-------
these model markets are small establishments: that receive 325,000 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
:aBBBBaBBBBBBei
Market
Model
A
B
C
D
E
BBaBeBlBeBlBeBBeeBBBBB
Market
Description11
Rural
Rural
Urban/
Suburban
Urban/
Suburban
Urban/
Suburban
BBeBBeBBBeeBe»BaBe»eei
Proportion of
Affected and
Unaffected
Facilities
Unaffected
Only
Affected Only
Unaffected
Only
Unaffected
Dominate
Affected and
Unaffected
Evenly
Distributed
BBeeeBBBBeeeeeeBBl
Total '
Number
Facilities15
1,543
1,606
1,157
10,432
8,073
Number of
Potentially
Affected
.Facilities0
0
1,606
0
287
'
4,038
Number of
Unaffected
Facilities3
1,543
0
1,157
10,145
4,035
Urban/
Suburban
Affected
Dominate
Total
7,683
30,494
4,298
10,229
3,385
20,265
aRural markets are defined as locales with population of 2.SOO or lass chat are not part of a
metropolitan statistical araa. For this analysis, rural markets have only one facility per
market are*.
bFacilities, are distributed co^Model Markets based on Che share of facilities located in
urban and curai areas (ABI. 1991), :he snare-or, facilities chat use ?CS. in the dry ciaanina
process (Safety-Kleen, 1986), and existing state regulations (Radian, 1991b).
C9otentially affected facilities are defined here as those that use PCS in the cleaning
process and do not have vent controls in place (Radian. 1991O. The total is equivalent co
the number of potentially affected facilities under Regulatory Alternatives I and II. Noce
that PCS facilities with baseline vent controls chat do not meet the requirements o£
Alternative III are not included in che estimate,of potentially affected facilities
.reported in this table.
^naf facted facilities either do not use PCE in the cleaning process or have baseline vent
controls.
4-20
-------
under the candidate alternatives. These facilities may incur casts because of
the regulation. However, as discussed in Section 4.2.2, na 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,
1991O and data on the share of facilities that use PCS (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 PCS. 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 PCS 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
with strict: PCS: emissions standard: or. about, 1..1S7. The remaining facilities
located in states with strict PCE emission standards (10,432) are assigned to
Market 0. Prica 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.
-------
regulations requiring vetic controls for commercial facilities are allocated co
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 £ and F are computed using the average ccst 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
f
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 Coin— orif» Tartar) Sector M3T-jff»f.^
One model market represents all facilities in the coin-operated sector.
Essentially two kinds of coin-operated plants are represented in the model
market: self-service and plane -operated. The distribution between tne 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:
-------
and. output adjustments computed for Market F define the maximum adjustments
for coin-operated facilities .
4.3.3. Tndug'ggial
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'
-------
-------
SECTION 5
FINANCIAL PROFILE. OF COMMERCIAL DRY CLEANING FIRMS
The dry cleaning NESHAP will potentially,impact business entities chat
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 325,000.
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 coat* equals marginal revenue (price in perfect
'competition) as long as marginal revenue at least covers average variable
5-1.
-------
coat. Economic failure describes the situation in which the decision maker
closes the facility if marginal revenue/price is below marginal cost.
Altaian (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 ean 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 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 for proposal. In addition, those
facilities that, might be affected have a near-perfect substitute for dry
cleaning—waterr 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 SPA. exempts small facilities. EPA
5-2.
-------
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
che potentially affected population. A financial impact analysis of this
industry is important for the following reasons:
• the 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
• most dry cleaning 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
A sole proprietorship consists of ona individual in business for himself
who- contributes-, all... of . the: equity capital,, takes all of. the risks, makes the
decisions, takes ther 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 13,322 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'
-------
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, -i^ (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 leads
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
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
-------
TABLE 5-1. LEGAL FORM OF' ORGANIZATION OF PRY CLEANING FIRMS—NUMBER AND
PERCENT
Legal•Organization
Total Firms Proprietorships Partnerships Corporations
Other
18,322* 8,494 (46.4%) 1,666 (9.1%) 3,147 (44.5%) 15 (0.1%)
27,332b 16,694 (61.1%) 1,803 (6.6%) 8,818 (32.3%) 17 «0.1%)
"Payroll firms only 1987.
51991 estimate; Payroll and non-payrolJ 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).
Source: 1987 Census of 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 Co fno ra-e i on ^
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. ara= 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 stocx. Second, institutional
5-5'
-------
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 jans.
5.4 DISTRIBUTION OF COMPANIES BY RECEIPTS SIZE
The U.S. has an estimated 27,332 commercial dry cleaning firms in 1991.
An estimated IS,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 wich 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 (23.5%), or industrial
launderers (67.3%).
Firm size is likely to be, 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.
-------
TABLE 5-2.' RECEIPTS OF DRY CLEANING FIRMS
Receipts. Range
($000)
<25
25-50
SO-75
75-100
subtotal
100-250
250-500
500-1,000
1,000-2,500
2,500-5,000
>5,000
subtotal
Total
No. pf Firms*
6,690
4,187
2,581
2,581
• 16,039
6,823
2,870
1,122
389
60
29
11,293
27,332
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
—
—
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
513,092
515,100
488,841
—
—
a!991 Estimate; Payroll and Non-Payroll Firms (includes plants that use PCE as
well as those that use other solvents.). Nonpayroll firms include 5625
below 25,000 in annual receipts and 1875 with 25,000 to,50,000 in annual
receipts (Radian, 1991a) .
Source: 1987 Census, of Service Industries, Subject Series (U.S. Department of
Commerce, 1990); Table 2-1.
TABLE 5-3. CONCENTRATION BY LARGEST DRY CLEANING FIRMS
4 Largest Firms
3 Largest. Firms
20 Largest Firms
50 Largest Firms
Percent of Industry Receipts*
2.4%
3.6%.
5.8%
9.1%
aPayroll. firms only, 1987.
Source: 1987 Census of Service Industries, Subject Series (U.S. Department of
Commerce, 1990b).
S"-T
-------
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, -he
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, L991a) are- owned., by a ingle-facility firms. Sven in the 5500K to SIM
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 $93,329 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
s-a;
-------
7ABLE 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
1.00
1.00
1..00
1.00
1.03
1.18
1.64
2.90
•7.07
22.45
Soure«: 1987 Canaus of Service Industries, Subject Sari«s (O.S. D«paronant of Contnarca,
1990b) ..
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.
S.S VERTICAL: INTEGRATION AND DT7ERSIFICATION:
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
perchloroechylene;. (PCE),- it could: be'.indirectly" and:, adversely, affected, by the,-
NESHAP if demand for PCS .diminishes after the regulation.
Ignoring for now that some dry cleaning fae-lin-i«»« also engage in
operations other, than dry cleaning, a dry cleaning firm 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
-------
dry cleaning services as inputs.. Forward 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 machinery, energy, and PCS, ail
dissimilar to dry cleaning services.
Intra-fi'rm diversification, sometimes referred to as horizontal
integration, is a potentially important dimension in firm-level impact
analysis for either or both of two reasons.
• First, a diversified firm could be indirectly as well as directly
affected by the NESHAP. For example, if a dry cleaning firm is
diversified in the manufacture of emissions control equipment (an
unlikely scenario), ic 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 mitigate 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
I
partly the reason for not including those firms at all in this rinanciax
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 (DfiB) 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
-------
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's 1989-1990 industry Norms and ifoy
gusinesa Ratios (Duns .Analytical Services, 1990) .
The D&B data base contains 991 commercial dry cleaning establishments.
Clark (1989) notes that the financial information provided to D&B 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 DSB's data base. Thus the
financial data reported therein are based on a possibly nonrepresentative
sample of firms.
Tnfiustiry Norms and Key Buainggs Rati-OS unfortunately does not
cnaracterize 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. 325,000 is only
about 7 percent, and that of-facilities with' annual receipts of 325,000 to
550,000 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 6,334 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 525,000 and $250,000, and that all
6,834 firms in above-average financial condition have annual receipts above
$100,000. ,
5-11
-------
TABLE 5-5. NUMBER 0? 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
2,531
6,323
2,370
1, 600
27,332
Baseline
Below Average
6,690
144
0
0
0
0 '
0
6,834
Financial
Average
0
4,043
2,531
2,531
4,459
f\
j
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 chere 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 ail 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. balances 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.
5-12:
-------
TABLE 5-6. NUMBER OF DRY CLEANING FIRMS, 3Y SIZE Aim 3ASELINE FINANCIAL
c^^^DITIc:•:
Receipts Range
(5000)
<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
1,673
1,047
. 645
645
1,706
718
400
6,334
Financial
Average
3,34.4
2,093
1,291
1,291
3, 411
1,434
800
13,664
Condition
Above Average
1,673
1,047
645 •
645
1,706
713
400
6,834
Source: Table 5r2 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 in 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 5100,000. 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 about $480 of total
assets for every $1,000 dollars, of sales. This ratio multiplied by the sales ~
5-13:
-------
estimate of 3367,510 yields estimated total assets of $177,257. DSB 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 nog 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." DSB 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 La. 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-aales 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 g^a^omanga include assets-to-sales of
approximately 70 percent, fixed assets-to-net worth of approximately 155
percent„ and a sacurn on. nee 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 uppor-quartile firm is not
possible. Again, constructing acQ—forma, financial statements of a firm that
yield, financial, ratios-, closely resembling, the; D&B,- upper-quartile ratio ia
5-14
-------
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,
• 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, usea, 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
-------
current or potential creditors are uneasy about the ability of che firm co
repay its debt. The debt ratio is a common measure of leverage that divides
all debt, long and shore 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 che value
of the firm to its owners, profitability-to-net worth is a measure of che
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 chat below-
average firms are only marginally able, at best, to meet current obligations
wich their cash and other current assets.
Also as expected, firms in better f-inancial. 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 capacicy
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 co financial scenario I of a posicive
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:, chan, 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 DfiB's judgment a low debt ratio is desirable. Because a main
5-16
-------
TABLE 5-7 . BASELINE FINANCIAL RATIOS OF DRY CLEANING FIRMS
Financial Condition
Below Average
Average
Above Average
Liquidity
Current ratio (times)
Activity
Fixed asset turnover
ratio (times)
0.80
2.30
1.73
5.56
5.10
7.54
Leverage
•Debt
ratio
(percent)
60
.00.
45
.90
15
.00
Profitability
profit to
profit to
profit
sales
assets
to NW
(percent)
(percent)
(percent )
1
1
3
.00
. 40.
.60
7
. 14
26
.00
.50
.80
13
32
38
.00
.50
.20
Sourc*: Duna Analytic*! S«rvic«», 1990.
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
inegn^iw and the abiitt'.y of dry cleaning firms to incur equipment and
operating costs required for compliance.* More profitable firms have more
incentive than-less profitaisle firms to comply because the annual returns ta
doing business- are greater. In the extreme, a single—facility firm earning
zero profit (price equals average variable cost) has no incsnijjza to comply
with a regulation imposing any positive cost unless it can pass along the
cleaning firms that are either unwilling or unable to comply with
the NESHAP must sell the facility, switch solvents, or discontinue their dry
cleaning operations- at the noncompliant. facility..
-------
* coat of the regulation to its customers. This same first is also less
o comply because i= 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 DS3'S
data base are only marginally profitable by all three measures. if some or
all of the estimated 6,630 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.
Hastsopouios (1991) clearly states that in maJcing 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
naw owners. Obtaining new capital from existing owners can be further
dichotomized, into internal and; external, financing;.. Usingr a; firm's- retained
5-13,
-------
earnings is equivalent to internal, equity financing. Obtaining additional
capital from the proprietor, one or more existing partners, or existing
sharer.olders constitutes external equity financing.
2ebt 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 tr.e 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
t
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
-------
the event of bankruptcy, the lenders have claim to the dissolved assets of ;.w.e
firm only after those of debt lenders.
Trade credit, loans or credit from equipment sellers, mortgage leans,
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:
where
WACC - WdMl-t) -Kd +
WACC - weighted average cost of capital
W
-------
t "• marginal effective state and federal corporation/ individual tax
rate
Kd -- 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
this analysis is his insistence that bank loans are not made to firms ar any
£031. unless expectations, are high that they. well, be repaid, according- to -he-
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..S: 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 before-tax debt costs are computed and employed:
• beat applicants: 7 percent
• typical health applicants: 7.5 percent
5-21
-------
» below-average bur still-sound applicants: 3.5 percent
Because debt interest, is deductible for state and federal income tax
purposes, the cost of debt has to be adjusted downward. An approximate
affective 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.
Jonas (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 SfiP 500 nominal equity yield is about 13 percent, which is an
estimate of the average cost of equity for all publicly traded stocks (Van
Home, 1980) .
Jones indicates that still another risJe 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,
-------
. Adding these dry cleaning firm equity risk premiums ana simultaneously
subtracting inflation premiums result in the following set cf real equity
;osts 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 15 percent for firms with Moody Bond Ratings of AAA. (the highest rating;,
3BB, and BB, respectively.
weighting the debt and. equity cost components is difficult for several
reasons.' First, market value weights are more theoretically correct than OGOK
value weights, but only the latter are observable for privately owned dry
cleaning firms (Bowlin, Martin, and Scott, 1990) . Second, target weights, r.c-t
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
-------
\
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.
-------
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 PCS 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 PCS emissions from dry-to-dry and
uncontrolled; transfer; machines-; by 95; percent*- (compared; to- uncontrolled:,
levels) . Transfer machines; with an RC in placa; ara 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 PCS emissions (compared to uncontrolled levels) .
6-1
-------
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
dry-to-dry
transfer
CA
CA
RC
CA
RC
CA
CA
RC
CA
no no
additional additional
control control
required required
CA
CA
RC
CA
CA
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'3 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
che-. firm- level, is: described:' in;- Section- 6.2.. racility-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
-------
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 6-1.1
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 costs
associated; with.- those, solvents;..
6-3
-------
:iose
Yea
Keep facility Sell facility
Yes
Operate
£ •
R '
G"
expected
periodic revenues (Price x Quantity)
periodic, costs, (variable, cost, plus* periodic.:,
repayment of principal and return on investment)
Figure 5-1. Responses to the Proposed Regulation
S-4
-------
If the expected costs of operating the complying facility exceed the
expected revenues, the owner of the facility closes 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 keep 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 economic
reasons, the owners will likely close the facility. These reasons might
include operating:coats that exceed projections, revenues that fall short of
projections, or both. If costs exceed revenues for -financial 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 onea—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, that facility ...
6-5
-------
5.3 FACILITY-LEVEL RESPONSES
The facility wich an uncontrolled PCE machine must either comply with
-he 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.
S . 3 . 1 Compliance Potion 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
i are., used to- compute: the:. NPC.' of: aach: control-, option:-
Control Option 1: Carbon Adsorber
n-1
-K 2- [OCA / a +• r)e-]
t-0
(6.1)
5-6
-------
Control Option 2: Refrigerated Condenser
"••1
NPCRC - KRC -r 2 [ORC / (1 + r) c ] if n'< 7
(6.2)
or;
n-l
KRC + 2 [oRC / (1 +' r)cj. + C(KRC / (1 + r)7)] if. n > 7
=-0
Control Option 3: Accelerated Purchase of New Dry-to-Dry Machine
14
NPG0D » KDD + 2 [ORC / (1 + r)t]- -
14
r)a] + 2 [oRC
r) =
(6.3)
c»n
KRC
where
" the net present cost of a CA
m the net present cost of an RC
"' the net present cost of accelerating the purchase of a new dry-
to-dry machine
* the capital cost of a CA
- the capital cost of an RC
™ the capital cost of a new dry-to-dry machine
"*' ther. incremental, operating, cost; of. a, CA.,
- 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-tq-dry- machine. Facility owners, replace
°CA.-
2This coat of capital differs by firm financial status. The discount
factor estimated for this analysis is 11 percent for firms in good financial
condition, 12.5 percent, for firms in average condition, and IS.4 percent for
firms,, in; poor: condition-., rot a>. more complete- discussion,: see Section 5.
6-T
-------
existing machines with new dry-to-dry machines equipped with 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 have 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
• Facility owners^ evaluate- the- cost of, the- control, options•• using a,
real, after-tax weighted average cost of capital (WACO, 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 ara given below:
3The mathematics of the cost formula require the notation of years 0-14,
where year 0 is the first year..
6-8
-------
Status
poor
average-
good.
WACC
IS.4%
12.5%
11.0%
Share of
Facilities
25%.
50%
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.
• Control devices purchased for existing machines in the commercial and
industrial 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- >
in controls.
• 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
Opt- ions rTnrter r.xr-h Regulators .Alternative
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- 'n1 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 far the'coin-
operated sector (see Equation (6.1)). The only difference is chat the age of
I 4
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 thair- 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-co-dry machines,. the> choices; are the; aame^ as; outlined: abovev and: the- cose-
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]) .
Tor 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 botsh. 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 co
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.
• The facility uses a solvent other 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-r
-------
The four size cutoffs are based on PCE consumption levels chat
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. Tor the same level
of annual receipts, the transfer machines consume more PCE than the
corresponding dry-to-dry machines. This difference o'ccurs 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: ther. 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 330,000 in annual receipts, it is estimated that no
facilities receive more than this amount from dry cleaning activities alone.
7-2
-------
TABLE 7-1. SIZE C'JTOFF LEVELS EASED ON CONSUMPTION OF PERCHLOROETKYLENE (PCE)
Size
Cutoff
None
i
2
3
4
Annual. Receipts from
Dry Cleaning
Activities"
($/yr)
N/A
25,000
50,000
75,000
100,000
Consumption of PCS by Machine
•Technology0 (kg/yr)
Dry-to-Dry
0
300
600
900
1,200
Transfer
0
400
300
1,200
1, 500
^Annual receipts are computed using a base price of 31.65-per kg of clothes
cleaned for- the coin-operated (self-service) sector, S6.34 per kg for che
coin-operated (plant-operated) and commercial sectors, and $2.00 per kg for
the industrial sector. These values refer to receipts from dry cleaning
activities only.
bThe consumption factor for dry-to-dry machines is 0.081 kg PCE per kg of
clothes cleaned. The consumption factor-for transfer machines is 0.115 kg
PCE' per kg clothes cleaned (Radian, 1990b).
Source:
Radian, 1991c.
7-3:
-------
TABLE 7-2. DISTRIBUTION OF AFFECTED" FACILITIES BY INDUSTRY SECTOR, MODEL
MARKET, AND SIZE CUTOFF: REGULATORY ALTERNATIVES I AND
Industry Sector
and Model Market
Total
Number of
Facilities
Number Affected Facilities
None
1
2
by Size
3
Cu--:f
4
Cain— Operate ecia
Self-Service
Plant -Operated
Total
Market
Market
Market
Market
Market
Market
Total
Tnrtnnrnn
,1 C
A
B
C
D
2
F
lid ,
2,
3,
1,
1,
1,
10,
8,
. 7,
30,
213
831
044
543
606
157
432
073
633
494
325
200
1,415
1,615
0
1,606
0
287
4,038
4,298
10,229
65
49
0
49
0
0
.0
214
3,000
3,193
6,407
65
0
0
0
0
0
0
146 .
2,055
2,187
4,388
65
0
0
0
0
0
0
115
1,621
1,725
3,461
65
0
0
0
0
0
0
38
1,250
1,330
2, 668
65
*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.
°The number of affected facilities under each size cutoff is based on the
share of facilities ac each income level, (see- Table 2-13), che average
annual output at each income level (see Table 2-7), and solvent consumption
factors (Radian, 1990b).
°The number of affected facilities under each size cutoff is based on the
total number of potentially affected facilities in each Model Marker (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, A:;D SIZE CUTOFF-. REGULATORY ALTERNATIVE iiia
Industry Sector
and Model Market
C s in-Gpe r mr seia
Self-Service
Plant -Operated
Total
Market A
Market B
Market C
Market • . D
Market E
Market F
Total
Tndust;riald
Total
number of •
Facilities
213
2,831
3,044
1,445
1,704
1,045 •
10,547
8,074
7,679
30,494
325
Number Affected Facilities
None
200
1,415
1, 615
0
1,704
0 •
1,394'
4,431
4,630
12,159
65
1
49
0
49 .
0
0
0
1,187
3,379
3,521
8,087
65
2
0
0.
0
0
o.
0
978
2,373
2 , 4.62
5,813
65
by Size
3
0
0
0
0
0
0
819
1,890
1,958
4, 667
65
Cutoff
4
0
0 .
0
0
0-
0
637
1,459
1,512
3,608
65
aSize cutoff levels are based on baseline consumption of perchloroethylene
(PCS). 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,..
Source:. Radian, 1991c..
-------
rABLE 7-4. DISTRIBUTION OF AFFECTED OUTPUT BY INDUSTRY SECTOR, MODEL MARKET,
AND SIZE CUTOFF: REGULATORY ALTERNATIVES I AND
Industry Sector
and Model Market
Co in— Opera-Red
Self-Service
Plant -Operated
Total
Commereial
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) °
None
535
985
3,520
0
3,819
0
4,750
67,141
71,447
147,157
34,130
1
220
0
220
0
0
0
4,576
64,673
68,320
133,068
34,180
2
0
0
. 0
. o
0
0
4,206
59,536
63,351
127,093
34,180
3
0
0
0
Q
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 average 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 che type of dry cleaning machine used. See Table "7-1
for description of cutoff levels.
-------
7ABLE' 7-5. DISTRIBUTION OF AFFECTED OUTPUT BY INDUSTRY SECTOR, MODEL MARKET,
. AND SIZE CUTOFF: REGULATORY ALTERNATIVE IIIa
Industry Sector
and Model Market
Ca in-Operated.0
Self-Service
P lant -Operated
Total
Commercial'3
Market A
Market S
Market C
Market D
Market Z
Market F
Total
Industrial0
Total
Output
(Mg/yr)
511
3,891
4,468
13,222
4,052
• 22,595
229,516
156,068
146,730
571,949
170,902
Total Affected Output by
Size Cutoff (Mg/yr) °
None
535
985
1,520
0
4,052
0
31,320
77,223
80,185
192,780
34,180
1
220
0
220
0
0
0
30,828
74,721
77,547
133,097
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
aTotal output and affected output values computed us'ing average 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 7-3.
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-.
7-7"
-------
The number of affected facilities represents about 53 percent of ail
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. In 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, foir» five8.- levels, oz;
output based on the- corresponding range of annual, receipts given below:
Annual Receipt; a Range
SO.'to: 25 thousand
$25 to SO thousand
$50 to 75 thousand
$75 to 100 thousand
Over $100: thousand
1.
2
3
4-
5'
7-a-
-------
TABLE 7-6. MODEL PLANT CAPITAL AND OPERATING COMPLIANCE COSTS FCR CARBON
ADSORBER CONTROLS (51989)d
Industry
Sector- and
Model'
Plant Number
Coin— Opg raged
1
2
Commercial
3
4
5
6
7
8
9
10
11
12
Industrial
13
14
15
CA
Capital
Costs ($)
3,601
2,540
6,760
5,760
c',760
6,976
6,760
6,760
6,976
6,760
6,760
-6,976
9,980
9,980
9,980
aNegative values indicate
^Output levels
1 under $25
2 $25 to $50
3 $50 to $75
correspond
thousand
thousand
thousand •
CA Operating
1
6,492
2,710
-
2,887
2,886
2,386
2,895
2,386
2,886
2,395
2,886
2,386
2,895
2,992
2,992
2,992
cost savings
2
6,466
2,703
2,827
2,827
2,827
2,835
2,826
2,326
2,835
2,326
2,326
2,834
2,922
2,922
2,922
Costs by Output
3
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
level (3/yr)-
4
6, 406
2, 688
2, 689
2, 688
2, 687
2, 596
2,686
2, 686
2, 695
2, 686
2,685
2, 693
2,747
2,747
2,747
5
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
due to reduced solvent consumption.
to average annual
receipts ranges
below:
4 $75 to $100 thousand
5 over1. 51' 00.-
thousand;.
Source: Radian, 1990a;.
7-9
-------
TABLE 7-7. MODEL PLANT CAPITAL AND OPERATING COMPLIANCE COSTS FOR
REFRIGERATED CONDENSOR CONTROLS IN THE COMMERCIAL SECTOR ($1989)d
RC
Plant Number Costs ($)
3 6,283-
4 6,283
5 6,283
6 8,424
7 6',283
8 6,283
9 8,424
10 . 6,283
11 ' • 8,675
12 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
aNegative values indicate cost savings due to
Add-on RC control devices are not built for
in the coin-operated and
bOutput levels correspond
1 under $25 thousand
2 $25 to $50 thousand
3 $50 to $75 thousand
4 $75 to $100 thousand
5 over $100 thousand
industrial
to average
sectors .
3
• 169
166
165
250
163
162
248
161
254
340
reduced
the size
4
103
100
98
183
95
93
179
92
184
270
5
-413
-423
-430
-345
-440
-444
' -358
-449
' -363
-278
solvent consumption.
machines typically used
annual receipts ranges below:
Source: Radian, 1990a.
7-10,
-------
tfote chat 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. :.*egative 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 31,500 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
macnines 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 annualizeci
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, ecruipment, are--, annuaiized,, 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 dfl_aot have built-in control devices.
Capital costs- are annualized over the life of. the CA (15 years) in the coin-
operated: sector.. For- the-purposes;- of."this; analysis,-it; is1, 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 (S1989)a
Industry Sector and
Model Plant Number
Coin— Operared
1
2
Commereial
3
4
5
6
7
8
9
10
11
12
13
14
15
Output Level0
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
5,364
5,864
5
7,462 .
3, 173
1,568
1,577
1,538
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 cleaning1macnine capital costs found in Table 2-riO.
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) .
"Output 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)a
Industry Sector and
Model Plant Number
Co •> n— Operate"!
1
2
3
4
5
6
7
8
9
'10
11.
12
Industrial
13
• 14
15
Output Level'3
1
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
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
dAnnualized coats 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 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).
°Outputv levelss correspond: tor averages 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:
7-13;
-------
using a real, after-tax weighted average cost of capital (WACO, 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 coses 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; bet 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:
-------
TABLE 7-10. MARKET ADJUSTMENTS COMPUTED FOR EACH SECTOR AND MODEL MARKET IN
THE DRY CLEANING INDUSTRY
Sector
Coin-Operated
Commercial
Commercial
Commercial
Commercial
Commercial
Commercial
Industrial
Model Market
A
• a
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
?,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. Price and Output; Ad"m a Emeriti
Economic impacts are quantified through estimated market adjustments in
price and output for the coin-operated sector and Model Markets E and F in the
commercial, sector. Figures 7-l_ depicts; the,- supply/demand;- relationship for a
representative market area in these sectors, Pre-regulatory equilibrium.
occurs at an output level of Qi and a price of PI per unit (kilogram) of
output. The supply curve (Si) is upward sloping with an elasticity of "£" 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- Si. to 32 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
-------
S/Q
Q/t
Figure 7-1. Price and Output Adjustments Due to a Market Supply Shift
these controls normally affect only supply-aide 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> ^s n°t
obvious from the' diagram, buc~ it can, be. computed, if. baseline, price and output:
values (Pi, Qi) , the demand elasticity CH), the supply elasticity (S), and the
supply shift parameter (1) are known. First, rewrite the inverse
supply/demand system in functional form as illustrated below:
)V
(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-lfi.
-------
E(P)
E(QS)
*-3f
(7.3)
E(P)
~ E(Qd),
(7.4)
where. £(•) - 3lrfi<«), r\ - 9Ln(Q
-------
Q2 -
}.
.(7.9)
All variables and parameters on the right hand side of Eqs. (7.8) and (7.S)
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 sectfeor 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
imnacts 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 7-13. The total
reduction in output for the commercial sector is from Model Markets E and F.
Cable 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- or: affected:, facilities-- and:, the-- shares 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.
-------
TABLE 7-11. PRICE ADJUSTMENTS FOR EACH SECTOR OF THE DRY CLEANING INDUSTRY BY
REGULATORY ALTERNATIVE, AND SIZE CUTOFF
Industry Sector
and Regulatory Baseline Price
Alt emative ($ / kg)
Size Cuto£fa
(Percent Chance from Baseline)
None 123
Coin—
Reg I, II, &
1.65
96.32
23.50
Coin—Operated
(olanr -operated^
Reg I, II, &
III'0
Commercial
Reg Ib
Reg II
Reg III
Tnduat-ria 1
•Reg I, II, &
6.34
6.34
6.34
6.34
2.00
1.07
c
c.
c
c
c
c
c
c
c
c
c
c
c
c
c
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.
bRegulatory Alternatives I, II, and III are identical for the Coin-Operated
and- Industrial Sectors.
cSee Table 7-12 for estimates of price adjustments- for the Commercial Sector.
dSecause unaffected facilities dominate the industry and dry cleaning accounts
for less than 8% of total output for the industry (including garments
cleaned in water), the Industrial sector will likely not adjust prices in
response to the alternatives.
T-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
Baseline
Price
' (S/kg)
Size Cutoff0
(oercentacre chancre from baseline)
None
1
2
3
4
Reg r
Market
Market
• Market
Market
Market
Market
A
B
C
D
E
F
6
6
6
6
6
. . 6
.34
.34
.34
.34
.34
.34
0
0
0
0
0.68
0.77
0
0
0
0
0.52
0.60
0
0
0
0
0.38
0.43
0
0
0
0
0.32
0.36 :
0
0
0
0
0.
0.
26
30
Re? IT
Market
Market
Market
Market
Market
Market
Reer TT
Market
Market
Market
Market
Market.
Market
A
B
C
D
E
F
T
A
B
C
D
£',
F
6
6
6
6
6
6
6
6
6
6
S
6
.34
.34
.34
.34
.34
.34
.34
.34
.34
.34
.34,
.34
0
0
0
0
0.85
0.96
0
0
0
0
0.98,
1.07
0
0
0
0
0.65
0.74
0
0
0
0
0.73.
0.35
0
0
0
0
0.47
0.53
0
0
0
0
0.58
0'. 63
0
0
0
0
0.40
0.45
0
0
0
0
0.49;
0.54
0
0
0
0
0.
0.
0
0
0
0
0.
0.
33
37
41
45
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.
bSize cutoff levels are based on baseline consumption of perchloroethylene
(PCS). 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 cucoff levels.
7-20
-------
TABLE 7-13. OUTPUT ADJUSTMENTS FOR EACH SECTOR OF THE DRY CLEANING INDUSTRY BY
REGULATORY, ALTERNATIVE AND SIZE CUTOFF*
• Industry Sector
and Regulatory
Alternative
Baseline
Output*
(Mg/yr)
Size Cutoff*
(Percentage Change from Baseline)
None .1 2 3
Co in— Opera tied
f self — s
Reg I, II, &
Coin—Operaged
(plant—nperared)
Reg I, II, &
577
3,891
-83.01 -25.52
-1.17
Commercial
Reg I
Reg II
Reg III
Industrial
Reg I, II, & •
571,
571,
571,
170,
949
949
949
902.
-0
-0
-0
0
.42
.52
.59
-0
-0
-0
0
.32.
.40
.47
-0
-0
-0
0
.23
.29
.35
-0
-0
-0
0
.19
.24
.29
-0.16
-0.20
-0.24
0
aTotal output includes output from facilities that use PCE and facilities that
use other solvents.
bSize cutoff levels are based on baseline consumption of perchloroethyiene
(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.
7-21
-------
TABLE 7-14. OUTPUT ADJUSTMENTS FOR MODEL MARKETS IN THE COMMERCIAL SECTOR BY
REGULATORY ALTERNATIVE AND SIZE CUTOFF*
Model Market
and Regulatory
Alternative
Re-cr I
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg Ic
Reg- TT
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg IIC
Recr TTT
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
3,319
25,476
227,709
155,823
145,898
571,949
13,222
4,052
22,595
229,515
146,730
156,068
571,949
Size Cutoff^
(percentace chancre from baseline)
None
0
0
'o
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
a 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.
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.
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 fcr 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 tc increase 0.93
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 1.0-6 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: ablevto 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 $€.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 -he r.ange
in social welfare that it generates. Welfare impacts often extenc. to ny
individuals and industries in an-economy. However, estimating ths--we. rs
impacts beyond the directly affected markets is generally cost-r:.-3hibi::.ve
because the resource costs of such a task may exceed the value zz the indirect
welfare effects that are measured.
Producer welfare impacts result from increased costs of production chat
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 a 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 Qj.
Assuming the cost-effective candidate NESHAP control increases the weighted
average unit production, costs;, in.-this: market,., ther 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 (P2r Q2)• The welfare changes attributable to the-candidate
NESHAP controls can be computed directly from Figure 7-2.
7-24
-------
J
S/Q
Q/t
Figure 7-2. Welfare Change Estimation
In a market environment, typically- consumers and producers of the good
or service derive welfare from a market transaction. The difference between
the maximum price consumers are willing to pay for a good or service and the
price they actually pay is referred to as consumer surplus. Consumer surplus
is measured as the area under the demand curve and above the price of the
product. Alternatively, producers derive a surplus from a market transaction
if the product price is above; the-. average, variable cost of production.
Producer- surplus is measured as 'the area above the supply curve and below che
market price. • • . •
The downward sloping industry demand curve above the baseline price of
P! in Figure, 7-2, indicates^ a.-positive; consumer surplus.. It is also evident
that' consumers-1 lose* some- of that- surplus- when .the-market- price- increases* from-.
?1: to P2. Specifically, the-loss in consumer surplus- is the sum of areas- A +
B + C, or the area'under the demand curve and between the equilibrium prices.
The- slope- and, position,: of' ther-market. supply curve indicates that, producers- are
also receiving a surplus at the baseline price. NESHAP 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 + D in 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
$2T4, 000 is predicted.
7-26
-------
rABLE 7-15. CONSUMER WELFARE IMPACTS FOR EACH SECTOR OF THE DRY CLEANING
INDUSTRY BY REGULATORY ALTERNATIVE AND SIZE CUTOFF ($ THOUSANDS)
Industry Sector
and. Regulatory
Alternative
Coin— <
Reg- I, II, &
JJone
-537
Size Cutoff0
-195
Cain—
f plant—
Reg I, II, &
'ill0
-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
Reg I, II,
Values are expressed in 1989 dollars and rounded to 3 significant digits.
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.
°SfpZ*_.CUtolf- lavei^ are, based^: on, baseline, consumption of perchloroerhvlene
(PCS) . 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
tor description of cutoff levels.
f°=
Coin-Operated
7-2T
-------
TABLE 7-16. CONSUMER WELFARE IMPACTS FOR MODEL MARKETS IN THE COMMERCIAL
SECTOR BY REGULATORY ALTERNATIVE AND SIZE CUTOFF (S THOUSANDS')'
Model'Market
and Regulatory
Size Cutoff"
Alternative
Rety I
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg I
Ra
-------
TABLE 7-17. PRODUCER WELFARE IMPACTS FOR EACH SECTOR OF THE DRY CLEANING
INDUSTRY BY REGULATORY. ALTERNATIVE AND SIZE CUTOFF (5 THOUSANDS)"3
Industry Sector
and Regulatory Size Cutoff"
Alternative None I 2 .
3 4
Coin— One rar ad
< self — sarvifie)
Reg I, II, &
-1,140
-193
' Co in— Operated.
(olant -oneraf aci>
Reg I, II, &
Commercial
Reg I
Reg II
Reg III
Tnduafria1
Reg I, II, 4
-4,320
-15,000
-19,800
-28,070
274
-8,110
-10,100
•17,300
274
-5,850
-7,230
-13,600
274
-4,900
-6,150
-11,800
274
-4,040
-5,070
-9,810
274
aValues are expressed in 1989 dollars and rounded to 3 significant digits.
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.
°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.
cRegulatory Alternatives I, II, and III are identical for the Coin-operated
and Industrial Sectors. .--.--
7-29':
-------
TABLE 7-18. PRODUCER WELFARE IMPACTS FOR MODEL MARKETS IN THE CC:-!MERCIAL
SECTOR BY REGULATORY ALTERNATIVE AND SIZE CUTOFF (S THOUSANDS)=
Model Market
Size Cutoffa
rf __ — _ _ ^
Alternative
Rgqr T
Market A
Market B
Market C
Market D
' Market E
Market F
Total Reg I
R«
-------
TABLE 7-19. KST WELFARE' IMPACTS FOR EACH SECTOR OF THE DRY CLEANING INDUSTRY
' 3Y REGULATORY ALTERNATIVE AND SIZE CUTOFF ($ .THOUSANDS)3
Industry Sector
and. Regulatory
Alternative
Size Cutoffb
None
Reg I, II, a
-1,670
-388
( plane -ope rar.ed.)
Reg I, II, a
Reg I
Reg II
Reg III
Reg I, II, &
-4,580
-29,000
-37,000
-47,600
274
-18,800
-23,400
-32,900
274
-13,600
•16,700
-25,100
274
11,400
14,200
21,600
274
'-9,360
-11,700
-18,000
274'
aValues are expressed in 1989 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.
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,...
7-31
-------
TABLE 7-20. NET WELFARE IMPACTS FOR MODEL MARKETS IN THE COMMERCIAL SECTOR BY
REGULATORY ALTERNATIVE AND SIZE CUTOFF (5 THOUSANDS)a
Model Market
Size Cutoff13
J ^
Alternative
Market A
Market B
Market C
Market D
Market E
Market F
Total Reg I
Re<7 IT
Market A
Market B
Market C
Market D
Market S
Market F
Total Reg II
Reg TTT
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
•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 co-3 significant" digits-. Details' may- noc. sum. ro 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.
bSize cutoff levels are based on baseline consumption of perchloroethylene
(PCE). The cutoff levels correspond to target levels of annual receipts and
di£farr depending: on: the- type1; of? dry- cleaning1 machine, used... See,- Table? 7-1.
for description of cutoff levels.
7-32,
-------
Aggregating the. welfare effects from each sector leads to an industry
estimate of the regulatory cost. The total industry welfare cost is estimated
to be $43,250,000 under Regulatory Alternative II with no size cutoff.
Consumers of dry cleaning services are projected to lose a relatively smaller
portion of their welfare (518,000,000) than producers ($30,000,000). With a
size cutoff corresponding to $100,000 in annual receipts (cutoff 4) welfare
impacts are considerably lower. Producers lose an.estimated $4,800,000 and
consumers lose $6,680,000 for a net welfare loss of $11,400,000.
7.3.3 PLantL Closures
To comply with a regulatory standard, facilities will normally incur
control costs and may have to reduce production levels, modify production
processes, or, as a last resort, shut down: In the short run, the decision to
shut down depends on the relationship between- the price of the service and the
average variable cost of production. The position of the average variable
cost curve is difficult to estimate without the aid of detailed financial data
including input'prices. As a result,, this section offers qualitative impacts
based on output adjustments for each sector. Closures measured in this way
provide an estimate of plant closures that is net of new plants entering the
market. In othe^: words, if the regulatory alternative results in 10 plant
closures and 7 plant start-ups, the value estimated in this analysis
corresponds to 3 net plant closures. Although this may tend to underestimate
the total number of plants closing, two additional assumptions have the effect
of;, making the-; estimates: worstr-case. in; terms;, of,, net. closures.. First, . it is
assumed that facilities do not reduce capacity utilization, but rather, the
entire output reduction is accounted for by facilities shutting down. In
addition, it is assumed that the smallest plants affected account for all the
plant, closures.
•Tables 7-21 and 7-22 show the number of facilities in each sector and
model market that would shut down in net if. the entire output reduction was
accounted for by the smallest facilities•• leaving; the industry. Net plant
closures will not likely reach these levels, but for policy evaluation this
worst-case analysis of net closures is 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 CUTOFF*
Industry Sector
and Regulatory
Alternative
None
Size Cutoff0
Co in-Qoe raced.
(self—service)
Reg I, II, &
Coin—Operated.
fplant—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
Reg I, II, &
aPrejected net closures are computed by dividing the estimated change in
output (Table 7-13) measured in leg per year by the minimum size affected
plant. Values reflect the assumption that plants do not reduce capacity
utilization.
'-'Size cutoff levels are based on baseline consumption of perchloroethylene
(PCS). 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.
°Regulatory Alternatives I, II, and III are identical for the Coin-Operated
and Industrial Sectors. .
7-34"
-------
ABLE 7-22. PROJECTED WORST-CASE NET PLANT CLOSURES IN EACH MODEL MARKET OF
THE COMMERCIAL SECTOR BX REGULATORY ALTERNATIVE AND SIZE CUTOFF*
Model Market
and Regulatory
Size Cutoff"
Alternative
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 ITT
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
0
0
0
204
217
421
0
0
0
0
243
250
493
2
0 -
0
0
0
71
76
147
0
0
0
0
88
94
182
0
0
0
0
109
112
221
3
0
0
0
0
43
45
38
0
0
0
0
53
57
110-
0
0
0
0
67
68
. 135
4
0
0
0
0
11
12
23
0
0
0
0
14
14
28
0
0
0
0
17
17
34
a?rojected--:net*; closures- are-.computed: by dividing- the* estimated', change- in.
output (Table 7-14) 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
(PCS) ., 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.
7-35-
-------
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 faciii._es in
this sector total 163 with no-size cutoff. This represents about 6 cei.ent of
the plant-operated facilities in the coin-operated sector. Because ary
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,A89 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.
7-3fi,
-------
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
(SICs): 7215 (Coin-operated laundries and dry cleaning), 7216 (Dry cleaning
plants, except rug cleaning), and 7213 (Industrial launderers). Nearly ail
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 .
Displacements. For reasons discussed in Section 4,' the NESHA?
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
MESHAP 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- cose; 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? thatr short-run, employment impacts', of. regulatory alternatives^
ara, proportional, to: pro jec ted., output affect's-;... An, estimated,: 1.76, 836. workers
7-38
-------
are on payroll at commercial dry cleaning plants in 1991. J- The worker
displacements of the three Regulatory Alternatives at various size cutoffs
iinplied by the methodology and assumptions are presented in Table 7-23.
( •
TABLE 7-23. PROJECTED WORKER DISPLACEMENTS*
Regulatory
Alternative
Size Cutoff
None
4
I
II
III
743
920
1,043
566
707
831
40?
513
619 '
336
424
513
283
354
424
Commercial dry cleaning sector, payroll employees only, assuming 1991
baseline employment of 176,836 workers and short-run output reductions from
Table 7-13.
piaii
*. Displaced workers suffer welfare lasses
through several mechanisms (see Hamermash, 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 markets (Abowd and Ashenfelter,
1981),., Abowtlv 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
1There were 163,369 payroll workers in the commercial sector in 1987
(U.S. Department of Commerce, 1990b). The 1991 estimate is computed based on
the 1987 value and a 2 percent annual growth rate (see Table 2-9).
7-39V.
-------
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 nag 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 311,504 (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,-to1 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 dsy cleaning facilities
7-40
-------
TABLE 7-24. PROJECTED WORKER DISPLACEMENT COSTS (5 MILLIONS)
Regulatory
Alternative
I
II
III
' . Size Cutoff
None 1 2
21..4 16.3 11.7
26.5 ' 20.4 14.8
30.0 23.9 17.8
3 4
9 ..7* 8.2 •
12.2 10.2
14.8 12.2
Commercial dry cleaning sector, payroll employees only,' assuming projected
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 chis 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 facilities would be affected by
the regulatory alternatives being considered. Within the commercial dry
cleaning;-sector-itself >, facilities-: that.:, use? solvents- other; chan PCS and.PCE
facilities that are, already in 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 faeilj.fri<»« 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 **•feezed 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
:nore 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 3100,000 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
-------
rABLE 7-25. NUMBER OF AFFECTED DRY CLEANING FIRMS BY SIZE AND BASELINE
FINANCIAL CONDITION, FINANCIAL SCENARIO I—REGULATORY ALTERNATIVES
I AND II
Receipts Range
(5000)
<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
. 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
"Number 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 7-2).
bAssumes 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.
7-43:1
-------
lABLE 7-26. NUMBER OF AFFECTED DRY CLEANING FIRMS BY SIZE AND BASELINE
FINANCIAL CONDITION, FINANCIAL SCENARIO I—REGULATORY ALTrSNATIVE
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
^Number 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 IK 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
17,0.
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
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
-------
'•ABLE 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
349
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
-------
TABLE 7-29. INSTALLED' PRICE OF CONTROL EQUIPMENT AND ANNUAL OPERATING COST, BY
REGULATORY ALTERNATIVE AND SIZE OF FIRM*
Receipts Range
($000)
<25
25-50
50-75
' 75-100
>100,
Regulatory
Alternative
. r
ii
in
i
ii
in
i
ii
in
r
ii
in
i1
ii
in
Equipment Price
($)
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
aAll costs are weighted-averages across affected facilities and firms. Costs
are computed using the distribution of facilities and firms reported in.
Tables 7-2, 7-3, and 7-25 through 7-28 and the costs reported in Tables 7-6
and 7-7.
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
-------
r bring it into compliance
greater than the scrap value, the owner will
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 $100,000 annual receipts, so do the note
payments . Note payments for firms in average and above-average financial
condition are' very similar because the interest rates are within one-half
percent of one another. Even though lenders are assumed to view firms in
below-average- financial condition as much, riskier- than; those in. average-
financial condition, they are assumed to view above-average firms as only
slightly less risky than average firms.
Firms that purchase control devices with cash have high initial cash
outlays but low. recurring-, annual, expenses., Firms-, that.. purchase.- control
devices with borrowed funds have low initial cash outlays but higher recurring
annual expenses. The initial cash outlays and recurring annual expenses
incurred by firms of different types and sizes are- presented in Table 7-31.
As described above, firms in average and above— average financial condition can
borrow funds and thus don't have to use cash to purchase control equipment.
Their recurring annual expenses, however, include interest and principal
payments on sevenryear/ notes? in-- addition; to annual, operating', costs-.. Firms in:
7-48
-------
TABLE 7-30. ANNUAL PRINCIPAL AND INTEREST PAYMENTS ON A SEVEN-YEAR NOTE 3Y
REGULATORY ALTERNATIVE, FIRM SIZE, AND INTEREST RATE ($)a
Regulatory Alternative
<525,000 Annual Receipts
11.0% note
11.5% note
525,000-50,000 annual receipts
11.0% note -
11^5% note
$50, 000-S75, 000 annual receipts
11.0% note
11.5% note ,
$75,000-3100,000 annual receipts
11.0% note
11.5% note
>$100,000 annual receipts
11.0% note
11.5% note
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
aS'even-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,
-------
r\BLE 7-31. INITIAL CASH OUTLAY REQUIREMENT* AND RECURRING ANNUAL EXPENSES0 3VT
FIRM SIZE, FINANCIAL CONDITION, AND REGULATORY ALTERNATIVE (3)
Firm Financial Condition
Receipts
Range
(SOOO)
<25
25-50
30-75
75-100
>100
Regulatory
Altern-
atives
T
II
III
T
II \
III
•I
' II
III
I
II
III
I
II
III
Below Average
Cash
Outlay
7,515
6,682
6,701
7,302
6,613
6,651
6,304
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
1,847
2,897
3,015
1, 653 '•
2,189
2,533
1,719
2,560
2,920
3,467
5,087
6,039
Abov-
Cash
c
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Average
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 ecpaipment 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 forma 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 sama 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 basa adequate cash to purchase control devices, their failures will
be caused by capital availability constraints (see discussion,below). The
iiabilitiss:: side •• or the" balance* aheec is- unax'Sacred' because"the- firms entar
into no new legal obligations.
The following adjustments are made to statements of firms of all sires
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: (se« 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 (prica) 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-sr.
-------
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) i*.«a the amount of principal payable this year (which is part of the
increase in notes payable). Because the assets of the firm ha--a increased by
the value (price) of the control equipment but the liabilities have increased
by that amount rlug 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 BEO.
forma statements in Appendix A are presented in Tables 7-33 through 7-37.
Financial ratio impacts on firms with annual receipts below 325,000 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-averaga firms.- is. unaffactad because, ciiey 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:, sizas; 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 coat of purchasing and operating control equipment is about the
same for most firms under 5100,000", the financial impacts are greater for the
smaller- firms-..
7-52
-------
TABLE 7-32. KEY FINANCIAL RATIOS
LIQUIDITY Cur-rant Ratio? total current assets divided by total current
liabilities. Measures the degree to which current
liabilities—legal obligations coining due within the
• year—are covered by current assets—assets that can be
readily converted into cash. Post-compliance ratios
'significantly below 0.8—the lower quartile
-------
r
TABLE 7-33. BASELINE AND AFFECTED FINANCIAL RATIOS:
-------
TABLE 7-34. BASELINE AND AFFECTED FINANCIAL RATIOS:
RECEIPTS*
525,000-50,000 FIRM
Baseline Financial Condition
Below Average Average Above Average
Liquidity
current ratio (times)
Baseline 0.80
RA I -0.24
RA II -0.14
RA III ' -0.14
Activity
fixed asset turnover ratio
(times)
Baseline 2.30
RA I 1.63
• RA II 1.67
RA III ' ' 1.67
Leverage • .
debt ratio (percent)
Baseline 60
RA I 60
RA II 60
RA III 60
Profitability
profit to sales (percent)
1.73
1.26
1.29
1.29
5.56
2.78
2.92
2.91
46
64
62
63
5.10
2.09
2.21
2.21
7.54
3.20
3.38
3.37
15
45
43
43
Baseline
RA. I,
RA II
RA III
profit to assets (percent)
Baseline;
RA- r
RA II
RA III
Profit to net-worth
(percent)
Baseline
RAV I'.
RA". II'
RA, III '
1.0
0.3
-2.6
-2.9
1..4..
0.5~
-3.3
-4.1
3.6
1..2,
-9 . 4
-10.. 4-
7.0
2.4
-0.1
-0.4
14.5..
3.7
-0.2
-0.7
26.3
10 -.2:
-0.6-
-1.8 '
13.0
8.5
5.9
5.6
32.5,
14.7"
10.5
10.0
38.2
26. S-
18.4
17.5
^Baseline- ratios->• are computed using data from Duns Analytical Services (1990)
Ratios under- aach: Regulatory Alternative' ara» computed using, coat, data, in,
Table 7-31 and data from Duns Analytical Services (1990).
7r-55.--
-------
:ABLE 7-35. BASELINE AND AFFECTED FINANCIAL RATIOS:
RECEIPTS*
550,000-75,000 FIRM
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
.31
.38
.86
Activity
fixed asset turnover ratio
(times)
Baseline 2.30
RA I , 1.87
RA II ' 1.89
RA III 1.88
Leverage
debt ratio (percent)
Baseline ' €0
RA I 60
RA II 60
RA III 60
Profitability
profit to sales (percent)
5.56
3.55
3.62
3.60
46
57
57
57
7.54
4.27
4.37
4.34
15
34
34
34
Baseline
RA I
RA II
RA III
profit to assets (percent)
Baseline
RA I
RA II
RA III
Profit to net-worth
(percent)
Baseline-1
RA I
RA II
RA III
1.0
0.7
-0.2
-0-.7
1.4
1.0
-0.3
-1.0
3..S:
2.6
-0.7
-2.4
7.0
4.5
3.7
3.2
14.5
7.8
6.5
5.6
26: 3
18.2
14'. 9
12.9
13.0
10.6
9.8
9.3
32.5
21.1
19.7
18.6
38.2,,
32.1
29.7
28.1
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:
RECEIPTS*
575,000-100,000 FIRM
Baseline Financial Condition.
Below Average Average Above Averace
Liquidity
current ratio (times)
Baseline 0.80
RA I 0.35
RA II 0.38
RA III 0.38
Activity
fixed asset turnover ratio
(times)
Baseline 2.30
RA I 1.95
RA II 1.98
RA III •• 1-.97
Leverage
debt ratio (percent)
Baseline 60
RA I. 60'
RA II 60
RA III 60
Profitability
profit to sales (percent)
1.73
1.49
1.50
1.50
5.56
3.87
3.97
3.96
46
55.
54
55
5.10"
3.14
3.23
7.54
4.74
4.88
4.87
15
31
30
30
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 ir
RA III
1.0
0.9
-0.2
-0.5
1.4.
1.2
-0.2
-0.8
3.S,
3.1,,
-0.6
-1.9
7.0
5 .2
4.3
3.9
14.5:
9.2
7.7
7.0
26.3
20.5-
16.9
15'. 4
13.0
11.2
10.3
9.9
3 2. 5
23.4
21.8
21.0
38.2
33.3-
31.0
29.9-
^Baseline 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).
7-57
-------
TABLE 7-37. BASELINE AND AFFECTED FINANCIAL RATIOS: >S100,000 FIRM REC2I?TS=
Baseline Financial Cor.::-tic -.
Below Average Average .-.:••-:ve .•• ?rage
Liquidity
current ratio (times)
Baseline
RA I
RA II
RA III
0.30
0.54
0.56
0.56
1.73
1.58
1.59
1.59
5.10
3.75
3.33
3.83
Activity
fixed asset turnover ratio
(times)
Baseline 2.30
RA I 2.09
RA II 2.10
RA III" ' 2.10
leverage
debt ratio (percent)
Baseline 60
RA I 60
RA II 60
RA III 60
Profitability
profit to sales (percent)
5.56
4.45
4,52
4.52
46
51
51
51
7.54
5.63
5.75
5.74
15
25
24
24
Baseline
RA I
RA II
RA III
profit to assets (percent)
3aseiine-
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
3.5.
3. ..7-..
1.8
0.9
7.0
6.1
5.6
5.2
' 14.5
11.5
10.7
10.2
26.3
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
dSaseline 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-het 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 time's 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. Ill 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 or-
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
-------
TABLE 7-38. PROJECTED FINANCIAL' FAILURES OF COMMERCIAL DRY CLEANING FIRMS BY
REGULATORY ALTERNATIVE AND SIZE CUTOFF, FINANCIAL SCENAR.'O I
-(NUMBER OF FIRMS AND PERCENT)4
Regulatory
Alternative
I
II
III
None
3,246
11.9%
4,872
17.8
5,292
19.4%
Size
<2S,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 ail dry'cleaning firms in U.S. in 1991. Assumes full
absorbtion of compliance costs. Financial failure is defined 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 £hat 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,, theyv are- substantially higner:
under any positive siza cutoff.
7-€0
-------
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
I
II
III
None
3,839
14.0%
5,478
20.0%
6,183
22.6%
Size
<2S,000
1, 448
5.3%
2,290
8.4%
2,787
10.2%
Cutoff ($000)
<50,000
1,027
3.8%
1,027
3.8%
1,365
5.0%
<75,000
334
3.1%
334
3.1%
1,126
4.1%
<100,000 '
669
2.4%
669
2.4%
905
3.3%
a?ercentage of all dry cleaning firms in U.S. in 1991. Assumes full
absorption of compliance costs. Financial failure is defined 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.
The effects of alternative size cut-offs on business failures are
illustrated graphically in Figures 7-3 through 7-8. These figures also
illustrate the types of estimated financial failures. Businesses in poor
financial condition are estimated to fail, unless they have sufficient cash to
purchase required control equipment (because they are assumed to be unable to
borrow money) . Failures of this type are referred to as capital atra-i i abiiit-y
failures-. Businesses--in: average- or-better, financial- condition can, borrow
money but still fail if expected revenues are insufficient to cover baseline
plus recurring regulatory costs—loan payments, recurring fixed control costs,
and variable control costs. These failures are referred to as profi
failures.
-------
u
J-t
en
O
O
o
XJ
nj
c
O
J_)
m
•H
3
0)
a
oo
in
ai
4J
a.
«5
O
O
U
Oi
D
O
O
o
co-
a
—H
(U
u
0)
cs
u
ro
C
O
o
o
o
o
o
o
o
o
o
o
o
o
-------
JJ
C
c
o
o
7-63
-------
W
JJ
JJ
m
o
O
n>
1-1
•»•(
(8-
(TJ
O
(Q
vo
en
CO
PO
oo
ui
voj
t-i
-------
SJ
C
U
J_*
w
r»
O
o
to
jj
u
CO
03
O
o
o
en
xj
a
U
(D
Cfi
3
C
3
U
N
-^
CD
-------
<0
CO
p-
en
CM
en
c
If)
r-i
o
o
in
r-
o
o
o
4J
a
u
OS
in
I
3
CJ
3
O
O
>
.*•*
4-1
4
1)
_1
—t
•4
3
cn
CD
a:
I
I
o.
•-(
u
in
c
-------
10
u
c
(0
o
o
(tl
-Q
03
O
ID
(M
O
O
in
c—
o
o
o
.U
—<
iJ
c
u
<0
o
u
"3
.—4
3
cn
(U
a
o
•H
1-1
c
u
CO
^
>""
o
£
(T3
c
,
a
«5
U
. •
<>» 3 «
sis-
CO
I
2
3
7-6T
-------
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 oe
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 rsgulaced industry will incur, a: significant adverse economic impacr: 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 (E?A,. 1982) :
• Annual compliance costs increase total costs of production for small
entities by mora 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-€8
-------
o
-H
U
C
0)
u
IT)
04
O
o
O
(0
OJ
a
0>
O
0)
a:
a
3
3
CJ
O
2
(U
OJ
o
4)
O<
iq
0)
D
13
U
3
(0
Cu
V)
<0
0)
—4
u -H
o
-------
o
o
<0
at
«
i
3
y
u
o
u
I
D
3
O
o
(U
T-l
2
Oi
c
o
•*4
AJ M
— « H-(
•a
c
o •*
u
^ m
ca e.
— i u
o a»
c w
(0 >H
e rt
4) O
e AJ
(0 3
M O>
rtj
-------
o
o
o
O -H
JJ "
^ as
«S G
•H M
O o
C JJ
(0 3
a» CT>
(Q 0)
-------
in
CN
O
o
o
co-
U
0>
3
1
3
O
m
O
cu
D
CU
en
0)
o
i-*
CD
IO
I
S-l
ITS
0)
'J
C/J
m
—^
o
CO
CJ •**•
J_)
-H rtj
(O C
—I U
O 0)
C iJ
(TJ ^
c «:
1-1
Cu >i
M
CD O
CO 3
81 31
m
—4
Cu.
7-72
-------
o
in
O
O
o
(O
JJ
a
—»
•H •
Cw
7*73
-------
o
o
o
03
a
o
OS
1-4
ta
3
I
3
CJ
O
-u
O
O
z
u
(T3
c
•H
03
3
a
•a
0)
u
o
•H
Cu
7-74
-------
• Capital costs of compliance represent a significant portion of
capital available to small entities, considering internal cash flow
plus external financing capabilities.
• The requirements of the regulation are likely to result in closures
of small entities.
Firms in the dry cleaning industry are classified as small or large
based on annual sales receipts (Code of Federal BeoTilationg. 1991). 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 failure 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 the impacts of the regulation. Under Regulatory
Alternative II approximately 17.. 8 percent of firms will experience financial
failure, and under Alternative III the share of firms that experience
financial failure is about 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 size,-,-cutoff; isi 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:, underi financial., scenario-,: Ii: and; the-, share, of. all.,, commercial,
firms that this number represents. Under Alternative I, about 14 percent of
commercial firms ara likely to experience financial failure with no size
cutoff to mitigate the impacts, of the: regulation. Undar 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 «hare 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.
7-7 6
-------
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 1990a; 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 PCS1
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:
«- Analyzed:, impact 3r, using,- a, mode.L plant approach; based' on 15 model
plants,- that, characterize• machine;- technology^-, machine capacity,
and operating practices of. typical dry cleaning machines.
Impacts: are measured; at multiple capacity utilization levels
for each model facility.
xThe regulatory alternatives apply to facilities that use PCE or
1,1,1-TCA. However, all facilities, that use 1,1,1-TCA are in compliance
with the candidate regulatory alternatives in the baseline. Therefore,
impacts are computed, only for-facilities, that use- ?C2.,
3-r
-------
* Analyzed impacts using an urban/rural model market: approach.
Model markets differentiate the market for dry cleaning
services by number of facilities in the market, she share of
affected and unaffected facilities in the market, the baseline
price of dry cleaning services, and the projected behaviora_
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 (WACO 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 chat 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
the costs computed for the economic analysis. Key elements of the
financial 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 350,000, that all firms in average
condition have annual receipts between 525,000 and S250,000,
and chat; all firms- in above-average- condition have receipts of
at least 5100,000. 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.
3-2,
-------
• Projected .changes in ownership due- to profitability impacts and
capital availability constraints.
The economic and financial impacts 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—-Alterative X 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.
The total annualized cost ,is estimated at $42.9 million under
Regulatory Alternative II with no cutoff. These regulatory costs result
in short-run price increases and output decreases 'representing less than
one percent deviation from baseline values. Producers and consumers are
projected to incur approximately $18 million and $25 million in welfare
losses, 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 She. smallest; aize? 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.
Than rasuita; 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 4,372. changes in
ownership are projected with no size cutoff. Nona 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 rema
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 selecttsd 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
Producer welfare losses
Consumer welfare losses
Net plant closures
Number worker displacements
Worker displacement costs
Projected changes-in ownership
$11.,5 million
34.3 million
$6.7 million
28
354
$10.2 million
0 - €69
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
cutof£_ (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
pro jectadj changes^ in-.-ownership., Under.-thai'financial, scenario^ III.
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.-S"-
-------
-------
, ' • SECTION. 9
REFERENCES
Abowd, John M. and Orley Ashenfelter. 1981., "Anticipated Unemployment,
Temporary Layoffs, and Compensating Wage Differentials." in studio* ,-n
Labor Krirfrprn, pp 141-170. Sherwin Rosen, ed. Chicago, IL: University
of Chicago Press.
Allen, R.G.D. 1962-. Mathematical Analysis for Eeonomial-.g London-
MacMillan & Co. .
Altman, Edward X.'. 1983. Corporate Financial pi.«i-T;«»flft pp 4-7. New York'
John Wiley and Sons.
American Business Information (ABI) . 1991. Data Base of Dry Cleaning
Facilities. Prepared for Research Triangle Institute.
Anderson, D. W., and Ram V. Chandran. 1987. "Market Estimates of Worker
Dislocation Costs." Economies Lai-nai-* 24:381-384.
Anderson, Donald W., Mims, Howard H., and Ross, A, Scott. 1987.
SUPDlv, Cost, and Availability nf Capital. and Clogure Analya-ia.
Prepared by Research Triangle Institute for U.S. Environmental
Protection Agency. September 30.
Bass, Archie. 1991. Commercial Loan Officer, Central Carolina Bank, March
22, 1991. Personal communication with Donald W. Anderson, Research
Triangle Institute.
Becker, Gary S. 1965. "A Theory of the Allocation of Time." Reprinted from
the Economic? .T^umal 75:493-517. September.
Behrens, Robert H. 1985. Commggeial T.oan Off leaf's Hanrihnnlf Boston:
Banker's Publishing Company.
Betchkal, Mark. 1987a. Institute of Industrial, Launderers, with. Lisa,
McNeilly. February 19, 1987. Personal, -communication,.- with. Research
Triangle Institute.
Betchkal, Mark. 1987b. Institute of Industrial Launderers, with Lisa
McNeilly. March 2, 1987. Personal communication with Research Triangle
Institute .
Blinder, Alan S.. 1988:. "The Challenge^ of . High Unemployment ..." Richard-. T .. Ely
. Lecture, printed, in: the,- Amar-ir'an- Tf-rmrmtir! • P^r-iow - 78 (2) :1-I5...
Bowlin, Oswald D., Martin, John D., and Scott, David F. 1990. Guide r.n
Finnnnifll ftnnlY^ll. pp 229-233. New York: McGraw-Hill.
Busier-, C., 1980., "Characteristics of: Dry- Cleaning- Solvents.,1" International
Fabricare Institute; Technical Bulletin No. T-536.
CheminnT Marfcp>i-^g
229(5) : 50, February 3.
1986. "Chemical Profile: Percfaloroethylene. *
9-1,
-------
Clark, Lyman H. 1989. Small Business Pinaneial Dat-a Baaea . Prepared for the
Office of Policy, Planning, and Evaluation, U.S.. Environmental
.Protection Agency.
adm of EVi
Regulations. 1991. "Business Credit and Assistance."
Dun
Revised as of January 1, 1991. National Archives and Records
Administration .
Coor, Kenneth R., Division President, and Grady Kenneth W., Production
Manager, Textilease Corporation* February 19, 1.991. Personal
communication with Brenda L. Jellicorse, Research Triangle Institute.
Deacon, Robert T. and Jon Sonstelie. 1985. "Rationing by Waiting and the
Value of Time : Results from a Natural Experiment . " Journal of
Political Economy 93(4) : 627— €47 .
Duns Analytical Services. 1990. Industry Mor-ms and K«»v Business Ration.
and Bradstreet Business Credit Services. 1989-1990.
Economic Reoorr o-ff tha President:. 1990. Washington, DC: United States.
Government Printing Office. February.
Faig, Kenneth. 1990. International Fabricare Institute. March 14, 1990.
Personal communication with Brenda L. Jellicorse, Research Triangle
Institute .
Faig, Kenneth. 1991. International Fabricare Institute, February 25, 1991.
Personal communication with Brenda L. Jellicorses . , Research Triangle
Institute .
Fftdagal Register. 1989. "Occupational Safety and Hesilth Administration, 29
CFR Part 1910, Air Contaminants." 54(12) :2812. January 19.
Fischer^ E. 1987. Editor of American Pryeleaneg. Fesbruary 6, 1987.
Personal communication with Lisa McNeilly, Research Triangle Institute.
Fisher, William., 1987. International Fabricare Institute, February 13, 1987.
Personal communication with Lisa McNeilly, Research Triangle Institute.'
Fisher, William. 1990a. . International Fabricare Institute. March 6, 1990.
Personal communication with Brenda L. Jellicors«, Research Triangle
Institute .
Fisher, William. 1990b. International Fabricare Institute, October 10, 1990.
Teleconference, with EPA staff , Radian Corporation staff, and Srenda L.
Jellicorae,. Research,- Triangle> Institute-...
Flaim, Paul O. 1984. "Unemployment in 1982: The Co«t to Workers and Their
Families." Mont;hlv t.ataOTr RarH.aw Feb. $30—37
Gordon, Robert J. 1978. Mae-goegonomigg .. pp 271-275.. Boston:, Little Brown
and Company.
Gronau, Reuben. 1977. "Leisure, Home. Production, and Work— the Theory of the
Allocation Of Time- Revisited." .Tom-na3 *f P<->lH-iga:i
a5<6);:1099-1123..
9-2
-------
Hamermesh, Daniel S. 1989. "What Do We Know About Worker Displacement in the
U.S.?" Industrial Balaf_inng 28(1) :51-59.
Hatsopoulos, George N. 1991. "Coat of. Capital: Reflections of a CEO."
Buainegg Bgonotnlgg . 26(2) :7?-13.
Houthakker, H. S., and Taylor, L. D. 1970. Consumer Demand in <-ho tTnit-o^
States: - Analysis and Pr-n-ieef inn*. Cambridge, MA: Harvard Univ. Press.
Of Suhsfifnres for
ol-rer,^ ^ nn,
ICF, Inc. 1986. .
Cleaning. Draft/Report to U.S. Environmental Protection Agency. April.
International Fabrioare Institute. 1989. "Results of IFI Survey of 1988
Operating Costs." TFT Pah'-rinaro Mou.q September.
Jones, C.P. Dr. 1991. Professor of Economics and Business, North Carolina
State University, March 22, 1991. Personal communication with Donald w.
Anderson, Research Triangle Institute.
Kooreman, Peter and Arie Kapteyn. 1987. "A Disaggregated Analysis of the
Allocation of Time within the Household. " Journal of
95(2) :223-249.
Martin, Stephen. 1982. "Industry Demand Characteristics and the Structure-
Performance Relationship." Jouimal of Economies and Bugine^.; 34:59—65
Mason, Charles, and Clifford Butler. 1987; "New Basket of. Goods and Services
being Priced in Revised CPI." Monthly Labor- R^TH^M. January.
Maxwell, Nan L. 1989. "Labor Market Effects from Involuntary Job Losses in
Layoffs, Plant Closings: The Role of Human Capital in Facilitating
Reemployment and Reduced Wage Losses . " ame^ican Journal of Economics
and Sflginlnrry 48 (2) : 129-141 .
Moore, Michael J. and W. Kip Viscusi. 1990. Compf»n«at:ian Mechanic for
Princeton, NJs Princeton University Press.
Radian. Corporation. 1990a. "National, Cost, Impacts ; of. Regulatory- Alternatives,
for the Hazardous Air Pollutant Dry Cleaning NESHAP . " Memorandum from
Carolyn Norris and Kim Kepford to U.S. Environmental Protection Agency,
Chemical and Petroleum Branch. January 25.
Radian Corporation. 1990b. "Revised Estimates of National Hazardous Air
Pollutant Consumption by the Dry Cleaning Industry." Memorandum from
Carolyn Norris and; Kim Kepford to U.S. Environmental Protection Agency,
Chemical • and: Petroleum Branch.. December:- 14 .
Radian Corporation. 1990cv "Revised Model Machine Selection for the Dry
Cleaning NESHAP." Memorandum from Carolyn Norris and Kim Kepford to
U.S. Environmental Protection Agency, Chemical and Petroleum Branch. -
December: 14.
Radian Corporation. 1990d. "Updated Control Costs and Cost-Effectiveness
Estimates for Hazardous Air Polluttant (HAP) Dry Cleaners." Memorandum
from Carolyn Norris and Kim Kepford to U.S. Environmental Protection
Agency;, Chemical and. Petroleum; Branch..., December 1.4 ..
9-3'
-------
Radian. Corporation. 1991a., "Documentation of Growth Rates for the Dry
Cleaning Industry." Memorandum from Carolyn Morris and Kim Kepford to
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
Cleaning Industry." Memorandum from Carolyn Norris and Kim Kepford to
U.S. Environmental Protection Agency, Chemical and Petroleum Branch.
March 1.
Safety— Kleen . 1986. Analyaia of Pry .Cleaning1 Industry.
Sherer, F.M. 1980. Industrial Market Structure and Economic Per-f ormanne .
2nd ed., Chicago: Rand McNally College Publishing Company.
Sluizer, Bud. 1990. Institute of Industrial Launderers, March 12, 1990.
•Personal communication with Brenda L. Jellicorse, Research Triangle
Institute.
Steinhoff, Dan and Burgess, John F. 1989. Small Business Management
Fundamentals. 5th ed. . New York: McGraw-Hill.
Tax Foundation 1991. Facsimile from Gregg Leong, The Tax Foundation,
Washington, DC.
Topel, Robert H. 1984. "Equilibrium Earnings, Turnover, and Unemployment:
New Evidence." Journal of Latinr- ffcnnrnniea 2 (4) : 500— 522 .
Torp, Richard. 1990. Coin Laundry Association, February 27, 1990. Personal
communication with Brenda L. Jellicorse, Research Triangle Institute.
Torp, Richard. 1991. International Fabricare Institute. February 8, 1991.
Personal communication with Kristy Mathews, Research Triangle Institute.
U.S. Department of Commerce, Bureau of Census. 1985. iQR? can ana of Service
industries. Miscellaneous Sub-Sects . Washington,, DC: U.S. Government
Printing Office. December.
U.S. Department of Commerce, Bureau of Census. 1988. rnunty and city Data
Boole 1988. Washington, DC: U.S. Government Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1989*. statistical
abstract n-f *he> rrn-itad states .. Washington,, D.C.: U.S. Government
Printing Office.
of.
U.S. Department of Commerce, Bureau of Census. 1990a. 1 gs?
Industries,. tJan gmpLnye^p Statist ies- Series. Washington, DC: U.S.
Government Printing Office. March.
U.S. Department of Commerce, Bureau of Census. 1990b. tq«7
of
Washington:., U.S.. Government Printing
Office. April.,
9-4
-------
U.S.. Department of, Commerce, Bureau of the Census. -ISSOc. 1987
of
Service Tnduatriea. Geographic Area Serieg . Washington, D.C.:
Government Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1990d. stat
Abstract of thg United states. Washington, DC: U.S. Government
Printing Office.
U.S.. Department of Commerce, Bureau of the Census. 1991. Current Population
Surveys Branch, April 15, 1991. Personal communication with Kristy
Mat hews, Research Triangle Institute.
U.S. Department of Commerce, Bureau of Economic Analysis. 1989b. Survey of
Current Buaineag . Washington, DC: U.S. Government Printing Office.
March.
U.S.Department of Labor, Bureau of Labor Statistics, 1991a. 19SO—i
consumer Expenditure Survey. Washington, DC: United States Government
Printing Office.
U.S. Department of Labor, Bureau of Labor Statistics. 1991b. Employment and
Earnings. Washington, DC: U.S. Government Printing Office. April.
U.S. Environmental Protection Agency. 1982. "EPA Implementation of the
Regulatory Flexibility Act." Memorandum from Anne M. Gorsuch to EPA
Administrators and Office Directors. February 9.
Van Home, James C. 1980. Financial Management and Policy. 5th ed.
Englewood Cliffs: Prentiss Hall.
9-5
-------
-------
TABLE A-l. BASELINE-FINANCIAL STATEMENTS OF DRY CLEANING
AVERAGE FINANCIAL, CONDITION
FIRMS IN BELOW-
Company Sales Range
Income Statement
Sales
cost of goods sold
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
aonr-currenf: liabilities:
total liabilities-
net worth.
capital
Total T,-Latiili-M«»*
< $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
S50-75K
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
IS', 51-4-
28,149
18,766
35,230
46.415
$75-100K
93,829
43,848 '
49,981
49,042
938
1, 666
6,478
8,144
4,387
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
<;<;. finn
> S100K
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
9^7. 9S7
A-l,
-------
TABLE A-2. BASELINE FINANCIAL STATEMENTS OF DRY CLEANING
FINANCIAL CONDITION
FIRMS IN AVERAGE
Company Sales Range
T rf -:100K
? '7,510
.61,337
206,173
130,448
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
95,395-
139,501
177,257
A-2
-------
TABLE A-3. BASELINE FINANCIAL STATEMENTS OF DRY CLEANING FIRMS IN ABOVE-
AVERAGE FINANCIAL CONDITION
. Company Sales Range
. Income Statement
Sales
cost of goods sold
gross profit
other expenses and
taxes
net profit
Balance sf]ppf
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:- payaole-
other current
liabilities
total current
liabilities
non-current. liabilitiast
total liabilities:
net. worth
capital:
Total T.iah-1 1 «t-ta^
4«*«J \T«&^ r.T._. - •- i_
< $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
5945
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
r,.3SB7-
2,433
13,785
IS"; 143:
16,218
350-75K
57,021
27,524
39,497
30,784
8,713
5,211
1,010
6,221
2,344
9,065
8,887
8,857
26,808
384
33.
459.
901
1,777
2,24'4-.,
4,021,
22,787
25,.03X
26,808
$75-10 OK
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
S43-
1,262
2,488
3,,14l:
5,630,
31,902
35,043'
37,532
BMMBI^HnBa
> S100K
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
2,51.7
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
T«Cnm* Sfal-.Mfnene
Sales
cost of goods
gross profit
other expenses' and taxes
net profit
Balanf?ft 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
rioces payable
other current liabilities
total current liabilities
non-current liabilities
total liabilities-
net worth
capital
"or*! ti*h,m~iM
SO-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-
4,366,
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.
S50-75K
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
46,315
$75-100K
93,329
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
IS, 474
32,349
63,800
90,554
154,354
102,903
193,457
257,257
-------
TABLE A-5. FINANCIAL STATEMENTS OF FIRMS IN AVERAGE FINANCIAL CONDITION-
REGULATORY ALTERNATIVE I '
Company Sales Range
Tneoma Sfa^mment
Sales
i
cost of goods
gross profit
other expenses and taxes
net profit
Balance .qhct«af|
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,ia»-H t«f 1 a.^
50-25K
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
550-75K
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
3,353
14,071
22,424
16,705'
30,777
$75-100K
93,829
41,191
52,638
47,789
4,349
8,191
3,439
11,630
5,069
16,699
24,214
11,676
52,589
2,082
181
4,071.
4,388
11,221
17,728
28,949
23,640
41,368
«^B»«=HKS=C
$ >100K
367,510
161,337
206,173
183,915
22,258
32,083
13,471
45,554
19,853
65,407
32,655
45,732
193,794
8,154
709
13,315.
19,144
•41,322
58,479
99,801
93,393
152, 472
1 O^ ~IQA
A-5
-------
TABLE A-6. FINANCIAL STATEMENTS OF FIRMS' IN ABOVE-AVERAGE FINANCIAL
CONDITION: REGULATORS ALTERNATIVE I
Company Sales Range
Tn.GQJfl^l S 1 3t@ITTSrtC
Sales
cost of goods
gross profit
other expenses and taxes
net profit
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, liabilitiasi
net worth
capital
Toeai Liabilities
and M«t Worth
SO-25K
17,736
7,234
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, 327-
545
2,625
7,913
10,538;
12, 982
20,895
23,520
S50-75K
67, 021
27,524
39,497
32,414
7,083
5,211
'1,010
6,221
2,344
9,065
- 15,691
8,H57
33,612
384
33
1,303.
901
3,221
8,352
11,. 574':
22,1339
30,391
33, S12"
$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
44,365
S >100K
•67,510
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
5,026
4,942
13,256
27,152
40,408
123,134
150,286
163,542
-------
TABLE A-7. FINANCIAL STATEMENTS OF FIRMS IN BELOW-AVERAGE FINANCIAL
CONDITION: REGULATORY ALTERNATIVE II
Company Sales Range
r n ^rtfl^o s T* *^1" pflic^n^*
Sales
cost of goods
gross profit
other expenses and taxes
net profit
Balance '100K
^ i^msB
367,510
171,74-6
195,764
193,894
1,871
-8,696
25,373
16,678
19,140
35,813
174,722
46,718
257,257
13,779
1,198
15,474-
32,349
63,800
90,554
154,354
102,903
193,457
257,257
A-7
-------
TABLE A-8. FINANCIAL STATEMENTS OF FIRMS IN AVERAGE FINANCIAL CONDITION:
REGULATORY ALTERNATIVE II
Company Sales Range
Tneome 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
tonal assets
accounts payable
loans payable
notes payable
other current liabilities
total current liabilities
non-current liabilities.
total liabilities-
net worth
capital
Total T,iabilH-TM
SO-2SK
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
S25-50K
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
15,349-;
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
16,746
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
S >100K
367,510
161,337
206,173
135,535:
20,638
32,083
13,471
45,554
19,853
55,407
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
and Net Wor-th
A-8
-------
TABLE A-9. FINANCIAL STATEMENTS OF FIRMS IN ABOVE-AVERAGE
CONDITION: REGULATORY ALTERNATIVE II
FINANCIAL
Company Sales Range
Tneoma St-af 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
1.S2-99K
and_Net_Wo reh
A-9;
-------
TABLE A-10. FINANCIAL STATEMENTS OF FIRMS IN BELOW-AVERAGE
CONDITION: REGULATORY ALTERNATIVE III
FINANCIAL
Company Sales Range
Tnf!OTtie 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
and N«»t Worth
SO-25K
17,736
3,288
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
S25-50K
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
550-75K
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
28V149
18,766
35,280
46,315
$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
65,630
5 >100K
357,510
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-IO;
-------
TABLE A-ll. FINANCIAL STATEMENTS OF FIRMS IN AVERAGE FINANCIAL CONDITION:
REGULATORY ALTERNATIVE III
Company Sales Range
Income Statement
Sales
cost of goods
gross profit
other expenses and taxes
net profit
Balance She*»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 Liabilif Jam
SO-25K
17,736
7,786
9,950
11,991
-2,C41
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
525-50K
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
S50-75K
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
$ >100K
367,510
161,337
206,173
186,487
19,636
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
-„,.„„,„ ejt-qfmnenr
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 N«»t Worth
SO-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
-
S25-50K
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
S50--75K
57,021
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
375-100K
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
S >100K
257,510
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
-------