United States
Environmental Protection
Agency
Office of Science
and Technology
Washington, DC 20460
EPA821-R-93-012
September 1993
Water
4>EPA Economic Impact Analysis
of Final Effluent
Limitations Guidelines and
Standards for the Pesticide
Manufacturing Industry
QUANTITY
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Economic Impact Analysis of
Final 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 the whole pesticide team for their professional
manner, conscientious effort, and contributions.
Credit must also be given to Abt Associates for their assistance and support in performing
the underlying economic analysis supporting the conclusions detailed in this report. Their study was
performed under Contracts 68-CO-0080, 68-03-3548, and 68-C3-0302. Particular thanks are given
to Randi Currier and Rob Sartain.
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TABLE OF CONTENTS
EXECUTIVE SUMMARY E.I
Introduction E.I
Methodology E.2
Baseline Results , E.4
Effects of Regulatory Compliance on Facilities E.4
Impacts on Direct Dischargers E.4
Impacts of PSES Regulations on Indirect Dischargers E.5
Effects of Regulatory Compliance on New Sources of Pesticide Manufacture . . E.6
Regulatory Flexibility Analysis E.6
Chapter 1: INTRODUCTION AND OVERVIEW 1.1
1.0 Background and Definitions 1.1
1.1 Structure of the Report 1.1
Chapter 2: DATA SOURCES 2.1
Chapter 3: PESTICIDE MANUFACTURERS PROFILE .... 3.1
3.0 Introduction 3.1
3.1 Categorization of Data 3.2
3.2 Sources of Demand for Chemical Pesticides 3.4
3.2.A Agriculture Market 3.7
3.2.B Industrial/Institutional/Commercial Market (I/I/C) 3.7
3.2.C Home/Lawn/Garden Market 3.10
3.3 Facility Characteristics 3.10
3.3.A Physical Characteristics 3.10
3.3.B Industry Output 3.12
3.3.C Production Characteristics 3.17
3.3.D Production Costs 3.18
3.3.E Employment Characteristics 3.22
3.3.F Revenues and Profit 3.26
3.3.G Capital Expenditures 3.31
3.3.H Production Capacity Utilization 3.34
3.4 Firm Characteristics 3.34
3.5 Industry Market Structure 3.37
3.5.A Barriers to Entry 3.37
3.5.B Vertical Integration 3.44
3.5.C Concentration 3.45
3.5.D Demand Elasticity and Product Substitution 3.48
3.6 International Trade 3.50
3.6.A U.S. Pesticide Imports and Exports 3.50
3.6.B U.S. Pesticide Industry in the World Market 3.52
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3.7 Analysis of Actual Facility Closures 3.55
3.8 Summary 3-59
Chapter 3 References 3.61
Chapter 4: FACILITY IMPACT ANALYSIS 4.1
4.0 Introduction 4.1
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 4.16
4.2.A Baseline Facility Closure Analysis 4.18
4.2.B Post-Compliance Facility Closure Analysis 4.19
4.3 Product Line Closure Analysis 4.25
4.4 Other Significant Financial Impacts 4.26
4.5 Facility Impacts 4-29
4.5.A Baseline 4.29
4.5.B Effects of Compliance with the Final Rule 4.29
Chapter 4 References 4.33
Chapter 5: COMMUNITY IMPACT ANALYSIS 5.1
5.0 Introduction 5.1
5.1 Methodology 5.2
5.1.A Primary Impacts on Employment 5.2
5.1.B Measuring Impact Significance 5.4
5.1.C Secondary Impacts on Employment 5.5
5.2 Results 5.6
5.2.A Impact of Best Available Control Technology Economically
Achievable (BAT) Regulations on Direct Dischargers 5.6
5.2.B Impact of Pretreatment Standards for Existing Sources (PSES)
Regulations on Indirect Dischargers 5.6
Chapter 5 References 5.8
Chapter 6: FOREIGN TRADE ANALYSIS 6.1
6.0 Introduction 6.1
6.1 Methodology 6.2
6.1.A Exports 6.2
6.1.B Imports 6.4
6.2 Results 6-4
6.2.A Impact of Best Available Technology Economically Achievable
(BAT) Regulations on Direct Dischargers ; 6.4
6.2.B Impacts of Pretreatment Standard for Existing Sources (PSES)
Regulations on Indirect Dischargers 6.5
Chapter 6 References 6.7
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Chapter 7: FIRM IMPACT ANALYSIS ........'. 7.1
7.0 Introduction 71
7.1 Analytic Approach . „ 72
7.1.A Firm Financial Performance 7.3
7.1.B Ability To Manage Financial Commitments 7.4
7.2 Analytic Procedure 75
7.3 Results 7 13
7.3.A Baseline Analysis 7 13
7.3.B Post-Compliance Analysis 7.14
Chapter 7 References 7 15
Chapter 8: SMALL BUSINESS IMPACTS 81
8.0 Introduction g 1
8.1 Methodology . . . g 1
8.2 Results .S.I
8.2.A Impact of Best Available Control Technology Economically
Achievable (BAT) Regulations on Direct Dischargers 8.1
8.2.B Impact of Pretreatment Standards for Existing Sources (PSES)
Regulations on Indirect Dischargers 8.2
Chapter 8 Reference 83
Chapter 9: IMPACTS ON NEW SOURCES 9.1
9.0 Introduction 91
9.1 New Source Performance Standards 9.1
9.2 Pretreatment Standards for New Sources 9.2
Appendix A: 1986 Pesticide Manufacturer Facility Census A.I
Appendix B: Mapping of Pesticide Active Ingredients Into Clusters B.I
Appendix C: Methodology for Estimating the Price Elasticity of Demand
for Pesticide Clusters c 1
Appendix D: Sensitivity Analysis: of Cost Pass-Through Ability . . . . . . . . . . . . . D.I
Appendix E: Compliance Costs as a Percentage of Facility Revenue E.I
Appendix F: Hypothetical Facility F.I
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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 final guidelines and standards applying
specifically to the pesticide manufacturing industry. The EIA builds on the analysis of impacts of the proposed
effluent guidelines for the industry, and incorporates changes resulting from public comments and EPA internal
review.
The EIA estimates the probable economic effect of compliance costs on facilities in terms of facility
closures, product line closures, profitability impacts, ability to incur debt, and compliance costs as a percentage of
facility revenues. Projected 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 73 pesticide manufacturing facilities, owned and operated by 49 firms that manufacture one or
more pesticide active ingredients (PAIs), are potentially subject to regulation1,2. At proposal, 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 final regulation corresponds to the
Treated Discharge Option. Because the costs of the Zero Discharge Option have not changed since proposal, the
impacts of this option are not reassessed in this document.
The economic impacts under the final 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
Based on data from the Section 308 Census, a total of 90 pesticide manufacturing facilities owned and operated
by 59 firms that manufacture one or more PAIs were potentially subject to regulation. However, EPA has
information indicating that 15 of these facilities have closed their in-scope PAI manufacturing operations since 1986.
Also, two facilities producing only Subcategory B in-scope PAIs are not counted as potentially subject to the
regulation.
2Although 73 facilities are potentially subject to the regulation, the EIA analyzed only 72 facilities for economic
impacts, the facility excluded from the economic analysis is an R&D facility with no revenues expected from the
manufacture of in-scope PAIs and no compliance costs.
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for compliance with Pretreatment Standards for Existing Sources (PSES) regulation. Each discharge category was
initially further analyzed by two subcategories: Organic Pesticide Chemicals Manufacturing (Subcategory A) and
Metallo-Organic Pesticide Chemicals Manufacturing (Subcategory B). EPA is not promulgating new limitations on
direct or indirect dischargers of Subcategory B PAIs. (Direct discharge of Subcategory B chemicals is already
limited to zero under BPT.)
Total BAT investment costs (capital and land) for the final regulation are projected to be $24.9 million,
with annualized costs of $18.2 million including depreciation and interest. Total investment costs for PSES for the
final regulation are projected to be $8.7 million, with annualized costs of $5.1 million including depreciation and
interest. The costs, presented in 1986 dollars, are based on the assumption that, whenever possible, facilities will
improve existing treatment rather than build new treatment.
Cost of Implementing BAT and PSES Regulations for Subcategory A*
(in millions of 1986 dollars)
BAT PSES
Capital Costs
Total Annualized Costs
$24.9
$18.2
$8.7
$5.1
*The reported costs are the full costs of compliance, some of which will
be borne by the government in the form of decreased tax receipts. The
analysis therefore overstates the burden of the regulations on industry.
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 HA 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-
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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). As an alternate check on the level
of impacts expected from the regulation, the analysis also compares facility annualized compliance costs to facility
revenue. : Typically, compliance costs that are greater than five percent of facility revenue are judged to be
indicative of a significant impact.
The evaluation of facility-level closure considers whether the portion of the facility involved in
manufacturing, and also formulating/packaging or performing contract work, for both in-scope pesticides (i.e., those
260 PAIs considered for regulation) and out-of-scope pesticides (all others) is expected to continue operations.3
A facility closure is projected to result from the regulation if the baseline after-tax cash flow is positive and the post-
compliance after-tax cash flow is negative (i.e., if a facility begins to lose cash due to the regulation).
A pesticide 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 (see appendix B), forty-four of which contain in-scope PAIs produced in 19864. For the purpose of this
analysis, a product line is defined as a cluster of PAIs. 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 a comparison, between each facility and the industry averages, of two key financial ratios: the "interest coverage
ratio"5 (earnings before interest and taxes divided by interest expense) and "return on total assets"6 (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.
The method of projecting facility closure has been changed since the proposed rule. At proposal, the analysis
used 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 incurred. Due to indications that the salvage
values reported by facilities In the Section 308 Census were not reliable, the final rule projects facility closure based
on an evaluation of baseline and post-compliance facility after-tax cash flows.
PAI #67 (biphenyl), in cluster F6, was considered in-scope at proposal but is not considered in-scope for the
final rule. The cluster count therefore decreased from forty-five to forty-four. See the Technical Development
Document for additional information.
5Also called "times interest earned."
Also called "return on investment."
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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 land disposal restrictions
and (2) compliance with the effluent guidelines for the Organic Chemicals, Plastics, and Synthetic Fibers (OCPSF)
industry. Of the 73 facilities potentially subject to the proposed effluent guidelines, 14 are projected to close in the
baseline analysis after incorporating the costs of RCRA and OCPSF regulations. Of the 14 facilities counted as
baseline facility closures, two have closed product lines since 1986 and three have undergone restructuring. An
additional 12 facilities are projected to close pesticide product lines in the baseline. Of these, four have closed
product lines since 1986 and another three have undergone restructuring.
Effects of Regulatory Compliance on Facilities
The economic impacts associated with the final rule are discussed below, by both discharge type and
subcategory.
Impacts on Direct Dischargers
Organic Pesticides Chemicals Manufacturing (Subcategory A)
For manufacturers included hi 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
$24.9 million and $18.2 million, respectively. The estimate of capital costs has increased by 67 percent since
proposal while the estimate of total annualized cost has increased by 24 percent. (See the Technical Development
Document for an explanation of changes in compliance cost estimates.) The change in compliance cost is the
aggregate effect of decreases in annualized compliance costs at four facilities and increases in annualized compliance
costs at four facilities. Most of the increase in total costs for direct dischargers is due to a substantial cost increase
at one facility. The estimated investment costs at this facility have increased from $1.6 million to $16.0 million,
with an increased in annualized costs from $2.0 million to $7.3 million. This change in estimated compliance costs
resulted from public comments by the facility. The Agency maintains that the actual compliance costs for this
facility would be lower than the estimates used in the final analysis. However, analysis using these higher cost
estimates ensures that EPA does not underestimate the burden of compliance at this facility.
None of the 28 direct discharge facilities covered under this subcategory are projected to close due to
compliance with BAT7. One of the facilities covered under this subcategory is projected to close a product line
7Twenty-eight direct discharge facilities are expected to bear costs. This count includes two facilities that are
known to have actually closed since 1986, but for which compliance costs are included to reflect the likely transfer
of the PAI production.
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as a result of the regulation. In addition, one other zero discharge facility that incurs only monitoring costs is
projected to close a product line. No facilities are expected to experience other significant financial impacts short
of facility or product line closure.
Given that the level of projected economic impacts has not changed since the proposal, the secondary
_ i
community and foreign trade impacts potentially associated with the regulation have not been re-estimated for the
direct dischargers. As presented at proposal, job losses totalling 31 full-time equivalents (FTE) 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 about two percent of the 49 firms owning facilities potentially subject
to regulation, 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 hi 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.
As an additional check on community impacts, foreign trade impacts, and firm-level impacts, EPA
examined the extent of the production decrease at the single facility bearing most of the increase hi compliance
costs. The revenue from in-scope pesticides produced at this facility is expected to fall by only about one percent,
so significant community or foreign trade Impacts are not expected. Further, analysis indicates that the firm owning
the facility is not expected to be significantly impacted by the rule.
Finally, the EPA compared annualized compliance costs with facility revenue. Compliance costs greater
than five percent of facility revenue are typically judged to be indicative of a significant economic impact. For the
28 direct discharging facilities with costs, the mean compliance costs as a percentage of revenue was 0.4 percent,
the median was less than one-tenth of one percent, and the highest value was 4.6 percent.
Metallo-Organic Pesticides Chemicals Manufacturing (Subcategory B)
No new limitations on direct dischargers are being promulgated 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 hi thas Subcategory, the total capital and annualized costs of compliance with
PSES are projected to be $8.7 million and $5.1 million, respectively. None of the 28 indirect discharging facilities
are projected to close entirely, close a product line, or experience other significant financial impacts due to
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compliance with PSES8. (At proposal, one facility was projected to close a product line. This facility has actually
closed and is counted as a baseline closure in the final rule.) Due to this decrease in total costs and impacts,
secondary community and foreign trade impacts associated with the regulation have not been re-estimated for
indirect dischargers. Instead, the estimates of these secondary impacts presented at proposal serve as high-end
estimates of the impacts. As presented at proposal, job losses totalling 97 FTEs were expected to occur as a result
of the product line closure 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. Two firms are expected to sustain significant financial impacts as a result of compliance with PSES.
Foreign trade in pesticide active ingredients is expected to fall by $16.1 million due to compliance with PSES. This
decrease in trade represented 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. Finally, EPA compared the annualized compliance costs with
facility revenue. For the 23 indirect discharging facilities with costs, the mean compliance costs as a percentage
of revenue was 0.7 percent, the median was 0.2 percent, and the highest value was 5.7 percent. The ratio of
compliance costs to facility revenue was greater than five percent for only one facility.
Subcategory B
No new limitations on indirect dischargers are being promulgated by the EPA 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 promulgating New Source Performance Standards (NSPS) and Pretreatment Standards for
New Sources (PSNS) for the organic pesticide chemicals manufacturing subcategory. These regulations are set
equal to BAT/PSES limitations for PAIs, modified to reflect a wastewater flow reduction of 28 percent in some
cases. The NSPS for priority pollutants is being set equal to the BAT limitations. The impact of the 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 final rule has been found to be economically achievable for existing
facilities, it is expected that compliance with this rule 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
8 Twenty-three of the 28 indirect discharging facilities covered under this subcategory are expected to bear
compliance costs.
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of small entities. The purpose of the Act is to ensure that regulatory agencies fit regulatory and information
requirements to the scale of the businesses, organizations, and governmental jurisdictions subject to regulation.
The effects of the BAT and PSES regulations on small businesses were separately considered. EPA defined
a small entity based on the Small Business Administration (SBA) standards. The SBA has established standards
based on employment at firms (including all affiliates and divisions) for each SIC group. For SIC 2869 (which
includes pesticide manufacturers) the SBA defines a small business as one employing less than 1,000 people.
Employment data for firms that own pesticide manufacturing facilities was obtained from Dun and Bradstreet's
Million Dollar Directory. Consistent with the other components of the EIA, significant impacts were defined as
facility closures, product line closures, or other significant financial impacts as previously discussed. Using these
measures, the results of the small business analysis are discussed below for the two discharge methods,
a. BAT. As previously discussed, it is projected that one direct discharging and one zero discharging
facility will close product lines due to BAT regulations. No facility closures or other significant financial impacts
are expected to occur. Both firms that are expected to experience facility product line closures have fewer than
1,000 employees. Because the number of small facilities significantly affected is not substantial, no regulatory
flexibility analysis is required; the EPA Administrator has certified to this effect.
b. PSES. No facilities are expected to close, close a product line, or experience another significant impacts
short of closure. Because no significant impacts are expected for any facilities, no regulatory flexibility analysis
is required; the EPA Administrator has certified to this effect.
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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.'
• 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).
dischargers and cover all pollutant categories.
These rules apply to new industrial direct
• 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 analogous 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 final BAT, NSPS,
PSES, and PSNS applying specifically to the pesticide manufacturing industry.
1.1 Structure of the Report
At proposal, two regulatory options were 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 final rule corresponds to the
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 have limited discharges from the facility site to POTWs or to surface
water only; discharges to other media could have remained constant or increased 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
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Treated Discharge Option. Because the costs of the Zero Discharge Option have not changed since proposal, the
impacts of this option are not reassessed in this document. The economic impacts are calculated separately 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 final rule 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 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
requested 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 final 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 final rule.4 Additional information on all data sources is presented in Chapter 2.
To fully evaluate the expected impacts of the final rule, six measures of impact are examined in the EIA:
• Impacts on facilities that manufacture pesticide active ingredients (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.
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
surface water to landfills, incinerators, or deep well injection sites.
^Baseline 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.
4Full title: Development Document for Best Available Technology, Pretreatment Technology, and New
Source Performance Technology for the Pesticide Chemical Industry: Final
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Figure 1.1
Economic Impact Analysis of Pesticides Manufacturing
Industry Effluent Limitations Guidelines:
Analytic Components
Facility Level
Analysis
Economic
Models
Facility
Closure
Analysis
Data Inputs
Other
Financial
Impacts
Comparison
of
Compliance
Cost to
Facility
Revenue
Facility
Impacts
Firm
Impacts
[23 Analytical Outputs
Employment
Impacts
Production
Losses
Community
Impacts
Foreign Trade
Impacts
Small Business
Impacts
New
Source
Impacts
I | Key Analytical Components
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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 cash flow to the baseline cash flow.
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 in financial
indicators of facilities' operating conditions between the baseline (i.e., pre-compliance) and post-compliance
scenarios. As an additional check on the expected impacts of compliance, annualized compliance costs are
compared to facility revenues. Typically, compliance costs in excess of five percent of facility revenue are
considered to be indicative of a significant economic impact.
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 tirade impacts may result
from changes in the domestic production of pesticides, because pesticides are traded in an international market.
Changes in the balance of trade are calculated based on both the estimated decreases in exported production and the
increases in 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 final regulation. The analysis is conducted by examining changes in the
financial indicators of a firm's operating conditions between the baseline and post-compliance scenarios.
An additional potential impact of the final regulation, evaluated using the results of the facility analysis,
is the impact on small businesses. The evaluation of impacts on small businesses has two steps. First, it is
determined whether the regulation is expected to significantly impact a substantial number of small businesses.
Impacts are defined as either a facility closure, a product line closure, or another significant financial facility impact
short of closure. Second, if a substantial number of small businesses are projected to sustain significant impacts,
alternative regulatory methods that mitigate or eliminate the economic impacts on small businesses would be
examined.
Finally, impacts of the PSNS and NSPS regulations on new sources of pesticide production are evaluated,
based on both the projected facility and firm impacts.
1.4
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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 ,in Chapter 6. A discussion of the expected impacts of the final 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. Chapter 9 describes the expected effects of the regulation
on new sources of PAI manufacture.
i
The report also includes six appendices. The first appendix contains the Section 308 Census of pesticide
manufacturing facilities. The second appendix presents the mapping of PAIs into clusters. The methodology by
which price elasticities of demand for PAI clusters are calculated is shown in Appendix C while Appendix D reports
on impacts under the assumption of zero cost-pass through. Appendix E presents the comparison of annualized
compliance cost to facility revenue. The final appendix details the analytical steps for calculation of facility impacts
for a hypothetical facility.
1.5
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Chapter 2: 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
included the 90 facilities that, in 1986, manufactured one or more of the 272 individual or classes of pesticide active
ingredients (PAIs) that were originally identified as within the scope of the regulation2'3. 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 facilities' 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 fonnulator/packager; this facility has closed since 1986. The other facility
performs only research and development.
federal Water Pollution Control Act, 33 U.S.C. 1318.
2The final manufacturer's effluent limitations include 260 in-scope PAIs or classes of PAIs rather than 272.
Three organic PAIs have been dropped from consideration for regulation: orthodichlorobenzene (#193),
paradichlorobenzene (#202), and biphenyl (#67). In addition, the following nine metallo-organic PAIs are no longer
considered for regulation under the final rule: oxydipheroxarsine (#6), cacodyllic acid (#72), bioquin (#88), copper
EDTA ((#89), methylarsenic acid salts and esters (#161), organo-arsenic pesticides (#188), organo-cadmium
pesticides (#189), organo-copper pesticides (#190), and organo-mercury pesticides (#191). See the Technical
Development Document for details on the exclusion of these PAIs.
EPA has information indicating that 15 of these facilities have closed their in-scope PAI manufacturing
operations since 1986. Therefore, the economic analysis does not include these 15 facilities. Also, since nine
metallo-organic PAIs are no longer considered for regulation under the final rule, two facilities producing
Subcategory B PAIs as their only in-scope products are no longer counted as potentially subject to the regulation.
2.1
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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 assume that all PAIs produced
by a single facility have the same profit margin.4 Twenty of the 88 facilities responding to Part B chose to provide
the PAI-specific cost and price data. Sixteen of the 73 facilities subject to regulation under the final rule provided
PAI-specific data.
The other major data input to the EIA was the estimated compliance costs of the regulation.5 At proposal,
the EPA evaluated compliance costs associated with two potential regulatory options: a Treated Discharge Option
and a Zero Discharge Option. The Treated Discharge Option limitations were based on biological treatment,
hydrolysis, activated carbon, chemical oxidation, resin adsorption, solvent extraction, incineration, and/or
recycle/reuse to control the discharge of PAIs in wastewater.6 Zero Discharge Option limitations would have
required no discharge of pesticide manufacturing process wastewater pollutants to surface water by using on-site
or off-site incineration and/or recycle/reuse. The final rule corresponds to the Treated Discharge Option. The
estimated costs associated with the Zero Discharge Option have not changed since proposal.
Three categories of compliance costs associated with pesticide manufacturing were evaluated for the Treated
Discharge Option: 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 impact analyses in this EIA,
including impacts on 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
4See Appendix A (Part B of the Census), page A.29, text preceding question 2-H.
5Full details of the compliance cost estimates can be found in the Technical Development Document.
6For some PAIs the Treated Discharge Option limits discharge to zero.
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to the U.S. Chemical Industry, the Census of Manufactures published by the Bureau of the Census, and the
International Trade Commission's (ITC) Synthetic Organic Chemicals. These documents, 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 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 las 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 in 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 fee 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, in addition to Parts A and B of 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/repackagiing. 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.1-2
The eight 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,
'Based on data from the Census, there were a total of 90 pesticide manufacturing facilities that manufactured
one or more of the 270 in-scope PAIs initially considered for regulation in 1986. However, EPA has information
indicating that 15 of these facilities have closed their in-scope PAI manufacturing operations since 1986 (the Census
base year). Also, metallo-organic (Subcategory B) PAIs are no longer considered for regulation under the final rule.
As a result, two additional facilities that produced only Subcategory B products are no longer counted as potentially
subject to the regulation. Therefore, a total of 73 pesticide manufacturing facilities that manufacture one or more
of the 260 in-scope PAIs covered by the final rule are potentially subject to regulation. The information presented
in the industry profile continues to be based on the original 90 facilities subject to regulation.
2Data from the 1988 Survey of the Pesticide Formulating, Packaging, and Repackaging Industry indicate that
in 1988, 51 of the pesticide manufacturers were engaged in formulating and packaging.
/
3.1
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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 discussion of the balance of trade for chemical pesticides and the nature
of foreign competition. Characteristics of facilities known to have discontinued PAI manufacturing since 1986 are
discussed in Section 3.7. Section 3.8 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 insect, 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 hi 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 fimgicides. The International Trade Commission's
Synthetic Organic Chemicals classifies pesticides into cyclic and acyclic fimgicides, 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 the 270 individual or classes of
PAIs initially considered for regulation) 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
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Table 3.1
Representative Classes of Pesticides and the Pests They Control
Class
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.
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.
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
3.3
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clusters.based upon differences in the sensitivity of product demand to changes in price (see Table 3.2).3 In
addition, although the OPP's cluster segmentation assigned each PAI 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 in Appendix B.
Although the economic impact analysis of the 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.4 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 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,
3Clusters 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
(tcrmiticides), 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
Cluster Primary Application
| Cluster Primal; Application
Herbicides used on:
I Fungicides used on:
H-l Broad spectrum of uses • F-l
H-2 Corn I F-2a
H-3 Soybeans, cotton, peanuts, alfalfa j F-2b
H-4 Sorghum, rice, and small grains j F-3
H-5a Oranges : F-4
H-5b Grapes I F-5
H-5c Fruit trees 1F-6
H-6 Sugarbeets, beans and peas j F-7
H-7 Drainage ditches, rights of way, forestry and j F-8
H-8 Turf JF-9
H-9a Vegetables j F-10
H-9b Tobacco !
H-10 Unclassified uses !
Broad spectrum of uses
Fruits and nuts
Grapes
Vegetables
Oranges
Seed treatments
Post-harvest fruit and vegetables
Grain storage
Ornamentals
Turf
Unclassified uses
Insecticides ased on/for/as;
Other Pesticides;
I-la Cotton j R-I
I-lb Soybeans, peanuts, wheat and tobacco ' '• R-2
I-2a Corn and alfalfa j R-3
I-2b Sorghum IR-4
1-3 Fruit, and nut trees, excluding oranges and : R-5
I-4a Oranges ! R-6
I-4b Grapes ! R-7
1-5 Vegetables ! R-g
[-6 Livestock and domestic animals : R-9
[-7 Non-agricultural sites (as repellent) j R-10
r8 Domestic bug control and for food processing j R-ll
1-9 As fumigants and nematicides | R-12
'-10 Termite control IR-13
1-11 Lawns, ornamentals, and forest trees j R-14
.-12 Mosquito larva j R-15
-13 Unclassified uses : TT-t
Industrial preservatives
Slimicides used in pulp and paper, cooling
Industrial microbiocides
Sanitizers used in dairies, food processing,
Synergists used as insecticide synergists,
Food preservatives
Wood preservatives, used for industrial,
Disinfectants
Water disinfectants
Plant regulators, defoliants, and desiccants
Preservatives, disinfectants, slimicides
Molluscides and misc. vertebrate control
Bird chemosterilants, toxicants, and
Dog and/or cat repellants
Rodent toxicants, anticoagulants, predator
3.5
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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.
3.6
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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, com, soybeans, cotton, and rice. Insecticides
were the second most commonly used pesticide type. In 1987, the most widely used insecticides were Carbaryl,
Malathiori, and Chlorpyrifos (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/Commcrcial 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, 1992).
The I/I/C market differs significantly from the agricultural market in several ways. First, the use of I/I/C
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 hi cooling towers varies
3.7
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,« - _^ ,
Pesticide Agricultural Production and Distribution1
Agent
Purpose
Registrant
Manufacturer
Formulator/Packager
Distributor
Dealer/Co-op/Repackager
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.
Synthesizes the active ingredient from raw materials.
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.
Acts as the "middle man;" buys pesticide from the
registrant/manufacturer/formulator and sells to the dealer.
Sells the pesticide to the user.2
1 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.
2 A user is defined as a farmer, government, commercial ground applicator, commercial aerial applicator,
etc.
Source: Based on a table in: Pesticide Containers: A Report to Congress, U.S. EPA, Office of
Pesticide Programs, May, 1992.
somewhat according to the climate (U.S. EPA, 1992). Second, I/I/C pesticides are generally used in smaller
quantities than agricultural chemicals. Third, I/I/C products in 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 in 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 in cooling towers, paper and textile mills, oil wells, metalworking coolants, etc.
3.8
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Figure 3.2
Production and Distribution Channels for the Industrial/
Institutional/Commercial and Home/Lawn/Garden Markets
Basic Pesticide Manufacturers
Independent
Formulators
i
Formulators/
Distributors
Distributors
Retailers
Contract
Fprmulators
"Tollers"
Consumer
Companies
Food Brokers, Etc.
Industrial,
Institutional &
Commercial Dealers
Home, Lawn
and Garden
Users
Source: Based on a diagram in: Pesticide Containers:
A Report to Congress, U.S. EPA, Office of
Pesticide Programs,
May, 1992.
Institutional
Users
Industrial
Users
Commercial
Users
Government
Users
3.9
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(U.S. EPA, 1992). 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 in 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
•i
The home/lawn/garden pesticide market includes pesticide products that are commonly used in and around
the home. These products include rodenticides, bisect 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 in containers that
ire 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.
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 in 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
3.10
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Percent
75%-
70-
65
60-
55-
50-
45-
40-
35
30^
25
20-
15-
10-
5-
0-
Figure 3.3
Facilities and In-Scope Pesticide Production
by Region, 19S6
52%
32%
35%
18%
30%
33%
Northeast/
North Central
Southeast/
South Central
Northwest/
Southwest
Region
H Percent of Facilities
F3 Percent of In-Scope Production
Source: Census.
3.11
-------
(35 percent).5 The northwest/southwest region has the second heaviest concentration (33 percent).6 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.7
3.3.B Industiy 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 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
southeast/south central region includes Alabama, Delaware, Florida, Georgia, Kentucky, Maryland,
Mississippi, North Carolina, South Carolina, Tennessee, Virginia, West Virginia,
*The 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
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Table 3.4
Pesticide Manufacturing Facilities by Facility Age, 19861
Number of Years
5 to 10 to 20 to 30 to
<5 < 10 < 20 < 30 < 40 40+
AH*
Pesticide
type (Number of Facilities)
Fungicides 0253 10
Herbicides 1455 14
Insecticides 13 3 4 33
Other Pesticides* 00 1 2 41
Multiple Types of 007788
Pesticides**
All in-scope Facilities 2 9 21 21 17 16
11
20
17
8
30
86***
* Refer to Table 3.2 for a description of other pesticides.
** Multiple types of pesticides include manufacturers that produce pesticides in more than
one of the groups outlined above.
*** Excluded from the 88 facilities that provided financial data are two facilities that did not
report facility age.
1 Facility age is the number of years the facility has been producing pesticides.
Source: Census
Number of Facilities by Facility Age, 1986
# Facilities
2
21
21
17
16
<5 5 to < 10 10to<20 20to<30
Age (Years)
30 to <40
40+
3.13
-------
>n
P S
to r)
g
i
JQ 8
oT
3.14
1 S
S
C*;
o\ —«
en co
* «?^
to ft
Si
CO
•ef
s s
OO
a s
s
c
1
•o
o
P
g
o o. S
cl
S
1 I
I 2 -S
1 I
S 1 "
S5 O ,,0
•a .S "S
•3 ^ 2
•3
o
1
1
I
SS -
1.1
Sourc
-------
Figure 3.4
Fungicide, Herbicide, and Insecticide Production1,
1980-1988
(in 1,000 pounds)
1,000 Pounds
1,000,000 —
900,000 —
200,000-
100,000-
Herbicide Production
(Including plant growth
regulators)
Insecticide Production
(Including rodenticides, soil
conditioners and fumigants)
i in in
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.
3.15
-------
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.
3.16
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mirrors planted acres.8 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).9
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.
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 ha 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 in 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
"All production and crop acres were divided by 1986 production and acres respectively, in order to display
production and acres on the same scale.
*The Conservation Reserve Program was a land retirement program aimed at retiring 40 to 45 million acres of
highly erodible crop land by 1990.
3.17
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Table 3.6 *
Distribution of Ik-Scope Pesticide Facility Production and Sales, 1986
Production Quantity
(million Ibs.)
«U 0.1 to Ito
<1 <6
AH
<2S
Number of
Facilities
Percent of
Total
8
18 35 14 15 90
9% 20% 39% 16% 17% 100%
Cumulative 9%
Percent
29% 68% 84% 101%'
Sales
(million $)
<$10
$10 to
<$50
2>$50 All
43 23 19 8S2
51% 27% 22% 100%
51% 78% 100%
1 Total does not equal 100% due to rounding.
2 Excluded from the 88 facilities that provided financial data are: one R&D facility and two
facilities that obtained pesticide revenues only from contract work or tolling and, therefore,
did not delineate in-scope vs. out-of-scope revenues.
Source: Census.
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
* ' 'in
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.10 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 revenues of less than
"Facility fixed costs were not broken down by pesticide-related vs. non-pesticide-related fixed costs in the
Census 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.
3.18
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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.
3.19
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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
• Manufacturing and Formulating and/or Packaging Out-of-Scope PAIs
03 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.
3.20
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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
• Depreciation
Q Fixed Overhead
H Research and Development
El Interest
D Other Expenses
Between $1 and $25 Million
;>;>;>;> Less than $1 Million
Facility size is measured by revenues from all pesticide-related activities.
Source: Census.
3.21
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Si 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).11 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.12 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 in 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 in 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.
12Facilities 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 in 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.
3.22
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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
EJ Labor Costs
H Contract Costs
ED Other Pesticide Costs
D Taxes
Facility size is measured by revenues from all pesticide-related activities.
Source: Census.
3.23
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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.
3.24
-------
'
S
«•
I
H
li
<,
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CM
C-4
\o
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Ov
iS
jo
s;
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vo 5
1
I
cu
1
I
Q
SS
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.11
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[S -n "3 »
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- « «i CO
3.25
-------
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)13, and SIC 28 (chemicals and allied products).
The figure shows a close correlation between employment trends in all manufacturing industries, and in 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." 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.
"Industrial 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.)
MIn-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.
3.26
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Table 3.8
Average Facility Employment Characteristics
by Facility Size, 1986
Full-Time Equivalents (FTEs)1
Size of
Facility
Average Average
„ pesticide Formulating Average Average
Number Average Manu- and Employment Non-
of Total factoring Packaging for Other Production
Average
Production
Employment/2
as %of
Average Total
Facilities Employment Employment Employment Production Employment Employment
Less than
$20M
$20M
to $74.9M
$75M
to $249.9M
$250M and
greater
Average for
All Size
Facilities
20
33
20
13
86/3
49
98
410
2,534
527
10
46
153
40
8
12
82
19
12
30
167
1,118
225
27
50
185
1,181
250
49%
49%
55%
53%
54%
1 FTEs are calculated by dividing total facility annual hours by 2,000. The average employment figures are the
arithmetic mean of FTEs across facility size.
Production employment figures include pesticide manufacturing, formulating and packaging, and other production
employment.
3 Excluded from the 88 facilities thaft provided financial data are an R&D facility and a facility that did not provide
employment data.
Source: Census
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.15 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
"Although 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.
3.27
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Figure 3.11
Employment Trends, 1975-1987
(1975 Base Year)
Number of Employees
Indexed to 1975
1.5-1
1.4-
SIC 2879
All Manufacturing
SIC 28
iSIC 2865, 2869
1 1 1 1 1 1 1 1 T
1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987
— 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
3.28
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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
M In-Scope Pesticide Chemicals
Q Other EPA Registered Pesticide Chemicals
D Pesticide Contract Work or Tolling 2
E3 Other Revenues
1 Facility size is measured by total facility revenues.
2 Tolling work maybe either in-scope or out-of-scope.
Source: Census.
3.29
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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
35% —1
30 —
25 —
20 —
15 —
ID-
S'
-5 —
-10%-
31.7%
Pesticide Type
16.5%
10.7%
8.6%
188888888888888888888888888?"
-2.8%
35% —|
25 —
20 —
15 —
10-
5 —
Fungicides Herbicides Insecticides Other
Pesticides
Revenues from In-Scope Pesticides
Multiple
Types of
Pesticides
22.6%
11.4%
6.7%
Profit as a Percent 25% •
of Sales
Less than $2 Million $2-$25 MHIion
Total Facility Revenues
20 —
15 —
10 —
5 —
0-
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.
-------
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.16 Many of the pesticides included in these
profit figures represent patent-protected chemicals produced by only one manufacturer. Although patented 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.
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 manufacturing1' 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 "ail manufacturing11 increased by approximately 20 percent.
16Production data collected hi 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. '
3.31
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Pesticide Capital Expenditures, 1975-
' " SIC 2879*
(in 1986 dollars)
Year
Capital
Expenditures
(million $)
Annual Percent
" Change
1975 342.6
1976 301.7
1977 340.9
1978 381.4
1979 280.8
1980 246.4
1981 263.3
1982 295.9
1983 145.0
1984 199.7
1985 192.6
1986 200.6
1987 224.1
Average Annual Change
73%
-12%
13%
12%
-26%
-12%
7%
12%
-51%
38%
4%
12%
-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
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.
3.32
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1986 Dollars Indexed
to 1975
Figure 3.14
Capita! Expenditures
in 1986 Dollars
(1975 Base Year)
1.5-
All Manufacturing
SIC 28
SIC 2879
' SIC 2865, 2869
°'° I I I I I I I I III|
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)
:«*.:.»«; ^ll Manufacturing
Source: Census of Manufacturers, 1987.
3.33
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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 hi 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 1984.17
The post-1982 decline in pesticide manufacturing capacity utilization may be attributable in part to the
Payment-in-Kind (PIK) program.18 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. 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.19
3.4 Firm 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.
17This is reasonable, since pesticide production is more closely related to agricultural production than to
measures of industrial activity.
"Recall that PIK took 48 million acres out of production in 1983.
19Per unit costs increase as quantities produced decrease. Producing larger quantities may therefore cost less
on a per unit basis.
3.34
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Table 3.10
U.S. Pesticide Production Capacity Utilization Rates, 1980-1989
(Percent)
Year
Herbicides Insecticides Fungicides
All
Pesticides
Annual
Percent Change
All Pesticides1
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
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
68.1
n/a2
-6%
10%
-33%
-4%
17%
7%
-5%
21%
Average
Annual
Change
4%
| 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.
? Projected.
Source: USDA Agricultural Resources: Situation and Outlook Report, AR-13, February
1989.
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 1986,59 firms produced in-scope pesticides in the United States. These firms owned 90 facilities, which
produced 136 individual or classes of in-scope PAIs. Hie number of PAIs manufactured by each firm varies
3.35
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Figure 3.15
Comparison of All Manufacturing Capacity Utilization
and Pesticide Production Capacity Utilization Rates
Capacity Utilization
Rate
90-i
80-
70-
60-
50-
40-
30-
20
,||IU||"« I IIIIIMIIM'II'
Mi
,,,111111"" Manufacturing
Insecticide
Pesticide
Herbicide
Fungicide
r^ i i i i i i i
1980 1981 1982 1983 1984 1985 1986 1987 1988
Years
Pesticide
Herbicide
Insecticide
Fungicide
mi All Manufacturing
Source: USDA Agricultural Resources: Situation and Outlook Report
AR-13 February, 1989.
Statistical Abstract of the United States , 1989.
3.36
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(see Figure 3.16). Approximately 41 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 71 percent 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, 41 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.
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
six percent of firms' sales.
3.5 Industry Market Structure
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
107 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.
3.37
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Figure 3.16
Number of Individual or Classes of In-Scope PAIs
Produced by Firms, 1986
123456789 10 11
Number of In-Scope PAIs Produced
Source: Census.
3.38
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Figure 3.17
Number of U.S. In-Scope Pesticide Manufacturing
Facilities Owned by Firms, 1986
Number of
Firms
10
2
Number of Manufacturing Facilities Owned by a Single Firm
Source: Census.
3.39
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Figure 3.18
Composition of Firm Sales, 1986
(Summed Across All Firms)
Pesticide1
Manufacturing
5%
Formulating and/1
or packaging
1%
Activity not related to In-Scope or
Out-of-Scope pesticides
94%
1 Includes in-scope and out-of-scope production activity.
Source: Census.
3.40
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Figure 3.19
*
Number of Firms that Produce an Individual PAI or
Class of PAI, 1986
Number of PAIs
150^
136 PAIs Produced
125-
100-
75-
50-
25-
3 PAIs produced by 4 or more firms
5 PAIs produced by 3 firms
21 PAIs produced by 2 firms
107 PAIs produced by only 1 firm
Source: Census.
3.41
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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.
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 addedrdepreciable 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).M 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
•I
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.21
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
''A higher ratio of value added by manufacturing to gross book value of depreciable assets may also result from
the use of older equipment.
2tThe 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.
3.42
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recover their R&D expenditures.22 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 not protect
profits. Nevertheless, patents for most pesticides are instrumental hi 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 hi
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 hi 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 $150,00023. 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.24 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.25 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.
^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.
23The 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
(HFRA, Section 4).
2*The Census collected total facility, not pesticide-specific, R&D costs.
MR&D costs were estimated based on firm size rather than facility size, because firm size is generally more
important than facility size hi determining the level of R&D.'
3.43
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" - Table 3.11
Research and Development Costs as a Percent of Total Facility Sales, i$86
" - ,- - JjyFirmSize1 '
Firm Size (Annual Revenues)
^
Facilities
Percent of R&D
Costs' to^Total
Facility Sales
Revenues less than $1 Billion
Revenues between $1 Billion and $6 Billion
Revenues greater than $6 Billion
All Facilities
46
26
12
842
3.3%
5.5%
3.7%
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.
3.5.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 the 90
in-scope PAI manufacturing facilities also engaged in formulating/packaging. When evaluated at the firm level,
these data reveal that 36 of the 59 firms represented in the Census have PAI formulating/packaging capabilities at
one or more of their in-scope PAI manufacturing facilities. In addition, four 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 59 firms represented in the Census, therefore, 40 (68 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).
3.44
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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.
Two main types of restructuring occurred in the United States in 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 in the world market, because U.S.
products were more expensive relative to foreign counterparts. The increase in 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 in 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).
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 in
1982 and 49 percent in 1987, indicating increased consolidation in the industry. 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.
^Based on parent firm information reported in the Census, 9 of the 90 facilities (10 percent) were owned by
foreign companies in 1986. Note: Foreign ownership was not explicitly requested in the Census, and was determined
based on the parent firm address reported hi the Census in conjunction with information presented in Dun and
Bradstreet's Million Dollar Directory.
3.45
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Figure 3.20
Number of Facilities Acquired by Firms
(From Jan. 81 to Dec. 86,
by Method of Acquisition)
Number of
Facilities
Purchase
Merger
Founded
Other Status1
Method of Acquisition
10f 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.
3.46
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Table 3,12
Share of Value of Pesticide Shipments Accounted for by the
4, S, 20, and 50 Largest Companies, 1972-1987
Year
1982
1977
1972
4 largest 8 largest 20 largest 50 largest
Total companies companies companies companies
-------
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 50 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 herbicide and fungicide markets are 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 in the quantity demanded.
Price elasticities of demand were estimated for each pesticide cluster in the analysis.27 In order to develop
the elasticity estimates, the EPA developed a comprehensive approach, including:
(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);2*
(3) Feasibility of employing non-chemical, non-biological pest control methods (Pimental, D., et al.,
1991).29 (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, l9S9a).x (The greater the
contribution of pesticides to the cost of production, the higher the expected price elasticity of
demand.);
»This section is based on detailed analyses of pesticide demand elasticities. See Appendix C for further details.
^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.
*>USDA (1989a). Economic Indicators of the Farm Sector: Cost of Production, 1987. February.
3.48
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Table 3.13
Share of Value of fa-Scope Pesticide Shipments Accounted
4, 8, and 20 Largest Sums, 1986
4 largest
firms
8 largest
firms
20 largest
firms
for by the
Total
Number of Facilities
All Pesticides 12
Fungicides 4
Herbicides 10
Insecticides 9
Concentration Ratio (Percent
All Pesticides 50
Fungicides 67
Herbicides 69
Insecticides 57
23
9
17
13
of Sales)
73
90
88
81
43
24
33
28
95
1001
99
99
90
30
39
36
,
100
100
100
100
Total Sales (Million $)
All Pesticides 1,945
Fungicides 278
Herbicides 1,695
Insecticides 531
2,844
375
2,169
749
3,645
415
2,456
919
3,884
416
2,463
928
1 Remaining six firms constitute less than 1% of total fungicide sales.
Source: Census.
(5) Analysis of the marginal productivity of pesticides (USDA, 1989, USDA, 1989a);31 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. Elasticity of demand for pesticide clusters with in-scope products in 1986 varies among
these clusters from -0.12 to -1.38 (see Appendix C, page 62). 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.
31USDA (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.
3.49
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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.
3.6.A U.S. Pesticide Imports and Exports
Table 3.14 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 3.15 and 3.16 show import and export values for herbicides and insecticides,
respectively.32 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.33 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 hi the 1980s (U.S. Department of Commerce, 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.
^Similar data is unavailable for fungicides.
MMuch of the decline hi exports and increase in imports'was due to the closing of one facility.
3.50
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Table 3.14
ILS. Import and Export Values for All Pesticides
(in thousand 1986 $)
Tear
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Average
Annual
Change
Source:
Value of
Imports
260,098
268,846
317,718
307,553
284,196
271,512
322,874
413,772
402,782
414,800
% Change
65%
3%
18%
-3%
-8%
-4%
19%
28%
-3%
3%
7%
Value of
Exports
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
United Nations International Trade Statistics
% Change
99%
7%
-6%
-9%
2%
1%
16%
-9%
6%
<1%
1%
Trade
Balance
978,410
1,052,050
923,329
824,872
872,810
902,071
1,034,361
817,683
897,192
891,159
% Change
111%
8%
-12%
-11%
6%
3%
15%
-21%
10%
1%
-1%
Yearbook, 1978-1987
Table%3.15
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:
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
Value of
Exports
462,023
494,605
495,111
460,619
470,692
526,205
586,791
212,157
197,936
233,650
—
Trade Statistics
% Change
NA
7%
<1%
-7%
2%
12%
12%
-64%
-7%
18%
-5%
Trade
Balance
373,556
347,850
334,187
302,327
304,296
406,438
429,222
(9,541)
5,410
49,787
—
% Change
NA
-7%
-4%
-10%
1%
34%
6%
-102%
157%
820%
-10%
Yearbook, 1978-1987
3.51
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• • , ;; Table 3.16 % j '-; , '"•'"•>"" ' i§ (|1.
U.S. Import and Export Values for Ihsedfcides
0n thousand 1986 $)
Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Average
Annual
Change
Source:
Value of
Imports
60,539
79,350
90,055
90,854
73,625
74,508
65,906
76,508
90,964
111,376
% Change
- Value of
Exports
NA 304,671
31%
13%
1%
-19%
1%
-12%
16%
19%
22%
9%
"United Nations International
358,331
301,474
294,367
289,169
268,194
345,073
239,421
251,425
204,867
Trade Statistics
« / fV
' % Change
NA
18%
-16%
-2%
-2%
-7%
29%
-31%
5%
-19%
-4%
Trade
Balance
244,132
278,981
211,418
203,513
215,544
193,686
279,167
162,913
160,461
93,491
'\ >i>
% Change*
NA
14%
-24%
-4%
6%
-10%
44%
-42%
-2%
-42%
-7%
Yearbook, 1978-1987
Table 3.17 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 in 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 3.17 also shows that imports have maintained approximately the same share of new
supply: 5 percent in 1978 and 6 percent in 1987.
3.6.B U.S. Pesticide Industry in the World Market
Table 3.18 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.
3.52
-------
•&,
£
I
'3
o
0
III
3.53
-------
Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Source:
U.S. Trade as a
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
Table 3.18
Percentage of the World Market Economy
1978-1987^
' % Change in
Share of
" " Imports
32.3%
.7%
26.6%
34.7%
-34.9%
-1.5%
12.5%
23.5%
-17.4%
-6.4%
U.S/Shareof
World Exports
> , •• if " -
26.2
26.3
24.7
30.5
27.1
27.4
29.3
26.8
24.9
23.4
" -. •:"*
for Pesticides,
f »*, &*•••> r* > , ,, >,
% Change in Share
of Exports
< « vt .fl
40.5%
.5%
-5.9%
23.4%
-11.3%
1.0%
7.0%
-8.5%
-7.0%
-6.4%
United Nations International Trade Statistics Yearbook, 1977-1987
The shift in the U.S. pesticide export position is due, hi 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 hi 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 3.19 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.34
In particular, the United Kingdom, Switzerland, Italy, and Brazil have increased their share of world pesticide
exports.
As indicated hi Table 3.18, 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
stated previously, some of the U.S. companies included hi the Census are owned by foreign entities.
3.54
-------
, Table 3.19
Value of Pesticide Exports for Leading Export Nations
as a Percent of the Total World Pesticide Exports, 1979-1987
Country
United
States
Germany
Fed. Rep.
United
Kingdom
France
Switzerland
Netherlands
Japan
Italy
Belgium
Brazil
TOTALS
1979
26.3
20.0
11.4
10.6
7.4
3.8
3.2
2.4
6.3
0.6
92.0
"
1980
24.7
18.2
11.3
9.6
7.3
4.7
3.2
2.7
8.8
0.6
91.1
1981
25.9
18.2
12.1
9.6
7.3
3.6
3.6
2.6
6.3
0.8
90.0
Source: United Nations International
f
1982
27.1
17.8
11.5
8.8
7.5
4.0
4.5
2.7
5.8
1.0
90.7
1983
27.4
18.8
12.1
10.0
8.3
4.8
5.8
3.2
0.9
1.1
92.4
Trade Statistics
1984
29.3
18.1
12.2
9.9
8.3
5.0
4.7
2.8
0.9
1.3
92.5
1985
26.8
18.5
13.9
10.1
8.2
5.5
3.7
2.8
1.3
1.0
91.8
1986
24.9
20.8
12.3
10.5
8.0
5.9
3.0
3.7
1.8
1.3
92.2
1987
23.4
18.5
14.1
10.6
9.2
5.8
3.4
3.7
1.9
1.5
92.1
Average
Annual
Change
-0.37%
-0.18%
0.34%
0.00%
0.23%
0.25%
0.02%
0.16%
-0.56%
0.12%
0.01
Yearbook, 1979-1987
and the United States. This expansion took place in major markets such as Brazil, India and eastern Europe.
Together with the development of pesticides manufactured in Taiwan and South Korea, this expansion further
increased the competition for products manufactured in western Europe and the United States (Shenton, 1989).
Table 3.20 shows the value of pesticide imports from leading importers to the United States as a percentage
of total U.S. pesticide imports. As seen in this table, although imports from western Europe still 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.
3.55
-------
Value of Pesticide Imports for Leading Importers to the United States
as a Percent of Total U.S. Imports, 1980-1987
Country
Canada
West
Germany
United
Kingdom
France
Switzerland
Netherlands
Japan
Italy
Brazil
TOTALS
1980
2.5
32.3
12.5
2.7
24.2
3.8
12.1
2.1
0.4
92.6
1981
3.8
22.1
NA
3.4
27.4
5.0
16.0
2.5
1.2
81.4
1982
2.8
23.3
8.4
3.0
34.1
4.2
9.1
2.7
2.8
90.4
Source: United Nations International
1983
3.9
21.1
10.9
3.7
26.2
4.1
9.4
2.6
6.3
88.2
1984
5.8
25.6
13.7
4.4
20.8
4.7
4.7
2.2
5.5
87.4
Trade Statistics
< V
^ v
, 1985
4.1
24.7
11.0
4.1
29.9
5.5
3.5
2.2
4.8
89.8
Yearbook,
1986
3.6
18.3
15.8
5.1
19.1
3.8
4.5
4.5
11.0
85.7
J987
4.7
17.7
16.0
6.4
11.5
3.1
4.4
3.4
12.1
79.3
Average
Annual
Change
0.31%
-2.09%
0.50%
0.53%
-1.82%
-0.11%
-1.09%
0.18%
1.68%
1982-1987
3.7 Analysis of Actual Facility Closures
The pesticide industry has undergone substantial restructuring since 1986. As mentioned earlier in the
Profile, factors such as the weakened dollar during die last half of the 1980s, the worldwide downturn in planted
agricultural acreage, and changes in consumer preferences have impacted facility operations. These factors have
influenced companies' decisions to not re-register or voluntarily cancel PAIs. Additionally, government actions such
as the re-registration requirements promulgated under the 1988 FEFRA amendments have led to pesticide
cancellations and use restrictions.
At least 15 of the 90 pesticide manufacturing facilities included in the Census have discontinued their
pesticide operations since 1986.35 In addition to the 15 facility closures, 12 facilities have closed PAI product
lines and 9 facilities were acquired by another company or underwent some type of restructuring. This section of
35 These facility closures reflect only the impacts with which EPA is familiar and are based on periodic contact
with the pesticide industry. A complete assessment of the financial impacts on pesticide manufacturers since 1986
would require contacting each facility in the industry. One of the 15 facilities has declared bankruptcy and may,
or may not, have actually discontinued pesticide operations. '
3.56
-------
the Profile focuses primarily on the Census facilities known to have incurred the most dramatic of these impacts -
discontinuation of pesticide operations.
EPA assessed patterns among the facilities that have ceased PAI manufacturing by analyzing the location
of the facilities, types of pesticides produced, facility size, mix of sales activity, and pesticide and total facility profit
margins in relation to the facilities in operation.36'37 Facility location and facility profit margins did not differ
substantially between facilities that have discontinued pesticide manufacturing activities and those in operation.
However, facility size (as measured by in-scope pesticide sales and total facility sales), mix of sales activity,
pesticide-level profit margins, and the types of pesticides produced, were notably different.
Average 1986 in-scope pesticide sales for facilities that have continued pesticide operations were $52.2
million, while facilities that have ceased pesticide production had average in-scope pesticide sales of $8.3 million
in 1986. Similarly, average 1986 facility sales were $163 million for open facilities and $36 million for closed
facilities. These data indicate that, on average, the size of in-scope pesticide and total facility operations for
facilities that have discontinued their pesticide operations were significantly smaller than those that continue to
produce PAIs. When comparing median in-scope pesticide and facility revenues, the difference between the two
groups of facilities are less dramatic: $13.8 million versus $3.4 million in 1986 in-scope pesticide revenues and
$43.4 million versus $34.8 million in 1986 facility revenue for open and closed facilities, respectively. These data
indicate that the largest facilities, both in terms of in-scope and total facility revenues, are not among the facilities
known to have discontinued pesticide operations.
Assuming that no new products or companies entered the industry after 1986 and that production at closed
facilities was not transferred, the pattern of closures indicates that industry concentration has increased. However,
to definitively conclude that industry consolidation has increased, information on the fate of production at the
affected facilities, as well as data on new products and producers, would be required. In the absence of such data,
information from secondary sources was used to assess changes in industry concentration since 1986.
A 1988 Chemical Week article entitled, "Environmental Concerns Force Global Changes in the Market,"
claimed that at least seven major agricultural chemical company buyouts occurred from 1985 to 1988. Much of
this consolidation was from U.S. firms selling to non-U.S. firms (Chemical Week, 1988). Industry experts cite the
cost of pesticide registration as one of the driving forces in industry consolidation, explaining that companies can
expect to spend $100 million per year on new product development and registration, and that sales of $1 billion per
36 Profit margins were calculated as pre-tax pesticide and facility profits as a percent of sales.
37 Financial data were not provided by one of the 15 facilities. Therefore, the financial information presented
in this section reflects data for 14 facilities.
3.57
-------
year are needed to cover these expenses (Chemical Week, 1988). This surge in research costs during the 1980s
led to the emergence of the theory of "critical mass," the amount of sales needed to support an R&D program (Sine,
1990). During the late 1980s, the pesticide industry began consolidating, in part, to obtain critical mass (Sine,
1990). This consolidation suggests that industry concentration has increased since 1986.
The mix of facility sales activity also differs considerably for open versus closed facilities. On average,
facilities that remain open obtained 45 percent of their revenues from non-pesticide operations in 1986, while
facilities that closed derived 63 percent of their revenues from non-pesticide operations in 1986. These data show
that facilities that discontinued PAI manufacturing were more involved in non-pesticide production.
Unlike facility profit margins, pesticide-level profit margins do differ substantially for open versus closed
facilities. Pesticide profit margins for facilities that closed averaged 8 percent, while open facilities averaged 11
percent. In contrast, facility level profit margins averaged 12 percent for open facilities and 15 percent for closed
facilities. Together with data on the mix of facility sales activities, facility profit margins indicate that non-pesticide
operations at facilities that discontinued PAI manufacturing were more profitable than pesticide activities.
The types of pesticides produced by individual facilities - herbicides only, insecticides only, fungicides only,
others only (see Table 3.2 for a list of other pesticides), and multiple types of pesticides - were examined to assess
whether facility closures were concentrated hi one of these broad market segments. Census data show that the
majority of the facilities that have continued PAI manufacturing produce multiple types of pesticides (34 percent),
followed by those that produced herbicides only, insecticides only, fungicides only, and others only (see
Table 3.21). In contrast, closures were equally concentrated among facilities that produced herbicides only,
fungicides only, and those producing multiple types of pesticides.
When analyzing the number of each pesticide type affected by both facility and product line closures, Census
data show that impacts fell disproportionately among fungicides - 35 percent of the in-scope fungicide PAIs produced
in 1986 were impacted by a facility or product line closure. In contrast, 19 percent of herbicide PAIs, the most
commonly produced in-scope PAI in 1986, were impacted.
EPA also examined facilities that discontinued PAI production and those that closed product lines and found
that 18 of the 98 (18 percent) PAIs manufactured by a single producer were impacted by these facility changes,
while 9 of the 33 (27 percent) PAIs produced by multiple facilities were impacted. These data indicate that closures
may be more likely to occur when there are multiple producers of a product. Similarly, when impacted PAIs were
examined at the cluster level, the data showed that PAIs classified in clusters with multiple in-scope PAIs hi 1986
were more likely to be impacted than clusters that included only one in-scope PAI. Specifically, none of the clusters
with a single in-scope PAI in 1986 contained PAIs that were impacted, while 74 percent of the clusters with multiple
3.58
-------
Table 3.21
Distribution of Facilities
by Type of Pesticide Produced
Pesticide Type
Herbicides Only
Fungicides Only
Insecticides Only
Other Pesticides Only
Multiple Types of Pesticides
Total1
1 Totals may not equal 100%
Source: Census.
% of All
Facilities
23%
13%
20%
9%
34%
100%
due to rounding.
% of Open
Facilities
23%
11%
21%
11%
34%
100%
% of Closed
Facilities
27%
27%
20%
0%
27%
100%
in-scope PAIs in 1986 were impacted. Together these data suggest that product competition, at both the PAI and
cluster level, increases the likelihood of closure.
Factors such as the public's perception of pesticide products and government actions including re-registration
and restricted uses have resulted in changes in the pesticide market since 1986. Census data indicate that factors
such as increased competition among products, facility size, and low pesticide profit level in relation to non-pesticide
operations may be characteristic of facilities known to have been affected by changing market conditions.
3.8 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 us
-------
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.
3.60
-------
Chapter 3 References
Chemical Week (1988). Environmental Concerns Force Global Changes in the Market. May 4, 1988.
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). 7957 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.
3.61
-------
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 (1992). Pesticide Containers: A Report to Congress. Office of Pesticide Programs,
May.
U.S. EPA, and Abt Associates, Inc. (1991). 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.
3.62
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Chapter 4: FACILITY IMPACT ANALYSIS
4.0
Introduction
This chapter presents the methodology for projecting impacts of the pesticide manufacturers effluent
limitations guidelines and standards at the facility level and describes the results of the analysis. A few
methodological changes have been implemented since the proposed rule in response to comments and internal EPA
review, resulting in no significant changes in projected impacts. The following text describes the methodology
employed hi the final analysis. Changes from the methodology used in the proposed analysis are footnoted and are
fully explained hi the Administrative Record.
As discussed hi 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) evaluation of facility after-tax cash flow to project facility closures;1
(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.2
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 hi 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.
In the economic impact assessment for the proposed rule, facility closure was evaluated by comparing
discounted cash flow to liquidation value. The analysis now projects a closure if a facility's after-tax cash flow is
negative. The new methodology was adopted because of the subjective and imprecise nature of estimates of
liquidation values. The questionable credibility of the estimates of liquidation value provided by respondents in the
Census became obvious as data from the pesticide formulator/packager/repackager Survey became available for
comparison. Full details on this methodological change are available in the Administrative Record.
2 .
Appendix E of this report compares compliance costs to facility revenue. This financial ratio was added to
the analysis following proposal of effluent limitations as an additional indicator of adverse financial impacts that
facilities may face due to the regulation. This ratio is not discussed in Chapter 4.
4.1
-------
Based on data from the Census, a total of 90 pesticide manufacturing facilities (owned and operated by 59
firms) that manufacture one or more in-scope PAIs were potentially subject to regulation. However, EPA has
information indicating that 15 of these facilities have closed their in-scope PAI manufacturing operations since 1986
(the Census base year). Also, metallo-organic (Subcategory B) PAIs are no longer considered for regulation under
the final rule. Two facilities producing only Subcategory B PAIs as their only in-scope products are no longer
counted as potentially subject to the regulation. Therefore, a total of 73 pesticide manufacturing facilities (owned
and operated by 49 firms) that manufacture one or more PAIs are potentially subject to regulation.3 Although 73
facilities are potentially subject to the regulation, the EIA only analyzes 72 facilities for economic impacts. The
facility excluded from the economic analysis is a research and development facility with no revenues expected from
the manufacture of in-scope PAIs and no compliance costs.
This chapter describes the economic models, and then discusses the methodologies for the facility closure
analysis, product line closure analysis, and other significant financial impact analysis. Finally, the facility-level
results are discussed.
4.1 Economic Model
Before presenting the specific model used hi 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 or increase quantities
produced, 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 and capacity
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.
Two of the 73 facilities that are counted as subject to the regulation have closed in-scope PAI operations since
1986 but the production either has been or may be transferred to another facility. To ensure that the costs to the
industry are not understated, EPA has retained these facilities in the analysis.
4.2 '
-------
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:
n/ =
- EC
where:
Qif
EC
if
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 Qif's, f= 1,N equals Q;);
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 Qif 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.
4.3
-------
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.
4.1.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
now effluent limitation guidelines and standards, require the following basic steps:
(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 com 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
4.4
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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 clusters.4 As reported in Section 3.1, the EPA's Office of Water made
minor adjustments to these pesticide clusters for this analysis. First, PAIs registered after 1980 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 56.5 Finally, PAIs were allocated to
more than one cluster when the PAI was known to be used hi substantial quantities for different end uses. The
adjusted PAI clusters were used as the basis for this EIA. The 260 PAIs, or classes of PAIs, considered for
regulation are mapped into the 56 separate clusters in Appendix B.6
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 kinds 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.
Tn 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.
Only 44 of these clusters had production of one or more of the 260 in-scope PAIs or classes of PAIs in 1986.
PAI #67 (biphenyl), in cluster F6, was considered in-scope at proposal but is not considered in-scope for the
final rule. The count of clusters with production in 1986 therefore decreased from forty-five to forty-four. See
the Technical Development Document for additional information.
4.5
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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.
PAI-specific data provided. Provision of PAI-specific prices hi the Census was optional. If these
data were provided, they were used in the analysis. Thirteen (18 percent) of the 72 pesticide
manufacturing facilities for which economic impacts are analyzed chose to provide price data on
their technical grade products.7
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 Doane's 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
PAIi is found to be twice the price of PAI2. If Facility A reported producing 200 pounds of PAIj
and 500 pounds of PAI2, with total in-scope revenues of $4,500, the analysis would calculate the
price of PAI2 as:
200(2p) +500(p) =$4,500
where p = the price of PAI2.
The solution for "p" is $5. PAIj would therefore be estimated to have a price of $10.
Thirteen of the 72 facilities provided PAI-specific data for technical products, eight facilities provided data
on formulated/packaged products, and two facilities provided data on intermediates. A total of sixteen of the 72
facilities provided PAI-specific data for at least one of these product groups.
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• 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.
• PAI-specific 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 in 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.9 This is represented by the equation:
/F=F x (IR/TR)
where:
IF
F
fixed costs associated with in-scope PAIs;
3-year average fixed costs for the entire facility;
X
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 final rule.
Q ,
Three-year averages were used in aia effort to modulate the variability of particular years and to create data
that represents a typical year.
4.7 .
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• 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 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.13
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.
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 of the 73 manufacturing facilities potentially subject to regulation 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). However, not all of these costs may have been borne by the pesticide manufacturers; a portion may have
been passed through to customers in the form of higher prices. As a reasonable simplification to reflect the cost
pass through, the analysis assumes that the burden of the cost increase is split evenly between the facilities and the
13The 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.
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customers. In other words, the facilities are assumed to bear 50 percent of the cost increase.14 These costs were
added to the baseline fixed costs of the affected facilities.
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 hi 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 hi SIC 2869.)
Of 73 facilities potentially subject to regulation, 25 are projected to 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 25 facilities total $97 million and $33 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 $16
million.'
Facility Compliance Costs
Full details of the methods by which the costs of complying with the final regulation were estimated can
be found in the Final 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 73 pesticide manufacturing facilities producing one or more of 260 PAIs,
or classes of PAIs, are potentially subject to regulation. At proposal, EPA evaluated compliance costs for pesticide
manufacturing 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
An alternate assumption, in which all RCRA compliance costs were borne by the manufacturers, might 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 could be projected.
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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to POTWs or surface water (Zero Discharge Option). The Treated Discharge Option limitations are based on the
use of biological treatment, hydrolysis, activated carbon, chemical oxidation, resin adsorption, solvent extraction,
incineration and/or recycle/reuse to control the discharge of PAIs in wastewater. The Zero Discharge Option was
based on on-site or off-site incineration and/or recycle/reuse.16 For both regulatory options, the economic impacts
on facilities were calculated separately for direct and indirect dischargers.17 Each discharge category was
analyzed further by two subcategories: organic pesticide chemicals manufacturing (Subcategory A) and metallo-
organic pesticide chemicals manufacturing (Subcategory B). Because EPA chose not to regulate Subcategory B at
this time and the Zero Discharge Option was found at proposal to not be economically achievable, only costs of the
Treated Discharge Option for Subcategory A are discussed. A full discussion of Subcategory B and the Zero
Discharge Option can be found in the EIA for the proposed rule (EPA, 1992).
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.1 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 hi 1986 dollars and are
based on the assumption that, whenever possible, facilities will build on existing treatment.
The costs and impacts of implementing the regulation 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 final regulatory option for Best Available Technology Economically Achievable (BAT) and Pretreatment
Standards for Existing Sources (PSES) for Subcategory A.
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 on facilities with zero discharge are reported with impacts on direct discharging facilities. 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.
18
For details on the weighted average cost of capital, see Section 4.2.B.
4.12
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Under the final rule, it is expected that 55 pesticide manufacturing facilities will incur compliance costs:
33 direct dischargers and 23 indirect dischargers (one facility is a joint discharger). Total BAT annualizsd costs
(applying to direct dischargers) are projected to be $18.2 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 for this
industry. Total annualized costs for PSES (applying to indirect dischargers) under the final rule are projected to
be $5.1 million for Subcategory A.19
Table 4.1
Costs of the
Direct
Dischargers***
Indirect
Dischargers
Number of facilities incurring costs
Capital and Land (MM$)
O & M (MM$)
Annualized Costs (MM$)
33
24.93
14.60
18.16
23
8.70
3.82
5.08
**
There are no costs for Subcategory B direct dischargers because direct discharge of
Subcategory B chemicals is already limited to zero under BPT. Regulations for Subcategory B
indirect dischargers are not proposed.
At proposal, projected costs were included regardless of whether a facility had closed pesticide
operations or was projected to close pesticide operations prior to incurring the costs of
compliance. The total costs were therefore overstated. For the final rule, costs are included
only for facilities not known to have actually closed and facilities that have closed but may have
transferred their production to another facility.
*** Impacts of requirements on zero discharge facilities are reported with impacts on direct
discharge facilities. 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.
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.
19,
The EPA is not proposing to regulate Subcategory B chemicals at this time.
4.13 *
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Pricing Rule to Estimate Post-compliance Prices20
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 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.
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.21 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.
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 profits. EPA conducted a sensitivity
analysis using this zero cost pass-through assumption. The results are reported in 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.
21Theoretically, 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. •
4.14
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To capture this effect, price increases for each market and each facility were calculated as:
where:
Cf,j
PC;
TJ
change in unit price for facility f, cluster j;
unit compliance costs for facility f, cluster];
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 hi unit price due to the installation of pollution control
equipment.22
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 hi 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 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.
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
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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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 in Appendix C.
A list of the elasticity estimates by cluster is presented in Appendix C, page 62, in order of increasing
elasticity of demand. As previously mentioned, 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 in 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
j , y, i ,,'i '
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.
4.2 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.23 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,
^Appendix E of this report compares facility compliance costs to facility revenue. This financial ratio was
added to the analysis following proposal of effluent limitations as an additional indicator of adverse financial impacts
that facilities may face due to the regulation. This ratio is not discussed in Chapter 4.
4.16
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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.
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 hi 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;
• 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.
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. The calculation compares the pre-compliance profitability of the
facility with the post-compliance profitability. Specifically, this calculation entails a comparison of pre-compliance
after-tax cash flow to post-compliance after-tax cash flow for the facility. In the majority of cases, a rational owner
would not continue operations if a facility's after-tax cash flow is negative.
The methodology used to project facility closures has been changed since the proposed rule. At proposal,
facility closures in both the baseline and post-compliance scenarios were evaluated by comparing facility discounted
cash flow to facility liquidation value. If the expected cash flows were less than the liquidation value of the facility,
the owner would be better off closing the facility.
After proposal, data from the Section 308 survey of pesticide formulating/packaging/repackaging facilities
became available for comparison with the data obtained from the Census. Using these two data sources, EPA
compared estimates of liquidation value, from the two questionnaires for the 45 pesticide manufacturing facilities
that had completed the financial portion of the Pesticide Formulating, Packaging and Repackaging Survey for 1988.
In the pesticide manufacturers Census, facilities were asked to estimate the liquidation value of the pesticide
4.17
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production and pesticide formulating/packaging lines and associated fixed assets, working capital, and real estate.
For comparison with the data available in the PFPR Survey, facility liquidation values were estimated by multiplying
the pesticide liquidation values by the ratio of facility revenue to pesticide revenue. The PFPR Survey was designed
such that facility liquidation values could be calculated as the quotient of tax assessment values of land, buildings,
equipment and machinery divided by the tax assessment percentage.
Of the 45 facilities, only 10 had gross facility liquidation values calculated from the two questionnaires that
were within a factor of two. While the two different approaches were not expected to give identical results, the
magnitude of the difference caused EPA to question the reliability of liquidation value estimates. Given these
discrepancies, EPA chose to evaluate facility closure as described above, i.e., based on after-tax cash flow. This
methodology does not require the use of liquidation values and corresponds to the methodology being considered
for use in evaluation of effluent limitations on the PFPR industry. Evaluation of after-tax cash flow is a simpler
methodology than comparing discounted cash flow to liquidation value and it avoids the uncertainty involved in using
estimates of liquidation value.24
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
Construction of the baseline facility closure analysis required estimation of 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 reflected in the Census data. Specifically, cash flow
for each facility was estimated as:
CFO = NI + DEP - OC(1-CT)
is methodological change does not alter the conclusion that the final rule is economically achievable.
4.18
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where:
CFO
NI
DEP
OC
CT
Cash flow;
Net income (i.e., after tax profits calculated from the Census);
Depreciation expenses (taken directly from the Census);
Cost of compliance with other EPA regulations first effective after 1986 (RCRA land
disposal restrictions and OCPSF effluent guidelines); and
Marginal corporate tax rate (assumed to be 34 percent).
4.2.B Post-Compliance Facility Closure Analysis
Facilities for which baseline cash flow was negative (i.e., those predicted to be baseline facility closures)
were not considered as potential facility closures in the post-compliance scenario. For the remaining facilities,
however, the post-compliance cash flow was evaluated to project facilities that would close due to the regulation.
Three factors are included when estimating cash flow in the post-compliance scenario:
• the compliance costs, including capital, land, and operating and maintenance;
• the resulting change in revenue associated with the new price and quantity; and
• the decrease in variable costs of production due to the reduction in quantity.
Facility changes in cash flow were calculated by summing the changes in annualized compliance costs,
revenue, and variable costs over all clusters produced at a facility. The post-compliance cash flow was then
calculated by adding the changes in cash flow to the baseline cash flow. The corresponding equation is:
PCCF=CF+£ (-
where:
PCCF =
CF
CCadj; =
the post-compliance facility cash flow;
facility baseline cash flow;
compliance cost adjustment to cash flow for cluster i;
the adjustment to cash flow due to the change in revenue for cluster i; and
the adjustment to cash flow due to the change in variable costs for cluster i.
4.19
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To calculate an adjustment to cash flow that includes the costs of compliance, capital and land, these costs must be
annualized. As described below, annualization of capital and land costs is accomplished by dividing these costs
costs by a "present value factor" which is constructed using an estimate of the cost of capital. Calculation of the
cost of capital and the present value factor are therefore discussed below followed by a discussion of the three
adjustments to cash flow.
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 to annualize the capital costs associated with the rule so that post-compliance changes in cost and
price can be projected.25
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 uses retained earnings. A second option involves acquiring
additional debt, through bonds, notes, or short-term commercial paper.26 Typically, acquiring debt is the less
expensive option because interest payments on debt reduce the firm's corporate tax burden and because debt offers
investors a less risky return than equity investment.27 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.
The 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.
26Debt 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).
07
Debt is a less risky investment than equity because debt is senior to equity. Interest payments are a
contractual obligation, paid before earnings are calculated and prior to declaration of dividends.
4.20
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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(E/A)+Y(l -CT)(DIA)
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 in 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 hi the
Compiistat 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?-* The calculated values taken from the Annual Statement Studies are 40.5 percent equity
financing and 59.5 percent debt financing.
28,
The "industrial chemical industry" includes SICs 2861, 2865, and 2869.
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The annual return on equity (R) was calculated as:
where:
i ss 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);29
(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.
/? = 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).30 Finally,
the marginal corporate tax rate (CT) is assumed to be 34 percent.31
The 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 dees 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 equal
to or higher than the cost of debt reported hi the Census for that facility, thereby increasing the projected burden
of compliance. Use of the WACCs is therefore conservative.
31
Because the firm, not the facility, tax rate is needed, use of the facility-level data from the Census was
inappropriate.
4.22 •
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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:
where:
RWACC=((l +WACQK1+G)) -1
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.
Present Value Factor
The real WACC is used to constnict a present value factor (PVF). Multiplication of annual costs by a PVF
discounts investments over a fixed time period. Correspondingly, division of present value costs by the PVF gives
annualized costs over a fixed period of time. As shown below, this analysis divided compliance capital and land
costs by the PVF to annualize these costs. The analysis uses a ten year discounting horizon as a conservative
estimate of the typical life of the pollution control equipment. The PVF is calculated as:
10
(1 + RWACC)1
where:
PVF
RWACC
present value factor;
the real weighted average cost of capital; and
number of years over which costs are discounted.
The three cluster level adjustments to cash flow 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 in taxes the facility
4.23
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will pay due to reduced profit. The facility will also pay reduced taxes as a result of depreciating capital
expenditures. An annualized cost of capital and land is calculated by dividing the estimated capital and land
investment by the present value factor. The present value factor is based on the WACC, as discussed in the
previous section. The operating and maintenance costs need no adjustments because they are annual costs. The
equation for the compliance cost adjustment is:
'PVF
,cpr?
10 '
where:
CCadjj «
OMj
CT
PVF
CPTj =
LAND: -
compliance cost adjustment to cash flow for cluster i;
operating and maintenance costs of compliance for cluster i;
marginal corporate tax rate;
present value factor;
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. The change in revenue will again be
partially offset by the corresponding change in taxes. The cluster-level adjustment to the baseline cash flow for the
change in revenue is shown by the equation:
/&Kffr«AP, x
i x A
-------
Adjustment for change in variable cost of production
The final adjustment to the baseline cash flow reflects the decrease in variable costs associated with
decreased production. Variable costs were assumed to decrease hi proportion to the decrease hi quantity of
pesticides produced. The decrease is again partially offset by an increase in taxes. The equation is:
where:
Qi
vq =
CT =
the adjustment to the cash flow due to the change hi variable costs for cluster i;
the change hi cluster i quantity from baseline to post-compliance;
the baseline quantity of cluster i;
the variable cost for cluster i; and
marginal corporate tax rate.
As previously discussed, a facility with negative after-tax cash flow hi the post-compliance scenario 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 Pharmaceuticals, may continue at the
location.
4.3 Product Line Closure Analysis
Facilities that did not close in 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 hi 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 (hi the short run), and would result hi an equal or lesser number of product
line closures. The calculation of unit prices and costs hi both the baseline and post-compliance scenarios was
described previously.
4.25
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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 closures. Such
results would not be contradictory, because the product line closure analysis evaluates closures based on estimated
unit prices and costs, while the facility closure analysis considers estimated facility cash flow.
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
arc 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 and condition.
Two financial indicators are examined hi this analysis: interest coverage ratio (ICR) and return on assets
(ROA).32 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 EBIT 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.
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-compliance
scenario. Significant financial impacts were evaluated by comparing each facility's post-compliance financial ratios
32The 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.
4.26
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to the lowest quartile ratios established for all in-scope pesticide manufacturing facilities. A significant impact is
said to result from the 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.33
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 hi 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(PREVITREV)
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 hi 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.
33The firm analysis is analogous to the "other significant impact analysis" for the facility level. See Chapter
7 for further details.
4.27 '
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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 EBIT:interest 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.
i'T
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 debt14
post-compliance total assets =
baseline total assets
plus compliance capital and land costs
^Compliance debt is the debt the firm is expected to incur in order to finance projected capital and land
expenses associated with the proposed regulation.
4.28
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4.5 Facility impacts
As discussed previously, 72 pesticide manufacturing facilities producing one or more of the 260 PAIs, or
classes of PAIs, potentially subject to regulation were evaluated for economic impacts under the final rule. The
EPA is regulating 120 of these chemicals and has projected compliance costs for 55 of the Subcategory A pesticide
manufacturing facilities. The economic impacts of the final rule on the facilities were calculated separately for
direct and indirect dischargers. Direct dischargers of Subcategory A chemicals were evaluated for compliance with
a BAT rule while indirect dischargers of Subcategory A chemicals were evaluated for compliance with a PSES rule..
Direct discharge of Subcategory B chemicals is already limited to zero under BPT. No BAT or PSES regulations
are being promulgated for Subcategory B chemicals at this time.
4.5.A Baseline
Fourteen of the 72 pesticide manufacturing facilities are projected to close in the baseline (see Table 4.2).
Of the 14 facilities counted as baseline facility closures, two have closed product lines since 1986 and three have
undergone restructuring. An additional 12 facilities are projected to close particular pesticide product lines in the
baseline. Four of these 12 facilities have closed product lines since 1986 and an additional three facilities have
undergone restructuring.
Table 4,2
Projected Baseline Closures
Facility Closures
Product Line Closures
14
12
4.5.B Effects of Compliance with the final Rule
The economic impacts projected to occur due to the final rule are discussed below. Because the Zero
Discharge Option was found at proposal to not be economically achievable, the estimates of compliance costs for
this option have not changed since proposal, and this option is not being promulgated, impacts due to this option
were not reassessed.
4.29
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Impacts of BAT Regulations on Direct Dischargers
Organic Pesticide Chemicals Manufacturing (Subcategory A)
Twenty-eight direct discharging and five zero discharging facilities producing Subcategory A chemicals are
expected to incur costs under this regulatory option.35 For manufacturers included in this Subcategory, the
incremental capital and annualized costs of complying with BAT limitations are expected to be $24.9 million and
$18.2 million, respectively. The estimate of capital costs has increased 67% since proposal while the estimate of
total annualized cost has increased by 24%. (See the Technical Development Document for an explanation of
changes in compliance cost estimates.) The change in compliance cost is the aggregate effect of decreases in
annualized compliance costs at four facilities and increases in annualized compliance costs at four other facilities.
Most of the increase hi total costs for direct dischargers is due to a substantial cost increase at one facility. The
estimated investment costs at this facility have increased from $1.6 million to $16.0 million, with an increase in
annualized costs from $2.0 million to $7.3 million. This change in costs resulted from public comments by the
facility. The Agency maintains that the actual compliance costs for this facility would be lower than the estimates
used in the final analysis. However, analysis using these higher cost estimates ensures that EPA does not
underestimate the burden of compliance at this facility.
No facilities are projected to close due to compliance with BAT (see Table 4.3). One direct discharging
facility is projected to close a product line as a result of the regulation. (One zero discharging facility, subject only
to monitoring costs, is also projected to close a product line.) No facilities are expected to experience other
significant financial impacts short of facility or product line closure.36
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 promulgated for the
metallo-organic pesticide chemicals manufacturing subcategory. There are therefore no associated costs or economic
impacts.
35
Impacts on zero discharge facilities are reported with impacts of direct discharge facilities. 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.
The projected impacts do not change under an assumption of zero cost pass through.
4.30
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Table 4.3
Impacts of the final Regulation, on Facilities
Direct Dischargers* indirect Dischargers
Facility Closures
Product One Closures
Other Financial Impacts
0
2
0
0
0
0
Impacts on zero discharge facilities are reported with impacts on direct
dischargefacilities. 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.
Impacts of PSES Regulations on Indirect Dischargers
Subcategory A
Twenty-three indirect discharging facilities producing Subcategory A chemicals are expected to incur costs
under the final rule. For manufacturers included in this Subcategory, the incremental capital and annualized costs
of complying with PSES limitations are expected to be $8.7 million and $5.1 million, respectively. The estimate
of capital costs has decreased 8 % since proposal while the estimate of total annualized costs has decreased by 15 %.
(See the Technical Development Document for an explanation of changes in compliance cost estimates.)
No facilities are projected to close entirely, close a product line, or experience other significant financial
impacts due to compliance with PSES. Therefore, the estimated impacts have decreased since the proposal. (At
proposal one facility was projected to close a product line. This facility has actually closed and is therefore not
included in the analysis of the final rule.)
Subcategory B
No new limitations on indirect dischargers are promulgated today for the metallo-organic pesticide
chemicals manufacturing Subcategory. Therefore, there are no associated costs or economic impacts. Estimates
of the costs if the Subcategory had been regulated were provided hi the ELA at proposal.
Impacts of BAT and PSES on Pesticide Formulating/Packaging/Repackaging Facilities and
End-Users
Effect of Possible Facility/Product Closures on PFPRs
As described above, no pesticide manufacturing facilities are projected to close due to BAT or PSES
regulations. Two manufacturing facilities are predicted to have product line closures due to the EAT rule. Neither
of these facilities provided PAI-specific costs or prices so EPA assumed average costs for each product and cannot
4.31
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predict which particular line would close. Only one of the active ingredients produced by these two facilities is
made by only one U.S. manufacturing facility. The other active ingredients would not be lost to the industry. The
product produced by a single facility is a fungicide used as a seed treatment. An estimated 10 pesticide
formulator/packager/repackager facilities out of approximately 2,500 facilities make products with that active
ingredient. If this was the product line that closed (which can not be predicted with accuracy without product
specific information), only 0.4 percent of all pesticide fonnulator/packager/repackagers would be affected. Further,
substitute products are available to pesticide formulator/packager/repackgers and the end user. Therefore, EPA does
not expect the possible closure of these product lines to have a significant effect on the pesticide
formulating/packaging/repackaging industry or end users of pesticides.
Effect of Pesticide Manufacturing Price Increases on PFPRs
A second possible impact of the pesticide manufacturing regulation on pesticide
formuiating/packaging/repackaging facilities would be to have active ingredient prices increase significantly.
In order to conservatively predict the severity of impacts that manufacturers would sustain in complying
with the regulations, the manufacturing economic impact analysis was performed assuming both partial cost pass
through to customers and a zero cost pass through (i.e., the worst case for manufacturers). To gauge the maximum
effect of the pesticide manufacturing regulation on the PFPR industry, it would have to be assumed that all
manufacturing cost increases related to the regulations were passed on to PFPR facilities.
Total annualized costs for the regulations are expected to be $23.24 million ($18.16 million for BAT and
$5.08 million for PSES, in 1986 dollars), spread over 1.15 billion pounds of active ingredients with the average
price increase being approximately $0.02 per pound of active ingredient assuming that the manufacturer passed the
full cost increase on to the pesticide formulator/packager/repackager. The average percentage of active ingredient
in formulated products varies from less than six percent in aerosol products to over 50 percent for agricultural
products, with the overall average being around 30 percent. This means that at the worst, if PFPR facilities passed
on the entire average price increase per pound of formulated product, this increase would vary from a small fraction
of a cent to slightly over one cent per pound depending on the percentage of active ingredient in a pound of
formulated product. Therefore, from the perspective of price increases, the pesticide manufacturing guideline will
not have a significant impact on pesticide formulating/packaging/repackaging facilities.
Not only are price increases for formulated products expected to be modest, the effect of such increases
on demand is expected to be minor. Based on the percentage of manufacturing facilities expected to incur
compliance costs, EPA estimates that only part of the costs of the manufacturing rule will be passed on to pesticide
formulator/packager/repackagers (neither extreme case presented above). The analysis estimated the effect of price
increases on demand based on the final demand for a formulated product. If PFPR facilities are able to pass
through a portion of the cost increase without a significant effect on demand for their product they would not be
4.32 '
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expected to face significant adverse economic impacts. As presented in Chapter 3, demand for 42 of the 45
pesticide clusters with production in 1986 is expected to have unit elasticity (i.e., elasticity equal to -1) or to be
inelastic. Therefore, demand for formulated products is expected to be relatively stable with respect to price
increases.
Percentage ofPFPR Business Affected
A preliminary analysis of the responses to the PFPR survey indicates that most of the facilities that
formulate, package or repackage in-scope pesticides obtain only a small percentage of their revenues from these
activities. The median percentage of revenue obtained from in-scope pesticides for all PFPR facilities is four
percent. Ninety percent of PFPR facilities obtain less than half of their revenue from in-scope PFPR activities.
In particular, almost three-quarters of the population of repackages for pesticides for the agricultural market obtain
15 percent or less of their revenue from this activity. Because activities other than pesticide
formulating/packaging/repackaging are the principal sources of revenues for the vast majority of PFPR facilities,
small increases in the prices of PAIs are not expected to result in significant impacts for most pesticide
formulator/packager/repackager facilities.
4.33
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Chapter 4 References
Doane Marketing Research (1987). Annual Marketing Survey. St. Louis, Missouri.
i
DPRA, Inc. (1990). Agchemprice; Current U.S.A. Prices of Non-fertilizer Agricultural Chemicals. January.
Manhattan, KS.
U.S. Department of Agriculture (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. Department of Commerce (1989, 1990, 1991). Bureau of the Census. Statistical Abstract of the United
States. Washington, D.C. January.
4.34
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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 final
regulation.
Following proposal, EPA thoroughly reviewed the details of the economic analysis in preparation for the
final rule. In response to this review and to public comments, the estimated compliance costs were adjusted and
the economic analysis was revised in several ways.' The changes, both separately and taken together, do not
significantly affect the number of impacts projected.
As presented in Chapter 4, under the final rule, no direct discharging facilities are projected to close due
to compliance with BAT and one facility, equal to 3 percent of the direct discharging facilities in Subcategory A,
is projected to close a product line. (One zero discharging facility, subject only to monitoring costs, is also
projected to close a product line.) No facilities are expected to experience other significant financial impacts short
of facility or product line closure. This level of impacts is equivalent to the level projected at proposal.
Under the final rule, no indirect discharging facilities are projected to close entirely, close a product line,
or experience other significant financial impacts due to compliance with PSES. This level of impact is lower than
the impacts projected at proposal for indirect discharging facilities. (At proposal, one indirect discharging facility
was projected to close a product line.)
Given that the level of projected impacts has stayed constant for direct dischargers and decreased for
indirect dischargers since proposal, community impacts were not re-estimated. Instead, the impacts reported at
proposal represent a conservative estimate of these impacts. The community impacts reported below are therefore
those reported in the EIA for the proposed rule.^
*The specific changes in the analysis since proposal are documented in the Federal Register notice of the final
rule, the administrative record, the technical development document, and at the relevant sections of the EIA.
2A single direct discharge facility accounted for most of the increase in estimated compliance costs since
proposal. (See Chapter 4.) The estimated investment costs at this facility increased from $1.6 million to $16.0
million, with an increase in annualized costs from $2.0 million to $7.3 million. The Agency maintains that the
actual compliance costs for this facility would be lower than the estimates used in the final analysis. However,
analysis using these higher cost estimates ensures that EPA does not underestimate the burden of compliance at this
facility. As an additional check on community impacts, the analysis examined the extent of the projected reduction
in in-scope revenue at this facility. The ira-scope revenue generated at this facility is expected to fall by only about
one percent due to the regulation, so significant community impacts are not expected.
5.1
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The impacts corresponding to both BAT and PSES compliance under the proposed rule are presented.3
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).4
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 reductions in revenue. 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 revenue.
5.1. A Primary Impacts on Employment
Primary impacts on employment are considered for facilities predicted to experience either a closure or a
decrease in in-scope PAI revenue 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.
3Impacts 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 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.
^Direct discharges of Subcategory B chemicals are already limited to zero under the Best Practicable Control
Technology Currently Available (BPT) regulation. BAT and PSES regulations are not being promulgated for
Subcategory B at this time.
5.2
-------
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.5 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.6 These calculations are shown below
algebraically.
Total pesticide production employee hours (TPH) are computed as:
where:
MH =
FH =
Annual employee hours spent in pesticide chemical manufacturing production; and
Annual employee hours spent in pesticide formulating/packaging.
Non-production employee hours related to pesticide production (TNH) are estimated as:
TNH=TPHx —
where:
N
P
Annual non-production employee hours spent at facility; and
Annual employee hours spent in all production at facility.
Total facility production hours (F), 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 seasonal! ty. "Annual hours" are estimated by
multiplying the average hours of the three months by 12.
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.
5.3
-------
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.7
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 in 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. 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 =
1FTE.
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 located.9 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 decline in
7Computed: (50 weeks/year)(40 hours/week) = 2000 hours/year.
sThe ratio of in-scope pesticide production volume to total facility production volume, although not the separate
numerator and denominator, is reported in the Census.
9MSAs are denned by the U.S. Office of Management and Budget.
5.4
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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 hi demand for inputs by the affected facility, and (2) induced impacts attributable to reductions in
consumption due to both primary and secondary losses hi earnings. Multiplier analysis is used to account 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.
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 hi the number of jobs given a change hi the number of jobs for a particular
industry.10 In this analysis, the industry directly affected is Chemicals and Selected Chemical Products.11 The
multiplier reported by BEA for this industry is 8.37.12 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).
10"Jobs" include both full- and part-time positions.
Multipliers based on direct employment changes are available at an aggregated industry level only.
The 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.
5.5
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5.2
Results
5.2. A Impact of Best Available Control Technology Economically Achievable (BAT) Regulations on Direct
Dischargers13
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 in 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 is not considered significant.
5.2.B Impact of Pretreatment Standards for Existing Sources (PSES) Regulations on Indirect Dischargers
For indirect discharging facilities, the effluent guidelines are not projected to result in any facility closures,
while one facility is expected to experience a reduction hi 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 hi the industry. The community employment level is not projected to decline
by more than one percent and, consequently, the estimated reduction in employment is not considered significant.
Among the modifications made to the economic analysis since proposal were changes in compliance costs
for several facilities. As a result of these changes, total annualized compliance cost increased by 24% for direct
dischargers. The change in compliance cost is the aggregate effect of decreases hi annualized compliance costs at
four facilities and increases in annualized compliance costs at four facilities. Most of the increase in total costs is
due to a substantial costs increase at one facility. Total annualized compliance costs for indirect discharging
facilities decreased by 14%.
As an additional check on community impacts under the final rule, EPA examined the extent of in-scope
revenue decrease at the single direct discharging facility bearing most of the increase in compliance costs. The
production at this facility is expected to fall by only one percent. Among indirect discharging facilities, the
estimated impacts have decreased since proposal. Therefore, the estimates of community impacts presented at
proposal serve as reasonable conservative estimates of the impacts due to compliance.
In summary, expected employment loss due to the BAT and PSES compliance is projected to be largely
contained within the pesticide industry. The estimated reduction in employment, 128 FTEs, is approximately one
percent of the total pesticide-related employment reported by all PAI manufacturers.
13Impacts of zero discharge requirements are discussed with direct discharge requirements.
5.6
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Table 5.1^
Community Impact
Employment Loss (FTEs)
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 requirements on zero dischargers are reported with impacts of requirements on
direct discharge. Zero dischargers may be subject to monitoring costs by the permitting
authority if they have any process wastewater. (No separate monitoring requirements are
contained in the proposed effluent guidelines for pesticide manufacturers). These
monitoring costs are included, in the analysis to capture ihe full cost to industry of
controlling process wastewater pollutants.
2 Impacts associated with Subcategory B PAIs are not shown, since regulations are not
being promulgated at this time for Subcategory B.
5.7
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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.
5.8
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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 hi 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 final effluent limitations
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.
Following proposal, EPA thoroughly reviewed the details of the economic analysis in preparation for the
final rule. In response to this review and to public comments, the compliance costs were adjusted and the economic
analysis was revised in several ways.' The changes, both separately and taken together, do not significantly affect
the number of impacts projected.
As presented in Chapter 4, under the final rule, no direct discharging facilities are projected to close due
to compliance with BAT and one facility, equal to 3 percent of the direct discharging facilities in Subcategory A,
is projected to close a product line. (One zero discharging facility, subject only to monitoring costs, is also
projected to close a product line.) No facilities are expected to experience other significant financial impacts short
of facility or product line closure. This level of impacts is equivalent to the level projected at proposal.
Under the final rule, no indirect discharging facilities are projected to close entirely, close a product line,
or experience other significant financial impacts due to compliance with PSES. This level of impact is lower than
the impacts projected at proposal for indirect discharging facilities. (At proposal, one indirect discharging facility
was projected to close a product line.)
Given that the level of projected impacts has stayed constant for direct dischargers and decreased for
indirect dischargers since proposal, foreign trade impacts were not re-estimated. Instead, the impacts reported at
proposal represent a conservative estimate of these impacts. The foreign trade impacts reported below are therefore
those reported in the EIA for the proposed rule.2
The specific changes in the analysis since proposal are documented in the Federal Register notice of the final
rule, the administrative record, the Technical Development Document, and at the relevant sections of the EIA.
2A single direct discharge facility accounted for most of the increase in estimated compliance costs since
proposal. (See Chapter 4.) The estimated investment costs at this facility increased from $1.6 million to $16.0
million, with an increase in annualized costs from $2.0 million to $7.3 million. The Agency maintains that the
actual compliance costs for this facility would be lower than the estimates used in the final analysis. However,
6.1
-------
The impacts corresponding to both BAT and PSES compliance under the proposed rule are presented.3
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).4
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.^ 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 predicted
to close under a regulatory option and for facilities predicted to have a decrease in in-scope PAI revenue 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 in 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 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 in PAI
analysis using these higher cost estimates ensures that EPA does not underestimate the burden of compliance at this
facility. As an additional check on foreign trade impacts, the analysis examined the extent of the projected reduction
In in-scope revenue at this facility. The in-scope revenue generated at this facility is expected to fall by only about
ons percent due to the regulation, so significant foreign trade impacts are not expected.
3Impacts 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 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.
^Direct discharges of Subcategory B chemicals are already limited to zero under the Best Practicable Control
Technology Currently Available (BPT) regulation. BAT and PSES regulations are not being promulgated for
Subcategory B at this time.
^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
« result of this assumption, effects of the regulation on foreign trade may be overstated.
6.2
-------
exports arc 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 in the Census.
Facility Closures with PAI-Spacific Information
If a facility is projected to close and PAI-specific export percentages were reported in 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.6 Algebraically, export revenue losses are computed as:
AIX =
where:
ADC = Change in export revenues for a facility;
AIV; = Facility revenues from PAI i; and
ADCPj = Percentage of PAI i production that is exported by the facility.7
Facility Closures without PAI-Specific Information
Although the provision of PAI-specific export data hi 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 hi export revenues is estimated by multiplying total facility pesticide sales by the
percentage of total production exported by a facility.8
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 hi exports are considered only for those facilities whose
in-scope revenues are expected to decrease by at least ten percent due to the regulation.
export data reported are expressed hi 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.
7For facilities projected to close, a full accounting of changes in exports would include changes in 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 in exports of PAIs alone are
considered in this section.
8The 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.
6.3
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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 hi cluster
revenues by the facility's cluster export percentage. If a facility is expected to close a product line, the percentage
'•»'„ i',n
change in production for that product line is 100 percent. The total decline in a facility's exports equals the sum
of ihe decline in exports for all affected clusters in that facility.
Facilities with Reduced Demand and No PAl-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. As a worst-case scenario, it is assumed that all lost revenue in 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 Impact of Best Available Technology Economically Achievable (BAT) Regulations on Direct
Dischargers10
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
'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
analysis. Details of the data review are contained in the Administrative Record.
10Impacts 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.
6.4
-------
(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 in in-scope revenues of ten percent
or greater produce PAIs in five 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 $5.4 million.
The changes in 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 in 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 is minor (less than one percent) in
comparison to both total U.S. pesticide trade and total U.S. merchandise trade.
6.2.B Impacts of Pretreatment Standard for Existing Sources (PSES) Regulations on Indirect Dischargers
No indirect discharging facilities are projected to close, and only one facility is expected to have a decline
in in-scope revenues of ten percent or greater. This facility reported 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.
The one indirect discharging facility expected to experience a decline in in-scope pesticide revenues of ten
percent or greater produces PAIs in three dusters. 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. With the
conservative assumptions incorporated in the analysis, PSES regulations 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.
In summary, neither BAT nor PSES regulations are projected to have a substantial impact on the U.S.
pesticide trade balance or the U.S. total merchandise trade balance. The $21.6 million decline in net pesticide
imports decreases the U.S. pesticide and merchandise trade balances by approximately 2 percent and one one-
hundredth of one percent, respectively.
6.5
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1 Table 6.1,^
Foreign Trade Impact
(in $ thousands)
Decline in Pesticide Exports
, w Discharger Type
Direct^
Indirect
Total
Subcategory A
Due to Plant Closures
Due to Reduced Production
Total Subcategoiy A
0
114
114
0
5,477
5,477
0
5,591
5,591
Increase in Pesticide Imports
.I,1
, Discharger Type
^Direct
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
Indirect
Total
Subcategory A
Due to Plant Closures
Due to Reduced Production
Total Subcategory A
5,522
5,522
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
in the proposed effluent guidelines for pesticide manufacturers). These monitoring costs are included
ia 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.
6.6
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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.
6.7
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6.8
-------
Chapter?: FIRM IMPACT ANALYSIS
7.0 Introduction
The firm analysis evaluates the impact of regulatory compliance on firms owning facilities subject to the
pesticide active ingredient (PAI) manufacturing effluent guidelines. 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's pesticide operations are strongly profitable while its non-pesticide operations are only marginally
profitable, or if a firm owns more than one pesticide manufacturing facility that would be subject to regulation; in
such cases, 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.
Following proposal, EPA thoroughly reviewed the details of the economic analysis in preparation for the
final rule. In response to this review and to public comments, the compliance cost estimates were adjusted and the
economic analysis was revised in several ways.2 The changes, both separately and taken together, do not
significantly affect the number of impacts projected.
As presented in Chapter 4, under the final rule, no direct discharging facilities are projected to close due
to compliance with BAT and one facility, equal to 3 percent of the direct discharging facilities in Subcategory A,
is projected to close a product line. (One zero discharging facility, subject only to monitoring costs, is also
projected to close a product line.) No facilities are expected to experience other significant financial impacts short
of facility or product line closure. This level of impacts is equivalent to the level projected at proposal.
Under the final rule, no indirect discharging facilities are projected to close entirely, close a product line,
or experience other significant financial impacts due to compliance with PSES. This level of impact is lower than
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.
2The specific changes in the analysis since proposal are documented in the Federal Register notice of the final
rule, the administrative record, the Technical Development Document and at the relevant sections of the EIA.
7.1 .
-------
the impacts projected at proposal for indirect discharging facilities. (At proposal, one indirect discharging facility
was projected to close a product line.)
The impacts corresponding to both BAT and PSES compliance under the final rule are presented.4 Only
those impacts associated with Subcategory A (Organic Pesticide Chemicals Manufacturing) chemicals are presented
because only Subcategory A chemicals are being regulated.5
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.
3Although the level of projected impacts has stayed constant for direct dischargers and decreased for indirect
dischargers since proposal, firm-level impacts were re-estimated because estimated investment costs at one facility
have increased from $1.6 million to $16 million since proposal (see Chapter 4). This change in estimated
compliance costs resulted from public comments by the facility. The Agency maintains that the actual compliance
costs for this facility would be lower than the estimates used in the final analysis. However, analysis using these
higher cost estimates ensures that EPA does not underestimate the burden of compliance at this facility.
^Impacts on zero discharge facilities are reported with impacts on direct discharge facilities. 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.
5Direct discharges of Subcategory B chemicals are already limited to zero under the Best Practicable Control
Technology Currently Available (BPT) regulation. PSES regulations for Subcategory B chemical are not being
promulgated at this time. For an analysis of the effects of PSES regulations on indirect dischargers of Subcategory
B chemicals, see the EIA published at proposal.
7.2 •
-------
(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 the regulation.
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.6 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 wasitewater 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:7
ROA =
EBIT
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.8 ROA
provides information about the quality of management, the competitive position of a firm within its industry, and
6For a further discussion of debt and equity financing, see Section 4.2.A.
7ROA is also known as "return on investment."
For credit analysis in particular, pre-tax ROA is important because interest payments are made from pre-tax
income.
7.3 .
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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).9 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,
arc 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.10 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 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):11
'RMA 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.
10For this discussion, a firm's operating cash flow is considered to be revenues minus costs, with the exception
of interest, lease expense and depreciation.
llrThe ICR is also known as "times interest earned."
7.4 . ''
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EBIT
Interest
Weakness in 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 in obtaining financing for treatment
investments. From 1982 to 1990, the median value of interest coverage for industrial chemicals firms (as defined
by RMA) 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 in the preceding section, the firm analysis is based on two financial measures: ROA and ICR.
Finn-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.12 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 final rule, detailed financial data were available for 20 of the 45 firms expected to incur costs; the
remaining 25 firms, closely-held or foreigB-owned entities, required the use of data obtained from RMA.
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 1986.13 For non-public-
reporting firms, firm-level revenues were obtained from the Census. Firm-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).
12Closely-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).
^Compustat, 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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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:
7.6
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PSOBUC REPORTING Tftsxss
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 Ifor 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:
7.7
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NON-POBUC-REPORTING
Baseline EDIT
Estimated EBIT = Firm Revenues X \<*«*** ****] x \ ML
[ Revenue J^ [Operating Profit\COMPUSIAT
Estimated EBJT = 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 EBTT/revenue ratio directly, gave an industry median operating profit/isveiaie ratio of 0.058 for
1986. The estimated average EBIT/revemie 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 EBlT/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.14 Specifically, the estimated ROA is 13.6 percent and the ICR is
f .'' "I
2.96. Although these values are the same in the baseline analysis for all non-public-reporting firms, they differ
I4If firm-level financial data were available for the non-public-reporting firms, the baseline ratio values could
be estimated more accurately.
7.8
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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 "vulnerable11 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 in 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 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.
7.9
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•»•'•' J pr^
Table 7.1j Determination of Firm-level financial t Viability , \[
ROA
Highest Quartile
Third Quartile
Second Quartile
Lowest Quartile
, ,„, Interest .Coverage Ratio
Lowest Quartite Median " ,'- Highest Quartile
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 tie baseline analysis. In 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.15 In the firm analysis, these values were summed over the facilities owned by each firm and
ussd to adjust the baseline components as shown below (see also Table 7.2 for the mathematical formulation of the
analysis):
15Discharge costs (e.g., the cost of sludge disposal) and monitoring costs are included within the operating and
maintenance cost category.
7.10
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Table 7.2: Calculation of Firm-Level Financial Measures in Post-Compliance Analysis J6
Finn Financial Performance (ROA)
Baseline ROA =
EBTT
Total Assets
Post-Compliance ROA =
EBIT -
A? * -i
~°2
+ F(AP * #j) - (pj * A#)]
Jbto/ Assets + c
where:
EBIT = Baseline earnings before interest and taxes
Oj = Baseline operating and maintenance expenses
02 = Compliance operating and maintenance expenses
Aq = Change in production quantity due to elasticity (qj - q^
qi = Baseline production quantity
<\2 = Post-compliance production quantity
Ap = Change in price due to elasticity (PJ - PZ)
Pl = 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
JEBZT - Utf * —
Post-Compliance ICR =
- °
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 _ \ (d * c) * 0.0593
Interest Payment L _ ^ + Q.0593)"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
16For 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.
7.11
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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)17
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
1'Depreciation associated with compliance capital expenditures is not subtracted from baseline EBIT since
depreciation is not a cash expense and therefore does not reduce the cash available to cover interest charges.
7.12
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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.
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).18 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).19
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 final rule.
7.3.A Baseline Analysis
Forty-five firms were projected to incur compliance costs under the final rule. In the baseline analysis,
two of these firms had an ROA below the first RMA quartile value. One of these firms was also the only one
whose ICR fell in the lowest RMA quartile. Because these firms were found to be "vulnerable" to financial failure
independent of regulatory action, they were excluded from the post-compliance analysis.
18Interest 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.
9Th& 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.
7.13 .
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7.3.B Post-Compliance Analysis
Under the final rule, compliance costs were projected for 45 pesticides manufacturing firms, two of which
were 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 either ROA or ICR in the lowest RMA
quartile in the post-compliance analysis, and were therefore said to incur significant financial impacts. None of
these three firms is publicly traded, so the results were obtained using the publicly available industry norms and the
firm-specific revenue data, as discussed in section 7.2. One of the impacted firms owns a facility projected to close
a product line post-compliance. Another of the impacted firms owns a facility projected as a baseline product line
closure. This firm may be in transition as its operations are reorganized. The third impacted firm owns no facilities
expected to incur impacts under the baseline or post-compliance scenarios.
7.14
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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.
7.15
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7.16
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Chapter 8: SMALL BUSINESS IMPACTS
8.0
Introduction
This chapter considers the expected effects of the 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 regulation is likely to have a significant impact on
a substantial number of small entities. If such an impact is expected, the EPA must prepare a Regulatory Flexibility
Analysis for the rule. If it is not expected that the rule would significantly impact a substantial number of small
entities, the EPA Administrator must certify this conclusion.
8.1 Methodology
This analysis considers whether the effluent limitations guidelines and standards are likely to have a
significant impact on a substantial number of small entities. At the outset, the term "small entity" was defined.
The analysis used the threshold for small businesses established by the Small Business Administration (SBA). The
SEA 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, then a Regulatory Flexibility Analysis would be conducted.
8.2 Results
8.2.A Impact of Best Available Control Technology Economically Achievable (BAT) Regulations on Direct
Dischargers
Under the final effluent limitations, no facility closures are projected for direct dischargers. One direct
discharging facility and one zero discharge facility 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 regulatory flexibility analysis is required. The EPA Administrator has certified to this effect.
8.1
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8.2.B Impact of Pretreatment Standards for Existing Sources (PSES) Regulations on Indirect
Dischargers
Under the final effluent limitations and guidelines, no indirect discharging facilities are expected to
close entirely, close a product line, or experience another significant impact short of closure. Therefore, no
regulatory flexibility analysis is required. The EPA Administrator has certified to this effect in the final rule.1
'Appendix E of the EIA for the proposed rule included additional information of the expected impact of the
effluent limitations on small businesses.
8.2
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Chapter 8 Reference
Dun's Marketing Services, Inc. (1991). Million Dollar Directory. New Jersey.
8.3
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8.4
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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. The final 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 promulgating NSPS under Subcategory A for the
conventional pollutants regulated under Best Practicable Control Technology Currently Available (BPT), 120
organic PAIs, and 28 priority pollutants. The EPA is promulgating 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. 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 hi wastewater discharge at new facilities. For other non-
conventional pollutants and conventional pollutants generated by Subcategory A, the final 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 for existing pesticide
manufacturers were found to be economically achievable, with some existing facilities already achieving a 28
percent wastewater flow reduction, the final 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
9.1
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occur through additional manufacture of currently produced PAls. 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 PAls, given the difficulty in predicting the nature of the treatability of new PAIs.
Subcategory B (Metallo-Organic Pesticide Chemicals Manufacturing)
The EPA is reserving NSPS for subcategory B chemicals because BPT already requires zero discharge
of process wastewater pollutants.
9.2 Pretreatment Standards for New Sources1
Subcategory A Chemicals
PSNS for the organic pesticide chemicals manufacturing subcategory are based on the final
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, 120
organic PAIs, and 24 priority pollutants covered under PSES.
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 final 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 in 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.
Zero discharge regulations were not considered for new sources due to the unacceptably large economic
impacts projected for existing sources at proposal. For more1 information see Chapter 9 of the EIA at proposal.
9.2
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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
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FomS
OI^BNo.: 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
Chief. Information Policy Branch (PM-223)
U.S. Environmental Protection Agency'
401 M Street, SW
Washington, DC 20460
°r
and
«*
Office of Management and Budget
Paoerwork Reduction Project
(2040-0111)
Washington. DC 20503
A.2
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
Part B Financial and Economic Information
Part B: General Instructions
The Pesticide Manufacturing Facility Census has three parts:
Introduction;
Pan A: Technical Information; and
Pan B: Financial and Economic Information.
The Introduction and Pan A were mailed seoarateiy and have been completed by your facility. This
package .contains the Part B questionnaire and its instructions. All recipients wno completed the
introduction and Pan A of the Pesticide Manufacturing Facility Census must complete Pan B at this time.
Througnout 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 dean Water Act (the Federal
Water Pollution Control Act. 33 U.S.C. 11251 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 Pan A of the Census questionnaire must complete
Pan 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 hookbt.
Completing the Census
Although Pan B may be completed by different officials, the individual who signed the certification for
Pan A should also certify all pans of the questionnaire by completing and signing the Pan B Certification
Statement located on page 3 of this questionnaire.
If the space aliened 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 CENSUS, FOR 1986
Part B Financial and Economic Information
GENERAL INSTRUCTIONS - Continued
When ana How to Return the Part B Questionnaire
^~he Pesticide Manutactunng Facility Census Pan B Questionnaire must De comoietea ana returned witmn
50 aays of receiot to:
Or. 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
ji '"' i
-uestions pertaining to any item in Pan 8 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)3825334
Provisions Regarding Data Confidentiality
Regulations governing the confidentiality of business information are contained in 40 CFR Part 2 Suboart B
ana 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 daim covering pan or all of the
information you submit, other than effluent data, as described in 40 CFR 2J203(b):
'(b) Method and time of asserting business confidentiality daim. A business which is
submitting information to EPA may assert a business confidentiality daim 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 dearly identified
by the business, and may be suomrtted 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 snouid so state.*
Information covered by a daim of confidentiality will be disdosed by EPA only to the extent and by means
of the procedures, set forth in 40 CFR Pan 2 Subpart B. In general, submitted records, reports, or
information protected by a business confidentiality daim may be disdosed to other employees, officers, or
authorized representatives of the United States concerned with carrying out the dean Water Act. or when
r9levant 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 CENSUS^ FOR 1986
Pan B Financial and Economic Information
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 fPesticides Report for Pesticide-Proaucmg
Establishments*). Check the box next to "Not Aoplicable' if this facility does not have an EPA FIFRA
Establishment Number
I2A 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 Numoer.
i i j . ; : i ' - ; j j 12 A ~ Not Applicable 123
Enter the facility mailing address.
!_l«J_;_LJ_i— I— LJ.
Street or P.O. Box
I4A
i
City or Town
I4B
i i I I I ...
; • ! ; . i - i i
I State | Zip Code
I4C I4D
Enter the address of the physical location of the facility if different from the mailing address.
I_!_I.J—I-J—I—!-J—1—!—'—!_!—!_!_l_!_l—I—I—I ISA
Street or Route Number
City or Town
I5B
Certification Statement
State | Zip Code
I5C I5D
1 certify that I nave 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:
I-J—I ' I—I—I ' I—I—I—I—I
Month Day Year
I6A
Signature of Certifying Official
Name of Certifying Official (please pnnt or type)
I_I_!_!_1_I—I-J—I—I-!-!-'—I.
Title
I6B
I6D
A.5
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSU$ FOR 1986
Part B Financial and Economic Information
INTRODUCTION - Continued
Review the Pesticide Acth/e Ingredients listed in Table i below ana circle all codes tnat corresoona
to active ingredients manufactured, formulated or packaged at this facility.
TABLE 1. PESTICJDE ACTIVE INGREDIENTS
"ODE
5
3
TO
t2
14
I4a
14C
i4d
:5
•JSa
iSb
iSc
16
•<6a
'.6b
16c
15d
17
17c
I7d
18
19
20
21
22
23
24
25
26
ACTIVE INGREDIENT
*..1-8is(chloropnenyi)-2.2.2-trichloroemanot
1.2-Oihydro-3.6-pyndazinedione
1.2-Ethylene dibromide
1.3.5-Triethylhexanydro-s-triazine
i .3-Dichloropropene
10.10'-Oxybisphenoxarsine
• -{3-ChJoroailyl)-3.5.7-triaza-i -azoniaaaamantane cnlonde
1 -(4^hloropnenoxy)-3.3-dimethyl.i-(i H-1.2.4-tnazoi-i-yl)-2-butanone
2.2'-MethyleneDis(3.4,6-trichloropnenoi)
2.2'-MethyleneDis(4,&dicNorophenoi
2.2>-Methylenaois(4*chlorophenol)
2.2-Oichlorovinyl dimethyl phosphate
2.3.5-Trimetrtytphenytmethytcarbamate
2.3.6-Tricnioropnenylac8tic acid or any salt or ester
2.4.5-Tricnioropnenoxyacetic acid or any salt or ester
2.4-Oichiorapnenoxyacetic acid or any salt or ester
2.4-Dichloropnanoxybutyric acxj or any salt or ester
2,4-Dichlcrc-6-(o-ch!oroanilino)-s-tnazme
2,4-0!ntrc-&octylpheny1crotonate. 2.6-Dinrtro-*-octylphenylcrotonate. and Nitrooctylphenols
(The octyl's are a mixture of 1-Methylheptyl, 1-EthythexyJ. ana •,-Propylpentyl)
2.6-Dichloro-4-nnroaniiine
2-Bromc-4-hydroxyacetophenone
2-Carbomethoxy-l-methyivinyi dimethyl phosphate, and related compounds
2-Chloroallyl diethyldithiocarbamate
2-Chlorc-1-(2,4-dichlorophenyl)vinyl diethyl phosphate
2-CWoro-4-((l -cyano-1 -methylethyl)amino)-6-ethylamino)-s-triazine
2-Chloro-N-isopropyiacetaniiide
A; 6
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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
2-Methyl-4-chlorophenoxyacetic acid or any salt or ester
2-n-Octyl-4-isothiazoiin-3-one
2-PivalyM .3-indandione
2-(2,4-Dichloropnenoxy)propionie acid or any salt or ester
2-(2-Methyt-4-cnioropnenoxyjpropiomc acid or any salt or ester
2-(4-Thiazoiyl)benzimidazole
2-(Methytthio)-*-(ethylamino)-(5-(li.2-dirnethyiproDyi)amino-s-triazine
2-
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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 INGREDIEffr
3-Qu!nofinof sulfate
Acepnate (O.S-Dimetnyl acerylphospnorarmdotriioate)
Acrfluoren (5-(2-Chloro^-(trifluorometnyl)prienoxy)-2-nitrobenzoic acid) or any salt or ester
AIacnlor(2-CWoro-2'.6'-diethyt-N-(metnoxymethyl)acetanilid6)
Aldicart) (2-Methyl-2-(methylthio)prooionaidenyde O-{methyicarbamoyl)oxime>
Alkyt" dimethyl benzyl ammonium cnionae w(50% C14.40% C',2,10% C16)
AJIethrln (all isomers and allethnn coil) •
Ametryn(2-{Ethy1amino)-4-(isopropyiamino)-6i(methylthlo)-s-trJazine)
Amltr3Z(N'-2.4-Dimethylpheny<)-N^((2.4^imetnylphenyi)imino|methyl)-N-methylmethanimidarniae)
Atraztne (2-Chloro-4-(ethy1aminoi-6-(isopropylamino)-s-tria2ine)
BendiocarO (2.2-DimethyM .3-benzoaioxoi-4-yl methytcamamate)
Benornyi (Methyl i-(butyicarbamoyi)-2-benzimidazoiecaroamate)
Benzene hexachloride
Benzyl benzoate
Beta-Thiocyanoethyl esters of mixed (any acids containing from 10-18 carbon atoms
B'rfanox (Methyl 5-(2,4-dichlorophenoxy)-2«nrtrobenzoate)
Biphanyl
Bromacil (5-Bromc-3-sec-Butyl-6-Methyluracil) or any salts or esters
CODE
51
52
£3
£4
=5
56
57
58
59
50
51
52
53
64
55
66
67
63
683
68b
68C
68d
69
5Sa
63b
69c
69d
70
71
72
72a
72b
72C
72d
73
74
75
76
77
78
78a
785
78C
78d
79
80
81
82
Bromoxynil (3,5-Dibromo-4-hydroxyDenzonrtriie) or any salt or ester
Butachlor (N-(Butoxymetnyl)-2-crilorc-2'.61-diethyiacetanilide)
b-Bromo-b-nitrostyrene (Note: b - beta)
Cacodylic acid (Dimethylarsenic acid) or any salts or ester
Captafol (cis-N-((i ,1,2^-Tetrachloroethyl)thio)-4-cydohexene-i .2-dicarooxirnide)
Captan (N-Trfchioromethythio-4-cydoh9xene-i ,2-dicarhoximide)
Carbaryl (1-NaDrtthytmethyicarbamate)
Carbofuran (2.3-Dlhydn>2,2-dlmethyl-7-benzofuranyl methylcarbamate)
CarbosuJfan(2,2-Dihydro-2ixdlmethyl-7-benzc^rar^(dibu^amino)thio)methylcxirt)amate)
Chloramben (3-Aminc-2.5-dichlorobenzoic: acid) or any salt or ester
Chiordane(Octachloro-4,7-metnanotetranydroindane)
Chloroneb (1,4-Oichloro-2.5-dimethoxybenzene)
Chloropicrin (Trichloronitromethane)
Chiorothalony (2.4.5.6-Tetrachloro-i ,3-dicyanobenzene)
A.8
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part E) Financial and Economic information
TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued
ACTIVE
33
34
35
36
37
38
39
90
91
92
32a
92b
92C
92d
93
94
95
96
97
98
S3a
980
98C
98d
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
115
117
118
119
120
121
122
Chloroxuron (3-(4-{4-ChIorophenoxy)prienyi)-l . 1 -dimethyiurea)
Chlorc-i-(2,4,5-trichlorophenyl)vinyl dimethyl phosphate
Chlorpyrifos methyl (0.0-Dimetnyl 0-(3.5.6-trichloro-2-pyridyl) phospnorothioate)
Chlorpyrifos (0,0-Diethyl 0-(3,5.6-tnchloro-2-pyridyl) phosphorthioate)
Coordination product of Manganese 16%, Zinc 2% and Ethyienebisdithiocaroamate 62%
Copper 8-quinolinolate
Copper ethyienediaminetetraacetate
Cyano(3-phenoxypnenyl)methyl 4-chioro-a-(i-rnethylethyl)benzeneacetate OCA)
Cycloheximide(3-(2-(3,5-Dimethy-2oxocyclohexyi)-2-hydroxyethyl)glutarimide)
Dalapon (2,2-Dichloropropionie acid) or any salt or ester
Decacruoro-bis(2,4-cyc!opentadiene-i -y<)
Demeton (O.O-Diethyl O-(and S-) (2-ethyithio)ethyl)phosphorothioate)
Desmedipham (Ethyl m-hydroxycaroanilate carbanilate)
Diammonium salt of ethylenebisdithiocarbamate
Dibromo-3-chloropropane
Dicamba (3.6-Dichloro-o-anisic acid) or any salt or ester
Dichlone (2,3-Dichioro-1.4-naphthoquinone)
Diethyl 4,41-opheny1enebis(3-triioalloprianate)
Diethyl diphenyl dichloroethane and related compounds
Diethyl dithiobis(thionoformate)
Oiethyl O-(2-isopropy(<6^nethyt-4^}yrimidinyl) phosphorothioate
Difluberuuron(N.(((4^hIorophen^)amino)(»rbon^)--2.6-difluoroben2arnide)
Diisobutylphenoxyethoxyethyl dimethyl benzyl ammonium chloride
Dimethoate (O.O-Dimethyl S-((methylcarbamoyl)methyl)phosphorothioate)
Dimethyl O-p-nitrophem/f phosphorothioate
Dimethyl phosphate ester of 3-hydroxy-N.N-dimethyl-cis-crotonamide
Dimethyl phosphate ester of a-methylbenzyl 3-hydroxy-cis-crotonate
Dimethyl tetraehioroterephtnaiata
Dimethyl (2^^-trichloro-l-hydroxyethyl) phosphorate
Dinoseb (2-sec-ButyM,6-dinttrophenoi)
Dioxathion (2,3-p-Oioxanedithiol S.S-bis(O.O-diethyl phosphorodrthioate))
Diphacinone (2-(Diphenytacetyl)-1,3-indandione)
Diphenamid (N,N-Dimethyl-2,2-diphenyiacetamide)
Diphenylamine
Dipropyi isocinchomeronate
Disodium cyanodithioimidocarbonate
Diuron (3-(3,4-Dichiorophenyl)-1,1-dimethyiurea)
Dodecyiguanidine hydrochloride
Dodine (Dodecyiguanidine acetate)
Endosulfan(Hexachlorohexaiahydromethano-2.4.3-benzodioxathiepin-3-oxide)
A.9
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 198i
Part B Financial and Economic Information
TABLE 1. PESTICIDE ACTIVE INGREDIENTS • Continued
5.0.0.5 ACTIVE INGREDIENT
* 23 Endotnall (7-Oxatoicycto(2 2 1 )heptane-2.3-dicarooxyiic acid) or any salt or ester
•23a _ , , —
I23b , _ —
•23C . —
:23d
'24
•25
126
'.27
128
129
130
131
132
t33
134
135
136
137
138
1383
13Sb
138C
138d
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
153a
153b
153C
153d
154
155
156
Endnn(Hexacnioroepoxyoctanyaro-enao.enao-aimetnanonaonthaiene>
Etralfluralin(N-Ethy»-N-(2-rnetnyl-2-DroDenyi)-2.6-dinitro-*-(trifluoromethy1)benzenearninei
Ethion (O.O.O'.O'-Tetraethyl S.S'-metnyiene bisohosonoroaithioate)
Ethoproo (0-Ethyl S.S-dipropyt phosonoroaithioatei
Ethyl 3-rrtetnyl-4"(methytthio)phenyi i-
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
',57
'.58
'59
ISO
161
I6la
I6ib
:6ic
TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued
ACTIVE INGREDIENT
Methoprene (lsopropyl(E.E)-i 1 -methoxy-3.7.11 -trimethyl-2.4-dodecadienoate)
Methoxychlor (2.2-bis(p-Methoxypnenv?M .1,1 -tncnloroethanei
Methyl benzethonium chloride
Methyl bromide
Methyiarsonic acid or any salt or ester
Methyldcoecylbenzyl trimethyi ammonium cnlorrae 80% ana methyldoaecyixyiyiene
bis(trimethylammoriium chlonde) 20%
Methylene Disthiocyanate
MethyU2.3-auinoxalinedithiol cydic S.S-dithiocarbonate
Metolachlor(2-Chloro-N-(2-ethyl^5-metny)phenyl)-N.(2-methoxy-l-rnethy1ethyl)acetarnide)
Mexacaroate (4-(Dimethylamino)-3.5-xy«yl methylcaroamate)
Mixture of 83 9% Ethylenebis(dlthiocaroamato) zinc and 161% Ethylenebisdithiocaroamate.
bimolecular and trimolecular cyaic annydrosulfides and disulfides
MonuronTCA * Monuron triehioroacetate
Monuron (3-{4-Chlorophenyl)-l ,i-dimethylurea»
N,N-Oiethyl-2-(l -naphathalenyloxyjpropionamide
N.N-Oietnyi-meta-totuamide and other isomers
Nabam (Oisodium salt of etirtylieneDisdlthiocartoamate)
Naled (i.2-Dibromo-2.2-dichloroethyl dimethyl phosphate)
Norea (3-Hexahydro^ J-metrtanoindan-5-yM .1 -dimethyfurea)
Norflurazon(4^hloro-5-(m8tfrylamino)-2-(a.a.a^rifluoiro-nvtc)lyl)^(2H)-pyridazinone)
N-1 -Naphthylphthalamic acid or any salt or ester
N-2-Ethyihexyt bicyctoheptsna dicarooximide
N-Butyl-N-ethy«-a.a.a-trifluorO"2.eKJinitro-p-toluidine
O.O.O.O-Tetraethyl dithtopyrophosphate
O.O.O.O-Tetrapropyl dithiopyrophosphate
0.0-Diethyl O-(3-cWoro-4-meihy*-2H3xo-2H-1-benzopyTan-7-yl) phosphorothioate
O.O-Diethyl O-{p-(methylsu«inyl)phenyl) phosphorothioate
O.O-Oieti~.y1 S-(2-(eUiytthio)ethyl) phospnorudithioate
0,0-DimethylO-(4-nitro-m-tolyl)phosphorothioate
O,O-DimethyIS-(phtha!imidoniethyl)phosphorodithloate
O.O-Oimethyl S-((4x«o-1 A3-benzotriazin-3(4H)-yl)methyl)phosphorodrthioate
O.O-Dimethyl S-((ethylsulfinyi)ethyl phosphorothioate
Organo-arsenic pesticides (not otherwise listed)
162
163
164
165
166
167
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
I88a
188b
188C
183d
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
ACTTVE INGREDIENT
Crgano-cadmium pesticides
SODE
'89
SSa
'89b
•S9c
•S9d
•90
:90a
*.90b
1900
•91
',91 a
i9ib
191C
t9id
•.92
I92a
192C
193
194
195
196
197
198
199
200
201
202
203
204
205
206
20Sa
206b
206C
206d
207
208
209
210
211
212
213
214
Organo-copper pesticides
Organc-mercury pesticides
Organc-tln pesticides
Ortnodichlorooenzene
Oryzalin (3,5-Dinitro-N4.N4-dlpropyisulfanilamide) (Note: N4 « M superscript 4)
Oxamyl (Methyl r4\N'
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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 INGBEDIEKT
Pidoram (4-Amino-3.5,6-trichloropicolinic acid) or any salts or esters
Piperonyl butoxide ((Butylcarbityi) (6-propyipiperonyt)ether)
Poly(oxyethy1ene(dimethy1iminio)etrjylene(dlmethyiiminio)ethyJenedichloride
Potassium dimethyldithiocarbamate
Potassium N-hydroxymethyl-N-metriyldithiocartjamate
Potassium N-methyldrthiocarbamate
Potassium N-(a-(nitroethyi)benzyi)ethylenediamine
Profenofos (0-(4-Bromo-2-chloroonenyi) O-ethyl S-propyl phosphorothioate)
Prometon(2.4-bis(lsopropylamino(-6-methoxy-s-triazine)
Prometryn (2.4-bis(lsopropylamino)-5-(methytthio)-s-triazine)
Propargrte (2-(p-tert-Butylprienoxy)cydonexyi 2-propynyl sulfrte)
Propazine (2-Chloro-4,6-bis(isopropytamino)-s-iriaztne)
Propionic acid
Propyt (3-dimethylamino)propyl caroamate hydrochloride
Pyrethrin coils
Pyrethrin I
Pyrethrin II
Pyrethrum (synthetic pyrethrin)
Resmethrin ((5-PhenyJmeth^)-3-furanyJ)methyl 2.2-dimethyl-3-
(2-methyi-i -propenyl)cydopropanecarboxyiate)
Rohnel (O.O-Dlmethyl O-(2.4,5-trichlorophenyl)phosphorothloate)
Rotenone
S.S.S-Tributylphosphorotrithioate
Siduron (i-(2-Methylcydohexyl)-3-phenylurea
SBvex (2-(2.4.5-Trichlorophenoxypropionic acid)) or any salt or ester
Simazine (2 Chloro-4.6-bisi(ethylamino)-s-tnazine)
Sodium bentazon (3-JsoprapyJ-iH.2.l.3-benzothiadiazin-4(3H)-one 2.2-dioxtde)
Sodium dimotrt-jltiithiocarbijmate
Sodium fluoroacetate
Sodium methyidithiocarbarnate
Sulfoxide (1.2-Methylenediaxy-^(2-(octylsulfidynyl)propyl) benzene
S-Ethyl cydohexyiethyithiocarbamate
S-Ethyt dipropylthiocarbamate
S-Ethyl hexahydro-1 H-azepine-1 -carbothioate
S-Propyi butylethytthiocartamate
S-Propyt dipropylthiocarbamate
•S-(2-Hydroxypropyl)thiomethanesulfonate
S-(0,O-Diisopropyl phosphorodithioate ester of N-(2-mercaptoethyl)benzenesulfonamide
Tebuthiuron (N-(5-{1,1 -Dimethylethyl)-! .S^-thiadiazol^-ylJ-N.N'-dimethylurea)
TemephostO.O.O'.O'-Tetramethyl-O.O'-thiodi-p-phenylenephosphorothioate)
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
2383
238b
238C
238d
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
A.13
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSU$ FOR 1986
Part B Financial and Economic Information
TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued
ACTIVE INGREDIENT
Terbacil (3-tert-Butyl-5-chloro-6-metnyiuracil)
Tarbufos (S-(((1.i-Dimethylethyi)thioimetnyi) O.O-diethyi phosphorodrthioate)
Tert3Uthylazine(2-(tert-Buty1ammo)-4-<:nloro-6-(ethy«amino)-s-tnazine
7erbutryn(2-(tert-Buty1amino)-4-(ethviamino)-6-(nnethylthio)-s-triazine)
Tetrachloropnenoi or any salt or ester
CODE
25*
255
256
257
258
258a
258b
258C
258d
259 '
260
261
262
263
264
265
2S5a
265b
265C
265d
266
267
268
269
270
271
272
Tetranydro-3.5-dlm«hyl-2H-l.3.5-thiaaiazine-2-thione
ThlopharuM-methyl (Dimethyl 4,41o.pnanyienebis(3-thioallophanate))
Thiram (Tetramethyrthiuram disuifide)
Toxaphene (technical chlorinated camonene (67^9% chlorine))
Tributyl phosphorotrithtoite
Trifluralin(a.a.a-Trifluro-2.6-dinitro-N.N-dipropvrt-p-toluidine)
Warfarin (3-(a-Acetonylbenzyl)-4-hyaroxycoumann) or any salt or ester
Zinc 2-mercaptobenzothiazotate
ZJneb (Zinc ethylenebisdtttiiocarbamate)
ZJram (Zinc dimethykjithkscarbamate)
(2,3,3-TrichloroaJlyl)dUsopropytthiocaroamate
(3-Phenoxyphenyl)mtmyl d-cis and tran" 2^-dimethyl-3-(2-methylpropenyl)cydopropanecarboxytate
•(Max. d-ds 25%; Min. trans 75%)
(4-Cydorwxene-i ,2-dlcarboximido)methyl 2^-dimethyl^}-
(2-mothylpropenyl)cydopropanecarooxylate
Isopropyl N-(3-chlo'rophenyl) carbamaw
A.14
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial *nd Economic Information
SECTION 1: FIRM FINANCIAL INFORMATION
l-A. Was this facility owned or controlled by a parent firm or firms on December 31 . 1 986?
veS ....... 1 (GO TO BOX 1 -A)
Kjn ' ........ 2 (SKIP TO SECTION 2, PAGE 18)
Sirt ' ................
30X 1-A
i If there is .more than one parent firm, sucn as in a joint venture, photocopy Section 1. ;
i pages 1 3 through 1 6. and complete all Section i questions for each parent firm.
1-B. Report the name, mailing address and DUNS number of the parent firm.
[1] Name of Parent Firm
• ,_ _-_i_i_!_!_l_ _______ l_i_l_I_l_l_i S1B1
[2] Mailing Address of Headquarters
I_'_J_!_I.J— O-J-J— !— !— I— I— I— i— I— '•— ! ' S1B2A
Street or P.O.Box
i i i i i i i i i_;.j_j_i— i—i-J-J— i—i
d&'^Tawn ----- "" i State I Zip Code
S1B2B S1B2C S1B2D
[3] What is the DUNS Nurritaer of the parent firm?
• l_i_! - I_LJ-J - l-J-J-l-J C Not A»*cable
S1B3A S1B3B
1-C Report the percentage of the parent firm's total 1986 sales (In dollars) generated by each of the
aSes !tt£d Tbeiow. (Ente,r Sro if the activity was not applicable. The sum of all percentages
must be 100%).
m Pare,*nru5f of sa!c« gansfated *>y manufacturing pesticides llstad (
in Table 1 . pages 4 through 1 2 ............................................... "§IcT — """"•— • -- ' —
[21 Percentage of sales generated by formulating or packaging pesticides (
listed in Table 1 . pages 4 through 12 ..................................... -SICZ"'"- -•"" ! — • — ' — ' '
13] Percentage of sales generated by other activities (SPECIFY) ........................... !_J-J— 1*
Total
100 %
A.15
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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)
NO ............................... 2 (SKIP TO QUESTION 1-E)
[1 ] In wnat year was this facility acquired by the parent firm?
Year S1D1
[2] How was this facility acquired by the parent firm? (CHECK ONE):
[j Purchase
~j Merger: Please list names of the companies that merged
S1D2
SiD2A
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ENVIRONM€NTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Pan B Financial and Economic Information
SECTION 1: FIRM FINANCIAL INFORMATION
•i-F. Reoort the names and EPA Fee-era! insecticide. Fungicide and Rodenticiae Act (F1FF.A1.
Establishment Numbers (as reported to tne EPA on Form 3540-16) for all other facilities owneo cr
controlled by the parent firm at which any of the pesticides listed on Table 1. pages 4 through 12.
were manufactured or formulated ana/or packaged. Check the cox next to 'Not Appncaoie' .f trs
facility does not have an EFA F1FHA Establishment Numoer. Check whether eacn facility was z.
manufacturer or formulator/packager of the pesticides listed on Table 1. If more space is recuirec
to grve a complete answer to this question, photocopy this page.
I I
S1F1A
Name of Facility .
^2 Not Applicable
EPA FIFRA Establishment Numoer
~ Manufacturer ~_
S1F1D
rormuiator/Packager
S1F1E
[2]
Name of Facility
!_'_'_!— I— I • I— I—! • j— :— i— i
EPA FIFRA Establishment Numoer
Not Applicable
Manufacturer
S1F2D
~ Formulator/Paekager
S1§2E
S1F2A
S1F3B
Name of Facility
EPA RFRA EstaoTishment^umber
[2 Manufacturer
S1F3D
Not Applicable
S1F3A
S1F3C
Formuiator/Packager
— S1F3E
[4]
_
Name of Facility
S1F4B !__'_' — !_l — I -'—I — 1 —
EPA FIFRA Establishment Numoer
Not Applicable
S1F4A
S1F4C
Manufacturer
S1F4D
[H Formuiator/Packager
S1F4E
A.17
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part 8 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 anv of the pesticides listed on Table i, pages 4 through 12.
were manufactured or formulated ana/or oacKaged. ChecK the box next to "Not Applicable" if the
facility does not have an EPA FIFRA Estaolishment Number. Check wnetner eacn facility was a
manufacturer or formulator/packager or the pesticides listed on Table 1. If more space is reauirea
to give a complete answer to this question, pnotocopy this page.
[1] I
S1F5B
Name of Facility
..-..
EPA FIFRA Establishment Numoer
_J-__ _ S1F5A
C NotAppiicable S1F5C
Manufacturer
31F5D
~ Formulator/Packager
S1F5E
12] l_'_!_l_l_i_l_l_!«:_:__
• Name of Facility
S1F6B I__'_J__I_I_I ' I_I_J ' I-J_J_J
EPA FIFRA Establishment Number
[3 Manufacturer
S1F6D
-J_l-.l— !—! S1F6A
D Not Applicable S1F6C
[j Formulator/Packager
S1F6E
[3] !_:_J_I_I_J_I_J_I_!_J_:!
Name of Facility
S1F7B |_LJ_I_LJ • I_LJ • I_S_
EPA FIFRA Estaolishment Number
____- S1F7A
D NotAppiicable S1F7C
Manufacturer
S1F7D
Formulator/Packager
S1F7E
W I— LJ-J- I—!— I— I— I— I
Name of Facility
S1F8B
EPA FIFRA Establishment Number
Q Manufacturer £
S1F8D
.!_1_!_LJ_I_!_I_!
! I n NotAppiicable
S1F8A
S1F8C
Formulator/Packager
S1F8E
A. 18
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS^ FOR 1986
Part B Financial and Economic information
SECTION 1: FIRM FINANCIAL INFORMATION
t-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)
i-l. Report the name, mailing address and DUNS number of the controlling firm.
[1] Name
Sill
[2] Mailing Address of Headquarters
l-l—I—l-l—1_ I—1—1
Street or P.O.Box
City or Town
SI
[3] DUNS Number
SII2A
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 i Comments. Reference entry by question numoer.
A.20
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
AH 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 oercentages
must be 100%).
Ml Production generated by manufacturing and/or formulating and packaging 32A1
' pesticide active ingredients listed in Table 1. pages 4 through 12 °
[2] Production generated by manufacture of intermediates that 3 ^
are sold _ °
[31 Production generated by manufacturing and/or formulating and packaging S2A3 _
EPA registered pesticides offl 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 — -°
• [5] Production generated by other activities (SPECIFY) :%
S2A5A( Variable), S2A5B (Description)
Total 1 ° 0%
2-B. Report the calendar year during which:
S2B1 .....
[1] Operations began at this facility
[2] Manufacturing and/or formulating/packaging
of either pesticide active ingredients or S2B2 i i
pesticide products began at this facBfty •_._!_"_
T 63*
[3} The most recent major expansion of plant and
equipment with respect to pesticides occurred S2B3 . • > i i
at this facility ; — —337'—
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 oaoe 21.
Question 2-C on page 21 requests facility Balance Sheet information. Please read the instructions
and definitions below before comoieting Question 2-C. The number in brackets, for example,
"[1] Inventories.' correspond to Balance Sheet entnes.
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 or, 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 facility level. Base the
estimate on your facility's share of sales. If you have estimated an amount tor 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 ail 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 terns (1 ] and [2].
A.22
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FAC8UTY CENSU^FOR 1986
Part IB Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-C. Instructions for reporting Balance Sheet information on pane 21 - continued
Noncurrent Assets: Retort 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 onginal cost of land.
Buildings/Plant: Report the cost of buildings including expansions ana
renovations ngt 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 Noncurrent Assets: Report all noncurrent assets, like investments in
capital stocks and bonds.
[4] Total Noncurrent Assets: Report the total noncurrent assets from each of the
items listed above that apply.
[5] Total Currant and Noncurrent 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 ail current liabBfties like accounts payable.
accrued expenses and taxes and the current portion of long-term debt.
Noncurrent Liabilities and Equity: Report all noncurrent liabilities that fail due beyond one
year.
m Long Term Debt and Other Noneurrent 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 tout liabilities.
The amount obtained ahould Include conirlbuted or ps& :•• capita! (preferred
and common stock) and retained earnings.
[9] Total Noncurrent Liabilities and Equity: Report the sum of items [7] and [8].
[101 Total Liabilities and Equity: Report the sum of Kerns (6] and (9).
A.23
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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. 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 Z2CA month to 32C2 month.
,
TAE
Current assets
(1] Inventories
[2] Other current assets
[3] Total current assets
•• Noncurrent assets •
*
W 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
| [g] Total noncurrent liabilities
i and equity
1 [10] Total liabilities and equity
3LE2-C. BALANCE SHEET
ASSETS
1985
(SOOO)
S2C1A
S2C2A
S2C3A
SZC4A " *"" •
S2C5A
LIABILITIES AND EQUITY
1985
($000)
S2C6A
S2C7A
S2C8A
S2C9A
S2C10A
1986
($000)
S2C1B
S2C2B
S2C3B
S2C4B
S2C5B
1986
($000)
S2C6B
S2C7B
S2C8B
S2C9B
_52Q.QB_
• ' , •'
.
1987
($000)
S2C1C
S2C2C
S2C3C
i
S2C4C
S2C5C
1987
($000)
S2C6C
i
S2C7C
S2C8C
S2C9C
S2C1QC
" ''!'• •
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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 tor reporting facility Income Statement Information on osae 24.
Question 2-0 on page 24 requests facility income and expense information. Please read tne
instructions and definitions below oefore 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 riot 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
[11
Sal* of Pesticide Chemical*: R*>no« tha total sales value of a!! pesticide chemicals.
This should Include ail pesticide active ingredients, intermediates, and finished
pesticide products. In cases where the pesticide chemical is not sold (there is no
known sales prica) but is transferred to another facflity owned by the company for
further processing and/or formulating/packaging, the facility share of sales generated
by tha final product should be allocated to tha facility. This share should 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].
[2] Revenue from Pesticide Contract Work or Tolling: Report the revenue from
pesticide contract work done by this facility for other facilities or firms.
(3] Other Revenue: Report all other revenues like the sales value of products and
services not reported in items [1 ] and [2].
[4] Total Facility Revenues: Report the sum of items [1 ] through [3].
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 lor reporting facility Income Statement information on cage 24 - continued
Expenses
Manufacturing Costs (Cost of Materials and Services Used): Include ail manufacturing
and/or formulating/packaging costs like direct materials, direct labor and indirect costs tnat
were either put Into production, usea as operating supplies, or used in repair ana
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
Interplant transfers to the facility, ana withdrawal from inventories.
Pesticides
[5]
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 ana 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
facfltty. induda tha total payments rnads 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] Nonpestlclde Costs: Report all other manufacturing costs not included
in items {5] through [8]. Include manufacturing costs associated with
nonpesticide chemicals or products. Report me types of cost for ftems
[5] through [8] for nonpesticide products and services.
Report the expenses listed below for the whole facility, not {ust pesticides.
110]
[11]
112]
[13]
[14]
[15]
[16]
Depredation: Report the depreciation on buildings, plant equipment.
and machinery at yeur facility.
Fixed Overheads: Report the total from all types of overhead. Include
rent, nonproduction utilities, selling costs, administration and general
expenses for your facitty.
Research and Development: Report all research and development
costs incurred during the year.
Interest: Report the total interest expense on all funds during the year.
Federal, State and Local Taxes: Report the total federal, state and
local taxes payable during the year.
Other Expenses: Report all other expenses not reported In items (10]
through [14].
Total Costs end Expenses: Report the sum of items (5] through [15].
A.26
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ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
Pert B Financial and Economic information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-0 Complete the facility Income Statements. Table 2-D below. Enter all information in thousanas of
' dollars as of December 31 for calendar years 1985. 1986. and 1987. If the facility fiscal year aoes
not correspond to the calendar year, please enter the months of the facility fiscal year below.
Facility 1986 fiscal year was from
^.onth to
S223
month.
TABLE 2-0. INCOME STATEMENTS
^i^—
REVENUES
[ 1 ] Sales of pesticide chemicals
[a] Pesticide chemicals
listed in Table 1
[b] Other registered pesticide
1985
(SOOO)
S2D1AA
1986
($000)
S2D1AB
1987
(SOOO)
S2D1AC
S2D1BA
S2D1BB
S2D1BC
I [2] Revenue from pesticide contract
S2D2A
S2D2B
S2D2C
wuiivwi twiiiiiy
[3] Other revenue
'
[4] Total facility revenues
S2D3A
S2D4A
EXPENSES
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
[1C] Depreciation
[11] Fixed overheads
[12] Research and development
[13] interest
[14] Federal, state and local taxes
[15] Other expenses
[16] Total costs and expenses
1985
($000)
S2D5A
«rv;a
S2D7A
S2D8A
S2D9A
S2D10A
S2D11A
S2D12A
S2D13A
S2D14A
S2.PJ5A
S2D16A
S2D3B
S2D4.B
S2D4C
1986
($000)
S2D5B
1987
($000)
S2D7B
S2D7C
S2D8B
S2P8C
S2D2B_
S2D9C.
S2D10B
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 facBity 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
Pail 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 ali products at a facility may affect the accuracy
of economic impact projections for some products.
___ YES ............................. 1 (SKIP TO SECnONJ>sK_
PAGE 38)
NO ............................... 2 (CONTINUE)
2-H. This section requests information on Table t Pesticide Active Ingredients produced at your facility
in 1986.
Instructions for completing Table 2-H Pesticide Production; Technical Grade Products, o. 30.
Column [1]
Column [2]
Column [3]
Column [4]
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 faculty, 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
Product Codt. 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.
B Table 1 Pesticide Active Ingredients produced at this facility in 1986 ana
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).
1986 Average Unit Production and Packaging Cost in Dollars. Provide the
average production cost tor 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, and any other pesticide costs.
Note that the column (3] entry corresponds to items (5] through [8] under question
2-D en p,
Express the costs in dollars. Do not include allocations for corporate overhead.
administrative expenses, research and development, capital costs or interest
expense.
1986 Average Unit 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 faculty. but Is transferred to another
facUity owned by the firm for further processing, the sales price of the final product
should be allocated to both faculties based on their share of the cpsts to produce
the product This is referred to as the "pereentaoe 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 Products - continued
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], cirde the code that corresponds to the unit of
measure you used to calculate the information you reported In columns (3). [4], [5]
and [71-
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 Year* (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
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSU^ FOR 1986
Pint 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 packagea on site as product group P01.
200 IPS 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:
$2.50/lb for technical grade
$4.00/ib for formulated product group P01
$4.25/Ib 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 facility owned by this company
Formulating and packaging at other facility not owned by this company
$i.SO/lb
S2.00/lb
SO.50/I b
$0.50/ib
$0.60 Ib
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 1 400 Ibs of Al 000 are sold as technical grade. The unit cost of production is Si.50/lb and the
unrt sales price te S2.50/lb. This corresponds to Product Code A on page 26.
Line 2 200 Ibs of Al 000 are transferred to another facBtty owned by this firm to be formulated and
packaged. The unit cost of production to this facility {remains Si.50/lb and the selling price of
the formulated product Is S4.00/Ib. 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/lb or 53.00/lb. This corresponds to Product Code B on page 26.
Formulated/Packaged Products (Table 2-J. p. 371
Line 1 200 Ibs of Al 000 are formulated/packaged on site by this facility. The total unit cost of the
formulated and packaged product is S2.00/lb ($1.50/lb for production plus $.50 for formulating
and packaging. Since ail unit costs are incurred by this facility, the total unit sales price of
$4.00/!b Is allocated to this facilty. 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.)
Line 2 200 Ibs of Al 000 are produced by this facilty and formulated/packaged by another firm under
contract to this facatty. This facility pays for the contract work. The total unit cost of the
formulated/packaged product is 52.10/lb ($1.50/1b for production plus S.60/lb for
formulating/packaging). Since all unit costs ant Incurred by this facility, the total unit sales
price of 54.00/lb is allocated to this facaity. This corresponds to Product Code B on page 35.
Line 3 200 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
52.16/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
lo this facility. This corresponds to Produce Code E on page 35. (Note: !' the ferity
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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-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSU^FOR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
24. During calendar year 1986. did this facility sail any
of pesticide products containing a pesticide active ingredient l.sted in Table
NO)
rme
(CIRCLE YES
YES
NO.
. >" "(READ THE INSTRUCTIONS"
BELOW AND COMPLETE
TABLE 2-1 ON PAGE 34)
.> (GO TO QUESTION 2-J ON
PAGE 35)
T«hln 3.1 Pesticide Production' Intf mediates.
Column [1]
Column [2]
Column [3]
Column 14]
column t j
Intermediate Nam«. Enter the name of every intermediate produced in 1 986 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.
Active Ingradtont Cod*. Enter the code for every Table 1 active ingredient
associated with your production of the intermediate listed in column (1 ].
Average Unit Production Cost in Dollars. Provide the average production cost for
one unit of the item reported In column {1J. 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 132! include allocations for corporate overhead.
administrative expenses, research and development, capital costs or interest
1986 Averafl. Un« Sales Price in Dollars. Report the average sailing price for one
^ ^ ^ ^ reported |n CQjurnn {1 j gxp^ ^ ^^3 pnce in dollars. If the
pesticide chemical is not sold when it leaves the faciity, but is transferred to another
facattv owned by «he 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
Instr
for completing Table 2-I Pesticide Production! Intermediates - continued
Column (5]
Column [6]
Column (7]
Column (8]
1986 Quantity Sold. In column (5], report the total quantity of the rtem reported in
column (1] that was produced at this facility during 1986 and sold.
UnR of Measure. In column (6J. 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
Sum Annual Quantity SoSd Over Three Years (1985-1987). Provwe the total
amount (sum) of the product reported in column {1 ] that was produced and sold by
this facility in 1985. 1986. and 1987 combined.
Percent Exported Over TTiree Years (1985-1987).
product In column [1] exported in 1985. 1986. and
percent of column [7] was exported.
Report the percent of the
1987 combined, i.e.. what
A. 36
-------
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OTECTION AGEN
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-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1980
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-J. During calenaar year 1986. did this facility produce any formulated or packaged products
containing a pesticide active ingredient listed in Table 1? (CIRCLE YES OR NO)
'ES > (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 ana
assign each group a numoer. For example, if your products contain two active
ingredients (say A and B). group all products containing only A into one group (call
it #1). group all products containing B into a second group (call it #2) ana 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 facflity.
E Table 1 pesticide products formulated/packaged by this facility from active
ingredients purchased from another firm.
A. 38
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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-J Pesticide Production: Formulated or Packaged Products -
continued
Column (51
Column [6]
Column [7]
Column [8]
Colurn^ J9]
1986 Average Unit Proauction and Formulating/Packaging Cost in Dollars.
Provide the average proauetion c^st fp1" """ ""t — inciuae sucn costs as material
-rntc j p thp rpsts et aii raw matpnais inrjudin packaging materials that were
used in the proauction ana /or rormuiation and packaging of pesticide products).
direct labor costs, tne costs or pesticide contract worx or tolling done for you bv
others, and any otner pesticiae costs.
Note that the column (5] entry corresponds to items (5] through [8] under question
2-0 on page 23.
Express the costs in dollars. Do not inauoe allocations for corporate overneao.
administrative expenses, researcn ana Development, capital costs or interest
exoense.
1986 Average Unit 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 purchased by your facility, but is transferred to your facility
from another facflity 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 and 29.
1986 Production Quantity. In column [5], report the total quantity of the item
reported in column {1 J that was formulated /packaged by this facility during 1986.
Unft 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 (5], [6]. [7]
and [8].
P - Pounds
T = Short tons
M - Metric tons
G B Gallons
Sum Annual Production Over Three Years (1985-1987). Provide the total amount
(sum* of the product reported in column [\] that was formulated/packaged by
facility in 1985. 1986. ana 1987 combined.
Column (10] Percent Exported Over Three Years (1985-1987). Report the percent of the
product exported in 1985. 1986. and 1987 combined. i.e.. what percent of column
[9] was exported.
A. 39
-------
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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 beiow. (Enter zero if the market is not applicable. The percentages snould sum to 100%).
Ill ^Agriculture (U.S.A.)
(31 Home, garden (U.SA) ....
[4] Export (Outside U.S.A.) ...
[5] Other markets (SPECIFY)
3IK3A V=riable)
'
Total
0 0 %
2-L. Facility Operations
Reoort 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 ...?.?.J«i ............. . ............ ___ ; _ ;
[2] The number of days part or all of the facility manufactured
pesticide chemicals [[[ . .................. .5.21*2. ............ _ . _ [ _ ;
(3] The number of days part or all of the faculty formulated /packaged
pesticide chemicals . ........................... . ....................... . ........................... .?.?L3.... _ : _ : _ :
2-M. Employee Information
In lines [1] through [4], report the total employee hours worked at this faculty in the months of
January 1986. May 1986 and November 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 1986.
[1] Total employee hours in pesti-
cide chemicals production
[2] Total employee hours in BfiSlh
eide formulating and packaging
[3j Total employ**; hours in other
production
[4] Total employee hours in ngn;
; production
[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
SZM2B
32M3B
S2M4B
S2M5B
S2M6B
November 1986
S2M1C
S2M2C
S2M3C
S2M4C
3ZM5C
-------
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-dosure 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
[11 Liquidation value (less dosure and post-dosure cost)
Closure and post-dosure cost
[2] Cost to convert to non-Table 1 pesticide active ingredients
or non-pesticide products
(SOOO)
S2NA1A
32NA1B
S2NA2
Pesticide formulating/packaging lines
[ 1 ] Liquidation value
[2] Cost to convert to non-Table 1 pesticide active ingredients
or non-pesticide products
S2NB1
S2NB2
2-O. Did this fac'drty have any property tax assessment for 1986?
vcc
* taW •••••••••••••••••••*•••••
S2O
.... ! (CONTINUE)
.... 2 (SWP 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.
Stats tax assessment value
mLmnd ..
^~"« *™ «««»«•«»«•••«"«"•»«-«««««««"•«««
[2] Biddings
[3] Equipment and machinery
[4] Total properly tax assessment value
Local tax assessment value
[5] Land
(6] Buildings
(7] Equipment and machinery
[8] Total property tax assessment value
(SOOO)
SP1
SP2
SP4
SP5
SP6
SP7
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CSNSU^FQR 1986
Part B Financial and Economic Information
SECTION 2: FACILITY FINANCIAL INFORMATION
2-Q. What was the 1986 assessed value oif the property expressed as a percentage of market value < 1986
level of assessment)? (Enter zero if the item was not aoplicaoie).
: 1 ] State assessment percentage .
[1] LULJI ai»^i£rnent percentage
2-R. Overall, what is the major source of competition tor pesticide products produced at this raciiity m
eacn of the three marxets listed below?
The same products means competing products containing identical or nearly identical pesticide
active ingredients or percentages of active ingredients but having different trade or prano names.
Substitute products means competing products oerrormtng tne same pesticidal functions Put
containing different pesticide active ingredients.
Competition
[ 1 ] Domestic producers of the
{2] Foreign producers of the
I [3] Domestic producers of the
[4] Foreign producers of the
Local
Regional
n
n
n
n
n
n
Market
National
D
n
n
n
n
n
International
D
D
i i
D
D
LJ
S2R1
S2R2
S2R3
A.43
-------
ENVIRONMENTAL PROTECTION AGENCY
PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
Part 6 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
SECTION 3: FACILITY CONTACT
Enter rne name, title, teleonone number ana aaaress (if different from the facility mailing aadressi cr tr.e
racnrrv representative to oe contacted wrtn Questions regaraing your resoonses to Part B:
Name (First ana Last)
Title
Teleonone Numoer
Address (if different from facility mailing aadressi:
Firm or Facility Name
Street or P.O. Box
City or Town
33 F
State
S3G
Zio Coce
CERTIFICATION: The information orovtaed in Part B of the Questionnaire, as weil as that proviaeo
in ail others, must be certified oy having the resoonsible individual for your facility commote ana sign
the Certification Statement Item 6 on cage 3 of this Questionnaire.
A.45
-------
A.46
-------
Appendix B: MAPPING OF PESTICIDE ACTIVE INGREDIENTS INTO CLUSTERS
This appendix lists the 56 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 260 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 column ("New PAIs") lists PAIs that have been registered since 1980, and were, therefore, not included
la the original OPP clusters.
B.I
-------
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B.28
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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 pries due to compliance with the effluent guidelines. (See Chapter 4.)
C.I
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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
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TABLE OF CONTENTS
1.0 Introduction 1
2.0 Price Elasticity of Demand for Agricultural Pesticides 3
2.1 Methodology , 3
2.2 Review of Empirics! 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-Agriculturally ... .55
4.0 Conclusions 59
References . > 61
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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 elasticity' 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 in the quantity demanded.
Measurements of the price elasticity of demand are of use in 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 in 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.S.A., 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.
"i!
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.D.A.) 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.DA.'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.D A.'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
clusters. 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,
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.
23 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 substituites, 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 in 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 in 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).
C-10.
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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.D.A. 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.
•Mi
where
PU,
PI
y
SCA
RE
+ a, In P, h + ^ In P{ + % In y + % In SCA + a5 In RE + In Fj + 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 in 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.D.A. annual pesticide surveys for 1968, 1971, and 1976. The input price indices, Plih 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
en 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 (Op). Exogenous variables included in his final aggregate demand
equation were:
x^w = adjusted and weighted price of pesticides (dollars per pound),
XT = acres of corn per farm, and
D^ = 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 in 1978).
The results of the regression analysis were as follows (standard errors are in parentheses):
InQ, = 2.212 - 1.464 In X,w +1.099 In x,. + 0.381 U, + e
(0.161) (0.064) (0.110)
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 their 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 in 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
P.. 14
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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:
PRODt, PPRODM
ACRE,
RPRICE;
a +b In PROD,.., +c In ACRE; +d In RPRICE, +f (IX,)
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 in 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
Intercept
Ln Acres
Ln Real
Price
Ln
Production
Previous
Year
Industrial
Production
Index
Herbicides
-12.93
(-3.51)
3.19
(4.02)
-0.67
(-2.49)
0.299
(1.88)
-0.00651
(-3.24)
Insecticides
R2=0.68
-3.49
(-1.32)
1.53
(2.90)
-0.32
(-2.51)
0.142
(0.57)
Fungicides
All Pesticides
R2=0.89
-1.46
(-0.47)
-6.42
(-2.26)
1.04
(2.02)
1.88
(3.02)
-0.35
(-2.07)
-0.49
(-2.37)
0.05
(0.18)
9.427
(1.84)
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
pyrcthroids 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 in 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 in wheat and grain sorghum, herbicides, especially 2,4-D, were utilized.
Using data on land utilization for 1969, Lacewel! and Masch constructed a linear programming model
for the five county region. For illustrative purposes, the change in the quantity of 2,4-D used in 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 in 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.
C.17
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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
R.,
Rj,
e,
C|
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
(0.796)
-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.DA. 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 -034. 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-
-------
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, j
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
y Milk -0.8255
-0.1092
-0.1467
-0.3688
-0.167
-0.2015
-0.9996
-0.4002-
-1.3780-
-0.2191
ts -0.2357-
-0.1371
-0.5584
-0.2516
-0.1964
-0.0388
-0.0385
stables -0.2102
-0.5612
: -0.3811
-0.6926
Ictail -0.7323
i, &nuts -0.1248
Truits & 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.2?
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Sources for Table 23:
Values for "own-price elasticity" were obtained from U.S.D.A. (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 grain" 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.D A. 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 Tercent of Use" equals "Pesticide use on crop"/"Pesticide use on cluster"
/16 "Weighted Elasticity" equals summation of ("percent of use" multiplied by "own-price elasticity1)
/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 cluster.
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.DA., 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 hi 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-Chemicall 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 dusters,
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, PimentePs 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, 25, and 2.6. Clusters in the "high substitutability" category can,
according to Pimentel et al., achieve at least a 40 percent reduction in pesticide use at an additional cost
of less than one dollar per hectare. Clusters in 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 in 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 in this analysis.
C.34
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Table 2.4
Non-chemical Substitutability for Pesticides by Cluster
Fungicides
High
Substitutability
Moderate
Substitutabilitv
Low
Substitutabilitv
soybeans
other vegetables
peaches
rice 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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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
dusters 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 in 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 clusters defined in this analysis separate agricultural chemicals into fungicides,
insecticides, and herbicides. The U.S.DA. 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
peanuts
cotton3
sugarbeets3
sorghum
corn3
rice0
wheat3
potatoes
barley3
tobacco
oats3
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
35
17
16
23
22
19
19
16
3
16
3
9
Insecticide
Costs as a
Percent of
Variable
3
3
13
5
3
2
1
2
6
0
7
0
""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.
H.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-
Dougias 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
Headlev's 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 in 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 in 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:
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 hi 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 in 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
cf 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 dusters 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
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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 dusters 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.
C.46.
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Cluster
Table 2.9
Summary of Elasticity Information
Elasticity of Feasibility Fraction of
Literature Food of Contribution to Marginal
Estimates Commodity Substitution Production Costs Productivity
Fungicides on: ^V "'-""',-''',- >< ->', ,,"ffe/4
vegetables MxA* , -0.38 ^ .Jos^'J %,
fruit & nut trees, ' ' X ' ' / , - \ ,,i- --., ~l,~- £
except oranges •.—&r9:2 (2$- - "' } , -0.23 - jajcxferate
•A" '**/* %?%&&* % --«•••> -•>'''•• ' W '% f - '••
oranges . J$LA.v ^ ^p* , ^ -1.00 0 ^moderate- t
grapes NvA*" , v " , -1.38 ' m<^$p|e. % s
„ **'•'•* •.'•s\'< -• -*yf *' ' % -
Herbicides on: "--,'' - -" "" -V**. ^VJfc V »"
sorghum, rice, ! -•''"--''•." ' ^Tj^^v ,;;,; „- -0.69 - ' -4ow""
oranges JSE,A. -1.00 s "- ^igh " ^
tree fruits, nuts & -'"'^ , „! s"'^ ^r^''c ' /,' /\
sugar cane JNxA. - -' •• -••••-' - ----- -0.20 *' % ' Jii^ *%
grapes H»K, "'^ " ' -•: ' , -1.38 ' j^oSeta^t
5'> ''* % "' " '"\? ' " ;/"-^ ' ""
vegetables H*J£"/' ' - " " -0.27 ^ ja^erate^
tobacco ^C' .."' 'V % ^ N.A. ^/^l^"",
sugar beets, beans N "-1; - " „ - / ; "^ ^T - '5' -
peas N.%, ' '? ,'" -0.12 '^-L,ifew
' -.v ^-%' -i.
N.A. '" ' $£&(. |
' ' s^
N.A. ^V^54j
/; "" ' •"
•*•_
"y •• Sf- -
Oon •• -• -•$/}- •&$£•'
• J^V/ w wX-OO •
0-7^ / ""-^^ aCO
. JJ *" ' <^$t+f}Q
0.19 '/ ,-%$iji.
N.A. "s - ^rf Ji
'-"r^^*^
,--#• - '
5- ^»v - ';
NA ^l
-------
Table 2.9 (cont.)
Summary of Elasticity Information
Literature
Estimates
Elasticity of Feasibility Fraction of
Food of Contribution to
Commodity Substitution Production Costs
Insecticides on:
vegetables
fruit & nut trees
exc. oranges
oranges
grapes
corn, alfalfa
sorghum
soybeans, peanuts,
wheat, & tobacco
cotton
N.A.
N.A.
N.A.
N.A.
0-02
0.03
0.03
0.13
Marginal
Productivity
(1) Burrows (1983), cotton only
(2) Pingali and Carlson (1985), apples only
(3) Miranowski (1980), corn only
(4) Huh (1978), com insecticides and herbicides
(5) U.S. EPA (1974), corn or soybeans, only
C.48
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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.DA. (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 hi 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 grains.
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.4S
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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 in 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 com 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.
L Herbicides used on tobacco
U.S.DA. 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 hi 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.
C.51
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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 substitutability
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.E)A.'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 duster, 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.DA. elasticity estimate for grapes.
x. 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.
C.53.
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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.
l!
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 grain storage
In the absence of more specific information, the elasticity of demand for fungicides on gram storage
is assumed to equal the elasticity of demand for grams. Elasticity estimates are available from Huang (1985)
for wheat and rice. Other stored grams 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 gram 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 grams (-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 hi this cluster is calculated based on the demand elasticity for the crops
C.54'
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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 in the same ratios as they were applied to the crops in 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.
C.55
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Table 2.10
Estimates of Elasticity of Demand for Clusters in the Agricultural Sector
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)
C.56
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3.0 PRICE ELASTICITY OF DEMAND FOR PESTICIDES USED NON-AGRICULTURALLY
Most of the pesticides included in this analysis are used in the agricultural sector; pesticides in 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 in 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 in 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 in
conjunction with herbicides to turf. It is therefore likely that an application system would be in 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 dusters 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 in this category. For each duster, 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 dusters 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 Encydopedia 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 stodc, or for fumigating
sod, principally for the control of plant parasitic nematodes. Given the application in 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.
C.58-
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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
C.59
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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.
1 '" '" ' ; , \
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 -035.
C.60
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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 clear 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.
C.6.1
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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
Sanitizers - 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
C.62
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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 hi The Practical Application
of Economic Incentives to the Control of Pollution: Tlie 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 hi
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 hi 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.DA. (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.SJDA. (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
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 discharge method. The predicted impacts
under a zero cost through assumption match the impacts predicted in the main text under an assumption of partial
cost pass through (compare Table D.I with Table 4.3).
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.I).
MetaUo-Organic Pesticide Manufacturing - (Subcategory B)
Direct discharge of Subcategory B chemicals in process wastewater is limited to zero under BPT. No
additional options were considered and no new limitations are promulgated 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 entirely or to close a product line under the no cost pass-through
assumption due to compliance with PSES.
D.I
-------
Metallo-Organic Pesticide Manufacturing - (Subcategory B)
Because no new limitations are promulgated 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.
Table D.I
Impacts of the Final Regulation on Facilities Under No Cost Pass-Through Assumption
Number of facilities with costs
Facility Closures
Product line Closures
* Zero discharging facilities are
Direct
Dischargers*
33
0
2
included with direct discharging facilities.
Indirect
Dischargers
25
0
0
D.2
-------
Appendix E: COMPLIANCE COSTS AS A PERCENT OF FACILITY REVENUE
As an additional evaluation of the economic impacts associated with the final BAT and PSES regulations,
the EPA compared annualized compliance costs with facility revenue for all facilities projected to incur costs under
the final rule. This comparison is a common gauge of achievability, with annualized costs in excess of five percent
of facility revenues typically indicating a significant impact. Costs as a percentage of revenue represents an
approximation of the percentage price increase that would result from 100 percent cost pass through, i.e., the
percentage increase in price needed to cover all treatment costs. The results are shown below, by discharge
category. Note that the facilities projected, to be baseline closures are included in this analysis.
Impacts of BAT Regulation on Direct Dischargers
For the 28 direct discharging facilities that are projected to incur costs under the final rule, the mean
compliance cost as a percentage of facility revenue was 0.4 percent, the median was less than one-tenth of one
percent, and the highest value was 4.6 percent. For the five zero discharge facilities with monitoring costs, the
mean compliance cost as a percentage of facility revenue was 0.2 percent, the median was 0.1 percent, and the
maximum was 3.9 percent. This comparison supports the conclusion that the regulation is economically achievable.
Impacts of PSES Regulation on Indirect Dischargers
For the 23 indirect discharging facilities that are projected to incur costs under the final rule, the mean
compliance costs as a percentage of revenue was 0.7 percent, the median was 0.2 percent, and the maximum was
5.7 percent. The ratio of compliance costs to facility revenue was greater than five percent for only one facility.
The comparison therefore supports the conclusion that the regulation is economically achievable.
The above results include facilities projected to close in the baseline, and indicate that were these facilities
to remain open and incur compliance costs, the rule would still be economically achievable. The cost to revenue
ratio for these facilities projected to close in the baseline is highlighted below.
Of the 14 facilities projected to close in the baseline that are not known to have actually closed to date, only
eleven would be projected to incur costs to comply with the final rule. Eight of the eleven facilities counted as
baseline closures and projected to incur compliance costs would be expected to incur only monitoring costs.
Therefore, only three facilities that are counted as baseline closures would incur significant costs if they remained
open. Monitoring costs are generally relatively low and would not, of themselves, be likely to significantly impact
a facility. For the eleven facilities that would be expected to incur costs, the highest annualized compliance costs
as a percentage of total facility revenue is estimated to be 0.2%. The mean compliance cost percentage of revenue
E.1
-------
is estimated to be about one-tenth of one percent. Therefore, even if the 14 facilities projected to close in the
baseline remained open, they would not be expected to be significantly impacted by the rule.
E.2
-------
Appendix F: EXAMPLE OF HYPOTHETICAL FACILITY CALCULATIONS
This appendix provides a detailed example of the facility-level analysis (described in Chapter 4 of the HA)
on a hypothetical facility. The example is intended to aid the reader in understanding the steps that EPA undertook
in the analysis. As in the actual analysis, economic impacts are calculated in four steps: 1) the baseline analysis,
2) post-compliance facility closure analysis, 3) product closure analysis, and 4) other significant impacts analysis.
The following tables duplicate the balance sheet and income statement as would have been provided in the
Census, with assumed values input for the hypothetical facility.
TABLE 2-C. BALANCE SHEET
ASSETS
Current Assets
[1] Inventories
[2] Other current assets
[3] Total current assets
Noncurrent assets
: [4] Total noncurrent assets;
[5] Total current and noncurrent assets
1985
($000)
29,750
33,000
62,750
86,850
149,600
1986
($000)
31,550
35,500
67,050
92,750
159,800
1987
($000)
32,000
37,100
69,100
101,800
170,900
LIABHITIES AND EQUITIES
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
37,100
46,250
66,250
112,500
149,600
38,900
48,750
72,150
120,900
159,800
41,250
54,000
75,650
129,650
170,900
F.I
-------
TABLE 2-D. INCOME STATEMENTS
REVENUES
[1] Sales of pesticide chemicals
[a] Pesticide chemicals listed in Table 1
[b] Other registered pesticide chemicals
[2] Revenue from pesticide contract work or tolling
[3] Other revenue
[4] Total facility revenues
1985
($000)
41,280
6,255
850
85,300
133,685
1986
($000)
43, ISO
8,070
1,000
86,475
139,325
1987
($000)
46,175
8,795
1,200
95,050
151,220
EXPENSES
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
16,190
2,030
950
2,815
57,210
15,900
2,220
1,150
4,450
55,490
16,700
2,225
1,100
3,540
57,075
Facility costs
[10] Depreciation
[11] Fixed overheads
[12] Research and development
[13] Interest
[14] Federal, state and local taxes
[15] Other expenses
[16] Total costs and expenses
8,750
11,180
6,065
1,810
4,670
12,195
123,865
7,580
12,675
6,540
1,795
6,460
9,990
124,250
8,080
13,355
6,700
1,995
7,845
11,500
130,115
F.2
-------
In addition to the balance sheet and income statement of the hypothetical facility, the following inputs were
used in the analysis:
• The facility produces three PAIs, designated A, B and C.
• These three PAIs are assigned to three clusters, designated X, Y and Z, as follows: 60 percent
of PAIA is in cluster Y, and 40 percent is in cluster Z, 100 percent of PAIB is in cluster Y, and
100 percent of PAI C is hi cluster X.
• The facility does not report PAI-specific data in the Census, and price data for all three PAIs is
available from a secondary source (had PAI-specific data been provided, it would have been used).
Production and price data are:
PAI
A
B
C
Production, in pounds
750,000
1,000,000
1,500,000
Price. $/lb.
2.00
10.00
20.00
The facility has fixed overhead of $ 3,480,000 to comply with OCPSF, and $ 1,220,000 to
comply with RCRA.
The estimated compliance costs are:
PAI
A
B
C
Capital Costs. $
80,000
200,000
400,000
Land Costs. $
600
700
1,200
Operating Costs. $
10,000
60,000
150,000
For each cluster, the estimated price elasticities of demand and percent of total U.S. production
expected to incur costs are:
Cluster
X
Y
Z
Elasticity
-0.60
-0.80
-0.30
Percentage of U.S. Production with Cost
0.60
0.75
0.40
The facility has a real weighted average cost of capital of 7.0 percent.
The marginal tax rate is 34 percent.
For determining other significant impacts, the lowest quartile value of the interest coverage ratio
is 1.1. The lowest quartile value for return on assets is 0.04.
F.3
-------
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*
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1
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$19,251,202
over
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variable cost
fe£
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ci to
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fixed
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costs for each
cluster
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culat
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st
is
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$101,46
nnual
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value factor.
9 .
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i
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co
inc
-------
u
g
1
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$0.070
s J
•
Cost - $6.174 / » +
• $6244 /tt
st-compliance unit cost for
post-compliance unit cost
ForcluslerY,
Pott-CompUmKC Unit
pos
the
Similarly, the
$16.564, an
$1.454.
1
i
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p, O
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i
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o
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i
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8
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= $2
= $3
Calc
facili
costs
' S
•?.
$52,468,000 - $23,
- $15.443,000 + $
$14,627,598
160,100.
59,
te baseline
as
-------
1
4,627^98 - $220,0
(3,540.5 » x $9.27
(7,678.7 U> x $3.4
(511.9 U> 0.928
($43,849, - 43,
,416,
36
$59
.499
20
99
.2
1
l
PH'
«,5
§
I
i
I
ysis
bas
Calculate line
interest coverage
ratio
Calculate baseline
return on assets
of
te amount
and land
nce cost t
borrowed
Ca
ca
co
wi
§!
a a e
IS'-
a & 8
® PH 1
— In -S
"3 P "3.
ill
post-
e EBIT
cul
pli
ate post-
ance inte
e
Cal
com
Cal
co
x
ets
Calculate p
compliance
Calculate post
compliance int
coverage ratio
Calculate post
compliance re
on assets
_
-------
I
-------
To summarize, potential impacts for the hypothetical facility are evaluated as follows.
« The facility is not predicted to be a baseline facility closure, since baseline cash flow is positive.
• The facility is not anticipated to incur a baseline product closure, since baseline cluster unit costs
are less than the estimated baseline cluster prices.
• The facility is not expected to close in the post-compliance scenario, since post-compliance cash
flow is positive.
• The facility is not estimated to incur a post-compliance product closure, since all post-compliance
unit cluster costs are less than the estimated post-compliance cluster prices.
• The facility is not predicted to incur any other significant financial impacts, since the post-
compliance interest coverage ratio and the post-compliance return on assets are both above the
lowest quartile values.
F.12
-------
|