vvEPA
United States
Environmental Protection
Agency
Office of Air Quality
Planning and Standards
Research Triangle Park, NC 27711
EPA-450/3-91-021
October 1991
Air
Economic Impact Analysis of
Regulatory Controls in the Dry
Cleaning Industry
Final
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Economic Impact Analysis
of Regulatory Controls in the
Dry Cleaning Industry
Emission Standards Division
U.S. F.n*; •"•-"•
77 Weil >- ..:••
U.S. Environmental Protection Agency
Office of Air and Radiation
Office of Air Quality Planning and Standards
Research Triangle Park, North Carolina 27711
October1991
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-------
(Disclaimer)
This report has been reviewed by the Emission Standards Division of the
Office of Air Quality Planning and Standards, EPA, and approved for
publication. Mention of trade names or commercial products is not intended to
constitute endorsement or recommendation for use. Copies of this report are
available through the Library Services Office (MD-35), U.S. Environmental
Protection Agency, Research Triangle Park, NC 27711, or from National
Technical Information Services, 5285 Port Royal Road, Springfield, .VA 22161.
iii
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CONTENTS
Section
Page
1 Introduction and Summary
2 Supply of Dry Cleaning Services
2.1 Profile of Suppliers by Industry Sector 2-l
2.1.1 Commercial Sector
2.1.2 Coin-operated Sector • ~ ,
^. —D
2.1.3 Industrial Sector
2.2 Production History and Trends
2.3 Production Processes
2-14
2.3.1 Machine Types
2.3.2 Solvents
2 — 16
2.3.3 Production Processes ... 0 , „
4. —1.0
2 . 4 Costs of Production
2.4.1 Costs of Production for Existing Facilities 2-24
2.4.2 Costs of Production for New Facilities 2-25
2.5 Model Facility Profile 2_27
3 Demand for Dry Cleaning Services -^
3 .1 Household Demand
3.1.1 Consumption and Trends 3_,
3.1.2 Characterization of Consumers 3_7
3.1.3 Household Demand Function . . . . _ 3_10
3.1.4 The Value of Time and the Full-Cost Model 3-1,4
3.1.5 Sensitivity To Price , 10
j —J.O
3.2 Industrial Demand
3.2.1 Consumption and Trends 3-1 Q
3.2.2 Characterization of Demanders 3_20
3.2.3 Derived Demand
IV
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CONTENTS (continued)
Section Page
3.2.4 Sensitivity to Price 3-21
4 Market Structure in the Dry Cleaning Industry 4-1
4.1 Facility Location Decision 4-1
4.1.1 Commercial Dry Cleaners 4-2
4.1.2 Coin-operated Dry Cleaners 4-3
4.1.3 Industrial Dry Cleaners 4-4
4.2 Market Structure 4-4
4.2.1 Market Structure in the Commercial Sector 4-5
4.2.2 Market Structure in the Coin-operated Sector 4-15
4.2.3 Market Structure in the Industrial Sector 4-18
4.3 Model Markets 4-19
4.3.1 Commercial Sector Markets 4-19
4.3.2 Coin-operated Sector Markets 4-22
4.3.3 Industrial Sector Markets 4-23
5 Financial Profile of Commercial 0ry Cleaning Firms 5-1
5.1 Firm Finances and Facility Economics 5-1
5.2 Population of Potentially Affected Firms 5-2
5.3 Legal Ownership of Commercial Dry Cleaning Facilities 5-3
5.3.1 Sole Proprietorship 5-3
5.3.2 Partnerships 5-4
5.3.3 Corporations 5-5
5.4 Distribution of Companies by Receipts Size 5-6
5.5 Distribution of Companies by Number of Facilities 5-8
5. 6 Vertical Integration and Diversification 5-9
5.7 Financial Characteristics of Firms in Regulated
Industry(ies) 5-10
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CONTENTS (continued)
Section
Page
5.8 Key Business Ratios of Dry Cleaning Firms 5_15
5. 9 Availability and Costs of Capital ; • 5_18
6 Responses to the Regulatory Alternatives C_T
6.1 Overview of Regulatory Alternatives g^
6.2 Firm-Level Responses , 0
b — £
6.3 Facility-Level Responses 6_6
6.3.1 Compliance Option Costs 6_6
6.3.2 Compliance Options Under Each Regulatory Alternative .. 6-9
7 Impacts of the Regulatory Alternatives
7-1
7.1 Affected Population 7 ,
7.2 Costs of Compliance -, 0
••••••••••>• •*.*.... / — o
7.3 Market Adjustments
7.3.1 Price and Output Adjustments 7_15
7.3.2 Welfare Effects 7_24
7.3.3 Plant Closures 'l-33
7.3.4 Employment Effects 7_37
7.4 Ownership Adjustments in Commercial Dry Cleaning Sector 7-41
7.5 Effects on Small Businesses 7_68
8 Conclusion
8—1
9 References
Appendix A
A-l
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CONTENTS (continued)
Table Page
1-1 Estimated Number of Dry Cleaning Plants by Industry Sector (1991) ... 1-3
1-2 Annualized Costs and Welfare Impacts of the Dry Cleaning NESHAP by
Regulatory Alternative and Size Cutoff ($1989) 1-7
1-3 Projected Worst-Case Net Plant Closures and Employment Effects of
the Dry Cleaning NESHAP 1-8
2-1 Distribution of PCE Dry Cleaning Machines and Facilities in the
Commercial Sector 2-3
2-2 1991 Distribution of Receipts for Commercial Dry Cleaning
Establishments: PCE and Non-PCE Establishments ($1989) 2-4
2-3 1991 Distribution of Receipts for Commercial Dry Cleaning
Establishments: PCE Establishments only ($1989) 2-4
2-4 1991 Distribution of Dry Cleaning Output in the Commercial Sector:
PCE and Non-PCE Establishments 2-5
2-5 1991 Distribution of Dry Cleaning Output in the Commercial Sector:
PCE Establishments Only 2-5
2-6 1991 Distribution of Receipts for Coin-Operated Establishments With ,
Dry Cleaning Capacity ($1989) 2-8
2-7 1991 Distribution of Dry Cleaning Output in the Coin-Operated
Sector 2-9
2-8 Annual Receipts, Average Base Price, and Total Output for
Commercial Dry Cleaners ($1989) 2-13
2-9 Annual Growth Rates by Machine Type and Sector (1986-1989) 2-13
2-10 Capital Costs of New Dry-to-Dry Machines ($1989) 2-22
2-11 Average Annual Operating Costs for Commercial Dry Cleaning Plants . . 2-23
2-12 Average Input Prices for PCE Dry Cleaning Facilities ($1989) 2-24
2-13 Model Plant Description and the Distribution of PCE Facilities by
Industry Sector and Income Level 2-28
vxi
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CONTENTS (continued)
Table
Page
3-1 Household Expenditures on Commercial Laundry and Dry Cleaninq
Services 1980-1989 ($1989) 3_5
3-2 Number and Median Income of Women in the Work Force 1980-1989
($1989) 3_7
3-3 Household Expenditures on Commercial and Coin-Operated Dry Cleaning
and Laundry Services by Income Category ($1989) 3-9
3-4 Household Expenditures on Commercial and Coin-Operated Dry Cleaning
and Laundry Services by Occupation Category 3-10
3-5 Household Expenditures on Commercial and Coin-Operated Dry Cleaning
and Laundry Services by Location Category 3-11
3-6 Regression Analysis 3-16
4-1 Data Used in the Supply/Demand Estimation 4_8
4-2 Parameter Estimates and Regression Statistics from the
Supply/Demand Estimation 4_10
4-3 Parameter Estimates and Regression Statistics from the
Supply/Demand Estimation (Time-Trend Specification) 4-11
4-4 Profile of Model Markets in the Commercial Sector 4-20
5-1 Legal Form of Organization of Dry Cleaning Firms—Number and
Percent
5-2 Receipts of Dry Cleaning Firms 5_7
5-3 Concentration by Largest Dry Cleaning Firms 5-7
5-4 Number of Commercial Dry Cleaning Facilities per Firm by Income
Category 5_9
5-5 Number of Dry Cleaning Firms, by Size and Baseline Financial
Condition ,_1 _
5-6 Number of Dry Cleaning Firms, by Size and Baseline Financial
Condition _ -
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CONTENTS (continued)
Table Page
5-7 Baseline Financial Ratios of Dry Cleaning Firms 5-17
6-1 Control Technology Options Under Each Regulatory Alternative 6-2
7-1 Size Cutoff Levels Based on Consumption of Perchloroethylene (' 3E) . . 7-3
7-2 Distribution of Affected Facilities by Industry Sector, Model
Market, and Size Cutoff: Regulatory Alternatives I and II 7-4
7-3 Distribution of Affected Facilities by Industry Sector, Model
Market, and Size Cutoff: Regulatory Alternative III. 7-5
7-4 Distribution of Affected Output by Industry Sector, Model Marker,
and Size Cutoff: Regulatory Alternatives I and II 7-6
7-5 Distribution of Affected Output by Industry Sector, Model Marke.,
and Size Cutoff: Regulatory Alternative III 7-7
7-6 Model Plant Capital and Operating Compliance Costs for Carbon
Adsorber Controls ($1989) 7-9
7-7 Model Plant Capital and Operating Compliance Costs for Refrigerated
Condensor controls in the Commercial Sector ($1989) 7-10
7-8 Model Plant Annualized Compliance Costs for Regulatory Alternative
I ($1989) '. 7-12
7-9 Model Plant Annualized Compliance Costs for Regulatory Alternatives
II and III ($1989) 7-13
7-10 Market Adjustments Computed for Each Sector and Model Market in the
Dry Cleaning Industry 7-15
7-11 Price Adjustments for Each Sector of the Dry Cleaning Industry oy
Regulatory Alternative and Size Cutoff 7-19
7-12 Price Adjustments for Model Markets in the Commercial Sector by
Regulatory Alternative and Size Cutoff (percentage change from
Baseline) . 7-20
7-13 Output Adjustments for Each Sector of the Dry Cleaning Industry by
Regulatory Alternative and Size Cutoff 7-21
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CONTENTS (continued)
Table
Page
7-14 Output Adjustments for Model .Markets in the Commercial Sector bv
Regulatory Alternative and Size Cutoff 1-22
7-15 Consumer Welfare Impacts for Each Sector of the Dry Cleaning
Industry by Regulatory Alternative and Size Cutoff 7-27
7-16 Consumer Welfare Impacts for Model Markets in the Commercial Sector
by Regulatory Alternative and Size Cutoff ($ thousands) 7-28
7-17 Producer Welfare Impacts for Each Sector of the Dry Cleaning
Industry by Regulatory Alternative and Size Cutoff {$ thousands)... 7-29
7-18 Producer Welfare Impacts for Model Markets in the Commercial Sector
by Regulatory Alternative and Size Cutoff ($ thousands) 7-30
7-19 Net Welfare Impacts For Each Sector of the Dry Cleaning Industry by
Regulatory Alternative and Size Cutoff ($ thousands) 7-31
7-20 Net Welfare Impacts for Model Markets in the Commercial Sector by
Regulatory Alternative and Size Cutoff ($ thousands) 7-32
7-21 Projected Worst-Case Net Plant Closures in Each Sector of the Dry
Cleaning Industry by Regulatory Alternative and Size Cutoff 7-34
7-22 Projected Worst-case Net Plant Closures in each Model Market of the
Commercial Sector by Regulatory Alternative and Size Cutoff 7-35
7-23 Projected Worker Displacements 7_3g
7-24 Projected Worker Displacement Costs ($ millions)
7-41
7-25 Number of Affected Dry Cleaning Firms By Size and Baseline
Financial Condition, Regulatory Alternatives I and II . . . '. 7-43
7-26 Number of Affected Dry Cleaning Firms By Size and Baseline
Financial Condition, Regulatory Alternative III • 7_44
7-27 Number of Affected Dry Cleaning Firms By Size and Baseline
Financial Condition, Regulatory Alternatives I and II 7-45
7-28 Number of Affected Dry Cleaning Firms By Size and Baseline
Financial Condition, Regulatory Alternative III 7_46
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CONTENTS (continued)
Table . Page
7-29 Installed Price of Control Equipment and Annual Operating Cost, by
Regulatory Alternative and Size of Firm 7-47.
7-30 Annual Principal and Interest Payments on a Seven-Year Note By
Regulatory Alternative, Fi-rm Size, and Interest Rate ($>a 7-49
7-31 Initial Cash Outlay Requirement and Recurring Annual Expenses By
Firm Size, Financial Concition, and Regulatory Alternative ($) 7-50
7-32 Key Financial Ratios 7-53
7-33 Baseline and Affected Financial Ratios: <$25,000 Firm Receipts.... 7-54
7-34 Baseline and Affected Financial Ratios: $25,000-50,000 Firm
Receipts 7-55
7-35 Baseline and affected Financial Ratios: $50,000-75,000 Firm
Receipts . 7-56
7-36 Baseline and affected Financial Ratios: $75,000-100,000 Firm
Receipts 7-57
7-37 Baseline and affected Financial Ratios: >$100,000 Firm Receipts... 7-58
7-38 Projected Financial Failures of Commercial Dry Cleaning Firms by
Regulatory Alternative and Size Cutoff, Financial Scenario I
(Number of Firms and Percent) 7-60
7-39 Projected Financial Failures of Commercial Dry Cleaning Firms by
Regulatory Alternative and Size Cutoff, Financial Scenario II
(Number of Firms and Percent) 7-61
xi
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CONTENTS (continued)
Figure
2-3 Market Supply Curve for Existing Facilities
Page
1-1 Number of Affected Dry Cleaning Facilities By Regulatory
Alternative and Size Cutoff
1-2 Potential Changes in Ownership by Size Cutoff, Financial
Scenario I
1-3 Potential Changes in Ownership by Size Cutoff, Financial
Scenario II
2-1 Typical Configuration of a Dry Cleaning Machine and the Various
Attachments
••»•••••••••••,»»,,. ...... ^ "~ X O
2-2 PCE Consumption by Sector for 1991. -, ,0
f.~ 1C
2-25
2-4 New Facility Costs Compared to Market Supply Curve for Existing
Facilities ...................... ^
......................................... 2—26
3-1 Total Annual Household Consumption of Commercial Dry Cleaning
Services (1980-1988) ........................... y
3-2 Annual Consumption of Commercial Dry Cleaning Services per
Household (1980-1988) ....................
4-1 Demand for Self-Service Dry Cleaning .................... 4_17
6-1 Responses to the Proposed Regulation ..................... 6_4
7-1 Price and Output Adjustments Due to a Market Supply Shrift .......... 7-16
7-2 Welfare Change Estimation ............................ 7_25
7-3 Capital Availability and Profitability Impacts, Financial
Scenario I—Regulatory Alternative I .......................... 7_62
7-4 Capital Availability and Profitability Impacts, Financial
Scenario I — Regulatory Alternative II -, ---,
........................ / — oo
Xll
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CONTENTS (continued)
Figure Page
7-5 Capital Availability and Profitability Impacts, Financial
Scenario I—Regulatory Alternative III 7-64
7-6 Capital Availability and Profitability Impacts, Financial
Scenario II—Regulatory Alternative III 7-65
7-7 Capital Availability and Profitability Impacts, Financial
• Scenario II—Regulatory Alternative III 7-66
7-8 Capital Availability and Profitability Impacts, Financial
Scenario II—Regulatory Alternative III 7-67
7-9 Baseline Financial Condition of Projected Business Failures,
Financial Scenario I—Regulatory Alternative I 7-69
7-10 Baseline Financial Condition of Projected Business Failures,
Financial Scenario I—Regulatory Alternative II 7-70
7-11 Baseline Financial Condition of Projected Business Failures,
Financial Scenario I—Regulatory Alternative III 7-71
7-12 Baseline Financial Condition of Projected Business Failures,
Financial Scenario II—Regulatory Alternative I 7-72
7-13 Baseline Financial Condition of Projected Business Failures,
Financial Scenario II—Regulatory Alternative II 7-73
7-14 Baseline Financial Condition of Projected Business Failures,
Financial Scenario II—Regulatory Alternative III 7-74
xiii
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SECTION 1
INTRODUCTION AND SUMMARY
Under the Clean Air Act Amendments of 1990, the U.S. Environmental
Protection Agency (EPA) is required to propose and promulgate a regulation to
control Hazardous Air Pollutant (HAP) emissions from dry cleaning facilities.
HAP's emitted from dry cleaning include perchloroethylene (PCE) and 1,1,1-
Trichloroethane (1,1,1-TCA). This report investigates the economic impacts
associated with three candidate regulatory alternatives and five size cutoff
levels considered for proposal. All plants that use 1,1,1-TCA are in
compliance with the proposed regulatory alternatives in the baseline. No
costs or economic impacts are projected for these facilities. Therefore, the
analysis of regulatory controls addresses impacts associated with the control
of PCE emissions only.
.This section provides a brief overview of the dry cleaning industry and
the impacts of the regulatory alternatives discussed in detail in the balance
of the report. A description of supply and demand for dry cleaning services
is provided in Sections 2 and 3, respectively. Section 4 describes market
structure and outlines an approach for analyzing market impacts of the
regulatory alternatives. The baseline financial profile of dry cleaning firms
is provided in Section 5. Section -6 describes the requirements .of the
candidate regulatory alternatives and outlines potential responses to the
regulatory alternatives. Section 7 reports projected economic and financial
impacts associated with each regulatory alternative and Section 8 summarizes
the analysis.
The dry cleaning industry is comprised of three sectors: commercial
(SIC 7216), coin-operated (SIC 7215), and industrial (SIC 7218). Commercial
facilities are. the most prevalent of the three types and are generally located
in shopping centers and near densely populated areas. Coin-operated plants
are typically part of a laundromat and provide dry cleaning either on a self-
service basis or by accepting items over the counter-similar to commercial
facilities. industrial plants usually rent uniforms and other items to their
industrial or commercial users and are generally larger than commercial and
coin-operated facilities.
1-1
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It is important to distinguish between the terms machine, facility,
plant, establishment, and firm used to describe the dry cleaning industry in
this analysis. A dry cleaning machine is a piece of equipment designed to
clean clothes or other items using a solvent mixture in place of water and
detergent. The terms facility, plant, and establishment are used
interchangeably and refer to a single physical location where dry cleaning
services are produced. Each facility may use one or more dry cleaning
machines in the production process. A dry cleaning firm is a legal entity
that owns one or more dry cleaning facilities.
Approximately 34,000 facilities offer dry cleaning services in the
United States. Of these,.about 28,000 use PCE in their cleaning process. The
commercial sector comprises approximately 90 percent of the industry with an
estimated 30,494 dry cleaning plants; 24,947 of these plants use PCE. The
industrial sector has 1,379 total plants, but only about 325 have dry cleaning
capacity. Approximately 40 percent, or 130, use PCE in their dry cleaning
operation. The U.S. has 27,180 coin-operated laundries. Of these 27,180
plants, an estimated 3,044 offer dry cleaning services. Table 1-1 summarizes
the total number of plants, the number of dry cleaning plants, and the number
of dry cleaning plants that use PCE by industry sector. In addition, the
number of potentially affected plants and potentially affected firms are
reported in Table 1-1. . Potentially affected entities include those that use
PCE in the dry cleaning process and do not have the control equipment required
under the most stringent regulatory scenario (Regulatory Alternative III with
no cutoff) . Potentially affected firms include those business entities that
own potentially affected facilities.
The three regulatory alternatives under consideration for proposal
.specify control equipment requirements for facilities by industry sector and
machine technology. An estimated 65 percent of dry cleaning plants or 21,954
have some type of baseline Control equipment in place. The 11,909 facilities
that do not have baseline control equipment in place would potentially incur
control costs under any of the alternatives considered. An additional 1,930
facilities have control equipment that does not meet the requirements of
Regulatory Alternative III. Therefore, under the most stringent regulatory
scenario, 13,839 facilities would be affected.
1-2
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TABLE 1-1. ESTIMATED NUMBER OF DRY CLEANING PLANTS BY INDUSTRY SECTOR (1991)a
Sector
Commercial
Coin-
Operated
Total
Number of
Plantsb
30,494
27,180
Number of
Dry
Cleaning
Plantsc
30,494
3,044
Number of
PCE Dry
Cleaning
Plants
24,947
3,044
Number of
Potentially
Affected
Plantsd
12,159
1,615
=======
Number of
Potentially
Affected
Firms6
10,744
e
Industrial
Total
1,379
59,053
325
33,863
130
28,121
65
13,839
^Includes facilities with payroll and those without payroll.
Includes plants in the coin-operated and industrial sectors that have dry
cleaning machines and those that do not have dry cleaning machines
clncludes dry cleaning plants that use PCE as well as those that use other
r0 PCE P^antS that d° not have ve"t controls required under the most
stringent regulatory scenario (Regulatory Alternative III with no cutoff)
Includes firms that own potentially affected plants. The number of
potentially affected firms that own coin-operated or industrial plants is not
?h^m^e? f" ! analysi5' Coin-operated plants will likely be exempt from
the regulation and industrial plants are expected to realize cost-savings
under each regulatory alternative considered. Therefore a firm financial
analysis is not performed for the coin-operated or industrial sectors.
Source: Radian (1991c); 1987 Census of Service Industries, Nonemployer
Statistics SeriesfU.S. Department of Commerce, 1990a); 1987 Census of Service
Industries, Subject Series (U.S. Department of Commerce, 1990b); Table 7-3.
Many facilities in the commercial and coin-operated sectors that are
potentially affected by the regulation are small establishments. It is
estimated that over 75 percent of potentially affected facilities receive less
than $100,000 in annual receipts1. The annualized control costs associated
Approximately 55 percent of affected machines represent output levels
corresponding_to $100,000 or less. The difference in the distribution of
affected machines and affected facilities is attributable to two assumptions
used to estimate impacts. First, it is assumed that uncontrolled machines
represent a larger share of lower income categories and a smaller share of
^nnennnnC°me cat*9ories' Second, it is assumed that facilities with over
$100,000 in annual receipts use multiple machines in their operations whereas
facilities below $100,000 receipts use only one machine. °peratlons wnereas
-------
with the regulatory alternatives range from $1,500 to $8,000 per plant. For
small facilities below $25,000 in annual receipts, these control costs may
represent more than one third of total receipts to the facility. To mitigate
the impacts on small facilities, size cutoffs based on PCE usage are
considered. These cutoffs correspond to target levels of annual receipts and
exempt facilities below a specified output level. Figure 1-1 shows the number
of affected facilities under each size cutoff by Regulatory Alternative. Note
that the number of affected facilities under each size cutoff is identical for
Alternatives I and II.
Because thousands of facilities in the dry cleaning industry are
potentially affected, analyzing regulatory impacts using a facility-specific
approach is not feasible. Therefore, a model plant approach based on fifteen
model plants that characterize the machine technology, machine capacity, and
operating practices of typical dry cleaning machines is used to estimate
impacts in the industry. Within each model plant category, impacts are
analyzed for plants operating at five output levels based on annual receipts.
Furthermore, impacts are analyzed using a model market approach that
differentiates the market for dry cleaning services by the number of
facilities in the market, the share of affected and unaffected facilities in
the market, and the projected behavioral response to the regulation. Eight
model markets are used to represent market conditions and market structure in
the dry cleaning industry including six model markets for the commercial
sector, one model for the coin-operated sector, and one model for the
industrial sector.
Regulatory impacts are projected using an integrated approach that
combines an economic impact analysis with a firm financial analysis. In the
economic impact analysis a methodological and empirical approach based on the
principles of applied welfare economics is used. Jlconomic impacts are
quantified through estimated market adjustments of price and output and
corresponding effects on consumer and producer welfare. The price and output
adjustments computed in this analysis are short-run effects. Almost all new
dry cleaning machines are equipped with built-in vent controls that satisfy
the requirements of the regulations. The current stock of uncontrolled
1-4
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14,000 T
12,000 -•
10,000 -
13,839
Number of
Affected
Facilities
8,000 -
6,000
4, 000
2,000
„, ] Regulatory
Alternatives I and II
Regulatory
Alternative III
25 50 75
Size Cutoff in Annual Receipts (SOOO)
100
Figure 1-1. Number of Affected Dry Cleaning Facilities By Regulatory
Alternative and Size Cutoff
Source: Tables 7-2 and 7-3.
1-5
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machines would have been replaced with controlled machines even in the
baseline. Consequently, long-run price and output adjustments are zero. In
addition, the effects of the candidate regulatory alternatives on employment
and plant closures are quantified as part of the economic impact analysis.
Financial impacts including capital availability and profitability impacts are
projected recognizing that firms differ by size and baseline financial health.
Table 1-2 reports the annualized costs, the producer welfare costs, and
the consumer welfare costs for the industry as a whole under each regulatory
alternative and size cutoff level. The annualized costs include the annual
operating costs of control equipment along with the annualized installed costs
of the equipment. The producer and consumer welfare costs are those projected
for the first year of the regulation. Lesser losses will be incurred in
fourteen subsequent years because existing uncontrolled machines are being
replaced with controlled machines upon retirement, even at baseline. Fifteen
years after the regulation takes effect, producer and consumer welfare costs
are zero assuming that the current stock of uncontrolled machines would be
replaced with controlled machines in the baseline over this time period.
Table 1-3 reports the projected worst-case net plant closures, projected
worker displacements, and worker displacement costs for the industry as a
whole under each regulatory alternative and size cutoff level. The plant
closure projections assume that the short-run industry output reductions are
achieved by closure of the smallest size facilities. The projected worker
displacements assume that layoffs are proportional to the short-run industry
output reductions. The projected worker displacement costs are based on the
projected displacements and are one-time (non-recurring) costs. Assuming (as
described above) that the long-run equilibrium level of dry cleaning services
is unaffected by the regulation, the long-run equilibrium employment will
likewise be unaffected. The output reduction used to estimate worker
displacement and displacement costs would have occurred in the baseline over
an estimated 15-year time period as owners of dry cleaning facilities replaced
retiring uncontrolled machines with controlled machines. Implicit in thet
estimated displacement costs is the assumption that this baseline output
1-6
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TABLE 1-2. ANNUALIZED COSTS AND WELFARE IMPACTS OF THE DRY CLEANING NESHAP BY
REGULATORY ALTERNATIVE AND SIZE CUTOFF ($1989)a
Cost or Impact Measure and
Regulatory Alternative
Annualized Costs ($106)
Regulatory I
Regulatory II
Regulatory III
Consumer Welfare Impacts ($106)
Regulatory I
Regulatory II
Regulatory III
Producer Welfare Impacts ($106)
Regulatory I
Regulatory II
Regulatory III
Size Cutoff in Annual Receipts ($000)
0 25 50 75 100
34.8
42.9
53.5
-14.6
-18.0
-20.3
-20.2
-25.0
-33.3
18.9
23.5
33.0
-10.8
-13.5
-15.8
-8.0
-10.0
-17.2
13.3
16.5
24.8
-7.7
-9.5
-11.5
-5.6
-7.0
13.3
11.1
13.9
21.3
-6.5
-8.1
-9.9
-4.6
-5.9
-11.5
9.1
11.5
17,7
-5.3
-6.7
-8.2
-3.8
-4.8
-9.5
'Annualized Costs and producer and consumer welfare losses incurred in first
year of regulation. Costs will be incurred in subsequent yetrs Jut
- current "
reduction— and corresponding reduction in employment— would have been
accounted for through attriticn rather than worker dislocation. In other
words, the present value of foregone future displacement is assumed to be
zero .
The firm financial analysis uses the costs estimated for the economic
impact analysis to project changes in the financial viability of dry cleaning
firms affected under each regulatory alternative. Estimated costs of capital
are developed for firms in poor, average, and good financial condition.
1-7
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TABLE 1-3. PROJECTED WORST-CASE NET PLANT CLOSURES AND EMPLOYMENT EFFECTS OF
THE DRY CLEANING NESHAP
Impact Measure and
Regulatory Alternative
Worst-Case Net Plant Closures3
Regulatory I
Regulatory II
Regulatory III
Size
0
1,354
1,599
1,768
Cutoff in
25
373
457
529
Annual
50
147
182
221
Receipts
75
88
110
135
($000)
100
23
28
34
Number Worker Displacements'*
Regulatory I 743 566 407 336 283
Regulatory II 920 707 513 424 354
Regulatory III 1,043 831 619 513 424
Worker Displacement Costs ($106)c
Regulatory I 21.4 16.3 11.7 9.7 8.2
Regulatory II 26.5 20.4 14.8 12.2 10.2
Regulatory III 30.0 23.9 17.8 14.8 12.2
aNet plant closures assuming all industry output reductions are achieved by
closures of smallest affected facilities.
bAssumes labor demand declines in proportion to equilibrium output reductions.
C0ne-time (non-recurring) worker displacement cost. The present value of
foregone future displacement is assumed to be zero.
1-8
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Capital availability constraints and profitability impacts are reported for
firms in the commercial sector that are affected under each regulatory
alternative. Figure 1-2 shows the potential changes in ownership by size
cutoff level under the regulatory alternatives assuming a positive
relationship between firm size and baseline firm financial condition, as might
be expected since smaller firms generally have significantly lower capacity
utilization than larger firms (financial scenario I).
Potential changes in firm ownership under an alternative assumption are
demonstrated in Figure 1-3. These projected impacts might result if the
number of firms in below-average, average, and above-average baseline
financial condition are proportionately distributed across firms of all sizes
(financial scenario II).
The total annualized cost ranges from $53.5 million under the most
stringent regulatory scenario to less than $10 million under the least
stringent. The estimated regulatory costs result in short-run price and
output adjustments that are relatively small (less than one percent deviation
from baseline values in most cases). The estimated loss in consumer welfare
ranges from $14.6 to $20.3 million with no cutoff. Producers lose an
estimated $20.2 to $33.3 million in welfare with no cutoff, in addition, more
than 3,000 potential changes in ownership are projected with no size cutoff.
However, the size cutoffs would mitigate the economic and financial impacts of
the regulatory alternatives. For example, with a cutoff level corresponding
to $100,000 annual receipts, consumer and producer welfare impacts under
Alternative II are $6.7 million and $4.8 million, respectively, and projected
changes in ownership are between 0 and 669.
1-9
-------
i
M
O
7,OOOT
6,000" "
5,000
Potential
Ownership 4,000
Changes
3,000
2,000"
1,000
5,292
Regulatory Alternative I
I I Regulatory Alternative II
[~~| Regulatory Alternative III
1,684
1,896
58
No Cutoff 25
Size Cutoff in Annual Receipts ($000)
Figure 1-2. Potential Changes in Ownership by Size Cutoff, Financial Scenario I
-------
•7,OOOT
6,000 -'
5,000
Potential
Ownership
Changes
No Cutoff
Regulatory Alternative I
I—I Regulatory Alternative II
Regulatory Alternative III
25 50 75
Size Cutoff in Annual Receipts ($000)
100
Figure 1-3. Potential Changes in Ownership by Size Cutoff, Fi
Financial Scenario II
-------
SECTION 2
SUPPLY OF DRY CLEANING SERVICES
The dry cleaning industry is a mature service industry involved in the
cleaning, pressing, and finishing of clothing and related products. This
section provides a profile of each sector of the industry, production history
and trends, an overview of the production process, and the estimated costs of
production.
2.1 PROFILE OF SUPPLIERS BY INDUSTRY SECTOR
The dry cleaning industry is composed of three sectors:
• commercial (SIC 7216),
• coin-operated (SIC 7215), and
• industrial (SIC 7218) .
Commercial facilities are the most prevalent of the three types and are
generally located in shopping centers and near densely populated areas. Coin-
operated plants are typically part of a laundromat and provide dry cleaning
either on a self-service basis or by accepting items over the counter-similar
to commercial facilities. Industrial plants usually rent uniforms and other
items to their customers and are generally larger than commercial and coin-
operated facilities.
2.1.1 Commercial Sector
Commercial dry cleaning facilities, the most familiar type of
establishment, provide services for households and include independently
operated neighborhood shops, franchises, and specialty cleaners. Commercial
dry cleaners provide full service dry cleaning, which includes spotting,
pressing, finishing, and packaging. in addition, many commercial dry cleaners
provide laundry'services for water-washable garments, rug cleaning services,
and minor alteration and repair services. On average approximately 85 percent
of the receipts at a commercial dry cleaning establishment are from dry
cleaning activities. The remaining 15 percent are from the auxiliary services
provided by the facility (U.S. Department of Commerce, 1991).
2-1
-------
Approximately 30,494 commercial dry cleaners operate in the U.S. Over
80 percent or about 24,947 commercial dry cleaners use perchloroethylene (PCE)
in their cleaning process. Table 2-1 shows the distribution of PCE
establishments, the distribution of PCE machines, and the corresponding number
of machines per facility for 5 income categories (based on annual receipts per
facility). This estimated total number of dry cleaning facilities and the
distribution of facilities by income level is based on the number and
distribution of PCE dry cleaning machines by design capacity, the average
number of machines per facility in the commercial sector I approximately 1.25)
(Radian 1990c), and the distribution of facilities reported in the 1987 Census
of Service Industries, Subject Series (U.S. Department of Commerce, 1990b).
In addition, it is assumed that facilities below $100 thousand in annual
receipts have one machine per facility.
Tables 2-2 and 2-3 show the 1991 distribution of annual receipts for all
commercial establishments and for establishments that use PCE, respectively.
Over three fourths of the total receipts to dry cleaning establishments were
earned by facilities with $100,000 or more in annual receipts. These
facilities represent only about one third of the total number of commercial
dry cleaning establishments. At the other end of the spectrum, small
facilities with below $25,000 in annual receipts account for more than 25
percent of the total number of facilities but only about 3 percent of total
receipts to commercial dry cleaners.
Dry cleaning output for the sector totals 571,984 Mg per year with
446,492 Mg from facilities that use PCE. Total output is computed by first
multiplying total annual receipts by the share of receipts from dry cleaning
activities (85%) to compute the receipts directly attributable to drycleaning.
This value is then divided by the estimated 1989 baseline price of $6.34 per
kilogram for dry cleaning services to compute total annual output measured in
kilograms of clothes cleaned. Tables 2-4 and 2-5 report 1991 estimated total
output and average output per establishment by income category.
2-2
-------
N)
10
TABLE 2-1. DISTRIBUTION OF PCE DRY CLEANING MACHINES AND FACILITY IN THE COMMERCIAL SECTOR
Annual Receipts
($000/yr)
0 -
25 -
50 -
75 -
over
25
50
75b
100b
100
Total
— —
Number of
Machine
6,822
4,270
2,632
2,632
15,076
31,432
PCE Machines Per
Establishment
I
1
1
1
1.75
is one for all non-PCE establishments
Number of PCE
Establishments
6,822
4,270
2,632
2,632
8,591
Number of PCE and
non-PCE
Establishments3
8,026
5,024
3,096
3,096
11,251
machin^ P« establishment
of Cc™etce,
-------
TABLE 2-2. 1991 DISTRIBUTION OF RECEIPTS FOR COMMERCIAL DRY CLEANING
ESTABLISHMENTS: PCE AND NON-PCE ESTABLISHMENTS ($1989)
Annual-
Receipts Number of
($000/yr) Establishments3 Percent
0-25
25-50
50-75
75-100
>100
Total
8,
5,
.3,
3,
11,
30,
026
024
096
096
251
494
26
16
10
10
36
100
.32
.47
.15
.15
.90
.00
Total
Annual
Receipts13
($000/yr)
' 142,
203,
207,
290,
3,421,
4,266,
350
679
528
539
966
062
Average Annual
Receipts Per "
Establishment0
Percent ($/yr)
3
4
4
6
80
100
.34
.77
.86 '
.81
.21
.00
17,
40,
67,
93,
304,
736
545
021
829
135
-
aSee Table 2-1.
bAverage annual receipts multiplied by number of establishments.
cBased on data reported in the 1987 Census of Service Industries, Subject
Series (U.S. Department of Commerce, 1990) for commercial dry cleaning
establishments with payroll converted to $1989 using the CPI for Apparel and
Upkeep.
TABLE 2-3. 1991 DISTRIBUTION OF RECEIPTS FOR COMMERCIAL DRY CLEANING
ESTABLISHMENTS: PCE ESTABLISHMENTS ONLY ($1989)
Annual
Receipts Number of
($000/yr) Establishments3 Percent
0-25
25-50
50-75
75-100
>100
Total
6,
4,
2,
2,
8,
24,
822
270
632
632
591
947
27
17
10
10
34
100
.35
.12
.55
.55
.44
.00
Total
Annual
Receipts*5
($000/yr)
120,
173,
176,
246,
2,612,
3,330,
998
127
399
958
824
305
Average Annual
Receipts Per
Establishment0
Percent ($/yr)
3
5
5
7
78
100
.63
.20
.30
.42
.46
.00
17,
40,
67,
• 93,
304,
736
545
021
829
135
-'
aSee Table 2-1.
bAverage annual receipts multiplied by number of establishments.
cBased on data reported in the 1987 Census of Service" Industries, Subject
Series (U.S. Department of Commerce, 1990) for commercial dry cleaning
establishments with payroll converted to $1989 using the CPI for Apparel and
Upkeep'.
2-4
-------
TABLE 2-4.
1991 DISTRIBUTION OF DRY CLEANING OUTPUT IN THE COMMERCIAL
SECTOR: PCE AND NON-PCE ESTABLISHMENTS
Annual
Receipts Number of
($000/yr) Establishments3 Percent
0-25
25-50
50-75
75-100
>100
Total
8,
5,
3,
3,
11,
30,
026
024
096
096
251
494
26
16
10
10
36
100
.32
.47
.15
.15
.90
.00
ss^ss^ss
Total
Annual
Outputb
(Mg/yr)
19,
27,
27,
38,
458,
571,
^H^^MS— •"—
085
307
823
952
781
948
Average Annual
Output Per
Establishment15
Percent (kg/yr)
3
4
4
6
80
100
.34
.77
.86
.81
.21
.00
2,
5,
8,
12,
40,
378
436
985
580
775
-
aSee Table 2-1.
"Receipts from Table 2-2 multiplied by the share of receipts from dry cleaning
activities (85%) divided by the 1989 base price (S6.34 per kg). cxeanin100
Total
Number of
Establishments3 Percent
6,822
4,270
2,632
2,632
8,591
24,947
ss^zffla^s-^=-^=— — — — --
27.35
17.12
10.55
10.55
34.44
100.00
Total
Annual
Outputb
(Mg/yr)
16,222
23,211
23,650
33,110
350,300
446,492
Average Annual
Output Per
Establishment5
Percent ,, , .
(kg/yr)
3.63
5.20
5.30
7.42
78.46
100.00
2,378
5,436
8,985
12,580
40,775
aSee Table 2-1.
"Receipts from Table 2-3 multiplied by the share of receipts from dry cleaning
activities (85%) divided by the 1989 base price ($6.34 per kg). e*nin9
The commercial sector baseline price is derived using International
Fabricare Institute (IFI) data on the average price to clean a two-piece man's
suit weighing one kilogram (Faig, 1990). Control cost estimates and other
financial data used in the economic impact analysis are measured in 1989
2-5
-------
dollars. However, the most recent base price estimate available for the
commercial sector is the average 1988 value ($5.92). The 1989 base price was
projected by first fitting a regression line to the natural logarithm of base
prices from 1973 to 1988 and a time trend. The slope of the regression line
(0.0707) is an estimate of the average growth rate of base prices over that
time period.
The projected 1989 base price is then calculated as the sum of the 1988
price plus the growth amount:
P1989 * P1988 * (1 + 0.0707) (2.1)
= $5.92 • (1 + 0.0707)
= $6.34
For the purposes of analysis, all facilities are assumed to charge $6.34 per
kilogram of clothes cleaned in the baseline. In following sections, price
changes due to the regulation are projected based on the price computed in
this section.
2.1.2 Coin-operated Sector
Facilities in the coin-operated sector also supply dry cleaning services
to households and are usually part of a laundromat. Water washing and drying
account for the majority of sales with dry cleaning offered as an auxiliary
service (Torp, 1990) . Approximately 10 percent of total receipts at coin-
operated laundries that offer dry cleaning services are from dry cleaning
activities.
Two types of dry cleaning services are available in this sector: self-
service and employee assisted dry cleaning. Self-service, coin-operated dry
cleaning, as the name suggests, requires the consumer to operate the dry
cleaning machine and does not include pressing, spotting, or other finishing
services. Employee assisted dry cleaning (referred to as plant-operated in
the balance of this report) is virtually indistinguishable from the service
provided by commercial dry cleaners except that the facility also offers coin-
operated laundry services. Consumers use coin-operated dry cleaners because
2-6
-------
they desire lower priced cleaning, have large items, or do not live near
commercial cleaners (ICF, 1986),
Census data indicate that 27,180 coin-operated laundries-including
facilities with and-without payroll-were operating in the U.S. in 1987 (U.S.
Department of Commerce, 1990a). Approximately 3,044 coin-operated laundries'
offer dry cleaning services. About 2,831 establishments offer plant-operated
dry-cleaning and another 213 establishments offer self-service dry cleaning
(Radian, 1991c). virtually all coin-operated laundries that offer dry
cleaning services use PCE in the cleaning process.
Table 2-6 shows the 1991 distribution of coin-operated establishments
with dry cleaning operations. The income distribution is based on the income
distribution of all coin-operated laundries with payroll including those
without dry cleaning capacity (U.S. Department of Coheres, 1990b) .
Establishments with over $100,000 in annual receipts account for approximately
14 percent of the establishments and more than half of the receipts at plants
with dry cleaning operations. Establishments that collect less than $25,000
in annual receipts account for about 17 percent of the plants and less than 4
percent of receipts at plants with dry cleaning operations. Nearly one half
of all plants in this sector with dry cleaning operations are in the $25 to
$50 thousand receipts range.
2-7
-------
TABLE 2-6. 1991 DISTRIBUTION OF RECEIPTS FOR COIN-OPERATED
ESTABLISHMENTS WITH DRY CLEANING CAPACITY ($1989)
Annual
Receipts Number of
($000/yr> Establishments3 Percent
0-25
25-50
50-75
75-100
>100
Total
523
1,451"
475
169
426
3,044d
17.19
47.70
15.61
5.49
14.00
100.00
Total
Annual
Receipts15
($000/yr)
9,248
58,706
31,835
15,669
140,571
256,029
Average Annual
Receipts Per
Establishment0
Percent ($/yr)
3.61
22.93
12.43
6.12
54.90
100.00
17,683
40,459
67,021
93,829
329,978
-
aThe distribution of establishments is based on the distribution of all coin-
operated laundries with payroll (including those without dry cleaning
capacity) reported in the 1987 Census of Service Industries (U.S. Department
of Commerce, 1991b).
bAverage annual receipts multiplied by the number of establishments.
cBased on data reported'in the 1987 Census of Service Industries, Subject
Series (U.S. Department of Commerce, 1990) for coin-operated laundries with
payroll converted to $1989 using the CPI for Apparel and Upkeep.
dRadian 1991a.
Projected 1991 annual receipts to coin-operated laundries with dry
cleaning operations total $256 million. However, only about. 10 percent or
$25.6 million in receipts are directly from dry cleaning activities in the
coin-operated sector. Dry cleaning output for this sector totals 4,298 Mg per
year. Output is computed based on an average price of $6.34 per kilogram of
clothes cleaned at plant-operated facilities and $1.65 per kilogram for self-
service facilities. Table "2-7 shows the total dry cleaning output and the
average output per establishment by income category for the coin-operated
sector.
2-8
-------
TABLE 2-7.
1991 DISTRIBUTION OF DRY CLEANING OUTPUT IN THE COIN-OPERATED
SECTOR
Annual
Receipts Number of
($000/yr) Establishments3 Percent
0-25
25-50
50-75
75-100
>100
Total
523
1,451
475
169
426
3,044C
17.19
47.70
15.61
5.49
14.00
100.00
Total
Annual
Outputb
(Mg/yr)
179
1,138
616
317
2,217
4,468
Average Annual
Output Per
Establishment53
Percent (kg/yr)
4.01
25.47
13.79
7.10
49.62
100.00
343
784
1,297
1,878
5,205
*The distribution of establishments is based on the distribution of all coin-
operated laundries with payroll (including those without dry cleanina
Ca "8? ^^ °f ""ic (U.S. Decent
"Receipts from Table 2-6 multiplied by the share of receipts from dry cleanina
act^ties (10%, divided by the 1989 base price. Base P?ice for coin- *
operated (self-service) is $1.65 per kg. Base price for coin-operated
(plant-operated is $6.34 per kg. See Table 2-13 for the share of plant-
operated and self-service establishments in each receipts category.
r\d.
Price information is unavailable for the coin-operated sector. Based on
conversations with industry officials, plant-operated facilities probably
charge the same price as commercial facilities or $6.34 per kilogram (Torp,
1990). A survey of two coin-operated facilities with self-service machine!
indicated that they both charge $6.00 to run one cycle in a 3.6 kilogram
capacity machine. Presumably, these facilities are representative of the
sector and $6.00 is the average price to use a 3.6 kilogram self-service coin-
operated machine. Thus, the average price to clean one kilogram of clothing
is calculated to be $1.65.
2.1.3 Industrial Sector
The industrial sector supplies items such as laundered uniforms, wiping
towels, floor mats, and work gloves to industrial or commercial users.
industrial laundries provide services for a diverse group of industrial and
2-9
-------
commercial users including auto service and repair shops, food processing
plants, manufacturing concerns, construction firms, hotels, restaurants,
security firms, banks, and real estate companies. The commercial or
industrial user usually rents the items from the industrial launderer who
provides pick-up, laundry, and delivery services for the consumer on a regular
basis (Coor and Grady, 1991).
Service agreements between the industrial launderers and their customers
to provide clean uniforms generally specify the number of changes per employee
and a schedule for delivery of the rented items. For example, the typical
agreement for uniform rental specifies that the industrial launderer provide
11 changes of clothing per employee per week including 5 clean suits left with
the customer, 5 dirty suits taken back to the laundry, and 1 transition suit
(the garment worn by the employee of the customer firm at the time of
delivery). Items are generally delivered and collected at the same time each
week (Coor and Grady, 1991).
According to Census data 1,379 industrial laundry facilities with
payroll were operating in 1987. Over 90 percent of these establishments
receive annual receipts over $100 thousand (U.S. Department of Commerce,
1990b). For this analysis, it is assumed that all industrial launderers with
dry cleaning capacity have annual receipts of over $100 thousand.
Approximately 325 industrial launderers have dry cleaning capacity. Of these
about 40 percent (or 130) use PCE and 60 percent (or 195) use petroleum
(Sluizer, 1990) .
Annual receipts for industrial facilities with dry cleaning capacity
total approximately $977 million. On average, about 35 percent of the
receipts at facilities with dry cleaning capacity are from dry cleaning
activities with the balance from water washing or other activities. Using an
average price of $2.00 per kilogram of clothes cleaned, the estimated total
dry cleaning output from commercial facilities is 170,901 Mg per year.
Price data are unavailable for the industrial sector. Therefore, a
small survey was conducted to determine the average price charged to provide
one clean uniform weighing approximately one kilogram. Prices ranged from
$1.75 to $2.25 per change. A representative from an industry trade
2-10
-------
association confirmed that these prices are representative of the prices
charged in the industry (Sluizer, 1990). The midpoint of the range ($2.00) is
assumed to be the average base price for the industry.
2.2 PRODUCTION HISTORY AND TRENDS
Although dry cleaning technology has existed for many years, the
industry did not experience widespread expansion until the 1960's. A deep
recession in the early 1970's eliminated part of the industry, but the late
1970's and early 1980's saw a resurgence of dry cleaners (Fischer, 1987).
During the 1950's, petroleum was the principle solvent in dry cleaning
plants. The 1960's brought a shift toward chlorinated solvents (e.g., PCE, F-
113) that has continued to the present. The main reason for the shift was the
widespread implementation of fire codes during' this period. In addition, an
existing new source performance standard (NSPS) for petroleum-based dry
cleaning restricts'the use of this solvent in new facilities. Because none of
the chlorinated solvents exhibit the flammable properties of petroleum, the
large number of plants built in shopping malls and suburban areas since the
1960's has been based on chlorinated-solvent technology (ICF, 1986).
Currently, a vast majority of all dry cleaners use PCE. However, demand
for PCE by the dry cleaning industry has been declining and is expected to
continue to decrease slowly due to greater recycling and lower solvent
emissions from equipment (ChBm.inftl MnrTrftflnff P-ror1-»r, 1986) . The economic
incentive for self-imposed emission reductions and solvent recycling has
persuaded several plants to install control devices and/or switch to more
efficient machines voluntarily.
No direct measurement of the quantity of clothes dry cleaned per year is
available for the dry cleaning industry." However, an estimate of aggregate
output can be derived through the quotient of total receipts for dry cleaning
activities and an average price per kilogram of clothes cleaned. Historical
information on average bise prices and total receipts is available only for
the commercial sector; statistics compiled for the industrial and coin-
operated sectors do not distinguish between those facilities that dry clean
and those that launder with water. The base price in the commercial sector is
2-11
-------
the price charged to clean a standard two-piece men's suit weighing one
kilogram. As seen in Table 2-8, the average base price and total annual
receipts measured in 1989 dollars increased by over 50 percent from 1974 to
1988. Total output for the sector measured in kilograms of dry cleaned
clothing declined from the mid 1970's to the early 1980's. From 1981 to 1988,
dry cleaning output increased by approximately one third.
Table 2-9 presents annual growth rates for each sector of the dry
cleaning industry. These estimates are based on machinery sales and are
therefore broken down by machine type as well as sector. Other factors
considered include machine life, current and historical sales data, and
replacement rate of the machinery. Predictions indicate that the commercial
sector will be the only sector to experience positive growth, at just over 2
percent per year. Both the industrial and coin-operated sectors are estimated
to show negative annual growth rates of approximately 5 percent and 7 percent,
respectively. These growth rates do not predict overall growth in output for
the coin-operated and industrial sectors, because dry cleaning activities
account for only a small portion of total output in these sectors.
Several factors have contributed to the trend away from coin-operated
dry cleaning. Because of environmental regulations, consumers are
increasingly aware of the hazards of operating coin-operated machinery and
handling the cleaning solvents. The decline is also due in part to more
expensive dry cleaning equipment, questionable returns on dry cleaning
activities in this sector, and the necessity of hiring an attendant. These
factors combine to make coin-operated dry cleaning operations unprofitable
(Torp, 1990) .
2-12
-------
TABLE 2-8.
ANNUAL RECEIPTS, AVERAGE BASE PRICE, AND TOTAL OUTPUT FOR
COMMERCIAL DRY CLEANERS ($1989)a
Year
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
Total
Annual Receipts
($106/yr)a
2,692
2,630
2,623
2,675
2,825
2,878
2,975
2,941
3,517
3,638
3,694
3,764
4,390
4,287
4,265
======
Average
Base Price
<$/kg)a
4.02
4.42
4.46
4.36
4.87
4.90
5.32
5.63
5.72
5.87
5.98
6.13
6.14
6.05
6.08
Includes receipts for facilities with payroll only.
Total Dry
Cleaning Output
(106 kg/yr)b
•••M^MV
570
506
499
521
493
499
475
444
522
527
525
522
608
603
596
All dollar figures
Index for Apparel and
BS)
(85%)
Source: Faig, 1990.
h of ^ceipts from dry cleaning activities
by average base price per kg.
TABLE 2-9.
ANNUAL GROWTH RATES BY MACHINE TYPE AND SECTOR (1986-1989)
Sector
Commercial
Coin-
Operated
Industrial
Dry-to-dry
9%
-7%
Transfer
Total
-7%
N/A_
2%
-7%
Note: Growth rates are estimates based on Section 114 information. Considered
at, 5esan;sirates WT mrhine iife'current saies data' **pi«*™™
and 5- and 10-year sales data. Total annual growth rate is weighted
according to the machine populations in each sector. "eignted
Source: Radian,1991a.
2-13
-------
The negative growth rate in industrial dry cleaning reflects increased
costs of dry cleaning due to state regulations as well as the advent of
polyester/cotton and polyester/wool blends that made water washable fabrics
feasible even for dress clothes. In the 1980's, industrial cleaning plants
have moved away from dry cleaning their output and toward laundering with new
detergent formulations. Between 1980 and 1985, the number of industrial
facilities that dry cleaned clothing dropped by approximately 50 percent (ICF,
1986). Virtually all the garments currently processed by industrial
launderers are water washable. However, some industrial launderers continue
to dry clean at least a portion of their water washable garments because dry
cleaning increases the life of the garment and enhances the garment's
appearance (Coor and Grady, 1991). An estimated 92 percent of the garments
cleaned by industrial facilities are laundered in water and detergent, and
this percentage is expected to continue to increase (Sluizer, 1990) .
2.3 PRODUCTION PROCESSES
Dry cleaning services generally include cleaning, pressing, and
finishing articles of clothing and other related products. In all three
sectors, the dry cleaning process is almost identical to laundering in water
except that a solvent, such as PCE, is used in place of water and detergent.
The coin-operated sector is the only one that does not regularly provide
pressing and finishing services. The processes, machinery,, and controls in
each sector of the dry cleaning industry are detailed in this section.
2.3.1 Machine Types
Two types of machines are commonly used in the dry cleaning industry:
dry-to-dry and transfer. Dry-to-dry machines combine washing and drying in
one machine and, therefore, do not have a separate machine for drying.
Transfer machines, like the traditional laundry machines for water washing,
consist of separate machines for washing and drying.
Most dry cleaning plants have one or more attachments to their dry
cleaning machine. These include solvent filters, distillers, and vent
controls. Figure 2-1 shows the typical configuration of a dry cleaning
2-14
-------
Solvent in JU:
Condensed Solvent
Figure 2-1.
Source: Safety-Kleen, 1986.
of . Dry cleaning Machine and the Various
machine and the various attachments. Solvent filters remove impurities from
the solvent and return the "clean" solvent to the solvent tank. Stills remove
any impurities left in the solvent after it is filtered as well as water and
detergent mixed with the solvent in the washing process through a distillation
process. Virtually all dry cleaning facilities have solvent filtration
systems and about 80 percent use stills. These devices extend the life of the
solvent and reduce the amount of solvent that must be purchased (Safety-Kleen
1986) .
Approximately 60 percent of all PCE dry cleaning machines have vent
control devices (Radian, 1991c) . Vent controls are attached to the dryer and
remove vaporized solvent from the dryer emissions. Vent control devices are
available in two basic types: carbon adsorbers (CA's) and refrigerated
condensors (RC's) . with the use of a CAf PCE emissions are trapped in a
carbon filter. The filter then undergoes a condensation process that
2-15
-------
eliminates the hazardous emissions. A typical CA lasts about 15 years and
reduces emissions by about 95 percent when operated properly. The second type
of control device, the RC, uses a refrigerated coil to cool PCE vapors. This
cooling process results in condensation of PCE emissions. The average life of
a RC is about 7 years. The emission reduction achieved by RC's differs
depending on the type of dry cleaning machine used. Refrigerated condensers
reduce vent emissions by 85 percent on transfer machines and by about 95
percent on dry-to-dry machines.
Over 90 percent of new dry-to-dry machines built for the commercial and
industrial sectors have built-in RC's (Federal Register. 1989). Add-on
control devices may be purchased and attached to machines that are not
equipped with vent controls from the manufacturer. A facility's selection of
control devices is constrained by the capacity of its dry cleaning machine.
Add-on RC's are not available for the very small machines built for the coin-
operated sector or for the large machines built for the industrial sector.
Both types of add-on devices are available to retrofit virtually all machines
built for the commercial sector.
Owners and operators of dry cleaning facilities purchase add-on vent
controls and attach them to their dryer for a variety of reasons. Some states
require dry cleaners to control their emissions using a vent control device.
Environmentally conscious owners may install vent controls even in the absence
of state regulations. Depending on the price paid for solvent and the amount
of solvent saved, some owners may realize a cost savings from reduced solvent
consumption with a vent control.
2.3.2 Solvents
Four solvents are currently in use in the dry cleaning industry: PCE,
fluorocarbon 113 (F-113), petroleum, and 1,1,1-trichloroethane (1,1,1-TCA).
Of these four, PCE is usually considered the most efficient cleaner. Five
main factors determine the suitability of a solvent for dry cleaning, each
with a range of acceptable values, as opposed to an absolute standard (Busier,
1980) :
2-16
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*" Unpleasant odor in garments after
stability is important to prevent damage to the metals used
in dry cleaning machinery. «ocu
A certain level -of volatility is desirable to permit rapid drying and
economical reclamation through distillation. arying and.
The solvent should be compatible with common detergents used in the
The importance of PCE to the dry cleaning process depends on the ease with
which it can be replaced by another comparable solvent. The potential for
solvent substitution should be evaluated against the criteria established for
the factors listed above.
F-113, petroleum, and 1,1,1-TCA can all theoretically be substituted for
PCE in the dry cleaning process. However, none of these solvents will perform
with the same degree of efficiency as PCE. Thus, an owner of a dry cleaning
plant will need to ponder various considerations associated with solvent
substitution. These factors include solvent prices, cleaning properties,
capital costs, and operating costs. An additional factor in the substitution
decision is the ease with which machinery designated for use with one solvent
can be converted to accept other solvents.
Although all three alternative solvents are used in some dry cleaning
plants, none are currently considered feasible for widespread" substitution for
PCE. F-113 most closely matches the cleaning abilities of PCE but is
unsuitable for certain garments and stains. In addition, the possibility of
regulations concerning ozone depletion may limit any immediate substitution.
Finally, the unit price of F-113 is considerably higher than the unit price of
PCE. Fire codes will probably prevent any substantial shift to petroleum, the
second solvent. The remaining solvent, 1,1,1-TCA, has yet to attract much
interest in this country. Its cleaning abilities are questionable because of
high solvent aggressiveness and instability. In addition, usage costs are
approximately ten times higher than for PCE (Fisher, 1990a) even though
trichloroethane users can achieve energy savings of 5 to 10 percent (Fisher,
1987) .
2-17
-------
Technically, one other substitute for PCE is available. Industrial dry
cleaners can switch to laundering garments with water and detergent for most
items. The commercial and coin-operated sectors do not have this flexibility
because the customer owns the item to be cleaned and, therefore, specifies the
cleaning method.
Approximately 28,000 of the 34,000 dry cleaning plants in the United
States use PCE as a cleaning solvent (see'Table 2-1). . Most of the remaining
plants use a petroleum-based solvent, and a small percentage use either F-113
or 1,1,1-TCA. Approximately 85 percent of total dry cleaning output from
commercial facilities is processed using PCE. Virtually all coin-operated
facilities with dry cleaning capacity use PCE. Solvent use in the industrial
sector is divided between PCE (40 percent) and petroleum (60 percent)
(Sluizer, 1990).
Figure 2-2 shows the percentage of total PCE consumed by each sector.
The commercial sector accounts for approximately 94.3 percent of total PCE
consumption by the dry cleaning industry. The industrial sector and the coin-
operated sector account for 4.6 percent and 1.1 percent of consumption,
respectively.
Coin-Operated Sector
(1.1%)
Industrial Sector
(4.6%)
Commercial Sector
(94.3%)'
Figure 2-2. PCE Consumption by Sector for 1991
Source: Radian, 1990b.
2-18
-------
2.3.3 Product j on
The flow of production is basically identical in coin-operated (plant-
operated) and commercial facilities. The production process begins when the
dry cleaning plant receives the soiled garment from the consumer. After a
garment enters the plant, a minimum of 10 steps of production are required to
produce a clean garment ready for delivery. These steps of production are
described below:
• lagging— Tagging typically involves attaching a tag to the garment
with a unique identification number for each customer. A record is
made of the customer's name, the corresponding tag number, any'
special instructions, and the promised delivery date.
• Initial Classifying— Garments are separated into three basic
categories at this stage of production: garments that require dry
cleaning but no pre-spotting, garments that require laundering but no
,pre-spotting, and garments that require pre-spotting.
• Applying Spotting Cheminn In— Garments stained with ink, paint, food
h^othe^SUbStan?eS are treated with solvents and other compounds '
before they are laundered or dry cleaned.
• Further Classifying— Garments are further classified by the type of
fabric and the color of fabric. This step is required because
garments with different fabric types and colors require different
treatment and can be damaged if they are processed with garments of
dissimilar fabric type or color.
• Waahing— In dry cleaning operations, garments are washed in a solvent
mixture comprised of solvent, water, and detergent. The correct
f!±!in"10n °f solvent' water' and detergent and the correct washing
temperature are vital to the successful removal of soil without
damaging the garment. The washing step ends with extraction of the
excess solvent mixture.
• Drying-After garments are washed and the excess moisture removed,
they are dried using heated air. Garments may be transferred to a
separate machine for drying (transfer machines) or dried in the same
Se m^h-^;"0^ machines> used to »"» the garments depending on
the machine technology employed by the facility.
flnd Finishing— Clean, dry garments are pressed and finished
Finishing includes replacing damaged or missing buttons, special
that
Hanoina—Gannents are placed on hangers in this step of the
production process.
2-19
-------
• Assembling—After they are placed on hangers, garments are sorted and
assembled by consumer identification number on the tag attached to
the garment and by promised delivery date.
• Packaging-—Assembled garments are packaged for delivery. Packaging
typically involves placing a plastic bag over the garments.
Garments are inspected periodically throughout the process described
above to determine the success in removing soil and the acceptability of the
pressing and finishing steps. Additional steps may be required for heavily
soiled garments, oversized items, or delicate garments that require special
handling. The production process ends with delivery of the* cleaned, pressed,
packaged garments to the consumer.
Production of clean clothes at coin-operated (self-service) facilities
involves the consumer as an active participant. The facility provides the
equipment used in the washing and drying process and the individual provides
the labor inputs required for the spotting, pressing, and finishing of the
garment. The process of producing clean clothes is similar to that described
above for commercial and coin-operated (plant-operated) facilities excluding
the tagging, assembling, and packaging steps.
Unlike customers in the commercial or coin-operated sector, customers of
industrial cleaners do not deliver the soiled items to the cleaning facility.
Rather, the industrial cleaner collects the soiled items from the commercial
or industrial user on a regular basis at no additional charge to the user.
The production process begins when the soiled garment enters the
industrial plant. The steps of production are similar to those described
above for commercial and coin-operated (plant-operated) facilities. A few
differences do exist, however. Garments cleaned by industrial facilities
generally contain a permanent identification number that identifies not only
the company purchasing the dry cleaning service but also the individual that
actually wears the garment, the route number, and the day of the week
scheduled for delivery of the cleaned items. The process generally requires
less classifying beyond the initial classifying because garments are more
homogeneous with regard to fabric type and color. In addition, the process is
generally more mechanized and larger in scope than the process at a typical
commercial or coin-operated (plant-operated) facility. The production process
2-20
-------
ends with the delivery of the cleaned item to the customer on the promised
delivery date.
2.4 -COSTS OF PRODUCTION
Costs of production in the dry cleaning industry can be classified as
either fixed or variable costs. Fixed costs are incurred regardless of the
level of production. Two types of fixed costs exist: those that occur only
once at the start-up of a business and those that regularly recur. Variable
costs depend on the level of production at a plant and fall to zero if the
plant ceases operations entirely. These three categories of costs are
described below:
(1) Fixed start-up costs: the costs associated with the decision to
open a dry cleaning plant,
(2) Fixed recurring costs: the costs associated with the decision to
operate the dry cleaning plant, and
(3) variable costs: the costs associated with the decision to operate
the dry cleaning plant at a given level of output.
The first category of costs includes most, if not all, capital costs as well
as long-term materials contracts and capacity investments. Table 2-10 shows
the capital costs of new dry-to-dry machines. In addition, some
administrative fees and initial building overhead costs, such as remodeling or
down payment, are included in this category of costs. These expenses are the
fixed costs that are incurred regardless of the level of production or whether
the firm operates at all. Total estimated start-up costs typically range from
$95 to $120 thousand (Faig, 1991).
Table 2-11 displays information on the second and third categories of
costs for commercial dry cleaning facilities by output level. On average,
total wages and salaries account for the largest portion of dry cleaning costs
followed by rent/building overhead expenses or total supply cost. The
majority of costs incurred by a dry cleaning plant are variable such as
solvent, labor, and energy costs. Table 2-12 provides unit price information
for the major inputs that contribute to the variable costs of operating a dry
cleaning facility.
2-21
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TABLE 2-10. CAPITAL COSTS OF NEW DRY-TO-DRY MACHINES ($1989)
Machine Capacity (kg/load)
11.3
13.6
15,9
20,4
22.7
27.2
45.4
63.5
113.4
Capital Cost ($)
26,046
27,820
29,594
42,171
44,040
47,040
65,255
104,000
157,000
Source: Radian, 1990a.
Dry cleaning plants have relatively small capital equipment costs,
although these vary between the sectors. In addition, the buildings used by
many plants are rented or easily transferable to other uses. As a result, the
relatively high variable cost to fixed cost ratio at most dry cleaning
facilities promotes a dynamic industry structure in which the less efficient
plants quickly terminate operations if losses become excessive.
The decision to open a new plant must be evaluated based on the costs
included in all three categories above., However, for existing facilities,
costs in category 1 are sunk and do not. affect the owner's decision to
continue operating. Production cost for existing and new facilities are
discussed below.
2-22
-------
TABLE 2-11.
AVERAGE ANNUAL OPERATING COSTS FOR COMMERCIAL DRY CLEANING
PLANTS
Annual Output (kg/yr)a
Cost Category
Fixed Recurring Costs
Wages and Salaries1"
Rent or Building Overhead
Depreciation
Interest and Bank Charges
Insurance
2,378
3,542
1,316
1,272
779
576
5,436
8,078
3,002
2,901
1,776
1,315
8,985
13,383
4,973
4,805
2,942
2,178
12,580
18,736
6,962
6,728
4,119
3,049
40,775
81,727
20,955
11,922
3,163
7,786
Variable Costs
Wages and Salaries
Total Supply Cost
Outside Work
Payroll taxes
Advertising
Utility-Fuel
Repairs and Maintenance
Utility-Electricity
Office Expense
Administrative Expense
Utility-Water and Sewage
Claims
Miscellaneous
Total Costs
3,024
1,541
1,437
541
435
360
312
268
259
241
117
92
908
6,898
3,515
3,277
1,234
991
821
712
611
591
550
267
210'
2,071
11,428
5,824
5,429
2,044
1,642
1,361
1,180
1,012
979
911
442
340
3,431
16,000
8,154
7,600
2,862
2,299
1,905
1,651
1,417
1,370
1,276
619
488
4,804
58,722
23,175
15,876
12,470
10,949
6,661
6,813
8,394
3,498
4,015
3,224
1,247
10,707
"lible 2-2thS 3Verage annual receiPts for five income categories reported in
blncludes owner's wages.
Source: International Fabricare Institute, 1989; Fisher, 1990b.
2-23
-------
TABLE 2-12. AVERAGE INPUT PRICES FOR PCE DRY CLEANING FACILITIES ($1989)
Input Price
Material
Perchloroethylene $0 . 683/kg
Energy
Electricity $0 . 0710/kWh
Steam $6.13/1000 Ib
Labor
Operating labor $5 . 94/hr
Maintenance labor $6 . 53/hr
Source: Radian,1990d.
2.4.1 Costs of Production for Existing Facilities
The short-run supply curve of an existing dry cleaning facility is the
portion of its marginal cost curve that lies above the minimum point of its
average variable cost curve. In other words, facilities will continue to
supply dry cleaning services in the short run as long as they can cover their
variable costs of production. The market supply curve is the horizontal
aggregation of the supply curves for all facilities in the market. This
aggregation is characterized in the step supply function (see Figure 2-3)
where the producer with the highest marginal cost in the market sets the
market price of dry cleaning services.
Lower cost producers are able to cover some or all of their fixed costs
because the market price is above their average variable cost. Differences in
the production costs across producers are attributed to differences in
management practices as well as differences in the productivity of capital
equipment. Assuming that the producti%^ity of dry cleaning equipment has been
increasing over time, owners of new equipment would tend to have lower
marginal costs than owners of older equipment, ceterus paribus.
2-24
-------
($/kg>
Market
Price
Market
Quantity
(kg/yr>
Figure 2-3. Market Supply Curve for Existing Facilities
An increase in the price of a variable input changes the facility's
average variable cost and its marginal cost. Changes in the marginal cost of
producing dry cleaning services would cause a shift in the supply of dry
cleaning services resulting in price and output adjustments at least in the
short run.
2-4.2 Costs of Product, on for Mew Faci Hr-if.fi
An entrepreneur contemplating construction of a new dry cleaning
facility won't invest unless he/she anticipates covering total costs. By
definition, total cost for a new facility includes fixed start-up costs
including a normal return, fixed recurring costs, and variable costs. If the
average total cost of opening a new dry cleaning plant is above the market
price, no new entry will occur. Conversely, if the average total cost is
below the market price, new entry will occur (see Figure 2-4). Therefore, any
2-25
-------
($/kg)
New
Facility
Costs
Market
Price
ATC
AVC
Market (kg/yr)
Quantity
Figure 2-4. New Facility Costs Compared to Market Supply Curve for
Existing Facilities
increase in the marginal costs of existing producers not affecting new
suppliers would have the effect of encouraging new entry into the market. The
entry of a new facility into .the market displaces the marginal existing
supplier. As the marginal suppliers are displaced in the market, price falls.
This process continues until price equals the average total cost of building a
new facility. Long-run price and output equilibrium? therefore, depends on
the average total cost of building a new facility. Once a new facility is
constructed, the fixed costs become sunk costs and only the variable costs are
relevant to the decision to continue operating the facility. The facility
continues to supply dry cleaning services as long as price exceeds average
variable cost.
2-26
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2.5 MODEL FACILITY PROFILE
The abundance of dry cleaning establishments precludes an approach that
investigates the impacts of candidate regulatory alternatives on a facility-
specific level. Ignoring the resource costs of collecting data for such a
large sample, computational time alone diminishes the feasibility of a
facility-specific approach. Consequently, a model plant approach is used in
which fifteen model plants represent the characteristics of average PCE
facilities in each sector. Table 2-13 presents operating parameters of the
model plants by industry sector, machine size, and process. In addition, the
distribution of PCE facilities represented by each model plant is reported for
five output levels. These output levels correspond to ranges of annual
receipts shown in Table 2-13.
The model plants were chosen to represent the variability in machine
size and technology that is present among existing facilities in the industry.
The coin-operated sector has basically only one machine size and design.
However, two model facilities in this sector are differentiated by the base
price charged for dry cleaning services and the type of service supplied
(self-service or coin-operated). Ten model plants for the commercial sector
and three model plants were selected for the industrial sector. Most of the
contemporary dry cleaning facilities are purchasing dr'y-to-dry machines to
save on. solvent costs, to comply with a recently promulgated worker exposure
regulation, and to reduce the.environmental impact of PCE emissions.
Nevertheless, some facilities continue to operate with transfer machines, and
that portion of the industry is represented through appropriate model plants.
2-27
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TABLE 2-13. MODEL PLANT DESCRIPTION AND THE DISTRIBUTION OF PCE FACILITIES BY INDUSTRY SECTOR AND INCOME LEVEL
N3
CO
Total Number
Facilities Number Facilities in Each Model Plant Category
Industry Machine Operating Per Model by Income Level ($000/yr)
Sector and Model Machine Capacity Days Per Plant
Plant Number Type (kg/load) Year Category
Coin-Operated
1 (self-service) dry-to-
dry
2 (plant- dry-to-
operated) dry
Total
Commercial
3
4
5
6
7
8
9
10
11
12
Total
dry-to-
dry
dry-to-
dry
dry-to-
dry
transfer
dry-to-
dry
dry-to-
dry
transfer
dry-to-
dry
dry-to-
dry
transfer
3.
3.
11.
13.
15.
15.
20.
22.
22.
27.
45.
45.
6
6
3
6
9
9
4
7
1
2
4
4
312.
250
250
250
250
250
250
250
250
250
250
250
213
2,831
3,044
2,639
1,766
9,761
7,665
2,753
2,317
1,918
91
1,605
726
24,947
0-25
42
481
523
1,355
901
1,838
2,748
0
0
0
0
0
0
6,822
25-50
117
1,334
1,451
527
440
1,336
1,403
353
183
28
0
0
0
4,270
50-75
38
437
475
430
274
888
628
296
91
25
0
0
0
2,632
75-100
16
153
169
155
0
532
603
645
284
219
57
137
0
2,632
>100a
0
426
426
128
76
2,584
1,142
730
880
823
34
1,468
726
8,591
CONTINUED
-------
TABLE 2-U.
ro
(CoSTlS DESCRIPTI°N AN° THE "STRIB07.IOH OF PCE FACILITIES BY INDUSTRY SECTOR
==================
Total Number
Mo
Source: Radian 1991c; Radian 1990c.
LEVEL
-------
SECTION 3
DEMAND FOR DRY CLEANING SERVICES
Two types of demand exist for dry cleaning services: household demand
and industrial demand. Household demand is characterized by individual
consumers purchasing dry cleaning services provided by commercial and coin-
operated facilities. Industrial demand is characterized by firms purchasing
dry cleaning, services to clean employee uniforms in production and service
establishments. Typically, employers rent these uniforms from an industrial
cleaner who provides regular cleaning and delivery services. The subsequent
sections discuss household demand and industrial demand in detail.
3.1 HOUSEHOLD DEMAND
As consuming units, households demand clean, pressed clothes. Because
some garments retire dry cleaning for proper care, households rely on dry
cleaning services provided by others to procure clean, pressed clothes. Two
types of dry cleaning services—commercial and coin-operated—are available to
households. Commercial facilities and coin-operated (plant-operated) provide
a complete service: garments are cleaned, pressed, and packaged for the
consumer. At self-service coin-operated facilities, consumers pay for using
dry cleaning machines, but they must clean and press their own clothes.
Despite some similarities in the influences of demand for these services,
these two sectors have experienced different growth patterns.
The subsections below discuss different facets of household demand. The
first two subsections explore consumption patterns and characteristics of the
consumers of dry cleaning services. The next subsection discusses the theory
of household production in the context of dry cleaned clothing. How consumers
value their time and their choice between coin-operated and commercial
facilities is presented in the fourth subsection. The final subsection
briefly examines consumer sensitivity to changes in the price of dry cleaning
services.
3.1.1 Consumption anri
Household consumption of commercial dry cleaning services can be
measured in terms of the total weight of clothes dry cleaned or in terms of
3-1
-------
total expenditures on dry cleaning services. Figure 3-1 shows that overall
consumption, measured by the total weight of clothing deemed, increased by
more than 25 percent from 1980 to 1988. However, on a per-household basis,
demand for dry cleaning services increased only 11 percent during this period.
Consumption per household reached its peak in 1986, when the average household
consumed almost 7 kilograms per year. This pattern is depicted in Figure 3-2.
Table 3-1 shows household consumption in terms of expenditures. These
data are calculated from the Consumer Expenditure Surveys (U.S. Department of
Labor, 1991a). The survey compiles average annual household expenditures for
a broad category called "Other Apparel Products and Services."1 This category
encompasses a wide range of goods and services, including material for making
clothes, shoe repair, clothing alterations and repairs, sewing supplies,
clothing rental, clothing storage, coin-operated laundry and dry cleaning,
commercial laundry and dry cleaning, watches and jewelry, and watch and
jewelry repair.
Expenditures on commercial laundry and dry cleaning services were
estimated in the following manner. Detailed information on the relative
weight of each category item (listed above) used to compile the Consumer Price
Index was available for the period 1982-1984 (Hanson and Butler, 1987). Based
on those relative weights, expenditures on laundry and dry cleaning services
(excluding coin-operated) made up about 25 percent of the category for those
years. The expenditures for each category item listed above were available
for 1989. Approximately 24 percent of the category expenditures were spent on
laundry and dry cleaning (excluding coin-operated). The expenditures reported
in Table 3-1 represent 25 percent of the "Other Apparel Products and Services"
category.2 Because the portion of the category attributed to laundry and dry
1The expenditures on apparel items come from the interview portion of
the Survey. Because the reported expenditures are based on the consumer's
memory, these data may not accurately reflect receipts at commercial dry
cleaning establishments.
2For the years 1980-1983, only data on urban consumers were available.
The expenditures estimated in Table 9-15 were adjusted to reflect all consumers
in the following manner. In 1989, urban consumers spent three times what rural
consumers did on commercial dry cleaning services; that relationship was
assumed to hold for the years 1980-1983. In addition, rural households were
assumed to comprise 16 percent of all households, which is approximately the
portion that they comprised for the years 1984-1986. The reported estimates
are a weighted average of urban consumer spending and rural consumer spending.
3-2
-------
700 T-
600
500 --
Millions 400
of
Kilograms
per
Year 300
200
100
I gSggffKWSf I
1980 1981 1982 1983 1984 1985 1986 1987 1988
Figure 3-1. Total Annual Household Consumption of Commercial Dry Cleaning
Services (1980-1988)
Source: Table 2-8
3-3
-------
6 --
Millions 4
of
Kilograms
per
Year 3
1980 1981
1982
1983 1984 1985 1986 1987 1989
Figure 3-2 Annual Consumption of Commercial Dry Cleaning Services per
Household (1980-1988)
aComputed by dividing total dry cleaning output (Table 2-8) by the total
number of households in the U.S. reported in Statistical Abstract of the
United States (U.S. Department of Commerce, 1991d); U.S. Department of
Commerce, 1991.
3-4
-------
TABLE 3-1. HOUSEHOLD EXPENDITURES ON COMMERCIAL LAUNDRY AND DRY CLEANING
SERVICES 1980-1989 ($1989) <-wwiwiw,
Average
Annual Household
Expenditures Increase
Year ($/Household/year)& (%)
Expenditures Total Annual
as a Share Household
of Income Expenditures Increase
($106/yr)c
1980
1981
1982
1983
1984
1985
1986'
1987
1988
1989
^••••-•••IwMHSS^SSSSS
62.18
57.58
55.96
58.95
62.95
67.70
66.75
68.49
67.35
66.50
-
-7.4
-2.8
5.3
6.8
7.5
-1.4
2.6
-1.7
-1,3
======————
0.15
0.14
0.14
0.14
0.14
0.15
0.15
0.15
0.14
0.14
—
5,022
4,757
4,675
4,947
5,377
5,876
5,905
6,129
6,132
6,173
-5.3
-1.7
5.8
8.7
9.3
0.5
3.8
.O'.l
0.7
Represents 25 Percent of "Other Apparel Products and Services." Original
data for 1980-1983 excluded rural consumers and were adjusted to incSde
rural consumers. Converted to 1989 dollars using all items CPI
Based on before tax income. Income calculated by multiplying national
personal income by the number of households.
Average household expenditures multiplied by number of households.,
198°-1D989 Consumer Expenditure Survey, U.S. (Department of Labor,
Unted Staes1111 Hi T °? °* *** President' "90, Statistical Abstract of the
Smmetce «5i) DeP"tment of Commerce, 1990d) ; U.S. Department of
cleaning expenditures remained fairly constant over time, the data
characterize commercial laundry and dry cleaning expenditures fairly well.
Approximately 85 percent of a typical consumer's commercial cleaning bill is
dry cleaning, as opposed to laundry (U.S. Department of Commerce, 1991).
Notice that, in 1980, households spent $62 a year on average; in 1989
that figure had increased to $67, an 8 percent increase. Aggregating across
the United States yields total expenditures of more than $5.0 billion in 1980
and $6.2 billion in 1989.
3-5
-------
Two main factors affecting the growth of dry cleaning consumption are
textile and lifestyle trends. During the 1970's, fashion trends demanded
easy-care fabrics. Because these fabrics, normally synthetic or a. synthetic
blend, do not necessarily require dry cleaning, consumption of dry cleaning
services decreased. Returning to more natural fibers and synthetic materials
that require dry cleaning for proper care led to increased consumption in the
1980's (Fischer, 1987).
The demand for. commercial dry cleaning services is also influenced by
general economic conditions as well as fashion trends. Prevailing economic
conditions influence the purchase of more expensive garments, which often
require dry cleaning for proper care. Another factor that increased household
demand for cleaning services is the increase in the number of women in the
work force. The impact on commercial cleaning comes from both the increased
opportunity cost of a working woman's time and the increase in the number of
women working outside the home. Table 3-2 shows the change in the number of
women in the work force and the median income for women for the period 1980-
1989.
Consumption at coin-operated facilities is also strongly affected by
general economic conditions, though sometimes for different reasons than
comme-rcial dry cleaning consumption. Historically, the cleaning volume at
coin-operated facilities plants has fluctuated with the economy.
Data on coin-operated consumption are sparse. However, the Census of
Services Industries does publish receipts for coin-operated laundry and dry
cleaning facilities. Caution must be exercised when applying these data to
the dry cleaning industry because the receipts include laundry receipts. In
1982, coin-operated laundry and dry cleaning establishments (with payroll)
across the United States took in $1,501 million in constant (1989) dollars
compared to $1,821 million in 1987 (U.S. Department of Commerce, 1990c). This
increase amounts to 21 percent. Receipts also increased in per-capita terms.
Per-capita expenditures expressed in constant dollars rose from $5.02 in 1982
to $6.83 in 1987.
3-6
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TABLE 3-2. NUMBER AND MEDIAN INCOME OF WOMEN IN THE WORK FORCE 1980-1989
($1989)
Year
1980
1981
1982
1983
1984
1985
1986 •
1987
1988
1989
Number of
Womena
(000)
42,117
43,000
43,256
44,047
45,915
47,259
48,706
50,334
51,696
53,027
By^""^^g^=g^g^^^^*-^^*"
Change
{%)
-
2.10
0.60
1.83
4.24
2.93
3.06
3.34
2.71
2.57
Median Incomeb
($1989)
17,443
16,994
17,558
18,038
18,406
18,730
19,057
19,173
19,439
N/A
====:
Change
(%)
•— •"«•— «-^«B.
-2.57
3.32
2.73
2.04
1.76
1.75
0.61
1.39
"Includes working women over the age of 16.
Source: Economic Report of the President, 1990.
3.1.2 Charactsrizai-i'on of
Although every individual probably owns at least a few garments that
require dry cleaning for proper care, individuals who use dry cleaning
services on a regular basis have identifiable characteristics. People's need
for dry cleaning services depends on the clothing they own and their '
occupation, which may dictate their clothing choices. White collar workers
are more likely to own clothing that requires dry cleaning for proper care.
Similarly, individuals in professional positions would utilize dry cleaning
3-7
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services more. By extension, individuals with higher incomes would be
expected to use dry cleaning services more often.
Consumer Expenditure Survey data for 1989 support these contentions.
Tables 3-3, 3-4, and 3-5 present data for two types of expenditures:
(1) expenditures on laundry and dry cleaning, excluding coin-operated and
(2) expenditures on coin-operated laundry and dry cleaning. These data are
compiled by income levels (see Table 3-3), occupation (see Table 3-4), and
location (see Table 3-5) . As indicated above, the expenditures for the
commercial sector are predominantly for dry cleaning services. This
assumption does not necessarily hold for the coin-operated sector, where the
majority of the expenditures are for laundry expenses. Caution must be
exercised when interpreting the coin-operated data.
As expected, expenditures on commercial dry cleaning increase with
income (see Table 3-3). An individual earning more than 550,000 a year spends
more than four times on dry cleaning than an individual earning less than
$30,000. These higher expenditures are induced by two factors. The first is
the need to dry clean most professional career clothing. The second is the
propensity for individuals with higher incomes to own luxury clothing (e.g.,
leather, suede), which requires dry cleaning for proper care. Also, as shown
in Table 3-3, coin-operated expenditures decline with income, although laundry
expenditures cannot be separated from the dry cleaning expenditures.
Figure 3-3 depicts this switch from coin-operated expenditures to
commercial expenditures as income rises. A point of further interest is that
expenditures on commercial cleaning are a relatively stable share of income
across all income levels. This stability suggests that any one income class
would not be more affected if prices increase.
Table 3-4 shows expenditures on commercial and coin-operated cleaning by
occupation classification. Individuals whose occupations fall in the
manager/professional category spend almost 83 percent more than any other job
category on commercial cleaning services. Individuals with, technical, sales,
or clerical positions spend more than $75 a year on commercial cleaning, which
is 135 percent more than any of the remaining categories.
3-8
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TABLE 3-3. HOUSEHOLD EXPENDITURES ON COMMERCIAL AND COIN-OPERATED DRY
CLEANING AND LAUNDRY SERVICES BY INCOME CATEGORY ($1989)
Income
Category13
<$000/yr)
5-10
10-15
15-20
20-30
30-40
40-50
over 50
comme
Cleaning
Average Annual
Expenditure
($/Household/yr)
17.40
18.57
30.57
42.06
62 .-13
90.75
175.93
ircial
Services3
Expenditures
as a Share of
Income*5 (%)
0.23
0.15
0.18
0.17
0.18
0.20
0.22
Coin-Operated
• Cleaning Services3
Average Annual
Expenditure
($/Household/yr)
45.90
42.14
41.92 .
43.76
. 35.06
23.95
15.81
Expenditures
as a Share of
Incomeb (%)
0.61
0.34
0.24
0.18
0.10
0.05
0.02
KL n h TK household expenditures are based only on those households
*£?* P^chase these services and do not take into account those households
that do not purchase each type of cleaning services. These estimates include
both laundry and dry cleaning expenses. Expenditures at commercial
establishments comprise mainly dry cleaning expenditures; only a small
portion of expenditures at coin-operated establishments constitute drv
cleaning expenditures. y
bBased on before-tax income.
Sl°9U91Ca7 198°~1989 Consumer Expenditure Survey (U.S. Department of Labor,
Finally, household cleaning expenditures differ greatly depending on the
geographic location (see Table 3-5). Urban consumers spend three times as
much on commercial cleaning than do their rural counterparts. This difference
in expenditures probably reflects occupation choices.
The Consumer F.yrenriJ,f,iirf Snrvry data reveal that the typical consumer of
commercial dry cleaning services is a manager or professional, earns more than
3-9
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TABLE 3-4.. HOUSEHOLD EXPENDITURES ON COMMERCIAL AND COIN-OPERATED DRY
CLEANING AND LAUNDRY SERVICES BY OCCUPATION CATEGORY
Commercial
Cleaning Servicesa
Occupation
Category
Manager/
Professional
Technical/
Average Annual
Expenditure
($/Household/yr)
138.28
75.68
Expenditures
as a Share of
Incomeb (%)
0.28
0.23
Coin-Operated
Cleaning Services3
Average Annual -
Expenditure .
($/Household/yr)
27.14
46.79
Expenditures
as a Share of
Incomeb (%)
0.06
0.14
Sales/
Clerical
Service
Workers
Construction/
Mechanics
Operators/
Labor
31.26
32.25
31.05
0.15
0.10
0.11
54.41
37.61
43.24
0.27
0.12
0.15
^Estimates of annual household expenditures are based only on those households
that purchase these services and do not take into account, those households
that do not purchase each type of cleaning services. These estimates include
both laundry and dry cleaning expenses. Expenditures at commercial
establishments comprise mainly dry cleaning expenditures; only a small
portion of expenditures at coin-operated establishments constitute dry
cleaning expenditures.
bBased on before-tax income.
Source: 1980-1989 Consumer Expenditure Survey (U.S. Department of Labor,
1991a).
$20,000 a year, and lives in an urban area. Making generalizations about the
coin-operated expenditure data is more difficult. But conversations with
coin-operated industry experts provide a picture of the typical consumer of
coin-operated dry cleaning. The typical patron is cost-conscious, probably in
the lower income brackets but may be in the lower middle class as well. This
patron is more likely to live in a rural location where commercial facilities
are not available (Torp, 1991). The data do not refute this description.
3.1.3 Household Demand Function
Like any demand function, household demand for dry cleaning services is
derived from utility maximization. Utility comes from commodities, not
directly from goods and services. Households combine goods and services with
time as inputs into a process that generates commodities. Thus, time spent on
3-10
-------
TABLE 3-5. HOUSEHOLD EXPENDITURES ON COMMERCIAL AND COIN-OPERATED DRY
CLEANING AND LAUNDRY SERVICES BY LOCATION CATEGORY
Commercial
Cleaning Services3
Coin-Operated
Location
Category13
Urban
Rural
Average Annual
Expenditure
($/Household/yr)
72.9
23.5
Expenditures
as a Share of
Income0 ( % )
0.22
0.10
- — . —
Average Annual
Expenditure
($/Household/yr)
37.24
16.90
Expenditures
as a Share of
Incomec (%)
0.11
0.07
Estinater of annual household expenditures are based only on those households
that purchase these services and do not take into account those household!
that do not purchase each type of cleaning services. These estinateT?---
both laundry and dry cleaning expenses. Expenditures at commercial
establishments comprise mainly dry cleaning expenditures; only a small
sndatures at eoan-rmAi-i-^ establishments constitute dry
bAn urban area is defined as an area within a Standard Metropolitan
^ri* ( SA) Or °"6 With a P°Pulation of more than 2,500 persons
persons" "" **** °Ut8ida °£ *" SMSA and with a Populatio^ of lesTtJan
cBased on before-tax income.
Source: ^£80-1989 Consumer Expenditure Survey (U.S. Department of Labor,
nonwork activities is crucial to producing commodities (Becker, 1965).
Commodities form the basis of the household utility function. That function
is maximized subject to a budget constraint and a time constraint, both of
which limit the goods, services, and commodities available to the household.
When choosing the combination of goods, services, and time that will be
used to produce any given commodity, the household makes its decision based on
the utility-maximizing option. Households have the option of substituting
time for goods or services in the event that such substitution yields more
utility. For example, a meal could be provided by combining groceries and"
time to produce a home-cooked meal or by eating out at a restaurant. How the
household makes these choices depends on its value of time.
3-11
-------
180-1
160'
140 '
Dollars
120 '
per
Household
100 -
80-
60 -
40-
20 -
0 .
M
l»
^
. '
4-
*i
4-
* •• '
<
f:
4-
%'
/-. s
^,»
^
4-
^ :
•• v
^
- >
' J
4-
1
;^j
: V i
;« /
,",
: __, "
j£
4
^ *,
X* i
•"^ :
•?***%
% "V
1 *
^ %,j
^
H V
A
V,
5-10 10-15
15-20 20-30 30-40 40-50 50 or more
Income Category ($000/yr>
COMMERCIAL D COIN-OP
Figure 3-3. Average Annual Expenditures on Dry Cleaning and Laundry Services
by Income Class ($1989)
Source: 1980-1989 Consumer Expenditure Survey, (U.S. Department of Labor,
1991a) .
3-12
-------
A household's production of clean, pressed clothing can be analyzed in
this framework. If the garment requires dry cleaning, the household, in
theory, has two choices: self-service dry cleaning (offered by self-service,
coin-operated facilities) or employee-assisted dry cleaning (offered by
commercial or coin-operated [plant-operated] facilities). In the balance of
this section, employee-assisted dry cleaning will be referred to as commercial
dry cleaning and self-service dry cleaning will be referred to as coin-
operated. In the coin-operated production process, consumers pay for using
the machines but clean and press the clothing themselves. In the commercial
cleaning process, consumers use their time to deliver and pick-up the garments
and pay for others to clean and press them. Although the market price of the
coin-operated method is lower, it requires more of consumers' time. Assuming
that consumer utility does not differ between clothes cleaned by household
production and clothes cleaned by a commercial cleaner, the household's
decision will depend on the opportunity cost of time.
A household production model similar to one developed by Gronau (1977)
is used to show how a household makes the decision to use commercial or coin-
operated dry cleaning. The household seeks to maximize the amount of cleaned,
pressed clothes, commodity Z, which is produced by combining dry cleaning
services, either commercial or coin-operated, (X) and consumption time (L).
2 = 2 (X, L) O.D
X includes both the value of market goods or commercially cleaned clothes (X,,,)
and the value of home goods or clean clothes produced by the consumer using
machinery and time (Xh).
x - *m + *h (3.2)
Home goods are produced by work at home: H represents the number of hours per
day spent producing clean clothing at home.
xh = f CH) (3.3)
3-13
-------
Utility is maximized subject to two constraints. The first is a budget
constraint where W is a wage rate, N is time spent on market work, and V is
other income.
^ = WN + V (3.4)
The second constraint is a .time constraint (T) .
T = L + H + N (3.5)
Equations (3.1), (3.2), and (3.3) are then combined and maximized
subject to equations (3.4) and (3.5) .
+ f(H)], L} + X(WN + V -X^ + 8(T - L - H - N) (3.6)
Z is maximized when the marginal rate of substitution between time and goods
is equal to the marginal product of home production and equ
-------
work performed by a member is a function of wage rate. In a study on Queuing,
Deacon and Sonstelie (1985) estimated the value of time to be roughly
equivalent to the after-tax wage.
Data are not available to measure the value of time to an individual who
chooses to use coin-operated dry cleaning facilities compared to an individual
who utilizes a commercial cleaner. However, using the Consumer
data 9ives an estimation of the relationship between dry cleaning
expenditures and income .
Data at the household level were available and included expenditures on
commercial and coin-operated dry cleaning, income, and other demographic
information such as education, type of employment, family size, and an
urban/rural designation. Two ordinary least squares (OLS) equations were
estimated—one for commercial dry cleaning expenditures and one for coin-
operated dry cleaning expenditures. The independent variables included income
and the dummy variables for the remaining demographic data.3 The coefficients
for income are very significant and have the expected signs in both models
(positive for commercial and negative for coin-operated) . Many of the other
demographic variables behave as expected. Unfortunately, the equations do not
explain all of the influences on dry cleaning expenditures very well. But the
equations do demonstrate the relationship between income and expenditures on
commercial cleaning. The results are presented in Table 3-6. Because income
plays such an influential role in consumers' choice of using commercial or
coin-operated dry cleaning facilities, consumers are likely to switch from
using a coin-operated facility to a commercial facility at a critical wage or
value of time. Above a certain wage, consumers are likely to value their time
enough to make the time-intensive coin-operated approach too costly when the
value of their time is included in the calculation. A full-cost model for dry
cleaning was developed that identifies the critical wage at which the switch
from coin-operated to full service occurs. The full cost of a commodity is
the sum of the prices of the goods and services consumed and of the time used
in producing these commodities. Direct costs are the prices of the goods and
3lhe data set consists of four quarters of household data. Dummv
variables for the quarters were also included in the equation to account for
differences in the miar-i-or-i,, .-=«,„*,„„„_ v-v-wuuc j.ui
differences in the quarterly responses
3-15
-------
TABLE 3-6. REGRESSION ANALYSIS6
Variables
Dependent Variable
Commercial
Expenditures
Coin-Operated
Expenditures
c
Income
Education Dummy
(1 if college graduate)
White Collar Dummy
(1 if manager or
professional)
Family Size
Urban Dummy
2nd Quarter Dummy
3rd Quarter Dummy
4th Quarter Dummy
Adjusted R2
F Value
-2.55
(-2.27)
0.0005
(41.77)b
11.03
(14.86)b
5.32
(8.50)b
-1.48
(-7.69)b
4.97
(5.42)b
-1.49
(-1.88)
-2.21
(-2.78)b
-2.05
(-2.61)b
0.160
442. 12b
4.79
(6.67)a
-0.0001
(-13.49)b
-2.25
(-4.75)b
2.26
(5.65)b
0.74
(6.01)b
5.40
(9.22)b
-0.05
(-1.11)
-0.78
(-1.55)
0.28
(0.56)
0.02
41.45b
Degression analysis performed using data from the 1989 Consumer Expenditure
Survey (U.S. Department of Labor, 1991a).
bDenotes significance at the one percent level.
services, and indirect costs are the total value of time. Indirect costs can
also be thought of as foregone income. Both direct and indirect costs are
included in the full cost of the commodity.
The full cost for dry cleaned clothing to the household, C, is defined
as follows:
p*q + t*d + s*r
(3.9)
3-16
-------
where
p - the unit price of dry cleaning services (commercial or coin-
operated),
q = the Quantity of dry cleaning,
t = the cost per mile of transportation to a dry cleaning facility,
d = the distance in miles to a dry cleaning facility,
s = the unit value or opportunity cost of time per hour, and
r = the time in hours required to drop off and pick up clothing (plus
facility, "eqUlrSd t0 Clean and Press clothing in a coin-opLated
This cost measures the cost of a single trip to a dry cleaner, which it will
vary with quantity because consumers can take one garment or many garments to
the cleaner in a single trip. in addition, the cost for coin-op consumers
will vary with quantity not only in terms of the cost of using the facility
but also with respect to the opportunity cost of time, which will also
increase with quantity.
The critical wage is based on the full cost of dry cleaning at
commercial and coin-operated facilities. The first component of the full cost
is the direct cost or the price charged by the dry cleaning facility. This is
$6.34 per kilogram for commercial facilities and $1.65 per kilogram for coin-
operated facilities (see Section 2 for explanation).
The second component is the opportunity cost of the time an individual
must spend to operate the machine and press the garment. That cost will vary
from individual to individual and will depend on that individual's wage rate.
One cycle in a 3.6 kilogram machine takes approximately 45 minutes to
complete, which converts to 0.20625 hours per kilogram. Assuming an
individual takes approximately 30 minutes to press a man's suit, total time
spent would be 0.70625 hours/kilogram.
Assuming that the distances to a commercial facility and a coin-operated
facility are the same eliminates any transportation costs from the calculation.
3-17
-------
The critical wage can then be calculated by solving the eolation below
for x.
$1.65 + 0.70625X = $6.34 (3.10)
0.70625x = $4.69
x = $6.64
For individuals earning less than $6.64/hour, using the coin-operated facility
would be more cost-effective. For individuals earning more than $6.64/hour,
using the commercial facility would be more cost-effective.
The foregoing analysis is contingent on the relative price of coin-
operated versus commercial dry cleaners. If the proposed regulation did not
affect the coin-operated sector but raised the price of commercial cleaning
services, then the critical wage at which consumers would switch from coin-
operated to commercial•would be higher. This higher wage implies that more
consumers would utilize coin-operated facilities.
The individual's choice assumes that both types of facilities are
readily accessible,.but this may not be the case for some smaller or rural
communities. These locations may have only one cleaning facility, and the
value of time may be irrelevant. Coin-operated facilities are not distributed
uniformly throughout the United States but tend to be concentrated in the
southeastern and mid-atlantic states. Despite the concentration of
facilities, consumers in these areas, depending on the elasticity of demand
for dry cleaning, may choose not to dry clean. The sensitivity to price of
dry cleaning is discussed below.
3.1.5 Sensitivity To Price
Consumers' sensitivity to the price of dry cleaning services depends on
other alternatives, which can vary from garment to garment. Some fabrics
require dry cleaning for proper care, whereas others can also be cleaned with
detergent and water. Specialty fabrics like leather, suede, and silk are
usually labeled "dry clean only." Consumers are often uncertain about which
fabrics can safely be laundered without being damaged. Therefore, the
importance of dry cleaning services to consumers varies with the ease with
3-18
-------
which another cleaning process can be substituted for dry cleaning and the
consumer's knowledge of the possibilities of substitution.
A few indirect substitutes are available to replace dry cleaning. In
the long run, consumers could replace the stock of clothes requiring dry
cleaning for proper care with water-washable garments. In the short run, they
could reduce the frequency of wearing dry-cleaned clothing or increase the
number of times a garment is worn before it is cleaned. The only direct
substitute available for dry cleaning is laundering with water and detergent,
but this method is not a perfect, substitute.
The price elasticity of demand is one way of measuring consumers'
sensitivity to price changes. Demand is said to be price elastic if an
increase (or decrease) in pries causes a proportionately greater decrease (or
increase) in purchases. Thus, elasticity of demand measures consumers'
responsiveness to price changes. Section 4 presents price elasticity
estimates and results.
3.2 INDUSTRIAL DEMAND
Many industries provide uniforms for their employees typically renting
these uniforms from an industrial launderer. The industrial customer is
charged a price per-uniform change and receives clean, delivered uniforms on a
regular basis. Unlike households, however, industrial customers are
indifferent to whether the uniforms are water washed or dry cleaned. They pay
the same price regardless of how the garment is cleaned.
Historically, changes in general economic conditions have affected
industrial cleaners less dramatically than coin-operated and commercial
sectors. As industrial production and employment increase, so does the demand
for industrial uniform rentals, the main item leased and cleaned by the
industrial sector (Betchkal, 1987a).
3.2.1 Consumption and Trgndp
Data are not available on the consumption of industrial dry cleaning
services. The fact that customers are indifferent to the cleaning method and
pay the same price for uniforms laundered in water and detergent as they do
3-19
-------
for uniforms cleaned in PCE probably explains the lack of information.
Furthermore, dry cleaning is typically a very small part of an industrial
launderer's business. Total industry receipts are availeible from the 1987
Census of Service Industries (U.S. Department of Commerce, 1990b). For the
years 1982 and 1987, receipts of industrial launderers totalled $2,435 million
and $2,947 million in constant (1989) dollars. This increase amounted to over
21 percent.
3.2.2 Characterization of Demanders
Customers of industrial cleaners encompass many industries. Industries
that typically rent uniforms include auto dealerships and independent garages,
construction, hotels, restaurants, security firms, food processing, and other
manufacturing industries. Even traditionally white collar industries such as
banking or real estate may rent blazers for their employees. Many types of
additional industries are likely to lease the other items offered by
industrial cleaners, such as mats, mops, towels, and cloths. All of these
firms use these products as inputs in their production process.
3.2.3 Derived Demand
Unlike the demand for commercial and coin-operated dry cleaning
services, the demand for industrial cleaning services is a. derived demand.
Customers of industrial cleaning view clean uniforms as inputs into their
production processes, so demand for these inputs is said to be derived because
it depends on the demand for the final good. Additional inputs are purchased
in anticipation of increasing production of the final good. As discussed in
Section 3.2.4, the elasticity of demand for an input is related to the
elasticity of demand for the final product.
In such a scenario, producers would maximize profits. Presumably, the
full-cost model for industrial dry cleaning services would be as follows:
C = p*q + T (3.11)
3-20
-------
where
p = the unit price of dry cleaning services
q = the quantity of dry cleaning services
T = transaction costs associated with purchasing dry cleaning services.
Transportation costs do not play a role here because industrial launderers
deliver the uniforms and do not charge different prices based on distance.
3.2.4 Sensitivity to Pr,igf»
The elasticity of demand for industrial' dry cleaning services is not
estimated for this analysis due to a lack of data. However, a theoretical
model is developed that expresses the elasticity within a range of values.
This model is based on the concept of the elasticity of substitution for
inputs and the cost share of inputs.
The elasticity of substitution measures the ease with which a producer
can substitute between inputs, holding final output constant. When
substitution is difficult (i.e., when changing the input mix does not improve
the efficiency of the inputs), the elasticity of substitution will be less
than one. In a fixed proportion production function, the elasticity of
substitution is zero because inputs must be used in a fixed ratio, and
altering that ratio would be inefficient. The customers of industrial dry
cleaners encompass many types of final products, so generalizing about the
elasticity of substitution with respect to inputs of clean uniforms is
difficult. However, clean uniforms will probably be used in fixed
proportions, or, at the very least, difficult to substitute. The elasticity
of substitution with respect to clean uniforms must fall between zero and one.
The second concept used in the model is the cost share of inputs. The
cost share simply represents the cost of a specific input as a percentage of
the total cost. The framework established by Allen'(1962) suggests a
theoretical estimation of the elasticity of demand for an input. In the
following equation, the elasticity is expressed as a proportional change.
3-21
-------
E(Qa) / E(Pa) = (AQa/Qa) / CAPa/Pa) (3.12)
where
a = inputs of clean uniforms
b = all other inputs
Qa = the quantity of clean uniforms
Pa = the price of clean uniforms
kfc = the cost share of all other inputs
5= the elasticity of substitution between uniforms and other inputs
ka = the cost share of clean uniforms
Xx = the elasticity of demand for the final product .
The cost share of all inputs other than clean uniforms is quite large,
and the cost share of clean uniforms is nearly zero. The elasticity of
substitution is most likely zero. Whatever the value of kb, the first term in
the above equation is zero or a very small number. ka will be nearly zero and
will limit the value of the second term of the equation to nearly zero. The
sum then is a small number, certainly less than one in absolute terms. Thus,
the elasticity of demand for industrial dry cleaning services is somewhat
inelastic.
One additional point merits mention. Empirical studies have shown that
the elasticity of demand for final goods is generally greater than demand for
intermediate goods (Martin, 1982} . The elasticity estimation of the demand
for dry cleaning services for households and for industrial consumers is
consistent with that finding.
3-22
-------
. ' SECTION 4
MARKET STRUCTURE IN THE DRY CLEANING INDUSTRY
A causal flow occurs from demand and su; ply conditions to market
structure and from market structure to conduct of firms (Sherer, 1980).
Economic theory provides a framework for analyzing the links between the
demand and supply conditions an industry face:, its market structure, and the
typical behavior of firms in that industry. :his section examines market
structure in the dry cleaning industry and de- elops an approach for estimating
the impacts of an increase in the cost of suprlying dry cleaning services due
to regulation. Certain aspects of market structure-including the existence
of barriers to entry, the number of sellers ir. a market area, and the
geographic distribution of consumers and producers—are particularly relevant
for determining the way consumers and suppliers would react to a'change in the
costs of providing dry cleaning services.
Fundamental to the analysis of market structure in the dry cleaning
industry is an understanding of the'geographic scope of the market area. To
facilitate this understanding, this section begins with a brief description of
the facility location decision, which is determined by the basic supply and
demand conditions outlined in previous sections. The section then describes
market structure in the three sectors prior to developing the model markets.
4.1 FACILITY LOCATION DECISION
Determinants of facility location differ by industry sector, in the
commercial and coin-operated sectors, dry cleaning markets are small in
geographic size. Depending on the number of sellers in a particular place and
the population density, markets may cover an area as small as a few city
blocks, in contrast, industria.1 facilities operate in geographic markets that
are much larger. Factors such as the income distribution of the customer
base, traffic patterns, and number of competing firms in an area contribute to
the location decision in each sector. The determinants of the facility
location characteristic of each industry sector are discussed below.
4-1
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4.1.1 Commercial Dry Cleaners
The service provided by commercial dry cleaners is effective, fast, and
requires little effort by the customer. These establishments sell a
convenience good that, like toothpaste and gasoline, does not typically
justify comparison shopping because the benefit of price comparison does not
compensate for the cost of the search (Sherer, 1980; Steinhoff and Burgess,
1989). An important determinant of the convenience of dry cleaning is the
proximity of the facility to the customer's home. The market that commercial
dry cleaners serve extends over a local area although the geographic size will
vary depending on population density.
The profit-maximizing dry cleaner evaluates multiple dimensions when
choosing the location of a new facility (Steinhoff and Burgess, 1989). Some
considerations are highly specific to the community and, while they are
crucial to the firm's potential success, have little bearing on the economic
impact analysis because they do not provide insight into the responses to
regulation. Among these dimensions are the availability of parking, types of
surrounding firms, traffic density, and side of the street for the facility.
Other dimensions such as rent, availability of labor, the local business
climate, and the share of the population in professional or managerial
occupation categories are also important to the potential for success, but
again they are unlikely to be significant for the, impact analysis.
The significant dimensions of the location decision for commercial dry
cleaning facilities are the size of the consumer base and the efficiency of
the existing firms. An increasing population in the area under consideration
may provide the basis for a new firm. In the absence of an expanding market,
the presence of inefficient firms may instead provide the basis. In either
case, the potential customer base must be at least large enough to generate
sufficient revenues to justify investment in the minimum size facility.
The minimum size facility implies a minimum population requirement,
which, because of limits on the size of dry cleaning equipment, may be several
thousand people (the population requirement would increase as average income
decreases). The technology of dry cleaning is "lumpy": dry cleaning machines
used by the commercial sector are available in about six sizes. The smallest
4-2
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machine used in this sector has a capacity of 11.3 kilograms per load. The
operation of a dry cleaning facility also requires labor for staffing the
front counter, preparing clothing for cleaning, operating the dry cleaning
machine, and processing the clean clothing for return to the customer. In
reality, labor is also unavailable in an infinitely divisible quantity.
Facility size is therefore imperfectly variable.
A potential owner of a dry cleaning facility confronts a definite lower
limit on the revenue that is necessary for profitable operation. In choosing
a location for a dry cleaning facility, the profit-maximizing potential owner
must consider the minimum customer base that this lower limit on revenue
implies. Owners who misjudge their customer base, either because of
miscalculation or over-confidence in their ability to attract customers away
from an existing facility, may be unable to cover their fixed costs or even
their variable costs. Inability to cover fixed costs can lead to financial
failure of the firm. Inability to cover variable costs can lead to closure of
the facility.
4.1.2 Coin-operated Dry Clftangrfi
Many of the determinants of the facility location decision that are
characteristic of the commercial sector are also characteristic of the coin-
operated sector. In particular, coin-operated laundries that offer plant-
operated services provide a convenience good that is virtually
indistinguishable from the service offered by the commercial sector. Like
commercial facilities, coin-operated facilities serve a local market area and
typically locate in places that are convenient to consumers.
One important difference does exist, however. As discussed in
Section 2, dry cleaning services are offered as an auxiliary to the regular
laundry operations at coin-operated facilities. Because dry cleaning activity
accounts for only about 10 percent of receipts at coin-operated facilities
with dry cleaning operations, the location decision is based on the
determinants relevant for locating a laundromat rather than for a dry cleaning
facility, once the decision to locate the coin-operated laundry is made, the
owner must decide whether to provide dry cleaning services in addition to the
regular laundry services. Relevant factors in this secondary decision include
4-3
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the proximity of other dry cleaning facilities, the size of the costumer base,
and the income distribution of residents within the community.
4.1.3 Industrial Dry Cleaners
Industrial cleaners serve a much larger geographic area than do
commercial or coin-operated cleaners. For example, the operator of one
industrial facility indicated that his facility served industrial and
commercial users located as far away as 100 miles (Coor and Grady, 1991) .
Services provided by industrial cleaners are not considered convenience goods.
Consumers in this sector view the services provided by industrial clfaners as
an input into their production process. Because the cleaner delivers the
cleaned items, consumers are generally more concerned with dependability of
service than with convenience.
The profit-maximizing industrial cleaner locates where costs of
production are minimized. According to one facility operator, the ideal
location is a small town that is centrally located to several large cities
where the customer base is located (Coor and Grady, 1991). Small towns
typically do not have the traffic congestion characteristic of larger cities.
Traffic congestion ties up delivery vehicles, which increases the cost of
delivery and may reduce customer satisfaction. In addition, small towns tend
to have less expensive land and building costs and labor costs. Because
industrial launderers clean most of the items they process in water and
detergent, a cheap, abundant water supply is also an important determinant of
location.
4.2 MARKET STRUCTURE
Within each sector of the industry many localized geographical markets
exist where only neighboring firms compete directly. These submarkets are
only loosely tied to a national market, but economic decisions by individual
firms are jointly related to national trends. The existing market structure
reflects fundamental market forces that are likely to be an enduring feature
of the dry cleaning industry. The economic impact analysis uses the
differences in market structure and pricing practices of dry cleaning
facilities to predict the market responses to the candidate regulatory
4-4
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alternatives. To simplify the analysis, a model market approach is used to
differentiate markets by
r the market sector,
• the number of suppliers in each market area, and
' alternative* SUppliers P°tentiaHy affected under each regulatory
An important economic impact associated with promulgation of the '
candidate regulations is the total welfare loss (gain) attributable to market
adjustments in the dry cleaning industry. A neoclassical supply/demand
analysis is developed for each sector and model market. The economic impacts
are analyzed for each sector and model market individually and the results are
then aggregated to determine total welfare effects.
4-2-1 Market Structm-P in r_hg (-y,mmprci*T Sector
Two basic market structures are prevalent in the commercial sector. The
first is a competitive structure, which is found predominantly in urban and
suburban"areas and characterized by the existence of many dry cleaning
facilities in each market area and no barriers to entry. Approximately 90
percent of the commercial facilities are in urban/suburban market areas. The
second type of market structure is characterized by a single, facility in a
rural market area. Because consumers are unwilling to drive long distances to
purchase dry cleaning services, the owner of a single facility in a remote
area does not behave as if in a perfectly competitive market.
Urban /Suburban Mfir)~M. Given the number of commercial facilities in
urban and suburban areas and the size distribution of those facilities, it is
assumed that a competitive market structure exists for these facilities. The
competitive model is based on the hypothesis that no facility individually'can
influence market equilibrium, but the behavior of all producers, taken together -
determines the position of the market supply curve. In addition, the cost of
producing the last unit of output, the marginal cost, along with market demand
determines equilibrium price and output. Furthermore, at a stable equilibrium
price, each individual facility can sell any level of output desired, with no
perceptible effect on equilibrium values. As a result, each facility faces an
4-5
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implicit demand curve that is perfectly, elastic (horizontal) at the current
market equilibrium price.
Initially, imposing controls on a facility will alter the costs of
producing the same level of output as before the control. This production
cost change will induce a shift of that facility's supply curve. Because the
supply curve for a well-defined market is the horizontal summation of
individual facility supply curves for all facilities participating in that
market, the shift in the market supply curve can be determined from knowledge
of facility-specific shifts. If the regulation results in a production cost
change for the marginal supplier within the market area, a change in the
equilibrium price and output will occur.
Precise estimates of the quantitative changes in price and output
require information on the position and slope of the market supply and market
demand curves both prior to and after the adjustment. Predicting the position
and slope of the market supply and demand curves is, therefore, crucial to-
estimating the economic impacts. The changes in price and output lead to
consumer and producer welfare changes that can be measured as areas within the
supply/demand plane. The neoclassical supply/demand analysis applied to this
study is introduced below.
The position of the market demand curve is critical to determining the
change in equilibrium price and output resulting from a regulatory-induced
shift in the market supply curve. The slope of the demand curve measures the
responsiveness of quantity demanded to a change in the price of the service.
The elasticity of demand .is a relative measure of demand responsiveness and as
a policy tool is generally preferred to the demand curve slope. The
elasticity of demand is measured as the percentage change in quantity demanded
of a good or service resulting from a one-percent change in its price. Post-
regulatory equilibrium price and output values and the resulting welfare
changes can be calculated if the baseline price and output values, the
relative shift of the market supply curve, and estimates of demand' and supply
elasticities are available.
A priori, predicting the elasticity of demand for commercial dry
cleaning services is difficult because many variables contribute to its value.
If data are unavailable to estimate a demand elasticity, a unitary elastic
4-6
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(T| - -1.0) demand curve could be used to estimate impacts, but considerable
uncertainty would be associated with the price and output adjustments and the
welfare loss estimates. Any market-measured value of the demand elasticity
would obviously be superior to an unsubstantiated simplification. The supply
and demand functions for the commercial dry cleaning sector are estimated
simultaneously to derive corresponding elasticity estimates.
A neoclassical supply/demand model is a system of interdependent
equations in which the price and output of a product are simultaneously
determined by the interaction of producers and consumers in the market. In
simultaneous equation models, where variables in one equation feed back into
variables in another equation, the error terms are correlated with the
endogenous (price, output) variables. In most circumstances, single-equation
ordinary least-squares estimation of individual equations in a simultaneous
equation model can lead to biased and inconsistent parameter estimates.
Furthermore, the supply and demand equations must be econometrically
identified prior to initiating a simultaneous equation regression procedure.
An equation is identified if obtaining values of the parameters from the
reduced-form equation system is possible. Put simply, identification requires
that at least one original exogenous (shifter) variable is contained in each
equation of the supply/demand system.
Section 2 presented data on average base prices and total output for the
commercial sector from 1974 to 1988. These data represent equilibrium points
of intersection between supply and demand curves for each of those years.
Estimating a supply or demand curve equation from these data would be
difficult because information is insufficient to completely identify the
supply/demand system. However, with the aid of intuitively acceptable supply
and demand shift variables, the price and output data can be used to
econometrically estimate the commercial sector supply and demand functions and
corresponding elasticities.
Gross population levels for the U.S. and the producer price index for
service industries from 1974 to 1988 were chosen as the demand and supply
shifters, respectively. Population levels are commonly used as demand shift
variables in regression equations. The producer price index is suitable for
the supply function because it is a good proxy for production costs. '
Table 4-1 lists the time-series data used in the supply/demand estimation.
4-7
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TABLE 4-1. DATA USED IN THE SUPPLY/DEMAND ESTIMATION
Year
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
Price
($/kg)a
4.02
4.42
4.46
4.36
4.87
4.90
5.32
5.63
5.72
5.87
5.98
6.13
6.14
6.05
6.08
Output
(106 kg/yr)b
570
506
499
521
493
499
475
444
522
527
525
522
608
603
596
P.P. Index
53.5
58.4
61.1
64.9
69.9
78.7
89.8
98.0
100.0
101.3
103..7
103.2
100.2
102.8
106.9
Population
(106)
213.9
216.0
218.0
220.2
222.6
225.1
227.8
230.1
232.5
234.8
237.0
239.3
241.6
243.9
246.1
aAll dollar figures converted to 1989 dollars through the Consumer Price Index
for Apparel and Upkeep.
bSee Table 2-8.
Source: Faig (1990); Survey of Current Business (U.S. Department of Commerce
1989b); Statistical Abstracts of the U.S. (U.S. Department of
Commerce 1989a).
Supply and demand equations for the commercial sector were
econometrically estimated by using the instrumental variables regression
procedure. Base price and total output were first converted to natural
logarithm form to ensure constant supply and demand elasticity estimates. The
structural models for the supply/demand system are the following:
4-8
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Supply: Ln(Qts) - ai + a2Ln(Pt) + 33PPIt + dt, (4.1)
Demand: Ln(Qtd) - bi + b2Ln(Pt) + bsPopt + ut, (4.2)
Ln(Qts> - Ln, (4.3)
where Q - output, P - price, Pop - population, and PPI - producer price index.
The supply equation (4.1), demand equation (4.2), and equilibrium condition
(4.3) determine the market price and the quantity supplied (demanded) when the
market is in equilibrium. For this reason, the variables Ln(Qts), Ln(Qtd),
and Ln(Pt) are endogenous because they are determined within the system of
equations, while Pop and PPI are exogenous variables. The parameter estimates
and regression statistics from the simultaneous system estimation are reported
in Table 4-2.
With Durbin-Watson statistics of 1.54 for both the supply and demand
equations, the null hypothesis of no serial correlation cannot be rejected at
the 0.01 level of significance. Overall, the significance of the parameter
estimates and the low standard errors indicate that base prices, dry cleaning
output, population levels, and the producer price index are effective in
predicting the supply /demand relationship.
Parameter estimates were also developed using a time variable instead of
population in an attempt to determine whether a simple time trend would be a
more suitable demand shifter. The results of that regression are reported in
Table 4-3. The parameter estimates are very similar to the regression with
population as an explanatory variable, but the population specification had a
slightly better fit. As a result, all future references to the elasticity
estimates will apply to the population specification.
The predicted elasticity of supply and demand can be derived directly
from the parameter estimates of the regression system. Regression equations
for the supply and demand functions appear in estimated form as
Ln(Qts) = -0.012 + 1.558Ln(Pt) - 0.023(PPIt), <4.4)
Ln(Qtd) - -6.351 - 1.086Ln(Pt) + 0.036 (Popt) . (4>5)
4-9
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TABLE 4^2. PARAMETER ESTIMATES AND REGRESSION STATISTICS FROM THE
SUPPLY/DEMAND ESTIMATION
Parameter Value
Supply Curve
Intercept 0.120
Price 1.558
P.P. Index -0.023
. Sum sq. res.
0.031
Demand Curve
Intercept -6.351
Price -1.086
Population 0.036
Sum sq. Res.
0.031
Std.
0
0
0
Std.
0
1
0
0
Std.
0
err.
.064
.291
.0.05
err.
.051
.289
.240
.007
err.
.051
t-stat 95% conf
1.882
5.361 0.924 to
-5.057 -0.033 to
DW test
1.54
-4.927
-4.530 -1.608 to
5.057 0.020 to
DW test
1.54
. int .
2.192
-0.013
-0.564
0.051
The first derivative of the supply equation with respect to the
logarithm of price (1.558) is an estimate of the supply elasticity for dry
cleaning services in the commercial sector. The interpretation of this
estimate is that the quantity supplied of dry cleaning services will increase
by 1.558 percent for every 1 percent increase in the price for that service.
The t-statistic value of 5.361 allows rejection of the null hypothesis so that
the estimate is not significantly different from zero at the 0.05 level of
significance.
The estimated elasticity of demand is the first derivative of the demand
equation with respect to the logarithm of price, or -1.086. The
interpretation of this value is that the demand for dry cleaning services will
decrease by 1.086 percent for every I percent increase in the price of that
service. The t-statistic value of -4.530 allows rejection of the null
hypothesis that the estimate is not significantly different from zero at the
0.05 level of significance.
4-10
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TABLE 4-3. PARAMETER ESTIMATES AND REGRESSION STATISTICS FROM THE
SUPPLY/DEMAND ESTIMATION (TIME-TREND SPECIFICATION)
Parameter Value
Supply CiiT-yft
Intercept 0.123
Price 1.512
P.P. Index -0.022
Sum Sq. Res.
0.345
Demand Cijrye.
Intercept 1.082
Price -0.989
Time 0.077
Sum Sq. Res.
0.345
^•"^•^•••"•»
Std.
0
0
0
Std.
0
0
0.
0.
Std.
0.
err.
.067
.305
.005
Err.
.054
.208
.239
.016
Err.
054
t-stat 95% conf. int.
1.825
4.959 0.848 to 2.176
-4.670 -0.033 to -0.012
DW test
1.46
5.198
-4.141 -1.509 to -0.469
4.670 0.041 to 0.112
DW test
1.46
The credibility of the demand elasticity estimate can be confirmed with
a demand elasticity point estimate computed by Houthakker and Taylor (1970).
These authors examined consumer demand relationships for many different goods
and services. The demand elasticity for a category of products they refer to
as "clothing upkeep and laundering in establishments" was estimated at 0.9293.
This value is contained in the 95 percent confidence interval for the demand
elasticity estimate reported in Table 4-2 (-1.608 to -0.564). In addition, it
is very close to the point estimate itself (-1.086).
If the regulation results in a change in the marginal supplier's cost of
providing dry cleaning services, then price and quantity impacts will occur in
the short run. Using the demand and supply elasticities estimated above,
projecting changes in short run equilibrium price and quantity associated with
each regulatory alternative is possible. As noted in Section 2, the baseline
4-11
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price in the market is equivalent to the marginal cost of providing dry
cleaning services (before the regulation) and the average total cost of
building a new facility. An increase in the marginal costs projected under
the regulatory alternatives would result in an increase in price in the short
run. As price rises above the average total cost of a new facility, new entry
is encouraged. The average total cost of the new facility, however, is not
affected under any of the alternatives considered because virtually all new
dry cleaning machines have built-in vent controls. Consequently, in the long
run, price and quantity adjustments are zero. In the absence of regulation,
the current stock of uncontrolled PCE machines would have been replaced by new
machines with vent controls, further supporting the position that long-run
price and output adjustments are zero. Therefore, price and output
adjustments in the balance of this analysis refer to short-run effects.
Not all commercial facilities in a market area are affected under the
candidate regulatory alternatives. Only those facilities that use PCE and
that do not have the required vent controls in the baseline will experience a
change in production costs. It is not known whether facilities that are
potentially affected are more or less likely to be the price-setting marginal
facility in the market. Without detailed information on individual supplier's
production costs, determining whether the marginal supplier will incur
regulatory costs is impossible. Therefore, it is assumed that the likelihood
of a shift in the marginal supplier's costs is directly related to the
proportion of facilities experiencing the cost increase.
Suppose that a given market area includes facilities that are
potentially affected by the regulation {PCE facilities that do not have the
required vent controls) as well as those that are unaffected (PCE facilities
that have the required vent controls or non-PCE facilities). If the
unaffected facilities dominate, then price and output adjustments are
unlikely. The impact in markets where unaffected facilities dominate falls
exclusively on the affected suppliers whose profits are reduced by the cost of
the regulation. Conversely if affected facilities dominate in a particular
market area, then the regulation is likely to result in an equilibrium price
and output adjustment for that market. Price would rise, but not by the full
amount of the cost increase, until demand and supply are in equilibrium. Put
4-12
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differently, the market supply curve will shift along a (stationary or
shifting) market demand curve with equilibrium changes in price and output
determined once the curves stabilize.
Rural Markftfn Considering the minimum-size customer base, as described
in Section 4.1.1, is critical for owners planning to open a facility in a
remote area served by a single facility. Areas with a lower population
density can sustain a lower density of dry cleaners than areas with a higher
population density. The existence of a minimum customer base explains the
pattern observed in the data set: sparsely populated areas are served by a
single facility and densely populated areas by multiple facilities.
The outstanding characteristic of the structure of the dry cleaning
industry in rural communities is the prevalence of markets that are served by
a single facility. Another salient characteristic of rural dry cleaning
facilities is that annual revenues are. typically below $25,000. The small
scale of the market in rural communities requires the operation of a minimally
sized facility. Consequently, the smallest facility would use an 11.3
kilogram machine. A new entrant would at a minimum add another 11.3 kilograms
of capacity. The only option available to a new entrant, therefore, is to
double (at the minimum) capacity in the market.
Although these single-facility markets are not perfectly competitive,
the ease of entry into the dry cleaning industry implies that the threat to
long-run profits from new entrants is keen and persistent. The optimal
pricing strategy is to set a profit-maximizing price that is low enough to
deter entry. Therefore, to model the economic impact of the proposed
regulations, it is assumed that the owners of firms in single-facility rural
markets follow a limit pricing strategy. The assumptions of potential large-
scale entry and output maintenance allow application of the theory of limit
pricing developed by Bain, Sylos-Labini, and Modigliani' (Sherer, I960).
Any price above the average total cost of a new facility would encourage
new entry into the market. The existence of a second facility in the market
would decrease the market share and the total revenue of the initial supplier.
Assuming that the productivity of dry cleaning equipment has been increasing
over time, owners of new equipment would tend to have lower marginal costs
4-13
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than owners of older equipment. Therefore, the market price would probably
decline with the entrance of a second facility, further decreasing the total
revenue of the existing supplier. Furthermore, if the assumption of increased
productivity is correct, owners of new facilities may be able to set prices at
a. level where initial suppliers would not be able to cover their costs of
production. If the price set by the new supplier fell below the variable
costs of production for the initial supplier, then the initial supplier would
cease operations. If the initial supplier could cover variable costs but not
all the fixed costs of production, then the facility would continue to operate
in the short run but would face potential financial failure. Facing this
potential erosion in profits and/or financial failure, the owner of an
existing facility is most likely to adopt the pricing strategy that presents
the strongest deterrent to a potential entrant to ensure that his market share
is not eroded.
Even in the pre-regulatory baseline, the new entrant's long-run average
cost curve already reflects the cost of compliance associated with the
candidate regulatory alternatives because the manufacturers of dry cleaning
machines have incorporated the requisite air pollution control devices into
the basic design (Federal Register, 1989). Therefore the pre-regulatory and
post-regulatory costs of potential r.ew entrants are the same, implying that
the limit price set by an existing facility would not change under any of the
regulatory alternatives.
Two types of rural markets must be analyzed: those with an unaffected
facility and those with a potentially affected facility. In market areas with
a single unaffected facility, costs do not change because the dry cleaning
machines either already comply with the alternatives or they use a solvent
other than PCE. Only in those market areas with a single potentially affected
facility where regulatory costs are projected, does a potential "exist for
economic impacts.
The theory of limit pricing to deter large-scale entry implies that the
i
established firm sets a price just below that at which a new entrant would
find entry profitable. An established dry cleaner cannot raise its price
without inducing entry and eroding its profits. Even when its costs rise, the
established owner does not have an incentive to adjust price and quantity
4-14
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because new entry would occur and the market price would fall. Therefore, in
rural, single-facility markets in which the alternatives considered for
proposal have an economic impact, the impact falls exclusively on the
established dry cleaners whose profits fall by the amount of the compliance
cost.
4.2.2 Market Rlrucil-urP -in i-h
Conversations with industry representatives indicate that a perfectly
competitive market structure is an accurate representation of current
conditions in the coin-operated sector. In addition, the characteristics of
supply and demand for coin-operated dry cleaning services and the determinants
of facility location decision are similar to those described for the
commercial sector, which is predominantly characterized by a competitive
market structure. Therefore, a competitive market structure is used to
estimate impacts in the coin-operated sector.
Coin-operated (plant-operated) facilities provide the same services to
the same consumers at approximately the same prices as commercial facilities.
Therefore the demand and supply elasticities estimated for the commercial
sector are used to compute impacts in this sector. The service offered by
self-service coin-operated facilities is different from that offered by
commercial facilities or plant-operated facilities. As described in
Section 2, the dry cleaning service offered by self-service facilities does
not include pre-spotting, pressing, or finishing. However, historical data on
price and output are not collected in a structured format for the coin-
operated sector. As a result econometrically estimating supply and demand
elasticities for self-service coin-operated dry cleaning is impossible. One
option is to assume that the elasticity estimates for the commercial sector
are representative of the market conditions characteristic of self-service dry
cleaning. Another option is to compute a rough estimate of demand elasticity
for self-service dry cleaning using the market price and output for self-
service dry cleaning and the market price for commercial dry cleaning. This
second option is described below.
First, a "choke price»~the price at which the quantity of self-service
coin-operated dry cleaning demanded is zero-is estimated. As discussed in
4-15
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Section 3, the consumer's full cost of obtaining dry cleaning services
includes the price paid to the supplier plus the consumer's opportunity cost
of time. Assuming that no consumer values time below the minimum wage rate,
the minimum opportunity cost of time is the product of the minimum wage rate
(4.25 per hour) and the time required to produce a clean suit ready to wear
(0.70625 hours). Under these assumptions, the minimum opportunity cost of
time associated with self-service dry cleaning is $3.00.
Commercial dry cleaning services, as well as the services offered by
plant-operated facilities in the coin-operated sector, are a perfect
substitute for the services offered by self-service coin-operated facilities.
In other words, if the consumer's full cost of producing clean clothing using
self-service cleaning rises above the full cost of producing clean clothing
using the services of a commercial cleaner, then the consumer will use the
services of the commercial cleaner. Presumably no consumer is willing to pay
more than $3.34 per kilogram—the commercial dry cleaning price ($6.34) less
the minimum opportunity cost of time ($3.00)—for self-service dry cleaning.
This is the choke price or the price above which quantity of self-service dry
cleaning demanded falls to zero.
Figure 4-1 shows the demand curve implied by the choke price and the
market price and quantity. This interpretation of the demand curve assumes
that demand is linear. This choke price combined with the market price and
quantity for self-service dry cleaning can be used to compute demand
elasticity in the following manner:
*» - ^* Q <4'6'
where r\ is the absolute value of demand elasticity, Q is the market quantity,
and P is the market price. Because demand is downward sloping, elasticity is
negative. At the. market price of $1.65 per kilogram, market quantity of
577,239 kilograms, and a choke price of $3.34, demand elasticity is -0.9476.
Because consumers have a perfect substitute for self-service dry
cleaning, even small increases in price are likely to result in large quantity
reductions. In other words, the existence of a perfect substitute implies
4-16
-------
$/Q
Choke
Price
Q/Time
Figure 4-1. Demand for Self-Service Dry Cleaning
that the demand for self-service dry cleaning is likely to be more elastic
than the demand for commercial or coin-operated (plant-operated) services.
The estimate computed above, however, implies that the demand for self-service
dry cleaning is slightly less elastic than the demand for commercial dry
cleaning. The reason for the counterintuitive result may lie in the
assumptions used to compute the demand elasticity.
First, the demand for self-service dry cleaning is assumed to be linear.
To the extent that this assumption does not specify the demand curve, the
elasticity estimate may also be miscalculated. In'addition, the minimum
opportunity cost of time may be underestimated. A higher opportunity cost of
time would yield a lower choke price and a higher elasticity estimate (in
absolute value). Because of these limitations, the demand and supply
elasticity estimates computed for the commercial sector are used to compute
impacts for self-service coin-operated facilities.
4-17
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Data are not available on the number of facilities in this sector
operating in markets where unaffected facilities dominate or vice versa.
Therefore it is assumed that each market area has the same distribution of
affected and unaffected facilities. Virtually all self-service dry cleaning
and more than half of the plant-operated facilities in the coin-operated
sector are uncontrolled. Therefore, the marginal cost of providing coin-
operated dry cleaning services is likely to increase resulting in price and
output adjustments for this sector.
The magnitude of the price and output adjustments in the coin-operated
sector is limited by the adjustments in the commercial sector. These
adjustments are computed separately for self-service and plant-operated
facilities because of the difference in the type of service offered and the
base price charged by these facilities. Plant-operated facilities are limited
in the price increase that may be passed along to consumers because these
facilities operate in markets dominated by commercial facilities. Price
effects at self-service facilities are also limited by the projected price
adjustments in the commercial sector. The post-regulatory price at self-
service facilities may not exceed the choke price based on the post-regulatory
price charged by commercial facilities. The post-regulatory choke price is
the post-regulatory commercial price less the estimated minimum opportunity
cost of time ($3.00) computed above.
4.2.3 Market Structure in the Industrial Sector
Industrial facilities also operate in perfectly competitive markets.
However, no price and output adjustments are likely to occur in this sector
for several reasons. First, water and detergent are near-perfect substitutes
for PCE because virtually all of the garments dry cleaned by industrial
facilities are water-washable. Because consumers do not dictate the cleaning
method used, facilities facing a regulatory cost with continued PCE usage
would likely substitute water washing for dry cleaning assuming sufficient
capacity is available. Second, industrial cleaners do not charge different
prices for garments cleaned in water and detergent and garments cleaned in PCE
(Coor and Grady, 1991); also, over 92 percent of the output from industrial
facilities is from regular laundry operations. This second factor is evidence
that the cost of producing the marginal unit of output in the market area is
4-18
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not likely to increase under any of the alternatives considered for proposal.
For these reasons, producers would not be able to pass along any regulatory
cost in the form of a price increase.
4.3 MODEL MARKETS
To facilitate computing impacts of the regulatory alternatives, actual
dry cleaning facilities have been allocated among model markets. The
methodology used to develop the model markets is discussed below.
4.3.1 Commercial Ser>.r>r Market s
Six model markets represent the commercial sector and are differentiated
by
• rural and urban areas,
• the proportion of affected and unaffected facilities •,
• the income distribution of facilities represented, and
• the behavioral response to a cost increase.
Data from American Business Information (ABI) (1991) compiled from
telephone yellow pages provided the location of commercial dry cleaning
establishments in the United States. Population data from the 1988 rit-y .nH
County PaM pppv (U.S. Department of Commerce, 1988) were merged with the
establishment data from ABI to determine the portion of facilities in rural
and urban areas .1 Additional data on the extent of current state regulations,
the percentage of facilities that use PCE in the dry cleaning process, and the
share of PCE facilities that have machines with baseline vent controls were
used to allocate facilities to each model market (Radian, 1991c; Safety-Kleen,
1986; Radian, 1991c) . '
Table 4-4 reports the total number of facilities and the number of
facilities potentially affected and unaffected by the regulation in each model
market of the commercial sector. An estimated 3,149 facilities (10.32 percent
of all commercial facilities) are located in rural areas. Rural markets are
represented by Model Markets A and B. It is assumed that all facilities in
that-
-------
these model markets are small establishments that receive $25,000 or less in
annual revenue. In addition, it is assumed that these small rural areas have
only one facility providing commercial dry cleaning services for the entire
market area. Market A represents those areas with a single facility that is
unaffected under the alternatives considered for proposal. No economic
impacts are estimated for markets represented by Market A. Market B
represents those areas with a single facility that is potentially affected
TABLE 4-4. PROFILE OF MODEL MARKETS IN THE COMMERCIAL SECTOR
Market
Model
A
B
C .
D
E
F
Total
Market
Description3
Rural
Rural
Urban/
Suburban
Urban/
Suburban
Urban/
Suburban
Urban/
Suburban
Proportion of
Affected and
Unaffected
Facilities
Unaffected
Only
Affected Only
Unaffected
Only
Unaffected
Dominate
Affected and
Unaffected
Evenly
Distributed
Affected
Dominate
Total
Number
Facilities15
1,543
1,606
1,157
10,432
8,073
7,683
30,494
Number of
Potentially
Affected
Facilities0
0
1,606
0
287
4,038
4,298
10,229
Number of
Unaffected
Facilities6
1,543
. ' 0
• 1,157
10,145
4,035
3,385
20,265
aRural markets are defined as locales with population of 2,500 or less that are not part of a
metropolitan statistical area. For this analysis, rural markets have only one facility per
market area.
facilities are distributed to Model Markets based on the share of facilities located in
urban and rural areas (ABI, 1991), the share of facilities that use PCE in the dry cleaning
process (Safety-Kleen, 1986), and existing state regulations (Radian, 1991b).
cPotentially affected facilities are defined here as those that use PCE in the cleaning
process and do not have vent controls in place (Radian, 1991c). The total is equivalent to
the number of potentially affected facilities under Regulatory Alternatives I and II. Note
that PCE facilities with baseline vent controls that do not meet the requirements of
Alternative III are not included in the estimate of potentially affected facilities
reported in this table.
dUnaffected facilities either do not use PCE in the cleaning process or have baseline vent
controls.
4-20
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under the candidate alternatives. These facilities may incur costs because of
the regulation. However, as discussed in Section 4.2.2, no price increase is
projected because facilities in this type of market practice limit pricing to
deter new entry.
The share of facilities assigned to Markets A and B is estimated using
data on the share of small facilities with baseline vent controls (Radian,
19910 and data on the share of facilities that use PCE (Safety-Kleen, 1986)
Of the 3,149 facilities in rural market areas, approximately 49 percent or
1,543 either have baseline vent controls or do not use PCE. These facilities
are assigned to Market A. The remaining 1,606 facilities are assigned to
Market B.
Urban/suburban commercial markets are represented by Model Markets C
through F. These model markets are characterized as having more than one
facility in each market area. Facilities of every income level operate in
market areas represented by these urban/suburban model markets. Market C
represents those urban/suburban markets where no commercial dry cleaning
facilities are affected under the alternatives considered for proposal
Market D describes those areas where the unaffected facilities dominate
Potentially affected and unaffected facilities represented in Market E are
roughly equivalent in number, and in Market F potentially affected facilities
dominate.
Approximately 38 percent of all commercial dry cleaning facilities or
about 11,589 facilities are located in states with stringent PCE requirements.
Markets C and D are used to characterize the market for commercial dry
cleaning services in these states. The number of facilities in markets
represented by Market C is assumed to be one tenth of the facilities in states
wxth strict PCE emissions standards or about 1,157. The remaining facilities
located in states with strict PCE emission standards (10,432) are assigned to
Market D. Price and quantity adjustments are assumed to be zero in these two
model markets where unaffected facilities dominate.
Those facilities located in states that regulate only very large
facilities are assigned to Market E. Market E represents 8,073 facilities or
about 26 percent of all commercial establishments. Locales with no state
4-21
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regulations requiring vent controls for commercial facilities are allocated to
Market F. In these two markets, some portion of the regulatory cost would be
passed on to consumers in the form of a price increase. The price increases
projected for Markets E and F are computed using the average cost increase per
unit of output (kilograms of clothes cleaned) for the model facilities in the
market area.
Facilities in each model plant category operating at each income level
are allocated proportionally to each model market described above based on the
total number of potentially affected and unaffected facilities assigned to
each market. For example, Market A represents 1,543 facilities with annual
receipts below $25,000. A total of 8,026 commercial facilities have annual
receipts below $25,000. Therefore 1,543 out of 8,026 or 19 percent of the
facilities receiving less than $25,.000 in each model plant category are
allocated to Market A. Facilities are allocated to Markets B through F in a
similar manner. Using the model plants to represent average facilities in
each market simplifies the analysis of impacts. Any shift in the model plant
supply curve is augmented by the number of facilities in the market to
determine the market supply curve shift.
4.3.2 Cpin—operated Sector Markets
One model market represents all facilities in the coin-operated sector.
Essentially two kinds of coin-cperated plants are represented in the model
market: self-service and plant-operated. The distribution between the two
kinds of plants was based on actual plant information (Radian, 1991c) . Seven
percent of the facilities (or 213) are self service, and the remaining 93
percent (2,831) are plant-operated.
In the coin-operated market, the price and output adjustments computed
for the regulatory alternatives are based on the average cost increase per
unit of output measured in kilograms of clothing cleaned. The price
adjustment in this sector is limited by the maximum adjustment computed for
the commercial sector as discussed in Section 4-.2.1. The highest price
adjustments for the commercial sector are projected in commercial Market F
where potentially affected facilities dominate. Consequently, projected price
4-22
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and output adjustments computed for Market F define the maximum adjustments
for coin-operated facilities.
4.3.3 'Industrial Sector Markka
One model market is used to compute impacts in the industrial sector.
As discussed in Section 4.2.3, any regulatory costs are not passed along to
the consumer in the form of price adjustments. Rather, the entire change in
costs is absorbed by the producers.
4-23
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-------
SECTION 5
FINANCIAL PROFILE OF COMMERCIAL DRY CLEANING FIRMS
The dry cleaning NESHAP will potentially impact business entities that
own commercial dry cleaning facilities. Behrens (1985) defines a business
entity as a legal being that is recognized by law as having the capacity to
conduct business transactions. The Census of Service Industries defines a
firm as a "business organization or entity consisting of one domestic
establishment or more under common ownership or control," and an establishment
is in turn defined to be "a single physical location at which business is
conducted."
A profile of the baseline financial condition of commercial dry cleaning
firms will facilitate an assessment of the affordability, cost, and firm
financial impacts of the dry cleaning NESHAP. The potential financial impacts
on small businesses are of particular concern for two reasons. First, the dry
cleaning industry is dominated by small businesses. Most firms have annual
receipts of less than $100,000, and many have receipts totaling under $25,000.
Second, the absolute control equipment costs are constant enough over machines
of various sizes that the capital requirements may be disproportionately high
for small businesses.
5.1 FIRM FINANCES AND FACILITY ECONOMICS
A facility, or establishment, is a site of land with a plant and
equipment that combine inputs like materials, energy, and labor to produce
outputs, like dry cleaning services. Firms are legal business entities that,
in this context, own one or more facilities. This distinction between
facilities and firms is an important one in economic and financial impact
analyses.
The conventional theory of the "firm" is really a theory of the
"establishment." The operator/manager of a facility-usually directly or
indirectly the owner of a firm-maximizes short-run profit by setting the rate
of output where marginal cost equals marginal revenue (price in perfect
competition) as long as marginal revenue at least covers average variable
5-1
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cost. Economic failure describes the situation in which the decision maker
closes the facility if marginal revenue/price is below marginal cost.
Altman (1983) draws the distinction between economic failure and
bankruptcy. Economic failure is the inability of invested capital (facility)
to continually cover its variable costs through revenues. Altman notes that a
firm can be an economic failure for years as long as it never fails to meet
its legal obligations because of the absence or near absence of enforceable
debt, thus continuing to operate as a firm. Alternatively, a firm may own
perfectly viable assets in an economic sense but earn insufficient profits to
meet enforceable debts.
Because viable facilities can be owned by nonviable companies and viable
companies can own nonviable facilities, a regulation that closes a facility
may leave the company that owns it virtually unaffected. Alternatively, a
regulation that would leave a facility viable after compliance may nonetheless
cause a firm to become bankrupt or force it to sell the facility. The number
of facilities closed by a regulation may exceed or be less than the number of
firms forced to sell facilities and/or go bankrupt.
5.2 POPULATION OF POTENTIALLY AFFECTED FIRMS
Facilities subject to regulation under the NESHAP are generally
classified in one of three four-digit Standard Industrial Classifications
(SICs) : 7215 (Coin-operated Iciundries and dry cleaning), 7216 (Dry cleaning
plants, except rug cleaning), and 7218 (Industrial launderers). Nearly all
industrial laundering facilities (SIC 7218) are already in compliance with the
regulatory alternatives considered for proposal. In addition, those
facilities that might be affected have a near-perfect substitute for dry
cleaning—water laundering. Consequently, the financial impacts on industrial
launderers are likely to be small, so these firms' finances are not
characterized in this report.
A financial profile of coin-operated dry cleaning firms is also not
presented, but for a very different reason. The economic impact analysis
indicates that each of the alternatives considered would cause substantial
price impacts and quantity impacts unless EPA exempts small facilities. EPA
5-2
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will thus probably exempt small coin-operated facilities, effectively
exempting them all. Consequently, coin-operated dry cleaning firms will
experience no financial impacts.
Effectively, this leaves commercial dry cleaning plants (SIC 7216) as
the potentially affected population. A financial impact analysis of this
industry is important for the following reasons:
" Jhe economic impact analysis indicates that a significant number of
facilities will be affected under each of the regulatory alternative
unless a size exemption is established;
• most commercial dry cleaning firms are single-facility firms, so an
affected facility is tantamount to an affected firm; and
* ^ dxy Cleanin9 firms have limited internal and external sources of
funds because they are small businesses.
5.3 LEGAL OWNERSHIP OF COMMERCIAL DRY CLEANING FACILITIES
Business entities that own commercial dry cleaning facilities-hereafter
"dry cleaning firms" or just »firms«-will generally be one of three types of
entities:
• sole proprietorships,
• partnerships, and
• corporations.
Each type has its own legal and financial characteristics that may have a
bearing on how firms are affected by the regulatory alternatives and on how
the firm-level analysis of the NESHAP might be approached.
5.3.1 Sole Propriet-m-sh-ip
A sole proprietorship consists of one individual in business for himself
who contributes all of the equity capital, takes all of the risks, makes the
decisions, takes the profits, or absorbs the losses. Behrens (1985) reports
that sole proprietorships are the most common form of business. Gill (1983)
reports that approximately 78 percent of businesses are sole proprietorships.
The 1987 Census of Service Industries reports that 8,494 of the 18,322 firms
with payroll in this industry, or 46 percent, are sole proprietorships. The
1991 population includes another 7,500 dry cleaning facilities are without
5-3
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payroll. Although no evidence is available, presumably most of these
nonpayroll facilities are small, are owned by single-facility firms, and are
sole proprietorships. Assuming that 7,500 nonpayroll, sole proprietorship
firms exist, of the 27,332 commercial dry cleaning firms in 1991, 16,694 (61
percent) are proprietorships (see Table 5-1).
Legally, the individual and the proprietorship are the same entity.
From a legal standpoint, personal and business debt are not distinguishable.
From an accounting standpoint, however, the firm may have its own financial
statements that reflect only the assets, liabilities, revenues, costs, and
taxes of the firm, aside from those of the individual.
Particularly relevant to the NESHAP analysis is that when a lender lends
money to a proprietorship, the proprietor's signature obligates him or her
personally and all of his/her assets. A lender's assessment of the likelihood
of repayment based on the firm and personal financial status of the borrower
is considered legal and sound lending practice because they are legally one-
and-the-same. The inseparability of the firm and the individual complicates
the assessment of credit availability and terms. Credit might be available to
a 'financially distressed "firm" if the financial status of the individual is
substantially strong to compensate. Alternatively, credit might be
unavailable to a financially health "firm" if the financial status of the
individual is sufficiently weak.
5.3.2 Partnerships
'About 8 percent of U.S. business entities are partnerships (Gill, 1983).
The 1987 Census of Service Industries reports that 1,666 of the 18,322 firms
with payroll in 1987 in this industry, or 9 percent, are partnerships. An
estimated 1,803 of all 27,332 dry cleaning firms operating in 1991 are
partnerships (see Table 5-1).
A partnership is an association of two or more persons to operate a
business. In the absence of a specific agreement, partnerships are general-
with each partner having an equal voice in management and an equal right to
profits, regardless of the amount of capital each contributes. A partnership
pays no federal income tax. All tax liabilities are passed through to the
5-4
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TABLE 5-1.
LEGAL FORM OF ORGANIZATION OF DRY CLEANING FIRMS—NUMBER AND
PERCENT
Legal Organization
Total
18,
27,
Firms
322a
332b
Proprietorships
8,494
16,694
ssasssssssssss
(46.4%)
(61.1%)
=====
Partnerships
1,666
1,803
(9.1%)
(6.6%)
Corporations
8,147
8,818
(44
(32
«-
.5%)
.3%)
" " •• -
Other
15
17
(0.
«o
1%)
.1%)
aPayroll firms only 1987.
"1991 estimate; Payroll and non-payroll firms assuming payroll firms "added" since 1987 are
distributed as 1987 payroll firms, and non-payroll firms are all proprietorships. There
are an estimated 7,500 nonpayroll firms (Radian, 1991a).
ST^ 1?Q8«-7CrSUS °f,Service Industries, Subject Series (U.S. Department of Commerce,
1990b); 1987 Census of Service Industries, Nonemployer Statistics (U.S. Department of
Commerce, 1990a).
individuals and are reflected on individual tax returns. Particularly germane
is that each partner is fully liable for all debts and obligations of the
partnership (Behrens, 1985). Thus, many of the qualifications and
complications present in analyses of proprietorships (e.g., capital
availability) are present—in some sense magnified—in analyses of
partnerships.
5.3.3 Corporations•
Even though only 14 percent of U.S. businesses are corporations, they
produce approximately 87 percent of all business revenues (Gill, 1983) . The
1987 Census of Service Industries reports that 8,147 of the 18,322 firms with
payroll in this industry, or 44 percent, are corporations. Including the
7,500 nonpayroll proprietorships, 32 percent of all dry cleaning firms
operating in 1991 are corporations (see Table 5-1).
Unlike proprietorships and partnerships, a corporation is a legal entity
separate and apart from its owners or founders. Financial gains from profits
and financial losses are borne by owners in proportion to their investment in
the corporation. Analysis of credit availability to a corporation must
recognize at least two features of corporations. First, they have the legal
ability to raise needed funds by issuing new stock. Second, institutional
5-5
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lenders (e.g., banks) to corporations assess credit worthiness solely on the
basis of the financial health of the corporation—not its owners. A
qualification of note is that lenders can require (as a loan condition) owners
to agree to separate contracts obligating them personally to repay loans.
5.4 DISTRIBUTION OF COMPANIES BY RECEIPTS SIZE
The U.S. has an estimated 27,332 commercial dry cleaning firms in 1991.
An estimated 19,832 (73 percent) of these are firms with payroll; the balance
(7,500 or 27 percent) includes firms without payroll. Estimating the
distribution of dry cleaning firms by receipts size assumes that all seasonal,
with-payroll firms have under $25,000 receipts and that 5,625 and 1,875
nonpayroll establishments are owned by as many nonpayroll firms with under
$25,000 receipts and $25,000-$50,000 receipts, respectively (Radian, 1990c).
These estimates are presented in Table 5-2. Approximately three-fifths
of all commercial dry cleaning firms have annual receipts of $100,000 or less.
Almost one-quarter of the total have annual receipts below $25,000 (assuming
all seasonal and most nonpayroll firms are included in this category). Only
about 2 percent of all dry cleaning firms have annual receipts over $1
million.
Industry concentration is a good summary indicator of firm size
distribution (see Table 5-3). The fifty largest commercial dry cleaning
companies earn only about 9 percent of total industry receipts. This "fifty
firm concentration ratio" is much lower than those for linen supply (63.1%),
coin-operated laundries (30.5%), power laundries (28.5%), or industrial
launderers (67.3%).
Firm size is likely to te a factor in the distribution of financial
impacts of the NESHAP on dry cleaning firms. Dry cleaning firms differ in
size for one or both of the following reasons:
• First, dry cleaning facilities vary widely by receipts (see
Section 9.1 and Table 9-27). All else being equal, firms with large
facilities are larger than firms with small facilities.
• Second, dry cleaning firms vary in the number of facilities they own.
All else being equal, firms with more facilities are larger than
those with fewer facilities (see Section 5.5).
5-6
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TABLE 5-2. RECEIPTS OF DRY CLEANING FIRMS
Receipts Range
($000)
<25
25-50
50-75
75-100
subtotal
100-250
250-500
500-1,000
1,000-2,500
2,500-5,000
>5,000
subtotal
Total
===========
No. of Firms3
6,690
4,187
2,581
2,581
16,039
6,823
2,870
1,122
389
60
29
11,293
27,332
aaaaagss
Receipts per
Firm
17,736
40,545
67,021
93,829
-
171,219
366,915
722,394
1,504,998
3,640,043
10,973,635
-
T
No. of
Establishments
6,690
4,187
2,581
2,581
16,039
7,032
3,382
1,836
1,130
424
651
14,455
30,494
======
Receipts per
Establishment
17,736
40,545
67,021
93,829
—
166,130
311,368
441,463
518,092
515,100
488,841
—
!! Estif"fte;
well as those that
and Non-Payroll Firms (includes plants that use PCE as
use other solvents.). Nonpayroll firms include 5625
and 1875 with 25'°°° to 5o'oo° i
VJ5?" industries, Subject Series (U.S. Department of
TABLE 5-3. CONCENTRATION BY LARGEST DRY CLEANING FIRMS
Percent of Industry Receipts8
4 Largest Firms
8 Largest Firms
20 Largest Firms
50 Largest Firms
aPayroll firms only, 1987.
2.4%
3.6%
5.8%
9.1%
IndUStries' Sub'ect Series
Department of
5-7
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5.5 DISTRIBUTION OF COMPANIES BY NUMBER OF FACILITIES
The financial impacts of the NESHAP on two firms of equal size might
depend significantly on their facility composition because substantial control
economies of scale exist. The costs of controlling larger machines are not
proportionately higher than the costs of controlling smaller ones. Also, the
effective impacts on more fully utilized dry cleaning machines are smaller
than on under-utilized dry cleaning machines. Because machine size and
utilization underlie facility receipts, facility impacts will be greater for
smaller than for larger facilities.
Control economies are facility-related rather than firm-related.
Hypothetically, a firm with ten uncontrolled facilities of a given size may
face approximately twice the control capital requirements of a firm with five
uncontrolled facilities of the same size. Alternatively, two firms with the
same number of facilities facing approximately the same control capital costs
may be financially affected very differently if the facilities of one are
larger than those of another.
An estimated 27,332 firms own 30,494 commercial dry cleaning
establishments in 1991: an average of 1.12 facilities per firm. An estimated
95 percent of all commercial dry cleaning firms own a single facility.
Table 5-4 reports the distribution of firms by number of dry-cleaning
establishments owned, assuming that all 7,500 nonpayroll establishments
(Radian, 1991a) are owned by single-facility firms. Even in the $500K to $1M
firm receipts range, the average number of facilities per firm is below two.
At the other extreme, 29 firms own about 22 facilities each.
The implication of this distribution are as follows. Up to a point,
firm receipts grow because machine sizes increase and/or machine capacity
utilization increases. Note that $75K-$100K firms have an average $93,829 of
receipts accruing to their single facility, while <$25K firms have an average
only $17,736 accruing to their single facility (Table 5-2). Since capital
costs of control devices are similar for machines of all sizes and utilization
rates, capital requirement impacts fall fairly proportionately as firm size
increases—up to a point (see Section 7). After some point, receipts per
5-8
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TABLE 5-4.
NUMBER OF COMMERCIAL DRY CLEANING FACILITIES PER FIRM BY
INCOME CATEGORY
Receipts Range ($000)
Facilities Per Firm
<25
25-50
50-75
75-100
100-250
250-500
500-1,000
1,000-2,500
2,500-5,000
>5,000
lndUStries'
1.00
1.00
1.00
1.00
1.03
1.18
1.64
2.90
7.07
22.45
=====
Series (U.S. Department of Conroerce,
establishment stabilize at about $500,000 (see Table 5-2) and firms grow only
by adding more facilities (see Table 5-3). Control economies of scale
essentially cease to exist for firms larger than $1 million.
5.6
VERTICAL INTEGRATION AND DIVERSIFICATION
Vertical integration is a potentially important dimension in firm-level
impacts analysis because a vertically integrated firm could be indirectly as
well as directly affected by the NESHAP. For example, if a dry cleaning firm
is vertically integrated in the manufacture and/or distribution of
perchloroethylene (PCE), it could be indirectly and adversely affected by the
NESHAP if demand for PCE diminishes after the regulation.
Ignoring for now that some dry cleaning facing, also engage in
operations other than dry cleaning, a dry cleaning JLirm is considered
vertically integrated if it also owns facilities that sell goods or services
used as inputs by the dry cleaning industry and/or facilities that purchase
5-9
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dry cleaning services as inputs. Forwarc integration is unlikely because
nearly all dry cleaning services are provided to individuals, not firms.
Backward integration is unlikely because the main inputs in the dry cleaning
industry are a building, dry cleaning macninery, energy, and PCE, all
dissimilar to dry cleaning services.
Intra-firm diversification, sometii^as referred to as horizontal
integration, is a potentially important dimension in firm-level impact
analysis for either or both of two reasor.s.
• First, a diversified firm could ta indirectly as well as directly
affected by the NESHAP. For exanple, if a dry cleaning firm is
diversified in the manufacture of emissions control equipment (an
unlikely scenario), it could be indirectly and favorably affected by
the NESHAP.
• Secondly, a diversified dry cleaning firm may own facilities in
unaffected industries like carpet cleaning, linen supply, power
laundering, or shoe repair—a more realistic situation. This type of
diversification would help mitigeze the' financial impacts of the
NESHAP.
Intra-facility diversification is also a relevant consideration because
dry cleaning facilities commonly engage in activities other than dry cleaning.
Many dry cleaning facilities do alterations work, repair shoes, clean
draperies, store garments, and sell other goods and services. This is another
type of diversification that could mitigate the impact, of the dry cleaning
NESHAP on certain dry cleaning firms. Indeed, the prominence and magnitude of
intra-facility diversification in the industrial dry cleaning industry is
partly the reason for not including those firms at all in this financial
impacts analysis.
5.7 FINANCIAL CHARACTERISTICS OF FIRMS IN REGULATED INDUSTRY(IES}
This section characterizes the financial condition of commercial dry
cleaning firms. Clark (1989) investigated.the suitability of available small
business financial data bases for EPA's use in its economic analyses. He
concludes that two main financial data bases are appropriate: Internal
Revenue Service (IRS) data and Dun and Bradstreet (D&B) data. Although each
of the data bases has its comparative merits, the Dun and Bradstreet data are
better for characterizing the finances of dry cleaning firms. The D&B data
5-10
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are more recent than the IRS data, are available for the dry cleaning
industry, and are probably based on a larger (though nonrandom) sample than
the IRS data. The financial condition of dry cleaning firms can be
characterized using Dun and Bradstreet•a 1989-1990 Industry Morm* anrt goy
Business Ratios (Duns Analytical Services, 1990).
The D4B data base contains 991 commercial dry cleaning establishments.
Clark (1989) notes that the financial information provided to D4B is supplied
by the businesses to obtain favorable credit ratings; therefore, the
businesses have an incentive to make their net worth and income look as good
as possible. Companies that are'not doing well financially have an incentive
to keep their financial information out of D&B's data base. Thus the
financial data reported therein are based on a possibly nonrepresentative
sample of firms.
Industry Norms and K^-y Buffing Pnfjp* unfortunately does not
characterize the finances of firms by firm size. Consequently, informal
assumptions are necessary to estimate the number of firms in each of the seven
receipts ranges in below-average, average, and above-average financial
condition. Two alternative assumptions are employed in this analysis.
One assumption (financial scenario I) reflects the high probability that
firms in below-average financial condition are disproportionately small since
the capacity utilization of their machines is so low. Dry cleaning machine
capacity utilization at facilities with annual receipts under 525,000 is only
about 7 percent, and that of facilities with annual receipts of $25,000 to
$50,000 is only about 15 percent. Capacity utilization approaches 80 percent
only when facility receipts approach $100,000.
Table 5-5 presents estimated numbers of firms by size and baseline
financial condition assuming a positive relationship between the two. The
result is that all 6,834 firms in below-average financial condition have
annual receipts below $50,000, that all 13,664 firms in average financial
condition have annual receipts between $25,000 and $250,000, and that all
6,834 firms in above-average financial condition have annual receipts above
$100,000.
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TABLE 5-5. NUMBER OF DRY CLEANING FIRMS, BY SIZE AND BASELINE FINANCIAL
CONDITION
Receipts Range
($000)
<25
25-50
50-75
75-100
100-250
250-500
>500
Total
Total
6,690
4,187
2,581
2,581
6,823
2,870
1,600
27,332
Baseline
Below Average
6,690
144
0
0
0
0
0
6,834
Financial
Average
0
4,043
2,581
2,581
4,459
0
0
13,664
Condition
Above Average
0
0
0
0
2,364
2,870
1,600
6,834
Source: Table 5-2 and Duns Analytical Services (1990), Financial Scenario I.
Table 5-6 uses the D&B data to characterize the population and shows the
number of dry cleaning firms in each of seven receipts categories and each of
three financial conditions under an alternative assumption that there is no
relationship between firm size and financial condition (financial
scenario II). Fifty percent of all firms are, regardless of size, allotted in
the "average financial condition" grouping, and 25 percent of all firms in
each of the "below-average" and "above-average" financial condition groupings.
Dun and Bradstreet data are employed to derive financial profiles of dry
cleaning firms in below-average, average, and above-average financial
conditions. Income statements and balance statements are the two basic
financial reports kept by firms. The former reports the results of a firm's
operation during a period of time—usually one year, in practice. The latter
is a statement of the financial condition of the firm at a point in time—
usually December 31 or the last day of the firm's fiscal year.
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TABLE 5-6.
Baseline Financial Condition '
Receipts Range
($000)
'<25
25-50
50-75
75-100
100-250
250-500
>500
Total
========
Total'
6,690
4,187
2,581
2,581
6,823
• 2,870
1,600
27,332
..
Below Average
1,673
1,047
645
645
1,706
718
400
6,834
Average
3,344
2,093
1,291
1,291
3, 411
1,434
800
13,664
Above Average
1,673
1,047-
645
645
1,706
718
400
6,834
Source: Table 5-2 and Duns Analytical Services (1990), Financial Scenario II.
The income statements and balance sheets of dry cleaning firms of
different sizes and financial conditions are presented in Appendix A
(Tables A-l through A-3,. The five sales categories are largely selected for
cut-off analysis purposes. ' All other lines ln the two statements derive
directly or indirectly, from "sales" relationships given in D&B. Several
examples will clarify how the statements are derived.
An estimated 11,293 dry cleaning firms have receipts over $100,000 The
estimated average receipts for these firms total $367,510, which is reported
as "sales" in the income statement. D&B reports that the average dry cleaning
firm in the data base has a net profit of 7 percent of sales. This ratio
multiplied by the sales estimate of $367,510 yields the estimated "net profit"
of $25,725 in the income statement. The three other lines in the income
statement are analogously derived by applying D&B ratios multiplied by sales.
Balance sheet items are derived in an analogous manner. D&B reports
that the average dry cleaning firm in the data base has afaout $48Q Qf ^^
assets for every $1,000 dollars of sales. This ratio multiplied by the sales
5-13
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estimate of $367,510 yields estimated total assets of $177,257. D&B reports
that the average dry cleaning firm has about $369 of current assets, $373 of
fixed assets, and $258 of other noncurrent assets per $1,000 of total assets.
These ratios multiplied by the total assets estimate yield the estimates
presented for those variables in the tables In the liabilities section of the
balance sheet, "total liabilities and net worth" must equal "total assets,"
and the component-parts are computed using D&B ratios multiplied by the total.
To project the potential financial impacts of the NESHAP on firms of
different sizes in below-average financial condition, baseline financial
profiles of representative less healthy firms are required. Unfortunately,
Dun and Bradstreet does not rank businesses in a particular industry in their
data base from "most healthy" to "least healthy" and then report the financial
ratios of the firm that falls in the lower quartile of that distribution.
Instead, D&B calculates each ratio of interest (e.g., current assets/current
liabilities) for the 991 firms and then ranks these ratios from "best" to
"worst." D&B then reports the lower quartile for each of these ratios
individually. Consequently, constructing the financial statement of the lower
quartile firm is not possible.
Constructing pro forma financial statements of a firm that yield
financial ratios closely resembling the D&B lower quartile ratios is possible.
Appendix A presents the income statements and balance sheets of dry cleaning
firms in below-average financial condition. D&B reports that the lower
quartile profit-to-sales ratio of commercial dry cleaning firms in its data
base is about one percent, which is consistent with the income statement
entries. Other lower-quartile ratios reported by D&B and employed in the
construction of these pro forma statements include assets-to-sales of
approximately 70 percent, fixed assets-to-net worth of approximately 155
percent, and a return on net worth of approximately 3.5 percent.
To project the potential financial impacts of the NESHAP on firms of
different sizes in above-average financial condition, baseline financial
profiles of representative healthy firms are required. For reasons described
above, constructing the financial statements of the upper-quzirtile firm is not
possible. Again, constructing pro forma financial statements of a firm that
yield financial ratios closely resembling the D&B upper-quartile ratio is
5-14
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possible. Appendix A presents the income statements and balance sheets of dry
cleaning firms in the same size categories, all in above-average financial
condition.
5.8 KEY BUSINESS RATIOS OF DRY CLEANING FIRMS
Financial ratio analysis is a widely accepted way of summarizing the
financial condition of a firm. Financial ratios include four fundamental
types:
• indicators of liquidity,
f
• activity,
• leverage, and
• profitability.
The baseline financial status of dry cleaning firms is characterized below by
means of financial ratio analysis.
Liquidity indicates the 'ability of the firm to meet its near-term
financial obligations as they come due. A common measure of liquidity is the
current ratio, which divides the firm's current assets by its current
liabilities. Current assets include cash, accounts receivable, inventories,
or other assets that represent or can be converted to cash within one year.
Current liabilities are essentially bills that must be paid within the year
(including current maturities of long-term debt). Higher ratios are generally
more desirable than lower ratios, because they indicate greater liquidity or
solvency.
Activity indicates how effectively the firm is using its resources. The
ratio of firm sales to fixed assets (plant and equipment), the fixed asset
turnover ratio, measures how well the firm uses its capital equipment to
generate sales. Higher ratios are generally more desirable than lower ratios.
Leverage indicates the degree to which the firm's assets have been
'supplied by, and hence are owned by, creditors versus owners. Leverage should
be in an acceptable range indicating that the firm is using enough debt
financing to take advantage of the lower cost of debt, but not so much that
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current or potential creditors are uneasy about the ability of the firm to
repay its debt. The debt ratio is a common measure of leverage that divides
all debt, long and short term, by total assets.
Profitability measures the return, usually as net income after all
costs, debt repayment, and taxes, to the firm over some time period, usually
one year. Profitability is most commonly, though perhaps not most relevantly,
expressed as a return to sales. Because net worth is a measure of the value
of the firm to its owners, profitability-to-net worth is a measure of the
annual return to owners expressed as a percent.
Financial ratio indicators of liquidity, activity, leverage, and
profitability among dry cleaning firms in below-average, average, and above-
average financial health are presented in Table 5-7. Clearly, as financial
status improves, firms become more liquid. Note particularly that below-
average firms are only marginally able, at best, to meet current obligations
with their cash and other current assets.
Also as expected, firms in better financial health generate more sales
with their plant and equipment. In the context of the dry cleaning industry,
this condition may indicate that firms with higher machine capacity
utilization are more financially sound than those with lower machine capacity
utilization. Sales per dollar of fixed assets are more than twice as high
among firms in average financial condition than among those in below-average
financial condition. This lends support to financial scenario I of a positive
relationship between firm size and financial health, that in turn underlies
the estimates presented in Table 5-5.
Leverage analysis of dry cleaning firms in the three different financial
states is more difficult than liquidity, activity, or profitability analysis.
The "mean firm" in the D&B data base is about 46 percent debt financed (and 54
percent equity financed). As explained above, less debt is not necessarily
"better" because a firm using too little debt is not minimizing its cost of
capital. .From a creditor's point of view though, less debt is probably better
than more debt, on balance. D&B reports are creditor-oriented, which probably
explains why in D&B's judgment a low debt ratio is desirable. Because a main
5-16
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TABLE 5-7. BASELINE FINANCIAL RATIOS OF DRY CLEANING FIRMS
Financial Condition
Below Average
Average
Above Average
Liquidity
Current ratio (times) 0.80
Activity
Fixed asset turnover 2.30
ratio (times)
Leverage
Debt ratio (percent) 60.00
Profitability
profit to sales (percent) 1.00
profit to assets (percent) 1.40
profit to NW (percent) 3.60
' ' =^=aa=g=s=aa5asB3=
Source: Duns Analytical Services, 1990.
1.73
5.56
45.90
7.00
14.50
26.80
5.10
7.54
15.00
13.00
32.50
38.20
objective of this analysis is to evaluate a dry cleaning firm's ability to
obtain and its cost of obtaining credit to purchase control equipment, this
interpretation is satisfactory.
Profitability analysis is useful because it helps evaluate both the
incentive and the ability of dry cleaning firms to incur equipment and
operating costs required for compliance.! More profitable firms have more
incentive than less profitable firms to comply because the annual returns to
doing business are greater. In the extreme, a single-facility firm earning
zero profit (price equals average variable cost) has no incenti™ to comply
with a regulation imposing any positive cost unless it can pass along the
^ ™~ Cleanin9 firms that are either unwilling or unable to comply with
the NESHAP must sell the facility, switch solvents, or discontinue tSeir dry
cleaning operations at the noncompliant facility.
5-17
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entire cost of the regulation to its customers. This same firm is also less
able to comply because it is less able to obtain a loan.
. The relationship between profitability and firm health is clearly
demonstrated in Table 5-7. One-quarter of the dry cleaning firms in D&B's.
data base are only marginally profitable by all three measures. If some or
all of. the estimated 6,690 commercial, dry'cleaning firms with annual receipts
under $25,000 are among the lower quartile in profitability, they are
generating annual profits of only several hundred dollars. Average dry
cleaning firms are seven times more profitable (related to sales) than below-
average firms, and above-average firms are about twice as profitable as
average firms.
These financial ratios suggest that the NESHAP requirements may have a
disproportionate impact on small firms and firms in below-average financial
health. -The financial ratios of below-average firms are sometimes
substantially worse than those of average firms. These baseline ratios will
be used as a basis of comparison in Section 7 when the potential financial
impacts, of the NESHAP on dry cleaning firms are considered,
5.9 AVAILABILITY AND COSTS OF CAPITAL
Without exception, affected dry cleaning facilities would have to
purchase control equipment to meet the regulatory alternatives or discontinue
dry cleaning operations ("closure"). In addition, many affected facilities
would incur recurring operating and maintenance costs that exceed their
solvent recovery credits. The availability and costs of capital to dry
cleaning firms of different sizes, types, and financial conditions will
influence the financial impacts of the dry cleaning NESHAP.
Hastsopoulos (1991) clearly states that in making investments, companies
use two sources of funds: equity and debt. Each source differs in its
exposure to risk, in its taxation, and its cost. Equity financing involves
obtaining additional funds from owners: proprietors, partners, or
shareholders. Partners and shareholders, in turn, can be existing owners or
new owners. Obtaining new capital from existing owners can be further
dichotomized into internal and external financing. Using a firm's retained
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earnings is equivalent to internal equity financing. Obtaining additional
capital from the proprietor, one or more existing partners, or existing
shareholders constitutes external equity financing.
Debt financing involves obtaining additional funds from lenders who are
not owners; they include buyers of bonds, banks, or other lending
institutions. Debt borrowing involves a contractual obligation to repay the
principal and interest on an agreed-upon schedule. Failure by the firm to
meet the obligation can result in legal bankruptcy.
The dry cleaning industry is dominated by small firms for whom selling
stocks and bonds is not a very realistic option. Steinhoff and Burgess (1989)
list a large number of sources of funding for small businesses, but most fit a
description of either debt or equity reasonably well:
• personal funds and/or retained earnings,
• loans from relatives and friends,
• trade credit,
• loans or credit from equipment sellers,
• mortgage loans,
• commercial bank loans,
• Small Business Administration loans,
• small business investment company loans,
• government sponsored business development companies,
• partners,
• venture capital funding, and
• miscellaneous sources.
Using personal funds and/or retained earnings, obtaining loans from
relatives and friends, obtaining funds from partners, and obtaining venture
capital funding effectively constitute equity financing because they generally
do not involve a legal contract for repayment. This type of borrowing is
considered more risky for the lender than for the borrowing firm because in
5-19
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the event of bankruptcy, the lenders have claim to the dissolved assets of the
firm only after those of debt lenders.
Trade credit, loans or credit from equipment sellers, mortgage loans,
commercial bank loans, Small Business Administration loans, small business
investment company loans, and government-sponsored business development
company loans generally constitute debt financing because they involve
contractual promises to repay the principal and some agreed-to interest. In
the event of firm bankruptcy, which can be initiated by a lender whose loan
terms are not being honored by the firm, debt lenders are paid out of the
assets of the firm before equity lenders. Thus, debt borrowing is considered
more risky for the firm's owners than equity borrowing.
One important difference then between debt and equity financing is its
cost. The expected or anticipated rate of return required by equity lenders
is higher than the required rate of return to debt lenders because of the
relative riskiness of equity. A second important difference between the two
sources of funds is tax related. Interest payments on debt are deductible to
the firm as a cost of doing business for state and federal income tax
purposes. Returns to owners are not tax deductible. Thus, borrowing debt has
a distinct tax-related cost advantage. For two reasons, then, the cost of
debt is normally lower than the cost of equity.
In this analysis, a simplifying assumption is made that dry cleaning
firms have two possible sources of .capital: bank loans (debt) and retained
earnings (equity). The availability and cost of capital is evaluated in that
context.
A firm's cost of capital is a weighted average of its cost of equity and
after-tax cost of debt:
WACC = Wd'd-t)'Kd + We'Ke, (5.1)
where
WACC = weighted average cost of capital
Wd = weighting factor on debt
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marginal effective state and federal corporation/individual tax
IT3tG
KQ- . = the cost of debt or interest rate
We = weighting factor on equity
Ke = cost (required rate of return) of equity.
A real (inflation-adjusted) cost of capital is desired, so employing the GNP
implicit price deflator for the seven year period 1982-1989 adjusts nominal
rates to real rates. Using an adjustment factor of 4 percent assumes that the
inflation premium on real rates for the next seven years is the actual rate of
inflation averaged over the last seven years (1990 Economic Report of the
President) .
Based on conversations with a business loan officer at a large
commercial bank (Bass, 1991), seven-year prime-plus variable interest rate
bank loans for control equipment are assumed to be available to qualifying
firms on the following cost terms :
• best applicants: prime plus one-half percent
• typical health applicants: prime plus one percent
• below-average but still-sound applicants: prime plus 2 percent
According to Bass, actual loan terms are negotiated on a case-by-case
basis, but the guidelines given above are reasonable. Particularly germane to
thas analysis is his insistence that bank loans are not made to firms at any
£0^ unless expectations are high that they well be repaid according to the
terms of the loan. This is why the risk premium spread from one-half percent
to 2 percent is so narrow.
Between 1982 and 1989 the prime rate varied around a mean of
approximately 10.5 percent, nominal. Using the inflation premium discussed
above, and assuming that the nominal prime rate will average about 10.5
percent over the next seven years, the expected xsal prime rate is about 6.5
percent. Then following Bass's guidelines for loan risk premium, the
following real bef ore-tax debt costs are computed and employed:
• best applicants: 7 percent
• typical health applicants: 7.5 percent
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• below-average but still-sound applicants: 8.5 percent
Because debt interest is deductible for state and federal income tax
purposes, the .cost of debt has to be adjusted downward. An approximate
effective marginal state and federal tax rate of 38 percent is computed using
data from The Tax Foundation (1991). Applying this rate to the real costs of
debt computed earlier derives after-tax real debt costs for dry cleaning firms
in three different financial conditions:
• above-average financial condition: 4.3 percent
• average financial condition: 4.7 percent
• below-average financial condition: 5.3 percent
The cost of equity, Ke, can be estimated by adding an equity risk
premium to a risk-free required rate of return (Jones, 1991) . Using the 1982-
1989 average return on 10-year federal treasury securities as the risk-free
rate, and assuming it is applicable for the next seven years, a nominal risk-
free, rate of 10 percent is obtained.
Jones (1991) reports that common practice is to use the Standard and
Poor 500 long-run average equity risk premium of about 8 percent as a first
basis for computing the cost of equity in conjunction with the risk-free rate.
Thus, the S&P 500 nominal equity yield is about 18 percent, which is an
estimate of the average cost of equity for all publicly traded stocks (Van
Horne, 1980).
Jones indicates that still another risk premium has to be added for
firms that are more risky than the S&P 500 average, and that dry cleaning
firms probably generally fall in this category. Even though the assumption is
\
necessarily arbitrary, dry cleaning firm equity risk premiums are employed as
follows:
• dry cleaning firms in above-average health: 0 percent
• dry cleaning firms in average health: 2 percent
• dry cleaning firms in below-average health: 6 percent
5-22
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Adding these dry cleaning firm equity risk premiums and simultaneously
subtracting inflation premiums result in the following set of real equity
costs for dry cleaning firms of different financial states:
• above-average financial condition: 14 percent
• average financial condition: 16 percent
• below-average financial condition: 20 percent
These estimates appear reasonable in view of a study by Anderson, Mims
and Ross (1987) which estimated real equity costs of 11 percent, 14 percent,
and 19 percent for firms with Moody Bond Ratings of AAA (the highest rating)',
BBB, and BB, respectively.
weighting the debt and equity cost components is difficult for several
reasons. First, market value weights are more theoretically correct than book
value weights, but only the latter are observable for privately owned dry
cleaning firms (Bowlin, Martin, and Scott, 1990) . Second, target weights, not
historical weights, are appropriately used for estimating the cost of capital
(Bowlin, Martin, and Scott, 1990) . Again, only historical weights are
observable. Third, marginal costs of capital, not historical average costs,
are appropriate hurdle rates for new investments (Bowlin, Martin, and Scott,
1990) .
For this analysis, the industry average debt/equity structure is the
optimal/target structure for all dry cleaning firms and book-value weights
approximate market-value weights (Bowlin, Martin and Scott, 1990). The debt
and equity weights of the mean dry cleaning firm in the Dun and Bradstreet
data base are 31 percent and 69 percent, respectively. Using these weights
and the component costs of capital derived above gives the weighted average
costs of capital for dry cleaning firms in the three financial states:
• above-average financial condition: 11 percent
• average financial condition: 12.5 percent
• below-average financial condition: 15.4 percent
These cost of capital estimates are not presented as actual costs to
particular firms. Likewise, they are not meant to imply that firms within a
5-23
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financial condition category all have the same cost of capital, or that
borrowed funds will necessarily be available to all firms. In particular,
recognize that 25 percent of all firms are in "below-average financial
condition." Within this range, some firms will be far more financially
distressed than others. The 15.4 percent real rate may overestimate the cost
of capital for some of these dry cleaning firms and underestimate some
unusually distressed firms.
Adequate control capital funds are probably unavailable through normal
channels to small, particularly distressed firms. Bass (1991) indicates that
most commercial banks will not lend money to financially distressed firms, and
retained earnings at small, distressed firms may be inadequate to pay for
control capital. Bass also stated that his institution, and others, won't
lend money to dry cleaning firms without first conducting an "environmental
audit" to protect the bank in the event that environmental contamination is
present or foreseeable at the time of the loan. One can never discount the
possibility that funds would be available from owners' personal funds, new
partners, friends, relatives, or other sources.
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SECTION 6
RESPONSES TO THE REGULATORY ALTERNATIVES
The regulatory alternatives considered for proposal require dry cleaning
facilities to install and operate vent control devices. Affected entities
will incur initial and recurring costs as a result of these requirements.
This section presents an overview of the requirements of the candidate
regulatory alternatives and a description of the potential firm-level and
facility-level responses to these requirements.
6.1 OVERVIEW OF REGULATORY ALTERNATIVES
Three regulatory alternatives are evaluated here. The main difference
in the control requirements among the alternatives is the treatment of
existing control mechanisms on transfer machines. Table 6-1 summarizes the
control equipment options for each of the regulatory alternatives by industry
sector and machine technology.
Dry cleaning machines emit PCE from two sources: vent emissions and
fugitive emissions. Fugitive emissions are controlled under each alternative
by requiring good work practices. The percentage reduction in fugitive
emissions attributable to good work practices is not quantified for this
analysis. Vent emissions are controlled under each alternative by air
pollution control devices. Control equipment required under Regulatory
Alternative I reduces vent emissions from dry-to-dry and transfer machines by
95 and 85 percent, respectively, compared to uncontrolled levels. For
machines in the commercial sector, Alternative I mandates using a carbon
adsorber (CA) or a refrigerated condenser (RC). Because of technical
constraints, all other machines must use a CA.. The control equipment required
under Regulatory Alternative II reduces vent PCE emissions from dry-to-dry and
uncontrolled transfer machines by 95 percent (compared to uncontrolled
levels). Transfer machines with an RC in place are not required to purchase
additional equipment under this alternative. Finally, control equipment
required under Regulatory Alternative III also results in a 95 percent
reduction in vent PCE emissions (compared to uncontrolled levels).
6-1
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TABLE 6-1. CONTROL TECHNOLOGY OPTIONS UNDER EACH REGULATORY ALTERNATIVE
Regulatory Alternative
Industry Sector and Machine Type
II
III
Coin-Operated
dry-to-dry
Commercial
dry-to-dry
transfer (uncontrolled)
transfer (RC controlled)
Industrial
CA
CA
'RC
CA
RC
CA
CA
RC
CA
no oo
additional additional
control control
required required
CA
CA
RC
CA
CA
dry-to-dry
transfer
CA
CA
CA
CA
CA
CA
CA = Carbon Adsorber
RC = Refrigerated Condenser
Source: Radian, 1990a.
Alternative III differs from Alternative II because it requires CA's on
transfer machines currently controlled with an RC.
Current owners of dry cleaning facilities with non-compliant machines
must decide to comply or exit the industry. That decisionmaking process at
the firm level is described in Section 6.2. Facility-level responses are
discussed in Section 6.3
6.2 FIRM-LEVEL RESPONSES
«
The dry cleaning NESHAP will potentially affect firms that own dry
cleaning facilities not in compliance with the regulatory alternatives
considered. A firm is a legal organization consisting of one domestic
6-2
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establishment or more under common ownership or control. An establishment is
a single physical location at which business is conducted—a site of land with
plant and equipment that combine inputs like materials, energy, and labor to
produce outputs, like dry cleaning services. Firms are legal business
entities that, in this context, own one or more facilities.
The owners of dry cleaning firms that own dry cleaning facilities
potentially affected by the regulatory alternatives have several ways they can
respond. The more important of these possible responses are depicted in
Figure 6-1.l
The current owners of dry cleaning firms operate dry cleaning facilities
whose periodic (e.g., annual) revenues cover or exceed their periodic average
variable costs. The owners of dry cleaning facilities that do not have the
. vent controls required under the candidate regulatory alternatives must assess
whether controlled facilities will continue to meet this same operating
criterion. These owners must evaluate their alternatives, assess the benefits
and costs of each, and respond in some manner. Owners generally respond in
the way that maximizes the net-present value of the firm.
The assessment of post-compliance costs and revenues is depicted in
Figure 6-1. The expected revenues (ER) of the complying -facility are
approximately the product of the expected price and the expected quantity. '
The expected costs (EC) are functionally related to the facility's current
variable costs plus costs of compliance. Compliance costs, in turn, include
the costs of purchasing, installing and operating control equipment, the costs
of financing the capital investment, less any solvent recovery credits.
technically, substituting other solvents for PCE is also an option
However, that choice is not addressed because of the higher operating cosis
associated with those solvents. "Plating costs
6-3
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Current Owners
Close
Keep facility Sell facility
Comply
Yes
Operate
E = expected
R = periodic revenues (Price x Quantity)
C = periodic costs (variable cost plus periodic
repayment of principal and return oh investment)
Figure 6-1. Responses to the Proposed Regulation
6-4
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If the expected costs of operating the complying facility exceed the
expected revenues, the owner of the facility C1OSM it. Altman (1983) defines
"economic failure" as the inability of invested capital to continually cover
its variable costs through revenues. For purposes of this discussion, owners
of dry cleaning firms are assumed to close facilities if they project that
annual revenues will be below annual variable costs. Furthermore, it is
assumed that once closed, facilities do not re-open.
If the expected revenues of operating the complying facility exceed the
expected costs, it is economically viable and the owners will likely k^ep the
facility or sail it. For this discussion, owners keep the facility if they
have and/or can borrow the funds required for the capital investment. If,
however, they neither have nor can borrow the required funds, they may decide
to sell the facility.
If the compliant facility is expected to remain profitable, it is
assumed that the current or new owners of the facility will comply with the
regulation in the manner that maximizes the net-present value of the firm. In
most circumstances, this is equivalent to responding in the least (net-
present) cost manner. If realized post -compliance revenues cover or exceed
realized costs, it is assumed that the firm continues to operate the facility.
If realized revenues are inadequate to cover realized costs, the owners will
likely close or sell the facility. If costs exceed revenues for
reasons, the owners will likely close the facility. These reasons might
include operating costs that exceed projections, revenues that fall short of
projections, or both. If costs exceed revenues for finan^.T reasons, the
owners may sell the facility. This could occur, for example, if the interest
rate (and required payments) on a variable rate loan rose to where revenues
were insufficient to cover the under-projected finance charges.
Because a viable dry cleaning firm can own viable facilities along with
non-viable ones— and other profitable non-dry cleaning assets as well— a
regulation that closes one or more dry cleaning facilities may leave the
company that owns it (them) virtually unaffected. Alternatively, because
viable facilities can be owned by non-viable (e.g., debt laden) companies, a
regulation that would leave a facility viable after compliance may nonetheless
force a firm to sell the facility.
6-5
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6.3 FACILITY-LEVEL RESPONSES
The facility with an uncontrolled PCE machine must either comply with
the regulation, switch solvents, or cease operations. As discussed in
Section 2, solvent substitution is unlikely. The following subsections
address the compliance options for facilities under each regulatory
alternative. Subsection 6.3.1 outlines the methods and assumptions used to
compute the costs (net present) associated with each compliance option and
subsection 6.3.2 identifies the options that satisfy the requirements of each
regulatory alternative by industry sector and machine type.
6.3.1 Compliance Option Costs
Three types of compliance options will satisfy the requirements of the
regulatory alternatives:
• retrofit with a CA
• retrofit with an RC
• accelerated purchase of a new dry-to-dry machine with a built-in vent
control
The choice that the facility owner makes depends on the sector, the
machine type, baseline vent controls, and its individual financial situation.
For the purposes of this analysis, it is assumed that the owner will choose
the least cost option that satisfies the requirements of the regulation.
To identify the lowest cost option, the incremental capital and
operating cost associated with each option is estimated. These costs vary by
machine type, capacity utilization, and the age of the machine. The net
present cost (NPC) of each available option is then computed. The following
methods are used to compute the NPC of each control option:
Control Option 1: Carbon Adsorber
n-1
= KCA + I* [OCA / (1 + r)1] (6.1)
t-0
6-6
-------
Control Option 2: Refrigerated Condenser
n-l
NPCRC - KRC +- 2 [ORC / (1 + r)*] if n < 7 (6 2)
t=*0
or
n-l
KRC + £ [0RC / (1 + r)t] + [(K
(1 + r))] if n > 7
t-0
Control Option 3: Accelerated Purchase of New Dry-to-Dry Machine
14
NPCDD = KDD + "£ [ORC / (1 +
t-o
f 14 1
j [KDD / d + D«] + I [ORC / (i + r,t] I
1 t-n J
6
t-n
where
NPCCA = the net present cost of a CA
NPCRC = the net present cost of an RC
NPCDD = the net present cost of accelerating the purchase of a new dry-
to-dry machine
KCA = the capital cost of a CA
KRC = the capital cost of an RC
KDD = the capital cost of a new dry-to-dry machine
°CA = the incremental operating cost of a CA
0RC = the incremental operating cost of an RC net of solvent recovery
r = the weighted average cost of capital2
n = the remaining life of the existing machine (cannot exceed 15)
t = the year (1991 is year 0)
Control option 3 represents the incremental cost associated with the
accelerated purchase of a new dry-to-dry machine. Facility owners replace
2?his cost of capital differs by firm financial status. The discount
factor estxmated for this analysis is 11 percent for firms in good financial
condition, 12.5 percent for firms in average condition, and 15 4 percent for
firms in poor condition. For a more complete discussion, see Section 5.
6-7
-------
existing machines with new dry-to-dry machines equipped w:ith built-in vent
controls even in baseline. Therefore, only the additional cost associated
with accelerating the purchase of a new dry-to-dry machine is included in the
cost calculations. Owners of transfer equipment that decide to accelerate the
purchase of a new dry-to-dry machine would incur lower baseline operating
costs because of greater solvent recovery associated with dry-to-dry machines.
This cost savings is not -included in the net present cost calculations
described above. If a credit for reduced baseline operating costs were
included in the calculations, a slightly larger share of the facilities would
be projected to choose option 3 as the least-cost compliance option. Because
these operating cost credits are not included, the annualized compliance costs
computed in Section 7 may be slightly overestimated.
In computing these costs, several assumptions are made:
• The distribution of the remaining life of existing machines is
rectangular. Dry-to-dry machines have a 15-year life; transfer
machines have a 20-year life.
• Virtually no new transfer machines have been sold in the last five
years. Therefore, one-fifteenth of the total population of machines
retires each year.
• In the absence of regulation, all machines would heive been replaced
by new dry-to-dry machines with built-in vent controls. The current
stock of uncontrolled machines would have been completely replaced by
these controlled machines within 15 years.
• Costs are computed for a 15-year period of analysis.3
• F.acility owners evaluate the cost of the control options using a
real, after-tax weighted average cost of capital (WACC), which
differs depending on their financial status. (See Section 5 for a
discussion of the method for computing the WACC.)
• The facility financial status, the WACC, and the share of facilities
in each financial status are given below:
3The mathematics of the cost formula require the notation of years 0-14,
where year 0 is the first year.
6-8
-------
Share of
WACC
poor 15.4% 25%
average 12.5% 50%
good 11.0% 25%
Operating costs are incurred at the beginning of each period. The
costs of control option 3 include the RC's operating costs because
most new dry-to-dry machines with vent controls use RC technology.
Purchased for existing machines in the commercial and
sectors are used only for the remaining life of the
existing machines or the remaining life of the control device, ,
whichever is shorter. Because new machines for these sectors come
equipped with built-in vent controls, the control device will not be
transferred to the new machine.
• Control devices purchased for existing machines in the coin-operated
sector are transferred to replacement machines. In general, new dry-
to-dry cleaning machines in this sector are not equipped with built-
ill COiljT
• Under option 2, machines with more than seven years of remaining life
must purchase an RC device in the first year and the eighth year
(These devices have a seven-year life.) Facilities with seven or
fewer years remaining life will purchase only one RC.
As indicated in Table 6-1, the regulatory alternative dictates the
compliance options that owners may consider. These options vary by machine
type and industry sector. Subsection 6.3.2 below identifies the options that
will satisfy the requirements of each regulatory alternative.
6'3'2 Compliance Qp1-ions rinrter paeh Rpgnlaf nry i^lternafiw
Under each of the regulatory alternatives, the owner of a coin-operated
facility has only one choice; a CA must be retrofitted to the machine.
Refrigerated condensers are not made for the size of the machines used in this
sector. Here the remaining life of the existing machinery is irrelevant.
The coin-operated facility will purchase a CA for its existing machines and
transfer the control device to replacement machines. The 'n- term shown in
Equation (6.1) is always 15 in this sector.
The facility owner in the commercial sector has three control options
under Alternative I. These options are the same for either a dry-to-dry
machine or a transfer machine. The first option is the installation of the
6-9
-------
CA. The cost computation is similar to that described above for the coin-
operated sector (see Equation (6.1)). The only difference is that the age of
existing equipment does matter. After the existing equipment wears out, it is
assumed that the facility owner will purchase a new dry-to-dry machine with an
internal vent control device. Because the purchase would occur in the absence
of regulation, the net present cost of the CA is calculated for only the
remaining years of life for the present machinery.
The second option available to the owner of a commercial facility is an
RC, whose NPC is described in Equation (6.2) . Again, the. NPC of the RC is
computed only for the remaining life of the dry cleaning machine.
The final option under this alternative is accelerating the purchase of
a new dry-to-dry machine with an internal control device. Even in the absence
of the regulation, the facility owner would probably have purchased a new dry-
to-dry machine with a built-in vent control device when his existing machine
required replacement. Therefore, the cost of the accelerated purchase only
includes costs associated with those years before the expiration of the
current machinery. Accordingly, the computation is seen in Equation (6.3).
Of these three options described above, facilities will select the least cost
option. Those facilities with older existing equipment are more likely to
choose option 3 than facilities with a longer remaining life. This selection
occurs because the incremental cost of accelerating the purchase of a new dry
cleaning machine is lower for these facilities. It is projected that facility
owners who choose to retrofit their existing equipment rather than to
accelerate the purchase of a new machine will choose option 2 because of the
lower NPC associated with this option.
For Regulatory Alternative II, the choices depend on machine type. For
dry-to-dry machines, the choices are the same as outlined above, and the cost
computations are outlined in Equations (6.1), (6.2), and (6.3). For owners of
uncontrolled transfer machines, the selection is narrowed to the CA or the
accelerated purchase of a new machine (Equations [6.1] and [6.2]). Owners of
RC-controlled transfers, however, would be allowed to continue to use their RC
with no additional control equipment required.
6-10
-------
For Alternative III, the owner of facilities with dry-to-dry machines
may choose between options 1, 2, and 3 (Equations [6.1], [6.2], and [6.3]).
For transfer machines, the facility can choose only between the CA and the
accelerated purchase (Equations [6.1] and [6.2]). Under this alternative,
owners of RC-controlled transfer machines or uncontrolled transfer machines
must retrofit with a CA or purchase a new dry-to-dry machine with a built-in
vent control. .
in the industrial sector, the choices are the same regardless of machine
type and regulatory alternative. Facilities may choose between the CA or
accelerating the purchase of a new machine (Equations [6.1] and [6.3]). The
RC is not an option under any alternative because they are not made for these
larger machines.
6-11
-------
SECTION 7
IMPACTS OF THE REGULATORY ALTERNATIVES
impacts of the regulatory alternatives are measured using an integrated
approach that considers both economic and financial impacts. A methodological
and empirical approach based on the principles of applied welfare economics is
used to compute the economic impacts of the alternatives. Economic impacts
are quantified through estimated market adjustments of price and output and
corresponding effects on consumer and producer welfare. In addition,
ownership impacts are estimated using financial data on the distribution of
firm viability. Changes in firm financial status and capital availability for
firms of different sizes and financial condition are estimated in the
financial analysis .
The approach is integrated by using inputs from each type of analysis to
compute impacts in the other. For example, financial impacts are based on the
costs computed in the economic analysis. In turn, economic impacts are based
on the costs of capital computed using data on the financial status of firms
in the industry.
7.1 AFFECTED POPULATION
The population, as defined here, includes only facilities with dry
cleaning equipment. Accordingly, coin-operated and industrial facilities
without dry cleaning machines are not included. Similarly, commercial drop
stations are not included.
Certain portions of the population would be unaffected under the
alternatives considered for three reasons.
' biaasinLr63 fv,SOlV!nt °thSr than PCE- This Distinction has the
biggest impact in the industrial sector.
• The facility already has the required control equipment in place.
• The facility is exempt because of a size cutoff based on PCE
consumption.
Thus, the affected population will vary with the regulatory alternatives and
the different cutoff levels.
7-1
-------
The four size cutoffs are based on PCE consumption levels that
correspond to target levels of annual receipts (from dry cleaning activities
only), shown in Table 7-1. If adopted, these size cutoffs would result in
certain facilities being excluded from the regulation. Notice the differences
between the dry-to-dry machines and the transfer machines. For the same level
of annual receipts, the transfer machines consume more PCE than the
corresponding dry-to-dry machines. This difference occurs because transfer
machines have higher fugitive emissions, resulting in more solvent required to
clean a given quantity of clothes (or to generate a given amount of receipts).
The population affected by the proposed regulatory alternatives can be
measured in two ways. The first is the number of facilities. Table 7-2 shows
the distribution of affected facilities by sector, model market, and cutoff
level under Regulatory Alternatives I and II. Table 7-3 shows the
distribution of affected facilities under Regulatory Alternative III.
Facilities with RC-controlled transfer machines are affected under Regulatory
Alternative III and unaffected under Regulatory Alternatives I and II.
Another method used to measure the share of the population potentially
affected under each alternative is based on the output of clothes cleaned per
year. Table 7-4 shows the distribution of affected output under Regulatory
Alternatives I and II. The distribution of affected output under Regulatory
Alternative III is reported in Table 7-5. The share of the population that is
affected differs, particularly in the commercial sector, depending on how the
population is measured. Under Regulatory Alternative II with- no size cutoff,
34 percent of commercial facilities are affected. These facilities represent
26 percent of total commercial output. This trend results from the prevalence
of baseline controls for large plants in this sector.
As noted in Section 6, all of the regulatory alternatives have the same
requirements and produce the same response in the coin-operated sector.
Therefore, no differences exist in the affected population under the three
alternatives. Furthermore, if cutoff levels 2, 3, or 4 are implemented as
part of the regulation, none of the coin-operated establishments will be
affected. It should be noted that while many coin-operated establishments
receive more than $50,000 in annual receipts, it is estimated that no
facilities receive more than this amount from dry cleaning activities alone.
7-2
-------
TABLE 7-1. SI2E CUTOFF LEVELS BASED ON CONSUMPTION OF PERCHLOROETHYLENE (PCE,
Size Cutoff
Annual Receipts from
Dry Cleaning
Activities3
<$/yr)
Consumption of PCE by Machine
Technology13 (kg/yr)
aAnnual receipts are computed using a base price of SI
cleaned for the coin-operated (self- °
v - *
kg of cl(=thes
activities only.
per kg clothes cleaned (Radian,
Source: Radian, 1991c.
re<=eipts from dry cleaning
t1' °;081 kg PCE «-' k' •*
transfer ™chine, is O.U5 kg
7-3
-------
TABLE 7-2. DISTRIBUTION OF AFFECTED FACILITIES BY INDUSTRY'SECTOR, MODEL
MARKET, AND SIZE CUTOFF: REGULATORY ALTERNATIVES I AND IIa
Industry Sector
and Model Market
Coin— Operated**
Self-Service
Plant-Operated
Total
Commercial0
Market A
Market B
Market C
Market . D
Market E
Market- F
Total
Industrial**
Total
Number of
Facilities
213
2,831
3,044
1,543
1, 606
1,157
10,432
8,073
7,683
30,494
325
Number Affected Facilities
None
200
1,415
1,615
0
1,-606
0
287
4,038
4,298
10,229
65
1
49
0
49
0
0
0
214
3,000
3,193
6,407
65
2
0
0
0
0
0
0
146
2,055
2,187
4,388
65
by Size
3
0
0
0
0
0
0
115
1,621
1,725
3,461
65
Cutoff
4
0
0
0
0
0
0
88
1,250
1,330
2,668
65
aSize cutoff levels are based'on baseline consumption of perchloroethylene
. (PCE). The cutoff levels correspond to target levels of annual receipts and
differ depending on the type of dry cleaning machine used. See Table 7-1
for description of cutoff levels.
bThe number of affected facilities under each size cutoff is based on the
share of facilities at each income level (see Table 2-13), the average
annual output at each income level (see Table 2-7), and solvent consumption
factors (Radian, 1990b).
cThe number of affected facilities under each size cutoff is based on the
total number of potentially affected facilities in each Model Market (see
Table 4-4), the share of facilities at each income level (see Table 2-13),
the average annual output at each income level (see Table 2-4), and solvent
consumption factors (Radian, 1990b).
dSee Table 2-13.
7-4
-------
TABLE 7-3. DISTRIBUTION OF AFFECTED FACILITIES BY INDUSTRY SECTOR, MODEL
MARKET, AND SIZE CUTOFF: REGULATORY ALTERNATIVE III-
Total
Industry Sector Number of
and Model Market Facilities
Co in— Qpq r a t p{jk
Self-Service 213
Plant-Operated 2,831
Total 3,044
Number Affected Facilities by Size Cutoff
None
200
1,415
1,615
1234
49 0 0 0
0 0 0 o
49 0 0 0
Market
Market
Market
Market
Market
Market
Total
Indust- fj 3
=saaaoBsa^K
A
B
C
D
E
F
id
SOBsna
1,445
1,704
1,045
10,547
8,074
7,679
30,494
325
urn — rnn-rm IT -
0
1,704
0
1,394
4,431
4,630
12,159
65
0
0
0
1,187
3,379
3,521
8,087
65
0
0
0
' 978
2,373
2,462
5,813
65
0
0
0
819
1,890
1,958
4,667
65
0
0
0
637
1,459
1,512
3,608
65
consumption factors (Radian, 1990b) . ' d solvent
dSee Table 2-13.
Source: Radian, 1991c.
7-5
-------
TABLE 7-4. DISTRIBUTION OF AFFECTED OUTPUT BY INDUSTRY SECTOR, MODEL MARKET,
AND SIZE CUTOFF: REGULATORY ALTERNATIVES I AND IIa
Industry Sector
and Model Market
Coin-Operated
Self-Service
Plant -Operated
Total
Commercial
Market A
Market B
Market C
Market D
Market E
Market F
Total
Industrial-
Total
Output
(Mg/yr)
577
3,891
4,468
13,222
3,819
25,476
227,709
155,823
145,898
571,949
170,902
Total Affected Output by
Size Cutoff (Mg/yr) b
None
535
985
1,520
0
3,819
0
4,750
67,141
71,447
147,157
34,180
I
220
0
220
0
0
0
4,576
64,673
68,820
138,068
34,180
2
0
0
0
0
0
0
4,206
59,536
63,351
127,093
34,180
3
0
0
0
0
0
0
3,928
55,636
59,200
118/764
34,180
4
0
0
0
0
0
0
3,588
50,969
54,231
108,788
34,180
aTotal output and affected output values computed using averagte output values
reported in Tables 2-5 and 2-7, the distribution of facilities in Table
2-13, and the distribution of affected facilities in Table 7-2."
bSize cutoff levels are based on baseline consumption of perchloroethylene
(PCE). The cutoff levels correspond to target levels of annual receipts and
differ depending on the type of dry cleaning machine used. See Table 7-1
for description of cutoff levels.
7-6
-------
TABLE 7-5. J^RIBUTION OF AFFECTED OUTPUT BY INDUSTRY SECTOR, MODEL MARKET
AND SIZE CUTOFF: REGULATORY ALTERNATIVE III- <"««.*,
Industry Sector
and Model Market
Coin-Op^ rat*»fib
Self-Service
Plant-Operated
Total
Commq i^e ia lb
Market A
Market B
Market C
Market D
Market E
Market F
Total
Indust-^--] fl ] c
_
Total
Output
(Mg/yr)
577
3,891
4,468
13,222
4,052
22,595
229,516
156,068
146,730
571,949
170,902
S58S^^SSS^^SE99
Total Affected Output by
Size Cutoff (Mg/yr) b
None
535
985
1,520
0
4,052
0
31,320
77,223
80,185
192,780
34,180
^^9!^^S^BWM^B^__
1
220
0
220
0
0
0
30,828
74,721
77,547
183,097
34,180
2
0
0
0
0
0
0
29,692
69,253
71,791
170,736
34,180
3
0
0
0
0
0
0
28,263
64,913
67,263
160,439
34,180
4
——«—-•«
0
0
0
0
0
0
25,973
59,491
61,652
147,117
34,180
7-7
-------
The number of affected facilities represents about 53 percent of all
coin-operated facilities with dry cleaning equipment. The impact is split
between plants with self-service equipment and those without. Those with
plant-operated equipment comprise the bulk of the affected population. With
no cutoff, 34 percent of the coin-operated output will be affected under the
candidate alternatives, the majority of which comes from plant-operated
machines. Again, the disparity indicates that the average size of facilities
affected is smaller than that for unaffected facilities.
In the industrial sector, size cutoffs would have no impact; all of the
industrial facilities with dry cleaning machines fall above the largest
cutoff. Also notice that .the affected population is the same share—20
percent—in terms of the number of facilities and output because the size
distribution of affected and unaffected plants does not differ.
7.2 COSTS OF COMPLIANCE
In Section 6 the control options available under each regulatory
alternative are identified and the method for determining which option owners
of affected facilities are likely to choose is outlined. ::n this section, the
methods and assumptions used to compute the annualized costs associated with
each regulatory alternative are discussed.
Tables 7-6 and 7-7 show the model plant capital and operating costs for
CA controls and RC controls, respectively. As noted before, coin-operated and
industrial plants do not have the option of retrofitting existing machines
with RC controls because these devices are not manufactured for the machine
sizes typically used in these two sectors. Capital costs are a function of
the machine size and do not differ with different levels of output. Operating
costs are a function of output level and are reported for five levels of
output based on the corresponding range of annual receipts given below:
Output Level . Annual Receipts Range
1 $0 to 25 thousand
* 2 $25 to 50 thousand
3 $50 to 75 thousand
4 $75 to 100 thousand
5 Over $100 thousand
7-8
-------
TABLE 7-6.
MODEL PLANT CAPITAL AND OPERATING COMPLIANCE
ADSORBER CONTROLS ($1989)*
FOR CARBON
*UK CARBON
Industry
Sector and
Model
Plant Number
CA
Capital CA Operating Costs by Output Level
Costs ($) i ~ " I '
•"• 2, 3 A
Coin— <">pgrfltf?f1
1
2
3
4
5
6
7
8
9
10
11
12
Induat.r-i.fl ]_
13
14
15
=====
8,601
3,540
6,760
6,760
6,760
6,976
6,760
6,760
6,976
6,760
6,760
6,976
9,980
9,980
9,980
^^KSSSSSSSSES^B
6,492
2,710
2,887
2,886
2,886
2,895
2,886
2,886
2,895
2,886
2,886
2,895
2,992
2,992
2,992
•S^BSSSSSSSSS^BBSSB
6,466
2,703
2,827
2,827
2,827
2,835
2,826
2,826
2,835
2,826
2,826
2,834
2,922
2,922
2,922
==—=———.—_._
6,436
2,695
2,758
2,758
2,757
2,766
2,757
2,757
2,765
2,756
2,756
2,764
2,837
2,837
2,837
•^ --
— — — — —
6,406
2,688
2,689
2,688
2,687
2,696
2,686
2,686
2,695
2,686
2,685
2,693
2,747
2,747
2,747
-— — SBK^^SS^^
($/yr)fa
6,140
2,618
2,141
2,138
2,137
2,145
2,134
2,133
2,142
2,132
2,129
2,138
-2,265
-8,147
-8,147
2 $25 to $50 thousand
3 $50 to $75 thousand
4 $75 to $100 thousand
5 over $100 thousand
Source: Radian, 1990a.
7-9
-------
TABLE 7-7. MODEL PLANT CAPITAL AND OPERATING COMPLIANCE COSTS FOR
REFRIGERATED CONDENSOR CONTROLS IN THE COMMERCIAL SECTOR ($1989)*
Model
Plant Number
3
4
5
6
7
8
9
10
11
12
RC
Capital
Costs ($)
6,283
6,283
6,283
8,424
6,283
6,283
8,424
6,283
8,675
10,811
RC Operating Costs by Output Level ($/yr)b
1
290
289
289
374
288
288
373
288
383
468
2
234
232
231
317
230
230
315
229
323
409
3
169
166
165
250
163
162
248
161
254
340
4
103
100
98
183
95
93
179
92
184
270
5
-413
-423
-430
-345
-440
-444
-358
-449
-363
-278
aNegative values indicate cost savings due to reduced solvent consumption.
Add-on RC control devices are not built for the size machines typically used
in the coin-operated and industrial sectors.
bOutput levels correspond to average annual receipts ranges below:
1 under $25 thousand
2 $25 to $50 thousand
3 $50 to $75 thousand
4 $75 to $100 thousand
5 over $100 thousand
Source: Radian, 1990a.
7-10
-------
Note that operating costs decline as output level increases because operating
costs are net of solvent recovery savings, and projected solvent recovery
savings (negative costs) rise faster than the positive cost components as
output increases. Negative values are indicated where solvent savings exceed
costs.
The CA capital costs average over $7,000 for commercial facilities with
dry-to-dry or transfer machines. Refrigerated condenser capital costs are
slightly lower than CA capital costs for dry-to-dry machines in the commercial
sector, carbon adsorber capital costs are about $1,500 lower than RC costs
for transfer machines in the commercial sector. However, CA annual operating
costs average $1,800 to over $2,000 dollars higher than RC operating costs for
machines of both types.
Using these cost inputs, the capital costs of new dry-to-dry machines
with built-in vent controls from Table 7-10, and the least cost options
identified in the net present cost analysis presented in Section 6, the
annualized compliance costs can be computed. Table 7-8 reports the annualized
costs of Regulatory Alternative I by model plant and output level. Table 7-9 '
reports the costs of Regulatory Alternatives II and III. The model plant
costs for facilities with dry-to-dry machines are the same for all
alternatives. Model plant costs for facilities with transfer' machines are
lower under Alternative I than under Alternatives II and III. Although the
costs per plant do not differ under Alternatives II and III, the number of
affected facilities with transfer machines is higher for Alternative III.
As noted previously, facility owners in the commercial and industrial
sectors will likely replace their existing machines with new dry-to-dry
machines that have built-in control devices. Therefore, capital costs "of
control equipment are annualized over the remaining life of the existing dry
cleaning machine rather than the life of the control device. New machines in
the coin-operated sector generally do_nat have built_in control deviceSt
Capital costs are annualized over the life of the CA (15 years) in the coin-
operated sector. For the purposes of this analysis it is assumed that the
distribution of the remaining life of existing machines is rectangular and
each year one fifteenth of the machines is replaced. Costs are annualized
7-11
-------
TABLE 7-8. MODEL PLANT ANNUALIZED COMPLIANCE COSTS FOR REGULATORY ALTERNATIVE
I ($1989)*
Industry Sector and
Model Plant Number
Coin-Operated
1
2
Commercial
3
4
5
6
7
8
9
10
11
12
Industrial
13
14
15
Output Levelb
1
7,814
3,264
2,271
2,289
2,307
2,946
2,436
2,450
3,125
2,471
3,397
4,075
6,110
6,110
6,110
2
7,788
3,258
2,215
2,232
2,249
2,889
2,378
2,391
3,067
2,412
3,338
4,016
6,039
6,039
6,039
3
7,759
3,250
2,150
2,166
2,183
2,822
2,310
2,324
2,999
2,344
3,269
3,947
5,955
5,954
5,954
4
7,728
3,242
2,084
2,099
2,116
2,755
2,242
2,255
2,930
2,275
3,199
3,877
5,865
5,864
5,864
5
7,462
3,173
1,568
1,577
1,588
2,227
.1,708
1,718
2,393
1,734
2,651
. 3,329
852
-5,029
-5,029
aAnnualized costs are computed using the control costs found in Tables 7-6 and
7-7 and the dry cleaning machine capital costs found in Table 2-10.
Discount rates vary by firm financial status: 15.4% for firms in poor
financial condition, 12.5% for firms in average financial condition, and
11.0% for firms in good financial condition. In the commercial and
industrial sectors costs are annualized over the remaining life of the dry
cleaning machine or the life of the control equipment, whichever is shorter.
In the coin-operated sector, costs are annualized over the'life of the
control equipment (15 years).
bOutput levels correspond to average annual receipts ranges below:
1 under $25 thousand
2 $25 to $50 thousand
3 $50 to $75 thousand
4 $75 to $100 thousand
5 over $100 thousand
7-12
-------
TABLE 7-9. MODEL PLANT ANNUALIZED COMPLIANCE COSTS FOR REGULATORY
ALTERNATIVES II AND III ($1989)*
Industry Sector and
Model Plant Number
Output Levelb
I
— «• — •_»_• <—
7,814 '
3,264
2,271
2,289
2,307
4,487
2,436
2,450
4,837
2,471
5,052
4,075
6,110
6,110
6,110
======
2
7,788
3,258
2,215
2,232
2,249
4,428
2,378
2,391
4,778
2,412
4,992
4,016
6,039
6,039
6,039
===—!———
3
"^^•'••^^•••••••••••••^MMH
7,759
3,250
2,150
2,166
2,183
4,360
2,310
2,324
4,708
2,344
4,922
3,947
5,955
5,954
5,954
••— ^ — -
4
—- — — —
7,728
3,242
2,084
2,099
2,116
4,291
2,242
2,255
4,638
2,275
4,851
3,877
5,865
5,864
5,864
5 .
"^— — •*— — •«•
7,462
3,173
1,568
1,577
1,577
3,749
1,708
1,718
4,087
1,734
4,296
3,329
852
-5,029
-5,029
••
.
to
2 $25 to $50 thousand
3 $50 to $75 thousand
4 $75 to $100 thousand
5 over $100 thousand
7-13
-------
using a real, after-tax weighted average cost of capital (WACC), that differs
depending on their baseline financial status. The share of facilities in each
financial status and the corresponding WACC is reported in Section 6.
In some instances it is more cost-effective to accelerate the purchase
of a new dry-to-dry machine with a built-in vent control than to retrofit the
existing machine. Annualized costs associated with this option are computed
by taking the net present cost computed in Eq. 6.3.in Section 6 and computing
the annualized value over the remaining life of the existing dry cleaning
machine.
7.3 MARKET ADJUSTMENTS
Regulatory controls are likely to disturb the current equilibrium in the
dry cleaning industry, resulting in price and output changes and corresponding
welfare impacts. Market price and output adjustments are calculated from
elasticity estimates, baseline price and output values, and control cost
estimates. In the coin-operated and industrial sectors and in Market Models
C, D, E, and F in the commercial sector market, impacts are computed based on
a competitive market model. Model Markets A and B in the commercial sector
represent markets with a single facility in the market area. Impacts in these
model markets are computed based on a monopoly model with limit pricing
behavior.
Table 7-10 shows the type of market adjustments computed for each sector
and model market. Price and output impacts are computed for the coin-operated
sector and commercial Markets E and F. No price and output impacts are
projected for the industrial sector or Model Markets A through D in the
commercial sector. In market areas where unaffected facilities dominate/
price and quantity impacts are likely to be zero. This is the case in the
industrial sector and in commercial Markets A, C, and D. Model Market B in
the commercial sector represents a single affected facility per market area.
This facility is not likely to raise prices under any of the alternatives
considered because to do so would encourage new entry into the market as
discussed in Section 4.
7-14
-------
Sector
Coin-Operated
Commercial
Commercial
Commercial
Commercial
Commercial
Commercial
Industrial
Model Ma
A
B
C
D
E
F
Price
Adjustments
yes
no
no
no
no
yes
yes
no
Output
Adjustments
yes
no
no
no
no
yes
yes
no
Welfare
Impacts
— —— — —
P,C
none
P
none
P
P/C
P,C
P
Key: "P" - producer welfare impacts.
"C" - consumer welfare impacts.'
All sectors and model markets with affected facilities will incur
producer welfare impacts. However, only those markets with price and output
adjustments have projected consumer welfare impacts.
7'3-1
and
Economic impacts are quantified through estimated market adjustments in
pr.ce and output for the coin-operated sector and Model Markets E and p in the
commercial sector. Figure 7-! depicts the supply/demand relationship for a
representative market area in these sectors. Pre-regulatory equilibrium
occurs at an output level of Ql and a price of PI per unit (kilogram, of
output. The supply curve (Sl) is upward sloping with an elasticity of -e- and
the demand curve (DI, is downward sloping with an elasticity of "T^
' Suppose that installing the cost-effective candidate control technology
results in a net cost increase for facilities in the representative market
The market supply curve will shift up from from a position such as Sl to S2 in
Figure 7-1 with a vertical shift distance equal to the weighted average
control cost per unit of output. Assuming that the market demand curve
remains stationary in response to technological controls is plausible because
7-15
-------
$/Q
Q/t
Figure 7-1. Price and Output Adjustments Due to a Market Supply Shift
these controls normally affect only supply-side variables such as production
costs. In addition, the candidate control devices will not lessen the quality
of the product, further justifying a stationary demand curve. Because the new
supply curve now intersects the downward sloping demand curve at a higher
point, equilibrium price will increase and equilibrium output will decrease.
The magnitude of the new equilibrium price/output combination (?2, Q2) is not
obvious from the diagram, but it can be computed if baseline price and output
values (Pi, Qi), the demand elasticity Cn), the supply elasticity (e), and the
supply shift parameter (1) are known. First, rewrite the inverse
supply/demand system in functional form as illustrated below:
P - P(QS, PPI, CT),
(7.1)
P - P(Qd, Pop),
(7.2)
where CT is the control technology that leads to the supply curve shift.
Next, convert the supply and demand functions to logarithmic form and take the
total differential:
7-16
-------
7|E +X«, • (7.3,
(7.4)
where EC) - 3LnC), TI - aLn(Qd) /9Ln(P) f e . 9Ln (Q*)/3Ln /ai«i(CTMaLn. The terms E(Pop) and E(PPI) are not included in the
above equations because they are exogenous variables and, therefore,
unaffected by policy changes.
The parameter Xs is the percentage shift of the marginal facility's
supply function given a change in the control technology. Assuming that there
i- no correlation between production costs and control costs, the shift in the
supply function of the marginal facility may correspond to the lowest control
costs (zero in markets with unaffected facilities) or highest control cost per
Kilogram of output estimated. For this analysis the supply snift is based on
the expected value of the percentage change in marginal costs for the given
market area. Measured along the price axis, the expected percentage-shift of
the supply function is equal to the weighted average control cost per unit of
output divided by the baseline price:
I,
.(7.5)
Because there are two equations and two unknowns, supply can now be set equal
to demand to solve for E(P):
E(P) + Xs, (?>6)
E(P)
e - r\) J ' (7.7)
By definition, E(P) - 9Ln(P) - (p2 - PI)/P! for "small" ch
Solving for the value of P2 from the expression above anl Tlll^gl^s
information into the equation for E(Q, produces the following formulas for P2
and Q2:
?2 - PI . { 1+ f—^—1 1
L L (E - Ti) J 5 • (7.8)
7-17
-------
All variables and parameters on the right hand side of Eqs. (7.8) and (7.9)
are known, so -the new equilibrium price/output combination can be computed
from this information.
• ' Baseline price and the projected price impacts are reported in
Table 7-11 for each sector of the dry cleaning industry under three regulatory
alternatives and five cutoff levels. Average price impacts for the entire
commercial sector are not reported in this table because the average impact
underestimates price adjustments for markets where affected facilities
dominate and overestimates adjustments with no affected or very few affected
facilities. Therefore price impacts in the commercial sector are presented by
model market in Table 7-12. Model Markets A and C do not experience price
impacts because no affected facilities are represented in these markets.
Facilities in Market B do not raise prices because' of limit pricing practices
to deter entry of new facilities. Prices do not change in response to the
regulatory alternatives in Market D because unaffected facilities dominate in
this market-model. Price impacts in Markets E and F represent the weighted
average price impacts for all facilities in these market models.
Total baseline output and projected output impacts corresponding to the
price impacts reported in Table 7-11 are reported in Table 1-13. The total
reduction in output for the commercial sector is from Model Markets E and F.
Table 7-14 reports the output adjustments for each market model in the
commercial sector. It is evident from Tables 7-11 through 7-14 that price and
output vary in magnitude among sectors and across size cutoff levels.
In the commercial and coin-operated sector, size cutoffs reduce the
number of affected facilities and the share of affected output. As the share
of affected output is reduced, the average compliance cost per kilogram of
output for the market area declines. All else equal, a lower compliance cost
per unit of output results in lower price and output adjustments. In the
commercial sector size cutoff levels affect price and output adjustments for
two additional reasons. First, the annual cost per affected facility declines
as the level of output increases because of increased solvent recovery savings
7-18
-------
Industry Sector
and Regulatory Baseline Price
Alternative ($/kg)
Reg Ir II, &
Reg I, ii, &
' d
Size Cutoff3
(Percent Chanqre from Basel
1.65
96.32 23.50
. (Plant, -opera 1-g.fji
Reg I, II, 4
Comm#» rfii g J
Reg Ib
Reg II
Reg III
lndu*i-r<-i
6.34
6.34
6.34
6.34
1.07 0 0 0 o
c C c c c
C r*
c c c c c
f* -»
c c c c c
2.00
differ depending on the ty of ry
for description of cutoff levels
percnioroethyl
°f/nnual ^Pt- and
used. See Table 7-1
*'
the Coin-Operated
cSee Table 7-12 for estates of price adjustments for the Commercial Sector
7-19
-------
TABLE 7-12. PRICE ADJUSTMENTS FOR MODEL MARKETS IN THE COMMERCIAL SECTOR BY
REGULATORY ALTERNATIVE AND SIZE CUTOFF (PERCENTAGE CHANGE FROM
BASELINE)3
Model Market
and Regulatory
Alternative
Ray I
Market A
Market B
Market C
Market D
Market E
Market F
Reg IT
Market A
Market B
Market C
Market D
Market E
Market F
Rey III
Market A
Market B
Market C
Market D
Market E
Market F
Baseline
Price
(S/kg)
6.34
6.34
6.34
6.34
6.34
6.34
6.34
6.34
6.34
6.34
6.34
6.34
6.34
6.34
6.34
6.34
6.34
6.34
Size Cutoffb
(percentage change from baseline)
None
0
0
0
0
0.68
0.77
0
0
0
0
0.85
0.96
0
0
0
0
0.98
1.07
1
0
0
0
0
0.52
0.60
0
0
0
0
0.65
0.74
0
0
0
0
0.78
0.85
2
0 .
0
0
0
0.38
0.43
0
0
0
0
0.47
0.53
0
0
0
0
0.58
_ 0.63
3
0
0
0
0
0.32
0.36
0
0
0
0
0.40
0.45
0
0
0
0
0.49
0.54
4
0
0
0
0
0.26
0.30
0
0
0
0
0.33
0.37
0
0
0
0
0.41
0.45
aAdjustments are zero, for facilities in Model Markets A and C because no
affected facilities are represented in these markets. Adjustments are zero
for facilities in Markets B and D due to full cost absorption by affected
facilities in these markets.
bSize cutoff levels are based on baseline consumption of perchloroethylene
(PCE). The cutoff levels correspond to target levels of annual receipts and
differ depending on the type of dry cleaning machine used. See Table 7-1
for description of cutoff levels.
7-20
-------
Industry Sector
and Regulatory
Alternative
Coin—Qp^^flfiftf)
(self—s*»T*yj.efa)
Reg I, ii, 4
Baseline
Output3
(Mg/yr)
Size Cutoff*5
(Percentage Change from Basoi
577
-83.01 -25.52 0
' 0
(lant—
Reg I, ii, &
3,891
-1.17
Reg I
Reg II
• Reg III
Indus^r-jal
571,949
571,949
571,949
-0.42
-0.52
-0.59
-0.32
-0.40
-0.47
-0.23
-0.29
-0.35
-0.19
-0.24
-0.29
-0.16
-0.20
-0.24
Reg I, II, 4
170,902
differ depending
for description of cutoff levels
^ "'
use PCS and facilities that
°*«™"- «=«iPt» and
used. See Table 7-1
Coin-operated
7-21
-------
TABLE 7-14. OUTPUT ADJUSTMENTS FOR MODEL MARKETS IN THE COMMERCIAL SECTOR BY
REGULATORY ALTERNATIVE AND SIZE CUTOFF*
Model Market
and Regulatory
Alternative
Reg I
Market A
Market B
Market C
Market D .
Market E
Market F
Total Reg Ie
Reg £1;
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg IIC
Reg Til
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg IIIC
Baseline
Output
(Mg/yr)
13,222
3,819
25,476
227,709
155,823
145,898
571,949
13,222
3,819
25,476
227,709
155,823
145,898
571,949
13,222
4,052
22,595
229,516
146,730
156,068
571,949
Size Cutoffb
(percentage change from baseline)
None
0
0
0
0
-0.74
-0.85
-0 . 42
0
0
0 .
0
-0.92
-1.05
-0.52
0
0
0
0
-1.06
-1.17
-0.59
1
0
0
0
0
-0.57
-0.65
-0.32
0
0
0
0
-0.71
-0.81
-0.40
0
0
0
0
-0.85
-0.93
-0.47
2
0
0
0
0
-0.41
-0.47
-0.23
0
0
0
0
-0.51
' -0..58
-0..29
0
0
0
0
-0.63
-0.68
-0.35
3
0
0
0
0
-0.34
-0.39
-0.19
0
0
0
0
-0.43
-0.49
-0.24
0
0
0
0
-0.54
-0.58
-0.29
4
0
0
0
0
-0.28
-0.32
-0.16
0
0
0
0
-0.36
-0.41
-0.20
0
0
0
0
-0.44
-0.48
-0.24
Adjustments are zero for facilities in Model Markets A and C because no
affected facilities are represented in these markets . Adjustments are zero
for facilities in markets B and D due to full cost absorption by affected
facilities in these markets. .
cutoff levels are based on baseline consumption of perchloroethylene
(PCE) . The cutoff levels correspond to target levels of annual receipts and
differ depending on the type of dry cleaning machine used. See Table 7-1
for description of cutoff levels.
GWeighted average output adjustments.
7-22
-------
(see Tables 7-8 and 7-9). in addition, the share of facilities with baseline
vent controls is significantly higher for large facilities than for small
facilities. These factors taken together result in lower average control cost
per kilogram of output and thus lower price and output adjustments at higher
cutoff levels.
Equilibrium price in the commercial market is estimated to increase 0.98
percent for markets where affected dry cleaners represent about half of all
facilities (Market E) under the most stringent regulatory scenario. Price
adjustments are projected to be about 1.07 percent for market areas where
affected cleaners dominate (Market F,. This amounts to pennies per kilogram
of clothes cleaned in either case.. Corresponding output adjustments in these
markets are about 1.06 percent and 1.17 percent, respectively.
As indicated in Section 4, owners of coin-operated dry cleaning
equipment are limited in the amount of a cost increase that can be passed
along to consumers in the form of a price increase. The maximum price that
can be charged -for self-service dry cleaning is equal to the maximum post-
regulatory commercial price less the minimum opportunity cost of time ($3 00',
estimated in Section 4. Under Regulatory Alternative III with no cutoff,
facilities in commercial Market F raise price to $6.41 per kilogram of clothes
cleaned. This represents the maximum projected post-regulatory price in the
commercial sector. Therefore, self-service coin-operated facilities cannot
raise prices above $3.41 per kilogram. Likewise, plant-operated facilities in
the coin-operated sector are not able to raise prices above the maximum post-
regulatory price in the commercial sector. The price and quantity adjustments
projected for the coin-operated sector are described below.
The self-service coin-operated sector would experience the most severe
equilibrium adjustment from baseline values. Projected equilibrium price
would increase from $1.65 to $3.24, or 96.32 percent with no cutoff.' Output
would decrease by 83.01 percent from 577 Mg per year to 98 Mg per year
Adjustments for plant-operated facilities are not as severe. Average price is
projected to increase by about 1.07 percent and output is expected to decrease
by 1.17 percent. Based on these estimated impacts, the average price at
Plant-operated facilities in this sector will rise from $6.34 to $6 41 and
output will decline from a total of 3,891 Mg per year to 3,846 Mg per year
7-23
-------
7.3.2 Welfare Effects
The determining costs of a regulatory policy are measured by the change
in social welfare that it generates. Welfare impacts often extend to many
individuals and industries in an economy. However, estimating the welfare
impacts beyond the directly affected markets is generally cost-prohibitive
because the resource costs of such a task may exceed the'value of the indirect
welfare effects that are measured.
Producer welfare impacts result from increased costs of production that
are fully or partially absorbed by the facility. Facilities that are unable
to pass along any price increase must absorb the total increase in costs.
Producer welfare impacts in these markets are equivalent to the costs of
control. This scenario describes facilities in commercial Markets B and D.
Facilities that are located in market areas where a price increase is likely
are able to pass along a portion of the increased costs of production. The
producer welfare impact in these markets is equivalent to some portion of the
compliance costs depending on the relative elasticity of supply and demand.
Consumers of dry cleaning services experience welfare impacts in markets
where price and output adjustments occur. Consumer welfare impacts in markets
represented by commercial Model Markets B and D are zero even though affected
facilities are in these market areas because price is not affected.
Figure 7-2 depicts the approach used to estimate welfare changes for a
representative market with price and output impacts. Baseline equilibrium
occurs at the intersection of the demand curve, DI, and supply curve, Si.
Price is at the level of PI, with a corresponding output level of Qi.
Assuming the cost-effective candidate NESHAP control increases the weighted
average unit production costs in this market, the supply curve will shift up
to a position such as 82• Control costs should not affect the demand
relationship in the industry; assuming the demand curve remains stationary is
plausible. The new equilibrium position is characterized by a price/output
combination of (P2, Q2)• The welfare changes attributable to the candidate
NESHAP controls can be computed directly from Figure 7-2.
7-24
-------
Q/t
Figure 7-2. Welfare Change Estimation
in a market environs, typically consumer, and producer, of the good
or service derive welfare fro. a market transaction. The difference between
the maximum price consumers are willing to pay for a good or service and the
pr.ce they actually pay i, referred to as consumer surplus, consumer surplus
« measured as the area under tne demand curve and above the price of the
product. Alternatively, producers derive a surplus from a market transaction
if the product price i, above the average variable cost of production
is
The downward sloping industry demand curve above the baseline price of
H - Figure ,-, indicates a positive consumer s»rplus. lt is also 'nUm'
that consumers lose some of th.f surplus when the market price increases from
Pi to ,2. specifically, the loss in consumer surplus is the sum of areas A +
B + C. or the area under the demand curve and between the esuilibrium prices
T e slope and position of the market supply curve indicates that producers are
also rece^ng a surplus at the baseline price. MSSHA, control costs cause
producers to lose the surplus area E . D and gain the area A. but the slope
7-25
-------
and position of the demand and supply curves assures a producer surplus loss
as the net effect.
The sum of the producer and consumer surplus losses is an estimate of
the loss in social welfare due to the candidate NESHAP control. The net
welfare loss is equal to the area E+B+C+Din Figure 7-2. Estimates of
the surplus changes for consumers and producers and the resulting change in
social welfare are presented in Table 7-15 through Table 7-20. These welfare
impacts are projected for the first year after the regulation is in effect.
Lesser losses will be incurred in 14 subsequent years because existing
uncontrolled machines are being replaced with controlled machines upon
retirement even at baseline. Estimated welfare impacts are zero fifteen years
after the effective date of the regulation assuming that the current stock of
uncontrolled dry cleaning machines would have been entirely replaced with
controlled machines in this time period.
Given the relative shifts in equilibrium price and output predicted for
self-service coin-operated facilities, the magnitude of the welfare change
estimate for the coin-operated sector is larger than either the commercial or
industrial sector value relative to the size of the sector. The estimated
change in social welfare of $6,250,000 is especially significant in comparison
to the size of the coin-operated sector. As discussed earlier, this sector of
the industry is the smallest with a declining growth rate in output and number
of plants that has continued for several years. In contrast to the estimated
Regulatory Alternative III welfare loss in the commercial sector
($47,600,000), this figure does not appear excessive; but the commercial
sector is more than 125 times as large in terms of yearly dry cleaning output.
Along the same lines, estimated price and output adjustments in the commercial
sector are relatively minor, leading to a welfare loss estimate that is modest
in comparison to the size of the sector.
Despite the predicted welfare loss in the coin-operated and commercial
sectors, producer and consumer surplus can actually increase if a regulatory
control leads to cost savings that cause the price of the product to fall
instead of rise. In such a case, social welfare would increase. This
scenario is applicable to the industrial sector where a gain in welfare of
$274,000 is predicted.
7-26
-------
TABLE 7-15. CONSUMER
IMpACTS FQR EACH ^^
INDUSTRY BY REGULATORY ALTERNATIVE AND SIZE CUTOFF (§
Industry Sector
and Regulatory
Alternative
Coin— Ope^af.Rfj
(s&l ft— sgryieg
Reg I, II, &
-537
-195
Coin—
Reg I, II, 4
Reg I, ii,
_262
Reg I
Reg II
Reg III
-13,800
-17,200
-19,500
-10,600
-13,300
-15,600
-7,700
-9,500
-11,500
-6,460
-8,080
-9,860
-5,320
-6,680
-8,180
7-27
-------
TABLE 7-16. CONSUMER WELFARE IMPACTS FOR MODEL MARKETS IN THE COMMERCIAL
SECTOR BY REGULATORY ALTERNATIVE AND SIZE CUTOFF ($ THOUSANDS)3
Model Market
and Regulatory
Size Cutoffb
Alternative
Rey I
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg I
Reg II
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg II
Reg TIT
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg III
None
0
0
0
0
-6,700
-7,130
-13,800
0
0
0
0
-8,340
-8,870
-17,200
0
0
0
0
-9,600
-9,930
-19,500
1
0
0
0
0
-5,160
-5,490
-10,600
0
0
0
0
-6,440
-6,850
-13,300
0
0
0
0
-7,680
-7,910
-15,600
2
0
0 •
0
0
-3,730
-3,970
-7,700
0
0
0
0
-4,600
-4,900
-9,500
0
0
0
0
-5,690
-5,830
-11,500
3
0
0
0
0
-3,130
-3,330
-(5,460
0
0
0
0
-3,920
-4,170
-8,080
0
0
0
0
-4,870
-4, 990
-9,860
4
0
0
0.
0
-2,580
-2,740
-5,320
0
0
0
0
-3,240
-3,440
. -6,680
0
. 0
0
0
-4,010
-4,130
-8,180
almpacts are zero for facilities in Model Markets A and C because no affected
facilities are represented in these markets. Impacts are zero for
facilities in Markets B and D due to full cost absorption by affected
facilities in these markets. Values are express in 1989 dollars and rounded
to 3 significant digits. Details may not sum to totals due to rounding.
Consumer welfare losses in first year of regulation. Costs will be incurred
in subsequent years but will decline over time. Recurring annual costs will
be zero 15 years after the effective date of the regulation assuming that
the current stock of uncontrolled machines would be replaced by controlled
machines in the baseline over this time period.
^Size cutoff levels are based on baseline consumption of perchloroethylene
(PCE). The cutoff levels correspond to target levels of annual receipts and
differ depending on the type of dry cleaning machine used. See Table 7-1
for description of cutoff levels.
7-28
-------
TABLE 7-17. PRODUCER WELFARE IMPACTS FOR EACH SECTOR OF THE DRY CLEANING
INDUSTRY BY REGULATORY ALTERNATIVE AND SIZE CUTCFF ($
Industry Sector
and Regulatory
Alternative
Coin— O ratter!
f —
Reg I, II, &
» )
None
-1,140
-193
Size Cutoffb
2
Coin—
Reg I, II, & -4,320
-! a
Reg I
Reg II
Reg III
-15,000
-19,800
-28,070
-8,110
-10,100
-17,300
-5,850
-7,230
-13,600
-4,9:0
-6,150
-11,800
-4,040
-5,070
-9,810
Reg I, II, &
274
274
274
274
"'
7-29
-------
TABLE 7-18. PRODUCER WELFARE IMPACTS FOR MODEL MARKETS IN THE COMMERCIAL
SECTOR BY REGULATORY ALTERNATIVE AND SIZE CUTOFF {$ THOUSANDS)3
Model Market
and Regulatory
Size Cutoff13
Alternative
Reg I
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg I
Reg- II
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg II
Reg III
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg III
None
0
-4,290
0
-824
-4,780
-5,090
-15,000
0
-6,630
0
-1,010
-5,890
-6,280
-19,800
0
-7,070
0
-7,160
-6,800
-7,040
-28,070
1
0
0
0
-627
-3,630
-3,860
-8,110
0
0
0
-782
-4,530
-4,830
-10,100
0
0
0
6,330
-5,420
-5,590
-17,300
2
0
0
0
-452
-2,620
-2,790
. -5,850
0
0
0
-558
-3,230
-3,440
-7,230
0
0
0
-5,480
-4,000
-4,100
-13,600
3
0
0
0
-378
-2,190
-2,330
-4,900
0
0
0
-473
-2,750
-2,920
-6,150
0
0
0
-4,840
-3,420
-3,500
-11,800
4
0
0
0
-310
-1,800
-1,920
-4,040
0
0
0
-389
-2,270
-2,410
-5,070
0
0
0
-4,070
-2,840
-2,900
-9,810
almpacts are zero for facilities in Model Markets A and C because no affected
facilities are represented in these markets. Values are express in 1989
dollars and rounded to 3 significant digits. Details may not sum to totals
due to rounding. Producer welfare losses in first year of regulation.
Costs will be incurred in subsequent years but will decline over time.
Recurring annual costs will be zero 15 years after the effective date of the
regulation assuming that the current stock of uncontrolled machines would be
replaced by controlled machines in the baseline over this time period.
cutoff levels are based on baseline consumption of pesrchloroethylene
(PCE) . The cutoff levels correspond to target levels of annual receipts and
differ depending on the type of dry cleaning machine used. See Table 7-1
for description of cutoff levels .
7-30
-------
TABLE 7-19. NET WELFARE IMPACTS FOR EACH SECTOR OF THE DRY CLEANING INDUSTRY
.BY REGULATORY ALTERNATIVE AND SIZE CUTOFF (* THOUSANDS)*
Industry Sector
and Regulatory
Alternative
"••••^••^^^^•""^^^••^••MM
Co 1 n — f)pq rfl t ftfi
( sel f —fif.Tviaf* \
Reg I, ii, 4
None
-1,670
.Size Cutoff*
2
-388
(plant— opf» rat- p
Reg I, II, &
-4,580
Reg I
Reg II
Reg ill
Indvisfrjpj 3]
Reg I, II, &
-29,000
-37,000
-47,600
-18,800
-23,400
-32,900
-13,600
-16,700
-25,100
-11,400
-14,200
-21,600
-9,360
-11,700
-18,000
274
274
274
7-31
-------
TABLE 7-20. NET WELFARE IMPACTS FOR MODEL MARKETS IN THE COMMERCIAL SECTOR BY
REGULATORY ALTERNATIVE AND SIZE CUTOFF (5 THOUSANDS)*
Model Market
and Regulatory
Size Cutoffb
Alternative.
Reg I
Market A
Market B.
Market C
Market D
Market E
Market F
Total Reg I
Reg II
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg II
Reg ITI
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg III
None
0
-4,290
0
-824
-11,600
12,300
-29,000
0
-6,630
0
-1,010
-14,200
-15,200
-37,000
0
-7,070
0
-7,160
-16,400
-17,000
-47,600
1
0
0
0
-627
-8,790
• -9,350
-18,800
0
0
0
-782
-11,000
-11,700
-23,400
0
0
0
-6,330
-13,100
-13,500
-32,900
2
0
0
0
-452
-6,350
-6,760
-13,600
0
0
0
-557
-7,840
-8,340
-16,700
0
0
0
-5,480
-9,700
-9,940
-25,100
3
0
0
0
-378
-5,320
-5,660
-11,400
0
0
0
-473
-6,660
-7,090
-14,200
0
0
0
-4,840
-8,290
-8,490
-21,600
4
0
0
0
-309
-4,380
-4,660
-9,360
0
0
0
-389
-5,500
-5,860
-11,700
0
0
0
-4,070
-6,880
-7,040
-18,000
a Impacts are zero for facilities in Model Markets A and C because no affected
facilities are represented in these markets. Values are express in 19&9
dollars and rounded to 3 significant digits. Details may not sum to totals
due to rounding. Net welfare impacts are the sum of producer and consumer
welfare impacts . Producer and consumer welfare losses in first year of
regulation. Costs will be incurred in subsequent years but will decline
over time. Recurring annual costs will be zero 15 years after the effective
date of the regulation assuming that the current stock of uncontrolled
machines would be replaced by controlled machines in the baseline over this
time period.
cutoff levels are based on baseline consumption of perchloroethylene
{PCE) . The cutoff levels correspond to target levels of annual receipts and
differ depending on the type of dry cleaning machine used. See Table 7-1
for description of cutoff levels.
7-32
-------
Aggregating the welfare effect, from each ,e=cor lead, to an industry
te Of the regulatory cost. The total industry welfare cost is estimta
to be S<3,250.000 under Keg^latory alternative XX with no si:e cut«f
consumers of dry cleaning services are projected to lose a relatively 'smaller
portion of their welfare ,«,. 000, 000, than producers ,530.000,000, with a
sxze cutoff corresponding to 3100,000 in annual receipts (cutoff 4, welfare
^act, are considerably lower. Producer, lose an estimated ,4,600,000 and
consumers lose 86,680,000 for a net welfare loss of 811,400,000.
7.3.3 Plani-
TO co,nply with a regulatory standard, facilities »ill nomaily inour
control cost, and ^ have to reduce production levels, modify production
resort'
Shut
on the relationship bet»een the price of the service and the
average variable cost of production. The position of the average variable
os . ^^ t ^^
^
n lud.ng lnput price,. ,s a result, thi, section of.ers qualitative impact,
based on output adjustments for each sector, closure, measured in thi,Tay
prov.de an estimate of plant closures that i, net of ne. plant, entering tie
«ar*et. X» other .ords, if the regulatory alternative results in 10 plant
clo,ures and , plant start-up,, the value e,ti™ated in thi, analysi,
totu - ^ "^ ^ ^ " underestimate
the total number of plants closing, t* additional assumption, have the effect
of »aklng the e,timate, worst-case in terms of net closures. Pirst, it i,
a,su«ed that facilities do not reduce capacity utili2ation, but rat er he
entre output reduction i, accounted ,or by facilities shutting do™. In
Table, 7-21 and 7-22 ,ho» the nu^r of facilities in each ,ector and
model market that would shut do™ in net if the entire output reduction wa!
accounted for by the smallest facilities leaving the industry. Het plant
dosures «u not lively reach these levels, but for policy evaluati „ thi,
worst-case analysis of net closures i, helpful.
7-33
-------
TABLE 7-21. PROJECTED WORST-CASE NET PLANT CLOSURES IN EACH SECTOR OF THE DRY
CLEANING INDUSTRY BY REGULATORY ALTERNATIVE AND SIZE CUTOFF3
Industry Sector
and Regulatory
Alternative
None
Size Cutoffb
Coin—Operated
(self—serviced
Reg I, II, &
Coin—Operated
(plant—operated)
Reg I, II, &
190
163
36
Commercial
Reg I
Reg II
Reg III
1,001
1,246
1,415
337
421
493
147
182
221
88
110
135
23
28
34
Industrial
Reg I,- II, &
aProjected net closures are computed by dividing the estimated change in
output (Table 7-13) measured in kg per year by the minimum size affected
plant. Values reflect the assumption that plants do not reduce capacity
utilization.
bSize cutoff levels are based on baseline consumption of perchloroethylene
(PCE). The cutoff levels correspond to target levels of annual receipts and
differ depending on the type of dry cleaning machine used. See Table 7-1
for description of cutoff levels.
cRegulatory Alternatives I, II, and III are identical for the Coin-Operated
and Industrial Sectors.
7-34
-------
TABLE 7-22. PROJECTED WORST-CASE NET PLANT CLOSURES IN EACH MODEL MARKET OF
THE COMMERCIAL SECTOR BY REGULATORY ALTERNATIVE AND SIZE CUTOFF3
Model Market
and Regulatory
Alternative •
Reg J
Market A
Market B
Market C
Market D
Market. E
Market F
Total Reg I
Reg TT
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg II
Reg TTJ
Market A
Market B
Market C
Market D
Market E
• Market F
Total Reg III
None
0
0
0
0
485
516
1,001
0
0
0
0
604
642
1,246
0
0
0
0
695
720
1,415
1
0
0
0
0
163
174
337
0
\J
0
0
. 0
204
217
421
H"*"^^MH>BNH^'^^MB^^^MMi
0
0
0
243
250
493
2
0
0
0
0
71
76
147
0
0
0
88
94
182
"^^^^"•••^••••^••••B
0
0
0
109
112
221
3
0
0
0
0
43
45
88
— •• — '
0
0
0
0
53
57
110
0
0
0
0
67
66
135
4
0
0
0
0
11
12
23
0
0
.0
0
14
14
28
0
0
0
0
17
17
34
7-35
-------
Once again, the self-service coin-operated facilities would experience
the most significant impacts with a potential for 190 net plant closures
without a size cutoff. This represents 89 percent of the self-serve
facilities. Projected worst-case net closures of plant-operated facilities in
this sector total 163 with no size cutoff. This represents about 6 percent of
the plant-operated facilities in the coin-operated sector. Because dry
cleaning represents only about 10 percent of a coin-operated laundry's total
receipts, this estimate of plant closure is defined as the estimated number of
coin laundries that would discontinue their dry cleaning line of business.
Given past history and recent trends of the coin-operated sector some "plant
closures" will probably occur, but it is uncertain whether they will be caused
by regulatory compliance costs or a naturally declining growth rate.
Model Markets E and F in the commercial sector represent markets in
which output reductions are likely. Based on the estimated output reductions
and the minimum affected plant size, potential net closures in these two model
markets total 1,415 under Regulatory Alternative III with no cutoff. However,
in each of these model markets estimated output reductions are less than 2
percent of total output.
In view of the size of the estimated output reduction, commercial plants
will probably adjust production levels without actually closing their
facilities. Evidence from Census data indicates that facilities do respond to
changes in the quantity demanded by increasing or reducing output per
facility. Census data indicate that commercial facilities with payroll were
operating at higher output levels on average in 1987 than in 1982. Using data
on average annual receipts, the number of plants, the base price, and the
share of receipts from dry cleaning activities, the average facility dry
cleaned 24,489 kilograms of clothing in 1982 and 28,335 kilograms in 1987.
One industry spokesman indicated that these changes do not reflect a trend
toward larger dry cleaning plants; rather, plants are operating at a higher
capacity utilization (Fisher, 1990a).
Finally, no plant closures are projected for the industrial sector in
view of the cost savings expected for this sector.
7-36
-------
7.3.4
The dry cleaning NESHAP may cause Short-run price impacts in the three
dry cleaning sectors being examined in this analysis. If the short-run effect
of a regulatory alternative is to increase the equilibrium price of dry
cleaning services (in a given sector), then the short-run market-clearing
output of services will be lower than the baseline output. If the market-
clearing output declines, so may the demand for labor services by operators of
dry cleaning facilities. Indeed, the reduction of labor demand may be
approximately proportional to the reduction in demand for dry cleaning
services. Current employees in dry cleaning facilities may incur a welfare
loss in the form of reduced pay or lost jobs. This section discusses the
anticipated employment effects of the dry cleaning NESHAP.
Facilities subject to regulation under the NESHAP are generally
classified in one of three four-digit Standard Industrial Classifications
: 7215 (Coin-operated laundries and dry cleaning), 7216 (Dry cleaning
Plants, except rug cleaning), and 7218 (Industrial launderers) . Nearly all
industrial laundering facilities (SIC 7218, are already in compliance with the
regulatory alternatives considered and those facilities that might be affected
have a near-perfect substitute for dry cleaning-water laundering, in
addition, facilities in this sector are projected to realize a cost savings
Consequently, the anticipated output impacts on industrial launderers are
likely to be zero, so employment effects in this sector are not considered
further.
The employment effects in the coin-operated dry cleaning sector are also
not presented, but for a very different reason. The economic impacts analysis
indicates that the NESHAP would cause substantial facility closures unless EPA
exempts small facilities. EPA will thus probably exempt small coin-operated
facilities, effectively exempting them all. Consequently, the employment
effects of the NESHAP are expected to be minor.
Effectively, this leaves commercial dry cleaning plants (SIC 7216) as
the potentially-affected population. Two employment effects of the NESHAP in
the commercial sectors are considered: employee displacements and employee
displacement costs. Displacements are job terminations that result from cut-
7-37
-------
backs at operating facilities and/or plant closures. Displacement costs are
welfare losses incurred by those workers displaced by the NESHAP.
Employee Displacements. For reasons discussed in Section 4, the NESHAP
will have no long-run price or quantity impacts relative to baseline. Briefly
stated, retiring controlled and uncontrolled dry cleaning machines are being
replaced at baseline by controlled machines, so the long-run baseline price of
dry cleaning services already reflects control costs.. Consequently, the
NESHAP causes no long-run quantity impacts either, implying no change in long-
run commercial dry cleaning sector employment.
The NESHAP may nonetheless cause short-run disturbances in price,
output, and employment in the commercial dry cleaning sector. Aggregate
short-run output reductions are projected to range from 0.42 percent of
baseline for Regulatory Alternative I to 0.59 percent of baseline for
Regulatory Alternative III. With market quantity impacts below one percent of
baseline under all alternatives, conceivably the market adjustment will occur
through output reductions at many facilities rather than through complete
closures at relatively few. if, however, facilities are affected in one or
more markets with baseline average variable costs relatively close to price,
then these facilities will likely close.
Annualized compliance costs under Regulatory Alternatives II and III are
in the neighborhood of $2,000 to $5,000 for most affected facilities (see
Table 7-9). An annualized cost of $4,500 represents 4.8 percent of receipts
of a facility with annual receipts of $94,000, 6.7 percent of receipts of a
$67,000 facility, 11 percent of receipts of a $41,000 facility, and 25 percent
of receipts of an $18,000 facility. Affected facilities in some markets will
be unable to pass along cost increases even in the short-run, and those in
other markets will be able to pass along cost.increases only for a short time
until new facilities open. Such facilities may be unable to absorb annualized
compliance costs as high as 25 percent of receipts. Some closures will likely
occur.
Because closures are likely to occur, and output reductions among
operating facilities can themselves result in worker displacements, this
analysis assumes that short-run employment impacts of regulatory alternatives
are proportional to projected output effects. An estimated 176,836 workers
7-38
-------
are on payroll at commercial dry cleaning plants in 1991.1 The worker
displacements of the three Regulatory Alternatives at various size cutoffs
implied by the methodology and assumptions are presented in Table 7-23.
TABLE 7-23. PROJECTED WORKER DISPLACEMENTS*
Regulatory
Alternative None
I 743
II 920
HI 1,043
1
566
707
831
Size Cutoff
2
407
513
619
3
336
424
513
4
283
354
424
aCb°a^ei7neal ^ ^nTo? S7e°tOr' P**"11 employees only, assuming
from
Bnplnvff P1lT>Tnn»nrnr
. Displaced workers suffer welfare losses
through several mechanisms (see Hamermesh, 1989; Maxwell, 1989; Blinder, 1988;
Flaim, 1984; and Gordon, 1978) : '
• foregone wages and benefits during job search,
• out-of-pocket search costs,
• diminished wages and/or job satisfaction at new jobs, and
• psychological costs.
Displacement risk, like risks of injury, risks of death, or otherwise
. unpleasant, working conditions, is a negative job attribute for which workers
receive compensation in competitive labor market, (Abowd and Ashenfelter,
1981, . Abowd and Ashenfelter (1981, found that the labor market compensates
anticipated layoffs and unemployment by 2 to 6 percent higher wages per year
Topel (1984, used a hedonic wage function to estimate that an anticipated one-
point increase in the probability of unemployment (e.g. from 6 per hundred
^ -tor in 1987
the 1987 value and a 2 percent annual gSlti'St
7-39 •
-------
workers to 7 per hundred workers) requires a 2.5 percent increase in wages to
compensate workers.
Anderson and Chandran (1987) developed and demonstrated a methodology to
compute a willingness-to-pay based estimate of worker displacement using
Topel's estimated compensating wage differential. Their method is analogous
to that used by economists to estimate the implicit value of a life using
labor market data (see Moore and Viscusi, 1990). The hedonic displacement
cost estimate conceptually approximates the one-time willingness-to-pay to
avoid an involuntary unemployment episode. Theoretically, it includes all
worker-borne costs net of any off-setting pecuniary or non-pecuniary
"benefits" of unemployment (e.g., unemployment compensation, leisure time
enjoyment). The hedonic displacement cost estimate is a net present
valuation.
Annual (1991) earnings in the (payroll commercial) dry cleaning industry
are $11,504 (U.S. Department of Labor, 1991b). Using Topel's compensating
differential estimate and the Anderson-Chandran methodology, dry cleaning
workers would demand an annual compensating differential of $288 ($11,504 *
.025) to accept a one-point increase in the probability of displacement. It
is assumed that they would be willing to pay an equivalent amount to avoid
such an increase in the probability of displacement. The implied statistical
cost of an involuntary layoff is thus $28,800 ($288/.01).
Regulatory Alternative II would displace a projected total of 920
workers (with no size cutoff). The displacement cost would be $26.5 million.
The estimated worker displacement cost of $26.5 million under Regulatory
Alternative II with no size cutoff falls to $10.2 million under size cutoff 4.
Table 7-24 shows the worker dislocation costs in the commercial sector under
each regulatory alternative and size cutoff.
As noted previously, worker displacement costs are computed based on the
estimated output reductions in the commercial sector. Output reductions occur
as facilities increase prices to cover the increased costs of production* due
to costs of control. An increase in production costs would have occurred even
in the absence of regulation, however, as owners of dry cleaning facilities
7-40
-------
TABLE 7-24. PROJECTED WORKER DISPLACEMENT COSTS ($ MILLIONS)*
Regulatory
Alternative
I .
II
III
None
21.4
26.5
30.0
1
16.3
20.4
23.9
•-'— 1
Size Cutoff
2 3 4
H.7 9.7 - 8.2
14.8 12.2 10.2
17.8 14.8 12.2
•Commercial dry cleaning sector, payroll employees only, assuming prelected
worker displacements from Table 7-23. One-time (non-recurring) cost
replaced retiring uncontrolled machines with controlled machines. Therefore,
the output reduction used to estimate worker displacement and displacement
costs would have occurred in the baseline over a 15 year time period (assuming
all uncontrolled machines would have been, replaced over this time period).
implicit in the estimated displacement costs is the assumption that this
baseline output reduction-and corresponding reduction in employment-would
have been accounted for through attrition rather than worker dislocation, in
other words, the present value of foregone.future displacement is assumed to
be zero.
7.4 OWNERSHIP ADJUSTMENTS IN COMMERCIAL DRY CLEANING SECTOR
To estimate the financial impacts of the regulatory alternatives on
businesses, estimating the number of firms they affect is necessary. As
explained in Section 7.1, not all dry cleaning facing would be affected by
the regulatory alternatives being considered. Within the commercial dry
cleaning sector itself, facilities that use solvents other than PCE and PCE
facilities that are already ir. compliance with the alternatives (perhaps
because of state regulations) will be unaffected by the NESHAP. This suggests
that some firms will also be unaffected by the NESHAP.
Affected firms and affected facilities are one-and-the-same for single-
plant firms (i.e., single-facility firms without an affected facility are
7-41
-------
themselves unaffected as business entities). In the case of multiplant firms,
the number of affected firms is harder to estimate. A six-facility firm, for
example, might have six affected facilities, six unaffected facilities, or any
combination of both. In this analysis, it is assumed that the proportion of
affected firms is identical to the proportion of affected facility«»« for all
firm sizes. The estimated total number of affected firms is probably not too
sensitive to this assumption because only 478 of 27,332 firms (1.75 percent)
have more than two facilities (see Tables 5-2 and 5-4 in Section 5).
Estimates of affected firms are presented in Tables 7-25 through 7-28.
Affected firms are categorized by size and baseline financial condition.
Tables 7-25 and 7-26 present estimates of affected firms by size and condition
assuming the financial scenario I relationship between firm size and
condition, while Tables 7-27 and 7-28 are based on the the financial scenario
II assumption.
The financial impact of a regulatory alternative on a firm depends
largely on the number and type of affected facilities it owns, if any.
Because large numbers of unaffected facilities and unaffected companies exist,
many firms are not affected. Because most firms own a single facility and
most facilities have a single machine, most affected firms are affected by the
capital and annual operating costs of a single control device. Others,
however, are financially affected by the capital and.annual operating costs of
two or more control devices because they own more than one machine in one or
more facilities.
The facility weighted-average equipment prices and annual operating
costs faced by firms in various receipts ranges under the three regulatory
alternatives are presented in Table 7-29. Equipment costs are similar under
all alternatives for firms under $100,000 annual receipts because they are
essentially "single-machine firms." Firms over $100,000 would face equipment
costs of $15,000 to $17,000, on average.
This analysis assumes that the owner(s) of an affected firm will try to
pursue a course of action that maximizes the value of the firm, subject to
7-42
-------
TABLE 7-25. NUMBER OF AFFECTED DRY CLEANING FIRMS BY SIZE AND BASELINE
"""""""T CONDITION, FINANCIAL SCENARIO I—REGULATORY ALTERNATIVES
Receipts Range
($000)
<25
25-50
50-75
75-100
100-250
250-500
>500
Total
— • — •
——-———-—-—-——— i ____
Baseline Financial Condition
Total
3..188
1,684
772
660
1,620
680
376
8,980
Below Average
3,188
58
0
0
0
0
0
3,246
Average
0
1,626
772
660
1,059
0
0
4,117
Above Average
0
0
0
0
561
680
376
1,617
receiPts ran*e computed based on the
the
average, average, and above-average f inancl conion in e^h
range is based on the distribution reported in Table 5-5 for all
7-43
-------
TABLE 7-26. NUMBER OF AFFECTED DRY CLEANING FIRMS BY SIZE AND BASELINE
FINANCIAL CONDITION, FINANCIAL SCENARIO I—REG5ULATORY ALTERNATIVE
III
Receipts Range
<$000>
<25
25-50
50-75
75-100
100-250
250-500
>500
Total
Baseline Financial Condition
Total
3,396
1,896
956
876
2,188
920
512
10,744
Below Average
3,396
65
0
0
0
0
0
3,461
Average
0
1,831
956
876
1,430
0
0
5,093
Above Average
0
0
0
0
758
920
512
2,190
aNumber of affected firms in each receipts range computed based on the
assumption that the.proportion of affected firms is identical to the
proportion of affected facilities (see Tables 2-2, 5-2, and 7-3) .
^Assumes a positive relationship between firm size and baseline financial
condition (Financial Scenario I). The share of affected firms in below-
average, average, and above-average financial condition in each receipts
range is based on the distribution reported in Table 5-5 for all firms.
7-44
-------
TABLE 7-27. NUMBER OF AFFECTED DRY CLEANING FIRMS BY SIZE AND BASELINE
FINANCIAL CONDITION, FINANCIAL SCENARIO II--REGULATORY
ALTERNATIVES I AND II
Receipts Range
($000)
<25
25-50
50-75
75-100
100-250
250-500
>500
Total
-»—•—-
Baseline Financial Condition
Total
3,188
1,684
772
660
1,620
680
376
8,980
Below Average
797
421
193
165
405
170
94
2,245
Average
1,594
842
386
330
810
340
188
4,490
Above Average
797
421
193
165
405
170
94
2,245
h ,H *"** ** ^ receiPts ""** computed baSed on the
that the proportion of affected firms is identical to the
proportion of affected facilities (see Tables 2-2, 5-2, and 7-2°.
£ft«ii ent °f affected fiims «• below-average, 50 percent of
avf™ ffif^T av"age' and 25 Percent of af-fected firr^ are above-
average financial condition in the baseline (Financial Scenario II)
7-45
-------
TABLE 7-28. NUMBER OF AFFECTED DRY CLEANING FIRMS BY SIZE AND BASELINE
FINANCIAL CONDITION, FINANCIAL SCENARIO II—REGULATORY ALTERNATIVE
III
Receipts Range
($000)
<25
25-50
50-75
75-100
100-250
250-500
>500
Total
Baseline Financial Condition
Total
3,396
1,896
956
876
2,188
920
. 512
10,744
Below Average
849
474
239
219
547
230
128
2,686
Average
1,698
948
477
438
1,094
460
257
5,372
Above Average
849
474
239
219
547
230
128
2,686
aNumber of affected firms in each receipts range computed based on the
assumption that the proportion of affected firms is identical to the
proportion of affected facilities (see Tables 2-2, 5-2, and 7-3).
bAssumes that 25 percent of affected firms are below-average, 50 percent of
affected firms are average, and 25 percent of affected firms are above-
average financial condition in the baseline (Financial Scenario II).
7-46
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TABLE 7-29. INSTALLED PRICE OF CONTROL EQUIPMENT AND ANNUAL OPERATING COST BY
REGULATORY ALTERNATIVE AND SIZE OF FIRM* '
Receipts Range
($000)
<25
25-50
50-75
75-100
>100
Regulatory
Alternative
I
II
III
I
II
III
I
II
III
I
II
III
I
II
III
Equipment Price
($)
7,515
6,682
6,701
7,302
6,613
6,651
6,804
6,451
6,550
7,334
6,780
6,829
16,538
15,222
15,274
Annual Operating
Cost ($)
338
1,789
1,838
272
1,471
1,580
186
789
1,121
137
1,098
1,447
-99
1,804
2,745
*^i ™^e "^":*™*? acros* a«ected facilities and
2
ami 7-7?
Costs
in ^
and the costs reported in Tables 7-6
uncertainties about actual costs of compliance and the behavior of other
firms. The owners' response options include
• closing the facility,
• bringing the facility into compliance with the regulation, and
• selling the facility.
If the expected post-compliance value of an affected facility is negative (or
simply lower than the "scrap value" of the facility), the owner of the plant
will likely close it. If the expected post-compliance value is positive and
7-47
-------
greater than the scrap value, the owner will either bring it into compliance
or sell it to another firm that will do so.
Whether the firm keeps or sells the facility depends on the financial
condition of the firm. If the firm has and/or can borrow sufficient funds to
make a facility compliant, it keeps the facility. If instead the firm has
inadequate funds and debt capacity, it sells or closes the facility. In this
analysis, it is assumed that firms in below-average financial condition cannot
borrow money. These firms either have sufficient cash and purchase the
control equipment, or they have insufficient funds and sell the facility to
another firm.
Firms in average or above-average financial condition are assumed to
borrow the required funds, though possibly some of them will use internal
funds instead of or in conjunction with borrowing. It is assumed that seven-
year bank notes at 11 percent interest are available to above-average firms,
and that similar notes at 11.5 percent interest are available to average
firms. The annual amortized (principal plus interest) payments on these
notes—available only to firms in above-average or average financial
condition—are presented in Table 7-30. Just as the control equipment costs
vary little across firms under $100,000 annual receipts, so do the note
payments. Note payments for firms in average and above-average financial
condition are very similar because the interest rates are within one-half
percent of one another. Even though lenders are assumed to view firms in
below-average financial condition as much riskier than those in average
financial condition, they are assumed to view above-average firms as only
slightly less risky than average firms.
Firms that purchase control devices with cash have high initial cash
outlays but low recurring annual expenses. Firms that purchase control
devices with borrowed funds have low initial cash outlays but higher recurring
annual expenses. The initial cash outlays and recurring annual expenses
incurred by firms of different types and sizes are presented in Table 7-31.
As described above, firms in average and above-average financial condition can
borrow funds and thus don't have to use cash to purchase control equipment.
Their recurring annual expenses, however, include interest and principal
payments on seven-year notes in addition to annual operating costs. Firms in
7-48
-------
TABLE 7-30. ANNUAL PRINCIPAL AND INTEREST PAYMENTS ON A SEVEN-YEAR NOTE BY
.REGULATORY ALTERNATIVE, FIRM SIZE, AND INTEREST RATE ($)*
Regulatory Alternative
$100,000 annual receipts
11.0% note
11.5% note
=^==^Bl^=!a=!^==^=^BBSHS==BS=^==
I
1,595
1,621
1,550
1,575
1,444
1,467
1,556
1,582
3,510
3,567
^— ^— — — ^— ^— --
II
1,418
1,441
1,403
1,426
1,369
1,391
1,439
1,462'
3,231
3,283
III
1,422
1,445
1,412
1,434
1,390
1,473
1,449
.1,473
3,241
3,294
•Seven-year notes at 11.5 percent interest available to firms in average
financial condition; 11 percent notes available to above-average firms
costs are computed using data from Table 7-29.
7-49
-------
TABLE 7-31. INITIAL CASH OUTLAY REQUIREMENT* AND RECURRING ANNUAL EXPENSES6 BY
FIRM SIZE, FINANCIAL CONDITION, AND REGULATORY ALTERNATIVE ($)
Firm Financial Condition
Receipts
Range
($000)
<25
25-50
50-75
75-100
>100
Reoulatorv Below Average
Altern-
atives
I
II
III
I
II
III
I
II
III
I
II
III
I
II
III
Cash
Outlay
7,515
6,682
6,701
7,302
6,613
6,651
6,804
6,451
6,550
7,334
6,780
6,829
16,538
15,222
15,274
Annual
Expense
338
1,789
1,838
272
1,471
1,580
186
798
1,121
137
1,098
1,447
-99
1,804
2,745
Average
Cash
Outlay
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Annual
Expense
1,959
3,230
3,283
1,647
2,897
3,015
1,653
2,189
2,533
1,719
2,560
2,920
3,467
5,087
6,039
Above Average
Cash
Outlay
0
0
0
0
0
0
0
0
0
. 0
0
0
0
0
0
Annual
• Expense
1,933
3,207
3,260
1,822
2,874
2,992
1,630
2,167
2,511
1,693
2,537
. 2,896
3,411
5,035
5,987
alnitial cash outlay equals cost of control equipment for firms in below-
average financial condition assuming they are unable to debt finance; zero
for average and above-average firms assuming debt financing (see
Table 7-29).
bRecurring annual expenses include annual operating cost (all firms) (see •
Table 7-29) plus seven-year note annual principal and interest payment for
average and above-average firms (see Table 7-30).
7-50
-------
below-average financial condition have large cash requirements because they
cannot borrow money but have only operating costs as recurring annual
expenses.
The firm financial impacts of the regulatory alternatives are assessed
by
• computing post-compliance pro fnrma income statements and balance
sheets of firms of different sizes and financial conditions;
• computing the implied post-compliance financial ratios of these
firms; and
• comparing baseline and post-compliance statements and ratios to
discern clearly adverse financial impacts.
The pro forma financial statements of affected firms are presented in
Appendix A. in all cases, revenues are assumed to be unaffected by the
regulatory alternatives. The following adjustments are made to statements of
firms of all sizes in below-average financial condition. In the annual income
statement, other expenses and taxes increase by the amount of the recurring
compliance costs, and net profits fall by the same amount. In the balance
sheet, cash declines by the price of the control equipment and fixed assets
rise by the same amount. These firms have simply "traded- cash for control
devices in an accounting sense, so total assets and total liabilities remain
unchanged. Because, in fact, none of the firms in below-average financial
condition i^B, adequate cash to purchase control devices, their failures will
be caused by capital availability constraints (see discussion below). The
liabilities side of the balance sheet is unaffected because the firms enter
into no new legal obligations.
The following adjustments are made to statements of firms of all sizes
in average and above-average financial condition. In the annual income
statement, other expenses and taxes increase by the amount of the recurring
compliance costs and the annual note payments (see Table 7-31), and net
profits fall by the same amount. In the balance sheet, cash is unaffected
because these firms borrow money for purchasing control equipment. Fixed and
total assets increase by the value (price, of the control equipment. On the
liabilities side of the balance sheet, total liabilities and net worth have to
increase by the same amount. Both current and non-current liabilities
7-51
-------
increase. Notes payable (this year) increase by the amount of the annual
principal and interest payment (from Table 7-30). Non-current liabilities
(which include bank notes) increase by the loan amount (control equipment
price) leaa the amount of principal payable this year (which is part of the
increase in notes payable). Because the assets of the firm have increased by
the value (price) of the control equipment but the liabilities have increased
by that amount plus interest costs, the net worth of the firm declines
somewhat. Financial ratios commonly used to measure financial viability are
described in Table 7-32.
The post-compliance (and baseline reference) financial ratios of
affected firms of different sizes and financial types derived from the pro
forma statements in Appendix A are presented in Tables 7-33 through 7-37.
Financial ratio impacts on firms with annual receipts below $25,000 are
presented first. All three regulatory alternatives will likely have
substantial adverse impacts on firms of this size, regardless of baseline
financial condition. The impacts of the alternatives on firms in below-
average and average financial condition are most apparent, but impacts even on
above-average firms may be substantial. The smallest-size, above-average
firms remain profitable under Regulatory Alternative I but may be unprofitable
under Alternatives II and III. Note that the debt ratios of average and
above-average firms increase very substantially because they borrow funds to
purchase control equipment.
The debt ratio of below-average firms is unaffected because they must
rely on cash rather than borrowed funds to purchase equipment, but liquidity
impacts are substantial.
Financial impacts diminish as firm size increases. Although the
baseline financial ratios of firms of all sizes in any given financial
condition are the same, the magnitudes of their flows and balances vary by
size. For example, even though firms of all sizes in average financial
condition have the same baseline profit-to-sales ratio (7.0), a firm with
twice the sales receipts of another has twice the annual profits as well.
Because the cost of purchasing and operating control equipment is about the
same for most firms under $100,000, the financial impacts are greater for the
smaller firms.
7-52
-------
TABLE 7-32. KEY FINANCIAL RATIOS
LIQUIDITY
ACTIVITY
Firari
total current assets divided by total current
T,K-i^:~~'S*i Measures the degree to which current
liabilities-legal obligations coming due within the
H!^"?rC covere<* bV current assets-assets that can be
readily converted into cash. Post-compliance ratios
significantly below 0.8-the lower quartile (LQ) ratio
for^dry cleaning firms (firms) in the Dun and
{D&B) data base_a-re considereci indicators of
Turnover Ratio: annual sales divided by fixed
assets. Measures how efficiently the firm uses its
plant and equipment to generate sales. Post-compliance
in thl D*fndLiCa£tly bel°W 2-30~the LQ ratio forfiSs
in the D4B data base-are considered indicators of
LEVERAGE
PROFITABILITY
Debt Ratio: total liabilities divided by total liabilities
plus net-worth. Measures the legal debt burden of the
rirm. Post-compliance ratios significantly above 60
percent-theLQ ratio for firms in the D&B data base-are
considered indicators of capital availability
constraints and thus business failure.
-tn-Salfls Ran-0; annual net profit divided by annual
sales, expressed as a percentage. Measures the excess
age. easures the ex
or _ annual revenues over annual accounting costs of
doing business. Post-compliance ratios significantly
below one percent-the LQ ratio for firms in the D&B
data base-are. considered indicators of business
£dix
Ratio
annual net profit divided by total
,™ n 3 3 Percenta^- Measures the return
current and non-current assets. Post-compliance
P«cent-the L? ra?io for
COnside"d indicators of
Pflt in.:
annual net profit divided by the
exP^ssed as a percentage Y
acc?unting return to the owners of the
n™K °mP liance ratios significantly below 3.6
percent-the LQ ratio for firms in the D4B data base-
are considered indicators of business failure
Van Home, 1980.
7-53
-------
TABLE 7-33. BASELINE AND AFFECTED FINANCIAL RATIOS: <$25,000 FIRM RECEIPTS3
Liquidity
current ratio (times)
Baseline
RA I
RA II
RA III
Activity
fixed asset turnover ratio
(times)
Baseline
RA I
RA II
RA III
Leverage
debt ratio (percent)
Baseline
RA I
RA II
RA III
Profitability
profit to sales (percent)
Baseline
RA I
RA II
RA III
profit to assets (percent)
Baseline
RA I
RA II
RA III
Profit to net-worth
(percent)
Baseline
RA I
RA II
RA III
Baseline
Below Average
0.80
-1.64
-1.37
-1.38
2.30
1.17
1.23
1.23
60
60
60
60
1.0
-0.9
-9.1
-9.4
1.4
-1.3
-13.0
-13.4
3.6
-3.2
-32.4
-33.4
Financial
Average
1.73
0 . 92
0.97
0.97
5.56
1.66
1.80
1.79
46
77
75
75
7.0
-4.0
-11.2
-11.5
14.5
-4,5
-13.0
-13.4
26.8
-19.1
-51.5
-52.9
Condition
Above Average
5.10
1.16
1.27
1.27
7.54
1.80
1.96
1.96
15
'-64
62
62
13.0
2.1
-5.1
-5.4
32.5
2.. 6
-6.5
-6.9
38.2
7.2
-17.0
-18.0
aBaseline ratios are computed using data from Duns Analytical Services (1990)
Ratios under each Regulatory Alternative are computed using cost data in
Table 7-31 and data from Duns Analytical Services (1990.) .
7-54
-------
TABLE 7-34. BASELINE AND AFFECTED FINANCIAL RATIOS:
RECEIPTS*
$25,000-50,000 FIRM
Liquidity
current ratio (times)
Baseline
RA I
RA II
RA III
Activity
fixed asset turnover ratio
(times)
Baseline
RA I
RA II
RA III
Leverage
debt ratio (percent)
Baseline
RA I
RA II
RA III
Profitability
profit to sales (percent)
Baseline
RA I
RA II
RA III
profit to assets (percent)
Baseline
RA I
RA II
RA III
Profit to net-worth
(percent)
Baseline
RA I
RA II
RA III
Baseline
Below Average
0.80
-0.24
-0.14
-0.14
2.30
1.63
1.67
1.67
60
60
60
60
1.0
0.3
-2.6
-2.9
1.4
0.5
-3.8
-4.1
3.6
1.2
-9.4
-10.4
«HHHMR9
Financial
Average
1.73
1.26
1.29
1.29
5.56
2.78
2.92
2.91
46
64
62
63
7.0
2.4
-0.1
-0.4
14.5
3.7
-0.2
-0.7
26.8
10.2
-0.6
-1.8
»-—•«—•••«•,
Condition
5.10
2.09
2.21
2.21
7.54
3.20
3.38
3.37
15
45
43
43
13.0
3.5
5.9
5.6
32.5
14.7
10.5
10 . 0
38.2
26.6
18.4
17.5
"Baseline ratios are computed using data from Duns Analytical Services (1990)
^anrS/T13^7 Alternati- •« computed "using cost data in
-31 and data from Duns Analytical Services (1990) .
7-55
-------
TABLE 7-35. BASELINE AND AFFECTED FINANCIAL RATIOS: $50,000-75,000 FIRM
RECEIPTS*
Baseline Financial Condition
Below Average Average Above Average
Liquidity
current ratio (times)
Baseline
RA I
RA II
RA III
0
0
0
0
.80
.22
.25
.24
1
1
1
1
.73
,.43
,.44
.44
5.
2.
2.
2.
10
81
88
86
Activity
fixed asset turnover ratio
(times)
Baseline 2.30 5.56 7.54
RA I 1.87 3.55 4.27
RA II 1.89 3.62 4.37
RA III 1.88 3.60 4.34
Leverage
debt ratio (percent)
Baseline 60 46 15
RA I 60 57 34
RA II 60 57 34
RA III 60 57 34
Profitability
profit to sales (percent)
Baseline
RA I
RA II
RA III
profit to assets (percent)
Baseline
RA I
RA II
RA III
Profit to net-worth
(percent)
Baseline
RA I
RA II
RA III
1
0
-0
-0
1
1
-0
-1
3
2
-0
-2
.0
.7
.2
.7
.4
.0
.3
.0
.6
.6
.7
.4
7
4
3
3
14
7
6
5
26
18
14
12
.0
.5
.7
.2
.5
.8
.5
.6
.8
.2
.9
.9
13
10
9
9
32
21
19
18
38
32
29
28
.0
.6
.8
.3
.5
.1
.7
.6
.2
.1
.7
.1
aBaseline ratios are computed using data from Duns Analytical Services (1990)
Ratios under each Regulatory Alternative are computed using cost data in
Table 7-31 and data from Duns Analytical Services (1990).
7-56
-------
TABLE 7-36. BASELINE AND AFFECTED FINANCIAL RATIOS:
RECEIPTS3
$75,000-100,000 FIRM
Liquidity
current ratio (times)
Baseline
RA I
RA II
RA III
Activity
fixed asset turnover ratio
(times)
Baseline
RA I '
RA II
RA III
Leverage
debt ratio (percent)
Baseline
RA I
RA II
RA III
Profitability
profit to sales (percent)
Baseline
RA I
RA II
RA III
profit to assets (percent)
Baseline
RA I
RA II
RA III
Profit to net-worth
(percent)
Baseline
RA I
RA II
RA III
•MMBIBBBBBM
Baseline
Below Average
0.80
0.35
0.38
0.38
2.30
1.95
1.98
1.97
60
60
60
60
1.0
0.9
-0.2
-0.5
1.4
1.2
-0.2
-0.8
3.6
3.1
-0.6
-1.9
BBBOBaOMBBCB
Financial
Average
1.73
1.49
1.50
1.50
5.56
3.87
3.97
3.96
46
55
54
55
7.0
5.2
4.3
3.9
14.5
9.2
7.7
7.0
26.8
20.5
16.9
15.4
asseatatBMacamacaaraa
Condition
Above Average
5.10
3.14
3.23
3.22
7.54
4.74
4.88
4.87
15
31
30
30
13.0
11.2
10.3
9 . 9
32.5
23.4
21 . 8
21 0
*. A » W
38.2
33.8
31.0
29.9
Baseline ratios are computed using data from Duns Analytical Services (1990)
Ratios under each Recrulatorv Alternative are computed "using cost data in '
. -* •* — -P. — —™ w^. * -w s*i«5 ^ umpi
Table 7-31 and data from Duns Analytical Services
(1990) .
7-57
-------
TABLE 7-37. BASELINE AND AFFECTED FINANCIAL RATIOS: >S100,000 FIRM RECEIPTS*
Baseline Financial Condition
Below Average Average Above Average
Liquidity
current ratio (times)
Baseline
RA I
RA II
RA III
0.80
0.54
0.56
0.56
1.73
1.58
1.59
1.59
5.10
3.75
3.83
3.83
Activity
fixed asset turnover ratio
(times)
Baseline 2.30 5.56 7.54
RA I 2.09 4.45 5.63
RA II 2.10 4.52 5.75
RA III 2.10 4.52 5.74
Leverage
debt ratio (percent)
Baseline 60 46 15
RA I 60 51 25
RA II 60 51 24
RA III 60 51 24
Profitability
profit to sales (percent)
Baseline
RA I
RA II
RA III
profit to assets (percent)
Baseline
RA I
RA II
RA III
Profit to net-worth
(percent)
Baseline
RA I
RA II
RA III
1.0
1.0
0.5
0.3
1.4
1.5
0.7
0.4
3.6
3.7
1.8
0.9
7.0
6.1
5.6
5.2
14.5
11.5
10.7
10.2
26.8
23.7
21.9
20.9
13.0
12.1
11.6
11.4
32.5
27.1
26.3
25.8
38.2
36.0
34.7
33.9
aBaseline ratios are computed using data from Duns Analytical Services (1990)
Ratios under each Regulatory Alternative are computed using cost data in
Table 7-31 and data from Duns Analytical Services (1990) .
7-58
-------
To illustrate, consider the impacts of Regulatory Alternative II on
profit-to-net worth of two firms in average financial condition—one with
annual receipts of $40,545 and the other of $93,829. Even though the sales of
the latter are 2.3 times those of the former, the cost of purchasing and
operating the control device is about the same for both (see Table 7-29). The
baseline profit-to-net worth ratio is 26.8 percent for both firms, but the
profits and net worth of the larger firm are 2.3 times higher than those of
the smaller firm. Thus, Regulatory Alternative II reduces estimated
profitability of the smaller firm to -0.6 percent but reduces estimated
profitability of the larger firm to 16.9 percent.
Once firm size reaches $75-100,000 in annual receipts, firms in average
and above-average financial condition are affected but remain reasonably
profitable, liquid, and properly leveraged under all three regulatory
alternatives. The projected financial impacts on even the largest firms in
below-average financial condition, however, remain significant. Table 7-37
indicates that large, below-average firms have estimated baseline
profitability ratios (to sales) of 1.0 percent. Regulatory Alternatives II
and III reduce profitability to 0.5 percent and 0.3 percent, respectively.
Regulatory Alternative I has a small profitability impact because operating
costs of the control capital are low (see Table 7-31). The below-average
model firm's estimated current ratio falls significantly from 0.80 to 0.54,
however, because control capital costs are high relative to cash balances.
Projected financial failures of businesses under the financial scenario
I are presented in Table 7-38. Business failures are thus dissolutions of
legal entities. In this context, businesses fail either because they do not
have and are unable to borrow sufficient funds to purchase control equipment
for the dry cleaning facility(ies) they own or because after making the dry
cleaning facility(ies) they own compliant, revenues would be insufficient to
meet legal financial obligations. Again, business failures are not
necessarily associated with_facility closures. Economically viable compliant
facilities may be sold rather than closed, because they still generate
revenues in excess of variable costs. Because the excess revenues may be
insufficient to pay existing and new legal obligations of some firms, however,
the facility may be sold to another, more financially viable firm.
7-59
-------
TABLE 7-38. PROJECTED FINANCIAL FAILURES OF COMMERCIAL DRY CLEANING FIRMS BY
REGULATORY ALTERNATIVE AND SIZE CUTOFF, FINANCIAL SCENARIO I
(NUMBER OF FIRMS AND PERCENT)3
Regulatory
Alternative
I
II
III
None
3,246
11.9%
4,872
17.8
5,292
19.4%
Size
<25,000
58
0.2%
1,684
6.2%
1,896
6.9%
Cutoff ($000)
<50,000
0
0%
0
0%
0
0%
<75,000
0
0%
0
0%
0
0%
<100,000
0
0%
0
0%
0
0%
Percentage of all dry cleaning firms in U.S. in 1991. Assumes full
absorbtion of compliance costs. Financial failure is d«jfined as (1) the
lack of sufficient funds or inability to borrow sufficient funds to purchase
the required control equipment or (2) insufficient revenues to meet legal
financial obligations due to increased costs of production.
Under financial scenario I that most firms in below-average condition
have annual receipts under $25,000 and all have receipts under $50,000, the
number of financial failures assuming no size cutoff ranges from 3,246 to
5,292, depending on the Regulatory Alternative. Projected failures are
substantially reduced with a $25,000 receipts cutoff, and zero with a $50,000
or higher cutoff.
Projected financial failures under financial scenario II with no
systematic relationship between firm size and financial condition are
presented in Table 7-39. While projected failures are only 11 percent to 17
percent higher (depending on the Regulatory Alternative) under the financial
scenario II assumption assuming no size cutoff, they are substantially higher
under any positive size cutoff.
7-60
-------
TABLE 7-39. PROJECTED FINANCIAL FAILURES OF COMMERCIAL DRY CLEANING FIRMS BY
REGULATORY ALTERNATIVE AND SIZE CUTOFF, FINANCIAL SCENARIO II
(NUMBER OF FIRMS AND PERCENT)a
Regulatory
Alternative
None
I 3,839
11.0%
II 5,478
28.0%
III 5,183
22.6%
Size
<25,000
1,448
5.3%
2,290
8.4%
2,787
10.2%
••^^M
Cutoff ($000)
-------
-J
a\
7,000 T
6,000 - -
5,000 - -
4,000 --
Potential
Ownership
Changes
3,000 - -
2,000 • -
1,000 - -
Capital Availability Constraints
| 1 Profitability Impacts
58
No Cutoff
Size Cutoff in Annual Receipts ($000)
25
Figure 7-3. Capital Availability and Profitability Impacts, Financial Scenario I--Regulatory Alternative I
-------
-J
I
o\
u>
Potential
Ownership
Changes
7,000 T-
6,000
5,000
4,000
3,000
2,000 -
f,000 -
4,872
1,626
No Cutoff
Capital Availability Constraints
I I Profitability Impacts
25
Size Cutoff in Annual Receipts ($000)
Figure 7-4. Capital Availability and Profitability Impacts, Financial
Scenario I—Regulatory Alternative II
-------
-J
I
01
4k
Potential
Ownership
Changes
7,000 -T-
6,000 - -
5,000 - -
4,000 --
3,000 - -
2,000 •-
1,000 --
HHH Capital Availability Constraints
I | Profitability Impacts
No Cutoff
Size Cutoff in Annual Receipts ($000)
Figure 7-5. Capital Availability and Profitability Impacts, Financial Scenario I—Regulatory Alternative III
-------
I
Ul
Potential
Ownership
Changes
7,000 7-
6,000 - -
5,000 - -
4,000 - - 3,839
3,000 - -
2,000 - -
1,000 -
1,594
Capital Availability Constraints
I I Profitability Impacts
669
No Cutoff 25 50 75
Size Cutoff in Annual Receipts ($000)
100
264
250
Figure 7-6. Capital Availability and Profitability Impacts, Financial Scenario II-Regulatory Alternative I
-------
I
9)
O\
Potential
Ownership
Changes
7,000 -r
6,000 - -
5,000 --
4,000 "
3,000 --
2,000 '-
1,000 - -
5,478
3,233
2,290
UM Capital Availability Constraints
| | Profitability Impacts
No Cutoff 25 50 "75
Size Cutoff in Annual Receipts ($000)
100
264
250
Figure 7-7. "Capital Availability and Profitability Impacts, Financial Scenario II—Regulatory Alternative II
-------
~J
«r>
Potential
Ownership
Changes
7,000 T
6,000
5,000 - -
4,000 - -
3,000 - -
2,000 - -
1,000 -
6,183
3,495
2,787
iiHl Capital Availability Constraints
I I Profitability Impacts
No Cutoff 25 50 75
Size Cutoff in Annual Receipts ($000)
100
358
250
Figure 7-8. Capital Availability and Profitability Impacts, Financial Scenario
II—Regulatory Alternative III
-------
Under financial scenario I, Regulatory Alternative I is projected to
result in failures only of firms in below-average financial condition at
baseline (see Figure 7-9). Regulatory Alternatives II and III, however, are
projected to result in failures of firms in both average and below-average
baseline financial condition, though there are no failures with a size cutoff
of $50,000 or higher (see Figures 7-10 and 7-11).
Under financial scenario II with no systematic relationship between firm
size and financial condition, a share of.projected closures are among firms in
average and above-average financial condition, but only with no size cutoff or
a $25,000 size cutoff. With any size cutoff of $50,000 or higher, all
projected closures are of firms in below-average financial condition (see
Figures 7-12 through 7-14).
7.5 EFFECTS ON SMALL BUSINESSES
The Regulatory Flexibility Act requires that special consideration be
given to the impacts of all proposed regulations affecting small businesses.
Obviously, small business effects within the industrial sector are not an
issue because production cost savings are predicted for this sector.
Therefore, the focus of the analysis of small business effects will be limited
to the coin-operated and commercial sectors.
The Small Business Administration (SBA) sets the standards for
classifying a business as small. If 20 percent of the small affected firms in
a regulated industry will incur a significant adverse economic impact then a
Regulatory Flexibility Analysis must be prepared or size cutoffs that mitigate
impacts on small facilities must be implemented. Criteria for determining
what is a "significantly adverse economic impact" on small business entities
are as follows (EPA, 1982) :
• Annual compliance costs increase total costs of production for small
entities by more than 5 percent.
• Compliance costs as a percent of sales for small entities are at
least 10 percent higher than compliance costs as a percent of sales
for large entities.
7-68
-------
I No Cutoff
01
U>
Size Cutoff in
im Below Average | |
25
Annual Receipts ($000)
Average fejk-j Above
Average
Figure 7-9. Baseline Financial Condition of Projected Business Failures, Financial Scenario I-
Regulatory Alternative I
-------
-J
I
No Cutoff 25
Size Cutoff in Annual Receipts ($000)
Below Average | | Average
Above Average
Figure 7-10. Baseline Financial Condition of Projected Business Failures, Financial Scenario I-
Regulatory Alternative II
-------
-J
No Cutoff
25
Size Cutoff in Annual Receipts ($000)
Below Average | | Average
Above Average
-------
-J
K)
NO Cutoff 25
Size Cutoff in Annual Receipts ($000)
DHH Below Average
CU
Average
[" | Above Average
Figure 7-12. Baseline Financial Condition of Projected Business Failures, Financial Scenario II-
Regulatory Alternative I
-------
-1
I
10
No Cutoff
25
Size Cutoff in Annual Receipts ($000)
Below Average | j Average
50
Above Average
of
Scenario II—
-------
-4
I
Ho Cutoff
25
Size Cutoff in Annual Receipts ($000)
Average | | Average
50
Above Average
Figure 7-14. Baseline Financial Condition of Projected Business Failures, Financial Scenario II-
Regulatory Alternative III
-------
.Capital costs of compliance represent a significant portion of
capital available to small entities, considering internal cash flow
plus external financing capabilities.
in closures
Firms in the dry cleaning industry are classified as small or large
based on annual sales receipts (Code nf FiM»r.i ».»«!.*«,».- 1M1, . For the
coin-operated sector small businesses are defined as firms earning less than
$3.5 million in annual receipts. Likewise commercial firms are classified as
small if they earn less than $2.5 million per year. By these definitions,
over 99 percent of coin-operated and commercial dry cleaning firms are small
(U.S. Dept. of Commerce, 1990b).
There are an estimated 27,332 commercial dry cleaning firms operating in
the U.S. Table 7-38 projects the number of commercial firms likely to
experience financial faille under financial scenario I and the share of all
commercial firms that this number represents. Under Alternative I, about 11.9
percent of commercial firms are likely to experience financial failure with no
size cutoff to mitigate tfae impacts of the regulation. Under Regulatory
Alternative II approximately 17.8 percent of firms will experience financial
failure, and under Alternative II! the share of firms that experience
financial failure is abo«t 19.4 percent. If a size cutoff equivalent to
$25,000 in annual receipts is included in the regulation, the share of firms
in the commercial sector -that experience financial failure decreases to 0.2,
6.2, and 6.9 percent under Regulatory Alternatives I, II, and III,
respectively. if any 3ize cutoff is included as part of the regulation, the
share of financial failures falls well below the 20 percent criterion under
all three alternatives.
Table 7-39 projects the number of commercial firms likely to experience
financial failure under financial scenario II and the share of all commercial
firms that this number represents. Under Alternative I, about 14 percent of
commercial firms are likely to experience financial failure with no size
cutoff to mitigate the impacts of the regulation. Under Regulatory
Alternative II approximately 20 percent of firms will experience financial
failure, and under Alternative III the share of firms that experience
financial failure is about 23 percent. If a size cutoff equivalent to $25,000
7-75
-------
in annual receipts is included in the regulation, the share of firms in the
commercial sector that experience financial failure decreases to 5, 8, and 10
percent under Regulatory Alternatives I, II, and III, respectively.
Unquestionably, self-service coin-operated facilities would incur the
largest percentage increase in production costs as a result of the NESHAP.
The majority of these facilities are relatively small entities, especially in
comparison to commercial and industrial plants. With no cutoff to mitigate
impacts, more than 20 percent of the facilities with dry cleaning capacity in
this sector would experience adverse economic impacts. However, if any size
cutoff above $25,000 is included in the regulation, virtually all coin-
operated laundries will be exempt.
7-76
-------
SECTION 8
CONCLUSION
This Economic Impact Analysis (EIA) examines the economic and
financial impacts associated with three regulatory alternatives
considered for proposal in the dry cleaning industry. In addition, five
size cutoff levels based on solvent consumption corresponding to target
levels of annual receipts are analyzed.
Of particular concern to EPA is the large number of small entities
potentially affected by the regulation. The commercial and coin-
operated sectors of the dry cleaning industry are comprised of thousands
of small facilities. According to Census data, approximately two-thirds
of commercial facilities and over 85 percent of coin-operated facilities
earn less than $100 thousand in annual receipts (U.S. Department of
Commerce I990a; U.S. Department of Commerce 1990b). The industrial
sector has much larger facilities with over 90 percent earning over $100
thousand in annual receipts. The alternatives do not apply to all
facilities in these three sectors. Only those facilities that use PCE*
and do not have the required control equipment are affected under the
alternatives analyzed. Over 12,000 potentially affected facilities are
in the commercial sector, and approximately 1,600 potentially affected
facilities are in the coin-operated sector. The industrial sector
includes only about 65 potentially affected facilities.
An integrated approach that considers both the economic and
financial impacts of the alternatives is used to address the concerns
regarding small business impacts. Key elements of the economic analysis
are listed below:
oapacity
1 1 l-llf "^±rrY 3f f "M"" aPply " £aoil"«s that use PCE or
1,1,1 J.UA. However, all facilities that use 111 -rra •
with the candidate regulatory alternatives'L^n^
impacts are computed only for facilities that use PCE
8-1
-------
• Analyzed, impacts using an urban/rural model market approach.
Model markets differentiate, the market for dry cleaning
services by number of facilities in the markeit, the share of
affected and unaffected facilities in the market, the baseline
price of dry cleaning services, and the projected behavioral
response to regulation.
• Estimated supply and demand elasticities using simultaneous
equation modelling techniques and recent time-series data.
• Estimated the weighted average cost of capital (WACC) for firms
in below-average, average, and above-average financial
condition. Computed annualized compliance costs using
engineering data and the WACC estimated for firms.
• Estimated short-run price and output adjustments and
corresponding consumer and producer welfare impacts using
applied welfare economics.
• Projected net plant closures based on the assumption that the
entire reduction in output is accounted for by the smallest
.size affected plants leaving the industry.
• Estimated one-time worker displacements and displacement costs
The financial analysis of affected dry cleaning firms is based on
costs computed for the economic analysis. Key elements of the
ncial analysis are listed below:
• Characterized the baseline distribution of commercial dry
cleaning firms by financial condition and firm size under two
financial scenarios. Financial scenario I assumes that since
capacity utilization is significantly lower at smaller firms,
all firms in below-average baseline financial condition have
annual receipts below 550,000, that all firms in average
condition have annual receipts between $25,000 and $250,000,
and that all firms in above-average condition have receipts of
at least $100,000. Financial scenario II assumes that 25
percent of all firms of all sizes are in below-average
condition, 50 percent are in average financial condition, and
25 percent are in above-average condition.
• Constructed pro forma, baseline financial statements and
financial ratios of commercial dry cleaning firms of different
sizes in below-average, average, and above-average financial
condition to allow assessment of the financial impacts of
regulatory alternatives with alternative size cutoffs.
• Evaluated the availability of funds to firms of different
baseline financial condition and different, output levels.
• Evaluated profitability impacts on firms by baseline financial
status and baseline output level.
8-2
-------
cfoitaie?v?rHe^n ownershiP due to Profitability impacts and
capital availability constraints.
constraints.
*
, The economic and financial intact s are computed for three
regulatory alternatives and five size cutoff levels. In all, fifteen
regulatory scenarios are considered. The analysis shows that including
a size cutoff significantly decreases economic and financial impacts. To
show -the mitigating influence of a size cutoff, two regulatory
scenarios— Alterntive I with no size cutoff and Alternative II with a
cutoff corresponding to $100,000 in annual receipts-are highlighted in
the balance of this section.
1;
.The total annualized cost is estimated at $42.9 million under
ReguJ&tory Alternative II with no cutoff. These regulatory costs result
in shprt-run price increases and output decreases representing less than
one ptercent deviation from baseline values. Producers and consumers are
projected to incur approximately $18 million and $25 million in welfare
lossefe, respectively. The minimal price and quantity adjustments
estimated indicate that impacts on consumers are relatively small.
impacts on producers, however, are not distributed across all producers
equally. The impacts that an individual dry cleaning firm may incur
depend on a combination of the market conditions, the baseline financial
condition of the firm, and the size of the firm.
Alternative II with no cutoff would result in an estimated 1600
net plant closures assuming that the reduction in output is entirely
accounted for by closure of the smallest size affected facility, in
addition, an estimated 920 employees in the commercial sector alone
would lose their jobs resulting in an estimated $26.5 million in one-
time worker displacement costs.
The results of the financial analysis indicate that small
businesses are likely to incur significant adverse impacts unless a size
cutoff is included in the regulation. For example, under Regulatory
Alternative II and financial scenario I, approximately 4,872 changes in
ownership are projected with no size cutoff. None of these projected
changes are for firms in above-average financial condition, and two-
thirds are for firms below-average condition. Under financial scenario
8-3
-------
II, about 14 percent of the approximately 5,500 changes in ownership
represent businesses in above-average baseline financial condition,
another 44 percent are in average financial condition, and the remaining
42 percent are in below-average financial condition.
The Regulatory Flexibility Act requires that special consideration
be given to the impacts of all proposed regulations affecting small
businesses. To comply with the guidelines set forth in the Act and to
help mitigate the impacts of the alternative selected for proposal, five
cutoff levels based on solvent consumption that correspond to target
levels of annual receipts are considered. The inclusion of a cutoff
level corresponding to $100,000 in annual receipts would result in the
following economic and financial impacts under Regulatory Alternative
II:
Annualized costs $11.5 million
Producer welfare losses $4.8 million
Consumer welfare losses $6.7 million
Net plant closures 28
Number worker displacements 354
Worker displacement costs $10.2 million
Projected changes in ownership 0 - 669
Impacts under Alternative II with no cutoff are significantly
higher than impacts with a cutoff corresponding to $100,000 in annual
receipts. Annualized costs, producer welfare losses, and consumer
welfare losses are reduced by about 73 percent compared to the impacts
with no cutoff. Projected net plant closures are reduced by over 98
percent. It should be noted that the 28 net plant closures projected
with the cutoff represent much larger plants on average (over $100,000
in annual receipts per plant) than the 1600 closures projected with no
cutoff (less than $25,000 in annual receipts per plant) . Worker
displacements and corresponding displacement costs would be reduced by
over 60 percent. Perhaps the most significant reduction in impacts is
seen in the projected changes in ownership. Under the financial
scenario I assumption that all firms in below-average financial
condition at baseline have annual receipts below $50,000, there are no
projected changes in ownership. Under the financial scenario II
8-4
-------
assumption, approximately 4,800 fewer changes are projected with a
cutoff, and all of those are in below-average condition at baseline.
EPA must propose a regulation that adequately reduces the level of
HAP emissions while considering the impacts on small businesses. This
EIA measures the small business impacts under each of the regulatory
alternatives and helps to provide quantitative support for selecting the
regulatory scenario that meets both criteria.
8-5
-------
SECTION 9
REFERENCES
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Temporary Layoffs, and Compensating Wage Differentials." In Studio
-------
Clark, .Lyman H. 1989. Small Business Financial Data Baaea. Prepared for the
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Protection Agency.
rode of Fedaral Re
-------
Hamermesh, Daniel S. 1989. "What Do We Know About Worker Displacement in the
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Hatsopoulos, George N. 1991. -Cost of Capital: Reflections of a CEO "
Business Economies . 26(2) :7— 13
Houthakker, H. S., and Taylor, L. D. 1970. canau>n*r n»n»mri
-------
Radian Corporation. 1991a. "Documentation of Growth Rates for the Dry
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U.S. Environmental Protection Agency/ Chemical and Petroleum Branch.
March 11.
Radian Corporation. 1991b. "Existing State Exemption Levels." Memorandum
from Kim Kepford to Brenda L. Jellicorse, Research Triangle Institute.
January 7 .
Radian Corporation. 1991c. "Modelling the Low Income Sector of the HAP Dry
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March 1.
Safety—Kleen. 1986. Analysis of Pry Cleaning Industry.
Sherer F.M. 1980. Industrial Market Sturugturp and Economic Performance.
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Steinhoff, Dan and Burgess, John F. 1989. Small Business Management
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Topel, Robert H. 1984. "Equilibrium Earnings, Turnover, and Unemployment:
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9-4
-------
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9-5
-------
-------
TABLE A-l. BASELINE FINANCIAL STATEMENTS OF DRY CLEANING FIRMS IN BELOW-
AVERAGE FINANCIAL CONDITION
Company Sales Range
Income Sf.afc*»m*»nt'
Sales
cost of goods sold
gross profit
other expenses and
taxes
net profit
Balance Sh*>«»t-
cash
accounts receivable
cash plus accounts
receivable
other current assets
total current assets
fixed assets
other non-current
assets
total assets
accounts payable
loans payable
notes payable
other current
liabilities
total current
liabilities
non-current liabilities
total liabilities
net worth
capital
Total Liab-M -it-i»«»
a«/-4 XT^s* Us^vt-U
< $25K
17,736
8,288
9,448
9,270
177
315
1,225
1,539
924
2,463
7,698
2,255
12,415
665
58
795
1,561
3,079
4,370
7,449
4,966
9,336
12,415
$25-50K
40,545
18,948
21,597
21,192
405
720
2,799
3,519
2,112
5,630
17,597
5,154
28,382
1,520
132
1,817
3,569
7,039
9,990
17,029
11,353
21,343
28,382
$50-75K
67,021
31,320
35,701
35,030
670
1,190
4,627
5,817
3,490
9,308
29,087
8,520
46,915
2,513
218
3,004
5,899
11,635
16,514
28,149
18,766
35,280
46,915
•••••••MB
$75-100K
93,829
43,848
49,981
49,042
938
1,666
6,478
8,144
4,887
13,031
40,722
11,928
65,680
3,518
306
4,206
8,259
16,289
23,119
39,408
26,272
49,392
65,680
•••••••••••1
> $100K
367,510
171,746
195,764
192,090
3,675
6,526
25,373
31,900
19,140
51,039
159,500
46,718
257,257
13,779
1,198
16,474
32,349
63,800
90,554
154,354
102,903
193,457
257,257
A-l
-------
TABLE A-2. BASELINE FINANCIAL STATEMENTS OF DRY CLEANING FIRMS IN AVERAGE
FINANCIAL CONDITION
Company Sales Range
Tneome Staf^ntent
Sales
cost of goods sold
gross profit
other expenses and
taxes
net profit
Balan.flf> Sheet
cash
accounts receivable
cash plus accounts
receivable
other current assets
total current assets
fixed assets
other non-current
assets
total assets
accounts payable
loans payable
notes payable
other current
liabilities
total current
liabilities
non-current liabilities
total liabilities
net worth
capital
Tnt-al Liabilities
< $25K
17,736
7,786
9,950
8,709
1,241
1,548
650
2,198
958
3,157
3,191
2,207
8,555
394
34
471
924
1,822
2,105
3,927
4,628
6,732
8,555
$25-50K
40,545
17,799
22,746
19,908
2,838
3,540
1,486
5,026
2,190
7,216
7,295
5,045
19,556
900
78
1,076
2,112
4,165
4,811
8,976
10,579
15,391
19,556
S50-75K
67,021
29,422
37,599
32,907
4,691
5,851
2,457
8,308
3,620
11,928
12,057
8,340
32,325
1,487
129
1,778
3,491
6,885
J7,952
14,837
17,488
25,440
32,325
$75-100K
93,829
41,191
52,638
46,070
6,568
8,191
3,439
11,630
5,069
16,699
16,880
11,676
45,255
2,082
181
2,489
4,888
9,639
11,133
20,772
24,483
35,616
45,255
> $100K
367,510
161,337
206,173
180,448
25,725
32,083
13,471
45,554
19,853
65,407
66,117
45,732
177,257
8,154
709
9,749
19,144
37,755
43,606
81,361
95,895
139,501
177,257
Worth
A-2
-------
TABLE A-3. BASELINE FINANCIAL STATEMENTS OF DRY CLEANING FIRMS IN ABOVE-
AVERAGE FINANCIAL CONDITION
Company Sales Range
Income fit-.atem^nt-
Sales
cost of goods sold
gross profit
other expenses and
taxes
net profit
Balance Sh*»^>f,
cash
accounts receivable
cash plus accounts
receivable
other current assets
total current assets
fixed assets
other non-current
assets
total assets
accounts payable
loans payable
notes payable
other current
liabilities
total current
liabilities
non-current liabilities
total liabilities
net worth
capital
Total Liahilit-ip.«?
.» « ^ %v.«.A. r.v_ t_ L.
< $25K
17,736
7,284
10,452
8,147
2,305
1,379
267
1,646
753
2,399
2,352
2,344
7,095
. 102
9
121
238
470
594
1,064
6,030
6,624
7,095
S25-50K
40,545
16,651
23,894
18,624
5,270
3,152
611
3,763
1,720
5,484
5,377
5,358
16,218
232
20
278
545
1,075
1,358
2,433
13,785
15,143
16,218
$50-75K
67,021
27,524
39,497
30,784
8,713
5,211
1,010
6,221
2,844
9,065
8,887
8,857
26,808
384
33
459
901
1,777
2,244
4,021
22,787
25,031
26,808
$75-100K
93,829
38,533
55,296
43,098
12,198
7,295
1,414
8,709
3,981
12,691
12,442
12,399
37,532
537
47
643
1,262
2,488
3,141
5,630
31,902
35,043
37,532
> $100K
367,510
150,928
216,582
168,806
47,776
28,574
5,538
34,112
15,594
49,706
48,732
48,566
147,004
2,105
183
2,517
4,942
9,746
12,305
22,051
124,953
137,258
147,004
A-3
-------
TABLE A-4. FINANCIAL STATEMENTS OF FIRMS IN BELOW-AVERAGE FINANCIAL
CONDITION: REGULATORY ALTERNATIVE I
Company Sales Range
Income Statement
Sales
cost: of goods
gross profit
other expenses and taxes
net profit
Balance Sheet
cash
accounts receivable
cash plus accounts
receivable
other current assets
total current assets
fixed assets
other non-current assets
total assets
accounts payable
loans payable
notes payable
other current liabilities
total current liabilities
non-current liabilities
total liabilities
net worth
capital
Total Liabilities
$0-25K
17,736 '
8,288
9,448
9,608
-161
-7,200
1,225
-5,975
924
-5,052
15,212
2,255
12,415
665
58
795
1,561
3,079
4,370
7,449
4,966
9,336
12,415
525-50K
40,545
18,948
21,597
21,464
133
-6,582
2,799
-3,783
2,112
-1,671
24,899
5,154
28,382
1,520
132
1,817
3,569
7,039
9,990
17,029
11,353
21,343
28,382
$50-75K
67,021
31,320
35,701
35,216
485
-5,614
4,627
-987
3,490
2,504
35,891
8,520
46,915
2,513
218
3,004
5,899
11,635
16,514
28,149
18,766
35,280
46,915
$75-100K
93,829
41,191
49,981
49,179
801
-5,667
6,478
811
4,887
5,697
48,055
11,928
65,680
3,518
306
4,206
8,259
16,289
23,119
39,408
26,272
49,392
65,680
$ >100K
367,510
43,848
195,764
191,990
3,774
-10,011
25,373
15,362
19,140
34,502
176,037
46,718
257,257
13,779
1,198
16,474
32,349
63,800
90,554
154,354
102,903
193,457
257,257
and Net Worth
A-4
-------
TABLE A-5. FINANCIAL STATEMENTS OF FIRMS IN AVERAGE FINANCIAL CONDITION-
REGULATORY ALTERNATIVE I
Company Sales Range
Tn<^pp^ staf'-puQ^n.t"
Sales
cost of goods
gross profit
other expenses and taxes
net profit
Balance Sh*»f»t-
cash
accounts receivable
cash plus accounts
receivable
other current assets
total current assets
fixed assets
other non-current assets
total assets
accounts payable
loans payable
notes payable
other current liabilities
total current liabilities
non-current liabilities
total liabilities
net worth
capital
Total Liabilities
-.«*-! IT A J_ T.t ._. __J_ W
$0-25K
17,736
7,786
9,950
10,667
-717
1,548
650
2,198
958
3,157
10,706
2,207
16,069
394
34
2,091
924
3,443
8,863
12,306
3,764
12,627
16,069
$25-50K
40,545
17,799
22,746
21,754
991
3,540
1,486
5,026
2,190
7,216
14,596
5,045
26,858
900
78
2,650
2,112
5,740
11,378
17,118
9,740
21,118
26,858
$50-75K
67,021
29,422
37,599
34,560
3,038
5,851
2,457
8,308
3,620
11,928
18,861
8,340
39,129
1,487
129
3,245
3,491
8,353
14,071
22,424
16,705
30,777
39,129
$75-100K
93,829
41,191
52,638
47,789
4,849
8,191
3,439
11,630
5,069
16,699
24,214
11,676
52,589
2,082
.181
4,071
4,888
11,221
17,728
28,949
23,640
41,368
52,589
$ >100K
367,510
161,337
206,173
183,915
22,258
32,083
13,471
45,554
19,853
65,407
82,655
45,732
193,794
8,154
709
13,315
19,144
41,322
58,479
99,801
93,993
152,472
193,794
A-5
-------
A-6. FINANCIAL STATEMENTS OF FIRMS IN ABOVE-AVERAGE FINANCIAL
CONDITION: REGULATORY ALTERNATIVE I
arapany Sales Range
Statement.
j
of goods
3 profit
~ expenses and taxes
profit
a Sheet
mts receivable
plus accounts
vable
: current assets
current assets
assets
non-current assets
assets
nts payable
p lyable
payable
current liabilities
current liabilities
urrent liabilities
Liabilities
orth
al
•.labilities
At Worth
$0-25K
17,736
7,284
10,452
10,079
373
1,379
267
1,646
753
2,399
9,867
2,344
14,609
102
9
1,716.
238
2,065
7,341
9,406
5,204
12,544
14,609
$25-50K
40,545
16,651
23,894
20,445
3,449
3,152
611
3,763
1,720
5,484
12,678
5,358
23,520
232
20
1,827
545
2,625
7,913
10,538
12,982
20,895
23,520
$50-75K
67,021
27,524
39,497
32,414
7,083
5,211
1,010
6,221
2,844
9,065
15,691
8,857
33,612
384
. 33
1,903
901
3,221
8,352
11,574
22,039
30,391
33,612
$75-100K
93,829
38,533
55,296
44,791
10,504
7,295
1,414
8,709
3,981
12,691
19,775
12,399
44,865
537
47
2,199
1,262
4,045
9,725
13,770
31,095
40,821
44,865
$ >100K
367,510
150,928
216,582
172,216
44,366
28,574
5,538
34,112
15,594
49,706
65,270
48,566
163,542
2,105
183
6,026
4,942
13,256
27,152
40,408
123,134
150,286
163,542
A-6
-------
TABLE A-7. FINANCIAL STATEMENTS OF FIRMS IN BELOW-AVERAGE
CONDITION: REGULATORY ALTERNATIVE II
FINANCIAL
Company Sales Range
Income Statement
Sales
cost of goods
gross profit
other expenses and taxes
net profit
Balance ?bfiftf
cash
accounts receivable
cash plus accounts
receivable
other current assets
total current assets
fixed assets
other non-current assets
total assets
accounts payable
loans payable
notes payable
other current liabilities
total current liabilities
non-current liabilities
total liabilities
net worth
capital
Total Liabilities
anri Mof- W/-\-.-t-K
$0-25K
17,736
8,288
9,448
11,059
-1,611
-6,367
1,225
-5,142
924
-4,219
14,379
2,255
12,415
665
58
795
1,561
3,079
4,370
7,449
4,966
9,336
12,415
tamamm^mmmm
$25-50K
40,545
18,948
21,597
22,663
-1,065
-5,893
2,799
-3,094
2,112
-982
24,209
5,154
28,382
1,520
132
1,817
3,569
7,039
9,990
17,029
11,353
21,343
28,382
$50-75K
67,021
31,320
35,701
35,828
-127
-5,261
4,627
-634
3,490
2,856
35,539
8,520
46,915
2,513
218
3,004
5,899
11,635
16,514
28,149
18,766
35,280
46,915
$75-100K
93,829
43,848
49,981
50,140
-160
-5,114
6,478
1,364
4,887
6,251
47,502
11,928
65,680
3,518
306
4,206
8,259
16,289
23,119
39,408
26,272
49,392
65,680
•••••••••MM
$ >100K
367,510
171,746
195,764
193,894
1,871
-8,696
25,373
16,678
19,140
35,818
174,722
46,718
257,257
13,779
1,198
16,474
32,349
63,800
90,554
154,354
102,903
193,457
257,257
A-7
-------
TABLE A-8. f SANCIAL STATEMENTS OF FIRMS IN AVERAGE FINANCIAL CONDITION:
r 3ULATORY ALTERNATIVE II
Compa • -• les Range ",
Income St , £, ,
Sales
cost of •
gross pr
other e: s and taxes
net proi .
Balance S
cash
account ~. ivable
cash pi . ounts
receivat)..
other ci assets
total cv assets
fixed af
other nc rent assets
total ass
account )le
loans p:-.
notes p
other c liabilities
total c liabilities.
non-cui labilities
total li -es
net wor
capital
Tnhal T,ii. &S.
and Net
30-25K
17,736
7,786
9,950
11,938
-1,988
1,548
650
2,198
958
3,157
9,872
2,207
15,236
394
34
1,911
924
3,263
8,114
11,377
3,859
11,973
15,236
$25-50K
40,545
17,799
22,746
22,804
-59
3,540
1,486
5,026
2,190
7,216
13,907
5,045
26,168
900
78
2,502
2,112
5,591
10,758
16,349
9,819
20,577
26,168
$50-75K
67,021
29, 422
37,599
35,096
2,503
5,851
2,457
8,308
3,620
11,928
18,509
8,340
38,777
1,487
129
3,169
3,491
8,277
13,754
22,031
16,74(5
30,500
38,777
$75-100K
93,829
41,191
52,638
48,630
4,008
8,191
3,439
11,630
5,069
16,699
23,660
11,676
52,035
2,082
181
3,951
4,888
11, 101
17,231
28,332
23,703
40,934
52,035
$ >100K
367,510
161,337
206,173
185,535
20,638
32,083
13,471
45,554
19,853
65,407
81,339
45,732
192,478
8,154
709
13,032
19,144
41,038
57,296
98,334
94,145
151,440
192,478
A-8
-------
TABLE A-9. FINANCIAL STATEMENTS OF FIRMS IN ABOVE-AVERAGE
CONDITION: REGULATORY ALTERNATIVE II
FINANCIAL
Company Sales Range
Income Statement-
Sales
cost of goods
gross profit
other expenses and taxes
net profit
Balance Sheet
cash
accounts receivable
cash plus accounts
receivable
other current assets
total current assets
fixed assets
other non-current assets
total assets
accounts payable
loans payable
notes payable
other current liabilities
total current liabilities
non-current liabilities
total liabilities
net worth
capital
Total Liabilities
av**4 KT*«4- &7A_4»U
SO-25K
17,736
7,284
10,452
11,353
-901
1,379
267
1,646
753
2,399
9,033
2,344
13,776
102
9
1,539
238
1,888
6,592
8,481
5,295
11,888
13,776
$25-50K
40,545
16,651
23,894
21,497
2,397
3,152
611
3,763
1,720
5,484
11,989
5,358
22,831
232
20
1,681
545
2,479
7,294
9,773
13,058
20,352
22,831
$50-75K
67,021
27,524
39,497
32,951
6,546
5,211
1,010
6,221
2,844
9,065
15,338
8,857
33,260
384
33
1,828
901
3,147
8,036
11,182
22,077
30,113
33,260
$75-100K
93,829
38,533
55,296
45,635
9,661
7,295
1,414
8,709
3,981
12,691
19,222
12,399
44,312
537
47
2,081
1,262
3,927
9,228
13,156
31,156
40,384
44,312
$ >100K
367,510
150,928
216,582
173,841
42,741
28,574
5,538
34,112
15,594
49,706
63,954
48,566
162,226
2,105
183
5,747
4,942
12,977
25,970
38,947
123,279
149,249
162,226
A-9
-------
TABLE A-10. FINANCIAL STATEMENTS OF FIRMS IN BELOW-AVEFLAGE FINANCIAL
CONDITION: REGULATORY ALTERNATIVE III
Company Sales Range
Income Statement:
Sales
cost of goods
gross profit
other expenses and taxes
net profit
Balanpe Sheet
cash
accounts receivable
cash plus accounts
receivable
other current assets
total current assets
fixed assets
other non-current assets
total assets
accounts payable
loans payable
notes payable
other current liabilities
total current liabilities
non-current liabilities
total liabilities
net worth
capital
Total T, •labilities
and. Net Wprth
$0-25K
17,736
8,288
9,448
11,108
-1,660
-6,386
1,225
-5,162
924
-4,238
14,399
2,255
12,415
665
58
795
1,561
3,079
4,370
7,449
4,966
9,336
12,415
$25-50K
40,545
18,948
21,597
22,772
-1,175
-5,931
2,799
-3,132
2,112
-1,020
24,248
5,154
• 28,382
1,520
132
1,817
3,569
7,039
9,990
17,029
11,353
21,343
28,382
$50-75K
67,021
31,320
35,701
36,151
-450
-5,360
4,627
-733
3,490
2,758
35,637
8,520
46,915
2,513
218
3,004
5,899
11,635
16,514
28,149
18,766
35,280
46,915
$75-100K
93,829
43,848
49,981
50,489
-509
-5,163
6,478
1,315
4,887
6,202
47,551
11,928
65,680
3,518
306
4,206
8,259
16,289
23,119
39,408
26,272
49,392
65,680
$ >100K
367,510
171,746
195,764
194,835
930
-8,747
25,373
16,626
19,140
35,766
174,773
46,718
257,257
13,779
1,198
16,474
32,349
63,800
90,554
154,354
102,903
193,457
257,257
A-10
-------
TABLE A-ll. FINANCIAL STATEMENTS OF FIRMS IN AVERAGE FINANCIAL CONDITION-
REGULATORY ALTERNATIVE III
Company Sales Range
Tnr?nm«» p^a^p^non*-
Sales
cost of goods
gross profit
other expenses and taxes
net profit
Balance Sh^f-
cash
accounts receivable
cash plus accounts
receivable
other current assets
total current assets
fixed assets
other non-current assets
total assets
accounts payable
loans payable
notes payable
other current liabilities
total current liabilities
non-current liabilities
total liabilities
net worth
capital
Total Liah-i ].itip^
and Net; Worth
$0-25K
17,736
7,786
9,950
11,991
-2,041
1,548
650
2,198
958
3,157
9,892
2,207
15,256
394
34
1,916
924
3,267
8,131
11,399
3,857
11,988
15,256
$25-50K
40,545
17,799
22,746
22,922
-177
3,540
1,486
5,026
2,190
7,216
13,945
5,045
26,207
900
78
2,510
2,112
5,600
10,792
16,392
9,815
20,607
26,207
^•••^••^•^•^
$50-75K
67,021
29,422
37,599
35,441
2,158
5,851
2,457
8,308
3,620
11,928
18,607
8,340
38,875
1,487
129
3,190
3,491
8,298
13,843
22,141
16,735
30,577
38,875
-^^ -^^^^^^^^^^ ^__^__
S75-100K
93,829
41,191
52,638
48,990
3,648
8,191
3,439
11,630
5,069
16,699
23,709
11,676
52,084
2,082
181
3,962
4,888
11,112
17,274
28,386
23,698
40,972
52,084
mtmmmmmfmtmm
$ >100K
367,510
161,337
206,173
186,487
19,686
32,083
13,471
45,554
19,853
65,407
81,391
45,732
192,530
8,154
709
13,043
19,144
41,049
57,342
98,391
94,139
151,481
192,530
A-ll
-------
TABLE A-12. FINANCIAL STATEMENTS OF FIRMS IN ABOVE-AVERAGE FINANCIAL
CONDITION: REGULATORY ALTERNATIVE III
Company Sales Range
y rt OOTH^* S i yi \ ^*^^Pn1*
Sales
cost of goods
gross profit
other expenses and taxes
net profit
Balance Sheet
cash
accounts receivable
cash plus accounts
receivable
other current assets
total current assets
fixed assets
other non-current assets
total assets
accounts payable
loans payable
notes payable
other current liabilities
total current liabilities
non-current liabilities
total liabilities
net worth
capital
Total Liabilities
and Net Worth
$0-25K
17,736
7,284
10,452
11,406
-954
1,379
267
1,646
753
2,399
9,053
2,344
13,796
102
9
1,544
238
1,893
6,610
8,503
5,293
11,903
13,796
325-50K
40,545
16,651
23,894
21,615
2,279
3,152
611
3,763
1,720
5,484
12,027
5,358
22,869
232
20
1, 689
545
2,487
7,329
9,815
13,054
20,382
22,869
$50-75K
67,021
27,524
39,497
33,295
6,202
5,211
1,010
6,221
2,844
9,065
15,437
8,857
33,358
384
33
1,849
901
3,167
8,124
11,292
22,067
30,191
33,358
$75-100K
93,829
38,533
55,296
45,994
9,302
7,295
1,414
8,709
3,981
12,691
19,271
12,399
44,360
537
47
2,092
1,262
3,938
9,272
13,210
31,151
40,423
44,360
$ >100K
367,510
150,928
216,582
174,793
41,790
28,574
5,538
34,112
15,594
49,706
64,006
48,566
162,278
2,105
183
5,758
4,942
12,988
26,017
39,004
123,273
149,290
162,278
A-12
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1. REPORT NO.
EPA 450/3-91-021
TECHNICAL REPORT DATA
please read Instructions on me reverse tie fore completmgi
4. TITLE AND SUBTITLE
2.
Economic Impact Analysis of Regulatory Controls in the
Dry Cleaning Industry
7. AUTHOR(S>
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
Office of Air Quality Planning & Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
12. SPONSORING AGENCY NAME AND ADDRESS
Director
Office of Air Quality Planning & Standards
U.S. Environmental Protection Agency
I Research Triangle Park, NC 27711
J15. SUPPLEMENTARY NOTES ~ ~
3. RECIPIENT'S ACCESSION NO.
5. REPORT DATE ' ~~
October 1991
6. PERFORMING ORGANIZATION CODE
10. PROGRAM ELEMENT NO.
11. CONTRACT/GRANT NOT"
68-D1-0143
13. TYPE OF REPORT AND PERIOD COVERED
4. SPONSORING AGENCY CODE
EPA/200/04
16. ABSTRACT ~~
Under authority of the 1990 Clean Air Act Amendments, a National Emission Standard
for Hazardous Air Pollutants (NESHAP) is being proposed to control perchloroethylene
r?TCS7^M ^^ aning/aCilitieS' Coi*-°perated (SIC 7215), commercial
(SIC 7216), and industrial (SIC 7218) sectors of the dry cleaning industry were
evaluated. This report analyzes three regulatory alternatives and five exemption
levels considered for proposal using an integrated approach that examines both
economic and financial impacts on dry cleaning facilities.
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
Air pollution
Dry cleaning
Hazardous air pollutants
Emission controls
Economic impact
b.lDENTIFIERS/OPEN ENDED TERMS
Air pollution control
c. COSATI Field/Group
13B
Unlimited
19. SECURITY CLASS {ThisReport)
Unclassified
21. NO. OF PAGES
L
20. SECURITY CLASS (This page)
Unclassified
231
22. PRICE
EPA Form 2220-J (R»v. 4-77) PREVIOUS EDITION is OBSOLETE
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