United States
         Environmental Protection
         Agency
Office of Science
and Technology
Washington, DC 20460
EPA821-R-93-012
September 1993
         Water
4>EPA  Economic Impact Analysis
          of Final Effluent
          Limitations  Guidelines and
          Standards for the Pesticide
          Manufacturing  Industry
                      QUANTITY

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

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                             ACKNOWLEDGEMENTS
       The most credit must be given to Dr. Thomas E. Fielding for his knowledge, experience,
cooperation, and leadership as project officer, and to the whole pesticide team for their professional
manner, conscientious effort, and contributions.
       Credit must also be given to Abt Associates for their assistance and support in performing
the underlying economic analysis supporting the conclusions detailed in this report. Their study was
performed under Contracts 68-CO-0080, 68-03-3548, and 68-C3-0302. Particular thanks are given
to Randi Currier and Rob Sartain.

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                              TABLE OF CONTENTS
EXECUTIVE SUMMARY	  E.I
      Introduction	E.I
      Methodology	E.2
      Baseline Results	,	E.4
      Effects of Regulatory Compliance on Facilities	E.4
             Impacts on Direct Dischargers	E.4
             Impacts of PSES Regulations on Indirect Dischargers  	  E.5
      Effects of Regulatory Compliance on New Sources of Pesticide Manufacture  . .  E.6
      Regulatory Flexibility Analysis   	E.6

Chapter 1: INTRODUCTION AND OVERVIEW	1.1
      1.0    Background and Definitions	1.1
      1.1    Structure of the Report   	1.1

Chapter 2: DATA SOURCES	2.1

Chapter 3: PESTICIDE MANUFACTURERS PROFILE	   .... 3.1
      3.0    Introduction	3.1
      3.1    Categorization of Data	3.2
      3.2    Sources of Demand for Chemical Pesticides	3.4
             3.2.A Agriculture Market	3.7
             3.2.B  Industrial/Institutional/Commercial Market (I/I/C)   	3.7
             3.2.C  Home/Lawn/Garden Market  	3.10
      3.3    Facility Characteristics	3.10
             3.3.A Physical Characteristics	3.10
             3.3.B  Industry Output	3.12
             3.3.C  Production Characteristics	3.17
             3.3.D Production Costs	3.18
             3.3.E  Employment Characteristics   	3.22
             3.3.F  Revenues and Profit	3.26
             3.3.G  Capital Expenditures	 3.31
             3.3.H Production Capacity Utilization	3.34
      3.4    Firm Characteristics	3.34
      3.5    Industry Market Structure	3.37
             3.5.A  Barriers to Entry	3.37
             3.5.B  Vertical Integration	3.44
             3.5.C  Concentration	3.45
             3.5.D  Demand Elasticity and Product Substitution	3.48
      3.6    International Trade  	3.50
             3.6.A  U.S. Pesticide Imports and Exports	3.50
             3.6.B  U.S. Pesticide Industry in the World Market	3.52

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      3.7    Analysis of Actual Facility Closures  	3.55
      3.8    Summary  	3-59
      Chapter 3 References  	3.61

Chapter 4: FACILITY IMPACT ANALYSIS  	4.1
      4.0    Introduction	4.1
      4.1    Economic Model	4.2
             4.1. A Generalized Model of the Pesticide Manufacturing Industry	4.2
             4.1.B Applied Model of the Pesticides Manufacturing Industry	4.4
      4.2    Facility Closure Analysis	4.16
             4.2.A Baseline Facility Closure Analysis	4.18
             4.2.B Post-Compliance Facility Closure Analysis	4.19
      4.3    Product Line Closure Analysis  	4.25
      4.4    Other Significant Financial Impacts  	4.26
      4.5    Facility Impacts  	4-29
             4.5.A Baseline	4.29
             4.5.B Effects of Compliance with the Final Rule	4.29
      Chapter 4 References   	4.33

Chapter 5: COMMUNITY IMPACT ANALYSIS	5.1
      5.0    Introduction	5.1
      5.1    Methodology	5.2
             5.1.A Primary Impacts on Employment	5.2
             5.1.B Measuring Impact Significance	5.4
             5.1.C Secondary Impacts on Employment	5.5
      5.2    Results	5.6
             5.2.A Impact  of Best  Available  Control  Technology Economically
                   Achievable (BAT) Regulations on Direct Dischargers	5.6
             5.2.B Impact  of Pretreatment Standards for Existing Sources (PSES)
                   Regulations on Indirect Dischargers	5.6
       Chapter 5 References  	5.8

Chapter 6:  FOREIGN TRADE ANALYSIS  	6.1
       6.0   Introduction	6.1
       6.1   Methodology	6.2
             6.1.A Exports	6.2
             6.1.B  Imports	6.4
       6.2   Results	6-4
             6.2.A Impact of Best  Available Technology Economically  Achievable
                    (BAT) Regulations on Direct Dischargers  ;	6.4
             6.2.B  Impacts of Pretreatment Standard for Existing  Sources (PSES)
                    Regulations on Indirect Dischargers	6.5
       Chapter 6 References  	6.7

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 Chapter 7: FIRM IMPACT ANALYSIS  ........'.	 7.1
       7.0   Introduction	   71
       7.1   Analytic Approach .  „	72
             7.1.A Firm Financial Performance  	7.3
             7.1.B Ability To Manage Financial Commitments	7.4
       7.2   Analytic Procedure   	  75
       7.3   Results	   7 13
             7.3.A Baseline Analysis	7 13
             7.3.B Post-Compliance Analysis	7.14
       Chapter 7 References	 7 15

 Chapter 8: SMALL BUSINESS IMPACTS	81
       8.0   Introduction	   g 1
       8.1   Methodology  . . .	       g 1
       8.2   Results	 .S.I
             8.2.A Impact  of Best Available  Control  Technology Economically
                   Achievable (BAT) Regulations on Direct Dischargers	8.1
             8.2.B Impact of Pretreatment Standards for Existing Sources (PSES)
                   Regulations on Indirect Dischargers	8.2
       Chapter 8 Reference	      83

Chapter 9: IMPACTS ON NEW SOURCES  	9.1
       9.0   Introduction	         91
       9.1    New  Source Performance Standards	9.1
       9.2    Pretreatment Standards for New Sources	9.2

Appendix  A:  1986  Pesticide Manufacturer Facility Census  	A.I
Appendix  B: Mapping of Pesticide Active Ingredients Into Clusters  	  B.I
Appendix  C: Methodology for Estimating the Price Elasticity of Demand
       for Pesticide Clusters	c 1
Appendix  D: Sensitivity Analysis: of Cost  Pass-Through Ability  .  . . . . .  . . .  . .  . .  D.I
Appendix  E: Compliance Costs as  a Percentage of Facility Revenue	  E.I
Appendix  F: Hypothetical Facility	  F.I

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                                         EXECUTIVE SUMMARY

 Introduction
         The Federal Water Pollution Control Act Amendments of 1972 established a comprehensive program to
 "restore and maintain the chemical, physical, and biological integrity of the Nation's waters" (section 101(a)). To
 implement the Act, the U.S.  Environmental Protection Agency (EPA) is to issue effluent limitations guidelines,
 pretreatment standards, and new source performance standards for industrial dischargers.  This Economic Impact
 Analysis (EIA) documents the assessment of the economic impacts of the final guidelines and standards applying
 specifically to  the pesticide manufacturing industry.  The EIA builds on the analysis of impacts of the proposed
 effluent guidelines for the industry, and incorporates changes resulting from public comments and EPA internal
 review.
         The EIA estimates the probable economic effect of compliance costs on facilities in terms of facility
 closures, product line closures, profitability impacts, ability to incur debt, and compliance costs as a percentage of
 facility revenues. Projected firm-level impacts, local community impacts, international trade effects, and the effect
 on new pesticide manufacturing facilities are also presented. A Regulatory Flexibility Analysis detailing the small
 business impacts is also included in the EIA for this industry.

         A total of 73 pesticide manufacturing facilities, owned and operated by 49 firms that manufacture one or
 more pesticide active ingredients (PAIs), are potentially subject to regulation1,2.  At proposal, the EPA analyzed
 the impacts of two possible regulatory options: a Treated Discharge Option (the proposed option) and a Zero
 Discharge Option based on on-site and off-site injection or incineration.  The final regulation corresponds to the
 Treated Discharge Option. Because the costs of the Zero Discharge Option have not changed since proposal, the
 impacts of this option are not reassessed in this document.

        The economic impacts under the final option were calculated separately for facilities discharging wastewater
 directly to surface water (direct dischargers)  and facilities discharging wastewater to a publicly owned treatment
works (POTW) (indirect dischargers).  Impacts on direct dischargers were calculated for compliance with a Best
Available Technology Economically Achievable (BAT) regulation; impacts on indirect dischargers were calculated
     Based on data from the Section 308 Census, a total of 90 pesticide manufacturing facilities owned and operated
by 59 firms that manufacture one or more  PAIs were potentially  subject to regulation.  However,  EPA has
information indicating that 15 of these facilities have closed their in-scope PAI manufacturing operations since 1986.
Also, two facilities producing only Subcategory  B in-scope PAIs are not counted as potentially subject to the
regulation.
    2Although 73 facilities are potentially subject to the regulation, the EIA analyzed only 72 facilities for economic
impacts,  the facility excluded from the economic analysis is an R&D facility with no revenues expected from the
manufacture of in-scope PAIs and no compliance costs.
                                                   E.I

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for compliance with Pretreatment Standards for Existing Sources (PSES) regulation. Each discharge category was
initially further analyzed by two subcategories: Organic Pesticide Chemicals Manufacturing (Subcategory A) and
Metallo-Organic Pesticide Chemicals Manufacturing (Subcategory B).  EPA is not promulgating new limitations on
direct or indirect dischargers of Subcategory B PAIs.  (Direct discharge of Subcategory B chemicals is already
limited to zero under BPT.)

        Total BAT investment costs (capital and land) for the final regulation are projected to be $24.9 million,
with annualized costs of $18.2 million including depreciation and interest.  Total investment costs for PSES for the
final regulation are projected to be $8.7 million, with annualized costs of $5.1 million including depreciation and
interest. The costs, presented in 1986 dollars, are based on the assumption that, whenever possible, facilities will
improve existing treatment rather than build new treatment.
                   Cost of Implementing BAT and PSES Regulations for Subcategory A*
                                        (in millions of 1986 dollars)
                                                                       BAT      PSES
                   Capital Costs
                   Total Annualized Costs
$24.9
$18.2
$8.7
$5.1
                   *The reported costs are the full costs of compliance, some of which will
                   be borne by the government in the form of decreased tax receipts.  The
                   analysis therefore overstates the burden of the regulations on industry.
 Methodology
         The costs and impacts of implementing the regulatory options are analyzed on an PAI-specific basis for
 each facility.  Building on the PAI-specific data,  the HA uses three primary impact measures:  facility closures,
 product line closures, and other significant impacts short of closure.  The analysis of significant impacts short of
 closure measures the effect of compliance costs on the ability of facilities to incur debt and on facilities' return on
 assets.  The analysis evaluates these impacts in a hierarchical manner that corresponds to the severity of the
 projected impact:  if a facility closes, product line closures and other significant impacts are not evaluated; if a
 facility sustains a product line closure, other significant impacts are not evaluated. The impacts are estimated for
 pesticide  manufacturing facilities incurring costs  using a combination of data from the Pesticide Manufacturing
 Facility Census for 1986 (hereinafter referred to as the Census) and secondary sources, such as Standard and Poor's
 Compustat  financial data, plus facility-specific compliance cost estimates developed by the EPA.   First, pre-
 compliance (baseline)  estimates of each of the three primary impact measures are calculated for each facility, to
 gauge the economic vitality of each facility prior to the proposed regulation.  If a facility fails one of the measures
 (e.g., a facility closes) in the baseline scenario, the model does not recount this same level of failure in the post-
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 compliance scenario.  The model does allow, however, for progressively severe impacts due to compliance (e.g.,
 a baseline product line closure may become a post-compliance facility closure).  As an alternate check on the level
 of impacts expected from the regulation, the analysis also compares facility annualized compliance costs to facility
 revenue. : Typically,  compliance costs that are greater than five  percent of facility revenue are judged to be
 indicative of a significant impact.

         The evaluation of  facility-level closure considers whether the portion  of the facility involved in
 manufacturing, and also formulating/packaging or performing contract work, for both in-scope pesticides (i.e., those
 260 PAIs considered for regulation) and out-of-scope pesticides (all others) is  expected to continue operations.3
 A facility closure is projected to result from the regulation if the baseline after-tax cash flow is positive and the post-
 compliance after-tax cash flow is negative (i.e., if a facility begins to lose cash  due to the regulation).

         A pesticide cluster is composed of PAIs that are close substitutes for each other for  a specific end-use.
 For example, insecticides used on corn is one cluster.  Fifty-six clusters were identified as  part of the impact
 analysis (see appendix B), forty-four of which contain in-scope PAIs produced in 19864. For  the purpose of this
 analysis, a product line is defined as a cluster of PAIs.  A baseline product line closure is projected if the unit cost
 (average variable cost plus average fixed cost per pound of PAI) of the product line exceeds the unit price (average
 price per pound of PAI). A post-compliance product line closure is projected if the product line remained open in
 the baseline, but showed unit costs exceeding unit price due to the addition of compliance costs.

         Short of closure, other  significant impacts of compliance with the effluent limitations are calculated based
 on a comparison, between each facility and the industry averages, of two key financial ratios:  the "interest coverage
 ratio"5  (earnings before interest and taxes divided by interest expense) and "return on total assets"6  (earnings
before interest and taxes divided by assets;). If either ratio for a facility falls in the lowest quartile for the industry
in the post-compliance but not the baseline scenario, it is said to sustain a significant  impact short of closure.
      The method of projecting facility closure has been changed since the proposed rule.  At proposal, the analysis
used  a net present value approach (which compares discounted cash flow to salvage value) to project whether
pesticide operations would remain open after regulatory costs are incurred.  Due to indications that the salvage
values reported by facilities In the Section 308 Census were not reliable, the final rule projects facility closure based
on an evaluation of baseline and post-compliance facility after-tax cash flows.
      PAI #67 (biphenyl), in cluster F6, was considered in-scope at proposal but is not considered in-scope for the
final rule.  The cluster count therefore decreased from forty-five to forty-four.  See the Technical Development
Document for additional information.
    5Also called "times interest earned."
     Also called "return on investment."
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Baseline Results
        The baseline economic analysis evaluated each facility's financial operating condition prior to incurring
compliance costs for this regulation. This analysis included the estimated costs associated with two significant EPA
regulations not in place in 1986 (the base year) and whose costs were therefore not reflected in the annual operating
expenses provided by the firm in the Census. Baseline cost additions include (1) RCRA land disposal restrictions
and (2) compliance with the effluent guidelines for the Organic Chemicals, Plastics, and Synthetic Fibers (OCPSF)
industry.  Of the 73 facilities potentially subject to the proposed effluent guidelines, 14 are projected to close in the
baseline analysis after incorporating the costs of RCRA and  OCPSF regulations.  Of the 14 facilities counted as
baseline facility closures, two have closed product lines since 1986 and three have undergone restructuring.  An
additional 12 facilities are projected to close pesticide product lines in the baseline.  Of these, four have closed
product lines since 1986 and another three have undergone restructuring.

Effects of Regulatory  Compliance on Facilities
        The economic impacts associated with the final rule are discussed below, by both discharge type and
subcategory.

         Impacts on Direct Dischargers
                 Organic Pesticides Chemicals Manufacturing (Subcategory A)
         For manufacturers included hi this subcategory, the incremental capital and annualized total costs (including
 capital, operating and maintenance, and monitoring costs) of complying with BAT limitations are expected to be
 $24.9 million and $18.2 million, respectively.  The estimate of capital costs  has increased by 67 percent since
 proposal while the estimate of total annualized cost has increased by 24 percent.  (See the Technical Development
 Document for an explanation of changes in compliance cost estimates.)  The change in compliance cost is the
 aggregate effect of decreases in annualized compliance costs at four facilities and increases in annualized compliance
 costs at four facilities.  Most of the increase in total costs for direct dischargers is due to a substantial cost increase
 at one facility.  The estimated investment costs at this  facility have increased from $1.6 million to $16.0 million,
 with an increased in annualized costs from $2.0 million to $7.3 million. This change in estimated compliance costs
 resulted  from public comments by the facility. The Agency maintains that the actual compliance costs for this
 facility would be lower than the estimates  used in the final analysis.  However, analysis using these higher cost
 estimates ensures that EPA does not underestimate the burden of compliance at this facility.

          None  of the 28 direct discharge facilities covered under this subcategory are projected to  close due to
 compliance with BAT7.  One of the facilities covered under this subcategory  is projected to close a product line
      7Twenty-eight direct discharge facilities are expected to bear costs.  This count includes two facilities that are
  known to have actually closed since 1986, but for which compliance costs are included to reflect the likely transfer
  of the PAI production.
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 as a result of the regulation.  In addition, one other zero discharge facility that incurs only monitoring costs is
 projected to close a product line. No facilities are expected to experience other significant financial impacts short
 of facility or product line closure.

         Given that the level of projected economic impacts has not changed since the proposal, the secondary
         _ i
 community and foreign trade impacts potentially associated with the regulation have not been re-estimated for the
 direct dischargers. As presented at proposal, job losses totalling 31  full-time equivalents (FTE) are expected to
 occur as a result of  the product line  closures and the decrease in demand  resulting from higher prices.  This
 employment loss represents less than one percent of employment in the pesticide-related portions of all pesticide
 manufacturing facilities. One firm, equal, to about two percent of the 49 firms owning facilities potentially subject
 to regulation,  is expected to experience significant financial impacts as a result of compliance with BAT. Foreign
 trade in PAIs  is expected to fall by $5.5 million due to compliance with BAT.  In 1986,  the United States was a
 net exporter of PAIs, with a trade balance of $897 million;  the decrease in PAI trade is projected to be less than
 one percent. When compared with U.S. net imports of $152 billion hi merchandise for 1986, compliance with the
 BAT regulation is seen to cause an increase in net imports of less than one one-thousandth of one percent.

         As an additional  check on community impacts, foreign trade impacts,  and firm-level impacts,  EPA
 examined the  extent of the production decrease at the single facility bearing most of the increase hi compliance
 costs.  The revenue from in-scope pesticides produced at this facility is expected to fall by only about one percent,
 so significant community or foreign trade Impacts are not expected. Further, analysis indicates that the firm owning
 the facility is not expected  to be significantly impacted by the rule.

         Finally, the EPA compared annualized compliance costs with facility revenue.  Compliance costs greater
 than five percent of facility revenue are typically judged to be indicative of a significant economic impact. For the
 28 direct discharging facilities with costs,  the mean compliance costs as a percentage of revenue was 0.4 percent,
 the median was less than one-tenth of one percent, and the highest value was  4.6 percent.

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

         Impacts of PSES  Regulations on Indirect: Dischargers
                Subcategory A
        For manufacturers included hi thas Subcategory, the  total capital and annualized costs of compliance with
PSES are projected to be $8.7 million and $5.1 million, respectively. None of the 28 indirect discharging facilities
are projected  to close entirely, close a product line,  or experience  other significant financial impacts due  to
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compliance with PSES8. (At proposal, one facility was projected to close a product line.  This facility has actually
closed and is counted as a baseline closure in the final rule.)  Due to this decrease in total costs  and impacts,
secondary community and  foreign trade impacts associated with the regulation have not been re-estimated for
indirect dischargers.  Instead, the estimates of these secondary impacts presented at proposal serve as high-end
estimates of the impacts.  As presented at proposal, job losses totalling 97 FTEs were expected to occur as a result
of the product line closure and the decrease in demand resulting  from higher prices.  This employment loss
represents less than one percent of employment in the pesticide-related portions of all pesticide manufacturing
facilities.  Two firms are expected to sustain significant financial impacts as a result of compliance with PSES.
Foreign trade in pesticide active ingredients is expected to fall by $16.1 million due to compliance with PSES.  This
decrease in trade represented about two percent of 1986 net exports of PAIs and about one-hundredth of one percent
of the 1986 net national trade imports of all goods.  Finally, EPA compared the annualized compliance costs with
facility revenue.  For the 23 indirect discharging facilities with costs, the mean compliance costs as a percentage
of revenue was 0.7 percent, the median was 0.2 percent, and the highest value was 5.7 percent.  The ratio of
compliance costs to facility revenue was greater than five percent for only one facility.

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

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

 Regulatory Flexibility Analysis
         The Regulatory Flexibility Act (5 U.S.C. 601 et seq.,  Pub. L.  96-354) calls for the EPA to prepare a
 Regulatory Flexibility Analysis (RFA) for proposed regulations having a significant impact on a substantial number
     8 Twenty-three of the 28 indirect discharging facilities covered under this subcategory are expected to bear
  compliance costs.
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 of small entities.   The purpose of the Act is to ensure that regulatory agencies  fit regulatory and information
 requirements to the scale of the businesses, organizations, and governmental jurisdictions subject to regulation.

        The effects of the BAT and PSES regulations on small businesses were separately considered. EPA defined
 a small entity based on the Small  Business Administration (SBA) standards.  The  SBA has established standards
 based on employment at firms (including all affiliates and divisions) for each SIC group.  For SIC 2869 (which
 includes pesticide manufacturers)  the SBA defines a small business as  one employing less than  1,000 people.
 Employment data for firms that own pesticide manufacturing facilities was  obtained from Dun and Bradstreet's
 Million Dollar Directory.  Consistent with the other components of the EIA, significant impacts were defined as
 facility closures, product line closures, or other significant financial impacts as previously discussed. Using these
 measures, the results of the small business analysis are discussed below for the two discharge methods,

        a.  BAT.  As previously  discussed,  it is projected that one direct discharging and one zero discharging
 facility will close product lines due to BAT regulations.  No facility closures  or other significant financial impacts
are expected to occur.  Both firms that are expected to  experience facility product line closures have fewer than
 1,000 employees.   Because the number of small facilities significantly affected is not substantial, no regulatory
flexibility analysis is required;  the EPA Administrator has certified to this effect.

        b. PSES.  No facilities are expected to close, close a product line, or experience another significant impacts
short of closure.  Because no significant impacts are expected for any facilities, no regulatory flexibility analysis
is required; the EPA Administrator has  certified to this effect.
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                            Chapter 1: INTRODUCTION AND OVERVIEW


 1.0     Background and Definitions


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

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

 implement these amendments,  the U.S.  Environmental Protection  Agency  (EPA) issues effluent limitations
 guidelines, pretreatment standards, and new source performance standards for categories of industrial dischargers.
 Specifically, the regulations that the EPA establishes are:


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

         •       Best Available Technology Economically  Achievable (BAT).  These rules  apply to existing
                industrial direct dischargers and the control of priority and non-conventional pollutant discharges.
                New Source  Performance  Standards (NSPS).
                dischargers and cover all pollutant categories.
These  rules apply  to  new  industrial direct
        •       Pretreatment Standards for Existing Sources (PSES).  These  rules apply to existing indirect
                dischargers  (whose discharges enter Publicly Owned Treatment Works, or POTWs).  They
                generally cover the control of toxic and non-conventional pollutant discharges that pass through
                the POTW or interfere with its operation.  They are analogous to the BAT controls.

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


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


1.1     Structure of the Report


        At proposal,  two  regulatory  options were evaluated:   one that would require treatment of process
wastewater pollutants (Treated Discharge Option), and  another that  would require no discharge of process

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

     The Zero Discharge Option would have limited discharges from the facility site to POTWs or to surface
water only; discharges to other media could have remained constant or increased as a result of changes in
discharge to surface water.  For example, pesticide manufacturing facilities could,  theoretically, achieve
compliance with a zero discharge effluent guideline by transferring the waste streams previously discharged to

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Treated Discharge Option.  Because the costs of the Zero Discharge Option have not changed since proposal, the
impacts of this option are not reassessed in this document.  The economic impacts are calculated separately for
direct and indirect dischargers. Direct dischargers would be required to comply with a BAT regulation; indirect
dischargers would be required to comply with a PSES regulation.

        This EIA describes both the methodology employed to assess impacts of the final rule and the results of
the analysis.  The overall structure of the analysis is summarized in Figure 1.1.  There are two main inputs to the
analysis: (1) data on industry baseline financial and operating conditions, and (2) projected costs of complying with
the regulation.   The industry baseline financial and  operating data are based  principally  on  the Pesticide
Manufacturing Facility Census for 1986 conducted under Section 308 of the Clean Water Act.3 The Census, which
requested facility-level data, was divided into two parts. Part A contained technical data, and Part B contained
economic and financial data.  The projected costs of compliance with the final regulation (the second major input
to the analysis) were developed by the EPA.  Details on the compliance cost estimates can be found in the Technical
Development Document for the final rule.4  Additional information on all data sources is presented  in Chapter 2.

        To fully evaluate the expected impacts of the final rule, six measures  of impact are examined in the EIA:

        •       Impacts on facilities that manufacture pesticide active ingredients (PAIs) covered by the regulation;
        •       Employment losses and associated community effects;
        •       Impacts on U.S. balance of trade;
        •       Impacts on firms that own facilities affected by the regulation;
        •       Impacts on pesticide facilities defined as small businesses; and
        •        Effects on the construction of new facilities and expansion of existing facilities.

        The EIA methodology is based upon a facility-level  impact analysis.  This analysis drives the other
components of the EIA (See Figure 1.1.)  The facility-level economic model estimates post-compliance revenues,
costs,  and profits.   The post-compliance financial data are then used to analyze three potential effects of the
surface water to landfills, incinerators, or deep well injection sites.
    ^Baseline conditions also include certain costs deemed necessary to comply with particular regulations
imposed under the Resource Conservation and Recovery Act (RCRA), and the effluent guidelines for the
Organic Chemicals, Plastics, and Synthetic Fibers (OCPSF) Industry.  Portions of these regulations took effect
after the base year of the Census, and imposed costs on certain pesticide manufacturers.  These costs are also
included in the analysis.
    4Full title: Development Document for Best Available Technology, Pretreatment Technology, and New
Source Performance Technology for the Pesticide Chemical Industry:  Final
                                                    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
                Facility
                Closure
               Analysis
       Data Inputs
 Other
Financial
Impacts
                         Comparison
                           of
                         Compliance
                          Cost to
                          Facility
                          Revenue
                                     Facility
                                     Impacts
                                     Firm
                                    Impacts
   [23  Analytical Outputs
                        Employment
                         Impacts
                        Production
                          Losses
            Community
             Impacts
           Foreign Trade
             Impacts
                                                               Small Business
                                                                 Impacts
                                          New
                                         Source
                                         Impacts
I  |  Key Analytical Components
                                    1.3

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increased costs on facilities: facility closure, product line closure, and other financial impacts short of closure.  The
analysis of facility closure is based on comparing the post-compliance facility cash flow to the baseline cash flow.
The product line closure analysis compares prices and costs of products to predict whether product lines remain in
production post-compliance.  The analysis of other significant financial impacts considers changes  in financial
indicators of facilities'  operating  conditions between the baseline  (i.e., pre-compliance) and post-compliance
scenarios.  As an additional check  on the expected impacts  of compliance,  annualized compliance costs are
compared to facility revenues.   Typically, compliance costs in  excess of five percent of facility revenue are
considered to be indicative of a significant economic impact.

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

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

        An additional potential impact of the final regulation, evaluated using the results of the facility analysis,
is the impact on small businesses.  The evaluation of impacts on small businesses has two steps.   First, it is
determined whether the regulation is expected to significantly impact a substantial number of small businesses.
Impacts are defined as either a facility closure, a product line closure, or another significant financial facility impact
short of closure.  Second, if a substantial number of small businesses are projected to sustain significant impacts,
alternative regulatory methods that mitigate or eliminate the  economic impacts on small businesses would be
examined.

        Finally, impacts of the PSNS and NSPS regulations on new sources of pesticide production are evaluated,
based on both the projected facility and firm impacts.
                                                   1.4

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

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

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

        The report also includes six appendices.  The  first appendix contains the Section 308 Census of pesticide
 manufacturing facilities.  The second appendix presents the mapping of PAIs into clusters. The methodology by
which price elasticities of demand for PAI clusters are calculated is shown in Appendix C while Appendix D reports
on impacts under the assumption of zero cost-pass through.  Appendix E presents the comparison  of annualized
compliance cost to facility revenue.  The final appendix details the analytical steps for calculation of facility impacts
for a hypothetical facility.
                                                   1.5

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                                      Chapter 2:  DATA SOURCES

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

         The Pesticide Manufacturing Facility Census for 1986, a census of pesticide manufacturing facilities
 conducted under Section 308 of the Clean Water Act,1 is the principal source of facility-level data. The Census
 included the 90 facilities that, in 1986, manufactured one or more of the 272 individual or classes of pesticide active
 ingredients (PAIs) that were  originally identified as within the scope of the regulation2'3.  Part A of the Census
 questionnaire requested the data necessary to perform the technical and treatment cost estimation analysis, including
 PAI-specific  production for 1986.  Part B of the Census  questionnaire requested detailed economic and financial
 data, including balance sheet and income statement information for 1985, 1986, and 1987. Three years of data were
 collected so that the EPA could construct a "typical" year upon which to base the impact analysis.  Part B was also
 designed to obtain information on facilities' cost of capital.  A copy of Part B of the Census is included as Appendix
 A.  A copy of Part A of the Census can be found in the Administrative Record.  Throughout the remainder of this
 document, the term "Census", if not further specified, will refer to Part B of the Pesticide Manufacturing Facility
 Census.

        Part A of the Census questionnaire was sent in July 1988; Part B was  mailed in January 1989.  Based on
 an initial review of Part A responses,  Part B was sent only to those facilities known to manufacture one or more
 of the PAIs within the scope of  the regulation.  Because Part B was sent to a reduced number of facilities, two
 facilities that were later determined to be manufacturing one or more of the PAIs subject to regulation were omitted.
 One was thought to be exclusively a fonnulator/packager; this facility has closed since 1986. The other  facility
performs only research and development.
    federal Water Pollution Control Act, 33 U.S.C. 1318.
    2The final manufacturer's effluent limitations include 260 in-scope PAIs or classes of PAIs rather than 272.
Three  organic  PAIs  have been dropped  from consideration for regulation:   orthodichlorobenzene (#193),
paradichlorobenzene (#202), and biphenyl (#67). In addition, the following nine metallo-organic PAIs are no longer
considered for regulation under the final rule: oxydipheroxarsine (#6), cacodyllic acid (#72), bioquin (#88), copper
EDTA ((#89),  methylarsenic acid salts  and  esters (#161),  organo-arsenic  pesticides (#188), organo-cadmium
pesticides (#189),  organo-copper pesticides (#190), and  organo-mercury pesticides (#191).   See the Technical
Development Document for details on the exclusion of these PAIs.
     EPA has information indicating that 15 of these facilities  have closed their in-scope  PAI  manufacturing
operations since 1986.  Therefore, the economic analysis does not include these 15 facilities.  Also, since nine
metallo-organic PAIs are no longer considered for regulation  under the final rule, two  facilities producing
Subcategory B PAIs as their only in-scope products are no longer counted as  potentially subject to the regulation.
                                                   2.1

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        In the proposed Census questionnaire sent to the Office of Management and Budget (OMB),  the EPA
proposed to request PAI-specific unit cost and price data. These data would permit the EPA to incorporate the
different unit costs, prices, and profit margins of PAIs in the impact analysis. The National Agricultural Chemicals
Association (NACA), the trade association representing numerous chemical manufacturing firms and individuals in
the 'industry, was reluctant to have the industry provide these detailed data and voiced objections to the OMB. OMB
subsequently rejected the proposed questionnaire.  As a compromise, the EPA allowed pesticide manufacturers a
choice in the final questionnaire.  Manufacturers could provide the PAI-specific data, or could elect to have their
facility's impact analysis done using averages. In this latter method, the EPA would assume that all PAIs  produced
by a single facility have the same profit margin.4 Twenty of the 88 facilities responding to Part B chose to provide
the PAI-specific cost and price data.  Sixteen of the 73 facilities subject to regulation under the final rule provided
PAI-specific data.

        The other major data input to the EIA was the estimated compliance costs of the regulation.5  At  proposal,
the EPA evaluated compliance costs associated with two potential regulatory options:  a Treated Discharge Option
and  a Zero Discharge Option.  The Treated Discharge  Option limitations were  based on biological treatment,
hydrolysis, activated carbon,  chemical oxidation,  resin adsorption, solvent extraction, incineration,  and/or
recycle/reuse to control the discharge of PAIs in wastewater.6   Zero Discharge Option limitations would have
required no discharge of pesticide manufacturing process wastewater pollutants to surface water by  using on-site
or off-site incineration and/or recycle/reuse. The final rule corresponds to the Treated Discharge Option.   The
estimated costs associated with the Zero Discharge Option have not changed since proposal.

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

        The Census data base and the compliance cost estimates were required for all impact analyses in this EIA,
including impacts on facilities, communities, foreign trade,  firms, small businesses, and  new sources.  The EPA
also used data from secondary sources in each of the chapters.  The profile of the pesticide industry relied on the
Annual Survey of Manufactures published by the U.S. Department of Commerce, Kline and Company's Kline Guide
     4See Appendix A (Part B of the Census), page A.29, text preceding question 2-H.
     5Full details of the compliance cost estimates can be found in the Technical Development Document.
     6For some PAIs the Treated Discharge Option limits discharge to zero.
                                                    2.2

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 to the  U.S.  Chemical Industry,  the Census of Manufactures published by  the Bureau of the Census, and the
 International Trade Commission's (ITC) Synthetic Organic Chemicals.  These  documents, together, provided
 production and aggregate industry data.  The profile also used import and export data from the United Nations'
 International Trade Statistics Yearbook.

         The facility impact analysis used secondary price data from the Annual Market Survey published by Doane
 Marketing Research and from Agchemprice published by DPRA, Inc. The facility impact analysis also employed
 data from the EPA's Office of Pesticides Programs (OPP). The OPP maintains data on PAI-specific sales, prices,
 and usage  from a number of proprietary sources.   The OPP data were among those used to estimate prices, and
 were also used to calculate the percentage of pesticide production that will not be covered by this regulation at this
 time.

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

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

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                         Chapter 3: PESTICIDE MANUFACTURERS PROFILE
3.0
        Introduction
        The following profile of the chemical pesticide industry describes the products,  facilities, and  firms
associated with pesticide active ingredient (PAI) manufacturing and sales.  It is intended to provide a backdrop for
the EIA by identifying and discussing key variables defining the market structure of the pesticide manufacturing
industry. The prevailing market conditions for pesticide products provide insight into firms' reactions to increased
costs due to regulatory compliance.

        The pesticide industry is organized vertically into  two major segments:  pesticide manufacturing and
pesticide formulating/packaging/repackagiing. Pesticide manufacturing involves the production of PAIs. PAIs are
not used directly for pest control, but are instead combined with solid, liquid and/or gaseous diluents before use.
PAIs are marketed in many formulations that may be either liquid or dry, and include a wide variety of solutions,
emulsions,  powders,  dusts,  granules, pellets,  and aerosols.  Formulating and  packaging therefore involves the
combination of active with inert ingredients, such as diluents, inorganic carriers, stabilizers, emulsifiers, aerosol
propellants or wetting agents; and packaging  the product in plastic, glass,  paperboard,  or  metal containers for
distribution and sale.  The concentration of  a PAI in a formulation may be high or  low.  Some formulations are
ready to use; others must be further diluted before use.  Repackaging involves transferring a single PAI or single
formulation from any marketable container to another marketable container without intentionally mixing any inerts,
diluents, solvents, other PAIs, or  other materials of any sort.  Data from the Census show that in 1986, 50 of the
90 pesticide manufacturing facilities (56 percent) also engaged in formulating and packaging, indicating that the
majority of pesticide manufacturers are vertically integrated.1-2

        The eight sections in this chapter focus on pesticide manufacturers, but some of the information presented
pertains to  both manufacturers of PAIs, and formulators/packagers/repackagers.  Section 3.1 categorizes the data
used to develop the profile.  Section 3.2 describes sources of demand for chemical pesticides in the United States.
Characteristics of pesticide manufacturing facilities, including physical characteristics, production costs, revenue,
    'Based on data from the Census, there were a total of 90 pesticide manufacturing facilities that manufactured
one or more of the 270 in-scope PAIs initially considered for regulation in 1986. However, EPA has information
indicating that 15 of these facilities have closed their in-scope PAI manufacturing operations since 1986 (the Census
base year). Also, metallo-organic (Subcategory B) PAIs are no longer considered for regulation under the final rule.
As a result, two additional facilities that produced only Subcategory B products are no longer counted as potentially
subject to the regulation.  Therefore, a total of 73 pesticide manufacturing facilities that manufacture one or more
of the 260 in-scope PAIs covered by the final rule are potentially subject to regulation. The information presented
in the industry profile continues to be based on the original 90 facilities subject to regulation.
    2Data from the 1988 Survey of the Pesticide Formulating, Packaging,  and Repackaging Industry indicate that
in 1988, 51 of the pesticide manufacturers were engaged in formulating and packaging.
                                                                             /
                                                   3.1

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profits, employment, labor productivity, and capital expenditures are described in Section 3.3. Section 3.4 examines
the organization of firms in the industry, including firm ownership and vertical industrial integration. Section 3.5
portrays the market structure of the pesticide industry, and includes discussions of barriers to market entry, demand
elasticity and product substitution, and firm concentration in the industry.  Section 3.6 provides an overview of
international trade in pesticides, including a discussion of the balance of trade for chemical pesticides and the nature
of foreign competition.  Characteristics of facilities known to have discontinued PAI manufacturing since 1986 are
discussed in Section 3.7.  Section 3.8 summarizes the information presented in the profile.

3.1 Categorization of Data

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

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

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

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                                                    Table 3.1
                         Representative Classes of Pesticides and the Pests They Control
       Class
 Target Pest
       Acaricide
       Algicide
       Attractant
       Avicide
       Bactericide
       Defoliant
       Dessicant
       Fungicide
       Growth regulator
       Herbicide
       Industrial Microbiocide
       Insecticide
       Miticide
       Molluscicide
       Nematicide
       Piscicide
       Predacide
       Repellents
       Rodenticide
       Silvicide
       Slimicide
       Sterliants
 Mites, ticks
 Algae
 Insects, birds, other animals
 Birds
 Bacteria
 Unwanted plant leaves
 Unwanted plant tops
 Fungi
 Insect and plant growth
 Weeds
 Microorganisms
 Insects
 Mites
 Snails, slugs
 Nematodes
 Fish
 Carnivorous animals
 Insects, birds, other animals
 Rodents
 Trees and woody vegetation
 Slime molds
Insects, other animals
      Source: Minnesota Department of Agriculture, Rinse and Win Brochure, 1989.
chemicals. For instance, all herbicides used on corn production were classified into the same cluster. Each cluster
therefore contains PAIs that may be roughly substituted for one another on major use sites.

        The EPA's Office of Water used the OPP's cluster segmentation to define individual markets for groups
of pesticides, because economic variables, such as demand elasticity, would not be meaningful for a market defined
as all pesticides.  The Office of Water expanded upon the OPP's cluster segmentation in two ways. First, PAIs
registered after 1980 were assigned to one of the 48 clusters.  Second,  the 48 clusters were  expanded to 56
                                                   3.3

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clusters.based upon differences in the sensitivity of product demand to changes in price (see Table 3.2).3  In
addition, although the OPP's cluster segmentation assigned each PAI to only one cluster, this analysis allowed for
a PAI to be assigned to more than one cluster if it had more than one important use.  The allocation of PAIs to
clusters can be found in Appendix B.

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

        The major markets for pesticides  are agriculture, industrial/institutional/commercial, and home/lawn/
garden.4     Agricultural  sales  account  for  approximately  70   percent  of  domestic  pesticide   sales.
Industrial/'institutional/commercial and home/lawn/garden  each constitute about 15 percent of U.S.  sales (see
Figure 3.1).

        Much of  the  pesticide application for the three markets  is  performed by commercial applicators.
Commercial applicators are trained professionals skilled in applying pesticides in an efficient and environmentally
safe manner. The National Pest Control Association estimated that in 1990 the commercial applicator industry
would contain 14,250 firms and have annual billings of $3.5 billion (National Pest Control Association, 1991).
Commercial applicators are contracted by the agricultural industry to apply pesticides to agricultural crops, as well
as to food products during storage and transit.  The industrial/institutional/ commercial sectors use the services of
commercial applicators to control pests  in many settings,  including schools, health care facilities,  prisons, food
processing establishments, hotels, restaurants, factories, and warehouses.  Household consumers  use commercial
applicators to manage pests that typically inhabit dwellings, such as termites, cockroaches, and mice, and to rid their
lawn and garden of pests.  Government entities use the services of commercial applicators to control mosquitos,
and to maintain vegetation around roads, and public recreational areas.   In  1985, residential services comprised
about 60 percent of the non-agricultural commercial applicator industry, commercial services constituted 25 percent,
     3Clusters were split when (1) there was a wide variety of price elasticities of demand among PAIs within a
 cluster, and (2) the PAIs among which demand elasticity varied had distinctive uses.  For example, the cluster that
 encompasses herbicides used on fruit trees was split into three clusters: herbicides used on grapes, herbicides used
 on oranges, and herbicides used on fruit trees (excluding grapes and oranges).
     Additional markets,  such as  stored  grain products  (elevators), seed  treatment, pest control operations
 (tcrmiticides), cattle,  golf courses,  utility right of ways, etc.,  also exist.  That level of detail, however, is not
 necessary in this discussion.
                                                    3.4   '

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                                                Table 3.2
                                           Pesticide Clusters
 Cluster   Primary Application
| Cluster  Primal; Application
 Herbicides used on:
                                                    I Fungicides used on:
 H-l      Broad spectrum of uses                     • F-l
 H-2      Corn                                      I F-2a
 H-3      Soybeans, cotton, peanuts, alfalfa            j F-2b
 H-4      Sorghum, rice, and small grains             j F-3
 H-5a     Oranges                                   : F-4
 H-5b     Grapes                                    I F-5
 H-5c     Fruit trees                                 1F-6
 H-6      Sugarbeets, beans and peas                  j F-7
 H-7      Drainage ditches, rights of way, forestry and  j  F-8
 H-8      Turf                                      JF-9
 H-9a    Vegetables                                 j  F-10
 H-9b    Tobacco                                   !
 H-10    Unclassified uses                            !
          Broad spectrum of uses
          Fruits and nuts
          Grapes
          Vegetables
          Oranges
          Seed treatments
          Post-harvest fruit and vegetables
          Grain storage
          Ornamentals
          Turf
          Unclassified uses
         Insecticides ased on/for/as;
         Other Pesticides;
I-la     Cotton                                     j R-I
I-lb     Soybeans, peanuts, wheat and tobacco   '     '• R-2
I-2a     Corn and alfalfa                            j R-3
I-2b     Sorghum                                   IR-4
1-3      Fruit, and nut trees, excluding oranges and    : R-5
I-4a     Oranges                                    ! R-6
I-4b     Grapes                                     ! R-7
1-5      Vegetables                                 ! R-g
[-6      Livestock and domestic animals               : R-9
[-7      Non-agricultural sites (as repellent)           j R-10
r8      Domestic bug control and for food processing j R-ll
1-9      As fumigants and nematicides                 | R-12
'-10     Termite control                             IR-13
1-11     Lawns, ornamentals, and forest trees          j R-14
.-12     Mosquito larva                              j R-15
-13     Unclassified uses                            : TT-t
          Industrial preservatives
          Slimicides used in pulp and paper, cooling
          Industrial microbiocides
          Sanitizers used in dairies, food processing,
          Synergists used as insecticide synergists,
          Food preservatives
          Wood preservatives, used for industrial,
          Disinfectants
          Water disinfectants
          Plant regulators, defoliants, and desiccants
          Preservatives, disinfectants, slimicides
          Molluscides and misc. vertebrate control
          Bird chemosterilants, toxicants, and
          Dog and/or cat repellants
          Rodent toxicants, anticoagulants, predator
                                                3.5

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                              Figure 3.1

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

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

  Note:   Census data were not used for this figure, because the question in the Census
         that refers to markets refers to total facility production, not pesticide production.
                                     3.6

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 and services to institutions, industries and the government represented 7, 6, and 2 percent respectively (Kline &
 Company, 1986).

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

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

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

3.2.B   Industrial/Institutional/Commcrcial Market (I/I/C)
        The I/I/C market includes many products, such as disinfectants, cleaning  supplies, and air conditioning
biocides, that are generally not perceived as pesticides by the public. In addition, products such as paint and wood
preservatives may  contain substantial amounts of pesticides. The I/I/C market is estimated to exceed $200 million
annually, with about 45 percent involving health care institutions (U.S. EPA, 1992).

        The I/I/C market differs significantly from the agricultural market in several ways. First, the use of I/I/C
products is generally more uniform across the country.  The need for disinfectants  in various parts of the United
States is approximately the same.  However, the use of pesticides for wood preservation and hi cooling towers varies
                                                   3.7

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                                       ,«     -                    _^   ,
                              Pesticide Agricultural Production and Distribution1
     Agent
Purpose
     Registrant


     Manufacturer

     Formulator/Packager
     Distributor
      Dealer/Co-op/Repackager
Registers the pesticide formulation with EPA.  Registration involves a
long, expensive R&D process to develop the pesticide, produce the data
required for registration, and proceed through the registration process.

Synthesizes the active ingredient from raw materials.

Produces the pesticide formulation by combining the active ingredient(s)
with other substances, including surfactants, clays, powders and
solvents; involves mixing or blending operations. Formulation may be
done in-house, by independent formulators, or by tollers who formulate
the product under contract to the manufacturer.

Acts as the "middle man;" buys pesticide from the
registrant/manufacturer/formulator and sells to the dealer.

Sells the pesticide to the user.2
      1  In many cases several steps are performed by one entity.  Large companies might register,
        manufacture, and formulate their pesticides.  Some distributors also formulate several pesticides.
        Additionally, a single facility might function as a distributor, dealer, and commercial applicator.
      2  A user is defined as a farmer, government, commercial ground applicator, commercial aerial applicator,
        etc.
      Source:     Based on a table in: Pesticide Containers: A Report to Congress, U.S. EPA, Office of
                  Pesticide Programs, May,  1992.                                          	
somewhat according to the climate (U.S. EPA, 1992).  Second, I/I/C pesticides are generally used in smaller
quantities than agricultural chemicals. Third, I/I/C products in general are usually less expensive per unit volume
of product than agricultural pesticides, because they are less concentrated.

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

        The distinction among industrial, institutional, and commercial pesticides is based on the setting in which
the pesticide is used. In some cases, the same formulation is used in different types of facilities. Typical industrial
end-users include personnel in food processing facilities and breweries.  Industrial pesticides, such as preservatives,
slimicides or biocides, are used in cooling towers, paper and textile mills, oil wells, metalworking coolants, etc.
                                                   3.8

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                              Figure 3.2


   Production and Distribution Channels for the Industrial/
 Institutional/Commercial and Home/Lawn/Garden Markets
          Basic Pesticide Manufacturers
    Independent
    Formulators
          i
                          Formulators/
                           Distributors
          Distributors
                               Retailers
            Contract
           Fprmulators
            "Tollers"
Consumer
Companies
 Food Brokers, Etc.
          Industrial,
        Institutional &
     Commercial Dealers
                               Home, Lawn
                               and Garden
                                  Users
Source:  Based on a diagram in: Pesticide Containers:
       A Report to Congress, U.S. EPA, Office of
       Pesticide Programs,
       May, 1992.
           Institutional
             Users
                                                        Industrial
                                                         Users
          Commercial
             Users
                                                       Government
                                                          Users
                               3.9

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(U.S. EPA,  1992).   Typical institutional end-users  include personnel  in hospitals, nursing homes, schools,
restaurants, hotels, and contract cleaning businesses that serve stores, apartment houses, office buildings,  and
garages (U.S. EPA et al., 1989). Commercial establishments use pesticides to protect landscaping and to maintain
cleanliness and health standards.  The federal, state and local governments use I/I/C chemicals on military bases,
and in hospitals and other government buildings.

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

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

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

3.3      Facility Characteristics

3.3.A   Physical Characteristics
         Figure 3.3, drawn from Census data, shows the geographic distribution of the PAI manufacturing facilities
and provides the percentage of  in-scope PAI production in each region.  Although pesticide facilities are located
in all regions of the country, the southeast/south central region of the country has the heaviest facility concentration
                                                    3.10

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Percent
 75%-
  70-
  65
  60-
  55-
  50-
  45-
  40-
  35
  30^
  25
  20-
  15-
  10-
   5-
   0-
                             Figure 3.3
         Facilities and In-Scope Pesticide Production
                        by Region, 19S6
                                               52%
32%
                    35%
       18%
                           30%
33%
            Northeast/
           North Central
                     Southeast/
                   South Central
 Northwest/
 Southwest
                                 Region
        H Percent of Facilities
        F3 Percent of In-Scope Production

        Source:  Census.
                                   3.11

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(35 percent).5 The northwest/southwest region has the second heaviest concentration (33 percent).6 Although the
southeast/south central region accounts for a larger percentage of facilities, the northwest/southwest region has the
largest share of in-scope pesticide production (52 percent).

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

3.3.B  Industiy Output
        Several factors have affected the demand for chemical pesticides. These include the decline in agricultural
acreage; the production of new, more highly concentrated pesticide products; more efficient application of pesticides;
the  increase in  pesticide resistance;  the increase in  environmental regulations;  and greater awareness  of
environmental issues on the part of both the seller and the buyer.  Although these factors have led to a contraction
in pesticide production and sales, profitability from pesticide sales in the industry appears  to have been  largely
unaffected by  the decline  in output  (Kline & Company,  1990).  Production characteristics  of the pesticide
manufacturing industry are outlined below.

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

         The most significant factor has been a decline in  agricultural acreage.   Figure 3.5, which plots total
pesticide production and total U.S. planted crop acres using  1986 as a base  year, shows how pesticide production
          southeast/south central  region  includes Alabama, Delaware,  Florida, Georgia,  Kentucky, Maryland,
 Mississippi, North Carolina, South Carolina, Tennessee, Virginia, West Virginia,
     *The northwest/southwest region includes all states west of the Mississippi River.
     'Many of the facilities in the Census did not begin pesticide production until many years after construction.
 Approximately 38 percent of the facilities have produced pesticides for more than 30 years, while less than 13
 percent of the facilities have produced pesticides for fewer than 10 years.
                                                    3.12

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Table 3.4
Pesticide Manufacturing Facilities by Facility Age, 19861
Number of Years
5 to 10 to 20 to 30 to
<5 < 10 < 20 < 30 < 40 40+
AH*
Pesticide
type (Number of Facilities)
Fungicides 0253 10
Herbicides 1455 14
Insecticides 13 3 4 33
Other Pesticides* 00 1 2 41
Multiple Types of 007788
Pesticides**
All in-scope Facilities 2 9 21 21 17 16
11
20
17
8
30
86***
* Refer to Table 3.2 for a description of other pesticides.
** Multiple types of pesticides include manufacturers that produce pesticides in more than
one of the groups outlined above.
*** Excluded from the 88 facilities that provided financial data are two facilities that did not
report facility age.
1 Facility age is the number of years the facility has been producing pesticides.
Source: Census
                       Number of Facilities by Facility Age, 1986
# Facilities
       2
21
                                                 21
17
                                             16
    <5        5 to < 10      10to<20      20to<30
                                    Age (Years)
                         30 to <40
          40+
                                      3.13

-------
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            to   r)
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i
                 JQ   8
                                  oT
                                          3.14
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                 s   s
                 OO
                                       a   s
                 s
 c

1
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 o
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g
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•3   ^       2
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                    1
                    1

                                                                I


                     SS -

                    1.1
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-------
                                     Figure 3.4

          Fungicide, Herbicide, and Insecticide Production1,
                                   1980-1988
                                  (in 1,000 pounds)
1,000 Pounds
  1,000,000 —
   900,000 —
   200,000-


   100,000-
                                                              Herbicide Production
                                                              (Including plant growth
                                                              regulators)
                                                          Insecticide Production
                                                          (Including rodenticides, soil
                                                          conditioners and fumigants)
i in in
 Fungicide Production
          1980  1981   1982  1983   1984  1985   1986   1987  1988

                                    Years
          Production data are reported in terms of manufactured PAIs.

          Source:  International Trade Commission, Synthetic
                  Organic Chemicals, 1980-1988.
                                     3.15

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                                   Figure 3.5

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

                                 Years

          Source:  International Trade Commission, Synthetic Organic
                  Chemicals, 1977-1987 and United States Department of
                  Agriculture, Agricultural Statistics 1984 and 1989.
                                    3.16

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 mirrors planted acres.8 Pesticide production was lowest in 1983, when the United States Department of Agriculture
 (USDA) implemented the Payment-In-Kind (PIK) program,  taking 48 million acres out of production. Although the
 number of planted acres increased after 1983, other USDA programs, such as the Conservation Reserve Program,
 continued to reduce agricultural acreage (Ribaudo, 1989).9

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

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

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

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

        The extent to which a facility is involved in pesticide-related activities vs. non-pesticide-related activities
 varies  slightly, depending upon the size of the facility (see Figure 3.7).  Smaller facilities (with total revenues of
 less that $20 million) devote approximately 31 percent of their production to non-pesticide related activities. Large
 and medium-sized facilities (with revenues greater than or equal to $20 million) are more diversified, with between
    "All production and crop acres were divided by  1986 production and acres respectively, in order to display
production and acres on the same scale.
    *The Conservation Reserve Program was a land retirement program aimed at retiring 40 to 45 million acres of
highly erodible crop land by 1990.
                                                   3.17

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                                             Table 3.6           *
                Distribution of Ik-Scope Pesticide Facility Production and Sales, 1986
                              Production Quantity
                                  (million Ibs.)
                   «U  0.1 to   Ito
                            <1   <6
                                      AH
                     <2S
      Number of
      Facilities
      Percent of
      Total
 8
18    35      14      15      90
9%    20%  39%    16%     17%    100%
      Cumulative    9%
      Percent
       29%   68%   84%   101%'
                                                         Sales
                                                      (million $)
<$10
                                             $10 to
                                             <$50
                                                                                 2>$50    All
                                                                  43      23      19     8S2
                                     51%     27%    22%    100%
                                                                 51%     78%    100%
      1 Total does not equal 100% due to rounding.
      2 Excluded from the 88 facilities that provided financial data are: one R&D facility and two
       facilities that obtained pesticide revenues only from contract work or tolling and, therefore,
       did not delineate in-scope vs. out-of-scope revenues.
      Source:  Census.
58 and 62 percent of production devoted to non-pesticide related activities. The composition of facility production
activity varies more dramatically among facilities when comparing chemical-related (including pesticides) production
activities to non-chemical-related production activities.  Large facilities (with total revenues greater than or equal
to $250 million) are more diversified, with 36 percent of production devoted to non-chemical-related activities. In
contrast, small and medium-size facilities (with total revenues of less than $250 million) devote between 5 and 10
percent of production to non-chemical-related activities.

3.3.D  Production Costs
                                                                        *              '              'in
        Production costs can be classified into two categories: fixed and variable. Fixed costs are independent of
the level of production and include depreciation on capital, fixed overhead, costs  for product research and
development (R&D), and interest on capital.  Figure 3.8 shows the composition of pesticide-related facility fixed
costs by facility size.10 In most cases, fixed overhead is the largest component of fixed costs. Depreciation is the
second largest component of fixed costs for facilities with revenues greater than or equal to $ 1 million.  While R&D
costs constitute the largest component of facility fixed costs for facilities with pesticide revenues of less than
    "Facility fixed costs were not broken down by pesticide-related vs. non-pesticide-related fixed costs in the
 Census because facilities maintained records of their fixed costs at the facility level.  During the pretest, it was
 determined that the respondent burden that would have been imposed by requiring facilities to break down costs
 were too great.  Consequently, the ratio of pesticide-related revenues to total facility revenues was applied to each
 of the categories of fixed costs to obtain estimates of pesticide-related fixed costs.
                                                  3.18

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                                  Figure 3.6

        Composition of Facility Production Activity, 1986
                        (Averaged Across All Facilities )
           Other Production
               Activity
                12%
   Manufacturing
  Chemicals Other
Than EPA-Registered
     Pesticides
       41%
        Manufacturing and
        Formulating and/or
     Packaging In-Scope PAIs
             41%
                                  Manufacturing and
                             Formulating and/or Packaging
                                  Out-of-Scope PAIs
                                        5%
        Manufacturing
    Intermediates to be Sold
(others included in in-scope PAIs)
             1%
       Source:  Census.
                                   3.19

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

1 Facility size is measured by total facility revenues.
 Source:  Census.
                             3.20

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                              Figure 3.8
 Composition of Pesticide-Related Facility Fixed Costs
                     by Facility Size1,1986
                     Greater than or equal to $50 Million
                             Between $25 and $50 Million
• Depreciation
Q Fixed Overhead
H Research and Development
El Interest
D Other Expenses
                                          Between $1 and $25 Million
                                                 ;>;>;>;>  Less than $1 Million
   Facility size is measured by revenues from all pesticide-related activities.
   Source:  Census.
                           3.21

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

        Variable costs depend upon the level of production.  These costs include pesticide material and product
costs, labor costs, contract or tolling costs, taxes, and other pesticide manufacturing costs (i.e., all other pesticide-
related operating costs not included in the aforementioned categories).11  Figure 3.9 shows the composition of
pesticide variable costs by  facility size. The figure shows that pesticide material and product costs are the largest
component of variable costs across all facility sizes. Labor costs, contract work, and other pesticide costs are small
in comparison.

        Figure 3.10 compares fixed and variable costs by facility size, to show the proportion of fixed costs to total
costs by facility size.  If fixed costs are a large proportion of total costs,  smaller firms may find it difficult to enter
the market.  The Census data suggest only minor differences in the ratio of fixed costs to total costs across facility
size, indicating that fixed costs are not likely to be a barrier to entry.12 For the category of smallest facilities (with
pesticide revenues of less  than $1 million), fixed costs comprise 27 percent of total costs.  For the category of
largest facilities (with pesticide revenues greater than or equal to $50 million), fixed costs comprise 41 percent of
total costs.   Very large facilities, which  often produce a  greater variety of pesticide types (e.g., insecticides,
fungicides, and herbicides) and PAIs may be more capital intensive, thereby facing a different set of cost constraints
than medium and small facilities.

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

         The data presented in Table 3.7 lend  further evidence that larger facilities tend to be more  diversified than
smaller facilities. As facilities increase in size, the percent of the labor dedicated to non-pesticide-related production
increases from 23 to 44 percent  of total facility employment.
     "Facility  taxes  were  not  broken  down by  pesticide-related  vs.  non-pesticide-related  in the  Census.
 Consequently, the ratio of pesticide-related revenues to total facility revenues was applied to total facility taxes to
 obtain estimates of pesticide-related taxes.
     12Facilities can recover costs incurred by introducing a new product to the market by adjusting the price once
 they have obtained patent protection. The fact that facilities may be willing to operate at a loss in the short run,
 knowing that they will ultimately recover their costs, mitigates the barrier to entry that is associated with large fixed
 costs such as R&D.
                                                    3.22

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                           Figure 3.9
Composition of Pesticide-Related Facility Variable
              Costs by Facility Size1,, 1986
                 Greater than or equal to $50 Million
                             Between $25 and $50 Million
                                           Between $1 and $25 Million
                                                        Less than $1 Million
• Pesticide Material and Product Costs
EJ Labor Costs
H Contract Costs
ED Other Pesticide Costs
D Taxes
 Facility size is measured by revenues from all pesticide-related activities.
 Source:  Census.
                               3.23

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

 E3 Variable Costs




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

 Source:  Census.

                                3.24

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      '
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«•
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              <,
            £

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                                     3.25

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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)13, and SIC 28 (chemicals and allied products).
The figure shows a close correlation between employment trends in all manufacturing industries, and in both the
agricultural chemical and organic chemical industries, as well as the chemical industry as a whole. Between 1980
and 1981, however, employment in the agricultural chemical industry increased, while the employment in the
organic chemical industry, chemical industry, and all manufacturing decreased.

3.3.F   Revenues and Profit
         Consistent with  the review of production data, examination of facility revenues reveals that facilities derive
a large percentage of their revenues from sources other than in-scope pesticide sales  (see Figure 3.12). Facilities
with revenues greater than or equal  to $250 million derive more than half their revenues (approximately 58 percent)
from sources other than in-scope pesticide sales, while facilities with revenues of less than $20 million obtain about
42 percent of their revenues from other sources." Although the proportion of revenues derived from sources other
 than in-scope pesticide sales varies across facility size, the figure illustrates diversity at the facility level  for all
 facility sizes.
     "Industrial organic chemicals include SIC 2865 (cyclic crudes and intermediates), SIC 2869 (industrial organic
 chemicals, n.e.c.), and SIC 2861 (gum and wood chemicals). The U.S. Industrial Outlook presents data for organic
 chemicals as industrial organic chemicals except gum and wood, i.e., SICs 2865 and 2869.  Consequently, for
 consistency in presenting data from secondary sources, organic chemicals are classified as SICs 2865 and 2869
 throughout this profile.  (Note: In 1986, SIC 2861 constituted only 5 percent of the value of shipments for SICs
 2861, 2865 and 2869 combined.)
     MIn-scope revenues are defined as the revenues derived from the sale of in-scope pesticide chemicals.   This
 definition excludes revenues from contract work or tolling, which may be entirely or partially attributable to in-scope
 pesticides. The figures presented may therefore be larger if a facility also obtains revenues from contract work or
 tolling.
                                                    3.26

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                                                       Table 3.8
                                      Average Facility Employment Characteristics
                                                 by Facility Size, 1986
                                             Full-Time Equivalents (FTEs)1
        Size of
        Facility
                          Average      Average
                         „ pesticide    Formulating    Average     Average
Number    Average       Manu-         and      Employment     Non-
   of         Total        factoring     Packaging    for Other    Production
                                                                  Average
                                                                 Production
                                                               Employment/2
                                                                  as %of
                                                               Average Total
Facilities  Employment  Employment  Employment   Production  Employment   Employment
     Less than
     $20M

     $20M
     to $74.9M

     $75M
     to $249.9M

     $250M and
     greater

     Average for
     All Size
     Facilities
      20


      33


      20


      13


    86/3
   49


   98


  410


2,534


  527
 10


 46


153


 40
 8


12


82


19
   12


   30


  167


1,118


  225
   27


   50


  185


1,181


  250
49%


49%


55%


53%


54%
     1  FTEs are calculated by dividing total facility annual hours by 2,000. The average employment figures are the
       arithmetic mean of FTEs across facility size.
       Production employment figures include pesticide manufacturing, formulating and packaging, and other production
       employment.
     3  Excluded from the 88 facilities thaft provided financial data are an R&D facility and a facility that did not provide
       employment data.

     Source:  Census
        On average, 1986 pre-tax in-scope pesticide facility profits equalled 13 percent of in-scope pesticide facility
sales.  Figure 3.13 presents 1986 pre-tax in-scope pesticide facility profits as a percent of in-scope pesticide sales

categorized by pesticide type, revenues  of in-scope pesticides, and total facility revenues.15 When profits were

broken down by pesticide type, facilities that produced only fungicides averaged the highest profit to sales ratio:

nearly 0.32.  This profit level contrasts with the profit to sales ratio of -0.03 for facilities that produced only

insecticides. Facilities that produce multiple types of pesticides (these also tend to be larger facilities) have pre-tax

profit to sales ratios of about 0.16.  When profits are broken down based on facilities' in-scope pesticide revenues,
the data indicate that larger facilities (with revenues greater than or equal to $25 million) were more profitable than
    "Although revenue information in the Census was broken down by in-scope vs. out-of-scope, facility costs were
not. In-scope-related facility costs were therefore calculated by applying the total cost figure to either the ratio of
in-scope pesticide revenues to total revenues or, where applicable, the ratio of in-scope pesticide revenues to total
pesticide-related revenues.
                                                  3.27

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                                Figure 3.11
                  Employment Trends, 1975-1987
                              (1975 Base Year)
Number of Employees
Indexed to 1975
  1.5-1
  1.4-
                                                                SIC 2879
                                                                All Manufacturing

                                                                SIC 28

                                                               iSIC 2865, 2869
                 1	1	1	1	1	1	1	1    T
     1975  1976 1977 1978 1979 1980  1981 1982 1983  1984 1985 1986  1987
     —  SIC 2879 (Agricultural Chemicals, n.e.c., and Formulation & Preparation of Pesticides)
     '""  SIC 2865, 2869 (Organic Chemicals, except gum & wood)
     —"  SIC 28 (Chemicals and Allied Products)
         All Manufacturing
 Source:  Census of Manufacturers, 1987
                                        3.28

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

Q Other EPA Registered Pesticide Chemicals

D Pesticide Contract Work or Tolling 2

E3 Other Revenues
   1 Facility size is measured by total facility revenues.
   2 Tolling work maybe either in-scope or out-of-scope.
     Source:  Census.
                                       3.29

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                                     Figure 3.13
   Pre-Tax In-Scope Pesticide Facility Profit as a Percent of
                     In-Scope  Pesticide Sales, 1986
Profit as a Percent
of Sales
Profit as a Percent
of Sales
35% —1
 30 —
 25 —
 20 —
 15 —
 ID-
   S'
                   -5 —
                -10%-
                          31.7%
                              Pesticide Type
                                                                           16.5%
                                      10.7%
                                                               8.6%
                                188888888888888888888888888?"

                                  -2.8%
35% —|


  25 —

  20 —

  15 —

  10-

   5 —
        Fungicides    Herbicides    Insecticides      Other
                                             Pesticides
                                       Revenues from In-Scope Pesticides
                                                                           Multiple
                                                                           Types of
                                                                          Pesticides
                                                                        22.6%
                              11.4%
                                                   6.7%
Profit as a Percent    25% •
of Sales
                          Less than $2 Million        $2-$25 MHIion

                                            Total  Facility Revenues
                  20 —


                  15 —

                  10 —

                   5 —

                   0-
                                                    Greater than or
                                                  Equal to $25 Million
                                                       20.8%
                                  13.8%
              10.1%
                         Less than $50 Million
                              $50-$250 Million
 Source:  Census.
                                                                     Greater than or
                                                                   Equal to $250 Million
 Note:    Revenue categorizations for in-scope revenues and facility revenues are broader than
          those that appear elsewhere in the profile, to prevent disclosure of confidential
          business information. In addition, the two facilities that changed ownership in 1986
          are not included in the information presented in this figure.

-------
smaller facilities (with revenues of less than $25 million) in 1986.  This information may indicate that larger
facilities, many of which produce several different types of pesticides, are more efficient.

        Industry experts, however, attribute the high profits in portions of the pesticide industry to the ability of
manufacturers to produce patent-protected pesticides with specific uses.16  Many of the pesticides included in these
profit figures represent patent-protected chemicals produced by only one manufacturer.  Although patented products
face competition from pesticides with the same end use, many manufacturers appear to have been successful at
differentiating their products.  Future profits, experts say, will most likely depend on producers' ability to develop
new patented products (Kline & Company, 1991).  Most  competition in the industry is among producers whose
products have similar biological activity.

3.3.G   Capital Expenditures
        Capital expenditures represent funding for additional capacity and/or automating or streamlining existing
facilities.  Table 3.9 shows that capital expenditures by the pesticide manufacturing industry varied significantly
from year to year between 1975 and 1987.  On average, capital expenditures decreased by 3 percent per year from
1975 to 1987.  Most of the decline took place in the late 1970s and early 1980s.  Annual (and, in some cases,
biennial) change appears to be cyclical, with downturns followed by upswings. The contraction in the demand for
pesticides may be partially responsible for the decline in capital expenditures in the industry.

        In  general, capital  expenditures  tend  to follow the  business  cycle.   Figure  3.14 compares capital
expenditures for all manufacturing, as an indicator of the business cycle, to capital expenditures in SIC 2879
(agricultural chemicals,  n.e.c.,  and pesticide formulations and  preparations),  SICs 2865 and 2869  (organic
chemicals, except gum and wood), and SIC 28 (chemicals and allied products). Agricultural chemicals and organic
chemicals both exhibit a cyclical trend, with an overall decrease in expenditures of approximately 35 percent from
1975 to 1987.  While exhibiting similar swings in capital expenditures to those of agricultural and organic chemicals,
the chemicals and allied products industry declined by only 20 percent between 1975 and 1987. Capital expenditures
in the manufacturing industry as a whole, like the agricultural chemical industry, appear to be cyclical. From 1978
to 1981, however, "all manufacturing1' maintained a fairly constant level  of capital expenditures,  while capital
outlays in the agricultural chemical industry declined. In addition, overall capital expenditures from 1975 to 1987
for "ail manufacturing11 increased by approximately 20 percent.
    16Production data collected hi Part A of the Census indicate that most clusters include production from multiple
facilities. In addition, data presented in Section 3.3.F of the profile shows that facilities experience a wide range
of profitability, suggesting that the pesticide market is competitive.  Conversely, few facilities produce the same PAI
within clusters, indicating that product differentiation exists within markets. These characteristics indicate that the
pesticide market is competitive with differentiated products. '
                                                   3.31

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                                Pesticide Capital Expenditures, 1975-
                                   '   "          SIC 2879*
                                       	(in 1986 dollars)
                            Year
      Capital
Expenditures
   (million $)
                                                           Annual Percent
                                                                 " Change
                            1975                  342.6
                            1976                  301.7
                            1977                  340.9
                            1978                  381.4
                            1979                  280.8
                            1980                  246.4
                            1981                  263.3
                            1982                  295.9
                            1983                  145.0
                            1984                  199.7
                            1985                  192.6
                            1986                  200.6
                            1987                  224.1

                         Average Annual Change
                           73%
                           -12%
                           13%
                           12%
                           -26%
                           -12%
                            7%
                           12%
                           -51%
                           38%
                            4%
                            12%

                            -3%
                         1 SIC 2879 includes establishments involved in
                           manufacturing or formulating agricultural chemicals,
                           n.e.c., and formulating and preparing pest control
                           chemicals.
                         Source:   Census of Manufactures, Preliminary Report,
                                   Industry Series, 1987
        In the Census, facilities provided the year of the most recent major expansion of facility or equipment with
respect to pesticide production. Almost 90 percent of the facilities indicated that they had made some sort of
expansion of facility or equipment related to pesticide production since 1960.  More than 80 percent of the facilities
invested in an expansion or improvement after 1970, while almost 40 percent of the facilities reported an expansion
or improvement after 1985.
                                                 3.32

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 1986 Dollars Indexed
 to 1975
                                  Figure 3.14

                          Capita! Expenditures
                              in 1986 Dollars
                               (1975 Base Year)
  1.5-
                                                            All Manufacturing
                                                            SIC 28
                                                            SIC 2879
                                                           ' SIC 2865, 2869
  °'°      I    I     I    I    I     I    I    I    III|
     1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987

                              Years

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

Source:  Census of Manufacturers, 1987.
                                   3.33

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3.3.H   Production Capacity Utilization
        Table 3.10 shows pesticide production capacity utilization rates from 1980 to 1989.  The data indicate that
production capacity utilization for all pesticides varied significantly during the decade, averaging approximately 68
percent for all pesticides.  At times, however,  some types of pesticides had much lower production capacity
utilization. During 1983 and 1984, for example, capacity utilization for insecticide production was particularly low,
declining to 29 percent hi 1984.  Figure 3.15 compares the capacity utilization rate for pesticide production to that
for all manufacturing.  The figure shows that the manufacturing capacity utilization trend runs counter to that for
pesticides. Capacity utilization for all manufacturing hit a low in 1982 and rose thereafter. Capacity utilization for
pesticide production, on the other hand,  peaked in 1982 and hit its lowest point in  1984.17

        The post-1982 decline in  pesticide manufacturing capacity utilization may be attributable in part to the
Payment-in-Kind (PIK) program.18 In addition, pesticide production capacity utilization rates  may fluctuate over
time because some pesticides are not produced on an annual basis. Rather, PAIs may be produced for a limited
time period (every second or third year) on what the industry commonly refers to as a campaign basis.  Although
many PAIs are produced annually, it is common industry practice to produce a specific PAI less frequently.  This
typically occurs when the pesticide is used on a low-volume specialty crop, or  for those pesticides with high
concentrations that allow for reduced volume.  During production, materials are fed into a  reactor  in order to
produce a desired chemical reaction;  labor and equipment are used to monitor the process to make sure that all
necessary conditions of production are met.

        Although the frequency of production is generally determined by product demand, the quantity produced
is typically a function of the volume required to make the run cost-efficient.  Due to start-up costs such as energy
and labor, costs per unit produced increase as quantities are reduced.  Total costs associated with the  minimum
volume a facility is willing to produce may be only slightly greater than total costs for production of much smaller
amounts of the pesticide.19

3.4     Firm Characteristics

        This profile has thus far focused primarily on characteristics of the facility. This section describes  the
 ownership structure of the industry and the way in which firms are organized.
     17This is reasonable, since pesticide production is more closely  related to agricultural production than to
 measures of industrial activity.
     "Recall that PIK took 48 million acres out of production in 1983.
     19Per unit costs increase as quantities produced decrease.  Producing larger quantities may therefore cost less
 on a per unit basis.
                                                   3.34

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                                                 Table 3.10
                       U.S. Pesticide Production Capacity Utilization Rates, 1980-1989
                                                  (Percent)
          Year
Herbicides     Insecticides     Fungicides
                      All
                      Pesticides
                      Annual
                      Percent Change
                      All Pesticides1
          1980
          1981
          1982
          1983
          1984
          1985
          1986
          1987
          1988
          19893
          Average
          Capacity
          Utilization
         77
         74
         84
         66
         67
         62
         64
         63
         75
         72

       70.4
  79
  72
  68
  33
  29
  56
  63
  61
  76
  76

61.3
  84
  68
  70
  71
  73
  66
  61
  59
  59
  63

67.4
  78
  73
  80
  54
  52
  61
  65
  62
  75
  81

68.1
            n/a2
            -6%
            10%
          -33%
            -4%
            17%
            7%
            -5%
           21%
Average
 Annual
 Change
                                                                                               4%
         | The rate for all pesticides may be higher than those for herbicides, insecticides, or fungicides.
           This difference is due to the inclusion of detailed information on capacity rates associated with
           pesticides either classified as rodenticides or unclassified.
         2 Not available.
         ? Projected.
         Source:   USDA Agricultural Resources: Situation and Outlook Report, AR-13, February
                   1989.
       The Census indicates that most in-scope pesticide facilities are owned or controlled by a parent firm (85
percent).  Although a number of smaller, single-facility firms control small portions of total production,  overall
production is becoming increasingly concentrated among large producers as a result of mergers and acquisitions.
Only 15 percent of the facilities are single entities not owned  or controlled by another firm as of December 31,
1986. Approximately 35 percent of all parent firms are controlled in turn by another company.  Large R&D costs,
including registration fees, may be a reason why the majority  of pesticide producers tend to be part of a larger,
multi-facility firm.

        In 1986,59 firms produced in-scope pesticides in the United States. These firms owned 90 facilities, which
produced 136 individual or classes of in-scope PAIs.   Hie number of PAIs manufactured by each firm varies
                                                 3.35

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                               Figure 3.15
    Comparison of All Manufacturing Capacity Utilization
     and Pesticide Production Capacity Utilization Rates
Capacity Utilization
Rate
 90-i


 80-


 70-


 60-


 50-


 40-


 30-
 20
,||IU||"«	I	IIIIIMIIM'II'
        Mi
,,,111111"" Manufacturing

        Insecticide
        Pesticide
                               Herbicide

                               Fungicide
            r^     i      i       i       i       i       i      i
   1980   1981   1982   1983   1984   1985   1986   1987   1988

                                  Years
            Pesticide
            Herbicide
            Insecticide
            Fungicide
        mi  All Manufacturing


        Source:  USDA Agricultural Resources: Situation and Outlook Report
                AR-13 February, 1989.
                Statistical Abstract of the United States , 1989.
                                3.36

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 (see Figure 3.16). Approximately 41 percent of the firms owning in-scope facilities in 1986 produced only one PAI,
 although one firm manufactured 11 PAIs.

        According to the Census data, approximately 71 percent of the firms owned only one in-scope pesticide
 manufacturing facility.  The remaining firms tended to own two or three in-scope pesticide producing facilities.
 Of these  firms, 41 percent produced the same pesticide at more than one of their in-scope facilities.  Figure 3.17
 presents the number of in-scope facilities owned by firms.

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

 3.5    Industry Market Structure

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

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

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

        A significant number of the PAIs in the Census are produced by only one firm. Given that patent protection
 exists for pesticide products,  it is possible that there is room for only one producer of each PAI, and that each
 producer maintains market power for that PAI.  Figure 3.19 exhibits data to support this assumption, revealing that
 107 of the 136 individual or classes of in-scope PAIs manufactured in 1986 were produced by only one firm. The
 concentration of individual PAI production among single firms may be countered, however, by the fact that some
pesticide products are substitutable. Consequently, individual firms that do not produce the same PAIs may produce
products that compete in the market place.
                                                  3.37

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                    Figure 3.16

Number of Individual or Classes of In-Scope PAIs
            Produced by Firms, 1986
  123456789    10  11
               Number of In-Scope PAIs Produced
 Source: Census.
                        3.38

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                            Figure 3.17


      Number of U.S. In-Scope Pesticide Manufacturing
               Facilities Owned by Firms, 1986
Number of
Firms
     10
                      2
               Number of Manufacturing Facilities Owned by a Single Firm
      Source:  Census.
                            3.39

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                          Figure 3.18



           Composition of Firm Sales, 1986
                   (Summed Across All Firms)
                                Pesticide1
                               Manufacturing
                                   5%
                                           Formulating and/1
                                            or packaging
                                                1%
    Activity not related to In-Scope or
       Out-of-Scope pesticides
               94%
1 Includes in-scope and out-of-scope production activity.

  Source:  Census.
                              3.40

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                            Figure 3.19
                                     *



    Number of Firms that Produce an Individual  PAI or

                       Class of PAI, 1986
Number of PAIs
            150^
                           136 PAIs Produced
            125-
            100-
             75-
             50-
             25-
3 PAIs produced by 4 or more firms

5 PAIs produced by 3 firms




21 PAIs produced by 2 firms
                                         107 PAIs produced by only 1 firm
        Source:  Census.
                             3.41

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

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

       Existence of Economies of Scale
                                                                           •I
       The relative capital intensity of the pesticide industry is one indication of the extent to which economies of
scale exist.  Although technology determines the minimum efficient size of a facility,  efficient scales of production
appear to vary widely across PAIs.  Comparing facilities that produce the same PAIs suggests that there is a large
difference in the quantities produced. Facilities can range in annual  output from a few thousand pounds to more
than 10 million pounds of the same PAL The fact  that there are vast  differences in the size of facilities producing
the  same  product indicates that  economies  of scale probably are not a major  factor within the pesticide
manufacturing industry.21

        Research and Development
        Large capital outlays for R&D represent another barrier to entry.  Research used to develop new, patented
products is considered to be key to chemical producers' success.   Patents are important to the pesticide industry
because they give producers a monopoly in the production of that pesticide and allow the producer to price a product
above cost.  Pesticide products carry a 17-year patent; firms need-this patent protection to price above costs to
     ''A higher ratio of value added by manufacturing to gross book value of depreciable assets may also result from
 the use of older equipment.
     2tThe analysis of economies of scale within the pesticide manufacturing industry is complex. Because multiple
 PAIs may be produced on the same line, using the same equipment, comparing production across individual PAIs
 may not provide definitive evidence on whether economies of scale exist.
                                                   3.42

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recover their R&D expenditures.22 Since different patented products may compete for the same use, however, pure
monopolies do not exist.

        Although patented products play an extremely important role in the industry, there are unpatented products
on the market that are profitable.  The existence of unpatented products signifies that patents alone do not protect
profits. Nevertheless, patents for most pesticides are instrumental hi recovering R&D costs, and are also a factor
in restricting market entry.

        Research and development costs are one of the fastest growing components of fixed costs that firms face.
In 1976, the average R&D costs of a single new pesticide were estimated at $10 million (1986 dollars), while hi
1987 the estimated costs to develop a single new pesticide were $40 million (1986 dollars) (U.S. Department of
Commerce, 1987). The increase in costs is partly due to more stringent toxicity tests performed hi compliance with
environmental regulations. Specifically, use restriction based on the amount of residue toxicity left on food products
places new pesticide products under greater scrutiny than existing pesticide products. According to industry experts,
it can take 10 years to bring a chemical pesticide from the R&D stage to registration  with the EPA (Rich, 1988).
To register a pesticide for a major food use, there is a  flat fee of $150,00023.  In order to support R&D and the
registration of new products, firms must be able to generate  sufficient pesticide sales.  The need for a large sales
volume may be one explanation for the  number of mergers and acquisitions in the 1980s.

        The Census data indicate that total average R&D costs for all firms represent about 4 percent of total facility
sales.24 Different levels of R&D are sustained, depending upon the size of firms.  Table 3.11 breaks down R&D
costs as a percent of total facility sales for three firm sizes.25  According to  the Census, firms with total revenues
of between $1 billion and $6 billion have the highest R&D expenses as a percent of sales. High R&D costs and
the uncertainty of product success may make it difficult for new firms to put up the capital and to absorb the risk
from R&D ventures.  These costs may bar entry, with the result that the industry becomes less competitive.
    ^After a pesticide product is patented, the manufacturer  must register the product for use.  Therefore,
manufacturers often have fewer than 17 years to recoup their R&D costs.
    23The annual maintenance fee is $425 for each registration up to 50 registrations; and $100 for each additional
registration, with the exception that no fee is charged for more than 200 registered products held by any registrant
(HFRA, Section 4).
    2*The Census collected total facility, not pesticide-specific, R&D costs.
    MR&D costs were estimated based on firm size rather than  facility size, because firm size is generally more
important than facility size hi determining the level of R&D.'
                                                   3.43

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                                             " - Table 3.11
                 Research and Development Costs as a Percent of Total Facility Sales, i$86
                                     " -  ,-  -  JjyFirmSize1                     '	
         Firm Size (Annual Revenues)
       ^
Facilities
                                                                                 Percent of R&D
                                                                                 Costs' to^Total
                                                                                 Facility Sales
         Revenues less than $1 Billion
         Revenues between $1 Billion and $6 Billion
         Revenues greater than $6 Billion
         All Facilities
      46
      26
      12
      842
3.3%
5.5%
3.7%
                                                                                       4.0%
         1 Average R&D to sales ratio across all facilities, by firm size.
         2 Excluded from the 88 facilities that provided financial data are four facilities that did not
           report firm revenues.
         Source:   Census.
3.5.B  Vertical Integration
       Vertical integration is the extent to which the different stages of production are organized in a single firm.
According to  the Census, both small and large firms tend to be vertically integrated, engaging in  the R&D,
manufacturing, and formulating/packaging of pesticides.

       Compared to developing and manufacturing PAIs, formulating/packaging is less expensive but often adds
considerable value to the end product  As mentioned previously, data from the Census indicate that 50 of the 90
in-scope PAI manufacturing facilities also engaged in formulating/packaging.  When evaluated at the  firm level,
these data reveal that 36 of the 59 firms represented in the Census have PAI formulating/packaging capabilities at
one or more of  their in-scope  PAI  manufacturing facilities.  In  addition,  four of the firms that do not
formulate/package PAIs at their in-scope PAI manufacturing facilities reported that they own other facilities at which
PAIs are formulated/packaged.  Of the 59 firms represented in the Census, therefore, 40 (68 percent) have both
PAI manufacturing and formulating/packaging capabilities.

       In addition to in-house formulating/packaging capabilities, many firms, both large and small, contract out
some aspects of the production process (tolling), typically the formulating/packaging process.  It is estimated that
approximately 80 percent of the formulated pesticide business is controlled by PAI manufacturers, either directly
with in-house capacity or indirectly through contracting (Kline & Company, 1990).
                                                  3.44

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

       Two main types of restructuring occurred in the United States in the 1980s. First, foreign firms  acquired
U.S. firms either in total or in part;25 second,  U.S. firms acquired or merged  with other domestic firms. Some
industry experts attribute the foreign component of restructuring to the volatility of the U.S. dollar from 1980 to
1990. The strong U.S. dollar prior to 1985 strengthened foreign firms' positions in the world market, because U.S.
products were more expensive relative to foreign counterparts. The increase in environmental controls implemented
in the United  States during the 1980s also contributed  to the price increase of U.S. products.   As the dollar
weakened after 1985, foreign firms  began purchasing production capacity in the United States.  As stated above,
mergers and acquisitions among U.S. firms may have resulted primarily from  the firms' need  to generate large
amounts  of  sales to support the rising costs of both R&D and environmental  compliance (U.S. Department of
Commerce;  1989d and Sine, 1990).

       In a concentrated industry, the dominant firm or firms are better able to influence market  outcomes to their
advantage. Industry concentration is frequently measured by concentration ratios, which are the percentage of total
sales accounted for by a given number of firms.  The Bureau of the Census calculates concentration ratios for the
top 4, 8, 20, and 50 producers of basic pesticides. These concentration ratios are displayed in Table 3.12. In SIC
28694  (pesticides  and other  synthetic  organic agricultural chemicals  except preparations),  the  top  four  firms
accounted for  54 percent of the value  of shipments in 1982.  In SIC 2879 (agricultural chemicals, n.e.c., and
pesticide preparations and formulations), the top four firms accounted for 39 percent of the value of shipments in
1982 and 49 percent in 1987, indicating increased consolidation in the industry. Examining concentration  ratios by
pesticide type in Table 3.12 shows the  fungicide preparations market to be the  most concentrated and insecticide
preparations to be the least concentrated.
    ^Based on parent firm information reported in the Census, 9 of the 90 facilities (10 percent) were owned by
foreign companies in 1986. Note: Foreign ownership was not explicitly requested in the Census, and was determined
based on the parent firm  address reported hi the Census in conjunction with information presented in Dun and
Bradstreet's Million Dollar Directory.
                                                  3.45

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                               Figure 3.20

            Number of Facilities Acquired by Firms
                         (From Jan. 81 to Dec. 86,
                         by Method of Acquisition)
Number of
Facilities
Purchase
Merger
Founded
                                                       Other Status1
                              Method of Acquisition
                  10f the two facilities that reported other, one indicated that the facility
                   was acquired through the contribution of capital by the parent
                   company; the other indicated that the facility was newly constructed.
           Source:  Census.
                                  3.46

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Table 3,12
Share of Value of Pesticide Shipments Accounted for by the
4, S, 20, and 50 Largest Companies, 1972-1987
Year

1982
1977
1972
4 largest 8 largest 20 largest 50 largest
Total companies companies companies companies

-------
       Concentration ratios based on sales of in-scope pesticides were calculated using the Census data.  These
ratios, shown in Table 3.13, indicate that the four largest firms account for 50 percent of the value of all in-scope
pesticide shipments.  Like the Bureau of Census data, examination of concentration ratios by pesticide type based
on  the data presented in Table 3.13 shows that the herbicide and  fungicide markets are the most heavily
concentrated, while the insecticide market is the least concentrated. The concentration ratios indicate that there may
be no dominant firm in the industry as a whole.  The pesticide industry is highly differentiated, however, meaning
that there may be dominant firms in individual pesticide markets.

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

        Price elasticities of demand were estimated for each  pesticide cluster in the analysis.27  In order to develop
the elasticity estimates, the EPA developed a comprehensive approach, including:

        (1)      Review of empirical studies of pesticide production and use;
        (2)      U.S. Department of Agriculture's analysis  of the price elasticity of demand for  food commodities
                 (USDA, 1985, 1989);2*
        (3)      Feasibility of employing non-chemical, non-biological pest control methods (Pimental, D., et al.,
                 1991).29  (The greater the  feasibility of substitution, the higher the expected  price elasticity of
                 demand.);
         (4)      An analysis of pesticides'  contribution to the cost of  production of a commodity, based  on
                 estimates of  the cost of production in the farm sector (USDA, l9S9a).x  (The greater the
                 contribution of pesticides to the cost of production, the higher the expected  price elasticity of
                 demand.);
     »This section is based on detailed analyses of pesticide demand elasticities. See Appendix C for further details.
     ^USDA (1985).  U.S. Demand for Food: A Complete System of Price and Income Effects., and U.S.D.A.
  (1989). Retail to Farm Linkage for a Complete Demand System of Food Commodities.
     »Pimentel, D., et al. (1991). Environmental and Economic Impacts of Reducing U.S.  Agricultural Pesticide
  Use. Pest Management in Agriculture.  CRC press.
     *>USDA (1989a). Economic Indicators of the Farm Sector: Cost of Production, 1987.  February.
                                                   3.48

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Table 3.13
Share of Value of fa-Scope Pesticide Shipments Accounted
4, 8, and 20 Largest Sums, 1986
4 largest
firms
8 largest
firms
20 largest
firms
for by the
Total
Number of Facilities
All Pesticides 12
Fungicides 4
Herbicides 10
Insecticides 9
Concentration Ratio (Percent
All Pesticides 50
Fungicides 67
Herbicides 69
Insecticides 57
23
9
17
13
of Sales)
73
90
88
81
43
24
33
28

95
1001
99
99
90
30
39
36
,
100
100
100
100
Total Sales (Million $)
All Pesticides 1,945
Fungicides 278
Herbicides 1,695
Insecticides 531
2,844
375
2,169
749
3,645
415
2,456
919
3,884
416
2,463
928
1 Remaining six firms constitute less than 1% of total fungicide sales.
Source: Census.



        (5)      Analysis of the marginal productivity of pesticides (USDA, 1989, USDA, 1989a);31 and
        (6)      Expert opinions within the OPP.

        The estimated price elasticities of demand vary significantly among the clusters, since each cluster faces
different market forces. Elasticity of demand for pesticide clusters with in-scope products in 1986 varies among
these clusters from -0.12 to -1.38 (see Appendix C, page 62).  Despite the wide range of demand elasticities among
pesticide clusters, 38 of the 45 have inelastic demand, i.e., the absolute values of the demand elasticities are less
than 1.  This indicates that demand at a, cluster level (although not necessarily at the PAI level) will not vary
significantly with moderate price increases.
    31USDA (1989). Retail to Farm Linkage for a Complete Demand System of Food Commodities., USDA (1989a).
Economic Indicators of the Farm Sector: Costs of Production, 1987.  February.
                                                  3.49

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3.6    International Trade

       The U.S. pesticide industry holds a sizable share of the world export market for pesticides: approximately
23 percent of the total value of shipments in 1987 (United Nations, 1987, and Department of Commerce,  1989d).
During the last decade, however, the margin between exports and imports has been declining, although the United
States remains a net exporter of pesticides.  Both the strong U.S. dollar from 1980 to  1985 and increasing foreign
competition contributed to the change in U.S. position. U.S. imports, although increasing, do not appear to threaten
the market power of domestic  firms.

3.6.A  U.S. Pesticide Imports and Exports
       Table 3.14 shows U.S. import and export values for pesticides from 1978 through 1987. The table shows
that pesticide imports increased more than exports over this period.  On average, the value of pesticide imports
increased by 7  percent,  while the value of pesticide exports increased by only  1  percent.  Although imports
increased substantially during the period, the United States maintained a positive trade balance.

       Similarly, Tables  3.15  and  3.16  show  import and  export  values  for herbicides and  insecticides,
respectively.32   Exports of herbicides, which comprise the  largest U.S. pesticide export, witnessed a dramatic
decline in the 1980s. In particular, the value of herbicide exports fell by 64 percent in real terms between 1984
and 1985.  In the same year,  herbicide imports increased by 41 percent to fill the vacuum left by a facility that
closed.33  In 1985,  the United States was a net importer of herbicides.  Over the ten year period from 1978 to
 1987,  exports of herbicides decreased by 5 percent per  year, while imports increased by  12 percent per year.
Although herbicides have been given the most research funding of all pesticide types, thereby exhibiting the most
 technological progress, they have also been the most susceptible to violations of intellectual property rights  due to
 the lack of patent protection outside the United States.  Of the three major groups  of pesticides, herbicides had the
 least favorable ratio of exports to imports hi the 1980s (U.S. Department of Commerce, 1989d).

        Insecticides  comprise the second largest component of U.S. pesticide  exports.   From 1978 to 1987,
 insecticide exports decreased by 4 percent as imports increased by 9 percent. In spite of these trends, insecticides
 showed a positive trade balance throughout the period.  Part of the decline in insecticide exports may be attributed
 to the decline in chlorinated hydrocarbon insecticide production.
     ^Similar data is unavailable for fungicides.
     MMuch of the decline hi exports and increase in imports'was due to the closing of one facility.
                                                   3.50

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Table 3.14
ILS. Import and Export Values for All Pesticides
(in thousand 1986 $)
Tear
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Average
Annual
Change
Source:
Value of
Imports
260,098
268,846
317,718
307,553
284,196
271,512
322,874
413,772
402,782
414,800

% Change
65%
3%
18%
-3%
-8%
-4%
19%
28%
-3%
3%
7%
Value of
Exports
1,238,508
1,320,896
1,241,047
1,132,425
1,157,006
1,173,584
1,357,235
1,231,455
1,299,974
1,305,959

United Nations International Trade Statistics
% Change
99%
7%
-6%
-9%
2%
1%
16%
-9%
6%
<1%
1%
Trade
Balance
978,410
1,052,050
923,329
824,872
872,810
902,071
1,034,361
817,683
897,192
891,159

% Change
111%
8%
-12%
-11%
6%
3%
15%
-21%
10%
1%
-1%
Yearbook, 1978-1987
Table%3.15
U.S. Import and Export Values for Herbicides
, , -,' {in thousand 1986 $}
-
Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Average
Annual
Change
Source:
Value of
Imports %
88,467
146,755
160,924
158,292
166,396
119,767
157,569
221,698
192,526
183,863


—

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


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


—
Trade Statistics

% Change
NA
7%
<1%
-7%
2%
12%
12%
-64%
-7%
18%


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


—

% Change
NA
-7%
-4%
-10%
1%
34%
6%
-102%
157%
820%


-10%
Yearbook, 1978-1987
3.51

-------
• • , ;; Table 3.16 % j '-; , '"•'"•>"" ' i§ (|1.
U.S. Import and Export Values for Ihsedfcides
0n thousand 1986 $)
Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Average
Annual
Change
Source:
Value of
Imports
60,539
79,350
90,055
90,854
73,625
74,508
65,906
76,508
90,964
111,376

% Change
- Value of
Exports
NA 304,671
31%
13%
1%
-19%
1%
-12%
16%
19%
22%
9%
"United Nations International
358,331
301,474
294,367
289,169
268,194
345,073
239,421
251,425
204,867

Trade Statistics
« / fV
' % Change
NA
18%
-16%
-2%
-2%
-7%
29%
-31%
5%
-19%
-4%
Trade
Balance
244,132
278,981
211,418
203,513
215,544
193,686
279,167
162,913
160,461
93,491

'\ >i>
% Change*
NA
14%
-24%
-4%
6%
-10%
44%
-42%
-2%
-42%
-7%
Yearbook, 1978-1987
       Table 3.17 presents U.S. pesticide exports as a percent of the value of total U.S. pesticide shipments, and
U.S. pesticide imports as a percent of new supply for 1978 to 1987. The table shows that pesticide exports as a
percent of the value of shipments have decreased over the period, from 25 percent in 1978 to 21 percent in 1987,
while the value of overall shipments increased over the same period.  These data, coupled with data from Table 3.5
showing a decrease in the quantity of pesticides produced and sold, indicate that U.S. producers have increased sales
to domestic markets.  Table 3.17 also shows that imports have maintained approximately the same share of new
supply:  5 percent in 1978 and 6 percent in 1987.

3.6.B U.S. Pesticide Industry in the World Market
       Table 3.18 shows U.S. trade in pesticides as a percentage of the world market economy for pesticides from
1978 to 1987. In 1978, U.S. pesticide exports accounted for 26.2 percent of the world export market. In 1981,
the U.S pesticides exports percentage peaked, capturing 30.5 percent of the world export market. In 1987, the U.S
share of the world pesticide market was 23.4 percent, the lowest percentage of the preceding ten years.
                                                  3.52

-------
•&,
£
I
'3
        o
        0
        
                   III
                         3.53

-------

Year
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
Source:
U.S. Trade as a
U.S. Share
of World
Imports
6.8
6.9
8.7
11.7
7.6
7.5
8.5
10.4
8.6
8.1
Table 3.18
Percentage of the World Market Economy
1978-1987^
' % Change in
Share of
" " Imports
32.3%
.7%
26.6%
34.7%
-34.9%
-1.5%
12.5%
23.5%
-17.4%
-6.4%
U.S/Shareof
World Exports
> , •• if " -
26.2
26.3
24.7
30.5
27.1
27.4
29.3
26.8
24.9
23.4
" -. •:"*
for Pesticides,
f »*, &*•••> r* > , ,, >,
% Change in Share
of Exports
< « vt .fl
40.5%
.5%
-5.9%
23.4%
-11.3%
1.0%
7.0%
-8.5%
-7.0%
-6.4%
United Nations International Trade Statistics Yearbook, 1977-1987
       The shift in the U.S. pesticide export position is due, hi part, to the increased strength of the dollar relative
to other currencies. As mentioned above, the. strong U.S. dollar from 1981 to 1985 caused U.S. products to be
more expensive than foreign products, thereby contributing to the decline.  Because exports and imports do not
respond immediately to changes in currency  exchange rates, it may take months,  even years,  for changes in
exchange -rates to have an impact.  The steady reduction in exports, resulting from the price increase of U.S.
products, may not be evident hi the trade statistics until after 1984 due to the length of contracts for pesticide sales.

       Foreign competition hi  the pesticides industry has increased substantially hi the last decade, causing a
deterioration hi the competitive position of U.S.  firms hi recent years.  Table 3.19 lists the leading pesticide
exporting countries hi the world  economy from 1979 to 1987. Although the United States remains the largest world
exporter of pesticides, its export lead has decreased as other countries' pesticide export markets have matured.34
In particular,  the United Kingdom, Switzerland, Italy, and Brazil have increased their share of world pesticide
exports.

        As indicated hi Table 3.18, the U.S. share of world imports for pesticides increased during the  1980s.
Between 1982 and 1984, the most dramatic expansion hi manufacturing facilities took place outside western Europe
        stated previously, some of the U.S. companies included hi the Census are owned by foreign entities.
                                                  3.54

-------
, Table 3.19
Value of Pesticide Exports for Leading Export Nations
as a Percent of the Total World Pesticide Exports, 1979-1987


Country
United
States
Germany
Fed. Rep.
United
Kingdom
France
Switzerland
Netherlands
Japan
Italy
Belgium
Brazil
TOTALS


1979
26.3

20.0

11.4

10.6
7.4
3.8
3.2
2.4
6.3
0.6
92.0
"

1980
24.7

18.2

11.3

9.6
7.3
4.7
3.2
2.7
8.8
0.6
91.1


1981
25.9

18.2

12.1

9.6
7.3
3.6
3.6
2.6
6.3
0.8
90.0
Source: United Nations International
f

1982
27.1

17.8

11.5

8.8
7.5
4.0
4.5
2.7
5.8
1.0
90.7


1983
27.4

18.8

12.1

10.0
8.3
4.8
5.8
3.2
0.9
1.1
92.4
Trade Statistics


1984
29.3

18.1

12.2

9.9
8.3
5.0
4.7
2.8
0.9
1.3
92.5


1985
26.8

18.5

13.9

10.1
8.2
5.5
3.7
2.8
1.3
1.0
91.8


1986
24.9

20.8

12.3

10.5
8.0
5.9
3.0
3.7
1.8
1.3
92.2


1987
23.4

18.5

14.1

10.6
9.2
5.8
3.4
3.7
1.9
1.5
92.1
Average
Annual
Change
-0.37%

-0.18%

0.34%

0.00%
0.23%
0.25%
0.02%
0.16%
-0.56%
0.12%
0.01
Yearbook, 1979-1987
and the United States.  This expansion took place in major markets such as Brazil, India and eastern Europe.
Together with the development of pesticides manufactured in Taiwan and South Korea, this expansion further
increased the competition for products manufactured in western Europe and the United States (Shenton, 1989).

       Table 3.20 shows the value of pesticide imports from leading importers to the United States as a percentage
of total U.S. pesticide imports.  As seen in this table, although imports from western Europe still comprise the
largest share of the U.S. import market, imports from other countries (such as Brazil) realized substantial increases
in exports;to the United States.
                                                  3.55

-------
Value of Pesticide Imports for Leading Importers to the United States
as a Percent of Total U.S. Imports, 1980-1987
Country
Canada
West
Germany
United
Kingdom
France
Switzerland
Netherlands
Japan
Italy
Brazil
TOTALS
1980
2.5
32.3

12.5

2.7
24.2
3.8
12.1
2.1
0.4
92.6
1981
3.8
22.1

NA

3.4
27.4
5.0
16.0
2.5
1.2
81.4
1982
2.8
23.3

8.4

3.0
34.1
4.2
9.1
2.7
2.8
90.4
Source: United Nations International
1983
3.9
21.1

10.9

3.7
26.2
4.1
9.4
2.6
6.3
88.2
1984
5.8
25.6

13.7

4.4
20.8
4.7
4.7
2.2
5.5
87.4
Trade Statistics
< V
^ v
, 1985
4.1
24.7

11.0

4.1
29.9
5.5
3.5
2.2
4.8
89.8
Yearbook,
1986
3.6
18.3

15.8

5.1
19.1
3.8
4.5
4.5
11.0
85.7
J987
4.7
17.7

16.0

6.4
11.5
3.1
4.4
3.4
12.1
79.3
Average
Annual
Change
0.31%
-2.09%

0.50%

0.53%
-1.82%
-0.11%
-1.09%
0.18%
1.68%

1982-1987
3.7    Analysis of Actual Facility Closures

       The pesticide industry has undergone substantial restructuring since 1986. As mentioned earlier in the
Profile, factors such as the weakened dollar during die last half of the 1980s, the worldwide downturn in planted
agricultural acreage, and changes in consumer preferences have impacted facility operations.  These factors have
influenced companies' decisions to not re-register or voluntarily cancel PAIs. Additionally, government actions such
as the re-registration requirements  promulgated under the 1988  FEFRA  amendments  have led to  pesticide
cancellations and use restrictions.

       At least 15 of the 90 pesticide manufacturing facilities included in the Census have discontinued their
pesticide operations  since 1986.35 In addition to the 15 facility closures, 12 facilities have closed PAI product
lines and 9 facilities were acquired by another company or underwent some type of restructuring.  This section of
    35 These facility closures reflect only the impacts with which EPA is familiar and are based on periodic contact
with the pesticide industry.  A complete assessment of the financial impacts on pesticide manufacturers since 1986
would require contacting each facility in the industry.  One of the 15 facilities has declared bankruptcy and may,
or may not, have actually discontinued pesticide operations. '
                                                   3.56

-------
the Profile focuses primarily on the Census facilities known to have incurred the most dramatic of these impacts -
discontinuation of pesticide operations.

        EPA assessed patterns among the facilities that have ceased PAI manufacturing by analyzing the location
of the facilities, types of pesticides produced, facility size, mix of sales activity, and pesticide and total facility profit
margins in relation to the facilities in operation.36'37 Facility location and facility profit margins did not differ
substantially between facilities that have discontinued pesticide manufacturing activities and  those in operation.
However, facility size  (as measured by in-scope pesticide sales  and total  facility sales), mix of sales activity,
pesticide-level profit margins, and  the types of pesticides produced, were notably different.

        Average  1986 in-scope pesticide sales for facilities that have continued pesticide operations were  $52.2
million, while facilities that have ceased pesticide production had average in-scope pesticide sales of $8.3 million
in 1986.  Similarly, average 1986  facility sales were  $163 million for open facilities and $36 million for closed
facilities.  These data indicate that, on average, the  size of in-scope pesticide and  total facility operations for
facilities that  have discontinued their pesticide operations were significantly smaller than those that continue to
produce PAIs. When comparing median in-scope pesticide and facility revenues, the difference between the two
groups of facilities are  less dramatic: $13.8 million versus $3.4 million  in 1986 in-scope pesticide revenues and
$43.4 million versus $34.8 million  in 1986 facility revenue for open and closed facilities, respectively. These data
indicate that the largest  facilities, both in terms of in-scope and total facility revenues, are not among the facilities
known to have discontinued pesticide operations.

        Assuming that no new products or companies entered the industry after 1986 and that production at closed
facilities was not transferred, the pattern of closures indicates that industry concentration has increased. However,
to definitively conclude that industry consolidation has  increased, information on  the fate of production at the
affected facilities, as well as data on new products and producers, would be required.  In the absence of such data,
information from secondary sources was used to  assess changes in industry concentration since 1986.

        A 1988 Chemical Week article entitled, "Environmental Concerns Force Global Changes in the Market,"
claimed that at least seven major agricultural chemical company buyouts occurred from 1985  to 1988.  Much of
this consolidation was from U.S. firms selling to non-U.S. firms (Chemical Week,  1988).  Industry experts cite the
cost of pesticide registration as one of the driving forces in industry consolidation, explaining  that  companies can
expect to spend $100 million per year on new product development and registration, and that sales of $1 billion per
    36 Profit margins were calculated as pre-tax pesticide and facility profits as a percent of sales.
    37 Financial data were not provided by one of the 15 facilities.  Therefore, the financial information presented
in this section reflects data for 14 facilities.
                                                   3.57

-------
year are needed to cover these expenses (Chemical Week, 1988).  This surge in research costs during the 1980s
led to the emergence of the theory of "critical mass," the amount of sales needed to support an R&D program (Sine,
1990).  During the late 1980s, the pesticide industry began consolidating, in part, to obtain critical mass (Sine,
1990).  This consolidation suggests that industry concentration has increased since 1986.

        The mix of facility sales activity also differs considerably for open versus closed facilities.  On average,
facilities that remain  open obtained 45 percent of their revenues from non-pesticide operations in 1986, while
facilities that closed derived 63 percent of their revenues from non-pesticide operations in 1986.  These data show
that facilities that discontinued PAI manufacturing were more involved in non-pesticide production.

        Unlike facility profit margins, pesticide-level profit margins do differ substantially for open versus closed
facilities.  Pesticide profit margins for facilities that closed averaged 8 percent,  while open facilities averaged 11
percent. In contrast,  facility level profit margins averaged 12 percent for  open facilities and 15 percent  for closed
facilities.  Together with data on the mix of facility sales activities, facility profit margins indicate that non-pesticide
operations at facilities that discontinued PAI manufacturing were more profitable than pesticide activities.

        The types of pesticides produced by individual facilities - herbicides only, insecticides only, fungicides only,
others only (see Table 3.2 for a list of other pesticides), and multiple types of pesticides - were examined to assess
whether facility closures were concentrated hi one of these broad market segments.  Census data show  that the
majority of the facilities that have continued PAI manufacturing produce multiple types of pesticides (34 percent),
followed  by those that produced herbicides  only,  insecticides only,  fungicides only, and  others  only  (see
Table 3.21).   In contrast,  closures were equally concentrated among facilities that produced herbicides only,
fungicides only, and  those producing multiple types of pesticides.

        When analyzing the number of each pesticide type affected by both facility and product line closures, Census
data show that impacts fell disproportionately among fungicides - 35 percent of the in-scope fungicide PAIs produced
in 1986 were impacted by a facility or product line closure. In contrast, 19 percent of herbicide PAIs, the most
commonly produced  in-scope PAI in 1986, were impacted.

        EPA also examined facilities that discontinued PAI production and those that closed product lines and found
that 18 of the  98 (18 percent) PAIs manufactured by a single producer were impacted by these facility changes,
while 9 of the 33 (27 percent) PAIs produced by multiple facilities were impacted. These data indicate that closures
may be more likely to occur when there are multiple producers of a product.  Similarly, when impacted  PAIs were
examined at the cluster level, the data showed that PAIs classified in clusters with multiple in-scope PAIs hi 1986
were more likely to be impacted than clusters that included only one in-scope PAI. Specifically, none of the clusters
with a single in-scope PAI in 1986 contained PAIs that were impacted, while 74 percent of the clusters with multiple
                                                    3.58

-------
Table 3.21
Distribution of Facilities
by Type of Pesticide Produced
Pesticide Type
Herbicides Only
Fungicides Only
Insecticides Only
Other Pesticides Only
Multiple Types of Pesticides
Total1
1 Totals may not equal 100%
Source: Census.
% of All
Facilities
23%
13%
20%
9%
34%
100%
due to rounding.
% of Open
Facilities
23%
11%
21%
11%
34%
100%

% of Closed
Facilities
27%
27%
20%
0%
27%
100%

 in-scope PAIs in 1986 were impacted.  Together these data suggest that product competition, at both the PAI and
 cluster level, increases the likelihood of closure.

        Factors such as the public's perception of pesticide products and government actions including re-registration
 and restricted uses have resulted in changes in the pesticide market since 1986.  Census data indicate that factors
 such as increased competition among products, facility size, and low pesticide profit level in relation to non-pesticide
 operations may be characteristic of facilities known to have been affected by changing market conditions.

 3.8    Summary

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

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

       Although the United States remains  a net exporter of pesticides, the value of pesticide exports decreased
while imports increased during the 1980s.   Factors such  as the strong dollar and the implementation of more
stringent environmental regulations in the United States, which made U.S. products more expensive relative to
foreign products, contributed to the deterioration of the United States's trade position in the mid-1980s. Although
competition from  western European countries is  still the most predominant  influence on the United  States's
competitive position in the  world pesticide market, there is increasing competition outside western Europe in
countries such as Brazil, Korea, and those in eastern Europe.
                                                  3.60

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                                         Chapter 3 References
 Chemical Week (1988).  Environmental Concerns Force Global Changes in the Market. May 4, 1988.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

U.S. Department of Commerce, International Trade Administration (1987).  7957 U.S. Industrial Outlook.
       Washington, D.C., January.

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

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

U.S. Department of Commerce, Bureau of the Census (1989b).  1987 Census of Manufactures.
       Washington, D.C., January.
                                                3.61

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

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

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

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

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

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

U.S. EPA (1992).  Pesticide Containers: A Report to Congress.  Office of Pesticide Programs,
       May.

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

U.S. International Trade Commission (1977-1988).  Synthetic Organic Chemicals, U.S. Production and Sales.
       Washington, D.C., Annual.
                                                  3.62

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                               Chapter 4:  FACILITY IMPACT ANALYSIS
 4.0
Introduction
         This chapter presents the methodology for projecting impacts of the pesticide manufacturers effluent
 limitations guidelines and  standards at the facility level  and describes  the results of the analysis.   A  few
 methodological changes have been implemented since the proposed rule in response to comments and internal EPA
 review, resulting in no significant changes in projected impacts.  The following text describes  the methodology
 employed hi the final analysis.  Changes from the methodology used in the proposed analysis are footnoted and are
 fully explained hi the Administrative Record.

         As discussed hi Chapter 1, the facility analysis is the principal building block of the entire economic impact
 assessment.  The facility impact analysis is characterized by the following:

         (1)      use of economic models to estimate pre- and post-compliance costs, prices, and  quantities for
                 groups of pesticide active ingredients (PAIs) produced by individual facilities;
         (2)      evaluation of facility after-tax cash flow to project facility closures;1
         (3)      comparison of unit prices to unit fixed costs plus unit variable  costs to project product  line
                 closures; and
         (4)      use of financial ratios to identify facilities that are expected to sustain significant financial impacts,
                 short of closure.2
The cost, price, and quantity outputs from the first step provide input to the facility closure,  product line closure,
and significant financial impact analyses of steps 2, 3, and 4.  The analysis evaluates these three impacts hi a
hierarchical manner:  if a facility closes, product line closures and other  significant impacts are not evaluated; if
a facility closes a product line, other significant impacts are not evaluated.  This hierarchy corresponds to  the
severity of the projected impact; i.e., a facility closure is more severe than a product line closure, which is more
severe than a significant financial impact.
      In the  economic impact  assessment for the proposed rule, facility closure was evaluated by comparing
discounted cash flow to liquidation value. The analysis now projects a closure if a facility's after-tax cash flow is
negative.  The new methodology was adopted because of the subjective and imprecise nature of estimates of
liquidation values. The questionable credibility of the estimates of liquidation value provided by respondents in the
Census became obvious as data from the pesticide formulator/packager/repackager Survey became available for
comparison.  Full details on this methodological change are available in the Administrative Record.
     2       .
      Appendix E of this report compares compliance costs to facility revenue.  This financial ratio was added to
the analysis following proposal of effluent limitations as an additional indicator of adverse financial impacts that
facilities may face due to the regulation. This ratio is not discussed in Chapter 4.
                                                   4.1

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        Based on data from the Census, a total of 90 pesticide manufacturing facilities (owned and operated by 59
firms) that manufacture one or more in-scope PAIs were potentially subject to regulation.  However, EPA has
information indicating that 15 of these facilities have closed their in-scope PAI manufacturing operations since 1986
(the Census base year). Also, metallo-organic (Subcategory B) PAIs are no longer considered for regulation under
the final rule.  Two facilities producing only Subcategory B PAIs as their only in-scope products are no longer
counted as potentially subject to the regulation. Therefore, a total of 73 pesticide manufacturing facilities (owned
and operated by 49 firms) that manufacture one or more PAIs are potentially subject to regulation.3  Although 73
facilities are potentially subject to the regulation, the EIA only analyzes 72 facilities for economic impacts.  The
facility excluded from the economic analysis is a research and development facility with no revenues expected from
the manufacture of in-scope PAIs and no compliance costs.

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

4.1     Economic Model

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

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

        Each facility must decide the quantity of each pesticide to produce, given certain technological and capacity
constraints. Some pesticides may have to be produced together if one is a byproduct of the manufacturing process
of another. Pesticides may  also be produced as by-products of other organic chemical  manufacturing.  Pesticide
manufacturing equipment may be flexible enough so that the facility may choose to use it to produce an alternate
product, perhaps with minor modifications. A producer may also elect to use a facility at a higher level of capacity
(perhaps by adding an additional  shift), thereby increasing the production of one or more pesticides.
      Two of the 73 facilities that are counted as subject to the regulation have closed in-scope PAI operations since
 1986 but the production either has been or may be transferred to another facility.  To ensure that the costs to the
industry are not understated, EPA has retained these facilities in the analysis.
                                                    4.2   '

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

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

        Data limitations, however, require that the model be simplified.  In particular, the entire production choice
set (of registered products) available to each facility is unknown.  Additional engineering studies of each facility's
production process, as well as analysis of firm-level pesticide registrations, would be necessary to relax this
assumption.  Given this limitation, it is assumed that a facility may respond to a new effluent guideline only by
decreasing current production of any or all of the pesticides currently manufactured. This assumption does not allow
for the production of new chemicals, i.e., those that were not being manufactured before the guidelines  were
introduced.  Neither does it allow one U.S. PAI manufacturer to benefit from the compliance costs and subsequent
decrease in PAI production of another manufacturer.  Note that this assumption is extremely conservative, since
it severely limits the options available to each facility and thus overstates the impact of the regulation.
                                                    4.3

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

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

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

These steps are explained below.

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

        The EPA's Office of Pesticides Programs (OPP) has undertaken a similar categorization exercise for its
regulatory purposes.  In 1980, the OPP defined pesticide markets to ensure that the EPA  regulated competing
products on roughly the same schedule, so that one pesticide does not have an unfair advantage over another.  As
                                                   4.4

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 described in Chapter 3, the pesticide markets were defined as clusters of PAIs that are substitutes for a specific end-
 use.   For example, insecticides used on corn is one market or cluster.  The OPP  assigned each of the PAIs
 registered in 1980 to one of 48 separate clusters.4  As reported in Section 3.1, the EPA's Office of Water made
 minor adjustments to these pesticide clusters for this analysis. First, PAIs registered after 1980 were assigned to
 clusters.  In addition, clusters were split when a wide range of price elasticities of demand were estimated to exist
 within a single cluster and it was possible to further differentiate corresponding PAI uses within the cluster (see
 Appendix  C).  Four clusters were split, increasing their number from 48 to 56.5  Finally, PAIs were allocated to
 more than one cluster when the PAI was known to be used hi substantial quantities for different end uses.  The
 adjusted PAI clusters were used as the basis for this EIA.  The 260 PAIs, or classes of PAIs, considered for
 regulation are mapped into the 56 separate clusters in Appendix B.6

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

        This situation suggests that the pesticide manufacturing markets can be characterized as competitive.  The
 market does not  appear  to be perfectly competitive, however, since few firms produce the same PAI; product
 differentiation exists within the markets.  For example, PAIs within a cluster may be differentially effective on a
 regional basis due to climate differences.  PAIs may also vary in their effectiveness on different varieties of pests
 and on different varieties of crops.  The structure of the pesticide markets can therefore generally be described as
 competitive with differentiated products (i.e., monopolistic components). In an industry with these characteristics,
 different prices may exist for products within a single market.  Firms must compete for customers in terms of both
 price and the kinds of products they sell.  Also, new firms may enter the industry with a new  product whose
 differentiation from its competitors' products may make it profitable.
     Tn the OPP's classification, each PAI appeared in only a single cluster, since the purpose of the classification
was to develop a regulatory schedule for each PAI.
      Only 44 of these clusters had production of one or more of the 260 in-scope PAIs or classes of PAIs in 1986.
      PAI #67 (biphenyl), in cluster F6, was considered in-scope at proposal but is not considered in-scope for the
final rule.  The count of clusters with production in 1986 therefore decreased from forty-five to forty-four. See
the Technical Development Document for  additional information.
                                                   4.5

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        Baseline Prices for Each Pesticide Cluster at Each Facility
        Baseline prices for each PAI cluster at each PAI manufacturing  facility served as  foundations of the
economic model. To estimate prices at the cluster level for each facility, prices were first estimated at the PAI level
for each facility in one of five ways, as described below.
                PAI-specific data provided.  Provision of PAI-specific prices hi the Census was optional. If these
                data were provided, they were used in the analysis. Thirteen (18 percent) of the 72 pesticide
                manufacturing facilities for which economic impacts are analyzed chose to provide price data on
                their technical grade products.7
                PAI-specific data not reported in the Census and only one in-scope PAI produced. In this case,
                reported in-scope revenues were divided by the production quantity of the PAI to obtain  the PAI
                price.
                PAI-specific data not reported in the Census, multiple PAIs are produced, and price data for all
                the PAIs are available from a secondary source.  Secondary data on prices were obtained from
                Agchemprice (DPRA, 1990), the Doane's Annual Market Survey (Doane Marketing Research,
                1987), telephone calls to PAI dealers, and EPA estimates. These secondary prices are reasonable
                indicators of the relative prices of the PAIs.  If used directly, however, the secondary prices may
                overstate the price the manufacturer receives for PAIs, because manufacturers may offer volume
                discounts or sell to a wholesale distributor.  Because most facilities in the Census reported their
                production of, and revenues from, in-scope PAIs, facility PAI prices were estimated using these
                Census data and the  relative, rather than the actual, PAI prices from secondary sources.  For
                example, assume Facility A produces two in-scope PAIs. From secondary sources, the price of
                PAIi is found to be twice the price of PAI2. If Facility A reported producing 200 pounds of PAIj
                and 500 pounds of PAI2, with total in-scope revenues of $4,500, the analysis would calculate the
                price of PAI2 as:
                                       200(2p) +500(p) =$4,500
                where p = the price of PAI2.
                The solution for "p" is $5.  PAIj would therefore be estimated to have a price of $10.
      Thirteen of the 72 facilities provided PAI-specific data for technical products, eight facilities provided data
on formulated/packaged products, and two facilities provided data on intermediates.  A total of sixteen of the 72
facilities provided PAI-specific data for at least one of these product groups.
                                                  4.6

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         •       PAI-specific data not reported, multiple PAIs are produced, and price data from a secondary
                 source is available for only some of the PAIs produced.  For those PAIs for which secondary price
                 data is not available, prices were  estimated by first dividing facility in-scope revenue by facility
                 in-scope production. Using these average prices, the analysis proceeded as described in the above
                 paragraph.

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

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

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

         Fixed  costs were reported on a facility-level in the Census, not on a PAI-specific or a pesticide-related
basis.  Fixed costs for all in-scope PAIs at a facility were estimated by multiplying 3-year average (1985, 1986,
and  1987) total facility fixed costs by the 3-year average percentage of facility revenues derived from sales of in-
scope pesticides.9 This is represented by the equation:


                                            /F=F x  (IR/TR)
where:
IF
F
fixed costs associated with in-scope PAIs;
3-year average fixed costs for the entire facility;
     X
      Prices were estimated in this manner for only one facility projected to incur compliance costs. This facility's
only pesticide-related revenues were for tolling. Due to the construction of the Census, tolling revenues cannot be
separated into sales of in-scope vs. other pesticides.  For this reason, the reported revenues could not be used to
estimate prices of in-scope PAIs. This facility incurs only monitoring costs under the final rule.
     Q                                                                                          ,
      Three-year averages were used in aia effort to modulate the variability of particular years and to create data
that represents a typical year.

                                                   4.7   .

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        •       On-site waste management train (up to 10 waste management procedures); and
        •       Off-site disposal train

For purposes of estimating costs associated with the land disposal restriction rules, the data were first scanned to
select only those components dealing with land disposal, e.g., landfill, surface impoundments, and waste piles.  The
RIAs for the first and last third of the Stage 3 Land Disposal Restrictions included total gallons of waste to be
treated and total incremental costs by baseline management practice and RCRA waste code.  This allows calculation
of unit (per gallon) costs for each RCRA waste by management practice.13

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

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

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

        Thirty of the 73 manufacturing facilities potentially subject to regulation incurred costs due to the RCRA
rules described above.  Total annualized RCRA costs for these  facilities are estimated to be $1.3 million (1986
dollars).  However, not all of these costs may have been borne by the pesticide manufacturers; a portion may have
been passed through to customers in the form of higher prices.  As a reasonable simplification to reflect the  cost
pass through, the analysis assumes that the burden of the cost increase is split evenly between the facilities and the
      13The RIA for the first third examined two alternatives and two scenarios within the first alternative.  The costs
 for Alternative A, Scenario I were used because this option was closest to the final rule.
                                                   4.10

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customers.  In other words, the facilities are assumed to bear 50 percent of the cost increase.14  These costs were
added to the baseline fixed costs of the affected facilities.

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

        Substantial overlap exists between facilities subject to the OCPSF effluent guidelines and those covered by
the proposed pesticide manufacturer effluent guidelines. (Manufacture of organic PAIs is included hi SIC 2869.)
Of 73 facilities potentially subject to regulation, 25 are projected to incur costs to comply with the OCPSF effluent
guidelines.  The estimated costs to comply with the pesticides effluent guidelines will be incremental to those of
meeting the OCPSF rule.  For this reason, OCPSF costs for all facilities affected by both rules are added to the
economic baseline.  Capital and annualized OCPSF costs for these 25  facilities total $97 million and $33 million,
respectively (1986 dollars).  Again, 50 percent pass-through to the customers is assumed.  As a result, additional
annualized fixed  costs for all pesticide manufacturing facilities due to OCPSF effluent guidelines total $16
million.'

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

        As discussed previously, a total of 73 pesticide manufacturing facilities producing one or more of 260 PAIs,
or classes of PAIs, are potentially subject to regulation.  At proposal, EPA evaluated compliance costs for pesticide
manufacturing facilities under two regulatory options: one  that would require treatment of process wastewater
pollutants (Treated Discharge Option) and another that would require no discharge of process wastewater pollutants
       An alternate assumption, in which all RCRA compliance costs were borne by the manufacturers, might result
in the projection of additional baseline closures in the current analysis.  As a result, fewer closures resulting from
the pesticide effluent guideline limitations and standards could be projected.
       Estimated costs of compliance may vary substantially from actual costs incurred, since companies frequently
meet regulatory requirements by means  other than those the EPA used  for estimating compliance costs.
                                                  4.11

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to POTWs or surface water (Zero Discharge Option).  The Treated Discharge Option limitations are based on the
use of biological treatment, hydrolysis, activated carbon, chemical oxidation, resin adsorption, solvent extraction,
incineration and/or recycle/reuse to control the discharge of PAIs in wastewater. The Zero Discharge Option was
based on on-site or off-site incineration and/or recycle/reuse.16  For both regulatory options, the economic impacts
on  facilities were  calculated separately for direct and indirect  dischargers.17   Each  discharge  category was
analyzed further by two subcategories:  organic pesticide chemicals manufacturing (Subcategory A) and metallo-
organic pesticide chemicals manufacturing (Subcategory B). Because EPA chose not to regulate Subcategory B at
this time and the Zero Discharge Option was found at proposal to not be economically achievable, only costs of the
Treated Discharge Option for Subcategory  A are discussed.   A full discussion of Subcategory B and the Zero
Discharge Option can be found in the EIA for the proposed rule (EPA, 1992).

        Three categories of compliance costs associated with pesticide manufacturing were evaluated: capital costs,
land costs, and operating and maintenance costs (including compliance self-monitoring and sludge disposal). The
capital and land costs were one-time "lump sum" costs; the operating and maintenance  costs were evaluated on an
annual basis.  Capital and land costs, annualized using the conservative assumption that they have a productive life
of ten years, were adjusted over the ten-year period  using the weighted  average  cost of capital.1    These
annualized capital  and land costs were added to operating and maintenance costs to produce total annualized costs.
For  facilities that both manufacture and formulate/package pesticides, the  compliance costs  apply  only  to the
manufacturing operations of the'facility.  All of the compliance cost estimates are presented hi  1986  dollars and are
based on the assumption that, whenever possible, facilities will build on existing treatment.

        The costs and  impacts  of  implementing the regulation were estimated on a PAI-specific basis for each
facility.  Table 4.1 presents the  capital and land, operation and maintenance,  and annualized costs associated with
the final regulatory option for Best Available Technology Economically Achievable (BAT) and Pretreatment
Standards for Existing Sources (PSES) for Subcategory A.
           Zero Discharge Option would limit discharges from the facility site to POTWs or to surface water only;
discharges to other media may remain constant or increase as a result of changes in discharge to surface water.
For example, pesticide manufacturing facilities  could, theoretically,  achieve compliance with a zero discharge
effluent guideline by transferring the waste streams previously discharged to surface water to landfills, incinerators,
or deep well injection sites.
       Impacts on facilities with zero discharge  are reported with impacts on direct discharging facilities.  Zero
dischargers may be subject to monitoring costs if they have any process wastewater.  Monitoring costs would be
imposed by the permitting authority (no separate monitoring requirements are contained in the proposed effluent
guidelines for pesticide manufacturers). These monitoring costs are included in the analysis to capture the full cost
to industry of controlling process wastewater pollutants.
     18
       For details on the weighted average cost of capital, see Section 4.2.B.
                                                    4.12

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         Under the final rule, it is expected that 55 pesticide manufacturing facilities will incur compliance costs:
 33 direct dischargers and 23 indirect dischargers (one facility is a joint discharger).  Total BAT annualizsd costs
 (applying to direct dischargers) are projected to be $18.2 million for Subcategory A.  There are no BAT costs
 associated with Subcategory B chemicals.   These chemicals are already limited by  Best  Practicable Control
 Technology  Currently Available  (BPT), which requires no discharge  of process  wastewater pollutants for this
 industry. Total annualized costs for PSES (applying to indirect dischargers) under the final rule are projected to
 be $5.1 million for Subcategory A.19
                                               Table 4.1
                                       Costs of the
                                                                          Direct
                                                                     Dischargers***
                                                                                       Indirect
                                                                                     Dischargers
    Number of facilities incurring costs
    Capital and Land (MM$)
    O & M (MM$)
    Annualized Costs (MM$)
                                                                        33
                                                                       24.93
                                                                       14.60
                                                                       18.16
 23
8.70
3.82
5.08
    **
      There are no costs for Subcategory B direct dischargers because direct discharge of
      Subcategory B chemicals is already limited to zero under BPT. Regulations for Subcategory B
      indirect dischargers are not proposed.
      At proposal, projected costs were included regardless of whether a facility had closed pesticide
      operations or was projected to close pesticide operations prior to incurring the costs of
      compliance.  The total costs were therefore overstated.  For the final rule, costs are included
      only for facilities not known to have actually closed and facilities that have closed but may have
      transferred their production to another facility.
***   Impacts of requirements on zero discharge facilities are reported with impacts on direct
      discharge facilities.  Zero dischargers may be subject to monitoring costs if they have any
      process wastewater.  Monitoring costs would be imposed by the permitting authority (no
      separate monitoring requirements are contained in the proposed effluent guidelines for pesticide
      manufacturers).  These monitoring costs are included in the analysis to capture the full cost to
      industry of controlling process wastewater pollutants.
        Post-compliance Costs for Each PAI Cluster at Each Facility
        As stated above, the compliance costs were estimated on a PAI basis for each facility.  To combine
compliance costs with other facility costs,  cluster-level compliance costs for each facility were calculated by
summing annualized PAI compliance costs for all PAIs within each cluster for each facility. Dividing total cluster-
level compliance costs for each facility by the cluster production quantity at  that facility yielded unit compliance
costs for each market and each facility. These costs were added to baseline unit costs to arrive at post-compliance
unit costs.
     19,
       The EPA is not proposing to regulate Subcategory B chemicals at this time.
                                                   4.13                      *

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        Pricing Rule to Estimate Post-compliance Prices20
        Changes in PAI prices and product demand are determined interactively in the market place. Typically,
a producer will raise prices based on the actions expected of competitors and the extent to which consumers will
decrease demand.  Consumers will then respond to the increased prices with a drop in demand based on several
factors, including the percent of their production cost contributed by the product and the availability of substitute
products.   Producers then examine the impact of the price increase and demand decrease  on profitability and
reevaluate their price.  Consumers again react.  This iterative process continues until producers believe they have
maximized profit.

        This analysis attempts to model an approximate end point of the supply and  demand interaction.   The
percentages of the compliance costs that are translated to price increases for each cluster depend on (1) the degree
of substitutability of alternative products, and (2) the extent of supplier price competition. Substitution among PAIs
is included by addressing impacts  on a cluster basis.   Substitution of PAIs with non-chemical alternatives  is
discussed in the following section on post-compliance quantities.

        A pricing rule was developed to take into account the effect of supplier competition  on the percentage of
compliance costs that are passed to the consumer.21 This rule is based upon the assumption that if production
bearing compliance costs makes up a small percentage of total cluster production, then  a price increase due to
regulation is unlikely.  If all production in a cluster is projected  to bear compliance costs, then all regulatory costs
are likely to be reflected in higher prices.
           analysis of impacts of the regulatory options incorporates the effects of facilities passing a portion of
the compliance costs to their customers.  An alternative method of analyzing impacts would be to assume that
pesticide manufacturers bear the entire burden of the cost increase in reduced profits.  EPA conducted a sensitivity
analysis using this zero cost pass-through assumption.  The results are reported in Appendix D.  For the main
analysis, however, the EPA presents impacts using the assumption of partial cost pass-through, because the EPA
believes that, in reality, pesticide manufacturing facilities will not bear the entire costs of the regulation.  The
analysis of zero pass-through (i.e., manufacturers bear all compliance costs) served as a theoretical construct to limit
the upper range of impacts of the regulation on  facilities.
     21Theoretically, the effects of supplier competition could be evaluated by modeling a supply curve in the pre-
and post-compliance scenarios.  This model was not used for the EIA because production cost data for pesticides
not included in the Census are unavailable. In addition, production cost functions within facilities are also unknown,
allowing only marginal costs of production to be estimated. •
                                                   4.14

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         To capture this effect, price increases for each market and each facility were calculated as:
where:
Cf,j
PC;
TJ
change in unit price for facility f, cluster j;
unit compliance costs for facility f, cluster];
total U.S. production of cluster j that incurs compliance costs; and
total U.S. production of cluster j.
The quantity of PAI production in each cluster incurring costs was calculated from the production data provided
in the Census (Parts A and B) and the estimated compliance costs. Total production of PAIs for each cluster was
calculated from the Census and other proprietary data. Post-compliance unit prices were calculated for each facility
and each cluster as  the baseline unit price plus the change hi unit price due to the installation of pollution control
equipment.22

        Post-compliance Quantities
        Having estimated post-compliance costs and prices,  the  remaining step solved  for  post-compliance
quantities.  An estimate of the price elasticity of demand for each cluster was used to predict changes in quantities
demanded given changes  in price.  The price elasticity of demand can be defined as the percentage change hi the
quantity demanded, divided by the percentage  change in price.  If consumers cut back their purchases to such a
large extent that any price increase reduces total revenues, then demand is said to be  elastic, i.e., customers are
sensitive to price changes.  If consumers cut back their purchases only slightly in response to higher prices, resulting
in an increase in revenues, demand is said to be inelastic, i.e., customers are not as sensitive to price changes. The
value of the price elasticity of demand is unbounded and may be positive or negative. It is  expected, however, that
price and demand are negatively correlated, i.e., an increase in price results in a decrease in the quantity demanded.
The price elasticity  of demand is therefore usually negative.

        The methodology for generating estimates of the elasticity of demand relied on  five sources.  First, the
EPA reviewed empirical studies of the price elasticity of demand for pesticides. Few such studies were located,
however, and the existing studies offer conflicting conclusions, most of them controversial.  Second, the EPA
reviewed the U.S. Department of Agriculture's (USDA, 1985) analysis of the price elasticity of demand for food
           pricing rule is not meant to be a perfect theoretical simulation of the price response to regulatory cost
increases. Given the uncertainty and limited availability of data on production functions and costs by facility and
PAI, use of the measure provides a reasonable basis for simulating the pricing response by producers.
                                                  4.15

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commodities.  The elasticity of demand for farm inputs can be derived from the elasticity of demand for farm
commodities because demand for production inputs must ultimately reflect demand for the end product. For this
reason, the USDA estimates of the elasticity of demand for food commodities provided the basis for estimating the
demand elasticity for PAI clusters.   Three additional factors were examined as indicators of how  the demand
elasticity for PAIs might vary from the demand elasticity for food: (1) the feasibility of employing non-chemical
or non-biological pest control methods, (2) the percent of production cost contributed by the PAIs of interest, and
(3) the productivity of expenditures for PAIs. The elasticity estimates generated from this process were reviewed
by OPP staff, whose comments were incorporated into the methodology.  A complete description of the process by
which the elasticity estimates were developed can be found in Appendix  C.

        A list of the elasticity estimates by cluster is presented in Appendix C, page 62, in order of increasing
elasticity of demand.  As previously mentioned, the elasticity estimates range from -0.12 (herbicides on sugar beets,
beans, and peas) to -1.38 (fungicides on grapes and herbicides on grapes).  The elasticity estimates vary substantially
within the fungicide, herbicide, and insecticide clusters;  the type of pesticide is not seen to affect the elasticity of
demand.

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

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

4.2      Facility Closure Analysis
         As previously discussed, the results of the  economic model described above are  used  to estimate three
potential  impacts of the proposed effluent limitations guidelines at the facility level.23  The first,  and  most severe,
potential  impact on a facility is facility closure.  For purposes of this EIA, a pesticide manufacturing facility is
defined as the portion of the facility involved in manufacturing and formulating/packaging, or performing contract
work for both in-scope and out-of-scope pesticides. A pesticide manufacturing facility, as defined for this analysis,
     ^Appendix E of this report compares facility compliance costs to facility revenue.  This financial ratio was
 added to the analysis following proposal of effluent limitations as an additional indicator of adverse financial impacts
 that facilities may face due to the regulation.  This ratio is not discussed in Chapter 4.
                                                    4.16

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does not include any non-pesticide related activity occurring at the physical facility.  A pesticide manufacturing
facility that is predicted to close may continue with  non-pesticide-related operations, such as production of other
organic chemicals.

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

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

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

        The calculation used to estimate whether or not a facility will close is intended to model the decision-
making process of the owners of the facility. The  calculation compares the pre-compliance profitability of the
facility with the post-compliance profitability.  Specifically, this calculation entails a comparison of pre-compliance
after-tax cash flow to post-compliance after-tax cash flow for the facility. In the majority of cases, a rational owner
would not continue operations if a facility's after-tax cash flow is negative.

        The methodology used to project facility closures has been changed since the proposed rule.  At proposal,
facility closures in both the baseline and post-compliance scenarios were evaluated by comparing facility discounted
cash flow to facility liquidation value. If the expected cash flows were less than the liquidation value of the facility,
the owner would be better off closing the facility.
        After proposal, data from the Section 308 survey of pesticide formulating/packaging/repackaging facilities
became available for comparison with the data obtained from the Census.  Using these two data sources, EPA
compared estimates of liquidation value, from the two questionnaires for the 45 pesticide manufacturing facilities
that had completed the financial portion of the Pesticide Formulating,  Packaging and Repackaging Survey for 1988.
In the pesticide manufacturers Census, facilities were asked to estimate the liquidation value of the pesticide
                                                   4.17

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production and pesticide formulating/packaging lines and associated fixed assets, working capital, and real estate.
For comparison with the data available in the PFPR Survey, facility liquidation values were estimated by multiplying
the pesticide liquidation values by the ratio of facility revenue to pesticide revenue. The PFPR Survey was designed
such that facility liquidation values could be calculated as the quotient of tax assessment values of land, buildings,
equipment and machinery divided by the tax assessment percentage.

        Of the 45 facilities, only 10 had gross facility liquidation values calculated from the two questionnaires that
were within a factor of two. While the two different approaches were not expected to give identical results, the
magnitude of the difference caused EPA to question the reliability of liquidation value  estimates.  Given these
discrepancies, EPA chose to evaluate facility closure as described above, i.e., based on after-tax cash flow. This
methodology does not require the use of liquidation values  and corresponds to the methodology being considered
for use in evaluation of effluent limitations on the PFPR industry. Evaluation of after-tax cash flow is a simpler
methodology than comparing discounted cash flow to liquidation value and it avoids the uncertainty involved in using
estimates of liquidation value.24

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

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

                                  CFO = NI + DEP -  OC(1-CT)
          is methodological change does not alter the conclusion that the final rule is economically achievable.
                                                   4.18

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where:
        CFO
        NI
        DEP
        OC

        CT
                        Cash flow;
                        Net income (i.e., after tax profits calculated from the Census);
                        Depreciation expenses (taken directly from the Census);
                        Cost of compliance with other EPA regulations first effective after 1986 (RCRA land
                        disposal restrictions and OCPSF effluent guidelines); and
                        Marginal corporate tax rate (assumed to be 34 percent).
4.2.B   Post-Compliance Facility Closure Analysis
        Facilities for which baseline cash flow was negative (i.e., those predicted to be baseline facility closures)
were not considered as potential facility closures in the post-compliance scenario.  For the remaining facilities,
however, the post-compliance cash flow was evaluated to project facilities that would close due to the regulation.

        Three factors are included when estimating cash flow in the post-compliance scenario:

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

        Facility changes in cash flow were calculated by summing the changes in annualized compliance costs,
revenue, and variable costs over all clusters  produced at a  facility.  The post-compliance cash flow was then
calculated by adding the changes in cash flow to the baseline cash flow. The corresponding equation is:
                               PCCF=CF+£ (-
where:
PCCF   =
CF
CCadj;   =
                the post-compliance facility cash flow;
                facility baseline cash flow;
                compliance cost adjustment to cash flow for cluster i;
                the adjustment to cash flow due to the change in revenue for cluster i; and
                the adjustment to cash flow due to the change in variable costs for cluster i.
                                                  4.19

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To calculate an adjustment to cash flow that includes the costs of compliance, capital and land, these costs must be
annualized.  As described below, annualization of capital and land costs is accomplished by dividing these costs
costs by a "present value factor" which is constructed using an estimate of the cost of capital.  Calculation of the
cost of capital and the present value factor are therefore discussed below followed by a discussion of the three
adjustments to cash flow.

        Cost of Capital
        The cost of capital is the rate at which a firm obtains funds for financing capital investments. The cost of
capital is required to annualize the capital costs associated with the rule so that post-compliance changes in cost and
price can be projected.25

        The cost of capital to a particular firm depends on how the investment is financed. One option, equity
financing, is taken when a firm issues stocks or uses retained earnings.  A  second option involves acquiring
additional debt, through bonds,  notes,  or short-term commercial paper.26  Typically, acquiring debt is the less
expensive option because interest payments on debt reduce the firm's corporate tax burden and because debt offers
investors a less risky return than equity investment.27  As a firm expands its debt holdings, however, the cost of
debt increases, forcing the  firm to reach an equilibrium between debt and equity financing.  It is assumed in this
analysis that firms use some combination of debt and equity to finance compliance costs. The measure of a firm's
overall cost of a capital investment, based  on the percentage values of debt and equity used  to finance the
investment, is termed the weighted average cost of capital  (WACC). Thus, the WACC is the average after-tax cost
of all funds used to finance a capital investment.

        The WACC can be presented in either nominal terms (i.e., not adjusted for inflation) or real terms (i.e.,
adjusting the nominal WACC for inflation).   This analysis uses the real cost  of capital to allow for  the use of
constant annual cash flows  (i.e., cash flows that are not inflated over time).  The two inputs to calculating the real
WACC - nominal WACC and the inflation rate - are discussed below.
       The cost of capital is determined by firm, rather than facility, characteristics.  As a key variable in the
facility level analyses, however, it is discussed in this section.
     26Debt capital is provided as a loan which creates a contractual obligation on the borrower to repay the loan
and contractually specified interest charges. Traditional sources of debt financing include commercial banks, non-
bank lending institutions, and the public capital markets. Except as provided by a security agreement, debt financing
does not provide the creditor any rights of ownership in the assets of the borrower. Equity capital represents a right
of ownership in the assets of the  firm seeking to finance a treatment  system (e.g., a corporation  or sole
proprietorship). Equity capital may be obtained as externally provided funds (through the sale of new equity) or
may be generated internally (from the cash flow provided by the firm's operations).
     07
       Debt is a less risky investment than equity because debt is senior to equity.   Interest payments are a
contractual obligation, paid before earnings are calculated and prior to declaration of dividends.
                                                   4.20

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


                                   WACC=R(E/A)+Y(l -CT)(DIA)

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

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

        The percentages of the investment that a firm is assumed to finance through equity (e/a) and debt (d/a) are
assumed to match the firm's historical mix of equity and debt investment. The values of these variables for each
firm are obtained from one of two sources. For each domestic public-reporting firm, the mix of debt and equity
is obtained from Standard and Poor's Compustat service for that firm in 1986. For all firms not included hi the
Compiistat data base, the mixture of debt and equity  financing was assumed to match the 1986 median mixture of
debt and equity financing for the "industrial chemical industry" as calculated from Robert Morris Associates' Annual
Statement Studies?-*  The calculated values taken from the Annual Statement Studies are 40.5  percent equity
financing and 59.5 percent debt financing.
     28,
       The "industrial chemical industry" includes SICs 2861, 2865, and 2869.
                                                 4.21

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        The annual return on equity (R) was calculated as:
where:
i       ss      the risk-free rate of return = 10.18 percent (calculated from the 1981-1990 average interest rate

                on 30-year U.S. Treasury Bonds as reported in Statistical Abstract of the United States, Bureau
                of the Census,  1989,  1990);29


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

                free investments = 8.0 percent, a standard value based on the Standard & Poor's 500.


/?       =      A measure of the  risk of an individual firm compared with the market.  Beta values are based

                directly on Value Line Investment Survey, Part I Summaries & Indexes (February 14, 1992) for

                publicly traded companies. For private firms, the median beta value calculated for the public PAI
                manufacturing firms was used.  This value is 1.056, indicating that the average risk of the public

                PAI manufacturing companies is close to the market average risk.


        The pre-tax interest rate on debt (Y) is assumed to be 10.95 percent.  This interest rate equals the 1981-

1990 average yield on AA 10-year industrial bonds (U.S. Department of Commerce,  1990 and 1991).30  Finally,

the marginal corporate tax rate (CT) is assumed to be 34 percent.31
       The variable i represents the risk-free component of the return on equity.  Equity has no maturity date;
therefore, i is best calculated as the return on long-term Treasury Bonds.

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

    31
       Because the firm, not the facility, tax rate is needed, use of the facility-level data from the Census  was
inappropriate.

                                                   4.22  •

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        Real WACC
        To allow the use of cash flows that are not adjusted for inflation, the real WACC was needed. The real
 WACC was estimated as:
where:
                                   RWACC=((l +WACQK1+G)) -1
RWACC
G
the real weighted average cost of capital; and
the rate of inflation = 4.74 percent.
The rate of inflation (G) is calculated as the mean annual inflation rate as reported by the unadjusted Consumer
Price Index between 1981 and 1990.

        Present Value Factor
        The real WACC is used to constnict a present value factor (PVF). Multiplication of annual costs by a PVF
discounts investments over a fixed time period.  Correspondingly, division of present value costs by the PVF gives
annualized costs over a fixed period of time. As shown below, this analysis divided compliance capital and land
costs by the PVF to annualize these  costs.  The analysis uses a ten year discounting horizon as a conservative
estimate of the typical life of the pollution control equipment. The PVF is calculated as:
                                              10
                                                 (1  + RWACC)1
where:
PVF
RWACC
present value factor;
the real weighted average cost of capital; and
number of years over which costs are discounted.
The three cluster level adjustments to cash flow are described below.

        Adjustment for compliance costs
        The compliance costs have three components:  operating and maintenance costs, capital costs, and land
costs. Operating and maintenance costs will be somewhat offset by the corresponding decrease in taxes the facility
                                                 4.23

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will  pay due to reduced profit.  The facility will also pay reduced taxes as a result of depreciating capital
expenditures.  An annualized cost of capital and land is calculated by dividing the estimated capital and  land
investment by the present value factor. The present value factor is based on the WACC,  as discussed in the
previous section.  The operating and maintenance costs need no adjustments because they are annual costs.  The
equation for the compliance cost adjustment is:
                                              'PVF
                                                                         ,cpr?
                                                                           10  '
where:
CCadjj  «
OMj
CT
PVF
CPTj   =
LAND: -
                compliance cost adjustment to cash flow for cluster i;
                operating and maintenance costs of compliance for cluster i;
                marginal corporate tax rate;
                present value factor;
                capital costs of compliance for cluster i; and
                land costs of compliance for cluster i.
        Adjustment for change in revenue
        The change in revenue contains two components:  the increase in revenue resulting from the increase hi
price and the decrease in revenue resulting from the decrease in quantity.  The change in revenue will again be
partially offset by the corresponding change in taxes. The cluster-level adjustment to the baseline cash flow for the
change in revenue is shown by the equation:
                            /&Kffr«AP, x
                                                        i  x A
-------
         Adjustment for change in variable cost of production
         The final  adjustment  to the baseline cash  flow reflects the decrease in variable costs associated with
 decreased production.  Variable costs were assumed to decrease hi proportion to the decrease hi quantity of
 pesticides produced.  The decrease is again partially offset by an increase in taxes. The equation is:
where:
Qi
vq     =
CT      =
the adjustment to the cash flow due to the change hi variable costs for cluster i;
the change hi cluster i quantity from baseline to post-compliance;
the baseline quantity of cluster i;
the variable cost for cluster i; and
marginal corporate tax rate.
As previously discussed, a facility with negative after-tax cash flow hi the post-compliance scenario was predicted
to close as a result of the regulation. The projection of closure refers only to the pesticide-related portion of the
facility.   Other operations, such as production of OCPSF chemicals or Pharmaceuticals,  may continue at  the
location.

4.3     Product Line Closure Analysis

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

        The evaluation of baseline and post-compliance product line closures is straightforward.  A product line
closure is predicted when  the unit total (i.e., fixed plus variable) cost of the product line (i.e., cluster) exceeds the
unit price.  Note that the comparison of price to total costs is very conservative.  A comparison of price to variable
costs only is a reasonable alternative (hi the short run), and would result hi an equal or lesser number of product
line closures.  The calculation of unit prices and  costs hi both the baseline and post-compliance scenarios was
described previously.
                                                   4.25

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        Given the methodologies used to calculate facility and product line closures, it is possible that a facility may
be projected to close all pesticide product lines, but the facility itself is not projected to close. In such a case, the
product line closure analysis serves as an alternate and complementary analysis of potential facility closures. Such
results would not be contradictory, because the product line closure analysis evaluates closures based on estimated
unit prices and costs, while the facility closure analysis considers estimated facility cash flow.
4.4     Other Significant Financial Impacts

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

        Other financial impacts were assessed based on financial indicators of operating performance and condition.
Two  financial indicators are examined hi this  analysis:  interest  coverage  ratio (ICR)  and return on assets
(ROA).32  The ICR and ROA gauge a facility's ability to continue doing  business long term, and  also indicate
a. facility's ability to qualify  for a loan or to attract  investors.  In  this way, the ratios are key indicators of a
facility's ability to finance costs associated with the proposed regulation.

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

        ROA is calculated as EBIT divided by assets. ROA is a measure of a facility's operating profitability and
asset management capability. This  ratio demonstrates the rate of return on the total investment in the facility.

         Other significant financial impacts are reported only for facilities that were not projected to  experience one
of the more severe impacts (e.g., a facility or product line closure) in either the baseline  or post-compliance
scenario.  Significant financial impacts were evaluated by comparing each facility's post-compliance financial ratios
     32The ICR is also known as "times interest earned;" the ROA is also known as the "return on investment.1
Additional information on these ratios can be found in Chapter 7.
                                                    4.26

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to the lowest quartile ratios established for all in-scope pesticide manufacturing facilities.  A significant impact is
said to result from the guidelines if a facility shifts into the lowest quartile of either the ICR or the ROA for all
pesticide manufacturing facilities due to the regulation.33

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

        Baseline Ratios
        The values marking the lowest quartiles for the ICR and ROA were determined by calculating the ratios
for all pesticide manufacturing facilities.  The three components used to calculate these two ratios were EBIT,
interest, and assets.  EBIT was calculated as three-year average revenues from pesticides minus three-year average
costs (except interest and taxes) associated  with pesticides. Pesticide-related revenues were taken directly from the
Census. Pesticide-related costs are composed of pesticide variable costs and pesticide fixed costs. Pesticide variable
costs were taken directly from the Census. Fixed costs (e.g., depreciation, fixed overheads, R&D, and other) are
not broken down hi the Census into those related or unrelated to pesticides, but are reported for the entire facility.
As a result, the percentage of  fixed costs generated by pesticide-related activity was  assumed  to match the
percentage of facility revenues from pesticide-related activity.

        The equation for calculating EBIT is therefore:

                                  EBIT=PREV- VC-FC(PREVITREV)
where:
EBIT
PREV
VC
FC
TREV
earnings before interest and taxes;
pesticide related revenue for a facility;
pesticide related variable cost for a facility;
total fixed costs (minus interest and taxes) for a facility; and
total facility revenues.
        Interest related to pesticides was calculated as the interest reported in the Census multiplied by the percent
of facility revenue from pesticides. Likewise, assets related to pesticides were calculated as assets reported hi the
Census multiplied by the percent of facility revenue from pesticides. EBIT divided by interest provided the ICR;
EBIT divided by assets gave the ROA.
     33The firm analysis is analogous to the "other significant impact analysis" for the facility level.  See Chapter
7 for further details.
                                                    4.27  '

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        Lowest Quartile Values
        The lowest quartile value for ROA was determined directly from the calculated baseline ROAs for all
pesticide manufacturing facilities.  Determination of the lowest quartile value for the interest coverage ratio,
however, required a decision on where to place firms reporting a zero interest payment.  A value of zero cannot
be used in the denominator of a ratio, so an assumption must be made regarding these cases for the ICR.   The
analysis ranked facilities reporting positive EBIT and zero interest as having interest coverage superior to any firm
reporting a positive interest value. If EBIT was negative and the reported interest expense was zero, the facility
                                                                       !:                    '
was assigned an EBIT:interest value of zero.  In effect, such a facility was seen as being worse off than a facility
with positive EBIT and a positive interest expense, but better off than a facility with negative EBIT and a positive
interest expense.  The EBIT:interest ratio marking the lowest quartile for pesticide manufacturing facilities is 1.13;
the lowest quartile ROA value is 0.04.
                                                                       i'T
        Post-compliance Ratios
        The post-compliance ratios for each facility with compliance costs that was not predicted to have a facility
or product line closure were calculated as follows:

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

                                post-compliance interest expense =
                                baseline interest expense
                                plus the current interest component of compliance debt14

                                post-compliance total assets =
                                baseline total assets
                                plus compliance capital and land costs
    ^Compliance debt is the debt the firm is expected to incur in order to  finance projected capital and land
expenses associated with the proposed regulation.
                                                   4.28

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 4.5     Facility impacts

         As discussed previously, 72 pesticide manufacturing facilities producing one or more of the 260 PAIs, or
 classes of PAIs, potentially subject to regulation were evaluated for economic impacts under the final rule.  The
 EPA is regulating 120 of these chemicals and has projected compliance costs for 55 of the Subcategory A pesticide
 manufacturing facilities.  The economic impacts of the final rule on the facilities were calculated separately for
 direct and indirect dischargers. Direct dischargers of Subcategory A chemicals were evaluated for compliance with
 a BAT rule while indirect dischargers of Subcategory A chemicals were evaluated for compliance with a PSES rule..
 Direct discharge of Subcategory B chemicals is already limited to zero under BPT. No BAT or PSES regulations
 are being promulgated for Subcategory B chemicals at this time.

 4.5.A   Baseline
         Fourteen of the 72 pesticide manufacturing facilities are projected to close in the baseline (see Table 4.2).
 Of the 14 facilities counted as baseline facility closures, two have closed product lines since 1986 and three have
 undergone restructuring.  An additional 12 facilities are projected to close particular pesticide product lines in the
 baseline.  Four of these 12  facilities have closed product lines since 1986 and an additional three facilities have
 undergone restructuring.
                                                Table 4,2
                                       Projected Baseline Closures
  Facility Closures
  Product Line Closures
14
12
4.5.B   Effects of Compliance with the final Rule
        The economic impacts projected to occur due to the final rule are discussed below.  Because the Zero
Discharge Option was found at proposal to not be economically achievable, the estimates of compliance costs for
this option have not changed since proposal, and this option is not being promulgated, impacts due to this option
were not reassessed.
                                                  4.29

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                Impacts of BAT Regulations on Direct Dischargers
                Organic Pesticide Chemicals Manufacturing (Subcategory A)
        Twenty-eight direct discharging and five zero discharging facilities producing Subcategory A chemicals are
expected to incur costs under this regulatory option.35  For  manufacturers included  in  this Subcategory,  the
incremental capital and annualized costs of complying with BAT limitations are expected to be $24.9 million and
$18.2 million, respectively. The estimate of capital costs has increased 67% since proposal while the estimate of
total annualized cost has increased by 24%.  (See the Technical Development Document for an  explanation of
changes in compliance cost estimates.)  The change in compliance cost is the aggregate effect of decreases in
annualized compliance costs at four facilities and increases in annualized compliance costs at four other facilities.
Most of the increase hi total costs for direct dischargers is due to a substantial cost increase at one facility.  The
estimated investment costs at this facility have increased from $1.6 million to $16.0 million, with an increase in
annualized costs from $2.0 million to $7.3 million.  This change in costs resulted from public comments by the
facility. The Agency maintains that the actual compliance costs for this facility would be lower than the estimates
used in the final  analysis.  However, analysis using  these higher cost estimates ensures  that EPA does  not
underestimate the burden of compliance at this facility.

        No facilities are projected to close due to  compliance with BAT (see Table 4.3). One direct discharging
facility is projected to close a product line as a result of the regulation. (One zero discharging facility, subject only
to monitoring costs, is also projected to close a product line.)  No facilities are expected to experience other
significant financial impacts short of facility or product line closure.36

                Metallo-Organic Pesticide Chemicals Manufacturing (Subcategory B)
        Direct dischargers of Subcategory B chemicals are limited to zero  discharge of  process wastewater
pollutants under BPT.  No additional options were considered and no new  limitations are promulgated for the
metallo-organic pesticide chemicals manufacturing subcategory. There are therefore no associated costs or economic
impacts.
     35
       Impacts on zero discharge facilities are reported with impacts of direct discharge facilities. Zero dischargers
may be subject to monitoring costs if they have any process wastewater.  Monitoring costs would be imposed by
the permitting authority (no separate monitoring requirements are contained in the proposed effluent guidelines for
pesticide manufacturers).  These monitoring costs are included in the analysis to capture the full cost to industry
of controlling process wastewater pollutants.

       The projected impacts do not change under an assumption of zero cost pass through.
                                                   4.30

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                                                Table 4.3
                               Impacts of the final Regulation, on Facilities
                                                         Direct Dischargers*    indirect Dischargers
       Facility Closures
       Product One Closures
       Other Financial Impacts
0
2
0
0
0
0
               Impacts on zero discharge facilities are reported with impacts on direct
               dischargefacilities.  Zero dischargers may be subject to monitoring costs if they have any
               process wastewater.  Monitoring costs would be imposed by the permitting authority (no
               separate monitoring requirements are contained in the proposed effluent guidelines for
               pesticide manufacturers).  These monitoring costs are included in the analysis to capture
               the full cost to industry of controlling process wastewater pollutants.
                Impacts of PSES Regulations on Indirect Dischargers
                Subcategory A
        Twenty-three indirect discharging facilities producing Subcategory A chemicals are expected to incur costs
under the final rule.  For manufacturers included in this Subcategory, the incremental capital and annualized costs
of complying with PSES limitations are expected to be $8.7 million and $5.1 million, respectively. The estimate
of capital costs has decreased 8 % since proposal while the estimate of total annualized costs has decreased by 15 %.
(See the Technical Development Document for an explanation of changes in compliance cost estimates.)

        No facilities are projected to close entirely, close a product line, or experience other significant financial
impacts due to compliance with PSES. Therefore, the estimated impacts have decreased since the proposal. (At
proposal one facility was projected to close a product line.  This facility has actually closed and is therefore not
included in the analysis of the final rule.)

                Subcategory B
        No new limitations on indirect dischargers are promulgated today for the metallo-organic pesticide
chemicals manufacturing Subcategory.  Therefore, there are no associated costs or economic impacts.  Estimates
of the costs if the Subcategory had been regulated were provided hi the ELA at proposal.
                Impacts of BAT and PSES on Pesticide Formulating/Packaging/Repackaging Facilities and
End-Users
        Effect of Possible Facility/Product Closures on PFPRs
        As described above, no pesticide manufacturing facilities are projected to close due to BAT or PSES
regulations. Two manufacturing facilities are predicted to have product line closures due to the EAT rule. Neither
of these facilities provided PAI-specific costs or prices so EPA assumed average costs for each product and cannot
                                                  4.31

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predict which particular line would close.  Only one of the active ingredients produced by these two facilities is
made by only one U.S. manufacturing facility. The other active ingredients would not be lost to the industry.  The
product produced  by a  single facility is  a fungicide used  as a seed treatment.  An estimated  10 pesticide
formulator/packager/repackager facilities out of approximately 2,500 facilities make  products with that active
ingredient.  If this was the product line that closed (which can not be predicted with accuracy without product
specific information), only 0.4 percent of all pesticide fonnulator/packager/repackagers would be affected. Further,
substitute products are available to pesticide formulator/packager/repackgers and the end user. Therefore, EPA does
not  expect  the possible  closure  of  these product  lines  to  have a  significant  effect on the  pesticide
formulating/packaging/repackaging industry or end users of pesticides.

        Effect of Pesticide Manufacturing Price Increases on PFPRs
        A   second   possible   impact   of   the   pesticide    manufacturing   regulation   on   pesticide
formuiating/packaging/repackaging facilities would be to have active ingredient  prices increase significantly.

        In order to conservatively predict the severity of impacts that manufacturers would sustain in complying
with the regulations, the manufacturing economic impact analysis was performed assuming both partial cost  pass
through to customers and a zero cost pass through (i.e., the worst case for manufacturers).  To gauge the maximum
effect of the pesticide manufacturing regulation on  the PFPR industry,  it would have to be assumed that all
manufacturing cost increases related to the regulations were passed on to PFPR facilities.

        Total annualized costs for the regulations are expected to be $23.24 million ($18.16 million for BAT and
$5.08 million for PSES,  in  1986 dollars), spread over 1.15 billion pounds of active ingredients with the average
price increase being approximately $0.02 per pound of active ingredient assuming that the manufacturer passed the
full cost increase on to the pesticide formulator/packager/repackager.  The average percentage of active ingredient
in formulated products varies from less than six percent in aerosol products to over 50 percent for agricultural
products, with the overall average being around 30 percent. This means that at the worst, if PFPR facilities passed
on the entire average price increase per pound of formulated product, this increase would vary from a small fraction
of a cent to  slightly over one cent per pound depending on the percentage of active ingredient in a pound of
formulated product.  Therefore, from the perspective of price  increases, the pesticide manufacturing guideline will
not have a significant impact on pesticide formulating/packaging/repackaging facilities.
        Not only are price increases for formulated products expected to be modest, the effect of such increases
on  demand is expected to be  minor.   Based  on the percentage of manufacturing facilities expected to incur
compliance costs, EPA estimates that only part of the costs of the manufacturing rule will be passed on to pesticide
formulator/packager/repackagers (neither extreme case presented above). The analysis estimated the effect of price
increases on demand based on the final demand for a formulated product.  If PFPR facilities are able to pass
through a portion of the cost increase without a significant effect on demand for their product they would not be
                                                   4.32  '

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expected to face significant adverse economic  impacts.  As presented in Chapter 3,  demand for 42 of the 45
pesticide clusters with production in 1986 is expected to have unit elasticity (i.e.,  elasticity equal  to -1) or to be
inelastic.  Therefore, demand for formulated products  is expected to be relatively stable with respect to price
increases.
        Percentage ofPFPR Business Affected
        A preliminary analysis of the responses to the PFPR survey indicates that most of the facilities that
formulate, package or repackage in-scope pesticides obtain only a small percentage of their revenues from  these
activities.  The  median percentage of revenue obtained from in-scope pesticides for all PFPR facilities is four
percent.  Ninety percent of PFPR facilities obtain less than half of their revenue from in-scope PFPR activities.
In particular, almost three-quarters of the population of repackages for pesticides for the agricultural market obtain
15  percent  or  less  of  their  revenue  from   this activity.    Because  activities  other  than  pesticide
formulating/packaging/repackaging are the principal sources of revenues for the vast majority of PFPR facilities,
small increases  in the prices of  PAIs are  not expected to result  in significant impacts for most  pesticide
formulator/packager/repackager facilities.
                                                   4.33

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                                        Chapter 4 References
Doane Marketing Research (1987).  Annual Marketing Survey. St. Louis, Missouri.

                                                                                                    i
DPRA, Inc. (1990).  Agchemprice; Current U.S.A. Prices of Non-fertilizer Agricultural Chemicals.  January.

     Manhattan, KS.


U.S. Department of Agriculture (1985). U.S. Demand for Food: A Complete System of Price and Income Effects.
     By Kuo S. Huang, National Economics Division, Economic Research Service.  Technical Bulletin No. 1714.


U.S. Department of Commerce (1989, 1990, 1991).  Bureau of the Census.  Statistical Abstract of the United

     States. Washington, D.C. January.
                                                 4.34

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

        Following proposal,  EPA thoroughly reviewed the details of the economic analysis in preparation for the
final rule.  In response to this review and to public comments, the estimated compliance costs were adjusted and
the economic analysis was revised in several ways.'   The changes, both separately and taken together, do  not
significantly affect the number of impacts projected.

        As presented in Chapter 4, under the final rule, no direct discharging facilities are projected to close due
to compliance with BAT and one facility, equal to 3 percent of the direct discharging facilities in Subcategory A,
is projected to  close  a product line.  (One zero discharging facility,  subject only to monitoring costs, is also
projected to close a product line.) No facilities are expected to experience other significant financial impacts short
of facility or product line closure.  This level of impacts is equivalent to the level projected at proposal.

        Under the final rule, no indirect discharging facilities are projected to close entirely, close a product line,
or experience other significant financial impacts due to compliance with PSES.  This level of impact is lower than
the impacts projected  at proposal for indirect discharging facilities.  (At proposal, one indirect discharging facility
was projected to close a product line.)
        Given that the level of projected impacts has stayed constant for direct dischargers and decreased for
indirect dischargers since proposal, community impacts were not re-estimated.  Instead, the impacts reported at
proposal represent a conservative estimate of these impacts. The community impacts reported below are therefore
those reported in the EIA for the proposed rule.^
    *The specific changes in the analysis since proposal are documented in the Federal Register notice of the final
rule, the administrative record, the technical development document, and at the relevant sections of the EIA.
    2A single direct discharge facility accounted for most of the increase in  estimated compliance costs since
proposal. (See Chapter 4.) The estimated investment costs at this facility increased from $1.6  million to $16.0
million, with an increase  in annualized costs from $2.0 million to $7.3 million.  The Agency maintains that the
actual  compliance costs for this facility would be lower than the estimates used in the final analysis. However,
analysis using these higher cost estimates ensures that EPA does not underestimate the burden of compliance at this
facility. As an additional check on community impacts, the analysis examined the extent of the projected reduction
in in-scope revenue at this facility. The ira-scope revenue generated at this facility is expected to fall by only about
one percent due to the regulation, so significant community impacts are not expected.
                                                   5.1

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        The impacts corresponding to both BAT and PSES compliance under the proposed rule are presented.3
Only those impacts associated with Subcategory A (Organic Pesticide Chemicals  Manufacturing) chemicals are
shown; no closures or other significant decreases in production are expected for manufacturers of Subcategory B
(Metallo-Organic Pesticides Chemicals Manufacturing).4

5.1     Methodology

        Community impacts are analyzed in two stages.  The first stage analyzes  the primary impact of facility
layoffs due to facility closures and other significant reductions in revenue.  If the primary employment losses
estimated in the first stage of the analysis are determined to be significant, the analysis is then taken to a second
stage that determines secondary impacts on the community employment level.  Secondary impacts arise from
reduced demand for inputs to the affected facility, and reduced consumption due to  losses in earnings. Secondary
impacts are assessed through multiplier analysis, which measures the extent to which employment levels in other
industries are affected by employment changes in a given industry. Secondary and primary employment losses are
summed to obtain the total impact on community employment levels resulting from pesticide facility closures and
other decreases in pesticide revenue.

5.1. A  Primary Impacts on Employment
        Primary impacts on employment are considered for facilities predicted to experience either a closure or a
decrease in in-scope PAI revenue of at least ten percent due to the regulation. All pesticide-related employment at
a facility is assumed to be lost in the case of facility closures.  The percentage of employment lost due to other
significant reductions in production is assumed to equal the percentage of revenues lost.
    3Impacts of zero discharge requirements are reported with impacts of direct discharge requirements.  Zero
dischargers may be subject to monitoring costs if they have any process wastewater.  Monitoring costs would be
imposed by the permitting authority (no separate monitoring requirements are contained in the effluent guidelines
for pesticide manufacturers). These monitoring costs are included in the analysis to capture the full cost to industry
of controlling process wastewater pollutants.
    ^Direct discharges of Subcategory B chemicals are already limited to zero under the Best Practicable Control
Technology Currently Available (BPT) regulation.  BAT and PSES regulations are not being promulgated for
Subcategory B at this time.
                                                   5.2

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        Facility Closures
        Employment loss resulting from a facility closure is assumed to equal the total annual pesticide-related
employment hours calculated from that facility's Census data.5  Total pesticide-related hours are calculated as the
sum of both pesticide-related production and non-production hours.  Pesticide-related production hours are obtained
directly from  the Census by adding pesticide manufacturing hours and pesticide formulating/packaging hours.
Pesticide-related non-production hours are estimated by computing the ratio of total non-production hours to total
production hours and multiplying the pesticide production hours by this ratio.6 These calculations are shown below
algebraically.
Total pesticide production employee hours (TPH) are computed as:
where:
MH    =
FH     =
                        Annual employee hours spent in pesticide chemical manufacturing production; and
                        Annual employee hours spent in pesticide formulating/packaging.
Non-production employee hours related to pesticide production (TNH) are estimated as:

                                             TNH=TPHx —
where:
        N
        P
                Annual non-production employee hours spent at facility; and
                Annual employee hours spent in all production at facility.
        Total facility production hours (F), used in the above equation, are computed as:
     Employment in the pesticide manufacturing industry tends to be seasonal. Facilities reported employee hours
for the months of January, May and November to account for this seasonal! ty.  "Annual hours" are estimated by
multiplying the average hours of the three months by 12.
         inclusion of pesticide formulating/packaging hours is conservative, because facilities that discontinue
manufacture of certain PAIs could purchase the PAIs and continue to formulate/package them.
                                                   5.3

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where:
        OPH   =
Annual estimate of employee hours spent in other production.
        Total pesticide-related employee hours lost due to a facility closure,  i.e., the sum of pesticide-related
production hours and pesticide-related non-production hours, are converted to full time equivalents (FTE), assuming
that 2000 hours = 1 FTE.7

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

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

5.1.B    Measuring Impact Significance
         The significance of facility  employment loss on the community is measured by its impact on the
community's level of employment as a whole. For purposes of this analysis, the community is defined as the
Metropolitan Statistical Area (MSA), in which the facility is located.9  The MSA is assumed to represent the labor
market area within which residents could reasonably commute to work.  If the facility is located in a Primary
Metropolitan Statistical  Area (PMSA) within the  MSA, then the PMSA population is used.  If a facility is not
located within an MSA, then the community is defined as a county (or township, for eastern states).  A decline in
    7Computed: (50 weeks/year)(40 hours/week) = 2000 hours/year.
    sThe ratio of in-scope pesticide production volume to total facility production volume, although not the separate
 numerator and denominator, is reported in the Census.
    9MSAs are denned by the U.S. Office of Management and Budget.
                                                   5.4

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

5.1.C  Secondary Impacts on Employment
        As stated above, if primary employment losses are found to have a significant impact on a community, then
secondary effects on employment levels are assessed by multiplier analysis.  Secondary effects arise from (1) the
reduction hi demand for inputs by the affected facility, and (2) induced impacts  attributable to reductions  in
consumption due to both primary and secondary losses hi earnings. Multiplier analysis is used to account for these
secondary effects, and provides a straightforward framework as long as the direct effects are small and a number
of other important limitations (e.g., constant returns to scale, fixed input ratios) hold.

        The  multiplier used in this analysis is based on input/output tables developed by the Department  of
Commerce, Bureau of Economic Analysis (BEA, 1986).  The  BEA multipliers are estimated via  the Regional
Industrial Multiplier System developed by the Regional Economic Analysis Division of the BEA.  The multipliers
reflect the total national change hi the  number of jobs given a change  hi the number of jobs for a particular
industry.10 In this analysis,  the industry directly affected is Chemicals and Selected Chemical Products.11 The
multiplier reported by BEA for this industry is 8.37.12  The change in total number of jobs is computed by:

                                          CTJ = 8.37 x CDCJ
where:
CTJ
CDCJ  =
Change in total jobs; and
Change in direct chemical industry jobs (FTEs).
    10"Jobs" include both full- and part-time positions.
      Multipliers based on direct employment changes are available at an aggregated industry level only.
      The use of this national multiplier may overstate the number of jobs affected within the community because
some of the inputs may be from sources outside the community or even outside the country.  No multipliers that
differentiate among the locations of inputs sources are known to exist.
                                                 5.5

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5.2
Results
5.2. A  Impact of Best Available Control Technology Economically Achievable (BAT) Regulations on Direct
        Dischargers13
        No direct discharging facilities are expected to close, while two facilities are expected to have a decline
in in-scope revenues of 10 percent or greater.  As shown in Table 5.1, total estimated employment loss is 31 FTEs,
less than  one percent of the  total pesticide-related employment figures  reported by all  PAI  manufacturers
(approximately 9,940 FTEs). The employment rates in the two affected communities are expected to decline by
less than one percent.  Therefore, the projected employment loss for direct dischargers is not considered significant.

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

        Among  the modifications made to the economic analysis since proposal were changes in compliance costs
for several facilities.  As a result of these changes, total annualized compliance cost increased by 24% for direct
dischargers. The change in compliance cost is the aggregate effect of decreases hi annualized compliance costs at
four facilities and increases in annualized compliance costs at four facilities.  Most of the increase in total costs is
due to a substantial costs increase  at one facility.  Total annualized compliance costs for indirect discharging
facilities decreased by 14%.

        As an additional check on community impacts under the final rule, EPA examined the extent of in-scope
revenue decrease at the single direct discharging facility bearing most of the increase in compliance costs.  The
production at this facility is expected to fall by only  one  percent.  Among indirect discharging facilities,  the
estimated  impacts have decreased since proposal.  Therefore,  the estimates  of community impacts presented at
proposal serve as reasonable conservative estimates of the impacts due to compliance.

        In summary, expected  employment loss due to the BAT and PSES compliance is projected to be largely
contained within the pesticide industry. The estimated reduction in employment, 128 FTEs, is approximately one
percent of the total pesticide-related employment reported by all PAI manufacturers.
    13Impacts of zero discharge requirements are discussed with direct discharge requirements.
                                                   5.6

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                                      Table 5.1^
                                 Community Impact
                              Employment Loss (FTEs)
                                                         Discharger Type
                                                 Direct1
            Indirect
            Total
Subcategory A2

FTE's Lost Due to Plant Closures

FTE's Lost Due to Reduced Production

FTE's Lost Due to Secondary Effects

Total Subcategory A FTE's Lost
 0.0

31.0

 0.0

31.0
 0.0

96.8

 0.0

96.8
  0.0

127.8

  0.0

127.8
1 Impacts of requirements on zero dischargers are reported with impacts of requirements on
  direct discharge. Zero dischargers may be subject to monitoring costs by the permitting
  authority if they have any process wastewater.  (No separate monitoring requirements are
  contained in the proposed effluent guidelines for pesticide manufacturers).  These
  monitoring costs are included, in the analysis to capture ihe full cost to industry of
  controlling process wastewater pollutants.
2 Impacts associated with Subcategory B PAIs are not shown, since regulations are not
  being promulgated at this time for Subcategory B.
                                        5.7

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                                         Chapter 5 References
Bureau of the Census (1986), Current Population Reports: Population and Per Capita Income Estimates for
  Counties and Incorporated Places, U.S. Department of Commerce.

Bureau of the Census (1988), Statistical Abstract of the United States, U.S. Department of Commerce.

Bureau of Economic Analysis (1986), Regional Multipliers, A  User Handbook for the Regional Input-Output
  Modelling System (RIMS II), U.S. Department of Commerce, May.

Bureau of Labor Statistics (1989), Handbook of Labor Statistics.
                                                 5.8

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                               Chapter 6: FOREIGN TRADE ANALYSIS
6.0
Introduction
        Pesticide active ingredients (PAIs) are traded in an international market, with producers and buyers located
worldwide. Changes hi domestic PAI production due to the regulation of effluent from PAI manufacturing facilities
may therefore affect the balance of trade. This chapter estimates the extent to which the final effluent limitations
for PAI manufacturers would affect the balance of trade.  To measure the significance of the expected changes in
exports and imports, these changes are compared with current U.S. exports and imports for the pesticide industry,
and with total U.S. merchandise trade.

        Following proposal, EPA thoroughly reviewed the details of the economic analysis in preparation for the
final rule.  In response to this review and to public comments, the compliance costs were adjusted and the economic
analysis was revised in several ways.'  The changes, both separately and taken together, do not significantly affect
the number of impacts projected.

        As presented in Chapter 4, under the final rule, no direct discharging facilities are projected to close due
to compliance with BAT and one facility, equal to 3 percent of the direct discharging facilities in Subcategory A,
is projected to close a product line.   (One zero discharging facility, subject only to monitoring costs, is also
projected to close a product line.) No facilities are expected to experience other significant financial impacts short
of facility or product line closure.  This level of impacts is equivalent to the level projected at proposal.

        Under the final rule, no indirect discharging facilities are projected to close entirely, close a product line,
or experience other significant financial impacts due to  compliance with PSES. This level of impact is lower than
the impacts projected at proposal for indirect discharging facilities. (At proposal, one indirect discharging facility
was projected to  close a product line.)
        Given that the level of projected impacts has stayed constant for direct dischargers and decreased for
indirect dischargers since proposal, foreign trade impacts were not re-estimated. Instead, the impacts reported at
proposal represent a conservative estimate of these impacts.  The foreign trade impacts reported below are therefore
those reported in the EIA for the proposed rule.2
     The specific changes in the analysis since proposal are documented in the Federal Register notice of the final
rule, the administrative record, the Technical Development Document, and at the relevant sections of the EIA.
    2A single direct discharge facility accounted for most  of the increase in estimated compliance costs since
proposal.  (See  Chapter 4.)  The estimated investment costs at this facility increased from $1.6  million to $16.0
million, with an increase in annualized costs from $2.0 million to $7.3 million.  The Agency maintains that the
actual  compliance costs  for this facility would be lower than the estimates used in the final analysis.  However,
                                                    6.1

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        The impacts corresponding to both BAT and PSES compliance under the proposed rule are presented.3

Only those impacts associated with Subcategory A (Organic Pesticide Chemicals  Manufacturing) chemicals are
shown; no closures or other significant decreases in production are expected for manufacturers of Subcategory B

(Metallo-Organic Pesticides Chemicals Manufacturing).4


6.1     Methodology


        Decreased production resulting from compliance with effluent guideline limitations may result in both

decreased U.S.  exports and  increased U.S.  imports of  PAIs.^   Exports may decrease as  previously exported
products are no longer manufactured;  imports may increase as domestic purchasers seek new sources of PAIs no

longer offered by a particular manufacturer.  Changes in exports and imports are considered for facilities predicted
to close under a regulatory option and  for facilities predicted to have a decrease in in-scope PAI revenue of at least

ten percent due to regulation.


6.1. A  Exports
        Changes in exports are considered only for those facilities expected to incur compliance costs, and who

also indicated in the Census that they exported a portion of their production in  1986. These changes are calculated
assuming that the foreign response to increased price matches the domestic response, i.e., foreign demand elasticities

equal domestic demand elasticities.  The analysis assumes that none of the decreased production of exported PAIs
is replaced by alternate U.S. products.   This "worst case"  assumption is  very  conservative and  is likely to

overestimate the reduction in exports.  If the impact on foreign trade is not significant in this worst-case scenario,
then more realistic scenarios would also indicate no significant impacts. The methods of estimating changes in PAI
analysis using these higher cost estimates ensures that EPA does not underestimate the burden of compliance at this
facility. As an additional check on foreign trade impacts, the analysis examined the extent of the projected reduction
In in-scope revenue at this facility.  The in-scope revenue generated at this facility is expected to fall by only about
ons percent due to the regulation, so significant foreign trade impacts are not expected.

    3Impacts of zero discharge requirements are reported with impacts of direct discharge requirements.  Zero
dischargers may be subject to monitoring costs if they have any process wastewater.  Monitoring costs would be
imposed by the permitting authority (no separate monitoring requirements are contained in the effluent guidelines
for pesticide manufacturers).  These monitoring costs are included hi the analysis to capture the full cost to industry
of controlling process wastewater pollutants.

    ^Direct discharges of Subcategory B chemicals are already limited to zero under the Best Practicable Control
Technology Currently Available (BPT) regulation.  BAT and PSES regulations  are not being promulgated for
Subcategory B at this time.

    ^Environmental laws in other countries are changing, often reflecting the changes in U.S. environmental laws.
This analysis conservatively assumes, however, that current foreign environmental laws will remain in effect. As
« result of this assumption, effects  of the regulation on foreign trade may be overstated.

                                                   6.2

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exports arc discussed below for four categories of facilities. Separate methods were required, depending on whether
the facility was projected to close and whether the facility chose to provide PAI-specific data in the Census.

        Facility Closures with PAI-Spacific Information
        If a facility is projected to close and PAI-specific export percentages were reported in the Census, the loss
hi exports is estimated as the product of the revenue from each PAI and the export percentage for that PAI, summed
over all PAIs produced.6 Algebraically, export revenue losses are computed as:
                                         AIX =
where:
        ADC    =      Change in export revenues for a facility;
        AIV;    =      Facility revenues from PAI i; and
        ADCPj  =      Percentage of PAI i production that is exported by the facility.7

        Facility Closures without PAI-Specific Information
        Although the provision of PAI-specific export data hi the Census was optional, all facilities were required
to provide the percentage of the facility's (total 1986 production that was exported. If PAI-specific information was
not provided by the facility, then the percentage  of exported PAI sales is  assumed to equal the percentage of
exported facility-level production. Revenues from pesticides and pesticide contract work are added to obtain total
pesticide-related sales.  The loss hi export revenues is estimated by multiplying total facility pesticide sales by the
percentage of total production exported by a facility.8

        Facilities with Reduced Demand and PAI-Specific Information
        Facilities incurring compliance costs and remaining open  may experience a decline in exports  due to
decreased demand resulting from price increases. Changes hi exports are considered only for those facilities whose
in-scope revenues are expected to decrease by at least ten percent  due to the  regulation.
         export data reported are  expressed  hi percentage of volume.  Because percentages of revenue are
unavailable, it is assumed that the percentage of revenues generated from exports is equal to the percentage of
volume exported.
    7For facilities projected to close, a full accounting of changes in exports would include changes in exports of
formulated/packaged pesticides as well as PAIs.  The single facility that reported PAI-specific data and is projected
to close, however, did not formulate/package PAIs hi 1986.  For this reason, changes in exports of PAIs alone are
considered in this section.
    8The facility-reported export data may not reflect actual exports for facilities that perform contract work,
because facilities may not know the trade status of such products.
                                                  6.3

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

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

6.1.B  Imports
        An analysis of changes in imports is performed for facilities projected to either close or lose at least ten
percent of in-scope pesticide revenues, and that also produce a PAI that was imported to the United States in  1986.
Because changes in revenues are evaluated for each facility at the cluster  level, the analysis of imports also focuses
on clusters.  Production of each cluster of PAIs was classified as replaceable by imports if any PAI within the
cluster was imported in 1986.   As a worst-case scenario, it is assumed that all lost revenue in  clusters with
imported PAIs (with the exception of revenue lost due to reduced exports) is replaced by imports.  This assumption
is very conservative and is likely to overestimate the increase in imports.  If this worst-case scenario does not result
in a significant impact on foreign trade, then neither would a more realistic scenario.

6.2     Results

6.2.A  Impact of  Best  Available Technology  Economically Achievable (BAT)  Regulations  on Direct
        Dischargers10
        No direct discharging facilities are projected to close, and two facilities are expected to have a decline hi
in-scope revenues  of ten percent or greater.  Of the two facilities affected, only one facility reported export data
    'import data from several sources were reviewed for this analysis.  Sources include the Office of Pesticides
Programs (OPP), the Bureau of the Census, and the International Trade Commission.  Data published by the Bureau
of the Census and the International Trade Commission were so highly aggregated that they were not useful for this
analysis. Details of the data review are contained in the Administrative Record.
    10Impacts of zero discharge requirements  are reported with impacts of direct discharge requirements.  Zero
dischargers may be subject to monitoring costs if they have any process wastewater. Monitoring costs would be
imposed by the permitting authority (no separate monitoring requirements are contained in the proposed effluent
guidelines for pesticide manufacturers). These monitoring costs are included in the analysis to capture the full cost
to industry of controlling process wastewater pollutants.
                                                   6.4

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(non-PAI-specific). Using the methods outlined above, it is estimated that exports from this facility could decline
by about $114,000 due to the regulation (see Table 6.1).

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

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

6.2.B   Impacts of Pretreatment Standard for Existing Sources (PSES) Regulations on Indirect Dischargers
        No indirect discharging facilities are projected to close, and only one facility is expected to have a decline
in in-scope revenues of ten percent or greater.   This facility  reported export data (non-PAI-specific).  Using the
methods outlined above, it is estimated that exports from this facility could decline by about $5.5 million due to the
regulation.

        The one indirect discharging facility expected to experience a decline in in-scope pesticide revenues of ten
percent or greater produces PAIs in three dusters.  The PAI production in each of these clusters is replaceable by
imports. In the worst-case scenario described above, imports are expected to rise by $10.6 million. With the
conservative assumptions incorporated in  the analysis, PSES regulations are projected to reduce the U.S. pesticide
trade balance from $897 million to $886 million, slightly more than a one percent decline.  The PSES regulation
would increase the total U.S. merchandise net imports by about one one-hundredth of one percent.

        In summary, neither BAT nor PSES regulations are  projected to have a substantial impact on the U.S.
pesticide trade balance or the U.S. total  merchandise trade balance.  The $21.6 million decline in net pesticide
imports decreases the U.S. pesticide and merchandise trade balances by approximately  2  percent and one one-
hundredth of one percent,  respectively.
                                                   6.5

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                                           1 Table 6.1,^
                                      Foreign Trade Impact
                                         (in $ thousands)
                                   Decline in Pesticide Exports
                                                              , w Discharger Type
                                                         Direct^
              Indirect
                 Total
 Subcategory A
 Due to Plant Closures
 Due to Reduced Production
 Total Subcategoiy A
    0
  114
  114
     0
 5,477
 5,477
     0
 5,591
 5,591
                                  Increase in Pesticide Imports
                                                                    .I,1
                                                                , Discharger Type
                                                        ^Direct
                                Total
Subcategory A
Due to Plant Closures
Due to Reduced Production
Total Subcategory A
    0
5,408
5,408
     0
10,632
10,632
     0
16,040
16,040
                             Net Decline in Pesticide Trade Balance   %
                                                                     .          •.
                                                                 Discharger Type
                                                                         Indirect
                                Total
Subcategory A
Due to Plant Closures
Due to Reduced Production
Total Subcategory A
5,522
5,522
16,109
16,109
     0
21,631
21,631
1 Impacts of zero discharge requirements are reported with impacts of direct discharge requirements.
  Zero dischargers may be subject to monitoring costs if they have any process wastewater.  Monitoring
  costs would be imposed by the permitting authority (no separate monitoring requirements are contained
  in the proposed effluent guidelines for pesticide manufacturers).  These monitoring costs are included
  ia the analysis to capture the full cost to industry of controlling process wastewater pollutants.
2 Subcategory B is not shown, since no closures or other significant decreases hi production are
  projected for this subcategory.
                                              6.6

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                                          Chapter 6 References
United Nations (1986).  Statistical Office.  International Trade Statistics Yearbook.  New York.

U.S. Department of Commerce (1988).   Bureau of the Census.   Statistical Abstract of the United States.
Washington, D.C. January.
                                                   6.7

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6.8

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

        Following proposal,  EPA thoroughly reviewed the details of the economic analysis in preparation for the
final rule. In response to this review and to public comments, the compliance cost estimates were adjusted and the
economic  analysis was  revised in several ways.2  The changes,  both separately and  taken  together, do not
significantly affect the number of impacts projected.

        As presented in Chapter 4, under the final rule, no direct discharging facilities are projected to close due
to compliance with BAT and one facility, equal to 3 percent of the direct discharging facilities in Subcategory A,
is projected to close  a product line.  (One zero discharging facility, subject only to monitoring costs, is  also
projected to close a product line.)  No facilities are expected to experience other significant financial impacts short
of facility or product line closure.  This level of impacts  is equivalent to the level projected at proposal.

        Under the final rule, no indirect discharging facilities are projected to close entirely, close a product line,
or experience other significant financial impacts due to compliance with PSES.   This level of impact is lower than
     Conversely, a firm may be able to reduce its cost of compliance by consolidating the manufacturing activities
and, therefore, the treatment investments required of several facilities. This would mitigate the projected impact
predicted by a facility-level analysis. While such cases are plausible, it is beyond the scope of this analysis to
identify them.
    2The specific changes in the analysis since proposal are documented in the Federal Register notice of the final
rule, the administrative record, the Technical Development Document and at the relevant sections of the EIA.
                                                   7.1   .

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the impacts projected at proposal for indirect discharging facilities. (At proposal, one indirect discharging facility

was projected to close a product line.)


        The impacts corresponding to both BAT and PSES compliance under the final rule are presented.4  Only
those impacts associated with Subcategory A (Organic Pesticide Chemicals Manufacturing) chemicals are presented

because only Subcategory A chemicals are being regulated.5


        The firm impact analysis is  organized into three sections.  The first section reviews the concepts used to

drive the  financial analysis.  The  second section describes the methodology that employs these concepts.  This

section also highlights some analytic difficulties encountered due to data limitations, and the steps required to

overcome them. The third part of the discussion presents the results of the firm analysis.


7.1     Analytic Approach


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


        (1)     The baseline analysis identifies firms whose financial condition, independent of regulatory action,
                is sufficiently weak to contraindicate the implementation of a treatment program required by a

                regulation.  Such firms would be at risk of financial failure even without regulatory costs.  For
                this reason, firms that fail  the baseline analysis are excluded from the post-compliance analysis.
    3Although the level of projected impacts has stayed constant for direct dischargers and decreased for indirect
dischargers since proposal, firm-level impacts were re-estimated because estimated investment costs at one facility
have increased from $1.6 million to $16 million since proposal  (see Chapter  4).   This change in estimated
compliance costs resulted from public comments by the facility. The Agency maintains that the actual compliance
costs for this facility would be lower than the estimates used in the  final analysis. However, analysis using these
higher cost estimates ensures that EPA does not underestimate the burden of compliance at this facility.
    ^Impacts on zero discharge facilities are reported with impacts on direct discharge facilities. Zero dischargers
may be subject to monitoring costs if they have any process wastewater.  Monitoring costs would be imposed by
the permitting authority (no separate monitoring requirements are contained in the proposed effluent guidelines for
pesticide manufacturers).  These monitoring costs are included in the analysis to capture the full cost to industry
of controlling process wastewater pollutants.

    5Direct discharges of Subcategory B chemicals are already limited to zero under the Best Practicable Control
Technology Currently Available (BPT) regulation.  PSES regulations for Subcategory B  chemical are not being
promulgated at this time.  For an analysis of the effects of PSES regulations on indirect dischargers of Subcategory
B chemicals, see the EIA published at proposal.

                                                    7.2  •

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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 the  regulation.

        The firm financial impact analysis is conducted from the perspective of creditors and equity investors who
would be the sources of capital to finance a firm's purchase  of treatment systems.6  To attract the financing for a
treatment program, a firm must demonstrate financial strength both before and, on a projected basis, after the
treatment program (baseline and  post-compliance, respectively).  The financial analysis presented in this report
simulates that performed by investors and creditors in deciding whether to finance  the installation of a pollution
prevention or wasitewater treatment system.  Two considerations that influence this  decision are (1) the financial
performance of the firm (particularly in relation to its competitors) and (2) the expected ability of the firm to manage
its financial commitments without risk of financial failure. These considerations, discussed below, form the basis
of this analysis.

7.1.A   firm Financial Performance
        If a firm's performance is weaker than that of its competitors,  the firm may not be able to provide the
expected investment return to its creditors and investors. Unless significant improvement in performance is likely,
investors and creditors will generally avoid providing financing to such firms. Alternatively, investors and creditors
may seek  higher returns (in the form  of higher interest rates or higher required returns on equity) to compensate
for the additional risk associated with  the capital they provide.  The higher cost of capital may in turn decrease the
likelihood that such firms will invest in the treatment options required for compliance with an effluent guideline.

        The measure of financial performance used in the firm analysis is  pre-tax return on assets (pre-tax ROA,
hereinafter referred to as "ROA"), computed as the ratio of earnings before interest  and taxes (EBIT) to assets:7
                                              ROA =
 EBIT
Assets
        ROA is a measure of the profitability of a firm's capital assets, independent of the effects of taxes and
financial structure.  It is perhaps the single most comprehensive measure of a firm's financial performance.8 ROA
provides information about the quality of management, the competitive position of a firm within its industry, and
    6For a further discussion of debt and equity financing, see Section 4.2.A.
    7ROA is also known as "return on investment."
     For credit analysis in particular, pre-tax ROA is important because interest payments are made from pre-tax
income.
                                                   7.3  .

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the economic condition of the industry in which the firm competes.  In addition, ROA incorporates information
about a firm's operating margin and asset management capability: the ratio of pre-tax income to sales (operating
margin), multiplied by the ratio  of sales to assets (asset turnover), equals ROA.  If a firm cannot sustain a
competitive ROA, on both a baseline and post-compliance basis, it will probably have  difficulty financing the
pollution control investment.  This is true regardless of whether financing is to be obtained as debt or equity.

        Illustrating typical ROA  values  from 1982 to 1990, the median ROA  for the U.S. industrial chemical
industry (as represented by SIC codes 2861, 2865,  and 2869) ranged from 10.1 percent to 18.9 percent (Robert
Morris Associates [RMA], 1991).9 At the 75 percent quartile, ROA ranged from 14.5 percent to 23.6 percent over
this same period (i.e., firms at this level were more profitable than 75 percent of those in the industry).  At the 25
percent quartile, which  is indicative of weak performance, ROA ranged from 7.2 percent to 13.4 percent.  The
computation of ROA, and the interpretation of the computed values as the basis for determining financial viability,
arc discussed in Section 7.2.

7.1.B   Ability To Manage Financial Commitments
        The second general area of concern to creditors and investors is the  extent to  which the firm can be
expected to manage its financial burdens without risk of financial failure.  In particular, if a firm's operating cash
flow does not comfortably exceed  its contractual payment obligations (e.g., interest and lease obligations), the firm
is seen  as vulnerable to a decline in sales or increase in costs.10  Either scenario may:   (1) sharply reduce or
eliminate returns to the equity owners of the firm; and/or (2) prevent the firm from meeting its contractual payment
obligations. In the first case, earnings might fall or become negative, with a consequent reduction or elimination
of dividends and/or reinvested earnings.   The market value of the firm's equity is also likely to fall, causing a
capital loss to investors.  In the second case, failure to make contractual credit payments will expose the firm and
its equity owners to the risk of bankruptcy, forced liquidation of assets, and probable loss of the entire equity value
of the firm.

        The ability to manage financial commitments is expressed by the ratio of EBIT to interest obligations, or
the interest coverage ratio (ICR):11
    'RMA provides financial statistics based on bank credit reports from public-reporting and non-public-reporting
firms in a variety of industries.  The RMA industry group that corresponds best to the pesticides manufacturing
industry is the "industrial chemicals" industry, which includes SIC codes 2861, 2865, and 2869.  The ROA values
are calculated from RMA's reported "operating profit/sales" ratio and "sales/asset" ratio.
    10For this discussion, a firm's operating cash flow is considered to be revenues minus costs, with the exception
of interest, lease expense and depreciation.
    llrThe ICR is also known as "times interest earned."
                                                   7.4   .                    ''

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                                                     EBIT
                                                    Interest
        Weakness in these characteristics of firm financial condition and performance, as would be indicated by
a low ICR, indicates vulnerability of the firm to financial failure and difficulty in obtaining financing for treatment
investments.  From 1982 to 1990, the median value of interest coverage for industrial chemicals firms (as defined
by RMA) ranged from 2.3 to 5.6.  Over the same period, the 75th percentile value ranged from 7.2 to 16.3, and
the 25th percentile value ranged from 1.0 to 2.2 (RMA, 1991).

7.2     Analytic Procedure

        As described in the preceding section, the firm analysis is based on two financial measures: ROA and ICR.
Finn-level data required to calculate these financial measures were obtained from public sources for domestic firms
subject to public reporting requirements.  In contrast, data for foreign-owned or closely-held domestic firms were
not publicly available.12 The only firm-specific data available for these firms were gross revenues obtained from
the Census.   Where  firm-level  data were not publicly  available, industry norms of financial condition and
performance were used as the basis for firm analysis.  For example, baseline financial measures  were developed
using median values for the industrial chemicals business sector reported by RMA.   As a result of these data
limitations,'the analysis for foreign-owned and closely-held domestic firms is less precise than for public-reporting
domestic firms.

        For the final rule, detailed financial data were available for 20 of the 45 firms expected to incur costs; the
remaining 25 firms, closely-held or foreigB-owned entities, required the use  of data obtained from RMA.

        As mentioned above, ROA is calculated by dividing EBIT by total assets. Data used to calculate ROA for
public-reporting firms were obtained from income statement compilations in Compustat for 1986.13  For non-public-
reporting firms, firm-level revenues were obtained from the Census. Firm-level values of assets, and EBIT for non-
public-reporting firms, were estimated from firm-specific revenues and RMA data (e.g., median values for assets
and EBIT as a percentage of revenues in 1986).
    12Closely-held firms are owned by only a few individuals.  They do not trade securities publicly and are
therefore not subject to public-reporting requirements under the rules of the Securities and Exchange Commission
(SEC).
    ^Compustat, a data base, provides financial information from SEC 10-K filings.  The 10-K document is the
form in which public-reporting firms are required to file detailed financial information annually with the SEC.  A
10-K document contains information similar to that contained in an annual.report but with additional detail.
                                                  7.5

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

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

        Computing Baseline Measures for Public-Reporting Firms
        Baseline data for public-reporting firms are taken from Compustat.  The three components of the two
financial ratios are described below:
                                                   7.6

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                                       PSOBUC REPORTING Tftsxss
                                             Baseline EBIT
     equals   Operating Income (operating revenues minus all production and operating costs, selling expenses,
             and general and administrative expense; but before taxes, interest and depreciation)
     minus   Depreciation and Amortization (non-cash cost items recognized as a charge against income and
             meant to reflect the consumption of wasting assets)
     minus   Losses from discontinued operations
      plus   Nonoperating Income.

                                         Baseline Total Assets
     equals   Total Current Assets
      plus   Net facility, property, and equipment
      plus   "Other" assets.

                                       Baseline Interest Expense
             Taken directly from Compustat, which lists interest expense as a single line item.
        Computing Baseline Measures Ifor Non-Public-Reporting Firms
        Baseline financial measures for non-public-reporting firms required firm-level values to be estimated on
the basis of: (1) firm-specific revenue information obtained in the Census; and (2) industry averages obtained from
RMA's 1991 Annual Statement Studies for the industrial chemicals business sector and Compustat. All values were
for the year 1986. The components of baseline financial ratios for non-public-reporting firms were estimated in the
following manner:
                                                  7.7

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                                     NON-POBUC-REPORTING
                                             Baseline EDIT
   Estimated EBIT = Firm Revenues X \<*«*** ****]   x  \	ML	
                                     [    Revenue    J^   [Operating Profit\COMPUSIAT
         Estimated EBJT = Firm Revenues x 0.058 x 1.18 = Firm Revenues x 0.068
  Firm revenues were taken from the responses of individual firms to the Census.  RMA, which did not
  provide an EBTT/revenue ratio directly, gave an industry median operating profit/isveiaie ratio of 0.058 for
  1986.  The estimated average EBIT/revemie ratio was determined by increasing the RMA operating
  profit/revenue ratio by the percentage amount by which EBIT exceeded operating profit for the public-
  reporting pesticides manufacturing firms included in the analysis. Based on Compustat data for the public-
  reporting firms in the analysis, EBIT was found to be 18 percent higher on average than operating profit.
  For the analysis of non-public-reporting firms, an EBIT/revenue ratio of 0.068 (i.e.,  1.18 x 0.058) was
  multiplied by firm-level revenue data to calculate firm-level EBIT. To summarize, for each $100 million in
  revenues, a non-public-reporting firm was assumed to have EBIT of $6.8 million.
                                         Baseline Total Assets
  Calculated using the median RMA revenue/assets ratio of 2.0 to 1.  A firm with $100 million in revenues
  was therefore assumed to have $50 million in assets.
                                       Baseline Interest Expense
  Calculated from the median RMA value of the EBlT/interest ratio, 3.0 to 1. Assuming that the estimated
  EBIT/revenue ratio for non-public-reporting pesticides firms is 0.068, an EBIT/interest ratio of 3.0 indicates
  that interest expense averages 2.3 percent of revenue for RMA firms (i.e., 0.068/3.0 = 0.0227 or
  approximately 2.3 percent). This value was multiplied by firm-level revenue data taken from the Census to
  estimate baseline interest expense for all non-public-reporting firms.  To summarize, for each $100 million
  in firm-level revenues, annual interest expense was estimated at $2.3 million.
        Because the baseline ratio values for all of the non-public-reporting firms in the analysis were calculated
using median RMA values, they are the same.14  Specifically, the estimated ROA is 13.6 percent and the ICR is
                                                                        f          .''  "I
2.96. Although these values are the same in the baseline analysis for all non-public-reporting firms, they differ
    I4If firm-level financial data were available for the non-public-reporting firms, the baseline ratio values could
be estimated more accurately.
                                                  7.8

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

        Evaluating Baseline Performance Measures
        To evaluate the baseline viability of the firms analyzed, the firm-specific values of baseline financial
performance were compared against the lowest quartile (i.e., 25th percentile) value in 1986  for the financial
performance measures as reported by RMA for the industrial chemicals business sector.  The lowest quartile value
for the ICR was 1.1; the lowest quartile for ROA was 8.8. Those firms for which the value of either the ROA or
the ICR was less than the first quartile value from RMA were judged to be  "vulnerable11 to financial failure,
independent of the application of a pesticides effluent guideline. Because both measures are judged to be critically
important to financial success and the ability to attract capital, failure with regard to either measure alone was
deemed adequate for the finding of "vulnerability" (see Table 7.1).  Because the  ratio values for non-public-
reporting firms were based on  the RMA median values rather than firm-specific data, none of the non-public-
reporting firms could be judged to be vulnerable in the baseline analysis.

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

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

        (2)     Using the 25th percentile values from RMA does not mean that 25 percent of the firms in this EIA
                will be judged to be in poor financial condition.  The firms in the RMA sample on  which the
                percentiles were calculated include those in the industrial chemicals business as a whole. The PAI
                manufacturing firms analyzed in this study are therefore a subset of the RMA sample.
                                                   7.9

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•»•'•' J pr^
Table 7.1j Determination of Firm-level financial t Viability , \[
ROA
Highest Quartile
Third Quartile
Second Quartile
Lowest Quartile
, ,„, Interest .Coverage Ratio
Lowest Quartite Median " ,'- Highest Quartile
Vulnerable
Vulnerable
Vulnerable
Vulnerable



Vulnerable



Vulnerable



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

        To recalculate ROA and ICR, the three baseline components (i.e., EBIT, total assets, and interest expense)
were adjusted to reflect compliance costs estimated at the facility level. In the facility analysis, compliance costs
were estimated in three categories:  capital costs (facility and equipment),  land costs, and annual operating and
maintenance costs.15  In the firm analysis,  these values were summed over the facilities owned by each firm and
ussd to adjust the baseline components as shown below  (see also Table 7.2 for the mathematical  formulation of the
analysis):
    15Discharge costs (e.g., the cost of sludge disposal) and monitoring costs are included within the operating and
maintenance cost category.
                                                   7.10

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         Table 7.2:  Calculation of Firm-Level Financial Measures in Post-Compliance Analysis J6
                                  Finn Financial Performance (ROA)
     Baseline ROA =
                        EBTT
                     Total Assets
     Post-Compliance ROA =
EBIT -
A? * -i
~°2
+ F(AP * #j) - (pj * A#)]
                                             Jbto/ Assets  + c
  where:
          EBIT = Baseline earnings before interest and taxes
          Oj    = Baseline operating and maintenance expenses
          02    = Compliance operating and maintenance expenses
          Aq   = Change in production quantity due to elasticity (qj - q^
          qi    = Baseline production quantity
          <\2    = Post-compliance production quantity
          Ap   = Change in price due to elasticity (PJ - PZ)
          Pl    = Baseline unit price
          P2    = Post-compliance unit price
          c     = Cost of compliance capital equipment and associated land requirements
                           Ability to Manage Financial Commitments (ICR)
     Baseline ICR =
                         EBIT
                    Interest Expense
                            JEBZT - Utf * —
     Post-Compliance ICR =
                                              - °
                                          Interest Expense + i
  where:
          ICR    = Interest Coverage Ratio
          i       = Average interest payment on debt for capital and land, assuming 10-year repayment,
                 where:
Average Annual _ \ (d * c) * 0.0593
Interest Payment   L _ ^ + Q.0593)"10
                                                                      10
          d      = Percent of compliance capital equipment and land assumed to be financed by debt
          d * c   = Debt financing required for compliance capital equipment and associated land
    16For firms with multiple plants, compliance costs and production quantities are summed.  In addition, the
average price (baseline and post-compliance) is weighted according to each plant's production quantity.
                                                7.11

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                                         Post-Compliance EBIT
    equals   Baseline EBIT
    minus   Compliance operating and maintenance costs (summed over facilities)
    minus   the change in variable production costs (assumed to decrease by the same percentage as
               production decreases  for each facility)
      plus    Change in revenues (based on price elasticity response and summed over facilities)17
                                      Post-Compliance Total Assets
    equals   Baseline Total Assets
      plus   Compliance capital and land costs (summed over facilities)
                                    Post-Compliance Interest Expense
    equals   Baseline interest expense
      plus   Annual interest expense for the debt component of compliance capital and land requirements
               (summed over facilities)
        The calculation of these values and the subsequent evaluation of post-compliance firm financial viability
were based on several secondary financial assumptions. These assumptions are outlined below:

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

        •      To be consistent with the facility analysis (in which capital equipment is assumed to have a ten-year
               useful life), a ten-year loan period was assumed for the debt used to finance compliance capital and
               land outlays.  To estimate a "steady state" interest payment burden on the firm, debt is assumed
    1'Depreciation associated with compliance capital expenditures is not subtracted from baseline EBIT since
depreciation is not a cash expense and therefore does not reduce the cash available to cover interest charges.
                                                  7.12

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                to be repaid on the basis of a constant annual payment amortization schedule over the ten-year
                period. This average annual interest payment is the value used for additional interest expense, and
                is used to calculate both post-compliance interest expense and the ICR.


                The interest charged on compliance-related debt is assumed to equal the average interest rate, 10.95

                percent, for AA-rated industrial debt with 10 years  to maturity, over the period 1981-1990, as

                reported by Salomon Brothers' An Analytical Record of Yields and Yield Spreads (U.S. Department

                of Commerce,  1990 and  1991).18 To convert this value to a real (i.e., inflation-free) rate, the rate

                was discounted on the basis of the average annual growth in the Consumer Price Index (CPI-U)

                for the period  1981-1990 (4.74 percent), resulting in a real interest rate of 5.93 percent (Survey
                of Current Business,  1991).19
7.3
Results
        Analyses of baseline and post-compliance financial viability were undertaken for those firms projected to
incur costs as the result of regulatory action.  The findings from this analysis are presented below, first for the
baseline and then for the final rule.


7.3.A  Baseline Analysis

        Forty-five firms were projected to incur compliance costs under the final rule.  In the baseline analysis,
two of these  firms had an ROA below the first RMA quartile value.  One of these firms was also the only one
whose ICR fell in the lowest RMA quartile. Because these firms were found to be "vulnerable" to financial failure
independent of regulatory action, they were excluded from the post-compliance analysis.
    18Interest rate information reported by individual facilities in the Census was not used for this analysis due to
difficulties of interpreting the reported values.  For example, a number of respondents reported that funds for capital
outlays were obtained from a parent firm at zero percent.  This reporting reflects internal accounting conventions
but does not accurately represent the interest cost borne by the firm for debt financing.  Other firms indicated that
interest costs were tied to the prime rate (e.g., prime rate or "prime rate plus one"). Such interest terms would
generally apply to  a working capital credit line or other short-term credit instrument.  However, the short-term
liability would usually be replaced by longer-term debt to match the expected life of the capital asset being financed.
The interest rates on  longer-term debt are usually higher than  short-term credit rates, so short-term rates may
understate potential interest costs.

    9Th& interest on debt, the inflation rate, and the mix of debt and equity assumed in the firm-level  analysis all
match the assumptions in Chapter 4 (the facility-level analysis). An assumption regarding the cost of equity is not
required in the firm-level analysis since it is not an input to the calculation of post-compliance EBIT, interest, or
assets.

                                                  7.13  .

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7.3.B   Post-Compliance Analysis
        Under the final rule, compliance costs were projected for 45 pesticides manufacturing firms, two of which
were found to be vulnerable to financial failure in the baseline analysis.  The post-compliance analysis was therefore
performed for only the remaining 43 firms. Three of these firms had either ROA or ICR in the lowest RMA
quartile in the post-compliance analysis, and were therefore said to incur significant financial impacts.  None of
these three firms is publicly traded, so the results were obtained using the publicly available industry norms and the
firm-specific revenue data, as discussed in section 7.2. One of the impacted firms owns a facility projected to close
a product line post-compliance. Another of the impacted firms owns a facility projected as a baseline product line
closure.  This firm may be in transition as its operations are reorganized. The third impacted firm owns no facilities
expected to incur impacts under the baseline or post-compliance scenarios.
                                                   7.14

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

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

U.S. Department of Commerce (1991).  Bureau of Economic Analysis, Survey of Current Business.  Washington,
      D.C.
                                                 7.15

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7.16

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                               Chapter 8: SMALL BUSINESS IMPACTS
8.0
Introduction
        This chapter considers the expected effects of the effluent limitations guidelines and standards for the
pesticide manufacturing industry on small businesses.  The Regulatory Flexibility Act (RFA) (Public Law 96-354)
requires the Environmental Protection Agency to determine if a regulation is likely to have a significant impact on
a substantial number of small entities. If such an impact is expected, the EPA must prepare a Regulatory Flexibility
Analysis for the rule.  If it is not expected that the rule would significantly impact a substantial number of small
entities, the EPA Administrator must certify this conclusion.

8.1     Methodology

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

8.2     Results

8.2.A  Impact of Best Available Control Technology Economically Achievable (BAT) Regulations on Direct
        Dischargers
        Under the final effluent limitations, no facility closures are projected for direct dischargers.  One direct
discharging facility and one zero discharge facility are expected to close product lines.   Employment data were
available for both of the firms owning facilities expected to close product lines. Both of these firms are considered
small based on the SBA size standard. Because two firms do not constitute a "substantial number of small entities,"
no regulatory flexibility analysis is required.  The EPA Administrator has certified to this  effect.
                                                   8.1

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8.2.B   Impact of Pretreatment Standards for Existing Sources (PSES) Regulations on Indirect
        Dischargers
        Under the final effluent limitations and guidelines, no indirect discharging facilities are expected to
close entirely, close a product line, or experience another significant impact short of closure. Therefore, no
regulatory flexibility analysis is required. The EPA Administrator has certified to this effect in the final rule.1
    'Appendix E of the EIA for the proposed rule included additional information of the expected impact of the
effluent limitations on small businesses.
                                                   8.2

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                                         Chapter 8 Reference




Dun's Marketing Services, Inc. (1991). Million Dollar Directory.  New Jersey.
                                                  8.3

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8.4

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                              Chapter 9: IMPACTS ON NEW SOURCES
9.0
Introduction
        In this chapter, two categories of regulation are considered based on the manner in which a new source
of pesticide active ingredients (PAIs) discharges wastewater.  Direct dischargers are regulated under New
Source Performance Standards (NSPS); indirect dischargers are regulated under Pretreatment Standards for New
Sources (PSNS). New facilities using either discharge method have the opportunity to incorporate the best
available demonstrated technologies, including process changes, in-plant controls, and end-of-pipe treatment
technologies, and to use facility site selection to ensure adequate treatment system installation.  Both NSPS and
PSNS represent the most stringent numerical values attainable through the  application of the best available
demonstrated treatment technologies for nonconventional, conventional, and priority pollutants.  The final NSPS
and PSNS regulations, and the reasonableness of the associated costs, are discussed below by chemical
subcategory.

9.1     New Source Performance Standards
        Subcategory A (Organic Pesticide Chemicals Manufacturing)
        The Environmental Protection Agency (EPA) is promulgating NSPS under Subcategory A for the
conventional pollutants regulated under Best Practicable Control Technology Currently Available (BPT),  120
organic PAIs, and  28  priority pollutants.  The EPA is promulgating NSPS effluent limitations guidelines that
equal Best Available Technology Economically Achievable (BAT) limitations, modified where appropriate to
reflect the wastewater flow reduction capability at new facilities. Based on a comparison of wastewater
generation and discharge practices at recently constructed vs. older pesticide manufacturing facilities, the EPA
concluded that 28 percent wastewater flow reduction had been demonstrated at some of the newer facilities.  For
this reason, the production-based mass limits developed for organic PAIs based on BAT treatment performance
data were modified to reflect the 28 percent reduction hi wastewater discharge at new facilities.  For other non-
conventional pollutants and conventional pollutants generated by Subcategory A, the final NSPS requires that the
BPT limitations for biological oxygen demand (BOD), chemical oxygen demand (COD), and total suspended
solids (TSS) be modified to reflect the 28 percent wastewater flow reduction demonstrated  at new facilities.

         The projected impact of the NSPS on new sources is expected to  be less burdensome than that of the
BAT regulations on existing sources.  Designing a new technology prior to facility construction is typically less
expensive than retrofitting a facility for a new technology.  Because the BAT technologies  for existing pesticide
manufacturers were found to be economically achievable, with some existing facilities already achieving a 28
percent wastewater flow reduction, the final NSPS are expected to be economically achievable. Moreover,
given the structure of the pesticide manufacturing industry, it is unlikely that expansions in the  industry will
                                                   9.1

-------
 occur through additional manufacture of currently produced PAls. Instead, it is more likely that new PAIs will
 be manufactured at any expanded or new facilities.  It is not possible to project NSPS guidelines for treatment
 of new PAls, given the difficulty in predicting the nature of the treatability of new PAIs.

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

 9.2     Pretreatment Standards for New Sources1
        Subcategory A Chemicals
        PSNS for the organic pesticide chemicals manufacturing subcategory are based on the final
 Pretreatment Standards for Existing Sources (PSES) technologies, modified where appropriate to reflect the 28
 percent flow reduction capability at new facilities.  As with Pretreatment Standards for Existing Sources
 (PSES), the PAI standards are production-based mass limits, while the priority pollutant standards are based on
 achievable concentrations. The EPA is proposing to establish PSNS for the same conventional pollutants, 120
 organic PAIs, and 24 priority pollutants covered under PSES.

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

        Subcategory B Chemicals
        Under Subcategory B, the EPA is reserving the right to set PSNS at  a later date.  For this reason,
economic impacts have not been calculated.
         Zero discharge regulations were not considered for new sources due to the unacceptably large economic
impacts projected for existing sources at proposal.  For more1 information see Chapter 9 of the EIA at proposal.
                                                  9.2

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               Appendix A:  1986 PESTICIDE MANUFACTURER FACILITY CENSUS


       This appendix includes Part B of the Pesticide Manufacturer Facility Census for 1986, which served as one
of the main data sources for the EIA.  Part B requested detailed economic and financial data from the facilities,
including balance sheet and income statement information for 1985, 1986, and 1987.  Part B was also designed to
obtain information on facility liquidation values and the cost of capital.
                                                 A.I

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                                            FomS
                                            OI^BNo.: 2040-0111
                                            Expiration Date: 4/30/90
            U.S. ENVIRONMENTAL PROTECTION AGENCY
      PESTICIDE MANUFACTURING FACILITY

                   CENSUS FOR 1986
         PART B.  FINANCIAL AND ECONOMIC INFORMATION
                        January 17,1989

Chief. Information Policy Branch (PM-223)
U.S. Environmental Protection Agency'
401 M Street, SW
Washington, DC 20460
                             °r
and
                                            «*
Office of Management and Budget
Paoerwork Reduction Project
 (2040-0111)
Washington. DC 20503
                              A.2

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

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

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

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

 Authority

 This mandatory census is conducted under the authority of Section 308 of the dean Water Act (the Federal
 Water Pollution Control Act. 33 U.S.C. 11251 et seq., as amended).  Late filing or failure otherwise to comply
 with these instructions may result In criminal fines, civil penalties and other sanctions as provided by law.
 Provisions concerning confidentiality of the data collected are explained below.

 Purpose

 The Pesticide Manufacturing  Facility Census questionnaire  is designed to collect data on pesticide
 manufacturing activities and waste treatment practices for the calendar year beginning January 1,1986 and
 ending December 31. 1986.  Part B requests financial and economic information for the calendar years
 1985.1986 and 1987.

 Who Must Respond

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

 Completing the Census

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

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

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

^~he Pesticide Manutactunng Facility Census Pan B Questionnaire must De comoietea ana returned witmn
 50 aays of receiot to:

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

 Questions on the Part B Questionnaire
                 ji                   '"'                  i
 -uestions pertaining to any item in Pan 8 mav oe directed to:

        Dr. Lynne Tudor  WH586
        U.S. Environmental Protection Agency
        Analysis and Evaluation Division
        401 M Street SW
        Washington. D.C. 20460
        (202)3825334

 Provisions Regarding Data Confidentiality

 Regulations governing the confidentiality of business information are contained in 40 CFR Part 2 Suboart B
 ana 43 Fed. Reg. 40001  (Sept 8, 1978).  Under these regulations, all records, reports, or information
 supplied to the EPA may be made public by the EPA without further notice if not accompanied by a
 business confidentiality claim.  You may assert a business confidentiality daim covering pan or all of the
 information you submit, other than effluent data, as described in 40 CFR 2J203(b):

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

 Information covered by a daim of confidentiality will be disdosed by EPA only to the extent and by means
 of the procedures,  set forth in 40 CFR Pan 2  Subpart B.  In general,  submitted records, reports, or
 information protected by a business confidentiality daim may be disdosed to other employees, officers, or
 authorized representatives of the United States concerned with carrying out the dean Water Act. or when
 r9levant to any proceeding under the Act  Effluent data are not eligible for confidential treatment.
                                                 A.. 4

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                       ENVIRONMENTAL PROTECTION AGENCY
               PESTICIDE MANUFACTURING FACILITY CENSUS^ FOR 1986
                       Pan B Financial and Economic Information
                               INTRODUCTION
Enter the name of this facility.
Enter the EPA Federal Insecticide. Fungicide and Rodertticide Act (FIFRA) Establishment Numoer
for this facility, as reported to the EPA on Form 3540-16 fPesticides Report for Pesticide-Proaucmg
Establishments*). Check the box next to "Not Aoplicable' if this facility does not have an EPA FIFRA
Establishment Number
                                     I2A           Not Applicable   I2B
Enter the DUNS Number of this facility. Check the box next to 'Not Applicable if this facility does not
have a DUNS Numoer.
i    i   j . ;   :   i   ' - ;   j  j        12 A           ~  Not Applicable    123
Enter the facility mailing address.

!_l«J_;_LJ_i— I— LJ.
Street or P.O. Box
                                I4A
                                                      i
City or Town
           I4B
 i   i   I   I   I   ...
 ;   •   !   ;  . i   -   i   i
       I State |    Zip Code
        I4C      I4D
Enter the address of the physical location of the facility if different from the mailing address.

I_!_I.J—I-J—I—!-J—1—!—'—!_!—!_!_l_!_l—I—I—I     ISA
Street or Route Number
City or Town
            I5B

Certification Statement
        State |    Zip Code
        I5C       I5D
1 certify that I nave personally 'examined and am familiar with the information submitted in all three
parts of the Census questionnaire and all attached documents, and that based on my inquiry of
those individuals immediately responsible for obtaining the information, I believe that the submitted
information is true, accurate and complete. I am aware that there are significant penalties for sub-
mitting false information, including the possibility of fine and imprisonment
Date Survey Completed:
I-J—I  '  I—I—I  '  I—I—I—I—I
 Month      Day         Year
I6A
Signature of Certifying Official
 Name of Certifying Official (please pnnt or type)

 I_I_!_!_1_I—I-J—I—I-!-!-'—I.
Title
                                                                   I6B
                                I6D
                                       A.5

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                               ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSU$ FOR 1986
                              Part B  Financial and Economic Information
                                 INTRODUCTION - Continued

      Review the Pesticide Acth/e Ingredients listed in Table i below ana circle all codes tnat corresoona
      to active ingredients manufactured, formulated or packaged at this facility.

                         TABLE 1.  PESTICJDE ACTIVE INGREDIENTS
"ODE
5

3

TO

t2

14
I4a

14C
i4d
:5
•JSa
iSb
iSc
16
•<6a
'.6b
16c
15d
17
17c
I7d
18
19

20
21
22
23
24
25
26
              ACTIVE INGREDIENT

*..1-8is(chloropnenyi)-2.2.2-trichloroemanot
1.2-Oihydro-3.6-pyndazinedione
1.2-Ethylene dibromide
1.3.5-Triethylhexanydro-s-triazine
i .3-Dichloropropene
10.10'-Oxybisphenoxarsine
• -{3-ChJoroailyl)-3.5.7-triaza-i -azoniaaaamantane cnlonde
1 -(4^hloropnenoxy)-3.3-dimethyl.i-(i H-1.2.4-tnazoi-i-yl)-2-butanone
2.2'-MethyleneDis(3.4,6-trichloropnenoi)
2.2'-MethyleneDis(4,&dicNorophenoi
2.2>-Methylenaois(4*chlorophenol)
2.2-Oichlorovinyl dimethyl phosphate
2.3.5-Trimetrtytphenytmethytcarbamate
2.3.6-Tricnioropnenylac8tic acid or any salt or ester
       2.4.5-Tricnioropnenoxyacetic acid or any salt or ester
2.4-Oichiorapnenoxyacetic acid or any salt or ester
2.4-Dichloropnanoxybutyric acxj or any salt or ester
2,4-Dichlcrc-6-(o-ch!oroanilino)-s-tnazme
2,4-0!ntrc-&octylpheny1crotonate. 2.6-Dinrtro-*-octylphenylcrotonate. and Nitrooctylphenols
    (The octyl's are a mixture of 1-Methylheptyl, 1-EthythexyJ. ana •,-Propylpentyl)
2.6-Dichloro-4-nnroaniiine
2-Bromc-4-hydroxyacetophenone
2-Carbomethoxy-l-methyivinyi dimethyl phosphate, and related compounds
2-Chloroallyl diethyldithiocarbamate
2-Chlorc-1-(2,4-dichlorophenyl)vinyl diethyl phosphate
2-CWoro-4-((l -cyano-1 -methylethyl)amino)-6-ethylamino)-s-triazine
2-Chloro-N-isopropyiacetaniiide
                                                 A; 6

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

               ACTIVE INGREDIENT

2-Methyl-4-chlorophenoxyacetic acid or any salt or ester
2-n-Octyl-4-isothiazoiin-3-one
2-PivalyM .3-indandione
2-(2,4-Dichloropnenoxy)propionie acid or any salt or ester
2-(2-Methyt-4-cnioropnenoxyjpropiomc acid or any salt or ester
2-(4-Thiazoiyl)benzimidazole
2-(Methytthio)-*-(ethylamino)-(5-(li.2-dirnethyiproDyi)amino-s-triazine
2-
-------
                        ENVIRONMENTAL PROTECTION AGENCY
               PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                       Part B Financial and Economic Information
           TABLE 1.  PESTICIDE ACTIVE INGREDIENTS - Continued

               ACTIVE INGREDIEffr

 3-Qu!nofinof sulfate
 Acepnate (O.S-Dimetnyl acerylphospnorarmdotriioate)
 Acrfluoren (5-(2-Chloro^-(trifluorometnyl)prienoxy)-2-nitrobenzoic acid) or any salt or ester
 AIacnlor(2-CWoro-2'.6'-diethyt-N-(metnoxymethyl)acetanilid6)
 Aldicart) (2-Methyl-2-(methylthio)prooionaidenyde O-{methyicarbamoyl)oxime>
 Alkyt" dimethyl benzyl ammonium cnionae w(50% C14.40% C',2,10% C16)
 AJIethrln (all isomers and allethnn coil) •
 Ametryn(2-{Ethy1amino)-4-(isopropyiamino)-6i(methylthlo)-s-trJazine)
 Amltr3Z(N'-2.4-Dimethylpheny<)-N^((2.4^imetnylphenyi)imino|methyl)-N-methylmethanimidarniae)
 Atraztne (2-Chloro-4-(ethy1aminoi-6-(isopropylamino)-s-tria2ine)
 BendiocarO (2.2-DimethyM .3-benzoaioxoi-4-yl methytcamamate)
 Benornyi (Methyl i-(butyicarbamoyi)-2-benzimidazoiecaroamate)
 Benzene hexachloride
 Benzyl benzoate
 Beta-Thiocyanoethyl esters of mixed (any acids containing from 10-18 carbon atoms
 B'rfanox (Methyl 5-(2,4-dichlorophenoxy)-2«nrtrobenzoate)
 Biphanyl
 Bromacil (5-Bromc-3-sec-Butyl-6-Methyluracil) or any salts or esters
 CODE

 51
 52
 £3
 £4
 =5
 56
 57
 58
 59
 50
 51
 52
 53
 64
 55
 66
 67
 63
 683
 68b
 68C
 68d
 69
 5Sa
 63b
 69c
 69d
 70
 71
 72
 72a
 72b
 72C
 72d
 73
 74
75
76
77
78
78a
785
78C
78d
79
80
81
82
Bromoxynil (3,5-Dibromo-4-hydroxyDenzonrtriie) or any salt or ester
Butachlor (N-(Butoxymetnyl)-2-crilorc-2'.61-diethyiacetanilide)
b-Bromo-b-nitrostyrene (Note: b - beta)
Cacodylic acid (Dimethylarsenic acid) or any salts or ester
Captafol (cis-N-((i ,1,2^-Tetrachloroethyl)thio)-4-cydohexene-i .2-dicarooxirnide)
Captan (N-Trfchioromethythio-4-cydoh9xene-i ,2-dicarhoximide)
Carbaryl (1-NaDrtthytmethyicarbamate)
Carbofuran (2.3-Dlhydn>2,2-dlmethyl-7-benzofuranyl methylcarbamate)
CarbosuJfan(2,2-Dihydro-2ixdlmethyl-7-benzc^rar^(dibu^amino)thio)methylcxirt)amate)
Chloramben (3-Aminc-2.5-dichlorobenzoic: acid) or any salt or ester
Chiordane(Octachloro-4,7-metnanotetranydroindane)
Chloroneb (1,4-Oichloro-2.5-dimethoxybenzene)
Chloropicrin (Trichloronitromethane)
Chiorothalony (2.4.5.6-Tetrachloro-i ,3-dicyanobenzene)
                                         A.8

-------
                       ENVIRONMENTAL PROTECTION AGENCY
               PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                       Part E) Financial and Economic information
           TABLE 1. PESTICIDE ACTIVE INGREDIENTS - Continued
               ACTIVE
33
34
35
36
37
38
39
90
91
92
32a
92b
92C
92d
93
94
95
96
97
98
S3a
980
98C
98d
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
115
117
118
119
120
121
122
Chloroxuron (3-(4-{4-ChIorophenoxy)prienyi)-l . 1 -dimethyiurea)
Chlorc-i-(2,4,5-trichlorophenyl)vinyl dimethyl phosphate
Chlorpyrifos methyl (0.0-Dimetnyl 0-(3.5.6-trichloro-2-pyridyl) phospnorothioate)
Chlorpyrifos (0,0-Diethyl 0-(3,5.6-tnchloro-2-pyridyl) phosphorthioate)
Coordination product of Manganese 16%, Zinc 2% and Ethyienebisdithiocaroamate 62%
Copper 8-quinolinolate
Copper ethyienediaminetetraacetate
Cyano(3-phenoxypnenyl)methyl 4-chioro-a-(i-rnethylethyl)benzeneacetate OCA)
Cycloheximide(3-(2-(3,5-Dimethy-2oxocyclohexyi)-2-hydroxyethyl)glutarimide)
 Dalapon (2,2-Dichloropropionie acid) or any salt or ester
  Decacruoro-bis(2,4-cyc!opentadiene-i -y<)
  Demeton (O.O-Diethyl O-(and S-) (2-ethyithio)ethyl)phosphorothioate)
  Desmedipham (Ethyl m-hydroxycaroanilate carbanilate)
  Diammonium salt of ethylenebisdithiocarbamate
  Dibromo-3-chloropropane
  Dicamba (3.6-Dichloro-o-anisic acid) or any salt or ester
  Dichlone (2,3-Dichioro-1.4-naphthoquinone)
  Diethyl 4,41-opheny1enebis(3-triioalloprianate)
  Diethyl diphenyl dichloroethane and related compounds
  Diethyl dithiobis(thionoformate)
  Oiethyl O-(2-isopropy(<6^nethyt-4^}yrimidinyl) phosphorothioate
  Difluberuuron(N.(((4^hIorophen^)amino)(»rbon^)--2.6-difluoroben2arnide)
  Diisobutylphenoxyethoxyethyl dimethyl benzyl ammonium chloride
  Dimethoate (O.O-Dimethyl S-((methylcarbamoyl)methyl)phosphorothioate)
  Dimethyl O-p-nitrophem/f phosphorothioate
  Dimethyl phosphate ester of 3-hydroxy-N.N-dimethyl-cis-crotonamide
  Dimethyl phosphate ester of a-methylbenzyl 3-hydroxy-cis-crotonate
  Dimethyl tetraehioroterephtnaiata
  Dimethyl (2^^-trichloro-l-hydroxyethyl) phosphorate
  Dinoseb (2-sec-ButyM,6-dinttrophenoi)
  Dioxathion (2,3-p-Oioxanedithiol S.S-bis(O.O-diethyl phosphorodrthioate))
  Diphacinone (2-(Diphenytacetyl)-1,3-indandione)
  Diphenamid (N,N-Dimethyl-2,2-diphenyiacetamide)
  Diphenylamine
  Dipropyi isocinchomeronate
  Disodium cyanodithioimidocarbonate
  Diuron (3-(3,4-Dichiorophenyl)-1,1-dimethyiurea)
  Dodecyiguanidine hydrochloride
  Dodine  (Dodecyiguanidine acetate)
  Endosulfan(Hexachlorohexaiahydromethano-2.4.3-benzodioxathiepin-3-oxide)
                                         A.9

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                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS FOR 198i
                              Part B Financial and Economic Information
                  TABLE 1. PESTICIDE ACTIVE INGREDIENTS • Continued

5.0.0.5                 ACTIVE INGREDIENT

* 23      Endotnall (7-Oxatoicycto(2 2 1 )heptane-2.3-dicarooxyiic acid) or any salt or ester
•23a     	_	,	,	—
I23b     	,	_	—
•23C	.	—	
:23d
'24
•25
126
'.27
128
129
130
131
132
t33
134
135
136
137
138
1383
13Sb
138C
138d
139
140
141
142
143
144
145
146
147
148
149

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

156
Endnn(Hexacnioroepoxyoctanyaro-enao.enao-aimetnanonaonthaiene>
Etralfluralin(N-Ethy»-N-(2-rnetnyl-2-DroDenyi)-2.6-dinitro-*-(trifluoromethy1)benzenearninei
Ethion (O.O.O'.O'-Tetraethyl S.S'-metnyiene bisohosonoroaithioate)
Ethoproo (0-Ethyl S.S-dipropyt phosonoroaithioatei
Ethyl 3-rrtetnyl-4"(methytthio)phenyi i-
-------
                      ENVIRONMENTAL PROTECTION AGENCY
             PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                     Part B  Financial and Economic Information
',57
'.58
'59
ISO
161
I6la
I6ib
:6ic
         TABLE 1.  PESTICIDE ACTIVE INGREDIENTS - Continued

              ACTIVE INGREDIENT
Methoprene (lsopropyl(E.E)-i 1 -methoxy-3.7.11 -trimethyl-2.4-dodecadienoate)
Methoxychlor (2.2-bis(p-Methoxypnenv?M .1,1 -tncnloroethanei
Methyl benzethonium chloride
Methyl bromide
Methyiarsonic acid or any salt or ester
Methyldcoecylbenzyl trimethyi ammonium cnlorrae 80% ana methyldoaecyixyiyiene
   bis(trimethylammoriium chlonde) 20%
Methylene Disthiocyanate
MethyU2.3-auinoxalinedithiol cydic S.S-dithiocarbonate
Metolachlor(2-Chloro-N-(2-ethyl^5-metny)phenyl)-N.(2-methoxy-l-rnethy1ethyl)acetarnide)
Mexacaroate (4-(Dimethylamino)-3.5-xy«yl methylcaroamate)
Mixture of 83 9% Ethylenebis(dlthiocaroamato) zinc and 161% Ethylenebisdithiocaroamate.
   bimolecular and trimolecular cyaic annydrosulfides and disulfides
MonuronTCA  * Monuron triehioroacetate
Monuron (3-{4-Chlorophenyl)-l ,i-dimethylurea»
N,N-Oiethyl-2-(l -naphathalenyloxyjpropionamide
N.N-Oietnyi-meta-totuamide and other isomers
Nabam (Oisodium salt of etirtylieneDisdlthiocartoamate)
Naled (i.2-Dibromo-2.2-dichloroethyl dimethyl phosphate)
Norea (3-Hexahydro^ J-metrtanoindan-5-yM .1 -dimethyfurea)
Norflurazon(4^hloro-5-(m8tfrylamino)-2-(a.a.a^rifluoiro-nvtc)lyl)^(2H)-pyridazinone)
N-1 -Naphthylphthalamic acid or any salt or ester
 N-2-Ethyihexyt bicyctoheptsna dicarooximide
 N-Butyl-N-ethy«-a.a.a-trifluorO"2.eKJinitro-p-toluidine
 O.O.O.O-Tetraethyl dithtopyrophosphate
 O.O.O.O-Tetrapropyl dithiopyrophosphate
 0.0-Diethyl O-(3-cWoro-4-meihy*-2H3xo-2H-1-benzopyTan-7-yl) phosphorothioate
 O.O-Diethyl O-{p-(methylsu«inyl)phenyl) phosphorothioate
 O.O-Oieti~.y1 S-(2-(eUiytthio)ethyl) phospnorudithioate
 0,0-DimethylO-(4-nitro-m-tolyl)phosphorothioate
 O,O-DimethyIS-(phtha!imidoniethyl)phosphorodithloate
 O.O-Oimethyl S-((4x«o-1 A3-benzotriazin-3(4H)-yl)methyl)phosphorodrthioate
 O.O-Dimethyl S-((ethylsulfinyi)ethyl phosphorothioate
 Organo-arsenic pesticides (not otherwise listed)
162

163
164
165
166
167

168
169
170
171
172
173
174
175 "
176
1763
176b
176C
176d
177
178
179
180
181
182
183
184
185
186
187
188
I88a
188b
188C
183d
                                        A. 11

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

                     ACTTVE INGREDIENT

        Crgano-cadmium pesticides
SODE

'89
 SSa
'89b
•S9c
•S9d
•90
:90a
*.90b
1900
•91
',91 a
i9ib
191C
t9id
•.92
I92a
192C
193
194
195
196
197
198
199
200
201
202
203
204
205
206
20Sa
206b
206C
206d
207
208
209
210
211
212
213
214
        Organo-copper pesticides
        Organc-mercury pesticides
         Organc-tln pesticides
         Ortnodichlorooenzene
         Oryzalin (3,5-Dinitro-N4.N4-dlpropyisulfanilamide) (Note: N4 « M superscript 4)
         Oxamyl (Methyl r4\N'
-------
                      ENVIRONMENTAL PROTECTION AGENCY
              PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                     Part B Financial and Economic Information
         TABLE 1.  PESTICIDE ACTIVE INGREDIENTS • Continued

              ACTIVE INGBEDIEKT

Pidoram (4-Amino-3.5,6-trichloropicolinic acid) or any salts or esters
Piperonyl butoxide ((Butylcarbityi) (6-propyipiperonyt)ether)
Poly(oxyethy1ene(dimethy1iminio)etrjylene(dlmethyiiminio)ethyJenedichloride
Potassium dimethyldithiocarbamate
Potassium N-hydroxymethyl-N-metriyldithiocartjamate
Potassium N-methyldrthiocarbamate
Potassium N-(a-(nitroethyi)benzyi)ethylenediamine
Profenofos (0-(4-Bromo-2-chloroonenyi) O-ethyl S-propyl phosphorothioate)
Prometon(2.4-bis(lsopropylamino(-6-methoxy-s-triazine)
Prometryn (2.4-bis(lsopropylamino)-5-(methytthio)-s-triazine)
Propargrte (2-(p-tert-Butylprienoxy)cydonexyi 2-propynyl sulfrte)
Propazine (2-Chloro-4,6-bis(isopropytamino)-s-iriaztne)
Propionic acid
Propyt (3-dimethylamino)propyl caroamate hydrochloride
Pyrethrin coils
Pyrethrin I
Pyrethrin II
Pyrethrum (synthetic pyrethrin)
Resmethrin ((5-PhenyJmeth^)-3-furanyJ)methyl 2.2-dimethyl-3-
    (2-methyi-i -propenyl)cydopropanecarboxyiate)
Rohnel (O.O-Dlmethyl O-(2.4,5-trichlorophenyl)phosphorothloate)
Rotenone
S.S.S-Tributylphosphorotrithioate
Siduron (i-(2-Methylcydohexyl)-3-phenylurea
SBvex (2-(2.4.5-Trichlorophenoxypropionic acid)) or any salt or ester
 Simazine (2 Chloro-4.6-bisi(ethylamino)-s-tnazine)
 Sodium bentazon (3-JsoprapyJ-iH.2.l.3-benzothiadiazin-4(3H)-one 2.2-dioxtde)
 Sodium dimotrt-jltiithiocarbijmate
 Sodium fluoroacetate
 Sodium methyidithiocarbarnate
 Sulfoxide (1.2-Methylenediaxy-^(2-(octylsulfidynyl)propyl) benzene
 S-Ethyl cydohexyiethyithiocarbamate
 S-Ethyt dipropylthiocarbamate
 S-Ethyl hexahydro-1 H-azepine-1 -carbothioate
 S-Propyi butylethytthiocartamate
 S-Propyt dipropylthiocarbamate
 •S-(2-Hydroxypropyl)thiomethanesulfonate
 S-(0,O-Diisopropyl phosphorodithioate ester of N-(2-mercaptoethyl)benzenesulfonamide
 Tebuthiuron (N-(5-{1,1 -Dimethylethyl)-! .S^-thiadiazol^-ylJ-N.N'-dimethylurea)
 TemephostO.O.O'.O'-Tetramethyl-O.O'-thiodi-p-phenylenephosphorothioate)
215
215a
215b
215C
215d
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233

234
235
236
237
238
2383
238b
238C
238d
239
240
241
242
243
244
245
246
247
248
249
250
251
252
 253
                                        A.13

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

             ACTIVE INGREDIENT

Terbacil (3-tert-Butyl-5-chloro-6-metnyiuracil)
Tarbufos (S-(((1.i-Dimethylethyi)thioimetnyi) O.O-diethyi phosphorodrthioate)
Tert3Uthylazine(2-(tert-Buty1ammo)-4-<:nloro-6-(ethy«amino)-s-tnazine
7erbutryn(2-(tert-Buty1amino)-4-(ethviamino)-6-(nnethylthio)-s-triazine)
Tetrachloropnenoi or any salt or ester
CODE

25*
255
256
257
258
258a
258b
258C
258d
259  '
260
261
262
263
264
265
2S5a
265b
265C
265d
266
267
268
269
270

271

272
Tetranydro-3.5-dlm«hyl-2H-l.3.5-thiaaiazine-2-thione
ThlopharuM-methyl (Dimethyl 4,41o.pnanyienebis(3-thioallophanate))
Thiram (Tetramethyrthiuram disuifide)
Toxaphene (technical chlorinated camonene (67^9% chlorine))
Tributyl phosphorotrithtoite
Trifluralin(a.a.a-Trifluro-2.6-dinitro-N.N-dipropvrt-p-toluidine)
Warfarin (3-(a-Acetonylbenzyl)-4-hyaroxycoumann) or any salt or ester
Zinc 2-mercaptobenzothiazotate
ZJneb (Zinc ethylenebisdtttiiocarbamate)
ZJram (Zinc dimethykjithkscarbamate)
(2,3,3-TrichloroaJlyl)dUsopropytthiocaroamate
(3-Phenoxyphenyl)mtmyl d-cis and tran" 2^-dimethyl-3-(2-methylpropenyl)cydopropanecarboxytate
    •(Max. d-ds 25%; Min. trans 75%)
(4-Cydorwxene-i ,2-dlcarboximido)methyl 2^-dimethyl^}-
    (2-mothylpropenyl)cydopropanecarooxylate
Isopropyl N-(3-chlo'rophenyl) carbamaw
                                       A.14

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                           ENVIRONMENTAL PROTECTION AGENCY
                   PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                           Part B Financial *nd Economic Information
                      SECTION 1: FIRM FINANCIAL INFORMATION
l-A.  Was this facility owned or controlled by a parent firm or firms on December 31 . 1 986?

                                    veS           .......  1 (GO TO BOX 1 -A)
                                    Kjn '           ........  2 (SKIP TO SECTION 2, PAGE 18)
           Sirt                      '   ................

                                      30X 1-A

     i  If there is .more than one parent firm, sucn as in a joint venture, photocopy Section 1.   ;
     i  pages 1 3 through 1 6. and complete all Section i questions for each parent firm.


1-B.  Report the name, mailing address and DUNS number of the parent firm.

      [1]   Name of Parent Firm

          • ,_ _-_i_i_!_!_l_ _______ l_i_l_I_l_l_i   S1B1


      [2]   Mailing Address of Headquarters

           I_'_J_!_I.J— O-J-J— !— !— I— I— I— i— I— '•— ! '  S1B2A
           Street or P.O.Box
           i  i   i   i   i  i  i   i   i_;.j_j_i— i—i-J-J— i—i
           d&'^Tawn ----- ""        i State I    Zip Code
                S1B2B                    S1B2C       S1B2D
      [3]   What is the DUNS Nurritaer of the parent firm?

         •   l_i_! - I_LJ-J - l-J-J-l-J          C Not A»*cable
             S1B3A                                    S1B3B
 1-C  Report the percentage of the parent firm's total 1986 sales (In dollars) generated by each of the
      aSes !tt£d Tbeiow. (Ente,r Sro if the activity was not applicable. The  sum of all percentages
       must be 100%).
       m Pare,*nru5f of sa!c« gansfated *>y manufacturing pesticides llstad               (
          in Table 1 . pages 4 through 1 2 ............................................... "§IcT — """"•—    • -- ' —
       [21 Percentage of sales generated by formulating or packaging pesticides           (
          listed in Table 1 . pages 4 through 12 ..................................... -SICZ"'"-  -•""    ! — • — ' — ' '

       13] Percentage of sales generated by other activities (SPECIFY) ...........................    !_J-J— 1*
           Total
                                                                           100 %
                                            A.15

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                             ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                            Part B Financial and Economic Information
                        SECTION 1: FIRM FINANCIAL INFORMATION
 1-D.  Did the parent firm acquire this facility after December 31 ,  1980?
                                        YES .............................  1 (CONTINUE)
                                        NO ...............................  2 (SKIP TO QUESTION 1-E)
      [1 ]   In wnat year was this facility acquired by the parent firm?
                                       Year           S1D1

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

     ~j  Merger: Please list names of the companies that merged
                                                                     S1D2
                                                              SiD2A
                                                              
-------
                            ENVIRONM€NTAL PROTECTION AGENCY
                    PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                            Pan B Financial and Economic Information
                       SECTION 1: FIRM FINANCIAL INFORMATION


•i-F.  Reoort the names and  EPA  Fee-era! insecticide.  Fungicide  and Rodenticiae Act  (F1FF.A1.
     Establishment Numbers (as reported to tne EPA on Form 3540-16) for all other facilities owneo cr
     controlled by the parent firm at which any of the pesticides listed on Table 1. pages 4 through 12.
     were manufactured or formulated ana/or packaged.  Check the cox next to 'Not Appncaoie' .f trs
     facility does not have an EFA F1FHA Establishment Numoer.  Check whether eacn facility was z.
     manufacturer or formulator/packager of the pesticides listed on Table 1. If more space is recuirec
     to grve a complete answer to this question, photocopy this page.
                                                       I   I
                               S1F1A
           Name of Facility .
                                                     ^2  Not Applicable
           EPA FIFRA Establishment Numoer

           ~  Manufacturer             ~_
               S1F1D
    rormuiator/Packager

     S1F1E
      [2]
           Name of Facility

           !_'_'_!— I— I • I— I—! • j— :— i— i
           EPA FIFRA Establishment Numoer
                  Not Applicable
               Manufacturer
               S1F2D
~  Formulator/Paekager
    S1§2E
                                S1F2A
 S1F3B

Name of Facility

EPA RFRA EstaoTishment^umber

[2  Manufacturer
 S1F3D
                  Not Applicable
                                                                          S1F3A

                                                                          S1F3C
                                            Formuiator/Packager
                                        —      S1F3E
      [4]
            _
            Name of Facility
 S1F4B      !__'_' — !_l — I -'—I — 1    —
            EPA FIFRA Establishment Numoer
                                                          Not Applicable
                                   S1F4A


                                   S1F4C
                Manufacturer

               S1F4D
 [H  Formuiator/Packager

   S1F4E
                                             A.17

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                            ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                            Part 8 Financial and Economic Information
                       SECTION 1: FIRM FINANCIAL INFORMATION
1-F.   Report the  names and  EPA  Federal  insecticide.  Fungicide  and Rodenticide Act (FIFRA)
      Establishment Numbers (as reported to the EPA on Form 3540-16) for all other facilities owned or
      controlled by the parent firm at which anv of the pesticides listed on Table i, pages 4 through 12.
      were manufactured or formulated ana/or oacKaged. ChecK the box next to "Not Applicable" if the
      facility does not have an EPA FIFRA Estaolishment Number.  Check wnetner eacn facility was a
      manufacturer or formulator/packager or the pesticides listed on Table 1.  If more space is reauirea
      to give a complete answer to this question, pnotocopy this page.
      [1]   I
S1F5B
           Name of Facility
            ..-..
           EPA FIFRA Establishment Numoer
                                                    _J-__ _    S1F5A

                                                     C NotAppiicable  S1F5C
               Manufacturer
                31F5D
                                      ~  Formulator/Packager
                                            S1F5E
      12]   l_'_!_l_l_i_l_l_!«:_:__
        •   Name of Facility

S1F6B     I__'_J__I_I_I ' I_I_J ' I-J_J_J
           EPA FIFRA Establishment Number
           [3  Manufacturer

           S1F6D
                                                    -J_l-.l— !—!     S1F6A

                                                     D Not Applicable   S1F6C
                                       [j  Formulator/Packager

                                          S1F6E
      [3]   !_:_J_I_I_J_I_J_I_!_J_:!
           Name of Facility

S1F7B     |_LJ_I_LJ • I_LJ • I_S_
           EPA FIFRA Estaolishment Number
                                                    ____-     S1F7A

                                                     D NotAppiicable  S1F7C
               Manufacturer

                  S1F7D
                                           Formulator/Packager

                                            S1F7E
      W   I— LJ-J- I—!— I— I— I— I
           Name of Facility
S1F8B
           EPA FIFRA Establishment Number

           Q  Manufacturer             £

                 S1F8D
.!_1_!_LJ_I_!_I_!

!   I        n NotAppiicable
                                                                        S1F8A

                                                                        S1F8C
                                           Formulator/Packager

                                           S1F8E
                                              A. 18

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


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

                                   ($000)

     [1]  1985 Revenue	      •   	

     [2]  1986 Revenue		

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

     [1]   Name
                                                                Sill
      [2]   Mailing Address of Headquarters
           l-l—I—l-l—1_ I—1—1
           Street or P.O.Box
           City or Town
            SI
      [3]   DUNS Number
                            SII2A
    State |    Zip Code
                                                        Not Applicable
                                           A.19

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

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

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

      Ml  Production generated by manufacturing and/or formulating and packaging          32A1
      '   pesticide active ingredients listed in Table 1. pages 4 through 12		°

      [2]  Production generated by manufacture of intermediates that                       3    ^
          are sold		_ °

      [31  Production generated by manufacturing and/or formulating and packaging         S2A3  _
          EPA registered pesticides offl listed in Table 1, pages 4 through 12		•_ *

      [4]  Production generated by manufacturing and/or formulating and packaging         S2A4  ^
          chemicals other than EPA registered pesticides		—  -°

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


          Total	     1  °  0%


 2-B.   Report the calendar year during which:
                                                     S2B1                     .....
        [1]  Operations began at this facility		



        [2]  Manufacturing and/or formulating/packaging
            of either pesticide active ingredients or        S2B2                        i   i
            pesticide products began at this facBfty		   •_._!_"_
                                                                                   T 63*


        [3}  The most recent major expansion of plant and
             equipment with respect to pesticides occurred   S2B3              .      •  >  i  i
             at this facility	    ; — —337'—
                                                A.21

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                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                              Part B Financial and Economic Information
                       SECTION 2: FACILITY FINANCIAL INFORMATION
2-C.  Instructions for reporting Balance Sheet information on oaoe 21.

      Question 2-C on page 21 requests facility Balance Sheet information. Please read the instructions
      and definitions  below before comoieting Question 2-C.  The number in brackets, for example,
      "[1] Inventories.' correspond to Balance Sheet entnes.


      Reporting Period

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


      Reporting Conventions

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

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


      Balance Sheet Definitions

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

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

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

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

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                              ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FAC8UTY CENSU^FOR 1986
                             Part IB Financial and Economic Information
                       SECTION 2: FACILITY FINANCIAL INFORMATION
2-C.  Instructions for reporting Balance Sheet information on pane 21 - continued


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

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

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

                  [5]    Total Currant and Noncurrent Assets: Report the sum of items (3] and [4].


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

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

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

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

                   [8]    Owner Equity. Report the difference between total  assets and tout liabilities.
                         The amount obtained ahould Include conirlbuted or ps& :•• capita! (preferred
                         and common stock) and retained earnings.

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

                   [101   Total Liabilities and Equity: Report the sum of Kerns (6] and (9).
                                                A.23

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

      Facility 1986 fiscal year was from    Z2CA month to 32C2    month.
,
TAE

Current assets
(1] Inventories
[2] Other current assets
[3] Total current assets
•• Noncurrent assets •
*
W Total noncurrent assets
[5] Total current and
noncurrent assets


Current liabilities
[6] Total current liabilities
Noncurrent liabilities and equity
[7] Long term debt and
other noncurrent liabilities
. [8] Owner equity
| [g] Total noncurrent liabilities
i and equity
1 [10] Total liabilities and equity


3LE2-C. BALANCE SHEET
ASSETS
1985
(SOOO)
S2C1A
S2C2A
S2C3A

SZC4A " 	 *"" •
S2C5A

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




1986
($000)
S2C1B
S2C2B
S2C3B

S2C4B
S2C5B


1986
($000)
S2C6B
S2C7B
S2C8B
S2C9B
_52Q.QB_
• ' , •'

.

1987
($000)
S2C1C
S2C2C
S2C3C
i
S2C4C
S2C5C


1987
($000)
S2C6C
i
S2C7C
S2C8C
S2C9C
S2C1QC
" ''!'• • 
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                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                              Part B Financial and Economic Information
                       SECTION 2: FACILITY FINANCIAL INFORMATION
2-D.  Instructions tor reporting facility Income Statement Information on osae 24.

      Question 2-0  on page  24  requests facility income and expense  information.  Please read tne
      instructions and definitions  below oefore completing Question 2-D. The numbers in brackets, for
      example. *(1] Sales of Pesticide Chemicals.' correspond to the entries on Table 2-D.

      Reporting Period

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

      Reporting Conventions

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

      If. for certain items, you do riot have amounts  at the facility level, you may use the Income
      Statements of the firm that owns and controls your facility to estimate the amounts at the facility
      level. If you need to estimate any items, estimate them based on your facility's share of sales. If you
      have estimated an amount for a particular Item, then place an asterisk (•) to the right of the entry.

      Income Statement Definitions

      Revenues
            [11
      Sal* of Pesticide Chemical*: R*>no« tha total sales value of a!! pesticide chemicals.
      This  should Include ail  pesticide  active ingredients,  intermediates, and  finished
      pesticide products.  In cases where the pesticide chemical is not sold (there is no
      known sales prica) but is transferred to another facflity owned by the company for
      further processing and/or formulating/packaging, the facility share of sales generated
      by tha final product should be allocated to tha facility. This share should be estimated
      based on its percent of total production costs.  Divide the sale of pesticide chemicals
      into the following categories:
      [a]    Pesticide chemicals listed In Table 1: Report revenues from the manufacture
            and/or formulating/packaging of pesticide active ingredients listed  in Table 1,
            pages 4 through 12 or intermediates produced during the manufacture of active
            Ingredients; listed in Table 1
      [b]    Other Registered Pesticide Chemicals:   Report  revenues from pesticide
            chemicals not reported in (la].
[2]    Revenue from  Pesticide Contract Work  or Tolling:  Report the revenue from
      pesticide contract work done by this facility for other facilities or firms.
(3]    Other Revenue:  Report all other revenues  like  the sales value of products and
      services not reported in items [1 ] and [2].
[4]    Total Facility Revenues: Report the sum of items [1 ] through [3].
                                                A.25

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

2.D.   Instructions lor reporting facility Income Statement information on cage 24 - continued

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

                        [5]
Material and Product Costs: Report the total cost of all raw materials
including packaging materials that were used In the production and/or
formulating/packaging of pesticide chemicals/products.  Include cost of
products bought ana sold.
Direct Labor Costs: Report the total cost including fringe benefits, of
all  direct labor that   can  be traced  to  the production and/or
formulating/packaging of pesticide chemicals/products..
Cost of  Pesticide Contract Work or Tolling:  Report the cost of all
contract work done for you by others using materials furnished by your
facfltty.  induda tha total payments rnads during the year for such work.
including freight out and In.
Other Pesticide Costs:  include all  other pesticide  related expenses.
such as effluent treatment and disposal, and energy used directly in
producing the product not Included In (5] through [7]-
                   Non Pesticides
                         [9]    Nonpestlclde Costs:  Report all other manufacturing costs not included
                               in items {5] through [8].  Include manufacturing costs associated with
                               nonpesticide chemicals or products. Report me types of cost for ftems
                               [5] through [8] for nonpesticide products and services.
                   Report the expenses listed below for the whole facility, not {ust pesticides.
                         110]
                         [11]


                         112]

                         [13]
                         [14]

                         [15]

                         [16]
 Depredation:  Report the depreciation on buildings, plant equipment.
 and machinery at yeur facility.
 Fixed Overheads: Report the total from all types of overhead.  Include
 rent, nonproduction  utilities, selling costs, administration and  general
 expenses for your facitty.
 Research and  Development:  Report all research and development
 costs incurred during the year.
 Interest: Report the total interest expense on all funds during the year.
 Federal, State  and Local Taxes:  Report the total federal, state and
 local taxes payable during the year.
 Other Expenses:  Report all other expenses not reported In items (10]
 through [14].
 Total Costs end Expenses: Report the sum of items (5] through [15].
                                                A.26

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

2-0   Complete the facility Income Statements. Table 2-D below.  Enter all information in thousanas of
   '   dollars as of December 31 for calendar years 1985. 1986. and 1987.  If the facility fiscal year aoes
      not correspond to the calendar year, please enter the months of the facility fiscal year below.
      Facility 1986 fiscal year was from
^.onth to
                                                     S223
                                                          month.
                          TABLE 2-0. INCOME STATEMENTS
                                      ^i^—
                                       REVENUES
[ 1 ]  Sales of pesticide chemicals

    [a]  Pesticide chemicals
         listed in Table 1

    [b]  Other registered pesticide
                                          1985
                                          (SOOO)
 S2D1AA
                  1986
                 ($000)
S2D1AB
                 1987
                 (SOOO)
                                S2D1AC
 S2D1BA
S2D1BB
                                                                         S2D1BC
I  [2]  Revenue from pesticide contract
                                         S2D2A
                 S2D2B
               S2D2C
wuiivwi twiiiiiy
[3] Other revenue
'
[4] Total facility revenues
S2D3A
S2D4A
EXPENSES
Manufacturing costs
[5] Pesticide material and product costs
[6] Pesticide direct labor costs
[7] Cost of pesticide contract work
[8] Other pesticide costs
[9] Nonpesticide costs
Facility costs
[1C] Depreciation
[11] Fixed overheads
[12] Research and development
[13] interest
[14] Federal, state and local taxes
[15] Other expenses
[16] Total costs and expenses
1985
($000)
S2D5A
«rv;a
S2D7A
S2D8A
S2D9A
S2D10A
S2D11A
S2D12A
S2D13A
S2D14A
S2.PJ5A
S2D16A
                                                          S2D3B
                                                          S2D4.B
                                S2D4C
                                                           1986
                                                           ($000)
                                                            S2D5B
                                   1987
                                  ($000)
                                                            S2D7B
                                   S2D7C
                                                            S2D8B
                                                                           S2P8C
                                                            S2D2B_
                                   S2D9C.
                                                            S2D10B
                                              A.27

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

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

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

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

                                   ___   YES .............................  1   (SKIP TO SECnONJ>sK_
                                                                    PAGE 38)
                                           NO ...............................  2   (CONTINUE)

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

Instructions for completing Table 2-H Pesticide Production; Technical Grade Products, o. 30.
Column [1]
Column [2]
Column [3]
Column [4]
                    Active Ingredient Code.  Enter the code for every Table 1 active ingredient that
                    your facility produced  In 1986 as a technical grade  product  If part of the
                    production was transferred to another faculty, list that part as a separate entry as
                    described by Product Code B.  If you need additional space to report, photocopy
                    the table before making any marks on it

                    Product Codt.  Enter the  code that  best describes the product reported  in
                    column (1].
                    Code Definition

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

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

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

                    1986 Average Unit Production and Packaging  Cost in Dollars.  Provide the
                    average production cost tor one unit of the item reported in column [1].  Include
                    such costs as material costs (I.e.. the costs of all raw materials, including packaging
                    materials that were used in the production and packaging of pesticide products).
                    direct labor costs, and any other pesticide costs.
                    Note that the column (3] entry corresponds to items (5] through [8] under question
                    2-D en p,
                     Express the costs in dollars.  Do not include allocations for corporate overhead.
                     administrative expenses, research and  development, capital costs or interest
                     expense.
                     1986 Average Unit Sales Price in Dollars. Report the average selling price for one
                     unit of the item reported in column [1].  Express the selling price in dollars. If the
                     pesticide chemical is not sold when it leaves the faculty. but Is transferred to another
                     facUity owned by the firm for further processing, the sales price of the final product
                     should  be allocated to both faculties based on their share of the cpsts to produce
                     the product  This is referred to as the "pereentaoe of cost procedure.* An example
                     of the percentage of cost procedure can be found on pages 28-30.
                                                A. 29

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                             ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                            Part B  Financial and Economic Information
                      SECTION 2:  FACILITY FINANCIAL INFORMATION
Instructions for completing Table 2-H Pesticide Production: Technical Grade Products - continued

      Column [5] '  1986 Production Quantity.  In column [5], report the total quantity of the item
                   reported in column (1 ] that was manufactured at this facility during 1986.

      Column (6]   Unit of Measure,  in column [6], cirde the code that corresponds to the unit of
                   measure you used to calculate the information you reported In columns (3). [4], [5]
                   and [71-
                   P - Pounds
                   T - Short tons
                   M- Metric tons
                   G * Gallons

      Column [7]   Sum Annual Production Over Three Years (1985-1987). Provide the total amount
                   (sum) of the product  reported in column (1] that was produced by this facility in
                   1985.1986. and 1987 combined.

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

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                             ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSU^ FOR 1986
                            Pint B  Financial and Economic Information
                      SECTION 2:  FACILITY FINANCIAL INFORMATION
             EXAMPLE OF PERCENT OF COST PROCEDURES
The following is an example of a hypothetical facility that both produces and formulates/packages active
ingredients. It demonstrates use of the 'Percentage of Cost Procedure/
Assume the facility produces 1.200 Ibs of active ingredient 000 In 1986. of which:
      400 Ibs are sold as technical grade.
      200 Ibs are formulated and  packagea on site as product group P01.
      200 IPS are formulated and packaged  by another facility owned by this company also as product
      group P01
      200 Ibs are formulated and packaged as product group P01 under contract by another facility not
      owned by this firm. The contract work is paid for by this plant.
      200 Ibs are combined with 100 Ibs of active ingredient 001 to formulate 300 Ibs of product group
      P02. Active ingredient 001  is purchased from another firm.
Unit sales are:
      $2.50/lb for technical grade
      $4.00/ib for formulated product group P01
      $4.25/Ib for formulated product group P02
Unit production, formulating and packaging costs are:
      Production of active ingredient 000
      Purchase of active ingredient 001
      Formulating and packaging on site
      Formulating and packaging at other facility owned by this company
      Formulating and packaging at other facility not owned by this company
$i.SO/lb
S2.00/lb
SO.50/I b
$0.50/ib
$0.60 Ib
                                               A. 31

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

Technical Grade Products (Table 2-H. p. 30)

Line 1     400 Ibs of Al 000 are sold as technical grade.  The unit cost of production is Si.50/lb and the
          unrt sales price te S2.50/lb. This corresponds to Product Code A on page 26.

Line 2     200 Ibs of Al 000 are transferred to another facBtty owned by this firm to be formulated and
          packaged.  The unit cost of production to this facility {remains Si.50/lb and the selling price of
          the formulated product Is S4.00/Ib. Since the production cost represents 3/4 of the total cost
          to produce the formulated product, the unit sales price for this facility is 3/4 of the total unit
          sales price of $4.00/lb or 53.00/lb. This corresponds to Product Code B on page 26.

Formulated/Packaged Products (Table 2-J. p. 371

Line 1     200 Ibs of Al 000 are formulated/packaged on site by this facility. The total unit cost of the
          formulated and packaged product is S2.00/lb ($1.50/lb for production plus $.50 for formulating
          and packaging.  Since ail unit costs are incurred by this facility, the total unit sales price of
          $4.00/!b Is allocated to this facilty.  This corresponds to Product Code A on page 35. (Note:
          This 200 Ibs is In addition to the  400 Ibs + 200 Ibs listed on Line  1 and Line 2 under Technical
          Grade Products.)

Line 2     200 Ibs of Al 000 are produced by this facilty and formulated/packaged by another firm under
          contract to this facatty.  This facility pays for the contract work.  The total unit cost of the
          formulated/packaged   product   is   52.10/lb   ($1.50/1b  for  production  plus  S.60/lb  for
          formulating/packaging).  Since  all unit costs ant Incurred by this facility, the total unit sales
          price of 54.00/lb is allocated to this facaity. This corresponds to Product Code B on page 35.

Line 3     200 Ibs of Al 000 are combined with 100 Ibs of Al 001 to  formulate 300 Ibs of products in
          Product Group P02. Al 001 Is purchased from another firm.  The total cost of production is
          52.16/lb (2/3 of $1.50 + 1/3  of $2.00 for active ingredients plus $.50 for formulating/
          packaging). Since this facility incurred the total unit cost, the total unit sales price is allocated
          lo this  facility. This corresponds to Produce Code  E on page 35.  (Note:  !' the ferity
          purchases active  ingredient 001  from another firm, and then formulates/packages it. this would
          be product group P03 and would also be assigned Product Code E
                                                A.32

-------

















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                      ENVIRONMENTAL PROTECTION AGENCY
              PESTICIDE MANUFACTURING FACILITY CENSU^FOR 1986
                      Part B Financial and Economic Information
                SECTION 2: FACILITY FINANCIAL INFORMATION
24.    During calendar year 1986. did this facility sail any
       of pesticide products containing a pesticide active ingredient l.sted in Table
       NO)
                                                                      rme
                                                                     (CIRCLE YES
                                   YES
                                   NO.
                                                            . >" "(READ THE INSTRUCTIONS"
                                                               BELOW AND COMPLETE
                                                               TABLE 2-1 ON PAGE 34)

                                                            .> (GO TO QUESTION 2-J ON
                                                               PAGE 35)
                  T«hln 3.1 Pesticide Production' Intf mediates.
Column [1]
Column [2]
Column [3]
        Column 14]
        column t j
                     Intermediate Nam«. Enter the name of every intermediate produced in 1 986 during
                     the manufacture of Table 1 Pesticide Active Ingredients and sold. Please include all
                     chemicals and codes that you listed in Part A of the Pesticide Manufacturing Facility
                     Census questionnaire.  If you need additional space to report, photocopy the table
                     before making any marks on It.

                     Active Ingradtont Cod*.   Enter the code for every Table 1 active ingredient
                     associated with your production of the intermediate listed in column (1 ].

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

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

                     Express the costs in dollars. Do 132! include allocations for corporate overhead.
                     administrative expenses,  research  and  development,  capital  costs or  interest
              1986 Averafl. Un« Sales Price in Dollars. Report the average sailing price for one
              ^ ^ ^ ^ reported |n CQjurnn {1 j  gxp^ ^ ^^3 pnce in dollars.  If the
              pesticide chemical is not sold when it leaves the faciity, but is transferred to another
              facattv owned by «he firm for further processing, the sales price of the final product
              should be allocated to both facilities based on their share of the costs to produce
              the product This is referred to as the 'percentage of cost procedure.
                                         A. 35

-------
                             ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSUS, FOR 1986
                            Part B Financial and Economic Information
                      SECTION 2: FACILITY FINANCIAL INFORMATION
Instr
           for completing Table 2-I Pesticide Production! Intermediates - continued
       Column (5]
       Column [6]
       Column (7]
       Column (8]
1986 Quantity Sold. In column (5], report the total quantity of the rtem reported in
column (1] that was produced at this facility during 1986 and sold.

UnR of Measure. In column (6J. circle the code that corresponds to the unit of
measure you used to calculate the information you reported in columns (3). [4], [5]
and (7].
p « Pounds
T = Short tons
M = Metric tons
G =* Gallons

Sum Annual  Quantity SoSd Over Three Years (1985-1987).  Provwe  the total
amount (sum) of the product reported in column {1 ] that was produced and sold by
this facility in 1985. 1986. and 1987 combined.
Percent Exported  Over TTiree Years (1985-1987).
product In column [1]  exported in 1985. 1986. and
percent of column [7] was exported.
                                               Report the percent of the
                                               1987 combined, i.e..  what
                                                A. 36

-------
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-------
                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1980
                              Part B Financial and Economic Information
                       SECTION 2: FACILITY FINANCIAL INFORMATION

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

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

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

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

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

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

                    Code Definition

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

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

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

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

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

-------
                              ENVIRONMENTAL PROTECTION AGENCY
                      PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                             Part B Financial and Economic Information
                       SECTION 2: FACILITY FINANCIAL INFORMATION
instructions for completing Table 2-J Pesticide Production:  Formulated or Packaged Products -
continued
Column (51
Column [6]
Column [7]
Column [8]
 Colurn^ J9]
                    1986 Average Unit  Proauction  and Formulating/Packaging  Cost in  Dollars.
                    Provide the average proauetion c^st fp1" """ ""t — inciuae sucn costs as material
                    -rntc  j p  thp rpsts et aii raw matpnais  inrjudin  packaging materials that were
                    used in the proauction ana /or rormuiation and packaging of pesticide products).
                    direct labor costs, tne costs or pesticide contract worx or tolling done for you bv
                    others, and any otner pesticiae costs.

                    Note that the column (5] entry corresponds to items (5] through [8] under question
                    2-0 on page 23.

                    Express the costs in dollars.  Do not inauoe allocations for corporate overneao.
                    administrative expenses, researcn ana  Development,  capital  costs  or  interest
                    exoense.

                    1986 Average Unit Sales Price in Dollars. Report the average selling price for one
                    unit of the Item reported in column (1|. Express the selling price in dollars. If the
                    pesticide chemical is not purchased by your facility, but is transferred to your facility
                    from another facflity owned by the firm for further processing, the sales price of the
                    final product should be allocated to both facilities based  on their share of the costs
                    to produce the product This is referred to as the "percentage of cost procedure.'
                    An example of the percentage of cost procedure can be found on pages 28 and 29.

                    1986 Production Quantity.   In column  [5], report  the total quantity of the item
                    reported in column {1 J that was formulated /packaged by this facility during 1986.

                    Unft of Measure.  In column (6], circle the code that corresponds to the unit of
                    measure you used to calculate the information you reported in columns (5], [6]. [7]
                    and [8].

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

                    Sum Annual Production Over Three Years  (1985-1987). Provide the total amount
                    (sum* of the product reported in column  [\]  that was formulated/packaged by
                    facility in 1985. 1986. ana 1987 combined.
       Column (10]  Percent Exported Over Three Years (1985-1987).  Report the percent of the
                     product exported in 1985. 1986. and 1987 combined. i.e.. what percent of column
                     [9] was exported.
                                                 A. 39

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

2-K.   Facility 1986 Markets

      Estimate the  percentage of this facility's total 1986 production that was delivered to the markets
      listed beiow.  (Enter zero if the market is not applicable. The percentages snould sum to 100%).
      Ill ^Agriculture (U.S.A.)
      (31  Home, garden (U.SA)  ....
      [4]  Export (Outside U.S.A.) ...
      [5]  Other markets (SPECIFY)
3IK3A   V=riable)
                                             '
          Total
                                                                                    0  0 %
2-L.   Facility Operations
      Reoort the operational information listed below for calendar year 1986.  (Enter zero if the category
      is not applicable).
      [1]  The number of days the entire facility was in operation ...?.?.J«i ............. . ............     ___ ; _ ;
      [2]   The number of days part or all of the facility manufactured
          pesticide chemicals [[[ . .................. .5.21*2. ............     _ . _ [ _ ;
      (3]   The number of days part or all of the faculty formulated /packaged
          pesticide chemicals . ........................... . ....................... . ........................... .?.?L3....     _ : _ : _ :
2-M.  Employee Information
      In lines [1] through  [4],  report the total employee hours worked at this faculty in the months of
      January 1986. May 1986 and November 1986 in the categories indicated. In lines (5] and [6], enter
      the average number of shifts run in the entire facility in a week, and the average number of hours per
      shift for the months of January 1 986. May 1 986 and November 1986.
      [1]  Total employee hours in pesti-
          cide chemicals production

      [2]  Total employee hours in BfiSlh
          eide formulating and packaging

      [3j  Total employ**; hours in other
          production

      [4]  Total employee hours in ngn;
         ; production

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

      [6]  Average number of hours per
          shift in the entire facility
January 1986
S2M1A
S2M2A
S2M3A
S2M4A
S2M5A
S2M6A
May 1986
S2M1B
SZM2B
32M3B
S2M4B
S2M5B
S2M6B
November 1986
S2M1C
S2M2C
S2M3C
S2M4C
3ZM5C

-------
                               ENVIRONMENTAL PROTECTION AGENCY
                       PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                              Part B Financial and Economic Information
                        SECTION 2: FACILITY FINANCIAL INFORMATION
2-N.  Estimate the liquidation values less closure and post-dosure costs of the pesticide production and
      pesticide formulating/packaging lines at this facility if you were to dose them permanently within the
      next three years.  Include the value of fixed assets, working  capital and  real estate in  your
      calculation of liquidation values. Report the estimates in thousands of dollars and enter zero dollars
      if the item is not applicable.
      Pesticide production lines
      [11  Liquidation value (less dosure and post-dosure cost)
               Closure and post-dosure cost	
       [2]  Cost to convert to non-Table 1 pesticide active ingredients
           or non-pesticide products	
                                                                                   (SOOO)
                                                                                S2NA1A
                                                                                32NA1B
                                                                                S2NA2
      Pesticide formulating/packaging lines
      [ 1 ] Liquidation value	
      [2] Cost to convert to non-Table 1 pesticide active ingredients
          or non-pesticide products	
                                                                               S2NB1
                                                                                S2NB2
2-O.  Did this fac'drty have any property tax assessment for 1986?
                                           vcc
                                           * taW •••••••••••••••••••*•••••
                   S2O
                                                             ....  ! (CONTINUE)
                                                             .... 2 (SWP TO QUESTION 2R)
2-P.  What was the 1986 property tax assessment value of the Items listed below? Report the values in
      thousands of dollars and enter zero if the item listed is not applicable.
      Stats tax assessment value
          mLmnd       .. 	
          ^~"« *™ «««»«•«»«•••«"«"•»«-«««««««"•«««
      [2]  Biddings	
      [3]  Equipment and machinery	
      [4]  Total properly tax assessment value
      Local tax assessment value
      [5]  Land	
      (6]  Buildings	
      (7]  Equipment and machinery	
      [8]  Total property tax assessment value
                                                                                   (SOOO)
                                                                                   SP1
                                                                                   SP2
                                                                                   SP4
                                                                                   SP5
                                                                                   SP6
                                                                                   SP7
                                                                                   
-------
                              ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CSNSU^FQR 1986
                             Part B  Financial and Economic Information
                       SECTION 2:  FACILITY FINANCIAL INFORMATION
2-Q.   What was the 1986 assessed value oif the property expressed as a percentage of market value < 1986
      level of assessment)? (Enter zero if the item was not aoplicaoie).
      : 1 ]  State assessment percentage .

      [1]  LULJI ai»^i£rnent percentage
2-R.   Overall, what is the major source of competition tor pesticide products produced at this raciiity m
      eacn of the three marxets listed below?

      The same products means competing  products containing identical  or nearly identical pesticide
      active ingredients or percentages of active ingredients but having different trade or prano names.
      Substitute products means competing products oerrormtng  tne same pesticidal functions Put
      containing different pesticide active ingredients.
Competition
[ 1 ] Domestic producers of the
{2] Foreign producers of the
I [3] Domestic producers of the
[4] Foreign producers of the




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S2R2
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                                                A.43

-------
                           ENVIRONMENTAL PROTECTION AGENCY
                    PESTICIDE MANUFACTURING FACILITY CENSUS FOR 1986
                          Part 6 Financial and Economic Information
                    SECTION 2: FACILITY FINANCIAL INFORMATION
Comments for Section 2. Reference entries by question number.
                                         A.44

-------
                             ENVIRONMENTAL PROTECTION AGENCY
                     PESTICIDE MANUFACTURING FACILITY CENSU$ FOR 1986
                             Part B Financial and Economic Information
                              SECTION 3:  FACILITY CONTACT
Enter rne name, title, teleonone number ana aaaress (if different from the facility mailing aadressi cr tr.e
racnrrv representative to oe contacted wrtn Questions regaraing your resoonses to Part B:
Name (First ana Last)
Title


Teleonone Numoer

Address (if different from facility mailing aadressi:
Firm or Facility Name
Street or P.O. Box
City or Town
           33 F
State
S3G
                                                       Zio Coce
   CERTIFICATION:  The information orovtaed in Part B of the Questionnaire, as weil as that proviaeo
   in ail others, must be certified oy having the resoonsible individual for your facility commote ana sign
   the Certification Statement Item 6 on cage 3 of this Questionnaire.
                                               A.45

-------
A.46

-------
         Appendix B: MAPPING OF PESTICIDE ACTIVE INGREDIENTS INTO CLUSTERS

        This appendix lists the 56 clusters used to define PAI markets in the EIA. As discussed in Chapters 3 and
4, the clusters were developed by EPA's Office of Water based on previous work by EPA's Office of Pesticide
Programs (OPP).  Individual PAIs that are included in each cluster are listed in three columns.  The first column
includes the 260 PAIs that were considered in-scope.  (The next column shows the Chemical Abstract Service
Number for the in-scope PAIs.)  Since the PAIs that will not be covered by the effluent guidelines may compete
with those that are covered, non-regulated PAIs have also been assigned to clusters.  Thus, the second PAI column
("Other PAIs on OPP List") includes those PAIs not considered for regulation at this time, but included in the
original OPP clusters. Many of these chemicals have already been regulated (see the header of the table for notation
indicating whether PAIs are covered by other regulations, as well as the production/marketing status of the PAIs).
The third column ("New PAIs") lists PAIs that have been registered since 1980, and were, therefore, not included
la the original OPP clusters.
                                                 B.I

-------
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B.28

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 Appendix C: METHODOLOGY FOR ESTIMATING THE PRICE ELASTICITY OF DEMAND FOR
                                   PESTICIDE CLUSTERS

       This appendix provides the complete methodology for estimating the price elasticity of demand for pesticide
clusters. The price elasticity of demand is used in the EIA to predict the change in demand given an increase in
PAI pries due to compliance with the effluent guidelines. (See Chapter 4.)
                                               C.I

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ESTIMATES OF THE PRICE ELASTICITY
OF DEMAND FOR PESTICIDE CLUSTERS

Prepared for:
Economic and Statistical Analysis Branch
Engineering and Analysis Division
Office of Science and Technology
Office of Water
U.S. Environmental Protection Agency
Washington, D.C. 20460

Prepared by:
Abt Associates Inc.
Cambridge, MA 02138

May 1991
 C.2

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                               TABLE OF CONTENTS

 1.0    Introduction  	1

 2.0    Price Elasticity of Demand for Agricultural Pesticides  	  3

       2.1    Methodology	,	3

       2.2    Review of Empirics! Studies of the Price Elasticity
             of Demand for Pesticides	6

       2.3    Price Elasticity of Demand for Food Commodities	22

       2.4    Feasibility of Non-Chemical Substitution	30

       2.5    Contribution to the Variable Cost of Production	36

       2.6    Productivity of Expenditures for Pesticides	38

       2.7    Conclusions - Agricultural Pesticides  	42

3.0    Price Elasticity of Demand for Pesticides Used
       Non-Agriculturally	   ...  .55

4.0    Conclusions  	59

References      . >	61
                                         C.31

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1.0   INTRODUCTION

      Purpose  of the Analysis
      Abt  Associates  has submitted  a draft  economic  impact  assessment (EIA) methodology  for  assessing
the costs of new  effluent  guidelines  for the pesticide  industry.   The draft  EIA methodology  relies on  the
use of price  elasticities  of demand for pesticide  clusters.   In this memorandum,  demand elasticities   for each
cluster  are  estimated  based  on  a review  of empirical  analyses,  the  elasticity  of  demand   for  food
commodities,  and a consideration  of the factors predicted by  microeconomic   theory to influence   elasticity
of demand.

      Definition   of the Price Elasticity of Demand
      In general,  the economic  concept  of elasticity measures the sensitivity   of the dependent variable  to
a change  in the  value of an independent  variable.   In particular,  the price  elasticity  of demand  measures
the sensitivity  of consumers  to  changes  in price.   (Since this is the  elasticity  measure of  concern for this
report we  may,  for convenience,   use the  term 'demand  elasticity'  in place  of the term 'price  elasticity   of
demand'.)

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

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

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

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

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

      The elasticity  of  demand for pesticides  may vary  significantly   between  the clusters,  since each  cluster
faces different  market  forces.  In particular,  a distinction  may be drawn between  the agricultural  end-uses
and  the non-agricultural   end-uses.   Agricultural  sales represent  approximately   70  percent of  the  total
expenditures  for conventional  pesticides   in  the U.S.,  with the  remainder   split about equally  between
commercial  and domestic  sales (U.S. EPA, 1988).   In contrast  to the non-agricultural    markets,  the basic
market  structure within which fungicides,   herbicides,   and insecticides  are used agriculturally   is somewhat
consistent  across users and some documentation  is  available  by  which  to estimate  the elasticity  of demand.
The price  elasticity  of demand  for pesticides  used agriculturally    will  be  analyzed   first,  followed  by a
discussion   of the elasticity of demand for  pesticides  used  in the non-agricultural   sector.
                                                     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.S.A., Agricola,   Agris  International,  and NTIS.  A search  for subject  matter containing  the
following  key words  was conducted:  price elasticity, or demand, or demand  elasticity,  and agricultural,  or
chemical,  or  pesticide,  or herbicide,  or fungicide,  or insecticide.  In addition  to the literature  search, Abt
Associates  sought  information  from  the U.S. EPA Office  of Pesticide  Programs,  the U.S. EPA  Office  of
Policy,   Planning,   and  Evaluation,   several  offices  of the  U.S.   Department   of Agriculture,   the  U.S.
International   Trade  Commission,   the  Chemical  Specialty   Manufacturers   Association,  the  National
Agricultural  Chemical  Association,  the World Bank, Resources for  the Future,  the editor  of the American
Journal  of Agricultural   Economics,   a market research firm,  Cornell  University,  North Carolina  State
University  (Dr. Gerald Carlson),  Texas A&M University  (Dr. Ron Lacewell),  Virginia Polytechnic  Institute
(Professor  George  Norton),   Iowa  State  University,   Stanford  University   (Dr.  Sandra  Archibald),   the
University  of Massachusetts  (Professor Joe Moffitt),  the University   of Arkansas (Professor Mark Cochran),
and  Harvard  University.
                                                                         "i!
      The literature  search and conversations  with the listed  expert  sources indicated  that  studies  of the
price elasticity   of demand  for pesticides  are  sparse,  and that the existing  analyses   offer  conflicting
conclusions  and are often controversial.    Further,  an attempt  at compiling  expert  opinions as to expected
elasticities  failed;  the lack of available  research on this issue precluded  compact,  ready answers  that  could
                                                  C.6

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

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

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

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

      Finally,  the productivity  of expenditures  for pesticides  is examined in  Section 2.6.   In theory,  if
pesticides  are  highly  productive   (i.e.,  the  costs of pest  damage  without  pesticides  greatly  exceeds  the
expenses  of pesticide  application),  a prescribed  pesticide  dosage  will  be applied  regardless of some degree
of price variation.   In other words, if pesticides  are highly  productive,   the demand  for pesticides  is likely
to be inelastic.
                                                     C.7 '

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

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

      The level of aggregation  of the pesticides  may influence  demand  elasticity by determining  the number
of close substitutes that are available.  According  to microeconomic  theory,   the  more narrowly  a product
is defined,  the more substitutes  that are likely  to be available.  For example,  more substitutes  are available
for pork chops than are available for meat.
      If a  product  has many  close substitutes,   it  is  likely   to  be characterized  by an  elastic  demand.
Consumers can  react to a price increase  by switching  products without much loss of utility.   If a product
has  a more limited  number of  substituites,  consumers  have  little  choice  but  to bear more  of the price

                                                     C.9-

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

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

      In determining  the  elasticity  of demand for clusters  of active  ingredients,   it may at  first  appear
reasonable  to bound the elasticity  of demand  for clusters  of pesticides  by using the elasticity  of demand  for
pesticides  as a group  as the  lower bound  and the  elasticity of  demand for individual  active  ingredients  as
an upper bound.  Since pesticides  as a group  will  include, all clusters  of pesticides,   it could  be argued that
a cluster will exhibit an elasticity  no lower than  the elasticity  of pesticides  as a group.  However,  since  the
elasticity  of  pesticides   as  a group represents  an average  of the elasticities   of clusters it can  not serve as a
boundary for any one cluster.   Similarly,   since the elasticities  of demand for individual   active ingredients
within a cluster will vary,  the elasticity  of any one active ingredient can not  act as  an upper boundary  for
the elasticity  of the cluster.   For  purposes  of comparison, however,  this  analysis   considers  the empirical
analyses  in two groups:  those which  consider  pesticides  as a group  and those  which  consider individual
active  ingredients.

      A  second  major variation  between  the regression   analyses  of  demand  elasticities  reviewed  in this
report is whether  the dependent variable  was measured   in units  of production  (e.g., pounds  produced  per
year) or in  units of use (e.g.,  pounds  applied  per acre per year).  Due to potentially  significant  inventories
of pesticides  and the dissimilar  market  structures of pesticide manufacturers  and packagers/formulators   of
pesticides,  units of production  and use may  result in different  estimates  of elasticity.   Further,  some studies
defined  the dependent  variable  in absolute  terms  while  others used the percent  of crop treated.  Also,  the
dependent   variable  was alternately  measured  in units of expenditure  (e.g., dollars)  and units of quantity
(e.g., pounds).
                                                     C-10.

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      Finally,  the studies differed   in the specification   of the model  (e.g., simultaneous  equations  vs. single
equation  models, inclusion  of an independent  variable for labor),  the time  period included,  and the region
of the country considered.  All of the factors  discussed  above contribute  to the difficulty   of comparing  the
empirical  studies.

      The results of the analyses  of elasticity  of demand, categorized  by their  definition   of the dependent
variable,  are described below.

Aggregated  dependent variable  measured  in  units of use
      Five analyses were located  which  estimated demand elasticity  for  pesticides as a group and  measured
the dependent variable in units  of pesticide use.  The studies are: Pingali  and  Carlson (1985),  Miranowski
(1980), U.S. EPA (1974),  Huh (1978),  and Burrows (1983). The results of these  studies are conflicting.   Huh
reports demand  for herbicides  and insecticides  used on corn  as elastic.  Contradicting  this result,  U.S. EPA
(1974) indicates  that demand for corn and soybean herbicides  and corn insecticides  is inelastic.  Miranowski
also concludes that demand for herbicides  used on corn is moderately  inelastic  when labor is not included
in the analysis.   However,  the price coefficient  in his equation  is not significantly  different  from negative
one.   When  Miranowski   includes  labor  in his model,  price  is insignificant,    suggesting  that  labor  is a
substitute  for herbicides   used  on corn.    Miranowski   did  not  find price  to be  a significant   factor in
predicting  the level of corn insecticides   used.   Therefore,   his model offers little further  insight into the
elasticity  of demand  for  insecticides.    Burrows  also found pesticide  price  to be insignificant   in explaining
demand for pesticides  and  mitacides  used  on cotton.  Finally,  Pingali  and Carlson estimate that the price
elasticity  of demand for insecticides and fungicides  used in orchards  to be significantly   different  from zero,
but  not significantly    different  from  negative  one.

      Pingali and Carlson estimated  price  elasticity of demand as part of a larger, multidisciplinary   study
over the  1976-1980 period for forty-seven   orchards  in Henderson County,  North Carolina.  To analyze the
effect   of errors in subjective   perception  on  the  demand   for pest  controls,   Pingali  and Carlson  ran a
simultaneous  model  of  pest populations  and  pest controls.   Their model involved  a five-equation   system
with  two  pest   population   equations  (insect  and  disease  infestation   levels),  two  pesticide   equations
(insecticides  and fungicides),  and one pruning  status or labor  equation.

      The variables used in the  pesticide equations  were  obtained  from  input demand  functions  developed
by Pingali  and  Carlson.   The  derived demand   functions  had  four  groups of variables:   biological,  input
prices, risk  aversion,   and human  capital.   The  levels  of insecticides  and fungicides  were given in terms of
pounds  of active  ingredients applied  per  acre of orchard.  The cost per unit of insecticides  and fungicides

                                                     c.n

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            were given  in dollars per  pound  of active  ingredients.   A two-stage  least  squares  estimate  of the system
            resulted in a price elasticity  of demand for insecticides  of -1.39.  The fungicide  price elasticity  of demand
            was estimated as -0.92.   The elasticities  of demand  for both insecticides   and fungicides  were found  to be
            significantly   less than zero but not significantly   different  from  negative  one.  The model can therefore be
            interpreted to confirm a negative correlation between price and demand; it does not,  however,  indicate with
            certainty  whether demand  is elastic or inelastic.

                  Miranowski  (1980) considered alternative  pest management  systems  for corn  production  with rising
            energy  prices.  He used historical  data from U.S.D.A.  agricultural  regions from  1968,  1971,  and  1976  to
            estimate derived  demand equations  for insecticide  and herbicide  treatment.  Separate  weighted least squares
            regression models for insecticide  and  herbicide  treatment  were developed  as follows:
            In ST.
                 •Mi
            where
            PU,
            PI
            y
            SCA
            RE
   + a,  In P, h + ^ In P{ + % In y + %  In SCA + a5 In RE + In Fj  + e
share of corn  treated with  insecticides  (i) or herbicides  (h),
price of insecticides   (i) or herbicides  (h),
price of fuel,
value of corn  output per acre,
share of corn  acres  in cropland  acres,
lagged  production-oriented   research and extension  expenditures,  and
farm wage rate.
                  Miranowski  obtained  data on insecticide  and herbicide  treatment,  as the share of corn  acres treated,
            from the U.S.D.A.  annual pesticide surveys  for 1968,  1971, and  1976.  The input price indices,  Plih and Pf,
            were derived  from data in  U.S.DA.'s  Agricultural  Prices  - Annual  Summary  (for  1967,  1972,  1977).

                  Miranowski  estimated price  elasticity  of demand for insecticides  as -0.78.   However,  the coefficient
            was not significantly   different  from zero.  He reported  results of two  herbicide  demand  models, one with
            and one without  the  price of labor. When the price of labor is not included in the analysis,  the coefficient
            en herbicide  price, -0.75, is significantly   less than zero but not significantly   different   from  negative  one.
            Therefore  the elasticity  of  demand may be either elastic  or inelastic,   but  only  moderately  so.

                  When the wage rate is held constant,  the herbicide  price  coefficient  is 0.03 and becomes  insignificant.
            Though the  results  of  the  model  with labor  held constant may  be consistent  with inelastic  demand for
            herbicides,   the coefficient   on  labor is positive and  significant,   suggesting  that labor and  herbicides  are
            substitutes.    The coefficients  of the price  of pesticides  in the two herbicide models suggest  that the price
            of labor and the price of pesticides  are co-linear.   Since  the coefficient  for the price of herbicides becomes

                                                                 C.12
.

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insignificant   when  labor is included  in the model,  it may be  the case that the labor  price  variable  is
dominating  the herbicide  price variable  with  the result  that  change in the dependent  variable  appears  to be
largely  a function  of the cost of labor rather  than  the price  of herbicides.  However,  when labor is absent
from the model, the coefficient  of the price  of pesticides probably  includes  some of the influence  of  labor
rate changes. The "true" elasticity  of demand is therefore likely  to fall between the two coefficients   of -0.78
and 0.03,  still indicating  inelastic demand.

      Huh (1978)  estimated  pesticide  price  elasticity  of demand  in his doctoral  dissertation.   Using cross-
sectional   farm  data from  Minnesota,   Huh modeled   pounds  of  active  ingredients   of herbicides   and
insecticides  used  on corn  per farm   (Op).  Exogenous  variables  included  in his final  aggregate  demand
equation  were:

      x^w    =     adjusted  and weighted price of pesticides (dollars  per pound),
      XT      =     acres of  corn  per  farm, and
      D^     =     a dummy variable  for crop  rotation  plan (0 when  farmer  did not intend  to plant  corn
                   again  in  1978,  1 when farmer intended  to plant some or all of corn  in 1978).

      The results of the regression analysis  were as follows  (standard  errors  are  in parentheses):

      InQ,   =     2.212  -  1.464  In X,w  +1.099 In x,.  + 0.381 U,  + e
                           (0.161)         (0.064)       (0.110)

      The coefficient   of  the price  of  pesticides   was  significantly   less than zero  and also  significantly
different   from  negative  one,  indicating  elastic demand.    However,   since  an  independent  variable   for
pesticide  substitutes  (e.g., labor) was not included,  the coefficient  on pesticide price  may include the effect
of changes  in labor  or other substitute  prices and  therefore  have a bias  towards  greater  elasticity.  Hub's
model is  therefore likely to overstate  the elasticity   of demand  to an unknown degree.

      As part of an analysis  of farmers' attitude towards  alternate crop protection   methods,  U.S. EPA (1974)
described  a survey of farmer sensitivity  to pesticide price changes.  Farmers in Iowa and Illinois responded
to the survey as follows:
                                                    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 their  soybean  acres
need  herbicides  each year.
(of soybean  growers)  would  not change  herbicide  use if
cost doubled
      The results indicate  that  the majority  of farmers surveyed  are insensitive  to price  changes.   Demand
for corn and soybean  herbicides  and corn insecticides  appears  to be inelastic.

      The final study in this category was conducted  by Burrows  (1983).  Burrows  tested the hypothesis  that
integrated   pest  management   (IPM)  will  significantly   reduce  pesticide   use.   He  also  examined   the
methodological   issue of simultaneity  between pesticide  use and IPM adoption.   Burrows considered  only
insecticides  and mitacides.   His  data were  drawn from a random  sample  of San  Joaquin  Valley  cotton
growers.  The observations contain  detailed information  on output,  pesticide and other input  use,  cost,  and
revenue for 47  growers spanning  a 5 year period  from  1970-1974.

      Burrows  performed  a Generalized  Least Squares (GLS) procedure  for both single  and simultaneous
equation  models.  The dependent  variable  is insecticide  and mitacide use measured in sales dollars per acre
of cotton  grown.  Explanatory  variables include  average pesticide  price per pound,  an IPM  consultant  fee
per  acre, and the expected yield  in pounds  per acre. Weather and cultural  practices  are included as proxies
for both the size of the pest population  and pesticide  persistence  in the environment.  A risk proxy,  the ratio
of acres planted  in cotton to total acres, is used assuming that,  for higher  ratio values,  risk-averse  growers
                                                     P.. 14

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will  be likely to use more pesticides  as insurance  against  crop loss.  Pesticide  price is a quantity-weighted
price index.

      In both the single and simultaneous models,  pesticide prices are insignificant.    Burrows  explained  that
this  may result from limited  degrees  of freedom  (there are only ten price observations).   He also  offered
an alternative   explanation  that expenditures   may  not be  sensitive   to price  when conflicting  sources  of
information  - personal experience,  pesticide  salespersons, IPM consultants,  and extension  representatives  -
 affect  the decision to spray.   Another  potential  explanation  is that if  the  expected  rate  of return  from
pesticide  use is high, price movements  over  a modest  range would  not have  much explanatory  value.  The
price elasticity  determined  by  the single  equation  model  is  approximately   unity,  -0.90.   The  elasticity
resulting  from  the  simultaneous  version,  of the model  is -1.23.   Since the coefficients  were  not significant,
these values  are inconclusive.

Aggregated  dependent  variable  measured  in  units of  production
      An  earlier  version  of  an economic  impact  assessment  of pesticide  effluent  guidelines  analyzed
aggregated pesticides  and measured the dependent variable  in units  of production  (U.S. EPA, 1985).   U.S.
EPA found  that the  price  elasticity   of demand for pesticides  as a group,  as well  as for  fungicides,
herbicides,  and insecticides  was significant  and  inelastic.   EPA estimated  pesticide  elasticity  of demand
based on the following  log-linear   function:
In PROD,

where:
PRODt,  PPRODM
ACRE,
RPRICE;
a +b In PROD,..,  +c In ACRE;  +d In RPRICE,  +f (IX,)


production  of pesticide  active  ingredients  in year t and t-1
acreage  of principal   crops  planted  in year t
real  unit  price for pesticide active  ingredient  in year  t
Industrial production  index in year t
      Elasticities  were calculated   for  herbicides,  insecticides,   fungicides,   and  all pesticides.    Pesticide
production  rates were obtained from U,S. International  Trade Commission,  Synthetic Chemicals.   The units
of production  were not given.  Pesticide  prices  were  average prices for  each product  group  and  for  all
pesticides  and were  calculated  from   U.S. International   Trade  Commission,   Synthetic   Chemicals  and
converted  to real prices  using the  GNP  Deflator.  Based on this model, EPA obtained  the following  results:
                                                     C.15

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Ln of
Production
                  Intercept
               Ln Acres
               Ln Real
               Price
              Ln
              Production
              Previous
              Year
                   Industrial
                   Production
                   Index
Herbicides
-12.93
(-3.51)
3.19
(4.02)
-0.67
(-2.49)
0.299
(1.88)
-0.00651
(-3.24)
Insecticides
R2=0.68
-3.49
(-1.32)
1.53
(2.90)
-0.32
(-2.51)
0.142
(0.57)
Fungicides
All Pesticides
R2=0.89
-1.46
(-0.47)

-6.42
(-2.26)
1.04
(2.02)

1.88
(3.02)
-0.35
(-2.07)

-0.49
(-2.37)
0.05
(0.18)

9.427
(1.84)
      T-statistics   are given in parentheses.  The analysis  indicated  that demand  is inelastic for each  of the

three pesticide  groups as well  as for pesticides  in general.  All price elasticities were significantly   less than

zero, and significantly  lower than one in  absolute  value,  except  for the coefficient   for herbicides  which is

not significantly   different  from negative  one.  The  model, therefore,   indicated  that the price elasticity  of

demand for  insecticides,   fungicides,    and all  pesticides  is  inelastic.   According  to  the model,  the price

elasticity  of  demand  for  herbicides  is near unity,  meaning that demand may be either elastic or inelastic.


      The analysis  suggested that the demand for herbicides is more elastic  than the demand for insecticides

or fungicides.   EPA explained  that during  the  1970's  herbicides  experienced  a large increase  in application

rates and the proportion  of acres treated and that "the coefficient   on acres in the  herbicide  equation  reflects

this".  The authors  also noted  that "one of the reasons the amount  of variation explained   by  the fungicide

equation  was so low was that a very large proportion of fungicides  were used for non-agricultural   purposes".

 The authors were unable  to explain  why  business  cycles  are important for herbicides   and  not for the other

two product  groups.  It should be noted that the study did not  include a variable  for  prices  of  substitutes

or  final products.   If these prices  are correlated  with  pesticide   prices,   the  coefficients   may  be biased.

Finally,  the  authors did not identify   the  type  of end-use  (e.g.,  agriculture,  commercial,   domestic)  of the

pesticides  included  in their  analysis.


      Another  factor that may  influence   the results  obtained  by  EPA is that  the dependent  variable  is

measured  by weight  (pounds).   This may  not  accurately  reflect  price  elasticities  since more effective  and
                                                      C.16

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expensive  pesticides   may  be  substituted  for  pesticides   requiring  higher  doses  to  be effective.    EPA
acknowledged  this  issue, stating that there has been a decrease in the amount of insecticides  produced  due
to the .substitution  of synthetic  pyrethroids  for more conventional  pesticide  ingredients.   The synthetic
pyrcthroids  are more powerful  than  conventional  pesticides, thus reducing  the weight of pesticides required
for pest control.  EPA asserted, however,  that  in  terms  of total insecticide  production,   these  impacts  are
small.

Active  ingredient  as dependent  variable;   measured  in units of use
      The following  three studies examined  demand elasticity  for specific  pesticides  and measured demand
in units  of  use:  Lacewell and Masch  (1972), Carlson (1977),  and Carlson  (1977a).  Lacewell  and  Masch
found  that  the demand  for  the herbicide   2,4-D was  inelastic.   Carlson's  price  coefficient   for  2,4-D  was
small  and negative,  but  not significant,   which  may  be consistent  with  price  inelasticity.    Carlson's
significant  price coefficients  for insecticide  active ingredients  indicated   that demand  is elastic in both the
short-run and the long-run.

      Lacewell  and Masch selected a five county  area in the Northern High Plains of Texas as the study area
to evaluate  the effect of a  tax  vs. a marketing   quota farm program  on the level  of chemicals  used  in a
specific  agricultural  region.   The primary  agricultural  crops of the area were grain sorghum  and wheat.  To
control weeds  in wheat  and grain  sorghum,  herbicides,  especially  2,4-D,  were  utilized.

      Using  data on land utilization  for  1969, Lacewel!  and Masch  constructed  a linear  programming  model
for the five  county  region.   For illustrative  purposes,  the  change in the quantity  of 2,4-D  used in response
to changes in the price of 2,4-D was investigated.   Requirements  for weed control were  assumed to  be  met
by one of three  weed  control  alternatives:  (1) use of 2,4-D, (2) use of 2,4-D  and dicamba,  and (3) use of
dicamba,  other chemicals   and  additional   tillage  operations.    The  price  of 2,4-D   was  increased   by
increments,   using  parametric  programming,  from  52 cents per pound  to $37.00  per pound,  at which point
the model predicted no  2,4-D would be used.  In response to a more marginal  price increase of 78 percent
(from  $0.52  to $0.93  per pound),  Lacewell and Masch predicted a decrease  in use  of 2,4-D  of 30 percent.
This translates  to an inelastic  demand  of approximately   -0.38.
      Carlson's two articles (1977 and 1977a) used the same log-linear  model to examine demand  elasticities
of particular  herbicides  and insecticides.   Carlson first considered  price  elasticity of demand  for pesticides
as part of a study to determine  the importance  of  pest  resistance  to insecticides in affecting  demand for
specific  compounds.  In his second article,  Carlson  illustrated  some  advantages  and disadvantages  of price
incentive  systems relative  to quantity  incentive  systems for pollution  control.

                                                     C.17

-------
       Carlson used individual  farm data on insecticide  use from several  cotton production  regions to test

 hypotheses  of decreasing  productivity   of insecticides   and substitutability   between  chemical  types.   His

 original estimation  model is
where
R.,
Rj,
e,
C|
             quantity  of a given  insecticide  purchased  in year  t (pounds  of actual  material),
             insecticide  price deflated  by an  index  of all agricultural  input prices,
             substitute  insecticide  price,
             resistance  index,
             agricultural  product  price  index,  and
             error term.
      The agricultural  product  price variable,  Q, was not statistically  significant   and was deleted from the

model.  A lagged  dependent  variable  was added to account for the assumed effects  of delayed  adjustments
to price changes.   Carlson used this model to analyze several of the largest  selling groups  of insecticides.
The specific  dependent  variables  and their  price  elasticities were as follows  (standard errors appear in
parentheses):
       Dependent  Variable

      (A) Domestic  and  foreign  sales of cyclic
      organophosphate  insecticides  (1953-1970)

      (B) Same as (A) except  divided  by  domestic
      cotton  acreage planted

      (C) Total sales of parathion  and methyl
      parathion  (1953-1970)

      (D) Domestic  sales of DDT (1945-1969)
      (E) Domestic  sales of DDT (1953-1969)
                                                            Price elasticity

                                                                 -1.461
                                                                 (0.796)

                                                                 -1.552
                                                                 (0.780)

                                                                 -1.06
                                                                 (0.273)

                                                                 -0.667
                                                                 (0.397)

                                                                 -1.091
                                                                 (0.625)
      Insecticide  price has the expected  negative  effect on insecticide   purchases.   Carlson concludes  that

sales of the compounds are quite responsive to price, indicating  that there are many substitute  pest controls

in the long run.  None of the coefficients,   however,  are significantly   different   from  negative  one, so the

model indicates  that elasticity  of demand  is unlikely  to be either highly elastic or highly  inelastic.
                                                    C.18-

-------
      In Carlson's subsequent  article (1977a) he reported  a slightly different  elasticity  for the parathion and
methyl  parathion group and also includes  the herbicide  2,4-D  in his analysis.  Further he reported  long-run
elasticities  for  DDT and 2,4-D.   The results  were  as follows:
      Dependent  Variable

      (F) Domestic  sales of parathion,  methyl
      parathion  (1953-1969)
      (G) Domestic  sales of 2,4-D (1950-1970,
      except  1965-68)  divided  by cropland  index
      (H) Same as (D) except  long-run
      (I) Same as (G) except  long-run
Price elasticity

     -0.945
     (0.339)
     -0.193
     (0.349)
     -1.53
     -0.594
             The analysis indicates  that the elasticity  of DDT increases  substantially  from the short-run  to
the long-run,  as would  be expected  as more  substitutes  may be developed  with time.  The  coefficient   for
2,4-D  shows demand to be inelastic,  but is insignificant.   Though this result  may be consistent with inelastic
demand,  it is inconclusive.

Active  ingredient  as dependent  variable:   measured  in  units of production
      Abt Associates located  no studies which  Fit this category.

Summary
      Table  2.1 summarizes  the empirical  studies  discussed  above; Figure 2.2 displays  the empirically-
derived  elasticity  estimates  graphically.  As can  be seen from  Figure  2.2,  elasticity  estimates ranged  from
approximately   zero to  -1.5.   While most  estimates  indicate  that  the  demand  for  pesticides is relatively
inelastic,  the results are inconclusive.   Since the studies  used different  models and, in particular,  different
dependent  variables,  variation   in the estimates  is expected.   The  number  of studies which  considered
clusters  of pesticides as the dependent  variable  was insufficient  to draw  reliable conclusions   as to the price
elasticity  of demand for clusters of  pesticides.  However,   the  results  of the analyses which  did define  the
dependent  variable  as  a cluster  of pesticides  will  be considered  in  the final  estimations  of demand
elasticities.
                                                    C.19.

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      23.   Price  Elasticity  of Demand for Food Commodities
      Given  that the empirical analyses are insufficient  to derive estimates  of demand elasticity  for clusters
of pesticides,  an alternative  method of estimation of the elasticity  was developed.  The method used in the
remainder  of Section  2 of this report relies on a consideration  of four factors:  (1) the price elasticity  of
demand  for food commodities,  (2) the availability  and relative  costs of non-chemical  pest management,  (3)
the  contribution   of  pesticides  to  the  variable   cost of farm  production,  and (4)  the  productivity    of
expenditures   on  pesticides.    Though these  sources will not  reveal  precise  quantifications   of the price
elasticity  of demand   for  pesticides,  they  can be used  to indicate  whether  demand  for the  pesticides  is
expected  to be elastic  or inelastic  and to construct approximate  estimates  of the elasticity  of demand.

      Since the demand  for  particular   inputs  to a product  is  in  part  derived  from demand for  the end
product,  the demand  for  pesticides  used  in the  agricultural  sector will be influenced   by the demand  for
food.  The demand elasticities  of food commodities,  developed  in this section,  are  used to provide  initial
estimates of the elasticity  of demand for  clusters of pesticides.

      Estimates of the direct  price elasticity for foods at the retail  level are taken from the U.S.DA.  report
entitled  "U.S. Demand for Food: A Complete  System  of Price and Income Effects" (1985), authored by Kuo
S. Huang.  Using  a constrained  maximum  likelihood  method,  Huang  developed statistical  procedures  for
estimating  a large-scale   demand  system  from  time-series  data.    He then applied his procedures  to  an
estimation  of a domestic food demand system  including  forty  food items and one non-food  item. The food
items, direct-price   elasticities,  and  standard  errors  of the estimates are listed in Table 2.2. The estimated
elasticities  ranged from  -0.0385  (cabbage)  to -1.378  (grapes).   Huang noted  that  an exact  t-test for  the
statistical significance   of the elasticity   estimates is not  applicable,  given  the  assumptions  of a maximum
likelihood  model.   For  the  purposes  of his analysis,   Huang  considered  an  estimate  to be  statistically
significant  if the  estimated  elasticity was larger than its standard  error.  While estimated  elasticities  with
relatively large standard  errors  may imply that the estimates  are not statistically  precise,  only four  of the
thirty-four    commodity  elasticity  estimates  used in this analysis   had a standard  error greater  than  the
elasticity  estimate (butter,  other fresh  fruits,  carrots, and cabbage).

      Huang also provided estimates of demand  elasticities  for the following aggregated food groups:  meat,
staples,  fats, fruits, vegetables,  processed fruits  and vegetables,  and desserts.  The direct  price elasticities
he obtained were  negative for all seven  food  categories,  with magnitudes  ranging from -0.08  to -034.  For
purposes of  the discussion  here, however,  the individual   food items must be reorganized  to correspond  to
the  crops included in the clusters.
                                                    C.25-

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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, j
Other process
Sugar
Ice Cream
(USDA, 1985)
Direct-Price
Elasticity
-0.6166
-0.7297
-1.3712
-0.5308
-0.6797
-0.1452
-0.3319
-0.2588
y Milk -0.8255
-0.1092
-0.1467
-0.3688
-0.167
-0.2015
-0.9996
-0.4002-
-1.3780-
-0.2191
ts -0.2357-
-0.1371
-0.5584
-0.2516
-0.1964
-0.0388
-0.0385
stables -0.2102
-0.5612
: -0.3811
-0.6926
Ictail -0.7323
i, &nuts -0.1248
Truits & vegetable -0.2089
-0.0521
-0.1212


Standard Error
0.0483
0.0327
0.2045
0.0608
0.1332
0.0225
0.1174
0.1205
0.2642
0.1026
0.1438
0.0689
0.1748
0.1469
0.1465
0.1334
0.1829
0.1067
0.5471
0.0656
0.0624
0.0636
0.0693
0.1816
0.0405
0.1436
0.1006
0.1072
0.1746
0.3677
0.0313
0.0921
0.0172
0.0848
Source:  U.S.D.A. (1985). U.S. Demand for Food: A Complete System of
Price and Income Effects.  By Kuo S. Huang.  National Economics
Division, Economic Research Service. Technical Bulletin No. 1714
                              C.26

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      To estimate  an average elasticity  for individual  crops in a cluster,  the elasticities  of the included  crops
are weighted  by  the quantity  of the relevant  pesticide  applied to that crop, as reported  in Pimentel  et al.
(1991).   This weighting  factor incorporates  the fact that pesticide  use varies  between crops; the elasticity
of demand  for  a crop with heavy  pesticide  use will  more greatly influence  the elasticity  of demand for the
relevant  cluster of pesticides  than will  the demand for a crop  with  low pesticide  use. The resulting elasticity
estimate  is not a measure  of the elasticity  of the entire cluster  of crops (unless the cluster consists  of only
one crop).  Rather,  it is a measure of  the weighted  average elasticity  of the individual  commodities  in the
cluster.    The  elasticity  of the  entire  cluster  will  be lower  than  the average  elasticity   of the  individual
commodities  due  to the reduction  in the number of substitutes.  For example,   people may easily substitute
beef for  pork  and therefore  these  individual  commodities  may have  relatively  high  elasticities.   However,
substitutes  for  all meats are less readily  available  and this category is likely to have  a lower  elasticity  than
the average  elasticity  of individual  meats.

      Since  the elasticity  of the demand  for food  commodities  is assumed to represent  the elasticity  of
demand  for pesticides,  this elasticity   will also be overstated.   The overestimation  of the value  of demand
elasticity  will  likely  result in an exaggerated  estimate  of the fraction  of cost increases that is borne by the
manufacturers.   In  the  absence of more appropriate  data, however,  this  value provides  a reasonable best
estimate  of the demand elasticity   for  clusters of pesticides.

      Table  2.3 displays  the average  elasticities  for the  clusters based  on Huang's analysis.  The elasticity
estimates for the clusters represented  range from -0.12 (herbicides on sugar beets, beans,  and  peas) to -1.38
(fungicides  on grapes, herbicides  on grapes, and insecticides  on grapes). This  range of values  indicates that
the demand for the food  clusters  varies from highly  inelastic  to somewhat  elastic.

      While the calculations  for most of the clusters  are straight-forward,    the estimation of elasticity  for
the six  clusters containing  crops that serve as animal  feed required an intermediate  step.  The elasticity  of
demand  for corn, sorghum,  soybeans,  and alfalfa -  all crops that are largely  used  for  animal  feed  - was
calculated from Huang's  estimates  of the elasticity  of demand for animal food products.
      An average  elasticity  for animal feed crops can be obtained  by weighting  the elasticity  of each animal
product by the amount  of that product  consumed.  Huang provides "the retail weight  equivalent  of civilian
food disappearance",   a measure  of consumption,   for each food item.  This weighting   calculation  yields an
elasticity of demand  for animal products  of -0.55.  However,  for this weighting  method to accurately  reflect
the elasticity  of demand for feed crops, it must be true that a unit of feed yields  equal units  of all included
animal  products.   This  is not the case.  The yield  rates  of dairy products and eggs are substantially  higher

                                                      C.2?

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Sources for Table 23:

Values for "own-price elasticity" were obtained from U.S.D.A. (1985).
Values for "pesticide Use" were obtained from Pimentel et al. (1991).

Notes to Table 2.3:

/I      THe price elasticity of demand for bananas is not included since a separate estimate of the quantity
        of herbicides applied to bananas is not available.  Also, fruit categories are only included if they can
        be assigned to a single cluster. For example, "fruit juice" is not included since it could include apple
        and orange juice, and therefore  overlap two clusters.

/2      Vegetable categories are only included if they can be assigned to a single cluster, for example,
        "other processed fruits and vegetables" is not included since the category overlaps two clusters.

/3      Crop is assumed to be fed to animals. See text for explanation of elasticity estimate.

/4      The elasticity estimate is for dried beans, peas, and nuts. No separate elasticity estimates for these
        foods are available.

/5      The elasticity estimate for sugar does not distinguish between sugar beets and sugar cane.

/6      Elasticity estimate is for wheat flour.

/7      Includes lemons, cherries, peaches, plums, and "other fruit"

/8      According to the 1989 "Agricultural Statistics" published by the U.S. Department of Agriculture, 34
        % of all tomato acreage is used to produce for the fresh market and 66% of the acreage is used to
        produce tomatoes for processing.  Pesticide use is split between fresh and processed markets using
        these percentages.  While this split will not be precise since production per acre and pesticide use
        may vary, it is used as a reasonable approximation.

/9      Includes cucumbers, peppers, sweet potatoes, and "other vegetables".

/10     The category "other grain" is excluded since elasticity estimates are not available.  Use of herbicides
        on "other grains" is relatively minor, at 2.7 million kgs per year.

/ll     Since estimates of the elasticity of cotton are not  included in the U.S.D A. report, cotton is not
        included in the elasticity estimate for  the cluster.  Herbicide use on cotton, estimate at 8.2 million
        kg/year, is small compared to herbicide use on soybeans.  Therefore, the elasticity estimate for the
        cluster should not be substantially affected by the absence of an elasticity estimate for cotton.

/12     Includes pecans and "other nuts"

/13     The analysis assumes that half of herbicides used on peas are used on canned peas with the
        remainder used on dried peas.

/14     Includes all herbicides applied to beans and one-half of herbicides applied to peas.

/15     Tercent of Use" equals "Pesticide use on crop"/"Pesticide use on cluster"

/16     "Weighted Elasticity" equals summation of ("percent of use" multiplied by "own-price elasticity1)

/17     Since estimate of the elasticity of demand for tobacco are not  included in the U.S.D A. report,
        tobacco is not included in the elasticity estimate for this cluster.  However, since about 80 percent  of
        the insecticides applied to crops in this cluster are applied to soybeans, peanuts, and wheat, the
        absence of an elasticity estimate for tobacco should not dramatically affect  the elasticity estimate for
        the cluster.
                                                 C.32

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than the yield rates of meats per  unit of food.  Therefore,  a weighted  average of the food  elasticities  based
on consumption   would  be biased towards  the elasticities of dairy products and  eggs.  That is, the elasticity
values for  dairy  products  and  eggs would influence  the resulting  average  elasticity  more heavily  than is
appropriate.

      As can be  seen from  Table 2.2, the elasticities  of demand  for  dairy products  and eggs are generally
lower than the elasticity  of demand for meats.  Weighting the elasticities by consumption is therefore  likely
to understate  the  elasticity  of demand for  feed crops.  To avoid  this underestimation,   the  elasticity  of
demand  for animal feed is calculated  based  only on the meat products.  The resulting  estimate of -0.69 is
conservative  in  that it  is likely  to somewhat  overstate  the elasticity   of demand  for  animal  products,   and
therefore  animal feed.   This  conservative  value,  however,   still indicates  that demand  for  feed crops is
inelastic.

      Huang's  report analyzed  demand elasticity  for foods  at the retail level.   U.S.DA.  has also analyzed
the elasticity  of demand for farm products by modeling  the quantity  of the farm product as an input in  food
processing  (U.S.DA., 1989).  The analysis  considers  eight  commodities:  beef and veal,  pork,  poultry,  eggs,
dairy,  processed  fruits  and vegetables,   fresh  fruit,  and fresh  vegetables.   U.S.DA.'s  results  are consistent
with  previous  findings,  and  show that all own-price   elasticities  are negative  and less than 1 hi absolute
values.   The authors found that,  with  the exception  of poultry,   farm-level   demands are nearly  as large as
the corresponding   retail elasticities  or somewhat  larger than  the  corresponding   retail elasticities.   Since
specific  commodity  elasticities  are not given and since  the findings  indicate  that  farm-level   elasticities  are
similar  to retail-level   elasticities,  this  analysis  uses the more  detailed  values for elasticities  that are given
in Huang's  report.

      2.4,   Feasibility   of Non-Chemicall   Substitution
      In order to further delineate variations  in the elasticities  of demand  exhibited  by each cluster, one can
examine  the market characteristics   that,  according  to microeconomic   theory, influence  the price elasticity
of demand.  These characteristics  include the availability   of substitutes  for the  product,  the  contribution
of the product to the cost of production,  and the productivity   of expenditures for the product.  This section
discusses  the availability  of substitutes   for clusters  of pesticides.    Section 2.5  considers  the impact of
pesticide  contribution  to the cost of production  while Section 2.6 evaluates the productivity   of expenditures
for pesticides.

      As discussed  earlier, demand elasticity  is, theoretically,   a function  of the availability   of substitutes,
among other  factors.  If a product has many close substitutes,  it is likely  to be characterized  by an elastic
                                                      C.33

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demand.  Substitutes  for a pesticide active ingredient  include an alternative  active  ingredient  as well as non-
chemical  substitutes.   In constructing  pesticide  clusters, U.S.  EPA's Office  of Pesticide  Programs  (OPP)
grouped all active  ingredients  which  are substitutes  for  each other.  The active  ingredients  included  in the
clusters  are both  chemical  and  biological.   Therefore,   substitutes  for a cluster include  only  cultural  and
environmental  pest control technologies1.

      Achievable  reduction in pesticide  use for specific   end-uses  has been studied  by Pimentel  et al. (1991).
Pimentel  considered  the costs  and benefits  of replacing  chemical  pest control  methods with  currently
available  biological, cultural,  and environmental   pest control technologies.   Since both the pesticide dusters,
as defined  by EPA, include  biological  pest control methods, the biological  alternatives  listed  by Pimentel
are not alternatives  to the clusters.   However, Abt Associates  knows of no analysis  which  considers  only
cultural and environmental  pest control  alternatives.  Further, the biological  pest control  methods constitute
only a small  minority  of the pesticides  within  the clusters.   Pimentel  et al.'s analysis is, therefore, used to
measure the relative substitutability   of the pesticide  clusters.

      In this report,  PimentePs  study is used to develop  a general  rating  of the degree  to which  pesticide
substitution  is feasible for each cluster.  The greater the feasibility  of substitution,   the higher the expected
elasticity  of demand for pesticides  in the cluster.  The ratings  are based on two criteria:  (1) the percentage
by  which  non-chemical   alternatives   can  replace  pesticides,  and  (2) the  projected  net cost  of  replacing
pesticides  with  a non-chemical   pest  control method.  Based on these criteria,  the  clusters  are grouped  into
three categories as shown in Tables  2.4, 25,  and 2.6.  Clusters in  the "high  substitutability"   category  can,
according  to Pimentel et al.,   achieve at least a  40 percent  reduction  in pesticide  use at an additional  cost
of less than one dollar per hectare.  Clusters in the "moderate substitutability"  category can achieve  at least
a 20 percent  reduction in pesticide  use at a cost no greater than five dollars per hectare.   Clusters  which  do
not qualify   for  either of these categories  are listed under the heading  "low substitutability".

      The clusters  defined by  OPP often group several of the crops that are listed in Table 2.4,  2.5, and 2.6.
To determine ratings  for the clusters,  the crop-specific   ratings were weighted   by  the pounds  of fungicide,
herbicide,   or insecticide  applied  to each crop, as  was  relevant  for the  cluster.   The cluster ratings,  as
developed  by Abt Associates   based on  Pimentel et al. are as follows:
       1 Most of the pesticide  clusters  include at least two active ingredients,   indicating  that chemical
   substitutes  exist for most active  ingredients.   The substitutability   between active ingredients  will  vary
   by region and with meteorological  conditions,  as well as with specific  crops.  A comparison  of the
   chemical substitutes  available for particular  active  ingredients  is not  undertaken  in this  analysis.
                                                     C.34

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                                                Table  2.4
                        Non-chemical   Substitutability  for Pesticides  by Cluster
                                                Fungicides
        High
        Substitutability
Moderate
Substitutabilitv
Low
Substitutabilitv
        soybeans
        other vegetables
        peaches
rice                        cotton
sugar beets                sweet corn
lettuce                     tobacco
carrots                     peanuts
potatoes                   tomatoes
onions
beans
cantaloupe
peppers
sweet potatoes
watermelons
apples
cherries
peas
pears
plums
grapes
oranges
grapefruit
lemons
"other"  fruit
pecans
"other"  nuts
cole
cucumbers
Source:  Abt Associates  estimates based on Pimentel  et al. (1991)
                                                    C.35

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                                                 Table 2.5

                         Non-chemical   Substitutabilitv  for Pesticides  by Cluster

                                                 Herbicides
         High
         Substitutabilitv

         tobacco
         potatoes
         tomatoes
         cucumbers
         apples
         plums
         oranges
         grapefruits
         lemons
         "other"  nuts
Moderate
Substitutabilitv

peanuts
sorghum
pasture
grapes
alfalfa
hay
beans
cherries
peaches
pears
"other"  fruit
pecans
Low
Substitutabilitv

corn
cotton
wheat
soybeans
rice
sugar beets
"other"  grain
lettuce
cole
carrots
sweet corn
onions
cantaloupe
peas
peppers
sweet potatoes
watermelons
"other"  vegetables
Source:  Abt Associates  estimates  based  on Pimentel et al. (1991)
                                                   C.36

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                                                 Table 2.6

                         Non-chemical  Substitutability  for Pesticides by Cluster

                                                Insecticides
         High
         Substitutabilitv

         sorghum
         hay
         tomatoes
         cherries
         peaches
         pears
         plums
         grapes
         "other"  fruit
         pecans
         "other"  nuts
         oranges
         grapefruit
         lemons
Moderate
Substitutabilitv

cotton
wheat
carrots
onions
cucumbers
beans
sugar beets
peas
watermelons
"other" vegetables
sweet potatoes
peppers
alfalfa
soybeans
rice
tobacco
peanuts
"other" grains
Low
Substitutabilitv

corn
lettuce
cole
potatoes
sweet corn
cantaloupe
Source:  Abt Associates  estimates  based on Pimentel et al. (1991)
                                                   C.37

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Low Substitutabilitv
fungicides  for use on vegetables
herbicides  for use on corn
herbicides  for use on soybeans,  cotton,  peanuts,  alfalfa
herbicides  for use on sugar beets, beans, and peas
insecticides  for use on corn  and alfalfa
insecticides  for use on vegetables

Moderate Substitutabilitv
fungicide  for use on fruit  and  nut  trees, except  oranges and grapes
fungicides  for use on oranges
fungicides  for use on grapes
herbicides  for use on vegetables
herbicides  for use on sorghum,  rice, small grains
herbicides  for use on grapes
insecticides  for use on cotton
insecticides  for use on soybeans,  peanuts,  wheat, and tobacco

High Substitutabilitv
herbicides  for use on tree fruits (except  oranges),  nuts,  and sugarcane
herbicides  for use on oranges
herbicides  for use on tobacco
insecticides  for use on grapes
insecticides  for use on oranges
insecticides  for use on fruit  and nut trees  excluding   oranges and grapes
insecticides  on sorghum

      As discussed  earlier, these  data can be used  to suggest  pesticide  clusters  for which  the demand
elasticity differs  substantially  from the demand elasticity for the associated  food commodities.  Demand for
the six  pesticide  clusters  with low Substitutability   may be inelastic  relative to the demand for the associated
foods.  In the seven cases of high Substitutability,   the demand  for  the pesticide cluster may be more elastic
than the demand for the  associated  foods.  The feasibility  of substitution  for pesticide  clusters is considered
in Section 2.7  in constructing   estimates  of the elasticity  of demand for the pesticide  clusters.
                                                    C.38

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      2.5.    Contribution  to the Variable  Cost of Production
      Economic theory  predicts  that  a producer's  sensitivity  to price will  increase  with the percentage  of
production  cost contributed  by that input.   To further distinguish  between  the elasticities  of demand  for
the different  clusters  of pesticides,  Abt Associates  has considered the extent to which  the pesticides  in the
dusters  contribute  to production  costs.

      The U.S.DA. publishes  cost-of-production    data summarizing  all operator  and landlord  costs and
returns  associated  with the production  of several individual   commodities  (U.S.DA.,  1989a).   The cost
estimates separate the cost of chemicals  and can be used to determine  chemical  costs as a percentage  of total
variable  costs  of production.    Cost of chemicals  is  included  in two categories:  "chemicals"  and "custom
application".    Both custom  operators  and  farmers  apply  pesticides.   The  category  "chemicals"   includes
agricultural  chemical  use by farmers and does not include  labor spent  in chemical  application.  Many custom
operators  charge a flat  rate and do not provide  a cost breakdown  between  labor and materials.   "Custom
application"  therefore includes  operator-applied   chemicals,  operator labor,  and farm operations  other than
chemical  application.    The  category  "custom   application"   was  included   in calculations  of  pesticide
contribution   to total cost  in order to ensure that all chemical  costs are included.  The estimate  of pesticide
contribution   to the cost of crop production will,  however,  be overstated.  These data are presented  in Table
2.7 for the commodities for which  the information was available.

      The pesticide  clusters  defined  in  this  analysis  separate agricultural  chemicals  into  fungicides,
insecticides,   and herbicides.   The U.S.DA.   report  does  not  separate the  costs of chemicals   into  these
categories.   In order to  divide  the  cost  of chemicals  between  each  of these types  of pesticides,   Abt
Associates  estimated  total  expenditures   for  each  pesticide  type for the  commodities  considered in  the
U.S.DA. report. Total expenditures  were calculated  by multiplying  the pounds of fungicide,   herbicide,
or insecticide  applied  to  a commodity  (from Pimentel et al, 1991)  by  the  average price  of the relevant
pesticide type  i.e.,  fungicides,   herbicides,  and  insecticides  (as reported in  Synthetic  Organic  Chemicals.
1988).  The  chemical  contribution  to variable  cost was then divided  between the three pesticide  categories
based on  the percent of  expenditures.    The percentages  of variable  production  costs for  fungicides,
herbicides,  and insecticides  by commodity  are listed  in Table 2.7.

      The crop-specific  estimates must be grouped into clusters for purposes of this analysis.  An estimate
of the contribution   of pesticide  to  variable cost for a cluster is made only if such  an  estimate  is available
for individual   crops contributing  at least 50 percent of the pesticide  use for  the cluster (based on Pimentel
et al., 1991).  Eight  clusters meet this qualification.   These  clusters  are listed below  in descending  order of
                                                    C.39-

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                                            Table 2.7


                 Fungicide. Herbicide, and Insecticide Contribution to Variable Costs of

                                            Production
Commodity

soybeans3
peanuts
cotton3
sugarbeets3
sorghum
corn3
rice0
wheat3
potatoes
barley3
tobacco
oats3
Chemical
Costs as a
Percent of
Variable
  Costs1

    37
    31
    29
    28
    25
    22
    20
    18
    16
    16
    10
     9
Fungicide
Costs as a
Percent of
Variable
Costs2

 0
12
 0
 0
 0
 0
 0
 0
 7
 0
 0
 0
                                                    Herbicide
                                                    Costs as a
                                                    Percent of
                                                    Variable
35
17
16
23
22
19
19
16
3
16
3
9
                     Insecticide
                     Costs as a
                     Percent of
                     Variable
 3
 3
13
 5
 3
 2
 1
 2
 6
 0
 7
 0
      ""Equals ("chemicals" + "custom operations")/"total variable cash expenses"
   »

      2Estimate by Abt Associates using pesticide prices from Synthetic Organic Chemicals, 1988 and
  pounds applied from Pimentel, D. et al, (in press), "Environmental and  Economic Impacts of
  Reducing U.S.  Agricultural Pesticides Use", Pest Management in Agriculture, CRC Press.

      3Source for percent of production costs - USDA, 1989. "Economics Indicators of the Farm Sector
  Costs of Production, 1987".  Economic Research Service.  February.

      ^Source for percent of production cost- USDA, 1988.  "1985 Potato Cost and Returns:  Fall
  Production Areas".  Potato facts special edition. Economic Research Service. September.

      5Source for percent of production cost - USDA, 1989. "Tobacco:  Situation and Outlook Report".
  Economic Research Service. September.
                                                 H.40

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the  percent  of the pesticide  contribution   to cost.   Based  only on contribution   to cost,  the order  also
corresponds  to expected  decreasing  price elasticity  of demand.  The clusters are:

1)    Herbicide used  on soybeans,  cotton,  peanuts,  alfalfa  (33 percent  of variable  cost)
2)    Herbicides  used on sorghum,  rice, small grains  (20%)
3)    Herbicides  used on corn  (19%)
4)    Insecticides  used on cotton (13%)
5)    Insecticides  used on soybeans, peanuts,  wheat,  and tobacco  (3%)
6)    Herbicides  used on tobacco  (3%)
7)    Insecticides  used on sorghum  (3%)
8)    Insecticides  used on corn and alfalfa   (2%)
      U.S.DA. did not estimate  the cost of production  for specialty  crops.  These data are compiled  at the
county  level  and collected  by  individual  states, but are not available  on a national level.  It is beyond the
scope of this study  to collect cost of production  data from each  county in each state for each crop.   Abt
Associates did, however,  obtain cost of production reports for specialty crops of interest from the states that
represented  a large percentage  of the planted  acreage  of each crop.  From  these reports  it was evident  that
the pesticide   contribution  to  cost  varied  significantly   between regions.   Therefore,  it was  decided  that
without  a statistically  valid national   sampling,  the  county-level    data could  not accurately  be used  to
represent national  cost  data.  No estimates of the  pesticide  contribution   to variable  costs of producing
specialty  crops are included  in this  analysis.

      The purpose  of considering  the  pesticide  contribution   to variable cost is to determine  whether the
demand elasticity for clusters of pesticides is likely  to  differ substantially from the elasticity  of demand for
the associated food  commodities  (calculated  is Section  2.3).   In particular,  for  the four  pesticide  clusters
where  chemicals  contribute  over ten  percent   of total variable  cash  expenses,   farmers  may be relatively
sensitive to pesticide  price  changes.   Therefore,  demand  for these pesticide clusters  may be more elastic than
demand for  the associated  food commodities.   This factor  is considered  in Section  2.7, along with  the other
available  data, to estimate  the elasticity of demand  for  each of the pesticide  clusters.

      2.6    Productivity  of  Expenditures  for  Pesticides
      The productivity   of an  input refers  to the  marginal  value  product  of  expenditure  for  the input
compared to the cost of the input. When the marginal value product exceeds the input cost, the input is said
to be productive.   If an  input  is highly  productive,  demand  for the input is theoretically  likely  to be
                                                     C.41

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insensitive  to small changes in price.  Three studies which examined  the productivity   of expenditures   for
agricultural  pesticides were located and  are discussed below.

      Headley (1968) estimated partial production  elasticities  for the following  input variables using Cobb-
Dougias  functions:    labor,  land and  buildings,  machinery,  fertilizer,   pesticides,  and  "other".  He then
compared the marginal   value production   of  expenditure  for  pesticides  to the marginal   factor  cost  of
pesticides  to determine  the extent of disequilibrium   in the use of pesticides  by farmers.   The results  of
Headlev's  study indicated  that the marginal value of a one-dollar  expenditure   for chemical pesticides  is
approximately  $4.00.  Headley noted  several limitations  of his  analysis,  including  that his  conclusions  are
based  on aggregative  analysis  and may not  apply  to local situations.

      Campbell  (1976) considered this same issue for a cross-sectional  sample of tree-fruit  farms in British
Columbia.  The statistical techniques  used by Campbell include  Ordinary  Least Squares and Factor Analysis
Regression.  The data used  in fitting Campbell's  regression  equation  were as follows:  the dependent  variable
was the value of output  of fruit;  the input variables  were the values of services  of land  and buildings  and
capital  equipment,   and the values  of inputs of irrigation  water,  labor,  fertilizers,  and pesticide  sprays.
Corresponding   to  Headley's  findings,   Campbell  found  that  the value  of a marginal  dollar's  worth  of
pesticides  was significantly   greater one dollar,  indicating  a relatively   inelastic  demand.   However,  as
Headley  did, Campbell  suggested  caution in the interpretation  of this result.  He noted  that it is possible
that his statistical   procedure  introduced   an upward  bias  to the estimate  since  the sample  data exhibited
fairly  high correlations  among some of the independent   variables,  including  pesticides.

      According to Lichtenberg  and Zilberman (1986),  however,  the studies  of  Headley and Campbell  are
methodologically   flawed.   Lichtenberg  and Zilberman  argue that econometric  measurements   of pesticide
productivity   that are derived from  standard  production  theory models contain  significant  upward biases that
result  in the overestimation  of pesticide  productivity.    The authors  claim that the constant  elasticity  of the
marginal effectiveness  curve  produced by a standard Cobb-Douglas   specification  will  not match the actual
behavior  of the marginal  effectiveness    curve.   The  correct  form  of  the  marginal   effectiveness   curve,
according to Lichtenberg and Zilberman,   will show an increase  in pesticide use in response to pest resistance
and a decrease  in  use only when pest resistance  is so widespread  that alternative measures are most cost
effective.   The true marginal  effectiveness   curve  will decline at an increasing  rate in the economic region.
Lichtenberg  and  Zilberman  cast doubt  on the  high  marginal  productivity  of pesticides  estimated  by
Campbell and Headley.
                                                    C.42

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      Given that these studies  do not provide  definitive   estimates  of the productivity   of pesticides  and do
not address the productivity   of  specific   pesticide  clusters,  we develop  simple  original  estimates  of the
productivity   of pesticide  clusters.   In  this  analysis,  the  productivity   of pesticides  (specified   as either
fungicides,  herbicides,   or insecticides)  on individual  food commodities  is calculated  as follows:

                                                      V X MP
where:
C
MP   =
P
the cost of pesticide  treatment  for  the food commodity  (dollars  per hectare),
the marginal  value product  from  the  pesticide application   (percent  of total production  value),
the productivity    of the pesticide  on the  food  commodity   (dollars per  hectare/dollars  per
hectare),  and
the production  value  of the  crop  (dollars per hectare  harvested).
      The data sources for the three input parameters were as follows.  The production  value of the crops
was obtained from U.S.DA.  (1989).  The cost of pesticide treatment  was taken from Pimentel et al. (1991).
No source of specific  estimates of the marginal  value product  associated  with fungicides,   herbicides,  and
insecticides  on crops was  located.  The analysis therefore  relied on the expertise  of the U.S. EPA Office of
Pesticide  Programs  (OPP) to estimate  the value  of this  parameter.  The OPP stated that it was  reasonable
to generalize  that the marginal  product  associated  with the use of fungicides,   herbicides,  or insecticides  on
a crop equaled ten percent  of the production  value of that crop  (telephone  communication,  Dave  Broussard,
OPP, 2/91).  Since no more precise  estimates  were available,  the analysis  adopted  this value.

      In reality,   there will be some variation  in the marginal value  product  of fungicides,   herbicides,  and
insecticides  on different  crops.  To the extent  that the marginal value  product for a pesticide type on a crop
is greater  than 10 percent, the analysis  will understate  productivity   and therefore  overstate the elasticity
of demand.  Similarly,  if  the marginal value product  for  a pesticide  type  on a crop is less than 10 percent,
the productivity   of the pesticide  will be overstated  and  the elasticity  of demand will  be underestimated.
      Weighted  averages   of  the  productivity   measures  for  pesticides  used on individual   crops  were
calculated to obtain measures  of productivity   for pesticide  clusters.   The weighting  factor  was the quantity
of pesticides  included  in  the  cluster applied  to each crop,  as determined  by Pimentel et al. (1991).
Table 2.8 displays  the productivity   measures  for  the  pesticide  clusters  for  which  the  information   was
available.

                                                     C.43

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                                       Table 2.8

                            Productivity of Pesticide Clusters
Cluster
           Productivity
    (Dollars of Marginal Product
per Dollars of Pesticide Expenditures')
Fungicides on:

 Fruit and nut trees, except oranges and grapes
 Grapes
 Vegetables
 Oranges

Herbicides on:

 Sorghum, rice, small grains
 Corn
 Soybeans, cotton, peanuts, alfalfa
 Sugar beats, beans, peas
 Vegetables
 Oranges
 Tree fruits (except oranges), sugar cane, nuts
 Grapes

Insecticides on:

 Cotton
 Sorghum
 Corn, alfalfa
 Vegetables
 Fruit and nut trees, except oranges and grapes
 Soybeans, peanuts, wheat, tobacco
 Oranges
 Grapes
                $5.81
                $9.83
                $12.37
                $12.54
                $0.88
                $1.11
                $2.68
                $2.72
                $17.85
                $17.91
                $19.29
                $61.43
                $0.72
                $1.24
                $3.69
                $7.92
                $8.51
                $13.08
                $15.04
                $37.80
                                        C.44

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      Note that  there is great variation  in the productivity   estimates.  The lowest productivity   estimate is
$0.72,  for insecticides  used on cotton; Herbicides  used on grapes  had the highest productivity,   at $61.43.
The wide  range  is due both to variability  hi the value of production  of crops and variability  in the cost of
applying  pesticides  to the crop.  For example, the value  of production  of cotton  is $487  per hectare while
the value  of  a hectare of grapes is $4,914 per hectare  (U.S.DA.,  1989).   In addition,  the average  cost of
insecticide  application  to cotton is about  $118 per hectare  while  the costs  of applying  herbicides to grapes
is $8 per hectare  (Pimentel et al., 1991).  However, it must  again  be recognized that due to lack of data,  the
analysis  assumes that the marginal value  of production  of insecticides   on cotton  and herbicides  on grapes
are identical.

      The productivity   of the clusters  is  considered   in the next  section, along with  the factors  previously
discussed,  in developing  estimates of the elasticity  of demand for each pesticide  cluster.  Demand  for  the
pesticide  clusters for which  productivity   is low can be  expected  to be elastic relative to the demand  for  the
associated  food  commodities,  ceteris paribus.  Similarly,  when a cluster of pesticides  is highly  productive,
demand  is likely  to be inelastic compared with demand  for the associated  food  commodities.

2.7.   Conclusions  - Agricultural  Pesticides
      Section  2 of this report estimates the  price elasticity  of demand  for twenty-four   pesticide clusters.
Estimates  of the  elasticity of  demand for clusters of  pesticides are based  on the price elasticity  of demand
for the associated food commodities.  However, the elasticity  of demand for an input  is not solely  a function
cf the demand for  the end product  (unless  input  ratios are assumed  to be  fixed).  Therefore,  the elasticity
estimates are adjusted as warranted by  consideration  of three factors:  (1) the feasibility  of substituting  non-
chemical  controls  for the pesticide cluster,  (2) the contribution  of the pesticide cluster  to the variable  cost
of crop production,   and (3) the productivity   of the  pesticide  cluster.  In addition,  the  literature estimates
of elasticity  are considered  when appropriate.

      Since the effect  of these factors is not easily quantified,  we  use this information  to adjust  the pesticide
elasticities  estimated  from the  demand for  crops rather  than to attempt  to pinpoint  the value  of  demand
elasticity.   Based on this information,  we identify dusters  for which  the  elasticity   of  the demand  for  the
food commodity  is likely to differ  substantially  from  the elasticity  of demand for  the corresponding  cluster
of pesticides.
      Note that  the effect of the factors  considered  may  cancel  each other.  For example,  the feasibility   of
non-chemical  substitution  for a cluster of pesticides  may be high,  indicating  that the elasticity  of demand
may be higher  for the cluster of pesticides  than for the associated  crops.  However,  if the productivity   of

                                                     C.45

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the pesticide  cluster  is also high,  less elastic demand is indicated  for  the  cluster of pesticides  than for  the
associated foods.  To decide whether  an adjustment   to the elasticity  of demand  for the food commodities
is warranted,  the net indication  of the factors is considered.  Factors that indicate  relatively  elastic demand
and factors  that indicate  relatively inelastic  demand cancel each other.   If,  on  net, two factors  indicate
relatively  elastic or inelastic demand, an adjustment  to the elasticity  estimate  is made.

      Table  2.9  summarizes the information  from the five  areas of research:  literature  estimates,  demand
elasticities of food commodities,   feasibility   of  substitution,   contribution   of chemicals  to production  costs,
and productivity   estimates.  The information  is  summarized for twenty-one  sectors of agricultural  pesticide
use. Three additional dusters  of  pesticides  are included  in the following   summary  of elasticity  of demand
for agricultural  pesticides:   fungicides   used on grain storage,  fungicides  used for seed  treatment,  and
fungicides -  post-harvest.    Since  these  clusters  differ  from  the other  agricultural  pesticide  clusters in that
the pesticides are not applied  to crops in the field,  they have not been  included  in the analysis to this point.
However,  since  the pesticides   in these clusters  are used  agriculturally,   elasticity  estimates are discussed  in
this section.   The best estimate of elasticity  for each of the twenty-four   agricultural  clusters is discussed
below.

a.    Fungicides  used on vegetables
      The elasticity   estimate  of -0.38   is taken directly  from  U.S.DA.'s  (1985) estimate  of the demand
elasticity  for retail vegetables,   weighted by the  amount of fungicides  applied to each type of vegetable.   No
adjustments   are made  since  the  substitutability   for fungicides  on  vegetables   is low and the marginal
productivity   of fungicides   on vegetables  is  moderate.

b.    Fungicides  used on fruit and nuts except oranges
      The elasticity  of demand for food  commodities in this  cluster,  based on a weighted-average   of the
elasticity  values estimated  by U.S.DA.  (1985),  is -0.23.    No adjustments   are made to  this  value are made
to arrive  at the elasticity of demand for  fungicides  applied to these food commodities.   No corrections  were
necessary  since the  substitutability   for fungicides   on fruit  and  nuts except citrus is moderate  as is the
marginal  productivity  of fungicides on fruit  and nut trees, except oranges.  The estimated  elasticity  of -0.23
indicates  less elastic  demand than does  the analysis  of Pingali  and Carlson (1985).  However, the elasticity
estimate  of Pingali  and Carlson   consider  only  apples  and is therefore   not  directly  comparable  to the
elasticity  estimate for the  cluster.  Both the  current  estimate and  the Pingali and Carlson estimate indicate
that demand  is inelastic.
                                                    C.46.

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Cluster
                                        Table 2.9
                           Summary of Elasticity Information
              Elasticity of   Feasibility    Fraction of
Literature        Food          of       Contribution to        Marginal
Estimates      Commodity   Substitution   Production Costs      Productivity
Fungicides on: ^V "'-""',-''',- >< ->', ,,"ffe/4
vegetables MxA* , -0.38 ^ .Jos^'J %,
fruit & nut trees, ' ' X ' ' / , - \ ,,i- --., ~l,~- £
except oranges •.—&r9:2 (2$- - "' } , -0.23 - jajcxferate
•A" '**/* %?%&&* % --«•••> -•>'''•• ' W '% f - '••
oranges . J$LA.v ^ ^p* , ^ -1.00 0 ^moderate- t
grapes NvA*" , v " , -1.38 ' m<^$p|e. % s
„ **'•'•* •.'•s\'< -• -*yf *' ' % -
Herbicides on: "--,'' - -" "" -V**. ^VJfc V »"
sorghum, rice, ! -•''"--''•." '  ^Tj^^v ,;;,; „- -0.69 - ' -4ow""
oranges JSE,A. -1.00 s "- ^igh " ^
tree fruits, nuts & -'"'^ , „! s"'^ ^r^''c ' /,' /\
sugar cane JNxA. - -' •• -••••-' - ----- -0.20 *' % ' Jii^ *%
grapes H»K, "'^ " ' -•: ' , -1.38 ' j^oSeta^t
5'> ''* % "' " '"\? ' " ;/"-^ ' ""
vegetables H*J£"/' ' - " " -0.27 ^ ja^erate^
tobacco ^C' .."' 'V % ^ N.A. ^/^l^"",
sugar beets, beans N "-1; - " „ - / ; "^ ^T - '5' -
peas N.%, ' '? ,'" -0.12 '^-L,ifew
' -.v ^-%' -i.
N.A. '" ' $£&(. |
' ' s^
N.A. ^V^54j
/; "" ' •"
•*•_
"y •• Sf- -
Oon •• -• -•$/}- •&$£•'
• J^V/ w wX-OO •
0-7^ / ""-^^ aCO
. JJ *" ' <^$t+f}Q
0.19 '/ ,-%$iji.
N.A. "s - ^rf Ji
'-"r^^*^
,--#• - '
5- ^»v - ';
NA ^l
-------
                                    Table 2.9 (cont.)
                           Summary of Elasticity Information
                         Literature
                         Estimates
Elasticity of   Feasibility   Fraction of
   Food          of       Contribution to
Commodity   Substitution   Production Costs
Insecticides on:
 vegetables

 fruit & nut trees
 exc. oranges

 oranges

 grapes

 corn, alfalfa
 sorghum

 soybeans, peanuts,
 wheat, & tobacco

 cotton
                                  N.A.

                                  N.A.


                                  N.A.

                                  N.A.

                                  0-02



                                  0.03


                                  0.03

                                  0.13
Marginal
Productivity
(1) Burrows (1983), cotton only
(2) Pingali and Carlson (1985), apples only
(3) Miranowski (1980), corn only
(4) Huh (1978), com insecticides and herbicides
(5) U.S. EPA (1974), corn or soybeans, only
                                               C.48

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c.    Fungicides   on oranges
      The elasticity  estimate  of -1.0  is taken  directly from U.S.DA.'s  (1985) estimate  of the demand  for
oranges.  No adjustments  are made since the substitutability  for fungicides   on citrus is moderate,  as is the
marginal  productivity   of fungicides   on oranges.

d.    Fungicides   on grapes
      The elasticity  estimate of -1.38  is again taken directly  from U.S.DA.'s  (1985) estimate of the demand
for retail  foods.   Since the feasibility  of substitution  for  fungicides  in this cluster  is  moderate  and  the
marginal  productivity   is moderate,  no adjustments  are  made.

e.    Herbicides  on sorghum,  rice, and small grains
      The best estimate of the  elasticity  of this food cluster is based  on the demand elasticity  of rice,  as
reported  by U.S.DA.  (1985)  and on  the demand  elasticity  of sorghum.  As  discussed  above,  the  elasticity
of demand for sorghum, generally  an animal  feed crop, was calculated  based on the elasticity  of demand
for animal meats.   To  estimate  an elasticity   for  the crops in this cluster,   the  two  crop elasticities   were
weighted  by the amount  of herbicides  applied  to each  crop (as reported in Pimentel  et al.,  1991).   The
resulting  elasticity  estimate is -0.44.

      However,  it is  likely  that the  elasticity  of demand  for  this  cluster   of  herbicides  will  exceed  the
elasticity  of demand for the associated crops.  Although  the feasibility  of substitution  for herbicides  hi this
cluster  is  moderate,  herbicides   contributed   a relatively  high  percentage  to total variable  costs,  and  the
marginal  productivity  of the herbicides is very low.   There is no precise method  by which  to translate these
factors  into an  estimate of  the  elasticity  of demand for herbicides  on sorghum,  rice,  and  small grains.
However,  to account  for  the low  marginal  productivity    and high contribution   to costs  of herbicides  on
sorghum,   rice, and  small  grains, demand on herbicides  on this cluster  is assumed to be more elastic than
demand for crops in this cluster.  The elasticity  estimate  is adjusted from -0.44  to -1.0.
f.     Herbicides  on soybeans,  cotton,  peanuts, and alfalfa
      As discussed  earlier  in this report,  assuming  that soybeans  and alfalfa  are fed to animals, the price
elasticity   of demand  for  the  crops in  this  cluster,   excluding   cotton,  is -0.67.    Since the  quantity  of
herbicides  applied  to cotton  is  small  In comparison  to the quantity  of herbicides  applied  to soybeans,
peanuts,  and  alfalfa,  the  exclusion  of  cotton  should not  substantially   affect   the elasticity   estimate2.
       2According to Pimentel et al. (1991), 8.2 million kgs. per year of herbicides are applied to cotton and 60.6
   million kgs. per year of herbicides are applied to soybeans, peanuts, and alfalfa combined.

                                                    C.4S

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Supporting the elasticity  estimate of -0.67,  U.S. EPA (1974) found  the demand for herbicides  on soybeans
to be inelastic.

      Three additional  factors present information  on the expected price elasticity  of demand for this cluster
of herbicides:   the feasibility  of substitution,   the  fraction  of contribution   to production  costs,  and the
marginal  productivity   of the herbicides.   The feasibility  of substitution  for this cluster of herbicides  is low,
influencing   the  demand  for  the  herbicides   to  be  inelastic.   However,   herbicides   (including   custom
application)  are estimated  to contribute  33 percent of the total cost of production  for this cluster.  This high
contribution  to variable  cost is likely to drive greater elasticity  of demand.  Also,  the marginal  productivity
of herbicides  in this cluster is estimated  as $2.68.   This return  on herbicide  use is fairly  low,  suggesting
somewhat elastic demand.

      Given  the  opposing  factors that influence  demand  for herbicides  in this  cluster,  it was judged  that
the estimated elasticity  of demand  for  the crops,  -0.67,    serves  well as an  estimate  of the elasticity   of
demand for the cluster of herbicides.

g.    Herbicides  on corn
      The estimate of  elasticity  of demand  for corn  herbicides  is -0.69.  This value is based on the average
elasticity   of meats as listed  in  U.S.DA.  (1985), since  the com  is  assumed  to be  used  as  animal feed.
Pesticides  in this cluster contributed  a relatively  high percentage  to total  variable  costs (19% including
custom application)  and the marginal productivity   of these pesticides  is low, at $1.11.  Both of this factors
indicate  elastic  demand.   However, the  feasibility  of substitution  for these  pesticides  is low,  indicating
inelastic  demand.  Therefore,  it was judged  that no additional  adjustment  to  the elasticity   estimate  was
warranted.

h.    Herbicides  on oranges
      The estimate  of the  elasticity  of demand for  herbicides  on oranges is -1.00, taken from U.S.DA.'s
estimate  of the elasticity  of demand for oranges. Although  the feasibility   of substitution for herbicides   on
oranges is high  (indicating   elastic demand), the marginal  productivity   of the  herbicides  is also fairly high
(indicating  inelastic  demand).  Therefore,  no adjustment  to the U.S.DA.  estimate of elasticity  of demand
for oranges is made.

i.    Herbicides  on tree fruits  ("except oranges"), nuts, and sugarcane
      The  elasticity  of  demand  for this cluster, based  on  the elasticity   of  demand  for retail  food,   is
estimated  as -0.20.   Pesticides  in this cluster have a high feasibility  of substitution  with  non-chemical  pest
                                                    C.50

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control methods, indicating  elastic  demand.   However,  the marginal productivity   of these pesticides  is also
moderately  high,   at $19.19,  indicating   inelastic  demand.   Therefore,  no  adjustments  are made  to  the
elasticity  estimate  for  retail food.

j.     Herbicides on grapes
      The price elasticity  of demand for herbicides  on grapes is estimated based on the elasticity  of demand
for grapes  at the retail level.  The estimated  elasticity  is -1.38.   Since the marginal  productivity   on  grapes
is extremely high,  the  elasticity  of  demand may be less than  -1.38.   However,  the marginal  productivity  is
the only  factor indicating   inelastic demand;   the feasibility  of substitution   for  herbicides  on grapes is
moderate.  Further,  the degree  of adjustment  to the elasticity  estimate  warranted  by the high  marginal
productivity  is unclear.  For these two reasons, this analysis  relies on the elasticity estimate for retail grapes.
However,  it should be noted that this value  may overstate elasticity,  and therefore  overstate the impact  of
the effluent  guidelines   on pesticide  manufacturers.

k.    Herbicides on vegetables
      The weighted-average    estimate  of demand  for vegetables  at  the retail level  is -0.27.   Since  the
feasibility  of substitution  is moderate and the marginal  productivity   is moderately  high for this  cluster,  the
elasticity  estimate  for food is used to represent  the elasticity  of demand for herbicides  used on these  foods.

L     Herbicides used  on tobacco
      U.S.DA.  did not estimate  the elasticity  of demand  for tobacco at the retail  level.   However,   the
addictive  nature of cigarette smoking probably  results in inelastic demand for tobacco.  It seems reasonable
to assume demand  for tobacco is as inelastic  as the least elastic demand for retail food, since people seldom
develop addictions  to specific  foods. Since U.S.DA.  found  that the elasticity  of demand for numerous food
commodities  was lower hi absolute  value than -0.20,  the elasticity  of  demand for  tobacco is estimated  as -
0.20.

      Since the feasibility  of substituting  a non-chemical  alternative  for herbicides  on tobacco  is high,
demand   for the herbicides  used on tobacco may be more elastic  than demand  for the tobacco  itself.
However,  the  costs of applying  herbicides  comprise only  3 percent  of the total variable costs of production.
Further,   the estimate of the marginal productivity  of herbicides   used  on tobacco is extremely high.  These
two factors indicate that  demand for herbicides  used on tobacco will be inelastic.   Given  these  opposing
factors, this analysis assumes  that the elasticity  of demand for herbicides  used on  tobacco  will match  the
elasticity  of demand for tobacco.   The  elasticity  estimate for this cluster  is therefore  -0.20.
                                                     C.51

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m.    Herbicides  on sugar  beets, beans,  and peas
      The estimate of the  elasticity  of demand  for  this  cluster is calculated  from  a weighted  average  of
U.S.DA.'s  (1985)  estimate  of demand  for  food at the  retail  level.   The value is -0.12.   No adjustments  are
made since the indications  regarding elasticity of  demand for the herbicides  conflict.   The  substitutability
for herbicides  on sugar  beets,  beans,  and peas is low,  indicating  relatively  inelastic demand,  while  the
marginal  productivity   of the herbicides  is low, indicating relatively  elastic demand.

n.    Insecticides  on vegetables
      The elasticity  for  this  cluster is  estimated  as  -0.33,   based on a weighted-average   of the  values
estimated  by U.S.DA.  (1985)  as the elasticities  of demand for vegetables.   No adjustments   are made to the
elasticity  estimate for vegetables.   The marginal productivity   of insecticides  in this cluster  is moderate, at
$7.92.  Although  the substitutability  for insecticides  on vegetables  is low, there is no quantitative   measure
of the extent  to which  the estimate  should be altered.  Further,  this is the-only factor  indicating that demand
is relatively  inelastic.   Therefore,  the  elasticity estimate  of  -0.33  is used in this analysis.

o.    Insecticides  on fruits  and nuts except  oranges
      The estimate of elasticity   of demand for the food commodities   in this cluster,  based on U.S.E)A.'s
(1985) estimates  of elasticity  of demand for food at the retail level,  is -0.21.   This value  differs  notably
from  the elasticity estimate  of Pingali and Carlson  (1985)  for insecticides  applied to apple  orchards.  Pingali
and Carlson estimated the elasticity of demand as  -1.39.   Since the authors  considered  only  apple orchards,
the estimates are not perfectly  comparable.   However,   since  apples receive  over 50 percent  of insecticides
applied to crops in this duster,  the differences between  the two  estimates is notable.

      The marginal  productivity  of these insecticides   is moderate and  does not suggest  that an adjustment
to the elasticity  estimate  for retail  food is required.  However,  the feasibility  of non-chemical  substitution
for these insecticides  is high, indicating  elastic demand.  To account for the high  feasibility  of substitution
and the elasticity estimate  of Pingali and Carlson,  the  elasticity  estimate  for  this cluster is adjusted from -
0.21 to -1.00.

p.    Insecticides  on oranges
      The U.S.DA.  estimate of the elasticity  of demand  for oranges at the retail level was  -1.00.  This value
is also  used  to represent  the elasticity  of demand  for insecticides   applied   to oranges.   Although  the
feasibility  of substitution  of insecticides  used on oranges is high  (indicating  relatively  elastic demand),  the
marginal   productivity    of  the  insecticides   is also  fairly  high  (indicating   relatively  inelastic   demand).
Therefore,  no adjustments   are made.
                                                    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.DA.  elasticity  estimate  for grapes.

 x.    Insecticides  on corn and alfalfa
      Since  a large  proportion  of production  of  each  of these  crops serves  mainly   as animal  feed,  an
 elasticity  estimate for the crops was developed based on the retail demand for  meat.  As discussed  above,
 the elasticity  for corn and alfalfa  is estimated  to be -0.69.   This elasticity  estimate is also used to represent
 the elasticity  of demand for insecticides   applied to these  crops.

      Three literature  values  describe  the elasticity  of demand  for  crops in this cluster.   U.S. EPA (1974)
 found the demand for corn insecticides to be inelastic.  Miranowski's  (1980) statistically   significant  estimate
 of the elasticity  of demand  for corn  insecticides  was -0.78.  Finally,  Huh (1978) estimated  the elasticity  of
 demand for corn insecticides  and herbicides as -1.46.  Since these literature estimates  conflict,  they do not
 indicate that an adjustment  to the elasticity estimate is needed.

      The feasibility   of substitution  on these crops is low, indicating  that demand is relatively  inelastic.  The
 low contribution  of insecticides  to the costs of production  of these crops  also indicates  that demand  for the
Insecticides   will  be relatively inelastic.   However,   the  marginal productivity   of insecticides  on  corn  and
 alfalfa  is fairly  low,  at $3.69.   Low productivity   is associated  with  elastic demand.   Given  the  opposing
factors,  no adjustment  is made to the estimate of the elasticity  of demand  for  corn and alfalfa.

s.    Insecticides  on  sorghum
      As was the case for corn and alfalfa,   the elasticity  of demand  for sorghum is calculated  based on the
 elasticity  of demand  for  meat,  since sorghum  is used  mainly  as a feed  crop.   The elasticity  estimate  for
 sorghum  is -0.69.   Although  the marginal  productivity   of  insecticides   on sorghum  is  low (indicating
 relatively  elastic demand)  and the  feasibility   of  substitution  is high  (also indicating  elastic demand),
 insecticides   contribute  only two  percent  of production  costs (indicating  inelastic  demand).  Given these
 opposing factors, no adjustment  to the sorghum  elasticity   estimate  is made.  The  elasticity  of insecticides
 used  on sorghum is estimated as -0.69.
                                                    C.53.

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t,     Insecticides  on soybeans,  peanuts,  wheat,  and tobacco
      The estimate  of  the elasticity  of demand  for soybeans,  peanuts,  and wheat is -0.56.   Although  an
estimate of the elasticity of demand for tobacco is not available,  this omission should not substantially  affect
the estimate since 80 percent of insecticides  used in this cluster are applied to soybeans,  peanuts,  or wheat.
The feasibility  of substitution,  fraction  of contribution   to production  costs, and marginal productivity  for
this  cluster of pesticides  do not suggest that an  adjustment  to the elasticity  of demand  for the food crops
is required.  The elasticity  estimate for  this pesticide  cluster  is therefore  -0.56.  This estimate is  consistent
•with  the finding  by U.S.  EPA (1974) that demand  for  soybeans is inelastic.
                                                                                                           l!
u.    Insecticides  on cotton
      No estimate of the elasticity  of demand for cotton was given  by U.S.DA.   However,  Burrows  (1983)
empirically  estimated  this elasticity.  Using a single equation  model,  Burrows  estimated  the elasticity  of
demand for cotton to be -0.9; with  a simultaneous  equation model,  Burrows  estimated  the elasticity as -1.23.
The average  of these two estimates is -1.06.

      Since the marginal  productivity  of  insecticides  on cotton is  extremely low,  at $0.72,  the demand for
.the insecticides  is  expected  to be elastic.   Further, the insecticides  contribute   a fairly  high  fraction,  13
percent, of the variable cash  costs of producing  cotton.  The feasibility   of substitution  for these insecticides
is moderate.  Since these  factors  are consistent  with the elasticity  estimate from Burrows,  the elasticity  of
demand for cotton insecticides  is  estimated  to be -1.06.

V.    Fungicides  on grain storage
      In the absence  of more specific  information,   the  elasticity of demand  for  fungicides  on gram  storage
is assumed to equal the elasticity of demand for grams.   Elasticity estimates are available  from Huang (1985)
for wheat  and rice.  Other stored  grams  may be fed to animals.   As  discussed  above,  an  estimate  for the
elasticity   of grains  fed to animals  was developed  as part of this analysis.   However,  since information  was
not located on the quantity of fungicides   applied  to each gram  and each end-use,  correct weighting  factors
for the different  elasticity estimates could not be developed  to estimate an average  elasticity  for all grains
treated with fungicides  in storage.   The elasticity  for this cluster is therefore estimated as a straight average
of the elasticity  of wheat  flour (-0.11), rice (-0.15),  and animal feed grams (-0.69). The resulting  elasticity
estimate for  fungicides  used  on grain in  storage  is -0.31.
W.    Fungicides  used for seed treatment
      Since  no specific information   on the elasticity of fungicides   used for seed treatment  was located,  the
elasticity  of demand for  fungicides  hi this cluster is calculated  based on the demand elasticity  for the crops

                                                    C.54'

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 constituting   the majority  of seed plantings,  and for which  an elasticity  estimate  was available.  These crops
 include  corn  (elasticity  estimated  as  -0.69),   wheat  (-0.11),  dried  beans, peas,  and nuts (-0.12),  and rice
 (-0.15).   Since  no information  was located on the quantity  of fungicides  applied  to  seeds  of each crop, a
 straight  average of the elasticities  was used to estimate  the demand  elasticity  for this cluster.  The resulting
 estimate  for this  cluster is -0.27.

.x.    Fungicides  - post-harvest
      The elasticity  of demand  for fungicides   applied  post-harvest   is  based on a weighted   average  of the
 elasticities  of demand for the crops to which  fungicides  are applied in the field.  These  crops are assumed
 to be vegetables,  fruit  and nut  trees,  and grapes,  as these were the crops included  in the four fungicide
 clusters  for which  the elasticity  of fungicides   used in field applications  was calculated.   Fungicides  are
 assumed  to  be applied to the crops after harvest in the same ratios  as they were  applied  to the crops  in the
 field.   These ratios are used to weight  the  demand  elasticities   for the individual  crops.   The resulting
 elasticity  estimate is -0.65.

      A  complete  list  of  Abt  Associates'  estimated  price  elasticities  of  demand  for clusters  defining
 agricultural   end-uses  is provided  in Table 2.10.
                                                      C.55

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                                               Table 2.10
                 Estimates of Elasticity of Demand for Clusters in the Agricultural Sector
                 fruit and nut trees except oranges
                 seed treatment
                 grain storage
                 vegetables
                 post-harvest
                 oranges
                 grapes
                                                                            Elasticity Estimate
-0.23
-0.27
-0.31
-0.38
-0.65
-1.00
-1.38
                Herbicides on:

                 sugar beets, beans, and peas
                 tobacco
                 tree fruits (except oranges, nuts, sugarcane)
                 vegetables
                 soybeans, cotton, peanuts, and alfalfa
                 corn
                 sorghum, rice, and small grains
                 oranges
                 grapes
-0.12
-0.20
-0.20
-0.27
-0.67
-0.69
-1.00
-1.00
-1.38
                Insecticides on:

                 vegetables
                 soybeans, peanuts, wheat, and tobacco
                 corn and alfalfa
                 sorghum
                 fruit and nut trees except oranges
                 oranges
                 cotton
                 grapes
-0.33
-0.56
-0.69
-0.69
-1.00
-1.00
-1.06
-1.38
Source: Abt Associates estimates based on Pimentel et al. (1991), USDA (1985), USDA (1989a), USDA
(1989b), USDA (1989c), Burrows (1983), Pingali and Carlson (1985), Miranowski (1980), Huh( 19878), U.S.
EPA (1974)
                                              C.56

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3.0    PRICE  ELASTICITY  OF DEMAND  FOR PESTICIDES  USED NON-AGRICULTURALLY

       Most of the pesticides  included  in this analysis  are used in the  agricultural  sector;  pesticides in non-
agricultural  clusters,  as defined  by OPP, constitute  less than 30 percent of total pesticide use by weight (U.S.
EPA,  1988).  However,  the non-agricultural    pesticides are described  by eighteen separate  clusters.  Unlike
in the agricultural  sector,  these clusters represent  eighteen distinct  and generally unrelated  end-uses, each
with  its own customers,  competitors,  and costs.  The literature search  described above  yielded  no studies
of the price  elasticity   of  demand  for pesticides  in the non-agricultural   sector.   Since the  scope of this
project does not  allow  for  the gathering and examination  of primary  data on elasticities  of demand for each
of these eighteen  markets  and since non-agricultural   pesticide  use represents  a relatively  small  percent  of
total pesticide  use, the demand elasticities  for the non-agricultural   sector are developed  based  on a reasoned
consideration  of two  factors.   Consistent  with  the analysis  of agricultural  pesticide  use, these factors are:
(1) the availability  of substitutes  for a cluster of pesticides,  and (2)  the contribution   of pesticides to the total
production  cost of the end-user.

      Based on the above  two factors,  the eighteen  non-agricultural   clusters  fit  into  two  categories:   (1)
pesticides  that contribute  a small  percentage  to total  cost  but have substitutes,   and (2) pesticides  that
contribute  a small  percentage of total production  costs and for which  there  are limited  substitutes.   There
were  no cases  in  which  it appeared  that pesticides  contributed  a substantial percentage  of  total production
costs.  The  two categories  and the clusters described  by them are listed below,  along with a brief  discussion
of the reasoning  behind  the cluster  categorization.

(1) Pesticides  contribute  a small  percentage  of total cost but substitutes  are available
     The two  non-agricultural   herbicide  clusters  are included  in this category:  (a) herbicides  on ditches,
rights  of way,  forestry,  and ponds,  and (b) herbicides  on  turf. The  available  substitute is labor, a viable
alternative  to  chemical  weed control.  To determine  the shift  to manual/mechanical   weed control given an
increase  in pesticides   price,  one  would   need  to know:   the cost  of herbicide  per  unit  of  area,  the
effectiveness   of herbicides, the labor cost of applying  herbicides per  unit  of area,  the labor cost  of manual
weed  control  per unit  of  area, and the effectiveness   of manual   weed  control.  Since  these  two clusters
together constitute  less than one percent of the pesticides  of interest (by weight) it was decided  not to invest
resources in the gathering  of these  data.
      Rather,  Abt Associates  considered  the cost structure of the end-users  of pesticides  in these  clusters.
Herbicides  used on ditches,  rights  of way,  forestry,  and ponds would generally be used by major industries
such  as railroads  and utilities   and by government  agencies,  such as state highway  departments.   The cost

                                                     C.57

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of herbicides would be an insignificant   percentage of their total production  costs.  Demand for this cluster
of herbicides  is  therefore  likely  to be inelastic.   While  herbicides  used on turf may  contribute  a greater
percentage   to  the total production  costs  (assuming  that these pesticides are  used,  for example,   on golf
courses  and turf  farms) the costs should  still  be relatively  small.  In addition,  fungicides  are applied  in
conjunction  with herbicides  to turf.  It is therefore  likely  that an application  system  would be  in place for
fungicides,   making the incremental  costs of herbicide  application   small.

      Based on the above  discussion,   this analysis  assumes  that  demand  for  the  two non-agricultural
herbicides  dusters is inelastic.   Although  the level of detail  of the  available  information  does not  result in
a quantitative   measure of the  elasticity,   such  a measure  is required.   Since  only one of the  two factors
considered   above  indicates  inelasticity  (percent  of production   costs),  while  the  other is inconclusive
(substitute  availability),  this analysis assumes that demand for these  two clusters  is only moderately inelastic,
and assigns a price elasticity of -0.66.   The sensitivity  analysis  will consider the  impacts  on active ingredient
manufacturers   if demand  for pesticides in these clusters  is perfectly   elastic.

(2) Pesticides contribute a small percentage of total  production  costs,  and there are limited  substitutes
      The remaining  sixteen non-agricultural    clusters  are grouped in  this category.   For each  duster,  the
cost of pesticides  appeared incidental  to the total cost of production  and no readily available, cost-effective
alternatives  to  the pesticides  were  known.   These two factors suggest  inelastic demand.  Further, only three
of the sixteen   dusters  in this category  constitute  more  than one percent (by weight)  of the pesticides  of
interest  in  this analysis.   Therefore,  little  additional  information   on the ultimate costs to manufacturers
would result from an investigation  of the  remaining thirteen  clusters.   The three  clusters  which included
at least one percent by weight  of the total pesticides  of concern  are listed below with a brief  discussion  of
their  categorization:

Insecticide  fumigants  and nematicides
      According  to Encydopedia  Britannica,  "Fumigation,  which requires some technical  skills  and certain
precautions  in  application,  is mostly feasible  for non-selective  quick  killing  of vermin  in large  commercial
operations.   For  the control  of household  pests it has been,  to a considerable   extent,  supplanted  by more
convenient  methods of extermination  such as the application  of powders  and residual  sprays".   Fumigants
are largely  used for killing insect pests of stored products,  for fumigating  nursery  stodc, or for fumigating
sod,  principally  for the control  of plant parasitic nematodes.   Given the application   in large  commercial
operations,  the contribution of  fumigants  and nematicides  to production  cost is likely  to be small.  Further,
since  the use of these  products  has become somewhat specialized,   it  is probable  that few substitutes  exist.
                                                     C.58-

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 Insecticides   for  termite  control
       Domestic and commercial  use of chemical  termite  controls  seems unlikely to contribute substantially
 to total consumer  or commercial  business  expenses.   Also,  while in the long-run,   wood could be replaced
 to some extent  as a building  material,   in the short-run  alternative  protection   from  and  eradication  of
 termites  is not readily available.   Further,  the cost of termite control can be viewed as insurance  against the
 much larger cost of destruction  of a building,   making  the cost of control appear  small.  For  the reasonably
 foreseeable  future, the demand  for chemical  termite  control is  likely  to be inelastic.

 Wood preservatives  - industrial,   commercial,   marine use
       The  wood  preservative  industry  developed  because   of  the need  for prolonging   the life  of  wood
 structures,   particularly   where  the structures   come  in contact   with ground.   Examples  of treated  wood
 include  railroad   ties,  telephone   poles,  and marine pilings.   Wood may be  chemically   treated  to protect
 against  fungicides,  insects,  and fire.  According  to U.S. EPA (1982),  expenditures  on wood preservative
 account  for "only  a small part" of  the annual  billion  dollar  preserved  wood  market.     Cost-effective
 alternatives  to chemical wood preservation  are not known. Demand for pesticides  in this cluster is therefore
 assumed to be inelastic.

       The remaining  clusters grouped in this  category are:

 •      Insect  repellents at non-agricultural   sites
 •      Domestic bug control  and food  processing  plants
 •      Mosquito larvacides
 •      Fungicides   on turf
 •      Industrial  preservatives   -  plastics,  paints,  adhesives,  textiles,  paper
 •      Synergist  - used as insecticide  synergists,  surfactants,   cheleating poultry  and livestock
 •      Plant  regulators, defoliants,  desiccants  - for all uses
*      Sanitizers  -  dairies,  food processing,   restaurants,   air  treatment
 •      Insecticides  on livestock and domestic animals
 •      Fungicides   - ornamentals
 •      Industrial  microbiocides.  cutting  oils, and oil well  additives
 •      Preservatives,  disinfectants,   and slimicides
 •      Slimicides  - pulp and paper, cooling  towers, sugar mills
 •      Fungicides   - ornamentals
 •      Industrial  microbiocides,  cutting  oils, and oil well  additives
 •      Preservatives,  disinfectants,   and slimicides
                                                      C.59

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      Ideally,  a quantitative  measure of the price elasticity  of demand  could be developed  for each of the
pesticides  clusters  listed above.  However,  the available data does not permit this  precision.   Since clusters
in  this  category  have  no  known  cost-effective    substitutes   and  since  the  pesticides  are  generally  an
insignificant   portion of total production  costs, demand  is expected to be moderately to highly  inelastic.  The
clusters  in this  category are assigned  a price elasticity  of demand  of  -0.33.   The sensitivity   analysis will
examine  the impact on manufacturers   in the demand is perfectly elastic.
                                                                         1 '"  '"                          ' ; ,  \
      Finally,  two  clusters  remain without  demand elasticity  estimates:  herbicides  for broad  spectrum use
and fungicides  for broad spectrum  use. The cluster "herbicides   for broad  spectrum  use" contains  only one
active ingredient,  2,4-D.  The price elasticity  of demand  for 2,4-D  was estimated by Lacewell  and Masch
(1972) and  by Carlson  (1977a,b).   Lacewell  and Masch  estimated  the elasticity  as approximately  -0.38.
Carlson  estimated  a short-run  elasticity of -0.19  and a long-run  elasticity  of -0.59.   Averaging   Carlson's
long-run  estimate and the estimate  of Lacewell  and Masch  results in an estimate  of elasticity  of demand for
2,4-D  of -0.48.   We use this value  as the price elasticity  of demand for broad spectrum  herbicides.

      The elasticity  estimate for broad spectrum  fungicides is calculated  simply by a weighted  average  of
the elasticity estimates  for all  of the other  fungicide  clusters.   The  weighting  is based on the quantity (by
weight)  of active  ingredient  applied  for the end-uses  described by each  cluster.   The resulting  elasticity
estimate is  -0.40.   This value  is in  good agreement with the elasticity  of demand  for  fungicides  estimated
by  U.S.  EPA (1985) as  -035.
                                                    C.60

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 4.0    CONCLUSIONS

       The estimated  elasticities for all 44 clusters  are listed in Table 4.0,  in order of increasing  elasticity  of
 demand.  As can be seen from  the table,  the elasticity  estimates  range from  -0.12  (herbicides  on sugar beets,
 beans,  and  peas) to -1.38  (fungicides   on grapes,  herbicides  on grapes,  and  insecticides   on grapes).   The
 elasticity  estimates  vary substantially  within the fungicide,  herbicide,   and insecticide  clusters;   the  type  of
 pesticide  is not predicted  to have a strong  influence  on the elasticity  of demand.

      The demand for pesticides  in all of the clusters except  four is expected to have  unit  elasticity  or to
 be inelastic.   Demand is expected to be  inelastic  for  the  three clusters of  pesticides applied  to grapes and
 for insecticides  applied to cotton.  The  main factor driving  the high elasticity for the grape  clusters is the
 high  elasticity  of demand  for  grapes at the retail  level.   Demand for  insecticides  on cotton is  expected  to
 be somewhat elastic  based on  literature  estimates  of the elasticity  and on the  low marginal  productivity   of
insecticides   applied  to cotton.

      As  should  be clear from sections   2 and 3,  the methodology  employed   to estimate  the elasticity   of
demand for the clusters  yields  reasonable best estimates  of elasticities  rather  than  certain quantifications.
The  estimates  are likely  to accurately  depict  whether demand  for a certain cluster  of pesticides  is extremely
or -only moderately  elastic or  inelastic;    the specific  numeric  value should not  be viewed  as definitive.
However, no estimates  of elasticity  of  demand  for clusters of pesticides  that are more reliable  than those
developed  through this  analysis are known.   To address  the uncertainty  implicit  in the estimates, a scenario
in which  the manufacturers  bear the total  costs of regulatory  compliance  will also be examined.
                                                      C.6.1

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                                                  Table 4.0
                      SUMMARY  OF ESTIMATES  OF  ELASTICITY   OF DEMAND
                         on sugar beets, beans, peas
                         on tree fruits  (except oranges), sugar  cane,  nuts
                         on tobacco
                         on fruit  and nuts trees  (except oranges)
                         for  seed treatment
                         on vegetables
                         on grain in storage
                         on vegetables
Cluster

Herbicides
Herbicides
Herbicides
Fungicides
Fungicides
Herbicides
Fungicides
Insecticides
Slimicides
Fumigants  and nematicides
Insecticides  on termites
Wood preservatives
Insect repellents  at non-agricultural   sites
Domestic bug control and food processing  plants
Mosquito larvacides
Fungicides  on turf
Industrial  preservatives
Insecticide  synergists  and surfactants
Plant regulators,  defoliants,   desiccants
Sanitizers  - dairies, food  processing,  restaurants,  air treatment
Insecticides  on livestock  and domestic  animals
Industrial  microbiocides,   cutting  oils, oil well addivites
Preservatives,  disinfectants,   and  slimicides
Fungicides  - ornamentals
Fungicides  on vegetables
Fungicides  - broad spectrum
Herbicides  - broad spectrum
Insecticides  on soybeans,  peanuts, wheat,  tobacco
Fungicides  - post harvest
Herbicides  on rights of way, drainage  ditches
Herbicides  on turf
Herbicides  on soybeans,  cotton, peanuts, alfalfa
Herbicides  on corn
Insecticides  on corn, alfalfa
Insecticides  on sorghum
Herbicides  on sorghum rice, small  grains
Herbicides  on oranges
Fungicides  on oranges
Insecticides  on fruit  and  nut trees,  except  oranges and grapes.
Insecticides  on oranges
Insecticides  on cotton
Fungicides  on grapes
Insecticides  on grapes
Herbicides  on grapes
                                                                          Elasticity  Estimate
-0.12
-0.20
-0.20
-0.23
-0.27
-0.27
-0.31
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.33
-0.38
-0.40
-0.48
-0.56
-0.65
-0.66
-0.66
-0.67
-0.69
-0.69
-0.69
-1.00
-1.00
-1.00
-1.00
-1.00
-1.06
-1.38
-1.38
-1.38
Source: Abt Associates  estimates
                                                    C.62

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                                            References

Burrows,  T.  (1983).   Pesticide  Demand  and Integrated  Pest Management:  A Limited  Dependent
Variable Analysis,   American  Journal  of Agricultural  Economics,  November.

Campbell,  H.  (1976).   Estimating  the  Marginal  Productivity   of Agricultural  Pesticides:  The Case of
Tree-Fruit   Farms in the Okanagan Valley.  Canadian Journal of Agricultural  Economics 24(2), 1976.

Carlson, G.  (1977). The Long Run Productivity   of Insecticides,   American  Journal of Agricultural
Economics,  59,  pp.  543-548,  August.

Carlson, G. (1977a). Economic Incentives for Pesticide  Pollution Control hi The Practical Application
of Economic Incentives to the  Control  of Pollution:   Tlie Case of British  Columbia,  ed. J. Stephenson.
Vancouver:   University  of British Columbia  Press.

Hall, D.C., and LJ.  Moffitt.   (1983).   Stochastically   Efficient  Economic  Thresholds  for Discrete
Choices.:  USDA-ERS   unpublished  manuscript.   Washington D.C.

Headley, J.C. (1968).   Estimating  the  Productivity   of Agricultural   Pesticides,  American  Journal  of
AGricultural  Economics,  50:13-23,  February.

Huh, Shing Haeng  (1978).  The Preventive   and Incidental   Demand for Pesticides:  An  Economic
Analysis  of the Demand  for Herbicides   and  Insecticides   Used  by  Selected  Corn  Producers  hi
Minnesota.  Thesis  submitted  to the Graduate School of the University   of Minnesota.   June.

Lacewell,  R. and W. Masch, (1972).  Economic Incentives  to Reduce the Quantity  of Chemicals Used
in Commercian  Agriculture.  Southern  Journal of Agricultural  Economics.  July.

Lichtenberg,  E. and D. Zilberman (1986).  The Econometrics of Damage Control:  Why Specification
Matters. American  Journal  of Agricultural   Economics.  May.

Miranowski,  J.  (1980).  Estimating  the  Relationship  between  Pest Management and Energy  Prices, and
the Implications  for Environmental  Damage. American Journal of Agricultural  Economics.  December.

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

Pingali, P. and  G. Carlson (1985).  Human Capital,  Adjustments  hi Subjective  Probabilities,  and the
Demand for Pest Controls.  American  Journal of Agricultural  Economics.  November.

U.S.DA: (1985).  U.S. Demand for Food: A Complete  System of Price  and Income  Effects.  By Kuo
S. Huang,  National  Economics Division,  Economic  Research  Service.   Technical  Bulletin  No. 1714.

U.S.DA. (1988).  1985 Potato Cost and Returns:  Fall Production Areas. Potato facts special edition.
Economic  Research Service.   September.

U.S.DA. (1989).  Retail to Farm Linkage for a Complete Demand  System of Food Commodities.  By
Michael K. Wohlgenant  and  Richard  C.  Haidacher.  Commodity   Economics Division,   Economic
Research  Service.   Technical  Bulletin  No. 1775.

U.S.DA. (1989a).  Economic Indicators of the Farm Sector:  Costs of Production,  1987.  ERS, USDA,
ECIFS7-3.   February.

U.S.DA! (1989b).  Tobacco:  Situation  and Outlook  Report.  Economic Research Service.  September.
                                                   ;.63

-------
U.SJDA.  (1989c).  Agricultural  Statistics  1989.  Washington.

U.S. EPA (1974).   Farmers'  Pesticide  Use Decisions  and Attitudes  on Alternate  Crop  Protection
Methods.   Washington.

U.S. EPA (1982).  Regulatory  Impact Analysis  Data Requirements  for  Registering  Pesticides  under
the Federal Insecticides,  Fungicides  and Rodenticide  Act.  Office of Pesticide  Programs.  August.

U.S. EPA (1985). Economic  Impact Analysis of Effluent  Limitations  Guidelines  and Standards  for
the Pesticide  Chemicals Industry.  September.

U.S. EPA (1988).  Pesticide  Industry Sales and Usage:  1988  Market Estimates.  Office  of Pesticide
and Toxic Substances.   February.
                                                 C.64

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            Appendix D:  SENSITIVITY ANALYSIS OF COST PASS-THROUGH ABILITY

        This appendix describes a sensitivity analysis of the percentage of compliance costs that, a manufacturer is
able to pass through to consumers. The model, as described in Chapter 4, assumes that producers can pass on a.
portion of compliance costs to customers in the form of price increases, to the extent allowed by producer price
competition and customer demand behavior.  To test the sensitivity of the closure analysis results to this assumption,
the worst-case assumption is made that facilities would bear the full costs of compliance (i.e. they could not pass
on any of the compliance costs to customers as price increases). This corresponds to an assumption that all clusters
have completely elastic demand elasticities, or that the percentage of total production subject to compliance costs
is close to zero.

        The results of this sensitivity analysis are presented below by discharge method.  The predicted impacts
under a zero cost through assumption match the impacts predicted in the main text under an assumption of partial
cost pass through (compare Table D.I with Table 4.3).

        Impacts of BAT regulations on direct dischargers
                Organic Pesticide Manufacturing - (Subcategory A)
        Under the no cost pass-through assumption, no facilities are projected to close due to compliance with BAT.
Two facilities are expected to close a product line as a result of the regulation (see Table D.I).

                MetaUo-Organic Pesticide Manufacturing - (Subcategory B)
        Direct discharge of Subcategory B chemicals in process wastewater is limited to zero under BPT.  No
additional options were considered and no new  limitations are promulgated for the  metallo-organic  pesticide
chemicals manufacturing subcategory. There are therefore no associated costs or economic impacts, and sensitivity
analysis need not be examined.

         Impacts of PSES regulations on indirect dischargers
                Organic Pesticide Manufacturing - (Subcategory A)
         No facilities are expected to close entirely  or to close a product line under  the no cost pass-through
 assumption due to compliance with PSES.
                                                   D.I

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                Metallo-Organic Pesticide Manufacturing - (Subcategory B)
        Because no new limitations are promulgated for the metallo-organic pesticide chemicals manufacturing
subcategory, no facility or product closures would be projected under the no cost pass-through assumption due to
compliance with PSES.
Table D.I
Impacts of the Final Regulation on Facilities Under No Cost Pass-Through Assumption

Number of facilities with costs
Facility Closures
Product line Closures
* Zero discharging facilities are
Direct
Dischargers*
33
0
2
included with direct discharging facilities.
Indirect
Dischargers
25
0
0

                                                   D.2

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           Appendix E:  COMPLIANCE COSTS AS A PERCENT OF FACILITY REVENUE

        As an additional evaluation of the economic impacts associated with the final BAT and PSES regulations,
the EPA compared annualized compliance costs with facility revenue for all facilities projected to incur costs under
the final rule.  This comparison is a common gauge of achievability, with annualized costs in excess of five percent
of facility  revenues  typically indicating  a significant impact.   Costs as a percentage of revenue represents an
approximation of the percentage  price increase that would result from 100 percent cost pass through, i.e., the
percentage increase  in price needed to  cover all treatment costs.  The results are shown below, by discharge
category.  Note that the facilities  projected, to be baseline closures are included in this analysis.

        Impacts of BAT Regulation on Direct Dischargers
        For the 28  direct discharging facilities that are projected to incur costs under the final rule, the mean
compliance cost as a percentage  of facility revenue was 0.4 percent, the median was less than one-tenth of one
percent, and the highest value was 4.6 percent.  For the five zero discharge facilities with monitoring costs, the
mean compliance cost  as  a percentage of facility revenue was 0.2 percent, the median was 0.1 percent, and the
maximum was 3.9 percent. This comparison supports the conclusion that the regulation is economically achievable.

        Impacts of PSES Regulation on Indirect Dischargers
        For the 23  indirect discharging facilities that are projected to incur costs under the final rule, the mean
compliance costs as  a percentage  of revenue was 0.7 percent, the median was 0.2 percent, and the maximum was
5.7 percent.  The ratio of compliance costs to facility revenue was greater than five percent for only one facility.
The comparison therefore supports the conclusion that the regulation is economically achievable.

        The above results include facilities projected to close in the baseline, and indicate that were these facilities
to remain open and incur compliance costs, the rule would still be economically achievable.  The cost to revenue
ratio for these facilities projected to close in the baseline is highlighted below.

        Of the 14 facilities projected to close in the baseline that are not known to have actually closed to date, only
eleven would be projected to incur costs to comply with the final rule.  Eight of the eleven facilities counted as
baseline closures and projected  to incur compliance costs would be expected to incur only monitoring costs.
Therefore, only three facilities that are counted as baseline closures would incur significant costs if they remained
open.   Monitoring costs are generally relatively low and would not, of themselves, be likely to significantly impact
a facility.  For the eleven facilities that would be expected  to incur costs, the highest annualized compliance costs
as a percentage of total facility revenue is estimated to be 0.2%.  The mean compliance cost percentage of revenue
                                                    E.1

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is estimated to be about one-tenth of one percent.  Therefore, even if the  14 facilities projected to close in the
baseline remained open, they would not be expected to be significantly impacted by the rule.
                                                     E.2

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             Appendix F:  EXAMPLE OF HYPOTHETICAL FACILITY CALCULATIONS

        This appendix provides a detailed example of the facility-level analysis (described in Chapter 4 of the HA)
on a hypothetical facility. The example is intended to aid the reader in understanding the steps that EPA undertook
in the analysis.  As in the actual analysis, economic impacts are calculated in four steps: 1) the baseline analysis,
2) post-compliance facility closure analysis, 3) product closure analysis, and 4) other significant impacts analysis.

        The following tables duplicate the balance sheet and income statement as would have been provided in the
Census, with assumed values input for the hypothetical facility.
TABLE 2-C. BALANCE SHEET
ASSETS
Current Assets
[1] Inventories
[2] Other current assets
[3] Total current assets
Noncurrent assets
: [4] Total noncurrent assets;
[5] Total current and noncurrent assets
1985
($000)
29,750
33,000
62,750

86,850
149,600
1986
($000)
31,550
35,500
67,050

92,750
159,800
1987
($000)
32,000
37,100
69,100

101,800
170,900
LIABHITIES AND EQUITIES
Current liabilities
[6] Total current liabilities!
Noncurrent liabilities and equity
[7] Long term debt and other noncurrent
liabilities
[8] Owner equity
[9] Total noncurrent liabilities and equity
[10] Total liabilities and equity
37,100

46,250
66,250
112,500
149,600
38,900

48,750
72,150
120,900
159,800
41,250

54,000
75,650
129,650
170,900
                                                 F.I

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TABLE 2-D. INCOME STATEMENTS
REVENUES
[1] Sales of pesticide chemicals
[a] Pesticide chemicals listed in Table 1
[b] Other registered pesticide chemicals
[2] Revenue from pesticide contract work or tolling
[3] Other revenue
[4] Total facility revenues
1985
($000)
41,280
6,255
850
85,300
133,685
1986
($000)
43, ISO
8,070
1,000
86,475
139,325
1987
($000)
46,175
8,795
1,200
95,050
151,220
EXPENSES
Manufacturing costs
[5] Pesticide material and product costs
[6] Pesticide direct labor costs
[7] Cost of pesticide contract work
[8] Other pesticide costs
[9] Nonpesticide costs
16,190
2,030
950
2,815
57,210
15,900
2,220
1,150
4,450
55,490
16,700
2,225
1,100
3,540
57,075
Facility costs
[10] Depreciation
[11] Fixed overheads
[12] Research and development
[13] Interest
[14] Federal, state and local taxes
[15] Other expenses
[16] Total costs and expenses
8,750
11,180
6,065
1,810
4,670
12,195
123,865
7,580
12,675
6,540
1,795
6,460
9,990
124,250
8,080
13,355
6,700
1,995
7,845
11,500
130,115
F.2

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        In addition to the balance sheet and income statement of the hypothetical facility, the following inputs were
used in the analysis:


        •      The facility produces three PAIs, designated A, B and C.

        •      These three PAIs are assigned to three clusters, designated X, Y and Z, as follows:  60 percent
                of PAIA is in cluster Y, and 40 percent is in cluster Z, 100 percent of PAIB is in cluster Y, and
                100 percent of PAI C is hi cluster X.

        •      The facility does not report PAI-specific data in the Census, and price data for all three PAIs is
                available from a secondary source (had PAI-specific data been provided, it would have been used).
                Production and price data are:
                PAI
                A
                B
                C
Production, in pounds
      750,000
     1,000,000
     1,500,000
            Price. $/lb.
             2.00
             10.00
             20.00
                The facility has fixed overhead of $ 3,480,000 to comply with OCPSF, and $ 1,220,000 to
                comply with RCRA.
                The estimated compliance costs are:
                PAI
                A
                B
                C
Capital Costs. $
  80,000
 200,000
 400,000
Land Costs. $
   600
   700
  1,200
Operating Costs. $
  10,000
  60,000
  150,000
                For each cluster, the estimated price elasticities of demand and percent of total U.S. production
                expected to incur costs are:
                Cluster
                  X
                  Y
                  Z
Elasticity
  -0.60
  -0.80
  -0.30
Percentage of U.S. Production with Cost
                0.60
                0.75
                0.40
                The facility has a real weighted average cost of capital of 7.0 percent.

                The marginal tax rate is 34 percent.

                For determining other significant impacts, the lowest quartile value of the interest coverage ratio
                is 1.1.  The lowest quartile value for return on assets is 0.04.
                                                  F.3

-------
            O
            a,
 B
w
        S

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            eu
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31

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 I

I
     I
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;5

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                                                     »
                                                   «
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                                                     •
                                                     ."§
                                                                9
                                                               ~i
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-------
W
 e
-S
Analysi
y
ree
I
60
.a
!
*
,832,03
        1
         t?
         X

g
g
      f
         »
$19,251,202
over
                  .
                  2°

              *  3
              g  |
              60  •§
Calculate in-
variable cost

                                   fe£

                                   OO  ^
                                   co O

                                   rS  ^
                                   S N

                                   J S
                                   ci  to
                                a  * -3
                                S  O  O
                              2 «
                           s^l
                           X> *•» r^
                                  'I
                      a
                     13
                               X
                               I
                               1
fixed
Calculate unit
costs for each
cluster

                                           oo" £

                                           ^ S
                                           *'3' oo

                                           X d
                                           fc. <«•
                                           «  -2
                                        S  *»— .g
.2
O3
3
"o


I
                                                 ll

                                          i  :
                                    1  1
ts
culat
able
Cal
                                                         CO v™4
                                                         x!a
                                                         S N
                                                        5
                                                        '1 §
                                                        3 .•a

                                                                        1

                                                                        1


                                                                        1
                                                                   o
                                                                  'S
                                                                        t
                                                                         §
                                                                                            VO
                                                     a
                                                                  *,
                                                                   i-
                                                           1
                                                        •S
                                                    2
                                                           1
                                                            j
                                                                   12

                                                                   o
                                                                    o
                                                                    4>
                                                                
-------
st
is
+'$66.0
$101,46
nnual
zed c
For clu
S
                                                                               .   S S
                                                                               oo o. cu

                                                                                                •S
                                                                                                •e
                                                                                                CO
                                                                                                OO
                                                                                                o
                                                                                                g
                                                                                                X
                                                                                                •i
                                                                                                 opN

                                                                                                   i
                    H.
                                                                                                -
                                                                                                 °<
                                                                                                    &
                                                                                                 >;§
                                                                                                f -S
                          .S -a
                          co  Ou
 g
?S

                                                   (L,
                                                                                          f
                                                                                          * <
                                                                                          O

Calculate pr
value factor.
                          9 .
                         •3 -
                                •"
                          3 J  8

i
u'S
                       8
                    *•&
                    o, 8
Cal
co
inc

-------
u
g

1
 §•

$0.070
         s J
         •
Cost - $6.174 / » +

• $6244 /tt

st-compliance unit cost for


post-compliance unit cost
ForcluslerY,

Pott-CompUmKC Unit
pos

the
Similarly, the

$16.564, an

$1.454.
      1
      i
      Ik
o 9
p, O



ll
3 s
                  5?

                  ;
                     .S C)
                     co 3
                 *
| *





U •§
                           •ii
                           I
                             §1
•3 »

r
                                 i
                                                   0 <- tN


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                                            ,     -

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                                                       ^*
                                              .
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                                              1 is
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o
1
            .2
          .2 N
          K>( IH
          rS  «
     i
     ti
8
  i
l
              ^

                                   •Q

                                       - 8
                        £

                        <
                                            J
                          S
= $2
= $3
Calc
facili
costs
                                    ' S
                                    •?.
$52,468,000 - $23,
- $15.443,000 + $
$14,627,598
160,100.

59,
te baseline
as

-------

                    1
4,627^98 - $220,0
(3,540.5 » x $9.27
(7,678.7 U> x $3.4
(511.9 U> 0.928
($43,849, - 43,
,416,
36
$59
.499
20

99
          .2
          1
                          l
                                       PH'

                                       «,5
              §
I
i

                                                    I
ysis
bas
Calculate line
interest coverage
ratio
Calculate baseline
return on assets
of
te amount
and land
nce cost t
borrowed
Ca
ca
co
wi
                                  §!
                                a a e
                                IS'-
                                a & 8
                                ® PH 1
                                — In -S
                                "3 P "3.
                                ill
post-
e EBIT
cul
pli
ate post-
ance inte
e
Cal
com
Cal
co
x
ets
Calculate p
compliance
Calculate post
compliance int
coverage ratio
Calculate post
compliance re
on assets
_

-------

I

-------
To summarize, potential impacts for the hypothetical facility are evaluated as follows.

«      The facility is not predicted to be a baseline facility closure, since baseline cash flow is positive.
•      The facility is not anticipated to incur a baseline product closure, since baseline cluster unit costs
        are less than the estimated baseline cluster prices.
•      The facility is not expected to close in the post-compliance scenario, since post-compliance cash
        flow is positive.
•      The facility is not estimated to incur a post-compliance product closure, since all post-compliance
        unit cluster costs are less than the estimated post-compliance cluster prices.
•      The  facility is  not predicted  to incur any other  significant  financial impacts, since the post-
        compliance interest coverage ratio and the post-compliance return on assets are both above the
        lowest quartile  values.
                                            F.12

-------