MATHTECH
          The Technical Research
          and Consulting Division of
             Mathematica, Inc.

   BENEFIT AND NET BENEFIT ANALYSIS OF

ALTERNATIVE NATIONAL AMBIENT ATTt QUALITY

    STANDARDS FOR PARTICOLATE MATTER
               VOLUME III

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     BENEFIT AND NET BENEFIT ANALYSIS OF

  ALTERNATIVE NATIONAL AMBIENT ATB QUALITI

      STANDARDS FOR PAKTICDLATE MATTER



                 VOLUME III
                Prepared for:

          Benefits Analysis Program
          Economic Analysis Branch
    Strategies and Air Standards Division
Office of Air Quality Planning and Standards

    U.S. ENVIRONMENTAL PROTECTION AGENCY
   Research Triangle Park,  North Carolina
                 March 1983

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     BENEFIT AND NET BENEFIT ANALYSIS OF ALTERNATIVE
       NATIONAL AMBIENT AIR QUALITY STANDARDS FOR
                   PARTICDLATE MATTER
                           By:
Ernest H. Manuel, Jr.
Robert L. Horst, Jr.
Kathleen M. Brennan
Jennifer M. Hobart
Carol D. Harvey
Jerome T. Bentley
Marcus C. Duff
Daniel E. Klingler
Judith K. Tapiero
                 With the Assistance of:
David S. Brookshire
Thomas D. Crocker
Ralph C. d'Arge
A. Myrick Freeman, III
William D. Schulze
James H. Ware
                     MATHTECH, INC.
                      P.O. Box 2392
              Princeton, New Jersey  08540
             EPA Contract Number 68-02-3826
                    Project Officer:
                     Allen C. Basala
                Economic Analysis Branch
          Strategies and Air Standards Division
      Office- of Air Quality Planning and Standards
          U.S. Environmental Protection Agency
      Research Triangle Park, North Carolina  27711
                       March 1983

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     The  analysis  and conclusions presented in this report  are
.those  of  the authors and should  not be  interpreted as necessarily
 reflecting  the  official  policies  of the U.S.  Environmental
 Protection Agency.

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                              EPA PERSPECTIVE
     There has been growing concern with the effectiveness  and  burden  of
regulations imposed by the Federal government.   In order to improve the
process by which regulations are developed,  Executive Order 12291  was
issued.  The order requires that Federal agencies develop and consider, to
the extent permitted by law, Regulatory Impact  Analyses (RIA) for  the
proposal and promulgation of regulatory actions which are classified as
major.  According to the order, a significant component of  the  RIA is  to  be
an economic benefit and benefit-cost analysis of the regulatory alternatives
considered.  Under the Clean Air Act,  the Administrator of  EPA  may not
consider economic and technological feasibility in setting  National  Ambient
Air Quality Standards (NAAQS).  Although this precludes consideration  of
benefit cost analyses in setting NAAQS, it does not necessarily preclude
consideration of benefit analyses for that purpose.

     In full support of the Executive Order, the EPA commissioned  Mathtech,
Inc. to accomplish an economic benefit and benefit-cost analysis of some  of
the alternatives that were thought likely to be considered  in the  development
of proposed revisions to the NAAQS for particulate matter (PM). The report,
entitled "Benefit and Net Benefit Analysis of Alternative National  Ambient
Air Quality Standards for Particulate Matter,"  documents the results of the
contractor's study.  One of the major objectives of the study was  to give a
better understanding of the complex technical issues and the resource
requirements associated with complying with  the spirit of the Order for the
NAAQS program.  In order to achieve this objective, the contractor was
given a wide range of latitude in the use of data, analytic methods, and
underlying assumptions.

     It is important to stress that the benefit analysis portion of the
Mathtech study has not had a role to date in the development of proposed
revisions to the NAAQS for particulate matter.   Staff recommendations
currently under consideration are based on the  scientific and technical
information contained in two EPA documents.   They are the "Air  Quality
Criteria for Particulate Matter and Sulfur Oxides" and the  "Review of  the
National Ambient Air Quality Standards for Particulate Matter:  Assessment
of Scientific and Technical Information, OAQPS  Staff Paper." These documents
have undergone extensive and rigorous  review by the public  and  the Clean
Air Scientific Advisory Committee in accordance with the Agency's  established
scientific review policy.  Although the Mathtech study reflects the
"state-of-the-art" in particulate matter benefit analysis,  the  approach and
results have not been subjected to a comparable extensive peer  review
process.  In addition, some EPA staff have raised questions regarding  the
approach taken in the analysis and the significance of the  results for
standard setting purposes under the Act.  These circumstances do not
necessarily preclude use of the benefit analysis in some manner after
appropriate peer review and further consideration of the questions that
have been raised.

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                                  PREFACE
     This report was prepared  for  the  U.S. Environmental  Protection Agency
by Mathtech,  Inc.   The report is organized  into  five  volumes containing a
total of 11 sections as follows:
     Volume I
         Section  1:
         Section  2:
The Benefit Analysis
The Net Benefit Analysis
     Volume II
         Section  3:
         Section  4:
         Appendix:
Health Effects Studies in the Epidemiology Literature
Health Effects Studies in the Economics Literature
Valuation of Health Improvements
     Volume III
         Section  5:
         Section  6:
         Section  7:
         Section  8:
Residential Property Value Studies
Hedonic Wage Studies
Economic Benefits of Reduced Soiling
Benefits of National Visibility Standards
     Volume IV
         Section  9:
         Section 10:
Air Quality Data and Standards
Selected Methodological Issues
     Volume V

         Section 11:
Supplementary Tables
                                     IV

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                            ACKNOWLEDGMENTS
     While preparing this report,  we had the benefit of advice, comments
and other assistance from many individuals.  Allen Basala, the EPA Project
Officer, and James Bain,  former Chief of the Economic Analysis Branch
(EAB),  were especially helpful.  They provided both overall guidance on
project direction  as well  as  technical review  and  comment on the report.
Others in EAB who assisted us included Thomas Walton,  George Duggan, and
John O'Connor, the current Chief  of EAB.

     Others  within EPA/OAQPS who reviewed parts of  the  report and assisted
in various ways  included Henry Thomas, Jeff  Cohen, John Bachman,  John
Haines, Joseph Padgett,  and Bruce Jordan.

     Several  individuals  within  EPA/OPA also  provided  comments  or assis-
tance at various stages of the  proj.ect.  These included Bart Ostro,  Alex
Cristofaro,  Ralph Luken,  Jon Harford, and Paul  Stolpman.

     Others outside EPA who  reviewed parts  of  the report  and provided
comments included V. Kerry Smith, Paul Portney, Lester Lave, Eugene Seskin,
and William Watson.  Other  Hathtech  staff who assisted  us in various  ways
were  Donald Wise,  Gary  Labovich, and  Robert J.  Anderson.   We  also
appreciate  the assistance of Al  Smith and  Ken  Brubaker  of Argonne National
Laboratory who conducted the parallel analysis  of  control  costs and air
quality impacts.

     Naturally,   it  was  not  possible  to  incorporate all  comments and
suggestions.  Therefore, the individuals listed above do not necessarily
endorse the  analyses or conclusions  of the report.

     The production of a report this  length in  several draft versions,  each
under a tight time constraint, is a job which taxes the  patience and sanity
of a secretarial  staff.   Carol Rossell had this difficult task and managed
ably with the assistance of Deborah Piantoni,  Gail Gay, and Sally Webb.
Nadine Vogel and Virginia Wyatt,  who share the  same burden at EAB,  also
assisted us  on several  occasions.

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                                 CONTENTS




                                VOLUME III









Section                                                               Page






   5.   RESIDENTIAL PROPERTY VALUE STUDIES




          Summary of Results 	    5-1




          General Background 	    5-4




          Methodology	    5-6




          Literature Review	    5-16




          Limitations of the Hedonic Technique 	    5-21




          Benefit Estimation 	    5-26




          Conclusion	    5-44




          References	    5-47




          Appendix 5A:  Sources of Data 	    5-51






   6.   HEDONIC WAGE STUDIES




          Summary of Results 	    6-1




          Introduction	    6-3




          Hedonic Wage Models:  Theoretical Construct 	    6-6




          Empirical Estimates of Hedonic Wage Models 	    6-17




          Benefits 	    6-30




          Concluding Remarks 	    6-50




          References 	    6-52
                                    VI

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                           CONTENTS (Continued)
Section
                                                                      Page
   6.   HEDONIC WAGE STUDIES (Continued)

          Appendix 6A:   Estimated Results from Hedonic Wage
                        Models 	    6~57
   7.   ECONOMIC BENEFITS OF REDUCED SOILING

          Introduction 	•	    7-1

               Overview	•	    7-1
               Scope of Analysis 	    7-2
               Summary of Results 	    7-5

          Models of Benefits from Reduced Soiling 	    7-9

               Introduction	    7-9
               PM Measures and Soiling 	    7-11
               Physical Damage Functions 	    7-13
               Behavioral Models of Reduced Soiling  Benefits 	    7-18
               Summary of Models	    7-56

          Benefits Calculations 	    7-62

               Introduction	    7-62
               Household Sector	    7-63
               Manufacturing Sector 	    7-97

          References 	    7-132
   8.   BENEFITS OF NATIONAL VISIBILITY STANDARDS

          Introduct ion	    8-1

          Estimating the Benefits of Urban Visibility
          Improvements	    8-5

               Introduct ion	    8-5
               Property Value Studies 	    8-7
               Direct Willingness-to-Pay Studies 	    8-10
               Biases in Direct Willingness-to-Pay Studies 	    8-12
               Prediction Relationships for Urban Areas 	    8-17
               Conclusions 	    8-22
                                    vn

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                           CONTENTS (Continued)
Section                                                               Page


   8.    BENEFITS OF NATIONAL VISIBILITY STANDARDS (Continued)

          Recreation Benefits 	    8-22

               Review of Studies 	    8-23

          The Existence Value of Protecting Visibility 	    8-42

               Studies of Existence Value 	    8-44
               Tentative Estimates of Existence Values for
                    Visibility	    8-49

          Nonaesthetic Benefits of Visibility Improvement 	    8-53

               National Benefit Estimate for Achieving a 13-,
                    20-. 30-Mile and a Nationwide 20 Percent
                    Standard 	    8-56

               Visual Range Regions Utilized in the Benefit
                    Calculation	    8-56

               Procedures for Data Collection	    8-62

               National Benefits for a 13-, 20-, 30-Mile and
                    20 Percent Improvement Standard 	    8-63

          References 	    8-66

          Appendix 8A:  How Visibility Changes Were Calculated
                        for the Property Value Studies 	    8-73
                                    Vlll

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                                  FIGUBES

                                VOLUME III
Figure No.
   5-1.   Implicit price schedule and bid functions ...............   5-13

   5-2.   Marginal implicit price schedule and demand price
          functions ...............................................   5-15

   5-3.   Alternative benefit estimates for a given change
          in air quality ...... ....................................   5-32
   6-1.   Indifference maps and equilibria for two workers ........   6—8

   6-2.   Iso-profit lines and equilibria for two firms ^ ...........   6-13

   6-3.   Illustration -of labor-market equilibrium ................   6-15

   6-4.   Implicit marginal price schedule for air quality and
          compensated supply functions for two workers ............   6-16

   6-5.   Illustration of benefits calculation for non-marginal
          changes in TSP levels ...................................   6-35


   7-1.   Processes leading to economic benefits ... ...............   7-10

   7-2 .   Example of economic surplus .............................   7-33

   7-3.   Demand and supply curves in the WJ analysis  .............   7-37

   7-4.   Household decision process ..... . ........................   7-40
   8-1.   Median summer visual range (miles) and isopleths for
          suburban/non-urban areas, 1974-76 	    8-57

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                                  TABLES

                                VOLUME III



Table No.                                                             Page
  5—1.    Property Value Benefits of Attaining Alternative
          Particulate Matter Standards 	   5-3

  5-2.    Review of Property Value Studies 	   5-22

  5—3.    Studies Considered in Estimating the Benefits of
          Particulate Matter Reduct ions 	   5-27

  5-4.    Comparison of TSP Elasticities Calculated from
          Residential Property Value Studies 	   5-30

  5-5.    Alternative Particulate Matter Standards 	   5-36

  5-6.    Estimated Benefits for Residential Property Value
          Studies - Benefits Occurring Bet-ween 1989 and 1995 -
          Scenario:  Type B PM10 - 70 AAM/250 24-hr	   5-37

  5-7.    Estimated Benefits for Residential Property Value
          Studies - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type B PM10 - 55 AAM	   5-38

  5-8.    Estimated Benefits for Residential Property Value
          Studies - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type B PM10 - 55 AAM/250 24-hr	   5-39

  5-9.    Estimated Benefits for Residential Property Value
          Studies - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type B PM10 - 55 AAM/150 24-hr	   5-40

  5-10.   Estimated Benefits for Residential Property Value
          Studies - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type B TSP - 75 AGM/260 24-hr	   5-41

  5-11.   Estimated Benefits for Residential Property Value
          Studies - Benefits Occurring Between 1987 and 1995 -
          Scenario:  Type B TSP - 150 24-hr	   5-42

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                            TABLES (Continued)
Table No.
  5-12.   Estimated Benefits for Residential Property Value
          Studies - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type A PM10 - 70 AAM/250 24-hr	   5-45

  5-13.   Benefits of Attaining Alternative Particulate Matter
          Standards 	   5-46
  6-1.    Summary of Estimated Benefits for Hedonic Wage Studies -
          Primary Standards 	   6-2

  6-2.    Summary of Estimated Benefits for Hedonic Wage Studies -
          Secondary Standards 	   6-2

  6-3.    Summary of Selected Hedonic Wage Models 	   6-25

  6-4.    Estimated Benefits for Marginal Changes in Air Quality
          Based on Results Reported by Rosen and Smith	   6—34

  6-5.    Data Sources and Transformations	   6-38

  6-6.    Air Quality Data:  Original Models and Current
          Benefits Analysis 	   6-40

  6-7.    Estimated Benefits for Marginal Changes in Air Quality ..   6-41

  6-8.    Estimated Benefits for Hedonic Wage Studies - Benefits
          Occurring Between 1989 and 1995 - Scenario:  Type B
          PM10 - 70 AAM/250 24-hr	   6-43

  6-9.    Estimated Benefits for Hedonic Wage Studies - Benefits
          Occurring Between 1989 and 1995 - Scenario:  Type B
          PMlO - 55 AAM	   6-44

  6-10.   Estimated Benefits for Hedonic Wage Studies - Benefits
          Occurring Between 1989 and 1995 - Scenario:  Type B
          PMlO - 55 AAM/250 24-hr	   6-45

  6-11.   Estimated Benefits for Hedonic Wage Studies - Benefits
          Occurring Between 1989 and 1995 - Scenario:  Type B
          PMlO - 55 AAM/150 24-hr	   6-46

  6-12.   Estimated Benefits for Hedonic Wage Studies - Benefits
          Occurring Between 1987 and 1995 - Scenario:  Type B
          TSP - 75 AGM/260 24-hr	   6-47
                                    XI

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                            TABLES (Continued)
Table No.                                                             Page
  6-13.   Estimated Benefits for Hedonic Wage Studies - Benefits
          Occurring Between 1987 and 1995 - Scenario:  Type B
          TSP - 150 24-hr	   6-48

  6-14.   Estimated Benefits for Hedonic Wage Studies - Benefits
          Occurring Between 1989 and 1995 - Scenario:  Type A
          PM10 - 70 AAM/250 24-hr	   6-51

  6-15.   Summary of Biases in Benefits Calculations:  Hedonic
          Wage Models 	   6-53

  6A-1.   Hedonic Wage Model Estimated by Rosen:  Effects of
          Individual—Specific Characteristics 	   6-58

  6A-2.   Hedonic Wage Models Estimated by Rosen:  Effects of
          Site-Specific Characteristics	   6-59

  6A-3.   Hedonic Wage Models Estimated by Smith . 1	   6-60
  7-1.    Summary of Estimated Benefits for Alternative Primary
          Standards 	   7-6

  7-2.  v  Summary of Estimated Benefits for Alternative
          Secondary Standards 	   7-6

  7-3.    Regression Results from Beloin and Haynie 	   7-14

  7-4.    Cleaning Activities in Cummings' Analysis 	   7-25

  7-5.    Total Per-Household Soiling Costs by Pollution Zone 	   7-27

  7-6.    SMSAs Included in MTH Analysis	,	   7-43

  7-7.    Goods Included in the MTH Analysis 	   7-44

  7-8.    Demand Equations with TSP	   7-46

  7-9.    Biases in Models of Soiling Studies 	   7-57

  7-10.   Air Quality Scenarios 	   7-63

  7-11.   Estimated Benefits for Cummings Soiling Study -
          Benefits Occurring Between 1989 and 1995 - Scenario:
          Type B PM10 - 70 AAM/250 24-hr	   7-66
                                    Xll

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                            TABLES (Continued)
Table No.
  7-12.   Estimated Benefits for Cummings Soiling Study -
          Benefits Occurring Between 1989 and 1995 - Scenario:
          Type B PM10 - 55 AAM/250 24-hr	   7-67

  7-13.   Estimated Benefits for Cummings Soiling Study -
          Benefits Occurring Between 1989 and 1995 - Scenario:
          Type B PM10 - 55 AAM/150 24-hr	   7-68

  7-14.   Estimated Benefits for Cummings Soiling Study -
          Benefits Occurring Between 1989 and 1995 - Scenario:
          Type B PM10 - 55 AAM	   7-69

  7-15.   Estimated Benefits for Cummings Soiling Study -
          Benefits Occurring Between 1987 and 1995 - Scenario:
          Type B TSP - 75 AAM/260 24-hr	   7-70

  7-16.   Estimated Benefits for Cummings Soiling Study -
          Benefits Occurring Between 1987 and 1995 - Scenario:
          Type B TSP - 150 24-hr	   7-71

  7-17.   Estimated Benefits for Cummings Soiling Study -
          Benefits Occurring Between 1989 and 1995 - Scenario:
          Type A PM10 - 70 AAM/250 24-hr	   7-73

  7-18.   Estimated Benefits for Watson and Jaksch Soiling
          Study - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type B PM10 - 70 AAM/250 24-hr	   7-76

  7-19.   Estimated Benefits for Watson and Jaksch Soiling
          Study - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type B PM10 - 55 AAM/250 24-hr	   7-77

  7-20.   Estimated Benefits for Watson and Jaksch Soiling
          Study - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type B PM10 - 55 AAM/150 24-hr	   7-78

  7-21.   Estimated Benefits for Watson and Jaksch Soiling
          Study - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type B PM10 - 55 AAM	   7-79

  7-22.   Estimated Benefits for Watson and Jaksch Soiling
          Study - Benefits Occurring Between 1987 and 1995 -
          Scenario:  Type B TSP - 75 AAM/260 24-hr	   7-80
                                    Xlll

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                            TABLES (Continued)
Table No.                                                             Page
  7-23.   Estimated Benefits for Watson and Jaksch Soiling
          Study - Benefits Occurring Between 1987 and 1995 -
          Scenario:  Type B TSP - 150 24-hr	    7-81

  7-24.   Estimated Benefits for Watson and Jaksch Soiling
          Study - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type A PM10 - 70 AAM/250 24-hr	    7-83

  7-25.   Estimated Benefits for Hathtech Household Expenditure
          Study - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type B PM10 - 70 AAM/250 24-hr	    7-86

  7-26.   Estimated Benefits for Hathtech Household Expenditure
          Study - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type B PM10 - 55 AAM/250 24-hr	    7-87

  7-27.   Estimated Benefits for Mathtech Household Expenditure
          Study - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type B PM10 - 55 AAM/150 24-hr	    7-88

  7-28.   Estimated Benefits for Mathtech Household Expenditure
          Study - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type B PM10 - 55 AAM	    7-89

  7-29.   Estimated Benefits for Mathtech Household Expenditure
          Study - Benefits Occurring Between 1987 and 1995 -
          Scenario:  Type B TSP - 75 AAM/260 24-hr	    7-90

  7-30.   Estimated Benefits for Mathtech Household Expenditure
          Study - Benefits Occurring Between 1987 and 1995 -
          Scenario:  Type B TSP - 150 24-hr	    7-91

  7-31.   Estimated Benefits for Mathtech Household Expenditure
          Study - Benefits Occurring Between 1989 and 1995 -
          Scenario:  Type A PM10 - 70 AAM/250 24-hr	    7-93

  7-32.   Extrapolation Biases in the Household Sector Models 	    7-95

  7-33.   Summary of Benefits from Reduced Soiling in the
          Household Sector 	    7-98

  7-34.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 344) - Benefits Occurring
          Between 1989 and 1995 - Scenario:  Type B PM10 -
          70 AAM/250 24-hr	    7-101
                                    xiv

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                            TABUS (Continued)
Table No.
  7-35.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 344)  - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 -
          55 AAM/250 24-hr	    7-102

  7-36.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 344)  - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 -
          55 AAM/150 24-hr	    7-103

  7-37.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 344)  - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 - 55 AAM .    7-104

  7-38.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 344)  - Benefits Occurring
          Between 1987 and 1995 - Scenario:   Type B TSP -
          75 AAM/260 24-hr	    7-105

  7-39.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 344)  - Benefits Occurring
          Between 1987 and 1995 - Scenario:   Type B TSP -
          150 24-hr	    7-106

  7-40.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 354)  - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 -
          70 AAM/250 24-hr	    7-107

  7-41.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 354)  - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 -
          55 AAM/250 24-hr	    7-108

  7-42.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 354)  - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 -
          55 AAM/150 24-hr	    7-109

  7-43.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 354)  - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 - 55 AAM .    7-110
                                    xv

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                            TABLES (Continued)
Table No.
  7-44.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 354)  - Benefits Occurring
          Between 1987 and 1995 - Scenario:   Type B TSP -
          75 AAM/260 24-hr	    7-111

  7-45.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 354)  - Benefits Occurring
          Between 1987 and 1995 - Scenario:   Type B TSP -
          150 24-hr	    7-112

  7-46.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 34) - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 -
          70 AAM/250 24-hr	    7-113

  7-47.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 34) - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 -
          55 AAM/250 24-hr	    7-114

  7-48.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 34) - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 -
          55 AAM/150 24-hr	    7-115

  7-49.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 34) - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 - 55 AAM .    7-116

  7-50.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 34) - Benefits Occurring
          Between 1987 and 1995 - Scenario:   Type B TSP -
          75 AAM/260 24-hr	    7-117

  7-51.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 34) - Benefits Occurring
          Between 1987 and 1995 - Scenario:   Type B TSP -
          150 24-hr	    7-118

  7-52.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 35) - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 -
          70 AAM/250 24-hr	    7-119
                                    xvi

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                            TABLES (Continued)
Table No.
  7-53.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 35)  - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 -
          55 AAM/250 24-hr	   7-120

  7-54.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 35)  - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 -
          55 AAM/150 24-hr	   7-121

  7-55.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 35)  - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type B PM10 - 55 AAM .   7-122

  7-56.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 35)  - Benefits Occurring
          Between 1987 and 1995 - Scenario:   Type B TSP -
          75 AAM/260 24-hr	   7-123

  7-57.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 35)  - Benefits Occurring
          Between 1987 and 1995 - Scenario:   Type B TSP -
          150 24-hr	   7-124

  7-58.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 344) - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type A PM10 -
          70 AAM/250 24-hr	   7-126

  7-59.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 354) - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type A PM10 -
          70 AAM/250 24-hr	   7-127

  7-60.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 34)  - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type A PM10 -
          70 AAM/250 24-hr	   7-128

  7-61.   Estimated Benefits for Mathtech Manufacturing
          Expenditure Study (SIC 35)  - Benefits Occurring
          Between 1989 and 1995 - Scenario:   Type A PM10 -
          70 AAM/250 24-hr.	   7-129

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                            TABLES (Continued)
Table No.                                                             Page
  1-62.   Summary of Benefits from Reduced Soiling in the
          Manufacturing Sector 	   7-131
  8-1.    1980 Annual National Benefits of Alternative
          Visibility Standards ....................................   8-5

  8-2.    Annualized Property Value Changes Per Mile of
          Visibility by Study and Area ............................   8-9

  8-3.    Annual Willingness to Pay Per Mile of Visibility
          By Study and Area .......................................   8-11

  8-4.    Low, Medium and High Estimated Residential Benefits
          Per Tear in 1980 Millions of Dollars for Visibility
          Policies of 13, 20, 30 Miles and a Nationwide 20%
          Improvement .............................................   8-21

  8-5.    Mean Incremental Willingness to Pay in 1980 Cents
          Per Mile ................................................   8-34
  8-6.    Visual Range Valuation Elasticities (02)
  8-7.    Low, Medium and High Estimated Recreational Benefits
          Per Tear in 1980 Millions of Dollars for Visibility
          Policies of 13, 20, 30 Miles and a Nationwide 20%
          Improvement ................................... . .........   8-43

  8-8.    National Existence Value Benefits from S.B.W.K. Study ...   8-48

  8-9. •   Recreation Areas Used in Existence Value Analysis .......   8-50

  8-10.   Estimated Existence Value Benefits Per Tear in 1980
          Millions of Dollars Per Visibility Policies of 13,
          20 and 30 Miles and a Nationwide 20% Improvement ........   8-54

  8-11.   Low. Medium and High Visibility Visual Range Values
          by Region ...............................................   8-61

  8-12.   Low, Medium and High Estimated Residential Aesthetic
          Benefits Per Tear in 1980 Millions of Dollars for
          Visibility Policies of 13, 20, 30 Miles and a
          Nationwide 20% Improvement ..............................   8-64
                                    XVlll

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                            TABLES (Continued)
Table No.                                                             Page
  8-13.   Low, Medium and High Estimated Recreational Benefits
          Per Year in 1980 Millions of Dollars for Visibility
          Policies of 13, 20, 30 Miles and a Nationwide 20%
          Improvement 	   8-65

  8-14.   Estimated Existence Value Benefits Per Year in 1980
          Millions of Dollars Per Visibility Policies of 13,
          20, 30 Miles and a Nationwide 20% Improvement	   8-67

  8-15.   Total Existence Value Index 	   8-68

  8-16.   Ranges for the Low, Medium and High Total Benefits Per
          Year in 1980 Millions of Dollars for Visibility
          Policies of 13, 20, 30 Miles and a 20% Nationwide
          Improvement 	   8-69
                                    xix

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             SECTION 5
RESIDENTIAL PROPERTY VALUE STUDIES

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                                SECTION 5
                   RESIDENTIAL PROPERTY VALUE STUDIES
SDMMAHY OF RESULTS

     One method that  has been used to estimate the benefits of air quality
improvements has involved  the  analysis of  residential property value
differentials.   The underlying hypothesis in this  method is that residen-
tial property values  will reflect not only housing  quality'but  also  site-
specific attributes such as  location,  neighborhood characteristics,  avail-
ability of services,  and environmental  amenities including air quality.
Under this hypothesis, property value differentials attributable to air
quality differences  reflect the household's economic valuation of  air
quality and therefore  can  be  used to  estimate the  economic value  of
improvements in air quality.

     The purpose of this section is  to develop estimates of the benefits
resulting  from reductions  in the ambient level  of suspended particulates
based on the results of representative studies employing the property value
technique.   Because  property value differentials  can measure only  the
effects of air pollution that are perceived  by the household, it should be
mentioned that the types of benefits  that are  measured  in this section are
any perceived health, physical  property, aesthetic, or psychic benefits
that can be attributed to  residing in an area with  relatively clean air.
Consequently,  it  is likely that the benefits estimated in  this section will
be larger than the benefits  estimated in Section 7 for household  soiling
and materials damage.   It  is unclear, however, whether  the estimates  pre-
sented  in this  section will exceed the  health benefits  presented  in
Sections 3 and 4.
                                   5-1

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     The benefits in this section were  estimated using the same air quality
data and air quality standards used throughout this study  —  implementation
of four particulate matter with a diameter of 10 um (PM10) and two total
suspended  particulate (TSP)  standards in  counties where  air quality data
are available.   The resulting  estimates  under these  alternative  standards
are summarized in Table 5-1.  The benefits reported  in this table are
reported  in terms  of the discounted present value in  1982 of a stream of
benefits  occurring between the  year  of standard attainment and 1995.*
These benefit  estimates  are stated in  1980 dollars and  assume a 10 percent
discount rate.  As Table  5-1 indicates,  under the  most  lax PM10 standard of
an annual  arithmetic average (AAM) of 70  ug/m  and  a 24-hour expected value
(EV)  of 250 ug/m ,  the discounted present value of benefits range  from  $3.4
to $11.4 billion and include  a point estimate of $6.9  billion.  The most
stringent  of  the PM10  standards  — an  AAM  of 55 ug/m3  and a 24-hour EV of
150 ug/m   results  in  benefits ranging from $7.6  to $25.4 billion with  a
point estimate of $15.3 billion.   The current primary standard  of  75 ug/m
annual geometric mean (AGM) and  260 ug/m   24-hour  maximum value  not to be
exceeded more  than  once  a year for TSP  are  estimated to  result in benefits
ranging from  $11.2 to $37.3 billion and  include a point  estimate of $22.4
billion.

     The benefits  estimated in  this  section should be  considered to be
general approximations of the  household benefits resulting from the attain-
ment of alternative particulate matter standards for the following reasons:

     •    Since  property value  differentials  only  reflect  the  value
          of  perceived differences in  air quality  levels,  any unper-
          ceived benefits resulting from  air quality  improvements
          will not be captured in the estimates reported  in this
          section.   Consequently,  the  benefits  reported in this
          section are underestimates of the  total  benefits of reduc-
          tions  in  the ambient level of particulate matter.
* The year of standard  attainment  for PM10  standards is 1989.   The year of
  standard attainment for TSP standards  is 1987.
                                   5-2

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                                Table  5-1

            PROPERTY VALUE BENEFITS  OF ATTAINING ALTERNATIVE
                      PARTICDLATE MATTER STANDARDS*

                       (Billions of  1980 Dollars)
Standard**
PM10 - 70 ng/m3
AAH and 250 (ig/m3
24-hour EV
PM10 - 55 fig/m3
AAH
PM10 - 55 (ig/m3
AAM and 250 fig/m3
24-hour EV
PM10 - 55 jig/m3
AAM and 150 (ig/m3
24-hour EV
TSP - 75 jig/m3
AGM and 260 |ig/m3
24-hour MAX
TSP - 150 fig/m3
24-hour MAX
Minimum
$ 3.4
6.0
6.0
7.6
11.2
14.9
Point Estimate
$ 6.9
11.9
12.0
15.3
22.4
29.7
Maximum
$11.4
19.9
20.0
25.4
37.3
49.6
 * Benefits are stated in terms of the  discounted present value in 1982  and
   assume a 10 percent  discount  rate.  Benefits for the PM10 standards  are
   accumulated from  1989  to 1995, while the benefits for the TSP standards
   are  accumulated from  1987 to  1995.

** AAM  - annual arithmetic mean;  AGM =  annual geometric mean; EV - expected
   value; MAX  = maximum value  not to be exceeded more than once a year.
         The  results of  studies on specific  cities in the early
         1960's  and 1970's are used to  estimate  the  benefits  of
         pollution reductions occurring between 1987  and 1995 for
         the  counties included in this analysis.   If the valuation
         of air quality improvements has  changed significantly since
         that time,  or if  the valuation of  air  quality  improvements
         differs between cities  and  counties,  the  use of these
                                   5-3

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         studies' results can  only be  considered as approximations
         of the benefits of attaining alternative particulate  matter
         standards.
         The assumptions  of the hedonic technique are assumed  to
         hold  in  the property  value market.   The  effect of this
         assumption is  unknown.
         The average of the particulate matter readings within a
         county is taken as  representative of the level  of  exposure
         of  all households  within a county.   The effect of this
         assumption on  the estimated benefits  is  unknown.
         The marginal  willingness to pay for air quality improve-
         ments of households residing  in single-family units  is
         assumed  to be  representative of all  households.   The  effect
         of this assumption  is  unknown.
         The marginal  willingness to pay for air quality improve-
         ments is assumed  to  decline  at a constant rate as air
         quality  improves.   The  effect of  this assumption  is
         unknown.
GENERAL BACKGROUND

     The analysis of residential property value differentials has been
widely used for estimating  the benefits  of  reductions in air pollution
levels.  This method assumes  that  the benefits of living  in a clean air
environment are capitalized into property values.   In other words, what
people are  willing to pay for air quality  improvements  can  be measured by
the observed  differences  in  the  value  of  residential properties that are
identical  in every respect except  air pollution exposure.

     Since  this method focuses on the decisions made in the housing  market,
the household  does not need to  know  the technical relationship between air
pollution  and physical damage.  The household, however, must be able to
perceive  the effect of different  levels of air quality,  and make decisions
in the housing market based on  that perception.  Consequently,  the types of
benefits that  are measured through  the  property value  method are any
perceived health,  physical property,  aesthetic, or psychic benefits that
are the result of  residing  in  an  area with relatively clean air.   Because
some  of  the   effects  of air  pollution  are probably not perceived by
                                   5-4

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households,  one  of  the disadvantages of property value studies is that they
cannot provide estimates of all  the  benefits  accruing  to  households  that
result from air  quality  improvements.  For example,  health effects that are
not perceived  by the household will not be captured by residential property
values.  Another  disadvantage  is  that residential property value studies
may only provide estimates of benefits that occur  at home.  For example,
benefits of air  quality  improvements  that occur at recreation areas and the
workplace may not  be measured  by residential property value differentials.*

     One of the  major  advantages  of property value differential analysis is
the ability to capture the value that households place on the aesthetic and
psychic amenities  of the place  where  they  reside.  Neither health studies
nor physical property  dose-response functions measure  the  aesthetic
benefits  of improvements in air quality.   In addition,  property value
studies can reflect the  choice of substitute activities  and goods  that are
used as a means of offsetting the  effects of pollution.  For example, if
the members of a household substitute indoor activities for outdoor activi-
ties on certain days  because of poor air  quality,  this reduced flow of
services from the property would be reflected in a lower property value.
It is possible,  however, that the purchase of goods to offset the effect of
pollution may result in  an  enhancement  of the property  value  in  a polluted
area.   If central  air  conditioning,  for example,  is bought by a household
in order to offset the  effects  of  pollution, the value of that house is
higher  relative to an  identical home without air conditioning  that is
exposed to the same level of pollution.  This can  be correctly reflected in
property value differentials if  air  conditioning  is identified as  one  of
the attributes of  housing.
* It is possible  that  property  values, in addition to reflecting the  value
  individuals place on amenities at  the home, may also reflect the value
  placed on amenities at  the workplace since once an individual  makes a
  residential  location decision,  the  choice of other locational amenities,
  such as  those  at the worksite,  are  limited.  See Cropper (1).
                                   5-5

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METHODOLOGY

     The  use  of  property value differentials  as  a  means  of  determining the
willingness to pay for air quality improvements has its underpinnings in
the hedonic price  technique.  This technique was originally developed by L.
M. Court  (2).  Griliches and Adelman  (3), Griliches (4),  Ohta and Griliches
(5),  Kain and Quigley (6), and others have used the technique to estimate
the value of changes in the quality  of consumer goods.   Generally  stated,
the hedonic  technique examines the  functional relationship between the
price of  a good and its characteristics.*   It has  been used extensively as
a means of estimating  the marginal willingness to pay  for environmental
quality  [Harrison-Rubinfeld (7); Nelson (8)].   In these studies,  housing
values are regressed on a set of housing  characteristics which  includes a
measure of air quality.

     Before explaining the hedonic technique, it is necessary to address
the question of whether predicted changes  in property values  are accurate
measures of the total benefits of air quality improvements.  Using a model
of locational choice, Polinsky and Shavell (9) have shown that  predicted
property value changes are accurate  measures of  these benefits  only under
certain  rather stringent  assumptions.  Their  explanation  proceeds as
follows:

     Assume  that there is a city inhabited by individuals with identical
utility  functions and equal incomes.**  People work in the center of the
city and reside in the area surrounding the center  city.  Air quality (AQ)
at a specific location increases with distance  (d) from the  center city.
 * See Chapter 1 of Griliches  (4)  for a summary of the  hedonic price
   technique.
** The model can be generalized to reflect the possibility that there is
   more than one utility function and unequal  incomes  within the  city.  In
   this case,  there  would  be  i consumer  groups (i = l,n) where each member
   of the i   group would have identical utility functions and incomes.
   This would  only  serve  to  complicate  the analysis  without  changing the
   results.  If  incomes are endogenous to the model, however, the following
   results will be altered.
                                   5-6

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Travel  cost (T)  to the center city is also  an increasing function of d.
Utility in this city  is a function of the  consumption of household  services
(H),  a composite  good (X),  and  the  level of air  quality  [AQ(d)]:

          U  = U(H. X. AQ(d))                                         (5.1)

The  consumer  desires to  maximize  his  utility subject to the budget
constraint:

           Y  =  p(d)H + X  + T(d)                                     (5.2)

where      Y  =  money income.
        p(d)  =  per unit  price  of housing  services at  a location with
                 distance  (d) from  the center city.
           H  -  household  services.
           X  =  the  composite  good with price  equal to 1.
        T(d)  =  commuting costs  to the center  city.

     By solving the  first-order conditions  of the  utility maximization
problem,  Equation (5.1) can be  stated in terms of an indirect  utility
function — utility as a function of the demand functions for  H,  X,  and  AQ:

           U  =  I[p(d), Y-T(d), AQ(d)]                               (5.3)

     Under the assumption  of  unrestrained  and costless  mobility  throughout
the city,  and identical utility  functions and income, a common equilibrium
level of utility,  U*,  will 1
his utility level  by moving.
level of utility,  U ,  will be obtained.  At U , no individual can increase
          U*  =  I[p(d), Y-T(d). AQ(d)]                               (5.4)
Implicit in this  relation  is  the equilibrium housing function:

        p(d)   =  P[U*, Y-T(d), AQ(d)].                                (5.5)
                                   5-7

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     It is important to note  that  the  equilibrium price of housing is  a
function of U* as well  as Y-T(d) and AQ(d).

     In Polinsky  and Shavell's  paper.  Equation  (5.5)  is  used  to  explain the
conditions under  which  a new property value  schedule can be  predicted  from
a change in air quality. If  it  is assumed that the city is small  and  there
is perfect and costless mobility among cities, then U  will be the  same
across all cities  and exogenous to the small  city.  If  air quality improves
within the small  city,  U  will  not  change and the change  in  property  value
is only  dependent on  the characteristics of d.  A regression equation
specifying the  relationship between p(d)  and  d  can be used in this  case to
predict the change in property  values resulting from a given change in air
quality.   If the city is either  large, or there  is  imperfect  mobility  among
cities, then U will be endogenous.  If air quality improves  within the
city, U will be affected.  In  this  case, the new property value schedule
cannot be predicted without first using a general equilibrium model to
determine the new level of U resulting from  the change  in air quality.

     In general,   therefore, property value equations that estimate the
change in property value for a given change  in air quality  can be  used to
predict  the new  property value schedule  for such changes only if the
following assumptions are met:

     •    The geographical  area under consideration must be  small.
     •    There must be perfect mobility  throughout,  and into and  out
          of,  the geographical  area.
     •    There must be no changes in input  and output  prices.

     The hedonic  technique, however, does not attempt  to  predict a new
property value  schedule resulting from a change in air  quality, but rather
estimates the  marginal  willingness  to pay for  air quality improvements by
observing  the housing market  in equilibrium.  In this method, the implicit
price of air quality  is identified  by examining the differentiated prices
within the housing market that result  from variations in existing air
quality.  Since  the housing market is  in  equilibrium, the  implicit price of
                                   5-8

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air quality can be shown to be equal to the marginal willingness to pay for
air quality.   The hedonic  technique  is therefore  useful  in  predicting the
benefits of marginal changes in air pollution.   Given certain conditions,
the implicit prices  estimated by the  hedonic  technique  and  other  relevant
variables  can be used to estimate  the inverse demand function for air
quality.*  Through the estimation of this demand curve, the benefits of
non-marginal changes  in air quality can also  be  predicted.

     The general  form of a hedonic equation relates  the  price  of a  good to
the characteristics  of  that  good.  As  applied to  the  housing  market, this
can be expressed  as:
          R£  =   r(Si, 1^, Q£)                                       (5.6)

where     R^  =   price of the i   residential  location.
          S^  =   a  vector  of structural  characteristics of the i
                 location.
          Nj  =   a  vector  of neighborhood characteristics of  the i
                 location.
          Q.  =   a  vector of environmental  characteristics of  the i
                 location with one element in the vector  being air quality
Note that housing price in the hedonic equation is a function only of the
characteristics  of the house, not of the household.

     The assumptions that are necessary in  order  for  the  hedonic  equation
to estimate the marginal willingness to pay for air quality  improvements
are:
* In  a  recent  study,  Palmquist  (9)  has  found that  without  strong
  assumptions,  it is not  possible to  identify the  demand  curve for a
  housing characteristic,  such as air quality, with data from  one city.
  Palmquist  has  suggested that the demand curve can be  identified by using
  data from  a number  of  cities.
                                   5-9

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         The housing market  must be in equilibrium.
         Individuals must be able to perceive the characteristics
         and attributes  of housing.
         A complete range  of houses with alternative characteristics
         must be available.
     The  partial derivatives  of property value with respect  to the housing
characteristics are interpreted as the marginal  implicit prices or the
additional amount that must be paid for a house with one more "unit" of a
particular  characteristic,  ceteris  paribus.   Since one of the  assumptions
of the property value  model  is that the housing market  is  in equilibrium,
the marginal  implicit price is  therefore equal to the  marginal  willingness
to pay for that characteristic.   In terms  of  the partial  derivative of
property value with respect to air quality (dR^/dq^), an estimate of the
equilibrium willingness to  pay for  marginal  air quality improvements is
obtained.

     In order to see why the partial derivatives  of the housing equation
variables are equal to the equilibrium marginal  willingness to pay for
housing characteristics,  it is helpful to develop  a model of  consumer
choice following Rosen (11).*

     Assume that there is a consumer whose utility is  dependent on the
consumption of a composite  good (X)  and  a vector  of housing characteristics
(H) where air  quality (h,)  is an element  in the vector:

           U   =  U(X,  hx, ... . hn)                                   (5.7)

The consumer has income (y) which can be  expressed  as:
* Rosen's paper dealt with both the consumption and production of a good
  that could be defined  in terms of its attributes and characteristics.
  Since  the hedonic price technique  only reveals the equilibrium  outcome of
  demand and supply conditions and not the underlying demand and supply
  functions,  and  since  the purpose  of  this paper  is to estimate the
  willingness to pay for air  quality improvements,  we will  limit our
  discussion to the consumer allocative decisions made for housing.
                                  5-10

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           y  =  X + p(H)                                            (5.8)

where   p(H)   =  price  of housing.
           X  =  the composite good with price equal to 1.

     Setting up the Lagrangian,  the consumer maximizes U subject to his
budget constraint.  The first  order  conditions can be expressed as:
       3U/3X  =  X    and    3U/3hi  =  XapfHJ/dhj                    (5.9)

where      X  =  the Lagrangian  multiplier.

     By combining equations,  it is found that in equilibrium the marginal
rate of substitution between  each  housing characteristic and the composite
good  is  equal to the  partial  derivative  of the  price of housing with
respect to that particular characteristic  (i.e.,  the implicit price of the
characteristic estimated by the  housing  equation):

          (3U/3hi)/OU/3X)  =   Op(H)/3hi)  .                        (5.10)

If X  is thought  of  as money  (the price of a dollar  is equal to  $1.00),
equilibrium is achieved when the  marginal rate of  substitution between h-
and money is equal  to the marginal implicit  price  of h...  Since the
marginal rate of  substitution between h^ and  money can also be viewed as
the marginal payment for h. with  money, the equilibrium conditions can be
expressed  as equating the marginal willingness to  pay  for h- (with money)
with its marginal implicit price.

     The  equating  of  the  marginal  implicit  price   and  the  marginal
willingness to pay can also be explained  by viewing  equilibrium  in terms of
a particular h..  Assume  that there is a level  of  consumer utility,  u, that
can be defined by the function:

          U(y  - 9; ht,  ...  , hQ)   =  u                               (5.11)
                                   5-11

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where     y - 9  =  X.

     For a given u,  Equation (5.11)  can be  thought of as  an indifference
curve relating the tradeoff between b^  and X.  A bid function:

          0(14,  ...  , hn; u,y)                                       (5.12)

can be derived  from  Equation (5.11) which  relates  the  alternative  expendi-
tures a consumer is  willing to make for h^ given a certain level of utility
and  income.  By  totally  differentiating (5.11),  we find that:

      aU/aX(dy - d9) + aU/ahj^dhj  + ...  + aU/ahndhn  =  du            (5.13)
Given the assumption  of a fixed level of  utility and income, du = 0 and dy
= 0.  If dhj, = 0 for k ^  i, Equation (5.13) reduces to:

          ae/ahi  =   ou/ah^/ou/ax)   .                            (5.14)

Viewing a particular h^h^ as air quality, Equation (5.14) shows that the
marginal rate of  substitution between air quality  and X (money) is  equal to
the  marginal implicit bid  for air quality (39/dh^) at a given level of
utility and income.

     Figure 5-1 shows the bid  function of consumer j  for  air quality while
holding everything else constant,  d-5^, l^. ... , hn; u , y ).   There are
a number of  different bid  functions reflecting  the different levels of
tastes,  preferences, and income of consumers.  This  function shows the
willingness to pay (bid) for air quality in terms of the amount of money
(X)  foregone, ceteris  paribus.  The minimum  implicit prices revealed in the
market  that must be paid for different levels of air quality while holding
h2  through hfl  constant  is  shown by p(hj^, hj, ...  ,  h*).   Equilibrium is
reached when d-^hj,  hj, ...  ,  hQ; u*, y*) is tangent to p(h1, hj, ...  ,
 $                                                            <
hQ); i.e., where the marginal willingness to pay  for air quality is equal
to its marginal implicit price.
                                   5-12

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      9rP
                                                  ..  hn*, u* ,  y*)
                                    h.  (air quality)
Figure 5-1.   Implicit price  schedule and bid functions
                             5-13

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     Since the hedonic equation can be expressed by p(h^,  .••  •  hn)'
marginal implicit  price  schedule for air quality (apOD/dh^)  can be easily
obtained by taking the derivative  of  the hedonic housing equation with
respect  to air quality (see Figure 5-2).  Assuming that the housing market
is in equilibrium  and following the above explanation,  this  schedule  will
also trace out the loci  of  marginal willingness to pay equilibria  for
different  levels of air quality by  consumers  with different  bid functions.
Note  that unless  all  consumers   have  identical  bid  functions  (i.e.,
identical  utility functions and  incomes),  the  hedonic  technique yields only
the equilibrium marginal willingness to pay of consumer j with bid function
9J.   This is  only one point on consumer j's demand price  function for h^
while holding utility constant  (i.e., the  inverse  compensated demand
function).  Consequently,   dp(H)/dh«   is not the inverse compensated demand
function for air quality and, in most cases, can only be used to approxi-
mate the benefits of marginal  improvements in air quality.  In order to
accurately estimate  the  demand curve for air  quality and predict  the
benefits of non-marginal  changes in  air quality when  consumers do not have
identical  utility  functions and incomes,  additional  information  and steps
are needed.

     In  this  section,  the  hedonic  studies  that have measured the relation-
ship between  residential property values and particulate matter will be
reviewed.  The ultimate  purpose of this review  is to determine which of
these studies can be used to  estimate some  of the economic benefits
resulting  from decreases  in  the ambient level of particulate matter.   The
studies  used in this section meet the following specific  criteria:

     •    Each study uses a properly  specified hedonic  model  and
          attempts to include as many of the characteristics of the
          residential  property as possible.
     •    The  relationship observed between particulate matter and
          residential property value is plausible in terms of the
          underlying theoretical construct.
     •    The  results can be used  to estimate  the benefits of parti-
          culate matter reductions.
                                   5-14

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                                        h, (air quality)
Figure 5-2.  Marginal implicit price schedule and
             demand  price functions.
                          5.15

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The next subsection will contain a review of the pertinent residential
property value  studies.  This will be  followed by  a  subsection  discussing
the limitations of the hedonic  price  technique.  The  next  subsection will
contain the methods used to calculate the benefits of reductions in the
ambient level  of particulate matter.  It will also contain the benefits
under alternative particulate matter standards.  Finally, the  section will
be ended with conclusions.
LITERATURE
     As previously mentioned,  all  of  the  studies that will be reviewed in
this section examine the relationship between residential property values
and some measure of particulate matter.  The measure of  particulate matter
most commonly used in these studies are sulfates (SO^), total suspended
particulates (TSP),  and dust fall.  Consequently,  studies including any of
these pollutants  will be examined.

     The first  study  undertaken  to  measure the  relationship  between
property values and the level of  air quality was done by Ridker  and Henning
(12).   Using 1960 cross-sectional census  tract data from the St.  Louis
metropolitan area,  the effect of air pollution levels  on property values
was estimated using  regression  analysis.   The dependent variable was the
median value (estimated by owner)  of  owner— occupied  single— family housing
units, and  the independent variables included those reflecting location
characteristics (e.g.,  accessibility to highway,  travel time to central
business district), property characteristics  (e.g., median number  of rooms,
houses  per  mile), neighborhood characteristics  (e.g.,  school quality,
persons  per housing unit),  median family income,  and an  air pollution
variable (an index  indicating the presence of S02,  SOg,  HjS, H2S04, and in
some cases  dustfall).  Different  linear  specifications were  tried  and a
significant  negative relationship was  found between the  dependent  variable
and the air pollution variable.  From these results, they concluded that
property values  could be expected to rise  at  least $83.00 and more  probably
                                   5-16

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$245,  if the measurement of SOj were to drop by 0.25  (ig/100 cm  /day.*  The
                                          •
elasticity  of the  pollution variable  could not  be computed from  the
information contained in the  study.**
     Zerbe  (19)  estimated  a property value  equation  for  Toronto  and
Hamilton,  Ontario.  The annual averages  of  sulfur dioxide and dustfall  were
the two pollution measures used.  Both linear and log-linear specifications
were employed; in the log-linear  specification, the elasticity of property
values with respect to sulfur  dioxide ranged from -0.061 to -0.121  for
Toronto and -0.081 for Hamilton."1"

     Crocker (20), in a  study of  the relationship  between home sale price
and the annual arithmetic  means for sulfur dioxide and total suspended
particulates in  Chicago,   found  a consistently significant negative
relationship between sale price  and particulate matter.  The elasticity of
home sale price  with respect to particulate  matter ranged from -0.25 to
-0.83 and  was generally  significant.  The coefficient  of SC^ was inconsis-
tently  signed and sometimes  insignificant.   However,  when SOj was  entered
separately into  the property value  equation,  it was generally negative  and
significant.

     Anderson and Crocker (25) estimated the relationship between  air
pollution  and median  property values (estimated by  owner)  for  three cities:
St. Louis,  Washington, D.C.,  and  Kansas  City.   Using separate equations  for
 * Freeman  (13) concluded that Ridker  and Henning's results were  over-
   interpreted  and  could not be used to  predict changes in property values
   when air quality changed because the demand curve for air quality had
   not been  identified.  This led to quite a debate in the literature over
   the  proper  interpretation  of the  derivative  of  the  air  quality
   variables.  [See Anderson and Crocker (14); Freeman (15);  Polinsky and
   Rubinfeld (16);  Small  (17); and Harrison and Rubinfeld (18).]
** In this section, the elasticity measures the percentage change in resi-
   dential property value  that  can  be  expected from a 1 percent change in
   the pollution variable.  In a  log-linear property value equation, the
   elasticity  is  equal  to  the coefficient of the pollution variable.
 + Information  on the  studies by  Zerbe (19), and Steele (21)  is taken from
   Freeman (22),  Waddell  (23), and  Appel (24).
                                    5-17

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owner-occupied and renter-occupied housing,  they found  a  significant
negative  relationship between the annual  arithmetic  means  of air pollution
and property values while- controlling for median family income,  percentage
of old units,  percent  of run-down units,  percent  of non-white population,
distance  to the  central city, and  median number  of  rooms.   The annual
arithmetic means of sulfur oxides  and  suspended particulates were the
pollution variables  considered in this study.   Using a  log-linear specifi-
cation,  they found that the  elasticity of sulfur  oxides  ranged  from -0.07
to -0.12,  while the elasticity of total  suspended  particulates ranged from
-0.06  to -0.17.  It  is  interesting to note that by using plausible interest
rates  (e.g., 8 to  12 percent),  the results for  the  owner  and renter
equations were roughly consistent.  This indicates  that the marginal
willingness to pay for  air  quality  improvements  estimated  for  households
living  in owner-occupied  residences  may be  similar  to the  marginal
willingness to pay of  households that rent their residences.

     Steele (21) did not find  a  significant relationship between property
values, as measured by mean value per room,  and SO* and particulates for
residences in Charlestown, South  Carolina.   However,  the relationship
between property values and pollution was plausibly  signed.

     Wieand (26) regressed per-acre housing  expenditures in St. Louis  (a
proxy for land values) on  property characteristics, neighborhood charac-
teristics, income,  and pollution  as  measured  by annual mean sulfation and
annual mean particulates.  Neither pollution variable was significant.

     Deyak and  Smith (27),  using  a log-linear  specification,  found a signi-
ficant relationship between  median property values of representative SMSAs
and total  suspended particulates.  Other  variables  included in  their best
equation were median family income  and  percent of  inferior housing units.
The elasticity  of  particulate matter was  quite  consistent and ranged from
-0.083 to  -0.088.   In  a later study on  the  owner- and renter-occupied
housing market for 85 cities which included  measures of local public
services  and taxes, Smith-Deyak (28) did not find a significant negative
relationship between air pollution and property  value.  Subsequent analysis
                                   5-18

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by Deyak showed that if cities with relatively low levels  of air pollution
                                                                *
were excluded from the analysis,  there was a significant  negative relation-
ship between air pollution and property value.

     Using the  data from Anderson  and Crocker's St.  Louis  study (25),
Polinsky and Rubinfeld (29) empirically estimated the equilibrium housing
market function developed by Polinsky and Shave11 (9) using a Cobb-Douglas
form of utility function.  Log-linear equations were developed for both
owned  and rented properties.   The  annual  arithmetic mean  of TSP was
negative and significantly different from zero at the 0.05 level for both
equations,  while the annual arithmetic  mean  of the sulfation variable was
negative and significantly different from zero at  the  0.10 level for the
homeowner  equation.   In the  owner—occupied property value equations,  the
elasticity of TSP was equal to -0.132 and the elasticity of sulfates was
equal to -0.063.

     Nelson (8)  also found a significant relationship between air pollution
and median census  tract  owner—occupied property values  in  Washington, D.C.
Several different  specifications were employed with the semi-log and log-
linear forms giving  the best  results.  It was concluded that an increase  of
10 ug/m  in the average monthly geometric mean from February to July of
total suspended particulates would  reduce  the mean value of property  by
$576 to $693.   In  the  most representative equations, the elasticity of  TSP
ranged from -0.078 to -0.116.   The estimated marginal willingness to pay
was then used to calculate a bid price function (i.e.,  an inverse demand
curve for air quality).  The  price elasticity of demand  for TSP in this
equation ranged from -1.2 to -1.4, indicating that  the  implicit price  of
TSP is quite responsive  to changes  in the level of TSP.  The  bid  price
function  estimated by Nelson,  however,  is only  a  first  attempt  at
estimating the demand curve for  air  quality.  In fact,  Palmquist (10)
suggests that unless some  rather stringent assumptions are met,  it is  not
possible to identify the demand  curve for air quality  using  the  data from
one city.   Consequently,  the true bid price function is probably more
complicated than the one estimated by Nelson.
                                   5-19

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     In a study  of  single-family owned residences in the Los Angeles  area,
Brookshire e_t .al.. (30) found a significant negative relationship between
the sale price of homes  and air pollution measures as  measured by nitrogen
dioxide (N0~) and total  suspended particulates.   Actual market transactions
          2t
for individual homes were used as the unit  of observation.  Both  the linear
and nonlinear specifications  employed showed  a significant  relationship
between home sale price  and pollution.  The average sale  price differential
attributable to a change in the level of pollution from "poor" to "fair"
ranged from $5,793  per home to $6,134 per home.  Elasticities could not be
calculated from  the information  contained in the study.   Brookshire .et,  al.
also estimated  an  inverse demand function for air quality,  but used NC^ as
the air quality  measure.

     In a study of Philadelphia property values, Peckham (31) found a
negative  relationship between 1969 air pollution levels  as measured by
particulates and sulfates,  and 1960  owner-occupied property values.  In the
log-linear equation,  the elasticity of  the sulfate variable was -0.096,
while  the elasticity of  the particulate variable was -0.116.

     Spore (32)  analyzed the  effect  of  air  pollution on  property  values in
Pittsburgh.   Sulfation and  dustfall  were the two pollution  variables
included in the analysis.  A significant  negative relationship between
property values  and air pollution was generally  exhibited in the  log-linear
equations that  were  estimated.   The  elasticity of  dustfall  in  these
equations ranged from -0.092  to  -0.149.

     The concentration of nitrogen  oxides (used as a proxy  fox  air pollu-
tion)  was found to be  negatively  related  to  median property  values in
Boston by Harrison  and Rubinfeld (18).   Besides air pollution, the housing
characteristics  that  were  included  in  the  equation were:   two  structural
variables,  eight neighborhood variables,  and  two accessibility  variables.
With  all the  independent variables at their mean levels, a  change in
nitrogen oxides (NO^) of 1 pphm was associated  with a change  in median
housing values  of  $1,613.  When the variable  for NOX  was replaced by a
variable  measuring particulate matter, particulate matter exhibited a
                                   5-20

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negative  and significant relationship with property value.  Evaluated at
the mean levels of the  variables, the elasticity of particulate  matter  was
-34.24.  No  explanation was given for the large  magnitude of this number.

     In a study of  the New York metropolitan area,  Appel (24) found a
significant negative relationship between the annual geometric mean of
total suspended particulates and mean property  values.   No other pollution
variables  were included in the study.  The  hedonic equation that performed
best was one  in which  the TSP variable  was entered in exponential  form.
This form conforms  to  .a  priori  expectations that  the  marginal  damages  of
pollution increase as pollution increases.  Evaluated at the mean,  the
elasticity  of TSP was equal to -0.039.   Other variables  included  in  the
best equation were the  mean number of rooms, the crime  rate, the percent of
non-white persons,  and minutes  of  time to the  central business district.
It  is  interesting  to  note  that when the  property value  equation was
estimated in  log—linear form,  the  elasticity of  the TSP variable was  equal
to -0.117.  This elasticity was not significant,  however.

     A summary of  the studies that have  found  a  significant negative
relationship between residential property values  and some measure of  parti-
culate matter is given in Table  5-2.

LIMITATIONS  OP THE HEDONIC TECHNIQUE

     Before  preceeding  with the calculation of benefits, it is important to
reiterate the limitations of using the hedonic technique and  the effect
these limitations have on the ability to predict the benefits  of improve-
ments in air  quality.  The hedonic  technique is capable of estimating  the
implicit price of  the  characteristics of a good that the  consumer is able
to perceive  accurately.   Since most characteristics of a good are tangible
and easily  perceived,  this  is not unreasonable.  Air quality, per se. is
not a tangible characteristic of housing  and it  is  possible that households
are unable to accurately perceive the effect of  air  quality on their resi-
dential property.   Even if households are cognizant of  some of  the effects
of air quality,  it  is  doubtful that they will be aware of  all of  its
                                   5-21

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                                                         Table 5-2



                                             REVIEW OF PROPERTY VALUE STUDIES
i
ro
Study
Ridker «
Henning
(12)
Zerbe
(19)
Crocker
(20)
Peckham
(31)
Anderson i
Crocker
(14)
Spore
(32)
Pollnsky «
Rublnfeld
(29)
City!
Dependent Variable
St. Louis i HPVa
Toronto and
Haul 1 ton, Canada |
HPV
Chicago | hone value
given by sale price
Philadelphia! HPV
Washington, DC, St.
Louis and Kansas
Cltyi HPV, median
gross rent and
median contract rent
Pittsburgh) HPV
St. Louis | HPV,
median gross rent
and median contract
rent
Pollutants Measured,
Hethod of Measurement
Index of sulfatlon
Annual average and median of
averages for sulfatlon (lead
candle) In both cities.
Annual average and median of
averages for dustfall In
Toronto .
Annual arithmetic mean for
sulfatlon (lead candle) and
participates
One-month average for sulfa-
tlon and arithmetic mean for
partlculates
Annual arithmetic mean for
concentrations of SO
(measured by lead caftdle)
and partlculates
Annual geometric means and
maximum monthly values for
sulfatlon (lead candle) and
dustfall
Same as Anderson i Crocker
(14)
Form
Linear
Linear and
"log-linear
Log-linear
Linear and
log-linear
Log-linear
Log-linear
Log-linear
Elasticities of Pollution
Variables (At Heans)
Not Available11
Log-linear equation:
Sulfatlont
Toronto -0.061 to -0.121
Hamilton -0.081
Equation with both
pollutants!
Partlculates:
-0.2 to -0.5
Sul ration i 0.06°
Log-linear equation:
Sulfatlon: -0.096
Partlculates: -0.116
Owner-occupied equation:
Sulf. Part.
D.C. -0.07 -0.06C
K.C. -0.08 -0.09^
S.L. -0.10 -0.12d
Sulfatlon: 0.03d
Dustfalli -0.12
Owner-occupied equation:
Sulf at Ion i -0.063°
Partlculatesi -0.132
Estimated Benefits;
Base Year
If sulfatlon falls by 0.25 /tg/100 cmZ/day,
MPV Increases by $186.50 to $245.00; 1960
At the mean, a decrease of 1 mg SO^/
100 cm /day would Increase mean property
value by a maximum of $97 In Toronto; 1961
At the mean, a reduction of 10 fig/m In
partlculates and 1 ppb of SO would lead
to a $350 to $600 Increase in the mean
value of property! 1964-1967
At the mean, a decrease in SO of 0.1 mg/
100 cm /day and a 10 /ig/ra /day In
suspended partlculates leads to an
Increase In the mean property value of
$600 to $750| 1960
At the mean, a reduction of 1 mg SO,/
100 cm /day and a 10 pg/ni /day In
suspended partlculates would Increase mean
property value of owner-occupied housing
by $300 to $700 In Washington, DC) 1960
A reduction of 0.005 ppm/day In SO^ and
5 tons/ml /month In dustfall Increases the
value of mean property by $150 to $200;
1970
A 5* reduction In sulfatlon i partlculate
levels In all areas of St. Louis would
lead to a predicted change In aggregate
property values of $55 million; 1960
(continued)

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                                                                         Table  5-2  (Continued)
Ul
i
Study
Deyak &
Smith (27)
Nelson
(8)
Brookshlre
et al.
(30)
Appel
(24)
Harrison 4
Rubinfeld
(18)
City,
Dependent Variable
SHSAj HPV
Washington, OC| MPV
South Coast Air
Basin In California!
sale price of
Individual houses
New York Metropoli-
tan Area; mean
property value of
single family owner-
occupied housing
Boston) MPV
Pollutants Measured,
Method of Measurement
Annual arithmetic average of
total suspended partlculates
Monthly geometric mean of
partlculates and arithmetic
average of means of oxldant
levels
Arithmetic average for
nitrogen dioxide and
partlculates
Geometric mean of suspended
partlculates
Mean concentrations for'
nitrogen oxides and partl-
culates calculated by a
dispersion model
Form
Log-linear
Log-linear
Linear and
semi -log
exponential
Exponential
Exponential
semi -log
Elasticities of Pollution
Variables (At Means)
Partlculates: -0.083
Partlculates:
-0.078 to -0.116
Oxldants:
-0.007 to -0.019
Not Available
Partlculates: -0.039
In separate equations —
Nitrogen oxldei -0.39
Partlculatesi -34.24
Estimated Benefits;
Base Year
Not reported
At the mean, a decrease In partlculates of
10 /ig/m Increases the value of mean
property by $576 to $693. At the mean, a
decrease In oxldants of 0.001 ppm
Increases the value of mean property by
$141 to $152 | 1970
At the mean, a decrease of 1 pphm In
nitrogen dioxide would result In an
Increase In the mean sale price of housing
of $2,010; 1977-1978
The average benefit (weighted by the
number of households exposed to alterna-
tive pollution levels) for a 1 /ig/n
reduction In suspended partlculates Is
$42.13; 1970
Mean value of property would Increase by
$1,613 If the oxldant concentration
decreased by 0.01 ppm; 1970
                  MPV Is median property value  of single-family  owner-occupied housing units in a census  tract.


                  The mean values of pollution  and property values were not reported; consequently,  the elasticities could not be estimated.


                  Not significantly different from zero at 0.05  level.
                  Not  significantly different  from zero at 0.01  level.

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effects.   Consequently, all  of  the effects  of  air quality  may not be
capitalized into  residential property values.   Application of  the  hedonic
technique  in order to estimate the effects  of  air pollution may therefore
result in an underestimate  of  the "true  benefits"  accruing to  residential
properties.   Although there  has  been some criticism that  the hedonic
technique  is invalid for predicting the benefits of air quality  improvement
because households  are  unable  to accurately perceive  any of the effects of
air pollution, the studies in  Table 5-2  appear to support the hypothesis
that households perceive at least some of the effects of  air pollution and
these effects  are  capitalized  into property  values.

     Benefits  estimated through hedonic  property value equations may only
provide estimates of  the perceived  benefits that  occur at the  residential
property.   Some  of the benefits from air quality  improvements  that occur
away from home (e.g.,  at the  workplace, recreational areas)  may not be
capitalized into residential property values.   Since a portion of the
household's time is  spent away  from home, it is possible  that only a
portion of the .total perceived benefits accruing to a household may be
predicted from the hedonic property value equations.  This must  be  kept in
mind when comparing benefits estimated by the hedonic technique to benefits
estimated by other methods.

     As mentioned in  the last  subsection,  the  assumptions  that  are
necessary in order to use the  hedonic equation to estimate the marginal
willingness to pay for  air quality improvements  are:

     •    Individuals must be  able to perceive the characteristics
          and  attributes of housing.
     •    The housing market must be in equilibrium.
     •    A complete  range of houses with alternative characteristics
          must be available.

     It is very unlikely that these  conditions will hold in the housing
market.  In this  study,  we  are mainly concerned with how  the  violation of
these assumptions will  affect  the  estimated air  pollution-property value
                                   5-24

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relationship.  The violation of  the first of these three assumptions has
already been addressed  in the  discussion on the difficulty of applying the
hedonic  technique  to  a  good possessing  a  characteristic such  as  air
quality.

     In order for the second assumption to be  met in the housing market,
households  must  be just  willing to hold the existing stock of  housing at
the prevailing prices.   Equilibrium will be  achieved only  if:   1)  all
households have complete  information on the prices and characteristics of
housing,  2)  transactions  and moving costs are  equal to zero,  and 3) housing
prices adjust instantaneously  to  changes in demand and supply.   According
to Freeman  (33),  divergencies  from equilibrium,  in most  cases,  will only
result in random  errors in marginal willingness-to-pay estimates.   Freeman
mentions, however, that  less  than  instantaneous adjustment in the housing
market to changes in demand or supply may result in biased estimates of the
air quality variable.   For example, if equilibrium is disrupted due to an
air quality change,  and transactions and moving costs  are non-zero,  house-
holds will  not move  unless the benefit is at least as great as the costs
involved in moving.   If air quality is consistently changing in one direc-
tion and  households consistently lag in their  adjustment  to that  change,
the observed marginal implicit price will  diverge  from  the  true  marginal
willingness to pay.  In this case,  the marginal implicit price  of  air
quality  identified in the hedonic property value  equation is  a  biased
estimate of the equilibrium marginal willingness to pay.

     Freeman also mentions that future  expectations  on housing  prices  may
result in biased  estimates of  the implicit price of air quality.  If house-
holds perceive that an improvement in air quality will take place in the
future and housing prices are affected by that perception,  the  market has
adjusted to the air  quality change  before  the change  actually takes  place.
If a hedonic price equation is specified for  the  housing market in an area
that has  already adjusted to a future  air quality change, the marginal
willingness to pay  for air quality would  be  underestimated.   This possi-
bility can be  tested by entering the pollution variables in  lagged form.
                                   5-25

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     It is quite  possible  that the third assumption may be violated due to
the nature of  the housing market.   Given the time necessary for the supply
of housing to adjust to changes in demand,  it is likely that some house-
holds will not be able to find housing with  all of the characteristics that
the household finds desirable.   In these households,  utility cannot be
maximized.  Whether this  is a problem that  will seriously affect  the
estimates of the  marginal  implicit price of air quality has not been inves-.
tigated at the present  time.   It is doubtful, however,  that  the  existence
of an incomplete  range  of  homes  in the  study area  will  make  the  estimated
relationship between air pollution and property values totally unreliable.

     Segmentation in the housing  market can also affect the  estimates of
the marginal implicit prices of housing  attributes.   Housing market segmen-
tation exists when the purchasers of housing participate in distinctly
separate housing submarkets even though the purchasers  are technically
participating  in  the same housing  market.  The submarkets may exist because
of racial discrimination,  cultural differences,  or geographic immobility.
Where housing market segmentation exists,   the structure of  the  prices of
housing  in each submarket will be different.   The specification of a
hedonic price function for one housing  market when submarkets exist will
result in  incorrect estimates of  the  marginal implicit prices of housing
attributes.  In order for the implicit prices of housing characteristics to
be correctly estimated, separate  equations for each submarket must be used.
Nelson (34) did not find that stratified samples for the Washington,  D.C.
area affected  the hedonic  price functions.   Further research  investigating
this  problem  is needed  before  anything  more conclusive  can be said
regarding the  effect of market segmentation on the  hedonic  price functions.

BENEFIT ESTIMATION

     The  studies reviewed in the Literature Review subsection provide
estimates of the  willingness  to pay for air quality improvements.  All of
these studies  have  included some form  of particulate  matter  in estimating
hedonic property value equations.  The  standards  under consideration in
this report are stated in terms of  particulate matter with a diameter of 10
                                   5-26

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|im (PH10) and total suspended particnlates  (TSP).   Since  it  is  assumed  that
PM10  is  equal to  approximately 0.55  of TSP  (see  Section 9),  only the
studies that specifically include TSP will  be considered in estimating the
benefits  accruing to  households as a  result of  these standards.

     Because of the relatively large  number of property  value  studies  that
have  included TSP as an explanatory variable, it was  possible to limit
further the selected  studies  to those that have included  at  least one other
pollutant besides TSP.  Limiting the selected  studies in  this way minimizes
the possibility that  particulate  matter is  proxying  for  the effects of the
general air pollution phenomenon.  Table 5-3 lists  the  studies  that will be
used for estimating benefits.

     For the purposes of this analysis,  it is assumed that the results of
these studies are representative of the relationship between  particulate
matter and property values in the counties  examined  in this study,  and  that
these results can be  used  to estimate the benefits  of achieving alternative
particulate matter standards.  It should be  noted that  there  are a'number
of reasons why a  strict comparison of  the  results of these studies is not
possible.   These  reasons can be  explained  by  referring to Table  5-2.
Although the majority of  these studies use  the median property value  of  a
census tract as  the  dependent variable in their  equations,  Crocker  (20)
uses the  sale price of individual homes.  Most studies have  concentrated on
                                Table 5-3
             STUDIES CONSIDERED  IN ESTIMATING THE BENEFITS OF
                       PARTICULATE MATTER REDUCTIONS
                        Crocker  (20)
                        Peckham  (31)
                        Anderson and Crocker (14)
                        Polinsky and Rubinfeld (29)
                        Nelson (8)
                                    5-27

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only the owner-occupied housing market,  while some  studies have also
estimated equations  for  the rental  market using median gross rent and
median contract  rent as dependent variables.

     The  results of the studies in Table 5-3  are based on data collected in
different years (e.g., 1960 and 1970 Census data).  This  is not likely to
be a problem in  comparing the studies because the demand for air quality
probably has  not  changed significantly over  the  years in  which these
studies were done.  However, the benefits estimated by the  studies using
data from different  years are obviously not comparable because of the
differences in property values due to  increases  in the price  level.  For
this reason, the comparison of the results  of the property value studies
will  be  based  on the estimated elasticities of  the TSP variables.

     As can  be  seen  in  Table  5-2,  all  of the studies examining  the
relationship between TSP  and residential property values  have  used a non-
linear functional form to estimate this  relationship.  The choice of a
nonlinear specification in  these studies can be viewed  as  being  twofold.
The hedonic  equation need not be linear if costless  repackaging of the
characteristics of the good is impossible.  In the housing market, it is
unlikely that  costless  repackaging  of the  characteristics of housing will
be common.  For example, two rooms  with four sides are  not equal to one
room with eight  sides.  In addition,  the hedonic equation may be nonlinear
in the air quality variable depending on the assumptions regarding the
marginal implicit  price of  air quality.  If  the hedonic equation is linear
in the air quality variable, its marginal  implicit price  is  constant over
the  entire  range  of  air quality.   Since  there is no variation in the
implicit price  of air quality,  it is not possible to identify the demand
for air quality.  In those hedonic studies  where both linear and nonlinear
specifications have been  tried  in order to measure  the implicit price of
air quality within a  specific  geographic area (e.g., SMSA), the nonlinear
specifications have given  more  satisfactory results.

     All of  the studies  listed in Table 5-3  have  used  a  log-linear
functional  form  to specify  a nonlinear relationship between  air pollution
                                  5-28

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and property  values.   A significant negative  relationship between air
pollution  and property values has been found in all  of  these studies.
Unlike the  marginal  implicit  price of air pollution in a linear  specifica-
tion,  the marginal implicit price in these specifications varies depending
on the  ratio  of the  property value to  the  pollution level.*  Of  more
interest, however, is the rate of change of  the marginal  implicit price
schedule.   It  is positive  for the  log-linear specification, implying that
the marginal implicit  price  (a negative)  is an  increasing  function of the
level of pollution.   Since pollution is something to be avoided (i.e., a
disamenity),  this means that the marginal  willingness to pay to avoid
pollution becomes less negative as  pollution increases; in other  words, the
marginal willingness to pay to avoid pollution is lower as the level of
pollution rises.  Intuitively, one would  expect that  the higher the level
of pollution,  the greater  the  marginal willingness to pay for  an improve-
ment  in air quality.

     Since the hedonic  technique examines the equilibrium relationship
between property values and air pollution, it is not  clear whether the
positive slope of  the  marginal implicit  price curve results from the fact
that people living  in  relatively  clean  air environments may  tend  to have
larger incomes,  and  consequently a higher equilibrium  marginal willingness
to pay for  additional air quality improvements,  than poorer people  who may
tend  to  live  in relatively dirty air environments.  Not enough empirical
research has been done  in this area, however,  to  justify  the hypothesis
that the marginal implicit price curve is negatively sloped.

     As  mentioned  in the Methodology  subsection,   the  hedonic  price
technique  yields  only the equilibrium willingness  to pay for marginal
improvements in air  quality.   The changes in  particulate matter  considered
in this analysis, however, will involve non-marginal changes.  In order to
accurately estimate the benefits  of these  changes  in air quality,  the
demand price  function of  consumers for air  quality must be known.  This
* The first derivative  of  the  log-linear specification,  (Property Value)
  a(Pollution)b, is b(Property Value/Pollution).
                                   5-29

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function  can  be  calculated using  the  implicit prices estimated by the
hedonic technique,  and  information  on air  quality  levels,  consumer income
and characteristics.   Nelson (8)  is the only researcher to specifically
estimate  a  demand price function  for  TSP.   However, Palmquist (10) has
shown  that  without  some rather limiting  assumptions,  the  inverse demand
function for TSP  cannot  be  identified based on data from one city.   Conse-
quently,  the benefits estimated in this  section will be  based solely on the
information yielded by  the  first stage  of the hedonic  price  technique.

     As previously mentioned,  the benefits of  achieving alternative parti-
culate matter standards will be  approximated using the results of the
studies  listed  in  Table  5-3.  Table  5-4 provides  a  comparison of the
elasticities  evaluated at  the mean TSP  level  projected under baseline
conditions  (i.e., without  implementation of particulate matter  standards)
and the mean  1980 residential property  value  for those  counties  that will
be used to calculate the benefits of particulate matter  reductions.  As can
be seen in  the table, the elasticities  range  from  -0.048 in Nelson's  study
to 0.5 in  Crocker's  analysis.  As just mentioned,  Nelson used measures of
the monthly mean of particulates from February to July in order to estimate
                                Table 5-4
              COMPARISON OF TSP ELASTICITIES CALCULATED  FROM
                    RESIDENTIAL PROPERTY VALUE STUDIES
                       Study
     Range
            Crocker (20)
            Peckham (31)
            Anderson and  Crocker  (14)
            Polinsky and  Rubinfeld  (29)
            Nelson (8)
-0.2   to -0.5
     -0.116
-0.06  to -0.12
     -0.132
-0.048 to -0.116
                                    5-30

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the relationship  between residential  property values and TSP.  Since this
averaging period may not represent  the annual  average of TSP,  Nelson's
estimates  will not be used  in  calculating the benefits  of particulate
matter reductions.  Except for Crocker, none of the  remaining studies being
considered  have  elasticities  in  excess of -0.20.  For  the purposes of this
study,  -0.20  will be used 'as  the  maximum  elasticity.    The  minimum
elasticity  that will be used in the calculation of benefits  is based on the
Anderson and Crocker  study,  and is equal to -0.06.  Since three studies
report elasticities in the range of -0.116  to  -0.132,  -0.12  will be used as
the point  elasticity in  calculating benefits.

     As previously mentioned,  the  range  of elasticities that will be used
to calculate the  benefits  of particulate matter  reductions is  based on
studies that estimate  a log-linear property value  equation.  The  marginal
implicit price  curve   for air  quality for  the  log-linear specification is
shown in Figure  5-3.  MPV'(P) is a plot of the derivative of the housing
equation with respect  to air  pollution (i.e., TSP) and also  traces out the
equilibrium willingness to pay to avoid air pollution. D(P) is the true
demand curve for air  pollution.   Since  air pollution is a "bad", a large
negative price  implies that  the  household  is  willing to pay large amounts
to avoid air pollution.  Note  that the marginal implicit price curve
resulting  from the log-linear  specification implies  that the  marginal
implicit price  of air  pollution increases (becomes less negative)  as pollu-
tion  increases.

     The benefits  of an  improvement  in air quality can be estimated by the
area  under  the demand  curve over  the  range of  improvement.   For the
improvement in  air quality  from  PQ to P^ that is shown  in  Figure 5-3, the
benefits are estimated  by  the  area APQP^C.  The demand curve for air
quality has  not been estimated in  these studies,  however,  and it  is neces-
sary to rely on information  contained in the hedonic price equations to
approximate the benefits of an improvement in air quality.   The benefits of
an improvement  in air  quality from  PQ to P^ can be approximated by the area
under the  marginal implicit  price  curve, APgP^B.   It must  be kept in mind
that this  approximation  implies  that  all households' marginal willingness-
                                   5-31

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                                          Pollution
                                       MPV'(P)
                                     D(P)
-$
  Figure  5-3,
Alternative benefit  estimates for a
given change in air  quality.
                          5-32

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to-pay functions are identical and  increasing in pollution abatement.*
Clearly,  this  is  an overestimate of the true  benefits of improvement.

     Freeman (35)  has suggested  two alternative  ways  of approximating the
benefits  of non-marginal  changes  in air  quality  when the  demand  curve for
air  quality is not  known.   By assuming that  the household's  marginal
willingness to pay for air quality is constant  over the entire range of air
quality, household benefits can be approximated by the area APgP^E.   The
benefits  represented  by this area can be  estimated by:
          T,   j-• ^.      ^Property Value /.„ ,,  ^.   v                  /-,,.*
          Benefits  =  	* •,-,<.•	 (APollution)                  (5.15)
                         SPollution
     For the log-linear specification, this is  equivalent to:
          _   _..      . Property Value /.„,,,..>                 fe -,*\
          Benefits  =  b —„ ,, •_,	 (APollution)                 (5.16)
                          Pollution
where            b   =   the  estimated  coefficient of the pollution variable
                       in the hedonic equation.
If the  true  demand curve for air pollution is D(P),  this approximation
technique will result in an overestimate if the marginal implicit price
function is  increasing in pollution abatement.  However, this technique
clearly will  result  in a closer approximation of  true benefits estimated by
the area AP0P^B.

     The other alternative is consistent with the .a priori  expectation that
the marginal  willingness  to pay for air pollution abatement' declines as air
quality improves.   One  point  on  the household's demand curve  for  air
* The marginal  implicit  price  function  will necessarily be increasing  in
  pollution  abatement only  for log-linear specifications.  The  linear
  specification of the property value equation will  result in a constant
  marginal implicit price.  Exponential, semi-log exponential, quadratic,
  and the Box-Cox transformation may yield marginal  implicit price curves
  that are decreasing  in  pollution abatement.
                                   5-33

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quality is known from the hedonic price  equation.   By assuming that the
                                             •
household's marginal willingness  to pay  for  air  pollution  abatement
declines linearly  from  that  point to a zero marginal willingness to pay
when air pollution has been completely abated,  benefits can be approximated
for a given improvement  in air quality.  For the  reduction in air pollution
from PO to P,  shown in Figure 5-3, household benefits  can be approximated
by the area ^PQP^D.  The benefits represented by this area can be easily
calculated as the difference in triangle OPQA and OPj^D.  This area can be
approximated by:

          Benefits  =  \ [(PQA •  OPQ)  - (P^ • OP1>]                 (5.17)

     For the log-linear specification,  this is equivalent to:

                                          (Pollution^2
          Benefits  =  -r Property Value
1 -
                                          (PollutionQ)
                                                      2
(5.18)
where   PollutionQ  =  initial pollution level
        Pollution^  =  pollution level  after  an  air quality change.

     Depending on the shape of the  actual demand curve for air quality, the
approximation of benefits under  the assumption of  a linearly declining
marginal willingness  to  pay curve can result  in  either an underestimate or
overestimate of true  benefits.

     Obviously,  either of  these  alternatives  will  result  in  closer
estimates of the true benefits of a given  air quality improvement than the
benefits estimated by the area under the  marginal  implicit price curve of
the  log-linear hedonic property value  specification.   The  linearly
declining marginal  willingness-to-pay  alternative, however,  is  consistent
with the .a priori assumption that  the household's marginal willingness to
pay for air pollution abatement declines as air  quality improves. Since it
seems  reasonable to expect that the marginal willingness to pay for air
quality improvements  will be decreasing,  the benefits  of  alternative
                                   5-34

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particulate  matter standards will be calculated using the linear to the
origin technique.

     Household  benefits  in a particular  county  will be calculated  for the
single-family owner-occupied household with  median property  value  that  is
exposed  to  the average  level  of pollution within the county.  For the
purposes  of  this analysis, the benefits accruing to this household  will  be
taken as  representative  of the benefits accruing to households residing  in
rental and multiple-dwelling units.   This may  lead to  an  overestimate  or
underestimate of  benefits  if  the  willingness  to pay for air quality
improvements tends to be different for the  households residing in these
types of structures.

     All of property value studies being considered in this section  have
estimated the particulate matter exposure of census tracts  within a  city
through the  interpolation  or dispersion modeling of city data.  Since  these
measures of exposure are more  likely  to  represent  the  average of  the
ambient level of particulate matter  monitored throughout the city than the
worst incidence of pollution throughout the  city,  county benefits  will  be
calculated using  the  average  of  the  annual arithmetic means of particulate
matter from all of the monitors within a county.

     A list of the sources of data used in the  calculation of benefits is
given in Appendix  5A.

     The  alternative  standards being considered  in  this  analysis are listed
in Table 5-5.  As the table shows, the standards are stated in terms  of
both the annual and 24-hour averages.  For each county,  the averaging  time
that is most stringent is  considered to controlling averaging time.   Since
the studies  being used in the calculation of benefits  are  stated  in  terms
of annual averages,  the  24-hour average is converted to an equivalent
annual average  if  the 24—hour average is the  controlling averaging time.
                                   5-35

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                                Table 5-5
                 ALTERNATIVE PARTICULATE MATTER STANDARDS
                               (in jig/m )
Standard
1
2
3
4
5
6
Measure of
Particulate
Matter
PM10
PM10
PM10
PM10
TSP
TSP
Annual
Mean*
70
55
55
55
75
—
24-Hour
Expected
Value
250
—
250
150
260**
150**
Attainment
Date
1989
1989
1989
1989
1987
1987
 * PM10 standards are ^stated in terms of the annual arithmetic mean while
   TSP standards  are  stated in terms of the annual geometric mean.
** Maximum value  not  to be exceeded more than once a year.
     The benefits of achieving alternative particulate  matter  standards  are
listed in Tables 5-6 through 5-11.  These benefits represent  the  benefits
that would be achieved when all counties included in the analysis are in
compliance with the standard for all years under consideration.*

     Table 5-6  reports  the discounted present  value  of the  benefits
resulting from a PM10  standard of 70 (ig/m3 annual arithmetic mean (AAM)  and
a 250 (ig/m3  24-hour expected value (EV).   Under  this standard,  benefits  are
estimated to be  in the range of $3.4 to $11.4 billion and include a point
estimate of  $6.9  billion.  The South Pacific region is the region  with  the
* In the language of Section 9,  these benefits represent "Type  B"  scenario
  benefits.
                                   5-36

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                         Table 5-6

ESTIMATED BENEFITS FOR:  RESIDENTIAL PROPERTY VALUE STUDIES

          Benefits Occurring Between 1989 and 1995
         Scenario:  Type B PM10 - 70 AAM/250 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Total U.S.
                                    0.0
                                    6.5
                                  113.3
                                  135.4
                                 1025.2
                                  337.4
                                   35.3
                                  121.4
                                 1481.7
                                  171.1
3427.2
          Point
         Estimate
              0.0
             13.0
            226.5
            270.8
           2050.3
            674.8
             70.5
            242.9
           2963.3
            342.2
         Maximum
             0.0
            21.6
           377.5
           451.3
          3417.2
          1124.6
           117.5
           404.8
          4938.9
           570.4
6854.3   11423.9
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent  Rate  of  Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
1371.8
2743.6
4572.7
                             5-37

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                         Table 5-7

ESTIMATED BENEFITS FOR:   RESIDENTIAL PROPERTY VALUE STUDIES

          Benefits Occurring Between 1989 and 1995
              Scenario:   Type B PM10 - 55 AAM
Federal Administrative Region   Minimum
          Point
         Estimate
          Maximum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Total U.S.
                                   59.4
                                   69.2
                                  205.6
                                  303.4
                                 1392.2
                                  525.0
                                  103.5
                                  253.8
                                 2809.9
                                  236.3
5958.3
118.8
138.5
411.2
606.9
2784.4
1050.0
207.0
507.6
5619.8
472.6
197.9
230.8
685.3
1011.4
4640.6
1750.0
345.0
846.1
9366.3
787.6
11916.6   19861.1
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
2385.0
 4770.0
7949.9
                             5-38

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                         Table 5-8

ESTIMATED BENEFITS FOR:  RESIDENTIAL PROPERTY VALUE STUDIES

         .Benefits Occurring Between 1989 and 1995
         Scenario:  Type B PM10 - 55 AAM/250 24-hr.
Federal Administrative Region  Minimum
          Point
        Estimate
         Maximum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
59.4
69.2
205.6
303.4
1397.1
535.4
104.3
253.8
2810.0
259.8
118.8
138.5
- 411.2
606.9
2794.1
1070.8
208.5
507.6
5620.0
519.7
197.9
230.8
685.3
1011.4
4656.9
1784.6
347.5
846.1
9366.6
866.1
Total U.S.
5998.0   11996.0   19993.3
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate  of Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
2400.9
4801.7
8002.9
                             5-39

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                         Table 5-9

ESTIMATED BENEFITS FOR:   RESIDENTIAL PROPERTY VALUE STUDIES

          Benefits Occurring Between 1989 and 1995
         Scenario:  Type B PM10 - 55 AAM/150 24-hr.
Federal Administrative Region   Minimum
          Point
         Estimate
          Maximum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.I.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Total U.S.
                                  226.2
                                  146.2
                                  389.3
                                  397.4
                                 1637.5
                                  644.6
                                  152.2
                                  387.9
                                 3135.8
                                  515.0
7632.2
452.4
292.3
778.7
794.9
3274.9
1289.3
304.3
775.9
6271.6
1030.0
754.0
487.2
1297 . 8
1324.8
5458.2
2148.8
507.2
1293.1
10452.7
1716.7
15264.4   25440.6
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
3055.0
 6110.0   10183.3
                             5-40

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                         Table 5-10

ESTIMATED BENEFITS FOR:  RESIDENTIAL PROPERTY VALUE STUDIES

          Benefits Occurring Between 1987 and 1995
         Scenario:  Type B TSP - 75 AGM/260 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
I
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Total U.S.
                                  185.1
                                  156.0
                                  569.9
                                  630.7
                                 2616.9
                                  891.4
                                  278.5
                                  493.3
                                 4757.7
                                  599.9
11179.4
Point
Estimate
370.3
311.9
1139.7
1261.3
5233.8
1782.9
557.0
986.6
9515.5
1199.9

Maximum
617.1
519.9
1899.5
2102.2
8723.0
2971.4
928.4
1644.3
15859.1
1999.8
22358.8   37264.7
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount.
Annualized Benefits
Between 1987 and 1995
Total U.S.
 3126.3
 6252.6   10421.1
                             5-41

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                         Table 5-11

ESTIMATED BENEFITS FOR:  RESIDENTIAL PROPERTY VALUE STUDIES

          Benefits Occurring Between 1987 and 1995
             Scenario:  Type B TSP - 150 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                  736,
                                  357,
                                  996,
Total U.S.
                                  839.0
                                 3260.1
                                 1126.4
                                  528.8
                                  699.6
                                 5332.3
                                  992.8
14869.3
Point
Estimate
1473.5
714.0
1993.1
1677.9
6520.3
2252.7
1057.7
1399.3
10664.6
1985.5

Maximum
2455.8
1190.0
3321.8
2796.5
10867.2
3754.5
1762.8
2332.1
17774.3
3309.2
29738.5   49564.2
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate  of Discount.
Annualized Benefits
Between 1987 and 1995
Total U.S.
 4158.2
 8316.4   13860.6
                              5-42

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largest  benefits.   Approximately  43  percent  of  the total  benefits
associated with, the  point  estimate  of this  standard accrue to this region.
Another  30 percent of total  benefits accrue  to  the  East  North Central
region.  With  the exception of  these two regions, none of  the  remaining
regions receive benefits in excess of $1  billion.  In fact,  the New England
region receives no benefits under this standard since  all of  the  region  is
estimated to be in  compliance  with  this standard.

     A more stringent PM10  standard of  55 (ig/m  AAM that is reported  in
Table 5-7 results in benefits  estimates  ranging from $6.0 to  $19.9 billion.
This range includes  a point estimate of $11.9 billion.   Again,  the  majority
of benefits accrue  to the  South  Pacific (47 percent) and East North Central
(23 percent) regions.  With the exception of the South Central and South
Atlantic regions  which  receive about 9 percent  and  5 percent,  respectively,
of the total benefits, each of the  remaining regions receive less than 5
percent of national benefits.

     Table 5-8 reports the  benefits estimated to accrue under the PM10
standard of 55  (ig/m3 AAM  and 250 (ig/nr 24-hour EV.   As  shown in  the table,
benefits  range from $6.0 to $20.0  billion and include $12.0 billion as a
point  estimate.   The  regional ranking  of  benefits remains  relatively
unchanged from the  benefits  reported  in Table 5-7.

                                              3                 3
     Benefits under  the PM10 standard of 55 jig/m  AAM and 150  jig/m  24-hour
EV reported in Table  5-9 range from $7.6  to  $25.4 billion.   The point
estimate for this range is $15.3 billion.  Based on the  point  estimate, the
South Pacific,  East  North  Central, South  Central, and North Pacific regions
each receive benefits in excess of $1 billion under  this  standard.

     The benefits under a TSP standard of 75 fig/m3 AGM and 260 ng/m3 24-
hour maximum value  not  to  be exceeded more than  once a year are reported  in
Table 5-10.  With a point estimate of $22.4 billion,  these  benefits  range
from  $11.2  to  $37.3 billion.   Under  this  standard, point  estimates  of
benefits in excess  of $1 billion accrue  to each of the following regions:
                                   5-43

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South Pacific, East North Central,  South Central, South Atlantic, North
Pacific,  and Middle  Atlantic.

     The last TSP standard considered in this report reflects a 150 jig/m
24-hour maximum value.  As indicated by Table 5-11,  the pdint estimate of
benefits under this  standard  is $29.7 billion.  The minimum  and maximum of
the range  surrounding this  point estimate  are  $14.9  and  $49.6 billion,
respectively.  Under this standard, the point  estimate of benefits for each
of the regions except the New York-New Jersey region  is greater than $1
billion.

     Finally,  Table 5-12 shows the benefits that  accrue  under  a 70/250 PM10
primary standard  when all  counties are not in attainment with the standard
throughout the 1989-1995 time horizon.*  This can occur because available
control options are exhausted prior to standard attainment.  This table can
be compared to Table 5-6 where all counties  were assumed to be in compli-
ance  with the same 70/250  PM10 standard.   As expected,  the benefits
estimates in Table 5-6 exceed those shown in Table 5-12.

CONCLUSION

     In  this  analysis,  the discounted present value of  the benefits
associated with reducing particulate matter levels to comply with alterna-
tive particulate  matter standards has been estimated using the results of
past property value differential studies.  Table 5-13 provides a summary of
these estimates in discounted present value  terms.  These  estimates range
from $3.4 to $11.4 billion  under the most lenient PH10 standard of 70 |ig/m3
AAM  and 250  |ig/m3  to  $7.6  to  $25.4 billion under the  strictest  PM10
standard of 55 (ig/m3 AAM and  150  (ig/m3 24-hour EV.  The estimated benefits
under the TSP standard of 75  (ig/m3 AGM and 260 (ig/m3 24-hour maximum range
from $11.2 to $37.3 billion while the benefits under the TSP standard of
150  ng/m   24-hour  maximum range  from $14.9  to $49.6  billion.   These
* In the language  of Section 9, these are "Type A"  benefits.
                                    5-44

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                         Table 5-12

ESTIMATED BENEFITS FOR:  RESIDENTIAL PROPERTY VALUE STUDIES

          Benefits Occurring Between 1989 and 1995
         Scenario:  Type A.PM10 - 70 AAM/250 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Total U.S.
                                    0.0
                                    6.5
                                  109.4
                                  113.8
                                  738.1
                                  232.8
                                   31.5
                                  116.0
                                  859.3
                                   62.1
2269.5
          Point
         Estimate
              0.0
             13.0
            218.7
            227 ..6
           1476.3
            465.6
             63.1
            231.9
           1718.5
            124.2
4538.9
         Maximum
             0.0
            21.6
           364.5
           379.3
          2460.4
           776.0
           105,
           386.
          2864.2
           207.1
    .1
    ,5
7564.9
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate  of  Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
 908.4
1816.8
3028.1
                             5-45

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                              Table 5-13

     BENEFITS OF ATTAINING ALTERNATIVE PARTICDLATE MATTER STANDARDS*
                       (Billions  of 1980 Dollars)
Standard
PM10 - 70 (ig/m3
AAM and 250 (ig/m3
24-hour EV
PM10 - 55 fig/m3
AAM
PM10 - 55 jig/m3
AAM and 250 (ig/m3
24-hour EV
PM10 - 55 jig/iii3
AAM and 150 ng/m3
24-hour EV
TSP - 75 jig/m3
AGM and 260 fig/m3
24-hour MAX
TSP - 150 (ig/m3
24-hour MAX
Minimum
$ 3.4
6.0
6.0
7.6
11.2
14.9
Point Estimate
$ 6.9
11.9
12.0
15.3
22.4
29.7
Maximum
$11.4
19.9
20.0
25.4
37.3
*
49.6
* Benefits  are  stated in terms of the discounted  present value  in  1982 and
  assume a 10 percent discount rate.   Benefits for  the PM10  standards are
  accumulated from 1989 to 1995 while the benefits for the TSP  standards
  are accumulated  from  1987 to 1995.
estimates are approximations of the benefits of meeting these standards  for
basically four reasons:
     1)    The  benefits  reported in  this  section represent  the
          benefits associated  with perceived differences in  air
          quality.  The unperceived benefits associated with  air
          quality improvements are  not reflected  in this  section.
          Consequently, these  benefits are underestimates of  the
          total benefits of reductions  in the ambient level of parti-
          culate  matter.
                                   5-46

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     2)   The  results of studies  on  spec ific cities in the early
         1960's and  1970's  are  used  to estimate  the benefits  of
         pollution reductions occurring from 1987 to 1995 for the
         counties  included in this analysis.  The effect  of  this
         assumption is unknown.

     3)   The  assumptions of the hedonic technique are assumed to
         hold in the property value market.   The effect  of  this
         assumption is unknown.

     4)   The  benefits of the air  quality improvements are  approxi-
         mated without knowledge of the true demand  curve for air
         quality.   The effect of this assumption is unknown.

     5)   The  marginal willingness to pay for air quality improve-
         ments of  households residing in  single-family units  is
         assumed to be representative of the marginal willingness to
         pay  of all households.   The effect  of this assumption is
         unknown.

     6)   The  average of the air pollution readings in a county is
         taken as  representative of the level of exposure of  all
         households in the  county.   The  effect of this assumption is
         unknown.
These estimates  are useful, however,  because  they provide some idea  of the
magnitude of the willingness to pay for improvements in residential air
quality  associated with  implementing  alternative particulate  matter

standards.
 1.   Cropper, M. L. et al.  Methods Development  for Assessing Air Pollution
     Control Benefits:  Vol.  4 - Studies on Partial Equilibrium Approaches
     to Valuation of Environmental Amenities.   Prepared  for  the U.S.
     Environmental Protection Agency.  University of California, Riverside,
     California, September 1, 1978.

 2.   Court,  L. H.   Entrepreneurial  and  Consumer  Demand  Theories for
     Commodity  Spectra.  Econometrica, 9(1):135-162,  April 1941;  9(2):241-
     297,  July-October 1941.

 3.   Griliches. Z.  and S. Adelman.  On an Index  of  Quality Change.   Journal
     of the American Statistical  Association, 56(296):535-548. September
     1961.

 4.   Griliches, Z.  (ed).  Price Indexes  and  Quality  Change.  Harvard
     University Press, Cambridge,  Massachusetts, 1971.
                                  5-47

-------
 5.  Ohta,  M.  and  Z.  Griliches.   Makes  and Depreciation in  the U.S.
     Passenger  Car Market.  Mimeographed,  Harvard  University, 1972.

 6.  Karn,  J.  F.  and J.  M.  Quigley.  Measuring  the  Value  of Housing
     Quality.   Journal of the American Statistical Association, 65:532-548.
     May  1970.

 7.  Harrison, D. and D.  L. Rubinfeld.   Hedonic Housing  Prices and the
     Demand for Clean Air.  Journal of Environmental Economics and Manage-
     ment, 5(1):81-102.   March 1978.

 8.  Nelson,  J. P.   Residential Choice, Hedonic Prices,  and  the Demand for
     Urban Air Quality.   Journal of Urban Economics, 5(3):357-369.  July
     1978.

 9.  Polinsky, A.  M. and  S. Shavell.  Amenities and Property Values in a
     Model of An Urban Area.   Journal of  Public Economics,  5(1-2): 119-129.
     January—February 1976.

10.  Palmquist, R. B.  The Demand for Housing Characteristics:  Reconciling
     Theory and Estimation.  Unpublished paper,  North Carolina  State
     University, December  1981.

11.  Rosen, S.   Hedonic  Prices and  Implicit  Markets:  Product Differ-
     entiation in Perfect Competition.   Journal of Political  Economy,
     82(l):34-55.  January/February 1974.

12.  Ridker, R. 6.  and J. A.  Henning.  The Determinants of Residential
     Property Values with Special Reference to Air Pollution.  Review of
     Economics  and Statistics, 49(2):246-257.   May  1967.

13.  Freeman, A. M.  III.  Air Pollution and Property  Values:  A Methodo-
     logical Comment.  Review of Economics and  Statistics,  53(4):415-416.
     November  1971.

14.  Anderson,  R.  J., Jr.  and  T. D. Crocker.   Air Pollution and Property
     Values:  A Reply.  Review  of  Economics and Statistics,  54(4):470-473.
     November  1972.

15.  Freeman,  A. M.  III.   Air Pollution  and Property Values:   A Further
     Comment.   Review of Economics and Statistics,  56(4):554-556.  November
     1974.

16.  Polinsky,  A. M. and D. L.  Rubinfeld.   The  Air Pollution and Property
     Value Debate.  Review of Economics and  Statistics,   57(1):106-110.
     February 1975.

17.  Small, K.  A.  Air Pollution and Property Values:  Further  Comment.
     Review of  Economics  and  Statistics,   57(1):111-113.  February  1975.
                                   5-48

-------
18.   Harrison, D. and 0. L. Rubinfeld.   Hedonic Housing Prices and the
     Demand  for Clean Air.   Journal  of  Environmental Economics and Manage-
     ment,  5(1):81-102.   March 1978.

19.   Zerbe,  R.,  Jr.  The  Economics  of  Air Pollution:   A Cost  Benefit
     Approach.  Toronto,  Ontario  Dept.  of  Public Health,  1969.

20.   Crocker, T.  D.   Urban Air Pollution Damage Functions:  Theory and
     Measurement.   Prepared for U.S. Environmental Protection Agency,
     Office  of  Air  Programs.   University  of  California,  Riverside,
     California, June 15, 1971.

21.   Steele,  W.   The Effect  of Air  Pollution on the  Value of Single-Family
     Owner-Occupied Residential Property in Charleston, South Carolina.
     Masters Thesis, Clemson University,  1972.

22.   Freeman, A. M.  III.   The Benefits  of Environmental  Improvement:
     Theory and Practice.   Johns Hopkins  University Press, Baltimore,
     Maryland,  1979.

23.   Waddell,  T.  E.   The Economic Damages  of Air  Pollution.   U.S. Environ-
     mental  Protection Agency,  Office of Research and Development, Research
     Triangle Park, North Carolina,  May 1974.

24.   Appel,  D.  Estimating the Benefits of Air Quality Improvement:  An
     Hedonic Price Index Approach  Applied to the New York Metropolitan
 •    Area.   Unpublished Ph.D. dissertation, Rutgers University,  1980.

25.   Anderson, R. J., Jr. and T. D. Crocker.  Air Pollution and Residential
     Property  Values.  Urban Studies,  8(3):171-180.  October  1971.

26.   Wieand, K. F.  Air Pollution and Property Values:  A Study of the St.
     Louis Area.  Journal of Regional  Science,  13(l):91-95.   April  1973.

27.   Deyak,  T.  A. and V. E.  Smith.  Residential Property Values and Air
     Pollution:  Some New  Evidence.   Quarterly Review  of Economics and
     Business,  14:93-100.   Winter 1974.

28.   Smith, V. K. and T. A. Deyak. Measuring the  Impact  of Air  Pollution
     on Property Values.   Journal of Regional  Science,  15(3):277-288.
     December 1975.

29.   Polinsky, A. M. and D.  L.  Rubinfeld.   Property Values  and the Benefits
     of Environmental Improvements:  Theory and Measurement.   In:  Public
     Economics  and  the Quality of Life, Lowdon Wingo and Alan Evans  (eds),
     Johns Hopkins University Press for Resources for the Future and the
     Centre  for Environmental Studies,  Baltimore,  Maryland, 1977.
                                   5-49

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30.  Brookshire,  D.  S.  e_t al.  Methods Development for Assessing Tradeoffs
     in Environmental  Management.  Vol. 2:  Experiments in Valuing Non-
     Market Goods:   A  Case  Study  of Alternative Benefit Measures of Air
     Pollution Control  in the South Coast Air Basin of  Southern  California.
     Prepared  for the U.S.  Environmental Protection  Agency.  University of
     Wyoming,  Laramie,  Wyoming, September 1,  1978.

31.  Peckham,  B.   Air Pollution and  Residential  Property  Values  in
     Philadelphia.   (Mimeo)  1970.

32.  Spore,  E.  Property Value  Differentials as  a Measure of  the  Economic
     Costs of  Air Pollution. Pennsylvania State University,  Center for Air
     Environment  Studies, University Park, Pennsylvania,  1972.

33.  Freeman, A. M.  III.  Hedonic  Prices, Property Values  and  Measuring
     Environmental Benefits:  A Survey of the Issues.   Scandanavian Journal
     of Economics,  1979.  pp. 154-173.

34.  Nelson, J. P.  Economic Analysis of Transportation Noise Abatement.
     Cambridge,  Massachusetts, 1978.

35.  Freeman, A.  M.  III.  Estimating Air Pollution Control Benefits from
     Land Value Studies. Journal of Environmental Economics  and Manage-
     ment, l(l):74-83.  May  1974.

36.  Bureau of  the  Census.   Population and Households by States  and
     Counties:   1980.   PC80-S1-2.

37.  Bureau of the  Census.   1980 Census of  Housing:   Selected Housing
     Characteristics by States and Counties.  October 1981.  HC80-S1-1.
                                   5-50

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                                APPENDIX 5A
                              SOURCES OF DATA


Households

     Number of households in a county in 1980.

     Source:  Bureau of the Census (36).


Property Value

     Property value of  single—family owner—occupied houses  in  a  county in
1980.

     Source:  Bureau of the Census (37).
                                    5-51

-------
      SECTION 6
HEDONIC WAGE STUDIES

-------
                                SECTION 6
                          HEDONIC WAGE STUDIES
SUMMARY OF RESULTS

     In this section, estimates of individual willingness-to-pay for air
quality derived from hedonic wage studies  are used to calculate benefits of
alternative standards  for particulate matter.   Hedonic  wage  studies relate
observed  wage  differentials to various  explanatory  factors,  including
individual-specific characteristics (such as education or prior experi-
ence),  job-specific characteristics (such  as risk of  injury  or death),  and
site-specific  characteristics (such as climate or air  quality).   The wage
differential which is attributable to variations  in air quality can be
measured and used to  approximate willingness-to-pay  for air quality.

     Estimated  coefficients from selected hedonic wage  studies are used to
calculate a range  of benefits  resulting under alternative particulate
matter standards;  a point  estimate  of benefits is also generated  for each
standard.   These  results  are  summarized  in  Tables 6-1 and 6-2,  which
present benefits associated with selected primary and secondary  standards,
respectively.*  These  estimates  represent the  present  discounted  value in
1982 of the stream  of  benefits which accrue  from  the attainment  date (1989
for PM10  standards;  1987  for the TSP standards) through 1995, using 10
percent as the  social discount rate  and expressed in  1980 dollars.

     Point benefits estimates  range from $19.8  billion for the most lax
PM10 primary standard  to  $58.9 billion under  the current TSP  primary
* See Section 9 for a more complete description of  the  particulate matter
  standards  under  consideration.
                                   6-1

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                               Table 6-1

       SUMMARY OF ESTIMATED BENEFITS* FOR HEDONIC WAGE  STUDIES —
                           PRIMARY STANDARDS
Standard**
PM10 70 AAM/250 24-Hour
PM10 55 AAM/250 24-Hour
PM10 55 AAM/150 24-Hour
TSP 75 AGM/260 24-Hour
Minimum
9,810
16,611
20,263
29,166
Point
19,815
33,554
40,929
58,914
Maximum
37,208
61,453
72,598
107,138
                               Table 6-2

        SUMMARY OF ESTIMATED BENEFITS* FOR HEDONIC WAGE  STUDIES —
                          SECONDARY STANDARDS"1"
Standard**
PM10 55 AAM
TSP 150 24-Hour
Minimum
16,534
36,376
Point
33,398
73,477
Maximum
61,239
126,840
 * Discounted present value  in millions of 1980 dollars  in  1982.

** The PM10 standards will be attained  in 1989, while the TSP standards
   will be attained in 1987.  Benefits for all standards  are accumulated
   from the  attainment year through 1995.

 + These estimates  include benefits which accrue  as  current (baseline)
   particulate matter levels are  reduced  to a secondary standard.
                                   6-2

-------
standard.   If the  secondary standards  are also  included,  then point

benefits  estimates  will  range  from  $33.4 billion to  $73.5 billion.


     Under most  standards, better than  70 percent of all benefits accrue to

the East North Central,  South Central, and South Pacific regions.  As the

particulate matter  standard becomes  more stringent, there is  some redistri-

bution of benefits  towards  the New  England, Middle Atlantic, New York-New

Jersey,  South  Atlantic, Midwest and  Mountain  regions.


     These benefits estimates  should be interpreted carefully,  since

several  assumptions are required in order to adapt both the hedonic wage

models and the data to  the procedures used in calculating  these benefits.

In particular, the  following points  should be noted:
          The  procedure  used  in  extrapolating benefits from  hedonic
          wage models assumes that  the  marginal willingness to pay
          for  air quality declines  linearly as air  pollution is
          abated from  the baseline  observation to  zero.   To the
          extent that  the actual  relationship  between  marginal
          willingness to  pay and particulate matter diverges from
          this linear approximation, the true  benefits of abating
          ambient particulate matter will diverge  from the  estimates
          presented above.   The  direction of any divergence  cannot be
          predicted.

          The  hedonic wage models from  which  these  benefits were
          estimated did not include  any measures of air pollution
          other  than TSP as explanatory varibles.  Thus,  the
          estimated coefficient on the TSP measure may also  proxy the
          effects of omitted air pollutants.   To the extent  that this
          occurs, the benefits presented  above may overstate the true
          benefits.
     These and other  caveats are discussed more fully in the  conclusion to

this section.


INTRODUCTION


     In the previous  section,  differentials in residential  property values

were used to approximate  individual willingness-to-pay for air quality.
                                   6-3

-------
This approach may not accurately reflect the benefits which accrue from
improvements in air quality for the following  reasons:*

     •    Residential property-value differentials may not reflect
          the value placed on  clean air in  non-residential areas
          (e.g.. the work place).
     •    Property  value estimates  of  the  benefits  of  clean  air  may
          be unreliable since air quality may not vary significantly
          within a  'city.

     As an alternative,  wage  differentials  may  reflect  willingness-to-pay
for air quality, on the premise that,  all other things equal,  an  individual
will  require more compensation for working in more highly polluted areas.
Thus, wage  differentials  attributable to  air pollution can reflect the
economic value  placed on clean air in the  work place.**  In addition,  since
                                         »
it is  assumed that labor  is mobile  and will  relocate  in  response to
disequilibrium  differentials, an  inter—city estimate of willingness-to-pay
for  air  quality is appropriate.  This  approach  has  the  advantage of
allowing more variation in air pollution levels and thus produces more
stable estimates of willingness-to-pay for air quality.

     It should  be  noted that wage differentials will  reflect only the
perceived health  and  welfare  effects of  improved  air quality.   Any
unperceived health effects,  for example, which are attributable to declines
in ambient air pollution levels  will  not be  accounted for  in  hedonic wage
models.  Also,  real wage differentials may reflect regional  differentials
in both nominal wages and prices of  local goods (e.g.,  property values)
which are linked to variations in air quality.   Real wage differentials
may,  in  this event,  also  measure  at  least some  of  the  value  placed on
improved  air quality in residential areas.
 * See Cropper  and Arriaga-Salinas (1).
** Wage differentials  estimated with a properly-specified model of  urban
   location may also reflect the value  placed on clean air  in residential
   areas.   See  Cropper  and  Arriaga-Salinas (1).
                                   6-4

-------
     There are  two distinct procedures  which can  be used to  measure

willingness-to-pay for air quality  from  wage  differentials:


     •    A labor supply curve can be estimated which  includes air
          quality as an  argument  in the individual  worker's  utility
          function [Cropper (2);  Cropper and  Arriaga-Salinas  (1)].

     •    A hedonic-wage model can be specified, relating observed
          wage levels to various  hypothesized determinants  of wage
          behavior,  including air quality.  [See, for  example, Rosen
          (4), Smith (8)].


     In each  case,  willingness-to-pay for air pollution is derived from the

estimated model.
     In this  chapter, hedonic wage  models are  used to calculate  the

benefits which result from  the  implementation of alternative  control

strategies  designed to meet specified primary and  secondary national

ambient air quality standards for particulate matter  (TSP).*   Following a

preliminary discussion of  the underlying theory  of  hedonic wage models,**

empirical  attempts to estimate hedonic wage models are briefly reviewed.

This discussion will focus on those  models  which include  air pollution as

an explanation of wage differentials.   Coefficients from  selected studies
are then used to calculate  the  benefits which result from the implementa-

tion of various  primary standards  governing ambient concentrations of

particulate matter.  These benefits are subject  to a number of  caveats

which are summarized at the conclusion of this section.
 * The discussion  in this  chapter  centers on  the use of compensating wage
   differentials to measure  the value  which an individual places on air
   quality.  This  differs  from  the wage compensation studies  discussed in
   the Appendix to Volume II,  which use wage differentials  associated with
   job risk to assess  the  statistical value  of life.

** This discussion  is limited to hedonic  wage models because the only
   attempts to assess  the  effect of air pollution on  wages  within  a  labor-
   supply framework  do not use  TSP as  a measure  of air  pollution  [Cropper
   (2);  Cropper and Arriaga-Salinas  (1)].   Thus,  the results of  these
   studies  cannot be  used to generate benefits estimates for changes  in the
   levels of particulates.
                                   6-5

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HEDONIC WAGE MODELS:  THEORETICAL CONSTRUCT

     Hedonic wage  models are a subset of the  hedonic price models  discussed
in the previous  section.  The reader is  referred  to that  section for a more
complete discussion of  the hedonic price technique and its  implications.  A
brief exposition of the hedonic wage  model,  adapted primarily from the
models  discussed in Rosen (3,4),  Thaler and Rosen (5), and Lucas  (6), is
offered below.

     Hedonic price techniques relate  the price of a good to  the character-
istics which comprise that good.  The  price of  each  good thus embodies a
series of  implicit  prices  placed  on these characteristics.   Hedonic  wage
models belong to this general  class  of  functions;  however,  in addition to
job  characteristics,  hedonic wage  functions  also  include  individual-
specific characteristics  (e.g.,  prior experience, education,  etc.) as  pre-
dictors of wage differentials.  According to Lucas (6),  individual-specific
characteristics  must be included because  of the basic difference between
the  labor market and the consumer goods  market.   Unlike  the sale of
consumer goods,  it cannot be assumed  that  entrepreneurs  are  indifferent to
the  identity of the workers  to  whom they "sell" j.obs.

     Hedonic wage models thus  relate observed  wage differentials to three
factors:

     •    Differences  in individual-specific  characteristics.
     •    Differences  in job-specific characteristics.
     •    Differences  in site-specific characteristics.

The  marginal implicit  price of air  quality  can  be  derived  from  properly-
specified hedonic wage models; this  is  illustrated below within a simple
theoretical construct  of the labor market.

     In  making their  labor-supply decisions,  workers  are assumed to
maximize a utility  function which  contains  the wage rate,  tastes and
                                   6-6

-------
preferences,* consumer goods and services,  and job characteristics,  which
can include factors relating specifically to the type of job (e.g.,  risk  of
personal injury  or  death)  as  well as site-specific characteristics (e.g.,
air quality, climate, etc.).  This utility function for worker a is  written
as follows:
         Ua  =  U(wJ, Z..,  Xa,  Oc, P)                                (6.1)

where  Ua  is utility of worker a.
       w?  is the wage accepted by worker a for the  i   job.
       Z.   is the vector of characteristics specific to the i   job.
       Xa  is a  vector  of  personal characteristics  associated with worker
           a.
       QC  is a  vector of goods and  services available  for  consumption.
        P  represents a measure of air pollution,  an inverse measure of  air
           quality.
If pollution is a disamenity and an increase in air pollution decreases
each worker's utility, then, ceteris paribus. a trade-off exists between
the acceptance wage (which has  a positive effect on utility) and air pollu-
tion.**  This trade-off is illustrated in Figure 6-1 by a set  of indiffer-
ence curves  for two  different workers; 9  and  9 .   Each curve  slopes
upward, indicating that, for  each worker, higher wages must accompany
higher  pollution levels  in  order to maintain a  constant  level of utility.
For each worker, a higher indifference curve is associated with a higher
 * These can be represented by  a vector  of  individual-specific charac-
   teristics [Lucas  (6)J.
** It is  not  necessary to assume  here  that  the worker  has detailed
   knowledge of  the technical  relationship between  air  pollution and
   personal health or property damage.   It is assumed that he perceives
   that  his well-being  is  diminished by  the presence of air  pollution.
   Thus, it is the perceived health and welfare effects  of air pollution
   which are considered here.   Effects of air pollution  which are not
   perceived by  the  worker  have  no impact  on this tradeoff between
   acceptance wage and air pollution.
                                  6-7

-------
w
                                                   W(P)
      Figure 6-1.  Indifference maps and equilibria
                   for two workers
                         6-8

-------
level of utility.   The  convex shape  of  each, indifference  curve  reflects a
diminishing marginal rate of substitution between wages and pollution.
Tastes for  clean air may vary among workers; in this example,  worker 1 has
a higher preference  for clean air  than worker 2  (i.e., worker  1  requires a
larger wage increase than  worker 2 to compensate for a given change in air
pollution levels).

     Each worker is faced with the  task of choosing that combination of
wage  rate and pollution which maximizes his utility function  [represented
by Equation (6.1)], subject  to  the opportunities offered by the market.
Market-determined equilibrium wage rates  associated with varying levels of
pollution  are  represented by W(P).   Each worker's utility is  maximized
where his marginal  rate of substitution between pollution and the  wage rate
(i.e., his  marginal  willingness-to-pay for  air  quality)** equals the rate
at which the market compensates for higher pollution levels  [i.e.,  the
marginal implicit price of clean air, or 3W(P)/3P],  Thus,  in  equilibrium,
worker  1 will  accept level  P.,  of pollution  in return for wage rate W,.
Worker 2 will  tolerate higher levels of pollution  in return for  wage rate
 2 *

     It is  also possible that air  pollution may  affect labor productivity,
and this has not been considered  in the discussion so far.  However, the
* Pollution  is  a disamenity,  and individuals must be paid in order to
  accept higher  levels of air pollution.  Thus, ceteris  paribus.  wages
  should be higher when air pollution  levels  are higher.  Conversely, when
  pollution levels  decline, (or when air  quality improves),  wages  will also
  decline.  This  decline  in wages  which must accompany an  improvement in
  air quality in order to maintain a constant level of utility indicates
  the worker's willingness-to-pay  for air quality.
                                   6-9

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productivity effects  of  air  pollution and other site-specific characteris-
tics have  received little theoretical discussion  to  date.*

     In order to complete the hedonic wage model, demand factors must be
introduced into the wage-determination process.  Below, a simple  model  of
labor  demand, based on the  procedure  used by Thaler and Rosen (5)  in
modeling job safety  and  labor demand,  is  used  to  illustrate  the  effect  of
air pollution on  the  wage offered by the firm.  This discussion abstracts
from the complications introduced when the influence of air  quality on the
firm's  location  decision is  considered.   Furthermore,  for purposes  of
clarity in exposition, it is  assumed that  each  individual firm can, through
some variation in its production  process, alter the ambient air quality in
its immediate vicinity.

     The firm combines a variety of inputs,  including labor  (L),  to produce
marketable output (Q).   Air pollution (P) may occur as  a by-product  of
this production process.   Ignoring all other inputs except labor,  the joint
production function for this  firm  is  written as follows:

          F(Q,P,L)  =  0                                             (6.2)

     The inverse of this  production function is specified as:

                Q  =  g(P,D                                        (6.3)

     It is assumed that  this production function exhibits the following
properties:
* One very recent  contribution in this area is presented by Roback (7)  who
  develops a general equilibrium model which includes both the amenity
  effects and  the productivity effects  of site-specific  characteristics.
  Her  general  result  indicates that  wages  will  rise as  air quality
  deteriorates if  air quality  has no  impact  on  labor productivity.
  However,  if air  pollution  adversely affects labor productivity,  then  the
  wage change  is  ambiguous.   Roback notes that the direction and strength
  of both the amenity and productivity effects of site-specific charac-
  teristics  can only be determined empirically.
                                   6-10

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         The marginal product  of  labor is positive  and diminishing.*

         The marginal product of labor varies inversely with the
         level of pollution.**

         The  level  of output  varies directly  with the level of
         pollution,  up to some very large  (and technically-
         determined) level  of  pollution (P).+  This implies  that, in
         order to lower pollution levels,  the firm must  divert some
         resources  away from  the production of marketable output.
         The transformation locus between output  Q and  air quality
         (the  inverse of the  level of pollution) is negative and
         concave.
     The cost of improved air quality is expressed as a function of the

level of air pollution,  or G(P).  These costs are positive and increasing

with the level of  air quality.


     The firm's profit function is written as follows:


                n  =  g(P,L) - w(P)L - G(P)                          (6.4)


where w(P) is the competitive wage  which must be paid at alternate air

pollution levels.


     The firm maximizes profit with respect to labor and pollution; the

resulting first-order conditions are written as  follows:
              S?  =  *(p)                                           (6-5)
          3Q   3G
 * 3Q/3L >  0, 32Q/3L2 < 0.

** 3Q/3L3P  < 0.

 + 3Q/3P >  0 for 0 1 P < P,  32Q/3P2 < 0.

++ 3G/3P <  0, 32G/3P2 > 0.


                                   6-11

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Equation  (6.5)  is  the familiar result  that labor  is  hired  until the
marginal product of labor equals the  competitive wage  rate.  Equation (6.6)
states that  air  pollution levels are  adjusted so  that  the  marginal  cost of
air pollution to the  firm  (i.e., the additional cost  of  hiring labor to
work  in a polluted  environment)  equals the marginal benefits  of  air pollu-
tion (i.e.,  additional  market  output and cost  savings which accrue because
anti-pollution devices are not  installed).

     An offer function can be specified which indicates the  wage paid to
the optimal number of workers at alternative pollution levels in order to
maintain a  constant  profit  level.   This  offer  function,  4(P,n),  is
expressed as:

          6   =   [g(P,D - G(P)  - n]/L                                 (6.7)


          '   -   IE                                                   <«••>

     This offer  function defines a set of iso-profit curves for  each  firm.
A set of iso-profit  curves for two different firms is illustrated in Figure
6-2.   These  curves  are positively-sloped,  indicating that  the cost savings
and increase in  revenues from marketable output which occur as  air pollu-
tion  levels increase  are matched by  the additional wage costs which the
firm  incurs in  order  to maintain its optimal labor  stock.* These  iso-
profit  curves differ  among firms as a result of differences  in optimal
technologies.   W(p), as  the market-generated locus of wage rates which
correspond  to each pollution level, also forms an envelope of these iso-
profit curves.   At the tangency  points between firms' iso-profit  curves and
W(P), the internal  trade-off between wages and  air pollution within the
* Mathematically,  3 0.  Thus, the marginal demand
  price for pollution, or the iso-profit curves in Figure  6-2,  is posi-
  tively sloped.  However, the second-order conditions do not produce an
  unambiguous result.  Thus, 32«f/3P2 -^ 0 [see Thaler and Rosen (5)].  The
  iso-profit  curves  in Figure 6-2 are based  on the assumption  that  3 ^/3P^
  < 0.
                                   6-12

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w
t
                                                       (P)
                                           P2        P
      Figure 6-2.   Iso-profit lines- and equilibria for
                   two firms
                         6-13

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firm (i.e.,  the slope  of  the  iso-profit curve)  equals the marginal implicit
price of  air quality determined by the  market.

     Equilibrium  in the labor  market is established where the worker's
marginal  willingness-to-pay for air  quality equals the  firm's  ability to
substitute wage payments for air quality while maintaining  an optimal
(i.e., profit-maximizing)  input stock.  W(P)  thus  defines a  series of
tangencies between workers' indifference  curves and  firms'  iso-profit
curves (see Figure 6-3).  Workers with high preferences  for  clean air
(e.g., worker  1) are employed by firms which produce lower levels of air
pollution.  Workers who do  not place  as high a value on  air  quality are
employed by firms which produce  higher  levels  of pollution (and are paid
higher wages).

     Hedonic wage  models relate observed equilibrium wage  rates,  generated
by the interaction of labor supply and  demand  decisions,  to  the  underlying
factors  which influence  these  decisions.  Thus,  estimates of hedonic wage
models trace out the market wage locus [i.e.,  W(P)].  The implicit marginal
price of each underlying characteristic  is expressed by the derivative of
W(P) with respect to that characteristic.   The implicit marginal price
schedule of air quality, 3W(P)/3P, is illustrated in Figure 6-4.  If the
labor market  is in equilibrium,  then this  implicit price  schedule also
represents the  marginal willingness-to-pay for air quality  of different
                                        •
sets of  workers  exhibiting different preferences  for air quality.

     Figure 6-4  also  indicates  the  compensated  supply price functions for
pollution derived from the indifference  curves for workers 1 and 2 in
Figure 6-3.  These supply price functions are  defined as 39/9P.  They show
the increase  in  wage  rate  which is  required to  compensate each  worker for
an  increase  in air pollution levels  while maintaining a constant level of
well-being (or his marginal willingness—to-pay for air  quality at each
level of pollution).  There  is only one point on each worker's compensated
supply price  function which  lies  in  the  implicit marginal price schedule;
this point corresponds to the equilibrium represented  by the tangency
between that worker's highest indifference curve and  the  market  wage  locus,
                                   6-14

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w
                                              W(P)
                                   P2        p
         Figure 6-3.  Illustration of labor-
                      market equilibrium
                         6-15

-------
                                             3W(P)
                                              3P
Figure 6-4.
Implicit marginal price schedule for air
quality and compensated supply functions
for two workers
                        6-16

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W(P).  Thus, the marginal implicit price schedule for air quality indicates

the equilibrium marginal willingness-to-pay for air quality at  each  level
of air pollution.


EMPUUCAL ESTIMATES OF HEDONIC WAGE MODELS


     Hedonic wage models relate wage differentials  to variations in  three

types of factors:
          Individual-specific  characteristics,  defined  as those
          characteristics which distinguish job-holders  or job-
          seeking  individuals.  These  characteristics include  such
          factors as education,  prior  experience,  race,  sez,  veteran
          status, union-membership,  etc.  as  possible variables  in
          explaining wage differentials.

          Job—specific characteristics,  i.e., distinct characteris-
          tics exhibited by various  types  of  jobs.  Some  examples  of
          job-specific characteristics are  risk  of injury or death,
          work of a repetitive nature,  or work within a  supervisory
          capacity.

          Site-specific characteristics,  or  characteristics asso-
          ciated with job location.  Since job-location decisions may
          be tied to  residential-location decisions,* site-specific
          characteristics generally  reflect the levels of amenities
          which  influence  residence choice.   Wage  differentials may
          compensate for variations  in  the levels  of  such amenities
          as pollution,  climate,  crowding, crime,  cultural opportuni-
          ties, etc.
Thus,  hedonic wage models are specified  in general functional form follows:


          W  =  F(Zlf  Z2,  Z3)


where      W  is the observed wage rate.**

         Z-.  is a vector  of individual-specific characteristics.
 * That is,  an individual  first  selects an area of residence;  this  decision
   constrains his  job  search  to  a  specific geographic area.

** Smith (8)  argues  that the  real wage rate,  and not the  nominal  wage rate,
   is the appropriate  dependent  variable.
                                   6-17

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         Z~   is a vector of job-specific  characteristics.
         Z,   is a vector of site-specific  characteristics.

     To date, most  estimates of hedonic wage models generally  include
individual-specific characteristics (i.e., Z^) and  either job-specific
characteristics  (i.e.,  Z2) or site-specific characteristics (i.e., Zj)  as
explanatory  variables [Smith (8)].   Attempts to link wage differentials  to
job-specific characteristics include studies conducted by Lucas (6), Thaler
and Rosen (5), Viscusi  (9), Hamermesh (10),  and Olson (11).  These models
have produced conflicting results regarding the effect of job-specific
characteristics on wage rates.   Brown (12) failed to confirm the hypothesis
that these  inconsistent estimates  result from a misspecified model  which
omits important  job  characteristics.

     Other investigators have used the hedonic wage model to impute the
values attached  to urban amenities  (i.e.,  the site—specific characteristics
defined above),  and  thus measure the  quality of  urban life  [see Rosen (4);
Meyer  and Leone (13); Getz  and Huang  (14);  Izraeli (15); Hoch (16)].
However,  there  are  many site-specific  characteristics which might
conceiveably affect wage rates,  and  several distinct methods of quantifying
some of these variables.*

     A general  comparison  of  hedonic  wage model estimates indicates that,
while  individual-specific  characteristics exert  a  consistent  and  predict-
able effect  on  wages,  the  impact of both job-  and site-specific character-
istics is less  consistent.  Comparisons  among  these models are difficult,
since they vary  in their data sources,  sample designs,  model specification,
and measurement of both wage rates and  explanatory variables.  However,
even within models which  include all three  general characteristics  as
* For example,  the climate can be measured by rainfall,  number of days of
  sunshine, high, low, and/or average  temperature, humidity, etc.   Each
  measures a somewhat different  climatic  effect; however, the  inclusion of
  all variables may not be feasible  where degrees  of freedom  are limited.
  In addition,  interrelationships among the different measures  may  cause
  collinearity  and thus blur the effect  of each  factor on  wages.
                                   6-18

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explanatory  variables,  coefficient  estimates for site-specific character-
istics  are  sensitive  to the  choice  of  the particular site-specific charac-
teristics  included  in the  model  [Rosen (4);  Smith (8)].

     It is not  the  intent of this review to  evaluate all  attempts  to
estimate hedonic-wage models.*  Instead, models  which include the effect of
air  pollution  (specifically,   ambient TSP  levels)  on wage  rates  are
evaluated for  use in  the calculation of benefits  obtained under alternative
particulate  matter  (PH) standards.   The selection of studies utilized in
these benefits calculations  is  based  on the criteria discussed in Section
1.   The studies  used  in this section meet the  following  specific criteria:
          They each use TSP as an explanatory variable.  This is an
          obvious prerequisite for use in benefits calculations.
          They each express  the dependent  variable  as  the  real  wage
          rate or earnings.  A  regional cost-of-living  deflator  will
          adjust earnings  for differences  in the prices  of  local
          goods- (e.g.,   property values)  which  may  also  reflect
          regional  variations  in  air quality.
           4
          Each model  is properly specified  in  that it  includes,
          either explicitly or implicitly,  explanatory variables
          measuring individual-, job-, and  site-specific  charac-
          teristics.
          Estimated coefficients produced by each  model are plausible
          in  terms  of  the underlying theoretical construct.
     Despite an abundance  of  empirical work relating wage differentials to
site—specific characteristics  within a hedonic—wage framework,  only  nine
studies have been identified  which analyze the  effect of ambient levels of
particulate matter on wages [Getz and Huang (14),  Izraeli (15,18), Meyer
and Leone  (13), National  Academy of Science (19), Hathtech (20), Roback
(7), Rosen (4),  and Smith (8)J.  Of these, only two studies are suitable
for use in benefits calculations [i.e., Rosen (4) and Smith (8)].
* The reader is  referred to Smith (17)  for  a  review  and critique of hedonic
  wage models.
                                   6-19

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     In their study of consumer preferences for site amenities, Getz and
Huang (14) analyze the  relationship between median annual earnings of white
males employed full-time and both  individual-specific variables and site-
specific variables,  including  air  quality.  A  principal  component*  of the
ambient levels of two pollutants  (sulfur  dioxide and TSP) is used as an
inverse measure of air quality; this relationship  is estimated for each of
nine urban occupational groups, as well  as  over all  occupations.  Getz and
Huang obtained mixed results; air pollution  generally exerted an insignifi-
cant effect  on median  earnings except for professionals, whose earnings are
significantly higher in the presence of higher levels of  air  pollution.
Other explanatory variables used in this  analysis include median worker age
and the proportion of  adults in each SMSA who are high school graduates as
human  capital variables,  and  a variety of site-specific characteristics,
such as the FBI violent  crime index,  the number of days of freezing
temperatures, the proportion of  teachers with  graduate degrees,  and a
principal component measure of the  per capita  number of  hospital  beds and
physicians.   Since the coefficients  estimated by Getz and Huang  do not
differentiate between  TSP and sulfur dioxide in measuring the effect of air
pollution on earnings,  they cannot  be used in the current benefits calcula-
tions.

     Meyer  and Leone  (13), NAS (19) and  Izraeli (15,18) focus on the role
which urban amenities  (or site-specific characteristics) play in explaining
wage or income differentials.  Using aggregate  data  for 39 SMSAs, Meyer and
Leone examine the impact of both TSP and  sulfur dioxide on several specifi-
cations of the dependent variable  (i.e.,  real median family  income and the
real wage rate for skilled workers,  for unskilled workers,  and for computer
analysts) within a log-linear  functional form.   In most  instances TSP
levels  exerted an insignificant  negative effect  on  earnings or  wages;
however, real wages for computer analysts were significantly higher in the
presence  of higher TSP levels.  Sulfur dioxide levels had a mixed but
always  insignificant effect on the  various dependent  variables.   The
* Principal components analysis is a  statistical device which  allows the
  investigator to  express two or more  collinear explanatory variables as a
  linear combination.
                                   6-20

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inclusion  of  two highly  correlated air pollution measures together  in the
same  model (i.e.,  sulfur dioxide  and TSP) may explain  the  resultant
insignificant coefficient estimates for these variables.  The NAS study
(19)  is a slightly expanded version of the models estimated by Meyer and
Leone. Additional site amenities, including NOj, are  included, and both
linear and log-linear specifications  are estimated over  various  subsets of
explanatory variables.  Once  again, the estimated coefficients for the air
quality variables are  generally not significant, and are  sometimes
negative.   TSP is  particularly problematic in this respect:  negative and
insignificant coefficients are often  observed.  The very small coefficient
values and the lack of significance  exhibited by  the air quality variables
may  be due to  collinearity among  these variables,  or between  these
variables  and other explanatory factors.

     The performance of other explanatory variables is mixed. Some consis-
tently exhibit the "wrong" sign, and many are  insignificant.   Given the
                  2
relatively high R s  which characterize  some of the specifications, the
evidence indicates a  severe problem with collinearity among the explanatory
variables.

     A similar model  was  estimated by Izraeli (15), using median real wage
rates as a dependent  variable from 67 SMSAs between 1964 and  1967.  Using
three  air pollution measures (i.e., TSP,  sulfur dioxide, and  benzene
soluble organic  matter), Izraeli obtained a  positive  and  significant
coefficient for sulfur dioxide; the coefficient in TSP was negative but
insignificant.  When TSP was used  as the only  pollution variable, the
estimated coefficient was positive but  not significantly different from
zero.  In  a recent study,  Izraeli (18) estimates a more refined model using
a greatly expanded  sample  which includes  237  SMSAs- in the year  1970.
Median annual  earnings  are related to variables  measuring the  median age
and schooling of the population, the number of weeks worked, the racial
composition of the population and a series  of environmental variables,
including  air quality, the crime rate, climate, and population size and
growth.  Air quality is measured by suspended particulate matter;  no
additional  air pollutants  are  included.   Separate equations are estimated
                                   6-21

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for males and females.   In  each case, TSP exerts a positive but insignifi-
cant effect on median earnings.

     Given the aggregate nature of the data used in the above-mentioned
studies, no job-specific characteristics can be explicitly  included as
explanatory  variables.   Similarly, individual-specific characteristics,
such as prior experience,  education,  etc.,  are difficult to  include in
their models.  However,  median schooling is used as an explanatory variable
in both  Meyer  and  Leone  (13) and NAS  (19).   Some  other  explanatory
variables,  such  as the median age or racial composition of the  population,
may also reflect the education or  experience levels  of the  population.
Since these models  do not  account for the impact of both  individual- and
job-specific  characteristics on  wage  or earnings differentials,  they cannot
be regarded  as properly—specified  hedonic wage models.  Thus, benefits
calculations  should not be based on  their  results.

     The remaining  studies [Mathtech (20),  Roback (7),  Rosen (4), Smith
(8)]  use  micro-data to relate wage rates  or earnings reported by surveyed
individuals to individual-specific  characteristics,  job-specific
characteristics, and site-specific characteristics.   The Hathtech  study
(20) used observations  drawn from the University, of Michigan Panel Study
of Income Dynamics for  the  1971 interview year for 699 counties.   The log
of the real wage,* is related to three measures of  pollution (i.e.,  TSP,
sulfur dioxide,  and nitrogen dioxide), entered in both linear and quadratic
terms.   Since pollution data is  available  only over 247 counties,  two
procedures  were  suggested to replace missing observations:

     •   The use of the means of the observed values.
     •   A method adapted from a technique developed by Dagenais
          (21),   which involves regressing  each  pollution  variable in
          all remaining  (non-pollution)  explanatory variables  and on
          relevant  auxiliary variables.   Values for the missing
* Defined as  hourly money  income from labor received by  the head of house-
  hold deflated by the BLS indicator of  comparative living  costs for i
  family of four at the lowest living standard.
                                   6-22

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         pollution variables are predicted using these regression
         results.
     Regression results are  reported using a restricted subsample where  all
missing observations are deleted, as well as for the full sample (i.e.,
1,395 observations),* using  the  two procedures outlined above to replace
missing pollution  variables.  Neither sulfur dioxide  nor  nitrogen dioxide
significantly  affect real  wages.   The  coefficient on  the  linear TSP
variable is positive  and significant only when the mean values of the
pollution variables  are used to approximate  missing observations; the
coefficient  on  the quadratic TSP term is negative and significant  in this
instance.  Thus, real  wages  will rise  at a declining rate as TSP concentra-
tions increase.  However,  the estimated elasticity of real  wage  with
respect to the TSP level is -0.16, measured at the mean value of TSP;**
this contradicts a. priori expectations regarding the  relationship between
the real wage  and TSP levels.   These implausible  results leads to the
rejection of these coefficients  as  a basis for benefits  calculations.+

     Rosen (4), Roback  (7), and Smith  (8) each use  micro-data from the
Current Population  Survey (CPS)    in their respective  analyses.  Each
includes TSP levels (measured at the annual  geometric mean) as  an explana-
tory variable  in conjunction  with  several other individual-specific,  job-
 * This sample  is  restricted to households which received no transfer
   income  and whose head worked 400 hours or more per year.
** This elasticity indicates the percentage change in the real wage rate
   which will  occur for  a  given  percentage change in TSP levels.   It thus
   measures  the  responsiveness of the real wage  to  changes in TSP levels.
   In this instance, a 10 percent increase in TSP levels  will lead to a  1.6
   percent decline  in the real wage.
 + The hedonic  wage model specified  in the  Mathtech study  was also
   estimated over various portions of the data set  based on sex, age,  and
   race.   Coefficients  estimated for air pollution variables  were seldom
   significantly different  from zero  in  these equations.
++ The Current Population Survey data allows the investigator  to link
   specific individual- and job-specific attributes  to  each surveyed
   individual;  this provides  better  control  over  these  explanatory
   variables than the more  aggregated data used in many previous  studies.
                                   6-23

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specific,  and  site-specific factors,  and each produces plausible estimates
for the TSP coefficients.   Roback's model uses nominal wage rates as the
dependent  variable,  while both Rosen and  Smith specify  the  dependent
variable as the real wage rate.

     Variations in nominal wages or earnings may be explained  in part by
regional differences  in  the cost  of  living.   Cost-of-living  differentials
may reflect the following:

     •    Regional differences  in the prices  of goods  which are
          exchanged between regions due, for example, to transport
          costs.
     •    Price  differentials for  nontraded goods  (e.g., housing)
          which are attributable to variations in environmental
          conditions across regions.

The  effect of these  regional variations  in cost  of  living  should be
accounted for in assessing the  relationship between wage  rates and air
quality.  Roback  (7) does not  account  for this factor; thus,   this study is
excluded from  further  consideration.

     Both Rosen  (4) and Smith (8) use  an appropriate data base to estimate
well-specified hedonic models which meet the  following criteria:

     •    Each includes TSP as an explanatory variable.
     •    Each specifies real  wage  or earnings as  the  dependent
          variable.
     •    Each includes,  either explicitly or implicitly,  explanatory
          variables measuring individual-,  job- and site-specific
          characteristics.
     •    Each produces a plausible relationship  between TSP and the
          real wage rate.

Thus, both  studies meet the general criteria for use  in benefits calcula-
tions.   A more detailed review of each study  is offered below; this discus-
sion is summarized in Table 6-3.
                                   6-24

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                                                         Table 6-3



                                          SUMMARY OF SELECTED HEDONIC WAGE MODELS
ro
in
Study
Rosen
(4)
smith
(8)
Sample
Males out of school
& reporting earn-
ings in 1969 over
19 major SMS As.
Obtained from 1970
CPS.
All surveyed indi-
viduals residing in
44 major SMSAs who
reported earnings
in 1977. Obtained
from 1978 CPS.
Dependent Variable;
Pollutant
Total real earnings
(wages & salaries +
self -employment
income); Annual
geometric mean for
particulates
(ug/m3).
Real wage rate;
Annual geometric
mean for particu-
lates (ug/m3).
Functional
Form
Semi -log
Semi-log
Estimated Coefficients;
Elasticities of
Pollution Variables
(At Means)
Estimated coefficients
range from 0.000553 to
0.0015; Elasticities
range from 0.0627 to
0.1702.
Estimated coefficients
range from 0.000615 to
0.00112; Elasticities
range from 0.0413 to
0.075.
Estimated
Benefits;
Base Year
A reduction in
levels by 1 ug/m3
leads to a change in
mean annual earnings
ranging from $4.92
to $13.35; 1970.
Reducing TSP levels
by 1 ug/m will
decrease mean annual
earnings by $6.05 to
$16.50; 1978.

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     In his  analysis of wage  rate differentials and urban  amenities, Rosen
used data  from  the  1970 CPS over 19  major SHSAs for males who were out of
school and who  reported earnings in  1969.  He uses a semi-log specification
where the dependent variable is the log of real annual earnings* and the
explanatory variables are entered in linear form.   In  addition to TSP
levels,  air  pollution is also measured by sulfur dioxide levels and by the
number of inversion days.   Individual-specific variables  include  schooling
and previous experience  (entered in both  linear and quadratic form), as
well as dummy variables for race, sex, marital status, veteran status, and
head-of-household.  Dichotomous variables  are also entered for  self-
employed  individuals, government  employees,  full-time employees,  and
individuals who worked  for  35  hours per week or less,  as  well  as  for
individuals  who were not  employed at any time during the  year and indivi-
duals who were  unemployed once during the year.   Job-specific  characteris-
tics are not explicitly recognized.  However, Rosen uses dummy  variables to
distinguish  across one—digit occupational and industry classes;  this  may
provide a  crude  measure of differences  in characteristics common to various
jobs across  the  sample.  Finally, measures  of  site-specific characteristics
include climate (i.e.,  the number of sunny  days,  the  number  of rainy days,
and the number  of days when the  temperature exceeded 90°F), crime  (measured
by the total crime rate), crowding  (i.e., population density, population
size, and whether  the surveyed  individual lives in the central  business
area),  and labor market conditions  (approximated by  the unemployment rate
and  the rate of population  growth).  In  addition to the  air pollution
variables  cited above, a measure of  water pollution is also included.

     Rosen  estimated his model using  all individual- and job-specific
characteristics and various combinations of the  site-specific attributes
discussed above.**  Estimated  coefficients for individual-specific  attri-
butes are  generally significant  and  of  the  same  sign and order  of  magnitude
 * Real annual earnings are defined here  as nominal earnings (wages and
   salaries, plus self-employed income) reported in the CPS divided by a
   cost-of-living index for either low-,  medium-,  or high-expenditure
   families,  depending on individual circumstances.
** Regression results reported by Rosen  are  listed in Appendix  6A.
                                   6-26

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as those  observed  in  other  studies  using  less restricted samples.  Results
                        •
for the site-specific variables are  less  robust.   Measures of climate,
crime,  and  market  conditions  generally  affect  wage  rates  in the expected
fashion;  however,  the size  of  the effect varies  with the particular set  of
site-specific  attributes  included in the model.  Significance levels are
also inconsistent across specifications; this probably reflects varying
degrees of multicollinearity among these  variables.   TSP had a consis-
tently positive and generally  significant  impact  on real  earnings.*
Reported  significant  estimates for the TSP coefficient range from 0.00055
to 0.00150;  this translates  to elasticity estimates  ranging  from 0.062  to
0.170.**  The measured impact  of changes  in TSP levels on real earnings  is
thus very slight:  at  the mean TSP level,  an increase  in TSP concentrations
by 10 percent will  increase  real earnings by less than 2  percent.

     Smith (8)  explicitly examines the effect  of both site-specific and
job-specific characteristics on real wage in his hedonic  wage model,  using
data obtained  from  the  1978 CPS  to  define the dependent  variable and
several  explanatory variables over 44 SMSAs.   Like Rosen, Smith uses  a
semi-log functional form  which specifies  the log of the real  wage rate   as
a function  of  several explanatory variables.   Individual-specific attri-
butes include years of schooling and years  of job experience (both  entered
in linear and  quadratic form), as well as a  series  of dummy variables
reflecting socio-economic characteristics.  Job-specific  characteristics
 * The number of inversion days  also  had  a positive and significant  impact
   on real wage.  The  results  for  sulfur  dioxide are less robust;  signifi-
   cant negative  coefficients  appear  in several instances.
** For the semi-log functional form,  the  elasticity of  the  real wage  with
   respect to TSP at a particular level of TSP is computed as  the product
   of the estimated coefficient on the TSP variable and the level of TSP.
   Thus,  the responsiveness  of the real wage to changes in TSP concentra-
   tions  varies  with the  level of  TSP.   Here  we calculate  elasticities  at
   the mean TSP  level  observed in the sample.
 + Defined as the nominal  hourly wage divided by  the  1977  Bureau  of Labor
   Statistics' cost-of-living index for families at an intermediate  living
   standard.  Since this index is only available for 27 SMSAs, Smith uses a
   procedure  suggested by  Cebula (22)  and  Cebula and Smith  (23)  to  estimate
   this index for the  remaining SMSAs.
                                   6-27

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include  injury rates, an  index of exposure to carcinogens,  an indicator  of
worker  knowledge  of  job hazards,*  and occupational dummy variables.
Smith's model also allows  for an interaction between experience and the
effect of product market uncertainty on  the firm's willingness to invest  in
on-the-job  training (OJT).   Another  set  of risk-interaction terms for race,
head of  household,  and  union  membership are  also  included  in  some regres-
sions.   In his preliminary analysis. Smith experimented with a large number
of site-specific characteristics, including various measures of climate,
cultural amenities (i.e.,  dummy  variables  indicating presence  of  symphony
or live  theatre,  the number of major  newspapers,  art museums,  and profes-
sional  sporting teams),  the  crime rate, the unemployment rate,  and the
number of hospitals.  Three measures of air pollution,  TSP, sulfur dioxide
and ozone,  were also considered.   Here,  Smith found that  TSP provided the
most consistent results.  The model was estimated over  all individuals  in
the sample and  separately for males and for  females;  Smith's  best results
using the most  robust  set  of site-specific, attributes are presented  in
Table 6A-3  in Appendix  6A.

     The site-specific attributes  which are  included in Smith's final
analysis are TSP levels, the unemployment rate, the crime rate,  and the
mean annual percentage of possible sunshine (i.e., the number of  hours  of
actual  sunshine/the number of  hours between sunrise and  sunset).
Generally,  the estimated coefficients for these  and  other explanatory
variables  are statistically significant and  their signs conform to both a.
priori expectations and the results of previous  studies.**

     Real wage rates are significantly higher for higher levels of TSP;
coefficient  estimates  over  the entire sample range  from 0.00083 to 0.00087.
 * Measured as the relative number of workers in each industry covered  by
   collective  bargaining agreements with provisions relating to health and
   safety conditions.
** Smith notes that his estimated coefficient for the unemployment rate
   differs  in  sign from estimates  reported by Rosen  (4).   He suggests that
   differences in  the industrial composition of the two samples may explain
   this inconsistency.
                                   6-28

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The elasticity of  the  real wage  rate with  respect to TSP;  calculated  at the
mean observed TSP levels,  varies from 0.056 to 0.058.   Smith's results for
men indicate  that elasticities  range from  0.0726 to 0.0750;  this confirms
Rosen's  finding  that  TSP levels  have  an extremely small,  albeit  statisti-
cally significant,  impact  on real  earnings.

     Generally, the hedonic wage  studies cited above  produce consistent
coefficient estimates for individual-specific attributes.   However,  there
is less agreement among these models  with respect  to coefficient estimates
for both job-specific and site-specific  attributes.   Smith (8) suggests
that the constraints imposed by data availability result in non-random
samples which,  in turn, produce diverse coefficient estimates.   He tests
the sensitivity of his site-specific and job-specific coefficient estimates
to sample  composition by deleting  selected SMSAs  and industry classes from
the sample and re— estimating his model.  He concludes that coefficients
estimated for site-specific  attributes are quite sensitive to sample compo-
sition, while job-specific effects  are less sensitive in this  regard.
                                                    *
     Before proceeding with benefits calculations based on  these results
derived by Rosen and by Smith,  some general remarks  concerning certain
unresolved empirical  issues  which arise within hedonic wage models are in
order.

     Estimates of hedonic  wage models  may be  subject to bias from at least
two sources:
          Bias resulting from the omission of  important  explanatory
          variables.
          Sample selection bias.
Investigators who attempt  to  estimate  hedonic  wage models are faced with
the  task of  choosing  among a  large  number  of  potential  explanatory
variables.   The  omission of  important variables from the model introduces a
bias of unknown  magnitude into the  estimates  of the model's parameters.
                                   6-29

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     Hedonic  wage models may also be biased as a result of sample trunca-
tion.  Many of the empirical models cited above are estimated over somewhat
restricted samples.*  Even when arbitrary restrictions are  not placed on
the data set, the sample is necessarily restricted to those individuals who
report wages  for the sample period  [Smith  (8)].   In either case,  a sample
selection bias is introduced into the   estimated coefficients.**  Addi-
tional assumptions  are  required  in order to use these coefficients to make
some inference  about  the behavior  of those  individuals not included in the
sample.
BENEFITS

     Benefits  calculations for the alternative  primary and secondary parti-
culate matter  standards described in Section 9  will be based on coefficient
estimates obtained by Rosen (4)  and Smith (8)  for their respective hedonic
wage  models.    These estimates will  be  used  to calculate individual
willingness-to-pay for air quality and  benefits  will be  extrapolated over
the labor force.

     In order to  use the coefficients estimated by Rosen and  Smith to
obtain an assessment  of benefits  over the  entire  labor force,  the following
assumptions are required:
          The estimated  coefficient on the TSP variable  in each study
          accurately  captures the effect of changes in TSP levels on
          real earnings.
          Benefits  based  on  these  coefficients reflect  the
          willingness-to-pay  for air quality of those individuals in
          the labor force who are not included in the samples from
          which these coefficients were estimated.
 * For example,  Rosen (4)  restricted his sample to males, and the Mathtech
   study (20) deleted all households which reported transfer  income or
   whose head worked less  than 400 hours per year.
** The reader is referred  to He c km an (24,25) and Gronau  (26) for a discus-
   sion of sample selectivity.  Wales and Woodland (27)  survey various
   methods of estimating labor supply functions using truncated  samples.
                                   6-30

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Measures  of TSP may  be correlated with, other  possible  explanatory
variables, including other types  of  air pollution,  which are  not considered
in the  model.   In  this  event,  the  estimated coefficient  for  the  TSP
variable may also capture the effect of these omitted variables on real
earnings.  It is assumed here that correlation between TSP and  excluded
explanatory  variables does not substantially  affect the  coefficient
estimates obtained  in the selected  studies.  This assumption  is certainly
tenuous,  particularly in view  of the sensitivity of  Rosen's  reported
results  for TSP as  different combinations of site-specific characteristics
are introduced within his model.  There is also strong evidence  that TSP
concentrations tend to be highly correlated with  other air pollutants.
Unfortunately,  very few hedonic  wage models report  results  for more than
one measure  of  air  pollution.  Those which include the  effect  of more than
one  air pollutant  in  real wages  or earnings  are deficient in  other
respects.*  It is possible  that the  estimated coefficient on TSP as the
sole pollutant in this type of model may also proxy the effects  of other
pollutants in real  wages  or earnings.  However,  it  is  difficult to assess
the extent to which this  might occur because neither of the studies with
well-specified models (Rosen and  Smith) report equations containing more
than one air  pollutant.

     The possibility of biased coefficient estimates due to sample selec-
tion or  truncation must also be .considered.   Smith  attempted  to measure the
bias  introduced by sample truncation  by applying  adjustment  indices
developed by Olsen  (28) to his  OLS estimates.**  The resultant  adjusted
coefficients  increased  in absolute  value by one to two percent,  while the
standard errors  of these coefficients  rose by one percent.  Smith concluded
that sample  truncation bias did not  pose a serious problem in his  analysis.
 * See,  for  example, Meyer and Leone  (13), MAS (19), or Izraeli  (15).
** Olsen (28) developed a  simple  approximation  to  the maximum likelihood
   estimators  for the truncated regression  model.   A table of conversion
   factors is applied to OLS estimators in order to produce  these approxi-
   mate maximum likelihood estimates.
                                   6-31

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     The hedonic-wage models  developed by Smith (8)  and Rosen (4)  were

estimated over different  samples,* using somewhat different model specifi-

cations.  Despite this,  the results  obtained within both models for TSP

corroborate  each other.   The  estimated  coefficients  appear to be quite

robust with respect to both the  sex  and  the residence  of  surveyed  indivi-

duals;** these  results support  the use of  these  coefficients in calculating

willingness-to-pay for air quality for every worker in the labor  force.


     Both Rosen and Smith use a semi-log  specification  of  the hedonic

wage function,  expressed  as follows:


          ln(W)  -  o +  0 TSP + XX                                   (6.9)


where     W  is the real  wage rate  (Smith) or real annual earnings (Rosen).

        TSP  is the annual geometric mean level of total suspended particu-
             lates.

          X  is a vector  of other explanatory variables.

 a, p and X  are estimated parameters.


     Estimates of this model reveal  the  equilibrium value  of air quality.

From this, the decline in real  wage rate or earnings which will accompany a

marginal  improvement in air quality;  (i.e., the  marginal  willingness-to-pay
 * Rosen used a sample of males over 19  major SHSAs drawn from the 1970
   CPS, while Smith used  a sample of all surveyed  individuals over 44 SMSAs
   drawn from the 1978 CPS.

** Smith  reports that the difference between coefficients estimated
   separately for males  and for  females is  not statistically signifi-
   cant.   His coefficient estimates for the  total  sample  over 44 SHSAs
   lie within the range  reported by Rosen for 19 SHSAs.

 + Here, it  is also assumed that the coefficients estimated by Smith (8)
   and Rosen (4) also apply to  those  individuals in the labor  force who
   were  not represented in these  studies because  they did not  report
   earnings for the sample periods.   In essence,  it  is  assumed that  these
   benefits calculations are not affected by sample selection bias.  As
   discussed above,  sample selection bias does not appear to influence the
   coefficient estimates  generated by  Smith (8).
                                   6-32

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for air quality) can be  estimated.   This marginal  willingness-to-pay  for
air quality is calculated as  the first  derivative  of real  wages  (or
earnings)  with respect  to air pollution.  For the  semi-log  function  given
in Equation (6.9),  the  marginal willingness-to-pay for air quality at  a
given level of real earnings  is defined  as:
If the estimated coefficient ({J) is positive,  then individuals  must  receive
higher wages in order to compensate for deteriorations in air quality.
Their marginal willingness-to-pay for air  quality,  or  the  decrease  in  the
wage rate which they would  accept  in order to  enjoy improved  air  quality,
is positive  and varies  in direct proportion with their wage level.   Indivi-
duals with higher real earnings will relinquish more of  their  earnings  in
response  to  a marginal  improvement in air quality as compared to those with
lower real earnings.   The  second derivative of Equation  (6.9)  is expressed
as follows:
                        W   >   0                                     (6.11)
Marginal willingness-to-pay  for  improved air quality will  increase at  an
increasing  rate as air quality deteriorates.  The  magnitude  of  this  effect
will also vary directly with the wage rate.

     Table  6-4 lists calculations of individual  marginal  willingness-to-pay
for air  quality  on  an annual basis using coefficients estimated by Smith
(8) and Rosen  (4).

     The implementation of  the  proposed  particulate  matter  standards will
probably result in a non—marginal change  in ambient TSP  concentrations.   In
order to calculate the precise effect of these non-marginal improvements  in
air quality on real wages (and thus on benefits), some knowledge  of the
supply  price   function for  TSP for each individual is  required.   This
                                    6-33

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                                Table 6-4

          ESTIMATED BENEFITS FOR MARGINAL CHANGES IN AIR QUALITY*
           BASED ON RESULTS REPORTED BY ROSEN (4) AND SMITH (8)
             Study
        Rosen (4)

        Smith (7)+

           Total  sample
           Males
           Females
                          Change in average annual real earnings**
                                 Minimum
$ 10.44
  13.22
  20.17
   7.70
                   Maximum
$ 28.33
  13.88
  20.84
   8.44
 * A change in TSP  levels of 1 jun/g  is assumed.

** Calculated at the  average wage or earnings levels reported for each
   sample and expressed in  1980  dollars.

 + The reported average nominal wage rates are divided by the cost-of-
   living index for a  4-person family at an intermediate  expenditure  level
   in order to calculate the real wage.  The change in the real wage as a
   result of the change in  TSP levels is then multiplied  by 2,080 hours in
   order to approximate annual real earnings.
function is not estimated within  the  hedonic  wage model.  However,  these

benefits can be approximated using a simple  technique suggested by Freeman
(29).


     Figure 6-5 illustrates the equilibrium marginal implicit wage that  the

sampled individuals will accept  in return  for  working under  varying  levels

of ambient TSP levels.   This marginal  implicit wage, W (TSP),  is  the first

derivative with respect  to TSP of the hedonic wage  model specified above as

calculated in Equation 6.10.   Each  point in W (TSP)  corresponds to  a

single point in each individual's supply  price function for TSP.  S(TSP)i

is the hypothesized supply price function  of individual  1,  indicating that

this worker must be paid higher real  wages  as compensation  for higher  TSP
levels in his work environment.
                                   6-34

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                                           Pollution
Figure 6-5,
Illustration of benefits calculation for
non-marginal changes in TSP levels
                          6-35

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     Benefits  calculations for non-marginal changes in TSP levels involve
measuring the area under S(TSP)j_ between the two pollution levels (i.e.,
area AP^B for a decline in TSP levels from Pj to P2).  Since SdSPJj is
not directly observable, this area is approximated by assuming that the
supply price  functions  for each  individual for TSP  decrease linearly from
the observation point  (i.e.,  point A)  to  zero as TSP  is continuously
abated.  Thus, benefits can be calculated as the difference between the
areas of two  triangles AP^O and CPjO, or

         Benefits  =  | [ (APX '  OP],) - (CP2 '  OP2>]                 (6.12)

For the semi-log specification of the hedonic wage model, benefits are
calculated as  follows:
          Benefits  =  *~-  P,  - =-                               (6.13)
where     P^   is the initial TSP level.
          ?2   is the TSP level after implementation of the appropriate
              control strategy.
and all other  variables are as defined above.
     Clearly,  the  accuracy of  this  approach  to  benefits calculations
depends on how closely the linear segment  CA approximates  the curvilinear
segment BA.   If segment CA falls  below  segment BA (as drawn in Figure  6-5),
then Equation (6.13)  will  underestimate the true  benefits obtained from the
given non-marginal change in TSP concentrations.   Conversely, if segment CA
lies above  segment  BA, the result is  an overestimate of these benefits.
Since segment BA is not observed, the  direction and extent of the bias in
benefits calculations resulting from this approach is unknown.

     The average  real wage  expressed in 1980 dollars is used to  calculate
individual  willingness-to-pay  for air quality; these benefits are multi-
plied  by average  annual  work hours  (i.e., 2,080 hours  per  year)  to
                                   6-36

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approximate  average  annual benefits per individual  at  the  attainment date.
Individual benefits are then  multiplied by the projected labor force at the
attainment  date in order to  extrapolate benefits over  the population.
These benefits  are  extended  over  the relevant time horizon under  the
assumption  of continuous growth in the labor force,  and  then discounted
back to 1982.

     Both Rosen and  Smith  use  data aggregated  to  the  SHSA level  in
estimating their models; however,  information on ambient TSP concentrations
is compiled by county.  In this study, benefits from  hedonic wage models
are calculated at  the  county level;  this facilitates  comparisons  with the
benefits estimates from health effects  studies  and property value studies
presented elsewhere  in  this report.

     The following information is required  for  benefits calculations  from
hedonic wage models:

     •   Real wage rates, expressed in 1980 dollars.*
     •   Projected changes in  ambient TSP concentrations for a given
         attainment date  resulting from the  attainment of a speci-
         fied standard.
     •   Projections of labor force  growth  into the future.

Data sources and the procedures used in transforming the  raw data into a
more suitable form are  summarized in Table 6-5.

     Benefits estimates are calculated for six alternative particulate
matter standards.  For  each standard,  minimum and maximum estimates provide
* Technically, the  real  wage at the  attainment date should be used.   In
  these  benefits calculations,  the average real wage in each county in 1978
  is used.  The  average nominal  wage rate  in 1978 for each  county  is
  deflated by the BLS  cost-of-living index for intermediate expenditure
  families in order to approximate this average real wage.   The consumer
  price  index  is then used to inflate  this  real  wage  to  1980 dollars.   The
  real wage is then extrapolated to the attainment date and beyond using
  income  projections  provided  by  the  Bureau  of  Economic  Analysis,
  Department  of  Commerce  (30).
                                   6-37

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                                            Table  6-5

                             DATA SOURCES  AND TRANSFORMATIONS
  Variable
              Source
                                                                        Comments
  Nominal
  wage
  rate
Payroll and employment information
for non-government* and Federal
government employees obtained from
County Business Patterns 1978 pub-
lished by the Bureau of Census (31)
The combined Federal government and non-
government payroll is divided by  the  total
number of employees reported by each  sector
in order to estimate average annual earnings
per individual.  This estimate is divided by
2,080 (i.e., no. of hrs. worked/year) as an
approximation to the average hourly wage.
  Cost-of
  living
  deflator
Index of comparative costs based on
an intermediate budget for a 4-
person family (Autumn 1977),
published by the Bureau of labor
Statistics.
This index is calculated for SMSAs and for
non-metropolitan areas within each census
region.  Generally the SMSA index associated.
with each county is used to deflate that
county's nominal wage rate.  When a defla-
tion factor is not available for an SMSA the
U.S. metropolitan index is used.  The
regional non-metropolitan index applies for
counties which are not part of an SMSA.
  Consumer
  Price
  Index
Bureau of Labor Statistics
Consumer price indices are published on a
regional basis for the 4 census divisions.
These regional price indices are used to
convert benefits estimates to 1980 dollars.
  Income
  growth
  projection
The real wage rate at the attain-
ment date is extrapolated from the
real wage rate in 1980 using income
growth projections to year 2000.
Continuous growth is assumed.
Growth rates are calculated from
projections of personal income to
year 2000 obtained from the U.S.
Bureau of Economic Analysis (30)
Income projections are available by state.
  TSP
  concentra-
  tions
Provided by EPA
PM10 concentrations are converted  to equiva-
lent TSP levels expressed as  the annual geo-
metric mean.  These TSP readings are then
transformed to an annual geometric mean to
conform with data used by Rosen  (4) and
Smith (8).  This data in its  original  form
represented the worst incidence of air
pollution within each area.   Data  was  trans-
formed in order to derive a measure of
average exposure throughout the county.  The
procedure used in this transformation  is
discussed in Section 9.
  Labor
  force
Labor force as of attainment date
is extrapolated from the labor
force in 1978 under the assumption
of a continuous growth rate.  Labor
force by county in 1978 is obtained
from unpublished data provided by
the Bureau of Labor Statistics.
The employment & population projec-
tions to 2000 used to calculate the
growth factor are provided by the
Bureau of Economic Analysis (32,33)
Employment projections  are  available  on an
SMSA level and are  used to  calculate  labor
force growth  for counties within these
SMSAs.  Population  projections  at the state
level are used to approximate labor force
growth  in rural counties.
* Excluding  self-employed individuals, railroad employees, farm  workers,  and domestic-service
  workers, and government employees at the state and local levels.
                                                 6-38

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a likely range of benefits, while a single point estimate indicates the
best defensible  benefits estimate.*  The choice of  coefficient for the
maximum benefits estimate  is based on  a  review  of  a  series  of  specifica-
tions estimated by Rosen  (4).   The coefficient was  selected  from that
specification which included  a  maximum number of explanatory variables,
most of which had  a  statistically significant  effect on real earnings.
This minimizes, to the extent possible,  the  impact  of omitted  explanatory
variables  on  the  estimated TSP coefficient.

     The point benefits estimate  is derived from results reported by Smith
(8).  In order to incorporate  the uncertainty which is  associated with this
estimate  due to  the  stochastic nature of the estimation procedure, the
lower bound of the  confidence interval  around this point estimate is used
to define the lower bound of  the reported range of benefits.**

     The coefficients on the TSP measure derived by both Smith and Rosen
were estimated  from air pollution  data at the SMSA level.   Table 6-6
summarizes the extent of ambient TSP exposure which each model assumed, and
the adjustments made to the design values  used in the current benefits
estimates in  order  to replicate these  conditions.

     Table 6-7 reports calculations of  individual willingness-to-pay for
air quality,  based on the  coefficients and the air pollution data which are
used in benefits estimates.   Estimates  of willingness  to pay for marginal
changes in concentrations of  particulate  matter  range  from $3.32 per year
to $10.98  per year, depending on the sample.  These  estimates appear to be
quite reasonable, and reflect a highly inelastic  relationship between real
wages and particulate concentrations.
 * The coefficients used  in  these calculations are given  as  follows:
   Minimum estimate 0.000431; Point estimate  0.000871;  Maximum  estimate
   0.000921.
** This procedure  is not used to  define the  maximum benefits estimates
   because  sufficient information is not  available to calculate a  statisti-
   cally-meaningful confidence  interval  around  the  coefficients  estimated
   by Rosen (4).
                                   6-39

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                                    Table 6-6

        AIR QUALITY DATA:  ORIGINAL MODELS AND CURRENT BENEFITS ANALYSIS
  Study
Geographic Area
 Monitor(s) Used in
   Original Study
Pollution Measure Used in
   Benefits Analysis*
  Rosen
   19 SMSAs
Monitor representing
worst incidence
within SMSA
Design value monitor in
county
  Smith
   44 SMSAs
Average over all
monitors within SMSA
Average over all monitors
in county
* See Section 9 for  a  complete description of the  air  quality data used in this
  benefits analysis.

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                                Table 6-7

         ESTIMATED BENEFITS FOR MARGINAL CHANGES  IN AIR QUALITY*
Standard
PM10 70 AAM/250 24-hr
(93 counties)
PM10 55 AAM
(161 counties)
PM10 55 AAM/250 24-hr
(163 counties)
PM10 55 AAM/ 150 24-hr
(297 counties)
TSP 75 AGM/260 24-hr
(282 counties)
TSP 150 24-hour
(499 counties)
Change in Annual Real Earnings**
Minimum
$3.32
3.52
3.52
3.65
3.62
3.71
Point"*"
$6.71
7.11
7.12
7.38
7.31
7.49
Maximum
$11.36
11.36
11.36
11.21
11.15
10.98
 * A change in ambient TSP levels of 1 ug/m3 (annual geometric mean) at the
   design value monitor  is assumed.  This is roughly equivalent to a change
   in ambient PM10 levels of  0.55 ug/m3.

** Calculated at  the  average  real  wage over all  affected  counties for each
   standard,  expressed  in 1980 dollars and multiplied by 2,080 hours in
   order  to  approximate  annual real earnings.   Thus,  these figures repre-
   sent a change  in average annual earnings.  Similar calculations by Smith
   (8) suggest that the range  of estimated benefits could be somewhat wider
   if these benefits were cumulated over  observed individual earnings.

 + The coefficients used here  reflect  the  relation between real wage rates
   and average exposure  to ambient  TSP.   Thus, for these  calculations,  a 1
   |ig/m3  change in ambient TSP at the design value monitor is translated
   into the equivalent change  in average exposure using the ratio of the
   mean average exposure to mean design value exposure for each standard.
                                   6-41

-------
     Benefits estimates  for three  alternative PM10  standards and  one
associated PM10 secondary  standard, as well as for the current TSP primary
and secondary standards,  are presented in Tables 6-8  through 6-13.   The
standards are arranged in  the following order:

     •   PM10 Primary Standard ~ 70 AAM/250  24-hour expected value
         (Table 6-8).
     •   PM10 Secondary  Standard  —  55 AAM (Table 6-9).
     •   PM10 Primary Standard — 55 AAM/250  24-hour expected value
         (Table 6-10).
     •   PM10 Primary Standard — 55 AAM/150  24-hour expected value
         (Table 6-11).
     •   TSP Primary Standard — 75 AGM/260 24-hour second-high
         (Table 6-12).
     •   TSP Secondary Standard —• 150 24-hour second-high (Table 6-
         13).
     In each  case, a range of  benefits, accompanied by a point estimate,  is
                               «
presented by air pollution control region and then aggregated over the
entire nation.  The stream of benefits is estimated from the attainment
date (1989 for PM  standards; 1987 for TSP  standards)  to 1995 and then
discounted back to 1982, using a 10 percent social discount  rate.  The
present discounted value of benefits in 1982 is expressed in millions  of
1980 dollars.

     Table  6-8  presents  estimates  of  the  benefits  which  will  accrue when
the most lax standard  (PM10 70 AAM/250 24-hour expected value)  is imposed.
Total  benefits estimates  range  from  almost $10  billion to about $37
billion,  with a point  estimate  of  about $19.8 billion.  About one-third  of
these benefits accrue to the South Pacific air pollution control region.
Another one-third will accrue to the East North Central  region,  while  the
South Central region will receive  about  12  percent  of the  total benefits.
The remaining benefits  are divided among the Middle Atlantic region (5
percent), the  South Atlantic region (5 percent), the Midwest region (2
percent),  the Mountain region (4 percent),  and  the North  Pacific region (5
                                   6-42

-------
                         Table 6-8

       ESTIMATED BENEFITS FOR:  HEDONIC WAGE STUDIES

          Benefits Occurring Between 1989 and 1995
         Scenario:  Type B PM10 - 70 AAM/250 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                    0.0
                                   10.0
                                  436.3
                                  533,
                                 2842.
                                 1230.8
                                  150.8
                                  382.8
                                 3697.8
                                  525.2
    ,6
    .2
Point
Estimate
0.0
20.3
881.4
1077.9
5741.1
2486.1
304.6
773.3
7469.3
1060.8

Maximum
0.0
40.1
1404.7
1850.5
14659.9
4875.1
547.0
1259.8
10848.6
1721.8
Total U.S.
9809.6
19814.9   37207.5
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate  of  Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
3926.6
 7931.4   14893.3
                              6-43

-------
                         Table 6-9

       ESTIMATED BENEFITS FOR:  HEDONIC WAGE STUDIES

          Benefits Occurring Between 1989 and 1995
              Scenario:   Type B PM10 - 55 AAM
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                  117.6
                                  121.9
                                  772.
                                 1183
                                 3994.
                                 1918.2
                                  426.2
                                  772.
                                 6557.
     .5
     .2
     .1
     .5
     .1
Total U.S.
                                  670.7
16534.1
Point
Estimate
237.6
246.3
1560.3
2390.0
8067.9
3874.7
860.8
1560.5
13245.1
1354.8

Maximum
362.3
541.1
2494.5
3889.2
21219.5
7294.4
1394.4
2287-7
19474.0
2281.6
33398.0   61238.5
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
 6618.2
13368.4   24512.4
                             6-44

-------
                         Table 6-10

       ESTIMATED BENEFITS FOR:  HEDONIC WAGE STUDIES

          Benefits Occurring Between 1989 and 1995
         Scenario:  Type B PM10 - 55 AAM/250 24-hr.
Federal Administrative Region   Minimum
                                  117.6
                                  121.9
                                  772.5
                                 1183.2
                                 4004.2
                                 1940.5
                                  428.0
                                  772.5
                                 6557.7
                                  713.0
                                16611.1
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Point
Estimate
237.6
246.3
1560.3
2390.0
8088.2
3919.7
864.6
1560.5
13246.1

Maximum
362.3
541.1
2494.5
3889.2
21252.2
7359.9
1399.8
2287.7
19475.5
                                            1440.3
           2390.5
Total U.S.
33553.6   61452.7
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate  of Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
                                 6649.1
13430.7   24598.1
                             6-45

-------
                         Table 6-11

       ESTIMATED BENEFITS FOR:  HEDONIC WAGE STUDIES

          Benefits Occurring Between 1989 and 1995
         Scenario:  Type B PM10 - 55 AAM/150 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South. Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Total U.S.
                                  381.4
                                  239.0
                                 1121.6
                                 1509.4
                                 4545.9
                                 2251.1
                                  597.4
                                 1159.4
                                 7239.5
                                 1217.8
20262.5
Point
Estimate
770.5
482.8
2265.5
3048.9
9182.4
4547.0
1206.8
2341.9
14623.3
2459.9

Maximum
1233.9
944.6
3533.7
4825.4
22940.6
8350.8
1898.4
3244.4
21613.7
4012.6
40929.1   72598.1
Discounted Present Value  in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
 8110.6
16383.0   29059.3
                              6-46

-------
                         Table 6-12

       ESTIMATED BENEFITS FOR:  HEDONIC WAGE STUDIES

          Benefits Occurring Between 1987 and 1995
         Scenario:  Type B TSP - 75 AGM/260 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                  369.7
                                  303.9
                                 1734.6
                                 2245.3
                                 6931.2
                                 2999.6
                                 1018.
                                 1393,
                                10816.
     .1
     .1
     .1
Total U.S.
                                 1354.6
29166.1
Point
Estimate
746.7
613.8
3503.7
4535.4
14000.7
6058.9
2056.5
2814.0
21848.0
2736.2

Maximum
1145.9
1190.8
5471.1
7156.6
36390.0
11855.5
3170.1
3947.9
32258.7
4551.2
58913.9  107137.7
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate  of Discount.
Annualized Benefits
Between 1987 and 1995
Total U.S.
 8156.3
16475.3   29961.0
                             6-47

-------
                         Table 6-13

       ESTIMATED BENEFITS FOR:  HEDONIC WAGE STUDIES

          Benefits Occurring Between 1987 and 1995
             Scenario:  Type B TSP - 150 24-hr.
Federal Administrative Region   Minimum
           Point
          Estimate
          Maximum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
1105.3
600.8
2588.2
2807.4
8262.0
3536.0
1672.8
1867.8
11858.1
2232.7
1213.5
5228.0
5670.8
16638.8
7142.5
3379.0
3772.9
23952.7
3475.5
2158.6
7933.7
8832.1
39904.8
12477.3
5015.8
5198.6
35013.4
Total U.S.
                                 2077.2
36375.5
            4195.7
           6829.8
73476.5  126839.6
Discounted Present Value  in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount.
Annualized Benefits
Between 1987 and 1995
Total U.S.
10172.4
20547.7   35470.7
                             6-48

-------
percent).  Tie New York-New Jersey region accounts  for a miniscule propor-
tion of total national  benefits (0.1 percent).   Tie entire New England
region is projected to be in compliance  with  tie standard by 1989; tins,  no
benefits will  accrne to tiis region  as a result of tie proposed  standard.

     Tie  remaining  tables  indicate  tie  benefits wiici are associated  witi
increasingly  stringent  PH10 standards, as well as witi tie current TSP
standard (wiici is more stringent tian tie strictest PM10 standard).  As
more stringent standards are applied,  benefits increase  accordingly.   Point
benefits estimates rise from $19.8 billion for tie most lax PH primary
standard to $73.5 billion under  tie current  TSP secondary  standard.  Tie
regional  distribution  of tiese benefits also varies as stricter standards
are  imposed.   Tie proportion of benefits  received by tie  East Norti
Central,  Souti Central, and Souti Pacific regions declines in favor  of tie
remaining areas.

     Tie very large  benefits reported in Tables  6-8  tirougi 6-13 are
consistent  witi  tie small marginal  effects observed in Table 6-7.  While
tie responsiveness of eaci individual's real  earnings  to  cianges  in tie
ambient levels of particulate matter is iigily inelastic,  tie effect  on
real earnings  of non-marginal cianges in particulate matter concentrations
cumulated over tie  entire labor force  is considerable.  For example,  assume
for tie moment tiat a  decrease in TSP levels by 1  ug/m will  cause tie  real
wage rate to fall by $0.005 per iour,  or by $10.40 per year.  A  very large
decline in ambient  levels of particulate matter —  say about 100 jig/m  —
could lead to a  decline in real  annual earnings  of rougily $1,000.  If a
regional  labor  force of 10,000 individuals is assumed, tien  tie total
benefits at tie attainment date alone will amount to some $10 million.
Tiis figure will,  of  course,  grow as tie labor force  expands  and as tie
effects of  improved air quality are  felt in tie future.

     Tie  benefits estimates presented in Tables 6-8 tirougi  6-13 are based
on tie assumption tiat all affected  counties  attain tie standard in tie
implementation year and  maintain it tirougi  1995.   As noted in  Section 9,
tiese are referred to as "B" scenarios.  An  alternative "A" scenario was
                                   6-49

-------
also considered.  In this scenario,  the appropriate control strategies used
to implement  the standards may not bring all counties  into attainment.
This can occur because available control  options are exhausted prior to
standard attainment.  In order to test the sensitivity of these benefits
estimates  to  the attainment assumption,  an alternate  set of benefits
estimates for  the -most lax PM10 standard (PH10 70  AAM/250  24-hour expected
value) are presented in Table 6-14.  The  assumption that  all counties
comply with the standard is relaxed,  i.e.,  residual nonattaimnent counties
are not forced into attainment.

     Table  6-14 should be compared  with Table 6-8, where  all  counties are
assumed to be  in compliance with the same PM10 standard.   As  expected,  the
benefits  estimates in Table 6-8 exceed those shown in Table 6-14.

     Benefits  estimates for the remaining particulate  matter standards when
the assumption of full compliance  is relaxed are presented  in  Section 11.
CONCLUDING
     In this section,  the  results reported in two hedonic wage  models
[Rosen (4)  and Smith (8)] are used to estimate the benefits which result
from the attainment of three  alternative primary standards  for ambient
levels of particulate matter.  Point estimates of  these benefits* range
from $73.5 billion under the most  stringent standard (the  current TSP
secondary  standard) to about $19.8 billion for the most lax PH10 primary
standard.

     The marginal willingness- to-pay  for  air  quality calculated  from the
data is quite reasonable, ranging from about $3.30  per year  to  over $11 per
year.   The  very  large benefits reported in Tables  6-8 through  6-13 reflect
two factors:
* Calculated  as the discounted present value of a stream of benefits  over a
  7-year (9-year)  horizon from an attainment date of 1989 (1987),  expressed
  in 1980 dollars  in  1982.
                                   6-50

-------
                         Table 6-14

       ESTIMATED BENEFITS FOR:  HEDONIC WAGE STUDIES

          Benefits Occurring Between 1989 and 1995
         Scenario:  Type A PM10 - 70 AAM/250 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Total U.S.
                                    0.0
                                   10.0
                                  422.0
                                  458.2
                                 2267.8
                                  829.2
                                  136.4
                                  364.8
                                 2073.4
                                  178.6
6740.4
Point
Estimate
0.0
20.3
852.3
925.6
4580.8
1675.0
275.6
736.9
4188.1
360.8

Maximum
0.0
40.1
1359.5
1553.4
10830.1
3278.4
485.1
1088.4
6064.2
600.0
13615.3   25299.1
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate  of Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
2698.0
 5449.9   10126.7
                             6-51

-------
         The effect of larger (i.e., non-marginal)  improvements  in
         air quality on real annual earnings.

         The extrapolation of  individual willingness-to-pay over the
         entire labor force in the  affected  areas.
     The benefits estimates cited above  are derived from the application of

the estimated relationship between real earnings and particnlate  matter

levels reported in Smith (8) and Rosen (4) to a set of data obtained from

other sources.  The assumptions which are  required  in  order  to  adapt these

models and data to the procedure used in the benefits calculations may bias

these calculations.  Some cautionary  remarks relating  to possible  sources

of bias are  summarized in Table 6-15.


REFERENCES
 1.  Cropper,  H.  L. and A.  S.  Arriaga-Sal inas.   Inter-City Wage
     Differentials  and the  Value of  Air Quality.    Journal of  Urban
     Economics,  8:236-254.   1980.

 2.  	.  Methods Development for  Assessing Air  Pollution  Control
     Benefits.  Vol.  IV,  EPA 600/5-79-001/d, February 1979.

 3.  Rosen,  Sherwin.  Hedonic  Prices and Implicit Markets.   Journal of
     Political  Economy,  82(l):34-35.   January/February 1974.

 4.  	.   Wage-Based Indices  of Urban Quality of Life.   In:   Current
     Issues in Urban Economics, P. Mieszkowski and  M.  Straszheim (eds.).
     Baltimore:  Johns Hopkins Press,  1979.

 5.  Thaler, R. and S. Rosen.  The Value of Saving a Life:  Evidence from
     the Labor Market.  In:  Household Production and Consumption.  N. E.
     Terleckyj, ed..  New York:  NBER,  1975.

 6.  Lucas,  R.  Hedonic Wage  Equations and Psychic Wages in  the Returns to
     Schooling.  American Economic Review, 67(3):549-558. September 1977.

 7-  Roback,  J.   The Value of Local Urban Amenities:   Theory  and
     Measurement.   Unpublished  Doctoral  Dissertation,  University of
     Rochester,  1980.

 8.  Smith,  V. K.   The Role of Site and .Job Characteristics in Hedonic Wage
     Models.  Forthcoming  in Journal  of  Urban Economics.

 9.  Viscusi,  W.   Wealth Effects  and Earnings Premiums for Job Hazards.
     Review  of  Economics and  Statistics, 60(3):408-416.   August 1978.
                                   6-52

-------
                               Table 6-15

     SUMMARY OF BIASES IN BENEFITS CALCULATIONS:  HEDONIC WAGE MODELS
             Source of Bias
  Direction of Bias
   Hedonic wage models can only measure
   perceived health, and welfare effects
   of  improved air quality.
Benefits underestimated
since unperceived health or
welfare effects omitted.
   Formula  for benefits  calculations based
   on procedure  suggested by Freeman (28)
   that  assumes  marginal willingness to
   pay for  air quality declines linearly
   from  initial  concentration of  ambient
   PM to zero as PM  is abated.
Cannot be predicted since
the true supply price
function for particulate
matter is not observed.
   TSP may be  highly  correlated with other
   site characteristics  (including other
   pollutants) which  are  omitted  from
   model.  Est.  coefficient on TSP may
   also include  some  of  the effect of the
   omitted variables  on  real earnings.
Cannot be predicted in
the absence of additional
information on the
omitted site characteris-
tics and their correla-
tion with TSP.
   Benefits  are  extrapolated over entire
   labor force while  the  coefficients on
   TSP were  estimated over  specific  sub-
   samples of the  labor force.*  It  is
   assumed that  unemployed  individuals
   place the same  value on  improved  air
   quality as employed individuals.
To the extent that
unemployed individuals
place a lower (higher)
value on air quality,
benefits are over-
estimated (under-
estimated) .
   Certain occupations not  included  in
   calculation of  county real wage rate.**
Unknown.
 * Rosen  (4)  used data  defined  over  employed  males  while  Smith  (8)
   estimated  his  model over  all employed  individuals  (both male  and
   female).  There is some  evidence that  the  response of  real wage  to
   changes in  air quality does  not vary  by  sex (8).   However,  neither study
   can measure  willingness-to-pay for air  quality among  the unemployed,
   although Smith shows that this  sample truncation does not seriously bias
   his coefficient estimates.

** These  data  exclude  self-employed  individuals,  railroad,  farm  and
   domestic-service workers,  and state and municipal employees.
                                    6-53

-------
10.   Hamermesh,  D.   Economic Aspects of Job Satisfaction.   In:   Essays  in
     Labor  Market and Population Analysis,  Ashenfelter  and  W.  Gates,  eds.
     New York:   Wiley,  1977.

11.   Olson, Craig A.  An Analysis  of  Wage Differentials Received by Workers
     on Dangerous Jobs.  Journal of Human Resources,  16(2): 167-185.  Spring
     1981.

12.   Brown, C.  Equalizing Differences in the Labor Market.  Quarterly
     Journal  of  Economics,  94(374) :113-134.  February  1980.

13.   Meyer,  J.  and  R,  Leone.  The Urban Disamenity Revisited.   In:  Public
     Economics  and the  Quality of Life,  L. Wingo and  A.  Evans,  eds.
     Baltimore:   Johns  Hopkins Press, 1977.

14.   Getz,  M.  and Y. Huang.   Consumer Revealed Preference for Environmental
     Goods.  Review  of Economics and  Statistics, 60(3) :449-458. August
     1978.

15.   Israeli, 0. Differentials in Nominal Wages and Prices. Unpublished
     doctoral  dissertation.  University of  Chicago,  1973.

16.   Hoch,  Irving.   Wages,  Climate  and the Quality of Life.  Journal  of
     Environmental  Economics and Management, l(4):268-295.  December 1974.

17.   Smith,  Robert  A.   Compensating  Wage Differentials  and Public Policy:
     A Review.   Industrial  and  Labor Relations Review,  32:339-352.  April
     1979.

18.   Izraeli, 0.   The  Effect of Environmental  Goods and  City Size  on
     Earning  Levels and Housing  Values  Across SMSAs:   Empirical  Evidence.
     Oakland University, Rochester, Michigan (presented at the Western
     Economics Association  Meeting,  San Francisco, California,  July 1981).
     25 pp.

19.   National Academy of Science.   Air Quality and Automobile Emission
     Control:  Volume  4 - The Costs and Benefits of Automobile Emission
     Control.  Prepared for the Committee on Public Works, United States
     Senate, Washington, D.C., U.S. Government Printing  Office,  September
     1974.  470 pp.

20.   Mathtech,  Inc.  Benefits  Analysis of Alternative Secondary National
     Ambient  Air Quality Standards  for  Sulfur Dioxide  and Total Suspended
     Particulates.  Prepared for the U.S.  Environmental Protection Agency
     (July  1981).   Draft Final Report.

21.   Dagenais,  M.   The Use of  Incomplete  Observations  in  Multiple
     Regression  Analysis.  Journal of Econometrics,  1:317-328.   1973.

22.   Cebula,  Richard  J.   Determinants of  Geographic  Living-Cost
     Differentials in  the United  States:  An Empirical  Note.    Land
     Economics,  56:477-481.  November 1980.
                                   6-54

-------
23.   Cebula,  Richard J. and Lisa Karen Smith.   An Exploratory Empirical
     Note on Determinants of Inter-Regional Living-Cost Differentials  in
     the  United States,  1970  and  1975.   Regional  Science  and Urban
     Economics,  11:81-85.   1981.

24.   Heckman, J.  The Common Structure of  Statistical Models of Truncation,
     Sample  Selection  and Limited Dependent Variables and  a Simple
     Estimator  for  Such  Models.  Annals of Economic and Social Measurement,
     5:475-492.   Fall 1976.

25.   	.   Sample  Selection Bias as a Specification Error.  Econometrica,
     47(1):153-162.   January 1979.

26.   Gronau,  R.  Wage  Comparisons  — a Selectivity  Bias.   Journal  of
     Political  Economy,  82(6):1119-1143.   November/December 1974.

27.   Wales,  T.  and A.  Woodland.  Sample  Selectivity and the Estimation  of
     Labor Supply Functions.   International Economic Review, 21:437-468.
     June 1980.

28.   Olsen,  R.  J.   Approximating a Truncated Normal Regression with the
     Method  of  Moments.   Econometrica,  48(5): 1099-1106.   July  1980.

29.   Freeman,  A.  Myrick.  The Benefits of  Environmental Improvement.
     Baltimore:  Johns Hopkins University Press, 1979.

30.   Bureau  of  Economic Analysis.   U.S. Department  of Commerce  News.  BEA
     80-74,'December 9,  1980.

31.   U.S. Bureau of  the  Censns.   County Business Patterns,  1978.

32.   U.S. Bureau of Economic Analysis.  OBER-BEA Regional Projections,  Vol.
     Ill,  SMSAs.  July  1981.

33.   U.S. Bureau of Economic Analysis.   Projections of Population 1976-
     2000.  March 23,  1981.
                                   6-55

-------
                               APPENDIX 
-------
                               Table 6A-1

               HEDONIC WAGE MODEL ESTIMATED BY ROSEN  (4):
             EFFECTS OF INDIVIDUAL-SPECIFIC CHARACTERISTICS
Explanatory
Variable
Race
(white = 1)
Head of Household
(yes - 1)
Married
(yes = 1)
Employed Full Time
(yes = 1)
Work 35 Hours/Week
or Less
(yes = 1)

Self- Employed
(yes = 1)

Government Employee
(yes = 1)

Never unemployed
During the Year
(yes = 1)

Unemployed Once
During the Year
(yes = 1)
Veteran
(yes = 1)

Sales
(yes = 1)
Crafts
(yes = 1)
Coefficient
0.150*

0.160*

0.085*

0.597*

-0.142*



0.160*


0.125*


0.050



-0.068


0.050*


-0.198*

-0.157*

Explanatory
Variable
Operatives
(yes = 1)
Laborer
(yes = 1)
Service
(yes = 1)
Durable Goods
(yes = 1)
Nondurables
(yes = 1)

Transport
(yes = 1)

Trade
(yes = 1)

Other Service
(yes = 1)

Public Administra-
tion
(yes = 1)

Education
Experience
t\
(Experience)

Log Weeks Worked
R2

Coefficient
-0.263*

-0.270*

-0.302*

-0.086*

-0.105*


-0.074*


-0.165*


-0.186*


-0.039



0.048*
0.015*

-0.0002*

0.835*
0.337

* Coefficient  has  a  t-statistic  >, 2.
                                    6-57

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                                                        Table 6A-2

                                        HEDONIC WAGE MODELS ESTIMATED BY ROSEN (4):
                                         EFFECTS OF SITE-SPECIFIC CHARACTERISTICS
Explanatory Variables
TSp(a)
Inversion Days
Water Pollution*3*
Rainy Days(a) (No.)
Sunny Days(a) (No.)
Temperature 90°F+'a'
(Number)
Crime Rate(b)
Unemployment Rate
Population Growth Rate
Population Density*13'
Population Level *c^
Live in Center City
(yes = 1)
Selected Regression Results: Estimated Coefficients
0.1060*


0.1220*


0.1070




-0.0943*
0.0681*


0.2860*


-0.0521
0.0457*



-0.0908*
0.0897*
0.0102*
-0.0868

-0.9620*
0.2390*
0.3160*




-0.0823*
0.1500*


0.0406

I
0.0270

0.4790*


-0.0910*
0.1100*


0.2250*


-0.1700
0.0539*
0.5330*


-0.0863*
0.0692*


0.2300*


0.1200


-0.1100*
0.0966*
-0.0960*
0.0602*


0.2720*


0.0614
0.0149

-0.0998*
0.0899*
-0.0950*
0.0553*


0.2950*

NA
0.0926
0.0095

-0.1120*
0.0967*
-0.9950*
0.0921*


0.1740*


0.0492

0.1950*
-0.0899*
0.1040*
-0.0864*
01
00
a Coefficient scaled up by 100.

* Coefficient has a t-statistic

NA = Not reported.
                                        2.
Coefficient scaled up by 10,000.

Coefficient scaled up by 10,000,000.

-------
I
Ul
                                                       Table  6A-3

                                       HEOONIC WAGE MODELS ESTIMATED BY  SMITH (8)*
Explanatory Variable
Intercept
Education
(no. of years)
(Education)2
Experience
(age - education - 6)
(Experience)2***
Race
(white = 1)
Sex
(male = 1)
Veteran
(yes = 1)
Unemployment Rate
Full
Sample
0.3411
(6.15)
0.0244
(3.88)
0.0013
(5.05)
0.0261
(32.44)
-0.0455
(-26.62)
0.0560
(5.85)
0.1663
(17.59)
0.0749
(7.80)
-0.0138
(-5.59)
Hale
0.6512
(8.98)
0.0308
(4.06)
0.0010
(3.30)
0.0309
(25.67)
-0.0532
(-22.26)
0.1118
(8.66)
—
0.0359
(3.60)
-0.0208
(-6.47)
Female
0.2021
(2.30)
0.0283
(2.60)
0.0009
(2.13)
0.0181
(15.94)
-0.0301
(-11.91)
-0.0261
(-1.86)
—
—
-0.0051
(-1.38)
Full
Sample
0.4038
(6.93)
0.0234
(3.72)
0.0013
(5.22)
0.0263
(32.60)
-0.0458
(-26.76)
0.0026
(0.13)
0.1662
(17.58)
0.0748
(7.80)
-0.0138
(-5.59)
Hale
0.6443
(8.24)
0.0304
(4.02)
0.0010
(3.30)
0.0310
(25.78)
-0.0535
(-22.41)
0.0734
(2.55)
—
0.0363
(3.65)
-0.0206
(-6.40)
Female
0.2328
(2.53)
0.0254
(2.34)
0.0011
(2.40)
0.0182
(16.05)
-0.0303
(-12.00)
-0.0329
(-1.10)
—
—
-0.0050
(-1.35)
                                                                                                 (continued)

-------
                                                 Table 6A-3  (Continued)
i
o^
o
Explanatory Variable
Professional
(yes = 1)
Manager
Sales
Clerical
Craftsman
Operative
Transport Equipment
Operator
Labor (Non-farm)
Service
Full
Sample
0.3471
(16.67)
0.3740
(17.32)
0.1491
(6.52)
0.2001
(10.15)
0.2646
(12.26)
0.0780
(3.62)
0.1225
(4.68)
0.0776
(3.25)
-0.0098
(-0.48)
Male
0.0871
(2.79)
0.1411
(4.45)
-0.0019
(-0.05)
-0.1009
(-3.06)
0.0171
(0.54)
-0.1465
(-4.40)
-0.1180
(-3.35)
-0.1288
(-3.81)
-0.2533
(-7.77)
Female
0.5650
(19.21)
0.5228
(16.19)
0.2010
(6.36)
0.3924
(15.45)
0.4495
(8.76)
0.2375
(8.18)
0.3694
(5.38)
0.2004
(3.99)
0.1702
(6.40)
Full
Sample
0.3499
(16.80)
0.3741
(17.33)
0.1479
(6.47)
0.1994
(10.11)
0.2625
(12.14)
0.0741
(3.44)
0.1236
(4.72)
0.0767
(3.21)
-0.0100
(-0.49)
Male
0.0903
(2.89)
0.1426
(4.50)
-0.0011
(-0.03)
-0.0975
(-2.96)
0.0184
(0.58)
-0.1496
(-4.50)
-0.1134
(-3.22)
-0.1293
(-3.82)
-0.2488
(-7.64)
Female
0.5678
(19.32)
0.5184
(16.05)
0.1981
(6.27)
0.3911
(15.39)
0.4432
(8.65)
0.2244
(7.69)
0.3587
(5.22)
0.1872
(3.72)
0.1683
(6.33)
                                                                                                 (continued)

-------
Table 6A-3 (Continued)
Explanatory Variable
Injury Rate (BLS)
Cancer Exposure Index
TSp(a)
(ug/m3)
Household Head
Union
OJT ' Experience
Crime Rate(b)
Sun (mean annual % of
possible sunshine)
Dual Job-Holder
Full
Sample
0.0114
(12.87)
0.0219
(2.76)
0.0871
(3.88)
0.1570
(18.08)
0.1832
(22.32)
-0.0012
(-0.98)
0.0943
(4.60)
-0.0015
(-2.62)
-0.0439
(-2.28)
Hale
0.0113
(10.65)
0.0283
(2.77)
0.1120
(3.85)
0.2318
(16.96)
0.1730
(17.09)
-0.0022
(-1.60)
0.0782
(2.94)
-0.0021
(-2.79)
-0.0408
(-1.71)
Female
0.0117
(7.66)
0.0089
(0.72)
0.0675
(1.97)
0.0694
(6.02)
0.1857
(13.47)
-0.0010
(-0.40)
0.1007
(3.24)
-0.0002
(-0.25)
-0.0256
(-0.82)
Full
Sample
0.0037
(1.47)
0.0231
(2.91)
0.0830
(3.70)
0.1641
(10.90)
0.1174
(7.01)
-0.0017
(-1.38)
0.0954
(4.66)
-0.0014
(-2.47)
-0.0426
(-2.21)
Hale
0.0122
(3.39)
0.0290
(2.84)
0.1084
(3.73)
0.3035
(11.69)
0.1035
(4.70)
-0.0022
(-1.65)
0.0797
(3.00)
-0.0020
(-2.71)
-0.0400
(-1.67)
Female
0.0100
(2.36)
0.0100
(0.81)
0.0615
(1.80)
0.1106
(4.74)
0.0971
(3.25)
-0.0021
(-0.82)
0.1007
(3.25)
-0.0001
(-0.13)
-0.0228
(-0.73)
                                               (continued)

-------
                                   Table 6A-3 (Continued)
Explanatory Variable
Workers' Knowledge of
Cancer Hazard
Injury Rate * Race
Injury Rate ' Head of
Household
Injury Rate * Union
R2
S
N
Full
Sample
4.3032
(6.01)



0.460
0.171
16.199
Hale
3.8789
(4.70)



0.462
0.160
9,105
Female
5.7079
(4.22)



0.322
0.173
7.094
Full
Sample
4.1518
(5.79)
0.0070
(3.01)
-0.0001
(-0.67)
0.0078
(4.53)
0.461
0.171
16.199
Hale
3.8538
(4.66)
0.0044
(1.49)
-0.0084
(-3.24)
0.0073
(3.55)
0.464
0.160
9.105
Female
5.52
(4.07)
0.0012
(0.30)
-0.0064
(-2.06)
0.0133
(3.76)
0.323
0.173
7.094
* t-statistics are given in parentheses.




a Coefficients are scaled up by 100.
Coefficients are scaled up by 100,000.

-------
              SECTION 7
ECONOMIC BENEFITS OF REDUCED SOILING

-------
                               SECTION 7
                  ECONOMIC BENEFITS OF KEDUCED SOILING
INTRODUCTION

Overview

     Soiling is the accumulation of  particulate matter on the  surface of an
exposed material.  This accumulation leads to changes in the  quality of
reflectance or transparency for materials  such as painted  surfaces and
glass so that these materials "look dirty" [Beloin and Haynie (1)].   In
addition,  textiles  can weaken and become faded with exposure to abrasive
particles, sunlight, and other pollutants  [Criteria Document (2)].   The
discoloration of fabrics is also considered  to be  a soiling effect.

     With the  implementation of PH control  programs,  particle  deposition
will decrease, and households and  firms may find that'they  can maintain
desired  levels  of cleanliness with fewer  resources  (e.g.,  labor  and
materials) than previously required.  As a consequence, less time and money
would have to be devoted to the variety  of cleaning tasks usually under-
taken.  Appropriate identification of these cost savings provides a measure
of the  benefits of reduced soiling.

     Two major questions are  addressed in this section:

     •    Can a consistent method be  identified  for calculating the
         economic benefits of  increased  cleanliness due  to  reduced
         ambient levels of particulate matter (PM)?
     e    What are the  monetary benefits of reduced soiling, given
         the alternative PM10 (i.e., PM <. 10 urn) and TSP scenarios
         described in Section 9?
                                   7-1

-------
     Much of this section focuses  on the  first of these questions.  Over
the past two decades, a number of studies have been completed which attempt
to assess the costs  associated with PM-related soiling damages.  Our objec-
tive is to review the basic approaches utilized  in  these  studies and to
assess their suitability  for the  calculation of benefits.  No new research
is attempted as part of this effort.  The evaluation of alternative studies
is based on how well the  studies  conform  to  certain  criteria which reflect
desirable  traits of a benefits analysis.  These criteria  include issues
such as the methodological soundness of the study,  consideration of all
relevant variables,  and the legitimacy of the empirical analysis.

     Most of the studies which examine  the benefits of reduced  soiling
focus  on the household  sector.   While none  of  the  household  studies
reviewed here  completely satisfies  all  the  criteria  we identify  as being
relevant,  a class of studies,  the  behavioral models, are sufficiently well-
specified  to warrant the calculation of benefits.   In our judgment, the
necessary analytical tools have been developed  to  the point where  reason-
able estimates of benefits from reduced soiling in the household sector can
be derived.

     While other sectors  of  the economy have received  less emphasis,  there
have been several attempts  to  identify benefits from reduced  soiling for
specific industries  in the  manufacturing sector and for commercial opera-
tions  associated with cleaning services.   Of these studies,  only the
industry analysis, which  examines the impact of changes in PM on production
costs, provides a basis for the calculation of benefits.   The results of
the non-household  sector  analyses will also be  reviewed in this section.

Scope of A*1*lysis

Evaluation Criteria  —

     The principal  objective of this  study is to review the analyses which
have attempted to measure the benefits of reduced soiling and to assess the
soundness of the estimates.  To  provide a  consistent  method for evaluating
                                    7-2

-------
the alternative studies,  several criteria, which, are  important for the

successful completion of any benefits  analysis,  are  identified.   These

criteria  help  identify  where  individual  analyses may be particularly weak

or strong,  and are  useful  in comparing  the plausibility of benefit

estimates generated from  different studies.   Thus,  the  criteria form a set

of conditions  by  which  the  range  of studies to  be  considered  can be
narrowed.


     Five general criteria are used to evaluate critically the various

studies  which  examine the benefits from reduced soiling.   These criteria
include:
     •    Theoretical basis for the  study — Is the study's  technical
         approach consistent with  the  requirements  for  calculation
         of  economic  benefits  as  prescribed  in  theoretical welfare
         economics?

     •    Consideration  of relevant  variables — Has  the  study
         accounted for  all  relevant variables  (e.g.,  economic,
         social, pollution, climate)  that may  influence cleaning
         decisions?

     •    Quality of input data — Does  the study use  data consistent
         with  the variables of  the  theoretical model and  are these
         data accurately measured?

     •    Legitimacy of the empirical analysis  — Were the  empirical
         techniques used  in  the  study  appropriate and  properly
         applied, and  were the results  correctly interpreted?

     •    Transferability of models  and/or results — Can the  study
         be  used as  a basis  for  estimating benefits  of reduced
         soiling given the implementation of various  PM control
         strategies?


Although  these  criteria are  fairly  stringent,   they represent the types of

conditions that must be satisfied if defensible,  quantitative estimates of

benefits  are  to be  derived.  In the next  major subsection,  these criteria

are used  to assess strengths and weaknesses of  the various  studies.
                                   7-3

-------
Coverage of Studies —

     An important issue  that  arises in the development of benefit  estimates
is the degree of  coverage associated with the alternative studies.  As
mentioned above, the literature concerned with the benefits of reduced
soiling has focused  primarily on household responses.  Clearly, many  other
sectors of  the  economy  may also be affected.   Commercial  establishments,
industrial plants,  and government facilities are  all  likely  to  experience
less soiling as  a result of reduced levels of ambient PH.  In this section,
benefits are calculated only for the household  sector  and two industries in
the manufacturing sector — SIC 344 (Fabricated  Structural  Metal  Products)
and SIC 354  (Metalworking Machinery).  Although a  limited number  of  studies
have been designed and undertaken for other sectors of the economy,  to date
these  efforts have not  found a statistically  significant  association
between PM  and  cleaning-related  cost data.  Consequently, no  benefits are
calculated for these  other sectors.  If these other sectors of the  economy
do derive benefits from reduced soiling, then  the estimates of benefits
developed in this  section should be  considered conservative  estimates of
total benefits from  reduced soiling.

Pollutant Measures —

     The development of physical and economic damage  functions for use in
benefits analysis  requires that specific assumptions be made  about measures
of air quality.   At  least  three  questions  arise  in the course  of  designing
a study, estimating  damage relationships,  and  calculating benefits.  These
questions are:

     •    What is (are)  the most appropriate  pollution variable(s)
          for the model?
     •    What variable(s)  is  (are) actually used by the  study?
     •    Will the non-marginal improvements in  ambient air  quality
          that  are   implied by  poll.ution controls  change   the
          composition of PM  and  consequently alter physical  effects
          and/or behavioral responses?
                                    7-4

-------
    These  questions  involve issues of both  a physical and statistical
nature.   The issues related  to  the  physical dimension include particle
size, deposition rates, and composition.  These three attributes  of PM help
define  the soiling potential of the pollutant.  The  statistical issues
include  choice  of  averaging time  and definitions of  spatial  and temporal
indices.

    The questions  posed above are directly  addressed in a  later
subsection.  Here,  we note that particle size plays a prominent role  in the
analysis  since  alternative  standards are  stated in  terms of  TSP and PM10.
However,  with either type of standard, data  are available for  both particle
size divisions.*  Consequently, benefit calculations for studies  which rely
on TSP as a measure of air pollution  can be  conveniently carried out  in the
units  of the original  study,  even when  the  standard  is  stated in terms of
PM10.  In  this case,  the  estimates represent  the benefits of the  TSP
reduction that  results from PM10  controls.
     Based on the review  in this section,  national benefits for reduced
soiling are estimated for the household sector and a part of the manu-
facturing  sector.  Benefit estimates for the household sector are  developed
from  three  studies:   Cummings  et al.  (5),  Watson  and Jaksch (6),  and
Mathtech (7).   Benefit  estimates  for the manufacturing  sector  are  derived
from a single study,  Hathtech  (8).

     Tables  7-1  and 7-2  summarize the benefit estimates of reduced  soiling
for six alternative  ambient air quality standards examined in the  report.
Table 7-1  reports the benefits  associated with three PM10 primary standards
and  the current TSP  primary  standard.  Table  7-2 shows  the  benefits
associated with two alternative secondary standards.   The  values in Table
7-2 represent the total benefits of  moving  from pre-control to post-control
* These data  are  generated  under the  assumption that  the  base year
  relationship of ambient PM10 to TSP is fixed  at 0.55 (4).
                                   7-5

-------
                                Table  7-1




     SUMMARY OF ESTIMATED BENEFITS FOR  ALTERNATIVE  PRIMARY STANDARDS*
Standard
PM10 70/250
PM10 55/250
PM10 55/150
TSP 75/260
Sector
Household
Manufacturing
Household
Manufacturing
Household
Manufacturing
Household
Manufacturing
Benefits
Minimum
0.73
0.73
1.30
1.32
1.62
1.48
2.41
2.43
Point
3.14
1.30
5.68
2.41
7.16
2.86
10.74
4.80
Maximum
13.85
9.45
25. 12
20.51
32.76
26.24
49.90
40.75
* Discounted present values  in 1982  in billions  of  1980  dollars.
                                 Table  7-2




    SUMMARY OF ESTIMATED BENEFITS FOR ALTERNATIVE  SECONDARY STANDARDS*
Standard
PM10 55
TSP 150
Sector
Household
Manuf actur ing
Household
Manufacturing
Benefits
Minimum
1.29
1.32
3.15
2.81
Point
5.64
2.41
14.21
5.81
Maximum
25.53
20.25
66.36
55.13
* Discounted present values in 1982  in billions  of  1980  dollars.
                                    7-6

-------
concentration  levels.   To  ascertain  the  incremental  benefits of  the
secondary standards,  given that the primary standards  are attained,  it  is
necessary to net out the benefits  associated with the relevant primary
standard.  In Table 7-2,  the  55  PM10 secondary standard should be  compared
with the 70/250 PM10 primary standard,  while the 150 TSP secondary standard
should be compared with the 75/260  TSP  primary standard.

     The benefit  numbers  reported here  represent total U.S. benefits.   The
estimates are reported as discounted present values in 1982 in billions  of
1980 dollars.  The real  discount rate is assumed to be  equal to 10 percent,
and the  estimates are developed over a horizon beginning at  the  start  of
1987 (TSP) or 1989 (PM10) and continuing  through to the  end of 1995.  For
the estimates reported here,  it  is assumed that all counties are in attain-
ment with the relevant standard over  the entire  horizon.  In the body of
the report,  estimates are  also derived when not all counties are assumed  to
be in compliance throughout the time period.   This  latter scenario  is
possible if available means of controlling emissions do not allow  for a
control level  sufficient for  standard attainment throughout the time
period.*

     Total  benefits from  reduced soiling in the household  sector  and a
subset of the manufacturing sector are estimated to  range from $1.46  to
$23.30  billion for the most lenient primary standard (70/250 PM10), and
from $4.84  to $90.65 billion  for the most  stringent primary  standard (TSP
75/260).   An important point  to  note is that even  though  the primary
standards are typically associated only  with health effects,  substantial
welfare  benefits  are also derived with  attainment  of the primary standards.

     For the two  ranges cited above, the resultant point estimate of  the
benefits associated with moving to a  secondary standard, given that  the
corresponding primary standard  is attained, are $3.61 and  $4.48 billion  for
PM10 and TSP, respectively.
* This  latter scenario is equivalent to the Scenario A described in Section
  9.
                                  7-7

-------
     Although  the numbers reported in Table 7-1 and 7-2  are  large in magni-
tude,  on  a  per-household  or per-plant  basis, they  appear  intuitively
                                                              q
reasonable.  For example, the per-household benefit of  a 1 jig/m  reduction
in TSP is  estimated to range from about $0.40  to $11.50 per year.   Similar
calculations  can be developed  for  the manufacturing sector.  The benefit
associated with a 1 jig/m^ reduction  in TSP for SIC 354 is estimated to be
approximately  $260  per plant  per year.   This amounts to about  0.019 percent
of the total production cost for an average plant in the industry.   For SIC
344,  these  numbers  are  $610 per plant  per year,  which  is  about 0.031
percent of the  total  cost for an average plant  in the industry.

     The estimates reported in Tables 7-1 and 7-2 are  subject to  certain
qualifications.  The major factors that limit the generality of the results
reported above  include:
          All  possible  benefit  categories  (sectors) are not included
          in the  analysis.  (Leads to an underestimate of benefits.)
          The  benefit  estimates are  sensitive  to a variety of model
          and  extrapolation biases.   These  are  discussed  in detail in
          the  report.   (Benefits affected in an unknown direction.)
          The  air quality data  used  in the benefits analysis  relies
          primarily on design value  monitor readings.   This implies
          that a  correction factor is required  in some  of  the studies
          to better characterize population and facility exposure.
          (Impacts  benefits in an unknown manner.)
          The  physical  effects  and behavioral  responses observed for
          a given composition  of PH do not  change when pollution
          controls are applied.  (Affects benefits  in  an unknown
          direction.)
          Air quality  is  assumed to improve  only in the subset of
          counties  included in  the air quality data file.  (Leads to
          an underestimate of benefits.)
These  limitations, as  well as others  that are  specific to particular
studies,  are  discussed in the body of the report.   While  it  is difficult to
assess whether the various limitations lead to an under- or overestimate of
benefits, there has been a conscious effort to adopt a more conservative
assumption when warranted.   As  used  here,  a  conservative assumption is one
                                   7-8

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that leads to benefit estimates that are lower in. magnitude relative to
estimates associated with a competing assumption.   On the  basis of our
review  and critique of  studies, our evaluation of biases,  and  the develop-
ment of ranges of benefits to reflect uncertainty,  we  believe the point
estimates  reported in  this section represent conservative  estimates of the
total benefits from reduced 'soiling.

MODELS  OF  BENEFITS FROM REDUCED SOILING

Introduct ion

     The object of an analytically proper analysis of  benefits from reduced
soiling is to provide an estimate  of  the income-equivalent change  in
welfare for a specified improvement  in some  measure of PM.  This subsection
describes  the general  approaches that have been used to estimate this value
and assesses the  success individual  studies  have  had  in  obtaining benefits
estimates.

     As noted in  the Criteria Document (2),   there are  several relationships
that must be formalized if an acceptable benefits analysis is to be con-
ducted.  These relationships  start with the transformation  function between
emissions  and  ambient concentrations,  and conclude with the specification
of the  relationship between economic damage  and the benefits associated
with reduced  soiling.   In this study,  the  factors  that translate emissions
into ambient concentrations are taken as  given.  Therefore,  the driving
force of  the analysis is  the expected change  in ambient concentration
levels associated with the  implementation  of alternative  air quality
standards.

     Figure 7-1  shows the linkages between ambient levels of PM and the
realization of economic benefits.  As ambient  levels  of PM decrease, it is
expected  that  there will  be  reductions  in  observed physical  effects  (e.g.,
dirtiness of windows),  which ultimately translate into reduced effort
required  to maintain  a given level  of  cleanliness.   In  turn,   this  reduced
effort means  a  cost  savings to households  and firms.   Under  certain
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                           Ambient levels of PM
                                    i
                             Physical damage
                        Physical damage functions
                                    i
                        Economic damage functions
                           Economic benefits of
                             reduced soiling
            Figure  7-1.  Processes leading to economic  benefits
conditions,  the cost savings  can be  interpreted  as the amount that house-
holds or firms are  willing to pay to have the improved air quality rather
than the air quality that existed prior to the PM reduction.   In  this  case,
the cost savings  are an estimate  of economic benefits.

     This subsection examines  the data  and methods that have  been used in
completing the steps identified in  Figure 7-1.  The discussion focuses
initially  on the air  quality  measures that are most  relevant  for an
analysis of soiling  damage.  Following this,  we begin  our  evaluation of the
various  models which have been  developed to  measure  economic benefits of
reduced  soiling.  In general,  there are two distinct classes of models:
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the physical damage  function models and the behavioral models.   Each of
these  model  types  is  evaluated  in terms of the criteria outlined  earlier,
and individual studies within each class are identified.  A detailed review
of selected  individual studies  is also presented to identify those  studies
best suited  for  the calculation of benefits.

PM Measures  and  Soiling

     As mentioned earlier,  several questions relating  to appropriate
measures of  air  quality arise in the course of designing and completing  an
air quality benefits analysis.  The questions reflect concern about both
physical and statistical characteristics  of  pollutants.

     With respect  to  the physical characteristics,  the Staff Paper  for PM
(3)  asserts  that the soiling of  textiles and vertical surfaces is generally
associated  with fine particle deposition.  On the other hand, horizontal
surfaces  are  considered to be more susceptible  to  deposition of large
particles.  However, it is  also pointed  out in Reference (3) that the
direct relation  between  increased soiling potential and increased particle
size may be mitigated by lighter color of  coarse  mode particles,   smaller
transport distances, and lower penetration rates of larger particles to
indoor  surfaces.  Because  of these offsetting factors, no concensus has
been reached on the most appropriate particle size division to use in an
analysis of  horizontal surface  soiling.

     With respect  to  the statistical issues,  it is a  commonly  held belief
that long-term measures  of  PH concentrations such as annual  means are most
representative of  soiling damage.   However,  this appears"to be based only
on qualitative evidence.   The real test of  this issue involves  an analysis
of behavioral  responses  to  long-term as  well  as episodic  PM measures.   In
fact,  one of  the  studies  examined  later  in this section finds   that  a
measure of  the 24-hour  second  highest reading is statistically the most
robust measure in  the economic function  that is  estimated.
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     The  other statistical issues that arise  in defining  air pollution
measures  involve definitions of  indices.   Since  air  quality varies in time
and space, it is necessary to construct  a  summary index that provides a
single-valued  representation  of air  quality  in a  given area.   The
procedures  used  to develop such  indices in the present study  are described
in Section 9.

     The  above  discussion indicates that no  concensus has been reached on
the most  appropriate pollution measure to use in a soiling study.  Despite
this conclusion,  it is the  case that physical characteristics  of  the
pollutant  will  affect  the  degree of physical impact  and  the'extent of
economic  response.  Although qualitative observations link ambient particle
loadings  and soiling,   attempts to  provide quantitative  measures  of
association  have not directly  addressed  the issues  of particle size,
deposition rates, and composition.  In fact,  the studies reviewed in this
section all use a statistical measure of ambient  TSP.  At issue then is
what biases may be introduced by using TSP  in  a statistical  relationship
when other measures of particulate matter  may be the factors that  are
appropriate for  the model posited.

     This is  a classic  errors-in-variables problem.  To determine what
biases may arise  in  such a situation,  it  is necessary  to know  the
underlying relationship between TSP and  the "true" measure.  If,  for
example, TSP is always exactly  two times greater than the  true measure,
then the coefficient of TSP will be biased in inverse proportion to  the
factor.  In this case, the coefficient will be one-half the value of  the
coefficient  in  the true model.   However, it is not necessarily the case
that benefits  will be  understated  as well.  In fact, benefits will be
unaffected if  the proposed model  is linear and all observations exhibit the
same  proportional relationship.   With nonlinear models  and/or more
complicated  relationships between TSP and the true variable, it becomes
more difficult  to  define precisely  the extent of  bias and  the ultimate
impact  on benefits.
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    Even if the estimation phase of the analysis is complicated using the
appropriate measure of pollution, problems can still arise once air quality
is  allowed  to  change.  With non-marginal changes  in ambient PM,  one would
expect the  composition of PM to differ after the change.  If this occurs
and behavioral  responses  are  sensitive  to  the  composition, then benefits
estiated from a model with observations for the pre-change  composition may
be  misstated.  Since the  composition of PH is not considered in the studies
reviewed here, the implicit assumption is that any changes in pollutant
composition will not alter the marginal responses.

Phsical
     To  date,  many of  the  studies that have attempted to identify the
benefits of reduced  soiling have relied on an analysis  plan like that
portrayed in Figure  7-1.   When each of the five  processes shown in the
figure is a direct part of the analysis, the methodology for determining
benefits  is typically classified as  the  damage function approach.  In this
approach, the first step involves  a  determination of exposure for  specific
materials at places with different levels of ambient particulate concentra-
tions.  Exposure to PM is then related to an objective  physical  measure
appropriate to the material under  review.  For example, with glass, the
measure  might  be the loss  of  transparency.  The relationship  between
exposure  and  physical  impact provides  an estimate  of  the  physical damage
function.

     The  foremost example  of  estimating physical damage functions for
soiling effects is Beloin  and Haynie (1).   In their study,  damage functions
are developed from data  collected  for six types  of  building materials:
painted  cedar siding,  brick, limestone,  concrete block,  asphalt  shingles,
and window glass.  Data  on PM concentrations  and soiling effects were
collected over time for a cross-section of five controlled sites around
Birmingham, Alabama.

     In the best  specifications,  they found that  soiling,  as  measured by
reduced  reflectance,  could be stated as  a  function of  PM levels and
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duration of exposure.  Physical damage functions for acrylic emulsion paint
and white  asphalt shingles are provided in Reference (1),  and these speci-
fications are reproduced in Table 7-3.   Note  that the constant term in the
left-hand side variable of each  regression equation  represents  an initial
reflectance value prior to exposure.   Thus,   the  regressions  can be inter-
preted as showing the expected change in reflectance for a given level of,
and exposure to,  TSP.

     Naturally,  in the development of physical  damage functions,  care  must
be taken to  control for factors  other  than PM that may contribute  to  a
change in the physical status of the material.   In soiling studies,  clima-
tological  parameters  would seem to be especially relevant explanatory
factors.  However,  because the sites in Beloin and Haynie (1) are  located
near one another,  the expectation is  that the microclimate is  similar  from
site  to  site.  Nevertheless,  Beloin and Haynie monitored temperature,
                                Table 7-3
                 REGRESSION RESULTS FROM BELOIN AND HAYNIE
Material
Acrylic Emulsion
Paint
White Asphalt
Shingles
Equation*
ln(92.5-y) = -0.311 + 0.345
+ 0.612
ln(41.8-y) = -4.881 +• 1.007
+ 0.595
In(TSP)
ln(t)
In(TSP)
ln(t)
  y  =  Measured percent reflectance.
TSP  =  Annual geometric mean of  total  suspended particulates (jig/m3).
  t  =  Exposure time in months.
* The R  for  the equations is 0.896  and 0.608, respectively.  The number of
  observations is 640 and 40, respectively.
Source:  Table IV,  Beloin and Haynie  (1).
                                    7-14

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rainfall, dew  duration, and relative humidity  to  test  for  significant
differences  in these  data across  sites.   Their  statistical  analysis
indicated  that  the  only difference  among  the variables was  a  higher level
of humidity at one of the sites.  In turn,  the higher humidity led to an
increase  in  mildew at the site.   Despite  this  finding, none of the recom-
mended specifications in Reference  (1) includes a  measure of humidity.
Consequently, as noted  in Reference (1), the increased mildew  formation is
confounded with soiling by TSP.

     Given relationships like those  shown  in  Table  7-3,  the  next step in
the damage function approach links the PM—related physical impacts to a
measure of economic  damage.  Ideally, an assessment of economic damage
would be  able to  account  for  the alternative preventative or  ameliorative
actions that  may  be taken by  individuals in response  to perceived physical
damage.  In terms of household  soiling, responses would likely take  the
form of more frequent and/or more  intense cleaning.   However,  preventative
measures  such as filtered air conditioners may  also be  employed.  As noted
in Reference  (1),  if  an  accurate assessment  of  soiling  damage is to be
developed, it  is important to  consider how  such decisions  are made by
individuals.   Unfortunately, the  mechanism  of  choice is usually not con-
sidered directly in the physical  damage function studies.

     In addition to  information  on  the  types of responses  that may be
observed,  an  analysis of soiling benefits  via the  damage function approach
must also consider  the  range of  tasks  and materials affected as well as the
unit value of the added cleaning costs.*  Not  only are these  information
requirements  burdensome,  but  the process  of  developing benefits estimates
with the  physical damage function  models implies some strong underlying
assumptions  and limitations.  For  example,  Waddell  [(10),  p. 24] mentions
that extrapolation  of  controlled study results to the real world ignores
the possibility  of nonconstant marginal products,  nonlinearities,  and
problems  of  aggregation and substitution.  Freeman (11) stresses the lack
* See  Geomet  (9) for an example  of  what is  required to develop an  inventory
  of materials in a sulfates materials damage  study.  A  similar effort
  would also  be necessary for a PM study.
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of consideration of consumer adjustments,  and the difficulty in assessing

the inventory of exposed area for the multitude of surfaces  that  may become

soiled.


Evaluation of Physical Duwge Function Studies —


     Many of these limitations can be related to the evaluation criteria

listed earlier.
          Theoretical basis  — Physical  damage  function  studies
          typically do not consider  the adjustment opportunities that
          are available  to economic decision-makers.  Estimates of
          cost-savings reflect only changes in expenditures required
          to return  a  material  to a "clean" state and do not provide
          accurate estimates of willingness to pay.

          Consideration of relevant  variables —  Oftentimes physical
          damage functions are determined from data collected in a
          controlled laboratory setting.  These conditions may not
          adequately reflect the conditions  in the  real  world.  In
          particular, climatic variables may act synergistically with
          PH to increase  the potential soiling effect.

          Quality of input data — Most data  used  in these analyses
          are micro  in nature and are well-suited to the analysis of
          specific materials.  However, data must be collected for
          many very specific tasks and/or  materials.  Furthermore,
          estimates  of exposed surface  areas  are usually  very rudi-
          mentary.

          Legitimacy  of  the  empirical  analysis — The estimation of
          physical and  economic  damage  functions typically involves
          simple linear regression techniques.  Little ji  priori
          evidence is available on the most appropriate specification
          for alternative damage functions.  Hisspecification of the
          damage functions can be especially important to  a benefits
          analysis when dealing  with non—marginal  changes in pollu-
          tion levels.

          Transferabilitv  of models — Individual physical damage
          functions can be adapted easily to a variety of alternative
          settings.   The major difficulties that arise  are:   the
          definition of damage  functions for each affected material;
          and  the  estimation of  the  exposed inventory of  each
          material.
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    The negative tone of  the  above evaluation is  reinforced by discussions
in both the Criteria Document (2)  and the  Staff  Paper (3).  The Criteria
Document reports  damage functions  for the Beloin and Haynie  (1) study
mentioned earlier,  and offers  an  example  of how repainting frequencies  can
be obtained from their model for  different PM levels.   However, the conclu-
sion reached in the Criteria Document  is  that:
     The  least reliable of the "significant" damage  functions are
     those for soiling  from particulate matter.  The  poorly under-
     stood deposition rates and poorly characterized chemical and
     physical properties make general  application of  the functions
     difficult, if not impossible.
The Criteria Document goes  on  to  say:
     The limitations of these and other physical damage functions
     hinder accurate estimates of total  material  damage  and  soiling.
     Coupled with  these limitations is the lack of material exposure
     estimates.  These problems presently  preclude complete  and
     accurate estimates of the costs of damage based on a physical
     damage function approach.
In our judgment, we agree with the  assessment that currently available data
limit the usefulness of calculating national benefits from reduced  soiling
when physical  damage  functions are required.*  However,  as will be  seen in
the discussion below, we  also believe  that recent research based on
behavioral  models  is  sufficiently  well-defined to warrant the calculation
of benefits.

     Although  the  data requirements of  the  physical  damage function
approach  may preclude its widespread use in estimating national benefits of
reduced  soiling,  the  research embodied  in  the  development of specific
damage functions  is  very important.  In general,  there is no & priori
economic reason for believing  that a particular  air  quality variable
affects  specific  economic choices.   As  a consequence,  the behavioral
* Lodge .et al. (12)  reach similar conclusions  in  their review of non-health
  effects of  PM.
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approach (described  below)  for  estimating  the  benefits  of  reduced  soiling
must rely on information  like that available in physical  damage  studies to
narrow the range of  alternative specifications  to  be  considered.

Behavioral Models of Reduced Soiling Benefits

     An alternative approach to  the  physical damage function studies is the
analysis of benefits based  on behavioral models.  Referring  back to Figure
7-1, it can be seen that a direct link between "ambient  levels  of  PM" and
"economic damage functions" is also indicated.  In this case,  the steps
dealing with physical  soiling  effects  are  bypassed.  The rationale behind
this way of characterizing the benefits  process is that damages from
soiling  may eventually be manifested in economic  choices.  As soiling
damage  effects  are perceived  by households and firms, they respond by
purchasing goods that help  to maintain  a desired level  of cleanliness.  In
deciding how much  of  their available  budget to allocate to cleaning-type .
goods, they indicate their willingness to pay for units of cleanliness.
Thus,  willingness to pay  for PH reductions may be observed  in  the economic
choices made by  households  and firms.

     Within the  class  of behavioral models, there  are  several  approaches
which can be distinguished.  These  include:

     •    Property value studies.
     •    Surveys of frequency  and  expenditures for cleaning activi-
          ties.
     •    Economic demand and/or supply models.

Property Vmlne Studies —

     The  hypothesis  underlying   the  property value studies  is that
structures in areas with relatively better air quality will have higher
values  than similar structures  in relatively dirty areas.  In  effect, much
like a swimming pool  or  an enclosed garage adds to the value  of  a home,
good air quality is also a positive attribute.  Many studies have been
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completed which employ this  approach,  yet the  Criteria Document (2) and
Staff Paper (3) provide only a limited discussion of the technique.  The
principal concerns identified  in  these reports are that:

     •   It is difficult to  identify  separately the benefits  of
         reduced soiling versus other  benefits such as health and
         aesthetics.
     •   It is difficult  to extrapolate results from  a single  city
         (study)  to obtain national estimates of benefits.

     Because of  the first concern, no additional discussion of property
value studies is provided in this  section.   However, further discussion can
be found  in Section 5.

Surveys of  Cleaning Expenditures —

     This type  of  study has been the  most popular for measuring reduced
soiling  benefits.*   Among  the  studies  included in  this group  are:
Michelson  and Tourin (13). Ridker (14),  Narayan and Lancaster (15), Booz,
Allen and  Hamilton (16),  Liu and Yu (17),  Brookshire et al.  (18),  and
Cummings  et al. (5).   In many cases, the  survey studies use some  variant of
a paired  cities approach to generate data for their  analysis.   For  example,
Michelson and Tourin  (13) compare the  frequencies of household maintenance
and cleaning activities in Steubenville,  Ohio (a dirty area)  with the
frequencies  of these  activities in Uniontown, Pennsylvania  (a clean area).
Under the  assumption  that  all  other  factors  that  may  influence  the
responses  of households to PM levels are  similar in the two cities, a
relationship between ambient PH and  frequency  of cleaning activities is
estimated.  Then, for  a given  cost per activity,  it is possible  to predict
how changes in PM impact on frequency  and  ultimately on total expenditures
* As noted in the  Criteria Document (2), the physical  damage function
  approach has been  the  most widely used method in materials  damage
  studies.   These studies  usually involve metal  corrosion or material
  deterioration.  For the soiling  effects considered in  this section, we
  believe  it  is  more  appropriate to view  the survey approaches  as  part of
  the behavioral class of models  since no  quantitative measure  of physical
  effect  is typically included in these analyses.
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for cleaning tasks.  In the Michelson and Tourin study cited above,  the
analysis indicated that annual' per capita costs for cleaning activities
were $84 (1967 dollars) higher in Steubenville than in Uniontown.  Since
the difference  in  annual mean  particulate concentration was approximately
115 (ig/m3,  the  value per unit change  in ambient PM  is estimated to be $0.73
per capita.

     The Narayan  and  Lancaster (15)  study  also used a paired  cities
approach.  In this study, the costs of maintaining a house were compared
for two cities in Australia.  This study, as well  as Michelson and Tourin
(13)  are faulted in the Criteria  Document (2) for potentially  suspect data.
In particular,  it  is  noted  that  attitudes of  the  respondents  to questions
directly connected to air pollution may have provided an incentive for  the
respondents  to misstate purposefully their true, historical responses.
This type of bias is,  of course,  possible in  any survey situation where
historical  data are collected  and respondents  have a desire  to influence
the study results. A procedure  for ameliorating this bias is to  collect
data on observed activities  or to  use data  collected for  reasons  not
directly related  to air pollution.  One  of  the economic studies  reviewed
below uses this procedure.

     Another example  of the paired  cities  approach is Brookshire et al.
(18).  In this experiment,  a more  sophisticated  survey instrument  was
designed to  test  for  the  presence of various biases.   In addition,  the
questionnaire  was better  suited to obtain estimates of  household
willingness  to pay  than earlier  studies which focused  only  on task
frequency and fixed unit costs.   Unfortunately, there are several  problems
with using this "contingent  valuation" method  in  a soiling effects study.
The major drawback is  that each respondent implicitly makes a choice of
what a  given change in PM level means in terms of actual soiling  effects.
In effect,  there is the presumption that individuals  can translate  changes
in PM  into the effect  on average household  cleanliness  and in  turn calcu-
late the  added cost  if PM levels  were to  increase.   Since different
individuals  may have different implicit "physical  damage functions", it is
difficult  to  isolate the value  of a unit  change in  cleanliness.
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     In the Brookshire .e_t  aJL. (18)  study,  the difficulty raised above was
overcome by assessing how changes  in visual range  affected individuals'
willingness  to  pay.  Studies have shown that scientific  measures of visual
range and  people's perception  of  visual range correlate well.   However,  in
valuing changes  in visual range,  it  is  difficult to  relate  changes  in
willingness  to  pay  directly to changes in PM levels alone,  since multiple
pollutants contribute to  visibility  impairment.  Furthermore, since  the
"bids" elicited in  the  survey did not distinguish among types of effects,
it is difficult  to ascribe a part of total benefits to reduced soiling
damages.   For this  reason,  Brookshire  et al. (18)  is  not  considered further
in this  section.

     In one  of  the  first studies of the economic costs of  air pollution,
Ridker (14) developed  a series of analysis plans to test for the signifi-
cance of  TSP in the cleaning  decisions of commercial and industrial  firms
as well  as  households.   The analyses  relied on  cross-sectional data
collected from both interurban  and intraurban areas.  In the analysis  of
interurban  data,  Ridker  looked at economic  data from three types  of
cleaning  services.  These included receipts  from laundry and cleaning
establishments,  the  costs of  cleaning  office and  apartment building
interiors,  and  the performance frequency  of contract  cleaning firms.   In
these studies,  the  hypothesis  tested was that cleaning costs per unit time
increased  with  higher  levels of suspended  particulates.

     For  each of  these  studies, the null  hypothesis  of no difference  could
not be rejected.   This  conclusion was based on a scan of scatter diagrams
and the  calculation of  rank correlation coefficients.  In order to  control
for  factors  that may confound  the identification of  a soiling effect,
Ridker partitioned  his  data to hold constant such factors as  climate,  per
capita income and interurban  price  (wage) differentials.   Even with  these
factors held constant, no clear association between cleaning costs  and
suspended  particulates was found.

     With the  intraurban  data,  Ridker  examined  supermarket sales  of
cleaning supplies  and maintenance procedures of firms with  branches  in
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different  parts  of  a city.   Again,  no  significant  association was  evident
from the data.

     Ridker  offers  several  possible  explanations for the  lack of  associa-
tion between TSP and cleaning  cost  data.  These include:   1)  the  levels of
air pollution experienced may  not be severe enough to lead to an  identifi-
able response;  2) measurement error may be an  important factor; 3) the
averaging  times  and statistical  aggregation of the pollution data may not
be appropriate;  and  4) there may be confounding variables  that prevent the
identification  of  an independent effect of air pollution.  Each of these
factors is  plausible and could  indeed lead  to  the  results  reported by
Ridker.

     Despite the finding of no association between cleaning  costs and
levels of suspended particulates,  Ridker's proposed analysis plans were
very innovative  and undoubtedly  had a  significant  impact  on  the design of
soiling cost studies  for  the following  decade.  This  is  especially  evident
with respect to the study of soiling costs for households.  In Reference
(14), Ridker  summarizes a soiling study  conducted  in Philadelphia, which is
similar in  design  to a study later conducted by Booz, Allen and Hamilton
(16).   In both  instances,  a  survey  instrument was  designed to elicit
information on cleaning expenditures, frequencies, and time durations for a
variety of household cleaning  activities.  The  specific attributes of this
type of household study are considered  in more detail below.

     The remaining  three studies in the group of  survey studies have an
element in  common.  Both Liu  and Yu (17) and Cummings et al. (5) use data
collected  by Booz,  Allen  and Hamilton  (BAH)  (16)  to estimate  the relation-
ship between frequency and ambient PM levels.   While the BAH study is
perhaps the best known of the  many soiling studies,  the data set suffers
from many inconsistencies.  This becomes especially evident in Cummings .et
al. (5), where a great deal of  effort goes  into constructing a sound data
base from  the original BAH information.
                                   7-22

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    Although, the  type  of data  collected  by BAH  is  similar  to that
collected for the paired city approaches described earlier,  the data are
not drawn from a sample within paired cities.   Rather, the data come from
four "pollution zones" in  the greater Philadelphia area.   Consequently, the
analysis  of the data must control for demographic characteristics that vary
across  pollution zones and may  influence household response  to alternative
PM  levels.

    Given the average frequency and aggregate  expenditure per task  in each
pollution zone, the analysis of soiling damages in References  (16), (17),
and (5) is carried out much in  the  same  fashion as described for the  paired
city studies.  There are,  however,  some changes of substance  that serve  to
alter the flavor of the analysis.  For example, Liu and Yu (17) use Monte
Carlo techniques to generate a larger data sample, while Cummings et al.
(5) incorporate the opportunity cost of labor into the  analysis.   Still,
the basic method of calculating  soiling damages  remains  fundamentally the
same in all  the survey studies.  For this reason,  we have chosen to  review
in  detail only  one of the studies  in  this group.  The study chosen  is
Cummings et  al.  (5).   In  our judgment,  this study is a good example  of the
type of analysis  that  can be carried out with task frequency  and expendi-
ture data and it offers some novel additions  which are  not  present  in the
other  survey  studies.  In addition,  since the Cummings study has only
recently been completed,  a  summary of this  work is not currently available
in  the most  recent Criteria Document.  Thus, a thorough  review  of the
Cummings analysis provides new information on the  status of household
soiling benefits  models.  Following  the  discussion  of the  Cummings  study,
the survey-type  studies will be  compared  against  the  evaluation  criteria
set forth earlier.

     CuBings et al. (5) — There are  essentially four  major divisions  in
the Cummings  report.  These can be  summarized  as:

     •   Background literature review of previous soiling  damage
         function  studies.
                                   7-Z3

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         A careful  analysis of the Booz,  Allen and Hamilton (BAH)
         data set with corrections  made  as appropriate.
         An analysis of a modified  version of the BAH soiling damage
         study.
         A contingent valuation study of soiling damage.
     The discussion here  focuses on the  third of these topics.   As  an
aside,  we note that the Cummings report asserts that  the fourth topic,  the
contingent valuation  method, is an infeasible  approach for estimating
particulate—related household soiling damages.

     The objective of  the Cummings soiling study  is to  determine  the
economic damages  associated  with soiling effects.  This is accomplished by
estimating expressions that predict how out-of-pocket expenditures  and
labor time are likely to vary as  ambient  PH changes.  As noted earlier,  the
data for  the  analysis are  drawn from  the BAH  study (16),  although  an
extensive  data-cleaning effort was undertaken  to  eliminate inconsistencies
in the BAH data base.

     The BAH survey was completed in 1970,  and involved 1,800 households in
the greater Philadelphia area.   For analysis purposes,  the region was
divided into four pollution zones  representing concentration levels of:

                          a
     •    Less than 75  ug/m .
     •    75 to 100 ug/m3.
     •    101  to 125 ug/m3.
                              a
     •    Greater than 125  ug/m .

Although  BAH  collected data  for  27  specific maintenance and  cleaning
activities, the Cummings study  focuses on the 11  particulate-sensitive
tasks in the BAH  study.  Table 7-4 summarizes these activities.  Note that
plausible cleaning tasks  such as dusting  and vacuuming  are not  included
since they were not part of the BAH survey.  This could lead to an under-
estimate of total benefits.
                                   7-24

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                               Table 7-4

                CLEANING ACTIVITIES  IN  CUMMINGS' ANALYSIS
                 Inside Activities

                     1.  Replace  air-conditioner filter
                     2.  Wash floor  surfaces
                     3.  Wax floor surfaces
                     4.  Wash windows  (inside)
                     5.  Clean Venetian blinds/shades

                  Outside Activities

                     1.  Clean/repair  screens
                     2.  Wash windows  (outside)
                     3.  Clean/repair  storm windows
                     4.  Clean outdoor furniture
                     5.  Maintain driveways, walks
                     6.  Clean gutters
Source:  Cummings et al.  (5),  Table  8.



    For each  task, data were collected on the number of households that
performed  the  task  (both households where all tasks were performed by  the
residents as well as households that hired outside help), as well as  the
mean annual  frequency.  These data were summarized by pollution zone.   In
order  to estimate damages, the following cost  equations were assumed:
         Cleaning costs incurred by those households that hire out-
         side help  (HIRE)

              Cl  '  al '  Nl '  Fl
         where a^, is the unit cost of having the task performed;  Nj
         is the number of HIRE households;  and Fj  is the mean annual
         frequency for the task in HIRE households.
(7.1)
         Cleaning costs incurred by do-it-yourself households  (DIT)

              Cj  =  N2 '  [V •  T + a2  '  F2]

         where  N2 is the  number  of DIY households performing the
         task; V is the imputed labor cost  per unit of time in DIY
         households; T is household time spent performing the task;
         a2  is  the  unit out-of-pocket cost  of  the task in  a  DIY
         home;   and F«  is the  mean annual frequency  for  DIY
(7.2)
                                   7-25

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          households.   The inclusion of a measure for the value of
          labor  is  a unique  feature  that distinguishes the Cummings
          study  from other benefits analyses  of reduced soiling.
Total costs are determined as the sum of Cj and Cj.  Note that since data
for N. and F. are summarized by pollution zone,  total cost estimates are
derived for each zone.  In addition, since F^ and possibly V and T will
vary with pollution levels, changes in PH will impact total costs.  This
provides the way in which soiling damages can be assessed for changes in
PM.

     Given data  for N- and F.,  the  next step in the Cummings analysis  is to
determine values for a^, aj, V, and T.   The unit cost parameters a^ and a~
are determined  from  the BAH study.   Unfortunately,  these costs  are provided
for  only  five  of the  11  particulate-sensitive  tasks  included  in the
Cummings analysis.   Consequently, costs must be attributed to the other
tasks on the basis of similarity with tasks  that  do have cost data.  This
is likely to be an important source of measurement error  in the  analysis.
However,  the direction of bias is unknown.

     In order  to develop  quantitative estimates for T  and V, a contingent
valuation survey was developed for the  Cummings study.  This questionnaire
was  used to obtain measures of the opportunity cost of household labor as
well as the time spent in cleaning  operations.   The  sample respondents were
drawn from each of the  four pollution zones in the  Philadelphia area,  with
each zone represented by 30 respondents.

     With data  identified for T and V by pollution  zone,  the next step
involved relating F, T, and V to PM.  Since the various pollution zones
include households  with diverse  incomes, this variable  should also be
controlled for  in  the  regression  analysis.   Cummings et  al. posit the
following relationships:
          F =  o0 + oxP + ojl                                      (7.3)

                          P2T                                      (7.4)

                                   7-26

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         V  =
XQ +
+ XjY
                                                                    (7.5)
where F, T, and V are as defined previously; P is ambient annual  average
TSP concentrations; and Y is household  income.  When these regressions were
run, approximately half  the specifications failed  to reject the  null
hypothesis that all the estimated coefficients were zero.   Three of the
frequency equations and  one  time equation had negative signs reported for
the pollution coefficient.   Thus, estimates of the task-specific F and T
functions are  only moderately successful.   Furthermore,  the  assumption of
linearity and  the consideration of only  two independent variables  may be
too restrictive.  Alternative  specifications  may yield very different
results.

     Because pollution levels have decreased from the  time of the original
BAH study Cummings et  al. were forced to make some  adjustments in the data
prior to the  determination of total costs by pollution  zone.  Given these
adjustments,  total  costs for  10  of the 11 particulate—sensitive  tasks  were
computed using Equations (7.1) and  (7.2).*  Table  7-5 summarizes  the
*
findings.
                                Table 7-5
           TOTAL PER-HOUSEHOLD SOILING COSTS BY POLLUTION ZONE
                             (1980 dollars)


HIRE Households
DIY Households
Pollution Zone (|ig/m )
40
$1,531
763
81
$2,887
905
102
$2,558
1,067
123
$2,683
1,386
Source:   Cummings .e_t jil. (5), Tables 18 and 19.
* The task of maintaining driveways was not considered.
                                   7-27

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     With, the per-household costs identified,  economic  damage estimates by
pollution zone can be derived by multiplying cost/task by the number of
households  performing  the  task in  a given  zone.   Note that  the term
"damage"  as used here is really  appropriate only in the  case where cleaning
costs are zero when particulate levels are  zero.  By labelling all cleaning
costs as economic damages, the implicit assumption is that all cleaning
occurs only  because of pollution.   A better way to describe  economic
damages  is the added costs incurred because  of increases in ambient PM
levels.   Thus,  it  is  the valuation  of changes  in  cleaning  activities that
is most relevant.

     The  economic  damages derived in the Cummings  study are on the order of
$300 million for each of the  pollution zones.   Although these numbers are
much larger  than  those found in BAH, this can be traced  to the value of
time which is included in Cummings analysis.

     The data on per-household cleaning  costs by pollution zone can be
related to the  corresponding  zone pollution level to identify an  economic
damage  function.   Given the Cummings data,  a linear specification was
estimated as:

          D   =  251.43 + 6.63P                                       (7.6)

where D represents damages in dollars per household per year,  and  P  is the
annual average  level of TSP.  The linear damage function  implies that
marginal damages are $6.63  (1980  dollars)  per  household per year  for each
microgram change in TSP levels.*  This is the information that would be
required to make extrapolations  to  national  benefits estimates.   Benefits
in county i  in a given year would be estimated  as:

         B£   =  6.63 • AP. •  H^                                      (7.7)
* Recall that  the marginal value in the Michelson and Tourin (12)  study was
  estimated to be $0.73 in 1967 dollars,  or about $2.50 in 1980 dollars.
                                   7-28

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where Bj^ is benefits in the ith county; AP is the change in TSP levels in
the  county, and H is the number of households in the county.*

     It  should be pointed out  that the linear damage function in Equation
(7.6) is  simplistic  and  as Cummings et  al. note,  "must  be  viewed as  little
more than of  expository value."  While it is true that a nonlinear  damage
function would be expected, we also feel that data problems  (e.g., unit
cost data), and  the several statistically  insignificant task frequency
regressions add to  the difficulty in accepting  the  damage function in
Equation (7.6).   While  the  derived  marginal  damage estimate  is  not  strong
enough to stand alone  as an indicator of economic damage, it does seem
reasonable to  use Equation  (7.7)  as part of the  information base available
for developing estimates of  reduced  soiling benefits.   In fact,  given the
framework of  the Cummings  study, it may  be possible to ascertain whether
this estimate should be an upper-  or lower-bound to an estimate derived
from  another  study.   This  may help  to  define  a  reasonable  range for
benefits from  reduced soiling.

     We  have  described  in  some detail  the major elements of the  Cummings
soiling  study.  In  the report,  an effort is also  made  to  clarify some
confusing issues in  the BAH  work and they close with  several recommenda-
tions for the future course  of  frequency studies.   First,  they believe  that
it  is more appropriate to define aggregate  tasks rather than  specific
tasks.  Second,  time spent  per unit time  in an activity is perhaps  a more
relevant variable  than frequency in the estimation of  the relationship
between activities  and pollution levels.  Cummings et al. believe  that  a
mixed approach  based on the contingent valuation  and soiling frequency
techniques would best  be able to obtain the data that would incorporate
these considerations.

     Evaluation of  Surrey  Studies — Several of the direct  survey studies
have been discussed briefly,  and one  study reviewed in some detail.   This
* This is also the equation used by SRI (19)  to estimate benefits from
  reduced soiling.
                                   7-29

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class of studies  is  now examined to see how well  they conform to the

evaluation criteria.
         Theoretical basis — With  the  exception of Brookshire et
         al. (18),  the survey studies  do not  collect data sufficient
         to obtain the appropriate measure of economic benefits.  In
         general,  frequency of cleaning  data and/or total  expendi-
         ture  data do not allow one to observe the variations in
         demand  or  supply that are required  for the proper  estima-
         tion  of benefits.   Consideration  of  variation in  total
         expenditures alone will yield  underestimates of "true"
         benefits.   See  Courant  and Porter  (20)  for  additional
         treatment of this issue.

         Consideration of  relevant variables  — Ideally,  the paired
         city  approaches are designed so that other potentially
         significant explanatory  variables in  the  frequency/PH
         regressions would be constant across study areas.  In those
         studies which use  the BAH data, however,  factors  such as
         income  should be included  in the  frequency specifications,
         since  this  attribute may  influence cleaning  decisions and
         will  differ across  the pollution zones  being  analyzed.

         Quality of input  data — Based on comments in the  Criteria
         Document (2),  this would appear to be of major concern.
         The principal  problem with the paired cities  studies is
         that  the  frequency data may contain  inherent  biases.
         Furthermore, the BAH data set also suffers from inconsis-
         tencies in  many of the  variables.   Thus,  since  these
         studies  typically collect  only expenditure data,  unit cost
         (price)  information is not directly  available from the
         surveys.  As a consequence, there is likely to  be measure-
         ment  error  in the cost data collected from outside  sources.

         Legitimacy of the  empirical analysis  — The frequency
         functions that are estimated are  usually simple linear
         specifications.  As with the physical damage functions,
         there is little  information available on the most appro-
         priate  functional form for these economic damage functions.
         In the  Cnmmings  study,  it  was noted that the null  hypothe-
         sis that  all parameters  in an equation were zero could not
         be  rejected  in almost  half  the  frequency  equations.
         Despite  this statistical problem,  cost  estimates were still
         derived for each task.   More work is required to give a
         better  theoretical  basis to the estimated equations.

         Transferability of models  —  As evidenced by Equations
         (7.6)  and  (7.7),  it  is  relatively  easy  to  identify the
         information  needed to  develop national  estimates  of
         benefits.  It is  perhaps wise  to  reiterate that the extra-
         polation relies  on a marginal damage estimate developed
                                   7-30

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         from  data  from a  single  city.   As  a  consequence,  an
         analysis of  benefits from  the Cummings  results must assume
         that  individuals in  other areas  will react to  soiling
         damage in a  fashion  similar to  that of Philadelphians.
    Overall,  the evaluation for the  survey studies  is  not  much more
optimistic than  that presented  for physical damage function  studies.
Indeed, the negative  comments quoted  earlier from the Criteria Document (2)
likely were meant for  these studies as well.  There are,  however, some
distinct advantages  to this  approach that should be  recognized.   First,  the
approach focuses on people's responses to  air pollution rather than  an
imputed value of expenditures for returning to some hypothetical clean
state.   Second, the  data  requirements would  seem  to be less burdensome.
With the survey studies,  it  is no longer necessary  to develop estimates  of
surface area exposed  or  to identify damage  functions for a variety  of
objects.

     In summary,  it does not appear that any one of the survey studies
could stand alone and provide a defensible  estimate of benefits.  However,
when used as  part of  a  general information base,  marginal  damages derived
from the survey  studies  may be  valuable  components of  a more general
benefits analysis.

Economic Deaaad and/or Supply Models —

     The  final class of behavioral models includes  those studies that  rely
on economic demand or supply functions  to estimate  benefits.   We are aware
of three models that can be  classified  in this manner.

     •    Watson and Jaksch  (16).
     •    Mathtech's  model of household expenditures (7).
     •    Mathtech's model  of cost and production  relationships in
         the manufacturing   sector (8).

Since these models differ  markedly, it  is not possible to provide a blanket
description  of  how  benefits estimates are developed.    However,   the
                                   7-31

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underlying  rationale on how economic benefits should be defined is the  same
in each case.  Basically,  the  value to society of a reduction  in  ambient PM
levels should be measured by  the aggregate  amount members of  society would
be willing  to  pay  to be in a  preferred state (i.e., cleaner air)  as opposed
to a less preferred state (i.e.,  dirtier air).  This willingness-to-pay
measure  includes two  components.  The first of these  is  the level of
reduced out-of—pocket cost that would accompany lowered PH levels.  This is
the measure captured by the  survey methods  described  in  the  previous  sub-
section.  The second component allows for the possibility that  the cost
savings may reduce prices and thus  stimulate  an  increase in the quantity of
goods  and  services demanded.  Since the increased quantity demanded is
direcly  attributable to improved air quality,  the  willingness-to-pay
measure  should include this   component as part  of  the benefit estimate as
well.

     Measures  of willingness to pay can be  ascertained from  knowledge of
demand and supply curves.  In Figure 7-2, the demand curve  is  given by D
and the supply (marginal  cost) curve is designated as  MC   If these curves
are thought of as  the  households'  demand and supply of cleanliness, then a
decrease in PM  levels may imply that each unit of cleanliness  could be
produced at lower  unit  cost than before.  That  is,  less resources  would be
required to attain the level of cleanliness that occurred before the air
quality  improvement.   In this  case, the supply curve would shift down to
MC'.  The shaded area  ABCO represents the change  in economic surplus, or
the amount consumers would  be willing to pay to have  the  improved air
quality.  Thus,  it is the correct  measure  of economic  benefits.

     The same  general principles for  measuring benefits are used in each of
the three  studies reviewed  in this subsection.   As noted above, however,
because the underlying  models that produce  the  relevant  demand  and supply
functions do differ, each of  the studies  will be evaluated individually.

     Watson and Jaksch (6) — The study by Watson and Jaksch (WJ)  is some-
what  unique in  that  it could easily be classified as a physical damage
function study,  a  survey  study, or  an economic demand/supply model study.
                                   7-32

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Price  per
unit
                                          Me
                                                   MC1
                             Q,
Q,   Quantity per  unit time
             Figure  7-2.   Example of economic surplus
                               7-33

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Elements of each type of study appear in. the WJ  analysis.  However,  since a
major distinguishing feature of their analysis is the use of demand and
supply curves (rather than cleaning frequency or expenditure), it seems
most appropriate to  include the study in this  subsection.

     The data  set used in the WJ analysis  is taken from the Booz,  Allen and
Hamilton (16)  study  discussed  earlier.  Since the WJ  study was  completed
prior to the Cummings e_t aJL (5) study, WJ did not have the benefit of the
data-cleaning  effort undertaken by Cummings et al.  Thus,  some caution is
warranted since  the  BAH data in its original form does contain inconsisten-
cies.

     The WJ reliance on the BAH data set did provide for an easy charac-
terization of the  demand curve.   In the original BAH analysis,  it  was
determined that  expenditure remained constant  across different levels of PH
for  a variety of tasks.  If  alternative levels of PM reflect different
states of household  cleanliness,  then this  implies that expenditures remain
constant across  units of cleanliness.  In this case,  the demand  curve for
cleanliness would  have  constant  unitary negative  elasticity (i.e.,  a
rectangular hyperbola).  This makes  specification of the demand function
straightforward.

     The assumption of unit  elasticity is  an important  part of the WJ
analysis.   However,  several recent commenters  have questioned  whether this
assumption can  legitimately be drawn from the BAH data.  Cummings et  al.
note that many of the tasks that were identified as  not  being  sensitive to
particulate matter involved materials damage-type impacts.  In addition,
Cummings .e_t .§_!. point out that activites identified in BAH as having low
unit values (and thus providing only small additions to total  cost) are
actually performed many times over the course  of a year so that total
annual costs may be high.   Rowe (21)  also  provides an example which shows
how  increasing  pollution coupled with a constant cleaning frequency can
imply expenditure  increases.   In this case,  outlays on  cleaning are
positively related to pollution levels and an  inelastic demand curve would
be implied.
                                   7-34

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    These comments indicate that the constant elasticity demand  curve used
by WJ  may impose  constraints on the model that are not  supported by avail-
able data.  If the demand  curve is suspected to be inelastic, then as Rowe
(21) points  out,  it  is likely that the WJ  benefits estimates  are  over-
estimates for  improvements  in air quality on this  point.

    Similar problems are possible  with the WJ supply (marginal  cost)
curve.   The  assumption  made  in  the WJ  case  1  (with which we  deal
exclusively) is that  the  marginal cost schedule rotates in proportion to
the ratio of the  final  and base pollution levels.

          MC  =  a •  (Pf/Pb)a '  Q                                    (7.8)

where HC is the  marginal cost per unit  of  cleanliness; Pf is  the  final
level of  pollution;  P^ is  the base level  of  pollution; Q is units  of
cleanliness; and a and a are parameters to be determined.

     Several comments can be made  about Equation (7.8).  First, WJ  argue
that the HC curve will be  increasing  in  cleanliness because  cleaning costs
and the opportunity cost  of labor will  increase  as pollution increases.
This would  imply that "a"  is positive.  However,  it  is not explicitly
recognized by WJ that there may be  economies of scale related to  certain
cleaning activities (e.g., buying larger boxes of soap) which could alter
the slope of the MC curve.  Indeed, even the linear representation of the
MC schedule  may be at odds with * priori expectations.   One would instead
expect the MC  curve to increase at an increasing rate.*

     In addition  to these  observations on the shape  of  the MC curve,  it is
also pertinent to examine  the  impact  of  pollution changes on  MC.   As
written.  Equation (7.8) implies  a  multiplicative rotation. The factor that
determines the degree of shift,  a.  is derived from several damage  function
studies.   In particular,  the painted cedar siding  equation from  Beloin and
* WJ recognize this  point during their discussion of the MC curve,
  treat  it  as linear for ease of presentation.
but
                                   7-35

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Haynie (1) yields estimates  of  o of 0.56 and 1.0.  The study by Esmen (22)
was used  to obtain an upper-bound estimate  for a of 2.0.  These factors
were used to  rotate  the MC schedules for each of the eight  cleaning and
maintenance tasks examined by WJ.   These tasks represent a subset of the
BAH activities.   They  include:   painting indoor and outdoor walls, washing
indoor  and outdoor windows,  washing  indoor walls,  wallpapering,  painting
tria,  and cleaning Venetian blinds.  As with the Cummings study, plausible
cleaning  activities such  as dusting and vacuuming are not considered
because they were not included  in the BAH survey.  As the earlier discus-
sion of physical damage functions noted, the  absence of  damage functions
for a variety of materials  makes it difficult  to measure benefits.  WJ rely
on functions developed for  only  two materials  to derive their estimates  of
a.  Thus,  the shape  and  rotation of  the  MC schedule may also contribute  to
a bias  in the benefits estimates.   With  so little empirical  evidence
available  for a,  it is difficult to assess whether the bias will lead to  an
overestimate or an underestimate of benefits.

     Figure 7-3  shows  the WJ  demand  and  supply curves for  cleanliness. The
shaded area OBF  represents the consumers  loss  from an  increase in pollution
to ?2 from P^.  Given the assumptions on the  demand  elasticity,  original
outlays OAB equal final outlays OEF.  The important point to  observe  is
that economic benefits are  generated  even though no additional outlays are
made.  Thus, the WJ analysis picks up a  component of benefits  that  could
not be observed in  the survey methods reviewed earlier.

     With specific  algebraic  expressions  for the  demand and supply curves,
it is possible to solve  for the  value  of economic surplus as a function of
the base  and final pollution levels,  the value of a, and total  expendi-
tures,  by pollution zone.   The derived expression is:
-ACW..   =  a In
                                                                    (7.9)
where    ACW^.   is  the  change  in welfare for task i in pollution zone  j.
           ?2i   is  the  final level of pollution in zone  j.
                                    7-36

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Price  per
unit
                                     MC(P2)
                                                  •MC(P1)
                                         A  Q units of  cleanliness
   Figure  7-3.-  Demand and supply curves in the WJ analysis
                               7-37

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           Pj^   is the initial level  of  pollution in zone j.

             a   is the factor  estimated from  the  damage function studies.
                It's value  is  0.56, 1.0, or 2.0.

           X..   is the outlay for task i in  zone j.


     Equation (7.9)  is the equation  needed to estimate national benefits.

Because of the  required  extrapolations,  we  believe  that separate  calcula-

tions by task and pollution zones are  not warranted.  Thus,  in any benefits

calculations, pollution values would be  county averages and total  expendi-

tures by county  would  be developed by multiplying the implied per-household

total cost (expenditures)  in Philadelphia  times the number of households  in

each  specific county. The necessary  data on household expenditures  in

Philadelphia  is  developed in WJ.


     In terms of the evaluation criteria,  the following comments can  be

made:
          Theoretical basis — The manner in which economic benefits
          are calculated  is appropriate for the single activity con-
          sidered in the study.  Questions do arise on the validity
          of the  assumptions implicit in the shapes of the demand and
          supply curves.

          Consideration  of relevant variables — The assumptions
          underlying the  demand curves  imply that specific household
          classes do  not have  to be  identified when estimating
          benefits.    This  occurs because of  the  constant  unit
          elasticity.

          Quality of  input data — With the BAH data  set  used to
          estimate benefits, there is some concern on  the reliability
          of the  data.  As noted earlier, Cummings .e_t .§_!. (5) devote
          some time to cleaning up inconsistencies  in  the BAH data.
          If possible, it would be appropriate to use the corrected
          BAH data in  any calculations  performed with Equation (7.9).-
          This may be limited by differences in tasks between the
          studies.

          Legitimacy  of the empirical analysis —  Despite  the fact
          that WJ use  demand  and  supply curves, no new estimation is
          performed  in this study. The  crucial aspect is whether the
          assumptions  made  by WJ  in  specifying the demand and supply
                                    7-38

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         relations are reasonable.   The  only  numerical  calculations
         involve evaluation of Equation (7.9).
         Transferabilitv of Model — This  can be readily accom-
         plished  using Equation (7.9).   Data on expenditures are
         available from WJ.  As with Cummings et al..  extrapolations
         to national benefits  estimates rely on data collected for
         only one city.
     In  general,  this evaluation coincides  with comments made  in  the
Criteria Document (2)  and the Staff Paper (3).  Both of these reports  are
cautious  in  accepting  the  estimated benefits of WJ because of  the  limited
number  of soiling functions which are available to represent a  variety of
cleaning tasks.  In addition,  we reiterate  comments made earlier that
additional empirical work is  required  to validate the assumptions  under-
lying the forms of the demand and supply schedules.  Though there are areas
where improvements are possible, the WJ study is an innovative approach to
estimating soiling benefits.   While it is difficult to rank WJ in relation
to Cummings  et  al. (5), it  seems prudent to view WJ as providing additional
information  for defining the probable  range  of  soiling benefits.

     Mathtech  Household Model  (7)  — In the  WJ  analysis,  household
decisions are examined for only one  of  the many commodities (services)  that
influence economic behavior,  the production  and consumption of cleanliness.
In addition to the  services provided by cleanliness, individuals also
require shelter, nutrition,  clothing, and other  services  to maintain a
desired  quality of life.   In the Mathtech household model (MTH),  the inter-
dependency of economic  decisions is recognized,  and systems of demand
equations are estimated.  That is, household demands for a variety of
services  are considered simultaneously.   This permits  consideration of  some
of the  adjustment opportunities available to individuals as air quality
improves.

     The basic  structure of  the MTH model  involves  a two-stage decision
process. Figure  7-4 outlines the major Components  of this process.   The
initial  decision  facing  the household  is the  allocation of  a fixed budget
among the many market goods  that may be purchased.  To determine these
                                   7-39

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       Environmental
        Conditions
Relative
 Prices
 Socioeconomic
Characteristics
                               Demand for
                              Market Goods
                           Index of Produced
                             Service Flows
                 Fignre 7-4.   Household decision process
"demands", various factors that are beyond the control of the household
must be  taken into account.   In particular,  the relative prices of  the
goods,  income, and various demographic factors all help shape the pattern
of household  demand.  Furthermore,  environmental variables such as ambient
concentrations may also influence  the demand for certain goods.   For
example,  the  demand for detergents or other cleaning products may be sensi-
tive to the level of PM concentrations.

     Once the allocation decision for market goods has been  made, condi-
tional on the factors mentioned  above,  the decision-making role of  the
household is  essentially  complete.  However,  there is a natural extension
to the  process that is important  for benefits analysis.  This additional
step represents the second stage mentioned above.

     The  idea behind the extension is that items  like  detergents are  not
demanded for their own sake, but rather for the  services they provide.
                                   7-40

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Thus,  it  may be instructive  to view  the  demand  for detergents as a derived
demand based on a more fundamental consumer demand for  cleanliness.   Viewed
in this way,  the allocation decision made by households with respect to
demands for detergents and other cleaning  items is consistent with  the
attainment  of  a particular  level  of  cleanliness.  Furthermore,  the  role of
air pollution  is clear.  Air pollution increases  the  cost of cleanliness by
increasing  the quantity  of detergents  and  other  cleaning items required to
obtain a unit of  cleanliness.   Conversely, a  reduction  in ambient  air
pollution can  lead to  a  reduction in the unit cost of cleanliness.

     In terms of Figure 7-4, this discussion implies  that  a link must be
established between an index of  produced  services such as  cleanliness  and
market goods.   This  link is  formalized in  the MTH model with the adoption
of "separability" assumptions that permit the many market goods that  may be
purchased  to  be  grouped naturally  into a  series of  categories.  These
categories represent  the various "produced" goods  described  above.   The
implication of the separability  assumption is that the various  items that
contribute to the production of nutrition (e.g., meats, dairy  products,
fruits, vegetables,  etc.) can be  analyzed independently of the  market goods
which contribute to the production of cleanliness (e.g., laundry products,
floor wax, etc.).   Thus, the separability assumption narrows the dimen-
sionality  of the system estimation effort. Furthermore, the assumption
plays an  important part in  the definition  of quantitative  indices for  the
unobserved  produced services.

     The  model described above is structured to deal directly with  house-
hold adjustments  to air  quality changes.  The focus  is on the value  house-
holds place on certain activities  rather than specific pollution-induced
damage.   A  physical  damage function between  pollution and objects that  may
be damaged is not included  in  the analysis.  Instead,  pollution enters  the
model as  a  proxy for damage.  It  is not  imperative that the type or  extent
of physical  damage be identified  explicitly.  The  knowledge that is
required is the value household  decision makers attach to activities or
services  that  may be sensitive to air  quality changes.
                                   7-41

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     A factor  that  can  limit  the  usefulness of this approach is that price
and quantity  data  are  not  available for the various produced  services.
Thus, it is not possible  to estimate directly demand relations  for these
non-market services.  Fortunately, the assumptions that permit the grouping
of market goods  are also  instrumental  in  establishing consistent  price and
quantity  indices for the produced services.  As would be expected, these
indices,  for any  particular  service,  depend  on the relative prices
(demands) for the  market  goods in the  service  category.   This  is an
important observation,  since  it implies that  the  aggregate  indices  may be
indirectly affected by  levels of  air quality  through  the demand  equations
of the market goods.

     Thus, with  information on  price and  quantity indices for the produced
services,  it is possible to  estimate  a system of demand equations  for these
services.   Furthermore,  since air quality indirectly affects the indices,
it is possible to ascertain the impact of  changes in air quality  on the
service indices.   This permits the identification of  economic benefits
associated with  the postulated air quality change.

     The data  used  to estimate  the HTH  model  are  drawn  primarily from the
1972-73 Consumer Expenditure Survey (23).  Expenditure data for over 100
current consumption items are  available for 24 large Standard Metropolitan
Statistical  Areas  (SHSA).  Price data for items  defined in Reference  (23)
are  taken primarily from Reference (24).  The  SHSAs included in the HTH
analysis are shown  in Table 7-6,  while  the goods eventually included  in the
analysis are  shown  in Table  7-7.  Note  that the 20 basic goods are grouped
into seven commodity aggregates.  This  is in  accord with the structure of
the decision process  outlined above.

     The goods included in the  HTH analysis account for approximately 40
percent  of  total   annual  consumption  expenditures.  Two categories of
commodity demand that may be affected by changes  in PM  levels are omitted
from  the  analysis.  Data and conceptual  problems  did not  permit the
analysis  of  recreational  (leisure) services nor  services generated by
property.   Since  labor-leisure  tradeoffs  and  location adjustments are two
                                   7-42

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                                Table  7-6




                      SMSAs  INCLUDED IN MTH ANALYSIS
         REGION  I:  NORTHEAST




            1.   Boston




            2.   Buffalo




            3.   New York  City




            4.   Philadelphia




            5.   Pittsburgh
REGION III:  SOUTH




   1.  Atlanta




   2.  Baltimore




   3.  Dallas




   4.  Houston




   5.  Washington, D.C.
         REGION II:  NORTH  CENTRAL




            1.   Chicago




            2.   Cincinnati




            3.   Cleveland




            4.   Detroit




            5.   Kansas City




            6.   Milwaukee




            7.   Minneapolis




            8.   St. Louis
REGION IV:  WEST




   1.  Denver




   2.  Honolulu




   3.  Los Angeles




   4.  San Diego




   5.  San Francisco




   6.  Seattle
Source:  Mathtech  (7), Table 4-1.
                                    7-43

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                                 Table  7-7
                     •

                    GOODS INCLUDED IN THE MTH ANALYSIS
                    Food
                              Cereal  and Bakery Products
                              Meat
                              Dairy Products
                              Fruits  and Vegetables
                              Miscellaneous Foods
                    Shelter
                        —    Home  Repair
                        —    Utilities

                    Home Operations

                        —    Laundry and Cleaning  Products
                        —    Other Household Products

                    Home Furnishings and Equipment

                        —    Textiles
                        —    Furniture
                        —    Major and  Minor Appliances
                        —    Housewares

                    Clothing

                        —    Clothing
                        —    Dry Cleaning

                    Transportation

                        —    Gas and Fuel
                        —    Other Vehicle  Operations

                    Health and Personal  Care

                        —    Personal Care
                        —    Non-Prescription Drugs
                        —    Non-Insured Medical Treatment
Source:  Mathtech (7),  Table 4-11.
                                    7-44

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plausible means of responding to perceived changes in PM levels,  failure to
recognize these adjustment possibilities  could lead  to  an  underestimate of
the households'  true willingness to pay.  In addition, lack of data pre-
cluded  the use of activity-specific measures  of  time  in the analysis.  This
can also  lead  to an underestimate of benefits.

     The  estimating forms for the demand specifications in the first stage
of the  decision process are derived from a Stone-Geary utility function.
This function  has  the mathematical  properties required for the two-stage
decision  process to be valid.  The demand system associated with this form
of the  utility function is known as the Linear Ezpenditure System.*

     In the  first  stage estimation,  the demands for two goods  are found to
be  sensitive  to PM  levels.  As  expected,  the demand for  laundry  and
cleaning products is directly related  to PM concentrations.  Also,  the
demand for  utilities  (gas and electricity) exhibits  a statistically signi-
ficant direct relation with PM.  A possible explanation for the  latter
finding  is that electricity is used in conjunction with other goods to
mitigate  the effects  of ambient PM.  These two equations are shown in Table
7-8. Recall from  the  discussion  above that these equations  represent only
a part  of the  decision process of the household.

     The  measure of PM included in the soiling studies  reviewed  previously
was  an annual arithmetic or geometric  mean measure  of TSP.  In the  MTH
study,  the measure of PM that  is found  to be most  robust in the various
specifications is  a  24-hour  averaging  time,  second-high TSP value for the
year.  The  index of TSP for the SMSA is  formed by taking the maximum of
second-high  concentrations across all sites  in the SMSA,

     An important feature  of  the MTH analysis  is  the variety of  checks
undertaken  to  test  the plausibility  and  sensitivity of  the results.  These
checks include:   consideration  of  an alternative  functional form (the
* A discussion of the properties of the Stone-Geary utility function and
  the LES  system  can  be  found  in  Stone  (25).
                                   7-45

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                                                            Table 7-8




                                                    DEMAND EQUATIONS WITH  TSP
o
Y31 = (1 - Al) • (CO + C



Y21 = (1 - Al) • (CO + Cl • S




1 • TX2HI) • (LAUCPI/MOPS) + Al - Al • ( DO + Dl
Coefficient
Al
CO
Cl
DO
Dl
Value
0.42462
-21.09430
0.01865
-32.13680
-10.13580
St. Er.
0.04504
12.46690
0.00647
16.82930
4.97639
X2HI) • (RPRPI/MSHELTER) + Al - Al • ((DO + Dl
Coefficient
Al
CO
Cl
DO
Dl
Value
0.23301
-32.23830
0.02393
-149.34600
0.12010
St. Er.
0.01562
15.55350
0.00977
47.04200
0.05375
• REGION) • (OHSEPI/MOPS)



• TX2HI) • (UTILPI/MSHELTER))




Definition of Acronyms
Y31 = Expenditure share of home operations budget on laundry and cleaning.
TX2HI = 24-hour averaging time, maximum .second-high concentration of TSP.
LAUCPI = Price index for laundry and cleaning goods.
MOPS = Total expenditure on home operations.
REGION = Dummy variable for region of country.
OHSEPI = Price index for other household service goods.
Y21 = Expenditure share of shelter budget on home repairs.
SX2HI = 24-hour averaging time, maximum second-high concentration of SO-.
RPRPI = Price index for home repairs.
MSHELTER = Total expenditure on shelter.
UTILPI = Price index for utilities.
                 Source:  Tables 4-16 and 4-17 of MTH (7).

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homogeneous  translog);  looking  at different  ways  to  enter pollution
variables  in  the  demand specifications;  comparisons of price elasticities
with elasticities from  other studies; and reasonableness checks for implied
TSP impacts.  These  tests add support  to the plausibility of the basic
results  in MTH,  and  lend  additional credence to benefit numbers generated
from the household model.

     Benefits  estimates  are derived  in the MTH study by comparing expendi-
ture functions before  and after the PM  change.*  This procedure differs
slightly from  the method described earlier, where benefits were defined  in
terms of the area to  the  left of the ordinary demand curve and  between the
marginal cost  curves  (see Figure 7-2).  In the MTH analysis,  the comparison
of expenditure functions is  equivalent  to measuring benefits  in  terms  of
the area to the left of  the compensated (i.e.,  utility constant) demand
curve and between the marginal  cost  curves.  For air quality improvements,
this latter way of measuring benefits will  lead to a  lower estimate than
that obtained  via the former method.

     The calculation of  benefits through  a comparison of expenditure
functions can  also be identified with the compensating variation measure  of
benefits.   In  particular:


          CV   -  E(P1,U) - E(P2,U)                                  (7.10)

where CV is compensating variation; E(*)  is the expenditure function; P   is
a vector of price  indices in  an  initial situation; P2  is  the vector  of
price indices  after a change in the indices due to an air. quality improve-
ment; and U  represents a constant level of the utility  index.  The  CV
measure  is interpreted as the amount a consumer would be willing to pay  (or
would have to be paid) in order  to be  indifferent between an  original
situation  and a new  situation with lower (higher) prices.   It is thus a
proper measure of economic benefits.
* For a discussion  of  expenditure functions,  see Diamond and McFadden (26),
                                   7-47

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     In terms of the evaluation criteria, the following  comments can be
                                                                   •
made:
          Theoretical basis — Estimates of economic benefits are
          derived in a manner that  is  consistent with  tenents of
          welfare economics.

          Consideration of relevant variables — The focus of MTfl on
          the  optimizing behavior of consumers  allows one to take
          advantage of & priori restrictions available  in conven-
          tional models of consumer behavior.  This helps narrow the
          scope  of  relevant economic variables.   However,  it may be
          the  case  that socioeconomic factors  or  other environmental
          measures  may also contribute  to  the explanation of varia-
          tion in quantity  demanded.  Because of the  complexity of
          the  system  estimation procedures,  only four  exogenous
          factors are analyzed  in  the various demand specifications.

          Quality of input data — The model  derived in MTH  requires
          observations on individual household behavior.   Available
          data are limited to average, SHSA-level observations for 24
          cities  in  two years.   Air pollution data  are also  averaged
          over the  entire SMSA.   It  is not clear what biases may be
          introduced by reliance  on  the aggregate  data.  A positive
          aspect  of  the economic input data is that it was collected
          for  purposes other than an  air  quality benefits  analysis,
          and   so  would  not be  subject  to the  inherent  biases
          suspected  in the survey  studies.

          Legitimacy of the empirical analysis — The MTH  study uses
          advanced econometric  techniques which  are appropriate to
          the  model being analyzed.  The choice   of functional forms
          for  the demand relations  is dictated  by the  choice of a
          particular utility  function.   The Stone-Geary function
          chosen for this  study  has been used  often in  economic
          analysis, is fairly easy to work with, and has  properties
          that are required for  the  two-stage  decision problem to be
          valid.  In addition,  a variety of plausibility checks were
          undertaken, including the specification of an alternative
          functional form.

          Transferabilitv of models — Benefits are calculated in the
          MTH model by comparing expenditure functions before and
          after pollution-induced  price  changes.   Although  the
          expression used to evaluate benefits is complex,  benefits
          can  be derived  in a  fairly straightforward manner for the
          scenarios  to be reviewed in this  report.  The  extrapolation
          to national  benefits  requires  assumptions  be  made on
          regional  prices  and  expenditures.  These variables are
          assigned values based  on the data observed for the  24 SMSAs
          used in the MTH analysis.
                                   7-48

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    Despite the  recent completion of  MTH,  the Criteria Document  (2)
contains a brief commentary  on  the  overall  Mathtech  effort.  The Document
notes  that  the  Mathtech models allow consideration of substitution  possi-
bilities and appear to  be  well based  from a theoretical  and empirical
perspective.  In addition to the concerns raised  above  with respect  to  the
quality of  the  input  data,  it is noted  in  the Criteria  Document that
additional  analysis  would be beneficial  in helping  to explain more  fully
some of the  behavioral adjustments implied by the models.   The  Staff  Paper
(3)  does not specifically address the  theoretical  or empirical methods
employed by  Mathtech.  However, additional detailed comments are  available
from  an EPA-sponsored  public  meeting held in July  1981  to  assess the
overall Mathtech study.*  At  this  meeting, a panel of leading environmental
economists  was  asked to review  and comment on each of  the sector analyses.
With respect to  the  household model,  the general  conclusion was  that this
was an excellent  piece  of  research.  Theoretical  and  econometric methods
were properly carried  out,  and  sufficient care was  given  to assessing  the
limitations  imposed  by required assumptions.  The major area of  concern was
with  the input data.  The  aggregate nature of the  data  and the limited
number of  sample points  made for  less than an optimal data set.

     Despite the data limitations, the general  consensus of  the review
panel members was that  the MTH model would generate the most  defensible
estimates  of benefits  from  among  currently available studies.  Because of
assumptions  made in the analysis,  it was recognized  that  benefits numbers
generated from the MTH  model would be conservative estimates of  willingness
to pay.

     This  evaluation  leads  us  to believe that reduced  soiling benefits
generated from the MTH model should be weighted most heavily in a compari-
son of benefits across  studies.   Benefit estimates derived from survey
studies like  Cummings  ejt al. (5) and the demand/supply  model  of Watson  and
Jaksch (6) can serve as  plausibility checks to the MTH numbers,  and help
define a probable  range of benefits from reduced  soiling for households.
* See Volume VI of Mathtech (27)  for a summary  of the public meeting.
                                   7-49

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     Hath tech. Manufacturing Model (8) —  The economic models discussed
earlier all focus on the household  sector.   Clearly, other  sectors  in  the
economy can also be  affected by soiling damage.   Commercial establishments,
industrial  plants,  and  government facilities may  all experience  decreases
in costs associated with soiling  damage as PM levels are reduced.  The
Mathtech manufacturing  analysis (MTM) examines the  benefits associated with
reduced PH levels for six manufacturing industries at the  3-digit level of
the Standard Industrial  Classification.*

     The MTM analysis uses  economic  techniques to derive a  general model of
production costs for manufacturing firms.   The hypothesis of  the analysis
is that soiling or contamination effects within manufacturing plants  add to
the total cost of production.  The costs of  production may  increase because
of 1) increased expenditures for cleaning, maintenance, and repairs; 2)
substitution to costlier materials  which are more  resistant to damage;  or
3) reduced  performance from  affected equipment or structures.

     Comparisons of  production  costs among  similar  types  of manufacturing
                     *
firms located in regions with different levels of PM permit  the estimation
of PM  effects.  Naturally,  in making such comparisons,  it is  important to
control for  other factors which  may cause  production costs to vary among
regions.  These factors may include variations in: wage rates, capital
costs and/or capital investment in—place,  taxes, prices for materials and
services,  etc.   Many of  these factors are controlled  for by  including them
in the analysis of  production cost variations.

     Also taken into account in  the analysis are  variations  in  climato-
logical conditions which may influence costs directly (e.g.,  through  varia-
tions  in heating and cooling  costs),  and which  may also  influence the
extent to which ambient  PM  causes physical and economic  damage.  The  clima-
tological factors considered  include ambient temperature and precipitation.
Since  SC^  levels may  act  in concert  with PM  to cause damage, ambient
measures of SOj are  also controlled  for in the analysis.
* The MTM study also looks  at  SC^ effects in the manufacturing  sector.
  These are not discussed here.
                                    7-50

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    The approach, described above results in an  estimate of the total costs
of production for  a  firm as a function of input prices,  the  level of
output,  and other exogenous factors.  An evaluation of costs before and
after  implementation of  a PM  control strategy provides  an estimate of
benefits.

    The formulation of the  model  in terms of production costs has several
advantages  relative   to  the more  standard  physical  damage  function
approach.*
         Data used  are'for  production costs actually incurred by
         firms as opposed to estimates  of what typical  maintenance
         and repair costs might be.
         The model  recognizes that firms can make choices  in  their
         response  to  pollution damage.   For example,  the model
         allows  for the  possibility that firms may substitute to
         costlier but  more damage—resistant  materials rather than
         incur air  pollution damages.
         Effects  for which damage functions have yet to be developed
         can still  be  measured.
         Subtle effects  such as efficiency losses  are captured.
     Data employed in the  estimation of the total cost functions are taken
from the 1972 Census of Manufactures (28).  Because of  confidentiality
restrictions, the Census Bureau does not  report data on individual  plants.
For this reason,  the MTM analysis focuses on county data for the 3-digit
SIC level.   An assessment  of  where PH  damage might be expected to be
greatest, coupled with data availability constraints,  limits the  MTM
analysis to six industries.  Estimation of the cost relationships  indicates
that PM,  specifically 24-hour average  second-high TSP values,  is an
important explanatory  variable in two  industries,  SIC  344 (Fabricated
Structural Metal Products) and SIC 354 (Metalworking Machinery).  Our
* Most  of  the physical damage function studies  that are based on  industry-
  related  impacts deal only with corrosion.   Models  of soiling referenced
  in the Criteria Document (2) and  Staff Paper (3) are  all household
  studies.
                                  7-51

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evaluation of  reduced  soiling benefits  is  limited to these two 3-digit SIC
classifications  and  the  2-digit SICs  (34 and 35) of which they are  a  part.
Since these industries  contribute only a small fraction to the value of
product  in the manufacturing sector,  estimates  of benefits are conservative
estimates of total benefits  in the sector.*

     An  important component  of  the  MTH analysis is a series of validation
checks which are performed to assess  the plausibility of the  model results.
These checks  include  an evaluation of various estimated economic para-
meters,  tests  for sensitivity of results to different methods for  including
pollution variables  in the models, reasonableness checks for implied pollu-
tion effects, and tests for spurious correlations due  to omission of own
pollution control costs.  These tests  add support  to the plausibility of
the basic results and  thus lend credence to benefit numbers generated from
the models.

     As  noted earlier,  the  Criteria  Document (2) briefly reviews the
overall  Hathtech effort,  with the  general  conclusion being  favorable  with
some  qualifications.  In addition,  the July 1981 Public Meeting also con-
sidered a draft version of the  MTH study.  The general conclusion at the
Public Meeting was that the MTM analysis is a careful and sophisticated
piece of research.  The assumptions  underlying both  the model structure and
the empirical analysis are made explicit  and plausibility checks are con-
ducted at many points  in the  study.**

     There were three  major  areas of concern.  First, general comments made
at the meeting by representatives of  the American Iron and Steel  Institute
(AISI) were that the relationship observed between  air pollution and manu-
facturing costs  should not be construed as  indicative of causation, but only
correlation.  The representatives cited  several factors which they felt
 * Even within a  particular SIC category,  data were not complete.  For
   example, in SIC 344,  57 counties  were included in the  analysis.   Plants
   in these counties  represented 38  percent of the industry output.
** See Reference (27)  for a summary  of  comments made at the Public  Meeting.
                                   7-52

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were "tme" causes of higher production costs:  age of plant, wage rate,
tax rates, and age of labor force.  It was asserted by the  AISI  representa-
tives  that these factors need to be included in the cost functions so that
included air  pollution variables do not  reflect the  impact  of  these
variables on  production cost.  Mathtech authors acknowledged that omitted
variables can lead to statistical bias in the  pollution coefficient, but
felt that the  manufacturing  sector analysis adequately  controlled for most
of the other  factors  influencing  production cost so that  the relationship
observed between air pollution and production cost  in the study  suggested a
cause-and-effect  relationship.   In addition to the  results  of  their
analysis, the Hathtech representatives noted that physical evidence from
other studies, and supporting anecdotal evidence collected  as part of their
study, were  both consistent with  the  kinds of  air  pollution effects
observed in their analysis.  This adds  further credence  to  the results
obtained  in the MTM  study.

     The  second major area  of concern involved the quality of  the data.
Members of  the review panel and the AISI representative commented on the
small number  of  observations available in the analysis,  and the  possibility
of  measurement  error associated with the  aggregate  data  and  proxy
variables.

     Finally, panel  members were concerned  with the magnitude of the
benefits  estimated  for SIC 344.   The implied effect of  a 1 |ig/m3  increase
in TSP is estimated to be  a  $90  to $340 increase in total  production costs
for a firm.   This  is  only 0.01  to 0.03  percent of average  total costs.
However, this one  industry accounted for  a large fraction of the total
benefits estimated  in the overall Hathtech  study.

     At the suggestion of  the review  panel, additional investigation of SIC
344 was undertaken.  One check involved informal interviews with managers
of some plants in SIC 344.  The purpose of the interviews was  twofold.  The
first  was to  look for evidence of damage  and/or behavioral  adjustments due
to PM deposition.  The second  objective was to determine whether plant
                                  7-53

-------
managers perceived  ambient  PM deposition  as affecting either their produc-
tion processes  or their production costs.

     Most of the managers  who were  interviewed reported no impacts on
either production processes  or  costs.  However, air  pollution effects may
not be readily  perceptible because of the predicted small changes in cost.
For example,  it was suggested that the effects of  temperature  and  moisture
may be larger than  those of PM, and yet it was found that managers did not
respond differently in cities with quite different  levels  of temperature
and humidity.   In  addition,  there was evidence  at  some plants of prior
adjustments  made to prevent metal  corrosion and contamination.  These
included the  use of coatings  on exposed  metal surfaces,  indoor  storage of
metal  inventories,  and  surface  cleaning before  painting  or welding.
However, these activities  might be undertaken for a  variety of  reasons so
that they could not be attributed  exclusively or primarily  to ambient PH.
The overall results  of the interviews were thus  inconclusive.

     As a second check, additional sensitivity analyses were performed for
SIC 344.  In one test, an outlying observation was eliminated from the
sample.  When  the  equations were re-estimated,  the implied effect of a
change in TSP increased by about 30 percent, but the estimated  coefficients
for TSP were  not as statistically significant.*  The implication  of this is
that  the  expressions for SIC  344 are  sensitive to sample composition.
Thus,  some  caution  is warranted  in  interpreting benefits  estimates
generated from  the  MTM cost equations.

     Because of the finding  for SIC 344, a similar  outlier analysis was
conducted for the other industry, SIC 354.   In this  case, elimination of
one outlying  data point resulted  in  a decrease  in the  predicted effect of
TSP on production costs, and  a  decline in statistical significance  for the
estimated TSP coefficients.   However,  examination of the data  for the
* The re-estimated coefficients  were  significantly  different  from  zero at
  the 10.6 percent level, while the  coefficients with the outlier were
  significant at  the  1  percent level.
                                   7-54

-------
outlying observation suggested  that  it was valid information,  and  thus the
original version of the  model was  retained.


    In terms of the evaluation criteria, the following observations  can be
made concerning the Mathtech manufacturing sector analysis:


    •    Theoretical  basis — Economic benefits are estimated  as
         differences  in total production cost  evaluated at two
         levels of ambient PM.  As a consequence, the model yields
         estimates consistent with the theoretical definition  of
         benefits.  Furthermore, the  structure of the model takes
         into account behavioral adjustments on  the  part of affected
         firms.

    •    Consideration of  relevant  variables — A variety  of
         economic and non—economic variables  are  controlled for  in
         the cost relationships.  However, data limitations pre-
         vented controlling  for some possibly relevant factors  such
         as local property taxes.   Omission  of these variables might
         bias  the coefficients of the air pollution variables  if
         these excluded variables tend to be correlated with air
         pollution.

    •    Quality of input  data —  Confidentiality restrictions  pre-
         clude the use of disaggregate plant data.  Consequently,
         data  for 3-digit SICs at  the county level are used.  Even
         with  this level of aggregation, lack  of appropriate data
         limit the number of  industries which  can be analyzed.   Of
         the six industries for which data are collected, only one
         has more than  SO  observations.   In terms  of specific
         variables,  proxies  are created for several of  the  desired
         variables,  including  the input price  data series.  This may
         introduce measurement error into the analysis.

    •    Legitimacy of Empirical  Approach — The  MTM analysis  uses
         econometric techniques  that are appropriate for the model.
         The functional form  chosen for the cost function analysis
         is the  transcendental  logarithmic function  (translog).
         This  is a very general function which  imposes  fewer main-
         tained assumptions  than other forms (e.g.,  Cobb-Douglas).

    •    Transferabilitv of  Model —  As  with the MTH model, the
         expression used to  evaluate benefits  is  complex.   Much  of
         the data needed to  evaluate benefits for this  study's
         scenarios must come from the MTM data base.
                                  7-55

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Sm«»«rv of Models

     In this  subsection, models which  describe the  impact of  reduced
soiling on household  and  manufacturing plant decisions have been reviewed.
To provide a point of reference for judging alternative studies,  a set  of
five evaluation criteria  were identified.   These  criteria  help define  the
desired structure  of  any  defensible benefits analysis  model.

     Another way  in  which studies  can  be evaluated is  in terms of the
biases implicit in the development and application of the  models.*  In  the
discussion of  each of the four  studies that have  been reviewed in detail,
the implications  of  specific assumptions have been noted.   Some of the
major  biases  associated with the models are summarized in Table 7-9.
Because each model contains biases that will impact benefits in an unknown
direction,  it  is not  possible to determine conclusively whether any parti-
cular  study will  provide an over-  or underestimate of "true" benefits.
Consequently,  an important element in reporting benefits should be a  range
of estimates reflecting,  in part, this degree of uncertainty.  It should  be
noted  that  the types  of  biases  listed in Table 7-9 refer only to biases
inherent in the models.   There are also biases  possible when the models are
used to provide estimates of national benefits.  These biases are discussed
in the next subsection.

     In the household sector,  three studies were evaluated.  Because  of
differences in study design  and model  bias it  is difficult  to make
meaningful comparisons  across studies.  For example, the Cummings model
accounts for changes  in out-of-pocket expenditures and the opportunity cost
of labor time in  cleaning,  the Watson  and Jaksch model yields benefits
estimates of a pure utility type;  and  the Hathtech household model captures
only the benefits  related to out-of-pocket expenditures.  At first glance,
it might be expected  that estimates derived from the Cummings study should
provide  an upper-bound  plausibility check for the  Mathtech household
estimates.   However,  this need not be true because of  the different biases
* In fact,  many of the  criteria reflect concern with the  question  of bias.

                                   7-56

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                                                        Table 7-9

                                           BIASES IN MODELS OF SOILING STUDIES
                 Model
              Description of Bias
   Expected Impact on
        Benefits
            Cummings et al.
-j
i
en
1.  Not all plausible cleaning tasks considered.
2.  Remaining measurement error in BAH data
    (e.g., unit cost data).

3.  Consideration of differentiated cleaning
    tasks.

4.  The use of a measure of the opportunity cost
    of leisure may be inappropriate since other
    activities that are equally undesirable may
    be substituted for cleaning when air quality
    improves.

5.  Adjustments to task frequency data of BAH.

6.  Use of linear specifications for frequency,
    labor and damage equations.

7.  Exclusion of possible relevant explanatory
    factors in various specifications.

8.  Calculation of benefits not based on varia-
    tions in demand and supply curves.
    Underestimate of
        benefits

        Unknown
Overestimate of benefits
  (possible jointness)

Overestimate of benefits
                                                                                    Overestimate of benefits

                                                                                            Unknown


                                                                                            Unknown
                                                                                        Underestimate of
                                                                                            benefits
                                                                                                 (cont inued)

-------
                                                  Table  7-9  (Continued)
                 Model
              Description of Bias
   Expected Impact on
        Benefits
            Watson & Jaksch
Ul
00
1.  Uncorrected BAH data employed in analysis.

2.  Not all plausible cleaning tasks considered.
                               3.   Consideration of  differentiated  cleaning
                                   tasks.

                               4.   Assumption of constant  unit  elastic demand.
5.  Assumption of linear, upward sloping
    marginal cost curve with multiplicative
    shift.

6.  Not all adjustment opportunities explicitly
    recognized.

7.  No adjustment for reallocation of labor time.
                               8.   Estimates  of  a  derived  from  limited  number  of
                                   studies.   Quality  of  estimates  questionable.

                               9.   Benefits calculated with  consumer  surplus
                                   measure.
        Unknown

    Underestimate of
        benefits

Overestimate of benefits
   (due to jointness)

 Possible overestimate
      of benefits

        Unknown
    Underestimate of
        benefits

    Underestimate of
        benefits

        Unknown
                                                        Small overestimate
                                                           of benefits
                                                                                                 (continued)

-------
                                      Table 7-9 (Continued)
     Model
              Description of Bias
                                                                                       Expected Impact on
                                                                                            Benefits
MTII
~J
Ui
1.  Problems associated with input data (e.g.,
    aggregate data for SMSAs, measurement error,
    small number of observations).

2.  Limited consideration of possible relevant
    explanatory variables.

3.  No consideration of location adjustment
    opportunities.

4.  No consideration of labor-leisure tradeoff
    possibilities.

5.  No adjustments for reallocation of labor
    time.

6.  Model does not capture benefits of a pure
    utility type.

7.  Stone-Geary utility function assumed.

8.  Separability assumptions imposed on groups
    of market goods.
                                                                                            Unknown
                                                                                Unknown
                                                                            Underestimate of
                                                                                benefits

                                                                            Underestimate of
                                                                                benefits

                                                                            Underestimate of
                                                                                benefits

                                                                            Underestimate of
                                                                                benefits

                                                                                Unknown

                                                                                Unknown
                                                                                     (continued)

-------
                                                  Table  7-9  (Continued)
                 Model
              Description of Bias
Expected Impact on
     Benefits
            MTM
-j
i
1.  Not all manufacturing sectors treated due to
    data limitations.

2.  Use of aggregate data.

3.  Approximate data on the prices and
    quantities of inputs and outputs.

4.  Exclusion of potentially relevant  variables.

5.  Translog cost function assumed.

6.  No adjustments permitted for plant
    relocation.
 Underestimate of
     benefits

     Unknown

     Unknown


     Unknown

     Unknown

 Underestimate of
     benefits

-------
associated with each, model.  As a consequence,  each of the three studies
must be viewed-as  contributing  individually  to the overall  stock of
knowledge  available for estimating benefits from reduced soiling.

    Based on the reviews in this  subsection,  it is possible  to make
subjective judgments  on the relative merits of  each  of the  studies.
Because of the model design and the manner in which benefits are calcu-
lated,  we  believe  the MTH study  should  be  weighted most heavily,  with the
other two  studies providing additional information  on the probable  range of
soiling benefits.   Furthermore, it  is our belief that the estimates derived
from the  MTH model will  likely be  conservative  estimates  of  the  benefits
from reduced  soiling  in the household  sector.   We are less sanguine  about
the relationship  to "true" benefits for the other two household soiling
studies.

     In the manufacturing  sector,  only  one  study  is  available for  benefits
estimation.  Based on the evaluation in  this section, we believe that it is
appropriate to view benefits numbers generated from the MTM model  as upper
bounds for the "true"  benefit numbers in SIC 344  and  SIC 354.   Note,
however,  in the context of the entire manufacturing sector, these estimates
will likely be conservative estimates  of the  total  benefits  from reduced
soiling in the sector.

     The  above  observation  is also pertinent with  respect to other sectors
of the economy.   In particular,  benefits from  reduced  soiling are not
reported  for  the commercial, government, and institutional sectors.  To our
knowledge, no studies have been  completed  which permit the calculation of
benefits  in these sectors.  Consequently, a  large part of  total benefits
from reduced  soiling may  be uncounted.
                                   7-61

-------
BENEFITS CALCULATIONS

Int r oduct i on

     In this subsection, benefits of  reduced soiling are calculated for the
household and manufacturing  sectors.   Benefits  in the  household  sector are
estimated using three  studies:  Cummings et al.  (5), Watson and Jaksch (6),
and the Mathtech household model (7).   Benefits estimates  in the  manu-
facturing sector are developed  entirely from the Mathtech manufacturing
model  (8).

     Benefits  estimates are calculated  on a  county-by-county basis.  The
counties  included in the  benefits analysis are determined as part of  a
research  effort conducted by another  EPA contractor.  This  other work
examines the costs of implementing alternative PH control strategies in
response to various  air  quality standards.  Any county that is  expected to
experience a reduction  in ambient levels of PH because of the PH control
programs  is included in  the benefits analysis.

     Although the  benefits calculations  are performed  with  county-specific
data,  for presentation purposes, benefit estimates  are  reported by EPA
Administrative Region.  The standard ten-region breakdown is employed.
Reporting estimates  in this fashion provides an  indication of  the distribu-
tion of benefits.

     Table  7-10  summarizes  the  scenarios to be evaluated  in this sub-
section.   As in the other  sections of this report,  the benefits from
reduced soiling associated  with  the  standards  shown in the table are pre-
sented as discounted present values  in 1982, in 1980 dollars.  Estimates of
annualized benefits  are  also provided.   A  discount rate of  10  percent and a
time  stream  of  benefits of 7  years  (PH10 standards) or 9  years (TSP
standards)  are also used.  A complete  description of such issues as non-
attainment status  for counties, maintenance of ambient  levels  after control
strategy implementation, and  the  impact of emissions growth on air quality
is contained in Section  9.
                                   7-62

-------
                               Table 7-10
                          AIR QUALITY SCENARIOS
Standard
Primary
Primary
Primary
Secondary
Primary
Secondary
Pollutant
PM10
PM10
PM10
PM10
TSP
TSP
Level
70
250
55
250
55
150
55
75
260
150
Averaging Time
Annual arithmetic mean
24-hour expected value
Annual arithmetic mean
24-hour expected value
Annual arithmetic mean
24— hour expected value
Annual arithmetic mean
Annual geometric mean
24-hour second high
24— hour second high
Implementation
Date
1989
1989
1989
1989
1989
1989
1989
1987
1987
1987
Household Sector
Coning*  et al. —
     In  Cummings et, al.  (5), the damage  function  is reported as:
               251.43 + 6.63P
(7.11)
where D is annual expenditures  per  household in 1980 dollars  and P is the
annual arithmetic mean  level of TSP.  As reported in our review of the
Cummings study,  this damage  estimate represents  both out-of-pocket expenses
and a measure  of the opportunity cost of  labor  in  cleaning  activities.

     As  part of  our review  of  this study,  an attempt was made to replicate
the results  of Cummings.   Data  were  presented  in  sufficient  detail in the
                                   7-63

-------
Cummings report to permit  reconstruction of the required variables.   One

reason this exercise was undertaken  was  that Cummings et a_l.  do not report

the standard error for the pollution coefficient.  In order to obtain a

range  of benefit estimates, our plan was to perturb the pollution coeffi-

cient by plus and minus two—standard deviations.  If one is willing to

accept the simple  regression form used by Cummings,  this perturbation

yields an estimate  of  the  95  percent confidence  interval for benefits  from

reduced soiling.


     The data required for replicating the  Cummings et  al. equation can be

developed from their Tables  7 and 20.  The following steps were  carried

out:
          Per-household estimates  of  cost were identified by task and
          pollution zone for the households that hired help  as  well
          as  households that performed the tasks  themselves.   Our
          estimates for this step coincided with those reported in
          Cummings.

          A weighted average, based on the distribution of  the two
          types  of households,  was calculated.   This gave an average
          per household expenditure for all houses that performed the
          tasks.  This weighted average was again estimated  by task
          and pollution  zone.

          In  order to  identify the average  expenditures  for all
          households  in a pollution  zone,  the  weighted  average
          estimates were  multiplied by a factor  representing the
          proportion of households  in the zone  that  performed the
          task.  In several  cases, the Cummings data was such that
          greater than 100 percent  of  households performed a task.
          In  these instances,  an upper bound of 100 percent was
          imposed.

          Damages  were aggregated across tasks by  pollution zone to
          obtain an estimate of total expenditures per household.
     Using this procedure,  the  following damage equation was estimated:
          D  =   131.28 + 8.85 '  P                                  (7.12)
               (143.67) (1.56)
                                   7-64

-------
Standard errors are given in parentheses.  This expression differs from the
one  estimated in Cummings et al.  Unfortunately, except for  the 100 percent
constraint imposed  in  Step  3,  it  is not possible  to  identify other causes
for  the observed  differences.   With  the  exception  of  the first  step
described above,  no additional  tables or  descriptions that  appear in
Cummings  are useful for making comparisons.

    Equation (7.12) is used  in  this subsection to calculate  benefits.   The
point  estimate of  marginal  damages  is  8.85,  with  a two-standard deviation
range of  about 5.72 to 11.98.   These  numbers represent  the dollar value
(1980  dollars)  of a 1 (ig/m   change in annual average TSP levels.   This  two-
standard  deviation range is used to identify a confidence interval for the
benefits estimates reported below.  Note  the  marginal damage estimate
reported in Cummings  (6.63) falls within this  range.  On the other hand,
this range does not include  the marginal damage estimate derived from the
Hichelson and Tour in (13) study.  Their value  (adjusted to 1980 dollars) is
$2.50.  This discrepancy is to be expected, however,  since the Cummings
study includes an estimate of  the  opportunity cost of labor in cleaning
activities in their damage relation.

     The  measure  of pollution used in the Cummings study is  the annual
geometric mean of TSP.   More precisely,  the midpoint of the  range of TSP in
each of the  four pollution zones of Philadelphia is associated with the
cost  data for  that zone.   Thus,  the  units of  the TSP coefficient in
Equation  (7.12)  reflect a  dollar value per unit of  average TSP annual
geometric mean concentrations.  That  is,  the index of pollution for the
analysis  is an average value.  Since the air quality data  generated for use
in this study rely  on design value monitors  that likely reflect the highest
values in a  given  county,  it  is appropriate to adjust the air quality data
to provide a representation of population exposure  in the county that is
more  consistent with the  structure of Equation  (7.12).  This adjustment
procedure  is discussed in detail in  Section  9.

     Tables  7-11 through 7-16 present the benefits estimates for each of
the  scenarios listed in Table 7-10.   These  benefits  represent the benefits
                                   7-65

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                        Table 7-11

      ESTIMATED BENEFITS FOR:  CUMMINGS SOILING STUDY

         Benefits Occurring Between 1989 and 1995
        Scenario:  Type B PM10 - 70 AAM/250 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                    0.0
                                    8.2
                                  289.9
                                  341.0
                                 1768.1
                                  699.0
                                  100.6
                                  235,
                                 2263,
    .2
    .5
          Point
         Estimate
              0.0
               .7
                8
                                  329.1
  12.
 447.
 526.6
2730.9
1079.7
 155.4
 363.2
3496.0
 508.3
         Maximum
   0.0
  17.2
 605.7
 712.3
3693.7
1460.3
 210.2
 491.3
4728.5
 687.5
Total U.S.
6034.7
9320.7   12606.6
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
2415.6
3730.9
5046.1
                            7-66

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                        Table 7-12

      ESTIMATED BENEFITS FOR:  CUMMINGS SOILING STUDY

         Benefits Occurring Between 1989 and 1995
        Scenario:  Type B PM10 - 55 AAM/250 24-hr.
Federal Administrative Region   Minimum
                                   83.2
                                  107.5
                                  565.0
                                  797.6
                                 2613.7
                                 1140.8
                                  270.8
                                  501.5
                                 4384.0
                                  471.7
                                10935.7
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                           Point
                                          Estimate
                                             128.5
                                             166.0
          Maximum
            173.9
            224.5
           1180.3
           1666.2
           5460.
           2383
            565.6
           1047.6
           9158.2
            985.3
                                                          .1
                                                          .1
Total U.S.
16890.2   22844.8
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
                                 4377.3
 6760.8
9144.2
                             7-67

-------
                        Table 7-13

      ESTIMATED BENEFITS FOR:  CUMMINGS SOILING STUDY

         Benefits Occurring Between 1989 and 199S
        Scenario:  Type B PM10 - 55 AAM/150 24-hr.
Federal Administrative Region   Minimum
           Point
          Estimate
          Maximum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Total U.S.
                                  282.2
                                  202.9
                                  814.9
                                 1047
                                 2998
                                 1365
                                  389
                                  754.8
                                 4954.9
                                  838.6
13648.4
435.8
313.4
1258.6
1617.9
4631.2
2108.3
600.8
1165.8
7652.9
1295.3
589.5
423.9
1702.3
2188.3
6263.8
2851.6
812.6
1576.8
10350.8
1751.9
21080.1   28511.7
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
 5463.2
 8437.9   11412.6
                            7-68

-------
                        Table 7-14

      ESTIMATED BENEFITS FOR:  CUMMINGS SOILING STUDY

         Benefits Occurring Between 1989 and 1995
              Scenario:  Type B PM10 - 55 AAM
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                    83.2
                                   107.5
                                   565.0
                                   797.6
                                 2601.5
                                 1116.3
                                   269,
                                   501,
                                  4383,
           Point
          Estimate
             128.5
             166.0
             872.7
            1231.9
            4018.1
            1724.2
             416.
             774.
            6770,
                                   439.2
             678.3
          Maximum
            173.9
            224.5
           1180.3
           1666.2
           5434.6
           2332.0
            562.7
           1047.6
           9157.6
            917.5
Total U.S.
10864.9
16780.9   22696.9
Discounted Present Value  in Millions  of 1980 Dollars  in 1982
Using a 10 Percent Rate of Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
 4349.0
 6717.0
9085.1
                             7-69

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                        Table 7-15

      ESTIMATED BENEFITS FOR:  CUMMINGS SOILING STUDY

         Benefits Occurring Between 1987 and 1995
         Scenario:  Type B TSP - 75 AAM/260 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Total U.S.
                                  287.0
                                  261.3
                                 1322.8
                                 1598.6
                                 4835.1
                                 1919.3
                                  671.6
                                  932.4
                                 8023.7
                                  917.2
20769.0
Point
Estimate
443.3
403.6
2043.0
2469.0
7467.8
2964.4
1037.3
1440.2
12392.6
1416.6

Maximum
599.6
545.9
2763.3
3339.4
10100.5
4009.4
1402.9
1947.9
16761.5
1916.1
32077.7   43386.4
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount.
Annualized Benefits
Between 1987 and 1995
Total U.S.
 5808.0
 8970.5   12133.0
                            7-70

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                        Table 7-16

      ESTIMATED BENEFITS FOR:  CUMMINGS SOILING STUDY

         Benefits Occurring Between 1987 and 1995
            Scenario:  Type B TSP - 150 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Total U.S.
                                  853.5
                                  522.7
                                 2007.8
                                 2086.1
                                 5858.1
                                 2304.4
                                 1178.4
                                 1333.8
                                 9149.9
                                 1540.9
26835.5
Point
Estimate
1318.2
807.3
3101.0
3222.1
9047 . 8
3559.2
1820.0
2060.0
14132.0
2379.9

Maximum
1783.0
1091.9
4194.2
4358.0
12237.5
4814.0
2461.6
2786.3
19114.1
3218.9
41447.5   56059.4
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount.
Annualized Benefits
Between 1987 and 1995
Total U.S.
 7504.5
11590.8   15677.0
                             7-71

-------
that would be achieved when all counties included in the analysis are in
compliance with the standard for all years under consideration.*   The first
three tables  present estimates for alternative PM10 primary standards.  The
order of presentation for the tables is from most lenient to most strin-
gent.  The minimum and maximum estimates are  obtained by perturbing the
coefficient in Equation (7.12) by minus  and  plus two  standard  deviations,
respectively.  From the tables, it can be seen that almost two-thirds of
total U.S. benefits will accrue in the East North Central and  South Pacific
regions.

     Table 7-14 presents estimates for a PM10 secondary standard of 55
    ^
Hg/m .  The table reports the total benefits  associated with attainment and
maintenance of this standard.  To identify the  incremental  benefits of the
secondary standard  conditional on a primary  standard being attained,  it is
necessary to  net out the benefits of the  primary standard.  For example, if
the 55 |ig/m  secondary  standard is associated with  the 70/250 |ig/m  primary
standard,  then the incremental  benefits  (discounted present value) of the
secondary standard  would be  $7.5  billion for the total U.S. point estimate
(i.e.,  16.8 - 9.3).  Although reduced soiling is a welfare benefit,  it is
important to note  that positive  welfare  benefits  will be generated in the
attainment of the  primary  standard.  This occurs  even though the primary
standard is based  only on health considerations.

     Tables 7-15 and 7-16 present benefits  estimates for the current TSP
primary and  secondary  standards.  As before, to  identify  the incremental
benefits of the secondary standard,  it  is necessary to net  out  the benefits
generated through  attainment of  the primary  standard.

     Finally,  Table 7-17  shows the benefits that accrue under a 70/250 PM10
primary standard when  all counties are not in attainment with  the  standard
throughout the  1989-1995 time  horizon.  This can occur because  available
means  of  controlling  emissions are exhausted prior to standard attainment
* In the  language of Section 9,  these benefits represent "B" scenario
  benefits.
                                    7-72

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                        Table 7-17

      ESTIMATED BENEFITS FOR:  CUMMINGS SOILING STUDY

         Benefits Occurring Between 1989 and 1995
        Scenario:  Type A PM10 - 70 AAM/250 24-hr.
                                           Point
Federal Administrative Region   Minimum   Estimate   Maximum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                    0.0        0.0       0.0
                                    8.2       12.7      17.2
                                  279.3      431.4     583.5
                                  274.9      424.5     574.2
                                 1267.2     1957.2    2647.1
                                  442.7      683.7     924.7
                                  '89.9      138.8     187.7
                                  220.9      341.2     461.5
                                 1221.3     1886.3    2551.3
                                  103.7      160.2     216.7
Total U.S.                       3908.0     6036.0    8163.9
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount.
Annualized Benefits
Between 1989 and 1995
Total U.S.
1564.3
2416.1
3267.8
                             7-73

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and/or because  of  emissions  growth.  This table can be compared to Table 7-
11 where all  counties were assumed to be in compliance with the  same  70/250
PM10  standard.  As expected, the benefits  estimates in Table 7-11 exceed
those shown in Table  7-17.

Watson, and Jmkseh  —

     In the Watson and Jaksch  study, benefits estimates  are  calculated by
task and pollution zone.  The expression used to  calculate benefits is:
           ACWi-   =   o  In
P2i
p-^-   ' X41                                 (7.13)
LPiJ
where    ACW-.   is  the  change in welfare in task i for pollution zone j.
           ?2-   is  the  final level of pollution in zone j.
           P,.   is  the  initial level of pollution in zone  j.
             a   is  a  factor estimated from damage function studies.
           X—   is  the  outlay for task i in zone j .

     For purposes of  extrapolation,  it does not seem warranted  to estimate
benefits at such a disaggregate  level  as task and zone.   Consequently, the
benefits derived below are calculated on a county basis only  and for the
aggregate of tasks.   In essence,  the subscript i can be deleted  in Equation
(7.13), and the j index refers to a county instead of a pollution zone.

     There are  five bits of information needed to determine benefits.  The
two  levels of  TSP  or PM are provided  to  us through  the  cost  of  control
study.  These data are representative of the highest value monitors.  As
with  the Cummings study, average TSP  indices  are  used  in WJ.  Consequently,
the design value data from the cost analysis must be adjusted to provide a
more  consistent match-up with the WJ model.

     The third  data  element that is required is an .estimate  of a.  This
factor is  taken from WJ, and three values are available:  0.56,  1.0, and
                                    7-74

-------
2.0.  These  three estimates provide  the variation needed to obtain  a
probable range of benefits for the Watson and Jaksch study.  The fourth
data element  required  is  an estimate of  per  household  expenditures.
Earlier  in this review,  it was mentioned that because of data difficulties
with, the Booz,  Allen and Hamilton data base, it  would be advantageous if
the Cummings "cleaned"  data  could be  used.   Unfortunately, upon closer
examination  this  did  not appear  to be  feasible.  The principal constraint
is that  only three of the eight cleaning/maintenance tasks examined in
Watson and Jaksch correspond to  those tasks included in Cummings.

     Using data provided in Watson and Jaksch,  a  per-household expenditure
estimate of $155.20 was calculated.   This figure is a  weighted average
across pollution zones for all  eight  cleaning/maintenance tasks.  Since
this value  is  in 1971 dollars, it was adjusted to 1980 dollars by the U.S.
average  Consumer  Price Index  for home  materials  repair.   In 1980 dollars,
the annual per  household expenditures  are estimated to be $333.68.

     The final variable  needed  to calculate benefits  is  the number of
households per county.  This variable and the necessary growth rates are
taken from  Reference  (29) and Reference (30),  respectively.

     The final form for estimating benefits from the Watson and Jaksch
study is:
-ACW.   =  o  In
                        P2j
                             •  X  • Nj                             (7.14)
where     X.  is the per-household  outlays in county j.
          N.  is the number of  households in county j.

The other variables are as defined previously,  except that j  indexes a
county rather  than a pollution zone.

     Tables  7-18  through  7-23 present  the benefits  estimates derived from
the Watson and Jaksch model for the scenarios described  in Table 7-10.   As
                                   7-75

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                         Table 7-18

  ESTIMATED BENEFITS FOR:  WATSON AND JAXSCH SOILING STUDY

          Benefits Occurring Between 1989 and 1995
         Scenario:  Type B PM10 - 70 AAM/250 24-hr.
Federal Administrative Region   Minimum
          Point
         Estimate
         Maximum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
I
New England
N.Y.-N.J.
Middle Atlantic
South. Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Total U.S.
                                    0.0
                                    5.6
                                  107.5
                                  146.3
                                  905.2
                                  321.3
                                   42.6
                                  106.5
                                  750.8
                                  151.7
2537.4
               .9
               .2
               .5
               .7
   0.0
  10.0
 191,
 261,
1616.
 573.
  76.1
 190.1
1340.7
 270.9
4531.1
    .9
    .5
    ,0
   0.0
  20.0
 383.
 522.
3233.
1147.4
 152.1
 380.2
2681.3
 541.8
9062.2
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount
Annualized Benefits
Between 1989 and 1995
Total U.S.
1015.7
1813.7
3627.4
                             7-76

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                         Table 7-19

  ESTIMATED BENEFITS FOR:  WATSON AND JAKSCH SOILING STUDY

          Benefits Occurring Between 1989 and 1995
         Scenario:  Type B PM10 - 55 AAM/250 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                   38.8
                                   69.5
                                  242.5
                                  386.0
                                 1373,
                                  570.
    .1
    .3
                                  126.8
                                  219.
                                 1683,
                                  232.3
          Point
         Estimate
             69.4
            124.1
            433,
            689,
1018,
 226,
2452.0
            391.2
           3006.5
            414.8
         Maximum
 138.7
 248.3
 866.0
1378.7
4904.
2036.
 453,
,0
.7
,0
           782.4
          6013.1
           829.6
Total U.S.
4942.1
8825.2   17650.5
Discounted Present Value in Millions of 1980 Dollars  in 1982
Using a 10 Percent Rate of Discount
Annualized Benefits
Between 1989 and 1995
Total U.S.
1978.2
3532.5
7065.1
                              7-77

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                         Table 7-20

  ESTIMATED BENEFITS FOR:  WATSON AND JAKSCH SOILING STUDY

          Benefits Occurring Between 1989 and 1995
         Scenario:  Type B PM10 - 55 AAM/150 24-hr.
Federal Administrative Eegion   Minimum
          Point
         Estimate
          Maximum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                  193.9
                                  131.1
                                  391.2
                                  531.8
                                 1579.5
                                  711.3
                                  193,
                                  339.
                                 1992.2
                                  476.1
    .1
    .7
346.3
234.1
698.6
949.7
2820.5
1270.2
344.8
606.6
3557.5
850.2
692.5
468.2
1397.1
1899.3
5640.9
2540.4
689.5
1213.2
7115.0
1700.5
Total U.S.
6539.9
11678.3   23356.7
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount
Annualized Benefits
Between 1989 and 1995
Total U.S.
2617.8
 4674.6
9349.1
                             7-78

-------
                         Table 7-21

  ESTIMATED BENEFITS FOR:  WATSON AND JAKSCH SOILING STUDY

          Benefits Occurring Between 1989 and 1995
               Scenario:  Type B PM10 - 55 AAM
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.I.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                   38.8
                                      ,5
                                      ,5
  69,
 242.
 386.0
1366.4
 555.4
 126.2
                                  219,
                                 1683
                                  214.6
          Point
         Estimate
  69.4
 124.1
 433.0
 689.3
2440.0
 991.9
 225.4
 391.2
3006.3
 383.2
         Maximum
 138.7
 248.3
 866.0
1378.7
4879,
1983
 450.
 782.4
6012.6
 766.4
                                                           .9
                                                           .7
                                                           .7
Total U.S.
4902.1
8753.7   17507.4
Discounted Present Value in Millions of 1980 Dollars  in 1982
Using a 10 Percent Rate of Discount
Annualized Benefits
Between 1989 and 1995
Total U.S.
1962.2
3503.9
7007.8
                              7-79

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                         Table 7-22

  ESTIMATED BENEFITS FOR:  WATSON AND JAKSCH SOILING STUDY

         •Benefits Occurring Between 1987 and 1995
          Scenario:  Type B TSP - 75 AGM/260 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
II
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                  151.9
                                  163.2
                                  622
                                  827
                                 2586
                                 1015.3
                                  336.5
                                  413
                                 3380
    .2
    .7
Total U.S.
                                  483.1
9979.6
Point
Estimate
271.3
291.4
1112.0
1476.8
4617.9
1813.0
601.0
737.9
6036.9
862.7

Max imam
542.5
582.7
2223.9
2953.5
9235.8
3626.1
1201.9
1475.7
12073.8
1725.5
17820.8   35641.6
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount
Annualized Benefits
Between 1987 and 1995
Total U.S.
2790.8
 4983.6
9967.2
                             7-80

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                         Table 7-23

  ESTIMATED BENEFITS FOR:  WATSON AND JAKSCH SOILING STUDY

          Benefits Occurring Between 1987 and 1995
             Scenario:  Type B TSP - 150 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                  598.0
                                  335.0
                                 1045.2
                                 1150.2
                                 3172.4
                                 1242.4
                                  670.
                                  657
                                 4143.3
                                  938.3
     .3
     .4
Point
Estimate
1067.9
598.2
1866.4
2054.0
5665.0
2218.6
1197.0
1174.0
7398.8
1675.5

Maximum
2135.8
1196.4
3732.8
4107.9
11330.0
4437.2
2394.0
2347.9
14797.6
3351.0
Total U.S.
13952.6
24915.3   49830.6
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount
Annualized Benefits
Between 1987 and 1995
Total U.S.
 3901.8
 6967.6   13935.1
                             7-81

-------
before, these benefits represent the benefits associated with complete
attainment and  maintenance  of  the  standards in all counties.   The ordering-
of the tables  is  also the same.  In particular,  the first three tables
represent  alternative PM10 primary standards, Table 7-21 shows the benefits
for the PM10 secondary standard,  and Tables 7-22 and 7-23 present benefit
estimates for  the current TSP primary and secondary standards, respec-
tively.  Finally, Table 7-24 shows  the benefits that accrue with the 70/250
PM10 scenario  when some of the counties remain  in  nonattainment due to
emissions growth.

     A comparison  of  the benefits derived from  the Cummings and Watson and
Jaksch models  reveals that the Watson and Jaksch  estimates are lower.
However,  because  of model  differences,  it is not possible to compare the
studies in a definitive manner.   Recall  that  the Cummings  study accounts
for benefits  derived from reduced out-of-pocket expenditures as well as the
value of  reduced labor time in cleaning activities,  while the Watson and
Jaksch model identifies adjustments  in the demand for cleanliness.  These
adjustments reflect actions based on "utility" considerations as well as
changes in the  cost of  cleanliness.

Mmthtech Household Model (MB)  —

     The basic analysis in MTH is limited to 24  SMSAs,  with these SMSAs
accounting for approximately 30 percent of the total U.S. population in
1976.   Since  the demand systems estimated in  MTH use SHSA-specific price,
income, and  demographic data,   extrapolation to other counties of the U.S.
requires assumptions  on these  data.  For example, since price data are not
available for  many parts of the country, proxies must be created.   The
proxies take  the form  of regional averages based on  the  data available in
the basic  study.

     The specific  assumptions made in performing  the  extrapolation include:

     •    Certain data are assigned to counties  based on the region
          in  which the  county  is  located.    In particular,  the
          following steps are taken:
                                   7-82

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                         Table 7-24

  ESTIMATED BENEFITS FOR:  WATSON AND JAKSCH SOILING STUDY

          Benefits Occurring Between 1989 and 1995
         Scenario:  Type A PM10 - 70 AAM/250 24-hr.
Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
I
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                    0.0
                                    5.6
                                  103.1
                                  111.0
                                  599.8
                                  192.0
                                   36,
                                   95,
                                  387.0
                                   41.0
    .6
    .9
Total U.S.
1571.9
          Point
         Estimate
   0.0
  10.0
 184.1
 198.2
1071.0
 342.9
  65.3
 171.3
 691.0
  73.2
2807.0
         Maximum
   0.0
  20.0
 368.2
 396.4
2142.1
 685.8
 130.7
 342.6
1382.0
 146.3
5614.1
Discounted Present Value in Millions of 1980 Dollars in 1982
Using a 10 Percent Rate of Discount
Annualized Benefits
Between 1989 and 1995
Total U.S.
 629.2
1123.6
2247.2
                              7-83

-------
         —   The  country  is  divided  into  two major  areas:
              Northeast plus  North Central and South plus  West.
         —   Averages by region are  computed  from  the 24 SMSA data
              for the following  items:
              a)   30-year average temperature
              b)   family size
              c)   average annual percent change  in the all-item
                   Consumer Price Index
              d)   average percent of total consumption expenditures
                   in the SHSA data
              e)   disaggregate and  aggregate price sets developed
                   in the basic  analysis of MTH.
     •   Certain data  are  assigned to  counties  based on county- or
         state-level data.  These data  include:
         —   Air quality data
         —•   Baseline county population numbers  are taken from
              Reference (29).   Conversions to household data are
              made by dividing by the regional family size values.
         —   County population projections are taken from Reference
              (30).
         —   State  income projections for 1985 and 1990 (current
              1972 dollars) are  obtained from  Reference (31).
     •   Certain data  on  assumptions are relevant for all counties.
         These  include:
         —   The parameters  of  the demand models in MTH.
         —•   The air quality scenario.
         —   The  parameters of the benefits  calculations (i.e.,
              discounted present value  in  1982,  in  1980 dollars;  10
              percent discount rate; 7-  or 9-year steam of benefits.

     Given these assumptions, benefit  estimates are developed  for each
county that experiences a reduction in PH concentrations.  Note that the
extrapolation  from  the original 24  SMSAs  in  the MTH study  is  only
geographic.  Recall  that the goods included  in the basic MTH  analysis
account for only about 40 percent of current consumption  expenditures.
With available  information,  it  is not possible  to extend the scope of the
MTH study beyond this subset of goods.
                                   7-84

-------
    Tables 7-25 through 7-30 present the benefit estimates of the MTH
model for the scenarios described in Table  7-10.  Since the measure of TSP
used in the MTH analysis is  the maximum of  all  site 24-hour average second-
high readings  in a county,  it is not necessary to adjust the design value
data to characterize population exposure.

    The ranges of benefits reported in the tables reflect  two plausible
sources of uncertainty in the MTH benefits equations.   First, since the
estimators for TSP are random variables with known standard error,  values
of the coefficient plus or minus  two standard deviations from the estimated
coefficient provide  a range  that reflects the stochastic  nature of the
estimator.  Note that this procedure  does not yield a  95  percent confidence
interval  for the MTH model.  Because of the across-equation  constraints
implied by the  estimated demand  systems,  a true  confidence  interval  would
have to account for covariances among the variables in the system.   The
procedure described above does not do this.  Analytical problems associated
with developing  the  statistically-preferred measure prevented its use for
this review.   Nevertheless,  the less rigorous approach does provide an
         *
indication of the uncertainty  associated with the pollution estimator.

    The  second way uncertainty  in the MTH  model  is  reflected in the
benefits calculations is through  variations in prices.   In the  original MTH
study,  price data were available for only 24 SMSAs.   An extrapolation to
other counties  in the country was made  by  defining two average price sets
for the United States.  One price  set is  constructed from the prices of
those MTH SMSAs  that  are in the West  and South; the other price set is the
average of prices of  SMSAs  in the Northeast and North Central  parts of the
country.   Taken together,  these average prices were  used to derive the
point estimates  of benefits in Tables 7-25  through 7-30.

    The minimum and maximum price set estimates are developed by using
price data from the  MTH SMSAs that  yield  the  lowest and highest marginal
valuations per unit change   in PM levels.  The  low price  set  is associated
with Atlanta; the high price set  is  associated  with New York City.
                                   7-85

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                          Table 7-25

ESTIMATED BENEFITS FOR:  MATHTECH HOUSEHOLD EXPENDITURE STUDY

           Benefits Occurring Between 1989 and 1995
          Scenario:  Type B PM10 - 70 AAM/250 24-hr.
  Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
        Point
       Estimate
        Maximum
0.0
0.2
4.7
5.7
35.8
10.0
1.4
3.5
31.5
6.6
0.0
1.7
35.4
40.3
271.0
72.0
10.2
25.1
231.0
48.0
0.0
3.2
67.4
76.7
513.7
136.5
19.4
47.5
440. 9
91.2
  Total U.S.
99.4
734.9
1396.5
  Discounted Present Value in Millions of 1980 Dollars in 1982
  Using a 10 Percent Rate of Discount.
  Annualized Benefits
  Between 1989 and 1995
  Total U.S.
39.8
294.2
 559.0
                               7-86

-------
                          Table 7-26

ESTIMATED BENEFITS FOR:  MATHTECH HOUSEHOLD EXPENDITURE STUDY

           Benefits Occurring Between 1989 and 1995
          Scenario:  Type B PM10 - 55 AAM/250 24-hr.
  Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
         Point
        Estimate
         Maximum
1.1
2.1
8.7
12.6
56.1
15.8
3.7
7.0
60.4
9.2
8.4
16.1
65.5
89.1
423.3
113.1
27.7
50.4
440.3
66.5
15.9
30.5
124.0
168.4
800.0
213.4
52.3
95.3
835.4
126.0
  Total U.S.
176.7
1300.3
2461.1
  Discounted Present Value in Millions of 1980 Dollars in 1982
  Using a 10 Percent Rate of Discount.
  Annualized Benefits
  Between 1989 and 1995
  Total U.S.
 70.7
 520.5
 985.1
                               7-87

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                          Table 7-27

ESTIMATED BENEFITS FOR:  MATHTECH HOUSEHOLD EXPENDITURE STUDY

           Benefits Occurring Between 1989 and 1995
          Scenario:  Type B PM10 - 55 AAM/150 24-hr.
  Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       .3
                                       ,5
  Total U.S.
  4.7
  4.1
 13.0
 15.9
 63,
 19.
  5.3
 10.0
 68.9
 16.1
220.7
         Point
        Estimate
  35.0
  30.5
  97.0
 112.5
 477.8
 140.0
  39.6
  71.6
 501.9
 116.1
1621.9
         Maximum
  65.8
  57.6
 183.4
 212.3
 902.8
 263.9
  74.8
 135.1
 951.1
 219.1
3065.9
  Discounted Present Value in Millions of 1980 Dollars in 1982
  Using a 10 Percent Rate of Discount.
  Annualized Benefits
  Between 1989 and 1995
  Total U.S.
 88.3
 649.2
1227.2
                              7-88

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                          Table 7-28

ESTDIATED BENEFITS FOR:  MATHTECH HOUSEHOLD EXPENDITURE STUDY

           Benefits Occurring Between 1989 and 1995
               Scenario:  Type B PM10 - 55 AAM
  Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
         Point
        Estimate
         Maximum
1.1
2.1
8.7
12.6
55.8
15.5
3.7
7.0
60.4
8.3
8.4
16.1
65.5
89.1
421.4
111.1
27.5
50.4
440.3
59.8
15.9
30.5
124.0
168.4
796.4
209.8
52.0
95.3
835.3
113.3
  Total U.S.
175.2
1289.5
2440.8
  Discounted Present Value  in Millions of 1980 Dollars in 1982
  Using a 10 Percent Rate of Discount.
  Annualized Benefits
  Between 1989 and 1995
  Total U.S.
 70.1
 516.2
 977.0
                               7-89

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                          Table 7-29

ESTIMATED BENEFITS FOR:  MATHTECH HOUSEHOLD EXPENDITURE STUDY

           Benefits Occurring Between 1987 and 1995
          Scenario:  Type B TSP - 75 AAM/260 24-hr.
  Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
         Point
        Estimate
         Maximum
3.4
4.5
19.9
23.0
103.3
28.1
8.9
12.1
108.9
17.3
25.8
33.7
148.2
161.4
in. 6
200.2
66.9
86.5
788.1
124.6
48.8
63.7
280.0
304.1
1467.6
377.0
126.4
163.2
1489.9
235.8
  Total U.S.
329.4
2413.1
4556.4
  Discounted Present Value in Millions of 1980 Dollars in 1982
  Using a 10 Percent Rate of Discount.
  Annnalized Benefits
  Between 1987 and 1995
  Total U.S.
 92.1
 674.8
1274.2
                              7-90

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                          Table 7-30

ESTIMATED BENEFITS FOR:  MATHTECH HOUSEHOLD EXPENDITURE STUDY

           Benefits Occurring Between 1987 and 1995
              Scenario:  Type B TSP - 150 24-hr.
  Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
  Total U.S.
431.1
         Point
        Estimate
                                               102.7
                                                69.8
                                               224.7
                                               218.2
                                               907.0
                                               247.6
                                               119.9
                                               123.3
                                               937.1
                                               204.0
3154.4
         Maximum
                     193.3
                     131.7
                     423.9
                     410.1
                    1711.4
                     466.0
                     226.0
                     232.0
                    1767.6
                     384.3
5946.4
  Discounted Present Value  in Millions  of  1980 Dollars  in 1982
  Using a 10 Percent Rate of Discount.
  Annualized Benefits
  Between 1987 and 1995
  Total U.S.
120.6
 882.1
1662.9
                               7-91

-------
     With the two adjustments for uncertainty,  there  are  a variety of ways
to define the overall range of benefits.  We have  chosen the option that
yields the widest range.   That  is,  the low estimate reflects  the low price
set in combination with a pollution coefficient  that is  two  standard devia-
tions below  the estimated coefficient; the point estimate reflects the
average (two-region)  price  set  in  combination  with the estimated coeffi-
cient; and the high  estimate reflects  an evaluation of  benefits for the
high price set and an estimator  two standard  deviations  above  the estimated
coefficient.

     The  benefit  calculations shown in Tables 7-25 through 7-30 are carried
out with SO*  levels  assumed  constant  at  the  current primary  standard level
(260 jig/m ).  In  the  MTH  analysis,  SO- is part  of an  interdependent system
of equations  so  that  its  value,  even if unchanging,  can  influence alloca-
tion decisions.   Consequently,  some measure  of  SOj  must be  included in the
extrapolation procedure. Because S02 data are not available for many of
the counties included in the PH10 scenarios,  the  SO-  concentrations are
                  a
fixed  at  260 ug/m   for  all counties.   To  test the sensitivity of  the
benefits  calculations to  this assumption, estimates were  derived for other
S02 levels.   The  estimates for the  alternative SO- levels  were quite close.
For example,  with all counties presumed  to be at 100 ug/m  ,  the  estimate of
total benefits  corresponding  to  the point estimate  in Table  7-25  is $733.8
million.   This  represents a  difference of only  $1.1  million.

     Finally, Table 7-31 shows the benefits associated with the 70/250 PM10
standard  when some  counties remain in nonattainment  after the  contol
strategy.   As before,  the  benefits  for this  option  are  about  65 percent of
the benefits  when all counties are  brought into attainment.

Synthesis of  Household Sector Benefits —

     Tables 7-11 through  7-31 of  this section report  the  benefits accruing
to the household sector from reduced soiling.  For each  study and each
scenario,  a range of benefit estimates is presented.   This  range  reflects.
                                   7-92

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                         Table 7-31

ESTIMATED BENEFITS FOR:  MATHTECH HOUSEHOLD EXPENDITURE STUDY

          Benefits Occurring Between 1989 and 1995
         Scenario:  Type A PM10 - 70 AAM/250 24-hr.
 Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
        Point
       Estimate
        Maximum
0.0
0.2
4.5
4.4
25.0
6.1
1.2
3.0
15.9
2.1
0.0
1.7
34.2
31.1
189.7
44.1
9.1
21.4
116.8
15.4
0.0
3.2
65.0
59.3
360.2
83.8
17.3
40.5
223.6
29.7
 Total U.S.
62.5
463.5
882.7
 Discounted Present Value  in Millions of 1980 Dollars in 1982
 Using a 10 Percent Rate of Discount.
 Annualized Benefits
 Between 1989 and 1995
 Total U.S.
25.0
185.5
353.3
                              7-93

-------
in part, the degree of uncertainty in the different studies due to model
biases and/or methodological  design.

     The level of benefits  estimates in the various tables may also be
affected by biases that occur in the extrapolation to national  benefits
estimates.   Examples  of  these types of biases are  shown  in Table 7-32.  As
can be seen from the table,  most of the identified biases are common to
each  of the studies.  Furthermore,  because the direction of bias is  not
known in all cases,  it is not possible  to  determine the impact of extrapo-
lation  on the soundness of the estimates.  Thus,  even if no model biases
were  present,  there would  still be uncertainty as  to whether a particular
benefit value was an  over-  or  underestimate of  the  "true" level of
benefits.

     Although  it  is  difficult to make objective  judgments on the soundness
of the various benefit estimates,  consideration of  the  evaluation criteria,
the model and extrapolation  biases,  and the  impact  of uncertainty can  all
help to provide a basis for  making subjective judgments on the  relative
merits  of each of the  studies.

     Because of model design and the manner in  which benefits are calcu-
lated,  we believe  the HTH study  should be weighted most heavily in  the
compilation of household benefits of reduced soiling.  Since the estimates
from  this study  fall at the low end of the range,  and there is reason to
believe that  the HTH benefits are conservative estimates, a reasonable
lower-bound estimate of overall  benefits for each scenario is the point
estimate of the  MTH study.

     The process  of choosing an upper—bound  estimate  is  more  speculative.
The option chosen involves adding the point estimates  from  the  Watson  and
Jaksch  (6) and Cummings (5) studies.   The rationale for  this  choice is
based on the types  of benefits identified  in the two studies.  In particu-
lar,   Cummings' model is designed  to reflect both  changes in out-of-pocket
expenditures  as  well  as  the opportunity cost of labor  time  spent in
cleaning activities,  while the Watson and Jaksch model captures behavioral
                                   7-94

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                                        Table 7-32

                    EXTRAPOLATION BIASES  IN THE HOUSEHOLD SECTOR MODELS
     Model
Gummings et al.
•^1

Ul
Watson & Jaksch
              Description of Bias
1.  The damage function is calculated from data
    for Philadelphia only.

2.  The damage function is based on a TSP range
    that is less than that in U.S. counties
    included in extrapolation.

3.  Air quality improvements are experienced
    only in the counties included in air quality
    data file.

4.  Population exposure calculated from improve-
    ments observed only at design value monitor.
1.  Demand and supply curves are based only on
    data from Philadelphia.

2.  Per-honsehold expenditures are assumed
    constant across all counties (equal to per-
    household expenditures in Philadelphia).

3.  The data underlying the unit elastic demand
    curve are drawn from a limited range of TSP
    values.
                                                                                        Expected  Impact  on
                                                                                            Benefits
                                                                                             Unknown
                                                                              Unknown
                                                                         Underestimate  of
                                                                             benefits
                                                                         Probable overesti-
                                                                          mate of benefits
                                                                                            Unknown
                                                                             Unknown
                                                                             Unknown
                                                                                  (cont inued)

-------
                                                 Table 7-32 (Continued)
                    Model
               Watson & Jaksch
                 (continued)
               Mathtech
vO
          Description of Bias
Air quality improvements are experienced
only in the counties included in the air
quality data file.

Population exposure calculated from improve-
ments observed only at design value monitor.
TSP elasticities for all counties are
generated from a model that includes only 24
SMSAs.

Price data are assigned to counties based on
regional averages.

The household model is estimated for a range
of TSP concentrations that is more narrow
than the range implied in the extrapolation.

Air quality improvements are experienced
only in the counties included in the air
quality data file.

Population exposure calculated from improve-
ments observed only at design value monitor.
Expected Impact on
     Benefits
 Underestimate of
     benefits
                                                                                        Probable overesti-
                                                                                         mate of benefits
     Unknown
                                                                                            Unknown
                                                                                            Unknown
                                                                                        Underestimate of
                                                                                            benefits
                                                                                        Probable overesti-
                                                                                         mate of benefits

-------
adjustments based  on utility and cost considerations.  Since the sum  of
benefits across these two studies  likely  contains double-counting,  we
believe that the sum should be viewed as an upper-bound estimate.  The
extent to which thesum is an upper-bound also depends on the accuracy  of
the  underlying  studies.   Unfortunately,  since  many of the biases  that are
present  in the two studies affect benefits in an unknown  direction, with  an
unknown magnitude,  it is  not possible to determine whether the studies  lead
to over- or underestimates  of  benefits.   However,  because  of  the  potential
for  double-counting,  it is our judgment that the sum of benefits from these
two studies provides  a reasonable upper-bound estimate  of the  benefits from
reduced soiling.

     The point  estimate  of  benefits  is  calculated  as  the geometric mean  of
the  lower-  and  upper-bound  estimates.   If equal weight were  given to  each
of the studies, then the logical choice for  the  point  estimate would  be
taken from  the  Watson and Jaksch results, since the results  of this study
fall between  Cummings and MTU.  However,  we believe  more weight  should  be
given to the HTH results.  Thus, we  have  chosen to use the  geometric mean,
which gives less weight  to  higher  values  (relative  to the arithmetic mean).

     Table 7-33 summarizes  the range of benefits  estimated for the house-
hold sector  for each of  the scenarios.   For  each scenario,  the upper-bound
estimate is about 20  times larger than the lower-bound estimate.   This  wide
range in benefits  estimates reflects,  in large part,  the uncertainty  that
is currently  present in the modeling  and estimation of household  soiling
models.

Manufacturing Sector

JUthtech Manufacturing Model (8) —

     The basic  analysis of the MTM study  was limited to  the analysis of six
3-digit SIC  industries comprising about  8.3  percent of  the value added  in
the  manufacturing  sector.  The  analysis  was  restricted to this  subset  of
industries because of data limitations.  From  this group of six industries.
                                    7-97

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                               Table 7-33
     SUMMARY OF BENEFITS FROM REDUCED SOILING  IN THE HOUSEHOLD SECTOR*
Standard
PM10 70/250
PM10 55/250
PM10 55/150
PM10 55
TSP 75/260
TSP 150
Benefit
Minimum
0.73
1.30
1.62
1.29
2.41
3.15
Point
3.14
5.68
7.16
5.64
10.74
14.21
Maximum
13.85
25.72
32.76
25.53
49.90
66.36
* Discounted present values in 1982 in billions of 1980 dollars.
PM was  found to be an important explanatory variable in the cost relation-
ships for two of the industries.  Thus, the coverage in the basic analysis
of MTM is small.

     Extrapolation of the MTM results  to more  complete  estimates of
national  benefits can be done  in  several ways.  First,  the geographic
coverage  of the two PM-sensitive  industries could be extended to other
areas of  the country.  However, since the original MTM data collection
effort included all  the county-level economic  data which were available, no
additional county  coverage could be obtained.

     The alternative  possibility is to extrapolate the  results for the  two
PM-sensitive industries  to other closely  related manufacturing  industries.
For example, instead of conducting the analysis at the 3-digit level,  one
might take the view that all 3-digit industries  in a given 2-digit group
can be treated similarly.  That is,  results for  SIC 354 could be treated as
                                   7-98

-------
representative of effects for all 3-digit SICs in the 2-digit SIC 35.  If
this approach is taken,  extrapolated benefits  could be  obtained for two 2-
digit SICs:  SIC 34 and SIC 35.

    The extrapolation to  other  industries  raises  at  least  two questions.
First,  can the effects identified in a subindustry (e.g.,  SIC 344)  be
viewed as  representative  of the effects in  the broader industry group?
Second,  if so,  how should the extrapolation be carried out?  The first
question cannot be answered definitively without actually conducting a
specific analysis  of other  subindustries  in each group.   Clearly,  there are
similarities among  the  various  3-digit industries  within a  2-digit group.
The similarities can include the use of common raw materials,  similar
processing techniques, and most  importantly,  the production of related end
products.  However,  the  industries  can  also be different in important ways,
and it  is  the latter fact  which guided the selection of an extrapolation
procedure.

    One possible extrapolation  procedure would be  to  apply the  estimated
models  from the MTH  basic  analysis  to data  for the corresponding  2-digit
industries.  Data and conceptual problems prevented this  alternative from
being applied.  Consequently, a less formal  extrapolation procedure was
adopted.  The procedure  involves  answering the following question:  If the
benefits of  improved  air quality at  the 2-digit level were  the same as at
the 3-digit level  in terms of the percentage  savings in production cost for
a given change  in  air quality, how  large would benefits be?  Note  that this
approach  does  not necessarily require  that the underlying production
technologies be the same — only that air quality benefits, on a percentage
basis, be  the same.

    In  order  to  calculate the extrapolated benefits using  the approach
described  above, data are  collected  on the value of shipments for  both 2-
digit and  3-digit industries on a county  basis.  With estimates of benefits
available  for the  3-digit industries, a normalizing factor is estimated  for
each county which  reflects the  dollar benefits,  per  dollar of value
shipped, per unit change in  air  quality.   In  order to establish a probable
                                   7-99

-------
range for  the factor,  the minimum,  maximum and average  across all counties
(in the  analysis) are  calculated.   Given county—specific  air quality data
and value  of  shipments  at  the 2-digit level,  the three  derived normalizing
factors can  be  used to calculate  a range of  benefits  for the 2-digit
industries.   Note that with the use of the more aggregate 2-digit industry
data, data are available for more  counties  than  in  the  analysis of 3-digit
industries.  Hence, extrapolation to the  industry level means that more
counties can  be  considered in the  2-digit  analysis.

     Benefits estimates  for  the two 3-digit SICs are given in Tables 7-34
through 7-45.  The first'six tables correspond to  SIC 344 and represent
benefits  for the six scenarios shown in Table  7-10.  As in the household
sector, these numbers represent the case where all counties are brought
into attainment throughout the analysis  period.   Furthermore, because
county average  second-high 24-hour average TSP data were used in the MTM
study, a correction factor to reflect facility exposure relative  to the
design value  monitor readings has  been utilized.*

     In the tables for the 3-digit  SICs,  note  that only  point estimates are
given.   The nonlinear  (in TSP)  system used to estimate the  cost functions
for  the various industries make  it difficult to  compute statistically
correct  confidence  intervals.

     Tables 7-40 through 7-45 present the benefit estimates for SIC 354.
In this  group of tables,  the more  lenient  standards  report no benefits for
several of the EPA regions. This does  not necessarily imply that no TSP
benefits  will occur in  these areas.  The zero  entries occur because the
necessary economic data were  not available  for counties in these areas.
Consequently, no benefits could b.e calculated.

     Tables 7-46 through 7-57 show  the benefit estimates  for the  two  2-
digit SICs.  As  before,  six tables are shown for each SIC, corresponding  to
* See Section 9  for  a  description of the process used to estimate this
  correction factor.
                                   7-100

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                           Table 7-34

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 344

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type B PM10 - 70 AAM/250 24-hr.
                                              Point
   Federal Administrative Region   Minimum   Estimate   Maximum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
           0.0
           0.0
          99.1
          67.9
         403.7
         140.2
           0.0
          12.3
          51.3
          32.9
            0.0
            0.0
            0.0
            0.0
            0.0
            0.0
            0.0
            0.0
            0.0
            0.0
   Total U.S.
0.0
807.4
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
0.0
323.2
0.0
                               7-101

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                           Table 7-35

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 344

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type B PM10 - 55 AAM/250 24-hr.
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.I.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
       Point
      Estimate
           7.7
          14.7
         221.5
         225.5
         756.9
         269.2
          39.6
          47,
          83,
    .1
    .9
          37.1
         Maximum
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
   Total U.S.
0.0
1703.1
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
0.0
 681.7
0.0
                               7-102

-------
                           Table 7-36

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC -344

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type B PM10 - 55 AAM/150 24-hr.
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
       Point
      Estimate
         Maximum
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
134.1
45.7
299.1
294.9
789.9
309.7
66.9
76.6
105.5
97.2
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
   Total U.S.
0.0
2219.5
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
0.0
 888.4
0.0
                                7-103

-------
                           Table 7-37

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 344

            Benefits Occurring Between 1989 and 1995
                 Scenario:  Type B PM10 - 55 AAM
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
       Point
      Estimate
           7.7
          14.7
         221.5
         225.5
         756.9
         266.4
          39.6
          47.1
          83.9
          37.1
         Maximum
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
   Total U.S.
0.0
1700.3
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
0.0
 680.6
0.0
                               7-104

-------
                           Table 7-38

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 344

            Benefits Occurring Between 1987 and 1995
            Scenario:  Type B TSP - 75 AAM/260 24-hr.
   Federal Administrative Region   Minimum
       Point
      Estimate
         Maximum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
          27.3
            .1
            .4
            .5
  36,
 427.
 413,
1482.2
 521.0
 129.0
  73.0
 164.6
  67.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
   Total U.S.
0.0
3341.6
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1987 and 1995
   Total U.S.
0.0
 934.5
0.0
                                7-105

-------
                           Table 7-39

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 344

            Benefits Occurring Between 1987 and 1995
               Scenario:  Type B TSP - 150 24-hr.
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
       Point
      Estimate
         Maximum
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
225.2
102.1
623.4
599.5
1527:6
502.7
271.0
125.5
116.7
149.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
   Total U.S.
0.0
4243.2
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1987 and 1995
   Total U.S.
0.0
1186.6
0.0
                                7-106

-------
                           Table 7-40

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 354

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type B PM10 - 70 AAM/250 24-hr.
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
       Point
      Estimate
           0.0
           2.4
          34.8
           1.2
         680.4
          10.2
           0.0
           0.0
           1.0
           0.0
        Maximum
            0.0
            0.0
            0.0
            0.0
            0.0
            0.0
            0.0
            0.0
            0.0
            0.0
   Total U.S.
0.0
730.0
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
0.0
292.2
0.0
                                7-107

-------
                           Table 7-41

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 354

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type B PM10 - 55 AAM/250 24-hr.
   Federal Administrative Region   Minimum
       Point
      Estimate
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
         Maximum
                     0.0
                     0.0
                     0.0
                     0.0
                     0.0
                     0.0
                     0.0
                     0.0
                     0.0
                     0.0
   Total U.S.
0.0
1321.3
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
0.0
 528.9
0.0
                               7-108

-------
                           Table 7-42

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 354

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type B PM10 - 55 AAM/150 24-hr.
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
       Point
      Estimate
          94.3
          61.2
          70.6
           4.8
        1219.0
          23.4
           5.4
           0.0
           1.9
           0.0
         Maximum
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
   Total U.S.
0.0
1480.5
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
0.0
 592.6
0.0
                               7-109

-------
                           Table 7-43

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 354

            Benefits Occurring Between 1989 and 1995
                 Scenario:  Type B PM10 - 55 AAM
   Federal Administrative Region   Minimum
       Point
      Estimate
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
         Maximum
                     0.0
                     0.0
                     0.0
                     0.0
                     0.0
                     0.0
                     0.0
                     0.0
                     0.0
                     0.0
   Total U.S.
0.0
1321.3
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
0.0
 528.9
0.0
                               7-110

-------
                           Table 7-44

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 354

            Benefits Occurring Between 1987 and 1995
            Scenario:  Type B TSP - 75 AAM/260 24-hr.
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
       Point
      Estimate
          20^8
          47.4
         122.1
           6.9
        2164.3
          41.0
          20.5
           0.0
           4.3
           0.0
         Maximum
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
             0.0
   Total U.S.
0.0
2427.2
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1987 and 1995
   Total U.S.
0.0
 678.8
                     0.0
                                7-lH

-------
                           Table 7-45

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 354

            Benefits Occurring Between 1987 and 1995
               Scenario:  Type B TSP - 150 24-hr.
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
       Point
      Estimate
         192.2
            .7
            .7
 107,
 157,
  10.0
2229.8
  40.2
  65.1
   0.0
   5.4
   0.0
         Maximum
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0-
   Total U.S.
0.0
2808.1
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1987 and 1995
   Total U.S.
0.0
 785.3
0.0
                                7-112

-------
                           Table 7-46

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 34

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type B PM10 - 70 AAM/250 24-hr.
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                       0.0
                                      136.9
                                      97.5
                                     1082.0
                                      166.8
                                      13.4
                                      20.6
                                      332.2
                                      65.2
          Point
         Estimate
              0.0
              0.0
            251.4
            179.0
           1986.0
            306.0
             24.6
             37.9
            609.9
            119.7
         Maximum
             0.0
             0.0
           368.8
           262.6
          2912.9
           448.8
            36.0
            55.6
           894.8
           175.6
   Total U.S.
1914.6
3514.5
                                                         5155.1
   Discounted Present Value  in Millions  of  1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
 766.4
1406.8
2063.5
                                7-113

-------
                           Table 7-47

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 34

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type B PM10 - 55 AAM/250 24-hr.
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                      47,
                                      50.
                                     263,
                                     257.8
                                    1910.7
                                     309.6
                                      78.
                                    -  67,
                                     750.
    ,5
    .7
    .4
    .0
    .4
    ,1
          Point
         Estimate
  88,
  94,
 491.9
               .5
               ,1
               ,0
                                      88.1
 481.
3568.
 578.
 145.6
 125.9
1401.0
 164.5
         Maximum
 139.4
 148.8
 772.6
 756.4
5604,
 907,
 228.
 197.7
2201.1
 258.5
.5
.7
.7
   Total U.S.
3823.3
7139.9   11215.4
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
1530.4
2858.0
4489.3
                                7-114

-------
                           Table 7-48

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 34

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type B PM10 - 55 AAM/150 24-hr.
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                     223.0
                                     120.4
                                     385.6
                                     359.1
                                    2519.6
                                     405.7
                                     152.3
                                     142.2
                                    1014.3
                                     185.7
          Point
         Estimate
            391.2
            211.2
            676.4
            630.0
           4420.3
            711.5
            267.2
            249.4
           1780.2
            326.0
         Maximum
           713.1
           384.9
          1232.8
          1148.2
          8056.5
          1296.9
           487.0
           454.6
          3244.6
           594.1
   Total U.S.
5507.9
9663.3   17612.7
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
2204.7
3868.0
7050.0
                                7-115

-------
                           Table 7-49

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 34

            Benefits Occurring Between 1989 and 1995
                 Scenario:  Type B PM10 - 55 AAM
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                      47.
                                      50.
                                     263.
                                    1910,
                                     308.
                                     257.8
                                      78.0
                                      67.
                                     750.
                                      88.1
          Point
         Estimate
             88.3
             94.3
            489.8
            479.5
           3553.1
            572.7
            145.0
            125.3
           1395.1
            163.8
         Maximum
           139.4
           148.8
           772.6
           756.4
          5604.
           903
           228.
           197,
          2201.
    ,5
    .3
    ,7
    .7
    .1
           258.5
   Total U.S.
3821.7
7107.1   11211.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
1529.8
2844.8
4487.5
                               7-116

-------
                           Table 7-50

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 34

            Benefits Occurring Between 1987 and 1995
            Scenario:  Type B TSP - 75 AAM/260 24-hr.
   Federal Administrative Region   Minimum
          Point
         Estimate
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                     144.8
                                     121.1
                                     566.
                                     466,
                                    3748.6
                                     585.2
                                     246.4
                                     152.6
                                    1542.9
                                     149.6
    ,5
    .3
  267,
  223,
 1046.2
  861.2
 6923,
 1080.
  455.0
  281.9
 2850.8
  276.4
               .5
               .7
               ,0
               .5
          Maximum
 385.0
 322.0
1505.3
1239.3
9961.4
1554.5
 654,
 405.
4103,
                         .7
                         .7
                         .2
                      397.9
   Total U.S.
7724.0
14266.3   20528.9
   Discounted Present Value in Millions  of 1980 Dollars  in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1987 and 1995
   Total U.S.
2160.0
 3989.6
5740.9
                                7-117

-------
                           Table 7^51

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 34

            Benefits Occurring Between 1987 and 1995
               Scenario:  Type B TSP - 150 24-hr.
   Federal Administrative Region   Minimum
                                     551.1
                                     267.3
                                     858.4
                                     660.4
                                    4775.9
                                     634.8
                                     457.8
                                     246.6
                                    1987.5
                                     296.6
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
TV
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Point
Estimate
1008.2
489.1
1570.5
1208.8
8738.0
1161.0
837.7
451.2
3638.9
543.1

Maximum
1663.5
807.0
2591.1
1994.5
14417.1
1915.6
1382.2
744.5
6003 . 8
896.0
   Total U.S.
10736.3
19646.6   32415.4
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1987 and 1995
   Total U.S.
 3002.4
 5494.2
9065.0
                               7-118

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                           Table 7-52

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 35

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type B PM10 - 70 AAM/250 24-hr.
   Federal Administrative Region   Minimum
          Point
         Estimate
         Maximum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                      10.3
                                     119.5
                                      61.4
                                    1148..7
                                     261.0
                                       0.0
                                      26.4
                                     460.8
                                      71.7
              0.0
             0.0
28.4
327.6
184.8
3148.3
715.2
0.0
72.5
1263.4
45.1
521.3
294.0
5008.4
1137.8
0.0
115.3
2009.9
            196.7
           313.0
   Total U.S.
2165.8
5936.9
9444.8
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
 866.9
2376.4
3780.5
                               7-119

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                           Table 7-53

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 35

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type B PM10 - 55 AAM/250 24-hr.
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                      26.5
                                     152.2
                                     262.3
                                     170.4
                                    2381.9
                                     502.9
                                      10.6
                                     146.2
                                    1054.5
                                      94.2
Point
Estimate
73.9
423.9
730.2
474.8
6631.7
1399.8
29.6
407.1
2937.4
262.6

Maximum
116.5
668.7
1151.9
749.0
10461.5
2208.2
46.7
642.2
4633.6
414.2
   Total U.S.
4801.7
13370.9   21092.5
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
1922.0
 5352.1
8442.8
                               7-120

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                           Table 7-54

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 35

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type B PM10 - 55 AAM/150 24-hr.
   Federal Administrative Region   Minimum
                                     202.6
                                     246.6
                                     341.2
                                     213.6
                                    2704.5
                                     594.6
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Point
Estimate
562.6
685.1
947.9
593.8
7514.9
1651.9
121.5
595.2
3372.8
535.0

Maximum
875.0
1065.5
1474.2
923.5
11686.4
2568.9
188.9
925.6
5244.9
831.9
   Total U.S.
5966.4
16580.8   25784.8
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
2388.2
 6636.9   10321.1
                                7-121

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                           Table 7-55

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 35

            Benefits Occurring Between 1989 and 1995
                 Scenario:  Type B PM10 - 55 AAM
   Federal Administrative Region   Minimum
          Point
         Estimate
          Maximum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                      26.5
                                     152.2
                                     262.3
                                     170.4
                                    2322.5
                                     480.9
                                      10.6
                                     146.2
                                    1054.5
                                      94.2
73.9
423.9
730.2
474.8
6466.2
1338.4
29.6
407.1
2937.4
262.6
116.5
668.7
1151.9
749.0
10200.4
2111.4
46.7
642.2
4633.6
414.2
   Total U.S.
4720.3
13144.1   20734.6
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
1889.4
 5261.3
8299.6
                               7-122

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                           Table 7-56

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 35

            Benefits Occurring Between 1987 and 1995
            Scenario:  Type B TSP - 75 AAM/260 24-hr.
                                              Point
   Federal Administrative Region   Minimum   Estimate
                     Maximum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
278.9
846.3
1604.4
879.3
13544.3
2363.6
232.6
638.6
5667.0
424.8
472.0
1432.2
2715.2
1488.1
22921. &
4000.0
393.6
1080.8
9590.4
718.9
   Total U.S.
11299.5
26479.7   44813.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1987 and 1995
   Total U.S.
 3159.9
 7405.1   12531.9
                               7-123

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                           Table 7-57

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 35

            Benefits Occurring Between 1987 and 1995
               Scenario:  Type B TSP - 150 24-hr.
   Federal Administrative Region   Minimum
                                     797.9
                                     627.0
                                    1022.9
                                     497.6
                                    7297.1
                                    1143.1
                                     332.6
                                     463.1
                                    3049.6
                                     384.7
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
rv
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
Point
Estimate
1811.8
1424.7
2323.9
1132.4
16577.0
2596.2
756.0
1051.8
6936.7
875.4

Maximum
2949.0
2318.8
3782.5
1843.0
26981.5
4225.8
1230.4
1711.9
11289.7
1424.7
   Total U.S.
15615.5
35485.9   57757.4
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1987 and 1995
   Total U.S.
 4366.9
 9923.6   16151.8
                               7-124

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 the six scenarios  described in Table 7-10.   These  tables  are  also  based  on
 complete attainment of the  various  standards, and the correction factor for
 facility exposure  has  been  incorporated  in the estimates.   In this  group  of
 tables, minimum and maximum estimates are presented in addition to the
 point estimates.   As described above,  these  bounding values  are calculated
 from the minimum and maximum values of the normalizing factor.

     Finally,  for comparative purposes.  Tables  7-58  through 7-61  show the
 benefits that would accrue for the two 2-digit SICs and the two 3-digit
 SICs when not all  counties are assumed  to be in attainment  throughout the
 analysis period.   The estimates in these tables  are given only for the
 70/250 PM10 standard.

     At the conclusion of  the section describing the benefits models, a
 table of model  biases was presented.   Of the  six biases listed for the MTM
 study, four were  identified as having an "unknown" impact on the benefit
 estimates.   As  a consequence,  it  is not possible to determine  conclusively
 whether the estimates shown above  represent over- or underestimates of some
 "true"  level of benefits for the SICs examined in MTM.  If production  costs
 of other industries are also  affected by  changes in TSP,  then  the estimates
 of this section represent conservative  estimates of benefits  for the  total
 manufacturing  sector.

     In addition to the model biases,  there are several  biases  that  arise
 in the course  of estimating  national benefits.  One of these biases, the
 concern with a limited number of  SICs,  was  mentioned above.   Other biases
.of this type include:

     •   A limited number of counties are included in the extrapola-
         tion since   confidentiality  restrictions  do  not  allow
         identification of needed economic  data in all cases.
     •   It is assumed that air quality improvements are experienced
         only  in  the counties included in the  air  quality data file.
     •   The procedure for benefits calculation assumes  a perfectly
         inelastic demand  for plant output.
                                    7-125

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                           Table 7-58

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 344

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type A PM10 - 70 AAM/250 24-hr.
   Federal Administrative Region   Minimum
       Point
      Estimate
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
                                       0.0
            .2
            .2
  0.0
  0.0
 95
 65.
255.9
 79.6
  0.0
 12.3
 31.3
  5.1
        Maximum
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
   Total U.S.
0.0
544.6
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
0.0
218.0
0.0
                                7-126

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ESTIMATED BENEFITS FOR:
  Table 7-59

MATHTECH MANUFACTURING EXPENDITURE STUDY
    SIC 354
            Benefits Occurring Between 1989 and 1995
           Scenario:  Type A PM10 - 70 AAM/250 24-hr.
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                     Point
                    Estimate
        Maximum
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
2.4
33.5
1.2
459.1
5.8
0.0
0.0
0.6
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
   Total U.S.
              0.0
502.7
0.0
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
              0.0
201.2
                                   0.0
                               7-127

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                           Table 7-60

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 34

            Benefits Occurring Between 1989 and 1995
           Scenario:  Type A PM10 - 70 AAM/250 24-hr.
   Federal Administrative Region   Minimum
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
TV
V
VI
VII
VIII
IX
X
New England
N.T.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                       0.0
                                       0.0
                                     132.9
                                      93.3
                                     784.7
                                     104.4
                                      12.1
                                      20.5
                                     160.9
                                      15.7
          Point
         Estimate
              0.0
              0.0
            231.8
            162.9
           1368.8
            182.1
             21.2
             35.7
            280.8
             27.5
         Maximum
             0.0
             0.0
           357.9
           251.4
          2112.6
           281.0
            32,
            55,
           433,
    .7
    .1
    .5
            42.4
   Total U.S.
1324.6
2310.7
3566.6
   Discounted Present Value in Millions of 1980 Dollars in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
 530.2
 924.9
1427.6
                                7-128

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                           Table 7-61

ESTIMATED BENEFITS FOR:  MATHTECH MANUFACTURING EXPENDITURE STUDY
                             SIC 35

            Benefits Occurring Between  1989  and 1995
           Scenario:  Type A PM10 - 70  AAM/250 24-hr.
   Federal Administrative Region   Minimum
          Point
         Estimate
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
REGION
I
II
III
IV
V
VI
VII
VIII
IX
X
New England
N.Y.-N.J.
Middle Atlantic
South Atlantic
E.N. Central
South Central
Midwest
Mountain
South Pacific
North Pacific
                                        0.0
                                        9.6
                                      107.3
                                       56.9
                                      756.2
                                      143.3
                                        0.0
                                       24.3
                                     .239.5
                                       18.6
              0.0
             27.0
            301,
            160.
           2124.
            402.
    .6
    .1
    .7
    .5
              0.0
             68.3
            673.0
             52.2
         Maximum
   0.0
  43.7
 488.7
 259.4
3442.6
 652.1
   0.0
 110.7
1090.5
  84.6
   Total U.S.
1355.7
3809.3
6172.2
   Discounted Present Value  in Millions  of  1980 Dollars  in 1982
   Using a 10 Percent Rate of Discount.
   Annualized Benefits
   Between 1989 and 1995
   Total U.S.
 542.7
1524.8
2470.6
                                7-129

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     Each of these  biases  is  expected to contribute to an underestimate of
benefits.   There  may also  be  a  bias  introduced by  the  assumption that  the
dollar benefit  per  microgram  reduction  is  the same for all industries in a
2-digit SIC grouping.  Although the minimum and maximum estimates were
developed to reflect the uncertainty surrounding the  assumption,  it is  not
possible  to determine whether the assumption is ezpeccted  to  lead to a
positive or negative effect on benefits.

Synthesis of Manufacturing Sector Benefits  —

     A total benefit estimate for industries in the two 2-digit industry
groups was developed as follows:
          A lower-bound estimate was calculated as the point estimate
          for SIC 354.
          An upper-bound estimate  was calculated as the sum of the
          point  estimates for the two 2-digit industries.
          A point estimate was calculated as  the geometric mean of
          the upper-  and lower-bound estimates.
     The choice  of  the lower-bound estimate was influenced by the fact that
TSP was not a significant explanatory factor at the 10 percent  level for
all specifications in SIC 344.   Thus,  a very conservative estimate of a
lower-bound value would  include benefits derived only from SIC 354.   Given
that these estimates are developed  directly from the data and equations in
the basic MTM analysis,  they are  the most defensible of the various benefit
numbers presented for  the manufacturing sector.

     Several options were possible  for  selecting  an upper-bound estimate.
The choice of the sum of the two 2-digit SIC point estimates reflects our
judgment that the various biases in the  MTM model and in the extrapolation
procedures will  likely  lead to an overestimate of benefits  at the 2-digit
level.   As was the  case  with the household sector,  the choice of an upper-
bound estimate is  more  speculative relative to the choice of the lower-
bound estimate.
                                   7-130

-------
    Finally, the point estimate is determined as the geometric mean of the
upper- and lower-bound estimates.  The  geometric  mean appears  to be a more
relevant statistical  index than the arithmetic mean  since  it weights lower
values  relatively greater.   This would conform to  our belief that more
confidence can be ascribed to the estimates presented as lower-bound values
in the  various  scenarios relative  to those identified as  upper-bound
values.

    Table 7-62  summarizes  the  estimated benefits of  reduced  soiling for
the manufacturing  sector.   As was pointed out in the review of  the MTM
study,  these estimates should  be  interpreted with  caution.   Although the
analytical model  and  empirical technique are carefully applied in  the MTM
analysis,  the  results were  found  to be  sensitive to influential  observa-
tions in the data.


                               Table 7-62
   SUMMARY OF BENEFITS FROM REDUCED SOILING IN THE MANUFACTURING SECTOR*
Standard
PM10 70/250
PM10 55/250
PM10 55/150
PM10 55
TSP 75/260
TSP 150
Benefit
Minimum
0.73
1.32
1.48
1.32
2.43
2.81
Point
1.30
2.41
2.86
2.41
4.80
5.81
Maximum
9.45
20.51
26.24
20.25
40.75
55.13
* Discounted present values in 1982 in billions of 1980 dollars,
                                   7-131

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REFERENCES
 1.  Beloin N. and F. Haynie.  Soiling of Building Materials.  Journal  of
     the Air Pollution  Control Association,  24:339, 1975.

 2.  U.S. Environmental Protection Agency.   Air Quality Criteria  for  Parti-
     culate Matter and Sulfur Oxides.  Volume IV, Review Draft December
     1981.

 3.  U.S. Environmental  Protection Agency.  Review  of  the  National Ambient
     Air Quality  Standards for Particulate Matter.   Draft Staff Paper,
     1982.

 4.  U.S. Environmental  Protection Agency.   Review  of  the  Relationships  of
     IP10,  IP15 and TSP.  Memo to H. Thomas of EPA, July 1981.

 5.  Cummings, R., H.  Burness  and R. Norton.  Methods Development for
     Environmental Control Benefits Assessment, Volume V:   Measuring  House-
     hold Soiling  Damages  from Suspended Air Particulates,  A Methodological
     Inquiry.   Draft Report,  January  1981.

 6.  Watson,  W. and J. Jaksch.  Air Pollution:   Household Soiling and
     Consumer Welfare Losses.  Forthcoming in Journal of Environmental
     Economics and Management, 1981.

 7.  Mathtech, Inc.  Benefits Analysis of Alternative Secondary National
     Ambient Air  Quality Standards for Sulfur Dioxide  and  Total  Suspended
     Particulates, Volume II.  Final  report  to  U.S. Environmental Protec-
     tion Agency,  May 1982.

 8.  Mathtech, Inc.  Benefits Analysis of Alternative Secondary National
     Ambient Air  Quality Standards for Sulfur Dioxide  and  Total  Suspended
     Particulates, Volume III.   Final report to U.S. Environmental Protec-
     tion Agency,  May 1982.

 9.  Geomet,  Inc.  Sulfur Dioxide and Sulfates Materials Damage  Study.
     Draft Final  Report prepared for U.S.  Environmental Protection Agency,
     Research Triangle  Park,  NC,  February  1980.

10.  Waddell,  Thomas E.  The Economic Damages  of  Air  Pollution.  Environ-
     mental Protection Agency 600/5-74-012, May  1974.

11.  Freeman, A. M.  The Benefits of Environmental  Improvement:   Theory and
     Practice.   John Hopkins  University Press, Baltimore, Maryland,  1979.

12.  Lodge,  J.  P.,  Jr., A. Waggoner,  D.  Klodt, and  C.  Crain.   Nonhealth
     Effects of Airborne Particulate Matter.  Atmospheric Environment, Vol.
     15, pp. 431-482.
                                    7-132

-------
13.  Michelson, I. and B. Tourin.  Report on Study of Validity  of Extension
    of Economic Effects  of Air Pollution Damage from Upper Ohio River
    Valley  to Washington,  D.C.  Area  Environmental Health  and  Safety
    Research Association,  August  1967.

14.  Ridker,  R.  Economic  Costs  of Air Pollution.   New York:   Frederick A.
    Praeger Press,  1967.

15.  Narayan R. and B. Lancaster.  Household Maintenance Costs  and Particu-
    late Air Pollution.  Clean  Air,  7:10-13, 1973.

16.  Booz, Allen and Hamilton, Inc.  Study to Determine Residential Soiling
    Costs of Particulate Air Pollution.   APTD-0715,  National Air Pollution
    Control Administration,  October  1970.

17.  Liu,  B.  and E.  Yu.   Damage  Functions for  Air Pollutants.   Report
    prepared for U.S. Environmental Protection Agency, February 1976.

18.  Brookshire,  D.,  R.  d'Arge,  W.  Schulze,   and M. Thayer.  Methods
    Development  for Assessing Tradeoffs  in Environmental Management,
    Volume II.  EPA-600/6-79-0016,  1979.

19.  SRI,  Inc.   An Estimate of the Non-Health Benefits of Meeting the
    Secondary National Ambient Air Quality Standards.  Prepared  for the
    National Commission on  Air  Quality,  January  1981.

20,  Courant P. and R. Porter.  Averting  Expenditure  and the Cost of Pollu-
    tion.   Journal of  Environmental  Economics  and Management 8(4),
    December  1981.

21.  Rowe, R. and L. Chestnut.  Issues in Visibility Benefit-Cost Analysis,
    Draft Report  for U.S.  Environmental Protection Agency,  August  1981.

22.  Esmen N.  A Direct Measurement Method for Dustfall.  Journal of the
    Air  Pollution Control Association,  23:34-36,  1973.

23.  U.S.  Bureau  of Labor   Statistics.   Consumer  Expenditure  Survey:
    Integrated Diary  and  Interview Survey Data,  1972-73.   Bulletin  1992,
    U.S.  Government Printing Office, Washington, DC,  1978.

24.  U.S. Bureau of Labor  Statistics.  Average Retail Prices  of Selected
    Commodities  and Services.   U.S. Government Printing Office, 1973.

25.  Stone, R.  Linear Expenditure Systems  and Demand  Analysis:  An Appli-
    cation to the Pattern of British Demand.  The Economic  Journal  64:511-
    527,  1954.

26.  Diamond  P.  and  D.  McFadden.   Some Uses of  the .Expenditure  Function in
    Public Finance.   Journal of Public  Economics,  3:3-21,  1974.
                                    7-133

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27.  Mathtech,  Inc.   Benefits Analysis  of Alternative Secondary National
     Ambient Air Quality Standards for Sulfur Dioxide and Total Suspended
     Particulates  - Volume  VI.   Final  Report to U.S. Environmental Protec-
     tion Agency,  Research Triangle Park,  NC, May 1982.

28.  U.S.  Department of  Commerce,   Bureau of  the Census.   Census  of
     Manufactures,  Vol.  II.   1972.

29.  U.S. Department of Commerce, Bureau of the Census.  City and County
     Data Book.  1977.

30.  Bureau of the Census.   Current Population Reports Series P-25,  Projec-
     tions of the  Population of the United States, 1977 to 2050.  No. 704,
     July 1977.

31.  U.S. Department  of  Commerce  News.  Projections  of  Personal Income  to
     the Year 2000.   December 9,  1980.
                                   7-134

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                SECTION 8
BENEFITS OF NATIONAL VISIBILITY STANDARDS

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                                SECTION 8
                BENEFITS OF NATIONAL VISIBILITY STANDARDS
INTRODUCTION

    Existing assessments of the benefits  of  air  pollution control have  to
a large extent focused on the health,  soiling, materials damage,  and vege-
tation damage effects of air  pollution.  However,  an  increasing body  of
economic research suggests that the aesthetic effects of air pollution are
also  important.  Visibility, defined  as  the ability to see distant vistas
nnobscured,  is affected by naturally occurring dust and humidity as well  as
by air pollution.   Based  on psychological.studies  of  perception and studies
of the value of clean air,  visibility appears to be the dominant aesthetic
impact  of air pollution.  Thus,  an  appropriate research  task is the estima-
tion  of the  value  of preserving or improving atmospheric  visibility  as  a
result  of air pollution  control programs.   An  assessment  of  the national
benefits of visibility protection has not  been accomplished to this point.
This  section attempts to make a very preliminary estimate of such benefits,
acknowledging that,  at  this point,  critical research still needs to  be
completed before such an estimate can be  considered to be  rigorous.   How-
ever,  the estimates  provided here do suggest  the order of magnitude  of
benefits to  be derived from a program of national visibility protection.

    Benefits are  calculated  for  four alternative visibility  standards
defined by a minimum  annual  allowable average visual range of 13 miles,  20
miles, 30 miles,  and a 20  percent improvement over existing levels.  In
making  these benefit  calculations we  assume,  for example,  for the 13-mile
standard  that all  areas  in the country  which now have an average annual
visibility below 13 miles  are  brought up to an  average  annual visual range
°f 13  miles.  Obviously, however,  reductions  in emissions necessary  to
                                   8-1

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bring the  worst areas up to a visual  range of 13 miles will,  as a side
effect,  improve  visibility in nearby areas already meeting  the  standard.
We do not  calculate the benefits of these secondary improvements in visi-
bility,  although ideally they should be included.

     The focus  on alternative visual range standards rather than concentra-
tions of PM was required because no  rigorous transformation  has been
defined between visual  range and levels of TSP or  PM10.  Scientific studies
of the optical  properties  of PM indicate that fine particles (FP) are the
most  dominant  size cut influencing light scatter.  Consequently,  an FP
standard may be  most  appropriate  for  identifying  the benefits  associated
with  changes in visual range.   Since the  scenarios examined  in this report
relate to PM10  and TSP only,  the benefits reported in this  section cannot
be added in  a  consistent fashion with the benefit numbers reported in the
other sections.  This section is most  properly viewed then only as a
synthesis of available  information of the benefits of alternative national
visibility standards.

     For calculating national visibility benefits  we utilize  three additive
categories of value:   1) residential, 2) recreation,  and 3)  existence.   In
a separate  subsection, we discuss in qualitative terms  some of the work
that has looked at visibility impairment and effects on air and highway
safety.   Residential benefits are  defined  as  those which  are derived from
visibility in and around consumers' homes and local communities.  Thus,
this category includes, for  example, benefits of unobscured  views from and
around homes, unpolluted  clear  blue skies,  and the benefits to recreation
undertaken near peoples' homes.   These  are  the benefits presumably captured
by the urban surveys of visibility benefits.*  Note that these surveys have
typically asked households  for  their willingness-to-pay for clean air and
then asked respondents  to  split  their  bid into component  parts,   including
as one  component visibility.   Also,  these studies  have compared  their
results  with property value studies  and,  in general, have found survey
results  to be  consistent with  the effects  of air pollution on property
* See, for example, Brookshire, d'Arge, Schulze and Thayer  (1).
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values.   However,  such studies  have  been  completed only for cities on the
West Coast (Los  Angeles  and San Francisco).  Although visual ranges are  of
the same  order  of magnitude as for eastern cities (less than 20 miles),
extrapolating  these results to the East is  not a desirable  procedure.
Fortunately, results  for East  Coast cities will be available  within one
year from a study underway  at the  University of Chicago under the overall
management of George Tolley.  Preliminary  pre-test results from  Chicago do,
however,  show roughly the  same value  of  visibility per  mile as  the West
Coast studies done by  the University  of Wyoming group.*

     The second  category of benefits,  recreation value,  is defined as  the
benefits  of  visibility improvement to recreation undertaken away from a
household's home  community.   For example,  a visit to the Grand Canyon
provides  enjoyment from being able to see far down the  canyon from  an
overlook.   To avoid  overlap  with residential benefits,  we assume that  half
of the average  of 60  days of recreation  undertaken by families  per year
takes place  away from  the home  community.  Again,  the only available
studies of the value of visibility to recreation have  been undertaken in
the western  United  States.  The  extrapolation to eastern recreation  is
quite weak because  visual  ranges  in the  western recreation areas studied
have been greater than 50 miles, while  in  eastern recreation areas they are
generally less  than  50 miles.  But,  such  an extrapolation  is necessary  to
obtain  an order of magnitude estimate  of the benefits of  visibility
standards.  Again, the need for new research is clear.   The University of
Chicago study mentioned  above should provide some preliminary evidence  in
this area for the  East Coast.

     The final  category  of  benefits, which  is examined quantitatively,  is
existence value.  Existence  value is defined as the benefit derived from
just knowing that the environment is  preserved.  Note that this value is
not associated with  use of the environment and probably applies  to "natural
wonders".  Thus, a household in  New York may be willing to pay to know that
visibility at the Grand Canyon is preserved even if they never intend to
* Op.  cit.  Brookshire et al. (1).
                                   8-3

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visit.   Only  one  study of the existence value  of  visibility protection has
been completed,  and that study focused on the Grand  Canyon.*   Thus,  to
provide  even a  rough order of magnitude  of  the benefits of visibility
standards, a weak extrapolation must again be made.  We have  chosen to
include six particularly well-known national parks for which we extrapolate
existence values for visibility based on  the Grand Canyon  results.   The
results  from the Grand  Canyon study indicated that the existence value
component  of visibility benefits is quite large.   Consequently,  this
category may be especially  important  in forming decisions on where future
research may  be  most valuable.

     Prior  to reporting  aggregate  national  benefit estimates,  we describe
in qualitative terms research on safety aspects of reduced visual range.
In the discussion,  it is suggested that cost of delays is probably a  more
appropriate measure  of better visibility benefits than lives  saved.  Unfor-
tunately, no  usable  study has yet to be completed which  permits a quantita-
tive evaluation  of these safety aspects.

     The final part  of this  section reports on  the overall benefits calcu-
lations,  giving  annual benefits  and discounted present values for different
attainment  paths  for alternative visibility standards.   We have also calcu-
lated  low, medium,  and high estimates for benefits based on differing
assumptions.  Table 8-1 presents  1980 annual benefits if the alternative
visibility standards could have been attained  in that year using our medium
estimates, except for existence values, where we only include six parks
(our low estimate).   Of  these estimates, as we have indicated above, the
residential  benefits are the most defensible.   We would  argue that an
appropriate  interpretation of Table 8-1 is  as  follows:  "the benefits  of a
20-mile visibility standard  may  be  of the  order  of magnitude of $7 billion
(residential benefits) and could easily be as high as  $15 billion (total
benefits).
* See Schulze, Brookshire et al.  (2).
                                   8-4

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                                Table 8-1
     1980 ANNUAL NATIONAL BENEFITS OF ALTERNATIVE VISIBILITY STANDARDS
                        (Millions of 1980 Dollars)
Standard
13 miles
20 miles
30 miles
20% Improvement
over current
visibility
Residential
Benefits
1,581
6,986
16,887
7,608
Recreation
Benefits
247
1,595
4,535
12,296
Existence
Value
Benefits
2,172
7,194
14,430
10,172
Total
4,000
15,775
35,852
30,076
ESTIMATING TBR BENEFITS OF URBAN VISIBILITY IMPROVEMENTS

Introduction

     During the past 15 years,  more than a score of econometric and survey
studies have been conducted  to  estimate the economic value of air quality.
These  studies have  usually adopted one of  two approaches.   First are
studies which use published data  on  housing  values  to  infer  a "price" for
air quality; and  second are  those  studies which  ask individuals to estimate
directly their willingness-  to-pay for  air quality  improvements.   With the
exception of one  study,  the  resulting valuations are for all aspects of air
pollution reduction,  inclusive of perceived human  health  effects,  vegeta-
tion impacts, corrosion, impacts on animals,  aesthetic considerations such
as visibility,  and other humanly conceived  effects.

     It is the purpose of this subsection  to review briefly  the  results
obtained in several  of  the property value and  willingness-to-pay approaches
for  assessing the  benefits  of  improved visibility  in urban-suburban
                                    8-5

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locations.  In Section 5, property value studies  were reviewed and benefits
evaluated  for the alternative air quality  scenarios  being examined  in this
report.   The  discussion  of  property value  studies in  this  section  focuses
on the attainment  of visual range standards and is  used  primarily as a
cross-check  on  the plausibility  of  the  residential visibility benefit
estimates derived from willingness-to-pay studies.   For this reason,  the
discussion  of  alternative property value  studies  is limited  in this
section.   There is,  however, extended  discussion of the various biases that
may be part  of the  direct willingness-to-pay  studies.

     In the property value  studies, the common  procedure is  to link
differences  in property values to various measures of air pollution  concen-
tration.  Since  measures  of various pollutants  are often  recorded  in
different averaging times or units,   the various property value studies
cannot be easily compared.  In addition, further complications arise in
that benefits are to be  estimated  for visual range standards.  In order to
assess the relative  monetary  magnitude of the property value estimates,  a
set of "standardization" equations  are developed here to relate various air
pollution concentrations   to  visual  range.    These  "standardization"
equations are likely to contain substantial errors for any one site or
pollutant condition  and therefore should be viewed with  caution  and  quali-
fications when comparing the various property value  studies.

     In the  review  of study  results,   willingness-to-pay studies are also
compared  as  to  their "implied" willingness-to-pay for visibility and other
aspects of air pollution. These  studies are more directly comparable since
in only one case was there a need to use the "standardization" equation
(3).

     Given  the  estimated  marginal willingness-to-pay  for  the  two
approaches,  we  next examine briefly the relationship of  the magnitudes of
benefits from the two types of studies.  We would expect that the property
value estimates  should exceed those derived  from the willingness-to-pay
studies.
                                   8-6

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     Following  the cross-check of approaches,  prediction equations are
developed  for estimating the  relationship  between visibility and
willingness-to-pay for  clean  air.  These equations are based exclusively  on
results obtained  in  a willingness-to-pay study  of Los Angeles air pollution
(1).  However, the prediction equations constructed from  the Los Angeles
study are shown  to be close to estimates derived from  other studies  of San
Francisco,  Chicago,  Boston, and Denver.

Property Value Studies

     Using  differences  in  property  values between  polluted  and less
polluted locations has become  a rather  common approach to  assess the
implied damages  of air pollution.  In addition,  a large  literature has
evolved on how the results of these  studies  should  be  interpreted and the
assumptions necessary to make  them valid in  estimating the  marginal
benefits of improved air  quality across an  urban area (4).*  However, no
one has  made  a  comparison of the  magnitude  of  estimates derived from the
various studies  for  alternative visibility  standards. The major problem in
doing so is the  difference in air pollution  data inclusive of type  of air
pollution,  measurement  methods applied,  averaging time,  and type  of  device
used to  measure  concentration.   In this study,  a set  of "standardization"
equations is developed  with reference only to urban  visibility.  The  calcu-
lations  are included in Appendix  8A.  By assuming a precise relationship
between equivalents  of  sulfation and particulates,  one  can  be converted to
the other in terms of impact  on visibility [see Trijonis (5)].  Next, using
data for Los Angeles,   a relationship is obtained between  nitrogen  oxides
and particulates  holding visibility constant  such that  an equivalent  effect
on visibility can be estimated  either  from concentrations  of particulates
or nitrogen oxides.  Finally,  using isopleth  maps for Los Angeles, a rela-
tionship is  obtained between the change in nitrogen oxide  concentrations
and changes in visibility. Let  S, P, and NO- represent concentrations of
* See Section 5 for a detailed description of the theory underlying the
  property  value  studies.
                                   8-7

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sulfation,  particulate matter, and nitrogen dioxide,  respectively,  and  let
V represent visual  range.  Then,  by calculation:

             S  =   oP
          AN02  =   PAP
            AV  =

where a is estimated  from  several  sources,  with P and y  estimated  directly
from Los Angeles data sources (see Appendix 8A).  Depending on particle
size and the presence of other pollutants, the $ estimated  from  Los Angeles
data may over- or  underestimate  the  relationship for  other urban  areas of
the U.S.   It is likely that the  estimate for y is larger for Los Angeles
than other areas because  of the above average  maximum distance of potential
visibility.   Thus,  the  same concentration of  N02  in  Los Angeles is likely
to have a greater  than  average  impact on visibility.  Given the rather
tenuous nature of  these calculations,  most  of  the property  value  studies
can be compared in visibility units of dollars per mile.   For example, a
property value study reporting  a "hedonic price" of  the change in  housing
value per unit change in sulf ation,  AHV/AS,  can be converted to AHV/AV by
simply noting that:
           AV  =  y  AS
or
          AHV  m  /AHV\ . /o_
          AV     Us /   UY
     Table 8-2  records the implied or direct willingness-to-pay per mile of
visibility for selected property  value studies.  The last  two studies
included  in Table 8-2 are especially  important, since willingness-to-pay
(contingent valuation)  studies  were   conducted  simultaneously.   The
remaining nine property  value studies  represent a sample  of the many
                                   8-8

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                                                          Table 8-2




                       ANNUALIZED PROPERTY VALUE CHANGES PER MILE OF VISIBILITY BY  STUDY AND AREA*
oo
Study
Ridker-
Henning (6)
Anderson-
Crocker (7)
Crocker
(8)
Harrison-
Rubinfeld (9)
Smith
(10)
Nelson
(11).
Zerbe
(12)
Peckham
(13)
Brookshire,
et al. (1)
SRI
(14)
Area,
Base Year
St. Louis,
1960
St. Louis,
1960
Chicago,
1964-1967
Boston,
1970
Wash., D.C.
1971
Wash., D.C.
1970
Toronto,
1961
Hamilton,
1961
Philadelphia,
1960
Los Angeles,
1979
San Francisco,
1981
in Property Value
US/Apoll.)
186.50-245.00/0.25 rag/100 cm2
(sulfation)
300.00-700.00/10 ug/m3+0.1 mg/100 cm2
(part. + sulfation)
350.00-600.00/10 ug/m3 + 1 ppb SO,
(part. + S02)
800.00/1 ppm NOx
430.00-500.00/10 ug/m3
(part.)
576.00-693.00/10 ug/m3 part.
141.00-152.00/0.01 ppm oxident
200.00-450.00/0.25 mg/100 cm2
(sulfation)
580.00-882.00/0.25 mg/100 cm2
(sulfation)
298.00/0.25 mg/100 cm2
(sulfation)
4468.00-6134.00/30% ANO2
14.73 -» 95.42/30% APSI Index
0.68 -» 31.55/30% AOzone
.A in Miles
of Visibility
11.49 mi
9.19 mi
9.19 mi
4.74 mi
(V = 14.22 mi)
4.60 mi
4.66 mi
11.49 mi
11.49 mi
11.49 mi
Fair -> Good 14 mi
Poor -» Fair 10 mi
Fair -» Good
2.4 mi
$ Per Mile
Per Yr. (1981)
5.66
16.38
14.57
38.84
22.30
38.56
8.43
18.97
7.81
39.27
75.47
6.14 -> 39.76
0.28 -» 13.15
                   * References.

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available  studies  of this type.   Note that  all nine appeared in the discus-
sion in Section 5.

     In each of the property value  studies,  property value differentials
are related to  ambient  concentrations of various  air pollutants.  Using the
procedure  documented  in Appendix  8A,  these ambient concentrations are
converted  to NO- equivalents and then  to  changes  in  visibility  utilizing
observed relationships  for  the  Los  Angeles Air Basin.   While  this conver-
sion procedure is not likely to  be  highly accurate, it does allow  a
comparison between studies on a per mile  or average basis.  Given the
qualifications  underlying the conversion procedure, the range of implied
willingness-to-pay over the various studies  is quite  small.   The  mean for
the sample of property value studies is $23.04,  with  a standard  deviation
of $19.94,  and a  range of  $.28 to  $40.00 per household/mile.   What is
interesting about the  property value  studies is that with one exception
they all  cluster  in the $5.00 to $40.00 per household/mile range  on an
annual  basis.   This suggests that a rough order of magnitude inference can
be made; namely, it  is  unlikely that households value  air pollution,  in all
of its  "perceived" dimensions, at more than $100.00 per mile  or  less than
$1.00 per  mile.

Direct  Willingness—to—Pay Studies

     Table 8-3  records  the  estimated annual  willingness-to—pay per  house-
hold for  miles of visibility  when families  were asked  directly  their
willingness-to-pay.  Since these studies  most often use pictures to portray
visual  range as part of the survey  process,  no "standardization"  equations
are required.   These studies encompass  a fairly broad typing of urban areas
ranging from Los  Angeles  and  Chicago down to  a small energy  impacted
community in  New Mexico.   The  estimates  ranged  from $1.54 per house-
hold/mile  to $40.25.  However,  the  Bresnock  study (3),  which  provides the
lower bound estimate,   utilized carbon monoxide  as the relevant measure of
pollution.  There appears to be  some  question as to whether carbon monoxide
is a good proxy for visual  range so that the estimated willingness-to-pay
may not adequately reflect  visibility changes.   Also,  the range in changes
                                   8-10

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                                                        Table 8-3

                           ANNUAL WILLINGNESS TO PAY PER MILE OF VISIBILITY  BY STUDY AND AREA
Study
Bresnock
(3)
Brookshire,
et al. (1)
SRI
(14)
EPRI/Blank,
et a^L. (15)
Tolley-
Randall (16)
Area,
Base Year
Denver ,
1977
Los Angeles,
1979
San Francisco,
1981
Farm ing ton,
1978
Chicago,
1981
WTP Per Year
5.70 for 18 pptra CO (low)
186.24 for 239 pptm CO (high)
312.00
70.56
i
ES- 91.08
CS-856.97
$200*
A in Miles
of Visibility
5.12 (low)
67.96 (high)
Fair -* Good 14 mi
Poor -* Fair 10 mi
2.4 mi
50 mi
9 mi
(V = 9)
$ Per Mile
Per Year (1981)
1.54 (low)
3.79 (high)
Fair -» Good 28.64
Poor -» Fair 40.25
29.40
ES- 2.35
CS-22.11
22.22
00
          This is a preliminary  estimate based  on a pre-test and given in a telephone conversation with one of the
          principle investigators.

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in visibility are perhaps too non-standardized across the various studies
to allow direct  comparison between  studies.  However,  the  range  of
estimates is within that  postulated earlier as  reasonable from the property
value studies.  The mean  is $18.57 with a standard deviation of  $14.57.

     According to theory, the property value  estimate should exceed the
willingness-to-pay estimate because  the property value differences are
represented  along  a rent  gradient,  while individual willingness-to-pay is
represented  by a  movement along  an indifference  curve.*  Thus,  individuals
are willing to pay less  than the current market price because  they have
already taken into consideration  their  individual "perceived" effects of
air pollution.   Comparing  the  means  of the  property  value studies  of
$23.04,  and  $18.57  for  the willingness-to-pay studies,  at  least the results
conform to  expectations from theory.  However,  this is a very  weak test
since we are comparing means  of estimates derived from different  times and
locations.

Biases in. Direct Willinpiess~to~PHT Stiulies

     Economists  have  argued that valuing public goods such as visibility
through a direct demand revealing process such  as willingness-to-pay  would
yield biased  results.   The principal  theoretical  support  for this conten-
tion is  the  possibility of strategic  bias.   However,  as  survey  techniques
to elicit contingent behavior or  bids  have come into use —  in part because
development of energy resources in formerly pristine environments  allows no
other techniques  to be used — other types of bias have come  to be regarded
as just as important.   These include information bias,  instrument  bias,
hypothetical bias and  traditional problems of  sampling,  interviewer,  and
nonrespondent bias.

     Beginning with Samuelson's  seminal work on public goods,  it has been
supposed that direct revelation  of  consumer  preferences  for such goods —
and,  of course,  environmental  quality is a  public good  —  would  be
* See D. S. Brookshire, M.  A. Thayer, W. D. Schulze, and R. C. d'Arge  (17).
                                   8-12

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impossible  (18).  In particular,  the free-rider  problem  would  give  indivi-
duals  incentives  to  misstate  their  preferences.   For  example,  if  residents
in Farmington,  New Mexico were asked how much they were willing  to pay to
clean up the air near a power plant, and  if  they  suspected that control
costs  would be borne by consumers  and owners  elsewhere,  these  local resi-
dents would have an  incentive  to overstate their willingness-t-o-pay.
Alternatively,  if residents suspected  that  they would be individually taxed
an amount equal to their own willingness-to-pay,  then a clear incentive
would exist to understate their  own  true  value, hoping that others would
bid more.

     Each approach  for asking willingness-to-pay will potentially generate
its own bias.   Thus,  if  recreators are told that the  average of their bids
to prevent  construction of a nearby power plant  would be used  to set an
entrance fee,  those  individuals who suspected  their bid  to be  greater than
the average bid would have an incentive  to overstate their willingness-to-
pay.  They, in fact, would have an incentive to raise the average bid as
close  as possible to their own true bid.  In other words, individuals would
have incentives to  misstate  their  own preferences  in an attempt to impose
their true  preferences on others.   It  would require  a substantial  amount of
information to actually behave in  this manner.  Of course, if  the respon-
dents to such a survey do not believe the  survey would have any impact on
policy or outcomes, then no incentives  for bias exist.   The hypothetical
nature of such surveys may then,  in actuality, aid  in eliciting bids which
are not strategically biased.   Alternatively,  since  payment  is  not
required,  a tendency to exaggerate  willingness-to-pay for a preferred
outcome might also  exist.

     Empirical evidence  thus far does  not support  the  existence  of
strategic bias among  consumers.   Bohm (19),  in an experimental  approach
utilizing actual  payments  for public  television, failed  to  find  strategic
bias significantly affecting the  outcome. Scherr and  Babb (20)  utilized
three  different mechanisms for valuing public  commodities and  found little
evidence  supporting  the  existence  of  strategic bias.  Smith (21),  in
laboratory  experiments,  also failed to find strategic bias as a significant
                                   8-13

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problem.   Studies  at  the University of Wyoming have  not encountered
problems with strategic bias.

     Since contingent  behavior or valuation is hypothetical, it is clear
that answers obtained through surveys are not based on information similar
to that which would  apply  if consumers  based  answers  on  real experiences.
One  is  an ex ante  response while  the  other is an  ex  post statement.
Typically,  consumers  do reevaluate decisions  on the basis of experience and
gained  knowledge.  Thus, an individual  or  household might respond to a
hypothetical decrease in environmental quality at  one  location with a low
bid,  thinking  that other  nearby  sites would  make  good substitutes.
However,  in a real situation the individual might have found that other
sites involved more  travel  costs  and were less satisfactory than  imagined.
The  information presented to the  respondent  in a  survey situation relating
to substitution  possibilities and alternative  costs may alter the  stated
willingness-to-pay.   The  respondent must be made aware  of proposed alterna-
tives in terms  of quality or quantity.   Other variants of information bias
might include giving the respondent information as to how other respondents
behaved, whether  in the aggregate their  bid  was  sufficient to achieve (or
not  achieve) the stated goal (e.g.,  prevention  of  visibility deterioration)
or alternative  sequencing of questions.

     Related to  information bias is instrument bias,  whereby characteris-
tics of the mechanism  for  obtaining willingness-to-pay possibly  influence
the  outcome.  Two  characteristics of  the  survey bidding approach are
vehicles  for payment and a starting point for initiation of the bidding
process.  Studies have  recognized that  the  mechanism  used to  collect the
bid  or  pay compensation may influence  its magnitude  (22).  That  is,  if the
recreator pays  a higher park entrance  fee rather  than  another type of tax,
his  bid for an environmental attribute may differ.   From economic  theory,
the  bid should  differ, if the  price of  the  commodity represented by the
bidding vehicle  changes, provided the recreator's  substitution possibili-
ties associated  with alternative  payment  mechanisms are different.  When a
payment vehicle  allows the  individual  to substitute over  a  wider range of
current commodities purchased, then the bid or compensation  should be
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related to  adjustments in disposable  income  or wealth, where the  individual
has the  greatest  latitude for  potential  substitution.   Practically,
however,  a believable payment mechanism related to  income adjustment, in
general,  cannot be  applied.   For example,  surveys are  often taken at
recreation sites  away from the  individual's  locale  or state.  In  this  case,
a wage tax may not be viewed as realistically payable by the recreator.
Thus,  there is a tradeoff between accuracy associated with a  less than
ideal  method of payment and the  believability of the vehicle for payment or
compensation.   The reduction  in  substitution possibilities for a more
believable  payment  mechanism is likely to reduce the contingent expenditure
or increase the compensation estimate.

    A second  type  of instrument bias  is starting point bias.  The contin-
gent valuation approach commences with questions on payment (and/or compen-
sation)  for hypothetical  changes  in  environmental  attributes.   Contingent
bidding surveys  to date have asked  the  recreator (or any type  of inter-
viewee) a  question  with  a "yes"  or "no"  answer  rather  than a question
requiring  explicit calculations.   It is presumed that the recreator can
more  accurately respond  to the  yes/no question  framework,  although to our
knowledge  this proposition has not been formally tested for individuals
responding  to  contingent  valuation questions.   Given the  proposition that
yes/no responses are desirable, often a starting bid or minimal level of
compensation has  been suggested.  The potential bias arises in suggesting a
starting point  from at least  two possible sources.  First, the bid itself
may suggest to the individual the approximate  range  of  "appropriate  bids".
Thus,  the  individual  may respond differently depending on  the  magnitude of
the starting bid.  Second, if  the individual values time highly, he may
become "bored"  or  irritated with going through  a  lengthy  bidding  process.
In consequence,  if the  suggested starting bid is substantially different
from  his   actual  willingness-to-pay,  the bidding process may  yield
inaccurate  or  only roughly approximate results.  The effect  of  these two
types  of starting point biases may substantially influence  the accuracy of
contingent valuation and therefore  the usefulness of this  approach for
assessment  of  environmental preferences.
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     Tie discussion on information bias suggested  that  the contingent
valuation approach will give answers dependent  upon the  information  or
"state  of  the  world" described.   The contingent valuation approach requires
postulating  a  change  in visibility such that it is believable  to  the  indi-
vidual  and accurately depicts a potential  change.  The change must be  fully
understandable to him; i.e., he must be  able  to  understand most, if not
all, of its ramifications.  The  individual also must  believe that the
change  might occur and  that  his  contingent valuation or behavioral  changes
will affect  both the possibility and magnitude  of  change  in visibility.   If
these conditions are not fulfilled,  the  hypothetical  nature  of  contingent
valuation  approaches will make their application  utterly useless.  A test
of hypothetical bias would  require that  the perturbation  proposed would
occur and  then the  respondents'  actual reaction would be evaluated in  terms
of the  previous hypothetical  statements of willingness-to-pay.  This, how-
ever,  makes  it extremely difficult  to measure  the  extent  of  hypothetical
bias within a contingent  experiment since  it depends not  only on the
structure  of the experiment,  but also  on  the "uncontrolled" factors of the
future.

     Any survey approach,  including the  contingent valuation  approach,   is
subject to  sampling bias, nonrespondent bias, and  interviewer bias.  Any  of
these certainly can  subject  the results  of  an  experiment to question even
if all  previously mentioned biases are nonexistent.   In a study by Rowe,
d'Arge  and Brookshire (23),  it was found  that from 40  to  50 percent of the
forecasted  differences between "true" bids and reported bids was due  to
various types  of biases.  That is, some types of bias  can potentially lead
to a 50 percent error in reported  bids.  Given these qualifications,  it  is
interesting to note that estimated willingness-to-pay  per mile tends  to
cluster around $10.00 to  $25.00 per household.

     We do  not attempt to  evaluate the  extent of  the  various biases
described  above for  the five direct willingness-to-pay studies  identified
in Table  8-2.  While  some  of  the studies did test  for the presence  of
alternative biases, it is difficult to  reach general conclusions on the
likely  magnitude of particular biases for contingent valuation studies
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since so much depends on questionnaire design.  Detailed discussions of the

biases and the manner in which they are  dealt  with in  questionnaire design
can be found  in Brookshire et al.  (1) and Rowe e_t al. (23).


     Before we present the  estimates  of visibility benefits  for urban
areas, we want to  highlight  some of  the major  assumptions which accompany

the development of  a benefits  number  from the  contingent valuation studies.
These include  the following:
     •    Willingness-to-pay  estimates derived  primarily  for West
          Coast cities are appropriate for the rest of the nation.
          (Probably  leads  to  upward bias.)

     •    That the depictions through photographs  in western  condi-
          tions are  appropriate for  other regions of the country.
          (Depends on locale,  but probably contributes an upward bias
          because  of greater  vistas in the  West.)

     «    That one-half of the total willingness-to-pay was due to
          aesthetic  considerations  of poor air quality throughout the
          nation.   (Bias  is probably upward  since health  considera-
          tions may be most important in many eastern urban areas.)

     •    That average willingness-to-pay does not differ markedly
          from marginal willingness-to-pay.   (Empirical estimates
          suggest  average  is  above  marginal,  so  bias  is likely to be
          upward for visibility improvements.)

     •    That the  techniques used  to  obtain  empirical estimates are
          unbiased.   (Bias, if present, is unknown quantitatively but
          likely to  be upward since there were no  budgetary  restric-
          tions imposed in discovering willingness-to-pay.)


     In summary, there are  many assumptions underlying the  extrapolation of

some  experimental  estimates  to nationwide  benefits  assessments.   Many of

the biases can be  at least  partially evaluated as to  direction, and in most

instances tend to be upward.


Prediction Relationships for Urban  Areas
     In order to develop national estimates of visibility values  in  urban-
suburban areas,  estimated prediction equations were  developed relating
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willingness-to-pay for  improved visibility to  income and  visibility
improvements.  The general forms  of the equations were:

          B  =  tiYaVP                                              (8.1)

          B'  =  iiYa(AV)P                                           (8.2)

where      B  =  willingness-to-pay  (in  1981  dollars) per  family per year
                for visibility of V  rather than no visibility.
          B'  =  willingness-to-pay  (in  1981  dollars) per  family per year
                for an air quality  improvement that  leads to a visibility
                change of  V.
          Y  =  yearly family or household income in 1981 dollars.
          V  =  miles of visibility  in an urban setting.
          AV  -  improvement in visibility in  miles, urban and suburban.
          TJ  =  a constant term.
        a, P  =  elasticities of income and  visibility associated with
                willingness-to-pay for  improved visibility.

    Two methods were developed to  estimate  the coefficients t\,  a,  and p.
Both involved starting with results from the Los Angeles experiment  (1).
In Method I,  (J was estimated by using an econometric estimate of AB/ANO*
from  Brookshire et  al..  and converted  to  AB/AV using the  results in
Appendix 8A,  and then recomputed in elasticity form using the means of  the
Los Angeles  data.   The coefficient  a was  obtained  from econometric
estimates found in Brookshire et al.   Finally, using the  means for  Los
Angeles  of B, V, and  Y,  the coefficient r\ was  computed.  The  complete
equation using Method I is then:

          B  =  o.497Y°-566V°-399                                  (8.3)

While  this  equation tended  to predict well around  the  means  for  Los
Angeles,  it  tended  to  not  predict well for large changes  in  visibility for
Los Angeles or other  cities examined.  The Boston study (9) contained an
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equation  where B = 0.279Y1*00V°'87 (although property value differences
were  used),   while  the  Denver  study  (3) had  an  implied  form  of  B =
0.204T '  *V *      (although  because of the rather  arbitrary  conversion  of
CO into N02 the 0 elasticity for  Denver should be  suspect).

     Method II involved using the  average of the natural logarithms  of  the
means of the household bid  and changes in visibility for the Los Angeles
experiment to estimate (J,  the elasticity for visibility.  Thus,  0  equals  In
312/ln  13 = 2.239.  Then this value along with  a  = 0.566  was substituted
into Equation (8.2),  again using  the means for Los  Angeles to estimate  the
constant term, n.  The resulting  equation  was:

          B'   =  0.0039Y°-566(AV)2'239                               (8.4)

     This equation tended to predict more accurately the  value of  changes
in visibility across various studies.  Using Method II,  0 was  calculated  to
be 2.239 for Los Angeles, 4.864 for San Francisco (14), 2.411 for Chicago
(16),  and  2.854  for Boston where  property value  differences  were  used (9).
Thus, the Los Angeles elasticity is  the lowest using Method II for the
studies where willingness-to—pay and visibility  measures could  be  inferred
from reported results, and also lower when compared with the Harrison  and
Rubenfeld property value  study for Boston.

     For estimation  purposes, both Equations  (8.3) and  (8.4)  are  utilized.
The fundamental  difference between  them is that Method I utilizes  the
differences  of  the logarithms  to predict,  while Method II  uses   the
logarithm of  the  difference (or  change in visibility),  both with  different
weights because of  the magnitude of the  constant  term.   As proposed visi-
bility improvements become larger.  Method I would  tend  to predict  less than
Method II, while  for very small changes the  reverse would be  true.   Method
I is calibrated to the Los Angeles data set,  but tends to underpredict  for
Boston,  overpredict for Denver,  and be reasonably close for  San Francisco.
Alternatively,  Method  II  consistently underpredicts, for  small visibility
improvements, for San Francisco,  Chicago,  and Boston.
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     Table 8-4 records national estimates using the equations resulting
from Methods I and II.   Note  that Method I yields  higher estimates  for
small predicted visibility improvements,  yet  lower ones when the visibility
change  becomes larger,  as was  postulated.  Furthermore, both  methods  tend
toward identical  predictions around the 20 percent visibility improvement.

     For prediction purposes. Method II is probably most  accurate  for
intermediate predictions of visibility improvement across all cities, while
Method I would be  more  accurate for extreme  changes and where the base
visibility was very high  or very low.

     It is  also  a useful  exercise to check  how  the benefit estimates
derived from Equations (8.1) and (8.2)  compare with some  gross  estimates  of
visibility developed  simply from  the  information given in Table 8-3.   It
appears reasonable to presume that, in the  aggregate within an urban
context,  where visibility is  typically in the range  of  seven  to  25  miles,
the value  that residents  place on  improved visibility ranges  from $5.00  to
$40.00  annually per mile  for an average household.  A "best estimate" value
would be about $19.00.   Given that there  are approximately 76  million U.S.
urban-suburban households and that the average  improvement of visibility of
20 percent  means a mileage increase of seven to ten miles, then urban
residential  benefits for  a  20  percent  improvement would be from $133.00 to
$190.00 per household each year (in 1981  dollars).  Nationally,  this
translates  into an estimate  of  from $10.0 billion to $14.4 billion per
year.   Thus,  in terms of  averages across  studies, it  is clear that benefits
to households in urban  areas are  likely to be  in the billions  per year  for
a 20 percent improvement in visibility per year.  Note, however,  that  this
is willingness-to-pay  for all effects  inclusive  of health effects  as
represented  by visibility.  In the Los Angeles study,  only about one-half
of willingness-to-pay can be attributed to aesthetics.  Therefore,  on a
national basis the aesthetic benefit  of  improved visibility of 20  percent
would  range from  $5.0  to  $7.2 billion per year based  on the aggregate
estimates  just given.  Note also that the ranges  for  a 20  percent improve-
ment (medium  estimates)  are entirely included in  the range  cited  earlier
for the total effects case  of  gross national averages.
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                               Table 8-4

  LOW, MEDIUM AND HIGH ESTIMATED RESIDENTIAL BENEFITS PER YEAR IN 1980
   MILLIONS OF DOLLARS FOR VISIBILITY POLICIES OF 13, 20, 30 MILES AND
                      A NATIONWIDE 20% IMPROVEMENT*

Policy

13 Miles
20 Miles
30 Miles
20%
Low


Eq. 1**
260
4,230
13,242
6,743

Eq. 2+
39.8
2,618
24,028
6,335
Medium


Eq. 1
1,581
6,986
16,887
7,608

Eq. 2
126
4,010
30,637
14,391
High


Eq. 1
1,893
7,908
18,534
8,555
. 	
Eq. 2
364
6,094
38,887
27,657
   * Let:
         B.  =  Benefits per year for  region i.

         Y.  =  Average annual household  income for region i.

         V-  =  Initial visibility in  region i.

         V?  -  New  visibility  imposed by the  policy alternatives  in
         H.
      region i.

      Number of households in region i.
'i  =  vi-vi-
 B  =  Benefits per year  =
      defined by Map 1.
                                             t where  the  regions are
   Then Low,  Medium and High  estimates were  calculated by  assuming a
   different  V*  (B^ = 0  for unaffected regions).
                                       °-566(V1i)0-399)
** Calculated  using:

          Bi   =  (0.497Y.0-566(V2)0-399  -  0.497Y.

 + Calculated  using:

          BJ^   =  (0.0039Yi°'566(AV.)2'239)  x Hi<

Note:   These numbers must be reduced by at least 50 percent to calculate
       the aesthetic benefits of improved visibility.
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     It is important to note  that  the values obtained here are  for all
perceived  effects of air pollution by  those who reside in urban areas.   It
was  impossible to separate out the  aesthetic value of visibility from
perceived  human health  and  other effects of reduced visibility.   However,
one study (1)  demonstrated that from 22 to 55 percent  of the total per-
ceived effect due to visibility changes is associated with aesthetic value.
This proportion would  likely be  lower in urban areas  with low  visibility
and higher in suburban areas with little or no  perceived health effect.   In
consequence, any national estimate  of the aesthetic benefit must take this
into account.   For example, the benefits estimates recorded in Table 8-4
for a 20 percent improvement in visibility nationwide,  for lust aesthetics.
must  be adjusted  downward to 22 to  55 percent  of their values.  Using
Equation  (8.1), a  20 percent  improvement would  yield national benefits
estimates of from $1.47 billion to $4.7 billion annually.   This is the
approximate range  observed  for the  aggregate estimates discussed earlier,
after the  correction had been introduced.

Conclusions

     The purpose of this brief analysis was  to  develop approximate yet
plausible  estimates of the  aesthetic value of  improved urban-suburban
visibility for  the U.S.  accomplished by using the results of  recent direct
willingness-to-pay studies.   The results were also cross-checked using
estimates  derived  from  property value studies.  From the studies analyzed,
it is clear that urban-suburban visibility values are positive  and are
likely to be in the billion dollar plus category. Given the substantial
uncertainties  as to precise willingness—to-pay, regional variations  in
tastes and preferences, and topography  that  are not  taken  into account,
little more can be reasonably proposed.

RECREATION BENEFITS

     In this subsection,  the  five  existing original empirical  studies
devoted to establishing measures  of  the value of  atmospheric  visibility  to
outdoor recreators  and tourists are  critically reviewed and synthesized.
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The results  of this  review are  used to arrive  at magnitudes  for the
constant b  and  the exponents p^  and 02  in the multiplicative expression:

          In(TWP)  =   ln(b) + px ln(Y)  +  02 ln(V)  ,                  (8.5)

where TWP  is the total willingness-to-pay  for  a single  outdoor recreation
activity day of the representative U.S. household, Y is that household's
annual income  in thousands of dollars,  and V is atmospheric  visual range in
miles.   The absence  of  any site-specific attributes  in this expression
implies that  visibility is to be valued independently of its  location.
Only  three of the five studies  have  thus far appeared in  the refereed
literature.  To generate their data,  all  five studies employ the contingent
valuation  techniques variously  reviewed,  evaluated,  and defended in
Brookshire  and Crocker (24) and Schulze ejt  ajL (25).

Review of Studies

Randall et  ml.  (22) —

     This  1974  study was  the  first  to apply the contingent valuation
technique to the problem of assessing the economic  benefits of atmospheric
visibility.  The study focused on the 1972 environmental  damages that a
2,080  MW  coal-fired power  plant  in Fruitland,   New Mexico,  and its
associated  raw material and transmission facilities, imposed on residents
of and visitors to the Four Corners region of the Southwest. More than 700
people were asked to  value  the "high," "intermediate," and "low" environ-
mental damages represented  by three  sets of photographs.  The three photo-
graphs  in each set depicted power plant stack emission conditions,  mine
spoil bank  conditions, and transmission line  conditions.

     The format and reporting of  Randall et.  al.  severely limits its  rele-
vance to a discussion of the value of atmospheric visibility to  outdoor
recreators.  Although the  authors assert (p.  141)  that respondents con-
sidered reduced visibility to be "... far  and  away the most  serious" of the
three  forms of  damage,  they provide  no information supporting  this
                                   8-23

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this conclusion.  Moreover,  if the  conclusion refers to the stack emissions
pictures,  the  reader cannot know how respondents  separated the visual
impact of the power plant and its  stacks from the visibility impact of its
emissions.   Further limiting the usefulness of the  study  for  determining
the separable value of visibility  is the authors'  failure  to  relate  stack
emissions to ambient pollution concentrations;  the  responsibility  to  form
this relation was left  to the  interviewee who did not have to  report  where
his imagination led him.  Having established a  measure of  the  compensating
surplus  values the interviewees attached  to various representations of
environmental damages,  the  authors,  when they reported these values,  chose
to  group residents and nonresidents,  thus making  it  impossible  to  distin-
guish the residential  portion of  the reported total valuation from its
outdoor  recreator component.   Finally, because respondents were asked to
express  their willingness—to—pay for environmental damage reductions in
terms of electricity bills  and/or  sales  taxes rather  than  income, the
reported values are,  by the Le  Chatelier principle [Silberberg  (26),
Chapter  9],  likely  to be biased downward somewhat.  Limiting the individual
to varying only his consumption of electricity, or the subset of marketed
goods subject to sales  taxes, reduces the alternative ways he has available
to maximize his gains from the visibility improvement.

     Whatever  the  interpretive difficulties Randall .e_t .§_!. pose,  they do
provide  for our purposes  some useful insights.   In particular,  they convey
information on  the  signs of 0, and 0~  "* Equation  (8.5).

     When those Randall et al. resident respondents who did not  live  on an
Indian reservation were requested  to express their willingness to pay
increased sales taxes for reductions in environmental damages,  they were
willing  to pay $35 annually for  a  reduction  from  high  to intermediate
damages, and  $50  annually  for a reduction  from  intermediate  to low
                                   8-24

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damages.*  Given that the visual range and color contrast  intervals are
equal across  the three damage levels,  this result  implies that the incre-
mental benefits of environmental quality improvement  are positive and
increasing, which means that  fk will be positive.

     Only when  reporting income  elasticities (p.  146)  do Randall e± al.
provide  separate results  for  their  sample  of 526 nonreservation area resi-
dents and  ISO recreators and tourists from  outside the area.   For the
nonreservation residents,  they estimated an income elasticity of 0.65  with
a standard error of 0.10  for  a move from high to intermediate damages, and
an income elasticity of 0.65  with  a standard error of 0.08 for  a  move  from
intermediate to low damages.  The 150 recreators and tourists, who  were
asked to bid  in terms of  user access fees,  had an estimated income  elasti-
city of 0.09  with a standard error of 0.15 for a move from high to inter-
mediate damages, and an income elasticity  of 0.16  with a  standard error of
0.11  for a move  from  intermediate  to low  damages.  These results indicate
the possible  range  in £,,  for subgroups of the survey population.

Brookshixe et. ml. (27) —

     As its authors note, this study was  inspired by Randall et al.  (22).
Starting from a pair  of photographs representing an undisturbed Lake Powell
environment,   83  outdoor  recreators and tourists in the region were asked,
in 1974, to state their willingness to pay daily access fees  to prevent the
construction of  a  2,400 MW coal-fired  power plant having no  visible
emissions from  700-foot  stacks,  and their willingness to pay daily access
fees to prevent  the same  power plant with readily perceived emissions and
* On p. 147. Randall et_ al.  state:   "Mean individual household willingness
  to pay for abatement, ...,  was about $50 annually to achieve situation B
  (intermediate damages)  and $85 annually to achieve  situation C (low
  damages)."  This statement is  inconsistent with  their detailed presenta-
  tion of  results.   The $35 and $50  figures we use are consistent with
  their detailed presentation.
                                    8-25

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visibility impacts  from being built.*  Equivalent surplus measures of value
were thus obtained.

     Some of the features limiting the usefulness of Randall e_t .§_!.  (22)
are also found  in Brookshire et al.  Again, although the high damage repre-
sentation depicted both stack emissions and resulting visibility impair-
ment,  no information is provided about the degree of impairment.   In
addition, because  respondents had to express their bids in  terms  of access
fees and were thereby constrained to adjust their access  to  Lake Powell
rather  than their  consumption of some other good, bids are likely to be
biased upward.**  This bias could be  accentuated by the requirement  that
respondents state their  bids  in  terms  of per day access fees:   degraded
visibility may result  either  in  a reduced willingness-to-pay  for  a visit,
reduced visit  length, and/or  reduced visits.   If, contrary to what they
would have preferred to  do, some respondents presumed that they were not
supposed to consider the  latter two adjustment possibilities,  their stated
utility losses  from the environmental  degradation would be exaggerated.
Since respondents  remained  uninformed about  what they were to assume with
respect to visit lengths and frequencies,  an  element of noncomparability is
present  across respondents' valuations.

     Brookshire  et  al. (p.  338) only present willingness-to-pay magnitudes
for two  cases:  1) a movement from a depiction  of an undisturbed  environ-
ment  to one with  a power plant having  no visible  emissions; and  2) a
movement from the same undisturbed environment to a representation  of a
power  plant with readily  perceived visibility impacts. Given additivity
across the cases,  the difference  in willingness-to-pay between these  two
cases is the willingness-to—pay to stop  a movement from the  no—visible-
emission power plant to  the visibility-impacted power plant case.   The
difference would then approximate the stated value  the  83 recreators and
 * The published form of Brookshire  et al.  only contains  line drawings of
   the photographs  that were used.
** Although one cannot tell from the published version,  the same bias is
   probably present in the user access  fee  results  of  Randall e_t al. (22).
                                   8-26

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tourists attached to visibility degradation independently of the visual
impact of the power plant.  Upon taking the difference,  a weighted mean bid
of $1.33  per day results.*  This  figure is less than half the weighted mean
bid for stopping  a movement from  the  undisturbed  environment case to the
no-visible-emission power plant  case.**  Brookshire £t al.  therefore imply,
contrary to the finding  in Randall e_t a_l. [(22), p. 141],  that the visual
impact of the  power plant contributed more to recreator losses than did the
reduction in atmospheric visibility.  However, if respondents  associated
the no-visible—emission impact  case with unavoidably  small reductions in
visibility, and if respondents were  indifferent  to the  presence of the
power plant, then the  weighted mean daily bid to  stop movement from the
undisturbed environment case to the "small" reduction  in visibility  case
was $1.81. Thus,  as  in Randall et al. (22), the results in Brookshire gjt
al. are consistent with f^ being positive.  As in  Randall et al.  (22), no
inferences can be made from Brookshire et al. about the magnitude of fU
since a quantitative  measure of  the visibility change  is not provided the
reader.

     Although  they do not explicitly provide income elasticity measures for
changes  in environmental quality, at least two of the Brookshire et al.
findings are consistent  with a small  magnitude for p^.  First, they test
for differences due to. an  income effect between compensating and  equivalent
surplus measures  of value.   No  statistically  significant differences  were
found.   Second, they varied the  income distributions across the  four group
classes  of their  respondents,  including residents.   The  variations had
"little impact" (p. 344) on an aggregate bid consisting of the product of
visitor days and bids summed across the four respondent groups.
 * Treating remote campers, developed campers,  and  motel  visitors  separ-
   ately,  the  weights  are  the product of each group's sample size and mean
   visitor days relative  to the sum of these products for the same three
   groups.
** The standard deviations of the mean bids for each group-case  combination
   are always less than 30 percent and generally less than 20 percent of
   the corresponding daily mean bid.  The  tails of the distributions  of the
   bids across cases thus have very little overlap.
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Rove et al.  (23)  —

     This study  is the immediate successor  to  the previous two studies.
Its method (a contingent valuation technique)  of  acquiring data on the 1976
value  of  atmospheric visibility  and its geographical focus  (the Four
Corners region)  was the same as its ancestors.  In its particulars, how-
ever, Rowe  et  al.  displays  considerable evolutionary progress.   The three
sets of two photographs employed  to  generate  data  for obtaining equivalent
surplus measures of atmospheric visibility contained no prominent man-made
features.  A fourth set of photographs  was  employed to obtain  an equivalent
surplus bid on the value  of  the visual  impact of the same power plant used
in Randall  et  al.  (22).  At the  three times the  visibility photos were
taken,  visual range in the region was  estimated  by air  traffic controllers
at the Farmington, New Mexico airport to  be  approximately 75, SO, and 25
miles.

     The study samples consisted of 26 outdoor recreationists at a  large,
man-made  reservoir near Farmington, and 93 Farmington  residents  who were
interviewed in their homes.  One  cannot  tell from the published report
whether the  former group was allowed  to adjust visitation rates and daily
access fees simultaneously.  The  authors separately report  (p. 10) the mean
equivalent  surplus bids for the  two groups.   Outdoor  recreationists were
willing to  pay  daily  user access fees of $3.00  (12.00 cents per mile) with
a standard deviation of $0.77 to maintain the  visibility represented by the
75-mile photographs  rather than that represented by the 50-mile photo-
graphs.   They were willing to pay $2.53 (10.12 cents per mile) per day with
a standard  deviation  of $0.65  to  maintain  the 50-mile rather than the 25-
mile visibility.  Thus, although the bid distributions across  the  photo-
graph sets do overlap  to a substantial degree,  improved  visibility, just as
is probably true in Randall et. al.  (22) and Brookshire  et_ al. (27),  causes
a progressively  greater willingness-to-pay for  further  improvements.  The
equivalent surplus bids of Farmington residents in Rowe  et al.  displayed
the  same  pattern.  Thus, if the outdoor  recreationist and  the  resident
samples in Rowe  et al. are considered as separate studies, four out of four
reviewed studies present  evidence that fa  i* positive.
                                    8-28

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     Not all the  evidence reported in Rowe e_t *!_. is so comforting to the
interpretations earlier given to Randall et_ al. (22)  and Brookshire et. al.
(27).  Outdoor recreation respondents  were only  willing to pay $4.06 per
day with a standard deviation of $1.05  to  maintain 75-mile rather than 25-
mile visibility.  This mean bid is only 73 percent  of  the sum of the two
bids over  the 75-to 50-mile  and the  50-  to  25-mile  intervals.  These
results  clearly  raise doubts  about  the  validity of the bid additivity
assumption earlier made for  the Brookshire et  al.  (27) study.

     Rowe et al.  provide empirical  evidence  supporting Randall's et al.
(22) contention that the impact of  emissions on visibility plays  a far
greater  role in respondents' bids than  does  the visual impact of the  power
plant and its  stacks.   The equivalent surplus bid of outdoor  recreationists
for maintaining 75-mile visibility rather  than having  25-mile visibility
and a power plant in the middle—distance  was only $4.56,  a figure  only
$0.50 greater than the offer of the same respondents for 75-mile rather
than 25-mile  visibility.   Similar results were obtained for Farmington
residents.

     Rowe et  al. do not report income elasticities for outdoor  recrea-
tionists.   As  calculated for a  one-step  move from 75-mile to 25-mile  visi-
bility,  the elasticities  they  report for Farmington residents  range  from
0.25 to  0.36.   Thus, as do the two previously reviewed studies,  Rowe et al.
find that stated willingnesses-to-pay for improvements in atmospheric visi-
bility are quite  unresponsive to  increases  in respondent incomes;  i.e.,  (J^
is again estimated to be positive but  small.  The authors do not  present
the income ranges to which these elasticities are meant to apply.  It is
quite conceivable that the low reported elasticity is due more to the small
income range that the respondents represented.

R*c (28) —

     Though not yet published,   Rae adds some new  dimensions to the use of
contingent  valuation techniques  to estimate the  economic value  of atmos-
pheric visibility.  This  1980 study was also placed in the Four Corners
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region,  specifically Mesa Verde National Park in southwest Colorado.  Visi-
bility impairment due to plume blight was distinguished during the data
generation exercise  from that due  to regional haze.  For the first time,
telephotometer measurements  made  at  the same times as  the photographs
allowed the  54 respondents' statements  to  be  related  to  objective measures
of visual range and color contrast.   No prominent man-made  features
confounded respondent interpretations of the depicted natural landscapes.
If differences  in  the natural landscapes are neglected,  the valuations
obtained from the two sets of  regional haze pictures can be compared with
the values established with the three Rowe et al. (23)  picture sets that
did not include  a power plant.  The  estimated visibility  valuations corres-
ponding to the two sets  of plume pictures used in Rae are  perhaps crudely
comparable to the  visibility conditions represented by the power plant
pictures  in Randall  et  al. (22), Brookshire et al. (27),  and  Rowe et al.
(23).

     The three  contingent valuation studies reviewed earlier say nothing
about the atmospheric visibilities and other attributes  at alternative
outdoor recreational  sites:  the respondents were  allowed to form their own
premises about the attributes of these other sites  and to keep these formu-
lations to themselves.   Thus the range of  implicitly considered substitu-
tion possibilities for the depicted  sites  in these three  studies could vary
widely from one respondent  to another.*  By the Le Chatelier principle, the
wider the range  of adjustment possibilities, the less  the value the indivi-
dual respondent will attach to impaired  visibility  at  the depicted  site.
Rae  (pp. 3.12-3.14) placed each of  his 54 respondents on nearly the same
footing by explicitly limiting  the  available substitutes  to nine, including
Arches,  Bryce  Canyon,  and Petrified Forest National  Parks,  San Juan
National  Forest, Glen Canyon National Recreation Area, Canyon De Chelly
National Monument, Salt Lake City, Las Vegas, Nevada, and the individual
* Since  the  range  of substitution possibilities the respondent weighs in
  his  answer is likely to be  that which  he considers  in his  actual
  behavior,  this variation across respondents can be considered an analyti-
  cal advantage  of  the  direct asking approach used in Randall  e_t  al.  (22),
  Brookshire et al. (27), Rowe et al. (23), and Schulze e_t al. (25).
                                   8-30

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respondent's most frequently visited site.  The simultaneous influences
upon the  value  of atmospheric visibility at Mesa Verde of the combinations
of respondent-perceived  attributes at each  of these sites were captured by
means of  a multinomial logit model (29).  Included among the stated attri-
butes of  Mesa  Verde was  a  daily access fee which formed  the basis  for
estimating compensating  surplus  measures  of improvements at Mesa  Verde in
atmospheric visibility.  Unfortunately,  the  author did not make  clear
whether  this fee was to be  an increment to or a substitute for existing
access fees.  Brookshire et  al. (27)  suffers from the  same  confusion.

     In contrast to the  direct inquiries  into marginal willingness-to-pay
that the  earlier reviewed  studies  employed,  the  multinomial logit  approach
in Rae infers  these willingnesses-to-pay  from  respondents'  stated  changes
in choice behavior,  including visitation rate changes.  Rae postulated that
each of his 54  utility—maximizing  respondents possessed a utility  function
U(s,x) +  w(s,x). where  s  is a vector  of  respondent  attributes, x  is  a
vector of site  attributes, v is a nonstochastic  function  reflecting "repre-
sentative tastes",  and w is  a function  that varies  randomly  in the sample.
Assuming that  the choice set of interest is  B =  (x^,  Xj,  ... , XQ),  and
that the w values are independently and randomly distributed over B,  the
probability that a  respondent will choose any x can be shown to be  (29):

                        v(s,x)
          p(xls.B)  =  —	
                      yeB
The choice probabilities  are  invariant  to  increasing monotonic transforma-
tions of UO)  [McFadden  (29)].  Given  a parametric specification of the
utility  indicator v, the  expression  for p  is  used to obtain parameter
estimates and is ultimately a basis to predict  respondent behavior,  given
the set of respondent-stated  individual and recreational site attributes.

     Because only two sets of slides  depicting regional haze  conditions
were presented to the respondents, the results of Rae,  by themselves,  can
provide no insight about  the magnitude  of pV   However,  because  the  "clear"
                                    8-31

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conditions  represented a visual range of 160 miles, the interval of  visual
ranges for  which valuations have been estimated in' the  Four Corners  region
is considerably extended by this study.  Rae's 54 respondents  in  the  summer
of 1980 were willing to pay $8.45  (8.88 cents  per mile)  in daily access
fees for an improvement  in visual range from 75  miles to 160 miles.

     Rae's  results are in agreement with Randall's et al. (22)  and Rowe's
e_t JLi- (23) finding that power plants  and their associated plumes impose
lesser insults than does general visibility  impairment.  An improvement
from a severe plume condition (color contrast = -0.04) to a clear condition
(color contrast = -0.24) was worth $4.00 in daily  access fees to Rae's
representative respondent.

     In his preferred specification from his  Table 4-2,  Rae  shows that
respondents with  an annual income  of $10,000 were willing to pay a daily
access fee  of $4.55 for an improvement  in visual range from 75 miles  to 160
miles; respondents with annual incomes  of  $25,000 were willing to pay $8.45
for this improvement;  and respondents  with  annual  incomes  of $40,000 were
willing to  pay $10.74.   For each income  category, the standard deviation of
the bid estimate  was about 30 percent  of the bid.   The  average 1980  income
of the respondents was $25,000.  .Using the  lower  income and bid in each
interval as the base, these  results imply that over the $10,000-$25,000
annual income  interval, the income elasticity of demand for the  visibility
improvement is 0.58,  while over the $25,000-$40,000 interval it is 0.45.
However, when the upper end  of each interval is used as the base,  these
elasticities  increase  to  0.77  and 0.55,  respectively.

Schulze et  al.  (2) —

     This study is the latest entry in the  rapidly evolving literature on
the economic value that outdoor  recreators  attach to  altered atmospheric
visibility.  Although it too employs a direct asking technique to obtain
compensating and equivalent surplus  measures,  it adds some  significant new
dimensions to  earlier treatments.  Teleradiometer measurements  of the
apparent green  contrast in the  five   sets of five photographs  that are used
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to represent  various  levels  of  Grand  Canyon and a regional visibility  for
outdoor  recreators are provided.   Tie  photographs used for outdoor  recrea-
tors represent  visual ranges  from  78  to 235  miles where  no  man-made
features are present.  Separate willingness-to-pay for visibility in  the
Grand Canyon  alone  and in the entire Southwest,  and for hazes and  for
plumes, are obtained.  Rather  than limiting the sample to an arbitrary
group of self-selected current users of the Grand Canyon, a stratified
random sample  of respondents was interviewed in the summer of 1980 at  their
homes in Albuquerque (61 people), Los Angeles (60 people), and Denver  (45
people).*   Respondents were  explicitly told that their bids were to be
interpreted as an  increment  to  a  $2.00 daily  access fee,  thus avoiding  the
possibility that  the bid might be regarded as a substitute for existing
daily access  fees,  but  unfortunately  retaining the possible Brookshire et
al.  (27)  and Rowe  et al.  (23)  confusions  between daily  access  fee
adjustments  and visitation  rate adjustments.  Visibility improvements in
the "Grand Canyon only" were tied solely to access fee  increases  in that
Park.  Regional visibility changes were said to involve identical daily
access fee  alterations for all regional National Parks.

     All compensating  surplus bids for the "Grand  Canyon  only" referred to
the willingness-to-pay  in excess  of a $2.00 daily access fee for  increased
visual ranges rather than having a 78-mile visual range.  Thus, if one is
to obtain the incremental bids across ranges,  one must  assume additivity of
the bids.  Making use  of this assumption. Table 8-5 presents the mean
incremental  bids in cents per mile  for each of the three cities.    The
visual ranges were calculated from the Schulze e_t .al. photographs by Dr.
Eric Walther  of  the Visibility Research Center at the  University of Nevada-
Las Vegas.  Each visual range  in the table is an unweighted mean of  the
"Grand Canyon haze only" visual  ranges in the  three photographs in  each of
the five sets.
* Another  334 people  in  these cities  were  interviewed  about  their
  willingness-to-pay to preserve  Grand Canyon and regional visibility inde-
  pendently of their desire to visit the area.
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                                Table 8-5
        MEAN  INCREMENTAL WILLINGNESS-TO-PAY IN 1980  CENTS PER MILE*

78 to 87 miles
87 to 140 miles
140 to 189 miles
189 to 235 miles
Albuquerque
16.22
1.38
1.84
5.00
Denver
16.33
1.96
2.57
5.52
Los Angeles
22.78
2.79
2.61
8.65
* Calculated from Schulze et al. [(2),  p.  66].
     With, the exceptions  of  the  move  from the 87-140 mile interval to the
140-189 mile interval for Los Angeles  respondents,  and  the moves  from the
78-87 mile  interval to the  87-140 mile  interval for respondents in all
three cities,  the rep-orted  incremental  willingnesses-to-pay of Table 8-5
display the  positive and increasing incremental valuations  for improved
visibility  that  appear  with varying degrees  of believability  in  the
previously reviewed studies.   The relatively high incremental bids reported
for the 78-87 mile  interval  are nevertheless quite disconcerting.  They
have no obvious and secure explanation.   However,  in distinct  contrast to
the four other contingent valuation studies reviewed  here, the standard
deviations of the  bids  in Schulze et al. are consistently about as large as
the mean bids.  The use of the 78-mile  visual  range  as the initial range in
the bidding  sequence might be another  clue to the extraordinarily high 78-
87 mile  bid.   If, for purposes of  the  interview,  respondents chose to
interpret the 78-mile visibility  as  complete darkness, then the mean incre-
mental willingness-to-pay over the 0-87 mile visual  range interval would be
1.68 cents,  1.71 cents, and  2.36 cents per mile  for Albuquerque,  Denver,
and Los Angeles,   respectively.   In  the absence of  a definitive  answer,  we
choose to treat the  incremental  values attached to the 78-87  mile  interval
                                   8-34

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as an aberration:  the evidence  for 02 being positive from the four earlier
studies  and the last three rows  of Table 8-5 dominates.

     Schulze &_t .§_!. also provide equivalent surplus statements of 1980
willingness-to-pay in excess of $2.00 daily access fee to prevent plume
blight in what would otherwise be (~ 150-200 miles) visual range Grand
Canyon scene.  The plume appears  as a horizontal brown band lying just over
some distant mountains.  The sky in the photographs is otherwise mostly
blue with some patchy cirrus clouds.   No  man-made  objects such as stacks or
power lines interfere.   Albuquerque respondents were  willing to pay an
average of $3.18  with a standard deviation of $3.26  to prevent  the plume;
Denver respondents  were  willing  to  pay  $4.80 with a standard deviation of
$6.98.  Since these willingness-to—pay magnitudes are similar to  the same
respondents' expressed  offers  to move  from haze-impaired 78- to 189-mile
visual ranges, support  is  offered for the findings of  Brookshire  et  al.
(27) on the relative contributions to total bids  of  stacks and plumes
versus atmospheric hazes.   However, Randall et al. (22) and Rowe  et  al.
(23) are  still  in place:   they contradict Brookshire et  al.  (27) and
Schulze et al. (2).   No dominating  evidence exists; the question of the
relative values  of the visual impacts of power plants and  stacks and the
visibility impairments of atmospheric hazes remains unsolved.

     By adding photographs  of vistas  in  Mesa Verde and Zion National Parks
to the already depicted Grand Canyon haze-impacted  vistas,  and  asking  the
respondent to consider the set o'f five photos as representative of visi-
bility in the  entire Southwest,  Schulze et al. obtain an  equivalent surplus
measure  of the value of  regional atmospheric visibility.   The  same 166
Albuquerque,  Denver,  and  Los  Angeles  respondents  were  asked  their
willingness-to-pay  to prevent a decline  in regional  visual  ranges from  a
five-photograph unweighted  average visual  range of 135 miles  to a similar
unweighted average  of 102 miles.  The highest and lowest visual ranges  in
the 135-mile set were 170 miles and 99 miles, respectively.  In the 102-
mile set,  the  highest range was 129 miles  and the  lowest  was  82  miles.
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                                                                             1 1
     All bids  for regional  visibility  were  to be expressed by respondents
as an addition to a daily $2.00 access fee  being charged at all regional
National  Parks.    Albuquerque respondents  were willing  to  pay $3.16
(standard deviation = $3.55) or 9.58 cents a mile to  keep regional visual
ranges  at the 135-mile average;  Denver respondents were willing to pay
$4.93 (standard deviation = $14.83) or 14.94 cents a mile; and Los Angeles
respondents  were  willing to pay $4.77  (standard deviation =  $7.60) or  14.45
cents a mile  [Schulze  et  al. (2), p. 68].  These per-mile  of regional
visual  range equivalent surplus valuations  are  five  to  seven  times larger
than the "Grand Canyon only" 87-  to 140-mile  visual range valuations of
Table 8-5.   This  is  to  be expected since all regional site substitutes for
the  Grand Canyon  are  also supposed to have had  their visual  ranges
similarly impaired.   Most importantly  perhaps,  the comparison between the
regional  calculations  and those  of  Table  8-5 provides  one  piece  of
empirical evidence  of the caution that must be exercised when studying the
value of atmospheric  visibility,  when extrapolating valuations from one or
more independent  site-specific studies to a  regional  or national valuation.
Extrapolations of  this sort will seriously underestimate the value of
atmospheric  visibility.

     Measures of jjj, the income elasticity of demand for visibility, are
also given  in Schulze  e_t a_l.  [(2), p. 78], provided  that the expressions
were  estimated  in  multiplicative  form.*   When this  interpretation is
attached to  the expressions  in  their Table 18, the income elasticity demand
for the "Grand Canyon only" visibility impairments was  only 0.05; for the
regional impairments it was 0.10.   These  elasticities,  if  indeed they are
such, are much lower  than  those reported in the earlier reviewed studies.
Given the uncertainty about their  actual  meaning,  we choose  to  disregard
them.
* Schulze ^t. il.. [(2), pp. 77-78] fail to indicate the functional form in
  which the bid expressions in their Table  18 were estimated.  It seems
  unlikely that  the expressions were linear  in the original variables  since
  b (Mean income/Mean bid at 140 miles)  = 0.05 (28,590/$2.77)  = 516.
                                   8-36

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A Synthesis —

     In the  preceding  pages,  we  have  reviewed  the only five  original
studies of which we are  aware that attempt to estimate the value to outdoor
recreationists and tourists  of atmospheric visibility.   Though all focus on
one or more areas  in the Southwest and all employ a contingent valuation
technique  to  generate data,  they can hardly be  regarded as perfect repli-
cates.   Differing sorts  of confounding problems  are present in each study:
some include  man-made objects in the photographs presented  to respondents,
while  others  do  not;  some refer only to qualitative visibility measures,
while  others  provide  actual  measures of the visual  ranges  and  color  con-
trasts the photographs represent;  and residents  and outdoor recreators are
considered separately  in most but not all studies.  Some  studies obtain
compensating surplus measures  of value,  while  others  choose the equivalent
measure.  If  income effects are  large,  substantial discrepancies in valua-
tions  of  the  same visual range  interval would  be expected; however, the
empirical  findings  in at  least  four  and perhaps five of the studies  imply
that income effects are  small  and perhaps even trivial.

     Several fairly obvious  faults  are common  to most and occasionally all
of the studies.  Host serious perhaps are  several sources of bias in the
estimated  magnitudes  of willingnesses-to-pay.   The  sign  of the  bias  will
differ according to whether the respondent is  confronted  with having to
minimize his losses or maximize his gains.   In no study are respondents
asked to  state their valuations  in terms of an  income  equivalent:  access
fees,  sales taxes,  and  electricity bills are  instead the bidding curren-
cies.   Adjustment possibilities  are thus unrealistically low; the expected
utility losses  from visibility  impairments will  therefore be exaggerated
and the utility gains from visibility  improvements  will be biased  downward.
Similar biases could arise in all five studies if  respondents, contrary to
their preferred adjustment mode,  assumed the number of outdoor recreational
activity days to be invariant.

     A third  source of  bias arises because only  local visual  ranges are
perturbed in all but the two most recent studies.  If the physical reality
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in the Southwest is that local visibility impairment due to hazes implies
regional  impairment,  respondents  were  then given  unrealistically high sets
of adjustment possibilities in all studies but Rae (28)  and  Schulze  et  al.
(2).   These excessively  large sets meant  that valuations  of  visibility
impairments were biased  downward,  with improvements being  biased upward.
If throughout  most  of North America visibility impacts tend  to be regional
in scope, the Southwest  regional willingness-to-pay in Rae (8.88 cents a
mile  for an improvement  from 75 to  160 miles)  and  Schulze  et  al.  (a
weighted three-city mean of 12.90 cents a mile to avoid an impairment from
135 to 102 miles) would seem most  apt  for  extrapolation  to  other regions.
Because  the  Schulze e_t a_l. study involves potential, past,  and current
outdoor recreators  in the region,  whereas  Rae samples  only currently active
recreators, the  12.90 cents per mile valuation of the former study could
include an option value  element.   In fact,  the 4.02 cents per mile differ-
ence  between the  two  studies is 45 percent of  the 8.88 cents per  mile
valuation of Rae.   The  sole existing empirical  study  of  the  magnitudes  of
option values in outdoor recreational  activities  of which we are aware  is
Greenley et al.  (30).  In a contingent  valuation study of water quality  in
the Platte River  Basin of Colorado, they found that option values increased
the current recreation use  values of water quality by  40  percent.  Thus,  if
a single per-mile value for atmospheric visual range  is to be used,  we opt
for the Schulze  et  al. figure of 12.90  cents per mile  in  1980 dollars.

     Other potential  sources  of confusion are present,  though their exact
impacts upon valuations are not readily identified.   With the sole  excep-
tion  of Schulze  e_t .§_!.,  those studies  that state  values in  terms of daily
access fees fail  to indicate to the respondent and/or  to the  reader whether
the stated fee is to be regarded  as an increment  to or as a  substitute for
existing fees.  Everyone treats  incremental  valuations as being strictly
additive across  visual  range  intervals  even though  there  is  weak empirical
evidence from Rowe et  al.  that this may be untrue.  Again,  with the sole
exception of Schulze  et  al.. the sample respondents have been self-selected
current  users.   This may account for  the wide disparity in the standard
deviations of the  valuations that Schulze  e_t a_l. report and the standard
deviations reported by  the  other  studies.
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     In spite  of some  confoundings,  biases,  and  confusions,  the  five
studies hold several patterns in common.  Most  obviously, the  income
elasticity of  demand by outdoor recreators and tourists for atmospheric
visibility,  though certainly  positive,  is quite low.  It  appears to lie
somewhere between  0.1 and 0.5, with the weight of evidence  leaning  more
toward 0.1.   A  reasonable  compromise  for  p,  is  thus perhaps 0.2.

     Only because of  a couple  of aberrations in Schulze .e_t al.  and a few
rather strained but  nevertheless supportive inferences  that must  be drawn
from  some   other   studies,  the empirical  evidence  that  incremental
willingnesses-to-pay increase with respect  to  improving  visibility is only
slightly  less  convincing.  Though highly inconvenient for much  of air
pollution policymaking (31),  this empirical finding  is  hardly surprising.
It is well known that visual  range and  color contrast in a relatively clean
atmosphere is affected much more  dramatically by a given addition of  fine
particles  than  is already dirty air.  The  empirical finding that  incremen-
tal valuations increase with  improved visibility simply  shows  that any
tendency toward decreasing incremental utility  of visibility  improvements
is insufficient to cancel out the contributions that increasing incremental
physical effects make to outdoor recreator  and tourist valuations.

     Assuming that the responsiveness of willingness-to-pay for local visi-
bility improvements is similar  to that for regional improvements,  several
of the reviewed studies provide enough information to yield rough  estimates
of the magnitude of {J~.   Defining  f^ as

        -    _   A(Villingness-to-pay)  .     Visual  range
        2       A(Visual range)         Willingness-to-pay    '

Table 8-6 gives estimates for this parameter,  the visibility valuation
elasticity,  as  calculated from  those studies that allow it.   Clearly,  the
willingness-to-pay for a given  increase in visual range  increases as visual
range  increases.  Once visual range has reached  150 miles or  so,  $2  almost
certainly  exceeds  unity.   There is no credible empirical evidence  whatso-
ever from  either the economics  or  the physical  science  literature on atmos-
                                   8-39

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                                Table 8-6

                VISUAL RANGE VALUATION ELASTICITIES (P2>
                               Using Lower Bound
                                  of Mileage
                               Interval as Base
               Using  Upper Bound
                  of  Mileage
               Interval  as Base
    From Rowe  e_t  al,.  (23)

        75  to  SO  miles
0.38
0.48
    From Rowe  et  al.  (23),
    and Rae  (28)*

        75  to  160 miles
0.73
0.81
    From Schulze  et al.
    [(2).  p.  66]

        Albuquerque

            87  to 140 miles
           140  to 189 miles
           189  to 235 miles

        Denver

            87  to 140 miles
           140  to 189 miles
           189  to 235 miles

        Los Angeles

            87  to 140 miles
           140  to 189 miles
           189  to 235 miles
0.82
1.17
2.96
1.15
1.43
2.67
1.18
1.03
3.46
0.87
1.12
2.45
1.08
1.27
2.00
1.53
0.96
2.25
* Rowe £t .§_!. (23) values were converted to 1980 dollars.  The Rowe et al.
  figure of $4.38 was used as the value  for  the 75-mile visual range, and
  the Rae  (28)  figure of $8.45 was  used as the  value for the  160-mile
  visual range.
                                   8-40

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pheric  visibility  that contradicts the pattern  displayed in Table 8-6.   In
regions with fairly high background visibility levels, small percentage
increases  in ambient  pollution  concentrations will cause even larger  per-
centage  losses in the  economic value attached to  atmospheric visibility.
If background visual ranges are  extremely lengthy,  the percentage losses  in
economic value can exceed the percentage  increases in ambient pollution  by
factors of 2 to 3. Even,  where background visual ranges are relatively
low, reductions  in visual  range  will  generate economic  losses,  though the
percentage  loss  is unlikely  to  be  more than  half  the percentage reduction
in visual range.

     The visibility valuation elasticities  of Table 8-6 do not extend down
to 13-,  20-,  and 30-mile median  summer visual ranges.  Nevertheless, given
the behavior of these elasticities display, it is not  a fearsome task  to
extend them.  Two points can be made.  First,  there probably is not much
variation  in the  positive magnitude of (^ over the 13-30 mile  interval.
Such variation  as there  is,  is  likely  exceeded by the noise factors
inherent in any economic benefits analysis.  Second, the positive magnitude
of $2 over the 13-30 mile  interval  is small.   A magnitude  of 0.2 is  as
reasonable  as any.  Thus, we propose using  the  following expression  to
estimate the value per outdoor activity day that the representative U.S.
household  attaches to  improvements  in visual range over the 13-30 mile
interval:

          In(TWP)   =  0.2 ln(Y)   +  0.2 ln(V)                         (8.6)

where TWP is the  household's total willingness-to-pay for a single outdoor
recreation activity  day,  T is  annual household  income in thousands  of
dollars,  and  V  is visual range in  miles.  To obtain household annual
willingness-to-pay, any  answer  obtained  to the above expression should  be
multiplied by 30, on  the assumption that the collection of  adults in the
representative U.S. household, whether it  has  complete darkness or 235-mile
visual range, will annually engage  in  30 days  of outdoor recreational
activity at  sites well removed from its immediate  home environs.   As
earlier noted, there is nothing whatsoever  in the literature  giving the
                                   8-41

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slightest  hint of the response of household outdoor activity days to varia-
tions in visual  range.

     A nationwide increase of 20 percent in median summer visual ranges
would result in 10- or 11-mile visual ranges throughout most of  the  Ohio
Valley  and Southeast and 100-mile ranges in the Four Corners  area.  The
rest of the nation would fall between these extremes.  On the basis of
Table'8-6,  we take the following expression to be a reasonable  representa-
tion for  a  20  percent increase in visual range  over that part of the
country currently  experiencing median summer visual ranges in excess  of 30
miles.

          In(TWP)  =  0.2 ln(T)   +  0.8 ln(V)                         (8.7)

As before,  the  values obtained from  the above expression  should be multi-
plied by 30 in order to obtain annual household willingness-to—pay.  It
should be noted that the  0.8 value attached to ln(V) is  the upper bound of
the values from Table 8-6 that one might reasonably attach to a  nationwide
average (based on land area) visual range of,  say,  50 miles.

     Table 8-7  summarizes the  low, medium,  and high estimated annual
recreational benefits for the  four alternative visibility policies.  For
the policy involving  a nationwide 20 percent  improvement  in visual range,
Equation (8.6)  is used when the initial level of atmospheric visibility is
less than 45 miles, while Equation (8.7) is  used when the initial visual
range is greater than or equal to 45 miles.

THE EXISTENCE VALUE OF PROTECTING VISIBILITY

     The notion of existence value was first developed by John Krutilla
(32).   Krutilla  argued that consumers may value  preservation of  a  pristine
natural environment even if  they do not  expect to use  that  particular
environment.   The  classic example is preservation  of  the  large  species of
whales. Even though an individual may, for example, never  see a  blue  whale
in person,  knowledge  that the species  survives is of real  value  and,  as an
                                   8-42

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                                Table  8-7

  LOW, MEDIUM AND HIGH ESTIMATED RECREATIONAL BENEFITS PER YEAR IN 1980
   MILLIONS OF DOLLARS FOR VISIBILITY  POLICIES OF 13, 20, 30 MILES AND
                      A NATIONWIDE 20% IMPROVEMENT*
Policy
13 Miles
20 Miles
30 Miles
20%
Low
59
1,048
3,566
7,392
Medium
247
1,595
4,535
12,296
High
455
2,201
5,606
17,448
* In the  following equation let:

       B.- =  Benefits per household per outdoor  recreational activity day
              in region  i.

       Y-  =  Household  income in region i.

       V*  =  Initial visibility in region i (see Table 8-4).

       V?  =  New visibility  imposed by the policy  alternatives in region
               i.
  Then Low,  Medium and High estimates  were calculated by assuming  a
  different  V*.
Estimates calculated using:

      B.  =  ^.0.2^2)0.02 _
  and
      Bi  =  (*i°'2(V?)0'8 * Y9-
                                            for y  <  45 mi
                                           for V  1 45 miles
  To obtain annual benefits per household, B^ was multiplied by 30, pre-
  suming therefore that  the  representative U.S.  household participates in
  30 outdoor recreational days annually.   This  result  was then multiplied
  by the number of households per region and then summed over regions to
  obtain total  national  benefits.
                                    8-43

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economic  measure, an  individual may be  willing to pay  to  preserve the
species.   Existence value can include two components.  The  first  is derived
from knowledge that the  environment  is  preserved in  the present.  The
second  is  derived  from  knowledge  that the environment will be preserved in
the future for the benefit of  future generations.  The latter of these two
types of existence values has been termed bequest value.

     On a purely  subjective basis,  existence values in  the present are
probably  associated with natural wonders of the environment  which are
widely known even by people  who cannot visit them.  Thus, a resident of New
York State may well know of the Grand Canyon and  of Yellowstone,  yet never
intend  to visit either  park, but  still be  willing to pay to preserve these
natural wonders, desiring simply to know that they remain pristine.

     The bequest  type  of  existence value may apply to far more ordinary
environments.   Thus,  parents may  wish to know  that a particular  stretch of
river will remain in a  natural  state  after their own death for use by their
own children or future  generations.   Hence,  existence value may  be  a more
                   .#
common phenomenon than is generally  supposed.

     Unfortunately,  to our knowledge,  only two studies  to  date  have
attempted to estimate  existence value.   The first of these studies values
water quality and  is  only  indirectly relevant to the problem of  estimating
the existence value of  visibility  (30).   The  second study attempts to value
preservation of air quality in the Grand Canyon and surrounding  parks and
is directly relevant for estimating the national benefit of visibility
protection (2).  We review these two studies in the following  subsection.

Studies of Existence  Value

     The Greenley, Walsh and Young  study (to be  referred  to henceforth as
the G.W.Y. study)  investigated the benefits  of  preventing  irreversible
water quality degradation due  to  mining  activity in  the  South Platte River
Basin of  Colorado.  G.W.Y. specifically  included questions  in their survey
to estimate existence and  bequest values for nonusers.  Two hundred and two
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households  in Denver and Fort Collins, Colorado were  interviewed using
either a sales tax  or  a  residential water-sewer fee as the vehicle for
collecting  the hypothetical payment  to  preserve the river.   The  recreation
derived user values (including  option value) averaged  $79 annually to
preserve  the river for the 80 percent of the sample that used or  expected
to use the river.  This same group  was willing to pay an additional $67
annually  for existence  and bequest values of preservation.   The  20  percent
of the sample  who were  nonusers were willing to pay,  on average, $42
annually  for preserving the South Platte,  a pure measure of existence and
bequest value.  This  latter group provides  a better measure  of  pure  exis-
tence value,  since individuals  who do not use and do not  plan to use a
particular  environment  can only have existence value for that environment.
If we accept $42 per year as the existence value  for  all of the sample,
including  users,  then existence values are about  53  percent of user values,
a significant increase  in the total preservation value.   Perhaps the most
surprising  aspect of the 6.W.7. study  is that positive existence values
were obtained at all.  The South  Platte is  not a wonder of the world, nor,
for that matter,  recognized on a national basis as an area for environ-
mental  concern.   Thus,  the ratio of  existence value to user value
(including  option value) of  0.53 may provide a lower  bound estimate of
existence  value  for  preservation of  environmental  quality for more well-
known natural environments.

     In contrast to the G.W.Y.  study  of the  South Platte, the study by
Schulze,  Brookshire,  Walther,  and Kelley (to be  referred to  as the S.B.W.K.
study)  of  the  value  of  visibility in the Grand Canyon region deals  with an
internationally recognized natural wonder.

     The  S.B.W.K.  study  can be summarized as follows.  During the summer of
1980,  over 600  people in  Denver, Los Angeles,' Albuquerque and Chicago were
shown  sets  of  photographs depicting both clear visibility  conditions and
regional  haze conditions.   Each  set consisted  of five photographs  ranging
from  poor  to excellent  visibility.  The  middle picture  in  each case
approximated average  visibility during the  summer (the season  of peak
visitation).  The vistas were three  different views from the Grand Canyon,
                                   8-45

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one view from Mesa Verde,  and one view from Zion.  The 8 x 10 inch textured
prints were placed on display boards,  each vista a separate row,  and each
row arranged with five photographs  from left to right in ascending order of
visual air quality (i.e.,  photograph A = "poor" visibility and photograph E
= "excellent" visibility).

     The survey participants were asked either 1) how much they would be
willing to pay  for visibility as shown in the five  sets  of photographs from
worst to  best  on the day of a visit to the Grand Canyon (an estimate of
user value), or 2) how much they would be willing to pay in  higher  electric
utility bills to  preserve the current  average  condition — middle picture
— rather than  allow  visibility  to  deteriorate,  on the average,  to  the next
worst condition as represented  in the photographs of the Grand Canyon or of
the region (an  estimate of total  preservation value).  They were  also asked
about their willingness-to-pay in the form of higher monthly electric power
bills to prevent  a plume  from being seen in a pristine  area.  To  represent
plume blight,  two photographs  were taken from  Grand Canyon National Park.
Both photographs  are  essentially identical  except one has a plume,  a narrow
gray  band,  crossing the entire vista in the  sky.   The source  was  not
industrial or municipal pollution, but a controlled burn in  the  area around
the Grand Canyon.  However,  the effect was comparable to what a large
uncontrolled industrial source might produce.

     The bidding  game was designed  to  reveal the  household's willingness-
to-pay for preserving visibility in specific locations as represented in
the  photographs.  For the  interviewees  asked  the  preservation value
questions in the survey,  the bids include both existence  value and user
value.

     The  survey  had  few  refusals, partly because of the  nature of the
interviews.   Typically,  interviews  were conducted  in  the  late afternoon or
early evening hours  in residential  neighborhoods.  Due to the  large size of
the display boards, most interviews were conducted on the  front  lawn of the
respondent's home.   Often, both husband and wife  participated jointly in
answering the questions.   This  was viewed by S.B.W.K. as  appropriate since
                                   8-46

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the principal  question was "how much would you be willing  to pay in higher
monthly electric  utility  bills  to  preserve visibility at the Grand Canyon
or in the entire  Grand  Canyon region?"  Household members would often
engage  in existence  discussion  before  giving  a  dollar amount.   Individual
bids ranged  from  an  average of $3.72 per month in Denver to $9.00 per month
in Chicago for preserving visibility at the Grand Canyon.  These  average
bids were increased  from  $2.89 to $7.10 per month per household in the four
cities if visibility preservation was to be extended to the Grand Canyon
region as a whole.  Prevention  of  a visible plume  in the Grand Canyon  was
worth on the average between  $2.84 and $4.32  per  month for the four cities
surveyed.

     The validity of these  survey results depends on  the perception by
individuals  of visibility conditions  as represented by  photographs.   The
S.B. W.K.  study  argued that a  linear relationship has been shown to exist
between perceived visibility  as  quantified by individuals  and  with scien-
tific measures of the apparent  contrast in the vista by a multiwavelength
teleradiometer.  This close linear  relationship  between perception of an
actual  vista and  the apparent contrast  of the vista also  extends to percep-
tion of visibility conditions represented by slides or 8 x 10  inch color
photographs as was  shown in the  research presented in Chapter 4 of  the
report by S.B.W.K.

     The benefit  estimates derived  from the interview results were extrapo-
lated from  the sample  population to the country  as a whole by applying
statistical  techniques to the results  of the  survey.  The  bids offered by
interviewees to preserve visibility were statistically related to income as
well as  other demographic characteristics.  Using an estimated linear
relationship of bids to population  characteristics,  the study estimated the
value of benefits to residents for  the  entire  nation.  This  was done by
substituting  the  average value for these characteristics for each state
into the relationship  and calculating the average value of the bid of a
person in that state.  This  value was then multiplied by the number of
households  in  the  state to get a total bid or  benefit.
                                   8-47

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     The  estimates of aggregate national benefits for preserving visibility
from the S.B.W.K. study  are  given in Table 8-8.   The estimates  shown in
Table  8-8  include  both user  and existence  values.   To estimate  the
existence value component alone,  we must net out  the user value  component.
Fortunately,  the S.B.W.K.  study does provide individual estimates of user
values for the Grand Canyon which are tied to entrance fees per carload.
Household bids per  visit to maintain visibility at level C (the current
summer average)  rather than have a worse  condition,  B,  on the day of a
visit, averaged  $1.08  (from Figure 15,  p.  66  of the S.B.W.K.  report)  for
the three cities (Albuquerque,  Los Angeles and Denver) where  the  Grand
Canyon user value question was asked.   Noting that the Grand Canyon had
2,131,700 individual visits   in 1979,  or  about  761,300  household  entrances
(we assume  one household is  equivalent to  one carload),  a total annual user
value bid would be  $1.08 x 761,300, or $822,204.  Thus, maintaining  visi-
bility at current average levels (C in the study)  as  opposed to allowing a
deterioration to level B,  a decrease in visibility as represented in the
photographs of 31.6. miles,  is worth  less than $1 million  to users.  This
contrasts with a  total  preservation value estimated  at $3,370 million  per
year for preventing  the  downward change  in visibility.  User value is,  at
least given the results of  the S.B.W.K. study, a negligible component of
                                Table  8-8
           NATIONAL EXISTENCE VALUE BENEFITS FROM S.B.W.K.  STUDY
                Yearly Benefits From
   Total
($  million)
           Preserving visibility at the Grand
           Canyon  (maintaining C vs.  B)
           Preserving visibility in the Grand
           Canyon  region (maintaining C vs. B)
           Preventing plume blight at the
           Grand Canyon
   3,370

   5,760

   2,040
                                   8-48

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the benefits of preserving visibility at the Grand Canyon.   Clearly, the
Grand Canyon mnst be an exceptional  case  in that virtually all of the
measured  benefits of visibility  preservation are derived from existence
value.  This is  in direct contrast to the G.W.Y. study of the  South Platte
which found existence values to be  only a fraction of user value.   However,
these results are  consistent with  * priori expectations  in  that  the'  rela-
tive size  of existence  values  would logically be related to  the  fame of a
particular environmental asset.  In crude terms,  willingness-to-pay for
existence  value  depends on knowledge of the state of an environmental  asset
independent of  the use  of that  asset.   It is unlikely that  most  residents
of the United States would be aware of the state of water  quality in the
South Platte, while it is highly likely that  many individuals across the
nation would be  aware of  a decline  in  air quality at the  Grand Canyon.

     In summary,  the two available studies  of  environmental existence
values provide  a clear  indication  that  existence values  may be important.
However,  it should be recognized that this  is a new area for benefits
research,  and extrapolations based  on  the one  study  done  for visibility in
the Grand  Canyon can only be highly tentative  at best.
     In assessing  existence values for visibility we assume that such
values apply only to nationally recognized recreation areas.  Table 8-9
presents  a list of  all  National  Parks and  in  addition those national
recreation areas  which have at least one million  visits per year.  The
table also gives  visits for 1979 and approximate  summer visibility  in
miles.*  Also included in the table  is an indication as to whether or not
•visibility is  classed as  important to the  recreation area.**
 * These data are  taken  from  the National Park Statistical Abstract (33).
** The  visibility "importance" ranking  and data are  taken from
   Protecting  Visibility;  An EPA  Report (34).
                                   8-49

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00
I
Ul
o
                                                         Table  8-9


                                     RECREATION AREAS USED  IN EXISTENCE VALUE ANALYSIS
Name of Area
1 Ac ad i a
2 Am is tad
3 Assateague Island
4 Blue Ridge Parkway
5 Boston
6 C & O Canal
7 Cabrillo
8 Cape Cod
9 Cape Hatteras
10 Chickasaw
11 Colonial
12 Delaware Water Gap
13 Fred Spot
14 Gateway
15 Geo. Washington Memorial Pkway.
16 Glacier
17 Glen Canyon
18 Golden Gate
19 Grand Canyon
20 Grand Teton
21 Great Smoky Mountains
22 Gulf Islands
23 Hawaii Volcanoes
24 Hotsprings
25 Independence
26 Indiana Dunes
27 Jefferson Memorial
28 Jefferson NEM
29 JFK Center for the Performing Arts
30 J.D. Rockefeller, Jr. Parkway
31 Lake Mead
32 Lake Meredith
33 Lincoln Memorial
34 Mammoth Cave
35 Mount Rainier
36 Mount Rushmore National Memorial
37 Muir Woods
Classification
NP
NRA
NS

NHP
NHP
NM
NS
NS
NRA
NHP
NRA
NMP
NRA

NP
NRA
NRA
NP
NP

NS
NP
NP
NHP
NL 51

NHS


NRA
NRA

NP
NP

NM
State
ME
TX
MD-VA
VA-NC
MA
MD-DC-WV
CA
MA
NC
OK
VA
PA-NJ
VA
NY-NJ
VA-MD
MT
AZ-UT
CA
AZ
WY
NC-TN
FL-MS
HI
AR
PA
IN
DC
MO
DC
WY
AZ-NV
TX
DC
KY
WA
SD
CA
1979
Individual
Visits
in 1000' s
2,787.4
1,261.8
1,674.9
11,700.2
1,777.4
2,832.6
1,196.4
3,922.0
1,516.9
1,434.5
7,172.1
2,002.5
1,010.7
8,773.1
4,678.0
1,446.1
1,656.0
11,321.1
2,131.7
2,446.2
8,019.8
2,965.0
1,632.6
1,118.8
2,002.3
1,606.2
2,328.1
1,859.4
4,130.6
1,356.0
6,155.1
1,849.4
3,352.3
1,384.9
1,516.7
1,245.4
1,227.2
Summer
Visibility
in Miles
38
29
10
12
15
10
15
15
9
17
8
14
12
10
12
48
72
14
75
70
10
8

15
9
10
12
15
12
75
70
34
12
9
25
55
14
Visibility
Classed as
Important
Yes
NO
No
No
No
No
No
NO
No
NO
No
No
No
No
No
No
No
No
Yes
Yes
Yes
No
Yes
No
No
No
No
NO
No
No
No
No
No
Yes
Yes
No
No
                                                                                                      (continued)

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                                                   Table  8-9 (Continued)
oo
i
Ul
Name of Area
38 Natchez Trace Parkway
39 National Capital Parks
40 Olympic
41 Ozark
42 Point Reyes
43 Rocky Mountain
44 San Juan
45 Shenandoah
46 Statue of Liberty
47 Valley Forge
48 Washington Monument
49 Whiskey town
50 White House
51 Yellowstone
52 Yosemite
53 Zion
54 Arches
55 Badlands
56 Big Bend
57 Bryce Canyon
58 Canyonlands
59 Capitol Reef
60 Carlsbad Caverns
61 Crater Lake
62 Everglades
63 Guadalupe Mountain
64 Haleakala
65 Isle Royale
66 Kings Canyon
67 Lassen Volcanic
68 Mesa Verde
69 Mount McKinley
70 North Cascades
71 Petrified Forest
72 Red Wood
73 Sequioa
74 Theodore Roosevelt
75 Virgin Islands
76 Voyageurs
77 Wind Cave
Classification


NP
NSR
NS
. NP
NHS
NP/4
NM
NHPl

NRA

NP
NP
NP
NP
NP8/
NP
NP
NP
NP
NP
NP
NP
NP
NP
NP
NP
NP
NP
NP
NP
NP
NP
NP
NP
NP
NP
NP
State
MS-TN-AL
DC-MD
WA
MO
CA
CO
PR
VA
NY-NJ
PA
DC
CA
DC
WY-MT-ID
CA
UT
UT
SD
TX
UT
UT
UT
NM
OR
FL
TX
HI
MI
CA
CA
CO
AK
WA
AZ
CA
CA
ND
VI
MN
SD
1979
Individual
Visits
in 1000 's
9,475.3
2,811.0
2,078.8
1,453.0
1,489.1
2,568.5
1,458.8
1,521.5
1,661.4
3,107.4
1,304.8
1,162.4
1,312.0
1,892.9
2,350.8
1,040.5
269.8
858.0
282.9
558.1
74.5
288.9
721.6
410.7
718.1
110.5
674.0
14.8
804.2
380.0
473.7
251.1
743.0
671.6
413.9
799.6
591.6
549.7
195.3
457.4
Summer
Visibility
in Miles
9
12
25
10
14
70

10
10
11
12
56
12
75
44
72
73
55
29
72
73
73
61
44
15
45

19
80
44
74
15
33
75
14
14
45

20
55
Visibility
Classed as
Important
No
No
Yes
No
Yes
Yes
No
Yes
No
NO
No
No
No
Yes
Yes
No
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

-------
     The method used to approximate  national existence values for visi-
bility can be  described as follows.  First, the monthly household  existence
value bid in dollars for visibility  at the Grand Canyon from the S.B.W.K.
study can be approximated as:

          Monthly bid  =  2.69 Y°'22

where Y is annual household income  in thousands of dollars.  This bid is
for preserving visibility conditions  C as opposed to B or an increment in
visibility of 31.6 miles.*  Thus, an estimate of an annual household bid
per mile of increased visibility at the Grand  Canyon is:
       Annual bid per mile  =  2'*?  *  12 Y°'22  =  1.02 Y°'22
                                o 1. o
Since we have argued that existence values are likely to be dependent on
the fame of a particular environment or  perhaps cultural asset,  we use as
an index of knowledge by consumers of a recreation area the ratio of visits
to each site  in Table 8-9 to  visits to  the Grand Canyon to  weight the value
of the visibility presented in the formula above.  Hence, we effectively
assume  that  if a site has one-tenth the visitation of  the Grand Canyon,
existence  values  for. visibility at  that site are worth one-tenth of the
value per  mile of the  Grand  Canyon.   While crude, we  feel  that  some
adjustment of this  sort  is essential  to approximate even an order of magni-
tude for existence  values.

     These  assumptions  imply that each household  in the U.S. will have an
existence value for visibility  improvement equal to
          n     Ri
          I  AV. =— 1.02 Y°'22
         L=l    ^C
* Conversion from apparent  contrast  of  the  photographs used  in  S.B.W.Z. to
  visibility in miles obtained from Eric Walther by personal  communication.
                                   8-52

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or the sum over n sites of the changes in visibility at each site i (AV-)
times the ratio  of recreation visits at site  i  to  the  recreation visits  at
the Grand Canyon (R^/RQ^)  times the annual value per mile of increased
visibility  at  the Grand  Canyon.

     Table 8-10 presents results for this calculation  aggregated across
households  with  varying  income levels across  the United States.  The  calcu-
lation is made first for all recreation  areas listed in Table 8-9.  Note
that this calculation includes presumed benefits for urban parks and  areas
where visibility has not been deemed an  important value as indicated  in
Table 8-9.  However,  one must question the evaluation that visibility  would
not be important at  the  Statue of Liberty,  the Washington Monument, or Lake
Powell.   In any case,  this provides  an upper bound  estimate  for the
benefits of policies  where  a  13-,  20-,  or  30-mile national visibility
standard is imposed  or  where visibility is improved everywhere  by 20 per-
cent.   Probably  a more  realistic assessment of existence value benefits  is
presented in the second  column of Table 8-10 where only those  areas  where
visibility  is  deemed an important  factor  are included in  the calculation.
Finally, it may be true that existence values are  only  important for the
most famous of national parks,  for  which  national  pride  or value to  future
generations become the dominant  values.  Using  only Acadia, the  Grand
Canyon,  the Grand Tetons,  the  Great Smoky Mountains,  Yellowstone and
Shenandoah  gives column three  of Table 8-10.   Note  that  the possible  range
of benefits exceeds one  order of  magnitude from  the high to the low
estimates shown in Table 8-10.  On the basis  of one  study of the existence
value of visibility,  such  a  wide  range  is an honest reflection of our
current knowledge.  At this point, we would recommend use of our  lower
bound estimate  as  the  most appropriate assumption of existence  value
benefits.

NONAESTBETIC BENEFITS OF VISIBILITY IMPROVEMENT

     To this point,  the  discussion  has  focused primarily on the  aesthetic
benefits associated with visibility improvements.  In this  section,  we
briefly review several  areas  where  benefits  of a nonaesthetic  type  may  be
                                   8-53

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                                                       Table  8-10


                         ESTIMATED EXISTENCE VALUE BENEFITS PER YEAR IN 1980  MILLIONS  OF  DOLLARS

                                   PER VISIBILITY POLICIES OF 13. 20 AND 30 MILES  AND
                                              A NATIONWIDE 20% IMPROVEMENT
oo
i
Ul
Policy
13 Miles
20 Miles
30 Miles
20%
All Recreation
Areas
21.418
81,131
173.330
56,653
All Recreation Areas Where
Visibility is a Factor
4,717
13.227
27,419
23,656
Only the Most
Famous Parks*
2,172
7,194
14,430
10,172
                      * Only Acadia,  Great Smokies, Shenandoah, Yellowstone, Grand Teton  and  Grand
                        Canyon are included in this calculation.

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generated because  of  better  visibility.  Specifically,  we consider various
safety aspects  of  improved visibility.   Unfortunately, no empirical studies
of the nonaesthetic benefits  of  visibility are available.  Conceptually,
however,  estimation of  such benefits is likely  to be  straightforward.
Clearly good  visibility is a requirement for  the  safe  operation of vehicles
on roads, water-ways, or  in  the air.   Additionally,  good  visibility may be
necessary for  productive activities such as surveying or possibly laser
transmission of messages.

     Focusing on safety  issues, it is clear that  the operation  of vehicles
may be hindered by bad visibility.   Econometric  techniques can be applied
to estimate  the effect of visibility conditions on  takeoffs and landings of
aircraft, road traffic,  and water traffic.   If  it can be shown that the
level  of these activities  is significantly affected by visibility (and
probably in a very nonlinear way), it  is a simple  matter to  estimate the
lost economic value associated with a  decrease in  these  activiites from a
reduction in visibility  from air pollution.   It  is  important to note that
although reductions in  transportation activities  result from safety con-
siderations,  a direct calculation  based on  lives saved from better  visi-
bility is probably inappropriate.   This  results from the obvious behavioral
response to delay travel rather than lose lives.  Obviously,  the cost of
delay is far  less  than the value of lives possibly lost in a transportation
accident. The  cost  of delays  induced by poor visibility  is thus an appro-
priate  measure of  the  benefits of better  visibility.  Finally,  it is
readily apparent that only when visibility  is reduced in the extreme are
significant  costs likely to be imposed.  Thus,  the possible  role of air
pollution in increasing the  frequency and  intensity  of fogs is likely to be
the most important component for benefits assessment.

     Some empirical estimates along  these lines should  be available
shortly.  The  University of Chicago group under the direction of George
Tolley (with Alan Randall and Glen  Blomquist, both of the University of
Kentucky, playing  important  roles  in the research)  is attempting to  econo-
metrically   relate  takeoffs  and  landings  of  aircraft to  visibility
                                   8-55

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conditions.*  Their model  includes  safety  as  well as aesthetic impacts in
that the desire to fly may be increased under good visibility conditions
since the flight  may be more pleasurable.  This  latter aspect may be of
specific importance for private  aviators.
NATIONAL BENEFIT ESTIMATE FOR ACHIEVING A 13-,  20-, 30-MILE AND A NATION-
WIDE 20 PERCENT STANDARD
     This section discusses  the procedures and data utilized in the calcu-
lation of national benefits for alternative visibility  standards.  Problems
that required attention were  identifying  the  areas  affected by each pro-
posed standard, specifying a mechanism to set upper and lower bounds to the
benefit  estimate,  defining the  appropriate unit  of analysis  for  data
collection,  choosing discount rates and time horizons,  and choosing attain-
ment goals.  The subsections that follow discuss each of these issues in
turn and present  the benefit estimates for each proposed  standard.

VisTiil t**ngo Regions Utilized in the Benefit Calculation

     In calculating  the willingness-to-pay for the  proposed 13-,  20-,  and
30-mile  standards,  as well as  nationwide improvement of 20 percent in
visual range,  the U.S. was divided into visibility regions.   Figure 8-1
presents  the median summer visual range in miles, designated by isopleths
for the years 1974-76.  The isopleths divide  the nation into visual range
regions of 10 miles  and less,  10-15 miles, 15-25 miles, 25-45 miles, 45-70
miles, and 70 miles or greater.   These visual  range regions form the basis
of the results to be presented later in this  section.   Also presented on
the map  in Figure 8-1 are the visual range levels at a multiple number of
sites in the U.S.   This information was utilized  in delineating  the regions
that represent an improvement from the existing level of visibility to a
proposed standard.

     Consider first  the delineation of the regions  affected -by a proposed
nationwide standard of 13 miles.  Inspection of Figure 8-1 reveals that
* These comments are based on a  telephone conversation with Alan Randall.
                                   8-56

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CO
(J1
      A A
P: Based on photographic
   photometry
It; basfcU on  neplielometry data
*; baitd on uncertain extrapolation of
   visibility frequency distribution

Additional  isopleths have been  added to
the figure.   See the text for an expla-
nation and underlying assumptions.
                                                                                                         II1
       Source:   U.S. Environmental Protection Agency (34).
                Figure  8-1.   Median Summer  Visual  Range (Miles)  and  Isopleths for Suburban/Non-Urban
                              Areas, 1974-76 (35)**

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many areas of  the  country  already  enjoy visibility  levels  in excess  of 13
miles.  For instance, all of the West, excluding coastal California,  and
much of New York and New England have  current visibility ranges of 15  miles
or greater.   Thus  the  task for the 13-mile national standard was  to
identify the  counties  that had current visual range levels below 13 miles.

     In identifying the  counties  and then  the number  of  households in each
county,  a new  set of isopleths  was drawn.   Consider  the  area  marked Region
I in Figure 8-1,  which  includes  major portions of the Southeast.   In this
region visual range, as  indicated by the various data points on the map,  is
at best 10 miles.  Thus for Region I,  all  of the  counties were identified
as an area that would  experience  an improvement in visual range as a result
of setting a  nationwide  minimum  standard of  13 miles.

     Examining the  states  of Illinois,  Missouri,  Arkansas,  and Louisiana,
one can see that  the current distribution of visual ranges is  from 10  to  13
miles.   Thus  for the 13-mile proposed standard  a  new  region was designated
— Region II  in Figure 8-1 — which identifies  areas which would experience
improvement from 10 miles to 13  miles.  Again, the counties were identified
in Region II.   Looking'at Florida and parts of  Georgia  and South Carolina,
Region II again represents  an area  which would  improve from 10 to  13  miles
in visual range.   While not drawn  on Figure 8-1,  similar "10— to  13-mile"
visual  range regions were designated  for the Southern California region,
especially in the South  Coast Air Basin area.  Again,  these counties are  in
areas that would  experience an  improvement in  visibility under a national
13-mile  standard.  In this  process the  counties  that would  experience  an
improvement  in visual range were identified nationwide  and utilized in the
calculation of the dollar benefits  of  a  national  visibility standard  of  13
miles.   Care  was taken  to identify major cities in designating a county as
an improved  area.  If a major city  within a county fell across the new
isopleths, the  county  was  arbitrarily placed into the  lower visual  range
category.

     The  process for  identifying  the counties that would experience  an
improvement under a  proposed  20-mile  standard was  identical to  that
                                    8-58

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followed for the 13-nile  standard.  However,  the counties identified  for
the 13-mile standard are already isolated and one only has to assume  the
improvement in visual  range  from the initial level to 20 miles rather  than
from the initial level to 13  miles.   Looking just  west of Region II,  a
Region  III  was  identified whereby the  assumed  initial level of visual range
was the "13-mile isopleth" chosen in identifying  affected counties  for  the
13-mile standard.  Thus, Region III in Figure 8-1 represents those counties
that would experience  an improvement  from  13  to  20  miles  under  a  20-mile
standard.   Revion IV represents the  counties that would  experience a visual
range improvement  from 15 miles to 20 miles.   New England and California
also have  areas where  the  same procedure was followed in identifying
counties that  would be affected  by  a 20-mile  nationwide  visual range
standard.   In  New  England, the corresponding regions are denoted by  a
" ' ".   In the West, the initial isopleths were  too narrow to illustrate
the boundaries.

     In identifying the counties affected by a 30-mile standard, Regions I,
II and II"  were assumed to improve to 30 miles  from  their initial levels.
Region III was assumed to have a visual range increase of 13 to 30 miles,
while Region IV's improvement was from 15  to 30 miles.  Region V went  from
20 to 30 miles.  As before,  however,  _a  new  region was identified  for
counties that had a visual range  of  at least 25 miles  and resulted in a  new
isopleth representing the 30-mile standard  — designated as Region VI.
Again, new isopleths as represented by Region VI were identified for the
West Coast and New England regions (denoted by " ' ").

     For the regions  identified thus far  for  the  13-,  20- and  30-mile
proposed national visual  range standards,  they were  in each case evaluated
for the visibility change that  would accompany a 20 percent nationwide
improvement in visibility.  That  is. Region I was  assumed to undergo a
change  of  10 to 13  miles; Region II,  10 to 13 miles; Region II, 13  to 15
miles,  etc.  This potentially introduces biases, but given the available
visual  range  data  base  it  would have been  inappropriate to  attempt a
further refinement.   In addition to assuming  that  the already  designated
regions realized a  20  percent  improvement,  the procedures for  identifying
                                   8-59

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affected counties  not  previously identified was continued.   Thus  a Region
                 •
VII,  just to the  west  of  Region VI was  identified as counties receiving  a
20 percent  improvement from  an  initial base visual range  of 30 miles.
Again, the appropriate areas  in  the  West Coast and in New England were
identified.  Finally, Region VIII was assumed to represent a 20  percent
improvement from  a base of 45 miles, while counties  in Region IX experience
20 percent improvements from a base of 70 miles.

     In designating visual range regions for the calculation of benefits of
alternative  standards,  an implicit assumption is  made for each region.
That  is,  within a region the visual range  is homogeneously  improved
throughout  the whole  region according to the initial base visual range
level.  Consider Region IV, where the base isopleth visual  range  level is
15 miles  and counties falling into this region are  assumed under the 20-
mile national standard not to exceed this level of visual  range.   In calcu-
lating  the  benefits,  the incremental visual range improvement  for the
region as a whole would be assumed to be 5 miles.  Clearly some counties in
the region have initial values  of visual range  greater than 15 miles.  Thus
under a 20-mile national standard these counties would experience  less than
a 5-mile  visual range improvement.   Consequently,  if a base visual range
for the region as  a  whole  of 15 miles were utilized  in the calculation of
benefits  for a 20-mile standard, the resulting dollar benefits would be
viewed as biased upwards.  Again, this is due to the  non—homogeneous nature
of the visual range  in the region.

     The  initial  value problem illustrates  the need to place upper and
lower bounds on the visual range benefit  estimates.    Table 8-11  presents  a
range of  initial  visual range values  for the nine regions.  These  were the
values utilized in the benefit calculations.   Since Region I is  the only
area  with visual range values below the surrounding isopleths,  a "low
value" or base value of 8 miles was chosen. For Region II through Region
IX the  low  value  corresponds to  the lowest visual range in the  region and
thus is the lower bound isopleth.  Again, the use of the "low value" will
represent an upper bound  of the visual range benefit estimates  for each
proposed standard in  each region.  The values chosen as "high"  for each
                                   8-60

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                                                       Table 8-11


                              LOW, MEDIUM AND HIGH VISIBILITY VISUAL RANGE VALUES BY REGION
Region
I
II
III
IV
V
VI
VII
VIII
IX
Low Initial Value Assumed
for Regions Visual Range-
Yields Upper Bound
Benefit Estimate
8
10
13
IS
20
25
30
45
70
Medium Initial Value Assumed
for Regions Visual Range-
Yield Intermediate
Benefit Estimate
9
11.5
14
17.5
22.5
27.5
37.5
57.5
80
High Initial Value Assumed
for Regions Visual Range-
Yield Lower Bound
Benefit Estimate
10
13
15
20
25
30
45
70
89
00
I

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region in Table 8-11 represent the  highest level of assumed initial overall
         1                                •
visual range  in  the  region.  This represents a lower bound to the benefit
estimate.  The medium values represent an  approximate average of visual
range values  for the region.   The results of utilizing the  bounds discussed
above is to enable  the calculation of a range rather than  a point estimate
for each proposed  standard.

Procedures  for Data Collection

     The estimation of a national household benefit value for improving
visibility  to 13, 20,  and  30 miles and for a  20 percent nationwide improve-
ment  required data  on average household income and the number of house-
holds.  Because the three  mile-specific  policies were  not  required for all
areas of the U.S., nine basic visibility regions  were defined (discussed
previously)  and  data  were collected by  region.  Unavoidably,  the regional
boundaries  did  not correspond to state  boundaries.  Therefore,  the appro-
priate unit for  observation was determined to be counties  (and independent
cities where appropriate).  This also caused a problem,  in that the visi-
bility boundary did not always correspond to  a county boundary.  The place-
ment  of  questionable counties was  determined by the  location of the major
cities,  thus ensuring  that the majority of the households would be placed
in the appropriate  region.   In the  rare instances where the visibility
boundary actually bisected  a major city,  the  city (and corresponding
county)  was placed  in  the lower visibility region.

     The counties of  each region  were identified by U.S. Bureau of the
Census  Maps (36).  Unfortunately,  the number of households and  average
income per household was  unavailable  at the county  level.  This required
that  county data be collected on the number of people and the income per
capita (37). This information was aggregated by state (note:   a single
state could contain several  regions,  thus requiring several aggregates).
These aggregates were then  transformed into the desired information by
using the state's ratio of individuals per  household (38).  From these, a
regional average was  calculated for each of  the nine  regions.
                                   8-62

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     All tlie data collected was for  1974.  In order to get approximations
for 1980,  the U.S. percentage change  in households and household  income was
applied to each  of the regional averages (39).  Hopefully, the described
procedures were able  to preserve interstate population  and  income patterns;
however, intrastate  changes were unavoidably lost.

National Benefits for « 13-. 2O-. 30-Mile  and 20 Percent Improvement
Standard

     The previous sections have discussed the delineation of counties  into
visual range regions.  This section will present  the  results by proposed
standard for three benefit categories —  residential,  recreation and  exis-
tence values.  The underlying  assumptions and benefit  expressions  utilized
in the calculation of benefits have  been  discussed in earlier sections.

     Table 8-12 presents  the residential  annual household benefits  for the
four standards for the lower  to higher bound benefit  estimate.   Following
the  discussion  in  the earlier  subsection, two  functional forms were
utilized.   (See footnotes  in the  table.)

     For achieving  the 13-mile standard the range  of annual residential
benefits is  from  $20 to $947  million.  The implementation of  a  nationwide
standard  of 20 miles yields  a yearly range of $2,309 to $3,954 million,
depending upon  the  calculation  procedure.   For  the  30-mile proposed
standard,  the  range  of yearly values is $6,621  to  $19,443 million.   If the
goal of a  20 percent  nationwide improvement is sought,  the  annual household
benefit range  is $3,168 to $13,289 million.

     Table 8-13 presents the range of estimates for household  recreation
benefits  for the alternative  standards.  Note that different  functional
forms were utilized for regions above and below  an initial visual range
level of  45  miles (see footnote in the  table).   The  lower bound benefit
estimate  for the  13-mile  standard is  $59  million  and  $967 million for the
20 percent improvement goal.
                                    8-63

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                               Table 8-12

       LOW,  MEDIUM AND HIGH ESTIMATED RESIDENTIAL AESTHETIC BENEFITS
      PER YEAR  IN 1980 MILLIONS OF DOLLARS FOR VISIBILITY POLICIES OF
            13, 20, 30 MILES AND A NATIONWIDE 20% IMPROVEMENT*

Policy

13 Miles
20 Miles
30 Miles
20%
Low


Eq. !••
130
2,115
6,621
3,372

Eq. 2
20
1,309
12,014
3,168
Medium


Eq. 1
791
3,493
8,444
3,804

Eq. 2
63
2,005
15,319
7,196
High


Eq. 1
947
3,954
9,267
4,278

Eq. 2
182
3,047
19,443
13,829
 * Table 8-4 did  not  represent an "aesthetics only" set of residential
   values.  In this section, the residential values from Table  8-4 have
   been reduced by 50 percent and thus only represent  aesthetic residential
   values.   In the following notes,  let:

        B^  =  Benefits per year for region i.

        Y   =  Average annual household income  for  region i.
  2
AV-
 BY
            =   Initial visibility in region i  (see Table 8-4).

            =   New  visibility imposed by the policy alternatives in region
               i.

            =   Number of households in region  i.

            ='V2-V1.
                                      9
            =   Benefits per year  -   E B^ where the regions are  defined
               by Map 1.              i=l
   Then Low,  Medium  and High estimates were calculated by assuming a
   different  V*  (Ei -  0  for unaffected regions).

** Calculated using:

       B.   =  [(0.497Y.°-566(V2)0-399 - 0.497Y.°-566(VJ)0-399) zH.]/2

 + Calculated using:

        B._  =  [(0.0039Yi°-566(AVi)2'239)  x E^/2
                                   8-64

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                               Table  8-13

  LOW, MEDIUM AND HIGH ESTIMATED RECREATIONAL BENEFITS PER YEAR IN 1980
   MILLIONS OF DOLLARS FOR VISIBILITY POLICIES OF 13, 20, 30 MILES AND
                      A NATIONWIDE 20% IMPROVEMENT*
Policy
13 Miles
20 Miles
30 Miles
20%
Low
59
1,048
3,566
7,392
Medium
247
1,595
4,535
12,296
High
455
2,201
5,606
17,448
* In the  following  equation let:

       B-   =  Benefits per household per outdoor recreational activity day
              in region i.

       Y.   =  Household  income  in region i.

       V*   =  Initial visibility in region i (see Table  8-4).

       V?   =  New  visibility imposed by the policy alternatives in region
  Then Low,  Medium and  High estimates were  calculated by assuming a
  different V.
  Estimates calculated using:

        B.  =  2(V2)0'02 - Y?-2^)0'2) for V* < 45 miles,

  and

        B.  =  (Y.°'2(Y2)0-8 - Y?-2(V^)°'8)  for V\ >. 45 miles.


  To obtain annual benefits per household, BI was multiplied by 30,  pre-
  suming  therefore  that  the  representative U.S.  household participates  in
  30  outdoor  recreational days  annually.  This result  was then multiplied
  by  the  number of households per region and then summed over regions to
  obtain  total national benefits.
                                     8-65

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     Tables 8-14  and  8-15 present  the existence  value benefits.   Tie
results in Table 8-14  are, as discussed earlier,  dependent upon an exis-
tence value index.  The process of calculating the index is shown in the
footnote  in Table  8-15.  Utilizing the  existence value index, the category
"Selected National Parks" represents, for each standard, the lower bound
existence  value  benefit  estimate.  For  instance,  a  20 percent  nationwide
improvement existence  value benefit is  $10,172 million.

     A complete range of benefit values for residential, recreation and
existence  value is  presented in Table 8-16.  Utilizing the values low.
medium and high for the initial assumed visual range 1'evels by region by
standard  and the alternative  functional  forms, a "low-low" to a  "high-high"
annual estimate  can  be constructed.  For  the  13-mile  standard,   the  lower
bound  annual  estimate  is $2,251  million and the upper bound is  $3,574
million.   These  values  in Table 8-16 thus represent  the  extreme bounds on
the annual visibility  estimates.

     In addition to  the  benefit numbers reported  in  Tables 8-12 through 8-
16,  a variety  of other calculations were  made to  reflect  different
discounting time horizons,   discount rates,  and  attainment  periods.
Estimates are  available for discounted present values  as well  as annualized
numbers.   Additionally, a variety  of  alternative aggregation procedures are
also utilized.  Tables reflecting these combinations of assumptions are
available  from the authors upon request.
 1.  Brookshire,  D.  S.,  R.  C.  d'Arge,  W.  D.  Schulze and  M.  A.  Thayer.
     Methods Development  for Assessing Tradeoffs in Environmental  Manage-
     ment - Vol.  II.  EPA-600/6-79-00Ib,  1979.
 2.  Schulze, W.  D., D. S. Brookshire,  E.  Walther  and K. Kelley.   The
     Benefits of Preserving Visibility in  the  National  Parklands of the
     Southwest - Vol. VIII of Methods Development for Environmental Control
     Benefits Assessment.  A  draft report  for the  U.S.  Environmental
     Protection Agency,  University of Wyoming, Laramie, WY,  1981.
 3.  Bresnock, A.  E.  Housing Prices, Income and Environmental Quality in
     Denver.   Unpublished  paper.
                                   8-66

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                               Table 8-14

     ESTIMATED EXISTENCE VALUE BENEFITS PER YEAR IN 1980 MILLIONS OF
        DOLLARS PER VISIBILITY POLICIES OF 13, 20, 30 MILES AND A
                       NATIONWIDE 20% IMPROVEMENT*
Policy
13 Miles
20 Miles
30 Miles
20%
Selected National Parks**
2,172
7,194'
14,430
10,172
 * Let:

        B  =  Benefits.

        A  =  Total  existence  value  index.

        H  =  Number of  households  in the U.S.

        Y  =  Household  income in thousands.

  Then the above  were calculated as:

        B  =   1.0215AH0>22H where  A varies according to Table  8-15.


** Arcadia, Great  Smokies,  Shenandoah, Yellowstone,  Grand Canyon,  and  Grand
  Teton.
                                     8-67

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                                Table  8-15

                       TOTAL EXISTENCE VALUE  INDEX*
                        (1980 Millions of Dollars)
                   Policy
                  Selected National Parks**
                  13  Miles

                  20  Miles

                  30  Miles

                   20%
                            13.42

                            44.75

                            89.51

                            63.27
        A
        A  =
      AVi
       Si
   2131.7
       Di
LAV.
                      Si
ID..
     V2131.7/

Total existence value index.

Change  in  visibility at  site  i compared  to  standard visi-
bility.

Visits to site i in thousands.

0 if (standard visibility - Vj)  < 0.

Visits to the Grand Canyon in thousands.

Dummy variable, depending upon importance  of visibility:

  D£  =  0    Visibility is not  a factor
  D   -  1    Visibility is a factor.
**
   D£ = 1 for Arcadia, Great  Smokies,  Shenandoah, Yellowstone, Grand Canyon
   and Grand Teton.
                                    8-68

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                                                      Table  8-16

                           RANGES FOR THE LOW. MEDIUM AND HIGH TOTAL BENEFITS PER YEAR IN 1980
                            MILLIONS OF DOLLARS FOR VISIBILITY POLICIES OF 13, 20, 30 MILES
                                           AND A 20% NATIONWIDE IMPROVEMENT*
oo

Policy

13 Miles
20 Miles
30 Miles
20%
Low


Low
2.251
10.357
24,617
20,732

High
2,361
9,551
30,010
20,936
Medium


Low
2,482
10,794
27,409
26,272

High
3,210
12,282
34,284
29.664
High


Low
2,809
12,442
29,403
31,898

High
3,574
13,349
39,479
41,449
                        * Calculated  from  Tables  8-4, 8-7, and 8-10.   The Low, Medium, and High
                          categories were obtained by  assuming different  initial  visibility
                          levels.   The Low  and High subcategories  indicate the range of the
                          estimates within each of the primary categories.

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 4.   Freeman, A. M.  The Benefits  of Environmental Improvement:  Theory and
     Practice.  Baltimore,  MD:  Johns  Hopkins  University Press,  1979.

 5.   Trijonis,  J.   Visibility in the Southwest:   An Exploration of the
     Historical Data Base.   Prepared by Technology Services Corporation for
     U.S. Environmental Protection Agency,  1977.

 6.   Ridker, R. 6. and J.  A. Henning.  The Determinants of Residential
     Property Values with Special Reference  to Air Pollution.  Review of
     Economics  and Statistics 49.   1967.

 7.   Anderson,  R.  J. and T. D. Crocker.   Air Pollution  and Residential
     Property Values.  Urban Studies  8.   1971.

 8.   Crocker, T.  D.  Urban Air Pollution Damage Functions, Theory and
     Measurement.   University  of  California,  Riverside,  available  through
     NTIS:PB, 1970.

 9.   Harrison,  D., Jr.  and  D. Rubinfeld.   Hedonic  Housing Prices  and the
     Demand for Clean Air.   Journal of Environmental Economics and  Manage-
     ment 5. 1978.

10.   Smith, B.  A.   Measuring  the Value of Urban Amenities.  Jo-urnal of
     Urban Economics 5.  1978.

11.   Nelson,  J.  P.  Residential Choice,  Hedonic Prices,  and  the  Demand for
     Urban Air  Quality.  Journal of Urban Economics  5.   1978.

12.   Zerbe,  R., Jr.   The  Economics  of Air  Pollution:  A Cost-Benefit
     Approach.  Ontario Department of  Public Health, Toronto,  Canada,  1969.

13.   Peckham,  B.    Air Pollution and  Residential  Property  Values  in
     Philadelphia.   1970.

14.   SRI International.  Measuring the Benefits of Air Quality Improvements
     in the San Francisco Bay Area.  SRI Project No. 8962, 1980.

IS.   Blank,  F.  et  al.  Valuation of Aesthetic  Preferences:   A Case  Study of
     the Economic  Value of  Visibility.  Electric Power Research Institute,
     No. RP7855-2, 1978.

16.   Tolley,  G. and A.  Randall.  Personal communication, November 1981.

17.   Brookshire, D.  S. ert !_1.  Valuing Public Goods:  A Comparison of the
     Survey  and Hedonic Approaches.  American Economic Review  (forth-
     coming) .

18.   Samuel son, P. A.  The Pure Theory of Public Expenditures.  Review of
     Economics  and Statistics 36(4).   November 1954.

19.   Bohm,  P.  An  Approach  to the Problem  of  Estimating Demand for Public
     Goods.   Swedish Journal of Economics 73.   March 1971.
                                   8-70

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20.  Scherr B. A. and E. M. Babb.  Pricing  Public Goods:  An Experiment
     with Two Proposed Pricing Systems.  Public Choice 23.   Fall  1975.

21.  Smith., V. L.  The  Principle of Unanimity and Voluntary Consent in
     Social Choice.   Journal of Political Economy  85:1125-1139.   December
     1977-

22.  Randall, A., B. Ives and  C. Eastman.  Bidding Games  for Valuation of
     Aesthetic  Environmental Improvements.   Journal of  Environmental
     Economics and Management 1.   1974.

23.  Rowe, R.  D.,  R. C. d'Arge  and D. S. Brookshire.  An Experiment  in the
     Economic  Value of Visibility.   Journal  of Environmental Economics and
     Management 7.  1980.

24.  Brookshire,  D.  S.  and T.  D. Crocker.   The Advantages of Contingent
     Valuation Methods for Benefit-Cost  Analysis.   Public  Choice  36.   1981.

25.  Schulze, W.  D., R.  C.  d'Arge  and D. S. Brookshire.  Valuing Environ-
     mental Commodities:   Some Recent Experiments.  Land Economics 27.
     1981.

26.  Silberberg,  E.   The Structure  of  Economics:   A Mathematical Analysis.
     New York:  McGraw Hill, 1978.

27.  Brookshire,  D.  S., B.  C.  Ives  and W. D.  Schulze.  The Valuation of
     Aesthetic Preferences.  Journal of  Environmental Economics and Manage-
     ment  3.  1976.

28.  Rae, D. A.  Visibility Improvement as  Mesa  Verde National Park:  An
     Analysis of the  Benefits and Costs of Controlling Emissions  in the
     Four Corners Area.  Interim Report, Research  Project  1742, Boston, MA:
     Charles Rivers Associates, Inc.,  1980.

29.  McFadden,   D.   Conditional Logit  Analysis  of  Qualitative  Choice
     Behavior.  In P.  Zarembka, ed.. Frontiers in Econometrics.   New York:
     Academic Press, 1973.

30.  Greenley,  D.  A., R.  G. Walsh and R.  A.  Young.    Option Value:
     Empirical Evidence from a Case Study of Recreation and Water Quality.
     Quarterly Journal of  Economics (forthcoming).

31.  Crocker,  T.  D. and B.  A. Forster.  Decision Problems in the Control of
   .  Acid Precipitation:   Nonconvexities and Irreversibilities.  Journal of
     the Air Pollution Control Association 31.  1981.

32.  Krutilla, J.  Conservation Reconsidered.  American Economic  Review 57.
     1967.

33.  National Park Service.   National Park  Statistical  Abstract.   U.S.
     Department of the Interior,  Statistical Office, Denver Service  Center,
     1979.
                                    8-71

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34.  U.S. Environmental Protection Agency.  Protecting Visibility:   An EPA
     Report to Congress.   EPA-450/5-79-008, Office  of Air, Noise,  and
     Radiation, Office of  Air Quality Planning and Standards, Research
     Triangle Park, NC, 1979.

35.  Trijonis,  J,  and D. Shepland. Existing  Visibil ity Levels in  the U.S.
     Prepared by Technology Service  Corporation for U.S.  Environmental
     Protection Agency, Research  Triangle Park, NC, 1979.

36.  U.S.  Bureau  of  the  Census.   County  and  City Data Book.   U.S.
     Government Printing Office,  Washington,  DC, 1978.

37.  U.S. Bureau of the Census.   Estimates of the Population of Counties
     and Metropolitan Areas:  July 1, 1974 and 1975.  Current Population
     Reports.  Series P-25,  No. 709, U.S.  Government  Printing  Office, 1977.

38.  U.S. Bureau of the Census.  Statistical Abstract of the United States:
     1980.   101st  edition,  Washington,  DC, 1980.

39.  U.S. Bureau of the Census.   Characteristics of the Population - Vol.
     1.   1970.
                                   8-72

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                               APPENDIX 8A

                 HOW VISIBILITY CHANGES 1ERE CALCULATED
                     FOR THE PROPERTY VALUE STUDIES
The dollar per mile  per year figures  were calculated in the  following
manner:

     a.    The figure  for A in property value  was  capitalized  with an
          interest rate of 10 percent and a life span of  30  years,  which
          gives  a  per-year bid for the given level  of  pollution change.

     b.    The figures for visibility were calculated by the  following
          equations:

            i)   1 mg/100 cm2  =  10 ug/cm2  (1).                       (8A)
                                >y
                Assume 10 ug/m  part  =  0.1 mg/
                since 0.1 mg/100 cm   =  1  ug/cm
100 cm2  sulfation (2)
                       3                 2
                 10  ug/m  part  =  1 ug/cm  sulfation                  (8B)

                                                        2
           ii)    Given  a A  in  sulfation of 0.25 mg/100 cm ,  we can convert
                 it  to  2.5 mg/cm  by Equation  (8A).


                             2'5 m/C1°2  by Equation  (8B).
                 10  ug/m       xug/m

                 x  =  25 mg/cm .

                 2.5 ug/cm  of sulfation is  equivalent to 0.25 ug/m   of
                 particulates.

           iv)    To  convert particulates (TSP), the following conversion
                 was used (3) :

                 AN02  =  A part x 0.097

                 AN02  =  -(25 ug/m3) (0.097)

                 AN02  =  -2.43 ug/m3.
            v)   Finally,  to convert to visibility (3):

                  AN0                  -2 43
                        - AV                  =  n'49 miles
                                    8-73

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     c.   To  calculate the WTP per mile per year:

         The yearly WTP was divided by A in miles and then adjusted to
         1981 dollars.


     d.   To  convert  the oxidant measure of pollution to N02 — * V (3):

          .... ,.   ..   .     ,  ,, ^ 1.41(Aoxidant)  _  AV
         AN02/Aoxidant  =  1.41 =? - _. 211 -  -  AV


         since   AN02  =  -0.211 AV.


         To  convert  C02 -* N02 -» V (3,4):

                                      0.06 ' AC02
         0.006  •  AGO, ' 10   =  NO, =5> - jrTTi -  =  AV
          To convert  SO- -» part -» V:

             10 ug/m3  =   1 ppb S02 (5) =5> 10 (ig/m3 + 1 ppb S02

                      =   20 (ig/m3

           AN02/ Apart  =   0.097

          0.097 Apart
            -0.211
REFERENCES
1.   Department of Health,  Education  and Welfare.  Air Quality Criteria for
     Sulfur Oxides.  No. AP-50, p. 166, 1969.

2.   Anderson,  R. J.  and T.  Crocker.  Air Pollution and  Residential
     Property Values.   Urban Studies  8:171-180.   1971.

3.   Derived using the ambient pollution components for Los Angeles as
     reported  in:   Brokshire  e_t a_l..  Methods Development for Assessing
     Tradeoffs  in Environmental Management - Vol.  II.  EPA-600/6-79-001b,
     February 1979.

4.   Bresnock,  A.  E.  Housing Prices, Income  and Environmental  Quality in
     Denver.   Unpublished paper.

5.   Crocker,  T.   Urban Air Pollution Damage Functions, Theory and Measure-
     ment.   University of California,  Riverside,  available  through NTIS:PB,
     pp. 197-668,  1970.
                                    8-74

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