Economic Impact Analysis of
  Proposed Effluent Limitations
Guidelines  and Standards for the
Pesticide Manufacturing Industry
     Dr. Lynne G. Tudor, Economist
  Economic and Statistical Analysis Branch
     Engineering and Analysis Division
     Office of Science and Technology
   U.S. Environmental Protection Agency
         Washington, DC 20460

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                             ACKNOWLEDGEMENTS
       The most credit must be given to Dr. Thomas E. Fielding for his knowledge, experience,
cooperation, and leadership as project officer, and to Maria D. Smith who always provided good
review and a fast turn around.  Credit must also be given to Paul Bangser for his good advice, Eric
Strassler for his help with the  questionnaire, and the whole pesticide team for their professional
manner conscientious effort, and contributions.
       Credit must be given to Abt Associates for their assistance and support in performing the
underlying analysis supporting the conclusions detailed in this report.  Their study was performed
under Contracts 68-CO-0080 and 68-03-3548. Particular thanks are given to Randi Currier and
Marianne Beauregard.

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                                         EXECUTIVE SUMMARY

  Introduction
          The Federal Water Pollution Control Act Amendments of 1972 established a comprehensive program to
  "restore and maintain the chemical, physical, and biological integrity of the Nation's waters" (section 101(a)).  To
  implement the Act, the U.S. Environmental Protection Agency (EPA) is to issue effluent limitations guidelines,
  pretreatment standards, and new source performance standards for industrial dischargers.  This Economic Impact
  Analysis (EIA)  documents  the assessment  of the  economic  impacts  of the  guidelines and standards applying
  specifically to the pesticide manufacturing industry.

         The EIA estimates the probable economic effect of compliance costs in terms  of facility closures, product
 line closures,  profitability impacts,  and ability to  incur  debt.  Firm-level impacts,  local  community impacts,
 international trade effects, and the effect on new pesticide manufacturing facilities are also presented. A Regulatory
 Flexibility Analysis detailing the small  business impacts is also included in the EIA for this industry.

         A total of 90 pesticide manufacturing facilities, owned and operated by 64 firms that manufacture one or
 more pesticide active ingredients (PAIs), are potentially subject to regulation.  The EPA analyzed the impacts of
 two possible regulatory options: a Treated Discharge Option (the proposed option) and a Zero Discharge Option
 based on on-site and off-site injection or incineration.  The economic impacts under each regulatory option were
 calculated separately for facilities discharging wastewater directly to surface water (direct dischargers) and facilities
 discharging wastewater to a publicly owned treatment works  (POTW)  (indirect dischargers). Impacts on direct
 dischargers  were calculated  for compliance  with a Best Available Technology Economically Achievable (BAT)
 regulation; impacts on indirect dischargers were calculated for compliance with Pretreatment Standards for Existing
 Sources (PSES) regulation.  Each discharge category was further analyzed by two subcategories: Organic Pesticide
 Chemicals Manufacturing (Subcategory  A) and Metallo-Organic Pesticide Chemicals Manufacturing (Subcategory
 B).

        The proposed regulation applies to Subcategory A and corresponds to the Treated Discharge Option. Total
 BAT investment  costs  (capital and land) for the proposed regulation  are projected to be $14.9 million, with
 annualized costs of $14.7 million including depreciation and interest.   Total investment costs for PSES for the
proposed regulation are projected to be $9.4 million, with annualized costs of $5.9 million including depreciation
and interest.
                                                   E.I

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                  Cost of Implementing BAT and PSES Regulations for Subcategory A
                                       (in millions of 1986 dollars)
                                                                      BAT
         PSES
                  Capital Costs
                  Total Annualized Costs
$14.9
$14.7
$9.4
$5.9
        The costs, presented in 1986 dollars, are based on the assumption that, whenever possible, facilities will
improve existing treatment rather than build new treatment.  Although 90 facilities are potentially subject to the
regulation, EPA analyzed only 88 facilities for economic impacts.  Financial data were not obtained for one facility
originally classified as a formulator/packager.  The other facility for which economic impacts were not calculated
was a research and development facility with no revenues associated with in-scope PAIs.  One of these facilities
is expected to incur no cost under the proposed regulation; the other is expected to incur only monitoring costs.

Methodology
        The costs and impacts of implementing the regulatory options are analyzed on an PAI-specific basis for
each facility.  Building on the PAI-specific data, the EIA uses three primary impact measures:  facility closures,
product line closures, and other significant impacts short of closure.  The analysis of significant impacts short  of
closure measures the effect of compliance costs on the ability of facilities to incur  debt and on facilities' return on
assets.  The  analysis evaluates these impacts in a  hierarchical  manner  that corresponds to the severity  of the
projected impact:  if a facility closes, product line closures  and other significant impacts are not evaluated; if a
 facility sustains a product line  closure, other significant impacts are not evaluated. The impacts are estimated for
 pesticide manufacturing facilities incurring costs using a combination of data from the Pesticide Manufacturing
 Facility Census for 1986 (hereinafter referred to as the Census) and secondary sources, such as Standard and Poor's
 Compustat financial data,  plus facility-specific compliance cost estimates developed  by the EPA.  First, pre-
 compliance (baseline) estimates of each of the three primary impact measures are calculated for each facility, to
 gauge the economic vitality of each facility prior to the proposed regulation. If a facility fails one of the measures
 (e.g., a facility closes) in the  baseline scenario, the model does  not recount this same level of failure in the post-
 compliance scenario. The model does allow, however, for progressively severe impacts  due to compliance (e.g.,
 a baseline product line closure may become a post-compliance facility closure).

          The facility-level closure analysis considers the portion of the facility involved in manufacturing, and also
  formulating/packaging or performing contract work, for both in-scope pesticides  (i.e., those 270 PAIs considered
                                                     E.2

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  for regulation) and out-of-scope pesticides (all others).1 A facility closure is projected to result from the regulation
  if the salvage value exceeds the present value of cash flow in the post-compliance, but not the baseline, scenario.

          A product line, or cluster, is composed of PAIs that are close substitutes for each other for a specific end-
  use.  For example, insecticides used on corn is one cluster.  Fifty-six clusters were identified as part of the impact
  analysis, forty-five of which contain in-scope PAIs produced in 1986. A baseline product line closure is projected
  if the unit cost (average variable cost plus average fixed cost per pound of PAI) of the product line exceeds the unit
  price (average price per pound of PAI).  A post-compliance product line closure is projected if the product line
  remained open in the baseline, but showed unit costs exceeding unit price due to the addition of compliance costs.

         Short of closure,  other significant impacts of compliance with the effluent limitations are calculated based
 on the comparison, between each facility and the industry averages, of two key financial ratios:  the "interest
 coverage  ratio"2 (earnings before interest and taxes  divided  by interest expense) and "return  on total assets"3
 (earnings before interest and taxes divided by assets).  If either ratio for a facility falls in the lowest quartile for the
 industry in the post-compliance but not the baseline  scenario, it is said to sustain a significant impact short of
 closure.
 Baseline Results
         The baseline economic analysis evaluated each facility's financial operating condition prior to incurring
 compliance costs for this regulation. This analysis included the estimated costs associated with two significant EPA
 regulations not in place in 1986 (the base year) and whose costs were therefore not reflected in the annual operating
 expenses provided by the firm in the Census.  Baseline cost additions include (1) RCRA costs for refining surface
 impoundments that treat, store, and dispose of hazardous wastes, and (2) compliance with the effluent guidelines
 for the Organic Chemicals, Plastics, and Synthetic Fibers (OCPSF) industry.  Of the 90 facilities potentially subject
 to the proposed effluent guidelines, 15 are projected to close in the baseline  analysis after incorporating the costs
 of RCRA and OCPSF regulations. In fact, three of these facilities have closed and another two have closed one
 or more product lines since 1986.  An additional 20 facilities are projected  to close pesticide product lines.  Of
 these, two have closed entirely, five have closed a pesticide product line, and two have changed ownership since
 1986.
or m    Pf T*     C ™•> W™6 deVel°Ped for omy that P°rtion °f the facility engaged in manufacturing one
o  more of the m-scope PAIs.)  The facility closure analysis uses a net present value approach (which compares
discounted cash flow to salvage value) to project whether pesticide operations would remain open after regulatory
costs are mcurred. The first step in the facility closure analysis was to project baseline costs and revenues over tZ
life of the faculty.  Projected  regulatory costs were then added to the baseline  costs; these post-compliance costs
were used to estimate a post-compliance cash flow.                                              P"«*uws ousts
    2Also called "times interest earned."
    3Also called "return on investment."
                                                   E.3

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Effects of Regulatory Compliance on Facilities
        The EPA analyzed the impacts of two possible regulatory options for BAT and PSES: a Treated Discharge
Option, and a Zero Discharge Option based on on-site or off-site injection or incineration. The economic impacts
associated with these two options are discussed below, by both discharge type and subcategory.

        Treated Discharge Option
                 Impacts on Direct Dischargers
                 Organic Pesticides Chemicals Manufacturing (Subcategory A)
        For manufacturers included in this subcategory, the incremental capital and annualized total costs (including
capital, operating and maintenance,  and monitoring costs) of complying with BAT limitations are expected to be
$14.9 million and $14.7 million, respectively.  No facilities are projected to close due to compliance with BAT.
One facility, equal to three percent of the 32 direct discharge facilities covered under this subcategory, is projected
to close a product line as a result of the regulation. (One other facility projected to close a product line, a zero
discharger, incurs only monitoring costs.) No facilities are expected to experience other significant financial impacts
short of facility or product line closure. Job losses totalling 31 full-time equivalents (FIE) are expected to occur
 as a result of the product line closures and the decrease in demand resulting from higher prices.  This employment
 loss represents less than one percent of employment in the pesticide-related portions of all pesticide manufacturing
 facilities.  One firm, equal  to 1.5 percent of the 64 firms in the industry, is  expected  to experience significant
 financial impacts as a result of compliance with BAT. Foreign trade in PAIs is expected to fall by $5.5 million due
 to compliance with BAT. In 1986, the United States was a net exporter of PAIs, with a trade balance of $897
 million;  the decrease in PAI trade is projected to be less than one percent.  When compared with U.S. net imports
 of $152  billion in merchandise for  1986, compliance with the BAT regulation is seen  to cause an increase in net
 imports of less than one one-thousandth of one percent.

                  Metallo-Organic Pesticides Chemicals Manufacturing (Subcategory B)
          No new limitations on direct dischargers are proposed by the  EPA for Subcategory B. Therefore, there
 are no associated  costs or economic impacts.

                  Impacts of PSES Regulations on Indirect Dischargers
                  Subcategory A
          For manufacturers included in this subcategory, the total capital and annualized costs of compliance with
  PSES are  projected to be $9.4 million and $5.9 million, respectively.  No facilities are projected to close due to
  compliance with PSES.  One facility, or three percent  of the 36  facilities in this subcategory, is projected to close
  a product line as a result of the regulation.  No facilities are estimated to experience  other significant financial
  impacts short of facility or product line closure.  Job losses totalling 97 FTEs are expected to occur as a result of
  the product line closures and the decrease in demand resulting from higher prices. This employment loss represents
                                                     E.4

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 less than one percent of employment in the pesticide-related portions of all pesticide manufacturing facilities.  Two
 firms are expected to sustain significant financial impacts as a result of compliance with PSES.  Foreign trade in
 PAIs is expected to fall by $16.1 million due to compliance with PSES.  This decrease in trade represents about
 two percent of 1986 net exports of PAIs and about one-hundredth of one percent of the 1986 net national trade
 imports of all goods.

                 Subcategory B
         No new limitations on indirect dischargers are proposed by the EPA for this subcategory.  Therefore, there
 are no associated costs  or economic impacts.

         Zero Discharge Option
                 Impacts of BAT Regulations on Direct Dischargers
                 Subcategory A
         Compliance with limitations based on the Zero Discharge  Option is expected to cost manufacturers of
 Subcategory A pesticides $1.13 million hi incremental capital costs  and $4.81 billion in annualized costs.  Total
 pesticide-related revenue for all 88 pesticide manufacturing facilities equaled $4.84 billion in 1986:  only slightly
 greater than the projected  annualized Zero Discharge Option compliance  costs for direct dischargers in this
 subcategory.

        Sixteen facilities (50 percent of the 32 direct discharge facilities in this subcategory) are projected to close
 due  to compliance  with this option.  Three additional facilities,  equal to ten percent of the 32 direct discharge
 facilities covered under  this guideline, are projected to close a product line.  (One of the facilities expected to close
 a product line, a zero discharger, would incur only monitoring costs.  Because the 32  facilities against which
 impacts are compared do not include zero dischargers, the percentage of facilities affected is overstated.) No
 facilities are expected to experience other significant financial impacts short of facility or product line closure. Job
 losses totalling 7,110 FTEs are expected to occur as a result of the facility closures, product line closures, and the
 decrease hi  demand resulting from higher prices. This employment  loss represents 72 percent  of employment in
 the pesticide-related portions of all pesticide manufacturing facilities. Seven firms, equal to about eleven percent
 of the 64 firms hi the industry, are expected to experience significant financial impacts as a result of compliance
with this option.  Foreign trade in PAIs is expected to fall by $2.4 billion, shifting the U.S. PAI balance of trade
 from $897 million in exports hi 1986  to $1.5 billion hi imports.   The U.S. national  net imports of merchandise
would increase by about two percent.

                Subcategory B
        No new limitations on direct dischargers  are proposed  for this subcategory.  Therefore, there are no
associated costs or economic impacts.
                                                   E.5

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                Impacts of PSES Regulations on Indirect Dischargers
                Subcategory A
        For manufacturers of organic pesticides, the total capital and annualized costs of compliance with the Zero
Discharge Option are estimated to be $1.1 million and $518.8 million, respectively.  Eleven facilities (31 percent
of the 36 facilities with indirect discharges in this subcategory) are projected to close if forced to comply with this
option.  Three facilities (8 percent of the 36 facilities with indirect discharges hi this subcategory) are projected to
close a product line.   No facilities are expected to experience other significant financial impacts short of facility
or product line closure.  Job losses totalling 802 FTEs are expected to occur as  a result of the facility closures,
product line closures, and the decrease in demand resulting from higher prices.  This employment loss represents
8 percent of employment in the pesticide-related portions of all pesticide manufacturing facilities. Seven  firms,
equal to about eleven percent of the firms in the industry, are expected to sustain significant financial impacts as
a result of compliance with this option.   Foreign trade in PAIs  is expected to fall  by $179.6 million due to
compliance with the Zero Discharge Option. This decrease in trade represents 20 percent of 1986 U.S. net PAI
exports and 0.12 percent of 1986 net national imports of all goods.

                Subcategory B
        No new limitations on indirect dischargers are being proposed for this subcategory. Therefore, there are
no associated costs  or economic impacts.

Effects of Regulatory Compliance on New Sources of Pesticide Manufacture
        The EPA is also proposing to establish New Source Performance  Standards  (NSPS) and Pretreatment
Standards  for New Sources (PSNS) for  the organic pesticide chemicals manufacturing  subcategory.   These
regulations are proposed to be equal to BAT/PSES limitations for PAIs, modified to  reflect a wastewater flow
reduction of 28 percent hi some cases.  The NSPS for priority pollutants is being set equal to the BAT limitations.
The projected impact of the proposed regulations on new sources is expected to be less burdensome than the impact
of the BAT/PSES regulations on  existing sources; designing a new technology prior  to facility construction is
typically less expensive than retro-fitting a facility for a new technology. Because compliance with the Treated
Discharge Option has been found to be economically achievable for existing facilities, it is expected that compliance
with this option will also be achievable for new sources.  NSPS/PSNS for metallo-organic pesticide chemicals are
not being proposed at this time. Therefore, there are no associated impacts on new sources.

Regulatory Flexibility Analysis
        The Regulatory Flexibility Act (5 U.S.C.  601  et seq., Pub. L.  96-354) calls  for the EPA to prepare a
Regulatory Flexibility Analysis (RFA) for proposed regulations having a significant impact on a substantial number
of small entities.  The purpose of the  Act is to ensure  that, while achieving EPA's statutory goals, the  EPA's
regulations do not impose disproportionate impacts  on small entities.
                                                   E.6

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        Both the Treated Discharge Option and the Zero Discharge Option were evaluated to determine their
impacts on small entities. The analysis concludes that a substantial number of small entities will not be impacted
significantly under the Treated Discharge Option. Although a substantial number of small entities would be expected
to be impacted significantly under the Zero Discharge Option,  that impact  would  not be expected to  fall
disproportionately on small entities.  Therefore, no alternative regulations for small entities were considered.
                                                  E.7

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 Chapter 1




 Chapter 2


 Chapter 3
Chapter 4
                                        TABLE OF CONTENTS
 Introduction and Overview  	             j j
 1.0     Background and Definitions	         j j
 1.1     Structure of the Report	      j'j


 Data Sources	                             2 i


 Pesticide Manufacturers Profile	         3 j
 3.0     Introduction	                         3 •,
 3.1     Categorization of Data	             32
 3.2     Sources of Demand for Chemical Pesticides  	     34
         3.2.A   Agriculture Market	3 g
         3.2.B   Industrial/Institutional/Commercial Market (I/I/C)  	3.8
         3.2.C   Home/Lawn/Garden Market	3.10
 3.3     Facility Characteristics  	   3  12
         3.3.A   Physical Characteristics  	3^2
         3.3.B   Industry Output  	    3  12
         3.3.C   Production Characteristics	3 15
         3.3.D   Production Costs	         3 2Q
         3.3.E   Employment Characteristics	3 24
         3.3.F   Revenues and Profit	         3 28
         3.3.G   Capital Expenditures  	      3 32
         3.3.H   Production Capacity Utilization	3,35
 3.4     Firm Characteristics	            3 39
 3.5     Industry Market Structure   	       3 42
         3.5.A  Barriers  to Entry	         3 42
         3.5.B  Vertical Integration	3 45
         3.5.C  Concentration	               3 48
         3.5.D  Demand  Elasticity and Product Substitution	3.50
 3.6     International Trade	               3  53
         3.6.A  U.S. Pesticide Imports and Exports	3.55
         3.6.B  U.S. Pesticide Industry in the World Market  	3.6!
 3.7     Summary	                  3  g<-
 Chapter  3 References	                         3  66

 Facility Impact Analysis	                  4 j
 4.0     Introduction	                      . \
 4.1      Economic Model	              4*2
         4.1. A  Generalized Model of the Pesticide Manufacturing Industry  ...4.2
        4.1.B   Applied Model of the Pesticides Manufacturing Industry	4.4
 4.2     Facility Closure Analysis	           4  19
        4.2.A  Baseline Facility Closure Analysis	4 20
        4.2.B   Post-Compliance Facility Closure Analysis	  4.26
 4.3     Product Line Closure Analysis	  	4 28
4.4     Other Significant Financial Impacts	       4 29
4.5     Facility  Impacts	4'32
        4.5.A   Baseline    ••••. ^ .................              '  432
        4.5.B   Effects of Compliance with  the Regulatory Options  	4.32
Chapter 4 References	              	-

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Chapter 5
Chapter 6
 Chapter 7
 Chapter 8
 Chapter 9
Community Impact Analysis   	     5.1
5.0     Introduction	5.1
5.1     Methodology  	5>1
        5.1.A   Primary Impacts on Employment	5.2
        5.1.B   Measuring Impact Significance	•• 5.3
        5.1.C   Secondary Impacts on Employment  	5.4
5.2     Results	5-5
        5.2.A   Treated Discharge Option	5.5
        5.2.B   Zero Discharge Option	5.5
Chapter 5 References	5.9

Foreign Trade Analysis  	     "•!
6.0     Introduction	^.1
6.1     Methodology  	6>1
        6.1.A   Exports  	6-2
        6.1.B   Imports  	6-3
6.2     Results	6-4
        6.2.A   Treated Discharge Option	6-4
        6.2.B   Zero Discharge Option	6-6
Chapter 6 References	•	6-9

                                                                            7 1
Firm Impact Analysis  	
7.0     Introduction	7-1
7.1     Analytic Approach	7.1
        7.1.A  Firm Financial Performance	• 7.2
        7.1.B   Ability to Manage Financial Commitments	7.3
7.2     Analytic Procedure	>	' -^
7.3     Results	7-12
        7.3.A  Baseline Analysis  	7.12
        7.3.B   Post-Compliance Analysis: Treated Discharge Option	7.12
        7.3.C  Post-Compliance Analysis: Zero Discharge Option  	    7.13
 Chapter 7 References	7-14

 Small Business Impacts  	      8-*
 8.0    Introduction	8>1
 8.1     Methodology	  . 8.1
 8.2    Results	8-5
         8.2.A   Treated Discharge Option	8-5
         8.2.B   Zero Discharge Option	8-5
 8.3     Conclusions	•  • 8<8
 Chapter 8 Reference	8-9

 Impacts on New Sources	     "•*
 9.0     Introduction	     ^-i
 9.1     New Source Performance Standards   	     9.1
 9.2     Pretreatment Standards for New Sources  	     9.2

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Appendix A




Appendix B




Appendix C




Appendix D




Appendix E
 1986 Pesticide Manufacturer Facility Census




Mapping of Pesticide Active Ingredients into Clusters




Methodology for Estimating the Price Elasticity of Demand for Pesticide Clusters




Sensitivity Analysis of Cost Pass-Through Ability




Details of Analysis of Impacts on Small Businesses

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                                           LIST OF TABLES
 Table 3.1
 Table 3.2
 Table 3.3
 Table 3.4
 Table 3.5
 Table 3.6

 Table 3.7
 Table 3.8

 Table 3.9
 Table 3.10
 Table 3.11

 Table 3.12

 Table 3.13

 Table 3.14

 Table 3.15
 Table 3.16
 Table 3.17
 Table 3.18

 Table 3.19

 Table 3.20

 Table 3.21

 Table 4.1
 Table 4.2

 Table 4.3
Table 4.4
Table 5.1
Table 5.2
 Representative Classes of Pesticides and the Pests They Control  .	     3.3
 Pesticide Clusters	         35
 Pesticide Agricultural Production and Distribution	     3.9
 Pesticide Manufacturing Facilities by Facility Age, 1986	    3.14
 Production and Sales of Pesticides,  1980-1988  	    3.15
 Distribution of In-Scope Pesticide Facility Production
 and Sales, 1986	               3 jg
 Total Facility Employment Characteristics by Facility Size,  1986	    3.27
 Average Facility Employment Characteristics by Facility Size,
 1986	    3 29
 Pesticide Capital Expenditures, 1975-1987, SIC 2879	    3.34
 U.S. Pesticide Production Capacity  Utilization Rates, 1980-1989	    3.37
 Research and Development Costs as a Percent of Total Facility
 Sales, 1986 by Firm Size	    3 47
 Share of Value of Pesticide Shipments Accounted for by the
 4, 8, 20,  and 50  Largest Companies, 1972-1982	    3.51
 Share of Value of In-Scope Pesticide Shipments Accounted for
 by the 4,  8, and 20 Largest Firms,  1986	    3.52
 Summary of Estimates of Elasticity  of Demand for Clusters with
 Production, 1986  	    3 54
 U.S. Import and Export Values for All Pesticides 	     3.57
 U.S. Import and Export Values for Herbicides	     3.53
 U.S. Import and Export Values for Insecticides	     3.59
 U.S. Pesticide Trade Compared to U.S.  Pesticide Shipments and
 New Supply, 1978-1987	     3 60
 U.S. Trade as a Percentage of the World Market Economy for
 Pesticides, 1978-1987  	      3 62
 Value of Pesticide Exports for Leading Export Nations as a
 Percent of the Total World Pesticide Exports, 1979-1987	    3.63
 Value of Pesticide Imports for Leading Importers to the United
 States as a Percent of Total U.S. Imports, 1980-1987	    3.54
 Costs of the Regulatory Options	          4 ^3
 Summary  of Estimates'of Elasticity of Demand for Clusters with
 Production, 1986   	    4 17
 Baseline Closures	      4 32
Impacts of the Regulatory Options on Facilities	    4.34
 Community Impact - Treated Discharge Option ...................     5.6
 Community Impact - Zero Discharge Option  	     5.7

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                 Table 6.1
                 Table 6.2
                 Table 7.1
                 Table 7.2

                 Table 8.1

                 Table 8.2
Foreign Trade Impact - Treated Discharge Option
Foreign Trade Impact - Zero Discharge Option
Determination of Firm-level Financial Viability
Calculation of Firm-Level Financial Measures in Post-Compliance
Analysis
Logistic Regression Analysis Zero Discharge Option:
Direct Dischargers
Logistic Regression Analysis Zero Discharge Option:
Indirect Dischargers
6.5
6.7
7.9
8.7

8.7
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                                          LIST OF FIGURES
 Figure 1.1

 Figure 3.1
 Figure 3.2

 Figure 3.3
 Figure 3.4
 Figure 3.5
 Figure 3.6
 Figure 3.7
 Figure 3.8

 Figure 3.9

 Figure 3.10

 Figure 3.11
 Figure 3.12
 Figure 3.13

 Figure 3.14
 Figure 3.15

 Figure 3.16

 Figure 3.17

Figure 3.18
Figure 3.19

Figure 3.20
Figure 8.1
 Economic Impact Analysis of Pesticides Manufacturing Industry
 Effluent Limitations Guidelines: Analytic Components	      1.3
 U.S. Market Demand for All Pesticides	      3.7
 Production and Distribution Channels for the Industrial/Institutional/
 Commercial and Home/Lawn/Garden Markets	    3.H
 Facilities and In-Scope Pesticide Production by Region, 1986	    3.13
 Fungicide, Herbicide and Insecticide Production,  1980-1988	    3.1?
 Pesticide Production and Total Planted Acres, 1977-1987   	    3.18
 Composition of Facility Production Activity, 1986	    3.21
 Composition of Facility Production Activity by Facility Size, 1986	    3.22
 Composition of Pesticide-Related Facility Fixed Costs by Facility
 Size, 1986  	    3 23
 Composition of Pesticide-Related Facility Variable Costs by
 Facility Size, 1986   	    3 25
 Ratio of Pesticide-Related Fixed Costs to Pesticide-Related Total
 Costs by Facility  Size,  1986  	    3_26
 Employment Trends, 1975-1987	    3.30
 Composition of Facility Revenue by Facility Size, 1986  .............    3.31
 Pre-Tax In-Scope Pesticide Facility Profit as a Percent of In-Scope
 Pesticide Sales, 1986	    3 33
 Capital Expenditures in  1986 Dollars	    3.35
 Comparison of All Manufacturing Capacity Utilization and Pesticide
 Production Capacity Utilization Rates	    3.35
 Number of Individual or Classes of In-Scope PAIs Produced by  Firms
 1986	
                                                                           3.40
Number of U.S. In-Scope Pesticide Manufacturing Facilities Owned
by Firms, 1986  	    3 41
Composition of Firm Sales, 1986  	    3 43
Number of Firms That Produce an Individual PAI or Class of PAI
1986	
                                                                           3.44
Number of Facilities Acquired by Firms	     3 49
Discontinuous Step Function	        84

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                              Chapter 1: INTRODUCTION AND OVERVIEW


  1.0     Background and Definitions


          The Federal Water Pollution Control Act Amendments of 1972 established a comprehensive program to

  "restore and maintain the chemical, physical, and biological integrity of the Nation's waters" (Section 101(a)).

  To implement these amendments, the U.S. Environmental Protection Agency (EPA) issues effluent limitations

  guidelines, pretreatment standards, and new source performance standards for categories of industrial
  dischargers.  Specifically, the regulations that the EPA establishes are:


                 Best Practicable Control Technology Currently Available (BPT).  These rules apply to existing
                 industrial direct dischargers, and generally cover control of conventional pollutant discharge.1

                 Best Available Technology Economically Achievable (BAT). These rules apply to existing
                 industrial direct dischargers and the control of priority and non-conventional pollutant
                 discharges.

                 New Source Performance Standards (NSPS). These rules apply to new industrial direct
                 dischargers and cover all pollutant categories.

                 Pretreatment Standards for Existing Sources (PSES).  These rules apply to existing indirect
                 dischargers (whose discharges enter Publicly Owned Treatment Works, or POTWs). They
                 generally cover the control of toxic and non-conventional pollutant discharges that pass through
                 the POTW or interfere with its operation.  They are analagous to the BAT controls.

                 Pretreatment Standards for New Sources (PSNS). These rules apply to new indirect
                 dischargers and generally cover the control of toxic and non-conventional pollutant discharges
                 that pass through the POTW or interfere with its operation.


 This Economic Impact Analysis (EIA) documents the assessment of the economic impacts of the proposed BAT,
 NSPS, PSES, and PSNS applying specifically to the pesticide manufacturing industry.


 1.1      Structure of the Report


         Two regulatory options are evaluated:  one that would require treatment of process wastewater

pollutants (Treated Discharge Option), and another that would require no discharge of process wastewater

pollutants to POTWs or surface water (Zero Discharge Option).2  The economic impacts are calculated
    'Conventional pollutants are defined as biochemical oxygen demand (BOD), total suspected solids (TSS) oil
and grease, and pH.  Other pollutants may also be regulated at the BPT level.

    'The Zero Discharge Option would limit discharges from the facility site to POTWs or to surface water
only; discharges to other media may remain constant or increase as a result of changes in discharge to surface
water.  For example, pesticide manufacturing facilities could, theoretically, achieve compliance with a zero
                                                  1.1

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separately for the two options and, within each option, for direct and indirect dischargers.  Direct dischargers
would be required to comply with a BAT regulation; indirect dischargers would be required to comply with a
PSES regulation.

        This EIA describes both the methodology employed to assess impacts of the proposed options and the
results of the analysis. The overall structure of the analysis is summarized in Figure 1.1.  There are two main
inputs to the analysis:  (1) data on industry baseline financial and operating conditions, and (2) projected costs
of complying with the proposed regulation.

         The industry baseline financial and operating data are based principally on the Pesticide Manufacturing
Facility Census for 1986 conducted under Section 308 of the Clean Water Act.3  The Census, which reported
facility-level data, was divided into two parts. Part A contained technical data, and Part B contained economic
and financial data. The  projected costs of compliance with the proposed regulation (the second major  input to
the analysis) were developed by the EPA.  Details on the compliance cost estimates can be found in the
Technical Development Document for the proposed rule.4  Additional information on all data sources is
presented hi Chapter 2.
 the EIA:
To fully evaluate the expected impacts of the proposed options, six measures of impact are examined hi


        Impacts on facilities that manufacture PAIs covered by the regulation;
        Employment losses and associated community effects;
        Impacts on U.S. balance of trade;
        Impacts on firms that own facilities affected by the regulation;
        Impacts on pesticide facilities defined as small businesses; and
        Effects on the construction of new facilities and expansion of existing facilities.
 discharge effluent guideline by transferring the waste streams previously discharged to surface water to landfills,
 incinerators, or deep well injection sites.
     3Baseline conditions also include certain costs deemed necessary to comply with particular regulations
 imposed under the Resource Conservation and Recovery Act (RCRA), and the effluent guidelines for the
 Organic Chemicals,  Plastics, and Synthetic Fibers (OCPSF) Industry.  Portions of these regulations took effect
 after the base year of the Census, and imposed costs on certain pesticide manufacturers.  These costs are also
 included in the analysis.
     "Full title: Technical Development Document for Proposed Effluent Limitations Guidelines,  New Source
 Performance Standards and Pretreatment Standards for the Pesticide Chemicals Point Source Category.

                                                      1.2

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                             Figure 1.1
Economic Impact Analysis of Pesticides Manufacturing
         Industry Effluent Limitations Guidelines:
                     Analytic Components
                 Facility Level
                   Analysis
                   Economic
                   Models
B               Facility
              Closure
              Analysis
 Other
Financial
Impacts
                   Product
                    Line
                   Closure
                   Analysis
                                    Facility
                                    Impacts
                         Employment
                           Impacts
                          Production
                           Losses
                                    Firm
                                   Impacts
             Community
              Impacts
            Foreign Trade
              Impacts
                                                              Small Business
                                                                Impacts
                                          New
                                         Source
                                         Impacts
      Data Inputs
         Analytical Outputs
I	I  Key Analytical Components
                                   1.3

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        The EIA methodology is based upon a facility-level impact analysis. This analysis drives the other
components of the EIA (See Figure 1.1.)  The facility-level economic model estimates post-compliance
revenues, costs, and profits.  The post-compliance financial data are then used to analyze three potential effects
of the increased costs on facilities:  facility closure, product line closure, and other financial impacts short of
closure.  The analysis of facility closure is based on comparing the post-compliance facility discounted cash
flow to the facility liquidation value.  The product line closure analysis compares prices and costs of products to
predict whether product lines remain in production post-compliance. The analysis of other significant financial
impacts considers changes hi financial indicators of facilities' operating conditions between the baseline (i.e.,
pre-compliance) and post-compliance scenarios.

         The impacts of the regulatory options on facilities drive other, secondary impacts, including those on
local communities and foreign trade.  The effects on communities are measured by the level of employment loss
expected to correspond to the decreased production of PAIs potentially subject to this regulation.  The
significance of the employment loss is evaluated by its impact on the community employment rate.  Foreign
trade impacts  may result from changes in the domestic production of pesticides, because pesticides are  traded in
an international market. Changes hi the balance of trade are calculated based on both the estimated decreases  in
exported production and the increases hi pesticide imports that result from meeting regulatory requirements.
The expected  changes in exports and Imports are compared with baseline (1986) exports and imports for the
entire pesticide industry, and with total U.S. merchandise trade (1986), to measure the significance of the
change.

         The effects of compliance costs are also evaluated at the firm level by considering changes in financial
indicators at the level of the parent company.  The firm analysis projects whether a  firm is capable of financing
the investment required to comply with the proposed regulation.  The analysis is conducted by examining
changes in  the financial indicators of a firm's operations conditions between the baseline and post-compliance
scenarios.

         Two additional potential impacts of the proposed regulation, using the results of both the facility and
 the firm analyses, are impacts on (1) small businesses and (2) new sources of pesticide production. The
 evaluation of impacts on small businesses is conducted hi three steps.  First, it is determined whether the
 proposed regulation is expected to impact a substantial number of small businesses significantly.  Impacts are
 defined as either a facility closure, a product line closure,  or another significant financial facility impact short of
 closure.  If a substantial number of small businesses are projected to sustain significant impacts,  the second
 stage of the analysis evaluates whether these impacts are expected to fall disproportionately on those businesses.
 Third, if the  regulatory burden on small businesses is disproportionate relative to that on larger businesses,
 alternative regulatory methods that mitigate or  eliminate the economic impacts on small businesses would be
 examined.
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        Impacts on the construction of new facilities and the expansion of existing facilities are examined in the
 final section of the EIA.

        The following chapter presents a description of the data sources consulted for this EIA.  Chapter 3
 profiles the pesticide industry, examining both the industry segments involved in PAI production and prevailing
 market conditions for pesticide products.

        Having set the stage for the analysis, each of the remaining chapters describes the data and
 methodology used to estimate one type of potential impact and the resulting impact estimates themselves.
 Chapter 4 details the methodology used to estimate the facility impacts. As stated above, facility impacts
 provide the methodological foundation for this EIA. First, the markets to be analyzed and the basic model of
 market structure are defined. Then, baseline and post-compliance costs, prices, and production quantities are
 estimated.  This chapter also describes the tests used to predict facility closure, product line closure, and other
 significant impacts.

        Chapter 5 describes the methodology for and results of the community impact analysis, based on the
 results of the facility analysis. Methods for estimating international trade effects, and the expected effects
themselves, are described hi Chapter 6.  A discussion of the expected impacts of the proposed regulation on
 firms owning pesticide manufacturing facilities is presented in Chapter 7.  Procedures for assessing  the impacts
on small businesses are presented in Chapter 8, along with the projected impacts themselves.  Finally, Chapter  9
describes the expected  effects of the regulation on new sources of PAI manufacture.
                                                  1.5

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                                      Chapter!:  DATA SOURCES

         This EIA employs data from many sources at differing levels of aggregation.  The various sources used
 are described below.

         The Pesticide Manufacturing Facility Census for 1986, a census of pesticide manufacturing facilities
 conducted under Section 308 of the Clean Water Act,1 is the principal source of facility-level data. The Census
 includes the 90 facilities that, in 1986, manufactured one or more of the 270 individual or classes of pesticide
 active ingredients (PAIs) that are within the scope of the proposed regulation.  Part A of the Census
 questionnaire requested the data necessary to perform the technical and treatment cost estimation analysis,
 including PAI-specific production for 1986.  Part B of the  Census questionnaire requested detailed economic and
 financial data, including balance sheet and income statement information for 1985, 1986, and 1987. Three
 years of data were collected so that the EPA could construct  a  "typical" year upon which to base the impact
 analysis.  Part B was  also designed to obtain information on facility liquidation values and the cost of capital. A
 copy of Part B of the Census is included as Appendix A.  A  copy of Part A of the Census can be found in the
 Administrative Record. Throughout the remainder of this  document, the term "Census", if not further
 specified, will refer to Part B of the Pesticide Manufacturing Facility Census.

        Part A of the Census questionnaire was sent in July  1988; Part B was mailed in January 1989.  Based
 on an initial review of Part A responses, Part B was sent only to those facilities known to manufacture one or
 more of the PAIs within the scope of the regulation.  Because Part B was sent to  a reduced number of facilities,
 two facilities that were later determined to be manufacturing one or more of the PAIs subject to regulation were
 omitted.  One  was thought to be exclusively a formulator/packager; the other performs only research and
 development.

        In the proposed Census questionnaire sent to the Office of Management and Budget (OMB), the EPA
proposed to request PAI-specific unit cost and price data.  These data would permit the EPA to incorporate the
 different unit costs, prices, and profit margins of PAIs in the impact analysis.  The National Agricultural
 Chemicals Association (NACA), the trade association representing numerous chemical manufacturing firms and
individuals in the industry, was reluctant to have the industry provide these detailed data and voiced objections
to the OMB. OMB subsequently rejected the proposed questionnaire. As a compromise, the EPA allowed
pesticide manufacturers a choice in the final questionnaire.  Manufacturers could provide the PAI-specific data,
or could elect to have their facility's impact analysis done using averages.  In this latter method, the EPA would
    'Federal Water Pollution Control Act, 33 U.S.C. 1318.
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assume that all PAIs produced by a single facility have the same unit cost, price, and profitability.2 Twenty of
the 88 facilities responding to Part B chose to provide the PAI-specific cost and price data.

        The other major data input to the EIA was the estimated compliance costs of the regulation.3 The
EPA considered compliance costs for the 90 facilities under two potential regulatory options:  a Treated
Discharge Option and a Zero Discharge Option. The Treated Discharge Option limitations are based on
hydrolysis, activated carbon, chemical oxidation, resin adsorption, solvent extraction, incineration, and/or
recycle/reuse to  control the discharge of PAIs hi wastewater.4 Zero Discharge Option limitations would require
no discharge of pesticide manufacturing process wastewater pollutants to surface water by using on-site or off-
site incineration and/or recycle/reuse.

        Three categories of compliance costs associated with pesticide manufacturing were evaluated for both
the Treated and. Zero Discharge Options: capital costs, land costs, and operating and maintenance costs.
Operating and maintenance costs include monitoring costs, required by permit writers to demonstrate
compliance, as well as the costs of sludge disposal.  All of the compliance cost estimates are presented in 1986
dollars and are based on the assumption that, whenever possible, facilities will build on existing treatment.  For
facilities that both manufacture and formulate/package PAIs, the compliance costs apply only  to the
manufacturing operations of the facility.

        The Census data base and the compliance cost estimates were required for all components of this EIA:
the industry profile, and the impact analyses for facilities, communities, foreign trade, firms,  small businesses,
and new sources.  The EPA also used data from secondary sources in each of the chapters. The profile of the
pesticide industry relied on the Annual Survey of Manufactures published by the U.S. Department of Commerce,
Kline and Company's Kline Guide to the U.S. Chemical Industry, and the International Trade Commission's
(ITC) Synthetic Organic Chemicals, which together provided production and aggregate industry data.  The
profile also used import and export data from the United Nations' International Trade Statistics Yearbook.

         The facility impact analysis used secondary price data from the Annual Market Survey published by
Doane Marketing Research and from Agchemprice published by DPRA, Inc. The facility impact analysis also
employed data from The EPA's Office of Pesticides Programs (OPP). The OPP maintains data on PAI-specific
sales, prices,  and usage from a number of proprietary sources.  The OPP data were among those used to
     2See Part B of the Census, page 26, text preceding question 2-H.
     3Full details of the compliance cost estimates can be found in the Technical Development Document.
     4For some PAIs the Treated Discharge Option limits discharge to zero.
                                                   2.2

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 estimate prices, and were also used to calculate the percentage of pesticide production that will not be covered
 by this regulation at this time.

         Data from the OPP also served as the basis for determining the substitutability among PAIs.  In 1980,
 the OPP defined pesticide markets to ensure that the EPA reviewed competing products on roughly the same
 schedule, so that one pesticide does not have an unfair advantage over another.  The pesticide markets were
 defined as clusters of PAIs that are substitutes for a specific end-use.  This classification was adapted and used
 as the basis for defining pesticide markets hi this EIA (see Appendix B).  In addition,  the facility-level analysis
 used the estimates of price elasticity  of demand developed hi the document entitled Estimates of the Price
 Elasticity of Demand for Pesticide Clusters (EPA,  1991; see Appendix C).

        The community impact analysis required the use of regional employment multipliers developed by the
 Bureau of Economic Analysis, population data from the Current Population Reports in Statistical Abstract of the
 United States (Bureau of the Census), and employment rates from the Bureau of Labor Statistics.  The foreign
 trade analysis used import data from the OPP and data on the U.S. trade balance from the International Trade
 Statistics Yearbook (United Nations)  and the Statistical Abstract of the United States.  The firm-level analysis
 was developed using financial statistics from Standard and Poor's Compustat and from Robert Morris
 Associates' Annual Statement Studies, hi addition to Parts A and B of Biof the Census.  The Compustat data
provided financial information on domestic firms subject to public reporting requirements, while the information
available through Robert Morris Associates was used for the remaining firms. Finally, the analysis of small
businesses required data from Dun and Bradstreet's Million Dollar Directory to calculate the number of
employees at the firm level.
                                                  2.3

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                           Chapter 3: PESTICIDE MANUFACTURERS PROFILE

  3.0     Introduction

          The following profile of the chemical pesticide industry describes the products, facilities, and firms
  associated with pesticide active ingredient (PAI) manufacturing and sales.  It is intended to provide a backdrop
  for the EIA by identifying and discussing key variables defining the market structure of the pesticide
  manufacturing industry.  The prevailing market conditions for pesticide products provide insight into firms'
  reactions to increased costs due to regulatory compliance.

          The pesticide industry is organized vertically into two major segments: pesticide manufacturing and
 pesticide formulating/packaging/repackaging.  Pesticide manufacturing involves the production of PAIs. PAIs
 are not used directly for pest control, but are instead combined with solid, liquid and/or gaseous diluents before
 use. PAIs are marketed in many formulations that may be either liquid or dry, and include a wide variety of
 solutions, emulsions, powders, dusts, granules, pellets, and aerosols.  Formulating and packaging therefore
 involves  the combination of active with inert ingredients, such as diluents, inorganic carriers, stabilizers,
 emulsifiers, aerosol propellants or wetting agents; and packaging the product in plastic, glass, paperboard, or
 metal containers for distribution and sale.  The concentration of a PAI in a formulation may be high or low.
 Some formulations are ready  to use; others must be further diluted before use.  Repackaging involves
 transferring a single PAI or single formulation from any marketable container to another marketable container
 without intentionally mixing any inerts, diluents, solvents, other PAIs, or other materials of any sort.  Data
 from the  Census show that in 1986, 50 of the 90 pesticide manufacturing facilities (56 percent) also engaged in
 formulating and packaging, indicating that the majority of pesticide manufacturers are vertically integrated.1

        The seven sections in this chapter focus on pesticide manufacturers, but some of the information
 presented pertains to both manufacturers of PAIs, and formulators/packagers/repackagers.  Section 3.1
 categorizes the data used to develop the profile. Section 3.2 describes sources of demand for chemical
 pesticides in the United States. Characteristics of pesticide manufacturing facilities, including physical
 characteristics, production costs, revenue, profits, employment, labor productivity, and capital expenditures are
 described in Section 3.3.  Section 3.4 examines the organization of firms in the industry, including firm
 ownership and vertical industrial integration. Section 3.5 portrays the market  structure of the pesticide industry,
 and includes discussions of barriers to market entry,  demand elasticity and product substitution, and firm
 concentration in the  industry.  Section 3.6 provides an overview of international trade in pesticides, including a
•   t               1988 SUrVey °fthe Pesticide Formulating, Packaging, and Repackaging Industry indicate that
in 1988, 51 of the pesticide manufacturers were engaged in formulating and packaging.  Since that time, however
4 of these manufacturers have discontinued production.
                                                   3.1

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discussion of the balance of trade for chemical pesticides and the nature of foreign competition.  Section 3.7
summarizes the information presented in the profile.

3.1 Categorization of Data

        The Federal Insecticide, Fungicide and Rodenticide Act (FIFRA) defines a pesticide as  "(1) any
substance or mixture of substances intended for preventing, destroying, repelling or mitigating any pest,  and
(2) any substance or mixture of substances intended for use as a plant regulator, defoliant, or desiccant."
Section 2(t) of FIFRA defines a pest as "(1) any bisect, rodent, nematode, fungus,  weed, or (2) any other form
of terrestrial or aquatic plant or animal life or virus, bacteria, or other microorganism (except viruses, bacteria,
or other microorganisms on or in living man or other living animals) which the administrator declares to be a
pest under Section 25(c)(l)."

        Other data sources used in this profile categorized pesticides in a variety of manners.  The Census  of
Manufactures (Bureau of the Census, 1986) classifies the pesticide industry primarily into two standard
industrial classifications (SICs). Establishments engaged primarily in the manufacture or formulation of
agricultural chemicals not elsewhere classified, and the formulation and preparation of pesticides,  are classified
as SIC 2879.   Establishments involved in the manufacture of pesticides, and other organic agricultural chemicals
that are PAIs used to formulate pesticides,  are classified as SIC 28694.  The Kline Guide to the U.S. Chemical
Industry classifies pesticides by three major types:  herbicides, insecticides, and fungicides.  The International
Trade Commission's Synthetic Organic Chemicals classifies pesticides into cyclic and acyclic fungicides,
herbicides and plant growth regulators; and insecticides, rodenticides, and related products such as seed
 disinfectants, soil conditioners, soil fumigants, and synergists. The U.N. International Trade Statistics Yearbook
 classifies pesticides  into disinfectants, insecticides, fungicides, and herbicides for retail sale as preparations  or as
 PAIs. The tables and graphs that present data from these sources refer to all pesticide production, both in-scope
 (including 270 individual or classes of PAIs) and out-of-scope (all non in-scope PAIs).  As an aid in
 understanding these categorizations, brief descriptions of the primary functions of pesticides are listed in
 Table 3.1.

         The market analysis for this profile relies on another classification of PAIs, based on the cluster groups
 established by the EPA's Office of Pesticide Programs (OPP).  In 1980, the OPP defined PAI markets to ensure
 that the EPA regulated competing PAIs on roughly the same schedule, so that one PAI did not have an unfair
 advantage over another.  Six hundred PAIs were classified into 48 clusters according to the major use of the
 chemicals. For instance, all herbicides used on corn production were classified into the same cluster.  Each
 cluster therefore contains PAIs that may be roughly substituted for one another on major use sites.
                                                     3.2

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 Class
                           Table 3.1
 Representative Classes of Pesticides and the Pests They Control
	                          Target Pest
Acaricide
Algicide
Attractant
Avicide
Bactericide
Defoliant
Dessicant
Fungicide
Growth regulator
Herbicide
Industrial Microbiocide
Insecticide
Miticide
Molluscicide
Nematicide
Piscicide
Predacide
Repellents
Rodenticide
Silvicide
Slimicide
Sterliants
                                 Mites, ticks
                                 Algae
                                 Insects, birds, other animals
                                 Birds
                                 Bacteria
                                 Unwanted plant leaves
                                 Unwanted plant tops
                                 Fungi
                                 Insect and plant growth
                                 Weeds
                                 Microorganisms
                                 Insects
                                 Mites
                                 Snails, slugs
                                 Nematodes
                                 Fish
                                 Carnivorous animals
                                 Insects, birds, other animals
                                 Rodents
                                 Trees and woody vegetation
                                 Slime molds
                                 Insects, other animals
Source: Minnesota Department of Agriculture, Rinse and Win Brochure, 1989.
                                      3.3

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        The EPA's Office of Water used the OPP's cluster segmentation to define individual markets for
groups of pesticides, because economic variables, such as demand elasticity, would not be meaningful for a
market defined as all pesticides. The Office of Water expanded upon the OPP's cluster segmentation in two
ways. First, PAIs registered after 1980 were assigned to one of the 48 clusters.  Second, the 48 clusters were
expanded to 56 clusters, based upon differences in the sensitivity of product demand to changes in price (see
Table 3.2).2 In addition, although the OPP's cluster segmentation assigned each PAT to only one cluster, this
analysis allowed for a PAI to be assigned to more than one cluster if it had more than one important use.  The
allocation of PAIs to clusters can be found hi Appendix B.

        Although the economic impact analysis of the proposed effluent guidelines is built on the individual
facility's production of PAIs that can be classified as belonging to one or more of these clusters, in the
remainder of this profile chapter EPA has aggregated the Census data to prevent disclosure of confidential
business information.  Information is generally presented in five categories:  fungicides, herbicides, insecticides,
multiple types of pesticides, and other pesticides.

3.2      Sources of Demand for Chemical Pesticides

         The major markets for pesticides are agriculture, industrial/institutional/commercial, and home/lawn/
garden.3  Agricultural  sales account for approximately 70 percent of domestic pesticide sales.
Industrial/institutional/commercial and home/lawn/garden each constitute about 15 percent of U.S. sales (see
Figure 3.1).

         Much of the pesticide application for the three markets is performed by commercial applicators.
Commercial applicators are trained professionals  skilled in applying pesticides in an efficient and
environmentally safe manner.  The National Pest Control Association estimated that in 1990 the commercial
applicator industry would contain 14,250 firms and have annual billings of $3.5 billion (National Pest Control
Association, 1991).  Commercial applicators are  contracted by the agricultural industry to apply pesticides to
agricultural crops, as well as to food products during storage and transit. The industrial/institutional/
commercial sectors use the services of commercial applicators to control pests in many settings, including
schools, health care facilities, prisons, food processing establishments, hotels, restaurants, factories, and
    Clusters were split when (1) there was a wide variety of price elasticities of demand among PAIs within a
 cluster, and (2) the PAIs among which demand elasticity varied had distinctive uses.  For example, the cluster that
 encompasses herbicides used on fruit trees was split into three clusters: herbicides used on grapes, herbicides used
 on oranges, and herbicides used on fruit trees (excluding grapes and oranges).
    Additional markets,  such as stored grain products (elevators), seed treatment, pest control operations
 (termiticides),  cattle, golf courses, utility right of ways, etc., also exist.  That level of detail, however, is not
 necessary in this discussion.

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                          Table 3.2
                     Pesticide Clusters
                            Pagel
 Cluster     Primary Application
  Herbicides used on;
 H-l          Broad spectrum of uses
 H-2          Corn
 H-3          Soybeans, cotton, peanuts, alfalfa
 H-4          Sorghum, rice, and small grains
 H-5a         Oranges
 H-5b         Grapes
 H-5c         Fruit trees
 H-6          Sugarbeets, beans and peas
 H-7          Drainage ditches, rights of way, forestry and ponds
 H-8          Turf
 H-9a         Vegetables
 H-9b         Tobacco
 H-10	Unclassified uses  	
  Insecticides used on/for/as:	
 I-la          Cotton
 I-lb          Soybeans, peanuts, wheat and tobacco
 I-2a          Corn and alfalfa
 I-2b          Sorghum
 1-3            Fruit, and nut trees, excluding oranges and grapes
 I-4a          Oranges
 I-4b          Grapes
 1-5            Vegetables
 1-6            Livestock and domestic animals
 1-7            Non-agricultural sites  (as repellent)
 1-8            Domestic bug control and for food processing plants
 1-9            As fumigants  and nematicides
1-10          Termite control
1-11         Lawns, ornamental's, and forest trees
1-12         Mosquito larva
1-13         Unclassified uses
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                          Table 3.2
                     Pesticide Clusters
                            Page 2
Cluster     Primary Application
  Fungicides used ont
F-l           Broad spectrum of uses
F-2a          Fruits and nuts
F-2b          Grapes
F-3           Vegetables
F-4           Oranges
F-5           Seed treatments
F-6           Post-harvest fruit and vegetables
F-7           Grain storage
F-8           Ornamentals
F-9           Turf
F-10          Unclassified uses
  Other Pesticides;
R-l           Industrial preservatives
R-2           Slimicides used in pulp and paper, cooling towers, and
R-3           Industrial microbiocides
R-4           Sanitizers used in dairies, food processing, restaurants,
R-5           Synergists used as insecticide synergists, sufacants,
              cheleating agents and carriers
R-6           Food preservatives
R-7           Wood preservatives, used for industrial, commercial
R-8           Disinfectants
R-9           Water disinfectants
R-10         Plant regulators, defoliants, and desiccants
R-ll         Preservatives, disinfectants, slimicides
R-12         Molluscides and misc. vertebrate control agents
R-13         Bird chemosterilants, toxicants, and repellants
R-14         Dog and/or cat repellants
R-15         Rodent toxicants, anticoagulants, predator control
U-l          Unclassified uses
                            3.6

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                               Figure 3.1

       U.S. Market Demand for All Pesticides1,1988
                          (Dollar Percentages)
                 Home, Lawn
                 and Garden
                    15%
   Industrial,
  Institutional,
Commercial and
  Government
      16%
                                                            U.S. Agriculture
                                                                 69%
        Includes both in-scope and out-of-scope PAIs.

 Source: Pesticide Industry Sales and Usage: 1988 Market Estimates, U.S. EPA, Office
        of Pesticides and Toxic Substances, February, 1988.

 Note:   Census data were not used for this figure, because the question in the Census
        that refers to markets refers to total facility production, not pesticide production.
                                3.7

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warehouses.  Household consumers use commercial applicators to manage pests that typically inhabit dwellings,
such as termites, cockroaches, and mice, and to rid their lawn and garden of pests. Government entities use the
services of commercial applicators to control mosquitos, and to maintain vegetation around roads, and public
recreational areas. In 1985, residential services comprised about 60 percent of the non-agricultural commercial
applicator Industry, commercial services constituted 25 percent,  and services  to institutions, industries and the
government represented 7, 6, and 2 percent respectively (Kline & Company, 1986).

3.2.A  Agriculture Market
        Agriculture forms the largest market for chemical pesticides.  The agricultural market is diverse in
terms of the types and amounts of pesticides used and in pesticide management practices, which vary
significantly among regions of the country, states,  and sometimes even counties.  This diversity is an important
distinction that separates agriculture from the other pesticide markets,  which tend to be more homogeneous
nationwide.

         Approximately 62 percent of all planted agricultural acres are treated with at least one type of pesticide
product (Pimental et al., 1986). Herbicides are the most commonly used type of pesticide in terms of quantity
of pesticide product applied.  In 1987, the herbicides that were  most widely used were Alachlor, Atrazine and
2,4-D (U.S. EPA, 1990).  These pesticides were used primarily on peanuts, corn, soybeans, cotton, and rice.
Insecticides were the second most commonly used pesticide type. In 1987, the most widely used insecticides
were Carbaryl, Malathion, and Chlorpyrirbs (U.S. EPA, 1990). These pesticides were used primarily on
 cotton, fruits, vegetables, nuts, and ornamentals.   Fungicides are applied to fewer acres  than herbicides or
 insecticides, but are generally applied to high-value fruit and vegetables. In 1987, Maneb and Captan were the
 most widely used fungicides (U.S. EPA, 1990).

         Table 3.3 provides a brief description of  the steps taken to move a PAI through process and distribution
 channels and then to the end user.  As indicated in Table 3.3, end users include farmers, government, and
 commercial applicators.  Farmers either purchase  and apply pesticide products themselves or pay commercial
 applicators to apply pesticides to their crops.  The government  uses agricultural chemicals to control vegetation
 around highways, roads, railroads, waterways, pipelines, power lines, government buildings, military
 complexes, and parking lots.

 3.2.B  Industrial/Institutional/Commercial Market (I/I/C)
         The I/I/C market includes many products, such as disinfectants, cleaning supplies, and air conditioning
 biocides, that  are generally not perceived as pesticides by the public.  In'addition, products  such as paint and
 wood preservatives may contain substantial amounts of pesticides.  The I/I/C  market is  estimated to exceed $200
 million annually, with about 45 percent involving health care institutions (U.S. EPA, 1991).
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                                      Table 3.3
                 Pesticide Agricultural Production and Distribution1
 Agent
                  Purpose
 Registrant
                  Registers the pesticide formulation with EPA.  Registration
                  involves a long, expensive R&D process to develop the
                  pesticide, produce the data required for registration, and
                  proceed through the registration process.
Manufacturer
                  Synthesizes the active ingredient from raw materials.
Formulator/Packager
                  Produces the pesticide formulation by combining the active
                  ingredient(s) with other substances, including surfactants,
                  clays, powders and solvents; involves mixing or blending
                  operations.  Formulation may be done in-house, by
                  independent formulators, or by tollers who formulate the
                  product under contract to the manufacturer.
Distributor
                  Acts as the "middle man;" buys pesticide from the
                  registrant/manufacturer/formulator and sells to the dealer.
Dealer/Co-op/Repackager    Sells the pesticide to the user.2
  In many cases several steps are performed by one entity.  Large companies might
  register, manufacture, and formulate their pesticides. Some distributors also
  formulate several pesticides. Additionally, a single facility  might function as a
  distributor, dealer, and commercial applicator.
  A user is defined as a farmer, government, commercial ground applicator, commercial
  aerial applicator, etc.
Source:
Based on a table in:  Pesticide Container Report to Congress, U.S. EPA,
Office of Pesticides and Toxic Substances, Draft, March 8,  1991.
                                           3.9

-------
        The I/I/C market differs significantly from the agricultural market in several ways.  First, the use of
l/JJC products is generally more uniform across the country.  The need for disinfectants in various parts of the
United States is approximately the same.  However, the use of pesticides for wood preservation and in cooling
towers varies somewhat according to the  climate (U.S. EPA,  1991).  Second, I/I/C pesticides are generally used
in smaller quantities than agricultural chemicals.  Third, I/I/C products hi general are usually less expensive per
unit volume of product than agricultural pesticides, because they are less concentrated.

        Another major difference between I/I/C and agricultural markets is that fewer manufacturers  of
pesticides used in the I/I/C market both register and formulate their pesticides; independent
formulators/packagers are  more predominant in the I/I/C market.  In addition, a greater variety of paths exist
between the formulators and end users.  This is evident in Figure 3.2, which illustrates distribution channels
within the I/I/C and home/lawn/garden markets.

         The distinction among industrial, institutional, and commercial  pesticides is based on the setting in
which the pesticide is used. In some cases,  the same formulation is used hi different types of facilities. Typical
industrial end-users include personnel in  food processing facilities  and breweries. Industrial pesticides, such as
preservatives, slimicides or biocides, are used hi cooling towers, paper and textile mills, oil wells, metalworking
coolants, etc. (U.S. EPA,  1991). Typical institutional end-users include personnel in hospitals, nursing homes,
schools, restaurants, hotels, and contract cleaning businesses  that serve stores, apartment houses, office
buildings, and garages (U.S. EPA et al., 1989). Commercial establishments use pesticides to protect
landscaping and to maintain cleanliness and  health standards. The federal, state and local governments use I/I/C
chemicals on military bases, and hi hospitals and other government buildings.

         Producers of pesticide products  used hi institutional  settings may sell directly to large users (e.g.,
 hospitals),  or they may use distributors at janitorial supply houses  to sell indirectly to smaller users.
 Institutional distributors usually sell general maintenance products (e.g., cleaning supplies and non-pesticide
 cleaners, as well as sanitizers  and disinfectants).  Similarly, producers of industrial and commercial pesticides
 may sell directly to the end-user or indirectly through a warehouse (U.S. EPA et al., 1983).

 3.2.C    Home/Lawn/Garden Market
          The home/lawn/garden pesticide market includes pesticide products that are commonly used  hi and
 around the home.  These  products include rodenficides, insect repellents, lawn and garden pesticides,
 disinfectants and other pesticidal cleaners, insecticides to protect pets and eliminate household pests, herbicides,
 fertilizers with herbicides/insecticides, and insect baits and traps.  In general, household pesticides are packaged
 hi containers that are smaller  than those used in the other markets and may also be less concentrated.  Some
 household  pesticides are seasonal (e.g., lawn and garden products), while others meet a demand that  remains
 fairly constant throughout the year.

                                                    3.10

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                             Figure 3.2

   Production and Distribution Channels for the Industrial/
  Institutional/Commercial and Home/Lawn/Garden Markets
         Basic Pesticide Manufacturers
   l
             life-House Forniolators";, —
    Independent
    Formulators
                             Contract
                            Formulators
                             "Tollers"
                  Consumer
                  Companies
          LJ
Formulators/
Distributors
          Distributors
                                  ±
                     I
                  Food Brokers, Etc.
I
                              Retailers
                           Industrial,
                         Institutional &
                      Commercial Dealers
                              Home, Lawn
                              and Garden
                                 Users
Source:  Based on a diagram in: Pesticide Container
       Report to Congress, U.S. EPA, Office of
       Pesticides and Toxic Substances, Draft,
       March 8, 1991.
                            Institutional
                              Users
                                                      Industrial
                                                       Users
                           Commercial
                              Users
                                                     Government
                                                       Users
                               3.11

-------
        The home/lawn/garden pesticide production and distribution chain, similar to the I/I/C chain, is
included in Figure 3.2.  The main difference between the household market and the other markets is that the
end user, the household consumer, purchases household pesticides from a wide variety of common retail
establishments.  These include grocery, drug, and discount stores, as well as home and garden shops and pet
supply companies.  The producer of household pesticide products can sell directly to the retail stores or
indirectly through a distributor warehouse.  Consumer companies, another distribution channel from
manufacturers to retail stores, make consumer products,  applying their label to the finished good.  Like
formulators, consumer companies can sell directly to retail establishments or indirectly through food brokers
who distribute products to retail stores.

3.3     Facility Characteristics

3.3.A  Physical Characteristics
        Figure 3.3, drawn from Census data, shows the geographic distribution of the PAI manufacturing
facilities and provides the percentage of in-scope PAI production hi each region.  Although pesticide facilities
are located  in all regions  of the country, the southeast/south central region of the country has the heaviest
facility concentration (35  percent).4  The northwest/southwest region has the second heaviest concentration (33
percent).5  Although the southeast/south central region accounts for a larger percentage of facilities, the
northwest/southwest region has the largest share  of in-scope pesticide production (52 percent).

         The Census also provides information on the age of pesticide facilities.  The data indicate that most of
the facilities are relatively old (i.e., constructed prior to 1970). The 1960s was the most active decade for
facility construction, with almost a quarter of the facilities constructed prior to 1970.  After 1980 only about 7
percent of existing facilities were constructed.  Table 3.4 presents the distribution of facilities by the number of
years in which they have produced pesticides.  This distribution is  shown for the five categories of pesticide
type-6

3.3.B   Industry Output
         Several factors have affected the demand for chemical pesticides. These include the  decline in
 agricultural acreage; the  production of new, more highly concentrated pesticide products; more efficient
     "The southeast/south central region includes Alabama, Delaware,  Florida, Georgia,  Kentucky, Maryland,
 Mississippi, North Carolina, South Carolina, Tennessee, Virginia, West Virginia,
     5The northwest/southwest region includes all states west of the Mississippi River.
     *Many of the facilities in the Census did not begin pesticide production until many years after construction.
 Approximately 38 percent of the facilities have produced pesticides for more than 30 years, while less than 13
 percent of the facilities have produced pesticides for fewer than 10 years.
                                                    3.12

-------
                      Figure 3.3

  Facilities and In-Scope Pesticide Production
                 by Region, 1986
Percent
75%-
70-
65-
60-
55-
50
45 "1
35
30
25-
20-
15-
10-
5-
0-


32%
."^ v^^^NX^ '
J^ -#S.S % %S*^^wS*'-\.
18%
•'!-''X--'X--'!--'.'-
Northeast/
North Central


35%
•N^^^vN"^^^. x>
30%


33%
^xM^^^^x
52%
•'X--'X'X-v\!-

, j
Southeast/ Northwest/
South Central Southwest
                         Region

 1  Percent of Facilities

 3  Percent of In-Scope Production
Source:  Census.
                         3.13

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Table 3.4
Pesticide Manufacturing Facilities by Facility Age, 19861
v,, Dumber of
5 to 10 to
<5 < 10 < 20
Pesticide
Type
Fungicides
Herbicides
Insecticides
Other Pesticides*
Multiple Types of
Pesticides**


% f
20 to
< ; 30
' '"", ,'&
Years
^
30 to
< 40 40+ All*
ft. V}.S J -
• i
(Number of Facilities)
0
1
1
0
0
All in-scope Facilities 2
* Refer to Table 3.2 for a description
** Multiple types of pesticides include
the groups outlined above.
*** Excluded from the 88 facilities that
facility age.
1 Facility age is
Source: Census
2
4
3
0
0
9
5
5
3
1
7
21
of other pesticides
manufacturers that
provided financial
the number of years the


3
5
4
2
7
21
produce
data are
1
1
3
4
8
17
pesticides in
two facilities
0 11
4 20
3 17
1 8
8 30
16 86***
more than one of
that did not report
facility has been producing pesticides.




# Faculties
       2
Number of Facilities by Facility Age, 1986

9           21	21           17
16
     <5       5 to < 10    10to<20    20 to <30
                               Age (Years)
                                  30 to <40   40+
                                    3.14

-------
  application of pesticides; the increase in pesticide resistance; the increase in environmental regulations; and
  greater awareness of environmental issues on the part of both the seller and the buyer. Although these factors
  have led to a contraction in pesticide production and sales, profitability from pesticide sales in the industry
  appears to have been largely  unaffected by the decline in output (Kline & Company,  1990).  Production
  characteristics of the pesticide manufacturing industry are outlined below.

         In 1988, total pesticide production was about 1.2 billion pounds.  Production declined by an average of
  two percent per year from  1980 to 1988 (U.S. Department of Commerce,  1987).  The volume of pesticides sold
  declined by four percent per year (see Table 3.5) (U.S. Department of Commerce, 1987). Figure 3.4 illustrates
  the decline in pesticide production for fungicides, herbicides, and insecticides  from 1980 to 1988. The graph
  shows  that herbicide production reached a trough in 1983, recovered somewhat, and then fell to a new low in
  1987.  Insecticide production declined to its lowest point in 1983 and recovered somewhat thereafter. Fungicide
 production was at its lowest point in 1987.

         The most significant  factor has been a decline in  agricultural acreage.  Figure 3.5, which plots total
 pesticide production and total  U.S. planted crop acres using 1986 as a base year, shows how  pesticide
 production mirrors planted acres.7 Pesticide production was lowest in 1983, when the United States
 Department of Agriculture (USDA) implemented the Payment-In-Kind (PIK) program, taking 48 million acres
 out of production. Although the number  of planted acres increased after 1983, other USDA programs, such as
 the Conservation Reserve Program, continued to reduce agricultural acreage (Ribaudo, 1989).8

        Also contributing to the decline  in pesticide production was the introduction of new, low-volume
pesticides such as postemergence herbicides.  Because these new pesticides are effective in significantly smaller
doses; the overall volume of pesticide production was reduced (Kline & Company,  1990).

 3.3.C  Production Characteristics
        Table 3.6 details the distribution of 1986 in-scope facility production and sales by facility size. The
Census  data indicate that, in terms of in-scope PAI production, most facilities (about 68 percent) are small- and
medium-sized, producing fewer than 6 million pounds of in-scope PAIs annually.  These facilities, however,
account for only ten percent of total in-scope pesticide production.
                                      divided * 19S6 productl°°
                                                                                  40 " 45 mi"ion — °f
                                                3.15

-------
        a


                                         '

                                                                        S   S
to
                                        s"  s
                                                     »"   a
                                                     invi
                                                                          R
                                                                          cl


                                                                s
                                                                     i
o\
YH
O
OO
a

I
                    a
                                        oCrf
                                            ^
                                                     
-------
                                     Figure 3.4

          Fungicide, Herbicide, and Insecticide Production1
                                    1980-1988
                                  (in 1,000 pounds)
1,000 Pounds
  1,000,000-1
   900,000-
                                                             Herbicide Production
                                                             (Including plant growth
                                                             regulators)
                                                          Insecticide Production
                                                          (Including rodenticides, soil
                                                          conditioners and fumigants)
                                                             Fungicide Production
         1980   1981   1982   1983   1984  1985   1986  1987   1988

                                   Years


         Production data are reported in terms of manufactured PAIs.

         Source:  International Trade Commission, Synthetic
                 Organic Chemicals, 1980-1988.
                                    3.17

-------
                                   Figure 3.5

           Pesticide Production and Total Planted Acres,
                                 1977-1987
                               (1986 Base Year)
Pounds Produced
Indexed to 1986
  1.3—1
  1.2-
   1.1-
   1.0-
                                                              Planted Acres
                                                            Pesticide Production
     1977  1978 1979 1980  1981  1982  1983  1984 1985  1986  1987

                                Years

          Source:  International Trade Commission, Synthetic Organic
                  Chemicals, 1977-1987 and United States Department of
                  Agriculture, Agricultural Statistics 1984 and 1989.
                                     3.18

-------
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                                 3.19

-------
        In terms of in-scope facility sales, the Census data indicate that the majority of facilities (51 percent)
are relatively small, with in-scope sales of less than $10 million (see Table 3.6).  Only 22 percent of all
facilities have annual in-scope pesticide sales greater than or equal to $50 million.

        For most facilities,  large and small, in-scope pesticide production makes up only a part of the facility's
production activity.  Figure 3.6, which presents the 1986 composition of production activity for facilities in the
Census, indicates that, on average, about 41 percent of facility production activity is devoted to the
manufacturing and/or formulating and packaging of In-scope pesticides.  The manufacture and/or formulating
and packaging of chemicals  other than EPA-registered pesticides account for another 41 percent of activity.
The remaining activities include:  other (i.e., non-chemical) production activity (12  percent); manufacturing
and/or formulating and packaging out-of-scope EPA-registered pesticides (5 percent); and  manufacturing
intermediates  (1 percent). All pesticide-related activities (in-scope and out-of-scope), on average, account for 47
percent of production activity.

         The extent to which a facility is involved hi pesticide-related activities vs. non-pesticide-related
activities varies slightly, depending upon the size of the facility (see Figure 3.7).  Smaller facilities  (with total
revenues of less that $20 million) devote approximately 31 percent of their production to non-pesticide related
activities.  Large and medium-sized facilities (with revenues greater than or equal to $20 million) are more
diversified, with between 58 and 62 percent of production devoted to non-pesticide  related activities.  The
composition of facility production activity varies more dramatically among facilities when comparing chemical-
related (including pesticides) production activities  to non-chemical-related production activities.  Large facilities
(with total revenues greater than or equal to $250 million) are more diversified, with 36 percent of production
devoted to non-chemical-related activities.  In contrast, small  and medium-size facilities (with total revenues of
less than $250 million) devote between 5 and 10 percent of production to non-chemical-related activities.
 3.3.D   Production Costs
         Production costs can be classified into two categories: fixed and variable.  Fixed costs are independent
 of the level of production and include depreciation on capital, fixed overhead, costs for product research and
 development (R&D), and interest on capital.  Figure 3.8 shows the composition of pesticide-related facility
 fixed costs by facility size.9  In most cases, fixed overhead is the largest component of fixed costs.
 Depreciation is the second largest component of fixed costs for facilities with revenues greater than or equal to
 $1 million. While R&D costs constitute the largest component of facility fixed costs  for facilities with pesticide
     'Facility fixed costs were not broken down by pesticide-related vs.  non-pesticide-related fixed costs in the
 Census.  This is because facilities maintained records of their fixed costs at the facility level.  During the pretest,
 it was determined that the respondent burden that would have been imposed by requiring facilities to break down
 costs were too great. Consequently, the ratio of pesticide-related revenues to total facility revenues was applied to
 each of the categories of fixed costs to obtain estimates of pesticide-related fixed costs.
                                                    3.20

-------
                                  Figure 3.6
        Composition of Facility Production Activity, 1986
                        (Averaged Across All Facilities )
           Other Production
               Activity
                12%
   Manufacturing
  Chemicals Other
Than EPA-Registered
     Pesticides
       41%
        Manufacturing and
        Formulating and/or
     Packaging In-Scope PAIs
             41%
                                  Manufacturing and
                             Formulating and/or Packaging
                                  Out-of-Scope PAIs
                                        5%
        Manufacturing
    Intermediates to be Sold
(others included in in-scope PAIs)
             1%
       Source:  Census.
                                      3.21

-------
                           Figure 3.7
   Composition of Facility  Production Activity
                  by Facility Size1,1986
               (Averaged Across Size Categories)
                 Greater than or
                  equal to $250 Million
                       Between $75 and $250 Million
                                         Between $20 and $75 Million
                                                      Less than $20 Million
• Manufacturing and Formulating and/or Packaging In-Scope PAIs
Q Other Production Activity
fl Manufacturing and Formulating and/or Packaging Out-of-Scope PAIs
Ejj Manufacturing Chemicals Other Than EPA-Registered Pesticides
D Manufacturing Intermediates to be Sold (others included in in-scope PAIs)

1 Facility size is measured by total facility revenues.
 Source:  Census.
                              3.22

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                             Figure 3.8
Composition of Pesticide-Related  Facility Fixed Costs
                    by Facility Size1,1986
                    Greater than or equal to $50 Million
                            Between $25 and $50 Million
                                         Between $1 and $25 Million



   Depreciation
   Fixed Overhead
   Research and Development
   Interest
   Other Expenses
                                         r^:>:>:>:>:>:>:>:>  Less than $1 Million
   Facility size is measured by revenues from all pesticide-related activities.
   Source:  Census.
                              3.23

-------
revenues of less than $1 million, R&D expenditures as a percent of total fixed costs (26.1 percent) are only
slightly greater than the percentage of fixed costs attributable to fixed overhead (25.7 percent).

        Variable costs depend upon the level of production. These costs include pesticide material and product
costs, labor costs, contract or tolling costs, taxes, and other pesticide manufacturing costs (i.e., all other
pesticide-related operating costs not included in the aforementioned categories).10  Figure 3.9 shows the
composition of pesticide variable costs by facility size.  The figure shows that pesticide material and product
costs are the largest component of variable costs across all facility sizes.  Labor costs, contract work, and other
pesticide costs are small in comparison.

        Figure 3.10 compares fixed and variable costs by facility size, to show the proportion of fixed costs to
total costs by facility size.  If fixed costs are a large proportion of total costs, smaller firms  may find it difficult
to enter the market.  The Census data suggest only minor differences in the ratio of fixed costs to total costs
across facility size, indicating that fixed costs are not likely to be a barrier to entry.u  For the category of
smallest facilities (with pesticide revenues of less than $1 million), fixed costs comprise 27 percent of total
costs.   For the category of largest facilities (with pesticide revenues greater than or equal to $50 million), fixed
costs comprise 41 percent of total costs. Very large  facilities, which often produce a greater variety of pesticide
types (e.g., insecticides, fungicides, and herbicides) and PAIs may be more capital Intensive, thereby facing a
different set of cost constraints than medium and small  facilities.

3.3.E  Employment Characteristics
         According to the Census data, the pesticide manufacturing industry supported a total of 3,432
production workers La 1986 (see Table 3.7). The thirteen largest facilities (all with revenues of greater than or
equal to $250 million) employed 58 percent of the total number of pesticide manufacturing production workers
in the industry. In contrast, the twenty smallest facilities (all with revenues of less than $20 million) employed
5 percent  of the total number of pesticide manufacturing production workers  in the Industry.

         The data presented La Table 3.7 lend further evidence that larger facilities tend to be more diversified
 than smaller facilities. As facilities increase In size,  the percent of the labor dedicated to non-pesticide-related
production increases from 23 to 44 percent of total facility employment.
     '"Facility  taxes were not broken down by  pesticide-related  vs. non-pesticide-related  In  the Census.
 Consequently, the ratio of pesticide-related revenues to total facility revenues was applied to total facility taxes to
 obtain estimates of pesticide-related taxes.
     "Facilities can recover costs incurred by introducing a new product to the market by adjusting the price once
 they have obtained patent protection.  The  fact that facilities may be willing to operate at a loss hi the short run,
 knowing that they will ultimately recover their costs, mitigates the barrier to entry that is associated with large fixed
 costs such as R&D.
                                                    3.24

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                            Figure 3.9
 Composition of Pesticide-Related Facility Variable
               Costs by Facility Size1,1986
                 Greater than or equal to $50 Million
                              Between $25 and $50 Million
                                           Between $1 and $25 Million
                                                        Less than $1 Million
• Pesticide Material and Product Costs
E3 Labor Costs
fl Contract Costs
E3 Other Pesticide Costs
D Taxes
 Facility size is measured by revenues from all pesticide-related activities.
 Source:  Census.
                               3.25

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                             Figure 3.10
Ratio of Pesticide-Related Fixed Costs to Pesticide-
                     Related Total Costs
                    by Facility Size1,1986
              Greater than or equal to $50 Million
              Fixed to total costs 41%
                           Between $25 and $50 Million
                           Fixed to total costs 32%
                                          Between $1 and $25 Million
                                          Fixed to total costs 29%
                                                        Less than $1 Million
                                                        Fixed to total costs 27%
 • Fixed Costs

 E3 Variable Costs
1 Facility size is measured by revenues from all pesticide related activities.

 Source:  Census.
                               3.26

-------


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-------
        Figure 3.9 shows that labor costs make up a relatively small portion of total pesticide variable costs,
suggesting that pesticide production is not a labor-intensive industry.  On average, pesticide manufacturing
facilities employed 527 employees (full-time equivalents, or FTEs), with 40 employees devoted to pesticide
manufacturing, 19 to formulating and packaging, 225 to other production, and 250 to non-production (see Table
3.8).  On average, production workers (for both pesticide and non-pesticide production) represented 54 percent
of total employment, with similar percentages for individual facility sizes.  This ratio is in reasonable agreement
with data  from the Census of Manufactures, which reports 1986 production employment to be 59 percent of
total employment for both SIC 2879 and SIC 2869.

        Figure 3.11 plots employment trends from 1975 to 1987 for  all manufactured goods against
employment in SIC 2879 (agricultural chemicals, not elsewhere classified [n.e.c.], in pesticide preparations and
formulations), SICs 2865 and 2869  (organic chemicals, except gum and wood)12, and SIC 28 (chemicals and
allied products).  The figure shows  a close correlation between employment trends in all manufacturing
industries, and hi both the agricultural chemical and organic chemical industries, as well as the chemical   \
industry as a whole. Between 1980 and 1981, however, employment in the agricultural chemical industry
increased, while the employment in the organic chemical industry, chemical industry, and all manufacturing
decreased.                                                                                          ;

3.3.F   Revenues and Profit
        Consistent with the review  of production data, examination of facility revenues reveals that facilities
derive a large percentage of their revenues from sources other than in-scope pesticide sales (see Figure 3.12).
Facilities with revenues greater than or equal to $250 million derive more than half their revenues
(approximately 58 percent) from sources other than in-scope pesticide sales, while facilities with revenues of
less than $20 million obtain about 42 percent of their revenues from other sources.13  Although the proportion
of revenues derived from sources other than in-scope pesticide sales varies across facility size, the figure
illustrates diversity at the facility level for all facility sizes.
    12Industrial organic chemicals include SIC 2865 (cyclic crudes and intermediates), SIC 2869 (industrial organic
chemicals, n.e.c.), and SIC 2861 (gum and wood chemicals).  The U.S.  Industrial Outlook presents data for organic
chemicals as industrial organic chemicals except gum and wood, i.e., SICs 2865 and 2869.   Consequently, for
consistency in presenting data from secondary sources,  organic chemicals are classified as SICs 2865 and 2869
throughout this profile.  (Note: In 1986, SIC 2861 constituted only 5  percent of the value of shipments  for SICs
2861, 2865 and 2869 combined.)
    13In-scope revenues are defined as the revenues derived from the  sale of in-scope pesticide chemicals.  This
definition excludes revenues from contract work or tolling, which may be entirely or partially attributable to  in-scope
pesticides. The figures presented may therefore be larger if a facility also obtains revenues from contract  work or
tolling.

                                                   3.28

-------
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                                          3.29

-------
                                 Figure 3.11

                  Employment Trends, 1975-1987
                              (1975 Base Year)
Number of Employers
Indexed to 1975
  1.5-,
                                                                SIC 2879




                                                               'All Manufacturing

                                                                SIC  28

                                                               iSIC  2865, 2869
    1975
                         I     I     I    I     I     I     I    I     I
         1976  1977 1978 1979 1980  1981 1982 1983 1984  1985 1986 1987

                                   Years

        SIC 2879 (Agricultural Chemicals, n.e.c., and Formulation & Preparation of Pesticides)
        SIC 2865, 2869 (Organic Chemicals, except gum & wood)
        SIC 28 (Chemicals and Allied Products)
        All Manufacturing

Source:  Census of Manufacturers, 1987.
                                      3.30

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                            Figure 3.12
            Composition of Facility Revenue
                   by Facility Size1,1986
                 Greater than or
                 equal to $250 Million
                           Between $75 and $250 Million
                                         Between $20 and $75 Million
                                                         Less than $20 Million
In-Scope Pesticide Chemicals

Other EPA Registered Pesticide Chemicals

Pesticide Contract Work or Tolling 2

Other Revenues
1 Facility size is measured by total facility revenues.
2 Tolling work maybe either in-scope or out-of-scope.
  Source:  Census.
                               3.31

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        On average, 1986 pre-tax in-scope pesticide facility profits equalled 13 percent of in-scope pesticide
facility sales.  Figure 3.13 presents 1986 pre-tax in-scope pesticide facility profits as a percent of in-scope
pesticide sales categorized by pesticide type, revenues of in-scope pesticides, and total facility revenues.14:
When profits were broken down by pesticide type, facilities that produced only fungicides averaged the highest
profit to sales ratio:  nearly 0.32.  This profit level contrasts with the profit to sales ratio of -0.03 for facilities
that produced only insecticides.  Facilities that produce multiple types of pesticides (these also tend to be larger
facilities) have pre-tax profit to sales ratios of about 0.16.  When profits are broken down based on facilities'
in-scope pesticide revenues, the data indicate that larger facilities (with revenues greater than or equal to $25
million) were more profitable than smaller facilities (with revenues of less than $25 million) in 1986. This
information may indicate that larger facilities, many of which produce several different types of pesticides, are
more efficient.

        Industry experts, however, attribute the high profits in portions of the pesticide industry to the ability of
manufacturers to produce patent-protected pesticides with specific uses.15  Many of the pesticides included in
these profit figures represent patent-protected chemicals produced by only one manufacturer.  Although patented
                                                                                                     I
products face competition from pesticides with the same end use, many manufacturers appear  to have been
successful at differentiating their products. Future profits,  experts say, will most likely depend on  producers'
ability to develop new patented products (Kline & Company, 1991). Most competition in the industry is among
producers whose products have similar biological activity.

3.3.G  Capital Expenditures
        Capital expenditures represent funding for  additional capacity and/or automating  or streamlining
existing facilities. Table 3.9 shows that capital expenditures by the pesticide manufacturing industry varied
significantly from year to year between 1975 and 1987.  On average, capital expenditures decreased by 3
percent per year from 1975 to 1987.  Most of the decline took place in the  late 1970s and early  1980s.  Annual
(and, in some cases, biennial) change appears to be cyclical, with downturns followed by upswings.  The
contraction in the demand for pesticides may be partially responsible for the decline in capital expenditures in
the industry.
     MAlthough revenue information in the Census was broken down by in-scope vs. out-of-scope, facility costs were
 not.  In-scope-related facility costs were  therefore calculated by applying the total cost figure to either the ratio of
 in-scope pesticide revenues to total revenues or, where applicable, the ratio of in-scope pesticide revenues to total
 pesticide-related revenues.                                                                           i
     15Production data collected in Part A  of the Census indicate that most clusters include production from multiple
 facilities.  In addition, data presented in  Section 3.3.F of the profile shows that facilities experience a wide range
 of profitability, suggesting that the pesticide market is competitive. Conversely, few facilities produce the same PAI
 within clusters, indicating that product differentiation exists within markets. These characteristics indicate that the
 pesticide market is competitive with differentiated products.
                                                   3.32

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                                     Figure 3.13
   Pre-Tax In-Scope Pesticide Facility Profit as a Percent of
                     In-Scope Pesticide Sales, 1986
Profit as a Percent
of Sales
Profit as a Percent
of Sales
Profit as a Percent
of Sales
35% —|
  30 —
  25 —
  20 —
  15 —
  10 —
   5 —
   0-
  -5 —
-10%-
31.7%
Pesticide Type
                                                                           16.5%
                                      10.7%
                                                               8.6%
                                                   -2.8%
                         Fungicides    Herbicides     Insecticides
                                               Other
                                             Pesticides
                                                Multiple
                                                Types of
                                               Pesticides
 35% —n
  30 —
  25 —
  20 —
  15-
  10 —
   5 —
   0-
                                      Revenues from  In-Scope  Pesticides
                                                                       22.6%
                              11.4%
                                                   6.7%
                         Less than $2 Million
25% —|

  20 —

  15 —

  10 —

   5 —

   0-
                                $2-$25 Million

                            Total Facility Revenues
                                          Greater than or
                                         Equal to $25 Million
                                                                       20.8%
                                                   13.8%
                              10.1%
                         Less than $50 Million
                              $50-$250 Million
Source:  Census.
                                          Greater than or
                                        Equal to $250 Million
 Note:    Revenue categorizations for in-scope revenues and facility revenues are broader than
         those that appear elsewhere in the profile, to prevent disclosure of confidential
         business information. In addition, the two facilities that changed ownership in 1986
         are not included in the information presented in this figure.
                                       3.33

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                  Table 3.9
   Pesticide Capital Expenditures, 1975-1987
                  SIC 2&791
               (in 1986 dollars)
  Year
     Capital
Expenditures
   (million $)
Annual
Percent
Change
  1975
  1976
  1977
  1978
  1979
  1980
  1981
  1982
  1983
  1984
  1985
  1986
  1987
       342.6
       301.7
       340.9
       381.4
       280.8
       246.4
       263.3
       295.9
       145.0
       199.7
       192.6
       200.6
       224.1
   73%
  -12%
   13%
   12%
  -26%
  -12%
    7%
   12%
  -51%
   38%
   -4%
    4%
   12%
Average Annual Change
                        -3%
1 SIC 2879 includes establishments involved in
 manufacturing or formulating agricultural
 chemicals, n.e.c., and formulating and preparing
 pest control chemicals.
Source:   Census of Manufactures, Preliminary
         Report, Industry Series,  1987
                    3.34

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         In general, capital expenditures tend to follow the business cycle.  Figure 3.14 compares capital
 expenditures for all manufacturing, as an indicator of the business cycle,  to capital expenditures in SIC 2879
 (agricultural chemicals, n.e.c., and pesticide formulations and preparations), SICs 2865 and 2869 (organic
 chemicals, except gum and wood), and SIC 28 (chemicals and allied products). Agricultural chemicals and
 organic chemicals both exhibit a cyclical trend, with an overall decrease in expenditures of approximately 35
 percent from 1975 to 1987.  While exhibiting similar swings in capital expenditures to those of agricultural and
 organic chemicals, the chemicals and allied products industry declined by only 20 percent between 1975 and
 1987.  Capital expenditures in the manufacturing industry as a whole, like the agricultural chemical industry,
 appear to be cyclical.  From 1978 to 1981, however, "all manufacturing" maintained a fairly constant level of
 capital expenditures, while capital outlays in the agricultural chemical industry declined.   In addition, overall
 capital expenditures from 1975 to 1987 for "all manufacturing" increased by approximately 20 percent.

        In the Census, facilities provided the year of the most recent major expansion of facility or equipment
 with respect to pesticide production.  Almost 90 percent of the facilities indicated that they had made some sort
 of expansion of facility or equipment related to pesticide production since 1960. More than 80 percent of the
 facilities invested in an expansion or improvement after 1970, while almost 40 percent of the facilities reported
 an expansion or improvement after 1985.

 3.3.H  Production Capacity Utilization
        Table 3.10 shows pesticide production capacity utilization rates from 1980 to 1989. The data indicate
 that production capacity utilization for all pesticides varied significantly during the decade, averaging
 approximately 68 percent  for all pesticides. At times, however, some types of pesticides had much lower
 production capacity utilization. During 1983 and 1984, for example, capacity utilization for insecticide
 production was particularly low, declining to 29 percent in 1984.  Figure  3.15 compares  the capacity utilization
 rate for pesticide production to that for all manufacturing. The figure shows that the manufacturing capacity
 utilization trend runs counter to that for pesticides.  Capacity utilization for all manufacturing hit a low in 1982
 and  rose thereafter.  Capacity utilization for pesticide production, on the other hand, peaked in 1982 and hit its
 lowest point in 1984.16
        The post-1982 decline in pesticide manufacturing capacity utilization may be attributable hi part to the
Payment-in-Kind (PIK) program.17  In addition, pesticide production capacity utilization rates may fluctuate
over time because  some pesticides are not produced on an annual basis. Rather, PAIs may be produced for a
limited time period (every second or third year) on what the industry commonly refers to as a campaign basis.
    "This is reasonable,  since pesticide production is more closely related to agricultural production than to
measures of industrial activity.
    17Recall that PIK took 48 million acres out of production in 1983.
                                                  3.35

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 1986 Dollars Indexed
 to 1975
                                  Figure 3.14
                          Capital Expenditures
                              in 1986 Dollars
                               (1975 Base Year)
1.5-

1.4-

1.3-

1.2-

1.1-



0.9-

0.8-

0.7-

0.6-

0.5-

0.4-
  0.0
                                                            All Manufacturing
                                                            SIC 28
                                                            SIC 2879
                                                           ' SIC 2865, 2869
         ~~l    I     I	1	1	1	1	1	1	1	1	1
     1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987

                              Years

    —  SIC 2879 (Agricultural Chemicals, n.e.c., and Formulation & Preparation of Pesticides)
    """  SIC 2865, 2869 (Organic Chemicals, except gum & wood)
    •—•  SIC 28 (Chemicals and Allied Products)
    *««;««  All Manufacturing                                                 :

Source:   Census of Manufacturers, 1987.
                                      3.36

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U.S. Pesticide


Year Herbicides
1980
1981
1982
1983
1984
1985
1986
1987
1988
19893
Average
Capacity
Utilization
77
74
84
66
67
62
64
63
75
72
70.4
Table 3,10
Production Capacity Utilization Rates, 1980-1989
(Percent)


Annual

All Percent Change
Insecticides Fungicides Pesticides All Pesticides1
79
72
68
33
29
56
63
61
76
76
61.3
84
68
70
71
73
66
61
59
59
63
67.4
78
73
80
54
52
61
65
62
75
81
Average
Annual
68.1 Change
n/a2
-6%
10%
-33%
-4%
17%
7%
-5%
21%
8%
4%
1 The rate for all pesticides may be higher than those for herbicides, insecticides, or
fungicides. This difference is due to the inclusion of detailed information on capacity rates
associated with pesticides either classified as rodenticides or unclassified.
2 Not available.
3 Projected.
Source: USDA
1989.

Agricultural


Resources: Situation and Outlook

Report, AR-13, February


3.37

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                              Figure 3.15
    Comparison of AH Manufacturing Capacity Utilization
    and Pesticide Production Capacity Utilization Rates
Capacity Utilization
Rate
 20
    1980   1981   1982   1983
            Pesticide
            Herbicide
            Insecticide
            Fungicide
            All Manufacturing
                                                             All
                                                             Manufacturing

                                                             Insecticide
                                                             Pesticide
                                                             Herbicide

                                                             Fungicide
 1	1	1	1	f
1984   1985   1986   1987   1988

    Years
        Source:  USDA Agricultural Resources: Situation and Outlook Report
                AR-13 February, 1989.
                Statistical Abstract of the United States , 1989.
                                 3.38

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 Although many PAIs are produced annually, it is common industry practice to produce a specific PAI less
 frequently. This typically occurs when the pesticide is used on a low-volume specialty crop, or for those
 pesticides with high concentrations that allow for reduced volume.  During production, materials are fed into a
 reactor in order to produce a desired chemical reaction; labor and equipment are used to monitor the process to
 make sure that all necessary conditions of production are met.

         Although the frequency of production is generally determined by product demand, the quantity
 produced is typically a function of the volume required to make the run cost-efficient.  Due to start-up  costs
 such as energy and labor, costs per unit produced increase as quantities are reduced. Total costs associated with
 the minimum volume a facility is willing to produce may be only slightly greater than total costs for production
 of much smaller amounts of the pesticide.18

 3.4      Finn Characteristics

        This profile has thus far focused primarily on characteristics of the facility.  This section describes the
 ownership structure of the industry and the way  in which firms are organized.

        The Census indicates that most in-scope  pesticide facilities are owned or controlled by a parent  firm (85
 percent).  Although a number of smaller, single-facility firms control small portions of  total production, overall
 production is becoming increasingly concentrated among large producers as a result of mergers  and acquisitions.
 Only 15 percent of the facilities are single entities not owned or controlled by another firm as of December 31,
 1986. Approximately 35 percent of all parent firms are controlled in turn by another company. Large  R&D
 costs, including registration fees,  may be a reason why the majority of pesticide producers tend to be part of a
 larger, multi-facility firm.
        In 1986, 64 firms produced in-scope pesticides in the United States.  These firms owned 90 facilities,
which produced 136 individual or classes of in-scope PAIs.  The number of PAIs manufactured by each firm
varies (see Figure 3.16).  Approximately 45 percent of the firms owning in-scope facilities in 1986 produced
only one PAI, although one firm manufactured 11 PAIs.

       According to the Census data, approximately three-quarters of the firms owned only one in-scope
pesticide manufacturing facility.   The remaining firms tended to own two or three in-scope pesticide producing
facilities.  Of these  firms, 44 percent produced the same pesticide at more than one of their in-scope facilities.
Figure 3.17 presents the number of in-scope facilities owned by firms.
    18Per unit costs increase as quantities produced decrease.  Producing larger quantities may therefore cost less
on a per unit basis.

                                                  3.39

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                         Figure 3.16

     Number of Individual or Classes of In-Scope PAIs
                 Produced by Firms, 1986
Number of Firms
 20-
 15-
 10-
  5-
  0
                                       8
9   10   11
                    Number of In-Scope PAIs Produced
      Source:  Census.
                             3.40

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                            Figure 3.17
       Number of U.S. In-Scope Pesticide Manufacturing
                Facilities Owned by Firms, 1986
Number of
Firms
      0
              Number of Manufacturing Facilities Owned by a Single Firm
      Source: Census.
                              3.41

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       Figure 3.18 shows the composition of 1986 firm sales activity. At the firm level, pesticides constitute a
small portion of sales.  On average, pesticide manufacturing and pesticide formulating/packaging combined
represent five percent of firms' sales.                                                               !

3.5    Industry Market Structure                                                                1

       Several factors play an important role in determining market structure, including (1) the barriers firms
face in entering and exiting  the market, (2) vertical integration, (3) the concentration of production, and (4) the
degree to which products are substitutable in consumption.  This section describes how these factors affect the
competitiveness  of the industry.

3.5.A Barriers to Entry                                                                         |
       Firms' abilities to enter and exit the market determine, in part, the competitiveness of the industry.  If
significant barriers to entry  exist, potential entrants may be dissuaded and existing firms may enjoy market
power.  If few barriers to entry exist, existing firms are more likely to face competition for market share.

        There are several types of entry barriers.  The most relevant to the pesticide industry are (1) capital
requirements, (2) economies of scale, and (3) R&D requirements, including registration costs.  Although; data
about barriers to entry are limited,  the available data reveal that market power exists for many firms in the
industry.                                                                                         ;

        A  significant number of the PAIs in the Census are produced by only one firm.  Given that patent
protection exists for pesticide products, it is possible that there is room for only one producer of each PAI, and
that each producer maintains market power for that PAI.  Figure 3.19 exhibits data to support this assumption,
revealing that 106 of the 136 individual or classes of in-scope PAIs manufactured in 1986 were produced by
only one firm.  The concentration of individual PAI production among single firms may be countered, however,
by the fact that some pesticide products are substitutable.  Consequently, individual firms that do not produce
 the same PAIs may produce products that compete in the market place.

        Capital Costs
        Firms require capital in order to begin, improve, or expand production.  The capital required to enter an
 industry may be sufficient to impede market entry.  There are no readily available data  on the amount of capital
 required for new construction or expansion of a pesticide chemical facility.  There are measures, however, that
 provide an indication of capital intensity in the industry.
                                                   3.42

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                                Figure 3.18

                Composition of Firm Sales, 1986
                        (Averaged Across All Firms)
                                      Pesticide
                                    Manufacturing
                                        4%    Formulating and/1
                                                 or packaging
                                                     1%
Activity not related to in-scope or
    out-of-scope pesticides
           95%
     Includes in-scope and out-of-scope production activity.
     Source:  Census.
                                    3.43

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                            Figure 3.19

    Number of Firms that Produce an Individual PAI or
                       Class of PAI, 1986
Number of PAIs
            150-,
            125
            100-
                           136 PAIs Produced
             75-
             50-
             25-
3 PAIs produced by 4 or more firms \
5 PAIs produced by 3 firms     ]


22 PAIs produced by 2 firms
                                          106 PAIs produced by only 1 firm ;
        Source:  Census.
                                3.44

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        The ratio of the value added by manufacturing to gross book value of depreciable assets provides a
 measure of the capital intensity of the industry. The data indicate that pesticide manufacturing is capital
 intensive, especially when compared to formulating/packaging and to all manufacturing.  SIC 2869, which
 includes the manufacture of basic pesticides and many other organic chemicals, had a value added: depreciable
 assets ratio of 0.51 in 1987; i.e., the value added represents 51 percent of the value of depreciable assets (U.S.
 Department of Commerce, 1989a). SIC 2879, industrial organic chemicals,  which includes primarily pesticide
 formulation, had a much higher ratio of 1.13, indicating less capital intensity (U.S.  Department of Commerce,
 1989a).19 SICs 20-39, which include all manufacturing, had a ratio of 1.34, demonstrating the relative capital
 intensity of pesticide production to manufacturing in general (U.S. Department of Commerce, 1989a).

        Existence of Economies of Scale
        The relative capital intensity of the pesticide industry is one indication of the extent to which economies
 of scale exist. Although technology determines the minimum efficient size of a facility, efficient scales of
 production appear to vary widely across PAIs.  Comparing facilities that produce the same PAIs suggests that
 there is a large difference in the quantities produced. Facilities can range in annual output from a few thousand
 pounds to more than 10 million pounds of the same PAL  The fact that there are vast differences, in the size of
 facilities producing the same product indicates that economies of scale probably are not a major factor within the
 pesticide manufacturing industry.20

        Research and Development
        Large capital outlays for R&D represent another barrier to entry.   Research used to develop new,
 patented products is considered to be key to chemical producers' success.   Patents are important to the pesticide
 industry because they give producers a monopoly in the production of that pesticide  and allow the producer to
price a product above cost.  Pesticide products carry a 17-year patent; firms need this patent protection to price
 above costs to recover their R&D expenditures.21  Since different patented products may compete for the same
 use, however, pure monopolies do not exist.

        Although patented products play an extremely important role in the industry, there are unpatented
products on the market that are profitable.  The existence of unpatented products signifies that patents alone do
    19A higher ratio of value added by manufacturing to gross book value of depreciable assets may also result from
the use of older equipment.
         analysis of economies of scale within the pesticide manufacturing industry is complex. Because multiple
PAIs may be produced on the same line, using the same equipment, comparing production across individual PAIs
may not provide definitive evidence on whether economies of scale exist.
    21 After a pesticide product is patented, the manufacturer must register the product for use.   Therefore,
manufacturers often have fewer than 17 years to recoup their R&D costs.
                                                   3.45

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not protect profits.  Nevertheless, patents for most pesticides are instrumental in recovering R&D costs, and are
also a factor in restricting market entry.

        Research and development costs are one of the fastest growing components of fixed costs that firms
face.  In 1976, the average R&D costs of a single new pesticide were estimated at $10 million (1986 dollars),
while in 1987 the estimated costs to develop a single new pesticide were $40 million (1986 dollars) (U.S.
Department of Commerce, 1987). The increase in costs is partly due to more stringent toxicity tests performed
in compliance with environmental regulations.  Specifically, use restriction based on  the amount of residue
toxicity left on food products places new pesticide products under greater scrutiny than existing pesticide
products.  According to industry experts, it can take 10 years to bring a chemical pesticide from the R&D stage
to registration with the EPA (Rich, 1988).  To register a pesticide for a major food use, there is a flat fee of
SISO.OOO22. In order to support R&D and the registration of new products,  firms must be able to generate
sufficient pesticide sales.  The need for a large sales volume may be one explanation for the number of mergers
and acquisitions in the 1980s.

        The Census data indicate that total average R&D costs for all firms represent about 4 percent of total
facility sales.23 Different levels of R&D are sustained, depending upon the size of firms.  Table 3.11 breaks
down R&D costs as a percent of total facility sales  for three firm sizes.24  According to the Census, firms with
total revenues of between $1 billion and $6 billion have the highest R&D  expenses as a percent of sales. High
R&D costs and the uncertainty of product success may make it difficult for new firms to put up the capital  and
to absorb the risk from R&D ventures.  These costs may bar entry, with the result that the industry becomes
less competitive.

3.5.B  Vertical Integration
        Vertical integration is the extent to which the different stages of production are organized in a single
firm.  According to the Census, both small and large firms tend to be vertically integrated, engaging in the
R&D, manufacturing, and formulating/packaging of pesticides.

        Compared to developing and manufacturing PAIs,  formulating/packaging is less expensive but often
adds considerable value to the end product.  As mentioned previously, data  from the Census indicate that; 50 of
     22The annual maintenance fee is $425 for each registration up to 50 registrations; and $100 for each additional
 registration, with the exception that no fee is charged for more than 200 registered products held by any registrant
 (HERA, Section 4).
     23The Census collected total facility, not pesticide-specific, R&D costs.
            costs were estimated based on firm size rather than facility size, because firm size is generally more
 important than facility size in determining the level of R&D.
                                                                                                   i
                                                   3.46                                           I

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                                Table 3.11
 Research and Development Costs as a Percent of Total Facility Sales, 1986
 	by Firm Size1   	

                                                             Percent of
                                                             R&D Costs to
                                              No» of         Total Facility
                                              Facilities       Sales
Firm Size (Annual Revenues)
Revenues less than $1 Billion
                                                  46
3.3%
Revenues between $1 Billion and $6 Billion
                                                  26
5.5%
Revenues greater than $6 Billion
                                                  12
3.7%
All Facilities
                                                  842
                                                                 4.0%
1 Average R&D to sales ratio across all facilities, by firm size.
2 Excluded from the 88 facilities that provided financial data are four facilities
 that did not report firm revenues.

Source:  Census.
                                    3.47

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the 90 in-scope PAI manufacturing facilities also engaged in formulating/packaging. When evaluated at the firm
level, these data reveal that 39 of the 64 firms represented in the Census have PAI formulating/packaging ,
capabilities at one or more of their in-scope PAI manufacturing facilities. In addition, five of the firms that do
not formulate/package PAIs at their in-scope PAI manufacturing facilities reported that they own other facilities
at which PAIs are formulated/packaged. Of the 64 firms represented hi the Census, therefore, 44 (69 percent)
have both PAI manufacturing and formulating/packaging capabilities.                                   ;

       In addition to in-house formulating/packaging capabilities, many firms, both large and small, contract
out some aspects of the production process (tolling), typically the formulating/packaging process. It is estimated
that approximately 80 percent of the formulated pesticide business is controlled by PAI manufacturers, either
directly with in-house capacity or indirectly through contracting (Kline & Company, 1990).

3.5.C  Concentration
        like many industries, the pesticide industry underwent significant restructuring in the 1980s.  According
to the International Trade Commission's Synthetic Organic Chemicals, the number of facilities producing _
pesticides declined by 23 percent from 1979 to 1988.  The Census indicates that between 1980 and 1986, 20 in-
scope pesticide facilities had parent firms that were purchased by or merged with other firms.  Although the
majority of the facilities did not change ownership status, the number of mergers and acquisitions is significant
in terms of overall production and sales.  Some of the industry's largest firms were restructured during this
period, concentrating production further. The number of mergers and acquisitions involving in-scope facilities
is shown in Figure 3.20.  Further concentration of the industry has occurred since 1986.
                                                                                                  I

        Two mam types  of restructuring occurred in the United States hi the 1980s.  First,  foreign firms
 acquired U.S. firms either in total or in part;25 second, U.S. firms acquired or merged with other domestic
 firms. Some industry experts attribute the foreign component of restructuring to the volatility of the U.S. dollar
 from 1980 to 1990. The strong U.S. dollar prior to 1985 strengthened foreign firms' positions hi the world
 market, because U.S.  products were more expensive relative to foreign counterparts.  The increase hi
 environmental controls implemented in the United States during the 1980s also contributed to the price increase
 of U.S. products.  As the  dollar weakened after 1985, foreign firms began purchasing production capacity, hi the
 United States. As stated above, mergers and acquisitions among U.S. firms may have resulted primarily from
 the firms' need to generate large amounts of sales to support the rising costs of both R&D and environmental
 compliance (U.S. Department of Commerce, 1989d and Sine, 1990).                                  ,
     "Based on parent firm information reported hi the Census, 9 of the 90 facilities (10 percent) were owned by
  foreign companies hi 1986. Note: Foreign ownership was not explicitly requested hi the Census, and was determined
  based on the parent firm address reported hi the Census hi conjunction with information presented hi Dun and
  Bradstreet's Million Dollar Directory.
                                                   3.48

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Number of
Facilities
                                Figure 3.20

            Number of Facilities Acquired by Firms
                         (From Jan. 81 to Dec. 86,
                         by Method of Acquisition)
           Purchase
Merger
Founded
                                                      Other Status1
                             Method of Acquisition
                  Of the two facilities that reported other, one indicated that the facility
                  was acquired through the contribution of capital by the parent
                  company; the other indicated that the facility was newly constructed
          Source:  Census.
                                 3.49

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       In a concentrated industry, the dominant firm or firms are better able to influence market outcomes to
their advantage.  Industry concentration is frequently measured by concentration ratios, which are the percentage
of total sales accounted for by a given number of firms.  The Bureau of the Census calculates concentration
ratios for the top 4, 8, 20, and 50 producers of basic pesticides.  These concentration ratios are displayed in
Table 3.12. In SIC 28694 (pesticides and other synthetic organic agricultural  chemicals except preparations),
the top four firms accounted for 54 percent of the value of shipments in 1982. In SIC 2879 (agricultural
chemicals,  n.e.c., and pesticide preparations and formulations), the top four firms accounted for 39 percent  of
the value of shipments. Examining concentration ratios by pesticide type in Table 3.12 shows the fungicide
preparations market to be the most concentrated and insecticide preparations to be the least concentrated.

       Concentration ratios based on sales of in-scope pesticides were calculated using the Census data. These
ratios, shown in Table 3.13, indicate that the four largest firms account for 42 percent of the value of all in-
scope pesticide shipments. Like the Bureau of Census data, examination of concentration ratios by pesticide
type based on the data presented in Table 3.13 shows  that the fungicide market is the most heavily concentrated,
while the insecticide market is the least concentrated.  The concentration ratios indicate that there may be no
dominant firm in the industry as a whole. The pesticide industry is highly differentiated, however, meaning that
there may be dominant firms in individual pesticide markets.

3.5.D  Demand Elasticity and Product Substitution
        Single firms dominate the production of specific pesticides.  For these firms to enjoy market power,
however, consumers must be unable to find substitutions for their products easily.  A common indicator of
substitutability in consumption is the price elasticity of demand, which shows  the percentage change in demand
given a percentage change in the price of a pesticide.  Price elasticity of demand is calculated by dividing the
percentage change in demand by the percentage change in price.  Numeric values associated with price
elasticities of demand are generally expressed relative to a one percent change in price.  For example, an
elasticity of -0.5 suggests that a 1 percent increase in price would result in a 0.5 percent decrease hi the quantity
demanded.                                                                                        ;

        Price elasticities of demand were estimated for each pesticide cluster in the analysis.26 In order to;
develop the elasticity estimates, the EPA developed a comprehensive approach, including:               i
         is section is based on detailed analyses of pesticide demand elasticities.  See Appendix C for further details.

                                                   3.50

-------
                                       Table 3.12
                Share of Value of Pesticide Shipments Accounted for by the
                      4, 8, 20, and 50 Largest Companies, 1972-1982
                 Total
 Year    (Mill. 1986$)
                            4 largest
                          companies
           8 largest
          companies
          20 largest
          companies
            50 largest
            companies
                  Synthetic Organic Pesticides. Not Formulated. SIC 2R6Q4
                 1832              54             76              93
1982
1977
1972
                 2285
                 1424
 65
 57
 80
 79
 93
 97
                                                                                   100
          Agricultural Chemicals, n.e.c.. and formulations and preparations, sic 287Q
 1982
 1977
 1972
                4919
                4191
                3438
 39
 37
 34
 58
 57
 51
 81
 76
 73
   91
   87
                            Insecticide Preparations. SIC 28795
 1982
 1977
 1972
                 991
                1284
                1039
 46
 45
 48
 71
 67
 67
 90
 87
  99
  98
  98
                            Herbicide Preparations. SIC 28796
 1982
 1977
 1972
               2710
               1825
               1243
 62
 65
 77
77
84
89
95
96
98
99+
99+
99+
                            Fungicide Preparations. SIC 28797
1982
1977
                358
                305
69
70
84
85
98
97
 100
99+
                         Other Pesticide Preparations, STC 2879R
1982
1977
                198
                186
49
47
65
66
87
91
99+
99+
                      Household Pestieida! Preparations. SIC 28799
1982
1977
                480
                353
53
56
70
71
89
89
  99
  99
Concentration Ratios from the 1987 Census expected to be available April 1992.

Source: Census of Manufactures, Concentration Ratios in Manufacturing, 1982.
                                       3.51

-------
                                   Table 3.13
        Share of Value of Ih-Scope Pesticide Shipments Accounted for by the
                         4, 8. and 20 Largest Firms, 19S6

j Number of Facilities
All Pesticides
Fungicides
Herbicides
| Insecticides
Concentration Ratio
	 — 	 	
All Pesticides
Fungicides
Herbicides
Insecticides
Total Sales (Million
	 — 	
All Pesticides
Fungicides
Herbicides
1 Insecticides
4 largest
firms

11
4
7
9
(Percent of
42
67
61
57
$)
1,640
278
1,510
531
8 largest
firms

18
9
14
13
Sales)
68
90
83
81

2,654
375
2,049
749
20 largest
firms

39
24
31
27

94
1001
99
99

3,634
416
2,448
918
Total
i
90
30 •
39
36 !

100 j
100 :
100 :
100

3,884 i
416 i
2,463 ;
928
1 Remaining six firms constitute less than 1 % of total fungicide sales.

Source:  Census.
                                          3.52

-------
         (1)      Review of empirical studies of pesticide production and use;

         (2)      U.S. Department of Agriculture's analysis of the price elasticity of demand for food
                 commodities (USDA, 1985, 1989);27

         (3)      Feasibility of employing non-chemical, non-biological pest control methods (Pimental, D., et
                 al., 1991).28 (The greater the feasibility of substitution, the higher the expected price
                 elasticity of demand.);

         (4)      An analysis  of pesticides' contribution to the cost of production of a commodity, based on
                 estimates of the cost of production in the farm sector (USDA, 1989a).29  (The greater the
                 contribution of pesticides to the cost of production, the higher the expected price elasticity of
                 demand.);

         (5)      Analysis of the marginal productivity of pesticides (USDA, 1989,  USDA, 1989a);30 and

         (6)      Expert opinions within the OPP.



        The estimated price elasticities of demand vary significantly among the clusters, since each cluster faces

 different market forces.  Table 3.14 shows that the estimates of elasticity of demand for pesticide clusters with

 in-scope products in 1986.  Elasticity of demand varies among these clusters from -0.12 to -1.38. Despite the

 wide range of demand elasticities among pesticide clusters, 38 of the 45 have inelastic demand,  i.e., the

 absolute values of the demand elasticities are less than 1.  This indicates  that demand at a cluster level (although

 not necessarily at the PAI level) will not vary significantly with moderate price increases.



 3.6    International Trade



        The U.S. pesticide industry holds a sizable share of the world export market for pesticides:

 approximately 23 percent of the total value of shipments in 1987 (United Nations, 1987, and Department of

 Commerce, 1989d).  During the last decade, however, the margin between exports and imports has been

 declining, although the United States remains a net exporter of pesticides. Both the  strong U.S.  dollar from

 1980 to 1985 and increasing foreign competition contributed to the change in U.S. position.  U.S. imports,

 although increasing, do not appear to threaten the market power of domestic firms.
    ^USDA (1985).  U.S. Demand for Food:  A Complete System of Price and Income Effects., and U.S.D.A.
(1989). Retail to Farm Linkage for a Complete Demand System of Food Commodities.

    ^Pimentel, D.,  et al. (1991).  Environmental and Economic Impacts of Reducing U.S. Agricultural Pesticide
Use.  Pest Management in Agriculture.  CRC press.

    2?USDA (1989a).  Economic Indicators of the Farm Sector: Cost of Production, 1987. February.

    ^SDA (1989). Retail to Farm Linkage for a Complete Demand System of Food Commodities., USDA (1989a).
Economic Indicators of the Farm Sector: Costs of Production, 1987. February.
                                                 3.53

-------
                       Summary of Estimates of Elasticy of Demand
                             for Clusters with deduction,
Cluster
Herbicides on sugar beets, beans, peas
Herbicides on tree fruits (except oranges), sugar cane, nuts
Herbicides on tobacco
Fungicides on fruit and nuts trees (except oranges)
Fungicides for seed treatment
Herbides on vegetables
Fungicides on grain in storage
Insecticides on vegetables
Slimicides
Fumigants and nematicides
Insecticides on termites
Wood preservatives
Insect repellents at non-agricultural sites
Domestic bug control and food processing plants
Mosquito larvacides
 Fungicides on turf
 Industrial preservatives
 Insecticide synergists and surfactants
 Plant regulators, defoliants, desiccants
 Sanitizers - dairies, food processing, restaurants,  air treatment
 Insecticides on livestock and domestic animals
 Industrial microbicides, cutting oils, oil well additives
 Preservatives, disinfectants, and slimicides
 Fungicides - ornamentals
 Insecticides on lawns, ornamentals and forest trees
 Unclassified uses
Elasticity Estimate
        -0.12
        -0.20
        -0.20
        -0.23 ;
        -0.27
        -0.27
        -0.31
        -0.33
        -0.33 ;
        -0.33
        -0.33 |
        -0.33
        -0.33 ;
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33 ;
        -0.33
        -0.33 •
        -0.33:
        -0.33;
         -0.33
         -0.33
         -0.33
         -0.33
                                               3.54

-------
                                          Table 3.14
                        Summary of Estimates of Elasticity of Demand
                              for Clusters with Production, 1986
                                           Page 2
 Cluster
                                                                        Elasticity Estimate
 Fungicides on vegetables
 Fungicides - broad spectrum
 Herbicides - broad spectrum
 Insecticides on soybeans, peanuts, wheat, tobacco
 Fungicides - post harvest
 Herbicides on rights of way, drainage ditches
 Herbicides on turf
 Herbicides on soybeans, cotton, peanuts, alfalfa
 Herbicides on corn
 Insecticides on corn and alfalfa
 Insecticides on sorghum
 Herbicides on sorghum,  rice, small grains
 Herbicides on oranges
 Insecticides on fruit and nut trees, except oranges and grapes
 Insecticides on oranges
 Herbicides - other agricultural uses
 Insecticides on cotton
 Fungicides on grapes
Herbicides on grapes
 -0.38
 -0.40
 -0.48
 -0.56
 -0.65
 -0.66
 -0.66
 -0.67
 -0.69
 -0.69
 -0.69
 -0.69
 -1.00
 -1.00
 -1.00
 -1.00
 -1.06
-1.38
-1.38
Source:  Estimates of the Price Elasticity of Demand for Pesticide Clusters, U.S. EPA and Abt
         Associates Inc., May 1991.
                                             3.55

-------
3.6.A  U.S. Pesticide Imports and Exports
       Table 3.15 shows U.S. import and export values for pesticides from 1978 through 1987.  The table
shows that pesticide imports increased more than exports over this period. On average, the value of pesticide
imports increased by 7 percent, while the value of pesticide exports increased by only 1 percent.  Although
imports increased substantially during the period, the United States maintained a positive trade balance.   .

        Similarly, Tables 3.16 and 3.17 show import and export values for herbicides and insecticides,
respectively.31  Exports of herbicides, which comprise the largest U.S. pesticide export, witnessed a dramatic
decline in the 1980s.  In particular, the value of herbicide exports fell by 64 percent in real terms between 1984
and 1985. In the same year, herbicide imports increased by 41 percent to fill the vacuum left by a facility that
closed.32  In 1985, the United States was a net importer of herbicides.  Over the ten year period from 1978 to
1987, exports of herbicides decreased by 5 percent per year, while imports increased by 12 percent per year.
Although herbicides have been given the most research  funding of all pesticide types, thereby exhibiting the
most technological progress, they have also been the most susceptible to violations of intellectual property rights
 due to the lack of patent protection outside the United States.  Of the three major groups of pesticides,
herbicides had the least favorable ratio of exports to imports in the 1980s (U.S. Department of Commerce,
                                                                                                  I
 1989d).

        Insecticides comprise the second largest component of U.S. pesticide exports. From  1978 to 1987,
 insecticide exports decreased by 4 percent as imports increased by  9 percent.  In spite of these trends,
 insecticides showed a positive trade balance throughout the period. Part of the decline in insecticide exports
 may be attributed to  the decline in chlorinated hydrocarbon insecticide production.

        Table 3.18 presents U.S. pesticide exports as a percent  of the value of total U.S.  pesticide shipments,
 and U.S. pesticide imports as a percent of new supply for 1978 to 1987. The table shows that pesticide exports
 as a percent of the value of shipments have decreased over the period, from 25 percent in 1978 to 21 percent hi
  1987, while the value  of overall shipments increased over the same period.  These data, coupled with data from
 Table 3.5 showing a decrease in the quantity of pesticides produced and sold, indicate that U.S. producers have
  increased sales to domestic markets. Table 3.18 also shows that imports have maintained approximately the
  same share of new supply:  5 percent in 1978 and 6 percent in 1987.
      31Similar data is unavailable for fungicides.
      32Much of the decline in exports and increase in imports was due to the closing of one facility.

                                                    3.56

-------
Table 3.15
U.S. Import and Export Values for All Pesticides
(in thousand 1986 $)

Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Average
Annual
Change
Source:
Value of

Imports % Change
260,098
268,846
317,718
307,553
284,196
271,512
322,874
413,772
402,782
414,800


—
United Nations International
65%
3%
18%
-3%
-8%
-4%
19%
28%
-3%
3%


7%
Trade
Value of
Trade
Exports % Change Balance % Change
1,238,508
1,320,896
1,241,047
1,132,425
1,157,006
1,173,584
1,357,235
1,231,455
1,299,974
1,305,959


—
Statistics Yearbook,
99%
7%
-6%
-9%
2%
1%
16%
-9%
6%
<1%


1%
1978-1987
gj-
978,410
1,052,050
923,329
824,872
872,810
902,071
1,034,361
817,683
897,192
891,159


—
^ ^ -™^_^__ ^_
111%
8%
-12%
-11%
6%
3%
15%
-21%
10%
1%


-1%

3.57

-------
Table 3.iF
U.S* Import and Export Values for Herbicides
(in thousand 1986$)

Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Average
Annual
Change
Source:
i •
Value of
Imports
88,467
146,755
160,924
158,292
166,396
119,767
157,569
221,698
192,526
183,863


—

% Change
NA
66%
10%
-2%
5%
-28%
32%
41%
-13%
-4%


12%
United Nations International Trade
Value of
Trade
Exports % Change Balance
462,023
494,605
495,111
460,619
470,692
526,205
586,791
212,157
197,936
233,650


—
Statistics Yearbook,
==^====
NA
7%
<1%
-7%
2%
12%
12%
-64%
-7%
18%


-5%
1978-1987
—
373,556
347,850
334,187
302,327
304,296
406,438
429,222
(9,541)
5,410
49,787


	
=====
I
j
% Change
NA
\ -7%
-4%
-10%
\ 1%
34%
> 6%
' -102%
i 157%
; 820%


-10%
=^
3.58

-------
Table 3.17
U.S, Import and Export Values for Insecticides
(in thousand 1 986 $)
Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Average
Annual
Change
Source:
Value of
Imports % Change
60,539
79,350
90,055
90,854
73,625
74,508
65,906
76,508
90,964
111,376

—
United Nations International
NA
31%
13%
1%
-19%
1%
-12%
16%
19%
22%

9%
Trade
Value of Trade
Exports % Change Balance % Change
304,671
358,331
301,474
294,367
289,169
268,194
345,073
239,421
251,425
204,867

—
Statistics Yearbook,
NA
18%
-16%
-2%
-2%
-7%
29%
-31%
5%
-19%

-4%
1978-1987
244,132
278,981
211,418
203,513
215,544
193,686
279,167
162,913
160,461
93,491

—

NA
14%
-24%
-4%
6%
-10%
44%
-42%
-2%
-42%

-7%

3.59

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-------
3.6.B  U.S. Pesticide Industry in the World Market
       Table 3.19 shows U.S. trade in pesticides as a percentage of the world market economy for pesticides
from 1978 to 1987.  In 1978, U.S. pesticide exports accounted for 26.2 percent of the world export market. In
1981, the U.S pesticides  exports percentage peaked, capturing 30.5 percent of the world export market.  In
1987, the U.S share of the world pesticide market was 23.4 percent, the lowest percentage of the preceding ten
years.

       The shift hi the U.S. pesticide export position is due, in part, to the increased strength of the dollar
relative to other currencies. As mentioned above, the strong U.S.  dollar from 1981 to 1985 caused U.S.
products to be more expensive than foreign products, thereby contributing to the decline.  Because  exports and
imports do not respond immediately to changes in currency exchange rates, it may take months, even years, for
changes in exchange rates to have an impact.   The steady reduction in exports, resulting from the price increase
of U.S. products, may not be evident in the trade statistics until after 1984 due to the length of contracts for
pesticide sales.

       Foreign competition hi the pesticides industry has increased substantially hi the last decade, causing a
deterioration hi the competitive position of U.S.  firms hi recent years.  Table 3.20 lists the leading pesticide
exporting countries hi the world economy from 1979 to 1987.  Although the United States remains the largest
world exporter of pesticides, its export lead has decreased as other countries' pesticide export markets have
matured.33 In particular, the United Kingdom, Switzerland, Italy, and Brazil have increased their share of
world pesticide exports.

        As  indicated hi Table 3.19, the U.S. share of world imports for pesticides increased during the 1980s.
Between 1982 and 1984, the most dramatic expansion hi manufacturing facilities took place outside western
Europe and  the United States.  This expansion took place hi major markets such as Brazil, India  and eastern
Europe.  Together with the development of pesticides manufactured hi Taiwan and South Korea,  this expansion
further increased the competition for products manufactured hi western Europe and the United States (Shenton,
1989).

       Table 3.21  shows the value of pesticide imports from leading importers to the United States as a
percentage of total U.S. pesticide imports. As seen hi this  table, although imports from western  Europe still
    33As stated previously, some of the U.S. companies included hi the Census are owned by foreign entities.
                                                   3.61

-------
Table 3.19
U.S. Trade as a Percentage of the World Market Economy
1978-1987
Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Source:
U.S. Share
of World
Imports
6.8
6,9
8.7
11.7
7.6
7.5
8.5
10.4
8.6
8.1
% Change in
Share of
Imports
32.3%
.7%
26.6%
34.7%
-34.9%
-1.5%
12.5%
23.5%
-17.4%
-6.4%
United Nations International Trade
U.S. Share of
World
Exports
26.2
26.3
24.7
30.5
27.1
27.4
29.3
26.8
24.9
23.4
Statistics Yearbook,
for Pesticides,
% Change in
Share of
Exports
40.5% '
.5%
-5.9% ;
23.4%
-11.3%
1.0% ;
7.0%
-8.5%
-7.0%
-6.4%
1977-1987
3.62

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comprise the largest share of the U.S. import market, imports from other countries (such as Brazil) realized
substantial increases in exports to the United States.

3.7     Summary

        During the  1980s the demand for U.S. pesticide products declined. This decline resulted from various
influences, including a decline in agricultural acreage, the introduction of highly concentrated products, more
effective application techniques,  and various environmental influences.  Although these factors resulted in a
contraction of pesticide production and sales, the industry as a whole has remained profitable.  Continued
profitability within the pesticide  manufacturing industry is most likely due to patent protection and producers'
ability to introduce new products with unique uses.

        Data collected in Part A of the Census indicate that the majority of PAIs are produced by only one firm.
Although the production data indicate that firms  have monopoly power for specific PAIs, this situation lends
itself to market power only if no substitutable products exist.  Further analysis of production by cluster reveals
that while most clusters include production from multiple facilities, few facilities produce the same PAI within
clusters. This information indicates that substitutable products exist in the pesticide manufacturing industry, and
suggests that the pesticide market is competitive  with differentiated products.

        The information presented in the profile  provides evidence that although barriers to entry exist in the
pesticide manufacturing industry (e.g., the high R&D costs required to introduce new products), they are
somewhat offset by patent protection. Firms may be willing to incur short-term losses stemming from the
introduction of a new product, knowing that with patent protection they will be able to recover their losses in
the long run.  Because firms require patent protection to  recover large outlays in R&D, it is likely that
competition within the industry will come in the  form of new products, where profits are somewhat protected,
rather than from new  producers  of existing products.

        Although the United States remains a net exporter of pesticides, the value of pesticide  exports decreased
while imports increased during the 1980s.  Factors such as the strong dollar and the implementation of more
stringent environmental regulations in the United States, which made U.S. products more expensive relative to
foreign products, contributed to the deterioration of the United States's trade position in the mid-1980s.
Although competition from western European countries is still the most predominant influence on the United
States's competitive position in the world pesticide market, there is increasing competition outside western
Europe in countries such as Brazil, Korea,  and those in eastern Europe.
                                                   3.65

-------
                                        Chapter 3 References


Kline & Company, Inc. (1986).  PCO Industry Thrives; Hits $2.5 Billion Mark. Pest Control Technology,
       December.

Kline & Company, Inc. (1990).  Kline Guide to the U.S. Chemical Industry, Fifth Edition.  New Jersey.,

Minnesota Department of Agriculture (1989). Rinse and Win Brochure.

National Pest Control Association, Inc. (1991).  Fact  Sheet.

Pimentel, D., et al. (1991). Environmental and Economic Impacts of Reducing U.S. Agricultural Pesticide
       Use.  Pest Management in Agriculture.  CRC press.

Pimental, P. and L. Levitan (1986).  Pesticide Amount Applied and Amount Reaching Pests.  Bioscience, 36,
       86.                                                                                    \

Ribaudo, Marc O. (1989).  Water Quality Benefits from the Conservation Reserve Program. Agricultural
       Economic Report No.  606, February.

Rich, Laurie, A. (1988).  Environmental Concerns Force Global Changes in the Market. Chemical Week,
       May.                                                                                  ;

Shenton, Tom (1989).  Crop Protection: An Agrochemical  Company Perspective. Chemistry and Industry,
       March.

Sine, Charlotte (1990). A Stronger Ag Chem Industry Emerges From the '80s. Farm Chemicals,  January.

United Nations, Statistical Office (1978-1987).  International Trade Statistics Yearbook.  New York.  Annual.

U.S. Department of Agriculture (1984).  Agricultural Statistics 1984. Washington, D.C.              ;

U.S. Department of Agriculture (1989).  Agricultural Statistics 1989. Washington, D.C.

U.S. Department of Agriculture (1989a).  Agricultural Resources Situation and Outlook Report, AR-13.
       Washington, D.C., February.

U.S. Department of Commerce, Bureau of the Census (1986).  1982 Census of Manufactures, Concentration
       Ratios in Manufacturing.  Washington, D.C.

U.S. Department of Commerce, International Trade Administration (1987).  1987 U.S.  Industrial Outlook.
       Washington, D.C., January.

U.S. Department of Commerce, Bureau of the Census (1989).  1987 Census of Manufactures, Preliminary
       Report Industry Series: Agricultural Chemicals.  Washington, D.C., July.

U.S. Department of Commerce, Bureau of the Census (1989a).  1987 Census of Manufactures, Preliminary
        Report Industry Series: Industrial Organic Chemicals.  Washington, D.C., July.

U.S. Department of Commerce, Bureau of the Census (1989b).  1987 Census of Manufactures.
        Washington, D.C., January.
                                                 3.66

-------
U.S. Department of Commerce, Bureau of the Census (1989c).  Statistical Abstract of the United States,  1989.
        Washington, D.C., January.

U.S. Department of Commerce, International Trade Administration (1989d).  1989 U.S. Industrial Outlook.
        Washington, D.C., January.

U.S. EPA, and ICF, Inc. (1980). Economic Profile of the Pesticide Industry. Office of Pesticide Programs,
        August.

U.S. EPA, and Mitre Corporation (1983). The Supply and Use Patterns of Disinfectants and Sanitizers at
        Selected Sites.  January.

U.S. EPA, International Sanitary Supply Association, Research Triangle Institute (1989). Meeting Summary.
        Research Triangle Institute, July.

U.S. EPA (1990). Pesticide Industry Sales and Usage: 1988 Market Estimates.  Office of Pesticides and Toxic
        Substances, February.

U.S. EPA (1991). Pesticide Container Report to Congress, Draft.  Office of Pesticides and Toxic Substances,
        March.

U.S. EPA, and Abt Associates, Inc. (1991a).  Estimates of the Price Elasticity of Demand for Pesticide
        Clusters.  May.

U.S. International Trade Commission (1977-1988). Synthetic Organic Chemicals, U.S. Production and Sales.
        Washington, D.C., Annual.
                                                3.67

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                               Chapter 4:  FACILITY IMPACT ANALYSIS
4.0
Introduction
        This chapter presents the methodology for projecting impacts of the proposed effluent limitations
guidelines and standards at the facility level and describes the results of the analysis. As discussed in
Chapter 1, the facility analysis is the principal building block of the entire economic impact assessment.  The
facility impact analysis is characterized by the following:

        (1)     use of economic models to estimate pre- and post-compliance costs, prices, and quantities for
                groups of pesticide active ingredients (PAIs) produced by individual facilities;
        (2)     application of a discounted cash flow analysis to project facility closures;
        (3)     comparison of unit prices to unit fixed costs plus unit variable costs to project product line
                closures; and
        (4)     use of financial ratios to identify facilities that are expected to sustain significant financial
                impacts, short of closure.
The cost, price,  and quantity outputs from the first step provide input to the facility closure, product line
closure, and significant financial impact analyses of steps 2, 3, and 4.  The analysis evaluates these  three
impacts in a hierarchical manner:  if a facility closes, product line closures and other significant impacts are not
evaluated; if a facility closes a product line, other significant impacts are not evaluated.  This hierarchy
corresponds to the severity of the projected impact; i.e., a facility closure is more severe than a product line
closure, which is more severe than a significant financial impact.

        The impacts are estimated for 88 of the 90 pesticide manufacturing facilities producing one or more of
the 270 PAIs or classes of PAIs considered for regulation. As discussed hi Chapter 2, 90 pesticide
manufacturing facilities completed Part A of the Census and 88 pesticide manufacturing facilities completed Part
B of the Census. Only one of the two pesticide manufacturing facilities from which Part B data were not
obtained is predicted to incur costs (for monitoring only) due  to the proposed regulation.

        This chapter describes the economic models, and then discusses the methodologies for the facility
closure analysis, product line closure analysis, and significant financial impact analysis.  Finally, the facility-
level results are  discussed.
                                                    4.1

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4.1     Economic Model

        Before presenting the specific model used in the analysis to estimate post-compliance costs, prices, and
quantities, a brief overview of the conceptual problem is provided.                                     !

4.1.A  Generalized Model of the Pesticide Manufacturing Industry
        The model of the pesticide manufacturing industry focuses  on the short run.  The focus on the short
run, by definition, limits facilities' and firms' options for responding to increased costs for pollution control and
is therefore conservative (i.e., it tends to overstate impacts).  For example, in the short run, firms cannot
register new products or make major modifications to physical plants.  They are free, however, to decrease
production, increase production (to the extent that capacity is underutilized), or change the production mix when
faced with new pollution control requirements.

        Each facility must decide the quantity of each pesticide to produce, given certain technological
constraints. Some pesticides may have to be produced together if one is a byproduct of the manufacturing
process of another.  Pesticides may also be produced as by-products of other organic chemical manufacturing.
Pesticide manufacturing equipment may be flexible enough so that the facility may choose to use it to produce
an alternate product, perhaps with minor modifications.  A producer may also elect to use  a facility at a higher
level of capacity (perhaps by adding an additional shift), thereby increasing the production of one or more
pesticides.

        In addition to incorporating the short run options, the model must capture the nature of regulatory
compliance costs and their effect on production decisions. Ideally,  these costs are a function of the production
mix. For example, additional controls may be required if a facility decides to produce pesticide i instead of
pesticide j.  A facility may also find that the same controls are required for two different pesticides, so that the
incremental control costs of producing pesticide i may be very  small as long as pesticide k is also  produced.

        Given all these considerations, the profit maximizing problem for  facility f can be depicted as:
                                                    4.2

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                                   = £ W  '=
where:
n,
EC,,
profit of facility f;
price of product i, a function of total industry production of product i (Q;), and
industry production of all products competing with product i;
production of product i by facility f (The sum of the Q^'s, f= 1,N equals QJ;
total cost to facility f of producing product i; and
total pollution control costs to facility f required under the proposed option to produce
product i.
Each facility in the industry attempts to maximize profits simultaneously.  The equilibrium solution is
represented by the matrix Q (total industry production), whose typical element Q^ represents facility f s
production of product i, that solves the profit maximizing problem for all facilities simultaneously.

        Data limitations, however, require that the model be simplified. In particular, the entire production
choice set (of registered products) available to each facility is unknown. Additional engineering studies of each
facility's production process, as well as analysis of firm-level pesticide registrations, would be necessary to
relax this assumption.  Given this limitation, it is assumed that a facility may respond to a new effluent
guideline only by decreasing current production of any or all of the pesticides currently manufactured.  This
assumption does not allow for the production of new chemicals, i.e., those that were not being manufactured
before the guidelines were introduced.  Neither does it allow one U.S.  PAI manufacturer to benefit from the
compliance costs and subsequent decrease in PAI production of another manufacturer. Note that this assumption
is extremely conservative, since it severely limits the options available to each facility and thus overstates the
impact of the regulation.

        This major simplification allows each market to be modeled separately, because the production
decisions no longer affect one another.  If a facility decides to decrease  the production of one chemical, it does
not "free up" capacity to produce another chemical.  As a result, the supply curve for chemical A does not shift
when the supply of chemical B changes.  It now becomes possible to find a new equilibrium in each market
separately and independently.  Built on this generalized model, the applied economic model of the pesticide
manufacturing industry is described below.
                                                   4.3

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4.1.B   Applied Model of the Pesticides Manufacturing Industry
        The construction of a model of the pesticides manufacturing industry, and the simulation of the effects
of new effluent limitation guidelines and standards, require the following basic steps:                   i

        (1)     Define the markets to be analyzed;
        (2)     Determine the basic model of market structure;
        (3)     Estimate baseline prices for each PAI cluster at each facility;                         ;
        (4)     Estimate baseline costs for each PAI cluster at each facility;
        (5)     Adjust baseline costs for other government regulations;
        (6)     Project facility compliance costs;
        (7)     Estimate post-compliance costs for each PAI cluster at each facility;
        (8)     Develop a pricing rule to estimate post-compliance prices for each PAI cluster at each facility;
                and                                                                              !
        (9)     Estimate a price elasticity of demand to solve for post-compliance quantities for each PAI
                cluster at each facility.
 These steps are explained below.

         Markets to be Analyzed
         A market is defined by competing products. Not all PAIs, however, compete with each other at the
 consumer level. For example, PAIs used as herbicides on corn do not compete with PAIs used as fungicides on
 residential gardens.  Neither do all PAIs used as herbicides compete with one another.  Because PAIs compete
 with each other individually or in groups rather than as a whole, separate PAI markets that capture this
 competitiveness are defined.                                                                       ;

         The EPA's Office of Pesticides Programs (OPP) has undertaken a similar categorization exercise for its
 regulatory purposes.  In  1980, the OPP defined pesticide markets to ensure that the EPA regulated competing
 products on roughly the same schedule, so that one pesticide does not have an unfair  advantage over another.
 As described in Chapter  3, the pesticide markets were defined as clusters of PAIs that are substitutes for a
 specific end-use.  For example, insecticides used on corn is one market or cluster.  The OPP assigned each of
 the PAIs registered in 1980 to one of 48 separate clusters1.  As reported hi Section 3.1, the EPA's Office of
 Water made minor adjustments to these pesticide clusters for this analysis.  First, PAIs registered after 1980
     'In the OPP's classification, each PAI appeared in only a single cluster,  since the purpose of the
  classification was to develop a regulatory schedule for each PAI.
                                                     4.4

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were assigned to clusters.  In addition, clusters were split when a wide range of price elasticities of demand
were estimated to exist within a single cluster and it was possible to further differentiate corresponding PAI uses
within the cluster (see Appendix C).  Four clusters  were split, increasing their number from 48 to 56.2
Finally, PAIs were allocated to more than one cluster when the PAI was known to be used in substantial
quantities for different end uses.  The adjusted PAI clusters were used as the basis for this EIA. The 270 PAIs,
or classes of PAIs, considered for regulation are mapped into the 56 separate clusters in Appendix B.

        Basic Model of Market Structure
        Assumptions made about market structure have important implications for empirical modeling.  For
example, the standard model of supply and demand (i.e., perfect competition) necessarily predicts at least one
facility closing if production costs  increase.  (When the supply curve shifts up to reflect the cost increase,
quantity must decrease and the marginal facility must close.) The production data contained in Part A of the
Census indicates that most clusters include production by several different facilities.  In addition, Part B of the
Census shows that the pesticide manufacturing facilities experience a range of profitability.

        This situation suggests that the pesticide manufacturing markets can be characterized as competitive.
The market does not appear to be perfectly competitive, however, since few firms produce the same PAI;
product differentiation exists within the markets.  For example, PAIs within a cluster may be differentially
effective on a regional basis due to climate differences.  PAIs may also vary in their effectiveness on different
varieties of pests and on different varieties of crops. The structure of the pesticide markets can therefore
generally be described as competitive with differentiated products (i.e., monopolistic components). In an
industry with these characteristics, different prices may exist for products within a single market.  Firms  must
compete  for customers in terms of both price and the lands of products they sell. Also, new firms may enter
the industry with a new product whose differentiation from its competitors' products may make it profitable.

        Baseline Prices for Each Pesticide Cluster at Each Facility
        Baseline prices for each PAI cluster at each PAI manufacturing facility served as foundations of the
economic model. To estimate prices at the cluster level for each facility, prices were first estimated at the PAI
level for each facility in one of five ways, as described below.
          45 of these clusters had production of one or more of the 270 PAIs or classes of PAIs in 1986.
                                                   4.5

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              PAI-speclfic data provided.  Provision of PAI-specific prices in the Census was optional. If
              these data were provided, they were used in the analysis.  Seventeen of the 88 pesticide
              manufacturing facilities (19 percent) chose to provide price data on their technical grade  .
              products.3
              PAI-specific data not reported in the Census and only one in-scope PAI produced. In this
              case, reported in-scope revenues were divided by the production quantity of the PAI to obtain
              the PAI price.                                                                      '.
              PAI-specific data not reported in the Census, multiple PAIs are produced, and price data for
              all the PAIs are available from a secondary source.  Secondary data on prices were obtained
              from Agchemprice (DPRA,  1990), the Annual Market Survey (Doane Marketing Research^
               1987),  telephone calls  to PAI dealers, and EPA estimates. These secondary prices are
              reasonable indicators of the relative prices of the PAIs.  If used directly, however, the
               secondary prices may overstate the price the manufacturer receives for PAIs, because    \
               manufacturers may offer volume discounts or sell to a wholesale distributor.  Because most
               facilities in the Census reported their production of, and revenues from, in-scope PAIs, facility
               PAI prices were estimated using these Census data and the relative, rather than the actual, PAI
               prices from secondary sources.  For example, assume Facility A produces two in-scope PAIs.
               From secondary sources, the price of PAIt is found to be twice the price of PAL,. If Facility
               A reported producing  200 pounds of PA^ and 500 pounds of PAL,, with total in-scope
               revenues of $4,500, the analysis would calculate the price of PAIj as:

                                        200(2/>) +500(p)=$4,500
               where p = the price of PAIj.

               The solution for "p" is $5. PAIj would therefore be estimated to have a price of $10.

               PAI-specific data not reported, multiple PAIs are produced, and price data from a secondary
               source is available for only some of the PAIs produced. For those PAIs for which secondary
               price data is not available, prices were estimated by first dividing facility in-scope revenue by
               facility in-scope production.  Using these average prices, the analysis proceeded as described
               in the above paragraph.
    Seventeen facilities provided PAI-specific data for technical products, nine facilities provided data on
formulated/packaged products, and two facilities provided data on intermediates.  A total of twenty facilities
provided PAI-specific data for at least one of these product groups.
                                                  4.6                                            !

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        •       PAI-speciftc data not reported, in-scope revenue not reported, secondary price information is
                available for all PAIs produced.*  In this situation, the secondary price information was used
                directly to estimate price.

Cluster-level prices for each facility were then generated as a weighted average of the PAI prices hi each
cluster. The weightings were based on the production quantities of each PAI at the facility.

        Baseline Costs for Each Pesticide Cluster and Facility
        Baseline (i.e., pre-compliance) costs were needed for the EIA.  Specifically, unit fixed costs and unit
variable costs by cluster were required for each facility. The methods of estimating fixed costs and variable
costs differed, as discussed below.

        Fixed costs were reported on a facility-level in the Census, not on a PAI-specific or a pesticide-related
basis.  Fixed costs for all in-scope PAIs at a facility were estimated by multiplying 3-year average (1985, 1986,
and  1987) total facility fixed costs by the 3-year average percentage of facility revenues derived from sales of
in-scope pesticides.5 This is represented by the equation:

                                              IF=F x (JRJTR)
where:
IF
F
IR
TR
fixed costs associated with in-scope PAIs;
3-year average fixed costs for the entire facility;
3-year average revenues from in-scope PAIs; and
3-year average total facility revenues.
Cluster-level fixed costs were then allocated based on the revenues for each cluster.  Unit fixed costs at the
cluster level were calculated as total cluster fixed costs divided by the in-scope cluster production quantity.
    "Prices were estimated in this manner for only one facility projected to incur compliance costs.  This
facility's only pesticide-related revenues were for tolling.  Due to the construction of the Census, tolling
revenues cannot be separated into sales of in-scope vs. other pesticides. For this reason, the reported revenues
could not be used to estimate prices of in-scope PAIs.  This facility incurs only monitoring costs under the
proposed option.
    5Three-year averages were used hi an effort to modulate the variability of particular years and to create  data
that represents a typical year.
                                                    4.7

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        Variable costs were estimated in one of two ways, depending upon whether the facility provided PAI-
specific data in the Census.6 For those facilities that provided PAI-specific unit variable costs, these costs were
multiplied by PAI-specific production to  obtain total variable costs for each PAL  These variable costs were
then summed within clusters.  The cluster variable costs were divided by total in-scope production for that
cluster to obtain an average unit variable cost for each cluster.
        If no PAI-specific data were provided, estimates of unit variable cost at the cluster level were generated
assuming a constant (average) profit margin across all pesticide products.  Facility pesticide-related variable
costs as a percent of facility pesticide sales were multiplied by the unit price of each PAI cluster at the facility
to arrive at that cluster's unit variable costs.  Algebraically, unit variable costs for each cluster at each facility
were calculated as:
                                           UVC. = P, x  (V/PR)
where:
V
PR
unit variable costs associated with cluster j;
price of cluster j;
3-year average variable (i.e., manufacturing) costs associated with pesticides; and
3-year average revenues from all pesticides.
Unit variable and fixed costs were summed to estimate cluster total unit costs for each facility.           !

        Baseline Cost Adjustments Due to Other Government Regulations                          .
        Since 1986, the principal year for which much of the Census data were collected, the EPA has  ,
promulgated two regulations whose compliance costs to facilities are not reflected in that data.  These
regulations are (1) Resource Conservation and Recovery Act (RCRA) land disposal restrictions  (40 CFR 268),
and (2) effluent guidelines for the Organic Chemicals, Plastics, and Synthetic Fibers (OCPSF) industry (40 CFR
414).7 The costs associated with these regulations are not reflected in the cost data reported by facilities in the
Census.  To accurately represent the costs faced by the pesticide manufacturing industry, the costs associated
with these regulations are added to reported facility fixed costs.  The procedure for allocating these  costs to PAI
clusters is identical to the allocation of facility reported fixed costs (discussed above).  The regulations and their
impacts on pesticide manufacturing costs are discussed below.
    6As previously discussed, provision of average unit variable costs by PAI code was optional in the Census.
Respondents who chose not to provide these data were informed by the Census that EPA would use financial
averages to represent all products at a facility.                                                        :
    Compliance costs for OCPSF include changes to the Economic Impact Analysis through January 21,1992.
                                                    4.8

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        The 1984 Hazardous and Solid Waste Amendments (HSWA) to RCRA had several new provisions,
some of which went into effect after 1986.  In particular, the Land Disposal Restrictions included in HSWA are
likely to have affected PAI manufacturers.  These regulations prohibit land disposal of hazardous waste until it
has been treated to the level achieved by the Best Demonstrated Available Technology (BOAT).

        Congress directed the EPA to write the rules in three stages. Stage 1 regulated solvents and dioxin and
was promulgated in 1986. Stage 2, signed July 8, 1987, regulated a group of wastes known as the  "California
List."  For Stage 3, the remaining hazardous wastes were divided into thirds, and signed into regulation on
August 17, 1988; June 23, 1989;  and May 8, 1990. Each of these rules became effective immediately upon
promulgation.

        Many pesticide manufacturers generate RCRA-listed wastes as a result of pesticide production, and will
therefore have incurred costs of complying with the land disposal restrictions since 1986.  For this reason, the
compliance costs estimated for the "California List" and the Stage 3 hazardous wastes were added to the
baseline fixed costs for PAI manufacturers8. The cost estimates were developed from two sources.  The 1986
Survey of Hazardous Waste Generators  (GENSUR), conducted by the EPA's Office of Solid Waste, was used
to determine the waste streams for pesticide manufacturing facilities.  These data were combined with cost data
from the Regulatory Impact Analyses (RIAs) for the land disposal rules.  Of the 90 facilities potentially covered
by the pesticide manufacturers effluent guidelines, 45 facilities were included in the GENSUR data base.  The
GENSUR data are organized by facility and waste stream. For each facility and waste stream, the following
data were available:

        •       RCRA waste codes (up to 10 codes per waste stream);
        •       Quantity of waste generated on-site and quantity disposed off-site;
        •       On-site waste management train (up to 10 waste management procedures); and
        •       Off-site disposal train

For purposes of estimating costs associated with the land disposal restriction rules, the data were first scanned to
select  only those components dealing with land disposal, e.g., landfill, surface impoundments, and waste piles.
The RIAs for the first and last third of the Stage 3 Land Disposal Restrictions included total gallons of waste to
    8Because Stage 1 of the rule became effective in 1986, the costs associated with this rule are assumed to be
 reflected in the Census data.
                                                   4.9

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be treated and total incremental costs by baseline management practice and RCRA waste code.  This allows
calculation of unit (per gallon) costs for each RCRA waste by management practice.9                    :

        For each pesticide manufacturing facility and waste stream, management and RCRA waste codes were
matched to the corresponding codes in the RIA to obtain unit costs for each facility, waste stream, and
management combination.  These unit costs were then multiplied by the appropriate quantities (e.g., gallons of
each waste at each facility managed, using each relevant method) to estimate a total cost for each RCRA rule.

        Because the middle third of the Stage 3 rule was not considered to be a major regulation (costs were
less than $100 million), compliance costs were not available in similar detail.  The available information
included total quantity of regulated waste generated and total incremental costs by baseline management practice
(i.e., not broken down by RCRA waste code).  It was therefore necessary to assume that the wastes covered by
this rule had the same unit costs.  Given the small number of wastes in this group, this assumption is not
expected to affect the analysis substantially.                                                          j

        Costs of complying with restrictions on land disposal  of the California List were available in a third
format. The RIA contained a table showing total land-disposed wastes and associated costs by four-digit SIC
codes.   SIC 2879 (pesticide and agricultural chemicals, not elsewhere classified) was among the industries
shown. An average unit cost was estimated by dividing total compliance costs by total regulated wastes that
were land disposed.  This unit cost was assumed to be constant across all RCRA wastes.

        Thirty-four pesticide manufacturing facilities incurred costs due to the RCRA rules described above.
Total annualized RCRA costs for these facilities are estimated to be $1.3 million (1986 dollars).  Not all of
these costs may have been borne by the pesticide manufacturers; however, a portion may have been passed
through to customers  in the form of higher prices.  Because no data on the portion of costs likely to be passed
through to customers  are readily available, the analysis assumes that the burden of the cost increase is split
evenly between the facilities  and the customers. In other words, the facilities are assumed to bear 50 percent of
the cost increase10.  These costs were added to the baseline fixed costs of the affected facilities.
    *The RIA for the first third examined two alternatives and two scenarios within the first alternative. The
costs for Alternative A, Scenario I were used because this option was closest to the final rule.
    10An alternate assumption, in which all RCRA compliance costs were borne by the manufacturers, would
result in the projection of additional baseline closures in the current analysis. As a result, fewer closures
resulting from the pesticide effluent guideline limitations and standards would be projected. EPA therefore
believes that the assumption of a 50 percent cost pass-through is conservative.
                                                   4.10

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        The final OCPSF Effluent Guidelines, issued November 1987, established effluent limitations guidelines
and standards for OCPSF process wastewater.  The regulations for direct dischargers covered about 60 priority
pollutants; those for indirect dischargers covered 47 priority pollutants. For purposes of the regulation, OCPSF
process wastewater  was defined to include establishments, or portions thereof, whose products are classified in
any one of five SIC codes: SIC 2821 (plastics and resin materials), SIC 2823 (cellulosic manmade fibers), SIC
2824 (non-cellulosic synthetic fibers), SIC 2865 (tar crudes, cyclic intermediates, dyes and organic pigments)
and SIC 2869 (industrial organic chemicals, not elsewhere classified). Most facilities were required to comply
with these regulations by November 5, 1990.

        Substantial overlap exists between facilities subject to the OCPSF effluent guidelines and those covered
by the proposed pesticide manufacturer effluent guidelines.  (Manufacture of organic PAIs is included in SIC
2869.)  Of 90 facilities in the Census, 55 also manufacture  compounds regulated under the OCPSF rule.  Thirty
of these pesticide manufacturers incur costs to comply with the OCPSF effluent guidelines.  The estimated costs
to comply with the pesticides effluent guidelines will be incremental to those of meeting the OCPSF rule. For
this reason, OCPSF costs for all facilities affected by both rules are added to the economic baseline.  Capital
and annualized  OCPSF costs  for these 30 facilities total $105 million and $36 million, respectively (1986
dollars).  Again, 50 percent pass-through to the customers is assumed.  As a result, additional annualized fixed
costs for all pesticide manufacturing facilities due to OCPSF effluent guidelines total $18 million.11

        Facility Compliance Costs
        Full details of the methods by which the costs of complying with the proposed regulation were
estimated can be found in the Technical Development Document (Chapter 8, Engineering Costs and Non-Water
Quality Aspects). A brief summary of the regulatory options and their associated costs is provided below.

        As discussed previously, a total of 90 pesticide manufacturing facilities producing one or more of 270
PAIs, or classes of PAIs, are potentially subject to regulation.  The EPA has projected costs for these 90
facilities under two regulatory options: one that would require treatment of process wastewater pollutants
(Treated Discharge Option) and another that would require  no discharge of process wastewater pollutants to
POTWs or surface water (Zero Discharge Option).  The Treated Discharge Option limitations would be based
on the use of hydrolysis, activated carbon, chemical oxidation, resin adsorption, solvent extraction, incineration
and/or recycle/reuse to control the discharge of PAIs hi wastewater.  The Zero Discharge Option is based on
    "Estimated costs of compliance may vary substantially from actual costs incurred, since companies
 frequently meet regulatory requirements by means other than those the EPA used for estimating compliance
 costs.
                                                   4.11

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on-site or off-site incineration and/or recycle/reuse.12 For both regulatory options, the economic impacts on
facilities were calculated separately for direct and indirect dischargers.13  Each discharge category was
analyzed further for two subcategories: organic pesticide chemicals manufacturing (Subcategory A) and
metallo-organic pesticide chemicals manufacturing (Subcategory B).

        Three categories of compliance costs associated with pesticide manufacturing were evaluated: capital
costs, land costs, and operating and maintenance costs (including compliance self-monitoring and sludge
disposal).  The capital and land costs were one-time "lump sum" costs; the operating and maintenance costs
were evaluated on an annual basis. Capital and land costs, annualized using the conservative assumption that
they have a productive life of ten years, were adjusted over the ten-year period using the weighted average cost
of capital.14 These annualized capital and land costs were added to operating and maintenance costs to produce
total annualized costs. For facilities that both manufacture and formulate/package pesticides, the compliance
costs apply only to the manufacturing operations of the facility.  All of the compliance cost estimates  are  |
presented in 1986 dollars and are based on the assumption that, whenever possible, facilities will build on
existing treatment.
        The costs and impacts of implementing the regulations were estimated on a PAI-specific basis for each
facility.  Table 4.1 presents the capital and land, operation and maintenance, and annualized costs associated
with the two regulatory options for Best Available Technology Economically Achievable (BAT) and      i
Pretreatment Standards for Existing Sources (PSES) by subcategory.  Under the Treated Discharge Option,  it is
expected that 61 pesticide manufacturing facilities will incur compliance costs: 32 direct dischargers and 30
indirect dischargers (one facility is  a joint discharger).  Under the Zero Discharge Option, 67 facilities are
projected to incur compliance costs: 35 direct dischargers and 33 indirect dischargers (again, one facility is  a
joint discharger).  Under the Treated Discharge Option, total BAT annualized costs (applying to direct
dischargers) are projected to be $14.7 million for Subcategory A.  There  are no BAT costs associated with
Subcategory B chemicals.  These chemicals are already limited by Best Practicable Control Technology
Currently Available (BPT), which requires no discharge of process wastewater pollutants. Total annualized
    iaThe Zero Discharge Option would limit discharges from the facility site to POTWs or to surface water
only; discharges to other media may remain constant or increase as a result of changes in discharge to surface
water.  For example, pesticide manufacturing facilities could, theoretically, achieve compliance with a zero
discharge effluent guideline by transferring the waste streams previously discharged to surface water to landfills,
incinerators, or deep well injection sites.
    'Impacts of zero discharge requirements are reported with impacts on direct discharge requirements.  Zero
dischargers may be subject to monitoring costs if they have any process wastewater.  Monitoring costs would be
imposed by the permitting authority (no separate monitoring requirements are contained in the proposed effluent
guidelines for pesticide manufacturers).  These monitoring costs are included in the analysis to capture the full
cost to industry of controlling process wastewater pollutants.
    MFor details on the weighted average cost of capital, see Section 4.2.A.
                                                   4.12

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                 4.13

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costs for PSES (applying to indirect dischargers) under the Treated Discharge Option are projected to $5.9
million and $120,000 for Subcategories A and B, respectively.15

        The costs associated with the Zero Discharge Option are substantially higher than those for the Treated
Discharge Option.  Total Zero Discharge Option BAT annualized costs are projected to be $4.8 billion for
Subcategory A.  Again, there are no costs associated with Subcategory B chemicals under the proposed BAT
guideline.  Total annualized costs for PSES under the Zero Discharge Option are estimated at $518.8 million
and $2.8 million for Subcategories A and B, respectively.                                             i

        Post-compliance Costs for Each PAI Cluster at Each Facility
        As stated above, the compliance costs were estimated on a PAI basis for each facility. To combine
compliance costs with other facility costs, cluster-level  compliance costs for each facility were calculated by
summing annualized PAI compliance costs for all PAIs within each cluster for each facility.  Dividing total
cluster-level compliance costs for each facility by the cluster production quantity at that facility yielded unit
compliance costs for each market and each facility.  These costs were added to baseline unit costs to arrive at
post-compliance unit costs.

        Pricing Rule to Estimate Post-compliance Prices16
        Changes in PAI prices and product demand are determined Interactively in the market place.
Typically, a producer will raise prices based on the actions expected of competitors and the extent to which
consumers  will decrease demand.  Consumers will then respond to the increased prices with a drop in demand
based on several factors, including the percent of their  production cost contributed by the product and the
availability of substitute products.  Producers then examine the impact of the price  increase and demand   i
decrease on profitability and reevaluate their price.  Consumers again react.  This iterative process continues
until producers believe they have maximized profit.

        This analysis attempts to model an approximate end point of the supply and demand interaction.  The
percentages of the compliance costs that are translated to price increases for each cluster depend on (1) the
degree of substitutability of alternative products, and (2) the extent of supplier price competition.  Substitution
among PAIs is included by addressing impacts on a cluster basis. Substitution of PAIs with non-chemical
alternatives is discussed in the following section on post-compliance quantities.
    15The EPA is not proposing to regulate Subcategory B chemicals at this time.
    "An analysis of economic impacts based on zero pass-through of compliance costs to consumers is
presented in Appendix D.
                                                  4.14

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         A pricing rule was developed to take into account the effect of supplier competition on the percentage
 of compliance costs that are passed to the consumer.17  This rule is based upon the assumption that if
 production bearing compliance costs makes up a small percentage of total cluster production, then a price
 increase due to regulation is unlikely. If all production in a cluster is projected to bear compliance costs, then
 all regulatory costs are likely to be reflected in higher prices.

         To capture this effect, price increases for each market and each facility were calculated as:
 where:
 APf!
 PCj
 T;
change in unit price for facility f, cluster j;
unit compliance costs for facility f, cluster j;
total U.S. production of cluster j that incurs compliance costs; and
total U.S. production of cluster j.
The quantity of PAI production in each cluster incurring costs was calculated from the production data provided
in the Census (Parts A and B) and the estimated compliance costs.  Total production of PAIs for each cluster
was calculated from the Census and other proprietary data.  Post-compliance unit prices were calculated for
each facility and each cluster as the baseline unit price plus the change in unit price due to the installation of
pollution control equipment.18

         Post-compliance Quantities
         Having estimated post-compliance costs and prices, the remaining step solved for post-compliance
quantities.  An estimate of the price elasticity of demand for each cluster was used to predict changes in
quantities demanded given changes in price. The price elasticity of demand can be defined as the percentage
change in the quantity demanded, divided by the percentage change in price.  If consumers cut back their
purchases to such a large extent that any price increase reduces total revenues, then demand is said to be elastic,
i.e., customers are sensitive to price changes.  If consumers cut back their purchases only slightly in response to
higher prices, resulting in an increase in revenues, demand is said to be inelastic,  i.e., customers are not as
    "Theoretically, the effects of supplier competition could be evaluated by modeling a supply curve in the
pre- and post-compliance scenarios.  This model was not used for the EIA because production cost data for
pesticides not included in the Census are unavailable.  In addition, production cost functions within facilities are
also unknown, allowing only marginal costs of production to be estimated.
    18The  pricing rule is not meant to be a perfect theoretical simulation of the price response to regulatory cost
increases.   Given the uncertainty and limited availability of data on production functions and costs by facility
and PAI, use of the measure provides a reasonable basis for simulating the pricing response by producers.

                                                 4.15

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sensitive to price changes.  The value of the price elasticity of demand is unbounded and may be positive or
negative.  It is expected, however, that price and demand are negatively correlated, i.e., an increase in price
results hi a decrease hi the quantity demanded.  The price elasticity of demand is therefore usually negative.

        The methodology for generating estimates of the elasticity of demand relied on five sources. First, the
EPA reviewed empirical studies of the price elasticity of demand for pesticides.  Few such studies were located,
however, and the existing studies offer conflicting conclusions, most of them controversial.  Second, the EPA
reviewed the U.S. Department of Agriculture's (USDA, 1985) analysis of the price elasticity of demand for
food commodities.  The elasticity of demand for farm inputs can be derived from the elasticity of demand for
farm commodities because demand for production inputs must ultimately reflect demand for the end product.
For this reason, the USDA estimates of the elasticity of demand for food commodities provided the basis for
estimating the demand elasticity for PAI clusters. Three additional factors were examined as  indicators of how
the demand elasticity for PAIs might vary from the demand elasticity for food: (1) the feasibility of employing
non-chemical or non-biological pest control methods, (2) the percent of production cost contributed by the PAIs
of interest, and (3) the productivity of expenditures for PAIs.  The elasticity estimates generated from this
process were reviewed by OPP  staff, whose comments were incorporated into the methodology.  A complete
description of the process by which the elasticity estimates were developed can be  found hi Appendix C.

         A list of the elasticity estimates by cluster is shown in Table 4.2, in order of increasing elasticity of
demand. As can be seen from the table, the elasticity estimates range from -0.12 (herbicides on sugar beets,
beans, and peas) to -1.38 (fungicides on grapes and herbicides on grapes).  The elasticity estimates vary
substantially within the fungicide, herbicide, and insecticide clusters; the type of pesticide is not seen to affect
 the elasticity of demand.                                                                           ;

         The demand for pesticides hi all but three of the clusters is  expected to have unit elasticity (i.e., -1) or
 to be inelastic.  Demand is expected to be elastic for fungicides and herbicides applied to grapes and for
 insecticides applied to cotton.  The main factor driving the high elasticity for the grape clusters is the high
 elasticity of demand for grapes  at the retail level. Demand for insecticides on cotton is expected to be
 somewhat elastic, based on both the literature estimates of the elasticity and the low marginal productivity of
 insecticides applied to cotton.

         The methodology employed to estimate the elasticity of demand for the PAI clusters yields reasonable
 best estimates of elasticities.  The estimates are a good indicator  of whether demand for a certain cluster of
 PAIs is extremely or only moderately elastic or inelastic;  the specific numeric values should not be viewed as
 definitive. The estimates of elasticity of demand for clusters of PAIs, developed through this analysis,  are the
 most reliable estimates known at this time.
                                                    4.16

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                                          Table 4.2
                       Summary of Estimates of Elasticity of Demand
                             .for Clusters with Production, 1986
                                            Page 1
Cluster
Elasticity Estimate
Herbicides on sugar beets, beans, peas
Herbicides on tree fruits (except oranges), sugar cane, nuts
Herbicides on tobacco
Fungicides on fruit and nuts trees (except oranges)
Fungicides for seed treatment
Herbides on vegetables
Fungicides on grain in storage
Insecticides on vegetables
Slimicides
Fumigants and nematicides
Insecticides on termites
Wood preservatives
Insect repellents at non-agricultural sites
Domestic bug control and food processing plants
Mosquito larvacides
Fungicides on turf
Industrial preservatives
Insecticide synergists and surfactants
Plant regulators, defoliants, desiccants
Sanitizers - dairies, food processing, restaurants, air treatment
Insecticides on livestock and domestic animals
Industrial microbicides, cutting oils, oil well additives
Preservatives, disinfectants, and slimicides
Fungicides - ornamentals
Insecticides on lawns, ornamentals and forest trees
Molluscides and misc. vertebrate control agents
        -0.12
        -0.20
        -0.20
        -0.23
        -0.27
        -0.27
        -0.31
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
        -0.33
                                          4.17

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                                        Table 4%
                      Summary of Estimates of Elasticity of Demand
                            for Clusters with Production, 1986
                                f           f/t
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 4.2     Facility Closure Analysis
         As previously discussed, the results of the economic model described above are used to estimate three
 potential impacts of the proposed effluent limitations guidelines at the facility level.  The first, and most severe,
 potential impact on a facility is facility closure.  For purposes of this EIA, a pesticide manufacturing facility is
 defined as the portion of the facility involved in manufacturing and formulating/packaging, or performing
 contract work for both in-scope and out-of-scope pesticides.  A pesticide manufacturing facility, as defined for
 this analysis,  does not include any non-pesticide related activity occurring at the physical facility.  A pesticide
 manufacturing facility that is predicted to close may continue with non-pesticide-related operations, such as
 production of other organic  chemicals.  Facility liquidation value, in the case  where other products are produced
 at the facility, refers to the liquidation value of the pesticide product lines and any related fixed assets, working
 capital, and real estate.

         A decision to close a facility is typically made at the firm level. The firm holds pesticide registrations
 and can consider transferring both pesticide and other products among facilities. In general, a facility owner
 (i.e., a firm)  faced with pollution control requirements must decide whether to make the additional investment in
 pollution control, to change  the products produced at the facility (both in-scope and out-of-scope), or to liquidate
 the facility. Because data on other products to which a facility may convert are unavailable or limited, this
 analysis assumes that either the pollution control investment is made or the facility is liquidated.  This
 simplification ignores the possibility that the pesticide product lines at some facilities may be used for the
 production of other chemicals.  The analysis is conservative in that it assumes that facility owners have very
 limited options.

        The evaluation of whether to close a facility is complex and involves a number of factors including:

        •       Present and expected profitability of the facility;
        •       Current market or salvage value of the facility;
        •       Required capital investment in pollution control technology equipment;
        •       Expected increase in annual operating costs due to pollution control requirements;  and
        •        Expected product price, production costs, and profitability of the facility after pollution control
                 equipment is installed and operating.

        In the majority of cases, a rational owner would decide to continue operations  if the discounted cash
flows are  greater than the current liquidation value of the facility.  If the expected cash flows are less than the
current liquidation value of the facility, the owner would be better off selling the facility.
                                                   4.19

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        The calculation used to estimate whether or not a facility will close is intended to model the decision-
making process of the owners of the facility. It compares the value of the facility if it is shut down to its value
if the necessary treatment were installed and operations continued.  Specifically, this calculation entails a
comparison of the present value of the cash flow (i.e., discounted cash flow) generated by the facility to the
liquidation value of the facility.  That is, it compares the value that the firm would receive from its future
stream of profits if it continued to operate the facility to the value that it would receive if it sold the facility for
its liquidation value. If the liquidation value of the facility is greater than the discounted cash flow, the facility
is considered to be a closure.

        The analysis of facility closure was conducted in two stages: baseline and post-compliance with the
proposed effluent limitations guidelines.  If, in  the baseline analysis, a facility was projected to close regardless
of the imposition of compliance costs, such a facility was not seen as financially viable. If a facility closed in
the baseline analysis, it was not considered in the post-compliance analysis. In other words, no economic
impacts of the proposed regulation on baseline  facility closures were predicted.

4.2.A  Baseline Facility Closure Analysis
        The steps in the construction of the baseline facility closure analysis involved the estimation of four
variables: facility cash flow, cost of capital, discounted cash flow (DCF), and liquidation value.  These   .
variables are discussed below.                                                                       ;

        Facility Cash Flow
        Facility cash flow consists of facility net income plus noncash expenditures.  Baseline,  or pre-    ,
compliance, facility cash flow was estimated based on data from the income statement reported  in the Census.
Cash flow was adjusted to account for the costs of complying with the RCRA land disposal restrictions and the
OCPSF effluent limitations guidelines. As discussed above, these rules (or portions thereof) were effective after
 1986, the base year for the analysis. The compliance costs associated with the rules  were therefore not
                                                                                                   f
reflected in the Census data.  Specifically, cash flow for each facility was estimated as:

                                    CFO=NI+IW.-CT)+DEP-OC(1-CT)
 where:
         CFO    =      Cash flow;
         MI      =      Net income (i.e., after tax profits calculated from the Census);
         IT      =      Interest expenses (taken directly from the Census);
                                                   4.20

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         CT

         DEP    =
         OC      =
Corporate income tax rate (calculated as taxes divided by before-tax profits calculated
from the Census);
Depreciation expenses (taken directly from the Census); and
cost of compliance with other EPA regulations first effective after 1986 (RCRA land
disposal restrictions and OCPSF effluent guidelines).
         Cost of. Capital
         The cost of capital is the rate at which a firm obtains funds for financing capital investments.  The cost
 of capital is required for two purposes: (1) to discount future cash flows for the facility closure analysis so that
 the present value  of cash flows can be compared to the facility liquidation value; and (2) to annualize the capital
 costs associated with the proposed rule so that post-compliance changes in cost and price can be projected.19

         The cost  of capital to a particular firm depends on how the investment is financed.  One option, equity
 financing, is taken when a firm issues stocks or retains earnings. A second option involves acquiring additional
 debt, through bonds, notes, or short-term commercial paper.20  Typically, acquiring debt is the less expensive
 option.  As a firm expands its debt holdings, however, the cost of debt increases, forcing the firm to reach an
 equilibrium between debt and equity financing. It is assumed in this analysis that firms use some combination
 of debt and equity to finance compliance costs. The measure of a firm's overall cost of a capital investment,
 based on the percentage values of debt and equity used to finance the investment, is termed the weighted
 average cost of capital (WACC).  Thus,  the WACC is the average after-tax cost of all funds used to  finance a
 capital investment.

        The WACC can be presented in either nominal terms (i.e., not adjusted for inflation) or real terms
 (i.e., adjusting the nominal WACC for inflation).  This analysis uses the real  cost of capital to allow  for the use
 of constant annual cash flows (i.e., cash  flows that are not inflated over time).  The two inputs to calculating the
 real WACC - nominal WACC and the inflation rate - are discussed below.
    19The cost of capital is determined by firm, rather than facility, characteristics.  As a key variable in the
facility level analyses, however, it is discussed in this section.
    ^Debt capital is provided as a loan which creates a contractual obligation on the borrower to repay the loan
and contractually specified interest charges.  Traditional sources of debt financing include commercial banks,
non-bank lending institutions, and the public capital markets.  Except as provided by a security agreement, debt
financing does not provide the creditor any rights of ownership in the assets of the borrower.  Equity capital
represents a right of ownership in the assets of the  firm seeking to finance a treatment system (e.g., a
corporation or sole proprietorship).  Equity capital  may be obtained as externally provided funds (through the
sale of new equity) or may be  generated internally  (from the cash flow provided by the firm's operations).

                                                 4.21

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        Nominal WACC
        The nominal WACC was calculated by weighting the cost of equity and the cost of debt by the
percentage of the investment expected to be financed by these two methods.  The equation used was:

                                      WACC=R(EfA)+Y(l -CT)(PIA)
where:
WACC =      nominal weighted average cost of capital;
R      =      after-tax return on equity;
E      =      amount of investment financed by equity;
A      =      total amount of the investment;
Y      =      pre-tax interest rate on debt;
CT     =      marginal corporate tax rate; and
D      =      amount of investment financed by debt.

The estimates of the nominal WACC vary by firm.  The sources of each of the variables hi the WACC equation
are discussed below.

        The percentages of the investment that a firm is assumed to finance through equity (e/a) and debt (d/a)
are assumed to match the firm's historical mix of equity and debt investment.  The values of these variables for
each firm are obtained from one of two sources.  For each domestic public-reporting firm, the mix of debt and
equity is obtained from Standard and Poor's  Compustat service for that firm in 1986.  For all firms not included
in the Compustat data base, the mixture of debt and equity financing was assumed to match the 1986 median
mixture of debt and equity financing for the  "industrial chemical industry" as calculated from Robert Morris
Associates' Annual Statement Studies.'11 The calculated values taken from the Annual Statement Studies are
40.5 percent equity financing and 59.5 percent debt financing.

        The annual return on equity (R) was calculated as:
     2lThe "industrial chemical industry" includes SICs 2861, 2865, and 2869.
                                                 4.22

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where:
i       =      the risk-free rate of return = 10.18 percent (calculated from the 1981-1990 average interest

                rate on 30-year U.S. Treasury Bonds as reported in Statistical Abstract of the United States,

                Bureau of the Census,  1989, 1990);22


(Rm-l)=        Typical risk premium,  or the rate of return on market portfolio minus the rate of return on risk

                free investments = 8.0 percent, a standard value based on the Standard & Poor's 500.
13
A measure of the risk of an individual firm compared with the market. Beta values are based
directly on Value Line Investment Survey, Part I Summaries & Indexes (February 14,  1992) for
publicly traded companies.  For private firms, the median beta value calculated for the public
PAI manufacturing firms was used. This value is 1.056, indicating that the average risk of the
public PAI manufacturing companies is close to the market average risk.
        The pre-tax interest rate on debt (Y) is assumed to be 10.95 percent.  This interest rate equals the
1981-1990 average yield on AA 10-year industrial bonds (U.S. Department of Commerce, 1990 and 1991).23
Finally, the marginal corporate tax rate (CT) is assumed to be 34 percent.24


        Real WACC
        To allow the use of cash flows that are not adjusted for inflation, the real WACC was needed.  The
real WACC was estimated as:
    22The variable i represents the risk-free component of the return on equity.  Equity has no maturity date;
therefore, i is best calculated as the return on long-term Treasury Bonds.

    ^Interest rate information reported by individual facilities in the Census was not used for this analysis due
to difficulties of interpreting the reported values.  For example, a number of respondents reported that funds for
capital outlays were obtained from a parent firm at zero percent.  This reporting reflects internal accounting
conventions but does not accurately represent the interest cost borne by the firm for debt financing.  Other firms
indicated that interest costs were tied to the prime rate (e.g., prime rate or "prime rate plus one").  Such
interest terms  would generally apply to a working capital credit line or other short-term credit instrument. The
short-term liabilities are usually replaced,  however, by longer-term debt to match the expected life of the capital
asset being financed.  The interest rate charged on longer-term debt is usually higher than that associated with
short-term credit rates,  so short-term rates may understate potential interest costs. The resulting WACC used
for each facility in the EIA is higher than the cost of debt reported in the Census for that facility, thereby
increasing the projected burden of compliance.  Use of the WACCs is therefore conservative.

    ^Because  the/I/TO,  not the facility, tax rate is needed, use of the facility-level data from the Census was
inappropriate.

                                                  4.23

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                                      RWACC=(£i+WACC)l(l
where:
RWACC
G
the real weighted average cost of capital; and
the rate of inflation = 4.74 percent.
The rate of inflation (G) is calculated as the mean annual inflation rate as reported by the unadjusted Consumer
Price Index between 1981 and 1990.

        Discounted Cash Flow
        The discounted cash flow (DCF) is the present value of a stream of annual cash flows.  In this analysis,
the ten-year DCF was compared to the liquidation value of each facility to predict facility closures.  The DCF is
calculated as:
                                                10
                                                       CF
                                               ti (l+RWACC)'
where:

DCF     =     facility present value cash flow over 10 years;
CF       =     facility annual cash flow;
RWACC =     the real weighted average cost of capital; and                                       :
i         =     number of years over which cash flows are discounted.

The time period over which cash flows are discounted, ten years, was chosen as a conservative estimate of the
average life of the pollution control equipment.                                                     I

         Liquidation Value
         Liquidation values for each facility were estimated based on data from the Census.  A facility's
liquidation value is defined as the gross value the facility would receive from selling its lines for pesticide
production and formulating/packaging.  The liquidation value includes the value of fixed assets, working;capital,
and real estate.25
     25The current analysis used gross rather than net liquidation values, thereby overstating the likelihood of
 facility closure.  The EPA expects that the EIA supporting the final rule will use net liquidation values.

                                                  4.24

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         For those facilities that reported the liquidation value of their pesticide production and
 formulating/packaging lines, this value was used as the facility liquidation value.  For facilities that could not
 provide this information in the Census, liquidation values were estimated using regression analysis based on the
 liquidation values provided by other pesticide manufacturing facilities.  Several different regression models were
 evaluated and are presented in the Administrative Record.

         The model used in the analysis has two independent variables:  (1) 1986 local property tax assessment
 of facility land, buildings, equipment, and machinery, and (2) 1986 facility inventories.  The liquidation value
 of a facility is dependent upon the market value of facility-owned land, buildings, and equipment as well as on
 the facility's inventories of products.  As the valuation of these assets increases, one would expect the
 liquidation value to increase, producing a positive coefficient for each of the independent variables. Given this
 model specification, the regression equation yielding the strongest results, as measured by goodness-of-fit tests,
 was:
                               LV = -12,906 + (0.417 x TA)  + (1.159 x INV)
where:
LV      =
TA
INV
facility liquidation value;
1986 local property tax assessment of facility land, buildings, equipment and machinery; and
1986 facility inventories.
The F value for this equation was 2099 with 46 degrees of freedom.26  The adjusted R-squared was 0.99.27
The standard error for the TA coefficient was 0.006 while the standard error for the INV coefficient was
0.391.K This equation was used in the analysis to estimate liquidation value for facilities that did not provide
this data in the Census.
    26The F statistic tests the overall significance of the regression.  The reported value leads to rejection of the
hypothesis that the coefficients of all of the independent variables are equal to zero, indicating that the variables
are useful in projecting liquidation values.
    ^Adjusted R2 indicates the proportion of variation explained by the regression model.  Values of R2 that are
close to 1 imply that most of the variability in the dependant variable is explained by the regression model.
          standard error for a regressor indicates the accuracy with which the coefficient of that regressor is
measured, given the other regressors in the model.  The reported standard errors indicate that the contribution
of the regressors is significant.

                                                   4.25

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4.2.B   Post-Compliance Facility Closure Analysis
        Facilities for which baseline DCF was less than the facility liquidation value (i.e., those predicted to
have baseline facility closures) were not considered as potential facility closures in the post-compliance scenario.
For the remaining facilities, however, the post-compliance DCF was compared with the facility liquidation value
to project facilities that would close due to the regulation.

        Although the liquidation values of the facilities do not change as a result of the regulation, post-
compliance DCFs must be calculated. Three factors are included when estimating the DCF in the post-
compliance scenario:

        •       the compliance costs, including capital, land, and operating and maintenance;
        •       the resulting change hi revenue associated with the new price and quantity; and
        •       the decrease in variable costs of production due to the reduction in quantity.

        Facility changes hi DCF were calculated by summing the present value of compliance costs, the present
value of the change in revenue, and the present value of the change hi variable costs over all clusters produced
at a facility.  The post-compliance DCF was then calculated by adding the changes hi cash flow to the baseline
DCF.   The corresponding equation is:                                                              j
 where:
 PCDCF  =
 DCF
 CCadji   =
the post-compliance facility discounted cash flow;
facility baseline discounted cash flow;
compliance cost adjustment to discounted cash flow for cluster i;
the adjustment hi the discounted cash flow due to the change m revenue for cluster i;
the adjustment hi the discounted cash flow due to the change hi variable costs for cluster i;
 The three cluster level adjustments are described below.

         Adjustment for compliance costs
         The compliance costs have three components:  operating and maintenance costs, capital costs, and land
 costs.  Operating and maintenance costs will be somewhat offset by the corresponding decrease hi taxes the
                                                    4.26

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 facility will pay due to reduced profit. A present value of operating and maintenance costs is generated by
 multiplying this value by a present value factor. The present value factor is based on the WACC, as discussed
 in the previous section.  The capital and land costs need no adjustments because they are already in the form of
 a present value.  The equation for the compliance cost adjustment is:


                               CCadj.=-((PVFx(.OM.x(l -CZ))) +CPT.+LANDJ
 where:
 CCadj;  =
 PVF
 OM;
 CT
 CPT;    =
 LAND,  =
 compliance cost adjustment to DCF for cluster i;
 present value factor (sum from i = 1 to 10 of I/ (1 +WACC)1);
 operating and maintenance costs of compliance for cluster i;
 corporate tax rate;
 capital costs of compliance for cluster i; and
 land costs of compliance for cluster i.
        Adjustment for change in revenue
        The change in revenue contains two components:  the increase in revenue resulting from the increase hi
price and the decrease in revenue resulting from the decrease in quantity.  Present values of both changes in
streams of revenue are needed to adjust the baseline DCF. The cluster-level adjustment to the baseline DCF for
the change in revenue is shown by the equation:

                                  Radj.=PVF((AP. x
where:
PVF    =
AP:
AQj
the adjustment to the DCF due to the change hi revenue for cluster j;
present value factor (sum from i = 1 to 10 of I/ (1 +WACC)');
the change in cluster j price from baseline to post-compliance;
the post-compliance quantity for cluster j;
the baseline price for cluster j; and
the change hi cluster j quantity from baseline to post-compliance.
        Adjustment for change in variable cost of production
        The final adjustment to the baseline DCF reflects the decrease hi variable costs associated with
decreased production. Variable costs were assumed to decrease hi proportion to the decrease hi quantity of
pesticides produced.  The equation is:
                                                 4.27

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                                                          x FC.)
where:
Cadjj    =
PVF    =
                the adjustment to the DCF due to the change in variable costs for cluster j;
                present value factor (sum from i = 1 to 10 of I/ (1 +WACC)1);
                the change hi cluster j quantity from baseline  to post-compliance;
                the baseline quantity of cluster j; and
                the unit variable cost for cluster j.
As previously discussed, a facility with a post-compliance DCF less than the facility liquidation value was
predicted to close as a result of the regulation. The projection of closure refers only to the pesticide-related
portion of the facility.  Other operations, such as production of OCPSF chemicals or pharmaceutical, may
continue at the location.

4.3      Product Line Closure Analysis

         Facilities that did not close hi either the baseline or the post-compliance scenario were analyzed for
possible product line closures.  The impact of a product line closure is less severe than that of a facility closure.
A facility that closes a product line may still profit from producing and formulating other pesticide products,
and may continue to operate while new products are registered or changes are made to the physical plant.  Like
the facility closures analyzed above, product line closures are evaluated in the baseline scenario first.  If a
facility is projected to close a product line hi the baseline, that facility is not re-evaluated for a product line
closure hi the post-compliance scenario.

         The evaluation of baseline  and post-compliance product line closures is straightforward.  A product line
 closure  is predicted when the unit total (i.e., fixed plus variable) cost of the product line (i.e., cluster) exceeds
 the unit price.  Note that the comparison of price to total costs is very conservative.  A comparison of price to
 variable costs only is a reasonable alternative (in the short run), and would result in an equal or lesser number
 of product line closures.  The calculation of unit prices and costs hi both the baseline and post-complianbe
 scenarios was described previously.

         Given the methodologies used to calculate facility and product line closures, it is possible that a facility
 may be projected to close all pesticide product lines, but the facility itself is not projected to close. In such a
 case, the product line closure analysis serves as an alternate and complementary analysis of potential facility
                                                   4.28

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 closures.  Such results would not be contradictory, because the product line closure analysis evaluates closures
 based on price and cost while the facility closure analysis also includes asset valuation.

 4.4     Other Significant Financial Impacts

         Facilities may sustain other significant financial impacts short of facility or product line closure.  These
 impacts are indicative of other less immediate, but also potentially damaging, effects that may occur as a result
 of compliance. For example, a firm may decide to keep  a facility in operation for several years, but may cease
 reinvestment in the facility's building and equipment, eventually closing it.  The impacts measured in this
 section are less severe than the closure of a facility or a product line, because the facility remains profitable
 with time to register new products, find ways to cut costs, or shift to other pesticide or non-pesticide products.

         Other financial impacts were assessed based on financial indicators of operating performance.  Two
 financial indicators are examined in this analysis:  interest coverage ratio (ICR) and return on assets  (ROA).29
 The ICR and ROA gauge a facility's ability to continue doing business long term, and also indicate a facility's
 ability to qualify for a loan or to attract investors. In this way, the ratios are key indicators of a facility's
 ability to finance costs associated with the proposed regulation.

         The ICR is calculated as earnings before interest and taxes (EDIT) divided by interest expense. This
 ratio provides a comprehensive measure of a facility's ability to meet its fixed cost obligations (e.g.,  short- and
 long-term debt) out of operating earnings.  Facilities must manage their fixed cost obligations in order to
 achieve profitability and raise additional capital. With that in mind, lenders and investors tend to avoid potential
 debtors/investments that have a high proportion of debt or other fixed obligations relative to operating earnings.

         ROA is calculated as EDIT divided by assets.  ROA is a measure  of a facility's operating profitability
 and asset management capability. This ratio demonstrates  the rate of return on the total investment in the
 facility.
                                                                                                      lance
        Other significant financial impacts are reported only for facilities that were not projected to experience
one of the more severe impacts (e.g., a facility or product line closure) in either the baseline or post-compli
scenario.   Significant financial impacts were evaluated by comparing each facility's post-compliance financial
ratios to the lowest quartile ratios established for all in-scope pesticide manufacturing facilities. A significant
    29The ICR is also known as "times interest earned;" the ROA is also known as the "return on investment.1
Additional information on these ratios can be found in Chapter 7.
                                                   4.29

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impact is said to result from the proposed guidelines if a facility shifts into the lowest quartile of either the ICR
or the ROA for all pesticide manufacturing facilities due to the regulation. w                           ;

        The analysis of other significant financial impacts was conducted in three steps:  (1) estimate the ICR
and ROA for all pesticide manufacturing facilities, (2) determine the lowest quartile values for the two ratios,
and (3) recalculate the post-compliance ICR and ROA for each facility.  These steps are discussed below.

        Baseline Ratios
        The values marking the lowest quartiles for the ICR and ROA were determined by calculating the ratios
for all pesticide manufacturing facilities.  The three components used to calculate these two ratios were EBIT,
interest, and assets.  EBIT was calculated as three-year average revenues from pesticides minus three-year
average costs (except interest and taxes) associated with pesticides.  Pesticide-related revenues were taken
directly from the Census.  Pesticide-related costs are composed of pesticide  variable costs and pesticide fixed
costs.  Pesticide variable costs were taken directly from the  Census. Fixed  costs (e.g., depreciation, fixed
overheads, R&D, and other) are not broken down in the Census into those related or unrelated to pesticides, but
are reported for the entire facility. As a result, the percentage of fixed costs generated by pesticide-related
activity was assumed to match the percentage of facility revenues from pesticide-related activity. The equation
for calculating EBIT is therefore:
                                      EBIT=PREV-VC-FC(PREV/TREV)
 where:
 EBIT   =
 PREV  =
 VC
 FC
 TREV  =
earnings before interest and taxes;
pesticide related revenue for a facility;
pesticide related variable cost for a facility;
total fixed costs (minus interest and taxes) for a facility; and
total facility revenues.
         Interest related to pesticides was calculated as the interest reported in the Census multiplied by the
 percent of facility revenue from pesticides. Likewise, assets related to pesticides were calculated as assets
 reported in the Census multiplied by the percent, of facility revenue from pesticides.  EBIT divided by interest
 provided the ICR; EBIT divided by assets gave the ROA.
      30The firm analysis is analogous to the "other significant impact analysis" for the facility level.  See Chapter
  7 for further details.
                                                     4.30

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        Lowest Quartile Values
        The lowest quartile value for ROA was determined directly from the calculated baseline ROAs for all
pesticide manufacturing facilities.  Determination of the lowest quartile value for the interest coverage ratio,
however, required a decision on where to place firms reporting a zero interest payment.  A value of zero cannot
be used in the denominator of a ratio, so an assumption must be made regarding these cases for the ICR.  The
analysis ranked facilities reporting positive EBIT and zero interest as having interest coverage superior to any
firm reporting a positive interest value.  If EBIT was negative and the reported interest expense was zero, the
facility was  assigned an EBITrinterest value of zero. In effect, such a facility was seen as being worse off than
a facility with positive EBIT and a positive interest expense, but better off than a facility with negative EBIT
and a positive interest expense.  The EBIT: interest ratio marking the lowest quartile for pesticide manufacturing
facilities is 1.13;  the lowest quartile ROA value is 0.04.

        Post-compliance Ratios
        The post-compliance ratios for each facility with compliance costs that was not predicted to have a
facility or product line closure were calculated as follows:

                                 post-compliance EBIT  =
                                 baseline EBIT
                                 minus compliance operating and maintenance costs
                                 minus the change in variable production costs
                                 plus the change in revenues

                                 post-compliance interest expense =
                                 baseline interest expense
                                 plus the current interest component of compliance debt?1

                                 post-compliance total assets =
                                 baseline total assets
                                 plus compliance capital and land costs
    31Compliance debt is the debt the firm is expected to incur in order to finance projected capital and land
 expenses associated with the proposed regulation.
                                                  4.31

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4.5     Facility Impacts
                                                                                                    t
        As discussed previously, a total of 90 pesticide manufacturing facilities produced one or more of the
270 PAIs, or classes of PAIs, potentially subject to regulation.  The EPA is regulating 122 of these chemicals
and has projected compliance costs for the pesticide manufacturing facilities under two potential regulatory
options:  a Treated Discharge Option and a Zero Discharge Option. The economic impacts of both these
options on the facilities were calculated separately for direct and indirect dischargers.  Each discharge category
was further analyzed for two subcategories: organic pesticide chemicals manufacturing (Subcategory A) and
metallo-organic pesticide manufacturing (Subcategory B).
4.5.A   Baseline
        Fifteen of the 90 pesticide manufacturing facilities are expected to close in the baseline (see Table 4.3).
Three of these 15  facilities have, in fact, closed since 1986, and another 2 of the 15 facilities have closed one or
more product lines since that time.  An additional 20 facilities are projected to close particular pesticide product
lines in the baseline.  Two of these 20 facilities have closed entirely; 5 of the facilities closed a pesticide
product line, and 2 of the facilities have changed ownership since 1986.
                                                Table 43
                                            Baseline Closures
  Plant Closures
      Subcategory A/Subcategory B*
  Product Line Closures
      Subcategory A/Subcategory B*
 15
15/0
 20
18/3
  *  Five facilities produce PAIs in both Subcategories A and B.  Two of these facilities have costs for both
     Subcategories.  Therefore, total closures may not equal the sum of Subcategory A and Subcategory B;
     closures.
4.5.B  Effects of Compliance with the Regulatory Options
        The economic impacts of the two regulatory options evaluated by the EPA are discussed below.
Having reviewed the costs, impacts, and pollutant removals associated with the two options, the EPA is
proposing the Treated Discharge Option.  The projected results of the Zero Discharge Option are shown for
comparison.  Although the EPA is not proposing further regulations for Subcategory B chemicals, the costs and
impacts that would result from regulation of Subcategory B chemicals are shown below.32
    32As discussed previously, the analysis of impacts of the regulatory options incorporates the effects of
 facilities passing a portion of the compliance costs to their customers. An alternative method of analyzing
 impacts would be to assume that pesticide manufacturers bear the entire burden of the cost increase in reduced
                                                   4.32

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        Treated Discharge Option
                Impacts of BAT Regulations on Direct Dischargers
                Organic Pesticide Chemicals Manufacturing (Subcategory A)
        Thirty-two direct discharging and zero discharging facilities producing Subcategory A chemicals are
expected to incur costs under this regulatory option (see Table 4.4).33  For manufacturers included in this
Subcategory, the incremental capital and annualized costs of complying with BAT limitations are expected to be
$14.9 million and $14.7 million, respectively. No facilities are projected to close due to compliance with BAT.
Two facilities are projected to close a product line as a result of the regulation.  No facilities are expected to
experience other significant financial impacts short of facility or product line closure.

                Metallo-Organic Pesticide Chemicals Manufacturing (Subcategory B)
        Direct dischargers of Subcategory B chemicals are limited to zero discharge of process wastewater
pollutants under BPT.  No additional options were considered and no new limitations are proposed for the
metallo-organic pesticide chemicals manufacturing Subcategory.  There are therefore no associated costs or
economic impacts.

                Impacts of PSES Regulations on Indirect Dischargers
                Subcategory A
        Twenty-seven indirect discharging facilities producing Subcategory A chemicals are expected to incur
costs under the Treated Discharge Option.  For manufacturers included in this Subcategory, the incremental
capital and annualized costs of complying with PSES limitations are expected to be  $9.4 million and $5.9
million, respectively. No facilities are projected to close due to compliance with PSES.  One facility, or 3
percent of the facilities  subject to regulation under this category, is projected to close a product line as a result
of the regulation.  No facilities are expected to experience other significant financial impacts  short of facility or
product line closure.
profits. EPA conducted a sensitivity analysis using this zero cost pass-through assumption.  The results are
reported hi Appendix D.  For the main analysis, however, the EPA presents impacts using the assumption of
partial cost pass-through, because the EPA believes that, in reality, pesticide manufacturing facilities will not
bear the entire costs of the regulation.  The analysis of zero pass-through (i.e., manufacturers bear all
compliance costs) served. as a theoretical construct to limit the upper range of impacts of the regulation on
facilities.
    33Impacts of zero discharge requirements are reported with impacts of direct discharge requirements.  Zero
dischargers may be subject to monitoring costs if they have any process wastewater. Monitoring costs would be
imposed by the permitting authority (no separate monitoring requirements are contained in the proposed effluent
guidelines for pesticide manufacturers).  These monitoring costs are included in the analysis to capture the full
cost to industry of controlling process wastewater pollutants.

                                                  4.33

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                                                       4.34

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                 Subcategory B
        Five Subcategory B facilities would be expected to incur costs under PSES if the EPA regulated these
chemicals.  The total expected capital costs would be $40,000, while the annualized costs would be $120,000.
No plant closures, product line closures, or other significant financial impacts would be expected to result from
these costs.

        Zero Discharge Option
                 Impacts of BAT Regulations on Direct Dischargers
                 Subcategory A
        Thirty-five direct discharging facilities producing Subcategory A chemicals would be expected to incur
costs under this regulatory option (see Table 4.4). For manufacturers included in this Subcategory, the
incremental capital and annualized costs of complying with BAT limitations is expected to be $1.1 million and
$4.8 billion, respectively.  Sixteen facilities would be projected to close due to  compliance with BAT.  Three
additional facilities would be projected to close a product line as a result of the regulation. No facilities would
be expected to experience other significant financial impacts short of facility or product line closure.

                 Subcategory B
        As discussed under the Treated Discharge Option, Subcategory B direct dischargers are already limited
to zero discharge of process wastewater pollutants under BPT.  No additional options were considered and no
new limitations are proposed for the metallo-organic pesticide chemicals manufacturing Subcategory.  There are
therefore no associated costs or economic impacts.

                 Impacts of PSES Regulations on Indirect Dischargers
                 Subcategory A
        Thirty indirect discharging facilities producing Subcategory A chemicals would be expected to incur
costs under the Zero Discharge Option. For manufacturers included in this Subcategory,  the incremental  capital
and annualized costs of complying with PSES limitations is expected to be $1.1 million and $518.8 million,
respectively. Eleven facilities would be projected to close due to compliance with PSES.  Three facilities under
this category would be projected to close a product line as a result of the regulation. No facilities would  be
expected to experience other significant financial impacts short of facility or product line closure.

                 Subcategory B
        Five Subcategory B facilities would be expected to incur costs under PSES.  The total capital costs
would be projected to be $80,000, while the annualized costs would be $2.8 million.  One facility is projected
to close as a result of the regulation/ An additional facility is expected to close a product line.  No facilities
would be expected to experience otlier significant financial impacts.
                                                  4.35

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                                        Chapter 4 References

Doane Marketing Research (1987).  Annual Marketing Survey. St. Ixmis, Missouri.
DPRA, Inc. (1990). Agchemprice;  Current U.S.A. Prices of Non-fertilizer Agricultural Chemicals. January.
     Manhattan, KS.
U S Department of Agriculture (1985). t/.5. Demand for Food:  A Complete System of Price and 7«cc^
     Effects. By Kuo S. Huang, National Economics Division, Economic Research Service. Technical Bulletin
     No. 1714.
U.S. Department of Commerce (1989, 1990, 1991). Bureau of the Census. Statistical Abstract of the United
      States.  Washington, D.C.  January.
                                                 4.36

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                            Chapter 5: COMMUNITY IMPACT ANALYSIS
5.0
Introduction
        This chapter evaluates community impacts resulting from both pesticide facility closures and other
significant reductions in pesticide active ingredient (PAI) production.  Community impacts are measured by the
level of employment loss expected to correspond to decreased production resulting from compliance with the
proposed regulation.

        The impacts corresponding to both the Treated Discharge Option and the Zero Discharge Option are
presented.  For each option, impacts are shown separately for direct dischargers (including zero dischargers)
and indirect dischargers.1  For the Treated Discharge Option, only those impacts associated with Subcategory A
(Organic Pesticide Chemicals Manufacturing) chemicals are shown; no closures or other significant decreases in
production are expected for manufacturers of Subcategory B (Metallo-Organic Pesticides Chemicals
Manufacturing).2 For the Zero Discharge Option, impacts are shown for both Subcategory A and Subcategory
B chemicals.

5.1     Methodology

        Community impacts are  analyzed in two stages. The first stage analyzes the primary impact of facility
layoffs due to facility closures and other significant production reductions. If the primary employment losses
estimated in the first stage of the analysis are determined to be significant, the analysis is then taken to a second
stage that determines secondary impacts on the community employment level.  Secondary impacts arise from
reduced demand for inputs to the affected facility,  and reduced consumption due to losses in earnings.
Secondary impacts are assessed through multiplier analysis, which measures the extent to which employment
levels in other industries are affected by employment changes in a given industry. Secondary and primary
employment losses are summed to obtain the total  impact on community employment levels resulting from
pesticide facility closures and other decreases in pesticide production.
    'Impacts of zero discharge requirements are reported with impacts of direct discharge requirements.  Zero
dischargers may be subject to monitoring costs if they have any process wastewater.  Monitoring costs would be
imposed by the permitting authority (no separate monitoring requirements are contained in the proposed effluent
guidelines for pesticide manufacturers).  These monitoring costs are included in the analysis to capture the full
cost to industry of controlling process wastewater pollutants.
    2Direct discharges of Subcategory B  chemicals are already limited to zero under the Best Practicable Control
Technology Currently Available (BPT) regulation.  Best Available Technlogy Economically Available (BAT)
regulations are therefore not considered for Subcategory B chemicals under either the Treated Discharge Option
or the Zero Discharge Option.
                                                  5.1

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S.l.A   Primary Impacts on Employment                                                         i
        Primary impacts on employment are considered for facilities predicted to experience either a closure or
a decrease in in-scope PAI production of at least ten percent due to the regulation. All pesticide-related
employment at a facility is assumed to be lost in the case of facility closures.  The percentage of employment
lost due to other significant reductions in production is assumed to equal the percentage of revenues lost.

        Facility Closures
        Employment loss resulting from a facility closure is assumed to equal the total annual pesticide-related
employment hours calculated from that facility's Census data.3  Total pesticide-related hours are calculated as
the sum of both pesticide-related production and non-production hours.  Pesticide-related production hours are
obtained directly from the Census by adding pesticide manufacturing hours and pesticide formulating/packaging
hours. Pesticide-related non-production hours are estimated by computing the ratio of total non-production
hours to total production hours and multiplying the pesticide production hours by this ratio.4 These calculations
are shown below algebraically.
Total pesticide production employee hours (TPH) are computed as:
where:
        MH     =      Annual employee hours spent in pesticide chemical manufacturing production; and
        FH      =      Annual employee hours spent in pesticide formulating/packaging.              >
Non-production employee hours related to pesticide production (TNH) are estimated as:
where:
        N
        P
                                             TNH =TPH x —
                                                          P
Annual non-production employee hours spent at facility; and
Annual employee hours spent in all production at facility.
         Total facility production hours (P), used in the above equation, are computed as:
    Employment in the pesticide manufacturing industry tends to be seasonal.  Facilities reported employee
 hours for the months of January, May and November to account for this seasonally.  "Annual hours" are
 estimated by multiplying the average hours of the three months by 12.
    'The inclusion of pesticide formulating/packaging hours is conservative, because facilities that discontinue
 manufacture of certain PAIs could purchase the PAIs and continue to formulate/package them.
                                                   5.2

-------
where:
        OPH    =
Annual estimate of employee hours spent in other production.
        Total pesticide-related employee hours lost due to a facility closure, i.e., the sum of pesticide-related
production hours and pesticide-related non-production hours, are converted to full time equivalents (FTE),
assuming that 2000 hours = 1 FTE.5

        Other Significant Reductions in Production
        Reductions in pesticide production that fall short of facility closure may also affect employment levels
at a facility.  In order to capture these impacts, this analysis calculates employment loss for any facility that is
projected to have at least a 10 percent reduction hi revenues from in-scope PAIs due to the proposed regulation.
The percentage of in-scope employment that is lost is assumed to equal the percentage of in-scope revenue that
is lost.

        Employee hours dedicated to in-scope pesticide work must be estimated because they are not reported
in the Census.  The ratio of in-scope pesticide hours to total facility-wide hours is assumed to equal the ratio of
in-scope pesticide production volume to total facility-wide production volume.  Facility-wide employee hours
and the ratio of in-scope pesticide production volume to total facility production volume are reported in the
Census.6 Hours related to production of in-scope pesticides are multiplied by the percentage loss of in-scope
revenues to estimate lost hours.  Employee hours lost are again converted to full time equivalents (FTE),
assuming that 2000 hours = 1 FTE.

5.1.B   Measuring Impact Significance
        The significance of facility employment loss on the community is measured by its impact on the
community's level of employment as a whole. For purposes of this analysis, the community is defined as the
Metropolitan Statistical Area (MSA), in which the facility is located7.  The MSA is  assumed to represent the
labor market area within which residents could reasonably commute to work. If the facility is located in a
Primary Metropolitan Statistical Area (PMSA) within the MSA,  then the PMSA population is used.  If a facility
is not located within an MSA, then the community is  defined as  a county (or township, for eastern states).  A
    5Computed: (50 weeks/year)(40 hours/week)  = 2000 hours/year.
         ratio of in-scope pesticide production volume to total facility production volume, although not the
separate numerator and denominator, is reported in the Census.
    7MSAs are defined by the U.S. Office of Management and Budget.
                                                   5.3

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decline in the community employment rate equal to or greater than one percent is considered significant. Data
necessary to determine the community impact from the employment loss include the community's population and
employment rate. The community population information used in this analysis is for 1986, as estimated by the
Bureau of the Census  (1986). Due to inconsistencies in MSA and county-level employment data, state   |
employment rates are used to represent community employment rates.  State employment rates are based on
1986 data from the Bureau of Labor Statistics (1989).

5.1.C   Secondary Impacts on Employment
        As stated above, if primary employment losses are found to have a significant impact on a community,
then secondary effects on employment levels are assessed by multiplier analysis.  Secondary effects arise from
(1) the reduction in demand for inputs by the affected facility, and (2) induced impacts attributable to reductions
hi consumption due to both primary and secondary losses in earnings.  Multiplier analysis is used to accotmt for
these secondary effects,  and provides a straightforward framework as long as the direct effects are small and a
number of other important limitations (e.g., constant returns to scale, fixed input ratios) hold.           '
                                                                                                I
        The multiplier used in this analysis is based on input/output tables developed by the Department of
Commerce, Bureau of Economic Analysis (BEA, 1986).  The BEA multipliers are estimated via the Regional
Industrial Multiplier System developed by the Regional Economic Analysis Division of the BEA. The
multipliers reflect the  total national change in the number of jobs given a change in the number of jobs for a
particular industry.8 In  this analysis, the industry directly affected is Chemicals and Selected Chemical
Products.9 The multiplier reported by BEA for this industry is 8.3710.  The change in total number of jobs is
computed by:

                                          CTJ = 8.37 x CDCJ
where:
CTJ
CDCJ   =
Change in total jobs; and
Change in direct chemical industry jobs (FTEs).
    8"Jobs" include both full- and part-time positions.
    'Multipliers based on direct employment changes are available at an aggregated industry level only.
    lcThe use of this national multiplier may overstate the number of jobs affected within the community
 because some of the inputs may be from sources outside the community or even outside the country. No
 multipliers that differentiate among the locations of inputs sources are known to exist.
                                                  5.4                                          i

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 5.2     Results

 5.2.A   Treated Discharge Option
         Impact of Best Available Control Technology Economically Achievable (BAT) Regulations on
         Direct Dischargers11
         Under the Treated Discharge Option, no direct discharging facilities are expected to close, while two
 facilities are expected to have a decline in in-scope revenues of 10 percent or greater. As shown hi Table 5.1,
 total estimated employment loss is 31 FTEs, less than one percent of the total pesticide-related employment
 figures reported by all PAI manufacturers (approximately 9,940 FTEs). The employment rates in the two
 affected communities are expected to decline by less than one percent.  Therefore, the projected employment
 loss for direct dischargers under the Treated Discharge Option is considered insignificant.

         Impact of Pretreatment Standards for Existing Sources (PSES) Regulations on Indirect
         Dischargers
         The proposed effluent guidelines under the Treated Discharge Option for direct dischargers are not
 projected to result in any facility closures, while one facility is expected to experience a reduction in in-scope
 pesticide revenues of at least ten percent. As indicated in Table 5.1, total expected employment loss is about 97
 FTEs, approximately one percent of total pesticide-related employment reported in the industry. The
 community employment levels are not projected to decline by more than one percent and, consequently, the
 estimated reduction hi employment is not considered significant.

 5.2.B   Zero Discharge Option
        Impact of BAT Regulations on Direct Dischargers
        Employment losses were  considered for 16 direct discharging facilities subject to closure and 3
 additional facilities expected to experience a decline hi in-scope revenues of at least 10 percent. Under the  Zero
 Discharge Option (see Table 5.2), employment losses due to the primary effects of facility closures  and reduced
production equal approximately 55 percent (5,461 FTEs) of total reported pesticide-related employment for  PAI
 manufacturers.  Only one community is expected to be significantly impacted by the loss of employment. This
 community,  located hi the Southeast,  is projected to lose 224 jobs within the pesticide industry and 1,649 jobs hi
other industries, representing 11 percent  of the community's total employment base.  Total expected
employment loss, from both primary and secondary effects, is predicted to be 7,110 FTEs for direct dischargers
under the Zero Discharge Option.
    "Impacts of zero discharge requirements are discussed with direct discharge requirements.
                                                  5.5

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                   fable 5.
Community Impact
           Employment Loss (FTEs)
                                                           Option
                                                        ^Discharger Type
                                                 Direct1
                                             Indirect
           Total
Subcategory A2
FTE's Lost Due to Plant Closures

FTE's Lost Due to Reduced Production

FTE's Lost Due to Secondary Effects

Total Subcategory A FTE's Lost
                                  0.0

                                 31.0

                                  0.0

                                 31.0
 0.0

96.8

 0.0

96.8
  0.0

127.8

  0.0

127.8
1 Impacts of zero discharge requirements are reported with impacts of direct discharge
  requirements.  Zero dischargers may be subject to monitoring costs if they have any
  process wastewater.  Monitoring costs would be imposed by the permitting authority (no
  separate monitoring requirements are  contained in the proposed effluent guidelines for
  pesticide manufacturers). These monitoring costs are included in the analysis to capture
  the full cost to industry of controlling process wastewater pollutants.
2 Subcategory B is not shown, since no closures or other significant decreases in production
  are projected for this subcategory under the Treated Discharge Option.	
                                         5.6

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                                      Table 5,2
                     Community Impact — Zero Discharge Option
                              Employment Loss (ETEs)
                                                         Discharger Type
                                                 Direct1
                              Total
 Subcategory A
 FTE's Lost Due to Plant Closures
 FTE's Lost Due to Reduced Production
 FTE's Lost Due to Secondary Effects
 Total Subcategory A FTE's Lost
5,289.2
  171.8
1,649.3
 7110.3
738.2
 64.0
  0.0
802.2
6,027.4
  235.8
1,649.3
7,912.5
Subcategory B2
FTE's Lost Due to Plant Closures
FTE's Lost Due to Reduced Production
FTE's Lost Due to Secondary Effects
Total Subcategory B FTE's Lost
    0.0
    0.0
    0.0
    0.0
  0.0
  3.9
  0.0
  3.9
    0.0
    3.9
    0.0
    3.9
Subcategories A and B
FTE's Lost Due to Plant Closures
FTE's Lost Due to Reduced Production
FTE's Lost Due to Secondary Effects
Total Employment Loss
5,289.2
  171.8
1,649.3
 7110.3
738.2
 67.9
  0.0
806.1
6,027.4
  239.7
1,649.3
7,916.4
1 Impacts of zero discharge requirements are reported with impacts of direct discharge
  requirements.  Zero dischargers may be subject to monitoring costs if they have any
  process wastewater. Monitoring costs would be imposed by the permitting authority (no
  separate monitoring requirements are contained in the proposed effluent guidelines for
  pesticide manufacturers).  These monitoring costs are included in the analysis to capture
  the full cost to industry of controlling process wastewater pollutants.
2 Subcategory B is already limited to zero direct discharge under BPT.
                                       5.7

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        Impact of PSES Regulations on Indirect Dischargers
        Organic Pesticide Chemicals Manufacturing (Subcategory A)                                  .
        Employment losses were considered for 11 facilities subject to closure and five facilities experiencing a
decline in in-scope revenues of at least 10 percent.12 Total expected employment loss among Subcategory A
indirect dischargers under the Zero Discharge Option is projected to be 802 FTEs, approximately eight percent
of total reported pesticide-related employment by PAI manufacturers (see Table 5.2). Community employment
levels did not show a significant change under the Zero Discharge Option for Subcategory A indirect
dischargers.

        Metdtto-Organic Pesticide Chemicals Manufacturing (Subcategory B)
        Employment losses for Subcategory B indirect dischargers under the Zero Discharge Option are
expected to total approximately 4 FTEs (less than one percent of the total reported pesticide-related
employment) by PAI manufacturers.  This change stems from two facilities that are expected to experience  a
decline in in-scope revenues of at least 10 percent.  There are no projected closures  under the Zero Discharge
Option for Subcategory B indirect dischargers.  Given that the decline in the employment rates for both of the
communities affected is less than one percent, the impacts are considered insignificant.

        In summary, total expected employment loss due to the Treated Discharge Option is only 128 FTEs.
Total employment losses expected under the Zero Discharge Option are projected to be nearly 62 times the
employment losses under the Treated Discharge Option.  Under the Zero Discharge Option, one community is
expected  to experience a significant decline in the employment rate (11 percent). This job loss may overstate
the community impact, however, since the use of a national multiplier cannot differentiate between input sources
within and outside this community.                                                                '
     12One impacted facility did not report any employment data. According to the records of a follow-up call,
  facility personnel indicated that employment information was unavailable in the format requested.

                                                    5.8

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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.
                                                5.9

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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 in domestic PAI production due to the regulation of effluent from PAI
 manufacturing facilities may therefore affect the balance of trade.  This chapter estimates the extent to which the
 two regulatory options for PAI manufacturers would affect the balance of trade.  To measure the significance of
 the expected changes in exports and imports, these changes are compared with current U.S.  exports and imports
 for the pesticide industry, and with total U.S.  merchandise trade.

         The impacts corresponding to both the Treated Discharge Option and  the Zero Discharge Option are
 presented. For each option, impacts are shown for direct dischargers  (including zero dischargers) and indirect
 dischargers.1  For the Treated Discharge Option, only those impacts associated with Subcategory A (Organic
 Pesticide Chemicals Manufacturing) are shown; no closures or other significant decreases in production are
 expected for Subcategory B (Metallo-Organic Pesticide Chemicals Manufacturing). For the Zero Discharge
 Option, impacts are shown for both Subcategory A and Subcategory B chemicals.2 The proposed rule,
 however corresponds to the Treated Discharge Option and does not include Subcategory B chemicals.

 6.1     Methodology

         Decreased production resulting from compliance with effluent guideline limitations may result in both
 decreased U.S. exports and increased U.S. imports of PAIs.3  Exports may decrease as  previously exported
 products are no longer manufactured; imports may increase as domestic purchasers seek new sources of PAIs no
 longer offered by a particular manufacturer.  Changes in exports and imports are considered for facilities
    'Impacts of zero discharge requirements are reported with impacts of direct discharge requirements.  Zero
dischargers may be subject to monitoring costs if they have any process wastewater.  Monitoring costs would be
imposed by the permitting authority (no separate monitoring requirements are contained in the proposed effluent
guidelines for pesticide manufacturers). These monitoring costs are included in the analysis to capture the full cost
to industry of controlling process wastewater pollutants.
    2Direct discharges  of Subcategory B chemicals are  already limited to zero under Best Practicable Control
Technology Currently  Available (BPT) regulation.   For this reason,  Best Available Technology Economically
Achievable (BAT) regulations are not considered for Subcategory B chemicals under either the Treated Discharge
Option or the Zero Discharge Option.
    Environmental laws in other countries are changing, often reflecting the changes in U.S. environmental laws.
This analysis conservatively assumes, however, that current foreign environmental laws will remain in effect.  As
a result of this assumption,  effects of the regulation on foreign trade may be overstated.
                                                   6.1

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predicted to close under a regulatory option and for facilities predicted to have a decrease in in-scope PAI
production of at least ten percent due to regulation.

6.1. A  Exports
         Changes in exports are considered only for those facilities expected to incur compliance costs, and who
also indicated in the Census that they exported a portion of their production hi 1986.  These changes are
calculated assuming that the foreign response to increased price matches the domestic response, i.e., foreign
demand elasticities equal domestic demand elasticities.  The analysis assumes that none of the decreased I
production of exported PAIs is replaced by alternate U.S. products.  This "worst case" assumption is very
conservative and is likely to overestimate the reduction in exports. If the impact on foreign trade is not
significant in this worst-case scenario, then more realistic scenarios would also indicate no significant impacts.
The methods of estimating changes hi PAI exports are discussed below for four categories of facilities.  :
Separate methods were required, depending on whether the facility was projected to close and whether the
facility chose to provide PAI-specific data hi the Census.

         Facility Closures with PAI-Specific Information
         If a facility is projected to close and PAI-specific export percentages were reported hi the Census, the
 loss hi exports is estimated as the product of the revenue from each PAI and the export percentage for that PAI,
 summed over all PAIs produced.4  Algebraically, export revenue losses are computed as:              i
                                          AIX =
                                                  i=l
 where:
          ADC    =       Change hi export revenues for a facility;
          AIV;    =       Facility revenues from PAI i; and
          ABCP|   =       Percentage of PAI i production that is exported by the facility.5
      The export data reported are expressed in percentage  of volume.   Because percentages of revenue are
  unavailable, it is assumed that the percentage of revenues generated from exports is equal to the percentage of
  volume exported.
      sFor facilities projected to close, a full accounting of changes in exports would include changes hi exports of
  formulated/packaged pesticides as well as PAIs.  The single facility that reported PAI-specific data and is projected
  to close, however, did not formulate/package PAIs hi 1986. For this reason, changes hi exports of PAIs alone are
  considered hi this section.
                                                     6.2

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         Facility Closures without PAI-Specific Information
         Although the provision of PAI-specific export data in the Census was optional, all facilities were
 required to provide the percentage of the facility's total 1986 production that was exported.  If PAI-specific
 information was not provided by the facility, then the percentage of exported PAI sales is assumed to equal the
 percentage of exported facility-level production.  Revenues from pesticides and pesticide contract work are
 added to obtain total pesticide-related sales.  The loss in export revenues is estimated by multiplying total
 facility pesticide sales by the percentage of total production exported by a facility.6

         Facilities with Reduced Demand and PAI-Specific Information
         Facilities incurring compliance costs and remaining open may experience a decline in exports due to
 decreased  demand resulting from price increases.  Changes in exports are  considered only for those facilities
 whose in-scope revenues are expected to decrease by at least ten percent due to the regulation.

         The decrease  in in-scope revenues for facilities with reduced demand is calculated on a cluster basis.
 Production-based weighted averages of the PAI-specific export data are calculated for each cluster at each
 affected facility. The  decline in exports for each cluster is  determined by  multiplying the facility's decline in
 cluster revenues by the facility's cluster export percentage.  If a facility is expected to close a product line, the
 percentage change in production for that product line is  100 percent.  The total decline  in a facility's exports
 equals the  sum of the decline in exports for all affected clusters in that facility.

         Facilities with Reduced Demand and No PAI-Specific Information
         As discussed above, if PAI-specific export data are unavailable, the facility-level export percentage is
 used. The decline in a facility's exports is estimated by multiplying the  decline in the facility's revenues by the
 percent of  the facility's total 1986 production that was exported.

 6.1.B   Imports
         An analysis of changes in imports is performed for facilities projected to either close or lose at least ten
 percent of  in-scope pesticide revenues, and that also produce a PAI that was imported to the United States in
 1986. Because changes in revenues are evaluated for each facility at the cluster level, the analysis of imports
 also focuses on clusters.  Production of each cluster of PAIs was classified as replaceable by imports if any PAI
 within the  cluster was imported in 1986.7  As a worst-case scenario, it is assumed that all lost revenue in
    The facility-reported export data may not reflect actual exports for facilities that perform contract work, because
facilities may not know the trade status of such products.
    'Import data from several sources were reviewed for this analysis.  Sources  include the Office of Pesticides
Programs (OPP), the Bureau of the Census, and the International Trade Commission. Data published by the Bureau
of the Census and the International Trade Commission were so highly aggregated that they were not useful for this
                                                   6.3

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clusters with imported PAIs (with the exception of revenue lost due to reduced exports) is replaced by imports.
This assumption is very conservative and is likely to overestimate the increase in imports.  If this worst-case
scenario does not result in a significant impact on foreign trade, then neither would a more realistic scenario.
6.2
Results
6.2.A  Treated Discharge Option
        Impact of Best Available Technology Economically Achievable (BAT) Regulations on Direct
        Dischargers8
        Under the Treated Discharge Option, no direct discharging facilities are projected to close,  and two
facilities are expected to have a decline hi in-scope revenues of ten percent or greater.  Of the two facilities
affected, only one facility reported export data (non-PAI-specific). Using the methods outlined above, it is
estimated that exports from this facility could decline by about $114,000 due to the regulation (see Table 6.1).

         The two direct discharging facilities expected to experience a decline hi in-scope revenues of tea
percent or greater under the Treated Discharge Option produce PAIs in five clusters.  The PAI production hi
each of these clusters is replaceable by imports.  In the worst-case scenario  described above, imports are
expected to rise by $5.4 million.

         The changes hi exports and imports expected to result from the BAT regulation are more meaningful
when compared to the trade balance of the pesticide industry and the total U.S. merchandise trade balance. In
 1986, U.S. exports of pesticides exceeded imports of pesticides by $897 million (United Nations, 1986).
 Considering all merchandise trade hi 1986, however, the U.S. had a negative net trade balance of $152 billion
 (U.S. Department of Commerce,  1988).  The change in pesticide trade due to the BAT regulation under the
 Treated Discharge Option is minor (less than one percent) hi comparison to both total U.S.  pesticide trade and
 total U.S. merchandise trade.

         Impacts of Pretreatment Standard for Existing Sources (PSES) Regulations on Indirect
         Dischargers
         Under the Treated Discharge Option, no indirect discharging facilities are projected to close, and only
 one facility is expected to have a decline hi in-scope revenues of ten percent or greater.   This facility reported
  analysis. Details of the data review are contained hi the Administrative Record.
     "Impacts of zero discharge requirements are reported with impacts of direct discharge requirements.  Zero
  dischargers may be subject to monitoring costs if they have any process wastewater.  Monitoring costs would be
  imposed by the permitting authority (no separate monitoring requirements are contained in the proposed effluent
  guidelines for pesticide manufacturers). These monitoring costs are included hi the analysis to capture the full cost
  to industry of controlling process wastewater pollutants.
                                                     6.4

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                                             Table fcl
                        Foreign Trade Impact — Treated Discharge Option
                                          (in $ thousands)
                                    Decline in Pesticide Exports
                                                                  Discharger Type
                                                          Direct1
               Indirect
                 Total
 Subcategory A

 Due to Plant Closures

 Due to Reduced Production

 Total Subcategory A
     0

   114

   114
      0

  5,477

  5,477
      0

  5,591

  5,591
                                   Increase in Pesticide Imports
                                                                  Discharger Type
                                                           Direct
               Indirect
                 Total
 Subcategory A

 Due to Plant Closures

 Due to Reduced Production

 Total Subcategory A
    0

5,408

5,408
     0

10,632

10,632
     0

 16,040

 16,040
                              Net Decline in Pesticide Trade Balance
                                                                 Discharger Type
                                                          Direct
              Indirect
                 Total
Subcategory A

Due to Plant Closures

Due to Reduced Production

Total Subcategory A
    0

5,522

5,522
     0

16,109

16,109
     0

21,631

21,631
1 Impacts of zero discharge requirements are reported with impacts of direct discharge requirements.
  Zero dischargers may be subject to monitoring costs if they have any process wastewater.  Monitoring
  costs would be imposed by the permitting authority (no separate monitoring requirements are contained
  hi the proposed effluent guidelines for pesticide manufacturers).  These monitoring costs are included
  in the analysis to capture the full cost to industry of controlling process wastewater pollutants.
2 Subcategory B is not shown, since no closures or other significant decreases  hi production are
  projected for this Subcategory under the Treated Discharge Option.
                                              6.5

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export data (non-PAI-specific). Using the methods outlined above, it is estimated that exports from this facility
could decline by about $5.5 million due to the regulation.                                             :
                                                                                                  1
        The one indirect discharging facility expected to experience a decline in in-scope pesticide revenues of
ten percent or greater under the Treated Discharge Option produces PAIs in three clusters.  The PAI production
in each of these clusters is replaceable by imports. In the worst-case scenario described above, imports are
expected to rise by $10.6  million. Even with the conservative assumptions incorporated in the analysis, PSES
regulations under the Treated Discharge Option are projected to reduce the U.S. pesticide trade balance from
$897 million to $886 million,  slightly more than a one percent decline.  The PSES regulation would increase the
total U.S. merchandise net imports by about one one-hundredth of one percent.

6.2.B  Zero Discharge  Option
        Impact of BAT Regulations on Direct Dischargers                                        i
        Under the Zero Discharge Option,  16 direct discharging facilities are projected to close, and 3 facilities
are expected to have a decline in in-scope revenues of 10 percent or greater.   Fourteen of these facilities
reported export data (only one facility reported PAI-specific data).  Using the methods outlined above,  exports
from these facilities are estimated to decline by about $529 million due to the regulation (see Table 6.2).

         The 19 direct discharging facilities impacted under the Zero Discharge Option produce PAIs in 29
 clusters.  The PAI production in each of these clusters is replaceable by imports. In the worst-case scenario
 described above, imports are expected to rise by $1.9 billion.

         These dramatic impacts of the BAT regulation under the Zero Discharge Option would shift the U.S.
 pesticide industry from a net export position to a net import position.  The change in pesticide trade would
 increase the total U.S. net merchandise imports by about two percent.

         Impacts of PSES Regulations on Indirect Dischargers
          Organic Pesticide Chemicals Manufacturing (Subcategory A)
         Under the Zero  Discharge Option, 11 Subcategory A indirect discharging facilities are projected to
 close, and 5 facilities are expected to have a decline in in-scope revenues of 10 percent or greater.  Ten of
 these facilities reported export data (non-PAI-specific). Using the methods outlined above,  exports from these
 facilities are estimated to decline by about $59 million due to the proposed regulation.                 :

          The 16 Subcategory A indirect dischargers impacted under the PSES regulations produce PAIs ;in 23
 clusters.  The PAI production in each of these clusters is replaceable by imports. In the worst-case scenario
 described above, imports are expected to rise by $121  million.  Based on the conservative assumptions '
 incorporated in the analysis,  PSES regulations applied to Subcategory A facilities under the Zero Discharge

                                                     6.6                                         !

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                                             Table 6.2
                           Foreign Trade Impact — Zero Discharge Option
                                          (in $ thousands)
                                    Decline in Pesticide Exports
                                                                     Discharger Time
                                                                 Direct*
                  Indirect
               Total
 Subcategory A
 Due to Plant Closures
 Due to Reduced Production
 Total__Subcategory A
 Subcategory B2
 Due to Plant Closures
 Due to Reduced Production
 Total Subcategory B	
   520,258
     8,399
  ..5.?M5.7.

         0
         0
         0
  57,843
     871
 ..58,71.1.
       0
      59
      59
   578,101
     9,270
	587..371

         0
        59
        59
                                    Increase in Pesticide Imports
                                                                     Discharger Type
                                                                  Direct
                  Indirect
               Total
Subcategory A
Due to Plant Closures
Due to Reduced Production
Subcategory B2
Due to Plant Closures
Due to Reduced Production
Total Subcategory B
 1,705,567
   197,884
..1,.903,451

         0
         0
         0
  96,963
  23,943
  1,802,53
  221,827
                                                                               ..120,906    2,024,35,
       0
   1,147
   1,147
        0
     1,147
     1,147
                               Net Decline in Pesticide Trade Balance
                                                                     Discharger Type
                                                                  Direct
                  Indirect
               Total
Subcategory A
Due to Plant Closures
Due to Reduced Production
Total Subcategory A
Subcategory B2
Due to Plant Closures
Due to Reduced Production
Total Subcategory B	
 2,225,825
  206,283
.2?432,108

         0
         0
         0
 154,806    2,380,63
  24,814    231,097
..179,620	2,611,72.
       0
   1,206
   1,206
        0
    1,206
    1,206
  Impacts of zero discharge requirements are reported with impacts of direct discharge requirements.  Zero
  dischargers may be subject to monitoring costs if they have any process wastewater.  Monitoring costs
  would be imposed by the permitting authority (no separate monitoring requirements are contained hi the
  proposed effluent guidelines for pesticide manufacturers).  These monitoring costs are included hi the
  analysis to capture the full cost to industry of controlling process wastewater pollutants.
2 Subcategory B is already limited to zero direct discharge under BPT.	
                                                i.7

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Option are projected to reduce the U.S. pesticide trade balance from $897 million to $717 million, a 20 percent
decline.  The PSES regulation would increase the total U.S. merchandise net imports by about one-tenth of one
percent.                                                                                           p

        Metallo-Organic Pesticide Chemicals Manufacturing (Subcategory B)
        Under the Zero Discharge Option, no Subcategory B indirect discharging facilities are projected to
close, and two facilities are expected to have a decline hi in-scope revenues of ten percent or greater.  Only
one of these facilities reported export data (non-PAI-specific).  Using the methods outlined above, it is estimated
that exports from these facilities will decline by about $59,000 due to the proposed regulation.

        The two Subcategory B indirect dischargers impacted under the PSES regulations produce PAIs in four
clusters.  The PAI production in each of these clusters is replaceable by imports. In the worst-case scenario
described above, imports are expected to rise by $1.1 million. Based  on the conservative assumptions
incorporated in the analysis, PSES regulations applied to Subcategory B facilities under the Treated Discharge
Option are projected to have minimal impact on both the U.S. pesticide trade balance and total U.S.     i
merchandise net imports.

        In summary, neither BAT nor PSES regulations under the Treated Discharge Option have a substantial
impact on the U.S. pesticide trade balance or the U.S. total merchandise trade balance.  Conversely, the impacts
of BAT and PSES regulations under the Zero Discharge Option could result in a $2.6 billion decline in the U.S.
pesticide trade balance,  leading to a trade deficit of $1.7 billion within the U.S. pesticide manufacturing •
industry. The impacts under the Zero Discharge Option are less dramatic when compared to the total U.S.
merchandise trade balance. The $2.6 billion increase in net imports would increase the U.S.  trade deficit by
approximately 1.7 percent.
                                                    6.8

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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.
                                                  6.9

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                                 Chapter 7:  FIRM IMPACT ANALYSIS
7.0
Introduction
        The firm analysis evaluates the impact of regulatory compliance on firms owning facilities subject to a
pesticide active ingredient (PAI) manufacturing effluent guideline.  Due to the differences between firms and
facilities,  the firm analysis may capture impacts not included in the facility analysis.  For example, some firms
may be in too weak a financial condition to undertake the treatment investment required for regulatory
compliance, even though the investment may appear to be financially desirable at the facility level. Such
circumstances may  occur if a firm owns more than one pesticide manufacturing facility that would be subject to
regulation; in that case, analysis at the individual facility level will not address the total impact of the financing
requirements on the firm.1 The regulatory action may therefore result in firms deciding to curtail pesticide
manufacturing activities at a facility, or a firm may restructure its finances or sell assets to allow the completion
of treatment investments.  Analysis of the economic impact of regulatory options at the firm level is therefore
an important component of the EIA.

        The firm impact analysis is organized into three sections.  The first section reviews the concepts used
to drive the financial analysis. The second section describes the methodology that employs these concepts.  This
section also highlights some analytic difficulties encountered due to data limitations, and the steps required to
overcome them. The third part of the discussion presents the results of the firm analysis.

7.1     Analytic Approach

        A firm's ability to comply with regulatory requirements  is assessed in two stages:

        (1)     The baseline analysis identifies firms whose financial condition, independent of regulatory
                action, is sufficiently weak to contraindicate the implementation of a treatment program
                required by a regulation.  Such firms would be at risk of financial failure even without
                regulatory costs.  For this reason, firms that fail the baseline analysis are excluded from the
                post-compliance analysis.
    'Conversely, a firm may be able to reduce its cost of compliance by consolidating the manufacturing activities
and, therefore, the treatment investments required of several facilities.  This would mitigate the projected impact
predicted by a facility-level analysis.   While such cases are plausible,  it is beyond the scope of this analysis to
identify them.
                                                   7.1

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        (2)     The post-compliance analysis identifies those firms, otherwise financially sound, whose  ,
                financial viability may be impaired by regulatory compliance. Such firms would be weakened
                by the financing burden and additional operating expenses of a treatment program.  These
                firms are characterized as likely to be significantly affected by a regulatory option.

        The firm financial impact analysis is conducted from the perspective of creditors and equity investors
who would be the sources of capital to finance a firm's purchase of treatment systems.2  To attract the
financing for a treatment program, a firm must demonstrate financial strength both before and,  on a projected
basis, after the treatment program (baseline and post-compliance, respectively).  The financial analysis presented
in this report simulates that performed by investors and creditors in deciding whether to finance the installation
of a pollution prevention or wastewater treatment system.  Two considerations that influence this decision are
(1) the financial performance of the firm (particularly in relation to its competitors) and (2) the expected ability
of the firm to manage its financial commitments without risk of financial failure.  These considerations,   ;
discussed below, form the basis of this analysis.                                                       '
                                                                                                   \
7.1.A  Firm Financial Performance                                                               :
        If a firm's performance is weaker than that of its competitors, the firm may not be able to provide the
expected investment return to its creditors and investors.  Unless significant improvement in performance is
likely, investors and creditors will generally avoid providing financing to such firms. Alternatively, investors
and creditors may seek higher returns (in the form of higher interest rates or higher required returns on equity)
to compensate for the additional risk associated with the capital they provide.  The higher cost  of capital may in
turn decrease the likelihood that such firms will invest in the treatment options required for compliance with an
effluent guideline.                                                                                  :

        The measure of financial performance used in the firm analysis is pre-tax return on assets (pre-tax
ROA, hereinafter referred to as "ROA"), computed as the ratio of earnings before interest and taxes (EBIT)  to
assets:3
    ^or a further discussion of debt and equity financing, see Section 4.2. A.
    3ROA is also known as "return on investment."
                                                   7.2

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                                              ROA =
                                                      Assets
        ROA is a measure of the profitability of a firm's capital assets, independent of the effects of taxes and
financial structure.  It is perhaps the single most comprehensive measure of a firm's financial performance.4
ROA provides information about the quality of management, the competitive position of a firm within its
industry, and the economic condition of the industry in which the firm competes.  In addition, ROA
incorporates information about a firm's operating margin and asset management capability: the ratio of pre-tax
income to sales (operating margin), multiplied by the ratio of sales to assets (asset turnover), equals ROA. If a
firm cannot sustain a competitive ROA, on both a baseline and post-compliance basis, it will probably have
difficulty financing the pollution control investment.  This is true regardless of whether financing is to be
obtained as debt or equity.

        Illustrating typical ROA values from 1982 to  1990, the median ROA for the U.S. industrial chemical
industry (as represented by SIC codes 2861, 2865,  and 2869) ranged from 10.1 percent to 18.9 percent (Robert
Morris Associates [RMA], 1991).5  At the 75 percent  quartile, ROA ranged from 14.5 percent to 23.6 percent
over this same period (i.e., firms at this level were more profitable than 75 percent  of those in the industry).
At the 25 percent quartile, which is indicative of weak performance,  ROA ranged from 7.2 percent to 13.4
percent.  The computation of ROA, and the interpretation of the computed values  as the basis for determining
financial viability, are discussed in Section 7.2.

7.1.B   Ability To Manage Financial Commitments
        The second general  area of concern to creditors and investors is the extent to which the firm can be
expected to manage its financial burdens without risk of financial failure. In particular, if a firm's operating
cash flow does not comfortably exceed its contractual payment obligations (e.g., interest and lease obligations),
the firm is seen as vulnerable to a decline in sales or increase in costs.6  Either scenario may:  (1) sharply
reduce or eliminate returns to the equity owners of the firm; and/or (2) prevent the firm from meeting its
contractual payment obligations. In the first case, earnings might fall or become negative, with a consequent
reduction or elimination of dividends and/or reinvested earnings.  The market value  of the firm's equity is also
    4For credit analysis in particular, pre-tax ROA is important because interest payments are made from pre-tax
income.
    5RMA provides financial statistics based on bank credit reports from public-reporting and non-public-reporting
firms in a variety of industries.  The RMA industry group that corresponds best to the pesticides manufacturing
industry is the "industrial chemicals"  industry, which includes SIC codes 2861, 2865, and 2869.  The ROA values
are calculated from RMA's reported  "operating profit/sales" ratio and "sales/asset" ratio.
    6For this discussion, a firm's operating cash flow is considered to be revenues minus costs, with the exception
of interest, lease expense and depreciation.
                                                   7.3

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likely to fall, causing a capital loss to investors.  In the second case, failure to make contractual credit payments
will expose the firm, and its equity owners to the risk of bankruptcy, forced liquidation of assets, and probable
loss of the entire equity value of the firm.

        The ability to manage financial commitments is expressed by the ratio of EBIT to interest obligations,
or the interest coverage ratio (ICR):7                                                                ;
                                                      EBIT
                                                    Interest
        Weakness hi these characteristics of firm financial condition and performance, as would be indicated by
a low ICR, indicates vulnerability of the firm to financial failure and difficulty hi obtaining financing for
treatment investments.  From 1982 to 1990, the median value of interest coverage for industrial chemicals firms
(as defined by RMA, see footnote 5) ranged from 2.3 to 5.6. Over the same period, the 75th percentile value
ranged from 7.2 to 16.3, and the 25th percentile value ranged from 1.0 to 2.2 (RMA, 1991).

7.2    Analytic Procedure

        As described hi the preceding section, the firm analysis is based on two financial measures: ROA and
ICR. Firm-level data required to calculate these financial measures were  obtained from public sources for
domestic firms subject to public reporting requirements.  In contrast, data for foreign-owned or closely-held
domestic firms were not publicly available.8 The only firm-specific data available for these firms were gross
revenues obtained from the Census.  Where firm-level data were not publicly available, industry norms  of
financial condition and performance were used as the basis for firm analysis.  For example, baseline financial
measures were developed using median values for the industrial chemicals business sector reported by RMA.
As a result of these data limitations, the analysis for foreign-owned and closely-held domestic firms is less
precise than for public-reporting domestic firms.

        For the Treated Discharge Option, detailed financial data were available for 20 of the 44  firms  \
expected to incur costs; the remaining 24 firms,  closely-held or foreign-owned entities, required the use of data
obtained from RMA. For the Zero Discharge Option, detailed financial data were available for 22 of the 48
firms expected to incur costs;  analysis for the remaining 26 firms is based on the industry norms obtained from
RMA.
    7The ICR is also known as "tunes interest earned."                                               r
    8Closely-held firms are owned by only a few individuals.  They do not trade securities publicly and are therefore
 not subject to public-reporting requirements under the rules of the Securities and Exchange Commission (SEC).
                                                   7.4

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         As mentioned above, ROA is calculated by dividing EBIT by total assets.  Data used to calculate ROA
 for public-reporting firms were obtained from income statement compilations in Compustat for 1986.9 For non-
 public-reporting firms, firm-level revenues were obtained from the Census.  Finn-level values of assets, and
 EBIT for non-public-reporting firms, were estimated from firm-specific revenues and RMA data (e.g., median
 values for assets and EBIT as a percentage of revenues in  1986).

         Dividing EBIT by interest expense yields the ICR. For public-reporting firms, data for this calculation
 were obtained from Compustat.  For non-public-reporting  firms, the data sources and calculation procedures are
 the same as those outlined for ROA.  That is, firm-specific interest and EBIT were calculated from firm-specific
 revenues from the Census and the RMA-reported median values for both interest, and EBIT as a percentage of
 revenues.

         Baseline EBIT, baseline total assets,  and baseline interest  expense are the components used to
 determine ROA and ICR. The data sources and calculations used  in this analysis differ depending on whether
 or not the required data are publicly available.  The calculation procedure for public-reporting firms and non-
 public-reporting firms are therefore presented separately.

         Computing Baseline Measures for Public-Reporting Firms
         Baseline data for public-reporting firms are taken from Compustat.  The three components of the two
 financial ratios are described below:
    9Compustat, a data base, provides financial information from SEC 10-K filings.  The 10-K document is the form
in which public-reporting firms are required to file detailed financial information annually with the SEC.  A 10-K
document contains information similar to that contained in an annual report but with additional detail.
                                                  7.5

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                                       PUBLIC REPORTING FWMS
                                             Baseline EBIT
    equals  Operating Income (operating revenues minus all production and operating costs, selling expenses,
            and general and administrative expense; but before taxes, interest and depreciation)
    minus  Depreciation and Amortization (non-cash, cost items recognized as a charge against income and
            meant to reflect the consumption of wasting assets)
    minus  Losses from discontinued operations
      plus  Nonoperating Income.
                                         Baseline Total Assets
    equals  Total Current Assets
      plus  Net facility, property, and equipment
      plus  "Other" assets.
                                       Baseline Interest Expense
            Taken directly from Compustat, which lists interest expense as a single line item.
        Computing Baseline Measures for Non-Public-Reporting Firms
        Baseline financial measures for non-public-reporting firms required firm-level values to be estimated on
the basis of:  (1) firm-specific revenue information obtained in the Census; and (2) industry averages obtained
from RMA's 1991 Annual Statement Studies for the industrial chemicals business sector and Compustat.  All
values were for the year 1986. The components of baseline financial ratios for non-public-reporting firms were
estimated in the following manner:                                                                 ,
                                                  7.6

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                                             Baseline EBTT
   Estimated EB1T = Firm Revenues x
                                      Operating Profit]
                                          Revenue
     EBTT
Operating Profit\COMPUSTAT
         Estimated EDIT = Firm Revenues x 0.058 x 1.18 = Firm Revenues x 0.068
 Firm revenues were taken from the responses of individual firms to the Census. RMA, which did not
 provide an EBIT/revenue ratio directly, gave an industry median operating profit/revenue ratio of 0.058 for
 1986.  The estimated average EBIT/revenue ratio was determined by increasing the RMA operating
 profit/revenue ratio by the percentage amount by which EBIT exceeded operating profit for the public-
 reporting pesticides manufacturing firms included in the analysis.  Based on Compustat data for the public-
 reporting firms in the analysis, EBIT was found to be 18 percent higher on average than operating profit.
 For the analysis of non-public-reporting firms, an EBIT/revenue ratio of 0.068 (i.e.,  1.18 x 0.058) was
 multiplied by firm-level revenue data to calculate firm-level EBIT. To summarize, for each $100 million in
 revenues, a non-public-reporting firm was assumed to have EBIT of $6.8 million.
                                          Baseline Total Assets
 Calculated using the median RMA revenue/assets ratio of 2.0 to 1.  A firm with $100 million in revenues
 was therefore assumed to have $50 million in assets.
                                        Baseline Interest Expense
 Calculated from the median RMA value of the EBIT/interest ratio, 3.0 to 1.  Assuming that the estimated
 EBIT/revenue ratio for non-public-reporting pesticides firms is 0.068, an EBIT/interest ratio of 3.0 indicates
 that interest expense averages 2.3 percent of revenue for RMA firms (i.e., 0.068/3.0 = 0.0227 or
 approximately 2.3 percent).  This value was multiplied by firm-level revenue data taken from the Census to
 estimate baseline interest expense for all non-public-reporting firms.  To summarize, for each $100 million
 in firm-level revenues, annual interest expense was estimated  at $2.3 million.
        Because the baseline ratio values for all of the non-public-reporting firms in the analysis were
calculated using median RMA values, they are the same.10 Specifically, the estimated ROA is 13.6 percent
and the ICR is 2.96. Although these values  are the same in the baseline analysis for all non-public-reporting
    10If firm-level financial data were available for the non-public-reporting firms, the baseline ratio values could
be estimated more accurately.
                                                   7.7

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firms, they differ across firms in the post-compliance analysis.  This is due to differences in the cost of  !
compliance for facilities, as well as to differences in the numerators and denominators of the baseline ICR and
ROA ratios (although not the ratios themselves) among the firms.

        Evaluating Baseline Performance Measures
        To evaluate the baseline viability of the firms analyzed, the firm-specific values of baseline financial
performance were compared against the lowest quartile (i.e., 25th percentile) value in 1986 for the financial
performance measures as reported by RMA for the industrial chemicals business sector. The lowest quartile
value for the ICR was 1.1; the lowest quartile for ROA was 8.8. Those firms for which the value of either the
ROA or the ICR was  less than the first quartile value from RMA were judged to be "vulnerable" to financial
failure,  independent of the  application of a pesticides effluent guideline. Because both measures are judged to
be critically important to financial success and the ability to attract capital, failure with regard to either measure
alone was deemed adequate for the finding of "vulnerability" (see Table 7.1). Because the ratio values for non-
public-reporting firms were based on the RMA  median values rather than firm-specific data, none of the non-
public-reporting firms could be judged to be vulnerable hi the baseline analysis.

        Two points addressing the methodology's limitations and interpretation should be considered:

        (1)     The 25th  percentile value is an arbitrary one for defining poor financial performance and
                condition. This approach assumes that the weakest one-fourth of firms in an industry are
                 automatically in poor financial condition and at risk of financial failure.  By definition, such
                 firms are  in poorer condition than 75 percent of their competitors.  In spite of this, some and
                possibly all firms in the lowest quartile might still be in good financial condition, particularly
                 during periods of stronger economic performance.  Alternatively, during a period of weaker
                                                                                                 i
                 economic performance, more than 25 percent of the firms in an industry might be in poor
                 condition and at risk of failure.  Although the 25th percentile values can provide insight into a
                 firm's ability (or lack thereof) to manage the financial requirements of regulatory compliance,
                 such an analytic procedure is imperfect.

        (2)      Using the 25th percentile values from RMA does not mean that 25 percent of the firms in this
                 EIA will  be judged to be in poor financial condition. The firms in the RMA sample on which
                 the percentiles were  calculated include those in the industrial chemicals business as a whole.
                 The PAI  manufacturing firms  analyzed in this study are  therefore a subset of the RMA sample.
                                                   7.8

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Table 74t Beterminatfon of Firm-level financial Viability
ROA
Highest Quartile
Third Quartile
Second Quartile
Lowest Quartile
Interest Coverage Ratio :
Lowest Quartile Median Highest Quartiles
Vulnerable
Vulnerable
Vulnerable
Vulnerable



Vulnerable



Vulnerable



Vulnerable
Note: Baseline firms in the indicated quadrants are labeled "vulnerable. " In the post-compliance analysis,
firms that move to these quadrants become vulnerable due to compliance costs and are said to sustain a
"significant impact. "
        The post-compliance analysis is undertaken only for those firms that were not found to be "vulnerable"
to financial failure in the baseline analysis.  La the post-compliance analysis, if either the re-computed ROA or
ICR for a firm was found to fall below the RMA first quartile value, then that firm was judged to be
"vulnerable" to financial failure as the result of regulatory action, and was said to sustain a "significant impact"
(see Table 7.1).

        To  recalculate ROA and ICR, the three baseline components (i.e., EBIT, total assets, and interest
expense) were adjusted to reflect compliance costs estimated at the facility level.  In the facility analysis,
compliance costs were estimated in three categories: capital costs (facility and equipment), land costs, and
annual operating and maintenance costs.11  In the firm analysis, these values were summed over the facilities
owned by each firm and used to adjust the baseline components as shown below (see also Table 7.2 for the
mathematical formulation of the analysis):
    "Discharge costs (e.g., the cost of sludge disposal) and monitoring costs are included within the operating and
maintenance cost category.
                                                   7.9

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         Tabte 7,2:  Calculation of UntirLeyel Financial Measures in Post-Compliance Analysis
                                  Firm Financial Performance (ROA)
     Baseline ROA  =
                        EBIT
                     Total Assets
     Post-Compliance ROA
EBIT -
A? * —
- °2 '
H [(A^ *
^2) - (Pi
* A#)]
                                             Total Assets •*• c
  where:
          EBIT = Baseline earnings before interest and taxes
          o,     = Baseline operating and maintenance expenses
          o2     = Compliance operating and maintenance expenses
          Aq    = Change in production quantity due to elasticity (qt - qj
          q,     = Baseline production quantity
          q2     = Post-compliance production quantity
          Ap    = Change hi price due to elasticity (pt - p^)
          p,     = Baseline unit price
          p2     = Post-compliance unit price
          c     = Cost of compliance capital  equipment and associated land requirements
                           Ability to Manage Financial Commitments (ICR)
     Baseline ICR =
                         EBIT
                    Interest Expense
                            EBIT -
     Post-Compliance ICR =
     + [(A/J * qz) - (PI  * A?)]
                                          Interest Expense + i
  where:
          ICR    = Interest Coverage Ratio                                                      ;
          i       = Average interest payment on debt for capital and land, assuming 10-year repayment,
                 where:
                          Average Annual
                          Interest Payment ~
(d * c) * 0.1095
                                             1 - (1 + 0.1095)
               -10
                        10
          d      = Percent of compliance capital equipment and land assumed to be financed by debt
          d * c   = Debt financing required for compliance capital equipment and associated land
    12For firms with multiple plants, compliance costs and production quantities are summed.  In addition, the
average price (baseline and post-compliance) is weighted according to each plant's production quantity.   :
                                                 7.10

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                                         Post-Compliance EBIT
    equals   Baseline EBIT
    minus   Compliance operating and maintenance costs (summed over facilities)
    minus   the change in variable production costs (assumed to decrease by the same percentage as
               production decreases for each facility)
     plus    Change in revenues (based on price elasticity response and summed over facilities)
                                      Post-Compliance Total Assets
    equals  Baseline Total Assets
      plus  Compliance capital and land costs (summed over facilities)
                                   Post-Compliance Interest Expense
    equals  Baseline interest expense
      plus  Annual interest expense for the debt component of compliance capital and land requirements
               (summed over facilities)
        The calculation of these values and the subsequent evaluation of post-compliance firm financial viability
were based on several secondary financial assumptions.  These assumptions are outlined below:

        •      The percentages of the investment that a firm is assumed to finance through equity (e/a) and
               debt (d/a) are assumed to match the firm's historical mix of equity and debt investment.  The
               values of these variables for each firm are obtained from one of two sources.  For each
               domestic public-reporting firm, the mix of debt and equity is  obtained from Standard and
               Poor's Compustat service for that firm in 1986.  For all firms not included in the  Compustat
               data base, the mixture of debt and equity financing was assumed to match the 1986 median
               mixture of debt and equity financing for the "industrial chemical industry" as calculated from
               RMA's Annual Statement Studies.  The calculated values taken from the Annual Statement
               Studies are 40.5 percent equity financing and 59.5 percent debt financing.

        •      To be consistent with the facility analysis (in which capital  equipment is  assumed to have a ten-
               year useful life), a ten-year loan period was assumed for the debt used to finance compliance
               capital and land outlays. To estimate a "steady state" interest payment burden on  the firm, debt
               is assumed to be repaid on the basis of a constant annual payment amortization schedule over
               the  ten-year period.  This average annual interest payment is the value used for additional
               interest expense, and is used to calculate both post-compliance interest expense and the ICR.
                                                 7.11

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               The interest charged on compliance-related debt is assumed to equal the average interest rate,

               10.95 percent, for AA-rated industrial debt with 10 years to maturity, over the period 1981-

               1990, as reported by Salomon Brothers' An Analytical Record of Yields and Yield Spreads

               (U.S. Department of Commerce, 1990 and 1991).13  To convert this value to a real (i.e.,'

               inflation-free) rate, the rate was discounted on the basis of the average annual growth in the

               Consumer Price Index (CPI-U) for the period 1981-1990 (4.74 percent), resulting in a real

               interest rate of 5.93 percent (Survey of Current Business,  1991).14
7.3
Results
        Analyses of baseline and post-compliance financial viability were undertaken for those firms projected

to incur costs as the result of regulatory action.  The findings from this analysis are presented below, first for

the baseline and then for the two regulatory options analyzed: the Treated Discharge Option and Zero

Discharge Option.                                                                                 '.
                                                                                                  i

7.3.A  Baseline Analysis
        Forty-eight firms were projected to incur compliance costs under at least one of the two regulatory

options.  In the baseline analysis, only one of these firms had an ROA below the first RMA quartile value.

This firm was also the only one whose ICR fell hi the lowest RMA quartile.  Because this firm was found to be

"vulnerable" to financial failure independent of regulatory action, it was  excluded from the post-compliance

analysis.


7.3.B  Post-Compliance Analysis: Treated Discharge Option
        Under the Treated Discharge Option, compliance costs were projected for 44 pesticides manufacturing

firms, one of which was found to be vulnerable to financial failure in the baseline analysis.  The post-

compliance analysis was therefore performed for only the remaining 43 firms.  Three of these firms had both
     "Interest rate information reported by individual facilities in the Census was not used for this analysis due to
 difficulties of interpreting the reported values. For example, a number of respondents reported that funds for capital
 outlays were obtained from a parent firm at zero percent.  This reporting reflects internal accounting conventions
 but does not accurately represent the interest cost borne by the firm for debt financing.  Other firms indicated that
 interest costs were tied to the prime rate (e.g., prime rate or "prime rate plus one").  Such interest terms would
 generally apply to a working capital credit line or other short-term credit instrument.  However, the short-term
 liability would usually be replaced by longer-term debt to match the expected life of the capital asset being financed.
 The interest rates  on longer-term debt are usually higher  than short-term credit rates, so short-term rates may
 understate potential interest costs.

     "The interest on debt, the inflation rate, and the mix of debt and equity assumed in the firm-level analysis all
 match the assumptions in Chapter 4 (the facility-level analysis). An assumption regarding the cost of equity is not
 required in  the firm-level analysis since it is not an input to the calculation of post-compliance EBIT, interest, or
 assets.                                                                                           :

                                                    7.12

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ROA and ICR in the lowest RMA quartile in the post-compliance analysis, and were therefore said to incur
significant financial impacts.

7.3.C  Post-Compliance Analysis:  Zero Discharge Option
        Under the Zero Discharge Option, compliance costs were projected for 48 pesticides manufacturing
firms. Again, one firm, found to be vulnerable to financial failure in the baseline analysis, was excluded from
the post-compliance analysis.  On a post-compliance basis, fourteen of the remaining 47 firms shifted into the
lowest RMA quartile for both ROA and ICR.  The finding of a substantially greater firm impact under the Zero
Discharge Option reflects the much higher level of compliance costs estimated for this option in comparison to
those estimated for the Treated Discharge Option.
                                                 7.13

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                                         Chapter 7 References
Robert Morris Associates (1991). Annual Statement Studies.  Philadelphia, PA.

U.S. Department of Commerce (1990, 1991).  Bureau of the Census, Statistical Abstract of the United States,
      An Analytical Record of Yields and Yield Spreads.

U.S. Department of Commerce (1991). Bureau of Economic Analysis, Survey of Current Business.
      Washington, D.C.
                                                  7.14

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                               Chapter 8: SMALL BUSINESS IMPACTS
8.0
Introduction
        This chapter considers the expected effects of the proposed effluent limitations guidelines and standards
for the pesticide manufacturing industry on small businesses.  The Regulatory Flexibility Act (RFA)  (Public
Law 96-354) requires the Environmental Protection Agency to determine if a proposed regulation is likely to
have a significant impact on a substantial number of small entities.  If such an impact is expected to be
disproportionately large compared to that on larger entities, then alternative regulatory methods to mitigate or
eliminate economic impacts on small businesses are examined.

8.1     Methodology

        This analysis proceeded in three stages. The first stage of the analysis considers whether the regulatory
options are likely to have a significant impact on a substantial number of small entities. At the outset, the term
"small entity" was defined.  The first stage of this  analysis used the threshold for small businesses established
by the Small Business Administration (SBA).   The SBA thresholds define small businesses based on revenue
and/or employment at firms (including all  affiliates and divisions) for each SIC group.  Pesticide manufacturers
are classified in SIC code 28694 (pesticide and other organic agricultural chemicals, composed of active
ingredients used to formulate pesticides).  The SBA size threshold for SIC 28694, given in terms of employment
only, is defined as firms employing fewer than 1,000 people.   Because firm employment data were not collected
in the Census, these data were taken from Dun and Bradstreet's Million Dollar Directory.  Firms meeting the
SBA definition of small entities were then analyzed for the likelihood of sustaining any significant impacts
resulting from regulatory compliance (e.g., facility closure, product line closure, or "other significant impact"
as defined in Chapter 4). If such an impact on a substantial number of small entities is indicated by the results
of the first analytical stage, then the analysis proceeds to the second stage.

        The second stage of the analysis examined whether the impacts on small businesses would be
disproportionately large. In contrast to  the first stage of the analysis, which defined an entity as  equivalent to a
firm, two additional definitions of an entity were considered: the facility (including pesticide- and non-pesticide-
related activities) and the pesticide-related portion of the facility (including activities related to the manufacturing
                                                   8.1

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of both in-scope and out-of-scope pesticide active ingredients [PAIs], pesticide
formulating/packaging/repackaging, and pesticide contract work or tolling).1 The second stage of the analysis

therefore considered five measures  of entity size:


                 •        firm revenues

                 •        total facility revenues

                 •        total facility employment

                 •        pesticide-related facility revenues

                 •        pesticide-related facility employment                                         ;


        To examine whether the economic impacts of the proposed guidelines were expected to fall

disproportionately on small businesses, the relationships between financial impacts, (e.g., facility and product

line closures), and the five measures  of entity size were examined using two analytical methods.2  First,

impacts vs. measures of entity size were plotted to provide a visual understanding of the relationship between
the two variables. Second, a series of logistic regressions was performed to test the hypothesis that an entity is

less likely to suffer adverse impacts commensurate with an increase in size.3 For both the plotting and   j

regression analyses, impacts  were translated into binary variables.  Entities expected to be impacted as a result

of the regulation were assigned a value of 0, while entities without impacts were assigned a value of 1.  The
relationship between impacts and entity size was  examined separately for direct and indirect dischargers.4 ?
    'The RFA states that the promulgating agency has the discretion to establish a new definition of a small entity
that it considers more appropriate for conducting a regulatory flexibility analysis if it is determined that the SBA
criteria are not suitable. Although the EPA agrees that the firm level is the appropriate one at which to examine
small business impacts in this industry, this  analysis also  considers other definitions  of a  small  entity for two
reasons. First, because earlier chapters evaluated the potential impacts of the regulatory options at the pesticide-
related level of the facility, small business impacts were also examined at this level. Second, assessing impacts at
the firm level based solely on employment would draw inconclusive results because firm employment data were not
publicly available for all firms represented in the Census.  The entire facility was also examined in this analysis as
a mid-point between the firm and the pesticide-related portion of the facility.

    2No "other significant impacts" (as defined in Chapter 4) were projected to occur under either the Treated
Discharge or Zero  Discharge Options.  "Other significant impacts" are therefore not included hi this analysis.

    3In a linear regression, the response values are unbounded.  In contrast,  response values  hi logistic regression
are bounded by 0 and 1. Given that the dependent variables used in the analysis are binary, logistic regression was
used hi the second  stage of this analysis.

    "Zero dischargers, deep well injectors, and  off-site incinerators were included with direct dischargers.;

    5The relationship between impacts and  entity size was also  analyzed combining the direct and indirect
dischargers.  The results of this analysis are shown hi Appendix E.                                    I

                                                    8.2

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         For the plotting analysis, each of the five measures of entity size were plotted against the two measures
 of impacts to determine whether a size (threshold) exists above which impacts are stable, i.e., the
 facility/product line(s) remain open.  If any of the plots resemble a discontinuous step function, as illustrated in
 Figure 8.1, this would indicate that small entities would be impacted disproportionately if the regulation was
 applied uniformly.

         The regressions examined the probability that a facility/product line would remain open as a function of
 entity size, using the following specification:
                                                       1+e
where:
the probability that an entity's facility/product line(s) will remain open for a given entity size
     = 1)  =
Yj       =       decision to close facility/product line(s);  l=open, 0=close
e        =       base of natural logarithms (2.71828)
X£       =       measure of entity size; and
Bi       =       the coefficient estimate for Xj.

         Altogether, ten regression analyses were performed, each using one of the five measures of entity size
as the independent variable and one of the two potential impacts (facility closure and product line closure) as the
dependent variable.6 7 Coefficient estimates from the regression models that were positive and statistically
different from zero would indicate that as entity size increases, the probability that a facility/product line will
remain open also increases.  This case would support the argument that a disproportionate number of small
facilities would suffer adverse economic impacts if the regulation was applied uniformly.
    6A11 of the five independent variables incorporated in the ten different regression models are expected to be
highly correlated because they all measure the same influence: entity size. For this reason, they were not examined
together within the context of one regression model.
    7A total of 30 plots resulted from  the analysis,  since the  10  regressions were examined for 3 discharge
categories: direct dischargers, indirect dischargers, and all dischargers combined.
                                                    8.3

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                       Figure 8,1
              Discontinuous Step Function
IMPACT
 Open 1
 Close 0
               XX   X
                        XXX XXX X X XXX XX X XXX X
XX X XX XXX XXX X XXX X XXX >
                                    X   XX
                             THRESHOLD        SIZE
                        8.4

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 8.2     Results

 8.2.A   Treated Discharge Option
         Impact of Best Available Control Technology Economically Achievable (BAT) Regulations on
         Direct Dischargers
         Under the Treated Discharge Option, no facility closures are projected for direct dischargers and two
 facilities are expected to close product lines.  Employment data were available for both of the firms owning
 facilities expected to close product lines. Both of these firms are considered small based on the SBA size
 standard. Because two firms do not constitute a "substantial number of small entities," no further analysis of
 direct dischargers under the Treated Discharge Option was required.

         Impact of Pretreatment Standards for Existing Sources (PSES) Regulations on Indirect
        Dischargers
        Under the Treated Discharge Option, no indirect discharging facilities are expected to close and only
 one facility is  expected to close a product line.  The firm owning this facility is not small based on the SBA size
 standard; therefore, no further analysis of indirect dischargers under the Treated Discharge Option is required.

 8.2.B  Zero  Discharge Option
        Impact of BAT Regulations on Direct Dischargers
        Under the Zero Discharge Option, 16 direct discharging facilities are expected to close and 3 facilities
 are expected to close a product line.  Firm employment date were available for 13 of the 19 facilities projected
 to incur significant adverse impacts.  Three of the 13 facilities for which firm employment data were available
 are small based on the SBA size standard. These three facilities are owned by three different firms.  Because
 firm employment data are not available for all firms, the results of the first analytical stage are somewhat
inconclusive.  To ensure that disproportionate impacts on small entities are fully considered, the impacts on
direct dischargers under the Zero Discharge Option are examined hi the second analytical stage.

        In the second stage of analysis, none of the ten plots (see Figures E.ll - E.20 in Appendix E) showing
the relationship between the two impact measures and five measures of entity size for direct dischargers show a
disproportionate impact on small entities.8 In fact, some plots show that larger entities bear a
disproportionately large portion of the impacts (see Figures E.16, E.17 and E.19).
    8A11 plots of entity size vs. impact can be found in Appendix E.
                                                  8.5

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        Table 8.1 presents the results of the ten regressions performed for direct discharging facilities under the
Zero Discharge Option.  To evaluate the data presented in Table 8.1, it is necessary to examine the coefficient
estimates and their associated p-values.9  If a coefficient is significantly different than 0 at the 90 percent  '
confidence level (p <. 10) and the coefficient is positive, then an increase in entity size is expected to increase
the probability that a facility/product line remains open.  If the coefficient is not significantly different than
zero, then entity size is not expected to have an impact on whether a facility/product line remains open.

        From the data shown hi Table 8.1, it is evident that small entities are not disproportionately subject to
facility/product line closures.  Although the estimated coefficients  for the size of the entity in eight of the ten
models are significant at the 90 percent confidence level (p< .10), the estimates are all negative. Applying any
of these estimates into  the logistic regression equation previously presented shows that larger facilities have a
higher probability than smaller facilities of closing.  For example, if model 4 (Zero Discharge Option) is used to
predict a facility closure, a facility with 100 pesticide-related FTEs would have a 0.39 probability of remaining
open, while a facility employing 200 FTEs would have a 0.30 probability of remaining open.

        Impact of PSES Regulations on Indirect Dischargers
        Under the Zero  Discharge Option,  11  indirect discharging facilities are expected to close and 3 facilities
are projected to close a product line. Firm employment data were available for 13 of the 14 facilities projected
to be impacted under the PSES regulation.   Seven of the 13 facilities for which employment data were available
are small based on the SBA size standard.  These seven facilities are owned by seven different firms.  Even
without complete firm employment data, a substantial number of small entities are expected to be impacted
significantly under the Zero Discharge Option. It is therefore necessary to advance to the second stage of the
analysis to further evaluate the, impacts on small entities for indirect dischargers under the Zero  Discharge
Option.

        In examining the ten plots (see Figures E.21 - E.30 in Appendix E) showing the relationship between
impacts and entity size for indirect dischargers, it does not appear that small indirect discharging facilities would
be impacted disproportionately under the Zero  Discharge Option.  None of the figures resemble the
discontinuous step function presented hi Figure 8.1.

        Table 8.2 shows the results of the ten regressions performed to examine the probability that an entity
would be adversely impacted as a function of entity size for indirect dischargers under the Zero  Discharge
Option.  The data presented in the table show that none of the coefficients are significant at the  90 percent
    *The p-value is the probability of obtaining the value of the coefficient if the true value were equal to zero.
Small values of p are interpreted as an indication that the coefficient is not equal to zero.
                                                   8.6

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Table 8.1
Logistic Regression Analysis
Zero Discharge Option: Direct Dischargers
Model
#
1
2
3
4
5
6
7
8
9
10
#of
Observations
45
45
44
46
46
20
20
20
21
21
Impact
(y,)
Facility Closure
Facility Closure
Facility Closure
Facility Closure
Facility Closure
Product Line Closure
Product Line Closure
Product Line Closure
Product Line Closure
Product Line Closure
Measure of Entity Size
(*,)
Pesticide Revenues
Facility Revenues
Firm Revenues
Pesticide Employment
Facility Employment
Pesticide Revenues
Facility Revenues
Firm Revenues
Pesticide Employment
Facility Employment
Coefficient
(ft)
-9.7E-9
-6.8E-9
-7.0E-11
-4.3E*
-1.6E-3
-2.0E-*
-LIE"8
-1.9E-10
-1.5E-2
-2.6E-3
Note: At the 95 percent confidence level p< .05 indicates that the coefficient is significant, while p<
indicates significance at the 90 percent confidence level. Coefficients that are in shaded sections are
significant to the 90 percent confidence level.
P
value
.0027
.0094
.1297
.0382
.0496
.0639
.0344
.1528
.0958
.0964
.10
Table 8.2
Logistic Regression Analysis
Zero Discharge Option: Indirect Dischargers
Model
#
1
2
3
4
5
6
7
8
9
10
Note: At
indicates
# of Impact
Observations (y,)
27
27
26
27
27
12
12
11
12
12
the 95 percent
significance at
Facility Closure
Facility Closure
Facility Closure
Facility Closure
Facility Closure
Product Line Closure
Product Line Closure
Product Line Closure
Product Line Closure
Product Line Closure
Measure of Entity Size
(x.)
Pesticide Revenues
Facility Revenues
Firm Revenues
Pesticide Employment
Facility Employment
Pesticide Revenues
Facility Revenues
Firm Revenues
Pesticide Employment
Facility Employment
confidence level p < .05 indicates that the coefficient is
the 90 percent confidence level.
Coefficient p
(0i) value
-2.6E-8
-2.7E-9
-6.2E-"
-1.8E-2
-8.3E^
3.8E-9
2.3E-8
5.3&9
-1.3E'2
7.4E-3
significant,
.3158
.6746
.4532
.1304
.5717
.9141
.4317
.4187
.5867
.4756
while p<. 10
8.7

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confidence level (p< .10).  This result indicates that among indirect discharging facilities, entity size does not
have a significant impact on whether a facility or product line remains open.

8.3 Conclusions
        Under the proposed option, the Treated Discharge Option, it is not necessary to consider an alternative
regulation for small businesses, since the regulation is not expected to have a significant impact on a substantial
number of small .entities.  In addition, no alternative regulations for small businesses need to be considered
under the Zero Discharge Option. Although a substantial number of small entities are expected to be impacted
significantly under this option, the impacts are not expected to be disproportionate in comparison to those on
larger businesses.
                                                     8.8

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                                         Chapter 8 Reference




Dun's Marketing Services, Inc. (1991).  Million Dollar Directory.  New Jersey.
                                                8.9

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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. (Zero discharge
regulations were not considered for new sources due to the unacceptably large economic impacts projected for
existing sources under this option.)  The proposed NSPS and PSNS regulations, and the reasonableness of the
associated costs, are discussed below by chemical subcategory.

9.1     New Source Performance Standards
        Subcategory A (Organic Pesticide Chemicals Manufacturing)
        The Environmental Protection Agency (EPA) is proposing NSPS under Subcategory A for the
conventional pollutants regulated under Best Practicable Control Technology Currently Available (BPT),  122
organic PAIs, and 28 priority pollutants.  The EPA proposes NSPS effluent limitations guidelines that equal
Best Available Technology Economically Achievable (BAT) limitations, modified where appropriate to reflect
the wastewater flow reduction capability at new facilities.  Based on a comparison of wastewater generation  and
discharge practices at recently constructed vs. older pesticide manufacturing facilities, the EPA concluded that
28 percent wastewater  flow reduction had been demonstrated at some of the newer facilities where appropriate.
For this reason, the production-based mass limits developed for organic PAIs based on BAT treatment
performance data were modified to reflect the 28 percent reduction in wastewater discharge at new facilities.
For other non-conventional pollutants and  conventional pollutants generated by Subcategory A, the proposed
NSPS requires that the BPT limitations for biological oxygen demand (BOD), chemical oxygen demand (COD),
and total suspended solids (TSS) be modified to reflect the 28 percent wastewater flow  reduction demonstrated
at new facilities.

        The projected impact of the NSPS on new sources is expected to be less burdensome than that of the
BAT regulations on existing sources.  Designing a new technology prior to facility construction is typically less
expensive than retrofitting a facility for a new  technology.  Because the BAT technologies proposed for existing
pesticide manufacturers were found to be economically achievable, with some existing facilities already
                                                   9.1

-------
achieving a 28 percent wastewater flow reduction, the proposed NSPS are expected to be economically
achievable.  Moreover, given the structure of the pesticide manufacturing industry, it is unlikely that expansions
in the industry will occur through additional manufacture of currently produced PAIs. Instead, it is more likely
that new PAIs will be manufactured at any expanded or new facilities.  It is not possible to project NSPS
guidelines for treatment of new PAIs, given the difficulty in predicting the nature of the treatability of new
PAIs.

        Subcategory B (Metallo-Organic Pesticide  Chemicals Manufacturing)                     ,
        The EPA is proposing to reserve NSPS for  subcategory B chemicals because BPT already requires zero
discharge of process wastewater  pollutants.

9.2     Pretreatment Standards for New Sources
        Subcategory A Chemicals
        Proposed PSNS for the  organic pesticide chemicals manufacturing subcategory are based on thfe
proposed Pretreatment Standards for Existing Sources (PSES) technologies, modified where appropriate to
reflect the 28 percent flow reduction capability at new facilities.  As with Pretreatment Standards for Existing
Sources (PSES), the PAI standards are production-based mass limits, while the priority pollutant standards are
based on achievable concentrations.  The EPA is proposing to establish PSNS for the same conventional
pollutants, 122 organic PAIs, and 26 priority pollutants proposed under NSPS.

         Similarly to NSPS, PSNS guidelines are expected to be economically achievable because the impact on
new sources should be less than that on  existing sources,  and the proposed PSES guidelines have been found to
be economically achievable.  In  addition, 28 percent reductions in wastewater flow have been demonstrated at
some facilities.  Also, as discussed above, it is more likely that new PAIs, rather than those currently produced,
will be manufactured at any expanded or new facilities.  The EPA does not believe it is possible to project
PSNS guidelines for treatment of new PAIs, owing  to the difficulty hi predicting the nature of the  treatability of
new PAIs.                                                                                     ;

         Subcategory B Chemicals
         Under Subcategory B, the EPA is reserving the right to set PSNS at a later date. For this reason,
economic impacts have not been calculated.                                                      l
                                                   9.2

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                APPENDIX A




1986 PESTICIDE MANUFACTURER FACILITY CENSUS

-------

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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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                                                      Form Approved
                                                      OtyBNo.: 2040-0111
                                                      Expiration Date: 4/30/90
                 U.S. ENVIRONMENTAL PROTECTION AGENCY
          PESTICIDE MANUFACTURING FACILITY

                          CENSUS FOR 1986
             PART B.  FINANCIAL AND ECONOMIC INFORMATION
                               January 17,1989
Public reporting burden for this collection of information is estimated to average 65 hours per response.
The reporting  burden includes time for reviewing instructions, gathering data, and completing and
reviewing the questionnaire.

Send comments regarding the  burden estimate or any other aspect of this collection of information.
including suggestions for reducing the burden, to:
     Chief, Information Policy Branch (PM-223)
     U.S. Environmental Protection Agency
     401 M Street, SW
     Washington, DC 20460
and
Office of Management and Budget
Paperwork Reduction Project
 (2040-0111)
Washington, DC 20503
                                        A.2

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                               ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
                              Part 8  Financial and Economic Information
Part B:  General Instructions
The Pesticide Manufacturing Facility Census has three parts:

       Introduction;
       Part A: Technical Information; and
       Part B: Financial and Economic Information.

The Introduction and Part  A were mailed separately and have been completed by your facility.  This
package contains the Part B questionnaire and its  instructions.  All recipients who completed the
introduction and Part A of the Pesticide Manufacturing Facility Census must complete Part B at this time.

Throughout this questionnaire  you will be asked about  the Pesticide Active Ingredients listed in Table  1.
pages 4 through 12, of this booklet.  It may be helpful to review the list and identify  active  ingredients
handled at this facility before completing the questionnaire.

Authority

This mandatory census is conducted under the authority of Section 308 of the Clean Water Act (the Federal
Water Pollution Control Act, 33 U.S.C. 1251 et seq., as amended).  Late filing or failure otherwise to comply
with these instructions may result in criminal fines, civil  penalties and other sanctions as provided by law.
Provisions concerning confidentiality of the data collected are explained below.

Purpose

The Pesticide  Manufacturing  Facility  Census  questionnaire is designed to  collect data on  pesticide
manufacturing activities and waste treatment practices for the calendar year beginning January 1,  1986 and
ending December 31, 1986. Part B requests financial  and economic information for the calendar years
1985,1986 and 1987.

Who Must Respond

All recipients who completed  the Introduction and Part A of the Census questionnaire must  complete
Part B at this time. The entire Pesticide Manufacturing Facility Census questionnaire must be completed by
all manufacturers of the Pesticide Active Ingredients listed in Table 1, pages 4 through 12, of this booklet.

Completing the Census

Although Part B  may be completed by different officials, the individual who  signed the certification for
Part A should also certify all parts of the questionnaire  by completing and signing the Part B Certification
Statement located on page 3 of this questionnaire.

If the space allotted for the answer to any question is  not adequate for your  complete response, please
continue the response in the Comments space at the end of each section. Reference the comments to the
appropriate question.
                                               A.3

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                               ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSU$ FOR 1986
                              Part B Financial and Economic Information
                             GENERAL INSTRUCTIONS - Continued
When and How to Return the Part B Questionnaire

"he Pesticide Manufacturing Facility Census Pan B questionnaire must be comoieted ana returned witnm
50 days of receipt to:

       Dr. Lynne Tuaor  WH586
       U.S. Environmental Protection Agency
       Analysis and Evaluation Division
       401 M Street, SW
       Washington, D.C. 20460

Questions on the Part B Questionnaire

Questions pertaining to any item in Part B mav oe directed to:

       Dr. Lynne Tudor  WH586
       U.S. Environmental Protection Agency
       Analysis and Evaluation Division
       401 M Street, SW
       Washington, D.C. 20460
       (202) 382 5834

Provisions Regarding Data Confidentiality

Regulations governing the confidentiality of business information are contained in 40 CFR Part 2 Subpart B
and 43 Fed.  Reg. 40001  (Sept. 8,  1978).  Under these regulations, all records, reports, or information
supplied to the EPA may be made public by the EPA without further notice if not accompanied by a
business confidentiality claim.  You may assert a business confidentiality claim covering part or all of the
information you submit, other than effluent data, as described in 40 CFR 2.203(b):

       *(b) Method and time of  asserting business confidentiality claim.  A business which is
       submitting information to EPA  may assert a business confidentiality claim covering the
       information by placing on (or attaching to) the information, at the time it is submitted to
       EPA, a cover sheet, stamped or typed legend, or other suitable form of notice employing
       language  such as  'trade secret,' 'proprietary,'  or 'company  confidential.'   Allegedly
       confidential portions of otherwise non-confidential documents should  be clearly identified
       by the business, and may be submitted separately to facilitate identification and handling
       by EPA.  If the business desires confidential treatment only until a certain date or until the
       occurrence of a certain event, the notice should so state."

Information covered by a claim of  confidentiality will be disclosed by EPA only to the extent, and by means
of the procedures, set forth in 40 CFR Part 2 Subpart B.  In  general, submitted records, reports, or
information protected by a business confidentiality daim may be disclosed to other employees, officers, or
authorized representatives of the United States concerned with carrying out the Clean Water Act, or when
relevant to any proceeding under the Act.  Effluent data are not eligible for confidential treatment.
                                                 A.4

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                       ENVIRONMENTAL PROTECTION AGENCY
                PESTICIDE MANUFACTURING FACILITY CENSU^ FOR 1986
                       Part B Financial and Economic Information
                                                                          :D
                               INTRODUCTION
Enter the name of this facility.
Enter the EPA Federal Insecticide, Fungicide and Rodertticide Act (FIFRA) Establishment Numoer
for this facility, as reported to the EPA on Form 3540-16 ("Pesticides Report for Pesticide-Producing
Establishments'). Check the box next to "Not Applicable" if this facility does not have an EPA FIFRA
Establishment Number

!_j	|	'_!_;-!__!_! -!_'_:_!  -2A        'Z Not Applicable   I2B

Enter the DUNS Number of this facility. Check the box next to "Not Applicable if this facility does not
have a DUNS Number.
j   I
                                  I3A
Enter the facility mailing address.
      .1.
                                                    Not Applicable
                                                                    I4A
Street or P.O. Box
City or Town
           I4B
                                           State
                                           I4C
                                                     Zip Code
                                                    I4D
Enter the address of the physical location of the facility if different from the mailing address.
                               !   I
                                                                     ISA
Street or Route Number

l_l_l_l_l_l_l_
City or Town
            I5B

Certification Statement
                                            State
                                            ISC'
                                                     Zil
I certify that I have personally examined and am familiar with the information submitted in all three
parts of the Census questionnaire and all attached documents, and that based on my inquiry of
those individuals Immediately responsible for obtaining the information, I believe that the submitted
information is true, accurate and complete. I am aware that there are significant penalties for sub-
mitting false information, including the possibility of fine and imprisonment.
Date Survey Completed:
                                    Month
                                                 I_J  -  I_J_I_I_J     I6A
                                                Day
                                                             Year
Signature of Certifying Official
 Name of Certifying Official (please print or type)

 I_I_I_I_I_I_I_I_I_I_U_U
 Title
                                                                    I6B
                                                                   I6D
                                        A.5

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                               ENVIRONMENTAL PROTECTION AGENCY
                       PESTICIDE MANUFACTURING FACILITY CENSU^ FOR 1986
                               Part B  Financial and Economic Information
 •;ODE
3
7
3
9
:o
:i
12
13
14
I4a
I4b
14C
I4d
15
15a
i5b
15c
15d
16
I6a
i6b
16c
i6d
17
I7a
i7b
17c
18
19

20
21
22
23
24
25
26
                            INTRODUCTION - Continued

Review the Pesticide Active ingredients listed in Table 1 below and circle all codes that correspond
to active ingredients manufactured, formulated or packaged at this facility.

                   TABLE 1.  PESTICIDE ACTIVE INGREDIENTS

                ACTIVE INGREDIENT                                           " ~

  1,1 -Bis(chlorophenyl)-2.2,2-trichloroethanoi                        '•
  1,2-Dihydro-3,6-pyridazinedione                                  ;
  1,2-Ethylene dibromide
  1,3,5-Triethylhexahydro-s-triazine
  1,3-Oichloropropene
  10,10'-Oxybisphenoxarsine
  1 -(3-Chloroa!lyi)-3,5,7-triaza-l -azoniaaaamantane cnloride
  1 -{4-Chlorophenoxy)-3,3-dimethyl-i -d H-1,2,4-triazol-l -yl)-2-butanone
 2,2'-Methylenebis(3,4,6-trichloropnenoi)
 2,2'-Methylenebis(4,6-dichlorophenoi
 2.2'-Methyleneois(4-chlorophenoJ)
 2,2-Dichlorovinyl dimethyl phosphate
 2,3,5-Trimethylphenyfmethyicarbarnate
 2,3,6-Trichlorophenylacetic acid or any salt or ester
 2,4,5-Trichloropnenoxyacetic acid or any salt or ester
 2,4-Oichiorophenoxyacetic acid or any salt or ester
 2,4-Dichlorophenoxybutyric acid or any salt or ester
 2,4-Dichloro-6-(o-chloroaniiino)-s-tria2ine
 2,4-Dintro-6-octylphenylcrotonate, 2.6-Dinitro-4-octylphenylcrotonate, and Nrtrooctylphenois
    (The octyl's are a mixture of 1-Methylheptyl, 1 -Ethylhexyi, and 1-Propylpentyl)
 2,6-Dichloro-4-nitroaniline
 2-Bromo-4-hyd roxyacetophenone
 2-Carbomethoxy-1 -methyjvinyl dimethyl phosphate, and related compounds
 2-Chloroallyl diethyldithiocarbamate
 2-Chloro-1-(2,4-dichlorophenyl)vinyl diethyl phosphate
 2-Chloro-4-((l -cyano-1 -methylethyl)amino)-6-ethylamino)-s-triazine
 2-Chloro-N-isopropytacetanilide
                                                 A.6

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                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                              Part B Financial and Economic Information
                  TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued
                      ACTIVE INGREDIENT
27
273
27b
27C
27d
28
29
30
30a
30b
30C
30d
31
3la
31b
31C
31d
32
33
34
343
34b
34C
34d
35
36
37
38
39
40
41
42
43
44
45
46
463
46b
46C
46d
47
473
 47b
 47C
 47d
 48
 49
 50
2-Methyl-4-chlorophenoxyacetic acid or any salt or ester
2-n-Octyl-4-isothiazo)in-3-one
2-Pivalyl-1,3-Indandione
2-(2.4-Dichlorophenoxy)propionic acid or any salt or ester
2-(2-Methyi-4-chloropnenoxy)propiomc acid or any salt or ester
 2-(4-Thiazolyl)benzimidazole
 2-(Methyithio)-4-(ethyiamino)-6-(1.2-dimethylpropyl)amino-s-triazine
 2-(m-Chlorophenoxy)propionic acid or any salt or ester
 2-(Thiocyanomethylthio)ben20thiazote
 2-((Hydroxymethyl)amino) ethanol
 2-((p-bhlorophenyt)phenylacetyl)-l ,3-indandione
 3,4,5-Trimethylphenyimethyicarbamate
 3,5-Dichloro-N-(1,1 -dimethyl-2-propynl)benzamide
 3^-Dimethyl-4-{methytthio)phenyldimethylcarbamate
 3',4'-Dichloropropionaniltde
 3-lodo-2-propynyt butylcarbamate
 3-(a-Acetonylfurfuryl)-4-hydroxycoumarin
 4,6-Dinitro-o-cresoi
 4-Amino-6-(l ,1 -dimethylethyl)-3-(methylthio)-l ,2,4-triazin-5(4H)-one
 4-Chlorophenoxyacetic acid or any salt or ester
 4-(2-Methyi-4-chlorophenoxy)butyric acid or any salt or ester
 4-(Dimethylamino)-m-tolyt methytcarbamate
 5-Ethoxy-3-(trichloromethyl)-l,2,4-thiadiazole
 6-Etnoxy-1,2-dihydro-2,2.4-trimethyl quinoiine
                                                 A.7

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                       ENVIRONMENTAL PROTECTION AGENCY
               PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                       Part B  Financial and Economic Information
           TABLE 1.  PESTICIDE ACTIVE INGREDIENTS - Continued

               ACTIVE INGREDIEOT

8-Quinolinol sulfate
Acephate (0,S-Dimethyl acetytphosDnoramidothloate)
Acifluoren (5-(2-Chloro-4-(trifluoromethyl)phenoxy)-2-nitrobenzoic acid) or any salt or ester
Alachlor(2-Chloro-2',6'-diethyl-N-(metnoxymethyl)acetaniiide)
Aldicarb (2-Methyi-2-(methylthio)prooionaldehyde O-(methylcarbamoyl)oxime)
Alkyt* dimethyl benzyl ammonium cnlonde '(50% C14, 40% C12, 10% C16)
Allethrin (all isomers and allethrin coil)  •
Ametryn (2-(Ethyiamino)-4-(isopropytamino)-6-(methylthlo)-s-tria2ine)
Amitraz (N'-2.4-Dimethylphenyl)-N-(((2,4-dimethy(phenyi)imino)methyi)-N-methylmethanimidamiae)
Atrazine (2-Chlorc-4-(ethyiamino)-6-(isopropy1amino)-s-triazlne)
Bendiocarb (2.2-DimethyM ,3-benzoaioxoM-yl methylcarbamate)
Benomyl (Methyl 1-(buty1carDamoyi)-2-benzimidazolecamamate)
Benzene hexachloride
Benzyl benzoate
Beta-Thiocyanoethyl esters of mixed fatty acids containing from 10-18  carbon atoms
Bifenox (Methyl 5-(2,4-dichlorophenoxy)-2-nitrobenzoate)
Biphenyl
Bromacil (5-Bromo-3-sec-Butyl-6-Methyluracil) or any salts or esters
Bromoxynil (3,5-Dibromo-4-hydroxybenzonitrile) or any salt or ester
Butachlor (N-(Butoxymethyl)-2-chloro-2',6'-diethy1acetaniiide)
b-Bromo-b-nrtrostyrene (Note: b » beta)
Cacodylic acid (Dimethylarsenic acid) or any salts or ester
Captafol (cis-N-((1,1,2.2-Tetrachloroethyi)thio)-4-cyclohexene-l ,2-dicarboximide)
Captan (N-Trichloromethylthio-4-cycfohexene-l ,2-dicarboximide)
Carbaryl (1-Naphthylmethylcarbamate)
Carbofuran (2,3-Dinydro-2,2-dirnethyi-7-benzofuranyl methylcarbamate)
Carbosulfan(2,2-Dihydro-2.2-dimethyl-7-benzofuranyl(dibutylamino)thio)rnethylcarbarnate)
Chloramben (3-Amino-2,5-dichlorobenzoic acid) or any salt or ester
Chlordane (Octachloro-4,7-methanotetrahyd roindane)
Chloroneb (1,4-Dichloro-2,5-dimethoxybenzene)
Chloropicrin (Trichloronitromethane)
Chlorothalonil (2,4,5,6-Tetrachioro-1,3-dicyanobenzene)
51
52
33
54
55
56
57
58
59
60
51
52
53
64
55
66
67
68
683
68b
58c
63d
69
59a
69b
59c
69d
70
71
72
723
72b
72C
72d
73
74
75
76
77
78
78a
78b
78c
78d
79
80
81
82
                                         A.8

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                       ENVIRONMENTAL PROTECTION AGENCY
               PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                       Part B Financial and Economic Information
           TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued

               ACTIVE INGREDIENT

Chloroxuron (3-(4-(4-Chlorophenoxyjphenyl)-l, 1 -dimethylurea)
ChIoro-l-(2,4,5-trichlorophenyl)vinyi dimethyl phosphate
Chlorpyrifos methyl (0,0-Dimethyl 0-(3.5,6-trichloro-2-pyridyl) phosphorothioate)
Chlorpyrifos (O.O-Diethyl O-(3,5.6-trichloro-2-pyridyl) phosphorthioate)
Coordination product of Manganese 16%, Zinc 2% and Ethyienebisdithiocaroamate 62%
Copper 8-quinolinolate
Copper ethyienediaminetetraacetate
Cyano(3-phenoxyphenyl)methyl 4-chioro-a-(l-methy!ethyl)benzeneacetate (9CA)
Cydoheximide(3-(2-(3,5-Dimethy-2-oxocyclohexyl)-2-hydroxyethyl)glutarimide)
 Dalapon (2.2-Dichloropropionic acid) or any salt or ester
  Decachlorc-bis(2,4-cyclopentadiene-i-yl)
  Demeton (O,0-Diethyl O-(and S-) (2-ethylthio)ethyl)phosphorothioate)
  Desmedipham (Ethyl m-hydroxycarbanilate carbaniiate)
  Diammonium salt of ethylenebisdithiocarbamate
  Dibromo-3-chloropropane
  Dicamba (3.6-Dichloro-o-anisic acid) or any salt or ester
  Dichione (2,3-Dichloro-1,4-naphthoquinone)
  Diethyi 4,4'-o-phenytenebis(3-thioallophanate)
  Diethyi diphenyl dichloroethane and related compounds
  Diethyi dithiobis(thionoformate)
  Diethyi O-(2-isopropyl-6-methyl-4-pyrimidinyl) phosphorothioate
  Difluben2uron(N-(((4-Chlorophenyl)amino)carbonyl)-2.6-difiuorobenzamide)
  Diisobutylphenoxyethoxyethyl dimethyl benzyl ammonium chloride
  Dimethoate (O.O-Dimethyl S-((methylcarbamoyl)methyl)phosphorothioate)
  Dimethyl O-p-nrtrophenyi phosphorothioate
  Dimethyl phosphate ester of 3-hydroxy-N,N-dimethyl
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                      ENVIRONMENTAL PROTECTION AGENCY
             PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                     Part B Financial and Economic Information
         TABLE 1.  PESTICIDE ACTIVE INGREDIENTS - Continued

             ACTIVE INGREDIENT

Endothall (7-Oxabicyc!o(2 2 1)heDtane-2,3-dicart>oxyiic acid) or any salt or ester
CODE

'23
'233
123b
'23C
:23d
•24
125
126
127
128
129
130
131
132
133
134
135
136
137
138
1383
138b
138c
138d
139
140
141
142
143
144
145
146
147
148
149

150
151
152
153
1533
153b
153C
153d
154
155

156
Endrin (Hexachloroepoxyoctahyaro-enao.enao-oimethanonapntnalene)
Ethalfluralin(N-Ethy4-N-(2-rnethyl-2-DroDenyl)-2.6-dinitro-4-(trifluorornethyl)benzeneamine)
Ethion (O.O.O'.O'-Tetraethyl S.S'-metnyiene bisohosphorodithioate)
Ethoprop (0-Ethyl S,S-dipropyl phosonoroaithioate)
Ethyl 3-methyl-4-(methylthio)phenyi 1 -fmethylethyl) phosphoramidate
Ethyl 4,4'-dichlorobenzilate
Ethyl diisobutylthiocarbamate
Famphur(0,0-DimethylO-(p-(dimetriyisulfamoyi)pheny1)phosphorothioate)
Fenarimoi (a-(2-Chlorophenyi)-a-(4-cnloropnenyl)-5-pyrimidinemethanol)
Fenthion (6,0-Dimethyl 0-(4-rnethylthio)-m-tdyi)phosphorothioate)
Ferbam (Ferric dimethyldithiocarbamate)
Ruometuron (1.1 -Dimethyl-3-(a,a.a-tnfiuoro-m-tdyi)urea)
Ruoroacetamide
Fdpet (N-((Trichioromethyl)thio)phthalimide)
Glyphosate (N-(Phosphonomethyl)glycine) or any salt or ester
 G!yphosine(N,N-bis(Phosphonomethy!)glycine)
 Heptachlor (Heptachlorotetrahydrcnt, 7-methanoindene)
 Hexadecyl cydopropanecarboxylate
 Hexazinone (3-Cyclohexyi-6-(dimethylamino)-1 -methyl-1,3,5-triazine-2.4(1 H,3H)-dione)
 Isofenphos (1 -Methyiethyt 2-{(ethoxy((l -methylethyl)amino)phosphinothioyl)oxy)ben2oate)
 Isopropalin (2,6-Dinitro-N,N-dipropylcumidine)
 Isopropyl N-phenyi carbamate
 Karbutilate (tert-Butylcaroamic acid ester of 3-(m-hydroxyphenyl)-l ,1 -dimethylurea)
 LJndane (gamma isomer of benzene hexachloride) 99% pure
 Linuron (3-(3,4-Dichlorophenyl)-l -methoxy-1 -methylurea)
 Malachite green (Ammonium (4-(p-(dimethy)amino)-alpha-phenyibenzylidine)-2I
    5-cydohexadien-l -y(kJene)-dimethy( chloride
 Malathion (O,O-Dimethyl dithiophospnate of diethyi mercaptosuccinate)
 Maneb (Manganese salt of ethytenebisdithiocarbamate)
 Manganese dimethyldithiocarbamate
 Mefluidide (N-(2,4-Dimethyl-5-(((trifluoromethyl)sulfonyl)amino)phenyl acetamide) or any salt or ester
 Methamidophos (O,S-Dimethyl phospnoramidothioate)
 Methidathion (O,O-Dimethyl phosphorodithioate, S-ester of 4-(mercaptomethyl)-2-methoxy-delta
    2-1,3,4-thiadiazolin-5-one)
 Methomyl (S-Methyl N-((metnylcarbamoyl)oxy)thioacetimidate)
                                       A.10

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                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                              Part 8  Financial and Economic Information
                  TABLE 1.  PESTICIDE ACTIVE INGREDIENTS - Continued

                      ACTIVE INGREDIENT

         Methoprene (lsopropy1(E,E)-l l-methoxy-3.7,1 i-trimethyl-2,4-dodecadienoate)
         Methoxychlor (2.2-bis(p-Methoxypnenyi)-l .1,1 -trichloroethane)
         Methyl benzethonium chloride
         Methyl bromide
         Methylarsonic acid or any salt or ester
168
169
170
171
172
173
174
175
176
1763
176b
176C
176d
177
178
179
180
181
182
183
184
185
186
187
188
188a
188b
188C
188d
Methyldodecylbenzyi trimethyi ammonium cnloride 80% and methyldoaecyixylytene
   bis(trimethylammoriium chloride) 20%
Methyfene bisthiocyanate
Methyl-2.3-quinoxalinedithioi cyclic S.S-dithiocarbonate
Metolachlor (2-Chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-l -methylethyl)acetamide)
Mexacarbate (4-(Dimethy1amino)-3.5-xytyl methylcarbamate)
Mixture of 83 9% Ethylenebis(dithiocarDamato) zinc and 161% Ethylenebisdithiocarbamate. '
   bimolecular and trimolecuiar cyclic annydrosulfides and disulfides
MonuronTCA » Monuron trichloroacetate
Monuron (3-(4-Chtorophenyl)-ltl-dimethylurea)
N,N-Diethyl-2-(l-naphatnaienyioxy)propionamide
N.N-Diethyi-meta-toluamide and other isomers
Nabam (Oisodium salt of ethylenebisdithiocarbamate)
Naled (1,2-D!bromo-2,2-dicnloroethyi dimethyl phosphate)
Norea (3-Hexahydro-4,7-methanoindan-5-yl-l ,1 -dimethylurea)
Norflurazon (4-Chloro-5-(methylamino)-2-(a,a,a-trifIuoro-m-tolyl)-3(2H)-pyndazinone)       '•
N-1 -Naphthylphtnalamic acid or any salt or ester
N-2-Ethylhexyl bicydoheptene dicarboximide
N-Butyl-N-ethyl-a.a.a-trifluoro-2,6-dinitro-p-toluidine
O.O.O.O-Tetraethyl dithiopyrophosphate
O.O.O.O-Tetrapropyldithiopyrophospnate
O.O-Diethyl O-(3-chloro-4-methyi-2-oxo-2H-l -benzopyran-7-yl) phosphorothioate
O,O-Diethyi O-(p-(methylsulfiny1)phenyt) phosphorothioate
O.O-Oiethyt S-(2-(ethylthio)ethyl) phosphorodithioate
O,O-DimethylO-(4-nitro-m-tolyl)phosphor6thioate
O,O-D!methyiS-(phthalimidomethy1)phosphorodithioate
O.O-Oimethyt S-((4-oxo-1,2,3-benzotriazin-3(4H)-yl)methyl)phosphorodithioate
O.O-Oimethyl S-((ethylsulfinyl)ethyl phosphorothioate
Organo-arsenic pesticides (not otherwise listed)
                                                A.11

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                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                              Part B Financial and Economic Information
                  TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued

                      ACTIVE INGREDIENT

         Organo-cadmium pesticides
 191 a
 191b
 191C
 191d
 192
 192a
 192b
 192C
•192d
 193
 194
 195
 196
 197
 198
 199
 200
 201
 202
 203
 204
 205
 206
 2063
 206b
 206C
 206d
 207
 208
 209
 210
 211
 212
 213
 214
         Organo-copper pesticides
         Organo-mercury pesticides
Organo-tin pesticides
Orthodichlorobenzene
Oryzalin (3,5-Dinitro-N4,N4-clipropyisuifanilamide) (Note: N4 = N superscript 4)
Oxamyl (Methyl N',N'-dimethyl-N-((methylcarbamoyl)oxy)-l-thiooxamidate)
Oxyfluorfen (2-Chlorc-1 -(3-ethoxy-4-nitrophenoxy)-4-(trifluoromethyl)benzene)
0-Ethyl O-(4-(methylthio)phenyl) S-propyi phosphorodithioate
0-Ethyi O-(4-(metnylthio)phenyO S-propyl phosphorothioate (9CA)
0-Ethyl O-(p-Nitrophenyl)phenytphosphonothioate
0-Ethyi S-phenyi ethytphosphonodithioate
0-lsopropoxyphenyt methylcaroamate
Paradichlorobenzene
Parathion (O,O-Diethyl O-(p-nitrophenyl)phosphorothioate)
Petxjimethalin (N-(1 -Ethylpropy1)-3.4^jimethyl-2,6-dinitrobenzenamine)
Pentachloronrtrobenzene
PentachlorophenoJ or any salt or ester
Perfluidone (1,1,1 -Trifluoro-N-(2-metnyl-4-(phenyisulfonyl)phenyl)methanesulfonamide)
Permethrin ((3-Phenoxyphenyl)methyl 3-(2.2-dichloroethenyl)-2.2-climethylcyclopropanecarboxyiate)
Phenmedipham (Methyl m-hydroxycarpanilate m-methyl carbanilate)
Phenothiazine
Phenylphenol
Phorate (O,O-Diethyl S-((ethylthioimethyl)phosphorodithioate)
Phosalone (O,O-Diethyl S-((6-chioro-2-oxobenzoxazolin-3-yl)methyl) phosphorothioate)
Phosphamidon (2-Chioro-N,N-diethyi-3-hydroxycrotonamide ester of dimethyl phosphate)
                                                A.12

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                      ENVIRONMENTAL PROTECTION AGENCY
              PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                      Part B  Financial and Economic Information
          TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued

              ACTIVE INGREDIENT

Pidoram (4-Amino-3.5,6-trichloropicolinic acid) or any salts or esters
Piperonyi butoxide ((Buty1carbity1)(6-propylpiperonyl)ether)
Poly(oxyethyiene(dimethyliminio)ethyiene(dimethyliminio)ethylenedichloride
Potassium dimethyidithiocarbamate
Potassium N-hydroxymethyl-N-methyldithiocarbamate
Potassium N-methyldithiocarbamate
Potassium N-(a-(nitroethyl)benzyi)ethylenediamine
Profenofos (O-(4-Bromo-2-chloroprienyi) 0-ethyl S-propyl phosphorothioate)
Prometon (2I4-bis(lsopropylamino)-6-methoxy-s-triazine)
Prometryn (2,4-bis(lsopropyiamino)-6-(methyfthio)-s-triazine)
Propargite (2-(p-tert-Butyiphenoxy)cyclohexyl 2-propynyl sulfrte)
Propazine (2-Chloro-4,6-bis(isopropylamino)-s-triazine)
Propionic acid
Propyt (3-dimethylamino)propyl carbamate hydrochioride
Pyrethrin coils
Pyrethrin I
Pyrethrin II
Pyrethrum (synthetic pyrethrin)
Resmethrin ((5-Phenyimethyl)-3-furanyi)methyi 2,2-dimethyl-3-
    (2-methyM-propenyi)cyclopropanecarboxyiate)
Rohnel (O,O-Dimethyl O-(2,4,5-trichlorophenyl)phosphorothioate)
Rotenone
S.S.S-Tributyl phosphorotrithioate
SkJuron (1 -(2-Methytcydohexyi)-3-phenyiurea
SBvex (2-(2.4.5-Trichiorophenoxypropionic acid)) or any salt or ester
Simazine(2_Ch!oro-4,6-bis(ethylamino)-s-triazine)
Sodium beritazon (3-lsopropyl-lH-2,1,3-benzothiadiazin-4(3H)one 2,2-dioxide)
Sodium dimethyidithiocarbamate
Sodium fluoroacetate
Sodium methyldithiocarbamate
SulfoxkJe (1,2-Methylenedioxy-4-(2-(octylsulftdynyl)propyl) benzene
S-Ethyl cydohexyiethylthiocarbamate
S-Ethyl dipropyithiocarbamate
S-Ethyl hexahydro-lH-azepine-1-carbothioate
S-Propyi butylethytthiocarbamate
S-Propyl dipropyithiocarbamate
S-(2-Hydroxypropyl)thiomethanesulfonate
S-(O,O-Diisopropyl phosphorodithioate ester of N-(2-mercaptoethyl)benzenesulfonamide
Tebuthiuron (N-(5-(1,1 -Dimethylethyl)-1,3,4-thiadiazol-2-yl)-N,N'-dimethy)urea)
Temephos(O,O,O',O'-Tetramethyi-O,0'-thiodi-p-phenyienephosphorothioate)
215
215a
215b
215c
215d
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233

234
235
236
237
238
238a
238b
238C
238d
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
                                       A.13

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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 INGREDIEMT

Terbacil (3-tert-8utyt-5-chloro-6-methyiuracil)
Terbufos (S-(((1,l-Dimethylethyi)thioimethyl) O.O-diethyl phosphorodithioate)
Terbuthylazine (2-(tert-Butylamino)-4-cnloro-6-(ethyiamino)-s-tna2ine
Terbutryn (2-(tert-8utylamino)-4-(ethy!annino)-6-(methylthio)-s-trJa2ine)
Tetrachloropheno) or any salt or ester
CODE

254
255
256
257
258
2583
258b
258C
258d
259
260
261
262
263
264
265
265a
265b
265C
265d
266
267
268
269
270

271

272
Tetrahydro-3.5-dlmethyl-2H-1,3,5-thiaaia2ine-2-thione
Thiophanate-methyl (Dimethyl 4,4'-o-pnenyienebis(3-thioallophanate))
Thiram (Tetramethytthiuram disuifide)
Toxaphene (technical chlorinated camphene (67-69% chlorine))
Tributyl phosphorotrithiorte
Trifluralin (a,a,a-Trifluro-2,6-dinitro-N.N-dipropy(-p-toluidine)
Warfarin (3-(a-Acetonylbenzyl)-4-hyaroxycoumarin) or any salt or ester
Zinc 2-mercaptQbenzotniazolate
ZIneb (Zinc ethylenebisdithiocarbamate)
Ziram (Zinc dimethyidithiocarbamate)
(2,3,3-Trichloroallyl)diisopropylthiocarbamate
(3-Phenoxyphenyl)methyl d-cis and tran" 2.2-dimethyl-3-(2-methylpropenyl)cyclopropanecarboxylate
    •(Max d-cis 25% ; Min. trans 75%)
(4-Cydohexene-l ,2-dicarboximido)methyl 2.2-dimethyl-3-
   (2-methyipropenyl)cyclopropanecarboxyiate
Isopropyl N-(3-chlorophenyi) carbamate
                                       A.14

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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-A.  Was this facility owned or controlled by a parent firm or firms on December 31, 1986?
            S1A
                                      YES	 1 (GO TO BOX 1-A)
                                      NO 	 2 (SKIP TO SECTION 2, PAGE 18)
                                        30X 1-A

       If there is ,more than one parent firm, such as in a joint venture, photocopy Section 1,
       pages 13 through 16. and complete all Section 1 questions for each parent firm.
1-B.  Report the name, mailing address and DUNS number of the parent firm.

      [1]   Name of Parent Firm
                                                                    S1B1
[2]   Mailing Address of Headquarters

     I   !   !   I   I   I  !_!_!_!_!_!_L
                                                                    S1B2A
            __
           Street or P.O.Box
           Citv nr town                      1 State

                 S1B2B                     S1B2C
      [3]   What is the DUNS Number of the parent firm?

         •   I_J_!-I_I_J_I-I_!_I_I_!
             S1B3A
                                                Zip Code

                                                S1B2D


                                                Q Not Applicable
                                                  S1B3B
 1-C.  Report the percentage of the parent firm's total 1986 sales (in dollars) generated by each of the
      activities listed beiow.  (Enter zero if the activity was not applicable.  The sum of all percentages
      must be 100%).

      [1] Percentage of sales generated by manufacturing pesticides listed
          in Table 1. pages 4 through 12	'sIcT	"""•"•-   l_J_l^_i%
       [2] Percentage of sales generated by formulating or packaging pesticides
          listed in Table 1, pages 4 through 12 ..................................... -sic? ..... "-

       [3] Percentage of sales generated by other activities (SPECIFY) .................
          Total
                                                                              1  00%
                                             A.15

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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)
           31D                        NO	 2 (SKIP TO QUESTION 1 -E)


     [1 ]   In what year was this facility acauired by the parent firm?

                                       l_L!J.I_i_l
                                           Year          S1D1

     [2]   How was this facility acquired by the parent firm? (CHECK ONE):     S1D2

          Q Purchase

          fj  Merger: Please list names of the companies that merged


                            J_!_:_J_!_i_!_l_J_!_l    siD2A


                            J_I__!_!_!_!_J_I_!_I    S1D2B


              l_l_l_J_l_l_l_!_l_l_i_l_l_!_J_J    S1D2C


          Q  Takeover

          Fj  Founded

          Q  Other (SPECIFY)	



1-E.  On December 31, 1986, did the parent firm own or control any other U.S. facilities at which any of
     the pesticides  listed  on Table  1, pages 4 through 12, were manufactured or formulated and/or
     packaged?
                   S1E                 YES	  1 (CONTINUE)
                                       NO	  2 (SKIP TO QUESTION 1-G)
                                            A.16

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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-F.  Report the names  and EPA Federal  Insecticide.  Fungicide and  Rodenticide  Act  (FjFRA'i
     Establishment Numbers (as reported to the EPA on Form 3540-16) for all other facilities owned cr
     controlled by the parent firm at which any of the pesticides listed on Table  1, pages 4 througn 12.
     were manufactured or formulated ana/or packaged.  Check the box next to "Not Applicable' if the
     facility does not have an EPA FIFRA Establishment Number.  Check whether each facility was a
     manufacturer or formulator/packager of the pesticides listed on Table 1.  If  more space is required
     to give a complete answer to this question, photocopy this page.                        :
     [1]
                                                                    SIFIA
           _
          Name of Facility .

          ! __'_'_'_!_! •!—'—' •'—
          EPA FIFRA Establishment Numoer
                                                       Not Applicable
              Manufacturer
              S1F1D
                                          Formulator/Packager
                                           S1F1E
S1F2B
          Name of Facility
            ___
           EPA FIFRA Establishment Numoer
                                                    _____    SIFZA

                                                    D  Not Applicable  S1F2C
              Manufacturer
              S1F2D
                                      ^j  Formulator/Packager
                                          S1§2E
     [3]    !_J_i_J_l_l_i_l_!_:_!
           Name of Facility
S1F3B      I_I_!_J_I__I ' I_J_I - I_J
           EPA FIFRA Establishment Number
                                              l_!_!_!_i_!_!_J      S1F3A

                                                    D  Not Applicable    S1F3C
              Manufacturer
                                          Formulator/Packager
                                             S1F3E
     W
          Name of Facility
S1F4B      l_i_!___-__  -__
           EPA FIFRA Establishment Numoer
                                                        Not Applicable
                                                                        S1F4A

                                                                        S1F4C
               Manufacturer

              S1F4D
                                       ]  Formulator/Packager

                                        S1F4E
                                           A.17

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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-F.   Report  the  names  and EPA Federal  Insecticide,  Fungicide  and  Rodenticide  Act  (FIFRA)
      Establishment Numbers (as reported to the EPA on Form 3540-16) for all other facilities owned or
      controlled by the parent firm at which any of the pesticides listed on Table  1, pages 4 through 12.
      were manufactured or formulated and/or Dacxaged. Check the box next to 'Not Applicable" if the
      facility does  not have an EPA FIFRA Estaolishment Number. Check whether each facility  was a
      manufacturer or formulator/packager of the oesticides listed on Table 1. If more space is required
      to give a complete answer to this question, pnotocopy this page.
      [1]
           Name of Facility
 S1F5B     l — '-'-J-J — M^LI-^
           EPA FIFRA Establishment Numoer

           Q  Manufacturer             ~
                 S1F5D
                                           J_l_l_!_J    S1F5A

                                              Not Applicable  S1F5C
                                rormulator/Packager
                                  S1F5E
      [2]
S1F6B
           Name of Facility
           EPA FIFRA Establishment Number

           Q  Manufacturer             Q  Formulator/Packager

           S1F6D                          S1F6E
                                          _i_l_l_l_l     S1F6A

                                           Q Not Applicable   S1F6C
      [3]
                                                                    I
S1F7B
Name of Facility

EPA RFRA Esta"bTishment"NumbeT

Q  Manufacturer             [

       S1F7D
                                                            S1F7A

                                              Not Applicable  S1F7C
                                           Formulator/Packager

                                             S1F7E
      [4]
S1F8B
Name of Facility

l_!_l_J_!_i-l_l_l-!_
EPA FiFRA Establishment Number

Q  Manufacturer             [

      S1F8D
                                              —.' — ' — > —'     S1F8A

                                              Not Applicable    S1F8C
                                            Formulator/Packager

                                            S1F8E
                                             A.18

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                                          ENVIRONMENTAL PROTECTION AGENCY
                                   PESTICIDE MANUFACTURING FACILITY CENSU^FOR 1986
                                          Part B Financial and Economic Information
                                      SECTION 1: FIRM FINANCIAL INFORMATION


               1 -G.  Report the total revenue of the parent firm for 1985,1986, and 1987 in thousands of dollars.

                                                  ($000)

                    [1]  1985 Revenue	      -	

                    [2]  1986 Revenue	  	

                    [3]  1987 Revenue	  	  	
               1-H.  Was the parent firm (listed on question 1 B) itself owned or controlled by another company?
                              SIH
YES 	  1 (CONTINUE)
NO	  2 (SKIP TO SECTION 2)
               1-1.   Report the name, mailing address and DUNS number of the controlling firm.

                    [1]   Name
                                                                               sin
                    [2]   Mailing Address of Headquarters
                         Street or P.O.Box
                                                                                   SII2A
                         City or Town
                           SI
                    [3]   DUNS Number
    State
Zip Code
                                                                       Not Applicable
.
                                                          A.19

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                          ENVIRONMENTAL PROTECTION AGENCY
                   PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                          Part B Financial and Economic Information
                      SECTION 1: FIRM FINANCIAL INFORMATION
Section 1 Comments. Reference entry by question number.
                                          A.20

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                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILJTY CENSUS FOR 1986
                              Part B Financial and Economic Information
                       SECTION 2: FACILJTY FINANCIAL INFORMATION
All of the information requested in Section 2 applies to this facility.

2-A.  Report the percent by quantity of total 1986 production volume generated by each of the following
      activities at this facility. (Enter zero if the activity was not applicable.  The sum of all percentages
      must be 100%).

      [1 ]  Production generated by manufacturing and/or formulating and packaging          S2A1
          pesticide active ingredients listed in Table 1, pages 4 through 12	     	;	;_!%

      [2]  Production generated by manufacture of intermediates that                       S2A2
          are sold	••	     _; _' _ %

      [3]  Production generated by manufacturing and/or formulating and packaging        S2A3
          EPA registered pesticides not listed in Table 1, pages 4 through 12		|	 %

      [4]  Production generated by manufacturing and/or formulating and packaging        S2A4
          chemicals other than EPA registered pesticides	     	j	j	|i %

      [5]  Production generated by other activities (SPECIFY)	_! _J _J %
          	S2A5A(Variable),   S2A5B (Description)  	            :

          Total	      1   0  0 %


2-B.  Report the calendar year during which:
                                                     eoni
       [1 ]   Operations began at this facility	.._	   i _!_!_'_
                                                                                   Year


       [2]   Manufacturing and/or formulating/packaging
            of either pesticide active ingredients or         S2B2
            pesticide products  began at this facility	   !	!	|	|._"
                                                                                   Year


       [3]   The most recent major expansion of plant and
            equipment with respect to pesticides occurred   S2B3
            at this facility	   I _ i _ I _ I _
                                                                                   Year
                                               A.21

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                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                              Part B Financial and Economic Information
                       SECTION 2: FACILITY FINANCIAL INFORMATION
2-C.  Instructions for reporting Balance Sheet information on oaae 21.
      Question 2-C on page 21 requests facility Balance Sheet information.  Please read the instructions
      and definitions below before completing Question 2-C.   The number in brackets, for example,
      '[1 ] Inventones,* correspond to Balance Sheet entries.
      Reporting Period

      Amounts for items in the Balance Sheets must be reported as of December 31, of calendar years
      1985. 1986 and 1987 or. the last day of the facility fiscal year. If your facility does not operate on a
      calendar year, you may substitute fiscal year data.


      Reporting Conventions

      Report all data for the facility. Report all dollar amounts in thousands.

      If, for certain items, you do not have amounts at the facility level, you may use the balance sheets of
      the firm that owns and controls your facility to estimate the amounts at the facHity level. Base the
      estimate on vour facility's share of sales.  If you have estimated an amount for a particular item, then
      place an asterisk (*) to the right of the entry.


      Balance Sheet Definitions

            Current Assets: Report current assets, including cash and other assets that are reasonably
            expected to be converted to cash, sold or consumed during the year.

                  [1]    Inventories:  Report the total value of all inventories owned by this facility
                        regardless of where the inventories are held.  Inventories consist of  finished
                        products, products  in the process  of being  manufactured, raw materials,
                        supplies, fuels etc.  Report inventories at cost or market value, whichever is
                        lower.

                  [2]    Other  Current Assets:   Report all other current assets such  as  prepaid
                        expenses like rent, operating supplies, and insurance; also include cash and
                        accounts receivable.

                  [3]    Total Current Assets: Report the sum of items [1] and [2].
                                               A.22

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                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSU^FOR 1986
                              Part 8 Financial and Economic Information
                       SECTION 2: FACILITY FINANCIAL INFORMATION
2-C.  Instructions for reporting Balance Sheet information on pane 21 - continued


            Noncurrent Assets: Report the total dollar value of all noncurrent assets, including physical
            items such as  property,  plant and  equipment; long-term  investments and  intangibles.
            Include:

                        Land: Report the original cost of land.
                        Buildings/Plant:  Report the  cost of buildings including expansions  and
                        renovations net of depreciation.
                        Equipment and Machinery:  Report the cost of all equipment and machinery
                        net of depreciation.
                        Intangibles:  Report intangibles including  franchises,  patents,  trademarks,
                        copyrights net of accumulated amortization.
                        Other Noncurrerrt Assets:  Report all  noncurrent assets,  like investments in
                        capital stocks and bonds.                                            i

                   [4]   Total Noncurrent Assets: Report the total noncurrent assets from each of the
                        Items listed above that apply.

                   [5]   Total Current and Noncurrerrt Assets:  Report the sum of items [3] and [4].


             Current Liabilities:  Report the total dollar value of all  current liabilities that fall due for
             payment within the year.

                   [6]    Total Current Liabilities:  Report all current liabilities like accounts payable,
                         accrued expenses and taxes and the current portion of long-term debt.

             Noncurrent Liabilities and Equity. Report all noncurrent liabilities that fall due beyond one
             year.

                   [7]    Long Term Debt and Other Noncurrerrt  Liabilities:  Report all long-term debt
                         such as bonds, debentures, and bank  debt, and all other noncurrent liabilities
                         like deferred income taxes.

                   [8]    Owner Equity:  Report the difference between total  assets and total liabilities.
                         The amount obtained should include contributed or paid  in capital (preferred
                         and common stock) and retained earnings.

                   [9]    Total Noncurrerrt Liabilities and Equity: Report the sum of items (7] and [8].

                   [10]  Total Liabilities and Equity: Report the sum of items [6] and [9].
                                                 A.23

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                            ENVIRONME NTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                            Part B Financial and Economic Information
                      SECTION 2: FACILITY FINANCIAL INFORMATION
2-C.   Complete the facility Balance Sheet: Table 2-C below. Enter all information in thousands of dollars
      as of December 31  for calendar years 1985, 1986, and 1987.  If the facility fiscal year does not
      correspond to the calendar year, please enter the months of the facility fiscal year below.

      Facility 1986 fiscal year was from    S2CA mOnth to S2C3    month.
TA

Current assets
[1] Inventories
[2] Other current assets
[3] Total current assets
Noncurrent assets
i
[4] Total noncurrent assets
[5] Total current and
noncurrent assets


Current liabilities
[6] Total current liabilities
Noncurrent liabilities and equity
[7] Long term debt and
other noncurrent liabilities
[8] Owner equity
[9] Total noncurrent liabilities
and equity
[10] Total liabilities and equity

3LE2-C. BALANCE SHEET
ASSETS
1985
($000)
S2C1A
S2C2A
S2C3A
S2C4A 	
S2C5A

LIABILITIES AND EQUITY
1985
($000)
S2C6A
S2C7A
S2C8A
S2C9A
S2C10A



1986
($000)
S2C1B
S2C2B
S2C3B
•S2C4B
S2C5B


1986
($000)
S2C6B
S2C7B
S2C8B
S2C9B
S2C10B



1987
($000)
S2C1C
S2C2C
S2C3C
	 -S2C4C' "-'-•- 	
S2C5C


1987
($000)
S2C6C
S2C7C
S2C8C
S2C9C
S2C10C

                                            A.24

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                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                              Part B Financial and Economic Information
                       SECTION 2: FACILITY FINANCIAL INFORMATION
2-D.  Instructions for reporting facility Income Statement Information on pace 24.

      Question 2-D  on page  24  requests facility  Income and exoense  information.  Please read  the
      instructions and definitions  below before completing Question 2-D. The numbers in  brackets, for
      example. '[1 ] Sales of Pesticide Chemicals,' correspond to the entries on Table 2-D.            :

      Reporting Period

      Amounts for items in the Income Statements must be reported as of December 31 of calendar years
      1985.1986 and 1987 or the last day of the facility fiscal year.  If your facility does not  operate on a
      calendar year basis, you may substitute fiscal year data.

      Reporting Conventions

      Report all data for the facility. Report all dollar amounts in thousands.

      If. for certain  items, you do not have amounts  at the facility level, you  may use the Income
      Statements of the firm that owns and controls your facility to estimate the amounts  at the facility
      level. If you need to estimate any items, estimate them based on your facility's share of sales. If you
      have estimated an amount for a particular item, then place an asterisk (*) to the right of the entry.

      Income Statement Definitions
       Revenues
             [1]
             [2]


             [3]


             W
Sale of Pesticide Chemical*:  Rpport the total sales value of a!! pssticids chemicals.
This should Include  all pesticide active  ingredients,  intermediates, and finished
pesticide products.  In cases where the pesticide chemical is not sold  (there is no
known sales price) but  is transferred to another facility owned by the company for
further processing and/or formulating/packaging, the fadlity share of sales generated
by the final product should be allocated to the facility. This share shquld be estimated
based on its percent of total production costs. Divide the sale of pesticide chemicals
into the following categories:
[a]    Pesticide chemicals listed In Table 1: Report revenues from the manufacture
       and/or formulating/packaging of pesticide active ingredients listed in Table 1,
       pages 4 through 12 or intermediates produced during the manufacture of active
       Ingredients listed in Table 1.
[b]    Other Registered Pesticide  Chemicals:  Report revenues from pesticide
       chemicals not reported In [la].
Revenue  from  Pesticide Contract Work or Toiling:  Report the revenue from
pesticide contract work done by this facility for other facilities or firms.

Other Revenue:  Report  all other revenues like  the sales value of products and
services not reported in items [1] and [2].
Total Facility Revenues: Report the sum of items [1 ] through (3J.
                                                 A.25

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                               ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                              Part B Financial and Economic Information
                        SECTION 2:  FACILITY FINANCIAL INFORMATION

2-D.   Instructions for reporting facility Income Statement information on oaoe 24 - continued

            Expenses

            Manufacturing Costs (Cost of Materials and Services Used):  Include all manufacturing
            and/or formulating/packaging costs like direct materials, direct labor and indirect costs that
            were either  put  into  production,  used as  operating  supplies,  or  used  in  repair and
            maintenance. Report total delivered cost after discounts and including freight of materials
            actually consumed or put into production during the year.  Include purchases,  cost  of
            interpiant transfers to the facility, and withdrawal from inventories.
                  Pesticides

                        [5]
                        [6]
                        [8]
Material and Product Costs:  Report the total cost of all raw materials
including packaging materials that were used in the production and/or
formulating/packaging of pesticide chemicals/products.  Include cost of
products bought and sold.

Direct Labor Costs:  Report the total cost, including fringe benefits, of
all  direct labor  that  can  be traced  to  the  production and/or
formulating/packaging of pesticide chemicals/products. •

Cost of Pesticide Contract Work or Tolling:  Report the cost of  all
contract work done for you by others using materials furnished by your
facility, include the total payments mads during the year for such work.
including freight out and in.

Other Pesticide  Costs:   Include all other pesticide related expenses.
such as effluent  treatment and disposal, and energy used directly in
producing the product, not included in [5] through [7].
                  Non Pesticides

                        [9]    Nonpesticidt Costs:  Report all other manufacturing costs not included
                              in items [5] through (8]. Include manufacturing costs associated with
                              nonpesticide chemicals or products. Report the types of cost for items
                              [5] through [8] for nonpesticide products and services.

                  Report the expenses listed below for the whole facility, not Just pesticides.

                        [10]  Depreciation:  Report the depreciation on buildings, plant, equipment,
                              and machinery at your facility.

                        [11]  Fixed Overheads:  Report the total from all types of overhead.  Include
                              rent, nonproduction utilities, selling costs, administration  and  general
                              expenses for your facility.

                        [12]  Research and Development:  Report all research and development
                              costs incurred during the year.

                        [13]  Interest: Report the total interest expense on all funds during the year.

                        [14]  Federal, State and Local  Taxes: Report the total federal, state and
                              local taxes payable during the year.
                        [15]  Other Expenses: Report all other expenses not reported  in items [10]
                              through [14].

                        [16]  Total Costs and Expenses: Report the sum of items [5] through [15].
                                                A. 26

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                             ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
                            Part B Financial and Economic Information
                      SECTION 2: FACILITY FINANCIAL INFORMATION

2-D.  Complete the facility Income Statements. Table 2-D below.  Enter all information in thousands of
     dollars as of December 31 for calendar years 1985, 1986, and 1987. If the facility fiscal year does
     not correspond to the calendar year, please enter the months of the facility fiscal year below.
      Facility 1986 fiscal year was from
                                     32:
^rnonth to
             S2DB
month.
                          TABLE 2-D. INCOME STATEMENTS
                                       REVENUES
:  [1] Sales of pesticide chemicals

     [a]  Pesticide chemicals
          listed in Table 1

:     [b]  Other registered pesticide
:          chemicals
                                           1985
                                          ($000)
                                         S2D1AA
                  1986
                 ($000)
                 S2D1AB
                1987
                (SOOO)
              S2D1AC
                                         S2D1BA
                 S2D1BB
              S2D1BC
[2] Revenue from pesticide contract
work or tolling
[3] Other revenue
[4] Total facility revenues

Manufacturing costs
[5] Pesticide material and product costs
[6] Pesticide direct labor costs
[7] Cost of pesticide contract work
[8] Other pesticide costs
[9] Nonpesticide costs
Facility costs
[10] Depreciation
[11] Fixed overheads
[12] Research and development
[13] Interest
[14] Federal, state and local taxes
[ 1 5] Other expenses
[16] Total costs and expenses
S2D2A
S2D3A
S2D4A
EXPENSES
1985
($000)
S2D5A
q?nfia
S2D7A
S2D8A
S2D9A
S2D10A
S2D11A
S2D12A
S2D13A
S2D14A
S2D15A
S2D16A
S2D2B
S2D3B
S2D4B

1986
($000)
S2D5B
«TVM
S2D7B
S2D8B
S2D9B
S2D10B
S2D11R
S2D12B
S2D13B
S2D14B
s*ni SR
S216B
S2D2C , i
q?mr
S2D4C

1987
($000) j
S2D6P
S2D7C
S2D8C
S2D9C
S2D10C
•wnnr
«TYI9r
S2D13C •
S2D14C
C9ni ^r
«nlfip
                                             A.27

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                            ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                            Part B Financial and Economic Information
                      SECTION 2: FACILITY FINANCIAL INFORMATION
2-E.   Did this facility borrow funds to finance a capital investment during calendar year 1986?
                 52E
YES 	  1 (CONTINUE)
NO	  2 (SKIP TO QUESTION 2-G)
2-F.    What was the 1986 interest rate charged? 	.?.?!.
2-G.   Enter the number of years over which a typical capital project is financed.
                                                                             32G
                                                                                 years
Comments for Section 2: Questions 2-A through 2-G. Reference entry by question number.
                                             A.28

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                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
                              Part B Financial and Economic Information
                       SECTION 2: FACILITY FINANCIAL INFORMATION

      Does the respondent choose to have the Agency assess economic impacts based on financial
      averages calculated from information submitted in Part A and  Part B (without data requested in
      Tables 2H, I, and J) of this census for all products within a given facility (manufacturing site)?

      Note:   The use of financial averages to represent all products at a facility may affect the accuracy
             of economic impact projections for some products.

                                   	   YES 	  1  (SKIP TO SECTION-2,-lL=.
                                                                   PAGE 38)
                                           NO	  2  (CONTINUE)

2-H.   This section requests information on Table 1 Pesticide Active Ingredients produced at your facility
       in 1986.

Instructions for completing Table 2-H Pesticide Production; Technical Grade Products. D. 30.

       Column [1]   Active Ingredient Code.  Enter the code for every Table 1 active ingredient that
                    your facility produced  in 1986  as a  technical  grade  product.   If part of the
                    production was transferred to another facility, list that part as a separate entry as
                    described by Product Code B. If you need additional space to report, photocopy
                    the table before making any marks on it.                                  :

       Column 12]   Product Code.   Enter the code that best describes the product reported  in
                    column [1].

                    Code Definition
                    A   Table 1 Pesticide Active Ingredients produced at this facility in 1986 to be sold
                         as technical grade products by this facility.

                     3   Table  1  Pesticide Active Ingredients produced at  this facility in  1986 and
                         transferred to another facility owned by this firm.

                     C   Table 1 Pesticide Active Ingredients produced at this facility in  1986 for another
                         firm (i.e., tolling).

       Column [3]   1986 Average Unit Production  and Packaging Cost  in Dollars.  Provide the
                     average production cost for one  unit of the item reported in column [1]. Include
                     such costs as material costs (!.e., the costs of all raw materials, including packaging
                     materials that were used in the production and packaging of pesticide products),
                     direct labor costs, and any other pesticide costs.

                     Note that the column [3] entry corresponds to items (5] through [8] under question
                     2-D on page 23.
                     Express the costs in dollars.  Do not  include  allocations for corporate overhead,
                     administrative expenses, research  and  development,  capital costs or interest
                     expense.
       Column [4]   1986 Average UnR Sales Price In Dollars.  Report the average selling price for one
                     unit of the item reported in column [1]. Express the selling price  in dollars.  If the
                     pesticide chemical Is not sold when it leaves the facility, but is transferred to another
                     facility owned by the firm for further processing, the sales price of the final product
                     should be allocated to both facilities based on their share of the costs to produce
                     the product  This is referred to as the "percentage of cost procedure.'  An example
                     of the percentage of cost procedure can be found on pages 28-30.
                                                A.29

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                             ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                             Part B Financial and Economic Information
                      SECTION 2: FACILITY FINANCIAL INFORMATION
Instructions for completing Table 2-H Pesticide Production: Technical Grade Product?
contini
      Column [5]   1986 Production Quantity.  In column [5], report the total quantity of the item
                   reported in column [1 ] that was manufactured at this facility during 1986.

      Column [6]   Unit of Measure, in column (6], circle the code that corresponds to the unit of
                   measure you used to calculate the information you reported in columns f31  f4i rsi
                   and [7].                                                      i M M J

                   P = Pounds
                   T = Short tons
                   M = Metric tons
                   G = Gallons

      Column [7]   Sum Annual Production Over Three Years (1985-1987). Provide the total amount
                   (sum) of the product reported in column [1] that was produced by this facility in
                   1985,1986, and 1987 combined.

      Column [8]   Percent  Exported Over Three  Years  (1985-1987).   Report the  percent of the
                   product  in  column [1] exported in 1985,  1986, and  1987 combined, i.e., what
                   percentage of column [7] was exported?
                                            A. 30

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                             ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
                            Part B  Financial and Economic Information
                      SECTION 2:  FACILITY FINANCIAL INFORMATION
             EXAMPLE OF PERCENT OF COST PROCEDURES
The following is an example of a hypothetical facility that both produces and formulates/packages active
ingredients. It demonstrates use of the "Percentage of Cost Procedure.'
Assume the facility produces 1,200 Ibs of active ingredient 000 in 1986. of which:
      400 Ibs are sold as technical grade.
      200 Ibs are formulated and packaged on site as product group P01.
      200 Ibs are formulated  and packaged  by another facility owned by this company also as product
      group P01
      200 Ibs are formulated  and packaged as product group P01 under contract by another facility not
      owned by this firm. The contract work is paid for by this plant.
      200 Ibs are combined with 100 Ibs of active ingredient 001 to formulate 300 Ibs of product group
      P02. Active ingredient 001 is purchased from another firm.
Unit sales are:
      S2.50/lb for technical grade
      $4.00/lb for formulated product group P01
      $4.25/lb for formulated product group P02
Unit production, formulating and packaging costs are:
      Production of active ingredient 000
      Purchase of active ingredient 001
      Formulating and packaging on site
      Formulating and packaging at other faculty owned by this company
      Formulating and packaging at other facility not owned by this company
$1.50/lb
$2.00/lb
$0.50/lb
$0.50/lb
$0.60 lb
                                              A.31

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                               ENVIRONMENTAL PROTECTION AGENCY
                       PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
                               Part B  Financial and Economic Information
                        SECTION 2: FACILITY FINANCIAL INFORMATION
                                 EXAMPLE (continued)
 Instructions for completing the 1985-1987 Pesticide Production Tables.  This facility would complete the
 Pesticide Production Table for Technical Grade Products and Formulated/Packaged Products as follows:

 Technical Grade Products (Table 2-H. p. 30)
 Line 1
 Line 2
          400 Ibs of Al 000 are sold as technical grade. The unit cost of production is $1.50/Ib and the
          unit sales price is $2.50/lb. This corresponds to Product Code A on page 26.

          200 Ibs of Al 000 are transferred to another facility owned by this firm to  be formulated and
          packaged.  The unit cost of production to this facility jremains $l.50/lb and the selling price of
          the formulated product Is $4.00/lb. Since the production cost represents 3/4 of the total cost
          to produce the formulated product, the unit sales price for this facility is 3/4 of the total unit
          sales price of $4.00/Ib or $3.00/Ib.  This corresponds to Product Code B on page 26.

Formulated/Packaged Products (Table 2-J. p. 371
LJnel
Line 2
Line 3
          200 Ibs of Al 000 are formulated/packaged on site by this facility.  The total unit cost of the
          formulated and packaged product is $2.00/lb ($1.50/lb for production plus $.50 for formulating
          and packaging.  Since all unit costs are incurred by this facility, the total unit sales price of
          $4.00/lb Is allocated to this facility. This corresponds to Product Code A on page 35. (Note-
          This 200 Ibs is in addition to the 400 Ibs + 200 Ibs listed on Line 1 and Line 2 under Technical
          Grade Products.)

          200 Ibs of Al 000 are produced by this facility and formulated/packaged by another firm under
          contract to this facility.  This facflity pays for the contract work.  The total  unit cost of the
          formulated/packaged  product  Is  $2.lO/lb  ($1.50/lb  for  production  plus $.60/lb  for
          formulating/packaging).  Since all  unit costs are incurred by this facility, the total unit sales
          price of $4.00/lb is allocated to this facility. This corresponds to Product Code B on page 35.

          200 Ibs of Al 000 are combined with 100 Ibs of Al 001 to formulate 300 Ibs of products in
          Product Group P02. Al 001 Is purchased from another firm.  The total cost of production is
          $2.16/lb  (2/3 of $1.50  +  1/3  of $2,00 for active  ingredients plus $.50 for formulating/
          packaging). Since this facility incurred the total unit cost the total unit sales price is allocated
          to this facility.   This corresponds  to Produce Code E on page 35.   (Note:   If the facility
          purchases active ingredient 001 from another firm and then formulates/packages it, this would
          be product group P03 and would also be assigned Product Code E.
                                               A. 32

-------
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                                                A.33

-------

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A. 34

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                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
                              Part B Financial and Economic Information
                       SECTION 2: FACILITY FINANCIAL INFORMATION
2-I.
During calendar year 1986, did this facility sell any Intermediates produced during the manufacture
of pesticide products containing a pesticide active ingredient listed in Table 1?  (CIRCLE YES OR
NO)
                                           YES .....».»........,..........>_ '(READ THETN5TRUCT10N5
                                                                BELOW AND COMPLETE
                                                                TABLE 2-1 ON PAGE 34)
                                           NO,
                                                     .>  (GO TO QUESTION 2-J ON
                                                         PAGE 35)
Instructions tor completing Table 2-1 Pesticide Production:  Intermediates.

       Column [1]   Intermediate Name. Enter the name of every intermediate produced in 1986 during
                    the manufacture of Table 1 Pesticide Active Ingredients and sold. Please include all
                    chemicals and codes that you listed in Part A of the Pesticide Manufacturing Facility
                    Census questionnaire.  If you need additional'space to report, photocopy the table
                    before making any marks on it.

       Column [2]   Active  Ingredient Code.  Enter the  code for every Table  1 active  ingredient
                    associated with your production of the intermediate listed in column [1].

       Column [3]   Average Unit Production Cost in Dollars. Provide the average production cost for
                    one unit of the item reported  in column [1].  Include such costs as material costs
                    (i.e., the costs of all raw materials, including packaging materials that were used in
                    the production and packaging of pesticide products), direct labor costs, the costs of
                    pesticide  contract work or tolling done for you by others, and any other pesticide
                    costs.

                    Note that the column [3] entry corresponds to items [5] through [8] under question
                    2-D on page 23.

                    Express the costs in dollars.  Do not include allocations for corporate overhead,
                    administrative expenses,  research and development,  capital costs  or interest
                    expense.

       Column [4]   1986 Average Unit Sales Price in Dollars. Report the average selling price for one
                    unrtof the item reported in column [1].  Express the selling price in dollars.  If the
                    pesticide  chemical is not sold when it leaves the facility, but is transferred to another
                    facility owned by the firm for further processing, the sales price of the final product
                    should  be allocated to both facilities based on their share of the costs to produce
                    the product This is referred to as the "percentage of cost procedure.'
                                               A.35

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                             ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSUS; FOR 1986
                            Part B Financial and Economic Information
                      SECTION 2: FACILITY FINANCIAL INFORMATION
Instructions tor completing Table 2-1 Pesticide Production: Intermediates - continued

      Column [5]   1986 Quantity Sold. In column {5], report the total quantity of the item reported in
                   column [1 ] that was produced at this facility during 1986 and sold.

      Column {6]   Unit of Measure. In column  [6], circle the code that corresponds to the unit of
                   measure you used to calculate the information you reported in columns (3], [4], [5]
                   and (7|.
                   P = Pounds
                   T = Short tons
                   M = Metric tons
                   G = Gallons

      Column [7]   Sum Annual Quantity Sold Over Three Years (1985-1987).  Provide the  total
                   amount (sum) of the product reoorted in column (1 ] that was produced and sold by
                   this facility in 1985,1986, and 1987 combined.

      Column (8]   Percent Exported Over Three  Years (1985-1987). Report the percent of the
                   product in column [1] exported in  1985, 1986, and 1987 combined, i.e.. what
                   percent of column [7] was exported.
                                              A. 36

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                                           A.37

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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-J.   During  calendar year  1986, did this  facility produce any formulated  or  packaged  products
      containing a pesticide active ingredient listed in Table 1? (CIRCLE YES OR NO)
                                          YES 	>  (READ THE INSTRUCTIONS
                                                               BELOW AND COMPLETE
                                                               TABLE 2-J ON PAGE 37)

                                          NO	>  (GO TO QUESTION 2-K ON
                                                               PAGE 38)
Instructions for completing Table 2-J Pesticide Production:  Formulated or Packaged Products.

       Column [1]   Product Group.  Group ail formulated/packaged products according to the active
                    ingredient(s) they contain, regardless of relative proportions or concentrations and
                    assign each group a number.  For example, if your products contain two active
                    ingredients (say A and B), group ail products containing only A into one group (call
                    it #1), group all products containing B into a second group (call  it #2)  and all
                    products containing both A and B into a third group (call it #3). Report dry and wet
                    formulations separately,   if you need additional space to report, photocopy this
                    table before making any marks on it.

       Column [2]   Active Ingredient Code.  For each product group formulated /packaged in 1986.
                    enter the code for every Table 1 active ingredient that it contained.

       Column [3]   Product or Trade Name.  Enter the trade name or name of the product.

       Column [4]   Product Code.   Enter the code that best describes  the  product reported  in
                    column (1].

                    Code Definition

                    A   Table 1 pesticide products produced and formulated/packaged at this facility
                        in 1986.

                    B   Table 1 pesticide products produced at this facility in 1986 and formulated/
                        packaged for you by another firm on a contract basis.

                    C   Table 1 pesticide products formulated/packaged by this facility in 1986, and
                        produced by another facility owned by the firm that owns this facility.

                    D   Table 1 pesticide products formulated/packaged by this facility on a contract
                        basis in 1986, for a firm other than the firm that owns this facility.

                    E   Table 1 pesticide  products formulated/packaged by this facility from active
                        ingredients purchased from another firm.
                                               A. 38

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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:
continued
                                                  Formulated or Packaged Products -
olumn [5]
                    1986 Average Unit  Production  and Formulating/Packaging Cost in Dollars.
                    Provide th& average  production rnst far nna unit — Include sucn costs as material
                    r-nvt
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                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                              Part B Financial and Economic Information
                       SECTION 2: FACILITY FINANCIAL INFORMATION

2-K.   Facility 1986 Markets

      Estimate the  percentage of this facility's total 1986 production that was delivered to the markets
      listed below.  (Enter zero if the market is not applicable. The percentages snould sum to 100%);

   ..  lll^Agriculture (U.S.A.) 	„.	i2£l	    _._'   v°
      [3]  Home, garden (U.S.A.) ........................................... 32JK2 .....

      [4]  Export (Outside U.S.A.) .......................................... Z2K&. .....

      [5]  Other markets (SPECIFY) .......... f.?.^A....;;..;ariable ) ......
                                     S2K53  'Description:
Total
                                                                                 1   0  0 %
2-L   Facility Operations
      Report the operational information listed below for calendar year 1986.  (Enter zero  if the category
      is not applicable).
      [1]  The number of days the entire facility was in operation ...?.?L1 ..........................    ; _ j _ | _ ;
      [2]   The number of days part or ail of the facility manufactured
          pesticide chemicals [[[ .§«&.?. ............    | _ ; _ j _ i
      [3]   The number of days pan or all of the facility formulated /packaged
          pesticide chemicals .......................... [[[ .?.?L3....    j _ « _ i _ ;

2-M.  Employee Information

      In lines [1] through [4], report the total employee  hours  worked at this facility in  the months of
      January 1986. May 1986 and Novemoer 1986 in the categories indicated. In lines [5] and [6], enter
      the average number of shifts run in the entire facility in a week, and the average number of hours per
      shift for the months of January 1 986. May 1 986 and November 1 986.
      [1]  Total employee hours in pesti-
          cide chemicals production

      [2]  Total employee hours in pesti-
          cide formulating and packaging

      [3]  Total employee hours in other
          production

      [4]  Total employee hours in non-
         ; pryfoction

      [5]  Average number of shifts run in
          the entire facility in a week

      [6]  Average number of hours per
          shift in the entire facility
January 1986
S2M1A
S2M2A
S2M3A
S2M4A
S2M5A
S2M6A
May 1986
S2M1B
S2M2B
S2M3B

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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-N.  Estimate the liquidation values less closure and post-closure costs of the pesticide production and
      pesticide formulating/packaging lines at this facility if you were to dose them permanently within the
      next three  years.  Include the value of fixed assets, working capital  and real estate in your
      calculation of liquidation values. Report the estimates in thousands of dollars and enter zero dollars
      if the item is not applicable.
      Pesticide production lines                                                      ($000)
      [1] Liquidation value (less closure and post-closure cost)	        32NA1A
               Closure and post-dosure cost	        S2NA1B
      [2] Cost to convert to non-Table 1 pesticide active ingredients
          or non-pesticide products	        S2NA2

      Pesticide formulating/packaging lines
      [1] Liquidation value	        S2NB1
      [2] Cost to convert to non-Table 1 pesticide active ingredients
          or non-pesticide products	        S2NB2	

2-O.  Did this facility have any property tax assessment for 1986?
                                           YES	  1 (CONTINUE)
                  520                     NO	  2 (SKIP TO QUESTION 2R)

2-P.  What was the 1986 property tax assessment value of the items listed below?  Report the values in
      thousands of dollars and enter zero if the item listed is not applicable.
      State tax assessment value                                                    ($000)
      [1 ]  Land	           SP1
      [2]  Buildings	           SP2	
      [3]  Equipment and machinery	      	003	
      [4]  Total property tax assessment value	      	SP4
      Local tax assessment value
      [5]  Land	           SPS
      [6]  Buildings	           SP6
      [7]  Equipment and machinery	           SP7.	
      [8] Total property tax assessment value	           SPB	
                                               A.42

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                             ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSU^FOR 1986
                             Part B Financial and Economic Information
                       SECTION 2: FACILITY FINANCIAL INFORMATION
2-Q.   What was the 1986 assessed value of the property expressed as a percentage of market value (1986
      level of assessment)? (Enter zero if the item was not applicable).
      [1 ]  State assessment percentage .

     •^2)—tocal assyysment percentage .
2-R.   Overall, what is the major source of comoetition ror pesticide products produced at this facility in
      each of the three markets listed below?

      The same proaucts means competing products containing identical or nearly identical pesticide
      active ingredients or percentages of active ingredients but having different trade or brand names.
      Substitute  products means competing products  oertormmg the  same  pesticidal functions  but
      containing different pesticide active ingredients.
                Competition
                                                                    Market
[1 ] Domestic producers of the
same products 	
[2] Foreign producers of the
gams products 	
[3] Domestic producers of the
substitute products 	
[4] Foreign producers of the
substitute products 	
[5] No competition . 	
[6] No market share 	

Local
Regional
	 n
	 n
	 L__I
	 n
	 n
	 L_4
	 n
	 n

National
n
n
n

n
n

International
n
n
n
n
n
n

                                                       S2R1
S2K2
S2F<3
                                               A.43

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                          ENVIRONMENTAL PROTECTION AGENCY
                   PESTICIDE MANUFACTURING FACILITY CENSU^FOR 1986
                          Part B Financial and Economic information
                    SECTION 2: FACILITY FINANCIAL INFORMATION
Comments for Section 2. Reference entries by question number.
                                         A.44

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                             ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSU^FOR 1986
                             Part B Financial and Economic Information
                              SECTIONS:  FACILITY CONTACT
Enter the name, title, telephone number and address (if different from the facility mailing address) of the
facility representative to De contacted with Questions regarding your resoonses to Part B:
              Last)
Tttie
                                          S3C
Telephone Number

Address (if different from facility mailing aadress):
Firm or Facility Name
Street or P.O. Box
!   '   :   I   I   s
City or Town
           33F
State I     Zip Code
S3G       S3K
   CERTIFICATION:  The information provided in Part B of the questionnaire, as well as that provided
   in all others, must be certified by having the responsible individual for your facility complete and sign
   the Certification Statement Item 6 on cage 3 of this questionnaire.
                                              A.45

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                     APPENDIX B




MAPPING OF PESTICIDE ACTIVE BVGREDD2NTS INTO CLUSTERS

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          Appendix B: MAPPING OF PESTICIDE ACTIVE INGREDIENTS INTO CLUSTERS

        This appendix lists the 56 PAI clusters used to define PAI markets in the EIA. As discussed in Chapters
3 and 4, the clusters were developed by EPA's Office of Water based on previous work by EPA's Office of
Pesticide Programs (OPP). Individual PAIs that are included in each cluster are listed in three columns. The
first column includes the  270 PAIs that were considered in-scope.  (The next column shows the Chemical
Abstract Service Number  for the in-scope PAIs.)  Since the PAIs that will not be covered by the effluent
guidelines  may compete with those that are covered, non-regulated PAIs have also been assigned to clusters.
Thus, the second PAI column ("Other PAIs on OPP List") includes those PAIs not considered for regulation at
this time, but included in the original OPP clusters. Many of these chemicals have already been regulated (see
the header of the table for notation indicating whether PAIs are covered by other regulations, as well as the
production/marketing status of the PAIs).  The third PAI column ("new PAIs") lists PAIs that have been
registered  since 1980 and were, therefore, not included in the original OPP clusters.
                                             B.I

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                   APPENDIX C

METHODOLOGY FOR ESTIMATING THE PRICE ELASTICITY
        OF DEMAND FOR PESTICIDE CLUSTERS

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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 price 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
 C.2

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                               TABLE OF CONTENTS




1.0   Introduction	



2.0   Price Elasticity of Demand for Agricultural Pesticides  	  3


                                                                             •a

      2.1    Methodology	J



      2.2    Review of Empirical Studies of the Price Elasticity


             of Demand for Pesticides	6




      2.3    Price Elasticity of Demand for Food Commodities	22




      2.4   Feasibility of Non-Chemical Substitution	30




       2.5   Contribution to the Variable Cost of Production	36




       2.6   Productivity of Expenditures for Pesticides	38




       2.7   Conclusions - Agricultural Pesticides  	42




 3.0   Price Elasticity of Demand for Pesticides Used

       Non-AgriculturaUy	55



                                                                            59
 4.0   Conclusions  	




 References     . i	
                                            C.3

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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  elasticity5   in place  of the term  'price elasticity  of
demand'.)

      The  price elasticity  of demand  estimates the degree  to which  a change in price  results in a change  in
the quantity  demanded.   It can be defined as the percentage  change in demand  divided  by the percentage
change  in price.  If consumers cut  back their  purchases to such a large extent  that any price  increase  reduces
total revenue, then demand is said to be elastic,  i.e.,  customers are sensitive  to price changes.  If consumers
cut back  their purchases only  slightly  in response  to higher  prices, resulting  in an  increase  in revenue,
demand  is said to be inelastic,  i.e., customers are not as sensitive  to price  changes.  The value of the price
elasticity  of demand is unbounded and may be positive  or  negative.   It is expected,  however,  that  price and
demand  are negatively  correlated,  i.e., an increase in price results  in a decrease  in the quantity  demanded.
The price  elasticity   of demand  is therefore   usually  negative.

      Four  possible values,  or ranges of values,  of the price elasticity of demand are of particular  interest.
First, if the absolute value  of the elasticity  of demand  is greater  than one, demand  is termed elastic.  In
other words,  the percentage change  in  demand is greater than the percentage  change  in price.    Second,
demand  is  said to be inelastic  when the absolute value of the elasticity  of demand  is less  than  one but
greater  than zero. Third,  if the value of the elasticity  of demand  is zero, demand  is said to be perfectly
inelastic.  That is, consumers  will  continue  to purchase a given quantity of  a good, despite any changes  in
price.  Finally,  if demand  and price  change by  equal  percentages,  the value of the  demand  elasticity   is
exactly  one, and  demand is said to have  unit elasticity.  Numeric values  are  generally   expressed  relative  to
                                                  C.4

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a one percent  change  in price.  For example,  an elasticity  of -1.5 means that a 1 percent increase in price
would result  in a 1.5 percent  decrease hi the quantity demanded.

      Measurements  of the price  elasticity   of demand  are of use hi predicting  the incidence  of a price
increase.  As the absolute value of the price elasticity  rises,  the proportion  of the cost increase that  can be
passed on to consumers declines.   If demand is perfectly  elastic,  no cost  pass through is possible.

      Market Definition
      In order to estimate the price elasticity  of demand for pesticides,   a clear definition   of the markets of
concern  must be developed.  In this analysis,  the markets are defined  to be 44 separate clusters of pesticides.
The clusters are groups of pesticide active ingredients  which  are close substitutes  for a given end-use.   For
example,  insecticides  used  on vegetables  is one of the clusters;  herbicides   used on turf  is another.

      The elasticity  of demand for pesticides  may vary significantly  between the clusters,  since each  cluster
faces different  market forces.  In particular,  a distinction  may  be drawn between  the agricultural   end-uses
and the non-agricultural  end-uses.   Agricultural   sales represent approximately   70 percent of the  total
expenditures   for conventional  pesticides  in the  U.S.,  with  the remainder  split  about equally  between
commercial  and domestic  sales (U.S. EPA, 1988).   In contrast  to the  non-agricultural   markets,  the basic
market structure within  which fungicides,  herbicides, and insecticides   are used agriculturally  is somewhat
consistent  across users and  some documentation  is available  by  which to estimate the elasticity  of demand.
The price  elasticity  of demand for  pesticides  used  agriculturally   will be analyzed  first,  followed  by  a
discussion  of the elasticity  of demand  for  pesticides  used hi the non-agricultural   sector.
                                                      C.5

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 2.0  PRICE  ELASTICITY  OF DEMAND FOR AGRICULTURAL   PESTICIDES

       Within the agricultural  pesticide  market there exist several industry  sectors including manufacturers,
 formulators   and packagers,   distributors,   and retailers  of pesticides.    The primary goal of this  analysis  is
 to estimate the elasticity  of demand  faced  by the manufacturers  of the active ingredients.  However,  most
 studies consider  the demand  elasticity  of the end-user rather than that of the formulator/packager   (usually
 the direct  customer   of  manufacturers).    This  analysis  will  assume  that  the  demand  elasticity  of the
 formulator/packager   is equal to the demand elasticity  of the end-user  since data  on formulator/packager
 demand elasticity were not located.  Assuming competitive  markets,  the long-run  elasticities  faced by the
 manufacturing  sector should be  similar to the elasticities  faced by  formulators/packagers.

       2.1  Methodology
       There is no one recognized  source of information   for the price  elasticity  of demand for pesticides;
 in  fact,  there is  an acknowledged   lack of  information  in this  area  of study.  Abt Associates  conducted  a
 thorough search  for analyses  of the price elasticity  of demand for  pesticides  and also sought expert  opinion
 as  to  the expected  elasticities.   The sources considered  included literature  searches using the  following
 databases  from  Dialog Information  Services:  Economic  Literature  Index,  Dissertation  Abstracts  Online,
 Agribusiness  U.SA.,  Agricola,  Agris International,  and NTIS.  A search for subject  matter containing  the
 following  key words  was conducted:  price elasticity,  or demand, or demand  elasticity,  and agricultural,  or
 chemical,  or pesticide,  or herbicide,  or fungicide,   or insecticide.  In addition to the literature  search, Abt
 Associates sought information  from  the  U.S. EPA Office of Pesticide  Programs,   the U.S. EPA  Office  of
 Policy,  Planning,   and Evaluation,   several  offices  of  the U.S. Department  of  Agriculture,  the  U.S.
 International   Trade   Commission,   the  Chemical   Specialty   Manufacturers   Association,   the  National
 Agricultural   Chemical  Association,   the World Bank,  Resources  for the Future,  the editor of the American
 Journal  of Agricultural   Economics,  a market  research  firm,   Cornell  University,   North Carolina  State
 University  (Dr. Gerald Carlson), Texas  A&M University  (Dr. Ron Lacewell),  Virginia  Polytechnic   Institute
 (Professor   George  Norton),  Iowa  State  University,   Stanford  University   (Dr.  Sandra  Archibald),   the
 University  of Massachusetts  (Professor  Joe Moffitt),  the University  of Arkansas (Professor Mark Cochran),
 and Harvard  University.

      The  literature  search and conversations  with the listed  expert  sources indicated that studies  of the
price  elasticity   of  demand  for  pesticides  are  sparse,  and  that  the  existing  analyses  offer  conflicting
conclusions  and  are often  controversial.   Further,  an attempt  at compiling  expert  opinions as to expected
elasticities  failed; the lack of available  research on this issue  precluded compact,  ready answers that could
                                                  C.6

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be conveyed  by telephone.   In order to develop the elasticity  estimates,  Abt Associates developed  a five-
pronged approach.

      First, as described in Section 2.2,  Abt Associates considered  the relevant  empirical  studies.   Though
these studies  do not comprehensively  answer  the question at hand for reasons that are presented below,  they
do provide  estimates  of demand  elasticity for selected clusters.  The second  input, and the main source of
data  from  which  pesticide  elasticities   are derived  in this  analysis,   is U.S. Department  of  Agriculture's
(U.S.DA.)  analysis of the  price  elasticity of demand for food  commodities  (U.S.DA.,  1985, 1989).   The
elasticity  of demand for farm inputs can be derived from the elasticity  of the demand  for farm commodities
since demand for  production inputs must ultimately  reflect  demand for the end product.   Though  the two
elasticities  may not correspond  exactly,  the  elasticity  of demand  for the food commodities  can serve as a
reasonable  proxy  for  the  elasticity   of demand  for  pesticides  in the  absence  of more  relevant   data.
U.S.D.A.'s  estimates  of elasticity  and the use of these estimates for purposes of this  analysis  are discussed
in Section  2.3.

      The other three factors used to estimate  the elasticity  of demand  for pesticides   are (1) the feasibility
of employing   non-chemical  or  non-biological  pest  control methods,  (2) the percent  of production   cost
contributed  by the pesticide of interest, and  (3) the productivity  of expenditures  for  pesticides.  Section 2.4
groups pesticide  clusters based  on the feasibility   of substituting  another  pest control method for chemical
and biological  pesticides.  The greater the feasibility  of substitution,  the higher the expected  price elasticity
of demand.  Since the clusters  group  chemical  and  biological  substitutes,   the  potential substitutes  for  a
cluster  of pesticides  are  cultural or  environmental  control  technologies,   such  as crop  rotation or the
introduction  of predatory  insects.  The rankings of the feasibility of non-chemical   substitution for  a cluster
of pesticides  are based on Pimentel et  al. (1991).

       The  analysis  of pesticide  contribution  to the cost of production of a commodity  is based on U.S.DA.'s
published  estimates  of the cost of production  in the farm sector  (U.S.DA.,  1989a,  1989b, 1988).    The
greater the contribution  to the cost  of production,   the higher the  expected  price   elasticity  of  demand.
Pesticide  contribution  to production  costs is reported in Section  2.5.

       Finally,  the productivity   of expenditures   for  pesticides  is examined  in  Section 2.6.  In theory, if
pesticides  are highly  productive  (i.e.,  the  costs of  pest  damage  without   pesticides   greatly  exceeds the
 expenses  of  pesticide application),  a prescribed  pesticide  dosage  will  be applied  regardless  of some degree
 of price variation.   In other words, if pesticides   are  highly  productive,  the demand  for pesticides  is likely
 to be inelastic.

                                                     C.7

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      Section  2.7  combines  the information  from the empirical  studies,  the elasticity of demand for food
commodities,  the substitutability  rankings,  the data on pesticide contribution  to production  cost, and the
measures of pesticide  productivity   to estimate  the price elasticity  of demand  for agricultural   pesticide
dusters.  The U.S.DA.  estimates  of the elasticity  of demand for food commodities  are used as the basis for
the final elasticity estimates.   The  other factors  are analyzed to determine  cases in which  the elasticity  of
demand  for food commodities  may vary substantially   from  the  elasticity  of demand for pesticides applied
to the food commodities.  In cases where  there is  a clear indication  that  the  elasticity  of demand  for the
food  commodities  and  the elasticity of demand  for the pesticides  applied  to the food  commodities  differ,
                                                                       .1
the elasticity  estimates  are adjusted in the appropriate  direction.

      Precise quantification   of the elasticity  of demand,  however,  is not revealed  through  the examination
of feasibility  of  substitution,  contribution  to costs,  and productivity   of  the  pesticides.   The results  only
indicate  whether  demand  for the pesticides  is likely to be more  or less elastic than demand  for the relevant
food  commodities.   Therefore, unless there is compelling  evidence  that the elasticities  of demand for food
and pesticides  applied  to food differ  substantially,  this analysis  relies  on the  estimates  of elasticity  of
demand for food commodities to  represent  the  elasticity of  demand  for pesticides  applied  to those  food
commodities.  It should  be clear that the resulting elasticity  estimates serve as indicators of the approximate
magnitude  of demand  elasticity  and not  as precise quantifications   of these elasticities.
       22    Review  of Empirical  Studies  of the  Price  Elasticity  of Demand For Pesticides
       The  empirical  analyses  of the price  elasticity   of demand  for  pesticides   can be separated  into
 econometric  analyses and other analyses.   The econometric  analyses  of demand  elasticity  employ  several
 different  dependent  variables.   Variations  in  the dependent  variable  influence   the resulting  demand
 elasticities.   In particular,  the dependent  variables  differ  in the level  of- aggregation  of pesticides   and in
 whether pesticides  are measured in units  of production  or units  of use.

       The level of aggregation  of the pesticides may influence  demand elasticity  by determining  the number
 of close substitutes   that are available.  According  to microeconomic  theory,  the more narrowly  a product
 is defined,  the more substitutes  that  are likely to be available.   For example,  more  substitutes  are available
 for pork  chops than are available for meat.

       If a  product   has many  close  substitutes,   it is likely  to be characterized   by  an  elastic  demand.
 Consumers  can react to a price  increase  by switching   products  without  much  loss of utility.   If a product
 has a more limited  number of substitutes,  consumers have  little choice  but  to  bear more  of the  price

                                                      C.9

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 increase.  For  chemical  pesticides  in general,  substitutes  include  only  labor  and other  non-chemical   pest
 control methods.   These are  also  the only  substitutes   for  fungicides,   herbicides,  or  insecticides   since
 pesticides  are generally  effective  against only either pathogens,  weeds,  or insects.   Since the clusters used
 in this analysis  were chosen to include  all close chemical  and biological  substitutes  for an end-use,  the only
 pest control  alternatives   are non-chemical  and non-biological.   Substitutes  for specific  active ingredients,
 however,  may include  other active  ingredients  in addition to the non-chemical,   non-biological   alternatives.

       For the  purposes  of  determining  the  incidence   of  the cost increase  resulting  from  new  effluent
 regulations,   the ideal price  elasticity of demand  is that  corresponding   to each pesticide  cluster.  However,
 few  of the  relevant  analyses   that  Abt  Associates  located  estimate  elasticity  of demand for  clusters  of
 pesticides.   Some of the analyses  reviewed  hi this report consider  pesticides  as a group  as the  dependent
 variable;   other studies   analyze  herbicides,   fungicides,   and  insecticides  separately  or study the demand
 elasticity   for pesticides   by crop.  Another  group  looks at specific  active ingredients.

       In determining  the  elasticity  of demand for  clusters  of active  ingredients,  it  may at first appear
 reasonable to bound the  elasticity  of demand  for  clusters of pesticides  by using the elasticity  of demand for
 pesticides  as a group  as  the  lower bound and the elasticity  of demand for  individual  active ingredients  as
 an upper bound.  Since  pesticides  as a group  will include all clusters  of pesticides,  it could be argued that
 a cluster will exhibit an  elasticity  no lower than the elasticity  of pesticides  as a group.  However,  since the
 elasticity   of  pesticides  as a group represents   an average  of the elasticities  of clusters it can not serve as a
 boundary  for any one cluster.   Similarly,  since the elasticities  of demand for  individual   active ingredients
 within  a cluster will vary,  the elasticity  of any one active ingredient  can not act as an upper  boundary  for
 the elasticity  of the cluster.   For  purposes  of comparison,  however,   this analysis  considers  the empirical
 analyses in two groups:  those  which  consider pesticides as a group and those  which  consider individual
 active ingredients.

      A second  major  variation  between  the regression   analyses  of demand  elasticities  reviewed  hi this
report is whether  the dependent variable  was measured  in units of production  (e.g., pounds produced per
year) or in units  of  use (e.g., pounds  applied  per acre per year).  Due  to potentially  significant  inventories
of pesticides  and the dissimilar  market structures  of pesticide  manufacturers  and packagers/formulators  of
pesticides,  units of production  and use may result in different  estimates of elasticity.   Further,  some studies
defined the dependent  variable  in absolute terms  while  others used  the  percent  of crop treated.  Also, the
dependent  variable  was  alternately   measured  in  units  of expenditure  (e.g., dollars)  and units of quantity
(e.g.,  pounds).
                                                    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.DA.  agricultural  regions  from  1968,  1971, and  1976 to
 estimate  derived  demand  equations  for insecticide  and herbicide treatment.  Separate weighted  least squares
 regression models  for  insecticide  and  herbicide  treatment  were developed  as follows:
In ST,ih
where
ST,,h
pi.h
y
SCA
RE
                       +a, hi F|h  +32 In Pf  +33 hi y +% In SCA  +a5 hi RE  + hi I>  -f e
                    share of corn treated with  insecticides  (i) or herbicides  (h),
                    price  of insecticides  (i) or  herbicides  (h),
                    price  of fuel,
                    value  of corn output  per acre,
                    share of corn acres hi cropland  acres,
                    lagged production-oriented   research and extension  expenditures,   and
                    farm  wage rate.
      Miranowski  obtained  data on insecticide  and herbicide treatment,  as the share of corn acres treated,
from the U.S.DA.  annual pesticide surveys  for  1968,  1971,  and  1976.  The input price indices,  I^h and Pf,
were derived  from data  in U.S.DA.'s  Agricultural  Prices - Annual Summary  (for  1967,  1972, 1977).

      Miranowski  estimated  price  elasticity of demand for insecticides  as -0.78.  However,  the coefficient
was not significantly  different   from zero.  He reported  results  of two herbicide  demand  models, one with
and one without  the price of labor. When the price of labor is  not included  in the analysis,  the coefficient
on  herbicide  price, -0.75, is significantly   less than zero but not significantly   different  from negative one.
Therefore  the elasticity   of demand may be either elastic or inelastic,  but only moderately  so.
      When the wage rate is held constant, the herbicide price coefficient  is 0.03 and becomes insignificant.
Though  the results  of the model with  labor held  constant  may be  consistent  with inelastic  demand  for
herbicides,  the  coefficient   on labor is positive  and  significant,   suggesting   that  labor  and herbicides  are
substitutes.   The coefficients   of the price of pesticides in the two  herbicide  models suggest that the price
of labor and the price of pesticides   are co-linear.  Since the coefficient  for the price of herbicides becomes

                                                     C.12

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insignificant   when  labor  is  included   in  the model,  it may  be the case  that  the  labor  price  variable  is
dominating  the herbicide price variable with the result that change  in the dependent variable  appears to be
largely  a function  of the cost of  labor  rather than  the price of herbicides.   However,  when labor is absent
from  the model, the coefficient  of the price  of pesticides  probably  includes  some of the influence  of labor
rate changes.  The "true" elasticity  of demand is therefore  likely to fall between  the two coefficients  of -0.78
and 0.03, still indicating  inelastic demand.

      Huh (1978)  estimated  pesticide  price  elasticity of demand in his  doctoral  dissertation.   Using cross-
sectional  farm  data from  Minnesota,  Huh modeled  pounds  of  active   ingredients  of  herbicides  and
insecticides   used  on corn  per farm (Q,-).  Exogenous  variables  included  in  his  final aggregate  demand
equation  were:

      Xjw   -     adjusted  and  weighted  price of pesticides  (dollars per  pound),
      •Xf    =     acres of corn  per farm, and
      Du    =     a dummy  variable  for crop rotation plan  (0 when  farmer  did  not intend  to plant corn
                   again  in  1978,  1 when  farmer  intended  to plant some or all of corn hi 1978).

      The results  of the regression  analysis  were  as follows  (standard  errors are in parentheses):
2.212 - 1.464 In X,w  +1.099  In 39  +0.381
        (0.161)        (0.064)       (0.110)
                                                                   +e
      The coefficient   of the price  of pesticides  was  significantly    less than  zero and  also  significantly
different   from  negative  one, indicating   elastic demand.   However,  since  an  independent  variable  for
pesticide  substitutes  (e.g., labor) was not included,  the coefficient  on pesticide price may include  the effect
of changes in labor or other  substitute  prices  and therefore  have a bias towards greater elasticity.  Hub's
model is therefore  likely  to overstate  the elasticity  of demand  to an unknown  degree.

      As part of an analysis  of farmers' attitude  towards alternate  crop protection  methods, U.S. EPA (1974)
described a survey  of farmer  sensitivity  to pesticide  price  changes.  Farmers in Iowa and Illinois responded
to the survey  as follows:
                                                     C.13

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      Percent  of Respondents
      Iowa         Illinois
       88            82
       62
       55
       29
       77
       96
       72
56
55
39
61
86
67
(of corn  growers)  believe  all  of their  corn  acres need
herbicides  each year
(of corn growers)  would not change herbicide  use  if cost
doubled
(of corn insecticide  users)  believe  all of their corn acres
need  insecticides
(of corn  growers)  believe  all  of their  corn  acres need
insecticides
(of corn insecticide  users)  would  not change  insecticide
use if cost doubled
(of soybean  growers)  believe  all  of then-  soybean  acres
need  herbicides  each year.
(of soybean  growers)  would not  change herbicide  use if
cost doubled
      The results indicate  that the majority  of farmers surveyed  are insensitive  to price  changes.   Demand
for corn  and soybean  herbicides  and  corn insecticides  appears  to be inelastic.

      The final study in this category was  conducted by Burrows  (1983).  Burrows  tested the hypothesis  that
integrated  pest management   (IPM)  will  significantly    reduce  pesticide  use.   He also  examined   the
methodological   issue of simultaneity  between  pesticide  use and IPM adoption.   Burrows  considered  only
insecticides  and mitacides.   His data were drawn  from  a random  sample of San Joaquin  Valley  cotton
growers.  The observations  contain  detailed  information   on output,  pesticide and  other input use, cost, and
revenue  for 47 growers  spanning a 5  year period from  1970-1974.

      Burrows  performed  a Generalized  Least Squares (GLS) procedure  for both single and simultaneous
equation  models. The dependent variable  is insecticide  and mitacide  use measured  hi sales dollars per acre
of cotton grown. Explanatory variables  include  average  pesticide  price per pound, an IPM consultant  fee
per acre,  and the expected  yield in pounds per acre. Weather and cultural practices  are included  as proxies
for both the size of the pest population  and pesticide persistence  in the environment.  A risk proxy,  the ratio
of acres  planted in cotton  to total acres,  is used assuming  that,  for higher ratio values,  risk-averse  growers
                                                     C.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:
 PROD^, PPROD^
 ACRE;
 RPRICE;
 EX;
a +b hi PRODM  +c hi ACRE;  +d hi RPRICE,  +f
production  of pesticide  active ingredients  in year t and t-1
acreage of principal  crops planted  in year t
real unit  price for pesticide  active  ingredient  in  year  t
Industrial  production  index  hi year t
       Elasticities   were  calculated  for herbicides,  insecticides,   fungicides,   and all pesticides.   Pesticide
 production rates were obtained  from U.S. International  Trade Commission,  Synthetic Chemicals. ,  The units
 of production  were  not given.   Pesticide  prices  were  average prices for each  product group and  for  all
 pesticides  and  were  calculated   from  U.S. International   Trade  Commission,  Synthetic   Chemicals  and
 converted  to real prices using the GNP Deflator.  Based on this model, EPA obtained the following  results:
                                                     C.15

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Ln of
Production
of
Intercept
                             Ln Real
Ln  Acres
Ln
Production
Previous
Year
Industrial
Production
Index
Herbicides
R2 =0.98
Insecticides
R2 =0.68
Fungicides
R2=0.35
All Pesticides
R2 =0.89
-12.93
(-3.51)
-3.49
(-1.32)
-1.46
(-0.47)
-6.42
(-2.26)
3.19
(4.02)
1.53
(2.90)
1.04
(2.02)
1.88
(3.02)
-0.67
(-2.49)
-0.32
(-2.51)
-0.35
(-2.07)
-0.49
(-2.37)
0.299
(1.88)
0.142
(0.57)
0.05
(0.18)
9.427
(1.84)
                                                                               -0.00651
                                                                               (-3.24)
      T-statistics   are given  in parentheses.  The analysis  indicated  that  demand is inelastic  for  each of the
three pesticide  groups  as well as for  pesticides  in general.  All price elasticities  were significantly  less  than
zero, and significantly   lower than  one in absolute  value,  except  for the  coefficient   for herbicides which is
not significantly   different  from  negative  one.  The model, therefore,  indicated  that the price  elasticity  of
demand for insecticides,   fungicides,   and all  pesticides  is inelastic.   According   to the  model, the price
elasticity  of demand  for  herbicides  is near unity,  meaning that demand  may be either  elastic  or inelastic.

      The analysis suggested  that the demand for herbicides is more elastic than the demand  for insecticides
or fungicides.   EPA explained that during  the  1970's herbicides   experienced   a large increase in  application
rates and the proportion of acres treated and that "the coefficient   on acres in the herbicide  equation reflects
this".  The  authors also noted that  "one of the  reasons  the amount of variation  explained  by the fungicide
equation was so low was that a very  large proportion of fungicides  were used for non-agricultural    purposes".
 The authors were unable  to explain  why  business  cycles  are important for herbicides  and not for the other
two  product groups.  It should be  noted  that the study did not include  a variable   for prices of substitutes
or final  products.  If  these  prices  are correlated  with  pesticide  prices,  the coefficients   may  be  biased.
Finally,  the authors did not  identify   the  type  of end-use  (e.g.,  agriculture,   commercial,  domestic) of the
pesticides  included in  their analysis.

      Another   factor that  may  influence   the  results  obtained  by  EPA is that the dependent  variable  is
measured by weight (pounds).   This may not  accurately  reflect  price  elasticities  since more effective   and
                                                    C.16

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expensive  pesticides   may  be  substituted   for pesticides  requiring   higher  doses  to be  effective.    EPA
acknowledged  this issue, stating that there has been a decrease in the amount  of insecticides  produced  due
to the substitution  of synthetic  pyrethroids  for more conventional   pesticide  ingredients.    The synthetic
pyrethroids  are more powerful  than conventional  pesticides,  thus reducing  the weight  of pesticides  required
for pest control.   EPA asserted, however,   that in terms  of  total insecticide   production,  these impacts are
small.

Active  ingredient  as dependent  variable;  measured  in units of use
      The following  three studies examined  demand elasticity  for specific  pesticides and measured  demand
in units of use:   Lacewell  and Masch (1972),  Carlson (1977),  and Carlson (1977a).   Lacewell  and Masch
found  that  the demand  for the herbicide  2,4-D  was  inelastic.   Carlson's  price coefficient  for 2,4-D  was
small  and  negative,  but  not  significant,   which  may  be  consistent  with  price  inelasticity.    Carlson's
significant  price  coefficients  for insecticide   active ingredients  indicated  that demand  is elastic  in both the
short-run  and the long-run.
      Lacewell  and Masch selected a five county area hi the Northern High Plains of Texas as the study area
to evaluate the  effect  of a tax  vs. a marketing quota farm  program on the level of  chemicals  used in  a
specific  agricultural  region. The primary  agricultural  crops of the area were grain  sorghum  and wheat.  To
control weeds hi wheat and grain sorghum, herbicides,  especially  2,4-D, were utilized.

      Using data on land utilization for  1969, Lacewell  and Masch constructed  a linear  programming  model
for the five  county region.  For illustrative   purposes,  the  change hi the quantity  of 2,4-D used  hi response
to changes  in the price of 2,4-D  was  investigated.   Requirements  for weed control  were  assumed to be met
by  one of three weed control  alternatives:    (1) use of 2,4-D,  (2) use of 2,4-D  and  dicamba,  and (3) use of
dicamba,  other  chemicals  and  additional  tillage  operations.    The  price  of  2,4-D   was increased  by
increments,  using parametric  programming,  from  52 cents per pound to $37.00  per pound,  at which point
the model predicted  no 2,4-D  would be used.  In response to a more marginal  price increase of 78 percent
(from $0.52 to $0.93  per pound),  Lacewell  and Masch predicted  a decrease  in use of 2,4-D  of  30  percent.
This  translates  to an inelastic  demand  of  approximately   -0.38.

      Carlson's two articles  (1977  and 1977a) used the same log-linear  model  to examine demand elasticities
of particular  herbicides  and insecticides.   Carlson first considered  price  elasticity  of demand for pesticides
as part of a study to determine  the importance of pest resistance  to insecticides  hi affecting  demand  for
specific  compounds.   In his second article,  Carlson illustrated  some  advantages  and disadvantages  of price
incentive  systems relative  to quantity  incentive systems for  pollution control.

                                                    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

Q    =
R,t   =
Rgt   =
6;     =
             quantity  of a given  insecticide   purchased  in  year  t (pounds of actual material),
             insecticide   price  deflated  by an index of all  agricultural  input prices,
             substitute  insecticide  price,
             resistance  index,
             agricultural  product  price  index,  and
             error term.
      The agricultural  product  price variable,  Q, was not statistically  significant  and was deleted from  the

model.  A lagged dependent  variable  was added to account for the assumed effects  of delayed adjustments

to price changes.  Carlson used this model to analyze  several  of the  largest selling  groups of insecticides.

The specific  dependent  variables  and their  price elasticities  were  as  follows  (standard  errors  appear  in

parentheses):
       Dependent  Variable

      (A) Domestic  and  foreign  sales of cyclic
      organophosphate  insecticides  (1953-1970)

      (B) Same as (A) except  divided  by  domestic
      cotton acreage planted

      (C) Total sales of parathion  and methyl
      parathion  (1953-1970)

      (D) Domestic  sales of DDT  (1945-1969)
      (E) Domestic  sales of DDT (1953-1969)
                                                            Price  elasticity

                                                                 -1.461
                                                                 (0.7%)

                                                                 -1.552
                                                                 (0.780)

                                                                 -1.06
                                                                 (0.273)

                                                                 -0.667
                                                                 (0.397)

                                                                 -1.091
                                                                 (0.625)
      Insecticide  price has the expected negative  effect  on insecticide  purchases.   Carlson  concludes  that

sales of the compounds are quite responsive  to price,  indicating  that there are many substitute  pest controls

in the long run.  None of the coefficients,   however,  are significantly   different  from negative one,  so the
model indicates  that elasticity  of demand  is unlikely to be either highly  elastic  or highly  inelastic.
                                                    C.18

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      In Carlson's  subsequent  article  (1977a)  he reported  a slightly different  elasticity  for the parathion and
methyl  parathion group  and also includes the herbicide  2,4-D  in his analysis.  Further  he reported long-run
elasticities for DDT and 2,4-D.   The results were  as follows:
      Dependent  Variable

      (F) Domestic sales of parathion,  methyl
      parathion  (1953-1969)
      (G) Domestic  sales of 2,4-D  (1950-1970,
      except  1965-68)   divided  by cropland  index
      (H) Same as (D) except  long-run
      (I) Same as (G) except  long-run
Price elasticity

     -0.945
     (0.339)
     -0.193
     (0.349)
     -1.53
     -0.594
            The analysis  indicates  that the elasticity  of DDT increases  substantially  from  the short-run  to
the long-run,   as would be expected as more substitutes  may  be developed  with time.   The coefficient  for
2,4-D  shows demand to be inelastic, but is insignificant.   Though this result  may be consistent  with inelastic
demand,  it is inconclusive.

Active  ingredient  as dependent  variable:  measured  in units  of production
      Abt Associates located no studies which  fit this category.

Summary
      Table  2.1 summarizes  the empirical   studies discussed  above; Figure 2.2 displays  the  empirically-
derived  elasticity  estimates graphically.   As can be seen from Figure 2.2,  elasticity  estimates  ranged from
approximately   zero to -1.5.   While most estimates  indicate  that the  demand  for pesticides  is  relatively
inelastic,  the results are inconclusive.   Since  the studies used  different  models and, in particular,  different
dependent  variables,  variation  in the estimates  is expected.   The number  of studies which considered
clusters  of pesticides as the dependent  variable  was insufficient   to draw reliable conclusions  as to the price
elasticity  of demand for  clusters of pesticides.   However,  the results of the analyses  which  did define  the
dependent  variable   as a  cluster   of  pesticides   will  be considered in  the final  estimations  of demand
elasticities.
                                                    C.19

-------
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      23.   Price Elasticity  of Demand  for  Food Commodities
      Given  that the empirical  analyses  are insufficient   to derive  estimates  of demand  elasticity  for clusters
of pesticides,  an alternative  method  of estimation of the elasticity was developed.   The method used in the
remainder  of Section 2 of this report relies  on a consideration  of four factors:  (1) the price elasticity  of
demand  for food commodities,   (2) the availability  and relative  costs of non-chemical  pest  management,  (3)
the  contribution  of  pesticides  to the variable   cost  of farm  production,  and  (4)  the  productivity   of
expenditures   on pesticides.   Though  these sources will not  reveal  precise  quantifications   of  the  price
elasticity  of demand  for pesticides,   they  can be used to indicate  whether  demand  for  the pesticides   is
expected to be  elastic or inelastic  and to construct  approximate  estimates  of the elasticity  of demand.

      Since the demand  for particular  inputs  to a product  is in part derived  from  demand for  the end
product,  the demand for pesticides  used in the  agricultural  sector  will be influenced  by the demand  for
food.  The demand elasticities   of  food  commodities,  developed  in this section, are used  to provide initial
estimates of the elasticity  of demand for clusters of pesticides.

      Estimates of the direct price elasticity  for foods  at the retail level are taken from the U.S.D.A. report
entitled  "U.S. Demand for  Food: A Complete System of Price and Income Effects"  (1985),  authored by Kuo
S. Huang.  Using a constrained  maximum  likelihood  method,  Huang  developed statistical  procedures  for
estimating  a large-scale  demand  system  from  time-series  data.    He then applied  his procedures  to  an
estimation  of a domestic  food demand system including  forty  food items and one non-food  item.  The food
items,  direct-price   elasticities,   and standard errors  of the estimates are listed in Table 2.2.  The  estimated
elasticities  ranged  from  -0.0385  (cabbage)  to -1.378   (grapes).   Huang noted  that an exact t-test for  the
statistical  significance  of the  elasticity  estimates is not applicable,  given  the  assumptions  of a maximum
likelihood  model.   For  the purposes  of  his analysis,  Huang  considered   an estimate to  be statistically
significant  if the estimated  elasticity  was  larger than  its standard  error.  While estimated  elasticities  with
relatively  large standard errors may imply  that the estimates  are not  statistically  precise,  only four of the
thirty-four   commodity   elasticity  estimates  used in  this analysis  had a standard error  greater  than  the
elasticity  estimate  (butter,  other fresh fruits, carrots,  and cabbage).

      Huang also provided  estimates  of demand  elasticities  for the following aggregated food groups:  meat,
staples, fats, fruits,  vegetables,  processed  fruits  and vegetables,  and desserts.  The direct  price  elasticities
he obtained were negative  for  all seven  food categories, with magnitudes  ranging  from -0.08 to -0.34.  For
purposes of the discussion  here, however,  the individual  food items must  be reorganized  to correspond   to
the crops included  in the clusters.
                                                   C.25

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                                 Table 2.2
                ESTIMATED DIRECT-PRICE ELASTICITIES
 Commodity

 Beef & veal
 Pork
 Other meats
 Chicken
 Turkey
 Eggs
 Cheese
 Fluid Milk
 Evaporated &
 Wheat Flour
 Rice
 Potatoes
 Butter
 Apples
 Oranges
 Bananas
 Grapes
 Grapefruits
 Other Fresh Fruits
 Lettuce
 Tomatoes
 Celery
 Onions
 Carrots
 Cabbage
 Other Fresh Vegetables
 Fruit Juice
 Canned Tomatoes
 Canned peas
 Canned Fruit cocktail
 Dried beans, p
 Other process*
 Sugar
 Ice Cream
(USDA, 1985)
Direct-Price
Elasticity
-0.6166
-0.7297
-1.3712
-0.5308
-0.6797
-0.1452
-0.3319
-0.2588
7 Milk -0.8255
-0.1092
-0.1467
-0.3688
-0.167
-0.2015
-0.9996
-0.4002-
-1.3780
-0.2191
3 -0.2357
-0.1371
-0.5584
-0.2516
-0.1964
-0.0388
-0.0385
ables -0.2102
-0.5612
-0.3811
-0.6926
tail -0.7323
& nuts -0. 1248
ruits & vegetable -0.2089
-0.0521
-0.1212


Standard Error
0.0483
0.0327
0.2045
0.0608
0.1332
0.0225
0.1174
0.1205
0.2642
0.1026
0.1438
0.0689
0^1748
0.1469
0. 1465
0.1334
0.1829
0.1067
0.5471
0.0656
0.0624
0.0636
0.0693
0.1816
0.0405
0.1436
0.1006
0.1072
0.1746
0.3677
0.0313
0.0921
0.0172
0.0848
Source:  U.S.D.A. (1985).  U.S. Demand for Food: A Complete System of
Price and Income Effects.  By Kuo S. Huang.  National Economics
Division, Economic Research Service.  Technical Bulletin No. 1714
                              C.26

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      To estimate  an average elasticity  for individual  crops in a cluster, the elasticities  of the included  crops
are weighted  by  the quantity  of the relevant  pesticide applied  to that crop, as reported  in Pimentel et al.
(1991).  This weighting  factor incorporates  the fact  that  pesticide use varies between  crops; the elasticity
of demand  for  a crop with heavy  pesticide  use will more greatly influence  the elasticity  of demand for the
relevant cluster of pesticides  than will  the demand for a crop with  low pesticide use. The resulting elasticity
estimate is not a measure of the elasticity  of the entire  cluster  of crops  (unless the cluster  consists  of only
one crop).  Rather,  it is a measure  of  the weighted average elasticity  of the individual  commodities  in the
cluster.  The elasticity  of  the  entire  cluster  will  be lower  than  the average elasticity  of the individual
commodities  due  to the reduction  in the number of substitutes.   For  example,  people  may  easily substitute
beef for pork and therefore  these individual  commodities  may have  relatively  high elasticities.   However,
substitutes  for all meats are less readily available  and this category is likely  to have a lower  elasticity than
the average elasticity  of  individual   meats.

       Since the elasticity   of the demand for  food commodities  is  assumed to represent  the  elasticity   of
demand for  pesticides,  this elasticity  will  also be overstated.    The overestimation  of the value of demand
elasticity will likely result  in an exaggerated  estimate of the  fraction  of cost increases that is borne  by  the
manufacturers.   In the absence  of more appropriate  data, however,  this  value  provides  a reasonable  best
estimate of the demand  elasticity  for clusters  of pesticides.

       Table  2.3 displays  the average  elasticities  for  the clusters  based on Huang's analysis.  The elasticity
 estimates  for the clusters represented  range from  -0.12  (herbicides on sugar  beets, beans, and peas) to -1.38
 (fungicides  on grapes, herbicides  on grapes,  and insecticides   on grapes). This range of values indicates that
 the demand  for  the food clusters  varies from highly inelastic  to somewhat  elastic.

       While the calculations  for most of the clusters are  straight-forward,    the estimation  of  elasticity  for
 the six clusters containing  crops that serve as animal feed required  an intermediate  step.  The elasticity  of
 demand for corn,  sorghum,  soybeans,  and  alfalfa - all  crops  that  are  largely used  for animal  feed -  was
 calculated  from  Huang's estimates of the elasticity  of demand for  animal  food  products.

       An average elasticity  for animal feed crops  can be obtained by weighting the elasticity  of each animal
 product by  the amount  of that product consumed.  Huang provides  "the retail weight equivalent of  civilian
 food disappearance",  a measure of consumption,   for each food  item.  This weighting  calculation  yields  an
 elasticity  of demand for animal products  of -0.55.  However,  for this weighting method to accurately reflect
 the elasticity  of  demand for feed  crops, it must be true that  a  unit of feed yields  equal units of all included
 animal products.   This  is not the case.  The yield  rates of dairy  products  and eggs are substantially  higher

                                                       C.27

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Sources for Table 2.3:

Values for "own-price elasticity" were obtained from U.S.DA. (1985).
Values for "pesticide Use" were obtained from Pimentel et al. (1991).

Notes to Table 2.3:

/I      The price elasticity of demand for bananas is not included since a separate estimate of the quantity
        of herbicides applied to bananas is not available. Also, fruit categories are only included if they can
        be assigned to a .single cluster.  For example, "fruit juice" is not included since it could include apple
        and orange juice, and therefore overlap two clusters.

/2      Vegetable categories are only included if they can be assigned to a single cluster,  for example,
        "other processed fruits and vegetables" is not included since the category overlaps  two clusters.

/3      Crop is assumed to be fed to animals. See text for explanation of elasticity estimate.

/4      The elasticity estimate is for dried beans, peas, and nuts. No separate elasticity estimates for these
        foods are available.

/5      The elasticity estimate for sugar does not distinguish between sugar beets and sugar cane.

/6      Elasticity estimate is for wheat flour.

/7      Includes lemons, cherries, peaches, plums, and "other fruit"

/8      According to the 1989 "Agricultural Statistics" published by the U.S. Department of Agriculture, 34
        % of all tomato acreage is used to produce for the fresh market and 66% of the acreage is used to
        produce tomatoes for processing.  Pesticide use is split between fresh and processed markets using
        these percentages.  While this split will not be precise since production per acre and pesticide use
        may vary, it is used as a reasonable approximation.

/9      Includes cucumbers, peppers, sweet potatoes, and "other vegetables".

/10     The category "other gram" is excluded since elasticity estimates are not available.   Use of herbicides
        on "other grains" is relatively minor, at 2.7 million kgs per year.

/ll     Since estimates of the elasticity of cotton are not included in the U.S.DA. report,  cotton is not
        included in the elasticity estimate for  the cluster. Herbicide use on cotton, estimate at 8.2 million
        kg/year, is small compared to herbicide use on soybeans. Therefore, the elasticity estimate for the
        cluster should not be substantially affected by the absence of an elasticity estimate for cotton.

/12     Includes pecans and "other nuts"

/13     The analysis assumes that half of herbicides used on peas are used on canned peas with  the
        remainder used on dried peas.

/14     Includes all herbicides applied  to beans and one-half of herbicides applied to peas.

/15     "Percent of Use" equals "Pesticide use on crop"/"Pesticide use on cluster"

/16     "Weighted Elasticity" equals summation of ("percent of use" multiplied by "own-price elasticity")

/17     Since estimate of the elasticity  of demand for tobacco are not included in the U.S.D A. report,
        tobacco is not included in the  elasticity estimate for this cluster. However, since about 80 percent of
        the insecticides applied to crops in this cluster are applied to soybeans, peanuts, and wheat, the
        absence of an elasticity estimate for tobacco should not dramatically affect the elasticity estimate for
        the duster.
                                                 C.32

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than the yield rates of meats per unit of food.  Therefore,  a weighted  average of the food elasticities based
on consumption  would be biased  towards the elasticities  of dairy products  and eggs.  That is, the elasticity
values for dairy  products and eggs  would  influence  the resulting  average  elasticity  more heavily  than is
appropriate.                                                                                          ;

      As  can be seen  from Table  2.2,  the elasticities  of demand  for  dairy  products and eggs are generally
lower than the elasticity  of demand for meats.  Weighting the elasticities  by consumption  is therefore likely
to understate  the elasticity  of demand  for  feed crops.  To avoid  this underestimation,   the elasticity  of
demand  for  animal  feed  is calculated  based only on the meat products.   The resulting estimate  of  -0.69 is
conservative   in  that it is  likely  to somewhat overstate  the  elasticity  of demand for  animal  products,   and
therefore  animal  feed.   This conservative   value,  however,  still indicates   that  demand  for  feed crops is
inelastic.

      Huang's report  analyzed demand  elasticity  for foods  at the retail level.  U.S.DA.  has also analyzed
the elasticity  of  demand for farm  products  by modeling  the quantity  of the  farm product  as an input  in  food
processing (U.S.D.A.,  1989).  The analysis  considers  eight  commodities:  beef and veal, pork,  poultry,  eggs,
dairy,  processed  fruits and  vegetables,  fresh fruit,  and fresh vegetables.   U.S.DA.'s  results  are consistent
with  previous findings,   and  show that  all  own-price   elasticities  are negative and less  than 1 in absolute
values.  The  authors found  that, with  the exception  of poultry,  farm-level   demands  are nearly  as  large as
the corresponding  retail  elasticities  or  somewhat  larger than  the  corresponding   retail  elasticities.   Since
specific commodity  elasticities are not given and since  the findings   indicate  that farm-level  elasticities  are
similar  to retail-level  elasticities,   this analysis  uses  the more detailed values  for elasticities  that are given
in Huang's  report.

      2.4.    Feasibility of Non-Chemical   Substitution
      In order to further delineate  variations  in the elasticities of demand exhibited  by each cluster,  one can
examine the  market characteristics  that, according  to microeconomic  theory, influence   the price elasticity
of demand.   These  characteristics   include  the availability   of substitutes  for the product,  the contribution
of the product to the cost  of production,  and the productivity  of expenditures  for the  product.  This section
discusses  the availability   of  substitutes for clusters of pesticides.    Section 2.5  considers  the  impact  of
pesticide  contribution  to the cost of production  while Section 2.6 evaluates  the productivity  of expenditures
for pesticides.

      As discussed earlier, demand elasticity  is, theoretically,   a function  of the  availability  of substitutes,
among other factors.  If  a product has many close substitutes,  it is  likely  to be characterized   by an elastic
                                                     C.33

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demand.  Substitutes  for a pesticide active ingredient  include an alternative  active  ingredient  as well  as non-
chemical  substitutes.   In constructing   pesticide  clusters,  U.S. EPA's Office  of Pesticide  Programs  (OPP)
grouped all active  ingredients  which  are substitutes  for  each other.  The active  ingredients  included  in the
clusters  are both  chemical  and  biological.   Therefore,   substitutes  for a cluster include  only  cultural  and
environmental  pest control technologies1.

      Achievable  reduction in pesticide  use for specific  end-uses  has been studied  by Pimentel  et al. (1991).
Pimentel  considered  the costs  and benefits  of  replacing  chemical  pest control  methods with  currently
available  biological, cultural,  and environmental   pest control technologies.   Since both the pesticide clusters,
as defined  by EPA, include  biological  pest control methods,  the biological  alternatives  listed  by Pimentel
are not alternatives  to the clusters.  However, Abt Associates  knows of no analysis  which  considers  only
cultural and environmental  pest  control alternatives.  Further,  the biological  pest control  methods constitute
only a small  minority  of the  pesticides  within  the clusters.  Pimentel  et al.'s analysis is, therefore,  used to
measure the  relative  substitutability   of the pesticide  clusters.

      In this report,  Pimentel's  study  is  used to  develop  a general  rating  of the degree  to which  pesticide
substitution  is feasible for each  cluster.  The greater the feasibility  of substitution,   the higher the expected
elasticity  of demand for pesticides  in the cluster.   The ratings  are based on two criteria:  (1) the percentage
by  which  non-chemical   alternatives   can  replace  pesticides,  and  (2) the  projected  net cost  of  replacing
pesticides  with  a non-chemical   pest control method.  Based on these criteria,  the clusters  are grouped  into
three categories  as shown  in  Tables 2.4,  2.5,  and 2.6.  Clusters in the "high  substitutability"   category  can,
according  to Pimentel  et al.,   achieve  at least a 40 percent reduction  hi pesticide  use at an additional  cost
of less than one dollar  per hectare.  Clusters hi the "moderate  substitutability"  category  can achieve  at  least
a 20 percent  reduction  in pesticide  use at a cost no greater than five dollars  per hectare.  Clusters  which do
not qualify   for  either of these categories  are listed under the heading  "low substitutability".

      The clusters  defined by OPP often  group several of the crops that are listed hi Table 2.4,  2.5, and 2.6.
To determine  ratings  for the  clusters,  the crop-specific    ratings were weighted  by  the pounds  of fungicide,
herbicide,   or insecticide  applied  to each crop,  as  was  relevant  for the cluster.   The  cluster ratings,  as
developed   by Abt Associates   based on Pimentel  et al. are as follows:
       1  Most  of the pesticide  clusters  include  at least  two active ingredients,   indicating  that chemical
   substitutes  exist for most active ingredients.   The substitutability   between  active  ingredients  will  vary
   by  region and with meteorological   conditions,  as well as with specific  crops.  A comparison  of the
   chemical  substitutes  available  for particular  active  ingredients  is not undertaken  hi this analysis.
                                                     C.34

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                         Non-chemical
          Table  2.4
  Substitutability  for Pesticides  by Cluster
          Fungicides
         High
         Substitutability
Moderate
Substitutabilitv
Low
Substitutabilitv
         soybeans
         other vegetables
         peaches
nee                        cotton
sugar beets                sweet corn
lettuce                     tobacco
carrots                     peanuts
potatoes                   tomatoes
onions
beans
cantaloupe
peppers
sweet potatoes
watermelons
apples
cherries
peas
pears
plums
grapes
oranges
grapefruit
lemons
"other"  fruit
pecans
"other"  nuts
cole
cucumbers
Source:  Abt Associates  estimates  based on Pimentel  et al.  (1991)
                                                    C.35

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                                                  Table 2.5

                         Non-chemical   Substitutabilitv  for Pesticides  by Cluster

                                                 Herbicides
         High
         Substitutabilitv

         tobacco
         potatoes
         tomatoes
         cucumbers
         apples
         plums
         oranges
         grapefruits
         lemons
         "other"  nuts
Moderate
Substitutabilitv

peanuts
sorghum
pasture
grapes
alfalfa
hay
beans
cherries
peaches
pears
"other"  fruit
pecans
Low
Substitutabilitv

corn
cotton
wheat
soybeans
rice
sugar beets
"other"  grain
lettuce
cole
carrots
sweet corn
onions
cantaloupe
peas
peppers
sweet potatoes
watermelons
"other"  vegetables
Source:  Abt Associates  estimates  based on Pimentel et al. (1991)
                                                   C.36

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r
                                                                   Table 2.6

                                          Non-chemical   Substitutability  for Pesticides  by Cluster

                                                                  Insecticides
                          High
                          Substitutabilitv

                          sorghum
                          hay
                          tomatoes
                          cherries
                          peaches
                          pears
                          plums
                          grapes
                          "other"  fruit
                          pecans
                          "other"  nuts
                          oranges
                          grapefruit
                          lemons
Moderate
Substitutabilitv

cotton
wheat
carrots
onions
cucumbers
beans
sugar beets
peas
watermelons
"other"  vegetables
sweet potatoes
peppers
alfalfa
soybeans
rice
tobacco
peanuts
"other"  grains
Low
Substitutabilitv

corn
lettuce
cole
potatoes
sweet corn
cantaloupe
                  Source:  Abt Associates  estimates  based on Pimentel  et al.  (1991)
                                                                     C.37

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Low Substitutabilitv
fungicides  for use on vegetables
herbicides  for use on corn
herbicides  for use on soybeans,  cotton,  peanuts,  alfalfa
herbicides  for use on sugar  beets, beans,  and peas
insecticides  for use on  corn and  alfalfa
insecticides  for use on  vegetables

Moderate Substitutabilitv
fungicide  for use on  fruit  and nut trees, except  oranges and grapes
fungicides  for use on oranges
fungicides  for use on grapes
herbicides  for use on vegetables
herbicides  for use on sorghum,  rice, small grains
herbicides  for use on grapes
insecticides  for use on  cotton
insecticides  for use on  soybeans,  peanuts,  wheat, and tobacco

High Substitutabilitv
herbicides  for use on tree fruits  (except  oranges), nuts,  and sugarcane
herbicides  for use on oranges
herbicides  for use on tobacco
insecticides  for use on  grapes
insecticides  for use on  oranges
insecticides  for use on  fruit  and  nut trees  excluding  oranges  and grapes
insecticides  on sorghum

      As discussed  earlier,  these  data can be used to suggest  pesticide  clusters  for which  the demand
elasticity  differs  substantially  from the demand elasticity  for the associated  food commodities.  Demand for
the six pesticide clusters with low substitutability   may  be inelastic  relative to the demand for the associated
foods.  In the seven cases of high substitutability,   the demand  for the  pesticide cluster may be more elastic
than the demand  for the associated foods.  The feasibility  of substitution  for pesticide  clusters is considered
in Section 2.7 in constructing  estimates  of the elasticity  of demand  for the pesticide  clusters.
                                                    C.38

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      2.5.    Contribution  to the  Variable Cost of Production
      Economic  theory  predicts  that a producer's  sensitivity  to price will increase with  the  percentage  of
production  cost  contributed  by that input.  To further  distinguish  between  the elasticities of demand for
the different  clusters of pesticides,  Abt Associates has considered  the extent  to which the pesticides  in the
clusters  contribute  to production  costs.

      The U.S.DA.  publishes  cost-of-production   data  summarizing  all operator  and  landlord  costs  and
returns  associated with the production  of several individual   commodities   (U.S.DA.,   1989a).  The cost
estimates separate the cost of chemicals  and can be used to determine  chemical costs as a percentage  of total
variable  costs of production.   Cost of chemicals  is included  hi two categories:   "chemicals"   and  "custom
application".    Both  custom  operators  and  farmers apply  pesticides.   The  category "chemicals"  includes
agricultural  chemical use by farmers  and does not include  labor spent in chemical  application.  Many custom
operators  charge a flat  rate and  do not provide  a cost breakdown  between  labor and  materials.   "Custom
application"  therefore includes operator-applied   chemicals,  operator labor, and farm operations other than
chemical   application.    The  category  "custom   application"   was  included  in  calculations   of pesticide
contribution   to  total cost  in order to ensure that all chemical  costs  are included.   The estimate  of pesticide
contribution   to  the cost of crop production will,  however,  be overstated.   These data  are presented in Table
2.7 for the commodities for which  the  information was available.

      The pesticide   dusters  defined  in  this  analysis   separate agricultural  chemicals  into fungicides,
insecticides,   and  herbicides.   The U.S.D.A.   report  does  not  separate  the costs of chemicals  into these
categories.    In  order  to  divide  the  cost  of chemicals  between  each  of  these  types  of pesticides,  Abt
Associates  estimated  total  expenditures   for  each pesticide  type for the commodities   considered   in the
U.S.DA.  report.  Total expenditures  were calculated  by  multiplying  the pounds of fungicide,   herbicide,
or insecticide  applied  to  a commodity  (from Pimentel  et al, 1991)  by the average  price of  the relevant
pesticide  type i.e.,  fungicides,  herbicides,  and  insecticides  (as reported in Synthetic  Organic  Chemicals.
1988).  The  chemical contribution  to variable  cost was then divided  between the three pesticide  categories
based on the percent   of expenditures.    The percentages  of  variable  production  costs for fungicides,
herbicides,  and  insecticides  by commodity  are listed in Table  2.7.

      The crop-specific  estimates  must be grouped into clusters  for purposes  of  this analysis.   An estimate
of the contribution  of pesticide  to variable cost  for a cluster is made only if such an estimate is available
for individual  crops contributing  at least 50 percent  of the pesticide  use for the cluster  (based on Pimentel
et al., 1991).  Eight  clusters meet this qualification.   These clusters  are listed  below in descending  order of
                                                    C.39

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                                            Table 2.7


                 Fungicide. Herbicide, and Insecticide Contribution to Variable Costs of

                                            Production
Commodity

soybeans3
peanuts3
cotton3
sugarbeets3
sorghum3
corn3
rice3
wheat3
potatoes4
barley3
tobacco5
oats
Chemical
Costs as a
Percent of
Variable
  Costs1

    37
    31
    29
    28
    25
    22
    20
    18
    16
    16
    10
     9
Fungicide
Costs as a
Percent of
Variable
Costs2

 0
12
 0
 0
 0
 0
 0
 0
 7
 0
 0
 0
Herbicide
Costs as a
Percent of
Variable
Costs2

35
17
16
23
22
19
19
16
3
16
3
9
Insecticide
Costs as a
Percent of
Variable
Costs2

 3
 3
13
 5
 3
 2
 1
 2
 6
 0
 7
 0
      1 Equals ("chemicals" + "custom operations")/"total variable cash expenses"

      2Estimate by Abt Associates using pesticide prices from Synthetic Organic Chemicals  1988 and
  pounds applied  from Pimentel, D. et al, (in press),  "Environmental and  Economic  Impacts  of
  Reducing U.S. Agricultural Pesticides Use", Pest Management in Agriculture, CRC Press.

      3Source for percent of production costs - USDA, 1989. "Economics Indicators of the Farm Sector
  Costs  of Production, 1987". Economic Research Service.  February.

      ^Source for percent of production cost- USDA, 1988.  "1985 Potato Cost and Returns-  Fall
  Production Areas".  Potato facts special edition.  Economic Research Service. September.

      5Source for percent of production cost - USDA, 1989. "Tobacco:  Situation and Outlook Report"
  Economic Research Service. September.
                                                C.40

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the percent  of  the pesticide  contribution   to cost.   Based  only on contribution   to cost,  the order  also
corresponds  to expected  decreasing price elasticity  of demand.  The clusters are:

1)    Herbicide  used  on soybeans,  cotton,  peanuts, alfalfa  (33 percent of variable  cost)
2)    Herbicides  used on sorghum,  rice, small grains (20%)
3)    Herbicides  used on corn (19%)
4)    Insecticides  used on cotton  (13%)
5)    Insecticides  used on soybeans,  peanuts, wheat,  and tobacco  (3%)
6)    Herbicides  used on tobacco  (3%)
7)    Insecticides  used on sorghum (3%)
8)    Insecticides  used on corn and alfalfa  (2%)
      U.S.DA. did not estimate the cost of production  for  specialty  crops.  These data are compiled  at the
county  level  and collected  by individual   states,  but are not available  on a national  level.   It is beyond the
scope  of this study  to collect cost of production  data from each county  in each state for  each crop.  Abt
Associates did, however,  obtain cost of production  reports for specialty  crops of interest from the states that
represented  a large percentage  of the planted acreage  of each  crop.  From  these reports it was evident  that
the pesticide   contribution  to cost varied  significantly   between  regions.   Therefore,  it was decided  that
without   a statistically  valid national  sampling,  the  county-level    data  could not  accurately  be used  to
represent  national  cost data.  No estimates of  the pesticide  contribution   to variable costs of  producing
specialty  crops are included  in this analysis.

      The purpose  of considering  the pesticide  contribution   to variable   cost is  to  determine  whether the
demand elasticity  for clusters of pesticides  is likely  to differ substantially  from the elasticity  of demand for
the associated  food commodities  (calculated  is  Section  2.3).   In  particular,  for the four pesticide  clusters
where  chemicals  contribute  over  ten percent  of total variable  cash  expenses,  farmers  may be relatively
sensitive  to pesticide  price  changes. Therefore,  demand  for these  pesticide  clusters may be more elastic  than
demand for the associated  food commodities.   This factor is considered in  Section 2.7, along with the other
available  data, to estimate the elasticity  of demand for each of the pesticide  clusters.

      2.6    Productivity  of Expenditures  for  Pesticides
      The productivity   of an input  refers  to the marginal  value  product  of expenditure  for the input
compared to the cost of the input.  When the marginal  value product  exceeds the input cost, the input  is said
to be productive.    If an input  is highly  productive,   demand for  the input  is theoretically  likely  to  be
                                                     C.41

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 insensitive  to small  changes in price.  Three studies  which  examined the productivity   of expenditures  for
 agricultural  pesticides  were located and are discussed  below.

       Headley (1968) estimated partial production elasticities  for the following  input variables  using Cobb-
 Douglas  functions:    labor,  land  and  buildings,  machinery,   fertilizer,   pesticides,   and  "other".   He then
 compared  the marginal  value  production   of  expenditure  for pesticides   to  the  marginal  factor  cost of
 pesticides  to determine  the extent  of disequilibrium   in the use of  pesticides  by  farmers.   The results of
 Headless  study  indicated  that the marginal value of  a one-dollar  expenditure  for chemical  pesticides  is
 approximately  $4.00.  Headley noted several limitations  of his analysis,  including   that his conclusions  are
 based on aggregative  analysis  and may  not  apply to  local situations.

       Campbell  (1976) considered  this same issue for a cross-sectional  sample of tree-fruit  farms in British
 Columbia.  The statistical  techniques used by Campbell  include Ordinary  Least Squares and Factor Analysis
 Regression.  The data used hi fitting  Campbell's  regression equation were as follows: the dependent  variable
 was the value of output  of fruit;  the input  variables  were the values  of services  of land and buildings  and
 capital equipment,  and the values  of inputs of irrigation  water,  labor,  fertilizers,  and pesticide  sprays.
 Corresponding  to Headley's  findings,    Campbell found  that  the  value of a marginal  dollar's  worth  of
 pesticides  was significantly  greater  one  dollar, indicating   a relatively   inelastic   demand.   However,  as
 Headley  did,  Campbell  suggested  caution in the interpretation  of this result.  He noted  that it  is possible
 that his statistical  procedure  introduced  an upward  bias to the estimate since the sample data exhibited
 fairly  high correlations  among  some of  the  independent  variables,  including  pesticides.

      According  to Lichtenberg and Zilberman  (1986),  however, the studies of Headley and Campbell  are
 methodologically   flawed.   Lichtenberg  and  Zilberman  argue  that econometric measurements  of pesticide
 productivity  that are derived from standard production theory models  contain significant  upward  biases that
 result  in  the overestimation  of pesticide  productivity.   The authors claim that the constant elasticity  of the
 marginal  effectiveness  curve produced by a  standard  Cobb-Douglas  specification   will not match  the actual
 behavior  of  the  marginal  effectiveness    curve.    The correct  form   of the  marginal  effectiveness   curve,
 according  to Lichtenberg  and Zilberman,   will show an increase hi pesticide use in response  to pest resistance
 and  a  decrease in use only  when  pest  resistance  is so widespread  that alternative  measures  are most cost
 effective.    The true marginal effectiveness  curve will decline at an increasing  rate in the economic  region.
Lichtenberg  and  Zilberman  cast  doubt  on  the high  marginal  productivity   of pesticides  estimated  by
Campbell  and Headley.
                                                    C.42

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      Given that these studies  do not provide definitive  estimates of the productivity  of pesticides  and do
not address the productivity   of specific  pesticide  clusters,  we  develop  simple original  estimates of  the
productivity   of pesticide   clusters.   In this analysis,  the  productivity   of  pesticides   (specified  as either
fungicides,  herbicides,   or insecticides)  on individual   food commodities is calculated  as follows:
                                               P -•
                                                      V x  MP
where:
C
MP  =
P
the cost of pesticide  treatment for the food  commodity  (dollars per hectare),
the marginal  value product  from  the pesticide  application  (percent of total production  value),
the productivity   of the pesticide  on the food  commodity  (dollars  per hectare/dollars  per
hectare),  and
the production  value  of the crop  (dollars per hectare harvested).
      The data sources for the three input parameters were as follows.   The production  value of the crops
was obtained from U.S.DA.  (1989).  The cost of pesticide  treatment  was taken from Pimentel  et al. (1991).
No source of specific  estimates of the marginal  value product  associated  with fungicides,  herbicides,  and
insecticides  on crops was located.  The analysis therefore relied on the expertise  of the U.S. EPA Office  of
Pesticide  Programs  (OPP) to estimate  the value  of this  parameter.  The OPP stated that it was reasonable
to generalize  that the marginal product  associated  with the use of fungicides,   herbicides,   or insecticides  on
a crop equaled  ten percent of the production  value of that crop  (telephone  communication,   Dave Broussard,
OPP, 2/91).  Since no more  precise  estimates  were available,  the analysis  adopted  this value.

      In reality,  there will be some variation  in the marginal  value  product  of fungicides,  herbicides,  and
insecticides  on different  crops.  To the extent  that the marginal value product for a pesticide  type on a crop
is greater  than 10 percent,  the analysis  will  understate  productivity   and therefore overstate  the elasticity
of demand.  Similarly,  if the marginal value  product for a pesticide  type on a crop is less than 10 percent,
the productivity   of the pesticide  will be overstated  and the elasticity  of demand will be underestimated.

      Weighted   averages  of the  productivity   measures   for  pesticides  used  on individual   crops  were
calculated to obtain measures of productivity   for pesticide  clusters.   The weighting  factor  was the quantity
of pesticides  included  in the cluster applied  to  each crop,  as determined  by  Pimentel et al. (1991).
Table 2.8 displays  the  productivity   measures  for  the  pesticide  clusters  for which  the  information  was
available.

                                                     C.43

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                                        Table 2.8

                            Productivity of Pesticide Clusters
Cluster
           Productivity
    (Dollars of Marginal Product
per Dollars of Pesticide Expenditures^
Fungicides on:

 Fruit and nut trees, except oranges and grapes
 Grapes
 Vegetables
 Oranges

Herbicides on:

 Sorghum, rice, small grains
 Corn
 Soybeans, cotton, peanuts, alfalfa
 Sugar beats, beans, peas
 Vegetables
 Oranges
 Tree fruits (except oranges), sugar cane, nuts
 Grapes

Insecticides on:

 Cotton
 Sorghum
 Corn, alfalfa
 Vegetables
 Fruit and nut trees, except oranges and grapes
 Soybeans, peanuts, wheat, tobacco
 Oranges
 Grapes
                $5.81
                $9.83
                $12.37
                $12.54
               $0.88
               $1.11
               $2.68
               $2.72
               $17.85
               $17.91
               $19.29
               $61.43
               $0.72
               $1.24
               $3.69
               $7.92
               $8.51
               $13.08
               $15.04
               $37.80
                                        C.44

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      Note that there is great variation in the productivity   estimates.  The lowest productivity   estimate is
$0.72,  for insecticides  used  on cotton;  Herbicides  used on grapes  had the highest productivity,   at $61.43.
The wide  range is due both  to variability   in the value  of production  of crops  and variability  in the cost of
applying  pesticides  to the crop.  For example,  the value of production  of cotton is $487 per hectare while
the value  of a hectare of grapes  is $4,914  per hectare  (U.S.DA.,  1989).   In addition,  the average  cost of
insecticide  application to cotton is about $118  per hectare  while the costs of applying herbicides  to grapes
is $8 per hectare (Pimentel et al., 1991).  However,  it must again be recognized  that due to lack of data, the
analysis  assumes that the marginal  value of production of insecticides  on cotton  and herbicides  on grapes
are identical.

      The productivity  of the clusters  is considered  hi the next section,  along with  the factors previously
discussed, in developing   estimates  of the elasticity  of demand  for each pesticide  cluster.  Demand for the
pesticide clusters  for which  productivity  is low can be expected  to be elastic  relative  to the demand for the
associated food commodities,  ceteris paribus.   Similarly, when  a cluster of pesticides  is highly productive,
demand  is likely  to be inelastic  compared  with demand for the associated  food  commodities.

2.7.  Conclusions  -  Agricultural   Pesticides
      Section  2 of this report estimates the price  elasticity   of  demand for twenty-four   pesticide  clusters.
Estimates of the elasticity  of demand for clusters  of pesticides  are based on the  price elasticity of demand
for the associated food commodities.   However,  the elasticity  of demand for  an input  is not solely a function
of  the demand  for the end product  (unless input  ratios are assumed to be fixed).  Therefore,   the  elasticity
estimates  are adjusted as warranted by consideration  of three factors:  (1) the feasibility  of substituting   non-
chemical  controls  for the  pesticide cluster,  (2) the contribution   of the  pesticide  cluster  to the variable  cost
of  crop production,   and (3) the productivity   of the pesticide  cluster.  In addition,  the literature   estimates
of  elasticity  are considered  when appropriate.

       Since the effect  of these factors  is not easily  quantified,  we  use this  information  to adjust  the pesticide
elasticities   estimated  from  the  demand  for crops  rather  than to attempt  to pinpoint  the value of demand
elasticity.   Based on this information,  we identify  clusters  for which  the elasticity   of  the demand for the
food commodity  is likely to differ  substantially  from the elasticity of demand for the corresponding  cluster
of pesticides.

       Note that the  effect of the factors  considered  may  cancel each other.   For example,  the feasibility  of
 non-chemical   substitution   for a cluster  of pesticides   may be high,  indicating   that the elasticity  of demand
 may be higher  for the cluster of pesticides  than for the associated  crops.  However,  if  the productivity  of

                                                      C.45                                          :

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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  clusters of pesticides  are included  in the  following  summary of elasticity  of demand
 for  agricultural   pesticides:   fungicides   used on grain  storage,  fungicides   used  for seed  treatment,   and
 fungicides  - post-harvest.   Since these  clusters  differ  from the other agricultural  pesticide  clusters in  that
 the pesticides are not applied to crops in the field,  they have not been included  in the analysis to this point.
 However,  since the pesticides  in these clusters  are used agriculturally,   elasticity  estimates are discussed in
 this section.   The best estimate  of elasticity   for each  of the twenty-four  agricultural  clusters is  discussed
 below.
a.
      Fungicides   used on vegetables
      The elasticity  estimate  of -0.38  is taken  directly  from  U.S.DA.'s  (1985)  estimate of  the  demand
elasticity  for retail vegetables,  weighted by  the amount  of fungicides  applied to each type  of vegetable.  No
adjustments   are  made  since  the  substitutability    for fungicides   on vegetables   is low and the  marginal
productivity   of fungicides   on vegetables   is moderate.
b.    Fungicides   used on  fruit  and nuts except oranges
      The elasticity   of demand  for food commodities   in this cluster, based on a weighted-average   of the
elasticity  values  estimated  by U.S.DA.  (1985), is -0.23.   No adjustments  are made to this value  are made
to arrive  at the elasticity  of demand for  fungicides  applied to these food commodities.  No corrections  were
necessary  since  the  substitutability   for  fungicides   on fruit  and  nuts except  citrus  is  moderate  as is the
marginal  productivity  of fungicides  on fruit and nut trees, except oranges.  The estimated elasticity  of -0.23
indicates  less  elastic  demand  than does  the analysis  of Pingali and  Carlson  (1985).  However,  the  elasticity
estimate  of Pingali   and Carlson  consider  only apples and is  therefore  not directly   comparable   to the
elasticity  estimate for the  cluster.   Both the current  estimate  and the Pingali and Carlson estimate  indicate
that demand  is inelastic.
                                                     n.46

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            Table 2.9
Summary of Elasticity Information
            Elasticity of   Feasibility   Fraction of



















Literature Food
Cluster Estimates Commodity
"ungicides on: •*.*'* •JT
vegetables J6&A... , r, -0.38
^"*'ps,i'A/ ^
fruit & nut trees, *£^ J^, "s" ^
except oranges l^x92T6& -0.23
Jr o %vv ^^.£v&. * *. <. *^ f
oranges SStifL^ s% -1.00
grapes ^^C>^s^-.» - -1.38
Serbicides on: '". '* Z^^r . "!' '"
sorghum, rice, ^--^T "" -^ " ""- "
small grains t^A* , -0.44
soybeans, cotton, ^s «^"\,1 ^
peanuts, alfalfa inei*|lis (5) ^ -0.67
corn Ji*tC -S.?S^> \ -0.69
¥'^|> _ ^
oranges T&A*, j^ ti^ -1.00
' ^X*S«f' vl "' " " "
tree fruits, nuts & ^" ^f^ "ix^% - -
sugar cane "&jA^ %„ ' -0.20
. j^^ Vdw ^ A ^X^- ^
•. ;• -iV.i,-fr ** W. f
grapes ^^V~V'^ , -^ ~L38
vegetables jIl^V^-X - °- r" _ ~0'27
tobacco -§*^, , v^ N.A.
sugar beets, beans ^Jv* , -
peas kX, "f"' " ~°-12
(1) Burrows (1983), cotton only
(2) Pingali and Carlson (1985), apples only
(3) Miranowski (1980), corn only
(4) Huh (1978), corn insecticides and herbicides
(5) U.S. EPA (1974), corn or soybeans, only
C.47
of Contribution to Marginal
Substitution Production Costs Productivity
, , , > , ,,:*, f
low N.A. $*%^?
iffS . N-
%«.-» 'v ••"-';"' ' ^ ,ff' :
jnOd^rAte N.A. ^$^+^83
'*'*.*'' &J£ :'^
moderate 0.20 ' f ,, $ft.§8
low 0.33 |2»6$
v v <• ' :
low *'" 0.19 $i*^i
"*
>^^ N.A. ^^
^ -• « 4- ff H* ^ t S ^
;E- ;i ••£
moderate N.A. $I7<8$
t " ,i, ^ '•
Jttgfe 0.03 , $5?^
low N.A. 'rf "$3L^



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Cluster
                                     Table 2.9 (cont.)
                           Summary of Elasticity Information
               Elasticity of   Feasibility   Fraction of
Literature         Food          of       Contribution to
Estimates      Commodity   Substitution  Production Costs
                                                                                        Marginal
Insecticides on: " •• •• ' ,'t
vegetables JS-A- '
fruit & nut trees HL3£ (£) , '^
exc. oranges ' '//,', '"',,; '' ^
oranges H»Xt, ,
grapes SLA* _.
corn, alfalfa -0,7$ £J>
keJasdc (5)
sorghum RA, , t ^
soybeans, peanuts, "'"' '"' '-- ~ff'f
wheat, & tobacco £&$!$.&& (5)
cotton -&£> -J^,(j> , -
-0.33 " ^\\&K '3
-0.21 ' 'Mgb ,
-1.00 ; ajgi'
-1.38 ' ^ iagb
-0.69 low
^ , '••"'"' ,'
-0.69 ^ ' lugfe
-0.56 nv04^faite
N.A. .jaoderafe:
N.A. : $fM,
NA % ^ft 5i
• /Ti • •" •»£ O+>^i
N.A. "$'15,04
N.A. ';- ^fl^J
0.02 ,, $3*^ |
•.•^. ^
0.03 ^ ' fix24
s^ '*"•"' ^ ':
OAO ^•^•'1 ftiff :
• UJ .fcpUiJJJ :
0.13 ^Oi735 ^
(1) Burrows (1983), cotton only
(2) Pingali and Carlson (1985), apples only
(3) Miranowski (1980), corn only
(4) Huh (1978), corn insecticides and herbicides
(5) U.S. EPA (1974), corn or soybeans, only
                                                C.48

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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.D.A. (1985)  and on the demand elasticity  of sorghum.  As discussed above,  the  elasticity
of demand  for sorghum,  generally  an animal  feed crop, was calculated  based  on the elasticity  of demand
for animal  meats.   To  estimate  an elasticity  for  the crops  in this cluster,  the two  crop elasticities  were
weighted  by the amount of herbicides  applied to each crop (as reported  in Pimentel  et al.,  1991).   The
resulting  elasticity  estimate  is -0.44.

      However,   it is likely  that  the  elasticity   of  demand  for  this  cluster  of herbicides  will  exceed  the
elasticity  of demand for the associated  crops.  Although  the feasibility  of substitution   for herbicides in this
cluster  is moderate,  herbicides  contributed  a relatively  high percentage   to total variable   costs, and  the
marginal  productivity   of the herbicides is very low.  There is no precise  method by which to translate  these
factors  into  an estimate  of the elasticity   of  demand  for  herbicides   on sorghum, rice,  and small grams.
However,  to account for  the  low marginal productivity  and high contribution  to costs of herbicides   on
sorghum,  rice, and small grains,  demand  on  herbicides  on this  cluster  is assumed to be more  elastic than
demand for crops  in this cluster.  The elasticity  estimate is  adjusted  from -0.44  to -1.0.
 f.     Herbicides  on soybeans,  cotton,  peanuts, and alfalfa
       As discussed  earlier in this report,  assuming  that soybeans  and  alfalfa  are fed to animals, the price
 elasticity  of  demand  for the  crops  in  this  cluster,   excluding   cotton,  is -0.67.    Since  the  quantity  of
 herbicides   applied  to cotton  is  small in comparison  to the  quantity  of  herbicides  applied  to soybeans,
 peanuts,   and  alfalfa,  the exclusion  of  cotton should  not  substantially  affect   the elasticity   estimate2.
       2According to Pimentel et al. (1991), 8.2 million kgs. per year of herbicides are applied to cotton and 60.6
    million kgs. per year of herbicides are applied to soybeans, peanuts, and alfalfa combined.

                                                      C.49

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 Supporting the elasticity  estimate  of -0.67,  U.S. EPA (1974)  found  the  demand for herbicides  on soybeans
 to be inelastic.

       Three additional  factors  present information  on the expected price elasticity  of demand for this cluster
 of herbicides:   the feasibility   of  substitution,   the fraction   of contribution   to production  costs,  and  the
 marginal  productivity   of the herbicides.   The feasibility   of substitution   for this  cluster of herbicides  is low,
 influencing   the  demand  for  the  herbicides  to be  inelastic.    However,  herbicides   (including   custom
 application)  are estimated  to contribute  33 percent  of the total cost of production for this cluster.  This high
 contribution  to variable  cost is likely  to  drive greater elasticity  of demand.  Also, the marginal  productivity
 of herbicides  hi  this cluster is estimated  as $2.68.   This  return on  herbicide  use  is fairly  low,  suggesting
 somewhat  elastic demand.

       Given  the  opposing  factors  that influence  demand for herbicides   in this cluster,  it was judged that
 the estimated  elasticity   of demand  for  the  crops,  -0.67,   serves well  as an estimate  of  the elasticity  of
 demand  for  the  cluster  of herbicides.

 g.     Herbicides  on corn
       The  estimate  of elasticity  of demand  for  corn herbicides  is -0.69.   This  value  is based  on the average
 elasticity  of meats  as  listed in  U.S.DA.  (1985),  since  the  corn  is assumed to  be used  as animal feed.
 Pesticides  in  this  cluster  contributed   a relatively  high  percentage   to total  variable  costs  (19%  including
 custom  application)  and  the marginal productivity  of these pesticides is low,  at $1.11.  Both of this factors
 indicate  elastic  demand.   However,   the feasibility   of substitution   for  these  pesticides  is  low,  indicating
 inelastic  demand.   Therefore,   it was judged that no  additional  adjustment   to  the elasticity  estimate was
 warranted.

 h.     Herbicides   on oranges
       The estimate  of the elasticity  of demand  for  herbicides  on oranges is -1.00,   taken from U.S.DA.'s
 estimate  of the elasticity  of demand for  oranges.  Although the  feasibility of substitution  for herbicides  on
 oranges  is high  (indicating  elastic demand),  the marginal  productivity  of the  herbicides  is also fairly  high
 (indicating  inelastic  demand).   Therefore,  no adjustment  to the U.S.DA. estimate  of elasticity  of demand
 for oranges  is made.
i-     Herbicides  on tree  fruits  ("except  oranges'),  nuts,  and sugarcane
      The elasticity  of demand  for this cluster,  based on  the elasticity   of demand  for  retail  food,  is
estimated  as -0.20.   Pesticides  in this cluster  have a high feasibility   of substitution  with  non-chemical   pest

                                                    C.50

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control methods,  indicating  elastic demand.  However,  the marginal  productivity  of these pesticides is also
moderately  high,  at $19.19,   indicating  inelastic  demand.   Therefore,   no adjustments   are  made to the
elasticity  estimate for retail  food.

j.     Herbicides  on grapes
      The price elasticity of demand  for herbicides  on grapes is estimated based on the elasticity  of demand
for grapes  at the retail level.  The estimated elasticity  is -1.38.   Since the marginal  productivity   on grapes
is extremely high, the elasticity  of demand may be less than -1.38.   However,   the marginal  productivity  is
the  only  factor  indicating   inelastic  demand;   the feasibility  of substitution   for  herbicides  on  grapes  is
moderate.  Further,  the degree  of adjustment  to the elasticity  estimate warranted  by  the high  marginal
productivity is unclear.  For these two  reasons, this analysis  relies on the elasticity  estimate for retail grapes.
However,   it should  be noted  that this  value  may  overstate elasticity,   and therefore  overstate the impact  of
the  effluent guidelines  on pesticide  manufacturers.

k.     Herbicides  on vegetables
       The  weighted-average   estimate  of demand  for vegetables  at the retail  level is -0.27.   Since the
feasibility   of substitution  is moderate  and the marginal productivity   is moderately  high  for  this cluster, the
elasticity   estimate for food  is used to represent the elasticity of demand for herbicides used on these foods.

1.     Herbicides  used on tobacco
       U.S.D.A.  did  not  estimate  the  elasticity  of  demand  for  tobacco  at the retail  level.   However, the
addictive  nature  of cigarette  smoking  probably results in inelastic  demand  for  tobacco.  It seems reasonable
to assume  demand  for tobacco is as inelastic as the least elastic  demand  for retail food, since people seldom
develop addictions   to specific foods.  Since U.S.DA.  found that the  elasticity  of demand  for numerous  food
commodities  was lower  in absolute value  than -0.20,   the elasticity  of demand  for  tobacco is estimated as -
0.20.

       Since the feasibility   of substituting  a non-chemical   alternative  for herbicides   on tobacco  is high,
 demand  for the herbicides   used on tobacco  may be  more  elastic  than  demand for  the tobacco  itself.
 However,  the costs of applying  herbicides  comprise only 3 percent of the total variable  costs of production.
 Further,  the estimate of the marginal  productivity  of herbicides  used on tobacco is extremely  high.  These
 two factors indicate that demand for  herbicides  used on tobacco  will  be  inelastic.  Given these  opposing
 factors,  this analysis assumes that the elasticity  of demand for herbicides   used on tobacco  will match  the
 elasticity   of demand for tobacco. The elasticity  estimate  for  this cluster  is therefore  -0.20.
                                                      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 substitutabilhy
for herbicides  on  sugar beets,  beans,  and  peas is  low,  indicating   relatively  inelastic  demand,  while the
marginal  productivity   of the herbicides is low, indicating  relatively  elastic demand.

n.    Insecticides  on vegetables
      The elasticity  for this cluster  is estimated  as -0.33,   based  on  a weighted-average    of the values
estimated  by U.S.DA. (1985) as the elasticities  of demand for vegetables.  No adjustments  are made to the
elasticity  estimate  for vegetables.  The marginal productivity   of insecticides   in this cluster is moderate,  at
$7.92.   Although  the  substitutability   for insecticides  on vegetables  is low,  there is no quantitative  measure
of the extent  to which  the estimate should  be altered. Further,  this is the  only factor indicating  that demand
is relatively  inelastic.   Therefore,  the elasticity estimate of  -0.33  is used in this analysis.

o.    Insecticides  on fruits   and nuts  except  oranges
      The estimate  of elasticity  of demand  for the  food commodities  in this cluster,  based  on U.S.DA.'s
(1985)  estimates  of elasticity of demand  for food at the retail  level,  is -0.21.  This  value differs  notably
from the elasticity  estimate   of Pingali  and Carlson (1985) for  insecticides  applied to apple orchards.  Pingali
and Carlson estimated the elasticity  of demand as -1.39.  Since the authors considered  only apple orchards,
the estimates  are not perfectly  comparable.   However,  since  apples receive over 50 percent  of insecticides
applied  to crops  in this  cluster,  the differences  between the two  estimates is notable.

      The marginal productivity   of these  insecticides  is moderate  and does not suggest  that an adjustment
to the elasticity  estimate for retail food is required.   However,  the feasibility   of non-chemical   substitution
for these insecticides  is high, indicating elastic demand.  To account for  the high  feasibility  of substitution
and the elasticity  estimate of Pingali  and  Carlson, the elasticity  estimate for this cluster is adjusted  from  -
0.21  to -1.00.

p.     Insecticides  on oranges
      The U.S.DA. estimate of the elasticity of demand  for  oranges at the  retail level was -1.00.   This  value
is  also used  to  represent the  elasticity   of demand for  insecticides   applied  to oranges.   Although  the
feasibility  of substitution of insecticides  used on oranges is high (indicating  relatively elastic  demand), the
marginal  productivity   of   the  insecticides   is also  fairly  high  (indicating   relatively   inelastic  demand).
Therefore,  no adjustments   are  made.
                                                      C.52

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q.    Insecticides  on grapes
      The U.S.DA.  estimate  of the elasticity  of demand for grapes at the retail  level was -1.38.   This value
is also used to represent  the elasticity  of demand for insecticides  applied to grapes.  Although the feasibility
of substitution  of insecticides  used on grapes  is high (indicating  relatively  elastic demand), the marginal
productivity   of the  insecticides  is also high, at $37.80 (indicating  relatively   inelastic  demand).  Therefore,
no adjustments are  made to  the U.S.D.A.  elasticity  estimate for grapes.

r.    Insecticides  on corn and alfalfa
      Since  a large  proportion  of production  of  each of  these  crops serves  mainly  as  animal  feed,  an
elasticity  estimate for the crops  was developed based on the retail demand for meat.  As  discussed  above,
the elasticity  for  corn and alfalfa  is estimated  to be -0.69.   This elasticity  estimate is also used to represent
the elasticity  of demand  for  insecticides  applied to these  crops.

      Three literature  values  describe  the elasticity  of demand  for  crops in this cluster.   U.S. EPA (1974)
found  the demand for corn insecticides  to be inelastic.  Miranowski's   (1980)  statistically  significant  estimate
of the elasticity  of demand for  corn insecticides  was -0.78.  Finally,  Huh (1978) estimated  the elasticity  of
demand  for corn insecticides   and herbicides as -1.46.  Since these literature  estimates  conflict,  they do not
indicate  that an adjustment  to the  elasticity estimate  is needed.

      The feasibility  of  substitution  on these crops is low, indicating  that demand is relatively inelastic.  The
low contribution  of insecticides  to the costs of production  of these crops also indicates that demand  for the
insecticides   will  be relatively inelastic.    However,   the  marginal productivity  of  insecticides  on corn  and
alfalfa is fairly  low, at $3.69.   Low productivity   is associated  with elastic  demand.  Given the opposing
factors,  no adjustment   is made to the estimate of the elasticity  of demand  for  corn and alfalfa.

s.    Insecticides  on sorghum
      As was the case for corn and alfalfa,  the elasticity of  demand for sorghum is calculated based on the
elasticity  of  demand for  meat,  since sorghum  is used mainly  as a feed crop.  The elasticity  estimate  for
sorghum  is -0.69.   Although  the  marginal  productivity   of insecticides   on sorghum  is  low  (indicating
relatively  elastic  demand)  and  the feasibility   of substitution  is high  (also indicating   elastic  demand),
insecticides   contribute   only  two  percent  of production  costs (indicating  inelastic  demand).  Given these
opposing factors,  no adjustment   to the sorghum  elasticity  estimate  is  made.  The  elasticity  of  insecticides
used on  sorghum is estimated as  -0.69.
                                                     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.

u.     Insecticides  on cotton
      No estimate of the elasticity  of demand  for  cotton  was given  by U.S.DA.   However, Burrows  (1983)
empirically  estimated  this elasticity.   Using a single equation model,  Burrows  estimated  the elasticity  of
demand for cotton to be -0.9;  with  a simultaneous equation  model,  Burrows  estimated the elasticity  as -1.23.
The average  of these two  estimates is -1.06.

      Since the marginal   productivity   of insecticides  on cotton is  extremely  low,  at $0.72, the demand for
the insecticides  is expected  to be elastic.   Further,  the insecticides  contribute   a fairly  high  fraction,  13
percent, of the variable  cash costs of producing  cotton.  The feasibility  of substitution  for  these insecticides
is moderate.  Since  these factors are consistent  with  the  elasticity  estimate from Burrows, the elasticity  of
demand for cotton  insecticides  is  estimated to be -1.06.

v.     Fungicides   on gram storage
      In the absence  of more specific  information,   the elasticity  of demand for  fungicides  on grain storage
is assumed to equal the elasticity  of demand  for grains.  Elasticity  estimates are available  from Huang (1985)
for wheat and  rice.  Other stored  grains may be  fed to  animals.   As discussed  above,  an estimate for the
elasticity   of grains fed to animals  was developed  as  part of this  analysis.   However,  since  information  was
not located on  the quantity of fungicides  applied to each grain and each end-use,  correct  weighting  factors
for the different   elasticity estimates could not be developed to estimate an average  elasticity  for all grains
treated with fungicides   in storage.   The elasticity for this cluster  is therefore  estimated  as a straight  average
of the elasticity  of wheat  flour  (-0.11),  rice (-0.15),  and animal feed grains (-0.69).  The resulting elasticity
estimate  for  fungicides   used on grain in storage is -0.31.
w.    Fungicides  used for seed treatment
      Since no specific information  on the elasticity  of fungicides  used  for seed treatment was located, the
elasticity  of demand for  fungicides   in this cluster is calculated  based on the demand  elasticity  for the crops

                                                    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  hi the same ratios as they were applied  to the crops hi the
field.   These  ratios  are used to weight  the demand  elasticities   for  the  individual   crops.   The resulting
elasticity  estimate  is -0.65.

      A  complete  list  of Abt Associates'  estimated  price elasticities  of demand   for  clusters  defining
agricultural  end-uses  is provided  in Table  2.10.
                                                      C.55

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                                               Table 2.10
                 Estimates of Elasticity of Demand for Clusters in the Agricultural Sector
                Cluster

                Fungicides on:

                  fruit and nut trees except oranges
                  seed treatment
                  grain storage
                  vegetables
                  post-harvest
                  oranges
                  grapes
Elasticity Estimate
   -0.23
   -0.27
   -0.31
   -0.38
   -0.65
   -1.00
   -1.38
                Herbicides on:

                  sugar beets,  beans, and peas
                  tobacco
                  tree fruits (except oranges, nuts, sugarcane)
                  vegetables
                  soybeans, cotton, peanuts, and alfalfa
                  corn
                  sorghum, rice, and small grains
                  oranges
                  grapes
   -0.12
   -0.20
   -0.20
   -0.27
   -0.67
   -0.69
   -1.00
   -1.00
   -1.38
                Insecticides on:

                 vegetables
                 soybeans, peanuts, wheat, and tobacco
                 corn and alfalfa
                 sorghum
                 fruit and nut trees except oranges
                 oranges
                 cotton
                 grapes
   -0.33
   -0.56
   -0.69
   -0.69
   -1.00
   -1.00
   -1.06
   -1.38
Source: Abt Associates estimates based on Pimentel et al. (1991), USDA (1985), USDA (1989a), USDA
(1989b), USDA (1989c), Burrows (1983), Pingali and Carlson (1985), Miranowski (1980), Huh( 19878), U.S.
EPA (1974)
                                              C.56

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3.0   PRICE ELASTICITY   OF DEMAND  FOR PESTICIDES   USED  NQN-AGRICULTURALLY

      Most of the pesticides  included  in this  analysis are used  in the agricultural  sector; pesticides  hi non-
agricultural  clusters,  as defined  by OPP, constitute less than 30  percent  of total pesticide use by weight  (U.S.
EPA, 1988).  However,  the non-agricultural    pesticides  are described by eighteen  separate clusters.   Unlike
in the agricultural  sector, these clusters  represent  eighteen  distinct  and generally  unrelated  end-uses,  each
with  its own customers,  competitors,  and costs.  The literature search  described  above yielded no studies
of the  price  elasticity  of demand  for pesticides  in the non-agricultural   sector.   Since the scope of this
project does not allow  for the gathering  and examination  of primary  data on elasticities  of demand  for each
of these eighteen  markets  and since non-agricultural   pesticide  use represents  a relatively  small percent  of
total pesticide  use, the demand elasticities for the non-agricultural   sector  are developed  based on a reasoned
consideration  of two  factors.   Consistent with  the analysis  of  agricultural  pesticide  use, these factors are:
(1) the  availability   of substitutes  for a cluster  of pesticides,  and (2) the contribution  of pesticides  to the total
production  cost of the end-user.

      Based on  the above  two factors, the eighteen  non-agricultural   clusters  fit into two  categories:  (1)
pesticides   that  contribute   a  small  percentage   to total cost but  have  substitutes,  and (2)  pesticides  that
contribute  a small percentage  of total production  costs and for which  there are limited substitutes.   There
were  no cases hi which  it appeared  that pesticides contributed  a substantial  percentage  of total production
costs.  The two categories and the clusters  described  by them are listed below,  along with a brief discussion
of the  reasoning  behind  the cluster  categorization.

(1) Pesticides contribute  a small percentage  of total cost but  substitutes  are available
     The two non-agricultural   herbicide clusters  are included  in this category:   (a)  herbicides on ditches,
rights  of way,  forestry,  and ponds,  and (b)  herbicides   on turf.  The available  substitute  is labor,  a viable
alternative  to chemical weed  control.  To determine  the shift  to manual/mechanical   weed control given  an
increase  in pesticides  price,  one   would  need to  know:  the  cost  of  herbicide   per  unit  of  area,  the
effectiveness   of herbicides,  the  labor  cost of applying  herbicides  per unit of area, the  labor cost  of manual
weed  control  per  unit  of area,  and  the effectiveness   of  manual  weed  control.   Since  these  two  clusters
together  constitute less than one percent of the pesticides of interest (by weight) it was decided  not  to invest
resources  in the gathering   of these  data.

       Rather,  Abt Associates  considered  the cost structure  of  the end-users  of pesticides  hi these  clusters.
Herbicides used on ditches, rights of way, forestry,  and ponds  would  generally  be used by major  industries
such  as railroads  and utilities  and  by government  agencies, such  as state highway  departments.    The cost
                                                      C.57

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 of herbicides  would  be an insignificant   percentage  of their  total production  costs.  Demand for this cluster
 of herbicides  is therefore likely to be  inelastic.  While herbicides  used  on turf may contribute  a greater
 percentage  to the total  production  costs (assuming  that  these pesticides  are  used,  for example,   on golf
 courses  and  turf  farms)  the  costs  should still be relatively  small.  In addition,  fungicides  are applied  hi
 conjunction   with herbicides   to turf.  It is therefore   likely  that an application  system  would be  hi place for
 fungicides,   making  the  incremental  costs of  herbicide  application   small.

      Based  on  the  above  discussion,   this  analysis  assumes  that  demand  for  the  two  non-agricultural
 herbicides  clusters is inelastic.   Although  the level  of detail of the available  information  does not result in
 a quantitative  measure  of the elasticity,  such  a measure  is required.   Since  only one of the  two factors
 considered   above  indicates   inelasticity  (percent   of  production  costs),  while  the  other  is inconclusive
 (substitute  availability),   this analysis  assumes  that demand  for these two clusters  is only moderately inelastic,
 and  assigns a price elasticity  of -0.66.  The sensitivity  analysis will consider the impacts  on active  ingredient
 manufacturers  if demand for pesticides  in  these clusters  is perfectly  elastic.

 (2) Pesticides  contribute   a small percentage  of total production   costs,  and there are  limited  substitutes
      The remaining   sixteen  non-agricultural   clusters  are grouped  hi  this category.  For each  cluster,  the
 cost of pesticides appeared incidental to the total cost of production  and no readily  available, cost-effective
 alternatives  to the  pesticides   were known.   These two factors suggest inelastic  demand.  Further,  only  three
 of the sixteen clusters in this category  constitute more than one percent  (by weight)  of the  pesticides  of
 interest  in this  analysis.   Therefore,  little  additional  information  on the  ultimate  costs to manufacturers
 would result from an investigation   of  the  remaining  thirteen  clusters.   The three clusters  which included
 at least one percent by weight  of the total pesticides of concern are listed  below with a brief  discussion  of
 their categorization:

 Insecticide  fumigants and nematicides
      According  to Encyclopedia  Britannica,   "Fumigation,   which  requires  some technical skills  and certain
 precautions  in application, is mostly  feasible  for non-selective   quick killing of  vermin  in large commercial
 operations.   For the  control  of household pests  it has been, to a considerable  extent, supplanted by  more
 convenient  methods  of extermination  such  as the application  of powders  and residual  sprays".  Fumigants
 are largely  used for killing insect pests of stored products,  for fumigating  nursery  stock,  or for  fumigating
sod,  principally  for  the  control of plant parasitic nematodes.   Given the  application  hi large commercial
operations,  the contribution   of fumigants  and nematicides  to production  cost  is likely to be small. Further,
since the use of these products  has become  somewhat  specialized,  it is probable that few  substitutes  exist.
                                                      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.

      Finally,  two  clusters  remain without  demand  elasticity  estimates:  herbicides  for broad spectrum use
and fungicides  for broad spectrum  use. The cluster  "herbicides  for broad spectrum  use" contains only one
active  ingredient,  2,4-D.   The price elasticity  of demand for 2,4-D  was estimated by Lacewell and Masch
(1972)  and by Carlson  (1977a,b).   Lacewell   and Masch  estimated  the elasticity  as approximately   -0.38.
Carlson  estimated  a short-run  elasticity of -0.19  and  a long-run   elasticity  of -0.59.  Averaging  Carlson's
long-run  estimate and  the  estimate  of Lacewell and  Masch results in an estimate  of elasticity  of demand for
2,4-D  of -0.48.   We use this value  as the  price elasticity   of demand for broad spectrum herbicides.

      The  elasticity  estimate for  broad spectrum  fungicides  is calculated   simply  by  a weighted average  of
the elasticity  estimates  for all  of the other fungicide   clusters.  The weighting  is based on  the quantity  (by
weight)  of active  ingredient  applied  for the end-uses  described  by each  cluster.  The resulting  elasticity
estimate is -0.40.   This  value  is in good agreement  with  the elasticity   of demand for fungicides  estimated
by  U.S.  EPA (1985)  as -0.35.
                                                    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 dear from  sections 2  and 3, the methodology  employed   to estimate  the elasticity  of
demand for the clusters  yields  reasonable best estimates  of elasticities  rather  than certain quantifications.
The estimates  are likely to accurately  depict whether  demand  for a certain cluster of pesticides  is extremely
or only  moderately  elastic  or inelastic;   the  specific   numeric  value  should not be viewed  as definitive.
However,  no estimates  of elasticity  of demand  for  clusters of pesticides  that are more reliable  than those
developed   through this analysis are known.  To address  the uncertainty  implicit  in  the estimates,  a scenario
in which  the manufacturers   bear the total costs  of regulatory  compliance  will also be examined.
                                                       C.61

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                                                  Table  4.0
                      SUMMARY OF ESTIMATES  OF ELASTICITY  OF DEMAND
                        on sugar beets,  beans, peas
                        on tree  fruits  (except oranges),  sugar cane, nuts
                        on tobacco
                        on fruit  and nuts trees  (except oranges)
                        for  seed treatment
                        on vegetables
                        on grain in storage
                         on vegetables
Cluster

Herbicides
Herbicides
Herbicides
Fungicides
Fungicides
Herbicides
Fungicides
Insecticides
Slimicides
Fumigants  and nematicides
Insecticides  on termites
Wood preservatives
Insect repellents  at non-agricultural   sites
Domestic  bug control and food processing  plants
Mosquito  larvacides
Fungicides  on turf
Industrial  preservatives
Insecticide  synergists  and surfactants
Plant regulators,  defoliants,   desiccants
Sahitizers  - dairies, food  processing,  restaurants,  air  treatment
Insecticides  on livestock   and domestic  animals
Industrial  microbiocides,   cutting  oils, oil well  addivites
Preservatives,  disinfectants,   and  slimicides
Fungicides  - ornamentals
Fungicides  on vegetables
Fungicides  - broad spectrum
Herbicides  - broad spectrum
Insecticides  on soybeans,  peanuts,  wheat,  tobacco
Fungicides  - post  harvest
Herbicides  on rights of way,  drainage  ditches
Herbicides  on turf
Herbicides  on soybeans,   cotton,  peanuts, alfalfa
Herbicides  on corn
Insecticides  on corn, alfalfa
Insecticides  on sorghum
Herbicides  on sorghum  rice,  small  grains
Herbicides  on oranges
Fungicides  on oranges
Insecticides  on fruit and  nut trees,  except  oranges and grapes
Insecticides  on oranges
Insecticides  on cotton
Fungicides  on grapes
Insecticides  on grapes
Herbicides  on grapes
                                                                           Elasticity  Estimate
-0.12
-0.20
-0.20
-0.23
-0.27
-0.27
-0.31
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.38
-0.40
-0.48
-0.56
-0.65
-0.66
-0.66
-0.67
-0.69
-0.69
-0.69
-1.00
-1.00
-1.00
-1.00
-1.00
-1.06
-1.38
-1.38
-1.38
Source: Abt Associates  estimates
                                                    C.62

-------
                                            References

Burrows,  T.  (1983).   Pesticide Demand  and Integrated  Pest Management:  A Limited  Dependent
Variable Analysis,  American Journal  of Agricultural   Economics,  November.

Campbell,  H. (1976).   Estimating  the Marginal  Productivity   of Agricultural  Pesticides:  The Case of
Tree-Fruit  Farms in the Okanagan Valley. Canadian  Journal of Agricultural   Economics 24(2),  1976.

Carlson,  G.  (1977). The Long  Run Productivity  of  Insecticides,   American Journal  of Agricultural
Economics,  59,  pp. 543-548,  August.

Carlson,  G. (1977a). Economic  Incentives for Pesticide  Pollution Control in The Practical Application
of Economic Incentives   to the Control of Pollution: The Case of British  Columbia,  ed. J. Stephenson.
Vancouver:   University  of British Columbia  Press.

Hall,  D.C., and LJ. Moffitt.   (1983).   Stochastically   Efficient  Economic Thresholds  for  Discrete
Choices.  USDA-ERS  unpublished  manuscript.   Washington D.C.

Headley,  J.C. (1968).   Estimating  the Productivity  of Agricultural   Pesticides, American Journal  of
AGricultural  Economics, 50:13-23,   February.

Huh,  Shing  Haeng  (1978).  The  Preventive   and Incidental  Demand  for  Pesticides:   An Economic
Analysis  of the  Demand  for  Herbicides  and  Insecticides  Used  by  Selected  Corn Producers  in
Minnesota.   Thesis submitted  to the Graduate School of the University  of Minnesota.  June.

Lacewell,  R. and W. Masch, (1972).  Economic  Incentives  to Reduce the Quantity  of Chemicals  Used
in Commercian  Agriculture.  Southern  Journal  of Agricultural   Economics.  July.

Lichtenberg,   E. and D. Zilberman  (1986).  The  Econometrics  of Damage  Control:   Why Specification
Matters.  American  Journal of Agricultural  Economics.   May.

Miranowski,  J.  (1980).  Estimating  the Relationship between Pest Management  and  Energy Prices,  and
the Implications   for Environmental  Damage. American Journal of Agricultural  Economics. December.

Pimentel,  D., et  al. (1991).  Environmental   and Economic  Impacts of Reducing  U.S. Agricultural
Pesticide Use, in ed. Pimentel,  D., Pest Management  in Agriculture.   CRC press.

Pingali,  P. and  G. Carlson  (1985).   Human Capital, Adjustments  in Subjective Probabilities,  and the
Demand  for Pest Controls.  American  Journal of Agricultural   Economics.  November.

U.S.DA. (1985).  U.S. Demand for  Food:  A Complete System of Price and Income Effects.   By Kuo
S. Huang,  National  Economics   Division,  Economic  Research Service.  Technical   Bulletin  No. 1714.

U.S.D.A. (1988).  1985  Potato Cost and Returns:  Fall Production Areas.  Potato facts special edition.
Economic Research  Service.  September.

U.S.DA. (1989).  Retail to Farm Linkage  for a Complete  Demand  System of Food Commodities.   By
Michael  K.  Wohlgenant  and Richard  C. Haidacher.   Commodity  Economics  Division,   Economic
Research Service.  Technical  Bulletin No. 1775.

U.S.DA. (1989a).  Economic Indicators  of the Farm  Sector: Costs of Production,  1987.  ERS, USDA,
ECIFS7-3.   February.

U.S.DA. (1989b). Tobacco:  Situation and Outlook Report. Economic  Research Service. September.
                                                   C.63

-------
U.S.D.A.  (1989c).   Agricultural   Statistics 1989.  Washington.

U.S.  EPA (1974).   Farmers'  Pesticide  Use Decisions  and Attitudes  on Alternate  Crop Protection
Methods.   Washington.

U.S. EPA (1982).  Regulatory  Impact  Analysis  Data  Requirements  for  Registering  Pesticides  under
the Federal Insecticides,   Fungicides  and Rodenticide   Act.  Office  of Pesticide  Programs.   August.

U.S. EPA (1985). Economic  Impact  Analysis  of Effluent   Limitations  Guidelines  and Standards for
the Pesticide  Chemicals  Industry.  September.

U.S. EPA (1988).  Pesticide  Industry  Sales  and Usage:  1988  Market Estimates.  Office  of  Pesticide
and Toxic Substances.   February.
                                                C.64

-------

-------
        APPENDIX D

SENSITIVITY ANALYSIS OF COST
   PASS-THROUGH ABILITY

-------

-------
             Appendix D: SENSITIVITY ANALYSIS OF COST PASS-THROUGH ABILITY

        This appendix describes a sensitivity analysis of the percentage of compliance costs that a manufacturer is
able to pass through to consumers.  The model, as described in Chapter 4, assumes that producers can pass on a
portion of compliance costs to customers in the form of price increases,  to the extent allowed by producer price
competition and customer demand behavior. To test the sensitivity of the closure analysis results to this assumption,
the worst-case assumption is made that facilities would bear the full costs of compliance (i.e.  they could not pass
on any of the compliance costs to customers as price increases).  This corresponds to an assumption that all clusters
have completely elastic demand elasticities, or that the percentage of total production subject to compliance costs
is close to zero.

        The results of this sensitivity analysis  are presented below by  regulatory  option and subcategory.  In
comparison  to the model used in the  EIA, there are no changes under the proposed option (Treated Discharge
Option).   Minor changes in results occur under the Zero Discharge Option:  one additional product line closure is
predicted for direct dischargers, while one additional facility closure and two additional product line closures would
be predicted for indirect dischargers (compare Table D.I with Table 4.4).

        Treated Discharge Option
                Impacts of BAT regulations on direct dischargers
                Organic Pesticide Manufacturing - (Subcategory A)
        Under the no cost pass-through assumption, no facilities are projected to close due to compliance with BAT.
Two facilities are expected to close a product line as a result of the regulation (see Table D. 1).

                Metallo-Organic Pesticide Manufacturing - (Subcategory B)
        Direct dischargers of Subcategory B chemicals  are  limited to  zero  discharge of process wastewater
pollutants  under BPT. No additional options were considered and no new limitations are proposed for the metallo-
organic pesticide chemicals manufacturing  subcategory.   There are therefore no associated  costs or economic
impacts, and sensitivity analysis need not be examined.

        Impacts of PSES regulations on indirect dischargers
                Organic Pesticide Manufacturing - (Subcategory A)
        No facilities are expected to close under the no cost pass-through assumption due to compliance with PSES.
One facility is projected to close a product line as a result of the regulation.
                                                     D.I

-------
                Metallo-Organic Pesticide Manufacturing - (Subcategory B)
        Because  no .new limitations are  proposed for the metallo-organic pesticide chemicals manufacturing
subcategory, no facility or product closures would be projected under the no cost pass-through assumption due to
compliance with PSES.

        Zero Discharge Option
                Impacts of BAT regulations on direct dischargers
                 Organic Pesticide Manufacturing - (Subcategory A)
        Sixteen facilities would be projected to close due to compliance with BAT limitations under the no cost
pass-through assumption.  Four additional facilities would be expected to close a product line as a result of the
regulation.

                Metallo-Organic Pesticide Manufacturing - (Subcategory B)
        As discussed under the Treated Discharge Option, Subcategory B direct dischargers are limited to zero
discharge of process wastewater pollutants under BPT.  No additional options  were considered and no new
limitations are proposed for the metallo-organic pesticide chemicals manufacturing subcategory.  Therefore, there
are no associated costs or economic impacts,  and sensitivity analysis need not be examined.

        Impacts of PSES  regulations on indirect dischargers
                 Organic Pesticide Manufacturing -  (Subcategory A)
        Twelve facilities would be expected to close due to compliance with PSES under the assumption of no cost
pass-through.  Five additional facilities would be projected to close a product  line as a result of this regulation.
                                                                    »
                 Metallo-Organic Pesticide Manufacturing - (Subcategory B)
        Under the no cost pass-through assumption, one facility would be projected to close due to compliance with
the PSES regulation. An additional  facility would be expected to close a product line.
                                                     D.2

-------
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-------
          APPENDIX E

DETAILS OF ANALYSIS OF IMPACTS
     ON SMALL BUSINESSES

-------

-------
              Appendix E: DETAILS OF ANALYSIS OF IMPACTS ON SMALL BUSINESSES
  The figures presented in this appendix illustrate the relationship between facility impacts and facility size that
  were examined in the second stage of the small business analysis.  This relationship was first examined by
  plotting the two financial impacts considered in the analysis:

                  •       facility closures
                  •       product line closures

 against five measures of facility size:

                  •       firm revenues
                  •       total facility revenues
                  •       total facility employment
                  •       pesticide-related facility revenues
                  •       pesticide-related facility employment

         The plotting exercise outlined above resulted in a total of 10 plots: two impacts by five measures of
 size.  The two impacts vs. the five measures of size were plotted for three populations: facilities that were
 classified as direct dischargers, facilities that were classified as indirect dischargers, and all facilities.  Plotting
 the  10 relationships for each of the three populations cited above resulted in a total of 30 plots.

         The 30 plots that are included hi  this appendix are arranged as follows.  Figures E.I - E.10 exhibit the
 relationship between facility impacts and facility size for all dischargers. The relationship between impacts and
 size for  facilities classified as direct and indirect dischargers are displayed in Figures E.ll - E.20, and Figures
 E.21 - E.30 respectively.

         Following the plotting exercise, 10 regressions were performed to examine the probability that a
 facility/product  line will remain open as a  function of entity size. Similar to the plotting exercise, each
 regression used  one of the five measures of entity size as the independent variable and one of the two impacts as
 the dependent variable.  The 10 regressions were performed for  three populations: facilities that were classified
 as direct dischargers, facilities that were classified as indirect dischargers, and all facilities, leading to a total of
30 regressions.

        Appendix E contains the results of the regression analyses that were performed  for all dischargers.
                                              E.I

-------
Results that were obtained when examining direct and indirect dischargers separately are presented and
discussed hi Chapter 8.
                                                  E.2

-------
Table E.I
Logistic Regression Analysis
Zero Discharge Option: All Dischargers
Model
#
1
2 -
3
4
5
6
7
8
9
10
#of
Observations
72
72
70
73
73
32
32
31
33
33
Impact
(y.)
Facility Closure
Facility Closure
Facility Closure
Facility Closure
Facility Closure
Product Line Closure
Product Line Closure
Product Line Closure
Product line Closure
Product Line Closure
Measure of Entity Size
(x.)
Pesticide Revenues
Facility Revenues
Firm Revenues
Pesticide Employment
Facility Employment
Pesticide Revenues
Facility Revenues
Firm Revenues
Pesticide Employment
Facility Employment
Coefficient
(ft)
-7.8E-*
-4.8E*
-6. IE'11
-4.4E3
-1.-3F3
-1.3E-8
-6.QE'9
-LIE'10
-9.9E-3
-1.2E'3
Note: At the 95 percent confidence level p < .05 indicates that the coefficient is significant, while p <
indicates significance at the 90 percent confidence level. Coefficients that are in shaded sections are
significant to the 90 percent confidence interval.
P
value
.0064
.0184
.1077
.0344
.0658
.0789
.0995
.1755
.1121
.3090
.10
E.3

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