United States
Environmental Protection
Agency
Office of Air Quality
Planning and Standards
Research Triangle Park, NC 27711
EPA 450/3-91-0215
September 1993
Air
Economic Impact Analysis of
Regulatory Controls in the
Dry Cleaning Industry

Final

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                               RTI Project Number 5428-33 DR
Economic Analysis of Air Pollution
Regulations:  Dry Cleaning Industry
                Emission Standards Division
              U.S. Environmental Protection Agency
               Office of Air and Radiation
            Office of Air Quality Planning and Standards
            Research Triangle Park, North Carolina 27711
                 September 1993

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                            (Disclaimer)
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                       5,35


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                             CONTENTS
Section
                                                              Page


  1   Introduction	*	


      1.1   Requirements and Impacts of the  Proposed
            Standards	               , _2


      1.2   Requirements of the Final Standards	.1-5


      1.3   Impacts  of the Final Standards. ,	  j__8

            1.3.1  Economic Impacts 	       30

            1.3.2  Financial Impacts	    2.-9



  2    Owners-'  Responses to the Final Standards	2-1


      2.1  Control Costs	            2-i


      2.2  Owners' Responses to  Control  Requirements	2-1


      2 .3  Annualized Control Costs	         2 -11



  3    Economic  Impacts	


      3 .1  Market Structure	               3_,

            3.1.1  Market  Structure in  the Commercial
                  Sector,	               -,_1

            3.1.2  Market  Structure- in- the-Industrial
                  Sector	             3 _7


     3.2   Affected Population	             3_7


     3 .3   Market, Adjustments	           3 _9  •

           3.3.1.  Price- and: Oucput. Adjustments-	j _o

           3 ..3 .2  Welfare- Impacts,	                      7 n
                                      *••-*,.,,....	O~"X.J

           3.3.3  Plant Closures	3_15  •

           3 .3 .-4.  Employment:. Effects	       *        i_i --
                                           ..  * .. . * •-• * . . »  . 4 . f f _ O"~-LO

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                       CONTENTS (cont inued)
Section
                                                              Page
      Financial Impacts	„	....4-1
      4.1   Financial Characteristics	4-2
            4.1.1  Distribution of Potentially Affected
                   Firms	4-2
            4.1.2  Baseline Financial Ratios	.4-4
      4.2   Ownership Adjustments	4-5
            4.2.1  Ratio Analysis 	4-8
            4.2.2  Changes In Ownership 	 	 4-8
      References
                                                              .R-l

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                             FIGURES
Figure
                                                             Page
 3-1   Price and Output Adjustments Due to Increased Costs
       of Production:  Urban/Suburban Markets	3-10

 3-2   Price and Output Adjustments Due to Increased Costs
       of Production:  Rural Markets	  2-12

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                              TABLES
Table


 1-1



 1-2


 1-3

 2-1



 2-2



 2-3


 2-4


 2-5




 2-6




 2-7




 2-8




 3-1


 3-2


 3-3
                                                       Page
 Control Technology Requirements Under the Proposed
 Standards By Industry Sector and Machine Type	1-3

 Economic Impacts of Proposed NESHAE	1-4

 Requirements of the Final  Standards	1-7

 Capital Costs of Control:   Vent Controls and Room
 Enclosures ($/facility/yr)	2-2


 Annual  Operating Costs  of Control:  Vent Controls
 and  Room Enclosures	.-	2-3


 Recordkeeping Costs for RC-Controlled Facilities	2-4

 Recordkeeping Costs for CA-Controlled Facilities	2-5

 Response Options  for Dry Cleaners Subject to
 Requirements  Beyond Recordkeeping Under  the  Final
 Standards	2-7


 Annualized Compliance Costs  Per Facility Due to  the
 Final Standards:  No Baseline Vent  Controls
 ($/facility/yr)	2-13


 Ann-  lized Compliance Cos 3  Per. Facilitv Due to  rhe
 Fini_ Standards :  RC Base^ne- Ventr Controls
 ($/facility/yr)	2-14


Annualized Compliance Costs  Per Facility Due to  the
Final Standards:  CA Baseline Vent  Controls
 ($/facility/yr)	2-15


 Profile-of Model Markers' in.  the--Commercial Sector. . . .' . .3-6

Number of  Facilities Affected by the Standards	3-8

Share of. Facilities'. Affected, by  the- Requirements  of
the Standards	            2-8
                            VI.

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                        TABLES- (continued)
Table



 3-4



 3-5


 4-1



 4-2


 4-3
Projected Price and Output Adjustments Due  to  the
Standards	
                                                      Page
                                                      .1-13'
Projected Welfare Impacts Due to the Standards ........ 3-14


With-Regulation Financial Statements by Baseline
Financial Condition.                                     _
Projected Financial Ratios with Regulation ............. 4-9


Potential Changes in Ownership Due to the Standards. . .4-11
                            VI1,

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                              SECTION 1
                            INTRODUCTION

      Under the Clean. Air Ace of 1990, the U.S. Environmental
 Protection Agency (EPA)  is required to propose and promulgate
 National  Emission Standards for Hazardous Air Pollutants  (NESHAP }
 In keeping with this requirement,  the EPA proposed a standard to
 control perchloroethylene (PCE)  air emissions from dry cleaning
 facilities on  December 9,  1991.  The purpose of this analysis is
 to evaluate the impacts  of the  final NESHAP for promulgation
 (referred  to as final standards  or standards in the balance of
 this report) .

     The  economic  and financial  impacts of the regulatory
alternatives considered  for proposal  were  estimated in the
Economic Impart Analysis of Rgmilaf.flrv CQnr.         '
Cleaning
                                              )"[.
                    (EPA, 1991) .  A copy of  the  1991  report appears
 in Appendix A of this report.  This report  follows  the same
 methodology and assumptions included in EPA's  1991  report.
     Public comments in response to the
                                                  NESHAP raised
 several issues, including concerns about possible air and water
 quality impacts, associated, with carbon adsorber  (CA) control
 devices and about the use of additional controls designed to
 reduce fugitive emissions from transfer machines.  The final
 scandards: evaluated:: in dhis-. report:, reflect:- EPA's response to these
 issues.  Furthermore,  the impacts reported in this analysis
'include recordkeeping costs,  which were not included in the
 impacts reported in Appendix A.

      The- standards- .outlined., in, this:, analysis- will, pocancially
 affect- dry  cleaners  in two  industry sectors:   commercial dry"
 cleaners  (SIC; 7216)  and: industrial dry cleaners (SIC 7218).   Coin-
 operated, facilities  (SIC," 7215) are not included in. this, analvsis
 because- no  facilities, in,, this, sector  are,- projected  to- incur
 impacts.  Commercial  facilities are the most prevalent  type  of  dry
 cleaners and are generally located in shopping centers  and near
                                i-r

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 densely populated areas.  Industrial plants usually rent uniforms
 and other items to their industrial or commercial users and are
 generally larger than commercial and coin-operated facilities.
 Appendix A contains a profile of the affected industry sectors.

      This section provides an overview of the proposed standards,
 a description of the requirements of the final standards,  and a
 summary of the impacts of the final standards.  Section 2  contains
 the estimated costs of the control requirements and describes the
 methods used to project owners'  responses to these control costs.
 Sections 3 and 4 describe the methods used to compute economic and
 financial impacts of the standards;  these sections also report the
 results of the analysis.

'1.1  REQUIREMENTS AND IMPACTS OF THE PROPOSED STANDARDS
      Under the proposed standards all existing facilities  with
 greater than $100,000 in annual  receipts are required to install
 vent  controls  to limit PCE emissions.   Control equipment
 requirements under the proposed.standards vary with the industry
 sector  and machine technology.   These control requirements  are
 shown in Table 1-1.   (See Appendix A for a description of  the
 machine technologies  identified  in Table 1-1.)

      The projected price  and  output  adjustments, welfare impacts,
 and plant  closures  due- to-the proposed standards are  shown- in.
 Table 1-2.   The  methods and assumptions  used to compute these
 impacts  are  described in  Appendix A.   The price and output
 adjustments  are  short-run effects.   Almost  all new dry cleaning
machines  are equipped with built-in  vent  controls  that  satisfy the
 requirements, of- the- proposed  standards.   The  current  scock- of,
uncontrolled machines  would have  been, replaced with" controlled
machines even  in  the  baseline.  Consequently,  long-run  price and
output adjustments are zero.

      The producer and consumer welfare costs reported  in Table 1-2
are projected  for the  first year of the- regulation.  Fewer  losses
will be  incurred in 14 subsequent: years as- a result of  replacing

                               1-2

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  TABLE 1-1.  CONTROL  TECHNOLOGY REQUIREMENTS UNDER THE PROPOSED
             STANDARDS  BY INDUSTRY SECTOR AND MACHINE TYPE
       Industry Sector and
           Machine Type
 Commereig],
    Dry-to-Dry
    Transfer (uncontrolled)
    Transfer (RC controlled)
 Industrial
    Dry-to-Dry
    Transfer
  Control Technology Requirement
CA or Refrigerated  Condenser (RC)
                CA
 No  additional control required
 Source: U.S. Environmental Protection Agency.  1991.' Economic Tm»,
                                                      _  -   . .  .  f* 3.112. 1
                 io        'S- Environmental  Protection Agency, Office
        of Air Quality Planning and Standards, EPA  450/3-91-02l!

 existing uncontrolled machines with controlled machines,  even at
 baseline.  Fifteen years after the regulation takes  effect,
 producer and consumer welfare costs are zero, assuming that the
 current stock of uncontrolled machines is replaced with  controlled
 machines in the baseline over this time period.  The plant  closure
 projections assume- chat the short-run- industry output reductions-
 are  achieved by closing the smallest affected facilities.

      The estimated regulatory costs of the proposed  standards
 result  in short-run price and output adjustments that are
 relatively- small; (less:. Chan', one, percent: in: absolute, value) .  The.
 estimated loss  in  consumer welfare-is  $6.7 million, for che^
 commercial,  sector.   Producers in  the commercial sector lose an
 estimated $4.8 million  in welfare.   Note that chese welfare losses
only consider the  costs  of  controlling  emissions.   The benefits
associated with changes  in  environmental quality  are  not  included
                                1-3

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 in the estimates of welfare impacts.  Under the proposed
 standards, 28 plant closures are projected for the commercial
 sector.

      The output reduction shown in Table 1-2 was used to project
•worker displacements resulting from the proposed standards.  The
 projected worker displacements assume that layoffs are
 proportional to the short-run industry output reductions.  Under
 the proposed standards,  it is projected that 354 workers will be
 displaced.  The projected worker displacement costs are based on
 the projected displacements and are one-time (nonrecurring) costs.
 Under the proposed standards,  projected worker displacement costs
 total $10.2  million..  Implicit in the estimated displacement costs
 is  the assumption that this baseline-, output reduction--and the
 corresponding reduction  in employment--would have been accounted
 for through  attrition  rather than through worker dislocation.   in
.other words,  the, present value of foregone future displacement is
 assumed to be zero.

      In addition to the  economic impacts,  EPA estimated  financial
 impacts due.to the  proposed standards  under two  financial
 scenarios:   Financial  Scenario  I, which  assumes  a positive
 relationship  between firm size  and baseline firm  financial
 condition  and Financial  Scenario  II, which  assumes that  the number
of-  firms, in. below-average,  average, and  above-average baseline
 financial  condition is proportionately distributed across firms  of
all sizes.  The  firm financial  analysis  used the costs estimated
for the economic impact  analysis to project changes in the
financial viability of affected dry cleaning firms.  Under
Financial. Scenario: I,, no: changes, in. ownership, are- projected.
Under Financial Scenario  II, 669 changes in, ownership are
projected because of capital availability constraints.

1.2   REQUIREMENTS OF- THE  FINAL STANDARDS'
     Three categories of  requirements contained in the final
standards were evaluated for this analysis:  vent control
                                1-5

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 requirements, room enclosure requirements, and recordkeeping
 requirements.  The control requirements vary by industry sector,
 type of dry cleaning machine, level of output, baseline vent
 control device,  and designation as a major or area source.  Major
 sources include facilities emitting 10 or more tons of PCE per
 year;  area sources include facilities emitting less than 10 tons
 of PCE per year.   For this analysis,  it was assumed that major
 sources include all industrial facilities and commercial
 facilities with greater than $100,000 in annual receipts that
 operate uncontrolled transfer machines.

      Table 1-3 contains the control technology requirements for
 dry cleaning machines under the final standards.  'All dry-to-dry
 and transfer machines at facilities with output levels
 corresponding to  more than $75,000 in annual receipts are subject
 to vent control and recordkeeping  requirements.   The vent control
 requirements specify that  uncontrolled facilities install RCs.
 Facilities above  $75,000 that use  CA  control devices in the
 baseline are nof required  to  purchase and install RC control
 devices until their CA requires  replacement.

     Room  enclosure requirements apply only to major sources  that
 use  transfer machines  or reclaimers (dryer used in combination
 with a  transfer washer or  dry-to-dry  machine).  In this analysis,
 it was  assumed that  room enclosures are raouired  for  all
 industrial  dry cleaners  wic.i  transfer  machines and for commercial
 facilities  over $100,000 in receipts operating transfer machines
without baseline control devices.  Room enclosure  requirements
 include a small CA  (approximately one-third the size of a CA used
 for process, vent-., controls) to capture- and, coneroi  fugitive
emissions from1 transfer- machines.

     The control requirements contained in Table 1-3 are  for
existing dry cleaning machines.  New dry-to-dry machines... are
subject to the same requirements (including RC vent controls and
recordkeeping) as existing dry-to-dry  machines-with one exception:
                                1-6

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 new dry-to-dry machines at major source facilities are required to

 install both RC and CA devices.   New transfer machines are

 effectively banned under the final standards through a requirement

 to  emit no emissions during clothing transfer.


 1.3   IMPACTS OF THE 'FINAL STANDARDS

      Impacts due to the final standards were projected using an

 integrated approach that combines an economic impact analysis with

 a firm  financial analysis.   The  approach was integrated by using

 inputs  from each type of analysis to compute impacts in the other.

 For  example,  financial impacts were  based on the  costs  computed in

 the  economic  analysis.   In turn,  economic impacts  were  based on

 the  costs  of  capital  computed using  data on  the financial  status
 of firms in the  industry.


 1.3.1   Economic
     The economic impacts of the standard were computed using  a
methodological and empirical approach based on the principles  of

applied microeconomics.  Economic impacts were quantified through
estimating market adjustments of price and output and estimating

corresponding effects on consumer and producer welfare.  The

effects of the standards on employment and plant closures were

also quantified as part of the economic impact analysis.   The key
elements of the economic analysis, are- as follows:

     • Analyzed impacts using a model plant approach that
       characterizes machine technology,  machine capacity,  and
       operating practices of typical dry cleaning machines.
       Impacts are measured at  multiple capacity utilization
       levels for each model plant.,

     • 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,
       tne  share of  affected and unaffected. facilities  in the
       market,  the baseline price of dry  cleaning services,  and
     .  the  projected behavioral response  to  regulation.

     •  Computed annualized compliance costs  using engineering data
       ana  an estimated weighted average  cost  of  capital  (WACO
       for  firms  in  below-average,  average-,,  and: above -aver age
                               1-8

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        financial condition  (consistent with the  distribution of
        financial condition used in Financial. Scenario  II).
       • Estimated short-run price and output adjustments  and
        corresponding consumer and producer  welfare  impacts  using
        applied microeconomics.  (Welfare impacts  computed in this
        analysis consider only the c^s£i of  controlling emissions'
        The benefits associated with changes in environmental
        quality are not included in the estimates  of welfare
        impacts.)         .
       • Projected net plant closures based on the  assumption  that
        the entire reduction in output is accounted for by the
        smallest  affected plants leaving the industry.
       * ™;!:mated one-time worker displacements and displacement
      The price and output adjustments projected for the final
 standards are all relatively small (<2. 5' percent in absolute
 value) .   in the commercial sector the net welfare impacts are an
 estimated -$25 million.   in the  industrial sector,  a welfare gain
 of  $607,000 is projected.   The oucput reduction in the commercial
 sector results in an  estimated 259 plant  closures and -$23.4
 million  in worker displacement costs.
 1-3.2.
     As previously mentioned,  the  financial  analysis  of  affected
dry cleaning firms was based on the costs computed for the
economic analysis.  Ownership,  ixnpaccs. were: escimaced.  using
financial data on the distribution of firm financial  health.  The
changes in firm financial status and capital availability for
firms of different sizes and financial condition were estimated 'in
the financial analysis.  Key elements. of the financial analvsis
are as; follows-:.-
             ial, scenarios..
                                  °f fUndS C° firms df Different
                          condition and different output .levels .
                               1-9

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     • Evaluated profitability impacts on firms by baseline
       financial status and baseline output level.
     • Projected changes in ownership due to profitability impacts
       and capital availability constraints.

     Projected changes in ownership due to the promulgation
requirements all result from capital availability constraints.
The estimated number of projected changes in ownership ranges from
0 to 834,  depending on the financial scenario.
                              1-10

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                              SECTION 2
              OWNERS' RESPONSES TO THE FINAL STANDARDS

      Owners of affected facilities have several options for
 responding to the standards.   This section reports the estimated
 control  costs associated with the control requirements of the
 final standards,  characterizes the owners'  options for responding
 to  these requirements,  and describes the methods used to project
 the owners'  responses.

 2.1  CONTROL COSTS

      The promulgation requirements evaluated in this report
 include  vent controls,  room enclosures,. and recordkeeping
 requirements (see Table 1-3).   Affected  entities will potentially
 incur initial and recurring costs  as a result of these
 requirements.  Tables 2-1  and  2-2  report  the capital (initial)  and
 annual operating  (recurring) costs  associated with the vent
 control  requirements  and room  enclosure requirements estimated  for
 facilities with $75,000  or more in  annual receipts.   Tables 2-3
 and 2-4  report the initial and recurring recordkeeping costs  for
 facilities with RC-controlled  machines and  CA-controlled machines,
 respectively.  Costs  reported  for recordkeeping  requirements
 include  leak detection and repair costs.  Costs  reported in Tables
 2-1  through  2-4 are net  of any solvent recovery  savings  associated
 with  the controls.

 2.2   OWNERS'  RESPONSES TO  CONTROL REQUIREMENTS
      The owners of dry cleaning facilities  potentially  affected.by
 the requirements of the:-standards: may respond:,, in:, several ways.,
Owners can invest in  the required, vent controls, and  room.
enclosures, switch solvents, accelerate  the purchase of  new dry-
 to-dry machines, with built in RCs-, or cease operations,.  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...
                                2-1

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      If the expected costs of operating the plant exceed the
 expected revenues,  the owner(s)  of the plant closes it.  If the
 expected revenues of operating the complying plant exceed the
 expected costs,  it is economically viable,  so the owner(s)  will
 likely keep the  plant or sell it.   Owner(s)  keep the plant 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 plant.   Potential
 changes in ownership due to capital constraints are discussed in
 Section 4.   The  discussion that follows,  however,  assumes that
 owner(s)  continue operating the plant.

      All dry cleaning facilities in the commercial and industrial
 sectors are  required to  perform recordkeeping activities.
 However,  owners  have several  choices for  complying with the venc
 control and  room enclosure requirements.  The choice that the
 plant owner(s) makes depends  on  the sector,  the machine type,  the
 level of  baseline control,  and financial  condition  of  the plant
 owner(s).  Assuming  that  the  owner(s) does not  cease operating in
 response  to  the  standards  leaves three  basic  options for  affected
 entities:   (1) invest  in  the  required vent control  device  (dry-to-
 dry and transfer) and/or  room  enclosure (transfer),  (2) accelerate
 the purchase of  a new  dry-to-dry machine with the required  venc
 controls, or (3)  switch solvents.   Solvent substitution is  not a
 cose-less opcion  for most:  dry  cleaners  for-many  reasons,  including
 higher  solvent prices, differences  in the cleaning properties of
 solvents, and the compatibility  (or  lack thereof) of alternative
 solvents with existing equipment.  Although other solvents  are
 used  in some dry  cleaning plants, none  are currently considered
 feasible  for-widespread, subscicution- for ?CE;  (EPA, 1991)
Consequently, solvent substitution is not considered further in
 this analysis.

     .Table 2-5 identifies  the relevant  response options- for all
 facilities required to meet requirements beyond recordkeeping.
Facilities required to meet requirements beyond the recordkeeping
requirements include- the- following:-
                                2-6

-------
 TABLE 2-5.  RESPONSE OPTIONS  FOR DRY  CLEANERS SUBJECT TO
             REQUIREMENTS BEYOND RECORDKEEPING UNDER  THE FINAL
             STANDARDS                            -        . .     .   -
 Industry
 Sector, and   Baseline
 Machine         Vent
 Technology    Control
                                  Annual Receipts per Facility
                             $75  to $100K
                                                       Over $100K
Commercial

 Dry-to-Dry     None


 Transfer       None


Industrial

 Dry-to-Dry     None


 Transfer       None


 Transfer       RC
 Transfer       CA
                            RC + RK(RC)

                            D/D(RC)  +•  RK(RC)

                            RC + RK(RC)

                            D/D(RC)  +  RK(RC)
                                                  RC  +  RK(RC)

                                                  D/D(RC)  + RK(RC)

                                                  RC  +  RK(RC) +  RE •

                                                  D/D(RC)  + RK(RC)


                                                  RC  +  RK(RC)

                                                  D/D(RC  +  CA) +  RK(RC)

                                                  RC  -i-  RK(RC)  -i- RE
                                                  D/D(RC

                                                  RK(RC)
                                                          +  CA)

                                                          -i-  RE
                                                  RK(CA) +  RE

                                                  D/D(RC +  CA)
RK(RC)
                                                                  RK(RC!
Notes :

  l'
              ™       ifc WaS assumed th*t no industrial facilities  have less
    than $100,000  in  annual receipts.
  2. Facilities, with less. than-. $75. 000' in. annual, receipts are subject »-o
    recorakeepxng  requirements only.   Consequently, these small facilities
    are not included  in  this table.
Definition  of Terms:

              = Purchasa and install a refrigerated condenser.
              = Perform recordkeeping activities  required  for  facilities
                with a refrigerated  condenser.
              ... Accelerate* the- purchase* of: a. new* dry-to-dry facility with  a
                ouilt-in refrigerated condenser.
              = Build a- room^ enclosure with, a small carbon  adsorber.
              = Accelerate^ the purchase -of a new. dry- to-dry facility with  a
                built-in refrigerated condenser and install  a small carbon
                adsorber .
              = Perform, recordkeeping.. activities'  required- for., facilities
                with a carbon adsorber.
 D/D(RC)
 D/D{RC + CA)
 RK(CA)
                                   2-7

-------
   •  commercial facilities with more than $75,000 in annual
      receipts  that operate uncontrolled dry-to-dry machines,
   •  commercial facilities with more than $75,000 in annual
      receipts  that operate uncontrolled transfer machines,
   •  industrial facilities that operate uncontrolled dry-to-dry
      machines,  and
   •  industrial facilities that operate either uncontrolled or
      controlled transfer machines.

      For  this  analysis,  it was  assumed that  owner(s)  will select
the least costly option  in present value  terms.   The  net  present
cost  (NPC) of each available option was computed using  data from
the control costs presented in  Tables  2-1  through 2-4 and the
capital costs of a new dry-to-dry machine  ('reported in  Appendix A,
Table 2-10).  The following equations were used  to compute the NPC
of the options  identified  in Table 2-5:
      • Installing and operating an RC

                     = KRC •*• 2   | ——"""-rr  I if n £ 7             (i;
                                 or
     •  Accelerating the purchase of a new dry-to-dry machine with
       an RC
                  NPCDD = KDD +  2  F   °re   1 .
                                t=0 Ld  +  r)fc J

                  / r  stay    T   V4 r  ore   J 1
                  1 Id +  D-  J + ttn [(I * r)t  J  J
     •  Performing the recordkeeping requirements
                                 14-, r

     •  Building  a  room enclosure  and installing  a small CA
                               2.,-3.

-------

                                                                     (4)
        Accelerating the purchase  of a new dry-to-dry machine with
        an RC and an add-on small  CA
                 NPCDD/CA .  KDD  +  KcA
                      f   *PP_ I    1 «  f   ORE    1
                      Id  + r)_» J + t=n Ld + r.)t J
                                                    (5)
where
      KRC
      ORC

      n

      t
      r
      NPCDD

      KDD
      KRK
      ORK.
the  net present cost  of  an RC
the  capital cost of an RC
the  incremental operating cost of an  RC net of
solvent recovery savings
the  remaining life of  the existing machine (cannot
exceed  15)
the  year (1991 is year 0)
the  WACC1
the  net present cost of  accelerating  the purchase
of a new dry-to-dry machine with a built-in RC
the  installed capital  cost  of a new dry-to-dry
machine with a built-in  RC
the  net present cost of  recordkeeping.- associated
with either a CA or an RC
the  initial cost of recordkeeping associated with
either  a CA or an RC
the  annually recurring, costs; of. recordkeeping..
associated: with: either* a: CA. or: an: RC; nee, of,
solvent recovery savings
     This  cost of capital differs by firm financial  status.   The discount
factor estimated  for this assessment  is 11 percent for firms in good financial
condition,  12.5 percent for firms in  average condition,  and 15.4 percent for
firms in poor condition.  For a more  complete discussion,  see Economic Tmnar^
AnalVSIS Of Recnilatorv CorH-rnlg in  *fre- Dry-meaning- TnHiigl-T-y (EPA  199!) in
Appendix A.
                                  2,-9

-------
              =  Che net present cost of building a room  enclosure
                 (RE) and installing a small CA

     KRE      =  the capital costs of a room enclosure

     KCA      =  the capital costs of a small CA

     ORE      »  the incremental operating costs of a room
                 enclosure

     OCA      =  the incremental operating cost of a small CA
                 including the solvent recovery savings

     NPCDD/CA  =  the net present cost of accelerating the purchase
                of a new dry-to-dry machine with a built-in RC and
                 installing•an add-on CA

In computing these costs,  several assumptions were 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 5  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.   The current  stock of
       machines would have been completely replaced by new
       machines within 15 years.

     • Costs are computed  for a 15-year  period of analysis.2

     •  Plant owner(s)  evaluate the cost  of the control  options
       using a real,  after-cax WACC, which differs  depending on
       their financial  status.   (See EPA,  1991  for  a  discussion of-
       the method for  computing the WACC.)

     •  The plant financial status,  the WACC, and  the  share  of
       facilities in each financial status are given  below:
        below average

           average

        above average
WACC"
15.4%
12.5%
11.0%
Share- of.
Facilif.i f p|
25%
50% '
25%
                               2-10

-------
      ••• Operating costs are incurred at the beginning  of  each
        period.  The costs of accelerating the purchase of  a  new
        dry-to-dry machine include the operating costs of an  RC
        because most new dry-to-dry machines with vent controls  use
        RC technology.
      •  RCs 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 RCs, the  add-on
        RC will not be transferred to the new machine.
      •  Machines with more than 7 years of remaining life must
        purchase an RC device in the first year and the eighth
        year._  (These control devices have a 7-year life.)
        Facilities, with 7 or fewer years of remaining  life will
        purchase only one RC.
      •••.The life of the room enclosure is equal to the remaining
        life of the transfer machine.

      Even in the absence of the standard,  it is projected  that
virtually all' owner (s)  of. dry  cleaning facilities would have
purchased new dry-to-dry machines with built-in vent  control
devices when  existing machines  required replacement.   Therefore,
the cost  of the  accelerated purchase  only  includes costs
associated with  those years before the expiration of  the  current
machinery.  Those  facilities with older existing equipment  are
more  likely to choose the  option to accelerate  the purchase of a
new., dry-to-dry machine- than, are.- 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 facilities with older  equipment.

2 . 3   ANNUALIZED:: CONTROL'..-COSTS::
      Once  the cost-minimizing., decision is-identified,  based on the
computations and assumptions outlined  in Section 2.2,  the
annualized costs- (AC) associated with,  each-decision can. be
computed.   The computations are relatively straightforward  for
facilities that purchase and invest.in the required vent  control
device and/or room enclosure.  Eq. (6) shows, the method for
computing-; these- costs;:,-
                               2,-11,

-------
      AC =
K
                                        KRK
                                                   I
                                                }/r J
                                                           ORK
where
     AC
     K
     n
          =  the  annualized compliance cost
          =  the  installed capital  costs  of an RC and/or a room
             enclosure
          »  the  annual  operating costs of  an RC and/or a room
             enclosure
          =  the  weighted  average cost of capital (described in the
             previous section)  •
          =  the  remaining life  of  the existing machine (cannot
             exceed  15 years) or the  remaining life  of the control
             equipment,  whichever is  shorter
          =  the  initial costs of recordkeeping
          =  the  annually  recurring costs of recordkeeping

     In some instances  it is less  costly to accelerate the
purchase of  a- dry-to-dry  machine.  Annualized  costs associated
with this option were computed  by  annualizing  the NPCDD  or the
NPCDD/CA computed in Eqs.  (2) or (5)  using  the  following equation:
              NPVDP or
                   or NPVnn/ra  ]   f _ KRK _ 1
                   1 + r)-«>/r J + [{I - (1 + r)-«}/r J
                                                                 (7)
where NPCDD and NPCDD/CA are as defined in Eqs.  (2\
other terms are as defined above.
                                                    or  (5) and all
     Table 2-6 reports the annualized costs for  facilities  without
baseline vent control devices.  Tables 2-7 and 2-8 report the
annualized costs, for- facilities with.baseline-RCs-and CAs,
respectively.  The values reported in Tables 2-6 through 2-8 were
used to compute the economic and financial impacts presented in
Sections 3 and 4 of this report.,-
                                2-12

-------
TABLE  2-6.  ANNUALIZED COMPLIANCE COSTS  PER FACILITY  DUE TO THE
            FINAL STANDARDS:   NO BASELINE VENT CONTROLS
            ($/facility/yr).
Industry Sector
and Machine
Capacity (kg/ load)
Commercial,
6.8
8.2
11,3-
13.6
15.9
15.9
20.4
22.7
22.7
27.2
45.4
45.4
Industrial
63.5
113.4
113.4
~""^ ES5S5S5SSSESS5SS
Notes :
Machine
Technology

Dry-to-Dry
Dry-to-Dry
Dry-to-Dry
Dry-to-Dry
Dry-to-Dry
Transfer.
Dry-to-Dry
Dry-to-Dry
Trans fez-
Dry- to -Dry
Dry-to-Dry
Transfer

Dry-to-Dry
Dry-to-Dry
Transfer
=========

Annual

$0 to $75K

345
345
345
345
345
345 '
345
345
345
345
345
345

—
—
===================

——^^"^"••^^•••^•M
Receints Per

$75 to $100K

4,874
4,897
2,442
2,429
2,445
3,189
2,571
2,582
3,253
2,603
3,520
4,214

__
• . 	
SESSS^^^^^^^^^^^^^H— — «•— •
— ' ' ii^^^sssaasaaaaa
•"••••MMKBBa
Facility

.Over $10 OK
"•• — •— — — — i— — i—
7,765
5,835
5,648
3,792
3,813
8,019
4,045
4,.066
8,454
2,063
2,971
6,735

2,673 -
-3,003
-8,5.44-
=============:
  '

is snorter.
                                  °r the !«• ««- the control, equipmen,
                        Recordkeeping costs are- annuaiized over 15 years
                                2..-13.

-------
 TABLE 2-7. ANNUALIZED COMPLIANCE COSTS PER FACILITY DUE TO THE
             FINAL STANDARDS:   RC BASELINE  VENT CONTROLS
              ($/facility/yr)
Industry Sector
and Machine
Capacity (kg/ load)
Commercial
6.8
8.2
11.3
13.6
15.9
15.9
20.4
22.7
22.7
27.2
45.4
45.4 '
Industrial
63.5
113.4
113.4
Annual Receiots Per Facilitv
Machine
Technology

Dry-to-Dry
Dry- to -Dry-
Dry- to -Dry
Dry-to-Dry
Dry- to -Dry
Transfer
Dry-to-Dry
Dry-to-Dry
Transfer
Dry-to-Dry
Dry-to-Dry
Transfer

Dry-to-Dry
Dry-to-Dry
Transfer
$0 to $75K

345
345
345
345
345
345
345
345
345
345
345
345

—
' —
— —
$75 to $100K

666
666
349
' 349
3.49
349
349
349
349
349
349
349

— _
—
_ _
Over $10 OK

1,300
983
983
666
666 .
666
666
666
666
349 '
349
349

358
358
-5.78=)'
Notes:

  1. Annualized compliance costs  in the commercial sector are computed using
     the capital and operating  costs presented in Table 2-3.   Costs  for the
     industrial sector are computed using values reported in  Tables  2-1 and
     ^ *~3 •
  2. Discount rates vary by firm  financial, status:  IS.4 percent  for firms in
     poor financial condition.  12.S percent: for- firms,in average- financial
     condition,  and 11 percent  for firms in good financial condition.
     ^r^r?ing^C°St3 af8 annualized ov** 15 years.  Room  enclosure costs
     are annualized over the remaining life of the dry cleaning machine.
                                   2-14

-------
 TABLE 2-8. ANNUALIZED COMPLIANCE COSTS  PER FACILITY DUE TO THE
            FINAL STANDARDS:  CA BASELINE VENT CONTROLS
            ($/facility/yr)
 Industry Sector
'and Machine
 Capacity (kg/load)
  Machine
Technology
Commercial
6.8
8.2
11.3-
13.6
15.9
15.9
20.4
22.7
22.7
27.2
45.4
45.4
Indus f-T-ifii
63.5
113.4
113 . 4
==============================
Notes :

Dry-to-Dry
Dry-to-Dry
Dry-to-Dry
Dry-to-Dry
Dry-to-Dry
Transfer
Dry-to-Dry
Dry-to-Dry
Transfer
Dry-to-Dry
Dry-to-Dry
Trans fez:

Dry-to-Dry
Dry-to-Dry
Transfer
SSTS^SSS^— S5^SSSS2^^^^^=^—
^^^^^^^^""•^^""•^^^•^••^••IM

             •^^•^^^^"••"SESBBSSESH;
   Annual  Receipts Per Facility

$0 to $75K  $75 to $1QQK Over  $100K
                                    345
                                    345
                                    345
                                    345
                                    345
                                    345
                                    345
                                    345
                                    345
                                    345
                                    345
                                    345
                            824
                            824
                            428
                            428
                            428
                            428
                            428
                            428
                            428
                            428
                            428
                            428
              sector are computed usi»g value. r.«aa
                            1,628
                            1,226
                            1,243
                              836
                              836
                              836
                              836
                              836
                              836
                              428
                              428
                              428

                              437
                              437
                           -5,706
                                                            -
                                                 .
                               2-15-

-------

-------
                              SECTION 3
                           ECONOMIC  IMPACTS

      Economic theory provides a framework for analyzing  the  links
 between the demand and supply conditions an industry faces,  the
 industry's market structure, and the typical behavior of firms in
 that industry.  This section examines market structure in the dry
 cleaning industry and develops an approach for estimating the
 impacts of an increase in the cost of supplying dry cleaning
 services.   A neoclassical supply/demand analysis was used to
 project market impacts due to the standards.   Price and quantity
 adjustments were computed using a model market, approach that
 considers  market structure in the commercial  and industrial
 sectors of the dry cleaning industry.   These  adjustments .were used
 to project consumer and producer welfare effects,  plant closures,
 and employment effects.

 3.1  MARKET STRUCTURE
     .Within each sector of the dry  cleaning  industry,  many
 localized  geographical  markets  exist where only  neighboring  firms
 compete directly.   Although submarkets  are only  loosely tied  to  a
 national market,  economic  decisions by  individual  firms are
 related to national trends.   The existing market structure
 reflects fundamental market forces that  are likely to be  an
 enduring feature  of the  dry cleaning: industry.  The, economic,
 impact  analysis uses the differences in  market structure  and
 pricing practices of dry cleaning facilities to predict the market
 adjustments in response  to  the standards.

 3 • ! • !   Market Sbrtirm-rg  jn  f.he Commercial .q^rrny
     Two basic market structures are prevalent in 'the commercial
 sector.  The. first  is a competitive structure, which is found
predominantly in- urban and suburban areas and. characterized; by-the=
existence of many dry cleaning facilities in each market area and
no barriers to entry.  Approximately 90 percent of the commercial
                                3-1

-------
 facilities are in urban/suburban market areas.  The second  type of
 market structure is characterized by a single plant in a rural
 market area  (see Appendix A for a discussion of market structure).
 Because consumers are unwilling to drive long distances to
 purchase dry cleaning services, the owner of a single plant in a
 remote area does not behave as if in a perfectly competitive
 market.

      Whan/Suburban MarKgt-,3.  For this analysis, it was assumed
 that a competitive market structure exists for commercial dry
 cleaning facilities located in urban and suburban areas.   The
 competitive model is based on the hypothesis that no plant
 individually can influence market equilibrium,  but the behavior of
 all producers taken together determines the position of the market
 supply curve.  In addition,  the cost of producing the last unit of
 output,  the marginal cost,  along with market demand determines
 equilibrium price and output.   Furthermore,  at  a stable
 equilibrium price,  each  individual  plant  can sell  any level  of
 output desired, with no  perceptible effect  on equilibrium  values.
 As  a result,  each plant  faces  an implicit demand curve that  is
 perfectly  elastic (horizontal)  at the  current market  equilibrium
 price.

      Initially,  imposing controls on a plant will  alter the  costs
 of  producing  the  same  level of output  as before-the controls.
 This  production cost change will induce a shift of that plant's
 supply curve.  Because the supply curve for  a well-defined market
 is  the horizontal summation of individual plant supply curves  for
 all  facilities participating in that market, the shift in the
 market supply curve-can. be determined:, from-, knowledge of. plant-
 specific shifts.

     The position of the market demand curve is critical to
determining the change in equilibrium., price-and output resulting,.
 from a shift in the market supply curve.  The slope of the demand
curve measures the responsiveness of quantity demanded to  a change
in the price of the. service,.  The elasticity of  demand- is  a
                                3-2

-------
  relative measure  of. demand responsiveness and. is measured as the
  percentage  change in quantity demanded of a good or service
  resulting from  a  one-percent change in its price.

       Price  and  output adjustments and the resulting welfare
  changes can be  calculated  if the  baseline price and output values,
  the relative shift  of the  market  supply curve,  and estimates of
  demand and  supply elasticities are  available.   Three of these
  components—the baseline price and  output values,  demand
  elasticity,  and supply elasticity—are  estimated in the Economic
  Impact Analysis of  Recnilafri^-y Controls  in t-.he DT-V rl aan-i nrr
  Industry (EPA,  1991)  contained in Appendix A.   Estimated baseline
 price is $6.34 per  kg in. the commercial sector  and $2.00  per kg in
 the industrial sector.  Baseline output levels  vary  with  each
 sector and model.market.   Demand and supply elasticity  estimates
 are -1.086 and 1.558, respectively.  The  final  component,  the
 relative shift  of the market supply curve, is based  on  the
 annualized costs of the standards computed in Section 2.
Rural
                      The outstanding characteristic of the
 structure of  the dry cleaning industry in rural communities is the
 prevalence of markets- that, are served by a single plant.  Anocher
 salient  characteristic of rural dry cleaning facilities is that
 annual revenues  are typically below $25,000.   The small scale of
 che market in rural communities requires the- operation of a
 minimally sized  plant.   The only option available to a new
 entrant,  therefore,  is  to double (at the minimum)  capacity in the
 market .

     Although ..these, single-plant: markets; are- not: perfectly
 competitive,  the ease of - entry  into  the  dry cleaning -industry
 implies  that  the- threat- to .long-run  profits from new entrants  is
keen and persistent.  The optimal, pricing  strategy  is.  to set a
prof it.-maximizingv price; chat  is: low-- enough .to: deter- entry.
Therefore, to model the economic  impact of the proposed
regulations, it was assumed that  the owners of firms in  single-
plant rural, markets, follow, a., limit-pricing' strategy.-  The-
                                3-3

-------
 assumptions of potential large-scale entry and output maintenance
 allow application of the theory of limit-pricing developed by
 Bain,  Sylos-Labini,  and Modigliani (Sherer,  1980) .

      Any price above the average total cost of a new plant would
 encourage new entry  into the market.   The existence of a second
 plant  in the market  would decrease the market share and the total
 revenue of the initial supplier.   Assuming that the productivity
 of dry cleaning equipment has been increasing over  time,  owners of
 new equipment would  tend to have  lower marginal costs than owners
 of older equipment.   Therefore,  the market price would probably
 decline with the -ntrance of a second plant,  further decreasing
 the total revenue of.the existing supplier.

     Furthermore, if the assumption of increased productivity is
 correct,  owners of new facilities may be  able to set prices at a
 level  where initial  suppliers would not be able to  cover  their
 costs  of production.   If the price set by the new supplier fell
 below  the variable costs of production for the initial  supplier,
 then the initial supplier would cease operations.   If  the initial'
 supplier could cover variable costs but not all the fixed costs of
 production,  then the plant would  continue to  operate in the short
 run but  would face potential financial failure.  Facing this
 potential erosion in profits and/or financial  failure,  the  owner
 of  an  existing plant is  most likely to adopt  the pricing  strategy
 that presents  the strongest  deterrent  to  a potential entrant  to
 ensure that his  market share is not eroded.

     Because  both new  and existing facilities  with  less than
 325,000  in annual., receipts, are-subject- to recordkeeping
 requirements under the final  standards, the new entrant's long-run
 average cost curve is. affected..  Therefore, the limit price set by
 an existing plant would potentially change in response  to che
 standards.

     To compute  the price and output adjustments and the resulting
welfare changes  for these,, rural (single facility) markets, the
                                3-4.

-------
baseline price and output values, the relative  shift  of  the
marginal cost curve, and estimates of demand elasticity  are
required.  As noted above, the baseline,price'and output values
and demand elasticity are estimated in  the Economir
Analysis of Regulatory Controls in the  Drv Cleaning;
                                                                EPA,
 1991) contained in Appendix A.  The relative shift of the marginal
 cost curve is based on the annualized costs of the standards
 computed in Section 2.
           Mare
                              TO facilitate computing impacts  of
 the standards,  actual dry cleaning facilities were allocated among
•model markets.   Six model markets represent the commercial sector
 and are differentiated by
      • • rural and urban areas, '
      • the proportion of facilities with 'baseline vent controls,
      • the income distribution  of facilities represented,  and
      .• the behavioral response  to a cost increase.

      Table 3-1  characterizes the model markets by 'share of
 facilities with baseline vent controls (due to state  regulation)
 and the total number  of  facilities  allocated to each  market (EPA,
 1991).

      Rural markets are represented, by  Model. Markets. A and  3.   it-
 was  assumed that  all  facilities  in  these  model  markets  are  small
 establishments  that receive  $25, 000. or less  in  annual revenue.   in
 addition,  it was  assumed  that these small rural  areas have  only
 one  plant  providing commercial dry cleaning  services  for the
 entire, market, area.  Market: A represents-., areas, that, have, a., single
plant .with  a vent  control in place in  the baseline.  Market B
represents  those areas with a single plant that  does not have a
baseline vent control.  These facilities, are only subject, co
recordkeeping requirements under the final standards, because of a
size cutoff for vent control and room enclosure requirements.
                                3-5

-------
 TABLE 3-1.
PROFILE OF MODEL MARKETS IN THE COMMERCIAL  SECTOR
Market
Model
A
B
C
D

E

F

Total
Source :
Market
Description3
Rural
Rural
Urban/ suburban
Urban/ suburban

Urban/ suburban

Urban/ suburban


U.S. Environmental
Share of Facilities with Total Number
Baseline Vent Controls
All facilities controlled
No facilities controlled
All facilities controlled
Controlled facilities
dominate
Controlled and uncontrolled
facilities evenly distributed
Uncontrolled facilities '
dominate

Protection Agency. 1991. Economic
Analysis of Rgmjlafcn-ry Canfr-rnis in fhe Dry rl eaninq Tnrh
Facilities
1,543
1,606
1,157
10,432

8,073

7,683

30,494
-act
asAry. Final
         report prepared for the U.S. Environmental Protection Agency, Office
         of Air Quality Planning and Standards, EPA 450/3-91-021.
 aRural markets are defined as locales with population of 2,500 or less that
  are  not part of a metropolitan statistical area.  For this assessment, rural
  markets have only one plant per market area.

      Urban/suburban commercial markets are represented by  Model
 Markets C through F.  These model  markets are characterized as
 having more than one plant 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 all facilities have baseline  vent
 control devices as a result of  stringent State regulation.
 Similarly,  Market D describes:  those-' areas, where mosc of che-
 facilities  have baseline  vent  controls  as a result of  State
 regulation  that mandates  vent  controls  for most facilities.   All
 of the impacts in Market  C  and  most  of  the impacts in  D are due to
 recordkeeping requirements.  Markets E  and F contain a mixture of
 facilities with and without baseline vent controls.   Impacts  in
Markets  E and F are the result  of recordkeeping,  vent control, and
 room  enclosure requirements.
                                 '3-6

-------
  3 • 1 • 2  Market
                                 Tndvig.1-rial gec1-nr.
       Like  commercial facilities located in urban /suburban areas,
 industrial  facilities  operate  in perfectly competitive markets.
 However, no price and  output adjustments  due to the standards are
 likely to occur in this sector for  two  reasons.   First,  water and
 detergent are near-perfect substitutes  for PCS because virtually
 all of the garments  dry cleaned by  industrial facilities are
 water -washable.  Because consumers  do not  dictate the cleaning
 method used, facilities facing  a control cost with continued PCE
 use would likely substitute water washing  for dry cleaning
 assuming sufficient  capacity is  available.

      Second, industrial cleaners  typically do not charge, different
 prices for garments cleaned in water and detergent  and garments
 cleaned in PCE;  also, over 92 percent of the  output from
 industrial  facilities is from regular laundry operations.  This
 second factor is evidence that  the- cost of producing  the marginal
 unit of .output  in the market  area is not likely to increase
 because of  the  standards.   For  these reasons, producers would not
 be able to  pass  along any of  the control costs in the form of a
 price  increase.,,

 3 . 2   AFFECTED POPULATION
      The- af.f actad, population  includes, facilities af f acted by
 recordkeeping requirements only and  facilities affected by some
 combination  of recordkeeping, vent control,  and room enclosure
 requirements.  The number of affected facilities varies depending
 on the model market analyzed.   Table 3-2 shows the number of
 affect ad,, facilities, in-  each, model, market under- each- type  of
 requirement .

     Table 3-3 shows  the- share  of the total, facilities in each
model market' potentially affected  by the type  of control
requirement.  Only 11 percent of  the facilities  in the commercial
sector are projected to incur costs beyond recordkeeping  costs
Approximately- 82: percent: of;, commercial, facilities:  use-  PCE in  the
                                3-7

-------
      TABLE  3-2.  NUMBER OF FACILITIES AFFECTED BY  THE STANDARDS
Industry Sector
/ and Model Market
Commercial
A
B
C
D
. E
F
Total Commercial
Industrial
Type of
Vent Control Room

0
0
0
115
1,621
1,725
3,461
65
Requirement
Enclosure

0
0
0
29
409
436
874 •
84

Recordkeeoing

1,071
1,606
843
7,682
6,979
6,766
24,947
130
TABLE 3-3.  SHARE OF FACILITIES  AFFECTED  BY THE REQUIREMENTS  OF THE
             STANDARDS
Type of Requirement
Industry Sector
and Model Market
Commerqi.a.1
A
3
C
D
E
F
Total Commercial,
Industrial
Total Number
of Facilities

1,543
1, 606
1,157
10,432
8,073
7,683
30,494
395
Vent
Control
(%)

0
0
0
1
20
22
11
16
Room
Enclosure
(%)

0
0
0
0
5
6
3
•21
Record-
keening
(%)

69
100
73
74
86
38
32
33
Notes:
  1. The cotal number of- facilities includes PCE  facilities as well as  chose
     that do not use PCE in the dry cleaning process  (see Appendix A).
  2. -The share affected is computed based on the  estimated number of affected
     facilities reported in Table 3-2.
                                   3-8

-------
 dry cleaning process and all of these PCS  facilities  are affected
 by the recordkeeping requirements.  In the industrial sector,
 approximately 16 percent of facilities are affected by the vent
 control requirements, 21 percent are affected by  the  room
 enclosure requirements, and 33 percent are affected by the
 recordkeeping requirements.

      Model Markets A through C were not projected to  incur impacts
 under the proposed standards because facilities in these  markets
 are either below the cutoff for vent control devices  (Markets A
 and B)  or have baseline vent controls (Markets B and C) ,  and
 because recordkeeping costs were not included when calculating
 impacts for the proposed standards.   However, recordkeeping costs
 were included in this analysis.   Consequently,  impacts were
 computed for facilities in all markets including Markets A' through
 C.   A higher proportion of the facilities in each of the-
 urban/ suburban model markets will  potentially incur impacts under
 the final  standards.

 3 . 3   MARKET ADJUSTMENTS
      The final standards are likely  to disturb the current
 equilibrium in the  dry  cleaning  industry,  resulting  in price and
 output  changes  and  corresponding welfare  impacts,  plant closures,
 and  employment  effects.  All, commercial- markets., are. projected, to
 incur price- and. output adjustments, and:, consumer' and: producer
 welfare  impacts.  However,  the industrial sector, is. projected to
 incur producer welfare impacts only.
3 • 3- . 1  Pric°- arid
     Incremencal, impacts; of... the, requirements--, were, quantified
through estimated market adjustments in price and  output  for both
urban/ suburban and- rural markets:- in.- the commercial, sector.   Figure
3-1. depicts- the supply /demand relationship, for a competitive
urban/ suburban market area.  Equilibrium prior to  the standards
occurs at an output, level, of Qx and a price- of PL per unit
                                3 -9:..-

-------
         S/Q
                                                        S-,
(e)
                                                        Si(e)
                                                        Q/t
Figure.3-1.  Price and Output Adjustments Due to Increased Costs of
             Production:   Urban/Suburban Markets
 (kilogram)  of  output.   The supply curve (Si)  is upward sloping with
an elasticity  of  -g- and the demand curve  (D)  is downward sloping
with an elasticity  of  "T\."

     Assuming  that  the standards  result in a net cost increase for
facilicies  in.  the. representative-urban/suburban, markec,  the markec
supply curve will shift up from a position such as Si to  S2  in
Figure 3-1.  The vertical  shift distance is equal to the  average
compliance  cost per unit of output due  to  the  standards.

     Assuming:  that; the.- markec demand'  curve.- remains: scationary in
response to technological  controls is-plausible- because- these
controls normally affect only supply-side  variables  such  as
production costs.   In. addition, the candidate  control devices will
not lessen the quality of  the product,  further justifying a
stationary demand curve.   If the new supply curve  (S2) now
intersects the downward sloping demand  curve at  a  higher  point
                                3-10

-------
  than the  baseline supply'curve (Si),  price increases and quantity
  decreases result.

       As noted  above,  the magnitude of the new equilibrium
  price/output combination (P2,  Q2) can be computed  if baseline price
  and  output values, the demand  elasticity,  the supply elasticity,
  and  the supply shift parameters are known.  Assuming that no
  correlation exists between production costs and  control costs,  the
  shift in  the supply function of the marginal  plant may correspond
  to the lowest control cost (zero in'markets' with unaffected
  facilities)  or highest control cost-per kilogram of  output
 estimated.  For this analysis,  the supply shift  was  based on the
•expected value of the percentage change in marginal  costs for the
 given market area.  Measured along the price  axis,, the  expected
 percentage shift of the supply function is equal to  the average
 compliance cost per unit of output divided by the baseline  price.

      Figure 3-2 depicts the demand and supply conditions  facing a
 single supplier in a rural market area.   The position of the
 marginal cost  curve is difficult  to estimate without using
 detailed data  on  input prices  at different output levels.
 However, such  data are not available..  For analytical convenience
 the marginal cost  curve (Md) is  assumed  to be horizontal over the
 relevant range.  The demand curve  (D)  is  downward sloping with an  .
 elas,tacrty of—T|—  As  in, the urban/suburban market,  equilibrium  	
prior to the standards  occurs at  an output  level'of Ql and a price
of P! per  unit  (kilogram)  of output. *

      An upward  shift in the  (horizontal) marginal cost curve (from-,
Mr. to, MC2)  .of,, a,monopoly supplier; in. rural, market results  in prica   ""
increases-,  and...quantity decreases.  As noted'previously,  suppliers
in these rural markets probably practice limit pricing to  deter
new-entry..  However,, the, standards result in higher long-run
average costs, for new entrants and a correspondingly  higher  limit
przce for current suppliers.  Consequently, price and. output
adjustments are projected for Markets A and B.
                               1-11,

-------
                                                   MC,
                  Q2   Ql
Figure 3-2.  Price and Output Adjustments Due  to  Increased Costs of
             Production:  Rural Markets
     Projected price and output adjustments due to  the  standards
are reported in Table 3-4.  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 only presented by model
market.

     Estimated; price-and output adjustments, due to  che.  standards
are less than 2.5 percent (in absolute value)  for all markets in
the commercial sector.   As noted above,  no price and output
adjustments are projected for the industrial sector.
                               3-12

-------
 TABLE 3-4. PROJECTED PRICE AND OUTPUT ADJUSTMENTS DUE  TO  THE
            STANDARDS
Industry Sector
and Model Market
Commercial Markgf.s
A
B'
C
D
• E
F
Total Commercial
Industrial
Baseline
Price
(S/kg).

6.
6.
6.
6.
6.
6.
6.
2.
=====

34
34
34
34
34
34
34
00
===
Price
Adjustment
(%)

2.
2.
0.
0.
o.
0.
. -
0.
=====

'29
29
15
17
57
63
-
00
Baseline
Output
(Mg/yr)

3
3
25
227
155
145
562
170

,669
,819
,477
,709
,823
,898
',396
,902 .
Output
Adjustment
(%)

-2
-2
-0
-0
-0
-0
-0
0
••"

.11
.11
.16
.18
.62
.69
.46
.00
"i."- ' '
 3.3.2   Welfare
      The costs of a regulatory policy are measured by the change
 in- social welfare that  it  generates.   The sum of the producer and
 consumer surplus  losses  is an  estimate of the loss in social
 welfare  due  to the standards.   The  estimates  do  not include the
 welfare  impacts associated with potential changes., in. environmental
 quality.  Note, that these  estimates or welfare: impacts are for- the
 costs of controlling emissions  only.   Benefits. resulting from
 changes  in environmental quality are  not  reflected in the
 estimated welfare  impacts.

      Producer- welfare impac.cs:. result: from: increased., coses,, or
production that are  fully  or partially absorbed,, by •• the plant.
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.  Facilities
that operate in markets where a price increase is likely  are able
to pass along a. portion of the increased costs of, production.  The
                               3-13

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 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 the industrial sector are zero because price
 and output is not affected.   Both sectors incur producer welfare
 impacts.

      Estimates of the surplus changes for consumers and producers
 and the resulting change in social welfare are presented in Table
 3-5.   In the  commercial sector,  estimated consumer welfare impacts
 are -$17.8 million.   Producer welfare impacts total approximately
 -$11.8  million.   In the industrial sector,  estimated  consumer
 welfare impacts  are zero (because price  and quantity  adjustments
 are zero),  and producer welfare  impacts  are a positive  $607,000
 because of a  projected net  savings due to the standards.
     TABLE 3-5. PROJECTED WELFARE IMPACTS DUE TO THE STANDARDS
Industry Sector
and Model Market
Consumer Welfare Producer Welfare
     Impacts           Impacts
     ($1,000)           ($1,000)
Net Welfare
  Impacts
   $1,000)
Commercial _ Marker. s
*
,n
B
C
D
E
C*
Total Commercial
Industrial'

-526
-548
.-239
-2,437
-5,643
-5,819
-15 , 212..
0

-11
-12
-167
-1,703
-3,969
-4,096
-9,958
607

-538
-560
-406
-4,141
-9,612.
-9 , 915
-25,170
607
     These welfare impacts are projected for the first year after
the regulation is, inv effect::.   Fewer- losses:, will, be- incurred, in.. 14
                               3-14

-------
 subsequent years because existing uncontrolled transfer  and dry-
 to-dry machines are being replaced with dry-to-dry machines with
 built-in vent controls -upon retirement even at baseline.

      Adding the producer and consumer welfare effects  leads to an
 estimate of the total control  cost for each sector.  In  the
 commercial sector net welfare  impacts due to the standards  are
 estimated to total— $25  million.   Net welfare impacts  in the
 industrial sector represent  a  gain of $607,000.

 3.3.3  Plant C
      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 using detailed     '<
 financial data including input prices.  As a  result,  this section
 offers qualitative impacts based on output adjustments for each
 sector.   Specifically,  it is assumed that the  entire  output
 adjustment is a result  of plant closures.

      It  should be noted that the estimates of  plant closures
 presented., in: this-., analysis, are-, based, on assumptions chat
 potentially  underestimate the gross or total number of. plant
 closures while  potentially overestimating the net plant closures.
 Because the  number of plant closures are presented as  net of  new
plants entering- the, market,  the- estimated number of. plant closures
do-, not:.. reflects a£cjLa, plant; closures L  However,  two assumptions
have, the;.:- effect: of-, making, the- estimates  worst-case- in  terms of net:
closures.  First,  it was  assumed that  facilities do not reduce
                        °hanses «- ownership presented in Section 4
                   f^ures as well as other changes in ownership such as
                   ^a*"*)  selling the plant to an owner in better
          -  ° lt"°'  Conse<3uently- the potential changes  in ownership
      sction     n "4 eXCSed ths' e*tin,ates of plant closure presented in
                                3-15.

-------
 capacity utilization, but  rather  the  entire  output  reduction was
 accounted  for by  facilities  shutting  down.   In  addition,  it  was
 assumed that.the  smallest  plants  projected to incur costs beyond
 recordkeeping costs account  for all the plant closures  in Markets
 C through  F.  in  Markets A and B, plants do  not  incur any impacts
• beyond recordkeeping costs.  Consequently, the  smallest plants  ($0
 to $25,000 in annual receipts) account for all  of the plant
 closures in Markets A and  B  because only the smallest plants  are
 represented in these markets.

      Under these  worst-case  assumptions, an  estimated 259  net
 plants are pr-jected to close in  the commercial sector as  a result
 of the standa is.  No plant  closures are projected  for the
 industrial sector in view of the  cost savings expected for this
 sector.

      Considering  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 plant.  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,  Che
 average plant dry cleaned 24,489  kilograms of clothing in 1982 and
 28,335 kilograms in 1987.   One industry spokesman indicated that
 these changes do not reflect  a trend toward  larger dry cleaning
 plants;  rather,  plants  are  operating at a higher capacity
 utilization. (Fisher,,  1990) .

 3.3.4  Employment  f.ffects

      The standards may  cause, short-run price  impacts in- the
 sectors  of  the dry cleaning industry examined in this assessment.
 If the  short-run effect  of  a  regulatory control  is to increase the
 equilibrium price  of dry cleaning  services, then  the short-run
                                3-16

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 market-clearing  output  of  services  will  be lower than the baseline
 output.  If  the  market-clearing  output declines,  the demand for
 labor services by operators of dry  cleaning facilities may also
 decline.  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.

      Facilities  in the  industrial sector are projected to realize
 a cost savings due to the solvent recovery  savings  associated with
 the standards.  Consequently,  the anticipated output  impacts  on
 industrial launderers are likely to be zero, so employment  effects
 in this  sector are not considered further..

      However,•in the commercial sector,   two  employment  effects of
 the standards are considered:   employee  displacements and employee
 displacement costs.   Displacements are job terminations that
 result from cut-backs at operating facilities and/or plant
 closures.   Displacement  costs  are welfare losses incurred by those
 displaced workers.  These employment impacts are short-run
 effects.  The primary effects  of  the standards are short-run
 effects because  it  is projected that virtually all dry cleaning
 machines are  being replaced at baseline by controlled, dry-to-dry
 machines.

     Because  closures  may occur and  output reductions among
 operating facilities can themselves  result in worker
 displacements, this analysis assumed that short-run  employment
 impacts of standards are proportional to  projected output  effects
 An estimated,  ITS, 336-, workers,, were, on: payroll, at.  commercial  drv
 cleaning plants in. 1991  (EPA.  1991).  Estimated,  worker-
 displacements computed as described.above  total  813.

     Displaced, workers;, suffer welfare lossesr.through, several
mechanisms (see Hamermesh, 1989;  Maxwell,  1989; Blinder, 1988;
Flaim,  1984;' and Gordon,  1978):  .       '
     •• foregone- wages: and: benefits-, during, :ob; search;..
                               3-17

-------
      • out-of-pocket search costs,
      • diminished wages and/or job satisfaction at new jobs, and
      • psychological costs.
      Displacement risk—like risk of injury, risk 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).  Abowd and
 Ashenfelter (1981)  found that the labor market compensates
 anticipated layoffs and unemployment by 2 to 6 percent higher
 wages per year.   Topel (1984)  used a hedqnic wage function to
 estimate that an anticipated one-point increase in the probability
 of unemployment  (e.g.,  from 6 per 100  workers to 7 per 100
 workers)  requires a 2.5 percent increase in wages to compensate
 workers.

      Anderson and Chandran (1987)  developed and demonstrated a
 methodology to compute a willingness-to-pay based estimate of
 worker  displacement using Topel' s. estimated compensating  wage
 differential.  Their method is  analogous to that used by
 economists  to  estimate the implicit  value of  a  life  using labor
 market  data (see Moore and Viscusi,  1990) .   The hedonic
 displacement  cost estimate conceptually  approximates  the  one-time  '
 willingness to pay  to avoid an  involuntary unemployment episode.
 Theoretically, it includes all  worker-borne costs  net  of  any off-
 setting pecuniary or nonpecuniary  "benefits"  of- unemployment
 (e.g.,  unemployment  compensation,  leisure  time  enjoyment).  The
 hedonic displacement  cost  estimate is a  net present valuation.

      Average annual  (1991)  earnings  in the (payroll,  commercial)
 dry cleaning industry, are-. $11, 504; (ir.S:.-  Department., of. Labor,,
 1991).  Using  Topel's compensating differential  estimate-and. the-
 Anderson-Chandran methodology,  this analysis projects that dry
 cleaning workers would demand an annual compensating, differential
 of $288 ($11,504  *0.025) to accept a one-point increase in the
probability of displacement.  it-was assumed that they would be
willing to pay an equivalent amount to avoid such an increase in
                               3-18

-------
the probability of displacement.  -Therefore, the implied
statistical cost of an involuntary layoff is $28,800  ($288/0.01)
The estimated worker displacement costs were computed by
multiplying the estimated nximber of workers displaced- by the
estimated cost of an involuntary layoff.  Worker displacement
costs computed in this way total $23.4 million.
                              3-19

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                             SECTION  4
                          FINANCIAL  IMPACTS

      The final standards will potentially affect business  entities
 that own dry cleaning facilities (see Appendix A for  a  financial
 profile of potentially affected firms in the commercial  sector).
 In the financial analysis, distinguishing between the terms  "firm"
 (or "company")  and "establishment"  (or "facility") is important.
 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."  An establishment,  in
 turn,  is defined as a "single physical location at which business
 is conducted."   In Section 2,  economic impacts.are evaluated using
 model  facility  data.   The focus of  this section,  however, is on
 potentially affected firms.

     Firms  in the industrial  sector are projected to  incur a cost
 savings  due to  the standards.   Consequently,  financial impacts
 were computed for firms  in the commercial  sector  only.  This
 analysis  assumes  that the owner(s)  of an affected firm will pursue
 a  course  of action that  maximizes the value  of  the  firm,  subject
 to uncertainties  about actual  costs of- compliance and the behavior
 of other  firms.   The  owners' response-options  include
     • closing the plant,
     •• bringing the plant- into compliances with:-, the-regulation, and
     • selling the plant.
 If  the expected post-compliance value of an affected plant  is
 negative  (or  simply lower than the  "scrap value" of the  plant),
 Che owner of- the. plant, will: likely-  closevi.tr..  If:- the.: expected:
 post-compliance value is positive- and-greater than, the scrap  '
 value,  the owner will aiLhj=r bring  it into compliance  QX.  sell it
 to another firm that will do. so.

   _ . whether the firm keeps or sells- the plant depends on the
 financial condition of the firm.  If the firm has and/or can
borrow sufficient funds, to. make, a, plant compliant, it:  keeps; the.
                               4-1

-------
 plant.  If instead the firm has inadequate funds and debt
 capacity,  it sells or closes the plant.  This section addresses
 these potential changes in ownership.

 4.1  FINANCIAL CHARACTERISTICS
      Firm financial impacts were computed for firms in three
 conditions of firm financial health:   below average, average, and
 above average.   This analysis 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 plant to  another firm.

      Firms in average or above-average financial condition were
 assumed to borrow the required funds,  though possibly some of them
 will  use internal funds instead of, or in conjunction with,
 borrowing.   It  was assumed that 7-year bank notes at 11  percent
 (nominal)  interest are available to above-average firms  and that
 similar notes at  11.5 percent interest are available to  average-
 firms  (see Appendix A for a discussion of the cost  of borrowing
 for firms  in different financial conditions)..   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 7-year notes  in addition.,  to annual  operating costs.
 Firms  in below-average financial condition have  large cash
 requirements  because  they cannot borrow money, but  they  have  only
 operating costs as  recurring  annual expenses.
411
TT «t JU . _
Distribution or Potentially Affect:ad
     Estimating the number of, firms, affected, is. necessary to
estimate the financial impacts of the standards.  As explained in
Section 1,  not all dry cleaners would be affected by the, standards
because plants that use solvents other than PCE will be unaffected
by the requirements.  Furthermore, the level of impacts incurred
by a firm may vary depending on whether facilities owned by the
                               4-2

-------
  firm are required to install vent controls,  build room enclosures,
  or simply perform recordkeeping requirements.  In this section, we
  focus on firms that own facilities projected to incur costs beyond
  the recordkeeping costs for two reasons.   First,  recordkeeping
  costs do not  include a large initial investment requiring the use
  of external funds or significant cash reserves.  Consequently, no
  capital  availability impacts would result from these costs.
 -Second,  profitability impacts from recordkeeping  costs are
 potentially significant only for the smallest firms  in poor
  financial  condition.   However/  most  of the firms  in  this  size
 category operate-in markets  (Markets A and B)  where  producers will
 be able  to pass all of these costs on to  consumers.   Consequently,-
 no profitability  impacts due to recordkeeping costs  are expected.
 In the balance of  this  section,  therefore, affected  firms  include
 those that own facilities required to invest  in vent controls or
 room enclosures in  addition  to  their recordkeeping costs.

      Affected firms and affected plants are one-and-the-same  for
 single-plant firms  (i.e., single-plant  firms without an affected
 plant are themselves unaffected as business entities).  In the
 case of multi-plant firms,  the number of affected firms is harder
 to estimate.   A six-plant firm, for example,  might have six
 affected plants,  six unaffected plants, or any combination of
 both.  In this assessment,  it was-assumed that all of the plants
 owned;,-by a: single firm are either- affected or unaffected and that
•all/plants -owned by a single firm are affected equivalently.    in
 addition, it was  assumed that the proportion  of affected linns is
 identical to the  proportion of affected plants for all firm sizes.
 The estimated  total number  of affected firms  is probably not too
 sensitive- to, these-, as sump cionsv because: only 478 of. 27', 332. firms
 (1.75 percent)  have-more- than two plants, (see- Appendix-A) .

      An. estimated  3,336 firms own facilities  projected' to  incur
 costs:, beyond: recordkeeping --costs under the final standards.   These
 affected  firms  include 660 businesses with $75,000  to $100,000 in
 annual receipts and 2,676 firms  with  more  than $100,000  in  annual
 receipts.  Under: Financial. Scenario- r.. which;,  assumes, a
                               4-3

-------
 relationship between size and baseline financial condition, no
 firms above $50,000 in annual'receipts are classified as below
 average in financial condition (see Appendix A for a discussion of
 the financial scenarios used in this analysis).   Consequently,
 under Financial Scenario I,  all of the affected firms are in
 average or above-average financial condition. . (A size cutoff
 exempts facilities below $75,000  in annual receipts from vent
 control and room enclosure requirements.)

      Under Financial Scenario II,  we assumed that no. relationship
 exists between firm size and financial condition.  Under this
 financial  scenario,  50  percent of  all firms,  regardless of size,
 are allotted to the "average financial condition" grouping,  and 25
 percent of all firms to each of the "below-average"  and "above-
 average" financial condition groupings.  Under Financial Scenario
 II,  834 firms  are in below-average financial  condition (3,336 *
 25%),  1,668  firms are in average financial condition (3,336  *
 50%),  and  834  firms  are in above-average financial  condition.
 (3,336 * 25%).

 4-1.2   Baseline Financial  Ratios

     Financial  ratios are  commonly used to measure a  firm's
 financial  viability.  Financial ratios computed for  this  analysis
 include four fundamental  types-:
     •  liquidity ratios
     • activity ratios
     • leverage ratios
     • profitability ratios
Baseline financial ratios were computed for potentially affected
dry cleaning firms using data from Duns Analytical Services  (1990)
for three categories of financial health (see Appendix A,
p. 5-17).  The changes that are made to the baseline financial
statements  in response to the requirements  of the standards result
in adjusted financial ratios for firms. , These adjusted ratios are
reported- in the following, subsection...
                               4-4.

-------
  4 . 2   OWNERSHIP ADJUSTMENTS

       The  firm financial  impacts  of  the  regulatory alternatives are
  assessed  by

       • computing with-regulation prg fgrma income  statements  and
                                °f different sizes  a™*  financial
                              With—sulation financial ratios of
                           and with-regulation statements and
               to discern clearly adverse financial impacts

 Table 4-1 shows the with-regulation financial statements of
 potentially affected firms in below-average, average, and above-
 average financial condition.

      The following adjustments were made to project the with-
 regulation financial statements of firms in below-average
 financial condition.  In the annual income statement,  other
 expenses and taxes increase by the amount of the annually
 recurring compliance costs,  and net profits fall by the same
 amount.   In the balance sheet,  cash' declines by  the price of the
 control  equipment  and fixed assets rise  by the- same- amount.   These
 firms  simply -trade"  cash for control  devices  in an accounting
 sense, so  total assets  and total  liabilities remain unchanged.

     The  following adjustments were made  to  project the with-
 regulation  financial  statements of. firms  in  average and above-
 average  financial  condition.  in  the annual  income  statement
 other expenses  and taxes  increase by the  amount  of  the recurrina
 compliance costs and. the, annual, note,, payments:, and  nee, orofics-'"
 fall by 'the same amount.  in the balance,  sheet,  cash is unaffected
because these firms borrow money, .for purchasing  control equipment
Fixed and total assets increase by the value- (price) of th-   •
control equipment.   On the liabilities- side, or the balance sheet,
                               4-5

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 total liabilities and net worth have to increase by the same
 amount.   Both current and noncurrent liabilities increase.   Notes
 payable  (this year)  increase by the amount of the annual principal
 payment.   Noncurrent liabilities (which'include bank notes)
 increase  by the loan amount  (control equipment price)  less  the
 amount of principal  payable  this year (which is part of the
 increase  in notes payable).

 4.2.1 Ratio Analysis
      Table 4-2 reports  the with-regulation financial ratios  of
 affected  firms of different  .sizes and financial types  derived from
 the  financial statements  presented  in Table 4-1.   The  impacts of
 the  regulation on firms in below-average and average financial
 condition are most 'apparent,  but impacts even on above-average
 firms may be substantial.  The  debt  ratios of average  and above-
 average firms increase very  substantially  because they borrow
 funds to  purchase control  equipment.   The  debt  ratio of below-
 average firms is  unaffected  because  they must rely on  cash rather
 than  borrowed funds  to purchase  equipment,  but  liquidity impacts
 are substantial.

 4.2.2  Changes  In Ownership

      Ownership  changes occur  either because businesses  do not  have
 and are unable  to borrow sufficient  funds  to  purchase  control
 equipment  for the dry cleaning plant(s) they  own  or  because after
making the dry  cleaning plant(s)  they own  compliant, revenues
would be  insufficient to. meet legal financial obligations.
Businesses in poor financial  condition, are, projected to  undergo a
change:of ownership unless- they have sufficient cash co purchase
required  control  equipment (because they are assumed to be unable
to borrow money).  Changes of this type result from  capital
availability  constraints.   Because none of the affected  firms  in
below-average financial condition have adequate cash to purchase
control devices (e.g., capital costs exceed cash reserves reported
                               4-8

-------

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 in the balance sheet),  these firms are projected to incur
 financial failure due to capital availability constraints.

      Businesses in average or better financial condition can
 borrow money but may still experience a change in ownership if
 expected revenues are insufficient to cover baseline plus
 recurring regulatory costs—loan payments,  recurring fixed control
 costs,  and variable control costs.  Ownership changes due to
 insufficient revenues  are categorized as prof itabillt-Y impacts.
 None of the firms in this analysis are projected to incur
 profitability impacts  that result in changes in ownership.

      Table 4-3  presents  the estimated changes in ownership due to
 the  standards.   All of these changes in ••ownership are due to
 capital availability impacts for firms in below average financial
 condition.   Under Financial Scenario I where there are no
 potentially affected firms in below-average condition,  the number
 of ownership changes is  0. •  Under Financial Scenario II,  where 25
 percent of  the  potentially affected firms are in below-average
 financial  condition, ownership  changes are  projected to be 834.

      The estimated number of ownership changes  presented here  is
 substantially higher than the estimated plant closures  (259)
 presented  in Section 3.   At  least  two  reasons explain  this
 difference.   First,  as noted in  Section 3,  plant  closures  are
 astimaced as  nee  rather  than- gross  closures- while potential
 changes  in ownership reflect  gross  plant  closures.   Second,
 ownership may change even if  the  facility doesn't close.   Firms in
 poor  financial  condition  may  sell  their affected dry cleaning
 facilities to another owner  in better  financial., condition.   In
 addition, ownership-changes-also- include-bankruptcies.  Although
 bankruptcy may  result in  a plant closure, it: may also simply
 result in a transfer of ownership to another owner without plant
 closure.,  if. the owner(s) decides to sell the plant or ownership
 is transferred because of bankruptcy, a change in ownership
occurred but the plant did not close.  Consequently, estimated
                               4-10

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   TABLE 4-3 .   POTENTIAL CHANGES IN OWNERSHIP  DUE TO  THE STANDARDS
 Annual Receipts Range  ($000)
 and Type of  Impact
 Financial
Scenario I
 Financial
Scenario II
75 to 100
Capital Availability
Profitability
100 to 250
Capital Availability
Profitability
250 to 500
Capital Availability
Profitability
Over 500
Capital Availability
Profitability .
Total
Capital Availability
Profitability
^^ 	 m-r- 	 	

0
0

0
0

0
0

0
0

0
0

165
0

405
0

170
0 .

94
0

834
0
Notes :
              h                 are Pro^ected "hen «im» in poor financial
              have insufficient funds to purchase the required  control
                      aSSUmed that firms in P°or financial condition cannot
  2.. Profitability -impaccs_ are- pro jeccaci when revenues, are insufficienc Co
     ^er the- cull costs  of- production including  control costs
  3. Financial Scenario I  assumes a positive correlation between firm size
     betwfon*^™  con^tion   Financial Scenario II assumes no correlation
     between firm size and financial condition.


changes in ownership may 'reasonably be  expected to exceed the

estimated: net: plant: closures:..
                                  4-11.

-------

-------
                              REFERENCES
 Abowd,  John M. and Orley Ashenfelter.  1981.   "Anticipated
      Unemployment, Temporary Layoffs, and Compensating Wage
      Differentials."  In Studies in Labor Markets T  pp  141-170.
      Sherwin Rosen, ed.  Chicago, IL:  University of Chicago
      Press.

 Anderson,  D. W.,  and Ram V. Chandran.  1987.   "Market  Estimates  of
      Worker Dislocation Costs."  Economics Let-f g-rs .  24:   381-384
                           »
 Blinder,  Alan S.   1988.   "The Challenge of High Unemployment  "
      Richard T. Ely Lecture printed in the American sermnmi a
             78(2') : 1-15.                            .
                                                      Kev
Duns Analytical Services.   1990.   Induat-.rv Norms
     Ratios.  Dun and Bradstreet  Business Credit  Services
     1990.
                                                             1989-
 Fisher, William.   March 6,  1990.   Telecon.  International
      Fabncare Institute. Personal communication with Brenda L
      Jellicorse,  Research Triangle Institute.

 Flaim,  Paul  O.   1984.   "Unemployment in 1982:  The Cost to Workers
      and  Their Families."  Monthly Labor Review  Feb.:30-37
Gordon, Robert J.  1978.  Macrogrnnnmi
     Little Brown and Company.
                                            pp.  271-275.   Boston:
Hamermesh, Daniel  S.   1989.   "What Do  We Know About Worke^
     Displacement  in the U.S.?"   Industrial R^iat-ir.^  28~(1):51-


Maxwell, Nan L.  1989.   "Labor Market  Effects from- Involuntarv Job
     Losses in  Layoffs,  Plane; Closings.:.  The Role' of: Human Canicai
     in Facilitating Reemployment and  Reduced Wage Losses . "  "
     American Journal  of PV-nnnmigs and Sogiolnrry  48 (2 ) : 129-141 .

Moore, Michael J.  and W.  Kip VlSCUSi.   1990.   Compenaafinn
     MSChaiuca  fQr Job Rinks.  Princeton,  NJ:  Princeton
     University  Press.

Radian Corporation   1990.  "National  Cost  Impacts  of  Regulatory
               «  f°r Bhe Hazard°^ Air- Pollutant  Dry  Cleaning
               Memorandum from Carolyn Norris and  Kim  Kepford to
     U.S. Environmental.  Protection Agency,  Chemical, and Petroleum
     Branch .  January 25 .

Radian Corporation    1993.  "Annual Respondent Burden  and Cost  of
     Reporting and Recordkeeping Requirements of the Promulgated
     Standard."  Memorandum from Carolyn Norris to  Brenda
     Jellicorse> Research. Triangles Institute^ Research.- ^r^
     Park, NC.  June  2.                                "

-------
Sherer, F. M.
               1980.  Industrial Market Structure and Economi
                   2nd ed. , Chicago:  Rand McNally College
     Publishing Company.

Topel,  Robert H.  1984.  "Equilibrium Earnings, Turnover, and
     Unemployment :  New Evidence . "  Journal of Labor Economics
     2(4} :500-522.

U.S. Department of Labor, Bureau of Labor Statistics.  1991.
     Employment and Barning-a .  Washington, DC:  U.S. Government
     Printing Office.  April.

U.S. Environmental Protection Agency.  1991.  Economic impact.
     Analysis of Reoulafcorv Confcrola in hhe Drv Cleaning Tnduaf.rv
     Final report prepared for the U.S. Environmental Protection
     Agency, Office of Air Quality Planning and Standards, EPA
     450/3-91-021.
                               R-2.

-------
                    Appendix  A

Economic  Impact  Analvaia  of  Recmlatorv  Control
                        in
            the  Drv Cleaning  Industrv

-------

-------
£EPA
          United States        Office of Air Quality
          Environmental Protection   Planning and Standards
          Agency           Research Triangle Park, NC 27711
                              October 1991
         Air
Economic Impact Analysis of
Regulatory  Controls in the  Dry
Cleaning Industry

Draft
                                r~\

-------

-------
 Economic Impact Analysis
of Regulatory Controls in the
    Dry Cleaning Industry
          Emission Standards Division
        U.S. Environmental Protection Agency
         Office of Air and Radiation
       Office of Air Quality Planning and Standards
      Research Triangle Park, North Carolina 27711
            October;i99i

-------

-------
                                     (Disclaimer)

      This report has been reviewed by the Emission Standards Division of the
Office of Air Quality Planning and Standards, EPA, and approved fsr
publication.  Mention of trade names or commercial products is not intended t:
constitute endorsement or recommendation for use'..  Copies of this report are
available through the Library Services Office (MD-35), U.S. Environmental
Protection Agency,  Research Triangle Park;  NC  27711,  or from National
Technical Information Services,  5285 Port Royal Road,  Springfield, VA  22161.

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                                   CONTENTS
Section
Page
   1  Introduction and Summary.
 1-1
   2  Supply of Dry Cleaning Services	2-1
       2.1   Profile of  Suppliers  by Industry Sector	 2-1
             2.1.1   Commercial  Sector	 2-1
             2.1.2  . Coin-operated  Sector	 2-6
             2.1.3   Industrial'.Sector	 2-9
       2.2   Production  History and Trends 	 2-11
       2.3   Production  Processes  	 2-14
             2.3.1   Machine Types	 2-14
             2.3.2   Solvents	 2-16
             2.3.3   Production  Processes	,	 2-18
       2. 4   Costs  of Production	 2-21
             2.4.1   Costs  of Production for.  Existing Facilities 	 2-24
             2.4.2   Costs  of Production for  New  Facilities	 2-25
       2.5   Model  Facility Profile	2-27

   3  Demand for Dry Cleaning Services	 3-1
       3.1   Household Demand	 3-1
             3.1.1   Consumption  and Trends	 3-1
             3.1.2,   Characterization, ofr Consumers*	 3-~
             3.1.3   Household Demand Function 	 3-10
             3.1.4   The  value of Time and the Full-Cost Model	3-14
             3 .1. S   Sensitivity  To Price-	3-13
       3.2   Industrial  Demand	 3-19
             3.2.1   Consumption  and Trends	3-19
             3.2.2   Characterization  of Pomanders 	 	 3-20
             3.2.3"   Derived- Demand		.	 3-20

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                             CONTENTS  (continued)
Section
                                                                           Page
             3.2.4   Sensitivity to Price
   4  Market Structure in the Dry Cleaning Industry.
       4.1   Facility  Location Decision 	
                                                                     4-1
             4.1.1  Commercial  Dry  Cleaners ...
             4.1.2  Coin-operated Dry  Cleaners
             4.1.3  Industrial  Dry  Cleaners ...
       4.2
     Market  Structure 	
     4.2.1   Market  Structure  in  the  Commercial Sector
     •4.2.2   Market  Structure  in  the  Coin-operated Sector
     4.2.3   Market  Structure  in  the  Industrial Sector ...
4 .3  Model Markets	
     4.3.1   Commercial Sector Markets 	
     4.3.2   Coin-operated Sector Markets 	
     4.3.3   Industrial Sector Markets	
  5  Financial Profile of Commercial Dry Cleaning Pi.

       5.1   Firm Finances and Facility Economics ....

       5..2,"  Population-:.of.- Potentially Affected; Firms
.  4 —J
.  4-4

.  4-4
  -r — 0
 4-15
 4-13

4-19
4-19
4-22
4-23


. 5-1

. 5-1
      5.3   Legal Ownership of Commercial Dry Cleaning Facilities
            5.3.1  Sole Proprietorship 	
            5.3.2  Partnerships 	
            5 .3,. 3.'  Corporations-:	
      5.4   Distribution- of  Companies- by Receipts Size .......
      •5.5   Distribution of  Companies  by Number of Facilities
      5. 6   Vertical. Integration  and Diversification	
      5.7  Financial Characteristics  of  Finns  in Regulated
           Industry(ies)	-	
                                                                  .  5-3
                                                                  .  5-3
                                                                  .  5-4


                                                                  .  5-6

                                                                  .  5-8

                                                                  .  5-9

                                                                  5-10

-------
                             CONTENTS  (continued.)
Saction
Page
       5.8   Key Business Ratios of Dry Cleaning Firms	  5-15

       5 .9   Availability and Costs of Capital	  5-18

   6  Responses to tha Ragulatory Alternatives	  6-1

       6.1   Overview of Regulatory Alternatives	  6-1
       6.2   Firm-Level Responses	  6-2
       6.3   Facility-Level Responses	•	  6-5
             6.3.1  Compliance Option Costs		  6-6
             6.3.2  Compliance Options Under Each Regulatory Alternative ..  6-9

   7  Impacts o£ the Regulatory Alternatives	  7-1

       7 .1   Affected Population	  7-1
       7 .2   Costs of Compliance	7-8
       7 .3   Market Adjustments	  7-14
             7.3.1  Price and Output Adjustments	  7-15
             7.3.2  Welfare-. Effects	  7-24
             7.3.3  Plane Closures	  7-23
             7.3.4  Employment Effects	"	7-37
       7.4   Ownership Adjustments in Commercial Dry Cleaning Sector 	  7-41

       7.5   Effects on Small Businesses 	  7-68

   8  Conclusion	  3-1

   9  Raierencas			  3-1

   Appendix A	4-	 .  A-l
                                       VI,

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                              CONTENTS (continued)
 Table
                                                                           Page
 1-3
 2-2
2-3
2-4
2-5
2-6
       Estimated  Number of  Dry Cleaning Plants  by Industry Sector (1991) ... 1-3

       Annualized Coses and Welfare  Impacts  of  the Dry Cleaning NSSKAP  by
       Regulatory Alternative  arid Size  Cutoff  (S1989) 	 1-7
Projected Worse-Case Net Plant Closures and Employment  Effects  of
the Dry Cleaning NESHAP	  l_g


Distribution of PCE Dry Cleaning Machines and Facilities  in  the
Commercial Sector	  2-3

1991 Distribution of Receipts, for Commercial Dry Cleaning
Establishments:  PCE and Non-PCE Establishments  ($1989) 	  ;-4
1991 Distribution of Receipts for Commercial Dry Cleaning
Establishments:  PCE Establishments only  ($1989) 	 2-4

1991 Distribution of Dry Cleaning Output in the Commercial Sector:
PCS and Non-PCE Establishments . .	 2-5

1991 Distribution of Dry Cleaning Output in the Commercial Sector:
PCE Establishments Only	•	2-5

1991 Distribution of Receipts for Coin-Operated Establishments with
Dry Cleaning Capacity ($1989)	 .	 2-3


1991 Distribution of. Dry Cleaning. Output,, in, the1 Coin-Operated
Sector	  "                 "               -_i
2-8   Annual Receipts, Average Base Price, and Total Output for
      Commercial Dry Cleaners  ($1989)  	 2-13

2-9   Annual Growth Rates by Machine Type and Sector U986-1989) 	 2-13

2-10  Capital Costs of New Dry-to-Dry Machines- ($1989)	 2-22

2-11  Average Annual Operating Coats for Commercial Dry Cleaning Plants . . 2-23

2-12  Average' Input Prices for PCS;: Dry- Cleaning; Facilities: ($1989)	 2-24

2-13  Model Plant Description and the Distribution of PCE Facilities by
      Industry Sector and Income Level	2-28
                                     vii

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                             CONTENTS  (continued.)
Tabla
                                                                     Page
3-1



3-2



3-3



3-4



3-5



3-6


4-1


4-2



4-3



4-4


5-1



5-2


5-3


5-4



5-5
Household Expenditures on Commercial Laundry and Dry Cleaning
Services 1980-1989  ($1989) 	  3-5
Number and Median Income of Women in the WorJc Force  1980-1989
($1989) 	
 3-7
Household Expenditures on Commercial and Coin-Operated Dry  Cleaning
and Laundry Services by Income Category  ($1989) 	  3-9

Household Expenditures on Commercial and Coin-Operated Dry  Cleaning
and Laundry Services by Occupation Category	 3-10

Household Expenditures on Commercial and Coin-Operated Dry  Cleaning
and Laundry Services by Location Category	 3-11

Regression Analysis	 3-16

Data Used in the Supply/Demand Estimation	  4-8

Parameter Estimates and Regression Statistics from the
Supply/Demand Estimation	'	 4-10

Parameter Estimates and Regression Statistics from the
Supply/Demand Estimation  (Time—Trend Specification) 	 4-11
Profile of Model Markets in,the Commercial. Sector
Legal Form of Organization of Dry Cleaning Firms—Number and
Percent	
4-20
 5-5
Receipts of Dry Cleaning Finns	  5-7

Concentration, by; Largest.. Dry- Cleaning- Firms'. .	
Number of Commercial Dry Cleaning Facilities per Finn by  Income-
Category	
 5-9
Number of Dry Cleaning Firms, by Size and Baseline Financial
Condition	  5-12
5-6   Number of Dry Cleaning Firms, by Size and Baseline Financial
      Condition	
                                                                     5-13
                                     viii

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                              CONTENTS (continued)
Table
                                                                           Page
5-7


6-1


7-1


7-2



7-3



7-4



7-5



7-6
Baseline Financial Ratios of Dry Cleaning Firms	•	 5-17

Control Technology Options Under Each  Regulatory  Alternative 	 6-2


Size Cutoff -avals Based on Consumption  of Perchloroethylene (PCE)  . . 7-3

Distribution of Affected Facilities by Industry Sector,  Model
Market, and Size Cutoff:  Regulatory Alternatives I  and II	 7-4
Distribution .of Affected Facilities by  Industry  Sector,  Model
Market, and Size Cutoff:  Regulatory Alternative III 	 7-5

Distribution of Affected Output by Industry  Sector, Model Market,
and Size Cutoff:  Regulatory Alternatives  I  and  II	 7-6

Distribution of Affected Output by Industry  Sector, Model Market,
and.. Size. Cutoff:  Regulatory Alternative. Ill	 7-7
Model Plant Capital and Operating Compliance Costs  for  Carbon
Adsorber Controls  ($1989)	•.	 7-9
7-7   Model Plant Capital and Operating Compliance  Costs  for Refrigerated
      Condenser controls in the. Commercial  Sector: ($1989) ................. 7-10

7-8   Model Plant Annualized Compliance Costs  for. Regulatory Alternative
      I  ($1989) .......................... ................................ 7-12


7-9-  Model Plant Annualized- Compliance Costs  for Regulatory Alternatives
      II and III  ($1989) .................................... ' ............. 7-13

7-10  Market Adjustments Computed for Each  Sector and Model  Market in the
      Dry Cleaning Industry ........... . ........ .......................... 7-15

7-111  Pricer Adjustments. for; Each. Sector.- of L ther Dry -Cleaning- Industry by
      Regulatory Alternative and Size Cutoff . ......... . .................. 7-19

7-12  Price Adjustments for Model Markets in the  Commercial  Sector by
      Regulatory Alternative and. Size. Cutoff  (percentage  change from
      Baseline):. ..... ...,..,„ ..... ........ . .- ..... .......... . _________ ..., ________ _____________ . . . .............. 7-20
7-13  Output Adjustments for Each Sector of the Dry Cleaning Industry by
      Regulatory Alternative a"ttd Size- Cutoff ........ ..................... 7-21

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                             CONTENTS  (continued)
Table
Page
7-14  Output Adjustments for Model Markets' in the Commercial  Sector by
      Regulatory Alternative and Size Cutoff	•	  7-22


7-15  Consumer Welfare Impacts for Each -Sector of the Dry Cleaning
      Industry by Regulatory Alternative and Size Cutoff	  7-27


7-16  Consumer Welfare Impacts' for Model Markets in the Commercial Sector
      by Regulatory Alternative and Size Cutoff  ($ thousands) 	  7-28


7-17  Producer Welfare Impacts for Each Sector of the Dry Cleaning
      Industry by Regulatory Alternative and Size Cutoff  (S thousands) . . .  7-29


7-18  Producer Welfare Impacts for Model Markets in the Commercial Sector
      by Regulatory Alternative and Size Cutoff  ($ thousands) 	  7-30

7-19  Net Welfare Impacts For Each Sector of the Dry Cleaning Industry by
      Regulatory Alternative and Size Cutoff (5 thousands) 	  7-31


7-20  Net Welfare Impacts for Model Markets in the Commercial Sector by
      Regulatory Alternative and Size Cutoff 1S thousands) 	  7-32


7-21  Projected Worst-Case Net Plant Closures in Each Sector of the Dry
      Cleaning Industry by Regulatory Alternative and Size Cutoff 	  7-34

7-22  Projected Worst-case Net Plant Closures in each Model Market of the
      Commercial Sector by Regulatory Alternative and Size Cutoff 	  7-35


7-23  Projected Worker Displacements	  7-39


7-24  Projected Worker Displacement Costs  (S millions) 	  7-41


7-25  Number of Affected Dry Cleaning Firms By Size and Baseline
      Financial Condition,- Regulatory Alternatives I and II	  7-43


7-26  Number of Affected Dry Cleaning Firms By Size and Baseline
      Financial Condition,  Regulatory Alternative III	7-44


7-27  Number of Affected Dry Cleaning Finns By Size and Baseline-
      Financial Condition,  Regulatory Alternatives I and II	7-45
7-28  Number of Affected Dry Cleaning Firms By Size and Baseline
      Financial Condition, Regulatory Alternative III	.7-46

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                              CONTENTS  (continued)
Table
                                                                           Page
7-29  Installed Price of Control Equipment  and Annual Operating Cost,  by .
      P.egulatory Alternative and Size of  Firm. .,	 7-4"

"-30  Annual Principal and  Interest Payments  on a  Seven-Year Note 3v
      P.egulatory Alternative, Firm Size,  and  Interest Rate (S) a	 7-49

7-31  Initial Cash Outlay Requirement and Recurring Annual Expenses  Bv
      Firm Size, Financial  Condition, and Regulatory Alternative  ($) .".... 7-50

7-32  Key Financial Ratios	 7-53


7-33  Baseline and Affected Financial Ratios:   <325,000  Firm Receipts.... 7-54

7-34  Baseline and Affected Financial Ratios:   $25,000-50,000  Firm
      Receipts	7-55


7-35  Baseline and affected'Financial Ratios:   $50,000—75,000  Firm
      Receipts	 7-56

7-36  Baseline and affected Financial Ratios:   $75,000-100,000  Firm
      Receipts	 7-57


7-37  Baseline and affected Financial Ratios:   >$10.0, 000" F.irm-Receipts.. .. 7-55

7-38  Projected Financial Failures of Commercial Dry Cleaning Firms by
      Regulatory Alternative and Size Cutoff, Financial  Scenario  I
      (Number of Firms, and Percent)	,.	 7-60

7-39  Projected Financial Failures of Commercial Dry Cleaning Firms by
      Regulatory Alternative and Size Cutoff, Financial  Scenario  II
      (Number of Firms and Percent)	•	 7-61

-------
                              CONTENTS (continued)
 Figure
Page
 1-1    Number of  Affected. Dry Cleaning Facilities By Regulatory
       Alternative  and Size Cutoff .'...:	 1-5
 1-2    Potential  Changes  in Ownership by Size Cutoff,  Financial
       Scenario I	
                                                                           1-10
 1-3    Potential Changes  in Ownership by Size Cutoff,  Financial
       Scenario II	;	 i-11


 2-1    Typical Configuration of  a  Dry Cleaning Machine and the Various
       Attachments	 2-15


 2-2    PCS Consumption by Sector for  1991	-. . . 2-18


 2-3    Market Supply Curve for Existing  Facilities	 2-25


 2-4    New Facility Costs Compared to Market  Supply Curve  for  Existing
       Facilities	  2-26


 3-1    Total Annual Household Consumption of  Commercial Dry Cleaning
       Services (1980-1988)	  3-3


 3-2    Annual Consumption of  Commercial  Dry Cleaning Services  per
       Household (1980-1988) 	  3-4


 3-3    Average Annual Expenditures  on Dry Cleaning and Laundry Services by
       Income Class ($1989) 	  2-12


 4-1    Demand for Self-Service Dry  Cleaning		  4-17


 6-1    Responses to the Proposed Regulation	  6-4


 7-1    Price: and. Outpuc: Adjustments; Due-- to- a, Market Supply- Shift	 .	.  7-15


 7-2    Welfare Change Estimation		7-25


7-3   Capital Availability and  Profitability Impacts,   Financial
      Scenario I-'-Regulatory- Alternative, I	7-62


7-4   Capital Availability and Profitability Impacts,   Financial
      Scenario I—Regulatory Alternative II	7-63
                                      xii

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                             CONTENTS  (continued)
Figure
Page
7-5   Capital Availability and Profitability Impacts, Financial
      Scenario I—Regulatory Alternative III	  7-64

7-6   Capital Availability and Profitability Impacts, Financial
      Scenario II—Regulatory Alternative III	  7-65

7-7   Capital Availability and Profitability Impacts, Financial
      Scenario II—Regulatory Alternative III	'.  7-66


7-3   Capital Availability and Profitability Impacts, Financial
      Scenario II—Regulatory Alternative III	  7-67

7-9   Baseline Financial Condition' of Projected Business Failures,
      Financial Scenario I—Regulatory Alternative I	  7-69

7-10  Baseline Financial Condition of Projected Business Failures,
      Financial Scenario I—Regulatory Alternative II	  7-70

7-11  Baseline Financial Condition of Projected Business Failures,
      Financial Scenario I—Regulatory Alternative III	  7-71

7-12  Baseline Financial Condition of Projected Business Failures,
      Financial Scenario II—Regulatory Alternative I	 .  7-72

7-13  Baseline Financial Condition of Projected Business Failures,
      Financial Scenario II—Regulatory Alternative II	7-73

7-L4-  Baseline; Financial- Condition- of Projected: Business Failures,
      Financial Scenario II—Regulatory Alternative III	  7-74
                                     xiii

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                                    SECTION 1
                            INTRODUCTION AND SUMMARY

       'Jnder the Clean Air Act Amendments of 1990, the U.S.  Environmental
 Protection Agency (EPA) is required to propose and promulgate  a  regulation  cc
 =cntrol Hazardous Air Pollutant  (HAP) emissions from dry cleaning  facilities.
 HAP's emitted from dry cleaning include perchloroethylene  (PCS)  and  1,1,1-
 Trichloroethane- (1,1,1-TCA).   This report investigates the  economic  impacts
 associated with three candidate regulatory alternatives and five size cutoff
 levels considered for proposal.  All plants that use 1,1,1-TCA are in
 compliance with the  proposed regulatory alternatives in the baseline.  :io
 costs or economic impacts are projected for these facilities.  Therefore,  the
 analysis of regulatory controls addresses impacts associated with the control
 cf PCS emissions  only.

       This section provides a brief overview of the  dry cleaning industry and
 the impacts of  the regulatory alternatives  discussed in detail in the balance
 of the report.  A description of supply and demand for dry-cleaning services
 is provided in  Sections 2  and 3,  respectively.   Section 4  describes market
 structure  and outlines  an  approach for analyzing market  impacts of  the
 regulatory alternatives.   The baseline financial profile of dry cleaning firms
 is provided in  Section  5.  Section 6  describes  the- requirements- of- the
 candidate  regulatory  alternatives  and outlines  potential responses  to the
 regulatory alternatives.   Section  7 reports projected  economic  and  financial
 impacts  associated, with each-  regulatory- alternative ana. Section- '8 summarises-
 the analysis.                                           .    -

       The  dry cleaning  industry  is comprised of  three  sectors:  commercial
 (SIC 7216), coin-operated  (SIC 7215),  and industrial  (SIC 7218).  Commercial
 facilities- are the-, most, prevalenr: or;. the:- three* types-and-are- generally Located
 in shopping centers and near  densely populated, areas.  Coin-operated  plants
are typically part of a laundromat and provide dry cleaning  either  on a  self-
service basis or by accepting items, over the- counter—similar to  commercial
facilities.  Industrial plants usually-rent uniforms: and;'.other; items'  to  their.
industrial or commercial users and are generally larger than commercial  and
coin-operated facilities.
                                      1-1

-------
       Zt  is  important  to distinguish between the terms machine, faciiir .-,
 plant, establishment,  and firm used to describe the dry cleaning indu:  •/ in
 this  analysis.   A dry  cleaning machine is a piece of equipment dealer.   -o
 clean clothes or other items using a solvent mixture in place of wac    and
 detergent.   The  terms  facility,  plant,  and establishment are used
 interchangeably  and refer to a single physical location where dry c  aning
 services  are produced.   Each facility may use one or more dry clear. :;g
 machines  in  the  production process.   A dry cleaning firm is a lega- entity
 chat  owns one or more  dry cleaning facilities.

       Approximately. 34,000 facilities offer dry cleaning services in the
 United States.   Of  these,  about  28,000  use PCS in their cleaning process.  The
 commercial sector comprises approximately 90 percent of the industry with an
 estimated 30,494  dry cleaning plants;  24,947 of these plants use PCS.   The
 industrial sector has  1,379 total  plants,  but only about 325 have dry cleaning
 capacity.  Approximately 40 percent,  or 130,  use PCE in their dry cleaning
 operation.   The  U.S. has 27,130  coin-operated laundries.  Of these 27,130
 plants, an estimated 3,044 offer dry cleaning services.   Table 1-1 summarizes
 the total number  of  plants,  the  number  of dry cleaning plants,  and the number
 of dry cleaning plants  that use  PCE  by  industry sector.   In addition,  the
 number of potentially  affected plants and potentially affected firms  are
 reported in  Table  1-1.   Potentially  affected entities include those chat use
 PCE in the dry cleaning process  and  do  not have the control equipment  required
 under che most stringent regulatory; scenario (Regulatory Alternative  III with
 no cutoff).  Potentially affected  firms  include those business entities chat
 own potentially affected facilities.

      The three regulatory alternatives  under consideration for proposal
 specify control, equipment, requirements,  for' facilities-- by industry, sector and.
machine technology.  An estimated.  65 percent of dry cleaning plants or 21,954
have some type of baseline control equipment in place.   The 11,909 facilities
that do not have baseline, control equipment  in place would potentially - incur
control coats under any of the alternatives  considered.   An additional. 1,930
facilities have control equipment that does  not meet the requirements  of
Regulatory Alternative  III.  Therefore,  under the most  stringent  regulatory
scenario,, 13,339- facilities  would be,-affected...
                                      1-2..

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 TABLE 1-1.  ESTIMATED NUMBER OF DRY CLEANING PLANTS BY  INDUSTRY  SECTOR (1991)a
Sector
Commercial
Coin-
Operated
•Total
Number of
Planes3
30, 494
27/180

Number of
Dry
Cleaning
Plants0 •
30,494
3,044

Number of
PCE Dry
Cleaning
Planes
24,947
3,044

Number of
Potentially
Affected
Plants'1
12,159
1,615

Number of
Potentially
Affectea
firms0
10,744
e

Industrial
.Total
1,379
59,053
325
33,863
130
28,121
65
13,839
e
e
 =Includes facilities  with payroll and those without payroll.
 -•Includes plants  in the coin-operated -and industrial sectors that have drv
 cleaning machines  and  those that  do  not  have  dry cleaning machines.
 clncludes dry-cleaning  plants  that use PCE as  well as those that use other
 solvents.
  Includes PCS  plants  that do not  have vent controls required under the most
 stringent regulatory scenario  (Regulatory Alternative  III with  no cutoff)
 elncludes firms that  own potentially  affected  plants.  The number of
 potentially affected firms that own  coin-operated or industrial plants  is  noc
 estimated for this analysis.  Coin-operated plants  will  likely  be exempt  from
 the regulation and industrial plants are  expected to realize cost-savings
 under each regulatory alternative considered.  Therefore  a  firm financial
 analysis is not performed for the coin-operated  or  industrial sectors.

 Source:   Radian  (1991c);  1987  Census  of Service Industries,  Nonemployer
 Statistics Series(U.S.  Department of Commerce, 1990a); 1987 Census of Service
 Industries,  Subject Series (U.S. Department of Commerce,  1990b);  Table  7-3.


      Many facilities in  the commercial and coin-operated sectors that are

 potentially affected by  the regulation  are small  establishments.  It  is

 estimated that over 75 percent of  potentially  affected  facilities receive  less

 than 5100,000  in annual  receipts1.  The annualized control costs associated
      Approximately 55 percent of affected machines represent output  levels
corresponding to 5100,000 or less.  The, difference in the, distribution of
affected machines' and: affected facilities- is.- attributable to  two  assumptions
used, to estimate impacts.  First, it" is assumed that uncontrolled machines
represent a larger share of lower income categories and a smaller share of
higher income categories.  Second, it is assumed that facilities  with  over
$100,000 in annual receipts use. multiple machines in their, operations  whereas
facilities', below- 3100,,000- receipts- use-? only- one, machine...
                                      1-3

-------
 with the regulatory alternatives range from 51,500 to 58,000 per plant.  For
 small facilities below 525,000 in annual receipts, these control costs may
 represent more than one third of total receipts to the facility.  To mitigate
 the  impacts  on small facilities,  size cutoffs based on ?CE usage are
 considered.   These cutoffs correspond to target levels of annual receipts and
 exempt facilities below a- specified output  level.   Figure 1-i shows the number
 of affected  facilities under each size cutoff by Regulatory Alternative.   Note
 --at the number of affected facilities under each size cutoff is identical for
 .-.  grnatives I and II.

       Because thousands of facilities in the dry cleaning industry are
 potentially  affected,  analyzing regulatory  impacts using a facility-specific
 approach is  not feasible.   Therefore,  a model plant approach based on fifteen
 model plants that characterize the  machine  technology,  machine  capacity,  and
 operating practices  of typical dry  cleaning machines is used to estimate
 impacts  in the industry,   within  each model plant  category,  impacts are
 analyzed for plants  operating at  five output levels based on annual receipts.
 Furthermore,  impacts  are analyzed using a model market  approach that
 differentiates the market  for dry cleaning  services by  the number  of
 facilities in the market,  the share of affected and unaffected  facilities  in
 the  market,  and the  projected behavioral response  to the  regulation.   Eight
 model markets  are used to  represent market  conditions and market structure in
 the  dry  cleaning industry  including six model markets for the commercial
 sector,  one  mociel for  the  coin-operated sector,  and one model for  nhe
 industrial sector.

      Regulatory impacts are  projected using an  integrated approach that
 combines an  economic impact analysis  with a  firm financial analysis.   In the
 economic impact' analysis- a> methodological and empirical, approach based on  the
 principles of  applied welfare economics is used.   Economic impacts  are
quantified through estimated market  adjustments  of  price  and output  and
corresponding  effects on consumer and. producer welfare.   The price  and output
 adjustments  computed in this  analysis are short-run effects.  Almost all new
dry cleaning machines are equipped  with built-in vent controls  that  satisfy
 the requirements  of the regulations.  The current stock of uncontrolled
                                      1-4.

-------
  Numoer or
  Affected
  "aciiiwies
            12,000  -•
            ::,ooo  -•
             3,000  -•
             o,000  -•
             -;,ooo  -•
             2,000  -•
                                                            r.-rcu^acc ry
                                                            Alcer.nacive
                                      25           50           75

                                 Size Cutoff in Annual Reeeiots (3000)
Tigura  1-1.  Number  of  Affected,. Dry Cleaning, ?acil
             Alternative and Size Cutoff

Source:   Tables 7-2 and 7-3.
ities  3v P.eauiacor.v
                                        1-5

-------
 machines would have been replaced with controlled machines  even  in che
 baseline.  Consequently, long-run price and output adjustments are zero.   in
 addition, the effects of the candidate regulatory alternatives on  eir.- .oyment
 and plant closures are quantified as part of the economic impact ana... -sis.
 Financial impacts including capital availability and profitability : ••.pacts  are
 projected recognizing that firms differ by size and baseline  financ_al health.

       Table 1-2 reports the annualized costs,  the producer  welfare costs, and
 the consumer welfare costs for the industry as a whole under each  regulatory
 alternative and size cutoff level.   The annualized costs- include the annual
 operating costs of control equipment along with the annualized installed costs
 of the equipment.  The producer and consumer welfare costs are those projected
 for the first year of the regulation.   Lesser losses will be incurred in
 fourteen subsequent years because existing uncontrolled machines are being
 replaced with controlled machines upon retirement,  even at baseline.  Fifteen
 years  after the regulation takes effect,  producer and consumer welfare costs
 are zero assuming that the current  stock  of uncontrolled machines would be
 replaced with controlled machines in the  baseline over this  time period.

       Table 1-3  reports  the projected  worst-case net  plant closures, projected
 worker displacements,  and worker displacement  costs  for the  industry as a
 whole  under each regulatory alternative and size cutoff level.   The plant
 closure projections  assume  that  the  short-run  industry output  reductions  are
 achieved by closure  of the  smallest:., size  facilities.   The  projected worker
 displacements  assume  that  layoffs are  proportional  to  the  short-run industry
 output  reductions.   The  projected worker displacement  costs  are based on  che
 projected displacements  and are  one-time  (non-recurring) costs.   Assuming (as
 described above)  that  the long-run equilibrium level of dry  cleaning services
 is  unaffected  by  the- regulation,  the': long-run-equilibrium-employment- will.
 likewise be unaffected.  The output  reduction used to  estimate worker
displacement and  displacement coats  would have occurred in the baseline over
an estimated 15-year time period as  owners of dry cleaning facilities replaced
retiring uncontrolled machines- with  controlled machines..   Implicit  in the
estimated displacement costs is  the  assumption that this baseline output
                                      1-9

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 TABLE 1-2.  ANNUALIZED COSTS AND WELFARE  IMPACTS  OF THE DRY CLEANING NESKAP BY
             REGULATORY ALTERNATIVE AND SIZE CUTOFF  ("S1989)a
Cost cr I~.pact Measure and

Regulatory Alternative
                                      Size Cutoff  in  Annual Receipts ($000)
                                      0
 25
50
                                                                 75
                                                                          100
 Annuaiized Costs (S106)

       Regulatory I                   34.8

       Regulatory II                 42.9

       Regulatory III                53.5

 Consumer Welfare Impacts ($106)
18.9 .    13.3 •     11.1       5.1

23.5     16.5      13.9      11.5

33.0     24.8      21.3      17.7
Regulatory I
Regulatory II
• Regulatory III
Producer Welfare Impacts (510s)
Regulatory I
Regulatory II
Regulatory III
-14,6
-18.0
-20.3

-20.2
-25.0
-33.. 3
-10.8
-13.5
-15.8

-8.0
-10.0
-17.2
-7.7
-9.5
-11.5

-5.6
-7.0
' 13.3
-6.5
-8.1
-9,9

-4.6
-5.9
-11.5
-5 . 3
-6.7
-8.2

-3.8
-4.8
-9.5
"Annualized Costs and producer and consumer welfare  losses  incurred in first
 year of regulation.  Costs will be incurred'in subsequent  years  but will
 decline over-time-.  Recurring-annual coats will- be  zero  15 years after cne
 errective? data.-of che- regulation assuming- that the-  current stock of
 uncontrolled machines would be replaced by controlled machines' in the
 baseline over this time period.



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.


      The firm-  financial, analysis uses  the- costs'- estimated: for the- economic

impact analysis to project changes in the  financial  viability of dry cleaning

firms affected under each regulatory alternative.  Estimated costs of capital

are-developed, for firms?, in-, poor-,., average,,  and;.good, financial, condition.
                                      1-7

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TABLE 1-3.  PROJECTED WORST-CASE NET PLANT  CLOSURES  AND EMPLOYMENT EFFECTS OF
            THE DRY CLEANING NESHAP
Impact Measure and
Regulatory Alternative
Worse-Case Net Plant Closures*
Regulatory I
Regulatory II
Regulatory III
Number Worker Displacements13
Regulatory I .:
Regulatory II
Regulatory III
Worker Displacement Costs (S10
Regulatory I
Regulatory II
Regulatory III
Size Cutoff in
0

1,354
1,599
1,768

25

373
457
529

743" 566
920
1,043
6,e
21.4
26.5
30.0
JNet plant closures assuming all industry
closures of smallest affected facilities
=Assumes labor demand declines-
in prooorr
707
831

16.3
20.4
23.9
Annual Receipts (SO 00)
50

147
182
221

407
513
619

11.7
14.8
17.8
output reductions are
•
ion to aouil
.ibrium out'
75

83
110
135

336
424
513

9.7
12.2
14.8
achieved
100

23
29
34

233
354
424

8.2
10.2
12.2
by
put reductions .
•One—cime  (non—recurring): worker displacement cost.  The present value 01
 foregone  future displacement is assumed to be zero.
                                      1-8-

-------
Capital availability constraints  and profitability  impacts  are reported for
firms in the commercial  sector  that  are  affected under  each regulatory
alternative.  Figure 1-2  shows  the potential  changes  in ownership by size
cutoff level under the regulatory alternatives  assuming a positive
relationship between firm size  and baseline firm financial  condition,  as might
be expected since smaller firms generally have  significantly lower capacity
utilization than larger  firms  (financial scenario I).

      Potential changes  in firm ownership under an  alternative assumption are
demonstrated in Figure 1-3.  These projected  impacts might  result if the
number of firms in below-average, average, and  above-average baseline
financial condition are proportionately  distributed across  firms  of all sizes
(financial scenario II).

      The total annualized cost ranges from $53.5 million under the most
stringent regulatory scenario to  less than $10  million  under the  least
stringent.  The estimated regulatory costs result in short-run price and
output adjustments that are relatively small  (less  than one  percent deviation
from baseline values' in most cases).  The estimated-loss in  consumer welfare
ranges from $14.6 to $20.3 million with  no cutoff.  Producers  lose  an
estimated $20.2 to $33.3 million  in  welfare with no cutoff.   In addition, more
than 3,000 potential changes in ownership are projected with, no,, size cutoff.
However,  the size cutoffs would mitigate the economic and financial  impacts of
the regulatory alternatives.  For example,  with a cutoff level  corresponding
co 3100,000 annual receipts, consumer- and producer welfare-impacts  under
Alternative II are $6.7 million and  $4.8 million, respectively, and projected
changes in ownership are betvcen 0 and 669.
                                      1-9

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                                   SECTION 2
                        SUPPLY OF DRY CLEANING SERVICES

      The dry cleaning  industry  is a mature  service industry involved in the
 cleaning, pressing, and finishing of clothing and related products.   This
 section provides a profile  of  each sector  of the  industry,  production history
 and trends, an overview of  the production  process,  and the estimated costs of
 production.

 2.1   PROFILE OF SUPPLIERS  BY  INDUSTRY SECTOR
      The dry cleaning  industry  is composed  of three  sectors:
      • commercial  (SIC 7216),                               .              •
      • coin-operated  (SIC  7215), and
      • industrial  (SIC 7218).

      Commercial facilities are  the most prevalent  of, the  three  types.and.are
 generally located in shopping  centers and  near densely populated areas.   Coin-
 operated plants are typically part of a laundromat  and provide dry cleaning
 either on a self-service basis or by accepting items  over  the counter—similar
 to commercial facilities.   Industrial plants  usually  rent  uniforms and other
 items to their customers and are generally larger than commercial and coin-
 operated facilities.

 2.1.1  Commercial Sector-
      Commercial dry cleaning facilities, the most  familiar  type of
 establishment,  provide services for households and  include independently
 operated neighborhood shops, franchises,  and  specialty cleaners.  Commercial
dry cleaners provide; full, services-dry cleaning;,, which, includes spotting,
pressing,  finishing, and packaging.   In addition,  many commercial dry cleaners
provide laundry services for water-washable garments,  rug cleaning services,
and minor alteration and repair services.  On average  approximately  85 percent
of the receipts at a commercial dry cleaning establishment are- from,dry
cleaning activities.  The remaining IS percent are  from the  auxiliary services
provided by the facility (U.S. Department of Commerce,  1991).
                                      2-1

-------
       Approximately 30,494  commercial dry cleaners operace in the U.S.   Over
 80  percent  or  about 24,947  commercial dry cleaners use perchloroethylene (PCS)
 in  their  cleaning process.   Table 2-1 shows  the distribution of PCE
 establishments,  the distribution of PCE machines,  and the corresponding number
 of  machines per  facility  for 5  income categories (based on annual receipts  per
 facility) .  This -estimated  total manner of dry cleaning facilities and  the
 distribution of  facilities  by income level is  based on the number and
 distribution of  PCE dry cleaning machines by design capacity,  the average
 number of machines  per facility in the commercial  sector (approximately 1.23)
 (Radian 1990c) ,  and the distribution of facilities reported in the 1987 Census
 of  Service  Industries, Subject  Series (U.S.  Department of Commerce,  1990b) .
 In  addition, it  is  assumed  that facilities below $100  thousand in annual
 receipts have  one machine per facility.

       Tables 2-2 and 2-3 show the 1991 distribution of annual  receipts  for  all
 commercial establishments and for establishments that  use PCE,  respectively.
 Over three fourths  of the total receipts  to  dry cleaning establishments were
 earned by facilities with 5100,000  or more in  annual receipts.  These
 facilities represent only about one  third of the total number  of  commercial
 dry cleaning establishments.  At  the other end of  the  spectrum, small
 facilities with below 325,000 in  annual  receipts account  for more  than  25
 percent of the total number  of  facilities  but  only  about  3 percent of total
 receipts to commercial dry cleaners .
      Dry cleaning output for the sector totals 571,984 Mg per year w
446,492 Mg from facilities that use PCE.  Total output is computed by first
multiplying total annual receipts by the share of receipts from dry cleaning
activities (85%) to compute the receipts directly attributable to drycleaning.
This, value is, then, divided by the estimated 1989 baseline price of 36.34 per
kilogram for dry cleaning services to compute total annual output measured in
kilograms of clothes cleaned.  Tables 2-4 and 2-5 report 1991 estimated total
output and average output per establishment by income category.
                                      2-2,

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 CJ
 0)
                                                                         3
                                                                         co
                                                                         to
                                                                         0)
 to

•a
 c
 0)
 u
                                                                         0)
                                                                         CO
                       to
                       3
                       n
                       C
                                                                    c    ™
                                                                    Ifl    ^
   CO
   
-------
TABLE 2-2.     1991  DISTRIBUTION OF  RECEIPTS  FOR COMMERCIAL DRY CLEANING
               ESTABLISHMENTS:   PCE  AND NON-PCE ESTABLISHMENTS (31989)
Annual
Receipts Number of
($000/y=) Establishments* Percent
0-25
25-50
50-75
75-100
>100
Total
8,
5,
3,
3,
11,
30,
026
024
096
096
251
494
26
16
10
10
36
100
.32
.47
.15
.15
.90
.00
Total
Annual .
Receipts^5
($000/yr)
142,
203,
207,
290,
3,421,
4,266,
350
679
528
539
966
062
Average Annual
Receipts Per
Establishment0
Percent ($/yr)
3
4
4
6
30
100
.34
.77
.86
.81
.21
.00
17,
40,
67,
93,
304,

736
545
021
829
135
—
'See Table 2-1.
3Average annual  receipts multiplied by number of establishments.
=3ased on data reported in the 1987 Census of Service Industries, Subject
 Series (U.S. Department of Commerce, 1990) for commercial dry cleaning
 establishments  with payroll converted to $1989 using the CPI for Apparel and
 Upkeep.
TABLE 2-3.     1991 DISTRIBUTION OF RECEIPTS  FOR COMMERCIAL  DRY CLEANING
               ESTABLISHMENTS:  PCE ESTABLISHMENTS  ONLY  ($1989)
Annual
Receipts Number of
($000/yr) Establishment sa Percent
0-25
25-50
50-75
75-100
>100
Total
6,322
4,270
2,632
2,632
3,591
24,947
27.35
17.12
10.55
10.55
34.44
100.00
Total
Annual
Receipts13
($000/yr)
120,998
173,127
176,399
246,958
2,512,824,.
3,330,305
Average Annual
Receipts Per
Establishment0
Percent ($/yr)
3.63
5.20
5.30
7.42
78.46
100.00
17,736
40,545
67,021
93,829
304,135
-
*See Table 2-1.
^Average annual  receipts multiplied by number of establishments.
-Based on data reported in the 1987 Census- of Service Industries, Subject
 Series (U.S. Department of Commerce, 1990) for commercial dry cleaning
 establishments  with payroll converted to $1989 using the CPI for Apparel and
 Upkeep.
                                      2.—4-

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 TABLE 2-4.     1991 DISTRIBUTION OF DRY CLEANING OUTPUT IN THE COMMERCIAL
                SECTOR:   PCE AND NON-PCE ESTABLISHMENTS

Annual
Receipts
(SOOO/yr)
0-25
25-50
50-75
75-100
>100
Total


Number of
Establishments4
8,026
5,024
3,096
3,096
11,251
30, 494



Percent
26.32
16.47
10.15
10.15
36.90
100.00
Total
Annual
Outputb
(Mg/yr)
19,085
27,307
27,823
38,952
458,781
571,948



Percent
3.34
4.77
4.86
6.81
80.21
100.00
Average Annual
Output Per
Establishment
(kg/yr>
2,378
5,436
. 8,985
12,580
40,775
-
 •''See Table 2-1.
 =Receipts from Table 2-2 multiplied by the share of receipts from dry cleaning
  activities  (85%)  divided  by the 1989 base price (36.34 per kg) .
 TABLE 2-5.     1991 DISTRIBUTION OF DRY CLEANING OUTPUT IN THE COMMERCIAL
               SECTOR:  PCE ESTABLISHMENTS ONLY
Annual
Receipts
($000/yr)
0-25
25-50
50-75
75-100
>100
Total
Number of
Establishments*
6,822
4,270
2,632
2,632
8,591
24,947
Percent
27.35
17.12
10.55
10.55
34.44
100.00
Total
Annual
Outputb
(Mg/yr)
16,222
23,211
23,650
33,110
350,300
446,492
Percent
3.63
5.20
5.30
7.42
78.46
100.00
Average Annual-
Output Per
Establishment*3
(kg/yr)
2,378
5, 436
8,985
12,580
40,775
-
aSee Table 2-1.
"Receipts from Table-2-3 multiplied, by the share-of receipts- from drvcisanina
 activities (85%)  divided by the .1989 base price  ($6.34 per kg) .


      The commercial, sector baseline;price is;derived- using; International

Fabricare Institute (IFI) data on the average price to clean a two-piece man's

suit weighing one kilogram (Faig, 1990) .  Control, cost estimates  and other

financial data used in. the-- economic  impact, analysis ara measured  in 1989
                                      2-5

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dollars.  However,  the most  recent  base price estimate available for the
commercial  sector  is  the  average  1988  value ($5.92).   7he 1989 base price was
projected by first  fitting a regression line to the natural logarithm of base
prices from 1973 to 1988  and a  time trend.   The slope of the regression line
 (0.0707) is an estimate of the  average growth rate of base prices over -hat
time period.

      The projected 1989  base price is then calculated as the sum of the 1988
price plus  the growth amount:

                          E>1989  -  P1988 •  (1 + 0.0707)                     (2.1)
                             - $5.92 •  (1  +  0.0707)
                                    = $6.34

For the purposes of analysis, all facilities are assumed to  charge $6.34 per
kilogram of clothes cleaned  in  the  baseline.   In following sections,  price
changes due to the regulation are projected based on  the price computed in
this section.

2.1.2  Co in—opera-gad  S&ef.ar
      Facilities in the coin-operated  sector also supply dry cleaning services
to households and are usually part  of  a laundromat.   Water washing and drying
account for the majority  of  sales- with dry  cleaning offered  as, an auxiliary
service (Torp,  1990).  Approximately 10 percent  of total receipts at  coin-
operated laundries that offer dry cleaning  services are  from dry cleaning
activities.

      Two types of dry cleaning services  are- available in this sector:   self-
service and employee assisted dry cleaning*.   Self-service-, coin-operated ary
cleaning,  as the name suggests, requires  the consumer to operate the  dry
cleaning machine and does  not include  pressing,  spotting,  or other finishing
services.   Employee assisted dry  cleaning (referred to as plant-operated in
the balance of this report)  is  virtually  indistinguishable from the service
provided by commercial dry cleaners except  that:  the facility also offers coin-
operated laundry services-..  Consumers-use-- coin-operated,  dry  cleaners  because
                                      2-6

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they desire lower priced cleaning, have large items, or do not live near
commercial cleaners  (ICF, 1386).

      Census data indicate that 27,180 coin-operated laundries—including
facilities with and without payroll—were operating in the U.S. in 1987  (U.S.
Department of Commerce, 1990a).  Approximately 3,044 coin-operated laundries
offer dry cleaning services.  About 2,831 establishments offer plant-operated
dry cleaning and another 213 establishments offer self-service dry cleaning
(Radian, 1991c).   Virtually all coin—operated laundries that offer dry
cleaning services use PCE in the cleaning process.

      Table 2-6 shows the 1991 distribution of coin-operated establishments
with dry cleaning operations.   The income, distribution is based on the income
distribution of all coin-operated laundries with payroll including those
without dry cleaning capacity  (U.S. Department of Commerce, 1990b).'
Establishments with over $100,000 in annual receipts account for approximately
14 percent of the establishments and more than half of the receipts at plants
with dry cleaning operations.   Establishments that collect less than 325,000
in annual receipts account for about 17 percent of the plants and less than 4
percent of receipts at plants  with dry cleaning operations.  Nearly one half
of all plants in this sector with dry cleaning operations are in the $25 to
550 thousand receipts range.
                                     2-7

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 7ABLS 2-6.     1991 DISTRIBUTION OF RECEIPTS FOR COIN-OPERATED
               ESTABLISHMENTS WITH DRY CLEANING CAPACITY .(51989)
Annual
Receipts
(SOOO/yr)'
0-25
25-50
30-75
75-100
>100
Total
Number of
Establishments3 Percent
523
1,451
475
1S9
426
3, 044d
17
47
15
5
14
100
.19
.70
.61
.49
.00
.00
Total
Annual
Receiptsb
(5000/yr)
9,
58,
31,
15,
140,
256,
248
706
835
669
571
029
Average Annual
Receipts Per •
Establishment c
Percent (5/yrj
3
22
12
6
54
100
.61
.93
.43
.12
.90
.00
17,683
40,459
67,021
93,829
329,978
-
 'The distribution of establishments is based on the distribution of all coin-
 operated laundries with payroll  (including those without dry cleaning
 capacity) reported in the 1987 Census of Service Industries  (U.S. Department
 of Commerce, 1991b).
=Average annual receipts multiplied by the number of establishments.
:3ased on data reported in the 1987 Census of Service Industries, Subject
 Series (U.S. Department of Commerce, 1990) for coin-operated laundries with
 payroll converted to  $1989 using the CPI for Apparel and Upkeep.
aRadian 1991a.
      Projected 1991 annual receipts to coin-operated laundries with dry
cleaning operations total 5256 million.  However, only about  10 percent or
525.6 million in receipts are directly from dry cleaning activities in the
coin-operated-, sector.  Dry cleaning.'output for this sector totals  4,298 Mg pe:
year.  Output is computed based on an average price of $6.34  per kilogram of
clothes cleaned at plant-operated facilities and $1.65 per kilogram for self-
service facilities.  Table 2-7 shows the total dry cleaning output and the
average output per establishment by income- category for the coin-operated
sector.
                                      2-9..

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 TABLE 2-7.     1991 DISTRIBUTION OF DRY CLEANING OUTPUT
               SECTOR
IN THE COIN-OPERATED
Annual
Receipts
(SOCO/yr)
C-25
25-50
50-75
75-100
>100
Total
Number of
Establishments3 Percent
523
1,451
475
169
426 •
3,044°
17.19
47.70
15.61
5.49
14.00
100.00
Total
Annual
Output5
(Mg/yr)
179
1,138
616
317
2,217
4,468
Average Annual
Output Per
Establishment^3
Percent (kg/yr)
4.
25.
13.
7.
49.
100.
01
47
79
10
62
00
343 '
784
1,297
1,878
5,205
-
.^The distribution of establishments is based on the distribution of  all  coin-'
 operated laundries with payroll  (including those without dry  cleaning
 capacity) reported in the 1987 census of service industries  (U.S. Department
 of Commerce, 1991b).
sReoeipts from Table 2-6 multiplied by the share of receipts from dry  cleaning
 activities  (10%) divided by the  19.89 base price..  Base price  for coin-
 operated (self-service) is 51.65 per kg.  Base price for coin-operated
 (plant-operated) is $6.34 per kg.  See Table 2-13 for the share of  plant-
 operated and self-service establishments in each receipts category.
=Radian 1991a.
      Price information  is unavailable  for the coin— operated sector.   Based on

conversations with industry officials,  plant -operated facilities  probably

charge the same price- as commercial, facilities- or  36.34  per kilogram (Torp,

1990) .  A survey of two  coin-operated facilities with self-service machines

indicated that they both charge $6.00 to run one cycle in  a 3.6 kilogram

capacity machine.  Presumably, these facilities are representative of the

sector and $6.00 is the  average price to use a 3.6 kilogram self-service coin-

operated machine.  Thus>. the- average? prica- to- clean- onei  kilogram: orV clothing

is calculated, to be $1.65.
2.1.3
      The industrial sector supplies items such as laundered uniforms,  wiping

towels, floor mats, and work gloves to industrial or commercial  users .

Industrial laundries provide services: for a, diverse group, of industrial, and
                                      2-9

-------
 commercial users including auto service and repair shops, food processing
 plants,  manufacturing concerns, construction firms, hotels, restaurants,
 security firms,  banks,  and real estate companies.  The commercial or
 industrial user usually rents the items from the industrial launderer who
 provides pick-up,  laundry,  and delivery services for the consumer on a regular
 basis (Coor and Grady,  1991).

       Service agreements between the industrial launderers and their customers
 to  provide clean uniforms generally specify the number of changes per employee
 and a schedule for delivery of the rented items.  For example,  the typical
 agreement for uniform rental specifies that the industrial launderer provide
 11  changes of clothing  per employee per week including 5 clean  suits left with
 the customer,  5  dirty suits taken back to the laundry,  and 1 transition suit
 (the garment  worn  by  the employee of the customer firm at the cime of
 delivery).   Items  are generally delivered and collected at the  same time each
 week (Coor and Grady, 1991) .

       According  to  Census data 1,379 industrial laundry facilities with
 payroll  were  operating  in 1987.   Over 90 percent of these establishments
 receive  annual receipts  over  $100 thousand (U.S.  Department  of  Commerce,
 1990b).   For  this  analysis,  it is assumed that  all  industrial launderers  wich
 dry cleaning  capacity have  annual receipts" of  over  $100  thousand.
 Approximately 325  industrial  launderers  have dry cleaning capacity.   Of these
 about  40  percent  (or  130) use  PCE and 60 percent  (or  195)  use petroleum
 (Sluizer,  1990).

      Annual  receipts for industrial facilities  with  dry cleaning  capacity
 total approximately $977  million.  On average,  about  35  percent of the
 receipts  at facilities with dry cleaning capacity are from dry  cleaning
 activities- with' the? balance: from  water- washing- or,- other: activities.   CJsing  an
average price  of $2.00 per-kilogram  of clothes  cleaned,  the.-estimated total
dry cleaning output from  commercial  facilities  is 170,901  Mg per year.

      Price data are unavailable  for  the  industrial sector..   Therefore, a
small survey was conducted to determine  the average price  charged  to  provide
one clean uniform weighing approximately one kilogram.  Prices  ranged from
$1.75 to  $2.25 per change-.,  A.  representative^ from,an.- industry trade-
                                     2-10

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 association confirmed that these prices are  representative of the prices
 charged in the industry  (Siuizer, 1990).  The midpoint  of  the range (52.00)  is
 assumed to be the average base price for the industry.

 2.2   PRODUCTION HISTORY AND TRENDS
       Although dry cleaning technology has existed for  many years,  the
 industry did not experience widespread expansion until  the  1960's.  A deep
 recession in the early 1970's eliminated part of the industry, but  the  late
 1970's and early 1980's saw a resurgence of dry cleaners (Fischer,  1987) .

       During the 1950's,  petroleum was the principle solvent  in dry cleaning
 plants.   The 1960's  brought a shift toward chlorinated  solvents  (e.g.,  ?CE, F-
 113)  that has continued to the present.   The main reason for  the shift  was the
 widespread'implementation of  fire codes'during this period.  'In addition, an'
 existing new source  performance standard (NSPS)  for petroleum-based dry
 cleaning restricts the  use of this  solvent in new facilities.  Because none of
 the. chlorinated solvents  exhibit  the flammable properties of petroleum,  che
 large  number of plants  built,  in shopping malls  and suburban areas since the
 1960's has  been based on  chlorinated-solvent  technology (ICF, 1986).

       Currently,  a vast majority  of  all  dry  cleaners  use PCE.  However,  demand
 for PCS  by  the  dry cleaning industry has been declining and is expected to
 continue  to  decrease  slowly due to greater recycling  and lower solvent
 emissions from  equipment  (Chemical Marks-finer  Reporter.  1986) .  The economic
 incentive-- for- self-imposed emission:-reductions - and solvent  recycling has
 persuaded several plants  to install  control devices and/or  switch to more
 efficient machines voluntarily.

       No direct measurement of the quantity of clothes dry  cleaned per year is
 available; for;, the-: dry- cleaning: industry.. However-,, an., estimate of. aggregate.
 output, can. be-derived,, through, the; quotient of total receipts for  dry cleaning
 activities and an average price per kilogram of clothes  cleaned.   Historical
 information on average- base-prices; and.total, receipts is available only  for
 the commercial sector; statistics compiled for the industrial and coin-
operated sectors do not distinguish between those facilities that  dry  clean
and those that, launder with water.  The base price in the commercial sector is
                                     2-11

-------
 the  price  charged to  clean a standard two-piece men's suit weighing one
 kilogram.   As  seen in Table 2-3,  the  average base price and total annual
 receipts measured in  1989  dollars increased by over 50 percent from 1974 to
 1988.   Total output for  the sector measured in kilograms of dry cleaned
 clothing declined from the mid 1970's  to  the early 1980's.   From 1981 to 1988,
 dry  cleaning output increased by  approximately one third.

      Table 2-9 presents annual growth rates for each sector of the dry
 cleaning industry.  These  estimates are based on machinery sales and are
 therefore broken  down  by machine  type  as  well as sector.   Other factors
 considered include  machine life,  current,  and historical sales  data,  and
 replacement rate  of the machinery.  Predictions  indicate that  the commercial
 sector will be the  only sector to  experience positive growth,  at just  over  2
 percent per year.   Both the  industrial  and coin-operated sectors are  estimated
 to show negative  annual growth rates of approximately 5 percent  and 7  percent,
 respectively.  These growth  rates  do not predict  overall growth  in  output for
 the coin-operated and  industrial sectors,  because  dry cleaning activities
 account for only  a small portion of total output  in these sectors.

      Several factors have contributed to the trend away from coin-operated
dry cleaning.  Because of environmental regulations,  consumers are
 increasingly aware of the hazards of operating coin-operated machinery  and
handling the cleaning solvents.  The decline is also  due in part to more
expensive dry cleaning equipment,  questionable returns on dry cleaning
activities  in this sector,  and the- necessity of hiring an attendant.  These
factors combine to make coin-operated dry cleaning operations unprofitable
 (Torp,  1990).
                                     2-12,

-------
 TABLE 2-3.    ANNUAL RECEIPTS,  AVERAGE BASE PRICE,  AND TOTAL OUTPUT FOR
               COMMERCIAL DRY  CLEANERS  (SI989)a
Year
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
Total
Annual Receipts
(S106/yr)a
2,692
2,630
2,623
2,675
2,825
2,878
2,975 .
2,941
3,517
3,638
2,694-
3,764
4,390
4,287
4,265
Average
Base Price
($/kg)a
4.02
4.42
4.46
4.36
4.87
4.90
5.32
5.63
5.72
5.87
5.98
6.13 '
6.14
6.05
6.08
Total' Dry
Cleaning Outout
(105 kg/yr)~b
570
506
499
521
493
499
475
444
522
5.27
525
522
608
603
596
 alncludes  receipts  for facilities  with payroll only.   All dollar figures
 converted to  1989  dollars  through the Consumer Price Index for Apparel and
 Upkeep.

 Total  sales multiplied by  share of receipts from dry cleaning activities
 (85%) divided by average base price  per  kg.

 Source:  Faig, 1990.
TABLE  2-9.     ANNUAL GROWTH RATES BY MACHINE TYPE AND SECTOR. (1986-1989)
                                Machine type
   Sector
                       Dry-to-dry
                 Transfer
Total
   Commercial

   Coin-
   Operated

   Industrial
9%
                           -3%
                   -7%

                   M/A


                   -5%
  2%.
                                                                   -5%
Note: Growth rates are- estimates: based-on. Section, 114.-.information.,  Considered,
 in these estimates were machine life, current sales data, replacement  rate,
 •and 5- and 10-year sales data.  Total annual growth rate is weighted
 according to the machine populations in each sector.

Source:  Radian,1991a.
                                     2-13

-------
       The negative growth rate in industrial dry cleaning reflects increased
 coats of dry cleaning due to state regulations as well as the advent of
 polyester/cotton and polyester/wool blends that made water washable fabrics
 feasible even for dress clothes.  In the 1980's, industrial cleaning plants
 have moved away from dry cleaning their output and toward laundering with new
 detergent formulations.  Between 1980 and 1985,  the number of industrial
 facilities that dry cleaned clothing dropped by approximately 50 percent (ICF,
 1986).   Virtually all the garments currently processed by industrial
 launderers are water washable.   However,  some industrial launderers continue
 to  dry clean at least a portion of their water washable garments because dry
 cleaning increases  the life of  the garment and enhances the garment ' s
 appearance (Coor and Grady,  1991) .   An estimated 92 percent of the garments
 cleaned by industrial facilities are laundered in water and detergent,  and
 this  percentage is  expected to  continue to increase {Sluizer,  1990) .

 2.3    PRODUCTION PROCESSES
       Dry cleaning  services  generally include cleaning,  pressing,  and
 finishing articles  of clothing  and other  related products .   In all three
 sectors,  the dry cleaning process  is  almost  identical  to laundering in  water
 except  that  a  solvent,  such  as  PCE,  is  used  in place of  water  and  detergent.
 The coin-operated sector  is  the only  one  that  does  not  regularly provide
 pressing  and finishing services.   The processes,  machinery,  and controls in
 each  sector of  the  dry cleaning industry  are  detailed  in this  section.

 2.3.1   Machine
      Two types of machines are commonly used in the dry cleaning  industry:
dry-to-dry and transfer.  Dry-to-dry machines combine washing and  drying  in
one machine and,, therefore^ do., notr, have-,, a. separate-/ machine, for. drying.
Transfer machines, like the traditional laundry machines for water washing,
consist of separate machines for washing and drying.

         Most dry cleaning plants- have- one- or more attachments to  their dry
    cleaning machine.  These include solvent filters, distillers,  and vent
   controls.  Figure 2-1  shows the typical  configuration  of  a  dry cleaning
                                     1-14,

-------
                                            Solvent in Ai.
                                      Conaansea Solvent
Figure 2-1. Typical Configuration of a Dry Cleaning. Machine and the .Various
            Attachments
Source:  Safety-Kleen, 1986.

machine and the various  attachments.   Solvent filters remove impurities from
the solvent and return the  "clean" solvent to the solvent tank.  Stills remove
any impurities left in the  solvent after it is filtered as well as water and
detergent" mixed:with  the-solvent" in the- washing" process through a distillation
process.  Virtually all  dry cleaning facilities have solvent filtration
systems and about 80  percent use  stills.   These devices extend the life of the
solvent and reduce the amount of  solvent  that must be purchased (Safety-Kleen,
1986) .

      Approximately. SO, percent, of all, PCS dry cleaning machines have vent
control devices (Radian,  1991c).   Vent controls are attached to the dryer and
remove, vaporized solvent  from- the- dryer emissions.   Vent .control devices are
available in two basic types:  carbon  adsorbers (CA's)  and refrigerated
condensers (RC's) .  With  the use  of  a  CA,  PCE emissions are trapped in a
carbon filter.  The filter  then undergoes a condensation process that
                                     2-15

-------
eliminates the hazardous emissions.  A  typical  CA lasts  about 15 years and
reduces emissions by about  95 percent when  operated properly.  The sec  j. type
of control device, the RC,  uses a  refrigerated  coil to cool PCS vapor;    This
cooling process results in  condensation of  PCE  emissions.   The average  _fe of
a RC is about 7 years.  The emission reduction  achieved  by RC's differ
depending on the type of dry cleaning machine used.   Refrigerated cor... nsors
reduce vent emissions by 85 percent on  transfer machines and by about  -'5
percent on dry-to-dry machines .

      Over 90 percent of new dry— to— dry machines  built for the commercial and
industrial sectors have built-in RC's f Federal  Rgg-is-eer. 1989) .   Add-on
control devices may be purchased and attached to machines  chat  are  not
equipped with vent controls from the manufacturer.  A  facility's  selection of
control devices is constrained by the capacity of its  dry  cleaning  machine .
Add-on RC's are not available for the very small machines  built for the  coin-
operated sector or for the large machines built for the industrial  sector.
Both types of add-on devices are available to retrofit virtually  all machines
built for the commercial sector.

      Owners and operators of dry cleaning facilities  purchase add-on vent
controls and attach them to thei-r dryer for a variety  of reasons.   Some  states
require dry cleaners to control their emissions using  a vent control device .
Environmentally conscious owners may install vent controls even in  the absence
of state regulations.  Depending on the price paid for solvent and  the amount
of solvent saved, some owners may realize a cost savings from reduced solvent
consumption with a vent control.
2.3.2
      Tour solvents are currently in use in the dry cleaning industry:   PCS,
fluorocarbon 113 (F-113) , petroleum, and 1, 1, 1-trichloroethane  (1,1,1-TCA).
Of these four, PCE is usually considered the most efficient cleaner.  Five
main factors determine- the- suitability of a solvent for dry cleaning, each
with a range of acceptable values, as opposed to an absolute standard  (Busier,
1980) :
                                     2.-16,

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        •  The solvent must be able to dissolve fats and oils without damaging
        » the most csrrmon fibers and dyes.
        •  The solvent should not leave an unpleasant odor in garments after
          drying.
        •  Chemical stability is  important to prevent damage tc the metals used
          in  dry cleaning machinery.
        •  A certain level of volatility is  desirable to permit rapid drying and
        .economical reclamation through distillation.
        ••  The  solvent  should be  compatible  with  common  detergents used in the
          process.

 The importance of ?CS to the dry cleaning process  depends  on  the  ease  with
 which it can be replaced by another comparable solvent.  The  potential  for
 solvent substitution should be evaluated -against the  criteria established .for
 zhe factors  listed above.

       F-113, petroleum,  and 1,1,1-TCA can all theoretically be substituted  for
 PCE in the dry cleaning process.  However, none of these solvents will perform
 with the  same degree of efficiency as PCS.  Thus,  an owner of a dry cleaning
 plant will need to ponder various considerations  associated with solvent
 substitution.  These factors include solvent prices,  cleaning properties,
 capital costs,  and operating costs.   An additional factor in the substitution
 decision  is  the ease with which machinery  designated for use with one solvent
 can be converted  to accept  other solvents.

       Although  all three alternative solvents are  used in some dry cleaning
 plants, none  are-currently  considered feasible,  for widespread;, substitution for
 PCE.   F-113 most  closely matches  the cleaning abilities  of  PCE but is
 unsuitable for  certain garments  and  stains.  In addition, the  possibility of
 regulations concerning ozone depletion may  limit any immediate substitution.
 Finally,  the  unit  price  of  F-113  is  considerably higher  than the  unit price  of
 PCS..   Fire- codes "ill probably, prevent;, any  substantial,, shift: to. petroleum,  the
 second solvent.  The remaining solvent,  1,1,1-TCA,  has yet  to  attract much
 interest in this country.   Its cleaning abilities are questionable because of
 high solvent  aggressiveness and instability. ' In addition,  usage  costs  are
 approximately ten times higher than for PCE  (Fisher, 1990a) even  though
trichloroethane users can achieve energy savings of 5 to 10 percent  (Fisher,
 1987) .                                          .      .             .

-------
      Technically, one other substitute for PCE is available.   Industrial dry
cleaners can switch to laundering garments with water and detergent  for most
items.  The commercial and coin-operated sectors do not have this flexibility
because the customer owns the item to be cleaned and, therefore, specifies the
cleaning method.
      Approximately 28,000 of the 34,000 dry cleaning plants in the  United
States use PCE as a cleaning solvent  (see Table 2-1).  Most of the remaining
olants use a petroleum—based solvent, and a small percentage use either F-113
or 1,1,1,-TCA.  Approximately 85 percent of total dry cleaning output from
commercial facilities is processed using PCE.  Virtually all coin-operated
facilities with dry cleaning capacity use PCE. Solvent use in the industrial
sector is divided between PCE (40 percent) and petroleum (60 percent)
(Sluizer, 1990).

      Figure 2-2 shows the percentage of total PCE consumed by each  sector.
The commercial sector accounts for approximately 94.3 percent of total PCE
consumption by the dry cleaning industry.  The industrial sector and the coin-
operated sector account for 4.6'percent and 1.1 percent of consumption,
respectively.
  Coin-Operated Sector
          (1.1%)
      Industrial  Sector
           (4.6%)
Commercial Sector
     (94.3%)
Figure 2-2. PCE Consumption by Sector for 1991
            Source:  Radian, 1990b.
                                     2-18

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2.3.3  Product i~ri P-r^g
      The flow of production is basically identical in  coin-operated (plant-

operated) and commercial facilities .  The production process  begins  when the

dry cleaning plant receives the soiled garment from the consumer.  After a

garment enters the plant, a minimum of 10 .steps of production  are  required to

produce a clean garment ready for delivery.  These steps of production  are

described below:

      • Iaaaina~Tagging typically involves attaching a tag to  the garment
        with a unique identification number for each customer.  A record is
        made of the customer's name, the corresponding  tag number, any
        special instructions, and the promised delivery date.

      • Initial Classifying — Garments are separated into three  basic
        categories at this stage of production:  garments that  require  dry
        cleaning- but no pre-spotting, garments that require, laundering  but no
        pre-spotting, and garments that require pre-spotting.

      • Applying Spotting Chemicals — Garmen-ga stained with ink, paint,  food,
        or other substances are treated with solvents and other compounds
        before they are laundered or dry cleaned.
                Classifying—Garments are further classified by the  type  of
        fabric and the color of fabric.  This step is required because
        garments with different fabric types and colors require different
        treatment and can be damaged if they are processed with garments  of
        dissimilar fabric type or color.

        Washing—Tn dry cleaning operations, garments are washed in  a solvent
        mixture comprised of solvent, water, and detergent.  The correct
        combination of solvent, water, and detergent and the correct washing
        temperature are vital to the successful removal of soil without
        damaging the garment.  The_ washing step ends with extraction of the
        excess •• solvent,: mixture.

        Drying—After garments are washed and the excess moisture removed,
        they are dried using heated air.  Garments may be transferred to  a
        separate machine for drying  (transfer machines)  or dried in  the same
        machine (dry-to-dry machines) used to wash the garments depending on
        the machine technology employed by the facility.

                         iiuaa~Clean>,. dry garments; are; pressed and  finished.
        Finishing includes replacing damaged.or missing buttons, special
        pressing (e.g;.,  pleated skirts), and any other special handling that
        may be required.

        Hangingr-Garment'S- ara-; placed, om hangers- in, this- step- of the
        production process.
                                     2-19

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                r: 7— After they are  placed on hangers,  garments are sorted and
        assembled by  consumer identification number on the tag attached to
        the garment and by promised delivery date.
      • Packaging— Assembled garments  are  packaged  for delivery.   Packaging
        typically involves placing  a plastic bag over the garments .

      Garments are inspected periodically  throughout  the  process  described
above to determine the  success in removing soil  and the acceptability  of  the
pressing and finishing  steps .  Additional  steps  may be  required for  heavily
soiled garments, oversized items, or delicate garments  that  require  special
handling.  The production  process ends with  delivery  of the  cleaned, pressed,
packaged garments to  the consumer.

      Production of clean  clothes at coin-operated  (self-service)  facilities
involves ' the consumer as an  active participant'.   The  facility  provides  the-.
equipment used in the washing and drying process  and  the  individual  provides
the labor inputs required  for the spotting,  pressing, and finishing  of  the
garment.  The process of producing clean clothes  is similar  to  that  described
above for commercial  and coin-operated (plant-operated) facilities excluding
the tagging, assembling, and packaging steps .

      Unlike customers  in  the commercial or  coin-operated sector, customers of
industrial cleaners do  not deliver the soiled items to the cleaning  facility.
Rather,  the industrial  cleaner collects the  soiled  items  from the commercial
or industrial user on a regular basis at no  additional charge to the user.

      The production process  begins-  when the soiled garment  enters the
industrial plant.  The  steps  of production are similar to  those described
above for commercial and coin-operated (plant-operated) facilities .  A  few
differences do exist,  however.  Garments cleaned by industrial  facilities
     • ?   -t '
generally contain, a permanent, identification, number that  identifies  not only
the company purchasing-  the- dry cleaning' service" but also  the individual cnat
actually wears the garment,  the route number, and the day of the week
scheduled for delivery  of  the cleaned items.  The process  generally  requires
less classifying beyond the. initial  classifying because- garments are more
homogeneous with regard to fabric type and color.   In addition, the  process is
generally more mechanized  and larger in scope than the process at a  typical
commercial, or.' coin-operated- (plant^operated)' facility.  The production  process
                                     2-20

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               ends with the delivery of the cleaned item to the customer on the promised
               delivery date.
\
      Costs  of production  in  the  dry cleaning industry can be classified as
aither  fixed or  variable costs.   Fixed costs  are incurred regardless of the
Level of production.   Two  types of  fixed costs exist:   those that occur only
once at the  start-up of a  business  and those  that regularly recur.  Variable
costs depend on  the level  of  production at  a  plant and fall to zero if the
plant ceases operations entirely.   These three categories of costs are
described below:
       (1)  Fixed start-up costs:   -the costs associated with the decision to
           open a dry cleaning plant,
       (2)  Fixed recurring costs:   the costs associated with the decision to
           operate the dry cleaning plant, and
       (3)  Variable costs:   the costs associated with the decision to operate
           the  dry cleaning plant  at a given level of output .

The first category of  costs includes most,  if  not  all,  capital costs as well
as long-term materials contracts  and capacity  investments.   Table  2-10  shows
the capital  costs of new dry-to—dry machines.   In  addition,  some
administrative fees and initial building overhead  costs,  such  as  remodeling or
down payment, are included in this category of  costs.   These expenses  are  the
fixed costs  that are incurred regardless  of the level  of  production  or  whether
the firm operates at all.  Total,  estimated. start-up costs, typically  range  f^ora
395 to $120  thousand (Faig, 1991)  .

      Table  2-11 displays information on- the second and third  categories of
costs for commercial dry cleaning facilities by output  level.   On  average,
total wages  and salaries account  for" the. largest' portion  of  dry cleaning costs
followed by  rent/building overhead expenses or  total supply  cost'.  The
majority of  costs incurred by a. dry cleaning plant are  variable such- as
solvent, labor, and energy costs.   Table  2-12 provides  unit price  information
for the major inputs that contribute to  the variable costs- of  operating a  dry
cleaning facility.
                                                   2-21

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         TABLE 2-10.  CAPITAL COSTS OF NEW DR¥-TO-DRY MACHINES  ($1989)
Machine Capacity (kg/ load)
11.3
13.6
15.9
20.4
22.7
27.2
45.4
63.5
113.4
Capital Cost ($)
26,046
27,820
29,594
42,171
44,040
. 47,040
65,255
104,000
157,000
Source:  Radian, 1990a.

      Dry cleaning plants have  relatively  small capital  equipment costs,
although these vary between the sectors.   In addition, the buildings  used by
many plants are rented or easily transferable to other uses.   As  a result,  the
relatively high variable cost to fixed cost ratio at most dry  cleaning
facilities promotes a dynamic industry structure in which the  less efficient
plants quickly terminate operations if losses become excessive.
      The decision to open a new plant must be evaluated based on the costs
included in all three categories above.  However, for existing facilities,
costs in category 1 are sunk and do not affect the owner's decision to
continue operating.  Production  cost for existing and new facilities  are
discussed below.
                                     2-22

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 TABLE 2-11.    AVERAGE ANNUAL OPERATING COSTS FOR COMMERCIAL DRY CLEANING
               PLANTS
Annual Output (kg/yr)a
Cost Category
Fixed Recurring Costs
Wages and Salaries*3
Rent or Building Overhead
Depreciation
Interest and Bank Charges
Insurance
Variable Costs
Wages and Salaries
Total Supply Cost
Outside Work
Payroll taxes
Advertising.
. Utility-Fuel
Repairs and Maintenance
Utility-Electricity
Office Expense
Administrative Expense
Utility— Water and Sewage
Claims
Miscellaneous
Total Costs
2,378

3,542
1,316
1,272
779
576

3,024
1,541
1,437
541
435.
360
312
268
259
241
117
92
908
17,019
5,436

8,078
3,002
2,901
1,776
1,315

6,898
3,515
3,277
1,234
991
821
712
611
591
550
267
210
2,071
38,820
3,985

13,383
4,973
4, -805
2,942
,2,178

11, 428
5,824
5,429
2,044
1, 642
1,361
1,130
1,012
979
911
442
340
3,431
64,313
12,580

13,736
6,962
5,728
4,119
3,049

15,000
3,154
7,600
2,862 .
2,299
1,905
1,651
1,417
1,370
1,276
619
488
4,304
90,038
40,775

81,727
20,955
11,922
3,163
7,786

58,722
23,175
15,876
12, 470
10, 949
6,661
6,813
8,394
3,498
4,015
3,224
1,24-7
10,707
291,392
aSased.. on,., the:,'average.: annual, receipts, for five- income- categories reported  in
 Tablet 2-2:...
"Includes owner's wages.

Source:  International Fabricare Institute,  1989; Fisher, 1990b.
                                      2-23

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 TABLE  2-12.   AVERAGE H-IPUT PRICES FOR PCE DRY CLEANING FACILITIES  (51989)
             Inpuc
                                      ?rice
            Material
            Perchloroethylene.
            Energy
                                    S0.683/kg
             Electricity.
             Steam	
                                  . $0.0710/kWh
                                  $6.13/1000 Ib
             Labor
             Operating  labor...
             Maintenance  labor
                                    ,55.94/hr
                                    ,56.53/hr
Source:  Radian, 19 9'0d.
2.4.1
f t °roduerion for
                                     inP'aiili.tiea
      The short-run supply  curve  of  an  existing dry cleaning facility is the
portion of its marginal  cost  curve that lies  above the minimum point of its
average variable" cost curve.   In  other  words,  facilities  will continue to
supply dry cleaning services  in the  short  run as long as  they can cover their
variable costs of production.  The market  supply curve is the horizontal
aggregation of the supply curves  for all facilities in the market .   This
aggregation is characterized, in the  step supply function  (see Figure 2-3)
where the producer with  the highest  marginal  coat in the  market sets the
market price of dry cleaning  services.

      Lower cost producers  are able  to  cover  some or all  of their fixed costs
because- the market, price- is. above-: their, average- variable  cost.   Differences in
the production coats across producers are  attributed to differences in
management practices as  well  as differences in the productivity of  capital
equipment.  Assuming that the productivity of  dry cleaning equipment has been
increasing over time, owners  of new  equipment  would tend  to have lower
marginal costs than owners  of older  equipment,
                                     2-24-

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        iS/kg)
       Market
       ?rice
                                                          Market    (kg/yr)
                                                         Quantity

            Figure 2-3. Market Supply Curve for Existing Facilities


      An increase  in  the  price  of  a  variable  input changes the facility's
average variable cost and its marginal  coat.   Changes in the marginal cost of
producing dry cleaning services  would cause a shift in the supply of dry
cleaning services  resulting  in  price and output- adjustments" ac least: in the
short run.

2.4.2  Costa of Producrf.ion for  htew Faeilitiga
      An entrepreneur, contemplating- construction, of: a,, new- dry cleaning
facility won't invest  unless he/she  anticipates  covering total costs.  By
definition, total  cost  for a new facility includes fixed start-up costs
including a--normal return, fixed, recurring costs,  and variable costs.  If the
average total cost of. opening- a  new  dry- cleaning  plant is- above-- the  market
price, no new entry will  occur.  Conversely,  if the average total cost is
below the market price, new entry  will occur  (see Figure 2-4).   Therefore,  any
                                     2-25

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                (S/.kg)

                New
              Facility
               Costs
 Market
 Price
ATC
          AVC
                                                                Market  (]eg/vr)
                                                               Quantity
Figure 2-4. New  Facility Costs  Compared to Market Supply Curve for
            Existing  Facilities

increase in the  marginal costs  of  existing producers not affecting new
suppliers would  have  the effect of encouraging new entry into the market.  The
entry of a new facility  into  che market displaces the marginal, -axxsting
supplier.  As the marginal  suppliers  are displaced in the market, price falls.
This process continues until  price equals the average total cost of building a
new facility.  Long-run  price and  output equilibrium,  therefore,  depends on
the average total cost of building a  new facility.   Once a.new facility is
constructed,, the fixed, costs,  become,-sunk; costs? and-only the-variable, costs are
relevant; to the decision to continue  operating the  facility.   The facility
continues to supply dry  cleaning services as  long as price exceeds average
variable cost.
                                     2-26.

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2.5   MODEL FACILITY PROFILE
      The abundance of dry cleaning establishments precludes  an  approach that
investigates the impacts of candidate regulatory alternatives on a  facility-
specific level.  Ignoring the resource coats of collecting data  for such a
large sample, computational time alone diminishes the feasibility of a
facility-specific approach.  Consequently/ a model plant approach is used ir.
which fifteen model plants represent the characteristics of average PCS
facilities in each sector.  Table 2-13 presents operating, parameters of  the
model plants by industry sector, machine size, and process.   In  addition, the
distribution of PCE facilities represented by each model plant is reported for
five output levels.  These output levels correspond to ranges  of  annual
receipts shown in Table 2-13.

      The model plants were chosen to represent the variability  in  machine
size and technology that is present among existing facilities  in  the industry.
The coin-operated sector has basically only one machine size  and design.
However, two model facilities in this: sector are differentiated by  the base
price charged for dry cleaning services -and the type of service supplied  .
(self-service or coin-operated).  Ten model plants for the commercial sector
and three model plants were selected for the industrial sector.  Most of the
contemporary dry cleaning facilities are purchasing dry-to-dry machines to
save on solvent costs,  to comply with a recently promulgated worker  exposure
regulation,  and to reduce the environmental impact of PCE emissions.
Nevertheless,- some facilities- continue to operate with transfer machines, and
that portion of the industry is  represented through appropriate model plants.
                                     2,-27

-------
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-------
                                   SECTION' 3
                       DEMAND FOR DRY CLEANING SERVICES

      Two types of demand exist for dry cleaning  services:   household demand
ar.d industrial demand.  Household demand is characterized by individual
consumers purchasing dry cleaning- services provided by commercial and coin-  .
ocerated facilities.  Industrial, demand is characterized by firms purchasing
drv cleaning services to clean employee uniforms  in production and service
establishments.  Typically, employers rent these  uniforms from an industrial
cleaner who provides regular cleaning-and delivery services.   The subsequent
sections discuss household demand and industrial  demand  in  detail.

3.1   HOUSEHOLD DEMAND
      As consuming units, households demand clean, pressed  clothes.   Because
some garments require dry cleaning for proper care, households rely on dry
cleaning services provided by others to procure clean, pressed clothes.  Two
types of, dry cleaning services—commercial, and. coin-operated—are available to
households.  Commercial facilities and. coin-operated  (plant-operated)  provide
a complete service:  garments are cleaned,  pressed, and  packaged for the
consumer.  At self-service coin-operated, facilities,  consumers pay for using
dry cleaning machines, but they must clean  and press  their  own clothes.
Despite some similarities in the influences o'f demand for these services,
these two sectors have experienced different growth patterns.

      The: subsections, below, discuss, different, facets, of.  household demand.  The
first two subsections explore consumption patterns and characteristics of the
consumers of dry cleaning services.  The next  subsection discusses the theory
of household production in the context of dry  cleaned clothing.  How consumers
value their, time: and, their choice between coin-operated  and commercial
facilities- ia_ presented, in:; the- fourth- subsection-., The. final subsection
briefly examines-consumer  sensitivity to changes  in  the-price of dry cleaning
services-.

3.1.1  f?ntiguiTTOt; Ion  anrt *T*y!»nd«
      Household consumption of commercial  dry cleaning services  can be
measured,-in-.terms-. o£:.the; total, weight o£. clotthesr; dry cleaned, or  in terms  of
                                       3-1.

-------
total expenditures on dry cleaning services.  Figure  3-1  shows  that overall
consumption, measured by the total weight of clothing cleaned,  increased by
nor® than 25 percent from, 1980 to 1988.  However, on  a per-household basis,
demand for dry cleaning services increased only  11 percent  during this ceriod.
Consumption per household, reached its peak in 1986, when  the- average household
consumed almost 7 kilograms per year.  This pattern is depicted in Figure 3-2.

      Table 3-1 shows household consumption in terms  of expenditures.   These
data are calculated from the consuma-p Expenditure Survevg  (U.S.  Department of
Labor, 1991a).  The survey compiles average annual household expenditures for
a broad category called "Other Apparel Products  and Services."1   This  category.
encompasses a wide range of goods and services,  including material for making
clothes, shoe repair, clothing alterations and repairs, sewing  supplies,
clothing rental, clothing storage, coin-operated laundry and dry cleaning,
commercial laundry and dry cleaning, watches and jewelry, and watch and
jewelry repair.

      Expenditures on commercial laundry and dry cleaning services were
estimated in the following manner.  Detailed information on  the  relative
weight of each category item (listed above) used to compile  the  Consumer  Price
Index was available for the period 1982-1984 (Manson  and Butler,  1987}.   Based
on .those relative weights,  expenditures on laundry and dry  cleaning services
(excluding coin-operated) made up about 25 percent of  the category for those
years.  The expenditures for each category item  listed above  were  available
for 1989.  Approximately 24 percent, of. the category expenditures were  spent  on
laundry and dry cleaning (excluding coin-operated).   The expenditures  reported
in Table 3-1 represent 25 percent of the "Other  Apparel Products and Services"
category.2  Because the portion of the category  attributed to laundry  and dry
      1The expenditures on apparel items coma from- the interview portion  of
the- Survey.  Because: the reported,expenditures are based on the consumer'3
memory, these data may not accurately reflect receipts at commercial dry
cleaning establishments.
      2?or the years 1980-1983, only data- on urban consumers were available.
The expenditures estimated in Table- 9-15 were- adjusted to reflect all  consumers
in the following manner.  In 1989, urban consumers spent three times what rural|
consumers did on commercial dry cleaning services; that relationship was
assumed to hold for the years 1980-1983.  In addition, rural households were
assumed to comprise 16 percent; of all households,, which is approximately  the
portion-, that; they-comprised; for,-the* yearsi 198.4—1986-'..  The;-reported  estimates
ara a weighted average of, urban consumer spending and rural consumer spending.
                                      3-2*.

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           700
           600 --
           500 --
 Millions  400
   o z,
 Kilograms
   per
  Year     300
           200
           100
                 1980    1981    1982   1983   1984    1985    1986   1987   1988

Figure 3-1.  Total Annual Household Consumption of Commercial Dry Cleaning
             Services (1980-1988)

Source:  Table 2-8
                                      3-3

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            6  --
  Millions  4
    of
 Kilograms
    per
   Year     3
                1980
1981   1982'   1983    1984    1985    1986
1987   1989
 Figure  3-2,  Annual Consumption, of  Commercial. Dry Cleaning Services per.
             Household (1980-1988)

 aComputed  by dividing total dry cleaning output (Table 2-8) by the total
. number of households, in the U.S. reported in Statistical Abstract of the
 United States  (U.S.  Department, of  Commerce,  1991d);  U.S. Department of
 Commerce, 1991'.
                                      3-4

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7ABLE. 3-1.  HOUSEHOLD EXPENDITURES ON COMMERCIAL LAUNDRY AND DRY CLEANING
            SERVICES 1980-1989  ($1989)
Average
Annual Household:
Expenditures ,-• Increase
Year {$ /Household/ Year ) a (%)
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
62
37
55
53
52
67
66
S3
67
66
.18
.58
.96
.95
.95
.70
.75
.49
.35
.50
-
-7.
-2.
5.
6.
7.
. -1.
2.
-1.
-1.

4
3
3
3
5
4
6
7
3
Expenditures
as a Share
of Income
0
0
0
0
0
0
0
0
0
0
.15
.14
.14
.14
.14
.15
.15
.15
.14
.14
Total Annual
Household
Expenditures
(5105/yr)c
•J /
4,
4,
4,
5,
5,
5,
6,
6,
6,
022
757
675
947
377
876
905
129
132
173
Increase
-
-5.
-1 .
5 .
8.
9.
0.
3.
0.
0.

3
7
8
7
3
5
a
i
7
Represents 25 percent o£ "Other Apparel Products and Services."   Original
 data for 1980-1983 excluded rural consumers and were adjusted to  include
 rural consumers.   Converted to 1989 dollars using all items CPI.
bSased on before tax income.  Income calculated by multiplying national
 personal income by the number of households.
cAverage; household, expenditures multiplied by number of households.

Sotirces:    1980-1989 Consumer Expenditure Survey, U.S.  (Department  of  Labor,
 1991a);  Economic Report of the President,  1990; Statistical Abstract of the
 United States,  (U.S.  Department of Commerce, 1990d); U.S. Department of
 Commerce,  1991)..
cleaning expenditures remained fairly constant over time,  the  data

characterize commercial laundry and dry cleaning expenditures  fairly well.

Approximately 85 percent; of: a. typical, consumer's, commercial cleaning bill is

dry- cleaning,- as opposed: to" laundry; (UiS... Department'  of; Commerce,  1991) .


      Notice that, in 1980, households spent  $62 a year on average;  in 1989

that:, figure- had, increased; to  567,, an, 3 percent, increase.   Aggregating across

the United States yields total expenditures of more than 35.0  billion in 1980

and $6.2 billion in 1989.
                                      3-5:

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      Two main factors affecting the growth of dry  cleaning consumption are
textile and. lifestyle trends.  During the  1970 's, fashion trends demanded
aasy-cara fabrics'.  Because these fabrics, normally synthetic or a synthetic
blend, do not necessarily require dry cleaning, consumption of dry cleaning
services decreased.  Returning to more natural fibers  and synthetic materials
chat require dry cleaning for proper care  led to increased consumption in che
1980's (Fischer, 1987).

      The demand for commercial dry cleaning services  is  also influenced by
general economic conditions as well as fashion trends.  Prevailing economic
conditions influence the purchase of more  expensive  garments,  which often
require dry cleaning for proper care.  Another factor  that increased household
demand for cleaning services is the increase in the  number of women in the
work force.  The impact on commercial cleaning comes from both the increased
opportunity cost of a working woman's time and the  increase in the number of
women working outside the home.  Table 3-2 shows the change in the number of
women in the work force and the median income for women for the period 1980-
1989.

      Consumption at coin-operated facilities, is also  strongly affected by
general economic conditions, though sometimes for different reasons  than
commercial dry cleaning consumption.  Historically,   the cleaning volume at
coin-operated facilities plants has fluctuated with  the economy.
      Oata on coin-operated consumption are- sparse.  However, the Census of
                  g does publish receipts for coin-operated laundry and dry
cleaning facilities.  Caution must be exercised when applying these data to
the dry cleaning industry because the receipts include laundry receipts .  In
1982,, coin-operated laundry- and. dry cleaning establishments  (with payroll)
across che- United States- took: in. 51,501'. million in. constant  (1989) dollars
compared' to 51,821 million in. 1987 (U.S. Department of Commerce, 1990c> .  This
increase amounts to 21 percent.  Receipts also increased in per-capita terms.
Per-capita expenditures; expressed in, constant; dollars rose from 55.02 in 1982
to 56.83 in 1987.
                                      3-s:

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 TABLE  3-2.  NUMBER AND MEDIAN INCOME  OF WOMEN IN THE WORK FORCE 1980-1989
             ($1989)
. Year
1980
1981
1982
1983
1984
1985
1986
. 1987
1988
1989
Number of
Women*
(000)
42,117
43,000
43,256
44,047
45,915
47,259
48,706
50,334
51,696
53,027
Change'
(%)
—
2.10
' 0.60
1.83
4.24
2 . 93
3.06
3.34
2.71
2.S7
Median Income^
($1989)
17,443
16,994
17,558
18,038
18,406
18,730
19,057
19,173*
19,439
N/A
Change
'(%)
— .
-2.57
3.32
2.73
.2.04
1.76
1.75
0.51
1.39
-
'Includes working women over the age of 16.
"Data includes women over the age of 15 with full-time employment. Converted
 to 1989 dollars using the all items CPI.
Source:  Economic Report of the President, 1990.
3.1.2
                        of
      Although every individual probably owns at least a few garments that
require^- dry cleaning- for; proper: carev,. individuals, -who -use, dry cleaning
services on a regular basis have identifiable characteristics .  People's need
for dry cleaning services depends on the clothing they own and their
occupation,, which may dictate their clothing choices.  White collar workers
are more likely to own clothing that requires dry cleaning for proper care.
Similarly, individuals in professional positions would utilize dry cleaning
                                      3-r

-------
services more.  3y extension, individuals, with higher incomes  would be
expected to use dry cleaning1 services more often.

                                  data for 1989 supporr these  contentions.
Tables 3-3, 3-4, and 3-5 presenc data for two types of expenditures:
(1)  expenditures on laundry and dry cleaning, excluding coin-operated and
(2)  expenditures on coin-operated laundry and dry cleaning.  These 'data  are
compiled by income levels  (see Table 3-3), occupation  (see Table  3-4), and
location (see Table 3-5) .  As indicated above, the expenditures for the
commercial sector are predominantly for dry cleaning services.  This
assumption does not necessarily hold for the coin-operated sector, where  the
.-aajority of the expenditures are for laundry expenses.  Caution must  be
exercised when interpreting the coin-operated data.

      As expected, expenditures on commercial dry cleaning increase with
income (see Table 3-3) .  An individual earning more than 350,000  a year  spends
more than four times on dry cleaning than an individual earning less  than
$30,000.  These higher expenditures are induced by two factors.   The  first is
the need to dry clean most professional career clothing.  The second  is the
propensity for individuals with higher incomes to own luxury clothing  (e.g.,
leather, suede), which requires dry cleaning for proper care.  Also,  as shown
in Table 3-3, coin-operated expenditures decline with income, although laundry
expenditures cannot be separated from the dry cleaning expenditures .

      Figure 3-3 depicts- this switch from coin-operated expenditures  to
commercial expenditures as income rises.  A point of further interest  is  that
expenditures on commercial cleaning are a relatively stable share of  income
across all income levels . , This stability suggests that any one income class
would not be more affected if. prices increase.

      Table 3-4 shows expenditures on commercial. and coin-operated cleaning by
occupation classification.  Individuals whose occupations fall in the
manager/professional category spend almost 83 percent more than any. other job
category on commercial cleaning services.  Individuals with technical, sales,
or clerical positions spend more than $75 a year on commercial cleaning,  which
is 135 percent more than any of the remaining categories.

-------
TABLE 3-3.  HOUSEHOLD EXPENDITURES ON COMMERCIAL AND COIN-OPERATED  DRY
            CLEANING, AND LAUNDRY SERVICES BY INCOME CATEGORY  (51989)
Commercial,
Cleaning Services3
Income
Category15
(SOOO/yr)
5-10
• 10-15
15-20
20-30
30-40
40-50
over 50
Average Annual
Expenditure
{$ /Household/ yr)
17.40
18.57
30.57
42.06
62.13
90.75
175.93
Expenditures
as a' Share of
Income^3 (%)
0.23
0.15
0.18
.0.17
0.18
0,20
0.22
Coin-Operat ed
Cleaning Services*
Average Annual
Expenditure
(S /Household/ yr)
45.90
42.14
41.92
43.76
35.06
23 . 95
IS. 81.
Expenditures
as a Share of
Income*5 ( % )
0.61
0.34
0.24
0.18
0.10
0.05
0.02
^Estimates of annual household expenditures are based only on those households
 that purchase these services and do not take into account those households
 that do not purchase each type of cleaning services.  These estimates include
 both laundry and dry cleaning expenses.  Expenditures at commercial
 establishments comprise mainly dry cleaning expenditures; only a small
 portion of, expenditures.-ar coin-operated establishments constitute dry
 cleaning- expenditures.
"Based on'before-tax income.

Source:  1980-1989 Consumer Expenditure Survey  (U.S. Department of  Labor,
 1991a) .
      Finally, . household cleaning expenditures- differ greatly  depending on the

geographic location  (see- Table 3-5) .  Urban consumers spend  three  times as

much on commercial cleaning than do their rural counterparts.   This  difference

in expenditures, probably reflects* occupation choices .
Tha. gon
-------
 TABLE 3-4.  HOUSEHOLD EXPENDITURES ON COMMERCIAL AND COIN-OPERATED  DRY
             CLEANING AND LAUNDRY SERVICES BY OCCUPATION CATEGORY
Commercial
Cleaning Services*
Occupation
Category
Manager/
Professional
Technical/
Average Annual
Expenditure
(S/Household/yr)
138.28
75.68
Expenditures
as a Share of
Incomeb (%)
0.28
0.23
Coin-Operated
Cleaning Servicesa
Average Annual
Expenditure
(S./ Household/ yr)
27.14
46.79
Expenditures
as a Share of
Incomeb ( % )
0.06
0.14
    Sales/
   Clerical
    Service
    Workers
 Construction/
   Mechanics
  Operators/
     Labor
31.25

32.25

31.05
0.1S

0.10

0.11
54.41

37.51

43.24
0.27

0.12

0.15
 'Estimates of annual household  expenditures  are  based only on those households
 that purchase these services and do not  take  into  account those households
 that do not purchase each type of cleaning  services.   These  estimates include
 both laundry and dry cleaning  expenses.   Expenditures  at  commercial
 establishments comprise mainly dry cleaning expenditures;  only  a small
 portion of expenditures at coin-operated establishments constitute' dry
 cleaning expenditures.
 bBased on before-tax income.
 Source:  1980-1989 Consumer Expenditure-Survey (U.S.  Department  of Labor,
 1991a).
$20,000 a year, and  lives  in  an  urban  area.   Making generalizations about the
coin-operaced expenditure  data is more difficult.   3ut  conversations with
coin-operated industry experts provide a  picture of the typical consumer of
coin-operated dry cleaning.   The typical  patron is  cost-conscious,  probably in
the lower income brackets  but may be in the  lower middle class  as  well.   This
patron is more likely to live- in. a  rural,  location where commercial  facilities
are-not, available- (Torp, 1991).  The-data do not refute this  description.

3.1.3  Houaehotrf Demand FiTnefrinn

      Like-any demand function,  household demand for dry cleaning services  is
derived from utility maximization.  Utility  comes from  commodities,  not
directly from goods and services.   Households combine goods and services with
tima asi inputs, into-- a.i process- chat  generates commodities'- Thus-, time  spent oh
                                     3-10.

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TABLE 3-5.  HOUSEHOLD EXPENDITURES ON COMMERCIAL AND COIN-OPERATED  DRY
            CLEANING AND LAUNDRY SERVICES BY LOCATION CATEGORY
                         Commercial
                     Cleaning- Services*
   Coin-Operated.
Cleaning Services*
Location
Category*5
Urban
Rural
Average Annual
Expenditure
($ /Household/ yr)
72.9
23.5
Expenditures
as a. Share of
Income6 (%)
0.22
0.10
Average Annual
Expenditure
{ S / Househo Id/ yr )
37.24
16.90
Expenditures
as a Share of
Income0 ( % )
0.11
0.07
Estimates of annual household expenditures are based only on those households
 that purchase these services and do not take into account those households
 that do not purchase each type of. cleaning services.  These estimates include
 both laundry and dry cleaning expenses.  Expenditures at commercial
 establishments comprise mainly dry cleaning expenditures; only a small
 portion of expenditures at. coin-operated establishments constitute dry
 cleaning expenditures.
3An urban area is defined as an area within a Standard Metropolitan
 Statistical Area (SMSA) or one with a population of more than 2,500 persons.
'A rural area is an area outside of an SMSA and with a population of less than
 2,500 persons
-Based on before-tax income.
Source:  1980-1989 Consumer Expenditure Survey  (U.S. Department of  Labor,
        1991a).
nonwork activities is crucial to producing commodities  (Becker,  1965).
Commodities form the basis of the household utility function.  That  function
is maximized subject to a budget constraint and a time constraint, both  of
which limit the goods, services, and commodities available to  the  household.

      When choosing the combination of. goods, services,  and  time, that will be
used to produce any given commodity, the household makes its decision based on
the utility-maximizing option.  Households have the option of  substituting
time for goods or services in the event that such substitution yields more
utility.  For example, a meal, could, be. provided by combining groceries and
time to produce a home-cooked;meal. or.  by eating: out at', a. restaurant-.  How. the
household makes these choices depends  on its: value of  time.
                                      3-11

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           180 T-
            ISO
           140 "
  Dollars  12
    per
 Household

           100 H-
            80--
            60 •-
            40--
            20 --
                  5-10     10-15
15-20    20-30    30-40    40-50   50 or more

  Income Category (SOOO/yr)
                                    COMMERCIAL  LJ COIN-OP
Figure 3-3. Average Annual Expenditures on Dry Cleaning and Laundry  Services
            by  Income  Class ($1989)

Source:  1980-1989 Consumer Expenditure Survey,  (U.S. Department of  Labor,
 1991a) ..
                                      3-12:

-------
      A household's production of clean, pressed, clothing  can be.analyzed in
this framework.  If, the garment requires dry cleaning, the household,  in
theory, has two choices:  self-service dry cleaning  (offered by  self-service,
coin-operated facilities) or employee-assisted dry cleaning  (offered by
commercial or coin-operated  [plant-operated] facilities).  In the balance of
this section, employee-assisted dry cleaning will be referred to  as  commercial
dry cleaning and self-service dry cleaning will be referred to as coin-
operated.  In the coin-operated production process, consumers• pay for  using
the machines but clean and press the clothing themselves.  In the commercial
cleaning process, consumers use their time to deliver and  pick-up the  garments
and pay for others to clean and.press them.  Although the  market  price of the
coin-operated method is lower, it requires more of consumers' time.  Assuming
that consumer utility does not differ between clothes cleaned by  household
production and clothes cleaned by a commercial cleaner, the household's
decision will depend on the opportunity cost of time.

      A household production model similar to one developed by Gronau  (1977)
is used to show how a household makes the decision to use  commercial or  coin-
•operated dry cleaning.  The household seeks to maximize the amount of  cleaned,
pressed clothes,'commodity Z, which is produced by combining dry  cleaning
services, either commercial or coin-operated, (X) and consumption time (L).
                                     Z  (X, L)
                                       (3.1)
X includes.both,the; value, of market goods- or commercially cleaned clothes
and the value of home goods or clean clothes produced by the consumer using
machinery and time  (Xh).
                                                                          (3.2)
Home goods are produced by work at home:  H represents the number  of  hours per
day spent producing clean clothing at homa.
Xh ~f(H)
                                                                          (3.3)
                                      3-13

-------
       Utility is  maximized sub jeer to two constraints . • The  first  is  a  budget
constraint   where W is  a wage race,  N is time spenc on market; varx, and V  is
other  income.
                                  X..J » WN +• V

The second constraint is a time constraint  (T) .

                                 T - L +• H +• M

      Equations (3.1),  (3.2), and  (3.3) are then  combined and maximized
subject to equations  (3.4) and  (3.5).
                                                                           (3.4)
                                                                           (3.5)
T f (H) ], LI
X(WN +'V -
                                                    '5(T  -  L  -  H  - W)
                                                                           (3.6)
Z is maximized when the marginal  rate  of substitution between time and goods
is equal to the marginal product  of  home production and equal to the wage
rate :
                          (dZ/dL) / (dZ/oX)
                                              f •  .
                                       (3.7)
In addition, the wage will equal  the  opportunity cost of time (W*)  and the
ratio of the marginal utilities of  time  and income.
                                 w - w*  - S  /  X
                                                                          (3.8)
      This model confirms earlier  observations  about  the relationship between
income and dry cleaning expenditures.   Because  the  opportunity cost of time i
higher for those with higher incomes, commercial expenditures should rise and
coin-operated expenditures should  fall  as  income rises.

3.1.4  The Va.lm» nf Tlmg anri f,hg Full—Coat; Madel
      Tha relationship between the value of time and  income or wages has been
well established, in, literature.  Becker (1965)  demonstrated that time
allocation is based on earnings.  An increase in earnings  results in a shift
away from time-intensive consumption to goods-intensive  consumption.  A later
study by Kooreman, and. Kapteyn.  (1987) confirmed, that the- amount of household
                                     3-14,

-------
work performed by  a member is  a, function of. wage race.   In a study on queuing,
Deacon and Sonstelie  (1985)  estimated the value of time to be roughly
equivalent to  the  after-tax: wage.

      Data are not available- to measure  the value of time to an individual who
chooses to use coin-operated dry cleaning facilities compared to an individual
who utilizes a commercial cleaner.   However,  using the  Consumer g^pendir_n>-o
sLIrvey data gives  an estimation of the relationship between dry cleaning
expenditures and income.

      Data at  the  household-level were available and included expenditures on
commercial and coin-operated dry cleaning,  income,  and  other demographic
information such as education,  type  of employment,  family size,  and an
urban/rural designation.   Two  ordinary least  squares (OLS)  equations  were
estimated—one for commercial  dry cleaning  expenditures  and one  for coin-
operated dry cleaning expenditures.   The  independent variables  included income
and the duoimy  variables for  the  remaining demographic data.3  The  coefficients
for income are very significant  and  have  the- expected signs in  both models
(positive  for  commercial  and negative  for coin-operated).   Many  of the other
demographic variables behave as  expected.   Unfortunately,  the equations do not
explain all of the influences  on dry cleaning expenditures  very  well.   But the
equations  do demonstrate  the relationship between  income  and expenditures  on
commercial cleaning.  The  results are  presented in  Table  3-6.  Because income
plays such an  influential  role  in consumers'  choice of using commercial or
coin-operated  dry cleaning facilities', consumers- are likely to  switch from
using a coin-operated facility  to a  commercial  facility  at  a  critical wage or
value of time.  Above a certain  wage,  consumers  are likely  to value their  time
enough to make- the time-intensive coin-operated approach  too  costly when the
value of their time is included  in the calculation.   A full-cost model for dry
cleaning was- developed, that, identifies, the- critical.wage  at which.the switch
from coin-operated to full service occurs.  The full, cost of  a commodity is
the sum of the prices of the goods and services  consumed and of  the time used
in producing these commodities.  Direct, costs: are  the prices, of  the goods  and
      3The data set consists of. four quarters, of household data.  Dummy
variables for the quarters were also included in the equation to account for
differences in. the quarterly^ responses-..
                                     . 3-15

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                              TABLE 3-6.   REGRESSION ANALYSIS*
                                               Dependent Variable
           Variables
 Commercial
Expenditures
C o in-Ope rat ed
 expenditures
C
Income
Education Dummy
(1 if college graduate)
White Collar Dummy
(1 if manager or
professional)
Family Size
Urban Dummy
2nd Quarter Dummy
3rd Quarter Dummy
4th Quarter Dummy
Adjusted R2
F Value
-2.55
(-2.27)
0.0005
(41.77)b
11.03
U4.86)b
5.32
(8.50)b
-1.48
(-7.69)b
.4.97
(5.42)b
-1.49
(-1.88)
-2.21
(-2.78)b
-2.05
(-2.61)b
0.160
442. 12b
4.79
(6.67)*
-0.0001
(-13.49)b
-2.25
(-4,75)b
2.26
(S.65)b
0.74
(6.01)b
5.40
(9.22)b
-0.05
(-1.11)
-0.78
(-1.55)
0.28
(0.56)
0.02
41.45b
'Regression analysis performed using data  from the  1989 Consumer Expenditure
 Survey  (U.S. Department of Labor,  1991a).
bDenotes significance at, che- one- percent level.
services, and indirect costs are  the  total  value  of  time.   Indirect costs can

also be thought of as foregone  income.   Both direct  and indirect costs are
included, in the- full cose, of the* commodity.,


      The full cost for dry cleaned clothing, .to the.  household,  C,  is defined
as • follows:
                                  p*q + t*d + s*r
                                    (3.9)
                                     3-16

-------
where
      p  - the unic price .o:l dry cleaning services  (commercial or coin-
           operated) ,
      q  - the quantity of dry cleaning,
      t  - the cost per mile of transportation  to a  dry  cleaning facility,
      d  = the distance in miles to a dry cleaning facility,
      s  » the unit value or opportunity cost of time  per hour,  and
      r  » the time in hours required to drop off and  pick up clothing (plus
           'the time required to clean and press clothing in a coin-operated
           facility).
This cost measures the cost of a single trip to a dry  cleane'r,  which  it will
varv with quantity because consumers can take one garment cr  many garments  to
the cleaner in a single trip.  In addition, the cost for coin-op consumers
will vary with quantity not only in terms of the cost  of using the facility
but also with respect to the opportunity cost of time, which  will also
increase with quantity.

      The- critical wage- is based-on the full cost of dry cleaning at
commercial, and coin-operated facilities.  The first  component of the  full cost
is the direct cost or the price charged by the  dry cleaning facility.   This is
$6.34 per kilogram for commercial facilities and SI.65 per kilogram for coin.-
operated facilities  (see Section 2 for explanation).

      The second component is the opportunity cost of  the time an individual
must spend to operate the machine and press the garment.   That cost will vary
from individual- to--individual- and. will, depend, on, that,  individual's, wage rate.
One cycle in a 3.6 kilogram machine takes approximately  45 minutes to
complete, which converts to 0.20625 hours per kilogram.   Assuming an
individual takes approximately 30 minutes to press a man's suit, total time
spent would.be- 0.70625° hours /kilogram.

      Assuming- that the- distances to a commercial facility and a coin-operated
facility are the same eliminates any transportation  coats from the calculation.
                                      3-17

-------
      The critical wage can then be calculated by solving the equation below
                         SI. 65 +• 0.7062SX - $6.34                         3.10)
                                 0.70625* -• $4.69
                                        x - $6.64

cor' individuals earning less than $6.64/hour, using the coin-operated  facility
would be more cost-effective.  For individuals earning more than  $6.64/hour,
using the commercial facility would be more cost-effective.

      The foregoing 'analysis is contingent on the relative price  of coin-
operated versus commercial dry cleaners.  If the proposed regulation did  not
affect the coin-operated sector but raised the price of commercial cleaning
services, then the critical wage at which consumers would switch  from  coin-
operated to commercial would be higher.  This higher wage implies that more
consumers would utilize coin-operated .facilities .

      The individual's choice assumes that both  types of facilities are
readily accessible, but this may not be  the case for some smaller or rural
communities.  These locations may have only one  cleaning facility, and the
value of time may be irrelevant.  Coin-operated  facilities are  not distributed
uniformly throughout the United States but tend  to be concentrated in  the
southeastern and, mid-atlantic  states.  Despite the concentration  of
facilities, consumers in these  areas, depending  on the elasticity of demand
for dry cleaning, may choose  not to dry  clean.   The sensitivity to price  of
dry cleaning is discussed below.
 3.1.5.
      Consumers '  sensitivity to  the price- of  dry cleaning services depends on
other alternatives,  which can vary from garment  to  garment.   Some fabrics
require dry  cleaning for- proper- care,  whereas- others  can also be cleaned with
detergent and water.   Specialty  fabrics like  leather,  suede,  and silJe are
usually labeled "dry clean only."  Consumers  are often uncertain about which
fabrics, can* safely be laundered, without being: damaged.  Therefore, the
importance of dry cleaning, services, to. consumers, varies, with the ease with
                                      3-13;

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which another, cleaning process can be substituted,  for dry cleaning and the
consumer's knowledge- of the possibilities of substitution.

      .-. few indirect substitutes- are available to  replace dry cleaning.   in
the Icng run, consumers could replace the stock of clothes  requiring dry
cleaning for proper care with water-washable garments.   In  che short run,  chey
could reduce the frequency of wearing dry-cleaned  clothing  or increase the
number of times a garment is worn before it is cleaned.   The  only direct
substitute available for dry cleaning is laundering with water and detergent,
but chis method is not a perfect substitute.

      The price elasticity of demand is one way of measuring  consumers'
sensitivity to price changes.  Demand is said to be price elastic if an
increase  (or decrease) in price causes a-proportionately greater  decrease  (or
increase) in purchases.  Thus, elasticity of demand measures•consumers'
responsiveness to price changes.  Section 4 presents price  elasticity
estimates and results.
3.2
INDUSTRIAL DEMAND
      Many industries provide uniforms for their employees  typically  renting
these uniforms from an industrial launderer.  The industrial customer is
charged a price per-uniform change and receives clean, delivered-uniforms  on  a
regular basis.  Unlike households, however, industrial customers  are
indifferent to whether the uniforms•are water washed or dry cleaned.   They pay
che- same price regardless of, how. the- garment, is; cleaned.

      Historically, changes in general economic conditions  have  affected
industrial cleaners less dramatically than coin-operated  and commercial
sectors.  As industrial production and employment increase, so does the demand
for: industrial, uniform; rentals-,, the; main-, itenr. leased: and; cleaned, by the.
industrial sector  (Betchkal, 1987a).

3.2.1-  Cnnsump-eian and Trends
      Data are not available on the consumption of  industrial dry cleaning
services.  The fact that, customers are indifferent  to the cleaning method and
pay the. same price for uniforms laundered  in water, and. detergent as they do
                                      3-19

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for uniforms cleaned, in  PCE  probably explains the lack of information.
Furthermore, dry cleaning is typically a very small part of an industrial
launderer's business.  Total industry receipts are available from the 1997
Canaua of Sgrviga  rndusgrleg (U.S.  Department of Commerce,  1990b) .   Tor the
years 1932 and 1987,  receipts, of  industrial  launderers totalled $2,435 million
and $2,947 million in constant (1989)  dollars.  This increase amounted to over
21 percent.

3.2.2  ("haraergriga-cion  of Demandera
      Customers of industrial cleaners  encompass many industries .   Industries
that typically rent  uniforms  include  auto dealerships and independent garages,
construction, hotels, restaurants,  security  firms,  food processing,  and other
manufacturing industries .  Even traditionally white collar  industries such as
banking or real estate may rent blazers  for  their employees.   Many  types of
additional industries are likely  to lease the other items offered by
industrial cleaners,  such as  mats, mops, towels,  and cloths.   All of these
firms use these products as  inputs  in their  production process .
3.2.3
               Damanfi
      Unlike the demand for commercial and coin-operated dry cleaning
services, the demand for industrial cleaning services is a derived demand.
Customers of industrial cleaning view clean uniforms as inputs into their
production processes, so demand for these inputs is said to be derived because
it depends on the demand for the1 final good.  Additional inputs are purchased
in anticipation of increasing production of the final good.  As discussed  in
Section 3.2.4, the elasticity of demand for an input is related to the
elasticity of demand for the final product.

      In, such, a. scenario, producers; would; maximize, prof its .  Presumaciy, nhe-
full-cost model for industrial dry cleaning services would be as follows:
                                                                         (3.11)
                                     3-20

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 where
       p » the unit price of dry cleaning services
       q * the quantity of, dry cleaning- services
       T - transaction costs associated with purchasing dry cleaning  services.

 Transportation costs.do not play a role here because industrial launderers
 deliver the uniforms and do not charge different prices based on distance.

 3.2.4  Sensitivity eo  Priee
       The elasticity of demand for industrial dry cleaning services  is not
 estimated for this analysis due to a lack of data.  However,  a theoretical
 model is developed that expresses  the elasticity within a range of values.
 This  model is based on- the  concept of the .elasticity of substitution for
 inputs  and the cost share of inputs.

      The elasticity of substitution measures the ease with which a producer
 can substitute between  inputs,  holding final output constant.   When
 substitution  is difficult (i.e., when changing the input  mix  does not improve
 the efficiency of  the inputs),  the elasticity of substitution will be less
 than  one.   In a fixed proportion production  function,  the elasticity of
 substitution  is zero because inputs must  be  used in a  fixed ratio,  and
 altering  that  ratio  would be inefficient.  The customers  of industrial dry
 cleaners  encompass many types  of final products,  so generalizing about the
 elasticity  of. substitution-with respect to inputs.- of. clean uniforms is
 difficult.  However, clean  uniforms will  probably be used in  fixed
 proportions,  or, at the  very least, difficult  to  substitute.   The elasticity
 of substitution with respect to clean uniforms must fall  between zero and one.

      The;, second- concept- used, in^ the-:, model, isr. that cosr, share  of  inputs.   The
cost: share-simply represents- the^ coat of*  a specific input as  a percentage of
the- total coat.  The frameworx eatabliahed by   Allen (1962) auggeats  a
theoretical, estimation of the elaaticity  of. demand for  an input.   In  the
following, equation,, the elaaticity ia  expressed aa  a. proportional  change.
                                     3-zr

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ECQa)
(AQa/Qa)  /  CAPa/Pa)
                                                                          (3.12)
 where
       a  -  inputs  of  clean uniforms
       b  -  all  other  inputs
       Qa * the quantity of clean uniforms
       ?a " the price of clean uniforms
       ko - the cost  share of  all other  inputs
       <5- the elasticity of substitution between uniforms and other, inputs
       ka - the cost  share of  clean uniforms
       A.X =• the elasticity of  demand for the  final  product.

       The  cost share of all inputs other than  clean uniforms is quite large,
 and the  cost share of clean uniforms  is nearly zero.   The  elasticity  of
 substitution is most likely zero.   Whatever  the value  of kj,»  the first term in
 the above  equation is zero  or a  very  small number.   ka will  be  nearly zero  and
 will limit  the value of the second term of the equation to nearly zero.   The
 sum then is a  small  number, certainly less than one in absolute terms.   Thus,
 the elasticity of demand for  industrial dry  cleaning services is somewhat
 inelastic.

      One additional point  merits  mention.   Empirical  studies have shown that
the elasticity of demand for  final goods  is  generally  greater than demand for
intermediate goods (Martin, 1982}.  The elasticity  estimation of the  demand
for dry cleaning: services for. households  and for industrial  consumers is
consistent  with that finding.
                                     3-22.

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                                   SECTION 4
                 MARKET STRUCTURE  IN  THE DRY  CLEANING INDUSTRY

      A causal flow occurs from demand and supply conditions to market
structure and from market structure to conduct of firms  (Sherer, 1980) .
Economic theory provides, a framework for analyzing the links between the
demand and supply conditions an industry faces, its market structure, and- the
typical, behavior of firms in that industry.  This section examines market
structure in the dry cleaning industry and develops an approach for estimating
the impacts of an increase in the cost, of supplying dry cleaning services due
to regulation.  Certain aspects of market structure—including the existence
of barriers to entry, the number of sellers in a market area, and the
geographic distribution of consumers and producers—are particularly relevant
for determining the way consumers and suppliers would react to a change in the
costs of providing dry cleaning services.

      Fundamental to the analysis of market structure in the dry cleaning
industry is an understanding of the geographic scope of the market area.  To
facilitate this understanding, this section begins with a brief description of
the facility location decision, which is determined by the basic supply and
demand conditions outlined in previous sections.  The section then describes
market structure in the three sectors prior to developing the model markets.
4.1
FACILITY LOCATION DECISION
      Determinants of.-facility location differ by industry sector.   In  the
commercial and coin-operated sectors, dry cleaning markets are  small in
geographic size.  Depending on the number of sellers in a particular place and
the population density, markets may cover an area as small as a few  city
blocks.  In contrast, industrial facilities operate in geographic markets  that
are- much, larger.- Factors such; as. the.; income; distribution of the: customer
base, traffic patterns, and number o£, competing firms in an area contribute to
the location decision in each sector.  The determinants of the  facility
location characteristic: of. each industry sector are discussed below.
                                      4-1:.

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4.1.1
      The service provided by commercial dry cleaners is effective,  fast,  and
requires little effort by the customer.  These establishments  sell  a
convenience good that, like toothpaste and gasoline, does not  typically
justify comparison shopping because the benefit of price comparison does  not
compensate for the cost of the search  
-------
machine used in this sector has a capacity of 11.3 kilograms per  load.   The
operation of a dry cleaning facility also requires labor  for staffing the
front counter, preparing- clothing for cleaning, operating the  dry cleaning  ,
machine, and. processing the clean clothing for return to  the customer.   In
reality, labor is also unavailable in an infinitely divisible  quantity.
Facility size is therefore imperfectly variable.

      A potential owner of a dry cleaning facility confronts a definite  lower
limit on the revenue that is necessary for profitable operation.'  In choosing
a location for a dry cleaning facility, the profit-maximizing potential  owner
must consider the minimum customer base that this lower limit  on  revenue
implies.  Owners who misjudge their customer base, either because of
miscalculation or over-confidence in their ability to•attract  customers  away
from an existing facility, may be- unable to cover their fixed costs or even
their variable costs.  Inability to cover fixed costs can lead to  financial
failure of the firm.  Inability to cover variable costs can lead  to closure of
the facility.

4.1.2  Coin—operated. Pry Cleaners
      Many of the determinants of.'the facility location decision  that are
characteristic of the commercial sector are also characteristic of the coin-
operated sector.  In particular, coin-operated laundries  that offer plant-
operated services provide a convenience good that is virtually
indistinguishable from the service offered by the commercial sector.  Like
commercial, facilities-,, coin-operated, facilities., serve- a., local market area  and
typically locate in places that are convenient to consumers.

      One important difference does exist, however.  As discussed in
Section 2, dry cleaning services are offered as an auxiliary to the regular
laundry operations at, coin-operated:facilities.  Because  dry cleaning activity
accounts for- only about. 10 percent, of. receipts at coin-operated facilities
with dry cleaning operations, the location decision is based on the
determinants relevant, for- locating a laundromat rather than for a dry cleaning
facility.  Onca the decision to locate the coin-operated  laundry  is made,  the
owner must decide whether to provide dry cleaning services in  addition to  the
regular laundry services.  Relevant factors, in this secondary  decision include
                                      4-3

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the proximity of other dry cleaning facilities, the size of  the  ccstumer base,
and the income distribution of residents within the community.
4 .
                  Prv
      Industrial cleaners serve a much larger geographic area  tr. ^n  do
commercial or coin-operated cleaners.  For example, the operator  of one
industrial facility indicated that his facility served industrial and
commercial users located as far away as 100 miles  (Coor and Grady,  1991) .
Services provided by industrial cleaners are not considered convenience  goods .
Consumers in this sector view the services provided by industrial cleaners  as
an input into their production process.  Because the cleaner delivers the
cleaned items, consumers are generally more concerned with dependability of
service than with convenience.

  '    The profit-maximizing industrial cleaner locates where costs  of
production are minimized.  According to one facility operator, the  ideal
location is a small town that is centrally located to several  large  cities
where the customer base is located (Coor and Grady, 1991) .  Small towns
typically do not have the traffic congestion characteristic of larger cities .
Traffic congestion ties up delivery vehicles, which increases  the cost of
delivery and may reduce customer satisfaction.  In addition, small  towns tend
to have less expensive land and building costs and labor costs.   Because
industrial launderers clean most of the items they process in  water  and
detergent,  a cheap, abundant water- supply is also an important determinant  of
location.
4.2
MARKET STRUCTURE
      Within, each,sector of. the industry many localized geographical markets
exist where only neighboring firms compete directly.  These submarkets are
only loosely tied to a national market, but economic decisions by individual
firms are jointly related to national trends.  The existing market structure
reflects fundamental market, forces; than, ara likely to be an enduring feature
of the dry cleaning industry.  The economic impact analysis uses the
differences in market.structure and pricing practices of dry cleaning
facilities, to; predicts, the- market, responses^ to, the- candidate regulatory
                                      4-4:

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alternatives.  To simplify the analysis, a model market  approach is  used to
differentiate markets by
      • the market sector,
      • the number of suppliers  in each market  area,  and
      • the share of suppliers potentially affected under each regulatory
        alternative .

      An important economic impact associated with  promulgation  of the
candidate regulations is the total welfare loss  (gain) attributable  to  market
adjustments in the dry cleaning  industry.  A neoclassical supply/demand
analysis is developed for each sector and model market .   The  economic impacts
are analyzed for each sector and model market individually and the results  are
then aggregated to determine total welfare 'effects.
4 . 2 . 1"  Marleef Sfrurrure  in the Camnf* Trial
      Two basic market structures are prevalent  in  the  commercial  sector.   The
first is a competitive structure, which is found predominantly in  urban and
suburban areas and characterized by the existence of many  dry cleaning
facilities in each market area and no barriers to entry.   Approximately 90
percent of the commercial facilities are in urban /suburban market  areas.   The
second type of market structure is characterized by a single  facility in  a
rural market area.  Because consumers are unwilling to  drive  long  distances to
purchase dry cleaning services, the owner of  a single facility in  a remote
area does not behave as  if in a perfectly competitive, market.

      •nrfaan Xs»hm?han Markets.  Given the number  of  commercial facilities in
urban and suburban areas and the size distribution  of those facilities, it is
assumed that a competitive market structure exists  for  these  facilities.   The
competitive model is based on, the hypothesis  that no facility individually can
influence- market equilibrium^ but" the- behavior,- of. all, producers taken together..
determines the position  of the- market supply curve. -In- addition,  the; cost of.
producing the last unit  of output, the marginal  cost, along with market demand
determines' equilibrium price and output.' Furthermore,,  at, a stable: equilibrium
•price, each individual facility can. sell any  level  of output desired, with no
perceptible effect on equilibrium values.  As a  result, each facility faces an
                                      4-5;

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implicit demand curve that is perfectly elastic,  (horizontal)  at  the  current
market equilibrium price.
                       »
      Initially, imposing, controls on a facility will alter  the  cqsts  of
producing the same level of output as before the control.  This  production
cost change will induce a shift of that facility's supply curve.   Because-the
supply curve for a well-defined market is the horizontal summation of
individual facility supply curves for all facilities participating in  that
market, the shift in the market supply curve can be determined from  knowledge
of facility-specific shifts.  If the regulation results in a  production cost
change for the marginal supplier within the market area, a change  in the
equilibrium price and output will occur.

      Precise estimates of the quantitative changes in price  and output
require information on the position and slope of the market supply and market
demand curves both prior to and after the adjustment.  Predicting  the  position
and slope of the market supply and demand curves is, therefore, crucial to
estimating the economic impacts.  The changes in price and output  lead to
consumer and producer welfare changes that can be measured as areas  within  the
supply/demand plane.  The neoclassical supply/demand analysis applied  to this
study is introduced below.

      The position of the market demand curve is critical to  determining the
change in equilibrium price and output resulting from a regulatory-induced
shift in the market supply curve.  The slope of the demand curve measures che
responsiveness of quantity demanded to a change in the price  of the  service.
The elasticity of demand is a relative measure of demand responsiveness and as
a policy tool is generally preferred to the demand curve slope.  The
elasticity of demand is measured as the percentage change in  quantity  demanded
of a good, or service resulting- from a. one—percent change- in its' price.  Post-
regulatory equilibrium price and output values and the resulting welfare
changes can be calculated if the baseline price and output values,  the
relative shift of the market supply curve, and estimates of demand, and supply
elasticities are available.

      A priori,  predicting the elasticity of demand for commercial dry
cleaning services, is difficult- because.- many variables, contribute to  its- value.
If data are unavailable- to estimate- a, demand elasticity,, a unitary elastic

                                      4-6

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 (•q *. -i.O) demand curve could, be used to estimate  impacts,  but considerable
 uncertainty  would be associated with the price  and output  adjustments and the
 welfare  loss- estimates.  Any  market-measured value of. the  demand elasticity
 would  obviously be  superior, to an unsubstantiated  simplification.   The supply
 and  demand functions- for the  commercial, dry  cleaning sector are estimated
 simultaneously to derive corresponding  elasticity  estimates.             .  .
       A  neoclassical supply/demand model is  a system of  interdependent
 equations  in which  the price  and output of a product are simultaneously
 determined by the interaction of producers and  consumers in the market.  In
 simultaneous equation models, where variables in one equation feed back into
 variables  in another equation, the error terms  are correlated with the
 endogenous (price,  output)  variables.   In most  circumstances,  single-equation
.ordinary least-squares estimation of individual equations  in  a simultaneous
 equation model can  lead to  biased and inconsistent parameter  estimates.
 Furthermore, the supply and demand equations must  be econometrically
 identiried prior to initiating a simultaneous equation regression  procedure.
 An equation  is identified if  obtaining  values of the* parameters from the
 reduced-form equation system  is possible.  Put  simply, identification requires
 that at  least one original  exogenous  (shifter)  variable  is  contained in each
 equation of  the supply/demand system.
       Section 2 presented data on average base  prices and  total output for the
 commercial sector from 1974 to 1988.  These  data represent  equilibrium points
 of intersection between supply and demand curves for each  of  those years.
 Estimating a supply or demand curve equation.from  these  data  would be
 difficult  because information is insufficient to completely identify the
 supply/demand system.  However, with the aid of intuitively acceptable supply
 and  demand shift variables, the price and output data can  be  used to
 econometrically estimate the  commercial, sector  supply and  demand functions and
 corresponding: elasticities„..
       Gross  population levels for the U.S. and  the producer price index for
 service  industries  from 1974  to 1988 were chosen as the  demand and supply
 shifters/-  respectively..  Population levels; are. commonly  used as demand shift
 variables  in regression equations.  The producer price index is suitable for
 the  supply function because it is a good, proxy  for production costs.
 Table  4-1  lists the time-series data used in the supply/demand estimation.
                                      4-7'

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             TABLE 4-1.  DATA USED IN THE SUPPLY/DEMAND  ESTIMATION
-ear
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
Price
($/kg)a
4.02
4.42
4.46
4.36
4.87
4.90
5.32
5.63
5.72
5.87
5.98
6.13
6.14
6.05
6.08-
Output
(106 kg/yr)*
570
506
499
521
493
499
475
444
522 .
527
525
522
60S
603
596
P.P. Index
53.5
58.4
61.1
64.9 '
69.9
78.7
89.8
98.0
100. 0
101.3
103.7
103.2
100.2
102.8
106.9
Popuiat-on
UO6}
213.'.'
216.0
213.0
220.2
222.6
225.1
227.8
230.1
232.5
234.8
237.0
239.3
241.6
243.9
246.1
aAll dollar figures converted to  1989 dollars through  the  Consumer Price  Index
*  for Apparel  and Upkeep.
°See Table 2-8.
.source :   raig (1990) ;  Survey of
                                        3uginega  (U.S. Department  of  Commerce
         1989b) ; S&agjgr.ieai Absrratrea of r_h» CJ.S.  (U.S. Department  of
         Commerce 198 9a) .
      Supply and demand equations for the commercial sector were
econometrically estimated by using the instrumental variables  regression
procedure.  Base price and total output were first converted to natural
logarithm form to ensure constant supply and demand elasticity estimates.
structural models for the supply/demand system are the following:
                                                                           The
                                      4-8

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      Supply:
      Demand:
Ln(Qts)

Ln (Qtd)
(4.1)
(4.2)
                              Ln(Qts)
(4.3)
where Q - output, P * price, Pop » population,  and PPI  - produced price index.
The supply equation  (4.1), demand equation  (4.2),  and equilibrium condition
(4.3) determine the market price and the quantity  supplied (demanded)  when the
market is in equilibrium.  For this reason, the variables Ln(Qts),  Ln(Qtd),
and Ln(Pt) are endogenous because they are determined within  the  system of
equations, while Pop and PPI are exogenous variables.   The parameter estimates
and regression statistics from the simultaneous system  estimation are  reported
in Table 4-2.  '                     .                      •

      With Durbin-Hatson statistics of 1.54 for both  the supply and demand
equations, the null hypothesis of no serial correlation cannot be rejected at
the 0.01 level of. significance.  Overall, the significance  of the parameter
estimates and the low standard errors indicate  that base prices,  dry cleaning
output, population levels, and the producer price  index are effective  in
predicting the supply/demand relationship.

      Parameter estimates were also developed using a time  variable instead of
population in an attempt to determine whether a simple  time trend would be a
more suitable demand, shiftar...  The-, results of "that  regression, are reported in  •
Table 4-3.  The parameter estimates are very similar  to the regression with
population as an explanatory variable, but: the  population specification had a
slightly better fit.  As a result, all future references to the elasticity
estimates will apply to the population specification.

      The predicted elasticity of supply and demand can be  derived directly
from the parameter estimates of the regression  system.   Regression equations
for the supply and demand, functions appear in estimated form as
      Ln(Qts) - -0.012 + 1.558Ln(Pt) - 0.023(PPIt),
(4.4)
              — -6.351. — 1..086La
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 TABLE 4-2.   PARAMETER ESTIMATES AND REGRESSION' STATISTICS FROM THE
             SUPPLY/DEMAND ESTIMATION
Parameter Value
Supply CUTTTB
Intercept 0.120
Price 1.558
P.P. Index -0.023
Sum sq. res.
0.031
Demand Curare
Intercept -6.351
Price -1.086
Population 0 . 036
Sum sq. Res .
0.031
Std. err.
.
0.064
0.291
0.005
Std. err.
0.051

1.289
0.240
0.007
Std. err.
0.051
t-atat 95% conf

1.882
5.361 0.924 to
-5.057 -0.033 to
DW test
1.54

-4.927 ' -
-4.530 -1.608 to
5.057 0.020 to
DW test
1.54
. int.


2.192
-0.013




-0.564 •
0.051


      The first derivative of the supply equation with  respect  to  the
logarithm of price  (1.558) is an estimate of the supply elasticity for  dry
cleaning services in the, commercial- sector.  The interpretation of chia
estimate is that the quantity supplied of dry cleaning  services- will increase
by 1.558 percent for every 1 percent increase in the price  for  that service.
The t-statistic value of 5.361 allows rejection of the  null hypothesis  so that
the estimate is not significantly different from zero at the  0.05  level of
significance.

      The estimated elasticity of demand is the first derivative of the demand
equation with respect to the logarithm of price, or -1.086.   The
interpretation of this value is that the demand for dry cleaning services will
decrease by 1.086 percent for every 1 percent increase  in the price of  that
service.  The t-statistic value of -4.530 allows rejection  of the  null
hypothesis that the--estimate, is: not. significantly different from zero at the
0.05 level of significance.
                                     4-10:

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 rABLE.4-3.  PARAMETER ESTIMATES  AND  REGRESSION STATISTICS FROM THE
            SUPPLY/DEMAND ESTIMATION (TIME-TREND SPECIFICATION)
Parameter Value .
Supply Curve
Intercept 0 . 123
Price 1.512
P.-P'. Index -0.022
Sum Sq. Res.
0.345
Opmanri Curve
Intercept 1.082
Price . -0.989
Time 0 . 077
Sum Sq. Res.
0.345
Std.

0
0
0
Std.
0

0
0
0
Std.
0
err.

.067 .
.305
.005
Err.
.054

.203.
.239 . '
.016
Err.
.054.
t-stat 95% conf

1.825
4.959 0.848 to
-4.670 -0.033 to
DW test
1.46

5.198
-4.141 -1.509 to
4.670 0.041 to
DW test
1.46
. int .


2.176
-0.012




-0.469
0.112


      The credibility of the demand elasticity estimate  can  be  confirmed with
a demand elasticity point estimate computed by HouthakJcer  and Taylor (1970) .
These>, authors, examined, consumer- demand relationships  for many different goods
and-services.  The demand elasticity for a category of products they refer to
as "clothing upkeep and laundering in establishments" was  estimated at 0.9293.
This value is contained in the -95 percent confidence  interval for the demand
elasticity estimate reported in .Table 4-2  (-1.608 to  -0.564).  In addition,  it
is: very closet to the? point- estimate; itself, {—1.086) .,

      If the regulation results in a change in the marginal  supplier's cost of
providing dry cleaning: services,., then priea and  quantity impacts will occur in
the short run.  Using the demand and supply elasticities- estimated above,
projecting changes in short run equilibrium price and quantity  associated with
each regulatory alternative is possible.  As noted in Section 2,  the.baseline
                                      4-ir;

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price in the market is equivalent to the marginal cost of providing dry
cleaning services (before the regulation) and the average total cost of
building a new facility.  An increase in the marginal costs projected under
the regulatory alternatives would result in an increase in price in the short
run.  As price rises above the average total cost of a new facility,  new entry
is encouraged.  The average total cost of the new facility,.however,  is not
affected under any of the alternatives considered because virtually all new
dry cleaning machines have built-in vent controls.  Consequently,  in the long
run, price and quantity adjustments are zero.  In the absence of regulation,
the current stock of uncontrolled PCE machines would have been  replaced by new
machines with vent controls, further supporting the position that  long-run
price and output adjustments are zero.  Therefore, price and output
adjustments in the balance of this analysis refer to short-run  effects.  .

      Hot all commercial facilities in a market area are affected  under the
candidate regulatory alternatives.  Only those facilities that  use PCS  and
that do not have the required' vent controls in the baseline will experience a
change in production costs.  It is not known whether facilities  that  are
potentially affected are more or less likely to-be the price-setting  marginal
facility in the market.  Without detailed information on individual supplier's
production costs, determining whether the marginal supplier will incur
regulatory costs is impossible.  Therefore, it is assumed that  the  likelihood
of a shift in the marginal supplier's costs is directly related to  the
proportion of, facilities experiencing the cost increase.

      Suppose that a given market area includes facilities that  are
potentially affected by the regulation (PCS facilities that do  not  have  the
required vent controls) as well as those that are unaffected (PCE  facilities
that have the required vent controls or non-PCS facilities).  If the
unaffected facilities dominate, then prica* and output, adjustments  are
unlikely.  The impact in markets, where unaffected facilities dominate falls
exclusively on the affected suppliers whose profits are reduced by the  cost of
the regulation.  Conversely, if. affected facilities dominate in  a particular
market area, then the regulation is likely to result in an equilibrium  price
and output adjustment for that market.  Price would rise, but not  by the full
amount of the cost increase, until demand and, supply are in equilibrium.  Put
                                     4-12

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 differently, the market  supply curve will shift, along a (stationary or
 shifting) market demand  curve with equilibrium changes in price and output
•determined once the  curves  stabilize.
           l Markets.   Considering the minimum-size customer base, as described
 in Section 4.1.1,  is  critical for owners  planning to open a facility in a
 remote area served by a  single facility.   Areas with a lower population
 density can sustain a lower  density' of dry cleaners than areas with a higher
 population density.   The existence of a minimum customer base explains the
 pattern observed in the  data set :.  sparsely populated areas are served by a
 single facility and densely  populated areas by multiple facilities.

      The  outstanding characteristic of the structure , of the dry cleaning
 industry in rural  communities is  the prevalence of markets that are served by
 a single facility.  Another  salient characteristic of rural dry cleaning
 facilities is that annual revenues are typically below 325,000.  The small
 scale of the market in rural communities  requires the operation of a minimally
 sized facility.  Consequently,  the smallest facility would use an 11.3
 kilogram machine.  A  new entrant  would at  a minimum add another 11.3 kilograms
 of capacity.  The only option available to a new entrant,  therefore, is to
 double (at the minimum)  capacity  in the market.

      Although these  single-facility markets are not perfectly competitive,
 the ease of entry into the dry cleaning industry implies that the threat to
 long-run,, profits; from; new: entrants- is.- keen; and persistent .   The, optimal-
 pricing strategy is to set' a profit-maximizing; prica.. that- is low enough to
 deter entry. • Therefore,  to  model the, economic impact. of the proposed
 regulations, it is assumed, that, the owners of  firms in single-facility rural
markets follow a limit pricing strategy.   The  assumptions of potential large-
 scale;. entry- and,. output. -Maintenance; allow? application- of ; the theory of. limit
pricing developed by  3ain, Sylos-Laaini, and- Hodigliani (Sharer,  1380).

      Any price above  the average total cost of a nev facility would encourage
new entry -into the- market.   The axiatanca; o£ a- second: facility in • the- market
would decrease the market share and the total  revenue of the initial supplier.
Assuming: that the productivity: of. dry cleaning equipment has been increasing
     time,, owners/ orV new  equipment would; tand  to- have lower, marginal, costs
                                     4-13'

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 Chan  owners  of  older  equipment.   Therefore,  the market, price would probably
 decline, with the  entrance, of  a  second facility,  further decreasing the total
 revenue of the  existing  supplier.   Furthermore,  if the assumption of increased
 productivity is correct,  owners  of  new facilities may be able to set prices at
 a  level where initial suppliers  would not  be able to cover their costs of
 production.  If the price set by the  new supplier fell below the variable
 costs of production for  the initial supplier,  then the initial supplier would
 cease operations.  If the initial supplier could cover variable costs but not
 all the fixed costs of production,  then the  facility would continue to operate
 in the  short run  but  would face  potential  financial failure,   racing this
 potential erosion in  profits  and/or financial  failure,  the owner of an
 existing facility is  most likely to adopt  the  pricing strategy that presents
 the strongest deterrent  to a  potential entrant to ensure that his market  share
 is not  eroded.

      Even in the pre-regulatory baseline, the new entrant's  long-run average
 cost  curve already reflects the  cost  of compliance associated with the
 candidate regulatory  alternatives because  the  manufacturers of  dry cleaning
 machines have incorporated the requisite air pollution  control  devices into
 the basic design  
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because new entry wouid" «ccur and the market price would fall.   Therefore,  in
ruralr single-facility markets in wni«h the alternatives considered, for
proposal have, an economic impact, the impact falls exclusively  on the
established dry.cleaners whose profits fall by the amount of  the compliance
cost.

4.2.2  Market Sfcrurrnr-a in r.h
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Section. 3, the consumer's full,cost,  of obtaining dry  cleaning services
includes the price paid to the supplier plus  the consumer's  opportunity cost
of time.  Assuming that no consumer  values time  below the  minimum waae rate,
the minimum opportunity cost of time is the product of the minimum w/ije rate
(4.25 per hour) and the time required to produce a clean suit ready -3 wear
(0.70625 hours).  Under these assumptions, the minimum opportunity cost of
time associated with self-service dry cleaning is $3.00.

      Commercial dry cleaning services, as well  as the services  offered by
plant-operated facilities in the coin-operated sector,  are a perfect
substitute for the services offered  by self-service coin-operated facilities.
In other words, if the consumer's full cost of producing clean clothing using
self-service cleaning rises above the full cost  of producing clean clothing
using the services of a commercial cleaner, then the  consumer will use che
services of the commercial cleaner.  Presumably  no consumer  is willing to  pay
more than $3.34 per kilogram—the commercial  dry cleaning  price  ($6.34)  less
the minimum opportunity cost of time ($3.00)--for self-service dry cleaning.
This is the choke price or the price above which quantity  of  self-service  dry
cleaning demanded falls to zero.

      Figure 4-1 shows the demand curve implied  by the choke  price  and the
market price and quantity.  This interpretation  of the demand curve  assumes
that demand is linear.  This choke price combined with the market  price  and
quantity for self-service dry cleaning- can be used to  compute demand
elasticity in the following manner:
1\  -
                                          *
                                       AP   Q
(4.6)
where T\ is the- absolute value- of demand elasticity, Q is the market quantity,
and P is the market price.  Because demand is downward sloping, elasticity is
negative.  At the market price of $1.65 per kilogram, market quantity of
577,239 kilograms, and. a choke price of $3.34, demand elasticity is -0.9476.

      Because consumers have a perfect substitute for self-service dry
cleaning, even small increases in price are likely to result in large.quantity
reductions.  In other- words, the existence of a perfect substitute implies

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                 9/Q
              Choke
              Price
                                                         Q/Time
                Figure  4-1.  Demand for Self-Service Dry Cleaning
that the demand for self-service dry cleaning is  likely to be more elastic
than the demand for commercial or coin-operated (plant-operated)  services.
The estimate computed above,  however,  implies that  the demand for self-service
dry cleaning is  slightly less elastic than the demand for commercial dry
cleaning.  The- reason for the counterintuitive result may lie- in the
assumptions used to compute the demand elasticity.

      First, the demand  for self-service dry cleaning is assumed to be linear.
To the-extent that  this  assumption does not specify the demand curve,  the
elasticity* estimate may  also-  be1 miscalculated.. In1  addition,, the- m-in-i'miTm:
opportunity cost of: tin* may  be- underestimated.   A  higher opportunity cost of
time would yield a  lower choke  price and a  higher elasticity estimate (in
absolute value).  Because  of  these limitations, the demand and supply
elasticity estimates computed for  the  commercial  sector are used to compute
impacts for self-service coin-operated facilities.
                                     4-1,7

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      Data axe not available on the number of facilities  in , this  sector
operating in markets where unaffected facilities dominate or vice versa.
Therefore it is assumed that each market area has the  same distribution of
affected and unaffected facilities.  Virtually all self-service dry cleaning
and more than half of the plant -operated facilities in the coin-operated
sector are uncontrolled.  Therefore, the marginal cost of providing coin-
operated dry cleaning services is likely to increase resulting in price and
output adjustments for this sector.

      The magnitude of the price and output adjustments in the coin-operated
sector is limited by the adjustments in the commercial sector.  These
adjustments are computed separately for self-service and  plant-operated
facilities because of the difference in the type of service  offered and the
base price charged by these facilities.  Plant-operated facilities  are  limited
in the price increase that may be passed along to consumers  because these
facilities operate in markets dominated by commercial  facilities.   Price
effects at self-service facilities are also limited by the projected prica
adjustments in the commercial sector.  The post-regulatory price  at  self-
service facilities may not exceed .the choke price based on the post-regulatory
price charged by commercial facilities.  The post-regulatory choke  price  is
the post-regulatory commercial price less the estimated minimum opportunity
cost of time (53.00) computed above.
4.2.3  Maritet; Sfer
                           r.he
      Industrial facilities also operate in perfectly competitive markets .
However, no price and output adjustments are likely to occur in this sector
for several reasons.  First, water and detergent are near-perfect substitutes
for PCS because virtually all of the garments dry cleaned by industrial
facilities are water-washable.  Because consumers do- not dictate- the cleaning
method used, facilities facing a regulatory cost with- continued PCS usage
would likely substitute water washing for dry cleaning assuming sufficient
capacity is available.  Second, industrial cleaners do not charge different
prices for garments cleaned in water and detergent and garments cleaned in PCS
(Coor and Grady, 1991) ; also, over 92 percent of the output from industrial
facilities is from regular laundry operations..  This second factor is evidence
that the cost of producing the marginal unit of output in the market area is
                                     4-18;

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not.  likely to  increase  under  any  of  the  alternatives  considered for proposal.
For  these reasons, producers  would not be  able  to  pass along any regulatory
cost in the form of  a price increase.
4.3
MODEL MARKETS
      To facilitate  computing  impacts of  the  regulatory alternatives,  actual
dry cleaning facilities have been allocated among model markets.   The
methodology used to  develop the model markets is  discussed below.

4.3.1  Commercial Sector Markets
      Six model markets represent the commercial  sector and are differentiated
by
      •  rural  and urban areas,
      •  the proportion of  affected  and unaffected facilities,
      •  the income distribution of  facilities represented,  and
      •  the behavioral response to  a cost increase.

      Data from American Business Information 
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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

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 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.

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 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:

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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'

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                                   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.

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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.

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will, thus probably exempt small coin-operated facilities,  effectively
exempting them all.  Consequently, coin-operated dry cleaning  firms  will
 ~\               ~        ,
experience no 'financial impacts.

      Effectively, this leaves commercial dry cleaning plants  (SIC 7216)  as
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'

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payroll.  Although no  evidence is available,  presumably most of these
nonpayroll facilities  are  small,  are owned by single-facility firms, and are
sole proprietorships.   Assuming that 7,500 nonpayroll,  sole proprietorship
firms exist, of the 27,332 commercial dry cleaning firms in 1991,  16, -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

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 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'

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lenders  (e.g., banks) to corporations assess credit worthiness  solely on the
basis of the financial health of the corporation-not  its  owners.   A
qualification of note is that lenders can require  (as  a loan condition)  owners
to agree to separate contracts obligating them personally to repay  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.

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                 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

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5.5   DISTRIBUTION OF COMPANIES BY NUMBER  OF  FACILITIES
      The financial impacts of the NESHAP  on  two  firms  of equal size might
depend significantly on their facility composition because substantial control
economies of scale exist.  The costs of controlling  larger machines  are not
proportionately higher than the costs of controlling smaller ones.   Also,  -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;

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 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

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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

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 are more recent than the IRS data, are available for the dry cleaning
 industry,  and are probably based on a larger  (though nonrandom)  sample  than
 the, IRS data.  The financial condition of dry cleaning firms can be
 characterized using Dun and Bradstreet'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

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 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:

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 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:

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 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

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 possible.   Appendix A presents the income, statements and. balance sheets of dry
 cleaning firms in the same size categories, all in above-average financial
 condition.

 5.8    KEY BUSINESS RATIOS OF DRY CLEANING FIRMS
       Financial ratio analysis is a widely accepted way of summarizing the
 financial  condition of a firm.  Financial ratios include four fundamental
 types:
       •  indicators of liquidity,
       •  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

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 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

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         TABLE 5-7 .  BASELINE FINANCIAL RATIOS OF DRY CLEANING FIRMS
                                             Financial Condition
                                Below Average
              Average
            Above Average
  Liquidity
         Current ratio (times)
  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..

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       * 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,

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 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

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 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

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       »  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

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                                               \
 financial condition category all have the same cost of capital, or that
 borrowed funds will necessarily be available to all firms.  In particular,
_recognize that 25  percent of all firms are in "below-average financial
 condition."   Within this range,  some firms will be far more financially
 distressed than others.   The 15.4 percent real rate may overestimate the cost
 of  capital for some of these dry cleaning firms  and underestimate some
 unusually distressed firms.

       Adequate control capital funds are  probably unavailable  through normal
 channels to  small,  particularly distressed firms.   Bass (1991)  indicates that
 most  commercial banks will not lend money to financially distressed firms,  and
 retained earnings  at small,  distressed firms may  be inadequate  to pay for
 control  capital.   Bass also  stated that his institution,  and others,  won't
 lend  money to  dry  cleaning firms without  first conducting an "environmental
 audit" to protect  the bank in the event that environmental contamination is
 present  or foreseeable at the time of  the loan.   One can never  discount the
 possibility  that funds would be  available from owners'  personal funds,  new
 partners,  friends,  relatives,  or other sources.

-------
                                   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

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    TABLE 6-1.  CONTROL TECHNOLOGY OPTIONS UNDER EACH REGULATORY ALTERNATIVE
                                                  Regulatory Alternative
      Industry Sector and Machine Type
                 II
            III
 Coin-Operated

    dry-to-dry

 Commercial

    dry-to-dry


    transfer (uncontrolled)


    transfer (RC controlled)




 Industrial

    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

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 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

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 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

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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

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                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

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       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

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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:

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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-

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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

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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

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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

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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':

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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

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 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,

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       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

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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"

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 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-

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       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

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 TABLE 7-29. INSTALLED' PRICE OF CONTROL EQUIPMENT AND ANNUAL OPERATING COST,  BY
             REGULATORY ALTERNATIVE AND SIZE OF FIRM*
Receipts Range
($000)
<25


25-50


50-75


' 75-100


>100,


Regulatory
Alternative
. 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

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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

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                       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

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 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.

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      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

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       •  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

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                                 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"-

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            , '        •               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.
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                                      9-1,

-------
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                                      9-2

-------
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                                      9-3'

-------
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                                      9-4

-------
U.S.. Department of, Commerce, Bureau of the Census.  -ISSOc.   1987
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      Englewood Cliffs:  Prentiss Hall.
                                     9-5

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-------
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

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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

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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;

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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;

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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

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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

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