesticides.
[10] Depreciation: Report the depreciation on buildings, plant, equipment,
and machinery at your facility.
[11] Fixed Overheads: Report the total from all types of overhead. Include
rent, nonproduction utilities, selling costs, administration and general
expenses for your facility.
[12] Research and Development: Report all research and development
costs incurred during the year.
[13] Interest: Report the total interest expense on all funds during the year.
[14] Federal, State and Local Taxes: Report the total federal, state and
local taxes payable during the year.
[15] Other Expenses: Report all other expenses not reported in items [10]
through [14].
[16] Total Costs and Expenses: Report the sum of items [5] through [15].
A. 26
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-D. Complete the facility Income Statements. Table 2-D below. Enter all information in thousands of
dollars as of December 31 for calendar years 1985, 1986, and 1987. If the facility fiscal year does
not correspond to the calendar year, please enter the months of the facility fiscal year below.
Facility 1986 fiscal year was from
32:
^rnonth to
S2DB
month.
TABLE 2-D. INCOME STATEMENTS
REVENUES
: [1] Sales of pesticide chemicals
[a] Pesticide chemicals
listed in Table 1
: [b] Other registered pesticide
: chemicals
1985
($000)
S2D1AA
1986
($000)
S2D1AB
1987
(SOOO)
S2D1AC
S2D1BA
S2D1BB
S2D1BC
[2] Revenue from pesticide contract
work or tolling
[3] Other revenue
[4] Total facility revenues
Manufacturing costs
[5] Pesticide material and product costs
[6] Pesticide direct labor costs
[7] Cost of pesticide contract work
[8] Other pesticide costs
[9] Nonpesticide costs
Facility costs
[10] Depreciation
[11] Fixed overheads
[12] Research and development
[13] Interest
[14] Federal, state and local taxes
[ 1 5] Other expenses
[16] Total costs and expenses
S2D2A
S2D3A
S2D4A
EXPENSES
1985
($000)
S2D5A
q?nfia
S2D7A
S2D8A
S2D9A
S2D10A
S2D11A
S2D12A
S2D13A
S2D14A
S2D15A
S2D16A
S2D2B
S2D3B
S2D4B
1986
($000)
S2D5B
«TVM
S2D7B
S2D8B
S2D9B
S2D10B
S2D11R
S2D12B
S2D13B
S2D14B
s*ni SR
S216B
S2D2C , i
q?mr
S2D4C
1987
($000) j
S2D6P
S2D7C
S2D8C
S2D9C
S2D10C
•wnnr
«TYI9r
S2D13C •
S2D14C
C9ni ^r
«nlfip
A.27
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-E. Did this facility borrow funds to finance a capital investment during calendar year 1986?
52E
YES 1 (CONTINUE)
NO 2 (SKIP TO QUESTION 2-G)
2-F. What was the 1986 interest rate charged? .?.?!.
2-G. Enter the number of years over which a typical capital project is financed.
32G
years
Comments for Section 2: Questions 2-A through 2-G. Reference entry by question number.
A.28
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
Does the respondent choose to have the Agency assess economic impacts based on financial
averages calculated from information submitted in Part A and Part B (without data requested in
Tables 2H, I, and J) of this census for all products within a given facility (manufacturing site)?
Note: The use of financial averages to represent all products at a facility may affect the accuracy
of economic impact projections for some products.
YES 1 (SKIP TO SECTION-2,-lL=.
PAGE 38)
NO 2 (CONTINUE)
2-H. This section requests information on Table 1 Pesticide Active Ingredients produced at your facility
in 1986.
Instructions for completing Table 2-H Pesticide Production; Technical Grade Products. D. 30.
Column [1] Active Ingredient Code. Enter the code for every Table 1 active ingredient that
your facility produced in 1986 as a technical grade product. If part of the
production was transferred to another facility, list that part as a separate entry as
described by Product Code B. If you need additional space to report, photocopy
the table before making any marks on it. :
Column 12] Product Code. Enter the code that best describes the product reported in
column [1].
Code Definition
A Table 1 Pesticide Active Ingredients produced at this facility in 1986 to be sold
as technical grade products by this facility.
3 Table 1 Pesticide Active Ingredients produced at this facility in 1986 and
transferred to another facility owned by this firm.
C Table 1 Pesticide Active Ingredients produced at this facility in 1986 for another
firm (i.e., tolling).
Column [3] 1986 Average Unit Production and Packaging Cost in Dollars. Provide the
average production cost for one unit of the item reported in column [1]. Include
such costs as material costs (!.e., the costs of all raw materials, including packaging
materials that were used in the production and packaging of pesticide products),
direct labor costs, and any other pesticide costs.
Note that the column [3] entry corresponds to items (5] through [8] under question
2-D on page 23.
Express the costs in dollars. Do not include allocations for corporate overhead,
administrative expenses, research and development, capital costs or interest
expense.
Column [4] 1986 Average UnR Sales Price In Dollars. Report the average selling price for one
unit of the item reported in column [1]. Express the selling price in dollars. If the
pesticide chemical Is not sold when it leaves the facility, but is transferred to another
facility owned by the firm for further processing, the sales price of the final product
should be allocated to both facilities based on their share of the costs to produce
the product This is referred to as the "percentage of cost procedure.' An example
of the percentage of cost procedure can be found on pages 28-30.
A.29
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
Instructions for completing Table 2-H Pesticide Production: Technical Grade Product?
contini
Column [5] 1986 Production Quantity. In column [5], report the total quantity of the item
reported in column [1 ] that was manufactured at this facility during 1986.
Column [6] Unit of Measure, in column (6], circle the code that corresponds to the unit of
measure you used to calculate the information you reported in columns f31 f4i rsi
and [7]. i M M J
P = Pounds
T = Short tons
M = Metric tons
G = Gallons
Column [7] Sum Annual Production Over Three Years (1985-1987). Provide the total amount
(sum) of the product reported in column [1] that was produced by this facility in
1985,1986, and 1987 combined.
Column [8] Percent Exported Over Three Years (1985-1987). Report the percent of the
product in column [1] exported in 1985, 1986, and 1987 combined, i.e., what
percentage of column [7] was exported?
A. 30
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
EXAMPLE OF PERCENT OF COST PROCEDURES
The following is an example of a hypothetical facility that both produces and formulates/packages active
ingredients. It demonstrates use of the "Percentage of Cost Procedure.'
Assume the facility produces 1,200 Ibs of active ingredient 000 in 1986. of which:
400 Ibs are sold as technical grade.
200 Ibs are formulated and packaged on site as product group P01.
200 Ibs are formulated and packaged by another facility owned by this company also as product
group P01
200 Ibs are formulated and packaged as product group P01 under contract by another facility not
owned by this firm. The contract work is paid for by this plant.
200 Ibs are combined with 100 Ibs of active ingredient 001 to formulate 300 Ibs of product group
P02. Active ingredient 001 is purchased from another firm.
Unit sales are:
S2.50/lb for technical grade
$4.00/lb for formulated product group P01
$4.25/lb for formulated product group P02
Unit production, formulating and packaging costs are:
Production of active ingredient 000
Purchase of active ingredient 001
Formulating and packaging on site
Formulating and packaging at other faculty owned by this company
Formulating and packaging at other facility not owned by this company
$1.50/lb
$2.00/lb
$0.50/lb
$0.50/lb
$0.60 lb
A.31
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
EXAMPLE (continued)
Instructions for completing the 1985-1987 Pesticide Production Tables. This facility would complete the
Pesticide Production Table for Technical Grade Products and Formulated/Packaged Products as follows:
Technical Grade Products (Table 2-H. p. 30)
Line 1
Line 2
400 Ibs of Al 000 are sold as technical grade. The unit cost of production is $1.50/Ib and the
unit sales price is $2.50/lb. This corresponds to Product Code A on page 26.
200 Ibs of Al 000 are transferred to another facility owned by this firm to be formulated and
packaged. The unit cost of production to this facility jremains $l.50/lb and the selling price of
the formulated product Is $4.00/lb. Since the production cost represents 3/4 of the total cost
to produce the formulated product, the unit sales price for this facility is 3/4 of the total unit
sales price of $4.00/Ib or $3.00/Ib. This corresponds to Product Code B on page 26.
Formulated/Packaged Products (Table 2-J. p. 371
LJnel
Line 2
Line 3
200 Ibs of Al 000 are formulated/packaged on site by this facility. The total unit cost of the
formulated and packaged product is $2.00/lb ($1.50/lb for production plus $.50 for formulating
and packaging. Since all unit costs are incurred by this facility, the total unit sales price of
$4.00/lb Is allocated to this facility. This corresponds to Product Code A on page 35. (Note-
This 200 Ibs is in addition to the 400 Ibs + 200 Ibs listed on Line 1 and Line 2 under Technical
Grade Products.)
200 Ibs of Al 000 are produced by this facility and formulated/packaged by another firm under
contract to this facility. This facflity pays for the contract work. The total unit cost of the
formulated/packaged product Is $2.lO/lb ($1.50/lb for production plus $.60/lb for
formulating/packaging). Since all unit costs are incurred by this facility, the total unit sales
price of $4.00/lb is allocated to this facility. This corresponds to Product Code B on page 35.
200 Ibs of Al 000 are combined with 100 Ibs of Al 001 to formulate 300 Ibs of products in
Product Group P02. Al 001 Is purchased from another firm. The total cost of production is
$2.16/lb (2/3 of $1.50 + 1/3 of $2,00 for active ingredients plus $.50 for formulating/
packaging). Since this facility incurred the total unit cost the total unit sales price is allocated
to this facility. This corresponds to Produce Code E on page 35. (Note: If the facility
purchases active ingredient 001 from another firm and then formulates/packages it, this would
be product group P03 and would also be assigned Product Code E.
A. 32
-------
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A.33
-------
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OTECTION AGEN
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A. 34
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-I.
During calendar year 1986, did this facility sell any Intermediates produced during the manufacture
of pesticide products containing a pesticide active ingredient listed in Table 1? (CIRCLE YES OR
NO)
YES .....».»........,..........>_ '(READ THETN5TRUCT10N5
BELOW AND COMPLETE
TABLE 2-1 ON PAGE 34)
NO,
.> (GO TO QUESTION 2-J ON
PAGE 35)
Instructions tor completing Table 2-1 Pesticide Production: Intermediates.
Column [1] Intermediate Name. Enter the name of every intermediate produced in 1986 during
the manufacture of Table 1 Pesticide Active Ingredients and sold. Please include all
chemicals and codes that you listed in Part A of the Pesticide Manufacturing Facility
Census questionnaire. If you need additional'space to report, photocopy the table
before making any marks on it.
Column [2] Active Ingredient Code. Enter the code for every Table 1 active ingredient
associated with your production of the intermediate listed in column [1].
Column [3] Average Unit Production Cost in Dollars. Provide the average production cost for
one unit of the item reported in column [1]. Include such costs as material costs
(i.e., the costs of all raw materials, including packaging materials that were used in
the production and packaging of pesticide products), direct labor costs, the costs of
pesticide contract work or tolling done for you by others, and any other pesticide
costs.
Note that the column [3] entry corresponds to items [5] through [8] under question
2-D on page 23.
Express the costs in dollars. Do not include allocations for corporate overhead,
administrative expenses, research and development, capital costs or interest
expense.
Column [4] 1986 Average Unit Sales Price in Dollars. Report the average selling price for one
unrtof the item reported in column [1]. Express the selling price in dollars. If the
pesticide chemical is not sold when it leaves the facility, but is transferred to another
facility owned by the firm for further processing, the sales price of the final product
should be allocated to both facilities based on their share of the costs to produce
the product This is referred to as the "percentage of cost procedure.'
A.35
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS; FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
Instructions tor completing Table 2-1 Pesticide Production: Intermediates - continued
Column [5] 1986 Quantity Sold. In column {5], report the total quantity of the item reported in
column [1 ] that was produced at this facility during 1986 and sold.
Column {6] Unit of Measure. In column [6], circle the code that corresponds to the unit of
measure you used to calculate the information you reported in columns (3], [4], [5]
and (7|.
P = Pounds
T = Short tons
M = Metric tons
G = Gallons
Column [7] Sum Annual Quantity Sold Over Three Years (1985-1987). Provide the total
amount (sum) of the product reoorted in column (1 ] that was produced and sold by
this facility in 1985,1986, and 1987 combined.
Column (8] Percent Exported Over Three Years (1985-1987). Report the percent of the
product in column [1] exported in 1985, 1986, and 1987 combined, i.e.. what
percent of column [7] was exported.
A. 36
-------
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A.37
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-J. During calendar year 1986, did this facility produce any formulated or packaged products
containing a pesticide active ingredient listed in Table 1? (CIRCLE YES OR NO)
YES > (READ THE INSTRUCTIONS
BELOW AND COMPLETE
TABLE 2-J ON PAGE 37)
NO > (GO TO QUESTION 2-K ON
PAGE 38)
Instructions for completing Table 2-J Pesticide Production: Formulated or Packaged Products.
Column [1] Product Group. Group ail formulated/packaged products according to the active
ingredient(s) they contain, regardless of relative proportions or concentrations and
assign each group a number. For example, if your products contain two active
ingredients (say A and B), group ail products containing only A into one group (call
it #1), group all products containing B into a second group (call it #2) and all
products containing both A and B into a third group (call it #3). Report dry and wet
formulations separately, if you need additional space to report, photocopy this
table before making any marks on it.
Column [2] Active Ingredient Code. For each product group formulated /packaged in 1986.
enter the code for every Table 1 active ingredient that it contained.
Column [3] Product or Trade Name. Enter the trade name or name of the product.
Column [4] Product Code. Enter the code that best describes the product reported in
column (1].
Code Definition
A Table 1 pesticide products produced and formulated/packaged at this facility
in 1986.
B Table 1 pesticide products produced at this facility in 1986 and formulated/
packaged for you by another firm on a contract basis.
C Table 1 pesticide products formulated/packaged by this facility in 1986, and
produced by another facility owned by the firm that owns this facility.
D Table 1 pesticide products formulated/packaged by this facility on a contract
basis in 1986, for a firm other than the firm that owns this facility.
E Table 1 pesticide products formulated/packaged by this facility from active
ingredients purchased from another firm.
A. 38
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
instructions for completing Table 2-J Pesticide Production:
continued
Formulated or Packaged Products -
olumn [5]
1986 Average Unit Production and Formulating/Packaging Cost in Dollars.
Provide th& average production rnst far nna unit — Include sucn costs as material
r-nvt
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-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-K. Facility 1986 Markets
Estimate the percentage of this facility's total 1986 production that was delivered to the markets
listed below. (Enter zero if the market is not applicable. The percentages snould sum to 100%);
.. lll^Agriculture (U.S.A.) „. i2£l _._' v°
[3] Home, garden (U.S.A.) ........................................... 32JK2 .....
[4] Export (Outside U.S.A.) .......................................... Z2K&. .....
[5] Other markets (SPECIFY) .......... f.?.^A....;;..;ariable ) ......
S2K53 'Description:
Total
1 0 0 %
2-L Facility Operations
Report the operational information listed below for calendar year 1986. (Enter zero if the category
is not applicable).
[1] The number of days the entire facility was in operation ...?.?L1 .......................... ; _ j _ | _ ;
[2] The number of days part or ail of the facility manufactured
pesticide chemicals [[[ .§«&.?. ............ | _ ; _ j _ i
[3] The number of days pan or all of the facility formulated /packaged
pesticide chemicals .......................... [[[ .?.?L3.... j _ « _ i _ ;
2-M. Employee Information
In lines [1] through [4], report the total employee hours worked at this facility in the months of
January 1986. May 1986 and Novemoer 1986 in the categories indicated. In lines [5] and [6], enter
the average number of shifts run in the entire facility in a week, and the average number of hours per
shift for the months of January 1 986. May 1 986 and November 1 986.
[1] Total employee hours in pesti-
cide chemicals production
[2] Total employee hours in pesti-
cide formulating and packaging
[3] Total employee hours in other
production
[4] Total employee hours in non-
; pryfoction
[5] Average number of shifts run in
the entire facility in a week
[6] Average number of hours per
shift in the entire facility
January 1986
S2M1A
S2M2A
S2M3A
S2M4A
S2M5A
S2M6A
May 1986
S2M1B
S2M2B
S2M3B
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-N. Estimate the liquidation values less closure and post-closure costs of the pesticide production and
pesticide formulating/packaging lines at this facility if you were to dose them permanently within the
next three years. Include the value of fixed assets, working capital and real estate in your
calculation of liquidation values. Report the estimates in thousands of dollars and enter zero dollars
if the item is not applicable.
Pesticide production lines ($000)
[1] Liquidation value (less closure and post-closure cost) 32NA1A
Closure and post-dosure cost S2NA1B
[2] Cost to convert to non-Table 1 pesticide active ingredients
or non-pesticide products S2NA2
Pesticide formulating/packaging lines
[1] Liquidation value S2NB1
[2] Cost to convert to non-Table 1 pesticide active ingredients
or non-pesticide products S2NB2
2-O. Did this facility have any property tax assessment for 1986?
YES 1 (CONTINUE)
520 NO 2 (SKIP TO QUESTION 2R)
2-P. What was the 1986 property tax assessment value of the items listed below? Report the values in
thousands of dollars and enter zero if the item listed is not applicable.
State tax assessment value ($000)
[1 ] Land SP1
[2] Buildings SP2
[3] Equipment and machinery 003
[4] Total property tax assessment value SP4
Local tax assessment value
[5] Land SPS
[6] Buildings SP6
[7] Equipment and machinery SP7.
[8] Total property tax assessment value SPB
A.42
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSU^FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-Q. What was the 1986 assessed value of the property expressed as a percentage of market value (1986
level of assessment)? (Enter zero if the item was not applicable).
[1 ] State assessment percentage .
•^2)—tocal assyysment percentage .
2-R. Overall, what is the major source of comoetition ror pesticide products produced at this facility in
each of the three markets listed below?
The same proaucts means competing products containing identical or nearly identical pesticide
active ingredients or percentages of active ingredients but having different trade or brand names.
Substitute products means competing products oertormmg the same pesticidal functions but
containing different pesticide active ingredients.
Competition
Market
[1 ] Domestic producers of the
same products
[2] Foreign producers of the
gams products
[3] Domestic producers of the
substitute products
[4] Foreign producers of the
substitute products
[5] No competition .
[6] No market share
Local
Regional
n
n
L__I
n
n
L_4
n
n
National
n
n
n
n
n
International
n
n
n
n
n
n
S2R1
S2K2
S2F<3
A.43
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSU^FOR 1986
Part B Financial and Economic information
SECTION 2: FACILITY FINANCIAL INFORMATION
Comments for Section 2. Reference entries by question number.
A.44
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSU^FOR 1986
Part B Financial and Economic Information
SECTIONS: FACILITY CONTACT
Enter the name, title, telephone number and address (if different from the facility mailing address) of the
facility representative to De contacted with Questions regarding your resoonses to Part B:
Last)
Tttie
S3C
Telephone Number
Address (if different from facility mailing aadress):
Firm or Facility Name
Street or P.O. Box
! ' : I I s
City or Town
33F
State I Zip Code
S3G S3K
CERTIFICATION: The information provided in Part B of the questionnaire, as well as that provided
in all others, must be certified by having the responsible individual for your facility complete and sign
the Certification Statement Item 6 on cage 3 of this questionnaire.
A.45
-------
APPENDIX B
MAPPING OF PESTICIDE ACTIVE BVGREDD2NTS INTO CLUSTERS
-------
-------
Appendix B: MAPPING OF PESTICIDE ACTIVE INGREDIENTS INTO CLUSTERS
This appendix lists the 56 PAI clusters used to define PAI markets in the EIA. As discussed in Chapters
3 and 4, the clusters were developed by EPA's Office of Water based on previous work by EPA's Office of
Pesticide Programs (OPP). Individual PAIs that are included in each cluster are listed in three columns. The
first column includes the 270 PAIs that were considered in-scope. (The next column shows the Chemical
Abstract Service Number for the in-scope PAIs.) Since the PAIs that will not be covered by the effluent
guidelines may compete with those that are covered, non-regulated PAIs have also been assigned to clusters.
Thus, the second PAI column ("Other PAIs on OPP List") includes those PAIs not considered for regulation at
this time, but included in the original OPP clusters. Many of these chemicals have already been regulated (see
the header of the table for notation indicating whether PAIs are covered by other regulations, as well as the
production/marketing status of the PAIs). The third PAI column ("new PAIs") lists PAIs that have been
registered since 1980 and were, therefore, not included in the original OPP clusters.
B.I
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-------
APPENDIX C
METHODOLOGY FOR ESTIMATING THE PRICE ELASTICITY
OF DEMAND FOR PESTICIDE CLUSTERS
-------
-------
Appendix C: METHODOLOGY FOR ESTIMATING THE PRICE ELASTICITY OF DEMAND FOR
PESTICIDE CLUSTERS
This appendix provides the complete methodology for estimating the price elasticity of demand for pesticide
clusters. The price elasticity of demand is used in the EIA to predict the change in demand given an increase in
PAI price due to compliance with the effluent guidelines. (See Chapter 4.)
C.I
-------
-------
ESTIMATES OF THE PRICE ELASTICITY
OF DEMAND FOR PESTICIDE CLUSTERS
Prepared for:
Economic and Statistical Analysis Branch
Engineering and Analysis Division
Office of Science and Technology
Office of Water
U.S. Environmental Protection Agency
Washington, D.C. 20460
Prepared by:
Abt Associates Inc.
Cambridge, MA 02138
May 1991
C.2
-------
TABLE OF CONTENTS
1.0 Introduction
2.0 Price Elasticity of Demand for Agricultural Pesticides 3
•a
2.1 Methodology J
2.2 Review of Empirical Studies of the Price Elasticity
of Demand for Pesticides 6
2.3 Price Elasticity of Demand for Food Commodities 22
2.4 Feasibility of Non-Chemical Substitution 30
2.5 Contribution to the Variable Cost of Production 36
2.6 Productivity of Expenditures for Pesticides 38
2.7 Conclusions - Agricultural Pesticides 42
3.0 Price Elasticity of Demand for Pesticides Used
Non-AgriculturaUy 55
59
4.0 Conclusions
References . i
C.3
-------
1.0 INTRODUCTION
Purpose of the Analysis
Abt Associates has submitted a draft economic impact assessment (EIA) methodology for assessing
the costs of new effluent guidelines for the pesticide industry. The draft EIA methodology relies on the
use of price elasticities of demand for pesticide clusters. In this memorandum, demand elasticities for each
cluster are estimated based on a review of empirical analyses, the elasticity of demand for food
commodities, and a consideration of the factors predicted by microeconomic theory to influence elasticity
of demand.
Definition of the Price Elasticity of Demand
In general, the economic concept of elasticity measures the sensitivity of the dependent variable to
a change in the value of an independent variable. In particular, the price elasticity of demand measures
the sensitivity of consumers to changes in price. (Since this is the elasticity measure of concern for this
report we may, for convenience, use the term 'demand elasticity5 in place of the term 'price elasticity of
demand'.)
The price elasticity of demand estimates the degree to which a change in price results in a change in
the quantity demanded. It can be defined as the percentage change in demand divided by the percentage
change in price. If consumers cut back their purchases to such a large extent that any price increase reduces
total revenue, then demand is said to be elastic, i.e., customers are sensitive to price changes. If consumers
cut back their purchases only slightly in response to higher prices, resulting in an increase in revenue,
demand is said to be inelastic, i.e., customers are not as sensitive to price changes. The value of the price
elasticity of demand is unbounded and may be positive or negative. It is expected, however, that price and
demand are negatively correlated, i.e., an increase in price results in a decrease in the quantity demanded.
The price elasticity of demand is therefore usually negative.
Four possible values, or ranges of values, of the price elasticity of demand are of particular interest.
First, if the absolute value of the elasticity of demand is greater than one, demand is termed elastic. In
other words, the percentage change in demand is greater than the percentage change in price. Second,
demand is said to be inelastic when the absolute value of the elasticity of demand is less than one but
greater than zero. Third, if the value of the elasticity of demand is zero, demand is said to be perfectly
inelastic. That is, consumers will continue to purchase a given quantity of a good, despite any changes in
price. Finally, if demand and price change by equal percentages, the value of the demand elasticity is
exactly one, and demand is said to have unit elasticity. Numeric values are generally expressed relative to
C.4
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a one percent change in price. For example, an elasticity of -1.5 means that a 1 percent increase in price
would result in a 1.5 percent decrease hi the quantity demanded.
Measurements of the price elasticity of demand are of use hi predicting the incidence of a price
increase. As the absolute value of the price elasticity rises, the proportion of the cost increase that can be
passed on to consumers declines. If demand is perfectly elastic, no cost pass through is possible.
Market Definition
In order to estimate the price elasticity of demand for pesticides, a clear definition of the markets of
concern must be developed. In this analysis, the markets are defined to be 44 separate clusters of pesticides.
The clusters are groups of pesticide active ingredients which are close substitutes for a given end-use. For
example, insecticides used on vegetables is one of the clusters; herbicides used on turf is another.
The elasticity of demand for pesticides may vary significantly between the clusters, since each cluster
faces different market forces. In particular, a distinction may be drawn between the agricultural end-uses
and the non-agricultural end-uses. Agricultural sales represent approximately 70 percent of the total
expenditures for conventional pesticides in the U.S., with the remainder split about equally between
commercial and domestic sales (U.S. EPA, 1988). In contrast to the non-agricultural markets, the basic
market structure within which fungicides, herbicides, and insecticides are used agriculturally is somewhat
consistent across users and some documentation is available by which to estimate the elasticity of demand.
The price elasticity of demand for pesticides used agriculturally will be analyzed first, followed by a
discussion of the elasticity of demand for pesticides used hi the non-agricultural sector.
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2.0 PRICE ELASTICITY OF DEMAND FOR AGRICULTURAL PESTICIDES
Within the agricultural pesticide market there exist several industry sectors including manufacturers,
formulators and packagers, distributors, and retailers of pesticides. The primary goal of this analysis is
to estimate the elasticity of demand faced by the manufacturers of the active ingredients. However, most
studies consider the demand elasticity of the end-user rather than that of the formulator/packager (usually
the direct customer of manufacturers). This analysis will assume that the demand elasticity of the
formulator/packager is equal to the demand elasticity of the end-user since data on formulator/packager
demand elasticity were not located. Assuming competitive markets, the long-run elasticities faced by the
manufacturing sector should be similar to the elasticities faced by formulators/packagers.
2.1 Methodology
There is no one recognized source of information for the price elasticity of demand for pesticides;
in fact, there is an acknowledged lack of information in this area of study. Abt Associates conducted a
thorough search for analyses of the price elasticity of demand for pesticides and also sought expert opinion
as to the expected elasticities. The sources considered included literature searches using the following
databases from Dialog Information Services: Economic Literature Index, Dissertation Abstracts Online,
Agribusiness U.SA., Agricola, Agris International, and NTIS. A search for subject matter containing the
following key words was conducted: price elasticity, or demand, or demand elasticity, and agricultural, or
chemical, or pesticide, or herbicide, or fungicide, or insecticide. In addition to the literature search, Abt
Associates sought information from the U.S. EPA Office of Pesticide Programs, the U.S. EPA Office of
Policy, Planning, and Evaluation, several offices of the U.S. Department of Agriculture, the U.S.
International Trade Commission, the Chemical Specialty Manufacturers Association, the National
Agricultural Chemical Association, the World Bank, Resources for the Future, the editor of the American
Journal of Agricultural Economics, a market research firm, Cornell University, North Carolina State
University (Dr. Gerald Carlson), Texas A&M University (Dr. Ron Lacewell), Virginia Polytechnic Institute
(Professor George Norton), Iowa State University, Stanford University (Dr. Sandra Archibald), the
University of Massachusetts (Professor Joe Moffitt), the University of Arkansas (Professor Mark Cochran),
and Harvard University.
The literature search and conversations with the listed expert sources indicated that studies of the
price elasticity of demand for pesticides are sparse, and that the existing analyses offer conflicting
conclusions and are often controversial. Further, an attempt at compiling expert opinions as to expected
elasticities failed; the lack of available research on this issue precluded compact, ready answers that could
C.6
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be conveyed by telephone. In order to develop the elasticity estimates, Abt Associates developed a five-
pronged approach.
First, as described in Section 2.2, Abt Associates considered the relevant empirical studies. Though
these studies do not comprehensively answer the question at hand for reasons that are presented below, they
do provide estimates of demand elasticity for selected clusters. The second input, and the main source of
data from which pesticide elasticities are derived in this analysis, is U.S. Department of Agriculture's
(U.S.DA.) analysis of the price elasticity of demand for food commodities (U.S.DA., 1985, 1989). The
elasticity of demand for farm inputs can be derived from the elasticity of the demand for farm commodities
since demand for production inputs must ultimately reflect demand for the end product. Though the two
elasticities may not correspond exactly, the elasticity of demand for the food commodities can serve as a
reasonable proxy for the elasticity of demand for pesticides in the absence of more relevant data.
U.S.D.A.'s estimates of elasticity and the use of these estimates for purposes of this analysis are discussed
in Section 2.3.
The other three factors used to estimate the elasticity of demand for pesticides are (1) the feasibility
of employing non-chemical or non-biological pest control methods, (2) the percent of production cost
contributed by the pesticide of interest, and (3) the productivity of expenditures for pesticides. Section 2.4
groups pesticide clusters based on the feasibility of substituting another pest control method for chemical
and biological pesticides. The greater the feasibility of substitution, the higher the expected price elasticity
of demand. Since the clusters group chemical and biological substitutes, the potential substitutes for a
cluster of pesticides are cultural or environmental control technologies, such as crop rotation or the
introduction of predatory insects. The rankings of the feasibility of non-chemical substitution for a cluster
of pesticides are based on Pimentel et al. (1991).
The analysis of pesticide contribution to the cost of production of a commodity is based on U.S.DA.'s
published estimates of the cost of production in the farm sector (U.S.DA., 1989a, 1989b, 1988). The
greater the contribution to the cost of production, the higher the expected price elasticity of demand.
Pesticide contribution to production costs is reported in Section 2.5.
Finally, the productivity of expenditures for pesticides is examined in Section 2.6. In theory, if
pesticides are highly productive (i.e., the costs of pest damage without pesticides greatly exceeds the
expenses of pesticide application), a prescribed pesticide dosage will be applied regardless of some degree
of price variation. In other words, if pesticides are highly productive, the demand for pesticides is likely
to be inelastic.
C.7
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Section 2.7 combines the information from the empirical studies, the elasticity of demand for food
commodities, the substitutability rankings, the data on pesticide contribution to production cost, and the
measures of pesticide productivity to estimate the price elasticity of demand for agricultural pesticide
dusters. The U.S.DA. estimates of the elasticity of demand for food commodities are used as the basis for
the final elasticity estimates. The other factors are analyzed to determine cases in which the elasticity of
demand for food commodities may vary substantially from the elasticity of demand for pesticides applied
to the food commodities. In cases where there is a clear indication that the elasticity of demand for the
food commodities and the elasticity of demand for the pesticides applied to the food commodities differ,
.1
the elasticity estimates are adjusted in the appropriate direction.
Precise quantification of the elasticity of demand, however, is not revealed through the examination
of feasibility of substitution, contribution to costs, and productivity of the pesticides. The results only
indicate whether demand for the pesticides is likely to be more or less elastic than demand for the relevant
food commodities. Therefore, unless there is compelling evidence that the elasticities of demand for food
and pesticides applied to food differ substantially, this analysis relies on the estimates of elasticity of
demand for food commodities to represent the elasticity of demand for pesticides applied to those food
commodities. It should be clear that the resulting elasticity estimates serve as indicators of the approximate
magnitude of demand elasticity and not as precise quantifications of these elasticities.
22 Review of Empirical Studies of the Price Elasticity of Demand For Pesticides
The empirical analyses of the price elasticity of demand for pesticides can be separated into
econometric analyses and other analyses. The econometric analyses of demand elasticity employ several
different dependent variables. Variations in the dependent variable influence the resulting demand
elasticities. In particular, the dependent variables differ in the level of- aggregation of pesticides and in
whether pesticides are measured in units of production or units of use.
The level of aggregation of the pesticides may influence demand elasticity by determining the number
of close substitutes that are available. According to microeconomic theory, the more narrowly a product
is defined, the more substitutes that are likely to be available. For example, more substitutes are available
for pork chops than are available for meat.
If a product has many close substitutes, it is likely to be characterized by an elastic demand.
Consumers can react to a price increase by switching products without much loss of utility. If a product
has a more limited number of substitutes, consumers have little choice but to bear more of the price
C.9
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increase. For chemical pesticides in general, substitutes include only labor and other non-chemical pest
control methods. These are also the only substitutes for fungicides, herbicides, or insecticides since
pesticides are generally effective against only either pathogens, weeds, or insects. Since the clusters used
in this analysis were chosen to include all close chemical and biological substitutes for an end-use, the only
pest control alternatives are non-chemical and non-biological. Substitutes for specific active ingredients,
however, may include other active ingredients in addition to the non-chemical, non-biological alternatives.
For the purposes of determining the incidence of the cost increase resulting from new effluent
regulations, the ideal price elasticity of demand is that corresponding to each pesticide cluster. However,
few of the relevant analyses that Abt Associates located estimate elasticity of demand for clusters of
pesticides. Some of the analyses reviewed hi this report consider pesticides as a group as the dependent
variable; other studies analyze herbicides, fungicides, and insecticides separately or study the demand
elasticity for pesticides by crop. Another group looks at specific active ingredients.
In determining the elasticity of demand for clusters of active ingredients, it may at first appear
reasonable to bound the elasticity of demand for clusters of pesticides by using the elasticity of demand for
pesticides as a group as the lower bound and the elasticity of demand for individual active ingredients as
an upper bound. Since pesticides as a group will include all clusters of pesticides, it could be argued that
a cluster will exhibit an elasticity no lower than the elasticity of pesticides as a group. However, since the
elasticity of pesticides as a group represents an average of the elasticities of clusters it can not serve as a
boundary for any one cluster. Similarly, since the elasticities of demand for individual active ingredients
within a cluster will vary, the elasticity of any one active ingredient can not act as an upper boundary for
the elasticity of the cluster. For purposes of comparison, however, this analysis considers the empirical
analyses in two groups: those which consider pesticides as a group and those which consider individual
active ingredients.
A second major variation between the regression analyses of demand elasticities reviewed hi this
report is whether the dependent variable was measured in units of production (e.g., pounds produced per
year) or in units of use (e.g., pounds applied per acre per year). Due to potentially significant inventories
of pesticides and the dissimilar market structures of pesticide manufacturers and packagers/formulators of
pesticides, units of production and use may result in different estimates of elasticity. Further, some studies
defined the dependent variable in absolute terms while others used the percent of crop treated. Also, the
dependent variable was alternately measured in units of expenditure (e.g., dollars) and units of quantity
(e.g., pounds).
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Finally, the studies differed in the specification of the model (e.g., simultaneous equations vs. single
equation models, inclusion of an independent variable for labor), the time period included, and the region
of the country considered. All of the factors discussed above contribute to the difficulty of comparing the
empirical studies.
The results of the analyses of elasticity of demand, categorized by their definition of the dependent
variable, are described below.
Aggregated dependent variable measured in units of use
Five analyses were located which estimated demand elasticity for pesticides as a group and measured
the dependent variable in units of pesticide use. The studies are: Pingali and Carlson (1985), Miranowski
(1980), U.S. EPA (1974), Huh (1978), and Burrows (1983). The results of these studies are conflicting. Huh
reports demand for herbicides and insecticides used on corn as elastic. Contradicting this result, U.S. EPA
(1974) indicates that demand for corn and soybean herbicides and corn insecticides is inelastic. Miranowski
also concludes that demand for herbicides used on corn is moderately inelastic when labor is not included
in the analysis. However, the price coefficient in his equation is not significantly different from negative
one. When Miranowski includes labor in his model, price is insignificant, suggesting that labor is a
substitute for herbicides used on corn. Miranowski did not find price to be a significant factor in
predicting the level of corn insecticides used. Therefore, his model offers little further insight into the
elasticity of demand for insecticides. Burrows also found pesticide price to be insignificant in explaining
demand for pesticides and mitacides used on cotton. Finally, Pingali and Carlson estimate that the price
elasticity of demand for insecticides and fungicides used in orchards to be significantly different from zero,
but not significantly different from negative one.
Pingali and Carlson estimated price elasticity of demand as part of a larger, multidisciplinary study
over the 1976-1980 period for forty-seven orchards in Henderson County, North Carolina. To analyze the
effect of errors in subjective perception on the demand for pest controls, Pingali and Carlson ran a
simultaneous model of pest populations and pest controls. Their model involved a five-equation system
with two pest population equations (insect and disease infestation levels), two pesticide equations
(insecticides and fungicides), and one pruning status or labor equation.
The variables used in the pesticide equations were obtained from input demand functions developed
by Pingali and Carlson. The derived demand functions had four groups of variables: biological, input
prices, risk aversion, and human capital. The levels of insecticides and fungicides were given in terms of
pounds of active ingredients applied per acre of orchard. The cost per unit of insecticides and fungicides
c.n
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were given in dollars per pound of active ingredients. A two-stage least squares estimate of the system
resulted in a price elasticity of demand for insecticides of -1.39. The fungicide price elasticity of demand
was estimated as -0.92. The elasticities of demand for both insecticides and fungicides were found to be
significantly less than zero but not significantly different from negative one. The model can therefore be
interpreted to confirm a negative correlation between price and demand; it does not, however, indicate with
certainty whether demand is elastic or inelastic.
Miranowski (1980) considered alternative pest management systems for corn production with rising
energy prices. He used historical data from U.S.DA. agricultural regions from 1968, 1971, and 1976 to
estimate derived demand equations for insecticide and herbicide treatment. Separate weighted least squares
regression models for insecticide and herbicide treatment were developed as follows:
In ST,ih
where
ST,,h
pi.h
y
SCA
RE
+a, hi F|h +32 In Pf +33 hi y +% In SCA +a5 hi RE + hi I> -f e
share of corn treated with insecticides (i) or herbicides (h),
price of insecticides (i) or herbicides (h),
price of fuel,
value of corn output per acre,
share of corn acres hi cropland acres,
lagged production-oriented research and extension expenditures, and
farm wage rate.
Miranowski obtained data on insecticide and herbicide treatment, as the share of corn acres treated,
from the U.S.DA. annual pesticide surveys for 1968, 1971, and 1976. The input price indices, I^h and Pf,
were derived from data in U.S.DA.'s Agricultural Prices - Annual Summary (for 1967, 1972, 1977).
Miranowski estimated price elasticity of demand for insecticides as -0.78. However, the coefficient
was not significantly different from zero. He reported results of two herbicide demand models, one with
and one without the price of labor. When the price of labor is not included in the analysis, the coefficient
on herbicide price, -0.75, is significantly less than zero but not significantly different from negative one.
Therefore the elasticity of demand may be either elastic or inelastic, but only moderately so.
When the wage rate is held constant, the herbicide price coefficient is 0.03 and becomes insignificant.
Though the results of the model with labor held constant may be consistent with inelastic demand for
herbicides, the coefficient on labor is positive and significant, suggesting that labor and herbicides are
substitutes. The coefficients of the price of pesticides in the two herbicide models suggest that the price
of labor and the price of pesticides are co-linear. Since the coefficient for the price of herbicides becomes
C.12
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insignificant when labor is included in the model, it may be the case that the labor price variable is
dominating the herbicide price variable with the result that change in the dependent variable appears to be
largely a function of the cost of labor rather than the price of herbicides. However, when labor is absent
from the model, the coefficient of the price of pesticides probably includes some of the influence of labor
rate changes. The "true" elasticity of demand is therefore likely to fall between the two coefficients of -0.78
and 0.03, still indicating inelastic demand.
Huh (1978) estimated pesticide price elasticity of demand in his doctoral dissertation. Using cross-
sectional farm data from Minnesota, Huh modeled pounds of active ingredients of herbicides and
insecticides used on corn per farm (Q,-). Exogenous variables included in his final aggregate demand
equation were:
Xjw - adjusted and weighted price of pesticides (dollars per pound),
•Xf = acres of corn per farm, and
Du = a dummy variable for crop rotation plan (0 when farmer did not intend to plant corn
again in 1978, 1 when farmer intended to plant some or all of corn hi 1978).
The results of the regression analysis were as follows (standard errors are in parentheses):
2.212 - 1.464 In X,w +1.099 In 39 +0.381
(0.161) (0.064) (0.110)
+e
The coefficient of the price of pesticides was significantly less than zero and also significantly
different from negative one, indicating elastic demand. However, since an independent variable for
pesticide substitutes (e.g., labor) was not included, the coefficient on pesticide price may include the effect
of changes in labor or other substitute prices and therefore have a bias towards greater elasticity. Hub's
model is therefore likely to overstate the elasticity of demand to an unknown degree.
As part of an analysis of farmers' attitude towards alternate crop protection methods, U.S. EPA (1974)
described a survey of farmer sensitivity to pesticide price changes. Farmers in Iowa and Illinois responded
to the survey as follows:
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Percent of Respondents
Iowa Illinois
88 82
62
55
29
77
96
72
56
55
39
61
86
67
(of corn growers) believe all of their corn acres need
herbicides each year
(of corn growers) would not change herbicide use if cost
doubled
(of corn insecticide users) believe all of their corn acres
need insecticides
(of corn growers) believe all of their corn acres need
insecticides
(of corn insecticide users) would not change insecticide
use if cost doubled
(of soybean growers) believe all of then- soybean acres
need herbicides each year.
(of soybean growers) would not change herbicide use if
cost doubled
The results indicate that the majority of farmers surveyed are insensitive to price changes. Demand
for corn and soybean herbicides and corn insecticides appears to be inelastic.
The final study in this category was conducted by Burrows (1983). Burrows tested the hypothesis that
integrated pest management (IPM) will significantly reduce pesticide use. He also examined the
methodological issue of simultaneity between pesticide use and IPM adoption. Burrows considered only
insecticides and mitacides. His data were drawn from a random sample of San Joaquin Valley cotton
growers. The observations contain detailed information on output, pesticide and other input use, cost, and
revenue for 47 growers spanning a 5 year period from 1970-1974.
Burrows performed a Generalized Least Squares (GLS) procedure for both single and simultaneous
equation models. The dependent variable is insecticide and mitacide use measured hi sales dollars per acre
of cotton grown. Explanatory variables include average pesticide price per pound, an IPM consultant fee
per acre, and the expected yield in pounds per acre. Weather and cultural practices are included as proxies
for both the size of the pest population and pesticide persistence in the environment. A risk proxy, the ratio
of acres planted in cotton to total acres, is used assuming that, for higher ratio values, risk-averse growers
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will be likely to use more pesticides as insurance against crop loss. Pesticide price is a quantity-weighted
price index.
In both the single and simultaneous models, pesticide prices are insignificant. Burrows explained that
this may result from limited degrees of freedom (there are only ten price observations). He also offered
an alternative explanation that expenditures may not be sensitive to price when conflicting sources of
information - personal experience, pesticide salespersons, IPM consultants, and extension representatives -
affect the decision to spray. Another potential explanation is that if the expected rate of return from
pesticide use is high, price movements over a modest range would not have much explanatory value. The
price elasticity determined by the single equation model is approximately unity, -0.90. The elasticity
resulting from the simultaneous version of the model is -1.23. Since the coefficients were not significant,
these values are inconclusive.
Aggregated dependent variable measured in units of production
An earlier version of an economic impact .assessment of pesticide effluent guidelines analyzed
aggregated pesticides and measured the dependent variable in units of production (U.S. EPA, 1985). U.S.
EPA found that the price elasticity of demand for pesticides as a group, as well as for fungicides,
herbicides, and insecticides was significant and inelastic. EPA estimated pesticide elasticity of demand
based on the following log-linear function:
In PROD,
where:
PROD^, PPROD^
ACRE;
RPRICE;
EX;
a +b hi PRODM +c hi ACRE; +d hi RPRICE, +f
production of pesticide active ingredients in year t and t-1
acreage of principal crops planted in year t
real unit price for pesticide active ingredient in year t
Industrial production index hi year t
Elasticities were calculated for herbicides, insecticides, fungicides, and all pesticides. Pesticide
production rates were obtained from U.S. International Trade Commission, Synthetic Chemicals. , The units
of production were not given. Pesticide prices were average prices for each product group and for all
pesticides and were calculated from U.S. International Trade Commission, Synthetic Chemicals and
converted to real prices using the GNP Deflator. Based on this model, EPA obtained the following results:
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Ln of
Production
of
Intercept
Ln Real
Ln Acres
Ln
Production
Previous
Year
Industrial
Production
Index
Herbicides
R2 =0.98
Insecticides
R2 =0.68
Fungicides
R2=0.35
All Pesticides
R2 =0.89
-12.93
(-3.51)
-3.49
(-1.32)
-1.46
(-0.47)
-6.42
(-2.26)
3.19
(4.02)
1.53
(2.90)
1.04
(2.02)
1.88
(3.02)
-0.67
(-2.49)
-0.32
(-2.51)
-0.35
(-2.07)
-0.49
(-2.37)
0.299
(1.88)
0.142
(0.57)
0.05
(0.18)
9.427
(1.84)
-0.00651
(-3.24)
T-statistics are given in parentheses. The analysis indicated that demand is inelastic for each of the
three pesticide groups as well as for pesticides in general. All price elasticities were significantly less than
zero, and significantly lower than one in absolute value, except for the coefficient for herbicides which is
not significantly different from negative one. The model, therefore, indicated that the price elasticity of
demand for insecticides, fungicides, and all pesticides is inelastic. According to the model, the price
elasticity of demand for herbicides is near unity, meaning that demand may be either elastic or inelastic.
The analysis suggested that the demand for herbicides is more elastic than the demand for insecticides
or fungicides. EPA explained that during the 1970's herbicides experienced a large increase in application
rates and the proportion of acres treated and that "the coefficient on acres in the herbicide equation reflects
this". The authors also noted that "one of the reasons the amount of variation explained by the fungicide
equation was so low was that a very large proportion of fungicides were used for non-agricultural purposes".
The authors were unable to explain why business cycles are important for herbicides and not for the other
two product groups. It should be noted that the study did not include a variable for prices of substitutes
or final products. If these prices are correlated with pesticide prices, the coefficients may be biased.
Finally, the authors did not identify the type of end-use (e.g., agriculture, commercial, domestic) of the
pesticides included in their analysis.
Another factor that may influence the results obtained by EPA is that the dependent variable is
measured by weight (pounds). This may not accurately reflect price elasticities since more effective and
C.16
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expensive pesticides may be substituted for pesticides requiring higher doses to be effective. EPA
acknowledged this issue, stating that there has been a decrease in the amount of insecticides produced due
to the substitution of synthetic pyrethroids for more conventional pesticide ingredients. The synthetic
pyrethroids are more powerful than conventional pesticides, thus reducing the weight of pesticides required
for pest control. EPA asserted, however, that in terms of total insecticide production, these impacts are
small.
Active ingredient as dependent variable; measured in units of use
The following three studies examined demand elasticity for specific pesticides and measured demand
in units of use: Lacewell and Masch (1972), Carlson (1977), and Carlson (1977a). Lacewell and Masch
found that the demand for the herbicide 2,4-D was inelastic. Carlson's price coefficient for 2,4-D was
small and negative, but not significant, which may be consistent with price inelasticity. Carlson's
significant price coefficients for insecticide active ingredients indicated that demand is elastic in both the
short-run and the long-run.
Lacewell and Masch selected a five county area hi the Northern High Plains of Texas as the study area
to evaluate the effect of a tax vs. a marketing quota farm program on the level of chemicals used in a
specific agricultural region. The primary agricultural crops of the area were grain sorghum and wheat. To
control weeds hi wheat and grain sorghum, herbicides, especially 2,4-D, were utilized.
Using data on land utilization for 1969, Lacewell and Masch constructed a linear programming model
for the five county region. For illustrative purposes, the change hi the quantity of 2,4-D used hi response
to changes in the price of 2,4-D was investigated. Requirements for weed control were assumed to be met
by one of three weed control alternatives: (1) use of 2,4-D, (2) use of 2,4-D and dicamba, and (3) use of
dicamba, other chemicals and additional tillage operations. The price of 2,4-D was increased by
increments, using parametric programming, from 52 cents per pound to $37.00 per pound, at which point
the model predicted no 2,4-D would be used. In response to a more marginal price increase of 78 percent
(from $0.52 to $0.93 per pound), Lacewell and Masch predicted a decrease in use of 2,4-D of 30 percent.
This translates to an inelastic demand of approximately -0.38.
Carlson's two articles (1977 and 1977a) used the same log-linear model to examine demand elasticities
of particular herbicides and insecticides. Carlson first considered price elasticity of demand for pesticides
as part of a study to determine the importance of pest resistance to insecticides hi affecting demand for
specific compounds. In his second article, Carlson illustrated some advantages and disadvantages of price
incentive systems relative to quantity incentive systems for pollution control.
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Carlson used individual farm data on insecticide use from several cotton production regions to test
hypotheses of decreasing productivity of insecticides and substitutability between chemical types. His
original estimation model is
where
Q =
R,t =
Rgt =
6; =
quantity of a given insecticide purchased in year t (pounds of actual material),
insecticide price deflated by an index of all agricultural input prices,
substitute insecticide price,
resistance index,
agricultural product price index, and
error term.
The agricultural product price variable, Q, was not statistically significant and was deleted from the
model. A lagged dependent variable was added to account for the assumed effects of delayed adjustments
to price changes. Carlson used this model to analyze several of the largest selling groups of insecticides.
The specific dependent variables and their price elasticities were as follows (standard errors appear in
parentheses):
Dependent Variable
(A) Domestic and foreign sales of cyclic
organophosphate insecticides (1953-1970)
(B) Same as (A) except divided by domestic
cotton acreage planted
(C) Total sales of parathion and methyl
parathion (1953-1970)
(D) Domestic sales of DDT (1945-1969)
(E) Domestic sales of DDT (1953-1969)
Price elasticity
-1.461
(0.7%)
-1.552
(0.780)
-1.06
(0.273)
-0.667
(0.397)
-1.091
(0.625)
Insecticide price has the expected negative effect on insecticide purchases. Carlson concludes that
sales of the compounds are quite responsive to price, indicating that there are many substitute pest controls
in the long run. None of the coefficients, however, are significantly different from negative one, so the
model indicates that elasticity of demand is unlikely to be either highly elastic or highly inelastic.
C.18
-------
In Carlson's subsequent article (1977a) he reported a slightly different elasticity for the parathion and
methyl parathion group and also includes the herbicide 2,4-D in his analysis. Further he reported long-run
elasticities for DDT and 2,4-D. The results were as follows:
Dependent Variable
(F) Domestic sales of parathion, methyl
parathion (1953-1969)
(G) Domestic sales of 2,4-D (1950-1970,
except 1965-68) divided by cropland index
(H) Same as (D) except long-run
(I) Same as (G) except long-run
Price elasticity
-0.945
(0.339)
-0.193
(0.349)
-1.53
-0.594
The analysis indicates that the elasticity of DDT increases substantially from the short-run to
the long-run, as would be expected as more substitutes may be developed with time. The coefficient for
2,4-D shows demand to be inelastic, but is insignificant. Though this result may be consistent with inelastic
demand, it is inconclusive.
Active ingredient as dependent variable: measured in units of production
Abt Associates located no studies which fit this category.
Summary
Table 2.1 summarizes the empirical studies discussed above; Figure 2.2 displays the empirically-
derived elasticity estimates graphically. As can be seen from Figure 2.2, elasticity estimates ranged from
approximately zero to -1.5. While most estimates indicate that the demand for pesticides is relatively
inelastic, the results are inconclusive. Since the studies used different models and, in particular, different
dependent variables, variation in the estimates is expected. The number of studies which considered
clusters of pesticides as the dependent variable was insufficient to draw reliable conclusions as to the price
elasticity of demand for clusters of pesticides. However, the results of the analyses which did define the
dependent variable as a cluster of pesticides will be considered in the final estimations of demand
elasticities.
C.19
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23. Price Elasticity of Demand for Food Commodities
Given that the empirical analyses are insufficient to derive estimates of demand elasticity for clusters
of pesticides, an alternative method of estimation of the elasticity was developed. The method used in the
remainder of Section 2 of this report relies on a consideration of four factors: (1) the price elasticity of
demand for food commodities, (2) the availability and relative costs of non-chemical pest management, (3)
the contribution of pesticides to the variable cost of farm production, and (4) the productivity of
expenditures on pesticides. Though these sources will not reveal precise quantifications of the price
elasticity of demand for pesticides, they can be used to indicate whether demand for the pesticides is
expected to be elastic or inelastic and to construct approximate estimates of the elasticity of demand.
Since the demand for particular inputs to a product is in part derived from demand for the end
product, the demand for pesticides used in the agricultural sector will be influenced by the demand for
food. The demand elasticities of food commodities, developed in this section, are used to provide initial
estimates of the elasticity of demand for clusters of pesticides.
Estimates of the direct price elasticity for foods at the retail level are taken from the U.S.D.A. report
entitled "U.S. Demand for Food: A Complete System of Price and Income Effects" (1985), authored by Kuo
S. Huang. Using a constrained maximum likelihood method, Huang developed statistical procedures for
estimating a large-scale demand system from time-series data. He then applied his procedures to an
estimation of a domestic food demand system including forty food items and one non-food item. The food
items, direct-price elasticities, and standard errors of the estimates are listed in Table 2.2. The estimated
elasticities ranged from -0.0385 (cabbage) to -1.378 (grapes). Huang noted that an exact t-test for the
statistical significance of the elasticity estimates is not applicable, given the assumptions of a maximum
likelihood model. For the purposes of his analysis, Huang considered an estimate to be statistically
significant if the estimated elasticity was larger than its standard error. While estimated elasticities with
relatively large standard errors may imply that the estimates are not statistically precise, only four of the
thirty-four commodity elasticity estimates used in this analysis had a standard error greater than the
elasticity estimate (butter, other fresh fruits, carrots, and cabbage).
Huang also provided estimates of demand elasticities for the following aggregated food groups: meat,
staples, fats, fruits, vegetables, processed fruits and vegetables, and desserts. The direct price elasticities
he obtained were negative for all seven food categories, with magnitudes ranging from -0.08 to -0.34. For
purposes of the discussion here, however, the individual food items must be reorganized to correspond to
the crops included in the clusters.
C.25
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Table 2.2
ESTIMATED DIRECT-PRICE ELASTICITIES
Commodity
Beef & veal
Pork
Other meats
Chicken
Turkey
Eggs
Cheese
Fluid Milk
Evaporated &
Wheat Flour
Rice
Potatoes
Butter
Apples
Oranges
Bananas
Grapes
Grapefruits
Other Fresh Fruits
Lettuce
Tomatoes
Celery
Onions
Carrots
Cabbage
Other Fresh Vegetables
Fruit Juice
Canned Tomatoes
Canned peas
Canned Fruit cocktail
Dried beans, p
Other process*
Sugar
Ice Cream
(USDA, 1985)
Direct-Price
Elasticity
-0.6166
-0.7297
-1.3712
-0.5308
-0.6797
-0.1452
-0.3319
-0.2588
7 Milk -0.8255
-0.1092
-0.1467
-0.3688
-0.167
-0.2015
-0.9996
-0.4002-
-1.3780
-0.2191
3 -0.2357
-0.1371
-0.5584
-0.2516
-0.1964
-0.0388
-0.0385
ables -0.2102
-0.5612
-0.3811
-0.6926
tail -0.7323
& nuts -0. 1248
ruits & vegetable -0.2089
-0.0521
-0.1212
Standard Error
0.0483
0.0327
0.2045
0.0608
0.1332
0.0225
0.1174
0.1205
0.2642
0.1026
0.1438
0.0689
0^1748
0.1469
0. 1465
0.1334
0.1829
0.1067
0.5471
0.0656
0.0624
0.0636
0.0693
0.1816
0.0405
0.1436
0.1006
0.1072
0.1746
0.3677
0.0313
0.0921
0.0172
0.0848
Source: U.S.D.A. (1985). U.S. Demand for Food: A Complete System of
Price and Income Effects. By Kuo S. Huang. National Economics
Division, Economic Research Service. Technical Bulletin No. 1714
C.26
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To estimate an average elasticity for individual crops in a cluster, the elasticities of the included crops
are weighted by the quantity of the relevant pesticide applied to that crop, as reported in Pimentel et al.
(1991). This weighting factor incorporates the fact that pesticide use varies between crops; the elasticity
of demand for a crop with heavy pesticide use will more greatly influence the elasticity of demand for the
relevant cluster of pesticides than will the demand for a crop with low pesticide use. The resulting elasticity
estimate is not a measure of the elasticity of the entire cluster of crops (unless the cluster consists of only
one crop). Rather, it is a measure of the weighted average elasticity of the individual commodities in the
cluster. The elasticity of the entire cluster will be lower than the average elasticity of the individual
commodities due to the reduction in the number of substitutes. For example, people may easily substitute
beef for pork and therefore these individual commodities may have relatively high elasticities. However,
substitutes for all meats are less readily available and this category is likely to have a lower elasticity than
the average elasticity of individual meats.
Since the elasticity of the demand for food commodities is assumed to represent the elasticity of
demand for pesticides, this elasticity will also be overstated. The overestimation of the value of demand
elasticity will likely result in an exaggerated estimate of the fraction of cost increases that is borne by the
manufacturers. In the absence of more appropriate data, however, this value provides a reasonable best
estimate of the demand elasticity for clusters of pesticides.
Table 2.3 displays the average elasticities for the clusters based on Huang's analysis. The elasticity
estimates for the clusters represented range from -0.12 (herbicides on sugar beets, beans, and peas) to -1.38
(fungicides on grapes, herbicides on grapes, and insecticides on grapes). This range of values indicates that
the demand for the food clusters varies from highly inelastic to somewhat elastic.
While the calculations for most of the clusters are straight-forward, the estimation of elasticity for
the six clusters containing crops that serve as animal feed required an intermediate step. The elasticity of
demand for corn, sorghum, soybeans, and alfalfa - all crops that are largely used for animal feed - was
calculated from Huang's estimates of the elasticity of demand for animal food products.
An average elasticity for animal feed crops can be obtained by weighting the elasticity of each animal
product by the amount of that product consumed. Huang provides "the retail weight equivalent of civilian
food disappearance", a measure of consumption, for each food item. This weighting calculation yields an
elasticity of demand for animal products of -0.55. However, for this weighting method to accurately reflect
the elasticity of demand for feed crops, it must be true that a unit of feed yields equal units of all included
animal products. This is not the case. The yield rates of dairy products and eggs are substantially higher
C.27
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Sources for Table 2.3:
Values for "own-price elasticity" were obtained from U.S.DA. (1985).
Values for "pesticide Use" were obtained from Pimentel et al. (1991).
Notes to Table 2.3:
/I The price elasticity of demand for bananas is not included since a separate estimate of the quantity
of herbicides applied to bananas is not available. Also, fruit categories are only included if they can
be assigned to a .single cluster. For example, "fruit juice" is not included since it could include apple
and orange juice, and therefore overlap two clusters.
/2 Vegetable categories are only included if they can be assigned to a single cluster, for example,
"other processed fruits and vegetables" is not included since the category overlaps two clusters.
/3 Crop is assumed to be fed to animals. See text for explanation of elasticity estimate.
/4 The elasticity estimate is for dried beans, peas, and nuts. No separate elasticity estimates for these
foods are available.
/5 The elasticity estimate for sugar does not distinguish between sugar beets and sugar cane.
/6 Elasticity estimate is for wheat flour.
/7 Includes lemons, cherries, peaches, plums, and "other fruit"
/8 According to the 1989 "Agricultural Statistics" published by the U.S. Department of Agriculture, 34
% of all tomato acreage is used to produce for the fresh market and 66% of the acreage is used to
produce tomatoes for processing. Pesticide use is split between fresh and processed markets using
these percentages. While this split will not be precise since production per acre and pesticide use
may vary, it is used as a reasonable approximation.
/9 Includes cucumbers, peppers, sweet potatoes, and "other vegetables".
/10 The category "other gram" is excluded since elasticity estimates are not available. Use of herbicides
on "other grains" is relatively minor, at 2.7 million kgs per year.
/ll Since estimates of the elasticity of cotton are not included in the U.S.DA. report, cotton is not
included in the elasticity estimate for the cluster. Herbicide use on cotton, estimate at 8.2 million
kg/year, is small compared to herbicide use on soybeans. Therefore, the elasticity estimate for the
cluster should not be substantially affected by the absence of an elasticity estimate for cotton.
/12 Includes pecans and "other nuts"
/13 The analysis assumes that half of herbicides used on peas are used on canned peas with the
remainder used on dried peas.
/14 Includes all herbicides applied to beans and one-half of herbicides applied to peas.
/15 "Percent of Use" equals "Pesticide use on crop"/"Pesticide use on cluster"
/16 "Weighted Elasticity" equals summation of ("percent of use" multiplied by "own-price elasticity")
/17 Since estimate of the elasticity of demand for tobacco are not included in the U.S.D A. report,
tobacco is not included in the elasticity estimate for this cluster. However, since about 80 percent of
the insecticides applied to crops in this cluster are applied to soybeans, peanuts, and wheat, the
absence of an elasticity estimate for tobacco should not dramatically affect the elasticity estimate for
the duster.
C.32
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than the yield rates of meats per unit of food. Therefore, a weighted average of the food elasticities based
on consumption would be biased towards the elasticities of dairy products and eggs. That is, the elasticity
values for dairy products and eggs would influence the resulting average elasticity more heavily than is
appropriate. ;
As can be seen from Table 2.2, the elasticities of demand for dairy products and eggs are generally
lower than the elasticity of demand for meats. Weighting the elasticities by consumption is therefore likely
to understate the elasticity of demand for feed crops. To avoid this underestimation, the elasticity of
demand for animal feed is calculated based only on the meat products. The resulting estimate of -0.69 is
conservative in that it is likely to somewhat overstate the elasticity of demand for animal products, and
therefore animal feed. This conservative value, however, still indicates that demand for feed crops is
inelastic.
Huang's report analyzed demand elasticity for foods at the retail level. U.S.DA. has also analyzed
the elasticity of demand for farm products by modeling the quantity of the farm product as an input in food
processing (U.S.D.A., 1989). The analysis considers eight commodities: beef and veal, pork, poultry, eggs,
dairy, processed fruits and vegetables, fresh fruit, and fresh vegetables. U.S.DA.'s results are consistent
with previous findings, and show that all own-price elasticities are negative and less than 1 in absolute
values. The authors found that, with the exception of poultry, farm-level demands are nearly as large as
the corresponding retail elasticities or somewhat larger than the corresponding retail elasticities. Since
specific commodity elasticities are not given and since the findings indicate that farm-level elasticities are
similar to retail-level elasticities, this analysis uses the more detailed values for elasticities that are given
in Huang's report.
2.4. Feasibility of Non-Chemical Substitution
In order to further delineate variations in the elasticities of demand exhibited by each cluster, one can
examine the market characteristics that, according to microeconomic theory, influence the price elasticity
of demand. These characteristics include the availability of substitutes for the product, the contribution
of the product to the cost of production, and the productivity of expenditures for the product. This section
discusses the availability of substitutes for clusters of pesticides. Section 2.5 considers the impact of
pesticide contribution to the cost of production while Section 2.6 evaluates the productivity of expenditures
for pesticides.
As discussed earlier, demand elasticity is, theoretically, a function of the availability of substitutes,
among other factors. If a product has many close substitutes, it is likely to be characterized by an elastic
C.33
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demand. Substitutes for a pesticide active ingredient include an alternative active ingredient as well as non-
chemical substitutes. In constructing pesticide clusters, U.S. EPA's Office of Pesticide Programs (OPP)
grouped all active ingredients which are substitutes for each other. The active ingredients included in the
clusters are both chemical and biological. Therefore, substitutes for a cluster include only cultural and
environmental pest control technologies1.
Achievable reduction in pesticide use for specific end-uses has been studied by Pimentel et al. (1991).
Pimentel considered the costs and benefits of replacing chemical pest control methods with currently
available biological, cultural, and environmental pest control technologies. Since both the pesticide clusters,
as defined by EPA, include biological pest control methods, the biological alternatives listed by Pimentel
are not alternatives to the clusters. However, Abt Associates knows of no analysis which considers only
cultural and environmental pest control alternatives. Further, the biological pest control methods constitute
only a small minority of the pesticides within the clusters. Pimentel et al.'s analysis is, therefore, used to
measure the relative substitutability of the pesticide clusters.
In this report, Pimentel's study is used to develop a general rating of the degree to which pesticide
substitution is feasible for each cluster. The greater the feasibility of substitution, the higher the expected
elasticity of demand for pesticides in the cluster. The ratings are based on two criteria: (1) the percentage
by which non-chemical alternatives can replace pesticides, and (2) the projected net cost of replacing
pesticides with a non-chemical pest control method. Based on these criteria, the clusters are grouped into
three categories as shown in Tables 2.4, 2.5, and 2.6. Clusters in the "high substitutability" category can,
according to Pimentel et al., achieve at least a 40 percent reduction hi pesticide use at an additional cost
of less than one dollar per hectare. Clusters hi the "moderate substitutability" category can achieve at least
a 20 percent reduction in pesticide use at a cost no greater than five dollars per hectare. Clusters which do
not qualify for either of these categories are listed under the heading "low substitutability".
The clusters defined by OPP often group several of the crops that are listed hi Table 2.4, 2.5, and 2.6.
To determine ratings for the clusters, the crop-specific ratings were weighted by the pounds of fungicide,
herbicide, or insecticide applied to each crop, as was relevant for the cluster. The cluster ratings, as
developed by Abt Associates based on Pimentel et al. are as follows:
1 Most of the pesticide clusters include at least two active ingredients, indicating that chemical
substitutes exist for most active ingredients. The substitutability between active ingredients will vary
by region and with meteorological conditions, as well as with specific crops. A comparison of the
chemical substitutes available for particular active ingredients is not undertaken hi this analysis.
C.34
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Non-chemical
Table 2.4
Substitutability for Pesticides by Cluster
Fungicides
High
Substitutability
Moderate
Substitutabilitv
Low
Substitutabilitv
soybeans
other vegetables
peaches
nee cotton
sugar beets sweet corn
lettuce tobacco
carrots peanuts
potatoes tomatoes
onions
beans
cantaloupe
peppers
sweet potatoes
watermelons
apples
cherries
peas
pears
plums
grapes
oranges
grapefruit
lemons
"other" fruit
pecans
"other" nuts
cole
cucumbers
Source: Abt Associates estimates based on Pimentel et al. (1991)
C.35
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Table 2.5
Non-chemical Substitutabilitv for Pesticides by Cluster
Herbicides
High
Substitutabilitv
tobacco
potatoes
tomatoes
cucumbers
apples
plums
oranges
grapefruits
lemons
"other" nuts
Moderate
Substitutabilitv
peanuts
sorghum
pasture
grapes
alfalfa
hay
beans
cherries
peaches
pears
"other" fruit
pecans
Low
Substitutabilitv
corn
cotton
wheat
soybeans
rice
sugar beets
"other" grain
lettuce
cole
carrots
sweet corn
onions
cantaloupe
peas
peppers
sweet potatoes
watermelons
"other" vegetables
Source: Abt Associates estimates based on Pimentel et al. (1991)
C.36
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r
Table 2.6
Non-chemical Substitutability for Pesticides by Cluster
Insecticides
High
Substitutabilitv
sorghum
hay
tomatoes
cherries
peaches
pears
plums
grapes
"other" fruit
pecans
"other" nuts
oranges
grapefruit
lemons
Moderate
Substitutabilitv
cotton
wheat
carrots
onions
cucumbers
beans
sugar beets
peas
watermelons
"other" vegetables
sweet potatoes
peppers
alfalfa
soybeans
rice
tobacco
peanuts
"other" grains
Low
Substitutabilitv
corn
lettuce
cole
potatoes
sweet corn
cantaloupe
Source: Abt Associates estimates based on Pimentel et al. (1991)
C.37
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Low Substitutabilitv
fungicides for use on vegetables
herbicides for use on corn
herbicides for use on soybeans, cotton, peanuts, alfalfa
herbicides for use on sugar beets, beans, and peas
insecticides for use on corn and alfalfa
insecticides for use on vegetables
Moderate Substitutabilitv
fungicide for use on fruit and nut trees, except oranges and grapes
fungicides for use on oranges
fungicides for use on grapes
herbicides for use on vegetables
herbicides for use on sorghum, rice, small grains
herbicides for use on grapes
insecticides for use on cotton
insecticides for use on soybeans, peanuts, wheat, and tobacco
High Substitutabilitv
herbicides for use on tree fruits (except oranges), nuts, and sugarcane
herbicides for use on oranges
herbicides for use on tobacco
insecticides for use on grapes
insecticides for use on oranges
insecticides for use on fruit and nut trees excluding oranges and grapes
insecticides on sorghum
As discussed earlier, these data can be used to suggest pesticide clusters for which the demand
elasticity differs substantially from the demand elasticity for the associated food commodities. Demand for
the six pesticide clusters with low substitutability may be inelastic relative to the demand for the associated
foods. In the seven cases of high substitutability, the demand for the pesticide cluster may be more elastic
than the demand for the associated foods. The feasibility of substitution for pesticide clusters is considered
in Section 2.7 in constructing estimates of the elasticity of demand for the pesticide clusters.
C.38
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2.5. Contribution to the Variable Cost of Production
Economic theory predicts that a producer's sensitivity to price will increase with the percentage of
production cost contributed by that input. To further distinguish between the elasticities of demand for
the different clusters of pesticides, Abt Associates has considered the extent to which the pesticides in the
clusters contribute to production costs.
The U.S.DA. publishes cost-of-production data summarizing all operator and landlord costs and
returns associated with the production of several individual commodities (U.S.DA., 1989a). The cost
estimates separate the cost of chemicals and can be used to determine chemical costs as a percentage of total
variable costs of production. Cost of chemicals is included hi two categories: "chemicals" and "custom
application". Both custom operators and farmers apply pesticides. The category "chemicals" includes
agricultural chemical use by farmers and does not include labor spent in chemical application. Many custom
operators charge a flat rate and do not provide a cost breakdown between labor and materials. "Custom
application" therefore includes operator-applied chemicals, operator labor, and farm operations other than
chemical application. The category "custom application" was included in calculations of pesticide
contribution to total cost in order to ensure that all chemical costs are included. The estimate of pesticide
contribution to the cost of crop production will, however, be overstated. These data are presented in Table
2.7 for the commodities for which the information was available.
The pesticide dusters defined in this analysis separate agricultural chemicals into fungicides,
insecticides, and herbicides. The U.S.D.A. report does not separate the costs of chemicals into these
categories. In order to divide the cost of chemicals between each of these types of pesticides, Abt
Associates estimated total expenditures for each pesticide type for the commodities considered in the
U.S.DA. report. Total expenditures were calculated by multiplying the pounds of fungicide, herbicide,
or insecticide applied to a commodity (from Pimentel et al, 1991) by the average price of the relevant
pesticide type i.e., fungicides, herbicides, and insecticides (as reported in Synthetic Organic Chemicals.
1988). The chemical contribution to variable cost was then divided between the three pesticide categories
based on the percent of expenditures. The percentages of variable production costs for fungicides,
herbicides, and insecticides by commodity are listed in Table 2.7.
The crop-specific estimates must be grouped into clusters for purposes of this analysis. An estimate
of the contribution of pesticide to variable cost for a cluster is made only if such an estimate is available
for individual crops contributing at least 50 percent of the pesticide use for the cluster (based on Pimentel
et al., 1991). Eight clusters meet this qualification. These clusters are listed below in descending order of
C.39
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Table 2.7
Fungicide. Herbicide, and Insecticide Contribution to Variable Costs of
Production
Commodity
soybeans3
peanuts3
cotton3
sugarbeets3
sorghum3
corn3
rice3
wheat3
potatoes4
barley3
tobacco5
oats
Chemical
Costs as a
Percent of
Variable
Costs1
37
31
29
28
25
22
20
18
16
16
10
9
Fungicide
Costs as a
Percent of
Variable
Costs2
0
12
0
0
0
0
0
0
7
0
0
0
Herbicide
Costs as a
Percent of
Variable
Costs2
35
17
16
23
22
19
19
16
3
16
3
9
Insecticide
Costs as a
Percent of
Variable
Costs2
3
3
13
5
3
2
1
2
6
0
7
0
1 Equals ("chemicals" + "custom operations")/"total variable cash expenses"
2Estimate by Abt Associates using pesticide prices from Synthetic Organic Chemicals 1988 and
pounds applied from Pimentel, D. et al, (in press), "Environmental and Economic Impacts of
Reducing U.S. Agricultural Pesticides Use", Pest Management in Agriculture, CRC Press.
3Source for percent of production costs - USDA, 1989. "Economics Indicators of the Farm Sector
Costs of Production, 1987". Economic Research Service. February.
^Source for percent of production cost- USDA, 1988. "1985 Potato Cost and Returns- Fall
Production Areas". Potato facts special edition. Economic Research Service. September.
5Source for percent of production cost - USDA, 1989. "Tobacco: Situation and Outlook Report"
Economic Research Service. September.
C.40
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the percent of the pesticide contribution to cost. Based only on contribution to cost, the order also
corresponds to expected decreasing price elasticity of demand. The clusters are:
1) Herbicide used on soybeans, cotton, peanuts, alfalfa (33 percent of variable cost)
2) Herbicides used on sorghum, rice, small grains (20%)
3) Herbicides used on corn (19%)
4) Insecticides used on cotton (13%)
5) Insecticides used on soybeans, peanuts, wheat, and tobacco (3%)
6) Herbicides used on tobacco (3%)
7) Insecticides used on sorghum (3%)
8) Insecticides used on corn and alfalfa (2%)
U.S.DA. did not estimate the cost of production for specialty crops. These data are compiled at the
county level and collected by individual states, but are not available on a national level. It is beyond the
scope of this study to collect cost of production data from each county in each state for each crop. Abt
Associates did, however, obtain cost of production reports for specialty crops of interest from the states that
represented a large percentage of the planted acreage of each crop. From these reports it was evident that
the pesticide contribution to cost varied significantly between regions. Therefore, it was decided that
without a statistically valid national sampling, the county-level data could not accurately be used to
represent national cost data. No estimates of the pesticide contribution to variable costs of producing
specialty crops are included in this analysis.
The purpose of considering the pesticide contribution to variable cost is to determine whether the
demand elasticity for clusters of pesticides is likely to differ substantially from the elasticity of demand for
the associated food commodities (calculated is Section 2.3). In particular, for the four pesticide clusters
where chemicals contribute over ten percent of total variable cash expenses, farmers may be relatively
sensitive to pesticide price changes. Therefore, demand for these pesticide clusters may be more elastic than
demand for the associated food commodities. This factor is considered in Section 2.7, along with the other
available data, to estimate the elasticity of demand for each of the pesticide clusters.
2.6 Productivity of Expenditures for Pesticides
The productivity of an input refers to the marginal value product of expenditure for the input
compared to the cost of the input. When the marginal value product exceeds the input cost, the input is said
to be productive. If an input is highly productive, demand for the input is theoretically likely to be
C.41
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insensitive to small changes in price. Three studies which examined the productivity of expenditures for
agricultural pesticides were located and are discussed below.
Headley (1968) estimated partial production elasticities for the following input variables using Cobb-
Douglas functions: labor, land and buildings, machinery, fertilizer, pesticides, and "other". He then
compared the marginal value production of expenditure for pesticides to the marginal factor cost of
pesticides to determine the extent of disequilibrium in the use of pesticides by farmers. The results of
Headless study indicated that the marginal value of a one-dollar expenditure for chemical pesticides is
approximately $4.00. Headley noted several limitations of his analysis, including that his conclusions are
based on aggregative analysis and may not apply to local situations.
Campbell (1976) considered this same issue for a cross-sectional sample of tree-fruit farms in British
Columbia. The statistical techniques used by Campbell include Ordinary Least Squares and Factor Analysis
Regression. The data used hi fitting Campbell's regression equation were as follows: the dependent variable
was the value of output of fruit; the input variables were the values of services of land and buildings and
capital equipment, and the values of inputs of irrigation water, labor, fertilizers, and pesticide sprays.
Corresponding to Headley's findings, Campbell found that the value of a marginal dollar's worth of
pesticides was significantly greater one dollar, indicating a relatively inelastic demand. However, as
Headley did, Campbell suggested caution in the interpretation of this result. He noted that it is possible
that his statistical procedure introduced an upward bias to the estimate since the sample data exhibited
fairly high correlations among some of the independent variables, including pesticides.
According to Lichtenberg and Zilberman (1986), however, the studies of Headley and Campbell are
methodologically flawed. Lichtenberg and Zilberman argue that econometric measurements of pesticide
productivity that are derived from standard production theory models contain significant upward biases that
result in the overestimation of pesticide productivity. The authors claim that the constant elasticity of the
marginal effectiveness curve produced by a standard Cobb-Douglas specification will not match the actual
behavior of the marginal effectiveness curve. The correct form of the marginal effectiveness curve,
according to Lichtenberg and Zilberman, will show an increase hi pesticide use in response to pest resistance
and a decrease in use only when pest resistance is so widespread that alternative measures are most cost
effective. The true marginal effectiveness curve will decline at an increasing rate in the economic region.
Lichtenberg and Zilberman cast doubt on the high marginal productivity of pesticides estimated by
Campbell and Headley.
C.42
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Given that these studies do not provide definitive estimates of the productivity of pesticides and do
not address the productivity of specific pesticide clusters, we develop simple original estimates of the
productivity of pesticide clusters. In this analysis, the productivity of pesticides (specified as either
fungicides, herbicides, or insecticides) on individual food commodities is calculated as follows:
P -•
V x MP
where:
C
MP =
P
the cost of pesticide treatment for the food commodity (dollars per hectare),
the marginal value product from the pesticide application (percent of total production value),
the productivity of the pesticide on the food commodity (dollars per hectare/dollars per
hectare), and
the production value of the crop (dollars per hectare harvested).
The data sources for the three input parameters were as follows. The production value of the crops
was obtained from U.S.DA. (1989). The cost of pesticide treatment was taken from Pimentel et al. (1991).
No source of specific estimates of the marginal value product associated with fungicides, herbicides, and
insecticides on crops was located. The analysis therefore relied on the expertise of the U.S. EPA Office of
Pesticide Programs (OPP) to estimate the value of this parameter. The OPP stated that it was reasonable
to generalize that the marginal product associated with the use of fungicides, herbicides, or insecticides on
a crop equaled ten percent of the production value of that crop (telephone communication, Dave Broussard,
OPP, 2/91). Since no more precise estimates were available, the analysis adopted this value.
In reality, there will be some variation in the marginal value product of fungicides, herbicides, and
insecticides on different crops. To the extent that the marginal value product for a pesticide type on a crop
is greater than 10 percent, the analysis will understate productivity and therefore overstate the elasticity
of demand. Similarly, if the marginal value product for a pesticide type on a crop is less than 10 percent,
the productivity of the pesticide will be overstated and the elasticity of demand will be underestimated.
Weighted averages of the productivity measures for pesticides used on individual crops were
calculated to obtain measures of productivity for pesticide clusters. The weighting factor was the quantity
of pesticides included in the cluster applied to each crop, as determined by Pimentel et al. (1991).
Table 2.8 displays the productivity measures for the pesticide clusters for which the information was
available.
C.43
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Table 2.8
Productivity of Pesticide Clusters
Cluster
Productivity
(Dollars of Marginal Product
per Dollars of Pesticide Expenditures^
Fungicides on:
Fruit and nut trees, except oranges and grapes
Grapes
Vegetables
Oranges
Herbicides on:
Sorghum, rice, small grains
Corn
Soybeans, cotton, peanuts, alfalfa
Sugar beats, beans, peas
Vegetables
Oranges
Tree fruits (except oranges), sugar cane, nuts
Grapes
Insecticides on:
Cotton
Sorghum
Corn, alfalfa
Vegetables
Fruit and nut trees, except oranges and grapes
Soybeans, peanuts, wheat, tobacco
Oranges
Grapes
$5.81
$9.83
$12.37
$12.54
$0.88
$1.11
$2.68
$2.72
$17.85
$17.91
$19.29
$61.43
$0.72
$1.24
$3.69
$7.92
$8.51
$13.08
$15.04
$37.80
C.44
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Note that there is great variation in the productivity estimates. The lowest productivity estimate is
$0.72, for insecticides used on cotton; Herbicides used on grapes had the highest productivity, at $61.43.
The wide range is due both to variability in the value of production of crops and variability in the cost of
applying pesticides to the crop. For example, the value of production of cotton is $487 per hectare while
the value of a hectare of grapes is $4,914 per hectare (U.S.DA., 1989). In addition, the average cost of
insecticide application to cotton is about $118 per hectare while the costs of applying herbicides to grapes
is $8 per hectare (Pimentel et al., 1991). However, it must again be recognized that due to lack of data, the
analysis assumes that the marginal value of production of insecticides on cotton and herbicides on grapes
are identical.
The productivity of the clusters is considered hi the next section, along with the factors previously
discussed, in developing estimates of the elasticity of demand for each pesticide cluster. Demand for the
pesticide clusters for which productivity is low can be expected to be elastic relative to the demand for the
associated food commodities, ceteris paribus. Similarly, when a cluster of pesticides is highly productive,
demand is likely to be inelastic compared with demand for the associated food commodities.
2.7. Conclusions - Agricultural Pesticides
Section 2 of this report estimates the price elasticity of demand for twenty-four pesticide clusters.
Estimates of the elasticity of demand for clusters of pesticides are based on the price elasticity of demand
for the associated food commodities. However, the elasticity of demand for an input is not solely a function
of the demand for the end product (unless input ratios are assumed to be fixed). Therefore, the elasticity
estimates are adjusted as warranted by consideration of three factors: (1) the feasibility of substituting non-
chemical controls for the pesticide cluster, (2) the contribution of the pesticide cluster to the variable cost
of crop production, and (3) the productivity of the pesticide cluster. In addition, the literature estimates
of elasticity are considered when appropriate.
Since the effect of these factors is not easily quantified, we use this information to adjust the pesticide
elasticities estimated from the demand for crops rather than to attempt to pinpoint the value of demand
elasticity. Based on this information, we identify clusters for which the elasticity of the demand for the
food commodity is likely to differ substantially from the elasticity of demand for the corresponding cluster
of pesticides.
Note that the effect of the factors considered may cancel each other. For example, the feasibility of
non-chemical substitution for a cluster of pesticides may be high, indicating that the elasticity of demand
may be higher for the cluster of pesticides than for the associated crops. However, if the productivity of
C.45 :
-------
the pesticide cluster is also high, less elastic demand is indicated for the cluster of pesticides than for the
associated foods. To decide whether an adjustment to the elasticity of demand for the food commodities
is warranted, the net indication of the factors is considered. Factors that indicate relatively elastic demand
and factors that indicate relatively inelastic demand cancel each other. If, on net, two factors indicate
relatively elastic or inelastic demand, an adjustment to the elasticity estimate is made.
Table 2.9 summarizes the information from the five areas of research: literature estimates, demand
elasticities of food commodities, feasibility of substitution, contribution of chemicals to production costs,
and productivity estimates. The information is summarized for twenty-one sectors of agricultural pesticide
use. Three additional clusters of pesticides are included in the following summary of elasticity of demand
for agricultural pesticides: fungicides used on grain storage, fungicides used for seed treatment, and
fungicides - post-harvest. Since these clusters differ from the other agricultural pesticide clusters in that
the pesticides are not applied to crops in the field, they have not been included in the analysis to this point.
However, since the pesticides in these clusters are used agriculturally, elasticity estimates are discussed in
this section. The best estimate of elasticity for each of the twenty-four agricultural clusters is discussed
below.
a.
Fungicides used on vegetables
The elasticity estimate of -0.38 is taken directly from U.S.DA.'s (1985) estimate of the demand
elasticity for retail vegetables, weighted by the amount of fungicides applied to each type of vegetable. No
adjustments are made since the substitutability for fungicides on vegetables is low and the marginal
productivity of fungicides on vegetables is moderate.
b. Fungicides used on fruit and nuts except oranges
The elasticity of demand for food commodities in this cluster, based on a weighted-average of the
elasticity values estimated by U.S.DA. (1985), is -0.23. No adjustments are made to this value are made
to arrive at the elasticity of demand for fungicides applied to these food commodities. No corrections were
necessary since the substitutability for fungicides on fruit and nuts except citrus is moderate as is the
marginal productivity of fungicides on fruit and nut trees, except oranges. The estimated elasticity of -0.23
indicates less elastic demand than does the analysis of Pingali and Carlson (1985). However, the elasticity
estimate of Pingali and Carlson consider only apples and is therefore not directly comparable to the
elasticity estimate for the cluster. Both the current estimate and the Pingali and Carlson estimate indicate
that demand is inelastic.
n.46
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Table 2.9
Summary of Elasticity Information
Elasticity of Feasibility Fraction of
Literature Food
Cluster Estimates Commodity
"ungicides on: •*.*'* •JT
vegetables J6&A... , r, -0.38
^"*'ps,i'A/ ^
fruit & nut trees, *£^ J^, "s" ^
except oranges l^x92T6& -0.23
Jr o %vv ^^.£v&. * *. <. *^ f
oranges SStifL^ s% -1.00
grapes ^^C>^s^-.» - -1.38
Serbicides on: '". '* Z^^r . "!' '"
sorghum, rice, ^--^T "" -^ " ""- "
small grains t^A* , -0.44
soybeans, cotton, ^s «^"\,1 ^
peanuts, alfalfa inei*|lis (5) ^ -0.67
corn Ji*tC -S.?S^> \ -0.69
¥'^|> _ ^
oranges T&A*, j^ ti^ -1.00
' ^X*S«f' vl "' " " "
tree fruits, nuts & ^" ^f^ "ix^% - -
sugar cane "&jA^ %„ ' -0.20
. j^^ Vdw ^ A ^X^- ^
•. ;• -iV.i,-fr ** W. f
grapes ^^V~V'^ , -^ ~L38
vegetables jIl^V^-X - °- r" _ ~0'27
tobacco -§*^, , v^ N.A.
sugar beets, beans ^Jv* , -
peas kX, "f"' " ~°-12
(1) Burrows (1983), cotton only
(2) Pingali and Carlson (1985), apples only
(3) Miranowski (1980), corn only
(4) Huh (1978), corn insecticides and herbicides
(5) U.S. EPA (1974), corn or soybeans, only
C.47
of Contribution to Marginal
Substitution Production Costs Productivity
, , , > , ,,:*, f
low N.A. $*%^?
iffS . N-
%«.-» 'v ••"-';"' ' ^ ,ff' :
jnOd^rAte N.A. ^$^+^83
'*'*.*'' &J£ :'^
moderate 0.20 ' f ,, $ft.§8
low 0.33 |2»6$
v v <• ' :
low *'" 0.19 $i*^i
"*
>^^ N.A. ^^
^ -• « 4- ff H* ^ t S ^
;E- ;i ••£
moderate N.A. $I7<8$
t " ,i, ^ '•
Jttgfe 0.03 , $5?^
low N.A. 'rf "$3L^
-------
Cluster
Table 2.9 (cont.)
Summary of Elasticity Information
Elasticity of Feasibility Fraction of
Literature Food of Contribution to
Estimates Commodity Substitution Production Costs
Marginal
Insecticides on: " •• •• ' ,'t
vegetables JS-A- '
fruit & nut trees HL3£ (£) , '^
exc. oranges ' '//,', '"',,; '' ^
oranges H»Xt, ,
grapes SLA* _.
corn, alfalfa -0,7$ £J>
keJasdc (5)
sorghum RA, , t ^
soybeans, peanuts, "'"' '"' '-- ~ff'f
wheat, & tobacco £&$!$.&& (5)
cotton -&£> -J^,(j> , -
-0.33 " ^\\&K '3
-0.21 ' 'Mgb ,
-1.00 ; ajgi'
-1.38 ' ^ iagb
-0.69 low
^ , '••"'"' ,'
-0.69 ^ ' lugfe
-0.56 nv04^faite
N.A. .jaoderafe:
N.A. : $fM,
NA % ^ft 5i
• /Ti • •" •»£ O+>^i
N.A. "$'15,04
N.A. ';- ^fl^J
0.02 ,, $3*^ |
•.•^. ^
0.03 ^ ' fix24
s^ '*"•"' ^ ':
OAO ^•^•'1 ftiff :
• UJ .fcpUiJJJ :
0.13 ^Oi735 ^
(1) Burrows (1983), cotton only
(2) Pingali and Carlson (1985), apples only
(3) Miranowski (1980), corn only
(4) Huh (1978), corn insecticides and herbicides
(5) U.S. EPA (1974), corn or soybeans, only
C.48
-------
c. Fungicides on oranges
The elasticity estimate of -1.0 is taken directly from U.S.DA.'s (1985) estimate of the demand for
oranges. No adjustments are made since the substitutability for fungicides on citrus is moderate, as is the
marginal productivity of fungicides on oranges.
d. Fungicides on grapes
The elasticity estimate of -1.38 is again .taken directly from U.S.DA.'s (1985) estimate of the demand
for retail foods. Since the feasibility of substitution for fungicides in this cluster is moderate and the
marginal productivity is moderate, no adjustments are made.
e.
Herbicides on sorghum, rice, and small grains
The best estimate of the elasticity of this food cluster is based on the demand elasticity of rice, as
reported by U.S.D.A. (1985) and on the demand elasticity of sorghum. As discussed above, the elasticity
of demand for sorghum, generally an animal feed crop, was calculated based on the elasticity of demand
for animal meats. To estimate an elasticity for the crops in this cluster, the two crop elasticities were
weighted by the amount of herbicides applied to each crop (as reported in Pimentel et al., 1991). The
resulting elasticity estimate is -0.44.
However, it is likely that the elasticity of demand for this cluster of herbicides will exceed the
elasticity of demand for the associated crops. Although the feasibility of substitution for herbicides in this
cluster is moderate, herbicides contributed a relatively high percentage to total variable costs, and the
marginal productivity of the herbicides is very low. There is no precise method by which to translate these
factors into an estimate of the elasticity of demand for herbicides on sorghum, rice, and small grams.
However, to account for the low marginal productivity and high contribution to costs of herbicides on
sorghum, rice, and small grains, demand on herbicides on this cluster is assumed to be more elastic than
demand for crops in this cluster. The elasticity estimate is adjusted from -0.44 to -1.0.
f. Herbicides on soybeans, cotton, peanuts, and alfalfa
As discussed earlier in this report, assuming that soybeans and alfalfa are fed to animals, the price
elasticity of demand for the crops in this cluster, excluding cotton, is -0.67. Since the quantity of
herbicides applied to cotton is small in comparison to the quantity of herbicides applied to soybeans,
peanuts, and alfalfa, the exclusion of cotton should not substantially affect the elasticity estimate2.
2According to Pimentel et al. (1991), 8.2 million kgs. per year of herbicides are applied to cotton and 60.6
million kgs. per year of herbicides are applied to soybeans, peanuts, and alfalfa combined.
C.49
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Supporting the elasticity estimate of -0.67, U.S. EPA (1974) found the demand for herbicides on soybeans
to be inelastic.
Three additional factors present information on the expected price elasticity of demand for this cluster
of herbicides: the feasibility of substitution, the fraction of contribution to production costs, and the
marginal productivity of the herbicides. The feasibility of substitution for this cluster of herbicides is low,
influencing the demand for the herbicides to be inelastic. However, herbicides (including custom
application) are estimated to contribute 33 percent of the total cost of production for this cluster. This high
contribution to variable cost is likely to drive greater elasticity of demand. Also, the marginal productivity
of herbicides hi this cluster is estimated as $2.68. This return on herbicide use is fairly low, suggesting
somewhat elastic demand.
Given the opposing factors that influence demand for herbicides in this cluster, it was judged that
the estimated elasticity of demand for the crops, -0.67, serves well as an estimate of the elasticity of
demand for the cluster of herbicides.
g. Herbicides on corn
The estimate of elasticity of demand for corn herbicides is -0.69. This value is based on the average
elasticity of meats as listed in U.S.DA. (1985), since the corn is assumed to be used as animal feed.
Pesticides in this cluster contributed a relatively high percentage to total variable costs (19% including
custom application) and the marginal productivity of these pesticides is low, at $1.11. Both of this factors
indicate elastic demand. However, the feasibility of substitution for these pesticides is low, indicating
inelastic demand. Therefore, it was judged that no additional adjustment to the elasticity estimate was
warranted.
h. Herbicides on oranges
The estimate of the elasticity of demand for herbicides on oranges is -1.00, taken from U.S.DA.'s
estimate of the elasticity of demand for oranges. Although the feasibility of substitution for herbicides on
oranges is high (indicating elastic demand), the marginal productivity of the herbicides is also fairly high
(indicating inelastic demand). Therefore, no adjustment to the U.S.DA. estimate of elasticity of demand
for oranges is made.
i- Herbicides on tree fruits ("except oranges'), nuts, and sugarcane
The elasticity of demand for this cluster, based on the elasticity of demand for retail food, is
estimated as -0.20. Pesticides in this cluster have a high feasibility of substitution with non-chemical pest
C.50
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control methods, indicating elastic demand. However, the marginal productivity of these pesticides is also
moderately high, at $19.19, indicating inelastic demand. Therefore, no adjustments are made to the
elasticity estimate for retail food.
j. Herbicides on grapes
The price elasticity of demand for herbicides on grapes is estimated based on the elasticity of demand
for grapes at the retail level. The estimated elasticity is -1.38. Since the marginal productivity on grapes
is extremely high, the elasticity of demand may be less than -1.38. However, the marginal productivity is
the only factor indicating inelastic demand; the feasibility of substitution for herbicides on grapes is
moderate. Further, the degree of adjustment to the elasticity estimate warranted by the high marginal
productivity is unclear. For these two reasons, this analysis relies on the elasticity estimate for retail grapes.
However, it should be noted that this value may overstate elasticity, and therefore overstate the impact of
the effluent guidelines on pesticide manufacturers.
k. Herbicides on vegetables
The weighted-average estimate of demand for vegetables at the retail level is -0.27. Since the
feasibility of substitution is moderate and the marginal productivity is moderately high for this cluster, the
elasticity estimate for food is used to represent the elasticity of demand for herbicides used on these foods.
1. Herbicides used on tobacco
U.S.D.A. did not estimate the elasticity of demand for tobacco at the retail level. However, the
addictive nature of cigarette smoking probably results in inelastic demand for tobacco. It seems reasonable
to assume demand for tobacco is as inelastic as the least elastic demand for retail food, since people seldom
develop addictions to specific foods. Since U.S.DA. found that the elasticity of demand for numerous food
commodities was lower in absolute value than -0.20, the elasticity of demand for tobacco is estimated as -
0.20.
Since the feasibility of substituting a non-chemical alternative for herbicides on tobacco is high,
demand for the herbicides used on tobacco may be more elastic than demand for the tobacco itself.
However, the costs of applying herbicides comprise only 3 percent of the total variable costs of production.
Further, the estimate of the marginal productivity of herbicides used on tobacco is extremely high. These
two factors indicate that demand for herbicides used on tobacco will be inelastic. Given these opposing
factors, this analysis assumes that the elasticity of demand for herbicides used on tobacco will match the
elasticity of demand for tobacco. The elasticity estimate for this cluster is therefore -0.20.
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m. Herbicides on sugar beets, beans, and peas
The estimate of the elasticity of demand for this cluster is calculated from a weighted average of
U.S.DA.'s (1985) estimate of demand for food at the retail level. The value is -0.12. No adjustments are
made since the indications regarding elasticity of demand for the herbicides conflict. The substitutabilhy
for herbicides on sugar beets, beans, and peas is low, indicating relatively inelastic demand, while the
marginal productivity of the herbicides is low, indicating relatively elastic demand.
n. Insecticides on vegetables
The elasticity for this cluster is estimated as -0.33, based on a weighted-average of the values
estimated by U.S.DA. (1985) as the elasticities of demand for vegetables. No adjustments are made to the
elasticity estimate for vegetables. The marginal productivity of insecticides in this cluster is moderate, at
$7.92. Although the substitutability for insecticides on vegetables is low, there is no quantitative measure
of the extent to which the estimate should be altered. Further, this is the only factor indicating that demand
is relatively inelastic. Therefore, the elasticity estimate of -0.33 is used in this analysis.
o. Insecticides on fruits and nuts except oranges
The estimate of elasticity of demand for the food commodities in this cluster, based on U.S.DA.'s
(1985) estimates of elasticity of demand for food at the retail level, is -0.21. This value differs notably
from the elasticity estimate of Pingali and Carlson (1985) for insecticides applied to apple orchards. Pingali
and Carlson estimated the elasticity of demand as -1.39. Since the authors considered only apple orchards,
the estimates are not perfectly comparable. However, since apples receive over 50 percent of insecticides
applied to crops in this cluster, the differences between the two estimates is notable.
The marginal productivity of these insecticides is moderate and does not suggest that an adjustment
to the elasticity estimate for retail food is required. However, the feasibility of non-chemical substitution
for these insecticides is high, indicating elastic demand. To account for the high feasibility of substitution
and the elasticity estimate of Pingali and Carlson, the elasticity estimate for this cluster is adjusted from -
0.21 to -1.00.
p. Insecticides on oranges
The U.S.DA. estimate of the elasticity of demand for oranges at the retail level was -1.00. This value
is also used to represent the elasticity of demand for insecticides applied to oranges. Although the
feasibility of substitution of insecticides used on oranges is high (indicating relatively elastic demand), the
marginal productivity of the insecticides is also fairly high (indicating relatively inelastic demand).
Therefore, no adjustments are made.
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q. Insecticides on grapes
The U.S.DA. estimate of the elasticity of demand for grapes at the retail level was -1.38. This value
is also used to represent the elasticity of demand for insecticides applied to grapes. Although the feasibility
of substitution of insecticides used on grapes is high (indicating relatively elastic demand), the marginal
productivity of the insecticides is also high, at $37.80 (indicating relatively inelastic demand). Therefore,
no adjustments are made to the U.S.D.A. elasticity estimate for grapes.
r. Insecticides on corn and alfalfa
Since a large proportion of production of each of these crops serves mainly as animal feed, an
elasticity estimate for the crops was developed based on the retail demand for meat. As discussed above,
the elasticity for corn and alfalfa is estimated to be -0.69. This elasticity estimate is also used to represent
the elasticity of demand for insecticides applied to these crops.
Three literature values describe the elasticity of demand for crops in this cluster. U.S. EPA (1974)
found the demand for corn insecticides to be inelastic. Miranowski's (1980) statistically significant estimate
of the elasticity of demand for corn insecticides was -0.78. Finally, Huh (1978) estimated the elasticity of
demand for corn insecticides and herbicides as -1.46. Since these literature estimates conflict, they do not
indicate that an adjustment to the elasticity estimate is needed.
The feasibility of substitution on these crops is low, indicating that demand is relatively inelastic. The
low contribution of insecticides to the costs of production of these crops also indicates that demand for the
insecticides will be relatively inelastic. However, the marginal productivity of insecticides on corn and
alfalfa is fairly low, at $3.69. Low productivity is associated with elastic demand. Given the opposing
factors, no adjustment is made to the estimate of the elasticity of demand for corn and alfalfa.
s. Insecticides on sorghum
As was the case for corn and alfalfa, the elasticity of demand for sorghum is calculated based on the
elasticity of demand for meat, since sorghum is used mainly as a feed crop. The elasticity estimate for
sorghum is -0.69. Although the marginal productivity of insecticides on sorghum is low (indicating
relatively elastic demand) and the feasibility of substitution is high (also indicating elastic demand),
insecticides contribute only two percent of production costs (indicating inelastic demand). Given these
opposing factors, no adjustment to the sorghum elasticity estimate is made. The elasticity of insecticides
used on sorghum is estimated as -0.69.
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t. Insecticides on soybeans, peanuts, wheat, and tobacco
The estimate of the elasticity of demand for soybeans, peanuts, and wheat is -0.56. Although an
estimate of the elasticity of demand for tobacco is not available, this omission should not substantially affect
the estimate since 80 percent of insecticides used in this cluster are applied to soybeans, peanuts, or wheat.
The feasibility of substitution, fraction of contribution to production costs, and marginal productivity for
this cluster of pesticides do not suggest that an adjustment to the elasticity of demand for the food crops
is required. The elasticity estimate for this pesticide cluster is therefore -0.56. This estimate is consistent
with the finding by U.S. EPA (1974) that demand for soybeans is inelastic.
u. Insecticides on cotton
No estimate of the elasticity of demand for cotton was given by U.S.DA. However, Burrows (1983)
empirically estimated this elasticity. Using a single equation model, Burrows estimated the elasticity of
demand for cotton to be -0.9; with a simultaneous equation model, Burrows estimated the elasticity as -1.23.
The average of these two estimates is -1.06.
Since the marginal productivity of insecticides on cotton is extremely low, at $0.72, the demand for
the insecticides is expected to be elastic. Further, the insecticides contribute a fairly high fraction, 13
percent, of the variable cash costs of producing cotton. The feasibility of substitution for these insecticides
is moderate. Since these factors are consistent with the elasticity estimate from Burrows, the elasticity of
demand for cotton insecticides is estimated to be -1.06.
v. Fungicides on gram storage
In the absence of more specific information, the elasticity of demand for fungicides on grain storage
is assumed to equal the elasticity of demand for grains. Elasticity estimates are available from Huang (1985)
for wheat and rice. Other stored grains may be fed to animals. As discussed above, an estimate for the
elasticity of grains fed to animals was developed as part of this analysis. However, since information was
not located on the quantity of fungicides applied to each grain and each end-use, correct weighting factors
for the different elasticity estimates could not be developed to estimate an average elasticity for all grains
treated with fungicides in storage. The elasticity for this cluster is therefore estimated as a straight average
of the elasticity of wheat flour (-0.11), rice (-0.15), and animal feed grains (-0.69). The resulting elasticity
estimate for fungicides used on grain in storage is -0.31.
w. Fungicides used for seed treatment
Since no specific information on the elasticity of fungicides used for seed treatment was located, the
elasticity of demand for fungicides in this cluster is calculated based on the demand elasticity for the crops
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constituting the majority of seed plantings, and for which an elasticity estimate was available. These crops
include corn (elasticity estimated as -0.69), wheat (-0.11), dried beans, peas, and nuts (-0.12), and rice
(-0.15). Since no information was located on the quantity of fungicides applied to seeds of each crop, a
straight average of the elasticities was used to estimate the demand elasticity for this cluster. The resulting
estimate for this cluster is -0.27.
x. Fungicides - post-harvest
The elasticity of demand for fungicides applied post-harvest is based on a weighted average of the
elasticities of demand for the crops to which fungicides are applied in the field. These crops are assumed
to be vegetables, fruit and nut trees, and grapes, as these were the crops included in the four fungicide
clusters for which the elasticity of fungicides used in field applications was calculated. Fungicides are
assumed to be applied to the crops after harvest hi the same ratios as they were applied to the crops hi the
field. These ratios are used to weight the demand elasticities for the individual crops. The resulting
elasticity estimate is -0.65.
A complete list of Abt Associates' estimated price elasticities of demand for clusters defining
agricultural end-uses is provided in Table 2.10.
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Table 2.10
Estimates of Elasticity of Demand for Clusters in the Agricultural Sector
Cluster
Fungicides on:
fruit and nut trees except oranges
seed treatment
grain storage
vegetables
post-harvest
oranges
grapes
Elasticity Estimate
-0.23
-0.27
-0.31
-0.38
-0.65
-1.00
-1.38
Herbicides on:
sugar beets, beans, and peas
tobacco
tree fruits (except oranges, nuts, sugarcane)
vegetables
soybeans, cotton, peanuts, and alfalfa
corn
sorghum, rice, and small grains
oranges
grapes
-0.12
-0.20
-0.20
-0.27
-0.67
-0.69
-1.00
-1.00
-1.38
Insecticides on:
vegetables
soybeans, peanuts, wheat, and tobacco
corn and alfalfa
sorghum
fruit and nut trees except oranges
oranges
cotton
grapes
-0.33
-0.56
-0.69
-0.69
-1.00
-1.00
-1.06
-1.38
Source: Abt Associates estimates based on Pimentel et al. (1991), USDA (1985), USDA (1989a), USDA
(1989b), USDA (1989c), Burrows (1983), Pingali and Carlson (1985), Miranowski (1980), Huh( 19878), U.S.
EPA (1974)
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3.0 PRICE ELASTICITY OF DEMAND FOR PESTICIDES USED NQN-AGRICULTURALLY
Most of the pesticides included in this analysis are used in the agricultural sector; pesticides hi non-
agricultural clusters, as defined by OPP, constitute less than 30 percent of total pesticide use by weight (U.S.
EPA, 1988). However, the non-agricultural pesticides are described by eighteen separate clusters. Unlike
in the agricultural sector, these clusters represent eighteen distinct and generally unrelated end-uses, each
with its own customers, competitors, and costs. The literature search described above yielded no studies
of the price elasticity of demand for pesticides in the non-agricultural sector. Since the scope of this
project does not allow for the gathering and examination of primary data on elasticities of demand for each
of these eighteen markets and since non-agricultural pesticide use represents a relatively small percent of
total pesticide use, the demand elasticities for the non-agricultural sector are developed based on a reasoned
consideration of two factors. Consistent with the analysis of agricultural pesticide use, these factors are:
(1) the availability of substitutes for a cluster of pesticides, and (2) the contribution of pesticides to the total
production cost of the end-user.
Based on the above two factors, the eighteen non-agricultural clusters fit into two categories: (1)
pesticides that contribute a small percentage to total cost but have substitutes, and (2) pesticides that
contribute a small percentage of total production costs and for which there are limited substitutes. There
were no cases hi which it appeared that pesticides contributed a substantial percentage of total production
costs. The two categories and the clusters described by them are listed below, along with a brief discussion
of the reasoning behind the cluster categorization.
(1) Pesticides contribute a small percentage of total cost but substitutes are available
The two non-agricultural herbicide clusters are included in this category: (a) herbicides on ditches,
rights of way, forestry, and ponds, and (b) herbicides on turf. The available substitute is labor, a viable
alternative to chemical weed control. To determine the shift to manual/mechanical weed control given an
increase in pesticides price, one would need to know: the cost of herbicide per unit of area, the
effectiveness of herbicides, the labor cost of applying herbicides per unit of area, the labor cost of manual
weed control per unit of area, and the effectiveness of manual weed control. Since these two clusters
together constitute less than one percent of the pesticides of interest (by weight) it was decided not to invest
resources in the gathering of these data.
Rather, Abt Associates considered the cost structure of the end-users of pesticides hi these clusters.
Herbicides used on ditches, rights of way, forestry, and ponds would generally be used by major industries
such as railroads and utilities and by government agencies, such as state highway departments. The cost
C.57
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of herbicides would be an insignificant percentage of their total production costs. Demand for this cluster
of herbicides is therefore likely to be inelastic. While herbicides used on turf may contribute a greater
percentage to the total production costs (assuming that these pesticides are used, for example, on golf
courses and turf farms) the costs should still be relatively small. In addition, fungicides are applied hi
conjunction with herbicides to turf. It is therefore likely that an application system would be hi place for
fungicides, making the incremental costs of herbicide application small.
Based on the above discussion, this analysis assumes that demand for the two non-agricultural
herbicides clusters is inelastic. Although the level of detail of the available information does not result in
a quantitative measure of the elasticity, such a measure is required. Since only one of the two factors
considered above indicates inelasticity (percent of production costs), while the other is inconclusive
(substitute availability), this analysis assumes that demand for these two clusters is only moderately inelastic,
and assigns a price elasticity of -0.66. The sensitivity analysis will consider the impacts on active ingredient
manufacturers if demand for pesticides in these clusters is perfectly elastic.
(2) Pesticides contribute a small percentage of total production costs, and there are limited substitutes
The remaining sixteen non-agricultural clusters are grouped hi this category. For each cluster, the
cost of pesticides appeared incidental to the total cost of production and no readily available, cost-effective
alternatives to the pesticides were known. These two factors suggest inelastic demand. Further, only three
of the sixteen clusters in this category constitute more than one percent (by weight) of the pesticides of
interest in this analysis. Therefore, little additional information on the ultimate costs to manufacturers
would result from an investigation of the remaining thirteen clusters. The three clusters which included
at least one percent by weight of the total pesticides of concern are listed below with a brief discussion of
their categorization:
Insecticide fumigants and nematicides
According to Encyclopedia Britannica, "Fumigation, which requires some technical skills and certain
precautions in application, is mostly feasible for non-selective quick killing of vermin in large commercial
operations. For the control of household pests it has been, to a considerable extent, supplanted by more
convenient methods of extermination such as the application of powders and residual sprays". Fumigants
are largely used for killing insect pests of stored products, for fumigating nursery stock, or for fumigating
sod, principally for the control of plant parasitic nematodes. Given the application hi large commercial
operations, the contribution of fumigants and nematicides to production cost is likely to be small. Further,
since the use of these products has become somewhat specialized, it is probable that few substitutes exist.
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Insecticides for termite control
Domestic and commercial use of chemical termite controls seems unlikely to contribute substantially
to total consumer or commercial business expenses. Also, while in the long-run, wood could be replaced
to some extent as a building material, in the short-run alternative protection from and eradication of
termites is not readily available. Further, the cost of termite control can be viewed as insurance against the
much larger cost of destruction of a building, making the cost of control appear small. For the reasonably
foreseeable future, the demand for chemical termite control is likely to be inelastic.
Wood preservatives - industrial, commercial, marine use
The wood preservative industry developed because of the need for prolonging the life of wood
structures, particularly where the structures come in contact with ground. Examples of treated wood
include railroad ties, telephone poles, and marine pilings. Wood may be chemically treated to protect
against fungicides, insects, and fire. According to U.S. EPA (1982), expenditures on wood preservative
account for "only a small part" of the annual billion dollar preserved wood market. Cost-effective
alternatives to chemical wood preservation are not known. Demand for pesticides in this cluster is therefore
assumed to be inelastic.
The remaining clusters grouped in this category
are:
Insect repellents at non-agricultural sites
Domestic bug control and food processing plants
Mosquito larvacides
Fungicides on turf
Industrial preservatives - plastics, paints, adhesives, textiles, paper
Synergist - used as insecticide synergists, surfactants, cheleating poultry and livestock
Plant regulators, defoliants, desiccants - for all uses
Sanitizers - dairies, food processing, restaurants, air treatment
Insecticides on livestock and domestic animals
Fungicides - ornamentals
Industrial microbiocides, cutting oils, and oil well additives
Preservatives, disinfectants, and slimicides
Slimicides - pulp and paper, cooling towers, sugar mills
Fungicides - ornamentals
Industrial microbiocides, cutting oils, and oil well additives
Preservatives, disinfectants, and slimicides
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Ideally, a quantitative measure of the price elasticity of demand could be developed for each of the
pesticides clusters listed above. However, the available data does not permit this precision. Since clusters
in this category have no known cost-effective substitutes and since the pesticides are generally an
insignificant portion of total production costs, demand is expected to be moderately to highly inelastic. The
clusters in this category are assigned a price elasticity of demand of -0.33. The sensitivity analysis will
examine the impact on manufacturers in the demand is perfectly elastic.
Finally, two clusters remain without demand elasticity estimates: herbicides for broad spectrum use
and fungicides for broad spectrum use. The cluster "herbicides for broad spectrum use" contains only one
active ingredient, 2,4-D. The price elasticity of demand for 2,4-D was estimated by Lacewell and Masch
(1972) and by Carlson (1977a,b). Lacewell and Masch estimated the elasticity as approximately -0.38.
Carlson estimated a short-run elasticity of -0.19 and a long-run elasticity of -0.59. Averaging Carlson's
long-run estimate and the estimate of Lacewell and Masch results in an estimate of elasticity of demand for
2,4-D of -0.48. We use this value as the price elasticity of demand for broad spectrum herbicides.
The elasticity estimate for broad spectrum fungicides is calculated simply by a weighted average of
the elasticity estimates for all of the other fungicide clusters. The weighting is based on the quantity (by
weight) of active ingredient applied for the end-uses described by each cluster. The resulting elasticity
estimate is -0.40. This value is in good agreement with the elasticity of demand for fungicides estimated
by U.S. EPA (1985) as -0.35.
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4.0 CONCLUSIONS
The estimated elasticities for all 44 clusters are listed in Table 4.0, in order of increasing elasticity of
demand. As can be seen from the table, the elasticity estimates range from -0.12 (herbicides on sugar beets,
beans, and peas) to -1.38 (fungicides on grapes, herbicides on grapes, and insecticides on grapes). The
elasticity estimates vary substantially within the fungicide, herbicide, and insecticide clusters; the type of
pesticide is not predicted to have a strong influence on the elasticity of demand.
The demand for pesticides in all of the clusters except four is expected to have unit elasticity or to
be inelastic. Demand is expected to be inelastic for the three clusters of pesticides applied to grapes and
for insecticides applied to cotton. The main factor driving the high elasticity for the grape clusters is the
high elasticity of demand for grapes at the retail level. Demand for insecticides on cotton is expected to
be somewhat elastic based on literature estimates of the elasticity and on the low marginal productivity of
insecticides applied to cotton.
As should be dear from sections 2 and 3, the methodology employed to estimate the elasticity of
demand for the clusters yields reasonable best estimates of elasticities rather than certain quantifications.
The estimates are likely to accurately depict whether demand for a certain cluster of pesticides is extremely
or only moderately elastic or inelastic; the specific numeric value should not be viewed as definitive.
However, no estimates of elasticity of demand for clusters of pesticides that are more reliable than those
developed through this analysis are known. To address the uncertainty implicit in the estimates, a scenario
in which the manufacturers bear the total costs of regulatory compliance will also be examined.
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Table 4.0
SUMMARY OF ESTIMATES OF ELASTICITY OF DEMAND
on sugar beets, beans, peas
on tree fruits (except oranges), sugar cane, nuts
on tobacco
on fruit and nuts trees (except oranges)
for seed treatment
on vegetables
on grain in storage
on vegetables
Cluster
Herbicides
Herbicides
Herbicides
Fungicides
Fungicides
Herbicides
Fungicides
Insecticides
Slimicides
Fumigants and nematicides
Insecticides on termites
Wood preservatives
Insect repellents at non-agricultural sites
Domestic bug control and food processing plants
Mosquito larvacides
Fungicides on turf
Industrial preservatives
Insecticide synergists and surfactants
Plant regulators, defoliants, desiccants
Sahitizers - dairies, food processing, restaurants, air treatment
Insecticides on livestock and domestic animals
Industrial microbiocides, cutting oils, oil well addivites
Preservatives, disinfectants, and slimicides
Fungicides - ornamentals
Fungicides on vegetables
Fungicides - broad spectrum
Herbicides - broad spectrum
Insecticides on soybeans, peanuts, wheat, tobacco
Fungicides - post harvest
Herbicides on rights of way, drainage ditches
Herbicides on turf
Herbicides on soybeans, cotton, peanuts, alfalfa
Herbicides on corn
Insecticides on corn, alfalfa
Insecticides on sorghum
Herbicides on sorghum rice, small grains
Herbicides on oranges
Fungicides on oranges
Insecticides on fruit and nut trees, except oranges and grapes
Insecticides on oranges
Insecticides on cotton
Fungicides on grapes
Insecticides on grapes
Herbicides on grapes
Elasticity Estimate
-0.12
-0.20
-0.20
-0.23
-0.27
-0.27
-0.31
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.38
-0.40
-0.48
-0.56
-0.65
-0.66
-0.66
-0.67
-0.69
-0.69
-0.69
-1.00
-1.00
-1.00
-1.00
-1.00
-1.06
-1.38
-1.38
-1.38
Source: Abt Associates estimates
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References
Burrows, T. (1983). Pesticide Demand and Integrated Pest Management: A Limited Dependent
Variable Analysis, American Journal of Agricultural Economics, November.
Campbell, H. (1976). Estimating the Marginal Productivity of Agricultural Pesticides: The Case of
Tree-Fruit Farms in the Okanagan Valley. Canadian Journal of Agricultural Economics 24(2), 1976.
Carlson, G. (1977). The Long Run Productivity of Insecticides, American Journal of Agricultural
Economics, 59, pp. 543-548, August.
Carlson, G. (1977a). Economic Incentives for Pesticide Pollution Control in The Practical Application
of Economic Incentives to the Control of Pollution: The Case of British Columbia, ed. J. Stephenson.
Vancouver: University of British Columbia Press.
Hall, D.C., and LJ. Moffitt. (1983). Stochastically Efficient Economic Thresholds for Discrete
Choices. USDA-ERS unpublished manuscript. Washington D.C.
Headley, J.C. (1968). Estimating the Productivity of Agricultural Pesticides, American Journal of
AGricultural Economics, 50:13-23, February.
Huh, Shing Haeng (1978). The Preventive and Incidental Demand for Pesticides: An Economic
Analysis of the Demand for Herbicides and Insecticides Used by Selected Corn Producers in
Minnesota. Thesis submitted to the Graduate School of the University of Minnesota. June.
Lacewell, R. and W. Masch, (1972). Economic Incentives to Reduce the Quantity of Chemicals Used
in Commercian Agriculture. Southern Journal of Agricultural Economics. July.
Lichtenberg, E. and D. Zilberman (1986). The Econometrics of Damage Control: Why Specification
Matters. American Journal of Agricultural Economics. May.
Miranowski, J. (1980). Estimating the Relationship between Pest Management and Energy Prices, and
the Implications for Environmental Damage. American Journal of Agricultural Economics. December.
Pimentel, D., et al. (1991). Environmental and Economic Impacts of Reducing U.S. Agricultural
Pesticide Use, in ed. Pimentel, D., Pest Management in Agriculture. CRC press.
Pingali, P. and G. Carlson (1985). Human Capital, Adjustments in Subjective Probabilities, and the
Demand for Pest Controls. American Journal of Agricultural Economics. November.
U.S.DA. (1985). U.S. Demand for Food: A Complete System of Price and Income Effects. By Kuo
S. Huang, National Economics Division, Economic Research Service. Technical Bulletin No. 1714.
U.S.D.A. (1988). 1985 Potato Cost and Returns: Fall Production Areas. Potato facts special edition.
Economic Research Service. September.
U.S.DA. (1989). Retail to Farm Linkage for a Complete Demand System of Food Commodities. By
Michael K. Wohlgenant and Richard C. Haidacher. Commodity Economics Division, Economic
Research Service. Technical Bulletin No. 1775.
U.S.DA. (1989a). Economic Indicators of the Farm Sector: Costs of Production, 1987. ERS, USDA,
ECIFS7-3. February.
U.S.DA. (1989b). Tobacco: Situation and Outlook Report. Economic Research Service. September.
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U.S.D.A. (1989c). Agricultural Statistics 1989. Washington.
U.S. EPA (1974). Farmers' Pesticide Use Decisions and Attitudes on Alternate Crop Protection
Methods. Washington.
U.S. EPA (1982). Regulatory Impact Analysis Data Requirements for Registering Pesticides under
the Federal Insecticides, Fungicides and Rodenticide Act. Office of Pesticide Programs. August.
U.S. EPA (1985). Economic Impact Analysis of Effluent Limitations Guidelines and Standards for
the Pesticide Chemicals Industry. September.
U.S. EPA (1988). Pesticide Industry Sales and Usage: 1988 Market Estimates. Office of Pesticide
and Toxic Substances. February.
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APPENDIX D
SENSITIVITY ANALYSIS OF COST
PASS-THROUGH ABILITY
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Appendix D: SENSITIVITY ANALYSIS OF COST PASS-THROUGH ABILITY
This appendix describes a sensitivity analysis of the percentage of compliance costs that a manufacturer is
able to pass through to consumers. The model, as described in Chapter 4, assumes that producers can pass on a
portion of compliance costs to customers in the form of price increases, to the extent allowed by producer price
competition and customer demand behavior. To test the sensitivity of the closure analysis results to this assumption,
the worst-case assumption is made that facilities would bear the full costs of compliance (i.e. they could not pass
on any of the compliance costs to customers as price increases). This corresponds to an assumption that all clusters
have completely elastic demand elasticities, or that the percentage of total production subject to compliance costs
is close to zero.
The results of this sensitivity analysis are presented below by regulatory option and subcategory. In
comparison to the model used in the EIA, there are no changes under the proposed option (Treated Discharge
Option). Minor changes in results occur under the Zero Discharge Option: one additional product line closure is
predicted for direct dischargers, while one additional facility closure and two additional product line closures would
be predicted for indirect dischargers (compare Table D.I with Table 4.4).
Treated Discharge Option
Impacts of BAT regulations on direct dischargers
Organic Pesticide Manufacturing - (Subcategory A)
Under the no cost pass-through assumption, no facilities are projected to close due to compliance with BAT.
Two facilities are expected to close a product line as a result of the regulation (see Table D. 1).
Metallo-Organic Pesticide Manufacturing - (Subcategory B)
Direct dischargers of Subcategory B chemicals are limited to zero discharge of process wastewater
pollutants under BPT. No additional options were considered and no new limitations are proposed for the metallo-
organic pesticide chemicals manufacturing subcategory. There are therefore no associated costs or economic
impacts, and sensitivity analysis need not be examined.
Impacts of PSES regulations on indirect dischargers
Organic Pesticide Manufacturing - (Subcategory A)
No facilities are expected to close under the no cost pass-through assumption due to compliance with PSES.
One facility is projected to close a product line as a result of the regulation.
D.I
-------
Metallo-Organic Pesticide Manufacturing - (Subcategory B)
Because no .new limitations are proposed for the metallo-organic pesticide chemicals manufacturing
subcategory, no facility or product closures would be projected under the no cost pass-through assumption due to
compliance with PSES.
Zero Discharge Option
Impacts of BAT regulations on direct dischargers
Organic Pesticide Manufacturing - (Subcategory A)
Sixteen facilities would be projected to close due to compliance with BAT limitations under the no cost
pass-through assumption. Four additional facilities would be expected to close a product line as a result of the
regulation.
Metallo-Organic Pesticide Manufacturing - (Subcategory B)
As discussed under the Treated Discharge Option, Subcategory B direct dischargers are limited to zero
discharge of process wastewater pollutants under BPT. No additional options were considered and no new
limitations are proposed for the metallo-organic pesticide chemicals manufacturing subcategory. Therefore, there
are no associated costs or economic impacts, and sensitivity analysis need not be examined.
Impacts of PSES regulations on indirect dischargers
Organic Pesticide Manufacturing - (Subcategory A)
Twelve facilities would be expected to close due to compliance with PSES under the assumption of no cost
pass-through. Five additional facilities would be projected to close a product line as a result of this regulation.
»
Metallo-Organic Pesticide Manufacturing - (Subcategory B)
Under the no cost pass-through assumption, one facility would be projected to close due to compliance with
the PSES regulation. An additional facility would be expected to close a product line.
D.2
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APPENDIX E
DETAILS OF ANALYSIS OF IMPACTS
ON SMALL BUSINESSES
-------
-------
Appendix E: DETAILS OF ANALYSIS OF IMPACTS ON SMALL BUSINESSES
The figures presented in this appendix illustrate the relationship between facility impacts and facility size that
were examined in the second stage of the small business analysis. This relationship was first examined by
plotting the two financial impacts considered in the analysis:
• facility closures
• product line closures
against five measures of facility size:
• firm revenues
• total facility revenues
• total facility employment
• pesticide-related facility revenues
• pesticide-related facility employment
The plotting exercise outlined above resulted in a total of 10 plots: two impacts by five measures of
size. The two impacts vs. the five measures of size were plotted for three populations: facilities that were
classified as direct dischargers, facilities that were classified as indirect dischargers, and all facilities. Plotting
the 10 relationships for each of the three populations cited above resulted in a total of 30 plots.
The 30 plots that are included hi this appendix are arranged as follows. Figures E.I - E.10 exhibit the
relationship between facility impacts and facility size for all dischargers. The relationship between impacts and
size for facilities classified as direct and indirect dischargers are displayed in Figures E.ll - E.20, and Figures
E.21 - E.30 respectively.
Following the plotting exercise, 10 regressions were performed to examine the probability that a
facility/product line will remain open as a function of entity size. Similar to the plotting exercise, each
regression used one of the five measures of entity size as the independent variable and one of the two impacts as
the dependent variable. The 10 regressions were performed for three populations: facilities that were classified
as direct dischargers, facilities that were classified as indirect dischargers, and all facilities, leading to a total of
30 regressions.
Appendix E contains the results of the regression analyses that were performed for all dischargers.
E.I
-------
Results that were obtained when examining direct and indirect dischargers separately are presented and
discussed hi Chapter 8.
E.2
-------
Table E.I
Logistic Regression Analysis
Zero Discharge Option: All Dischargers
Model
#
1
2 -
3
4
5
6
7
8
9
10
#of
Observations
72
72
70
73
73
32
32
31
33
33
Impact
(y.)
Facility Closure
Facility Closure
Facility Closure
Facility Closure
Facility Closure
Product Line Closure
Product Line Closure
Product Line Closure
Product line Closure
Product Line Closure
Measure of Entity Size
(x.)
Pesticide Revenues
Facility Revenues
Firm Revenues
Pesticide Employment
Facility Employment
Pesticide Revenues
Facility Revenues
Firm Revenues
Pesticide Employment
Facility Employment
Coefficient
(ft)
-7.8E-*
-4.8E*
-6. IE'11
-4.4E3
-1.-3F3
-1.3E-8
-6.QE'9
-LIE'10
-9.9E-3
-1.2E'3
Note: At the 95 percent confidence level p < .05 indicates that the coefficient is significant, while p <
indicates significance at the 90 percent confidence level. Coefficients that are in shaded sections are
significant to the 90 percent confidence interval.
P
value
.0064
.0184
.1077
.0344
.0658
.0789
.0995
.1755
.1121
.3090
.10
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