Guidelines for Preparing
 

Economic
 

Analyses
 

December 17, 2010 

(updated May 2014) 

National Center for Environmental Economics
 

Office of Policy
 

U.S. Environmental Protection Agency
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table of Contents
 

Acronyms and Abbreviations..................................................................................... viii
 

Glossary...............................................................................................................xi
 

1 Introduction....................................................................................................... 1-1
 

1.1 Background to the Guidelines for Performing Economic Analyses ................................................................1-1
 

1.2 The Scope of the Guidelines.................................................................................................................................1-2
 

1.3 Economic Framework and Definition of Terms .............................................................................................1-3
 

1.4 Organization of the Guidelines ...........................................................................................................................1-5
 

2 Statutory and Executive Order Requirements for Conducting Economic Analyses .................... 2-1
 

2.1 Executive Orders ....................................................................................................................................................2-2
 

2.1.1	 Executive Order 12866, “Regulatory Planning and Review”.......................................................2-2
 

2.1.2	 Executive Order 12898, “Federal Actions to Address Environmental Justice 


in Minority Populations and Low-Income Populations” ..............................................................2-3
 

2.1.3	 Executive Order 13045, “Protection of Children from 


Environmental Health Risks and Safety Risks”...............................................................................2-3
 

2.1.4	 Executive Order 13132, “Federalism” ...............................................................................................2-3
 

2.1.5	 Executive Order 13175, “Consultation and Coordination with 


Indian Tribal Governments”................................................................................................................2-3
 

2.1.6	 Executive Order 13211, “Actions Concerning Regulations that 


Significantly Affect Energy Supply, Distribution, or Use”............................................................2-4
 

2.2 Statutes ....................................................................................................................................................................2-4
 

2.2.1	 The Regulatory Flexibility Act of 1980 (RFA) as amended by The Small Business 


Regulatory Enforcement Fairness Act of 1996 (SBREFA), (5 U.S.C. 601-612) .....................2-4
 

2.2.2	 The Unfunded Mandates Reform Act of 1995 (UMRA) (P.L. 104-4) .....................................2-4
 

2.2.3	 The Paperwork Reduction Act of 1995 (PRA) (44 U.S.C. 3501)...............................................2-5
 

3 Statement of Need for Policy Action.......................................................................... 3-1
 

3.1 Problem Definition ...............................................................................................................................................3-1
 

3.2 Reasons for Market or Institutional Failure .....................................................................................................3-1
 

3.3 Need for Federal Action.......................................................................................................................................3-2
 

4 Regulatory and Non-Regulatory Approaches to Pollution Control ....................................... 4-1
 

4.1 Evaluating Environmental Policy .......................................................................................................................4-1
 

4.1.1	 Efficiency .................................................................................................................................................4-1
 

4.1.2	 Cost-Effectiveness..................................................................................................................................4-2
 

4.2 Traditional Command-and-Control or Prescriptive Regulation ................................................................4-2
 

4.2.1	 Technology or Design Standards........................................................................................................4-3
 

4.2.2	 Performance Standards .........................................................................................................................4-4
 

4.3 Market-Oriented Approaches.............................................................................................................................4-5
 

4.3.1	 Marketable Permit Systems..................................................................................................................4-5
 

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4.3.2 Emission Taxes........................................................................................................................................4-9
 

4.3.3 Environmental Subsidies ................................................................................................................... 4-10
 

4.3.4 Tax-Subsidy Combinations............................................................................................................... 4-11
 

4.4 Other Market-Oriented or Hybrid Approaches .......................................................................................... 4-12
 

4.4.1 Combining Standards and Pricing Approaches ........................................................................... 4-12
 

4.4.2 Information Disclosure...................................................................................................................... 4-13
 

4.4.3 Liability Rules...................................................................................................................................... 4-14
 

4.5 Selecting the Appropriate Market-Based Incentive or Hybrid Approach .............................................. 4-15
 

4.5.1 The Type of Market Failure............................................................................................................... 4-15
 

4.5.2 The Nature of the Environmental Problem................................................................................... 4-15
 

4.5.3 The Type of Pollutant Information that is Available and Observable ..................................... 4-16
 

4.5.4 Uncertainty in Abatement Costs or Damages.............................................................................. 4-16
 

4.5.5 Market Competitiveness ................................................................................................................... 4-17
 

4.5.6 Monitoring and Enforcement Issues............................................................................................... 4-17
 

4.5.7 Potential for Economy-Wide Distortions...................................................................................... 4-17
 

4.5.8 The Goals of the Policy Maker......................................................................................................... 4-18
 

4.6 Non-Regulatory Approaches............................................................................................................................ 4-18
 

4.6.1 How Voluntary Approaches Work.................................................................................................. 4-19
 

4.6.2 Economic Evaluation of Voluntary Approaches .......................................................................... 4-19
 

4.7 Measuring the Effectiveness of Regulatory or Non-Regulatory Approaches......................................... 4-21
 

5 Baseline........................................................................................................... 5-1
 

5.1 Baseline Definition................................................................................................................................................5-1
 

5.2 Guiding Principles of Baseline Specification ...................................................................................................5-2
 

5.3 Changes in Basic Variables...................................................................................................................................5-6
 

5.3.1 Demographic Change...........................................................................................................................5-6
 

5.3.2 Future Economic Activity....................................................................................................................5-6
 

5.3.3 Changes in Consumer Behavior .........................................................................................................5-6
 

5.3.4 Technological Change ..........................................................................................................................5-7
 

5.4 Compliance Rates..................................................................................................................................................5-8
 

5.4.1 Full Compliance.....................................................................................................................................5-9
 

5.4.2 Under-Compliance ...............................................................................................................................5-9
 

5.4.3 Over-Compliance............................................................................................................................... 5-10
 

5.5 Multiple Rules ..................................................................................................................................................... 5-11
 

5.5.1 Linked Rules ........................................................................................................................................ 5-11
 

5.5.2 Unlinked Rules.................................................................................................................................... 5-11
 

5.5.3 Indirectly Related Policies and Programs ...................................................................................... 5-12
 

5.6 Partial Benefits to a Threshold ......................................................................................................................... 5-12
 

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5.7 Behavioral Responses ......................................................................................................................................... 5-14
 

5.7.1 Potential for Cost Savings................................................................................................................. 5-14
 

5.7.2 Voluntary Actions............................................................................................................................... 5-15
 

5.8 Conclusion ......................................................................................................................................................... 5-16
 

6 Discounting Future Benefits and Costs ....................................................................... 6-1
 

6.1 The Mechanics of Summarizing Present and Future Costs and Benefits ...................................................6-2
 

6.1.1 Net Present Value...................................................................................................................................6-2
 

6.1.2 Annualized Values..................................................................................................................................6-3
 

6.1.3 Net Future Value ....................................................................................................................................6-3
 

6.1.4 Comparing the Methods......................................................................................................................6-4
 

6.1.5 Sensitivity of Present Value Estimates to the Discount Rate ........................................................6-4
 

6.1.6 Some Issues in Application ..................................................................................................................6-4
 

6.2 Background and Rationales for Social Discounting.......................................................................................6-6
 

6.2.1 Consumption Rates of Interest and Private Rates of Return .......................................................6-6
 

6.2.2 Social Rate of Time Preference ...........................................................................................................6-7
 

6.2.3 Social Opportunity Cost of Capital ..................................................................................................6-8
 

6.2.4 Shadow Price of Capital Approach ....................................................................................................6-8
 

6.2.5 Evaluating the Alternatives ............................................................................................................... 6-10
 

6.3 Intergenerational Social Discounting ............................................................................................................. 6-11
 

6.3.1 The Ramsey Framework .................................................................................................................... 6-12
 

6.3.2 Key Considerations ............................................................................................................................ 6-14
 

6.3.3 Evaluating Alternatives ...................................................................................................................... 6-16
 

6.4 Recommendations and Guidance ................................................................................................................... 6-18
 

7 Analyzing Benefits............................................................................................... 7-1
 

7.1 The Benefits Analysis Process ..............................................................................................................................7-1
 

7.2 Economic value and types of benefits ...............................................................................................................7-6
 

7.2.1 Human Health Improvements............................................................................................................7-8
 

7.2.2 Ecological Benefits.............................................................................................................................. 7-15
 

7.2.3 Other Benefits ..................................................................................................................................... 7-20
 

7.3 Economic Valuation Methods for Benefits Analysis ................................................................................... 7-21
 

7.3.1 Revealed Preference Methods .......................................................................................................... 7-21
 

7.3.2 Stated Preference................................................................................................................................. 7-35
 

7.3.3 Combining Revealed and Stated Preference Data....................................................................... 7-44
 

7.4 Benefit Transfer ................................................................................................................................................... 7-44
 

7.5 Accommodating Non-monetized Benefits.................................................................................................... 7-49
 

7.5.1 Qualitative Discussions ..................................................................................................................... 7-49
 

7.5.2 Alternative Analyses ........................................................................................................................... 7-50
 

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Table of Contents 

8 Analyzing Costs .................................................................................................. 8-1
 

8.1 The Economics of Social Cost ............................................................................................................................8-1
 

8.1.1 Partial Equilibrium Analysis................................................................................................................8-2
 

8.1.2 General Equilibrium Analysis .............................................................................................................8-4
 

8.1.3 Dynamics.................................................................................................................................................8-6
 

8.1.4 Social Cost and Employment Effects ................................................................................................8-6
 

8.2 A Typology of Costs..............................................................................................................................................8-7
 

8.2.1 Alternative Concepts of Cost..............................................................................................................8-7
 

8.2.2 Additional Cost Terminology.............................................................................................................8-8
 

8.2.3 Transitional and Distributional Costs...............................................................................................8-9
 

8.3 Measurement Issues in Estimating Social Cost................................................................................................8-9
 

8.3.1 Evaluating Costs Over Time ...............................................................................................................8-9
 

8.3.2 Difficulties in Valuing Social Cost .................................................................................................. 8-12
 

8.3.3 Use of Externally-Produced Cost Estimates.................................................................................. 8-13
 

8.4 Models Used in Estimating the Costs of Environmental Regulation...................................................... 8-13
 

8.4.1 Compliance Cost Models ................................................................................................................. 8-14
 

8.4.2 Partial Equilibrium Models .............................................................................................................. 8-15
 

8.4.3 Linear Programming Models............................................................................................................ 8-16
 

8.4.4 Input-Output Models........................................................................................................................ 8-17
 

8.4.5 Input-Output Econometric Models ............................................................................................... 8-18
 

8.4.6 Computable General Equilibrium Models.................................................................................... 8-19
 

9 Economic Impact Analyses..................................................................................... 9-1
 

9.1 Statutes and Policies ..............................................................................................................................................9-1
 

9.2 Conducting an Economic Impact Analysis......................................................................................................9-2
 

9.2.1 Screening for Potentially Significant Impacts ..................................................................................9-3
 

9.2.2 Profile of Affected Entities...................................................................................................................9-3
 

9.2.3 Detailing Impacts on Industry ............................................................................................................9-5
 

9.2.4 Detailing Impacts on Governments and Not-for-Profit Organizations.................................. 9-11
 

9.2.5 Detailing Impacts on Small Entities................................................................................................ 9-14
 

9.3 Approaches to Modeling in an Economic Impact Analysis....................................................................... 9-15
 

9.3.1 Direct Compliance Costs.................................................................................................................. 9-15
 

9.3.2 Partial Equilibrium Models .............................................................................................................. 9-17
 

9.3.3 Computable General Equilibrium Models.................................................................................... 9-18
 

10 Environmental Justice, Children’s Environmental Health 


and Other Distributional Considerations ..................................................................10-1
 

10.1 Executive Orders, Directives, and Policies.................................................................................................... 10-1
 

10.2 Environmental Justice ....................................................................................................................................... 10-4
 

10.2.1 Background Literature....................................................................................................................... 10-4
 

10.2.2 Analyzing Distributional Impacts in the Context of Regulatory Analysis............................. 10-6
 

10.2.3 Relevant Populations.......................................................................................................................... 10-9
 

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10.2.4 Data Sources.......................................................................................................................................10-12
 

10.2.5 Scope and Geographic Considerations........................................................................................10-12
 

10.2.6 Defining the Baseline.......................................................................................................................10-14
 

10.2.7 Comparison groups ..........................................................................................................................10-14
 

10.2.8 Measuring and estimating impacts................................................................................................10-15
 

10.3 Children’s Environmental Health ................................................................................................................10-20
 

10.3.1 Childhood as a Lifestage.................................................................................................................10-20
 

10.3.2 Analytical Considerations...............................................................................................................10-21
 

10.3.3 Intersection Between Environmental Justice and Children’s Health.....................................10-22
 

10.4 Other Distributional Considerations..........................................................................................................10-22
 

10.4.1 Elderly..................................................................................................................................................10-22
 

10.4.2 Intergenerational Impacts ...............................................................................................................10-22
 

10.5 Conclusion .......................................................................................................................................................10-23
 

11 Presentation of Analysis and Results ......................................................................11-1
 

11.1 Presenting Results of Economic Analyses..................................................................................................... 11-1
 

11.1.1 Presenting the Results of Benefit-Cost Analyses.......................................................................... 11-2
 

11.1.2 Presenting the Results of Cost-Effectiveness Analyses................................................................ 11-4
 

11.1.3 Presenting the Results of Economic Impact Analyses and Distributional Analyses............. 11-9
 

11.1.4 Reporting the Effects of Uncertainty on Results of Economic Analyses................................ 11-9
 

11.2 Communicating Data, Model Choices and Assumptions, and Related Uncertainty ......................... 11-9
 

11.2.1 Data......................................................................................................................................................11-10
 

11.2.2 Model Choices and Assumptions..................................................................................................11-10
 

11.2.3 Addressing Uncertainty Driven by Assumptions and Model Choice ...................................11-11
 

11.3 Use of Economic Analyses .............................................................................................................................11-11
 

Appendix A Economic Theory 

A.1 Market Economy...................................................................................................................................................A-1
 

A.2 Reasons for Market or Institutional Failure ....................................................................................................A-4
 

A.3 Benefit-Cost Analysis...........................................................................................................................................A-6
 

A.4 Measuring Economic Impacts............................................................................................................................A-7
 

A.4.1 Elasticities ...............................................................................................................................................A-7
 

A.4.2 Measuring the Welfare Effect of a Change in Environmental Goods..................................... A-10
 

A.4.3 Single Market, Multi-Market, and General Equilibrium Analysis .......................................... A-12
 

A.5 Optimal Level of Regulation........................................................................................................................... A-14
 

A.6 Conclusion ........................................................................................................................................................ A-16
 

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Table of Contents 

Appendix B Mortality Risk Valuation Estimates...............................................................B-1
 

B.1 Central Estimate of Value of Statistical Life ....................................................................................................B-1
 

B.2 Other Value of Statistical Life Information .....................................................................................................B-3
 

B.3 Benefit Transfer Considerations.........................................................................................................................B-3
 

B.4 Adjusted Associated with Risk Characteristics ...............................................................................................B-3
 

B.5 Effects on Willingness to Pay Associated with Demographic Characteristics .........................................B-4
 

B.6 Conclusion..............................................................................................................................................................B-6
 

Appendix C Accounting for Unemployed Labor in Benefit-Cost Analysis..................................C-1
 

References..........................................................................................................D-1
 

Author Index ........................................................................................................ E-1
 

Subject Index ....................................................................................................... F-1
 

List of Figures 

Figure 6.1 Distribution of Net Benefits over Time....................................................................................................6-3
 

Figure 7.1 Benefits of an Environmental Improvement ............................................................................................7-7
 

Figure 8.1 Competitive Market Before Regulation ...................................................................................................8-3
 

Figure 8.2 Competitive Market After Regulation......................................................................................................8-3
 

Figure 8.3 Labor Market with Pre-Existing Distortions ...........................................................................................8-5
 

Figure 9.1 Pollution Abatement Costs as a Percentage of Total Revenues for 


Industries with Highest Pollution Abatement Costs in 2005..............................................................9-7
 

Figure 9.2 Pollution Abatement Costs are a very Small Percentage of Total Manufacturing Costs................9-7
 

Figure A.1 Marginal and Total Willingness to Pay ....................................................................................................A-1
 

Figure A.2 Marginal and Total Cost .............................................................................................................................A-2
 

Figure A.3 Market Equilibrium......................................................................................................................................A-3
 

Figure A.4 Utility Possibility Frontier ..........................................................................................................................A-4
 

Figure A.5 Negative Externality.....................................................................................................................................A-5
 

Figure A.6 Demand Curve for Tuna .............................................................................................................................A-8
 

Figure A.7 Indifference Curve..................................................................................................................................... A-11
 

Figure A.8 Change in Optimal Consumption Bundle........................................................................................... A-11
 

Figure A.9 Benefits and Costs of Abatement ........................................................................................................... A-13
 

Figure A.10 Maximized Net Benefits........................................................................................................................... A-14
 

Figure A.11 Efficient Level of Pollution ...................................................................................................................... A-15
 

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Table of Contents 

List of Tables 

Table 7.1 Types of Benefits Associated With Environmental Policies: 


Categories, Examples, and Commonly Used Valuation Methods.......................................................7-9
 

Table 8.2 Major Attributes of Models Used in the Estimation of Costs........................................................... 8-13
 

Table 8.3 Input-Output Table for the United States ............................................................................................ 8-17
 

Table 9.1 Potentially Relevant Dimensions to Economic Impact Analyses........................................................9-2
 

Table 9.2 Commonly Used Profile Sources for Quantitative Data.......................................................................9-6
 

Table 9.3 Indicators of Economic and Financial Well-Being of Government Entities.................................. 9-13
 

Table 11.1 Template for Regulatory Benefits Checklist ......................................................................................... 11-4
 

Table 11.2 Template for Quantified Regulatory Benefits....................................................................................... 11-5
 

Table 11.3 Template for Dollar-Valued Regulatory Benefits ................................................................................. 11-6
 

Table 11.4 Template for Summary of Benefits and Costs....................................................................................... 11-7
 

Table B.1 Value of Statistical Life Estimates..............................................................................................................B-2
 

List of Text Boxes 

Text Box 4.1 Coase Solution...........................................................................................................................................4-4
 

Text Box 7.1 Estimating Benefits From Reducing Carbon Dioxide Emissions: 


Text Box 7.6 The Benefits and Costs of the Clean Air Act 1990 to 2010: 


Text Box 7.7 Benefits Transfer: Water Quality Benefits in the 


Text Box 4.2 U.S. Acid Rain Trading Program for Sulfur Dioxide .........................................................................4-6
 

Text Box 4.3 Water Quality Trading of Nonpoint Sources ................................................................................... 4-20
 

Text Box 5.1 Technological Change, Induced Innovation, and the Porter Hypothesis .....................................5-8
 

Text Box 5.2 Sequencing Unlinked Rules ................................................................................................................. 5-13
 

Text Box 6.1 Potential Effects of Discounting.............................................................................................................6-5
 

Text Box 6.2 Social Rate and Consumption Rates of Interest.................................................................................6-6
 

Text Box 6.3 Estimating and Applying the Shadow Price of Capital .....................................................................6-9
 

Text Box 6.4 Alternative Social Discounting Perspectives..................................................................................... 6-11
 

Text Box 6.5 Applying these Approaches to the Ramsey Equation ..................................................................... 6-14
 

Text Box 6.6 What’s the Big Deal with The Stern Review? .................................................................................... 6-18
 

The Social Cost of Carbon ......................................................................................................................7-2
 

Text Box 7.2 Integrating Economics and Risk Assessment.......................................................................................7-5
 

Text Box 7.3 Non-Willingness to Pay Measures...................................................................................................... 7-13
 

Text Box 7.4 Spatial Correlation ................................................................................................................................. 7-31
 

Text Box 7.5 Value of Time .......................................................................................................................................... 7-35
 

Reduced Acidification in Freshwater Adirondack Lakes............................................................... 7-47
 

Combined Animal Feeding Operations Rule .................................................................................. 7-48
 

Text Box 7.8 Structural Benefit Transfer with an Application to Visibility....................................................... 7-49
 

Text Box 8.1 The Sulfur Dioxide Cap-and-Trade Program — A Case Study.................................................... 8-11
 

Text Box 8.2 The Pollution Abatement Costs and Expenditures Survey ........................................................... 8-20
 

Text Box 10.1 Social Welfare Functions and Inequality Indices.......................................................................................10-16
 

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Acronyms and Abbreviations 

Acronyms and Abbreviations
 

ABC Air Benefit and Cost (Group) 

AC annualized costs 

ACN AirControlNET 

ADP Action Development Process 

BAT best available technology 

BCA benefit-cost analysis 

BLS Bureau of Labor Statistics 

BMP Best Management Practice 

BPT best practicable technology 

CA conjoint analysis 

CAA Clean Air Act 

CAFO Combined Animal Feeding Operations 

CAAA Clean Air Act Amendments 

CAIR Clean Air Interstate Rule 

CAMR Clean Air Mercury Rule 

CBO Congressional Budget Office 

CE certainty equivalent 

CEA cost-effectiveness analysis 

CEM continual emissions monitoring 

CEQ Council on Environmental Quality 

CERCLA Comprehensive Environmental Response, Compensation and Liability Act 

CFC chlorofluorocarbons 

CFOI Census of Fatal Occupational Injuries 

CFR Code of Federal Regulations 

CGE computable general equilibrium 

CO carbon monoxide 

CO2 carbon dioxide 

COI cost of illness 

CPI Consumer Price Index 

CR contingent ranking 

CS compensating surplus 

CV contingent valuation 

CV compensating variation 

DALY disability-adjusted life year 

DICE Dynamic Integrated model of Climate and the Economy 

DOE Department of Energy 

DOT Department of Transportation 

DWL deadweight loss 

EA economic analysis 

EBIT earnings before interest and taxes 

EEAC Environmental Economics Advisory Committee 

EIA economic impact analysis 

ELG Effluent Limitation Guidelines 

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Acronyms and Abbreviations 

EO Executive Order 

EPA Environmental Protection Agency 

ES equivalent surplus 

EV equivalent variation 

EVRI Environmental Valuation Reference Inventory 

FINDS Facility Index Data System 

FR Federal Register 

FTE full-time equivalent employment 

GDP gross domestic product 

GHG greenhouse gases 

GIS Geographic Information System 

HCFC hydrochlorofluorocarbon 

Hg mercury 

IAM integrated assessment model 

ICR Information Collection Request 

IEc Industrial Economics, Inc. 

IMPLAN Impact Analysis for Planning 

IPCC Intergovernmental Panel on Climate Change 

IPM Integrated Planning Model 

LP linear programming 

MAC marginal abatement cost curve 

MD marginal external damage curve 

MR marginal revenue 

MPC marginal private costs 

MSC marginal social costs 

MSD marginal social damages 

NAAQS National Ambient Air Quality Standards 

NAICS North American Industrial Classification System 

NB net benefits 

NEI National Emissions Inventory 

NEPA National Environmental Policy Act 

NESHAP National Emission Standard for Hazardous Air Pollutant 

NFV net future value 

NH3 ammonia 

NIOSH National Institute of Occupational Safety and Health 

NOAA National Oceanic and Atmospheric Administration 

NOx nitrogen oxide 

NPDES National Pollutant Discharge Elimination System 

NPV net present value 

NWPCAM National Water Pollution Control Assessment Model 

OAQPS Office of Air Quality Planning and Standards 

OCC opportunity cost of capital 

OECD Organization for Economic Cooperation and Development 

OGC Office of General Counsel 

OIRA Office of Information & Regulatory Affairs 

OLS ordinary least squares 

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Acronyms and Abbreviations 

OMB Office of Management and Budget 

OSHA Occupational Safety and Health Administration 

OTEA Office of Trade and Economic Analysis 

PACE Pollution Abatement Costs and Expenditures 

PAOC pollution abatement operating cost 

PM2.5 particulate matter, 2.5 microns in diameter or less 

PM10 particulate matter, 10 microns in diameter or less 

POTW publicly-owned (wastewater) treatment work 

PRA Paperwork Reduction Act 

PVC present value of costs 

QA quality assurance 

QALY quality-adjusted life year 

R&D research and development 

RAPIDS Rule and Policy Information Development System 

RACT Reasonably Available Control Technology 

RCRA Resource Conservation and Recovery Act 

RDD random digit dialing 

REMI Regional Economic Models, Inc. 

RFA Regulatory Flexibility Act 

RIA regulatory impact analysis 

RUM random utility maximization 

S&P Standard & Poor’s 

SAB Science Advisory Board 

SAM social accounting matrix 

SBA Small Business Administration 

SBREFA Small Business Regulatory Enforcement Fairness Act 

SCC social cost of carbon 

SIC Standard Industrial Classification 

SISNOSE significant economic impact on a substantial number of small entities 

SO2 sulfur dioxide 

SWC Survey on Working Conditions 

TAMM Timber Assessment Market Model 

TMDL Total Maximum Daily Loadings 

TRI Toxics Release Inventory 

TSLS two-stage least squares 

UMRA Unfunded Mandates Reform Act 

UPF utility possibility frontier 

USC United States Code 

VOC volatile organic compounds 

VSL value of statistical life 

VSLY value of a statistical life-year 

WTA willingness to accept 

WTP willingness to pay 

x Guidelines for Preparing Economic Analyses | May 2014 
Glossary 

Glossary 

Annualized value 

An annualized value is a constant stream of benefits 

or costs. The annualized cost is the amount that a 

party would have to pay at the end of each period t 

to add up to the same cost in present value terms as 

the stream of costs being annualized. Similarly, the 

annualized benefit is the amount that a party would 

accrue at the end of each period t to add up to the 

same benefit in present value terms as the stream of 

benefits being annualized. 

Baseline 

A baseline describes an initial, status quo scenario that 

is used for comparison with one or more alternative 

scenarios. In typical economic analyses the baseline is 

defined as the best assessment of the world absent the 

proposed regulation or policy action. 

Benefit-cost analysis (BCA) 

A BCA evaluates the favorable effects of policy 

actions and the associated opportunity costs of 

those actions. It answers the question of whether the 

benefits are sufficient for the gainers to potentially 

compensate the losers, leaving everyone at least as well 

off as before the policy. The calculation of net benefits 

helps ascertain the economic efficiency of a regulation. 

Benefits 

Benefits are the favorable effects society gains due 

to a policy or action. Economists define benefits 

by focusing on changes in individual well-being, 

referred to as welfare or utility. Willingness to pay 

(WTP) is the preferred measure of these changes as it 

theoretically provides a full accounting of individual 

preferences across trade-offs between income and the 

favorable effects. 

Benefit-cost ratio 

A benefit-cost ratio is the ratio of the net present 

value (NPV) of benefits associated with a project 

or proposal, relative to the NPV of the costs of the 

project or proposal. The ratio indicates the benefits 

expected for each dollar of costs. Note that this 

ratio is not an indicator of the magnitude of net 

benefits. Two projects with the same benefit-cost 

ratio can have vastly different estimates of benefits 

and costs. 

Cessation lag 

Cessation lag is the time interval between the 

cessation of exposure and the reduction in risk. 

See latency for a definition of a related but distinct 

concept. 

Command-and-control regulation 

Command-and-control regulation requires polluters 

to meet specific emission-reduction targets defining 

acceptable levels of pollution. This type of regulation 

often requires the installation and use of specific 

types of equipment to reduce emissions. Commandand-control regulations usually impose the same 

requirements on all sources, although new and 

existing sources, taken as groups, are frequently 

subject to different standards. 

Compliance cost 

A compliance cost is the expenditure of time 

or money needed to conform to government 

requirements such as legislation or regulation. 

In the case of environmental regulation, these 

direct costs are associated with: (1) purchasing, 

installing, and operating new pollution control 

equipment; (2) changing a production process 

by using different inputs or different mixtures 

of inputs; and (3) capturing waste products and 

selling or reusing them. 

Consumption rate of interest 

Consumption rate of interest is the rate at which 

individuals are willing to exchange consumption 

over time. Simplifying assumptions, such as the 

absence of taxes on investment returns, imply that 

the consumption rate of interest equals the market 

interest rate, which also equals the rate of return on 

private sector investments. 

Cost-effectiveness analysis (CEA) 

CEA examines the costs associated with obtaining 

an additional unit of an environmental outcome. 

It is designed to identify the least expensive way of 

achieving a given environmental quality target, or 

the way of achieving the greatest improvement in 

some environmental target for a given expenditure of 

resources. 

Guidelines for Preparing Economic Analyses | May 2014 xi 
Glossary 

Costs 

Costs are the dollar values of resources needed to 

produce a good or service; once allocated, these 

resources are not available for use elsewhere. Private 

costs are the costs that the buyer of a good or service 

pays the seller. Social costs, also called externalities, are 

the costs that people other than the buyers are forced 

to pay, often through non-pecuniary means, as a 

result of a transaction. The bearers of social costs can 

be either particular individuals or society at large. 

Distributional analysis 

Distributional analysis assesses changes in social 

welfare by examining the effects of a regulation across 

different subpopulations and entities. Two types 

of distributional analyses are the economic impact 

analysis (EIA) and the equity assessment. 

Economic efficiency 

Economic efficiency refers to the optimal production 

and consumption of goods and services. This 

generally occurs when prices of products and services 

reflect their marginal costs, or when marginal benefits 

equal marginal costs. 

Economic impact analysis (EIA) 

An EIA examines the distribution of monetized 

effects of a policy, such as changes in industry 

profitability or in government revenues, as well 

as non-monetized effects, such as increases in 

unemployment rates or numbers of plant closures. 

Elasticity of demand 

Elasticity of demand measures the relationship 

between changes in quantity demanded of a good and 

changes in its price. It is calculated as the percentage 

change in quantity demanded that occurs in response 

to a percentage change in price. As the price of a 

good rises, consumers will usually demand a lower 

quantity of that good. The greater the extent to which 

quantity demanded falls as price rises, the greater 

is the price elasticity of demand. Some goods for 

which consumers cannot easily find substitutes, such 

as gasoline, are considered price inelastic. Note that 

elasticity can differ between the short term and the 

long term. For example, if the price of gasoline rises, 

consumers will eventually find ways to conserve their 

use of the resource. Some of these ways, like finding 

a more fuel-efficient car, take time. Hence gasoline 

would be price inelastic in the short term and more 

price elastic in the long term. 

Elasticity of supply 

Elasticity of supply measures the relationship between 

changes in quantity supplied of a good and changes 

in its price. It is measured as the percentage change 

in quantity supplied that occurs in response to a 

percentage change in price. For many goods the 

quantity supplied can be increased over time by 

locating alternative sources, investing in an expansion 

of production capacity, or developing competitive 

products that can substitute. One might therefore 

expect that the price elasticity of supply will be 

greater in the long term than the short term for such 

a good, that is, that supply can adjust to price changes 

to a greater degree over a longer period of time. 

Emissions tax 

An emissions tax is a charge levied on each unit of 

pollution emitted. 

Environmental justice 

Environmental justice is the fair treatment and 

meaningful involvement of all people regardless of 

race, color, national origin, or income with respect to 

the development, implementation, and enforcement 

of environmental laws, regulations, and policies. Fair 

treatment means that no group of people, including 

racial, ethnic, or socioeconomic groups should bear a 

disproportionate share of the negative environmental 

consequences resulting from industrial, municipal, 

and commercial operations or the execution of 

federal, state, local, and tribal programs and policies. 

Meaningful involvement means that: (1) people have 

an opportunity to participate in decisions about 

activities that can affect their environment and/or 

health; (2) the public’s contribution can influence the 

regulatory agency’s decision; (3) their concerns will 

be considered in the decision-making process; and 

(4) the decision makers seek out and facilitate the 

involvement of those potentially affected.1 

1 Definition taken from http://www.epa.gov/compliance/environmentaljustice/ 

index.html (accessed December 22, 2010). 

xii Guidelines for Preparing Economic Analyses | May 2014 
Glossary 

Equity assessment 

An equity assessment examines the distribution 

of benefits and costs associated with a regulation 

across specific sub-populations. Disadvantaged or 

vulnerable sub-populations, for example low-income 

households, may be of particular concern. 

Expert elicitation 

Expert elicitation is a formal, highly-structured and 

well-documented process for obtaining the judgments 

of multiple experts. Typically, an elicitation is 

conducted to evaluate uncertainty. This uncertainty 

could be associated with: the value of a parameter 

to be used in a model; the likelihood and frequency 

of various future events; or the relative merits of 

alternative models. 

Externalities 

An externality is a cost or benefit resulting from an 

action that is borne or received by parties not directly 

participating in the action. 

Flow pollutant 

A flow pollutant is a pollutant for which the 

environment has some absorptive capacity. It does not 

accumulate in the environment as long as its emission 

rate does not exceed the absorptive capacity of the 

environment. Animal and human wastes are examples 

of flow pollutants. 

Hotspot 

A hotspot is a geographic area with a high level of 

pollution/contamination within a larger geographic 

area of low or “normal” environmental quality. 

Kaldor-Hicks criterion 

The Kaldor-Hicks criterion is really a combination 

of two criteria: the Kaldor criterion and the Hicks 

criterion. The Kaldor criterion states that an activity 

will contribute to Pareto optimality if the maximum 

amount the gainers are prepared to pay is greater 

than the minimum amount that the losers are 

prepared to accept. Under the Hicks criterion, an 

activity will contribute to Pareto optimality if the 

maximum amount the losers are prepared to offer to 

the gainers in order to prevent the change is less than 

the minimum amount the gainers are prepared to 

accept as a bribe to forgo the change. In other words, 

the Hicks compensation test is conducted from the 

losers’ point of view, while the Kaldor compensation 

test is conducted from the gainers’ point of view. The 

Kaldor-Hicks criterion is widely applied in welfare 

economics and managerial economics. It forms an 

underlying rationale for BCA. 

Latency 

Latency is the time interval from the first exposure 

of a pollutant until the increase in health risk. See 

cessation lag for a definition of a related but distinct 

concept. 

Leakages 

A leakage is the displacement of pollution from one 

location to another as a result of the imposition of 

tighter pollution controls. Under tradable permit 

systems, leakages occur when pollution is displaced to 

an area not affected by a cap on allowed emissions. 

Marginal benefit 

The marginal benefit is the benefit received from an 

incremental increase in the consumption of a good or 

service. It is calculated as the increase in total benefit 

divided by the increase in consumption. 

Marginal cost 

The marginal cost is the change in total cost that 

results from a unit increase in output. It is calculated 

as the increase in total cost divided by the increase in 

output. 

Marginal social benefit 

The marginal social benefit is the marginal benefit 

received by the consumer of a good (marginal private 

benefit) plus the marginal benefit received by other 

members of society (external benefit). 

Marginal social cost 

The marginal social cost is the marginal cost incurred 

by the producer of a good (marginal private cost) 

plus the marginal cost imposed on other members of 

society (external cost). 

Market failure 

Market failure is a condition where the allocation of 

goods and services by a market is not efficient. Causes 

of market failure include: externalities, concentration 

of market power, information asymmetry, 

transactions costs, and the nature of the good (e.g., 

Guidelines for Preparing Economic Analyses | May 2014 xiii 
Glossary 

public goods). For environmental conditions, 

externalities are the most likely causes of the failure 

of private and public sector institutions to correct 

pollution damages. 

Market permit systems 

A market permit system is a system under which 

emissions sources are required to have emissions 

permits matching their actual emissions. Each permit 

specifies how much the source is allowed to emit and 

is transferable among firms. 

Market-based incentives 

Market-based incentives include a wide variety of 

methods for environmental protection. Instruments 

such as taxes, fees, charges, and subsidies generally 

“price” pollution and leave decisions about the level of 

emissions to each source. Another example is the market 

permit system, which sets the total quantity of emissions 

and then allows trading of permits among firms. 

Meta-analysis 

Meta-analysis is a statistical method of pooling data 

and/or results from a set of comparable studies of 

a problem. Pooling in this way provides a larger 

sample size for evaluation and allows for a stronger 

conclusion than can be provided by any single study. 

Meta-analysis yields a quantitative summary of the 

combined results. 

Net benefits 

Net benefits are calculated by subtracting total costs 

from total benefits. 

Net future value 

Net future value is similar to NPV, however, instead 

of discounting all future values back to the present, 

values are accumulated forward to some future time 

period — for example, to the end of the last year of a 

policy’s effects. 

Net present value (NPV) 

The NPV is calculated as the present value of a stream 

of current and future benefits minus the present value 

of a stream of current and future costs. 

Non-use value 

Non-use value is the value that an individual may 

derive from a good or resource without consuming 

it, as opposed to the value obtained from use of the 

resource. Non-use values can include bequest value, 

where an individual places a value on the availability 

of a resource to future generations; existence value, 

where an individual values the mere knowledge of 

the existence of a good or resource; and paternalistic 

altruism, where an individual places a value on others’ 

enjoyment of the resource. 

Opportunity cost 

Opportunity cost is the value of the next best 

alternative to a particular activity or resource. 

Opportunity cost need not be assessed in monetary 

terms. It can be assessed in terms of anything that is of 

value to the person or persons doing the assessing. For 

example a grove of trees used to produce paper may 

have a next-best-alternative use as habitat for spotted 

owls. Assessing opportunity costs is fundamental to 

assessing the true cost of any course of action. In the 

case where there is no explicit accounting or monetary 

cost (price) attached to a course of action, ignoring 

opportunity costs could produce the illusion that 

the action’s benefits cost nothing at all. The unseen 

opportunity costs then become the implicit hidden 

costs of that course of action. 

Quality-adjusted life year (QALY) 

QALY is a composite measure used to convert 

different types of health effects into a common, 

integrated unit, incorporating both the quality and 

quantity of life lived in different health states. This 

metric is commonly used in medical arenas to make 

decisions about medical interventions. 

Shadow price of capital 

The shadow price of capital takes into account the 

social value of displacing private capital investments. 

For example, when a public project displaces private 

sector investments, the correct method for measuring 

the social costs and benefits requires an adjustment 

of the estimated costs (and perhaps benefits as well) 

prior to discounting using the consumption rate of 

interest. This adjustment factor is referred to as the 

“shadow price of capital.” 

Social cost 

From a regulatory standpoint, social cost represents the 

total burden a regulation will impose on the economy. 

It can be defined as the sum of all opportunity 

xiv Guidelines for Preparing Economic Analyses | May 2014 
Glossary 

costs incurred as a result of the regulation. These 

opportunity costs consist of the value lost to society 

of all the goods and services that will not be produced 

and consumed if firms comply with the regulation and 

reallocate resources away from production activities 

and towards pollution abatement. To be complete, 

an estimate of social cost should include both the 

opportunity costs of current consumption that will 

be foregone as a result of the regulation, and also the 

losses that may result if the regulation reduces capital 

investment and thus future consumption. 

Social welfare function 

A social welfare function establishes criteria 

under which efficiency and equity outcomes are 

transformed into a single metric, making them 

directly comparable. A potential output of such a 

function is a ranking of policy outcomes that have 

different aggregate levels and distributions of net 

benefits. A social welfare function can provide 

empirical evidence that a policy alternative yielding 

higher net benefits, but a less equitable distribution 

of wealth, ranks better or worse than a less efficient 

alternative with more egalitarian distributional 

consequences. 

Stock pollutants 

A stock pollutant is a pollutant for which the 

environment has little or no absorptive capacity, such 

as non-biodegradable plastic, heavy metals such as 

mercury, and radioactive waste. A stock pollutant 

accumulates through time. 

Subsidies 

A subsidy is a kind of financial assistance, such as a 

grant, tax break, or trade barrier, that is implemented 

in order to encourage certain behavior. For example, 

the government may directly pay polluters to reduce 

their pollution emissions. 

Tax-subsidy 

A tax-subsidy is any form of subsidy where the 

recipients receive the benefit through the tax 

system, usually through the income tax, profit tax, 

or consumption tax systems. Examples include 

tax deductions for workers in certain industries, 

accelerated depreciation for certain industries or 

types of equipment, or exemption from consumption 

tax (sales tax or value added tax). 

Total cost 

Total cost is defined as the sum of all costs associated 

with a given activity. 

Use value 

Use value is an economic value based on the tangible 

human use of some environmental or natural 

resource. 

Value of statistical life (VSL) 

VSL is a summary measure for the dollar value of 

small changes in mortality risk experienced by a 

large number of people. VSL estimates are derived 

from aggregated estimates of individual values for 

small changes in mortality risks. For example, if 

10,000 individuals are each willing to pay $500 for a 

reduction in risk of 1/10,000, then the value of saving 

one statistical life equals $500 times 10,000 — or $5 

million. Note that this does not mean that any single 

identifiable life is valued at this amount. Rather, 

the aggregate value of reducing a collection of small 

individual risks is, in this case, worth $5 million. 

Value of statistical life year (VSLY) 

The VSLY is the estimated dollar value for a year of 

statistical life. In practice this metric is often derived 

by dividing the VSL by remaining life expectancy. 

This approach is controversial in that it assumes that 

each year of life over the life cycle has the same value, 

and it assumes that the value of a statistical life equals 

the present discounted value of these annual amounts. 

Willingness to accept (WTA) 

WTA is the amount of compensation an individual is 

willing to take in exchange for giving up some good or 

service. In the case of an environmental policy, WTA 

is the least amount of money that an individual would 

accept to forego an environmental improvement (or 

endure an environmental decrement). 

Willingness to pay (WTP) 

WTP is the largest amount of money that an 

individual or group would pay to receive the benefits 

(or avoid the damages) resulting from a policy 

change, without being made worse off. In the case 

of an environmental policy, WTP is the maximum 

amount of money an individual would pay to obtain 

an improvement (or avoid a decrement) in an 

environmental effect of concern. 

Guidelines for Preparing Economic Analyses | May 2014 xv 
xvi Guidelines for Preparing Economic Analyses | May 2014 
Chapter 1 

Introduction
 

T

he Guidelines for Preparing Economic Analyses are part of a continuing effort by 

U.S. Environmental Protection Agency (EPA) to develop improved guidance 

on the preparation and use of sound science in support of the decision-making 

process. This document builds on previous work first issued in December 

of 1983 as the Guidelines for Performing Regulatory Impact Analysis (U.S. 

EPA 1983) and later revised in the late 1990s. In September of 2000, the EPA issued its 

Guidelines for Preparing Economic Analyses (Guidelines) (U.S. EPA 2000b), revised to reflect 

the evolution of environmental policy making and economic analysis that had accrued 

over the decade and a half since the original guidelines were released. At the time of release, 

EPA committed to periodically revise the Guidelines to account for further growth and 

development of economic tools and practices. 

In an effort to fulfill that commitment, this document incorporates new literature published 

since the last revision of the Guidelines. It describes new Executive Orders (EOs) and recent 

guidance documents that impose new requirements on analysts, and fills information gaps by 

providing more expansive information on selected topics. Furthermore, a loose-leaf format 

has been adopted to facilitate the incorporation of new information in the future. This new, 

more flexible format, in addition to the electronic release of the document, will allow future 

updates and additions without requiring a wholesale revision of the document. 

1.1 Background Agency.1 However, new guidance documents and 

While economic analysis can provide valuable handbooks on how to comply with a number of 

insights into the setting of Agency priorities and plans EOs and statutes have been issued both within and 

for meeting them, the focus of this document is on outside the Agency in the last several years. The 

the conduct of economic analysis to support policy Office of Management and Budget (OMB), for 

decisions and meeting the requirements described instance, released its Circular A-4 in 2003 to replace 

by related statutes, EOs, and recommendations both its “Best Practices” document (OMB 1996) and 

in guidance materials. With a few exceptions, the its “OMB Guidelines” (OMB 2000). Circular A-4 

collection of EOs and statutes that govern the provides recommendations to federal agencies on 

conduct of economic analysis and distributional the development of economic analyses supporting 

analysis has remained largely unchanged since 2000. regulatory actions. As such, it greatly influences the 

EO 12866, directing federal agencies to perform conduct of economic analysis and the development 

a benefit-cost analysis (BCA) for economically of new analytic tools and approaches within the 

significant rules (those with an economic impact of Agency. The OMB recommendations, as well as other 

$100 million or more), still provides the primary 1 EO 13422, a 2007 amendment to EO 12866, contributed to the formal impetus for much of the formal BCA within the benefit-cost framework by requiring agencies to “identify in writing 

the specific market failure (such as externalities, market power, lack of 

information) or other specific problem that [the regulation] intends to 

address ... as well as assess the significance of that problem.” However, 

EO 13422 was revoked in January 2009 through EO 13497. 

Guidelines for Preparing Economic Analyses | December 2010 1-1 
1-2 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 1 Introduction

guidance documents, are referenced in the revised 

Guidelines where appropriate. 

As a result of these modifications and updates 

the new, revised Guidelines will ensure that 

EPA’s economic analyses are prepared to inform 

the policy-making processes and satisfy OMB’s 

requirements for regulatory review. The new 

Guidelines also seeks to establish an interactive 

policy development process between analysts 

and decision makers through an expanded set 

of cost, benefit, economic impacts, and equity 

effects assessments; an up-to-date encapsulation of 

environmental economics theory and practice; and 

an enhanced emphasis on practical applications. 

Underlying these efforts is the recognition that 

a thorough and careful economic analysis is 

an important component in informing sound 

environmental policies. Preparing high-quality 

economic analysis can greatly enhance the 

effectiveness of environmental policy decisions 

by providing policy makers with the ability to 

systematically assess the consequences of various 

actions. An economic analysis can describe the 

implications of policy alternatives not just in terms 

of economic efficiency, but also in terms of the 

magnitude and distribution of an array of impacts. 

Economic analysis also serves as a mechanism for 

organizing information carefully. Thus, even when 

data are insufficient to support particular types of 

economic analysis, the conceptual scoping exercise 

can provide useful insights. 

It is important to note that economic analysis is 

but one component in the decision-making process 

and under some statutes it cannot be used in 

setting standards. Other factors that may influence 

decision makers include enforceability, technical 

feasibility, affordability, political concerns, and 

ethics, to name but a few. Nevertheless, economic 

analysis provides a means to organize information 

and to comprehensively assess alternative actions 

and their consequences. Provided early in the 

regulatory design phase, economic analysis can 

help guide the selection of options. Ultimately, 

good economic analysis based on sound science 

should lead to better, more defensible rules. 

1.2 The Scope of the 

Guidelines 

The scope of the Guidelines is on economic analysis 

typically conducted for environmental policies 

using regulatory or non-regulatory management 

strategies. Separate guidance documents exist 

for related analyses, some of which are inputs to 

economic assessments. No attempt is made here 

to summarize these other guidance materials. 

Instead, their existence and content are noted in 

the appropriate sections. 

As with the 2000 Guidelines, the presentation 

of economic concepts and applications in 

this document assumes the reader has some 

background in microeconomics as applied to 

environmental and natural resource policies. To 

fully understand and apply the approaches and 

recommendations presented in the Guidelines, 

readers should be familiar with basic applied 

microeconomic analysis, the concepts and 

measurement of consumer and producer surplus, 

and the economic foundations of benefit-cost 

evaluation. Appendix A provides the reader with 

a brief review of economic foundations and the 

Glossary defines selected key terms. 

These Guidelines are designed to provide 

assistance to analysts in the economic analysis 

of environmental policies, but they do not 

provide a rigid blueprint or a “cookbook” for 

all policy assessments. The most productive and 

illuminating approaches for particular situations 

will depend on a variety of case-specific factors 

and will require professional judgment. The 

Guidelines should be viewed as a summary of 

analytical methodologies, empirical techniques, 

and data sources that can assist in performing 

economic analysis of environmental policies. 

When drawing upon these various resources, 

there is no substitute for reviewing the original 

source materials. 

In all cases, the Guidelines recommends adhering 

to the following general principles as stated by 

OMB (1996): 

‘“Analysis of the risks, benefits, and costs 

associated with regulation must be guided 
Guidelines for Preparing Economic Analyses | December 2010 1-3 

Chapter 1 Introduction

by the principles of full disclosure and 

transparency. Data, models, inferences, and 

assumptions should be identified and evaluated 

explicitly, together with adequate justifications 

of choices made, and assessments of the effects 

of these choices on the analysis. The existence 

of plausible alternative models or assumptions, 

and their implications, should be identified. 

In the absence of adequate valid data, properly 

identified assumptions are necessary for 

conducting an assessment.” 

“Analysis of the risks, benefits, and costs 

associated with regulation inevitably also 

involves uncertainties and requires informed 

professional judgments. There should be 

balance between thoroughness of analysis 

and practical limits to the agency’s capacity 

to carry out analysis. The amount of analysis 

(whether scientific, statistical, or economic) 

that a particular issue requires depends on the 

need for more thorough analysis because of the 

importance and complexity of the issue, the 

need for expedition, the nature of the statutory 

language and the extent of statutory discretion, 

and the sensitivity of net benefits to the choice 

of regulatory alternatives.”’ 

Economic analyses should always strive to be 

transparent by acknowledging and characterizing 

important uncertainties that arise. In addition, 

economic analyses should clearly state the 

judgments and decisions associated with 

these uncertainties and should identify the 

implications of these choices. When assumptions 

are necessary in order to carry out the analysis, 

the reasons for those assumptions must be stated 

explicitly and clearly. Analysts must take care 

to avoid double counting of benefits and costs 

when there are overlapping regulatory initiatives. 

Further, economic analyses of environmental 

policies should be flexible enough to be tailored 

to the specific circumstances of a particular 

policy, and to incorporate new information 

and advances in the theory and practice of 

environmental policy analysis. 

1.3 Economic Framework and 

Definition of Terms 

The conceptually appropriate framework for 

assessing all the impacts of an environmental 

regulation is an economic model of general 

equilibrium. The starting point of such a model 

is to define the allocation of resources and 

interrelationships for an entire economy with 

all its diverse components (households, firms, 

government). 

One of the first methodological questions an 

analyst must answer when conducting economic 

analysis is: who has “standing?” The most inclusive 

answer allows all persons who may be affected by 

the policy to have standing, regardless of where 

(or when) they live. For domestic policy making, 

however, the norm is to limit standing to the 

national level. This decision is based on the fact 

that authority to regulate only extends to a nation’s 

own residents who have consented to adhere to 

the same set of rules and values for collective 

decision making, as well as the assumption that 

most domestic policies will have negligible effects 

on other countries (Kopp et al. 1997, Whittington 

et al. 1986). 

OMB’s Circular A-4 gives the following guidance 

to agencies with regard to conducting economic 

analyses in support of rulemakings: “Analysis 

should focus on benefits and costs that accrue 

to citizens and residents of the United States. 

Where you choose to evaluate a regulation that 

is likely to have effects beyond the borders of the 

United States, these effects should be reported 

separately” (OMB 2003, p. 15). Potential 

regulatory alternatives are then modeled as 

economic changes that move the economy from 

a state of equilibrium absent the regulation (the 

baseline) to a new state of equilibrium with the 

regulation in effect. The differences between 

the old and new states are measured as changes 

in prices, quantities produced and consumed, 

income and other economic quantities. These 

measurements can be used to characterize the net 

welfare changes for each affected group identified 

in the model. Analysts can rely on different 

outputs and conclusions from the general 

equilibrium framework to assess issues of both 
1-4 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 1 Introduction

 

 

 

 

efficiency and distribution. These issues often take 

the form of three distinct questions: 

1. Is it theoretically possible for the “gainers” 

from the policy to fully compensate the 

“losers” and still remain better off ? 

2. Who are the gainers and losers from the 

policy and associated economic changes? 

3. How did a particular group, especially a 

group considered to be disadvantaged, fare as 

a result of the policy change? 

The first question is directed at the measurement 

of efficiency, and is based on the Potential 

Pareto criterion. This criterion is the foundation 

of BCA, requiring that a policy’s net benefits 

to society be positive. Measuring net benefits 

by summing all of the welfare changes for all 

affected groups provides an answer to this 

question. Net benefits are derived by summing 

all of the benefits that accrue as a result of a 

policy change (including spillover effects) 

less costs imposed by the policy on society 

(including externalities). Since spillovers and 

externalities by definition are not captured in 

market transactions, counting private costs and 

private benefits accruing to market participants 

is not sufficient for estimating social benefits and 

costs. The policy that maximizes net benefits is 

considered the most efficient.2 

The last two questions are related to the 

distributional consequences of the policy. Because 

a general equilibrium framework provides for the 

ability to estimate welfare changes for particular 

groups, these questions can be pursued using the 

same approach taken to answer the efficiency 

question, provided that the general equilibrium 

model is developed at an appropriate level of 

disaggregation. 

Although a general equilibrium framework can, 

in principle, provide the information needed to 

address all three questions, in practice analysts have 

limited access to the tools and resources needed 

Appendix A gives a conceptual overview of this discussion. See in 

particular Section A.3 on BCA. 

to adopt a general equilibrium approach.3 More 

often, EPA must resort to assembling a set of 

different models to address issues of efficiency and 

distribution separately. However, the limitations 

on employing general equilibrium models have 

greatly diminished in recent years with advances 

in the theory, tools and data needed to use 

the approach. Chapter 8 contains additional 

information on general equilibrium models. 

Analysts should weigh the need for additional 

precision against the cost of employing general 

equilibrium models over other methods. In doing 

so analysts should consider the size, impact, and 

complexity of the question at hand. In general, the 

more detailed methods are justified by questions 

with larger and more complex impacts. This 

question is considered in each of the chapters on 

specific models. 

The Guidelines follows more traditional practices 

and adopts conventional labels to distinguish 

models or approaches used to answer questions on 

the efficiency and distribution of environmental 

regulations. For purposes of this document, 

the presentation separates the concepts and 

approaches into the following three general 

categories: 

• the examination of net social benefits using a 

benefit-cost analysis (BCA); 

• the examination of impacts on industry, 

governments, and non-profit organizations 

using an economic impacts analysis (EIA); and 

• the examination of effects on various subpopulations, particularly low-income, 

minority, and children, using distributional 

analyses. 

This division is necessary not only because of data 

and resource limitations, but because analysts often 

lack models that are sufficiently comprehensive 

to address all of these dimensions concurrently. 

Within a BCA, for example, EPA is generally 

unable to measure benefits with the same models 

3 The general equilibrium framework will at least capture all “market” 

benefits and costs, but may not include non-market benefits, such as 

those associated with existence value. In practice, models of general 

equilibrium may be unable to analyze relatively small sectors of the 

economy. For more on general equilibrium analysis see Chapter 8, 

Section 4.6. 

2 
Guidelines for Preparing Economic Analyses | December 2010 1-5 

Chapter 1 Introduction

used for estimating costs, necessitating separate 

treatment of costs and benefits. Further, when 

estimating social costs there are cases in which 

some direct expenditures can be identified, but 

data and models are unavailable to track the 

“ripple” effects of these expenditures through 

the economy. For most practical applications, 

therefore, a complete economic analysis is 

comprised of a BCA, an EIA, and an equity 

assessment. 

BCA evaluates the favorable effects of policy 

actions and the associated opportunity costs of 

those actions. The favorable effects are defined 

as benefits. Opportunities foregone define 

economic costs. While conceptually symmetric, 

benefits and costs are often evaluated separately 

for “traditional” environmental problems (e.g., 

emissions of pollutants from point sources into 

air and water) due to practical considerations. 

Analysts may organize the analysis of benefits 

differently from the analysis of costs, but they 

should be aware of the conceptual relationship 

between the two. Assessing the effects of 

environmental policy is inherently a complex 

process in which results from various disciplines 

are integrated to predict environmental outcomes 

and their economic consequences. As EPA 

addresses increasingly complex environmental 

problems (e.g., climate change), so in turn 

will be the models needed to track the various 

processes to describe and capture policy effects. 

Computable general equilibrium (CGE) models 

for these types of policies will become increasingly 

important. 

Once the change in pollution levels resulting from 

a policy is predicted, this change is translated into 

health outcomes or other outcomes of interest 

using information provided by risk assessors. 

Benefits analyses then apply a variety of economic 

methodologies to estimate the value of these 

anticipated health improvements and other sources 

of environmental benefits. Social cost analyses 

attempt to estimate the total welfare costs, net of 

any transfers, imposed by environmental policies. 

In most instances, these costs are measured by 

higher costs of consumption goods for consumers 

and lower earnings for producers and other 

factors of production. Some of the findings of a 

social cost analysis are inputs for benefits analyses, 

such as predicted changes in the outputs of 

goods associated with a pollution problem. More 

information on analyzing benefits can be found in 

Chapter 7 while details on estimating social costs 

can be found in Chapter 8. 

The assumptions and modeling framework 

developed for the BCA can describe gains and 

losses to assess efficiency. However the BCA 

framework often limits detailed examination 

of the gainers and losers and the impacts on 

disadvantaged sub-populations. To estimate these 

two categories of impacts analysts rely upon EIA 

and equity assessments, which use a multiplicity of 

estimation techniques. Chapters 9 and 10 provide 

information on how these analyses relate to BCA 

and detail estimation techniques. 

Note that none of these three types of analyses 

(BCA, EIA, and equity assessment) address 

the cost-effectiveness of a policy option. 

Cost-effectiveness analyses (CEA) report the 

estimated costs needed to achieve a specific 

goal or an additional unit of environmental 

improvement. Costs-per-life-saved and costsper-ton-of-pollution-reduction are examples of 

cost-effectiveness measures. When comparisons 

are made across policies, CEA can be used to help 

identify the least costly approach to achieving a 

specific goal.4 

1.4 Organization of the 

Guidelines 

The remainder of this document is organized into 

ten main chapters as follows: 

• Chapter 2: Statutory and Executive Order 

Requirements for Conducting Economic 

Analyses reviews the major statutes and 

other directives mandating certain economic 

assessments of the consequences of policy 

actions; 

4 Note that CEA is not covered extensively in this document. Additional 

sources for details on CEA include IOM (2006) and Boardman et al. 

(2006). 
1-6 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 1 Introduction

• Chapter 3: Statement of Need for the 

Proposal provides guidance on procedures 

and analyses for clearly identifying the 

environmental problem to be addressed, and 

for justifying federal intervention to correct 

the problem; 

• Chapter 4: Regulatory and Non-Regulatory 

Approaches to Consider discusses the variety 

of regulatory and non-regulatory approaches 

analysts and policy makers ought to consider 

in developing strategies for environmental 

improvement; 

• Chapter 5: Baselines provides a definition 

of baseline and discusses how analysts should 

approach conducting a baseline analysis; 

• Chapter 6: Analysis of Social Discounting 

presents a review of discounting procedures 

and provides guidance on social discounting 

in conventional contexts and over very long 

time horizons; 

• Chapter 7: Analyzing Benefits provides 

guidance for assessing the benefits of 

environmental policies including various 

techniques of valuing risk-reduction and other 

benefits; 

• Chapter 8: Analyzing Costs presents the 

basic theoretical approach for assessing the 

costs of environmental policies and describes 

how this can be applied in practice; 

• Chapter 9: Economic Impact Analyses and 

Equity Assessment provides guidance for 

performing a variety of different assessments 

of the economic impacts of environmental 

policies; 

• Chapter 10: Environmental Justice, 

Children’s Environmental Health and 

Other Distributional Considerations 

discusses key analytical issues and 


considerations to keep in mind when 


performing distributional analyses; and 


• Chapter 11: Presentation of Analysis and 

Results concludes the main body of the 

Guidelines with suggestions for presenting 

the quantified and unquantified results of the 

various economic analyses to policy makers. 
Chapter 2 

Statutory and Executive Order 

Requirements for Conducting 

Economic Analyses 

Agencies are subject to a number of statutes and executive orders (EOs) that 

direct the conduct of specific types of economic analyses.1

 Many of these 

directives are potentially relevant for all of EPA’s programs while others 

target individual programs. This chapter highlights directives that may apply 

to all of EPA’s programs.2 

The scope of requirements for economic analysis can vary substantially. In some cases, a 

statute or EO may contain language that limits its applicability to only those regulatory 

actions, or rules, that fall above a specified threshold in significance or impact. Economic 

analysis may be necessary to determine if a regulatory action exceeds a significance or 

impact threshold, and thus falls in the class of regulatory actions targeted by the statute 

or EO. If a regulatory action must comply with the requirements of a given statute or 

EO, additional economic analysis (e.g., analysis of benefits and costs as required by EO 

12866), procedural steps (e.g., consultation with affected state and local governments 

as required by EO 13132), or a combination of economic analysis and procedural steps 

may be required. This chapter describes the general requirements for economic analysis 

contained in selected statutes and EOs, identifies thresholds beyond which a regulatory 

action must follow additional economic analysis requirements, and provides further 

direction for analysts seeking guidance on compliance with the statute or EO.3

 For 

each EO or statute highlighted in this chapter, references to applicable OMB and EPA 

guidelines are provided. Another resource for determining the type and scope of economic 

analysis required for a rule is a program’s Office of General Counsel (OGC) attorney.4 

Requirements of the statutes and EOs that do not necessitate economic analysis are not 

covered in this chapter. 

1	 For the text statutes and EOs appearing in this chapter, and guidance specific to them, or for more information on their implications for EPA rule development, 

visit the Action Development Process (ADP) Library on EPA’s intranet http://intranet.epa.gov/adplibrary (accessed April 28, 2004, internal EPA document). 

Many of the citations for other applicable guidelines included in this section can be found at that site. Alternatively, information on statutes and EOs can easily 

be found using http://usasearch.gov/. 

2	 Statutory provisions that require economic analysis but apply only to specific EPA programs are not described here. However, analysts should carefully 

consider the relevant program-specific statutory requirements when designing and conducting economic analyses, recognizing that these requirements may 

mandate specific economic analyses. 

3	 Note that for some statutes and EOs, requirements for proposed regulatory actions may vary slightly from the requirements for final regulatory actions. 

4	 See U.S. EPA (2005b) for more information. 

Guidelines for Preparing Economic Analyses | December 2010 2-1 
Chapter 2 Statutory and Executive Order Requirements for Conducting Economic Analyses

 

 

 

2.1 Executive Orders 

2.1.1 Executive Order 12866, 

“Regulatory Planning and 

Review” 

Threshold: Significant regulatory actions. A 

“significant regulatory action” is defined by 

Section 3(f )(1)-(4) as one that is likely to result in 

a rule that may: 

• Have an annual effect on the economy of 

$100 million or more or adversely affect in 

a material way the economy, a sector of the 

economy, productivity, competition, jobs, 

the environment, public health or safety, 

or State, local, or tribal governments or 

communities; 

• Create a serious inconsistency or otherwise 

interfere with an action taken or planned by 

another agency; 

• Materially alter the budgetary impact of 

entitlements, grants, user fees, or loan programs 

or the rights and obligations of recipients 

thereof; or 

• Raise novel legal or policy issues arising out of 

legal mandates, the President’s priorities, or 

the principles set forth in this Executive order. 

Any one of the four criteria listed above can 

trigger a regulatory action to be defined as 

“significant;” a regulatory action that meets the 

first criteria is generally defined as “economically 

significant.” While the determination of economic 

significance is multi-faceted, it is most often 

triggered by the $100 million threshold. This 

threshold is interpreted as being based on the 

annual costs or benefits of the proposed or finalized 

option. If one rule option poses costs or benefits 

in excess of $100 million, but the rule option to 

be proposed or finalized has costs and benefits 

that fall below the $100 million range, the rule 

is not considered economically significant. 

The same definition applies whether the rule is 

regulatory or deregulatory in nature. In the case 

of a deregulatory rule with cost savings, transfers 

should not be netted out. For example, if there are 

additional costs in one market and cost savings in 

another, they should not be combined to get “net” 

cost savings. If one company loses $100 million in 

business to another company, that is sufficient for 

an economic significance determination, even if 

the net effect is zero. The EO is silent on whether 

the threshold should be adjusted for inflation. As 

such, nominal values have been used in practice, 

implying that as inflation increases the threshold 

becomes more stringent. 

Requirements contingent on threshold: A 

statement of the need for the proposed action and 

an assessment of social benefits and costs (Section 

6(a)(3)(B) are required. The requirements for 

BCA increase in complexity and detail for 

economically significant rules (i.e., those that fall 

under the definition in the first bullet above). 

For these rules, the EO requires that agencies 

conduct an assessment of benefits and costs of the 

action, that benefits and costs be quantified to the 

extent feasible, and that the benefits and costs of 

alternative approaches also be assessed (Section 

6(a)(3)(C)).5 

Guidance: Chapters 3 through 8 of this document 

provide guidance for meeting these requirements. 

OMB’s Circular A-4 (2003) provides guidance to 

federal agencies on the development of regulatory 

analysis of economically significant rules as required 

by EO 12866. More specifically, Circular A-4 is 

intended to define good regulatory analysis and 

standardize the way benefits and costs of federal 

regulatory actions are measured and reported. 

Chapter 9 of this document describes methods for 

analyzing and assessing distributional effects of a 

rule through EIA. Chapter 10 addresses how to 

assess environmental justice implications.6 

5	 EO 13422 and amended EO 12866 formerly required analysts to 

“identify in writing the specific market failure (such as externalities, 

market power, lack of information) or other specific problem” and 

extended the BCA requirement to “significant” guidance documents. 

Although EO 13497, issued in January 2009, revoked EO 13422 

together with any “orders, rules, regulations, guidelines, or policies” 

enforcing it, a subsequent memo issued by then Director of OMB Peter 

R. Orszag offering guidance on the implementation of the new EO 

indicated that “significant policy and guidance documents...remain 

subject to OIRA’s review.” 

6	 In its Statement of Regulatory Philosophy, EO 12866 states that 

agencies should consider the distributional and equity effects of a rule 

(Section 1(a)). 

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Chapter 2 Statutory and Executive Order Requirements for Conducting Economic Analyses

2.1.2 Executive Order 12898, 

“Federal Actions to Address 

Environmental Justice in Minority 

Populations and Low-Income 

Populations” 

Threshold: No specific threshold; Agencies 

are required to “...identify and address 

disproportionately high and adverse human health 

or environmental effects of its programs, policies, 

and activities on minority populations and lowincome populations...” 

Requirements contingent on threshold: No 

specific analytical requirements. 

Guidance: EPA issued interim guidance for 

considering environmental justice in the Action 

Development Process (U.S. EPA 2010); EPA 

and the Council on Environmental Quality 

(CEQ) have prepared guidance for addressing 

environmental justice concerns in the context 

of National Environmental Policy Act (NEPA) 

requirements [U.S. EPA 1998a and CEQ (1997)]. 

These materials provide guidance on key terms in 

the EO. Chapter 10 of this document addresses 

environmental justice analysis. 

2.1.3 Executive Order 13045, 

“Protection of Children from 

Environmental Health Risks and 

Safety Risks” 

Threshold: Economically significant regulatory 

actions as described by EO 12866 that 

involve environmental health risk or safety 

risk that an agency has reason to believe may 

disproportionately affect children. 

Requirements contingent on threshold: An 

evaluation of the health or safety effects of the 

planned regulation on children, as well as an 

explanation of why the planned regulation is 

preferable to other potentially effective and 

reasonably feasible alternatives the agency is 

considering. 

Guidance: EPA has prepared guidance for rule 

writers on compliance with EO 13045 (U.S. 

EPA 1998b). EPA’s Children’s Health Valuation 

Handbook (U.S. EPA 2003b) discusses special 

issues related to estimation of the value of health 

risk reductions to children. Guidance in Chapter 

10 of this document addresses equity analyses 

focused on children. 

2.1.4 Executive Order 13132, 

“Federalism” 

Threshold: Rules that have “federalism 

implications” due to either substantial compliance 

costs or preemption of state or local law. Rules 

with federalism implications are defined as those 

rules “that have substantial direct effects on the 

States [including local governments], on the 

relationship between the national government 

and the States, or on the distribution of power 

and responsibilities among the various levels of 

government.” Rules may be considered to impose 

substantial compliance costs on state or local 

governments unless the costs are expressly required 

by statute or there are federal funds available to 

cover them. 

Requirements contingent on threshold: 

Submission to OMB of a Federalism Summary 

Impact Statement and consultation with elected 

officials of affected state and local governments. 

Guidance: Specific guidance on EO 13132 can 

be found in the internal EPA document Guidance 

on Executive Order 13132: Federalism (U.S. EPA 

2008c). 

7 

2.1.5 Executive Order 13175, 

“Consultation and Coordination 

with Indian Tribal Governments” 

Threshold: Rules and policy statements that 

have tribal implications; that is, those that 

have “substantial direct effects on one or more 

Indian tribes, on the relationship between the 

Federal Government and Indian tribes, or on the 

distribution of power and responsibilities between 

the Federal Government and Indian tribes.” 

7 This document is located at http://intranet.epa.gov/adplibrary/ 

documents/federalismguide11-00-08.pdf (accessed March 4, 2010, 

internal EPA document). 

Guidelines for Preparing Economic Analyses | December 2010 2-3 
Chapter 2 Statutory and Executive Order Requirements for Conducting Economic Analyses

Requirements contingent on threshold: To 

the extent practicable and permitted by law, 

if a regulatory action with tribal implications 

is proposed and imposes substantial direct 

compliance costs on Indian tribal governments, 

and is not required by statute, then the agency 

must either provide the funds necessary to pay the 

tribal governments’ direct compliance costs, or 

consult with tribal officials early in the process of 

regulatory development and provide to OMB a 

Tribal Summary Impact Statement. 

Guidance: A tribal guidance document is 

currently under development by EPA’s Regulatory 

Management Division.8

 Guidance in Chapter 9 of 

this document addresses equity analyses focusing 

on minority populations. 

2.1.6 Executive Order 13211, 

“Actions Concerning Regulations 

that Significantly Affect Energy 

Supply, Distribution, or Use” 

Threshold: Rules that are a significant regulatory 

action under EO 12866 and that are likely to 

have significant adverse effects on the supply, 

distribution, or use of energy. 

Requirements contingent on threshold: 

Submission of a Statement of Energy Effects to 

OMB. The Statement of Energy Effects addresses 

the magnitude of expected adverse effects, 

describes reasonable alternatives to the action, and 

describes the expected effects of such alternatives 

on energy supply, distribution, and use. 

Guidance: EPA has prepared guidance on 

what effects might be considered significant in 

Memorandum on Energy Executive Order 13211 

— Preliminary Guidance (2008d). OMB has 

guidance for implementing EO 13211 as well.9 

8	 Please check the ADP Library on EPA’s intranet, http://intranet.epa. 

gov/adplibrary (accessed April 8, 2010, internal EPA document) for the 

status of this guidance. 

9 U.S. EPA 2008d, Memorandum on Energy Executive Order 13211 — 

Preliminary Guidance, located at http://intranet.epa.gov/adplibrary/ 

statutes.htm#energy under the heading “Preamble Template” 

(accessed July 8, 2008, internal EPA document). OMB’s guidance for 

implementing EO 13211 is located at http://www.whitehouse.gov/omb/ 

memoranda/m01_27.html (accessed July 8, 2008). 

2.2 Statutes 

2.2.1 The Regulatory Flexibility 

Act of 1980 (RFA), as Amended by 

The Small Business Regulatory 

Enforcement Fairness Act of 1996 

(SBREFA) (5 U.S.C. 601-612) 

Threshold: Regulations that have a significant 

economic impact on a substantial number of small 

entities, including small businesses, governments 

and non-profit organizations. 

Requirements contingent on threshold: 

Preparation of a regulatory flexibility analysis, 

and compliance with a number of procedural 

requirements to solicit and consider flexible 

regulatory options that minimize adverse 

economic impacts on small entities. 

Guidance: EPA has issued specific guidance for 

complying with RFA/SBREFA requirements 

in the internal document EPA Final Guidance 

for EPA Rulewriters: Regulatory Flexibility Act 

as amended by the Small Business Regulatory 

Enforcement Fairness Act (2006c).10 

2.2.2 The Unfunded Mandates 

Reform Act of 1995 (UMRA) 

(P.L. 104-4) 

Threshold one (Sections 202 and 205 of UMRA): 

Regulatory actions that include federal mandates 

“that may result in the expenditure by State, local, 

and tribal governments, in the aggregate, or by the 

private sector, of $100 million or more (adjusted 

annually for inflation) in any one year.”11 

Requirements contingent on threshold one: 

Section 202 of UMRA requires preparation 

of a written statement that includes the legal 

authority for the action; a BCA; a distributional 

analysis; estimates of macroeconomic impacts; 

and a description of an agency’s consultation with 

elected representatives of the affected state, local, 

or tribal governments. Section 205 of UMRA 

10	 U.S. EPA 2006c, available at http://intranet.epa.gov/adplibrary 

(accessed May 1, 2008, internal EPA document). 

11	 Note that the threshold in this case is “adjusted annually for inflation” 

as opposed to the threshold under EO 12866. 

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Chapter 2 Statutory and Executive Order Requirements for Conducting Economic Analyses

requires an agency to consider a reasonable number 

of regulatory alternatives and select the least 

costly, most cost-effective, or least burdensome 

alternative, or to publish with the final rule an 

explanation of why such alternative was not 

chosen. 

Threshold two (Section 203 of UMRA): 

Regulatory requirements that might “significantly” 

or “uniquely” affect small governments. 

Requirements contingent on threshold 

two: Agencies must solicit involvement from, 

and conduct outreach to, potentially affected 

small governments during development and 

implementation. 

Guidance: EPA has issued Interim Guidance 

on the Unfunded Mandates Reform Act of 1995, 

(1995b), and OMB provides general guidance on 

complying with requirements contingent on each 

of the two thresholds under UMRA.12 

2.2.3 The Paperwork Reduction 

Act of 1995 (PRA) (44 U.S.C. 3501) 

Threshold: Actions (both regulatory and nonregulatory) that include record-keeping, reporting, 

or disclosure requirements or other information 

collection activities calling for answers to identical 

questions imposed upon or posed to ten or more 

persons, other than federal agency employees. 

Requirements contingent on threshold: The 

agency must submit an information collection 

request (ICR) to OMB for review and approval 

and meet other procedural requirements including 

public notice. Note that 1320.3(c)(4)(ii) states 

that “any collection of information addressed 

to all or a substantial majority of an industry 

is presumed to involve ten or more persons.” 

However, OMB guidance on this issue indicates 

that if agencies have evidence showing that this 

presumption is incorrect in a specific situation 

(i.e., fewer than 10 persons would be surveyed), 

the agency may proceed with the collection 

without seeking OMB approval. Agencies must 

12	 See U.S. EPA 1995b available at http://intranet.epa.gov/adplibrary/ 

statutes/umra.htm (accessed December 21, 2010). 

be prepared to provide this evidence to OMB on 

request and abide by OMB’s determination as to 

whether the collection of information ultimately 

requires OMB approval. 

Guidance: Both guidance and templates for 

completing an ICR and associated Federal Register 

(FR) notices can be found on EPA’s intranet site, 

“ICR Center.”13 

13 See http://intranet.epa.gov/icrintra/ (accessed April 14, 2004, internal 

EPA document). 

Guidelines for Preparing Economic Analyses | December 2010 2-5 
2-6 Guidelines for Preparing Economic Analyses | December 2010 
 

 

 

 

 

 

 

 

 

 

 

Chapter 3 

Statement of Need for Policy Action
 

Aclear statement of need for policy action is an essential component in economic analyses 

of environmental policy prepared for economically significant rules.1

 This chapter 

discusses the key elements that comprise this statement: 

• Problem Definition: Section 3.1 provides components to include in a definition of the 

environmental problem to be addressed; 

• Reasons for Market or Institutional Failure: Section 3.2 identifies factors relevant to 

an analysis of the reasons existing legal and other institutions have failed to correct the 

problem; and 

• Need for Federal Action: Section 3.3 describes items to consider in preparing a 


justification of the need for federal intervention instead of other alternatives. 


The statement of need for policy action should also describe any statutory or judicial 

requirements that mandate the promulgation of particular policies or the evaluation 

of specific effects pertaining to the action. In some instances, statutes prohibit the use 

of certain types of analysis in policy making. In these cases, the guidance presented in 

Guidelines should be applied in a manner consistent with such mandates. 

3.1 Problem Definition 

The problem definition discussion should briefly 

review the nature of the environmental problem to 

be addressed. The following considerations are often 

relevant: 

• The primary pollutants causing the problem and 

their concentration; 

• The media through which exposures or damages 

take place; 

• Private and public sector sources responsible for 

creating the problem; 

• Human exposures involved and the health effects 

due to those exposures; 

EO 12866 states that “Federal agencies should promulgate only such 

regulations as are required by law, are necessary to interpret the law, 

or are made necessary by compelling need, such as material failures of 

private markets to protect or improve the health and safety of the public, 

the environment, or the well-being of the American people...” (emphasis 

added). EO 13422 extended the requirements in EO 12866 to guidance 

documents, but has since been revoked. 

• Non-human resources affected and the 


resulting outcome;
 

• Expected evolution of the environmental 

problem over the time horizon of the analysis; 

• Current control and mitigation techniques; 

• The amount or proportion (or both) of the 

environmental problem likely to be corrected by 

federal action. 

3.2 Reasons for Market or 

Institutional Failure 

After defining the problem, the statement of need 

should examine the reasons why the market and 

other public and private sector institutions have 

failed to correct it. This identification is an important 

component of policy development because the 

underlying failure itself often suggests the most 

appropriate remedy for the problem. 

Guidelines for Preparing Economic Analyses | December 2010 3-1 

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3-2 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 3 Statement of Need for Policy Action

OMB’s Circular A-4 discusses three categories 

of market failure, including externalities, 

market power, and inadequate or asymmetric 

information.2 Circular A-4 also points out that 

there may be other social purposes for regulation 

beyond correcting market failures, such as 

improving government function, removing 

distributional unfairness, or promoting privacy and 

personal freedom. Externalities are the most likely 

cause of the failure of private and public sector 

institutions to completely correct environmental 

damages. However, information asymmetries and 

pre-existing government-induced distortions can 

also be responsible for these problems. 

Externalities occur when the market does not 

compensate for the effect of one party’s activities 

on another party’s well-being. Externalities 

can occur for many reasons, for example, high 

transaction costs can make it difficult for injured 

parties to ensure that polluters internalize the cost 

of damage through bargaining, legal, or other 

means. Externalities can also result when activities 

that pose environmental risks are difficult to 

link to the resulting damages, such as those that 

occur over long periods of time or those that are 

transferred from one location to another. 

Consistent with EO 12866, the statement of need 

should assess the significance of the problem. 

Economic analyses should explore, for example, 

why transaction costs are high or what information 

asymmetries exist. Similar analyses are appropriate 

for situations where other factors are responsible 

for the failure of the market or public and private 

sector institutions to adequately address an 

environmental problem. 

3.3 Need for Federal Action 

The final component of the statement of need for 

policy action is an analysis of why a federal remedy 

is preferable to actions by private and other public 

sector entities, such as the judicial system or state 

and local governments.3

 Federal involvement is 

often required for environmental problems that 

cross jurisdictional boundaries (for instance, 

international environmental problems). In some 

cases, federal involvement is mandated by statute 

or EO as described in Chapter 2. This analysis 

should justify the basis for federal involvement by 

comparing it to the performance of a variety of 

realistic alternatives that rely on other institutional 

arrangements. This component of the statement 

of need for policy action should verify that the 

proposed action is within the jurisdiction of the 

relevant statutory authorities, and that the results 

of the policy will be preferable to no action. 

Finally, the statement of need should identify any 

aspects of the regulations being proposed that are 

necessitated by statutory requirements rather than 

being discretionary, as this may have an influence 

on the development of the economic analysis and 

presentation of the results. 

3 See EO 13132 on “Federalism” for introductory statements regarding 

2 For further discussion of market failure, see Perman et al. (2003), principles of federalism, and a section describing the special 

Hanley et al. (2001), and Nicholson (1995). requirements for preemption. 
Chapter 4 

Regulatory and Non-Regulatory 


Approaches to Pollution Control
 

This chapter briefly describes several regulatory and non-regulatory approaches 

used in environmental policy making. The goals of this chapter are to introduce 

several important analytic terms, concepts, and approaches; to describe the 

conceptual foundations of each approach; and to provide additional references 

for those interested in a more in-depth discussion.1

 Specifically, this chapter 

discusses the following four general approaches to environmental policy making: (1) 

command-and-control regulation; (2) market-based incentives; (3) hybrid approaches; and 

(4) voluntary initiatives. While command-and-control regulations have been a commonly 

used method of environmental regulation in the United States, EPA also employs the 

three other approaches. Market-based incentives and hybrid approaches offer the regulated 

community an opportunity to meet standards with increased flexibility and lower costs 

compared to many command-and-control regulations, while voluntary initiatives may allow 

environmental improvements in areas not traditionally regulated by EPA. The chapter also 

includes a discussion of criteria used to evaluate the effectiveness of regulatory and nonregulatory approaches to pollution control. 

4.1 Evaluating 

Environmental Policy 

Once federal action is deemed necessary to address an 

environmental problem, policy makers have a number 

of options at their disposal to influence pollution 

levels. In deciding which approach to implement, 

policy makers must be cognizant of constraints and 

limitations of each approach in addressing specific 

environmental problems. It is important to account 

for how political and information constraints, 

imperfect competition, or pre-existing market 

distortions interact with various policy options. Even 

when a particular approach is appealing from a social 

welfare perspective, it may not be consistent with 

statutory requirements, or may generate additional 

concerns when considered along with other 

Baumol and Oates (1988), particularly Chapters 10-14; Kahn (1998); 

Kolstad (2000); Sterner (2003); and Field and Field (2005) are useful 

references on the economic foundations of many of the approaches 

presented here. 

existing regulations. While any policy option under 

consideration must balance cost considerations with 

other important policy goals (including benefits), 

economic efficiency and cost-effectiveness are two 

economic concepts useful for framing the discussion 

and comparison of the regulatory options presented 

in the remaining sections of this chapter. 

4.1.1 Economic Efficiency 

Economic efficiency can be defined as the 

maximization of social welfare. An efficient market is 

one that allows society to maximize the net present 

value (NPV) of benefits: the difference between 

a stream of social benefits and social costs over 

time. The efficient level of production is referred 

to as Pareto optimal because there is no way to 

rearrange production or reallocate goods in such 

a way that someone is better off without making 

someone else worse off in the process. The efficient 

Guidelines for Preparing Economic Analyses | December 2010 4-1 

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4-2 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

level of production occurs without government 

intervention in a market characterized by no 

market failures or externalities (see Appendix A 

for a more detailed discussion of efficiency and for 

a graphical representation of the efficient point 

of production). Government intervention may 

be justified, however, when a market failure or 

externality exists (see Appendix A), in which case 

the government may attempt to determine the 

socially optimal point of production once such 

externalities have been internalized. Said differently, 

government analysts may evaluate which of the 

various policy approaches under consideration 

maximizes the benefits of reducing environmental 

damages, net the resulting abatement costs. 

Conceptually, the socially optimal level is 

determined by reducing emissions until the benefit 

of abating one more unit of pollution (i.e., the 

marginal abatement benefit) — measured as a 

reduction in damages — is equal to the cost of 

abating one additional unit (i.e., the marginal 

abatement cost).2

 In the simplest case, when 

each polluter chooses the level at which to emit 

according to this decision rule (i.e., produce at a 

level at which the marginal abatement benefit is 

equal to the marginal abatement cost), an efficient 

aggregate level of emissions is achieved when the 

cost of abating one more unit of pollution is equal 

across all polluters. Any other level of emissions 

would result in a reduction in net benefits. 

This definition of efficiency describes the simplest 

possible world where a pollutant is a uniformly 

mixed flow pollutant — the pollutant does 

not accumulate or vary over time — and the 

marginal damages that result are independent 

of location. When pollution levels and damages 

vary by location, the efficient level of pollution is 

achieved when marginal abatement costs adjusted 

by individual transfer coefficients are equal across 

all polluters. Temporal variability also implies an 

The idea that a given level of abatement is efficient — as opposed to 

abating until pollution is equal to zero — is based on the economic 

concept of diminishing returns. For each additional unit of abatement, 

marginal social benefits decrease while marginal social costs of that 

abatement increase. Thus, it only makes sense to continue to increase 

abatement until the point where marginal benefits and marginal costs 

are just equal. Any abatement beyond that point will incur more 

additional costs than benefits. 

adjustment to this equilibrium condition. In the 

case of a stock pollutant, marginal abatement costs 

are equal across the discounted sum of damages 

from today’s emissions in all future time periods. 

In the case of a flow pollutant, this condition 

should be adjusted to reflect seasonal or daily 

variations. Under uncertainty, it is useful to think 

of the efficient level of pollution as a distribution 

instead of as a single point estimate. 

The reality of environmental decision making is 

that Agency analysts are rarely in the position 

to select the economically efficient point of 

production when designing policy. This is partly 

because the level of abatement required to reduce 

a particular environmental problem is often 

determined legislatively, while the implementation 

of the policy to achieve such a goal is left to the 

Agency. In cases where the Agency has some 

say in the stringency of a policy, its degree of 

flexibility in determining the approach taken varies 

by statute. This may limit its ability to consider 

particular approaches or to use particular policy 

instruments. It is also important to keep in mind 

analytic constraints. In cases where it is particularly 

difficult to quantify benefits, cost-effectiveness 

may be the most defensible analytic framework. 

4.1.2 Cost-Effectiveness 

The efficiency of a policy option differs from 

its cost-effectiveness. A policy is cost-effective 

if it meets a given goal at least cost, but costeffectiveness does not encompass an evaluation 

of whether that goal has been set appropriately 

to maximize social welfare. All efficient policies 

are cost-effective, but it is not necessarily true 

that all cost-effective policies are efficient. A 

policy is considered cost-effective when marginal 

abatement costs are equal across all polluters. In 

other words, for any level of total abatement, each 

polluter has the same cost for their last unit abated. 

4.2 Traditional Commandand-Control or Prescriptive 

Regulation 

Many environmental regulations in the United 

States are prescriptive in nature (and are often 

2 
Guidelines for Preparing Economic Analyses | December 2010 4-3 

Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

referred to as command-and-control regulations).3 

A prescriptive regulation can be defined as a 

policy that prescribes how much pollution an 

individual source or plant is allowed to emit and/ 

or what types of control equipment it must use to 

meet such requirements. Such a standard is often 

defined in terms of a source-level emissions rate. 

Despite the introduction of potentially more costeffective methods for regulating emissions, this 

type of regulation is still commonly used and is 

sometimes statutorily required. It is almost always 

available as a “backstop” if other approaches do 

not achieve desired pollution limits. 

Because a prescriptive standard is commonly 

defined in terms of an emissions rate, it does not 

directly control the aggregate emission level. In 

such cases, aggregate emissions will depend on 

the number of polluters and the output of each 

polluter. As either production or market size 

increase, so will aggregate emissions. Even when 

the standard is defined in terms of an emission 

level per polluting source, aggregate emissions will 

still be a function of the total number of polluters. 

When abatement costs are similar across 

regulated sources, a source-level standard may 

be reasonably cost-effective. However when 

abatement costs vary substantially across 

polluters, reallocating abatement activities so 

that some polluters have stricter standards than 

others could lead to substantial cost savings. If 

reallocation were possible (e.g., through a nonprescriptive approach), a polluter facing relatively 

high abatement costs would continue to emit at 

its current level but would pay for the damages 

incurred (e.g., by paying a tax or purchasing 

permits), while a polluter with relatively low 

abatement costs would reduce its emissions. 

Note that regulators can at least partially 

account for some variability in costs by allowing 

Goulder and Parry (2008) refer to these as “direct regulatory 

instruments” because they feel that “command-and-control” has a 

“somewhat negative connotation.” Ellerman (2003) refers to them 

as prescriptive regulations. We follow that convention here. Notable 

exceptions to this type of regulation in the U.S. experience include 

the phase-down in lead content in gasoline, which allowed trading of 

credits among refineries and offset programs applied in non-attainment 

areas. For more information on early applications of market incentives, 

see U.S. EPA (2001b). 

prescriptive standards to vary according to size 

of the polluting entity, production processes, 

geographic location, or other factors. Beyond 

this, however, a prescriptive standard usually does 

not allow for reallocation of abatement activities 

to take place — each entity is still expected to 

achieve a specified emissions standard. Thus, while 

pollution may be reduced to the desired level, 

it is often accomplished at a higher cost under a 

prescriptive approach.4 

It is common to “grandfather,” or exempt, older 

polluters from new prescriptive regulations, 

thereby subjecting them to a less stringent standard 

than newer polluters. Grandfathering creates 

a bias against constructing new facilities and 

investing in new pollution control technology or 

production processes.5

 As a result, grandfathered 

older facilities with higher emission rates tend to 

remain active longer than they would if the same 

emissions standard applied to all polluters. 

The most stringent form of prescriptive regulation 

is one in which the standard specifies zero 

allowable source-level emissions. For instance, EPA 

has completely banned or phased out the use or 

production of chlorofluorocarbons (CFCs) and 

certain pesticides. This approach to regulation 

is potentially useful in cases where the level of 

pollution that maximizes social welfare is at or 

near zero.6 

Two types of prescriptive regulations exist: 

technology or design standards; and performancebased standards. 

4.2.1 Technology or 

Design Standards 

A technology or design standard, mandates the 

specific control technologies or production 

4	 See Tietenberg (2004) for a discussion of empirical studies that 

examine the cost-effectiveness of prescriptive air pollution regulations. 

Of the ten studies included, eight found that prescriptive regulations 

cost at least 78 percent more than the most cost-effective strategy. 

5	 For a discussion of grandfathering, see Helfand (1991). 

6	 For cases where the optimal level of pollution is at or near zero, the 

literature also indicates that market-based incentives can sometimes 

be useful as a transition instrument for the phasing-out of a particular 

chemical or pollutant. See Sterner (2003) and Kahn (1998). 

3 
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

Text Box 4.1 - Coase Solution 

Government intervention for the control of environmental externalities is only necessary when parties cannot work 

out an agreement between themselves. Coase (1960) outlined conditions under which a private agreement between 

affected parties might result in the attainment of a social welfare maximizing level of pollution without government 

intervention. First, property rights must be clearly defined. In situations where the resource in question is not 

“owned” by anyone, there are no incentives to negotiate, and the offending party can “free ride,” or continue to 

pollute, without facing the costs of its behavior. 

When property rights have been allocated, a social welfare maximizing solution can be reached regardless of which 

party is assigned the property rights, although the equity of the assignment may vary. Take for example a farm 

whose pesticide application to its crops contributes pollution to the well water of nearby homeowners. If property 

rights of the watershed are assigned to the homeowners, then the farm may negotiate with the homeowners to allow 

it to continue to use the pesticide. The payment need not be in the form of cash but could be payments in kind. If 

property rights of the watershed are given to the farm, then the homeowners would have to pay the farm to stop 

applying the pesticide. 

In each case, the effectiveness of the agreement is contingent on meeting additional conditions: bargaining must 

be possible, and transaction costs must be low. These conditions are more likely to be met when there are only a 

small number of individuals involved. If either party is unwilling to negotiate or faces high transaction costs, then no 

private agreement will be reached. Asymmetric information can also hinder the ability of one or more party to come 

to an agreement. Going back to the example, consider a case where there are many farms in the watershed using the 

pesticide on their crops. Clearly homeowners would have more difficulty in negotiating an agreement with every farm 

than they would when negotiating with one farm. 

processes that an individual pollution source must 

use to meet the emissions standard. This type of 

standard constrains plant behavior by mandating 

how a source must meet the standard, regardless 

of whether such an action is cost-effective. 

Technology standards may be particularly useful 

in cases where the costs of emissions monitoring 

are high but determining whether a particular 

technology or production process has been put in 

place to meet a standard is relatively easy. However, 

since these types of standards specify the abatement 

technology required to reduce emissions, sources 

do not have an incentive to invest in more costeffective methods of abatement or to explore new 

and innovative abatement strategies or production 

processes that are not permitted by regulation. 

4.2.2 Performance-based 

Standards 

available methods to comply with the standard. 

At times, the available methods are constrained 

by additional criteria specified in a regulation. 

Performance-based standards that are technologybased do not specify a particular technology, but 

rather consider what is possible for available and 

affordable technology to achieve when establishing 

a limit on emissions.7 

In the case of a performance-based standard, 

the level of flexibility a source has in meeting 

the standard depends on whether the standard 

specifies an emission level or an emission rate (i.e., 

emissions per unit of output or input). A standard 

that specifies an emission level allows a source to 

7	 As an example, Reasonably Available Control Technology (RACT) 

specifies that the technology used to meet the standard should 

achieve “the lowest emission limit that a particular source or source 

category is capable of meeting by application of control technology 

that is reasonably available considering technological and economic 

feasibility.” RACT defines the standard on a case-by-case basis, 

A performance-based standard also requires 	 taking into account a variety of facility-specific costs and impacts on 

air quality. EPA has been restrictive in its definition of technologies that polluters meet a source-level emissions meeting this requirement and eliminates those that are not 

standard, but allows a polluter to choose among commercially available (see Swift 2000). 
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

 

 

 

 

choose to implement an appropriate technology, 

change its input mix, or reduce output to meet 

the standard. An emission rate, on the other hand, 

may be more restrictive depending on how it is 

defined. If the emissions rate is defined per unit 

of output, then it does not allow a source to meet 

the standard through a reduction in output. If the 

standard is defined as an average emissions rate 

over a number of days, then the source may still 

reduce output to meet the standard. 

The flexibility of performance-based standards 

encourages firms to innovate to the extent 

that they allow firms to explore cheaper ways 

to meet the standard; however, they generally 

do not provide incentives for firms to reduce 

pollution beyond what is required to reach 

compliance.8

 For emissions that fall below the 

amount allowed under the standard, the firm 

faces a zero marginal abatement cost since the 

firm is already in compliance. Also, because 

permitting authority is often delegated to the 

States, approval of a technology in one state 

does not ensure its use is allowed in another. 

Firm investment in research to develop new, less 

expensive, and potentially superior technologies 

is therefore discouraged.9 

4.3 Market-Oriented 

Approaches 

Market-oriented approaches (or market-based 

approaches) create an incentive for the private 

sector to incorporate pollution abatement into 

production or consumption decisions and to 

innovate in such a way as to continually search 

for the least costly method of abatement.10 

Market-oriented approaches can differ from 

more traditional regulatory methods in terms 

of economic efficiency (or cost-effectiveness) 

and the distribution of benefits and costs. In 

particular, many market-based approaches 

8	 For a theoretical analysis of incentives for technological change, see 

Jung et al. (1996) and Montero (2002). Empirical analyses can be 

found in Jaffe and Stavins (1995), and Kerr and Newell (2003). 

9	 See Swift (2000) and U.S. EPA (1991) for a detailed discussion of how 

emission rate-based standards hinder technological innovation. 

10	 The incentive to innovate means that the marginal abatement cost 

curve shifts downward over time as cheaper abatement options are 

introduced. 

minimize polluters’ abatement costs, an objective 

that often is not achieved under command-andcontrol based approaches. Because market-based 

approaches do not mandate that each polluter 

meet a given emissions standard, they typically 

allow firms more flexibility than more traditional 

regulations and capitalize on the heterogeneity 

of abatement costs across polluters to reduce 

aggregate pollution efficiently. Environmental 

economists generally favor market-based policies 

because they tend to be least costly, they place 

lower information burden on the regulator, and 

they provide incentives for technological advances. 

Four classic market-based approaches are discussed 

in this section: 

• Marketable permit systems; 

• Emission taxes; 

• Environmental subsidies; and 

• Tax-subsidy combinations. 11 

While operationally different (e.g., taxes and 

subsidies are price-based while marketable 

permits are quantity-based), these marketbased instruments are more or less functionally 

equivalent in terms of the incentives they put in 

place. This is particularly true of emission taxes 

and cap-and-trade systems, which can be designed 

to achieve the same goal at equivalent cost. The 

sections that follow discuss each of these marketbased approaches in turn. 

4.3.1 Marketable Permit Systems 

Several forms of emissions trading exist, including 

cap-and-trade systems, project-based trading 

11	 The literature on applied market-based approaches for environmental 

protection should be consulted, along with the references they contain, 

for information concerning the design, operation, and performance of 

these approaches. Anderson and Lohof (1997) and Stavins (1998a, 

2000b) compile information on both the theory and empirical use of 

economic incentives. Newell and Stavins (2003) generate rules-ofthumb designed to make it easy for policy makers to determine when 

market-based incentives may result in cost savings over commandand-control regulations. Harrington et al. (2004) compare the costs 

and outcomes of command-and-control and incentives-based 

regulatory approaches to the same environmental problem in the 

United States and Europe. Additional sources include Sterner (2003), 

Stavins (2003), Tietenberg (1999, 2002), U.S. EPA (2004a, 2001a), 

OECD (1994a, 1994b), and proceedings published under the “Project 

88” forum, Stavins (1988, 1991). 
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

Text Box 4.2 - Acid Rain Trading Program for Sulfur Dioxide (SO2) 

In 1995, Title IV of the 1990 Clean Air Act Amendments established a cap-and-trade system for SO2 emissions to 

address the problem of acid rain. Two hundred and sixty three of the highest emitting SO2 units of 110 electricitygenerating plants were selected to participate in the first phase of the trading program. Emissions of SO2

 in 1995 

were initially limited to 8.7 million tons for those facilities. Of the plants that participated, most were coal-fired units 

located east of the Mississippi River. Under this system, allowances were allocated to units on a historical basis, after 

which they could use the allowances, sell them to other units, or “bank” the allowances for use in subsequent years. 

Continual emission monitoring (CEM) systems have allowed the government to easily monitor and enforce emission 

restrictions in accordance with the allowances. The second phase of the program, initiated in 2000, imposed a 

national SO2 emissions cap of 10 million tons and brought almost all SO2 generating units into the system. 

Initial evaluations of the first phase of implementation suggest that the SO2 trading system has significantly reduced 

emissions at a relatively low cost. In fact, allowance prices have been considerably lower than predicted, reflecting 

lower than expected marginal costs. A significant level of trading has occurred and has resulted in savings of over $1 

billion per year as compared to command-and-control alternatives. Emissions in 1995 were almost 40 percent below 

the 10 million ton limit. The evaluations demonstrated that one reason for such large reductions in SO2 emissions 

below the allowable limit is the ability to bank allowances for future use. The success of the program has continued 

into the second phase, with recent estimates of the full U.S. Acid Rain Program’s benefits [including SO2 trading and 

direct nitrogen oxide (NOx) controls] reaching upwards of $120 billion annually in 2010 with annual costs around 

$3 billion (in 2000$); a benefit to cost ratio of about 40 to 1. Trends over the life of the program show that while 

electricity generation has grown steadily and SO2 and NOx emissions have fallen substantially, electricity retail prices, 

until very recently, have declined in real terms. Percent Change Relative to 1990 

50% 

40% 

30% 

20% 

10% 

0% 

-10% 

-20% 

-30% 

-40% 

-50% 

-60% 

1990199519961997199819992000200120022003200420052006 

Electricity Retail Price N0 Emissions x

S02

 Emissions Electricity Generation 

Source: U.S. EPA 2007a 

For more information, see Burtraw and Bohi (1997), Schmalensee et al. (1998), Stavins (1998b, 2003), Carlson et al. 

(2000), Chestnut and Mills (2005), and U.S. EPA (2007a). 

systems and emissions rate trading systems. The at lower costs have an incentive to do so. Each of 

common element across these programs is that these systems is discussed in turn below.12 

sources are able to trade credits or allowances so 12 For a more detailed discussion of the various systems and how to 

that those with opportunities to reduce emissions design them, see U.S. EPA (2003c). 
Guidelines for Preparing Economic Analyses | December 2010 4-7 

Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

4.3.1.1 Cap-and-Trade Systems 

In a cap-and-trade system the government sets the 

level of aggregate emissions, emission allowances 

are distributed to polluters, and a market is 

established in which allowances may be bought or 

sold. The price of emission allowances is allowed 

to vary. Because different polluters incur different 

private abatement costs to control emissions, 

they are willing to pay different amounts for 

allowances. Therefore, a cap-and-trade system 

allows polluters who face high marginal abatement 

costs to purchase allowances from polluters with 

low marginal abatement costs, instead of installing 

expensive pollution control equipment or using 

more costly inputs. Cap-and-trade systems also 

differ from command-and-control regulations in 

that they aim to limit the aggregate emission level 

over a compliance period rather than establish an 

emissions rate. 

If the cap is set appropriately, then the equilibrium 

price of allowances, in theory, adjusts so that 

it equals the marginal external damages from 

a unit of pollution. This equivalency implies 

that any externality associated with emissions is 

completely internalized by the firm. For polluters 

with marginal abatement costs greater than the 

allowance price, the cheapest option is to purchase 

additional units and continue to emit. For polluters 

with marginal abatement costs less than the 

allowance price, the cheapest option is to reduce 

emissions and sell their permits. As long as the 

price of allowances differs from individual firms’ 

marginal abatement costs, firms will continue to 

buy or sell them. Trading will occur until marginal 

abatement costs equalize across all firms.13 

Generally, allowances initially sold at auction 

represent income transfers from the purchasers to 

the government in the amount of the price paid for 

the allowances. The collection of revenue through 

this method of allowance allocation gives the 

government the opportunity to reduce pre-existing 

13 The U.S. Acid Rain Program established under Title IV of the 1990 

Clean Air Act Amendments is a good example of a marketable permit 

program. For economic analyses of this program see Joskow et al. 

(1998), Stavins (1998b), Ellerman et al. (2000), and Chestnut and 

Mills (2005). For more information on the program itself see Text 

box 4.2 and EPA’s (2008a) Acid Rain Website at http://www.epa.gov/ 

acidrain (accessed April 5, 2004). 

market inefficiencies, to reduce distributional 

consequences of the policy, or to invest in other 

social priorities. Allowances may also be allocated 

to polluters according to a specified rule. This 

represents a transfer from the government to 

polluting firms, some of which may find that the 

value of allowances received exceeds the firm’s 

aggregate abatement costs. 

The distribution of rents under cap-and-trade 

systems should be considered when comparing 

these systems with more traditional regulatory 

approaches. If the allowances are auctioned or 

otherwise sold to polluters, the distributional 

consequences will be similar to those experienced 

when regulating using taxes. If allowances 

are distributed for free to polluters, however, 

distributional consequences will depend on the 

allocation mechanism (e.g., historical output 

or inputs), on who receives the allowances, 

and on the ability of the recipients to pass 

their opportunity costs on to their customers. 

If new entrants must obtain allowances from 

existing polluters, then the policy maker should 

also consider potential barrier-to-entry effects. 

Differing treatment applied to new versus existing 

polluters can affect the eventual distribution of 

revenues, expenses, and rents within the economy. 

Additional considerations in designing an effective 

cap-and-trade system include “thin” markets, 

transaction costs, banking, effective monitoring, 

and predictable consequences for noncompliance. 

The United States’ experience suggests that a 

market characterized by low transaction costs and 

being “thick” with buyers and sellers is critical if 

pollution is to be reduced at the lowest cost. This 

is because small numbers of potential traders in a 

market make competitive behavior unlikely, and 

fewer trading opportunities result in lower cost 

savings. Likewise, the number of trades that occur 

could be significantly hindered by burdensome 

requirements that increase the transaction costs 

associated with each trade.14 

14 This is also often the case for bubbles and offsets. See O’Neil (1983) 

for an evaluation of an early example of a permit-trading program in 

the United States and the main reasons for its failure. 
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

Cap-and-trade systems should also be sensitive to 

concerns about potential temporal or spatial spikes 

(i.e., hotspots — areas in which the pollution 

level has the potential to increase as a result of 

allowance trading). This may happen, for example, 

in an area in which two facilities emit the same 

amount of pollution, but due to differences 

in exact location and site characteristics, one 

facility’s impact on environmental quality differs 

substantially from that of the other polluter. 

While one potential solution to this problem is 

to adjust trading ratios to equalize the impact 

of particular polluters on overall environmental 

quality, determining the appropriate adjustments 

to these ratios can be costly and difficult. Other 

possible solutions include zone-based trading and 

establishing pollution “floors.” 

Two recent reviews of the literature (Burtraw et 

al. 2005 and Harrington et al. 2004) find little 

evidence of spatial or temporal spikes in pollution 

resulting from the use of market-based approaches. 

In fact, market-based approaches have led to 

smoothing of emissions across space in some cases. 

These results come primarily from studies of the 

SO2

 and NO x trading programs and if the marketbased policy is not carefully designed, the results 

may not transfer to other pollutants that have 

more localized effects. 

Banking introduces increased flexibility into 

a trading system by allowing polluters to bank 

unused permits for future use. A firm may reduce 

emissions below the allowance level now, and 

bank (or save) remaining allowances to cover 

excess emissions or sell to another polluter at a 

later time. In this way, polluters that face greater 

uncertainty regarding future emissions, or that 

expect increased regulatory stringency, can bank 

allowances to offset potentially higher future 

marginal abatement costs. 

For a cap-and-trade system to be effective, reliable 

measurement and monitoring of emissions 

must occur with predictable consequences for 

noncompliance. At the end of the compliance 

period, emissions at each source are compared 

to the allowances held by that source. If a source 

is found to have fewer allowances than the 

monitored emission levels, it is in noncompliance 

and the source must provide allowances to cover its 

environmental obligation. In addition, the source 

must pay a penalty automatically levied per each 

ton of excess emissions.15 

4.3.1.2 Project-Based Trading Systems 

Offsets and bubbles (sometimes known 

as “project-based” trading systems) allow 

restricted forms of emissions trading across 

or within sources to allow sources greater 

flexibility in complying with command-andcontrol regulations such as emission limits or 

facility-level permits. An offset allows a new 

polluter to negotiate with an existing source to 

secure a reduction in the latter’s emissions. A 

bubble allows a facility to consider all sources 

of emissions of a particular pollutant within 

the facility to achieve an overall target level of 

emissions or environmental improvement. While 

offsets and bubbles are mostly used to control air 

pollution in non-attainment areas, they have been 

historically hindered by high administrative and 

transaction costs because they require case-by-case 

negotiation to convert a technology or emission 

rate limit into tradable emissions per unit of 

time, to establish a baseline, and to determine 

the amount of credits generated or required (U.S. 

EPA 2001a). 

4.3.1.3 Rate-Based Trading Systems 

Rather than establish an emissions cap, the 

regulatory authority under a rate-based trading 

program, establishes a performance standard or 

emissions rate. Sources with emission rates below 

the performance standard can earn credits and 

sell them to sources with emission rates above 

the standard. As with the other trading systems, 

sources able to improve their emissions rate at 

low cost have an incentive to do so since they can 

sell the resulting credits to those sources facing 

higher costs of abatement. However, emissions 

may increase under these programs if sources 

increase their utilization or if new sources enter 

the market. Therefore, the regulating authority 

15	 Notably, the U.S. Acid Rain Trading Program has nearly 100 percent 

compliance and requires only about 50 EPA staff to administer. 
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

may need to periodically impose new rate 

standards to achieve and maintain the desired 

emission target, which in turn may lead to 

uncertainty in the long term for the regulated 

sources. Rate-based trading programs have been 

used in the United States to phase out lead in 

gasoline (1985) and to control mobile source 

emissions (U.S. EPA 2003c). 

4.3.2 Emissions Tax 

Emissions taxes are exacted per unit of pollution 

emitted and induce a polluter to take into account 

the external cost of its emissions. Under an 

emissions tax, the polluter will abate emissions up 

to the point where the additional cost of abating 

one more unit of pollution is equal to the tax, and 

the tax will result in an efficient outcome if it is set 

equal to the additional external damage caused by 

the last unit of pollution emitted. 

As an example of how an emissions tax works, 

suppose that emissions of a toxic substance are 

subject to an environmental charge based on 

the damages the emissions cause. To avoid the 

emissions tax, polluters find the cheapest way to 

reduce pollution. This may involve a reduction 

in output, a change in inputs to production, the 

installation of pollution control equipment, or 

a process change that prevents the creation of 

pollution. Polluters decide individually how 

much to control their emissions, based on the 

costs of control and the magnitude of the tax. 

The polluting firm reduces emissions to the 

point where the cost of reducing one more unit 

of emissions is just equal to the tax per unit of 

emissions. For any remaining emissions, the 

polluter prefers to pay the tax rather than to 

abate further. In addition, the government earns 

revenue that it may use to reduce other pollution 

or reduce other taxes, or may redistribute to 

finance other public services.16 While difficult to 

implement in cases where there is temporal and/ 

or spatial variation in emissions, policy makers can 

more closely approximate the ambient impact of 

emissions by incorporating adjustment factors for 

16	 For more information on how the government can use revenues from 

taxes to offset distortions created by other taxes, see Goulder (1995) 

and Goulder et al. (1997). 

seasonal or daily fluctuations or individual transfer 

coefficients in the tax. 

Despite the apparent usefulness of such a tax, 

true emissions taxes — those set equal or close to 

marginal external damages — are relatively rare in 

the United States.17 This is because taxing emissions 

directly may not be feasible when emissions are 

difficult to measure or accurately estimate, when it 

is difficult to define and monetarily value marginal 

damages from a unit of emissions (which is needed 

to properly set the tax), or when taxes are applied 

to emissions that are difficult to monitor and/or 

enforce. In addition, attempts to measure and tax 

emissions may lead to illegal dumping.18 Other 

considerations when contemplating the use of 

emission taxes include the potential imposition of 

substantially different cost burdens on polluters 

as compared with other regulatory approaches, 

political incentives to set the tax too low, and 

the collection of revenues and distribution of 

economic rents that result from such programs. 

User or product charges are a variation on 

emission taxes that are occasionally utilized in 

the United States. These charges may be imposed 

directly upon users of publicly operated facilities 

or upon intermediate or final products whose 

use or disposal harms the environment. User or 

product charges may be effective approximations 

of an emissions tax for those cases in which the 

product taxed is closely related to emissions. 

User charges have been imposed on firms 

that discharge waste to municipal wastewater 

treatment facilities and on non-hazardous solid 

wastes disposed of in publicly-operated landfills. 

Product charges have been imposed on products 

that release CFCs into the atmosphere, that 

utilize more gasoline (such as cars), or require 

more fertilizer. In practice, both user and product 

charges are usually set at a level only sufficient to 

recover the private costs of operating the public 

system, rather than being set at a level selected to 

create proper incentives for reducing pollution to 

the socially optimal level. 

17	 These taxes are called “Pigovian” after the economist, Arthur Pigou, 

who first formalized them. See Pigou (1932). 

18	 See Fullerton (1996) for a discussion of the advantages and 

disadvantages of emission taxes. 
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

Taxes and charges facilitate environmental 

improvements similar to those that result from 

marketable permit systems. Rather than specifying 

the total quantity of emissions, however, taxes, 

fees, and charges specify the effective “price” of 

emitting pollutants. 

4.3.3 Environmental Subsidies 

Subsidies paid by the government to firms or 

consumers for per unit reductions in pollution 

create the same abatement incentives as emission 

taxes or charges. If the government subsidizes the 

use of a cleaner fuel or the purchase of a particular 

control technology, firms will switch from the 

dirtier fuel or install the control technology to 

reduce emissions up to the point where the private 

costs of control are equal to the subsidy. It is 

important to keep in mind that an environmental 

subsidy is designed to correct for an externality 

not already taken into account by firms when 

making production decisions. This type of subsidy 

is fundamentally different from the many subsidies 

already in existence in industries such as oil and 

gas, forestry, and agriculture, which exist for other 

reasons apart from environmental quality, and 

therefore can exacerbate existing environmental 

externalities. 

Unlike an emissions tax, a subsidy lowers a firm’s 

total and average costs of production, encouraging 

both the continued operation of existing polluters 

that would otherwise exit the market, and the 

entry into the market by new firms that would 

otherwise face a barrier to entry. Given the 

potential entrance of new firms under a subsidy, 

the net result may be a decrease in pollution 

emissions from individual polluters but an increase 

in the overall amount.19 For this reason, subsidies 

and taxes may not have the same aggregate social 

costs, or result in the same degree of pollution 

control. A subsidy also differs from a tax because it 

requires government expenditure. Analysts should 

always consider the opportunity costs associated 

with using public funds. 

19	 See Sterner (2003) for a more in-depth discussion of how subsidies 

work and for numerous examples of subsidy programs in the United 

States and other countries. 

It is possible to minimize the entry and exit 

of firms resulting from subsidies by redefining 

the subsidy as a partial repayment of verified 

abatement costs, instead of defining it as a per 

unit payment for emissions reductions relative to 

a baseline. Under this definition, the subsidy now 

only relates to abatement costs incurred and does 

not shift the total or average cost curves, thereby 

leaving the entry and exit decisions of firms 

unaffected. Defining the subsidy in this way also 

minimizes strategic behavior because no baseline 

must be specified. 20 

Instead of pursuing a per unit emissions subsidy, 

the government may choose to lower the 

private costs of particular actions to the firm or 

consumer through cost sharing. For example, if 

the government wishes to encourage investment 

in particular pollution control technologies, the 

subsidy may take the form of reduced interest 

rates, accelerated depreciation, direct capital 

grants, and loan assistance or guarantees for 

investments. Cost-sharing policies alone may 

not induce broader changes in private behavior. 

In particular, such subsidies may encourage 

investment in pollution control equipment, rather 

than encouraging other changes in operating 

practices such as recycling and reuse, which 

may not require such costly capital investments. 

However, in conjunction with direct controls, 

pollution taxes, or other regulatory mechanisms, 

cost sharing may influence the nature of private 

responses and the distribution of the cost burden. 

As is the case with emissions taxes, subsidy rates 

also can be adjusted to account for both spatial and 

temporal variability. 

A government “buy-back” constitutes another type 

of subsidy. Under this system, the government 

either directly pays a fee for the return of a 

product or subsidizes firms that purchase recycled 

materials. For instance, consumers may be offered 

20	 Strategic behavior is a problem common to any instrument or 

regulation that measures emissions relative to a baseline. In cases 

where a firm or consumer may potentially receive funds from the 

government, they may attempt to make the current state look worse 

than it actually is, in order to receive credit for large improvements. If 

firms or consumers are responsible for paying for certain emissions 

above a given level, they may try to influence the establishment of that 

level upward in order to pay less in fines or taxes. 
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

a cash rebate on the purchase of a new electric or 

push mower when they scrap their old one. The 

rebate is earned when the old gasoline mower is 

turned in and a sales receipt for the new device 

is provided.21 Buy-back programs also exist to 

promote the scrapping of old, high-emission 

vehicles. 

Environmental subsidies in the United States have 

been used to encourage proper waste management 

and recycling by local governments and businesses; 

the use of alternative fuel vehicles by public bus 

companies, consumers, and businesses; and land 

conservation by property owners using costsharing measures. While most of these subsidies 

are not defined per unit of emissions abated, they 

can be effective when the behavioral changes they 

encourage are closely related to the use of products 

with reduced emissions. 

4.3.4 Tax-Subsidy Combinations 

Emission taxes and environmental subsidies can 

also be combined to achieve the same level of 

abatement as achieved when the tax and subsidy 

instruments are used separately. One example of 

this type of instrument is referred to as a depositrefund system in which the deposit operates as a 

tax and the refund serves as a partially offsetting 

subsidy. As with the other market instruments 

already discussed, a deposit-refund system creates 

economic incentives to return a product for reuse 

or proper disposal, or to use a particular input in 

production, provided that the deposit exceeds the 

private cost of returning the product or switching 

inputs. 

Under the deposit-refund system, the deposit is 

applied to either output or consumption, under 

the presumption that all production processes of 

the firm pollute or that all consumption goods 

become waste. A refund is then provided to the 

extent that the firm or consumer provides proof 

of the use of a cleaner form of production or 

of proper disposal. In the case where a depositrefund is used to encourage firms to use a cleaner 

input, the deposit on output induces the firm to 

21	 For more information on the Office of Air’s Small Engine Buy-back 

reduce its use of all inputs, both clean and dirty. 

The refund, however, provides the firm with an 

incentive to switch a specific input or set of inputs 

that result in a refund, such as a cleaner fuel or a 

particular pollution control technology. 

A tax and offsetting subsidy combination 

functions best when it is possible to discern 

a direct relationship between an input, or 

output, and emissions. For instance, a tax on the 

production or use of hydrochlorofluorocarbons 

(HCFCs) combined with a refund for HCFC 

recycled or collected in a closed system is a 

good proxy for a direct emissions tax on ozone 

depletion.22 

The most common type of tax-subsidy 

combination is the deposit-refund system, which 

is generally designed to encourage consumers to 

reduce litter and increase the recycling of certain 

components of municipal solid waste.23 The most 

prominent examples are deposit-refunds for items 

such as plastic and glass bottles, lead acid batteries, 

toner cartridges and motor oil. Other countries 

have implemented deposit-refund systems on 

a wider range of products and behaviors that 

contribute to pollution, including the sulfur 

content of fuels (Sweden), product packaging 

(Germany), and deforestation (Indonesia). Taxsubsidy combinations have also been discussed in 

the literature as a means of controlling nonpoint 

source water pollution, cadmium, mercury, and the 

removal of carbon from the atmosphere.24 

The main advantage of a combined tax and subsidy 

is that both parts apply to a market transaction. 

Because the taxed and subsidized items are easily 

observable in the market, this type of economic 

instrument may be particularly appealing when 

it is difficult to measure emissions or to control 

illegal dumping. In addition, polluters have 

an incentive to reveal accurate information on 

abatement activity to qualify for the subsidy. 

22	 See Sterner (2003) for a more detailed description of this and other 

examples of tax-subsidy combinations. 

23	 For example, Arnold (1995) analyzes the merits of a deposit-refund 

system in a case study focusing on enhancing used-oil recycling. 

Sigman (1995) reviews policy options to address lead recycling. 

Program see U.S. EPA (2006c).	 24 See U.S. EPA (2004a), Fisher et al. (1995), and O’Connor (1994). 
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

 

 

 

Because firms have access to better information 

than the government does, they can measure and 

report emissions with greater precision and at a 

potentially lower cost. 

Disadvantages of the combined tax-subsidy system 

may include potentially high implementation and 

administrative costs, and the political incentive 

to set the tax too low to induce proper behavior 

(a danger with any tax). Policy makers may 

adjust an emissions tax to account for temporal 

variation in marginal environmental damages, 

but a tax on output sold in the market cannot 

be matched temporally or spatially to emissions 

during production. In addition, to the extent 

that emissions (e.g., SO2

 from power plants) 

are easily and accurately monitored, other 

market incentives may be more appropriate. If a 

production process has many different inputs with 

different contributions to environmental damages, 

then it is necessary to tax the inputs at different 

rates to achieve efficiency. Likewise, if firms are 

heterogeneous and select a different set of clean 

inputs or abatement options based on firm-specific 

cost considerations, then the subsidy should 

be adjusted for differences in these production 

functions.25 A uniform subsidy combined with 

an output tax may be a good proxy, however, 

when there is limited heterogeneity across inputs’ 

contribution to emissions and across firms. 

Conceptually similar to the tax-subsidy 

combination is the requirement that firms post 

performance bonds that are forfeited in the 

event of damages, or that firms contribute upfront funds to a pool. Such funds may be used 

to compensate victims in the event that proper 

environmental management of a site for natural 

resource extraction does not occur. To the extent 

that the company demonstrates it has fulfilled 

certain environmental management or reclamation 

obligations, the deposited funds are usually 

refunded. Financial assurance requirements have 

been used to manage closure and post-closure 

care for hazardous waste treatment, storage, and 

disposal facilities. Performance bonds have also 

25	 The main advantages and disadvantages of deposit-refund systems are 

discussed in U.S. GAO (1990); Palmer, Sigman, and Walls (1997); and 

been required in extraction industries such as 

mining, timber, coal, and oil.26 

4.4 Other Market-Oriented or 

Hybrid Approaches 

In addition to the four classic market-based 

instruments discussed above, several other marketoriented approaches are often discussed in the 

literature and are increasingly used in practice. 

Often, these approaches combine aspects of 

command-and-control and market-based incentive 

policies. As such, they do not always present the 

most economically efficient approach. Either the 

level of abatement or the cost of the policy is likely 

to be greater than what would be achieved through 

the use of a pure market-based incentive approach. 

Nevertheless, such approaches are appealing to 

policy makers because they often combine the 

certainty associated with a given emissions standard 

with the flexibility of allowing firms to pursue 

the least costly abatement method. This section 

discusses the following market-oriented approaches: 

• Combining standards and pricing approaches; 

• Liability rules; and 

• Information as regulation. 

4.4.1 Combining Standards and 

Pricing Approaches 

Pollution standards set specific emissions limits, 

thereby reducing the probability of excessively 

high damages to health or the environment. Such 

standards may impose large costs on polluters. 

Emissions taxes restrict costs by allowing polluters 

to pay a tax on the amount they emit rather than 

undertake excessively expensive abatement. Taxes, 

however, do not set a limit on emissions, and 

leave open the possibility that pollution may be 

excessively high. Some researchers suggest a policy 

that limits both costs and pollution, referred to 

as a “safety-valve” approach to regulation, which 

combines standards with pricing mechanisms.27 

In the case of a standard and tax combination, 

the same emissions standard is imposed on all 

26	 For more information on the use of financial assurance or performance 

bonds, see Boyd (2002). 

Fullerton and Wolverton (2001, 2005).	 27 See Roberts and Spence (1976) and Spence and Weitzman (1978). 
Guidelines for Preparing Economic Analyses | December 2010 4-13 

Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

polluters and all polluters are subject to a unit tax 

for emissions in excess of the standard. 

While a standard and pricing approach does not 

necessarily ensure the maximization of social 

welfare, it can lead to the most cost-effective 

method of pollution abatement. This policy 

combination has other attractive features. First, if 

the standard is set properly, the desired protection 

of health and the environment will be assured. This 

feature of the policy maintains the great advantage 

of a standards approach: protection against 

excessively damaging pollution levels. Combining 

approaches allows for more certainty in the 

expected environmental and health effects of the 

policy than would occur with a market-based 

approach alone. Second, high abatement cost 

polluters can defray costs by paying the emissions 

fee instead of cleaning up. This feature preserves 

the flexibility of emissions taxes: overall abatement 

costs are lower because polluters with low 

abatement costs reduce pollution while polluters 

with high abatement costs pay taxes. 

4.4.2 Information Disclosure 

Requiring disclosure of environmental information 

has been increasingly used as a method of 

environmental regulation. Disclosure strategies are 

most likely to work when there is a link between 

the polluting firm and affected parties such as 

consumers and workers.28 Disclosure requirements 

attempt to minimize inefficiencies in regulation 

associated with asymmetric information, such as 

when a firm has more and better information on 

what and how much it pollutes than is available to 

the government or the public. By collecting and 

making such information publicly available, firms, 

government agencies, and consumers can become 

better informed about the environmental and 

human health consequences of their production 

and consumption decisions. In some cases, the 

availability of this information may also encourage 

more environmentally benign activities and 

discourage environmentally detrimental ones. For 

example, warning labels on hazardous substances 

28	 See OMB (2010b) for guidance issued to regulatory agencies on the 

use of information disclosure and simplification in the regulatory 

process. 

that describe safe-handling procedures or the risks 

posed by the product may encourage hazardous 

substance handlers to take greater precautions, 

and/or may encourage consumers to switch to 

less damaging substitutes for some or all uses 

of the substance. Similarly, a community with 

information on a nearby firm’s pollution activity 

may exert pressure on the firm to reduce emissions, 

even if formal regulations or monitoring and 

enforcement are weak or nonexistent.29 

Requirements for information disclosure need 

not be tied explicitly to an emissions standard; 

however, such requirements are consistent 

with a standard-based approach because the 

information provided allows a community to easily 

understand the level of emissions and the polluters’ 

level of compliance with existing standards or 

expectations. As is the case with market-based 

instruments, polluters still have the flexibility 

to respond to community pressure by reducing 

emissions in the cheapest way possible. 

The use of information disclosure or labeling rules 

has other advantages. When expensive emissions 

monitoring is required to collect such information, 

reporting requirements that switch the burden 

of proof for monitoring and reporting from the 

government to the firm might result in lower 

costs, because firms are often in a better position 

to monitor their own emissions. If accompanied 

by spot checks to ensure that monitoring 

equipment functions properly and that firms 

report results accurately, information disclosure 

can be an effective form of regulation. Without 

the appropriate monitoring, however, information 

disclosure might not result in an efficient outcome. 

While information disclosure has its advantages, 

it is important to keep three caveats in mind 

when considering this method for environmental 

regulation. First, the use of information as 

regulation is not costless: U.S. firms report 

spending approximately $346 million per year 

29	 For more information on how information disclosure may help to 

resolve market failures, see Pargal and Wheeler (1996), Tietenberg 

(1998), Tietenberg and Wheeler (2001), and Brouhle and Khanna 

(2007). 
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

to monitor and report releases.30 Any required 

investments in pollution control are in addition 

to this amount. Second, the amount of pressure 

a community exerts on an emitting plant may 

be related to socioeconomic status. Poorer, lesseducated populations tend to exert far less pressure 

than communities with richer, well-educated 

populations.31 Third, information disclosure may 

not result in a socially efficient level of pollution 

when consumers either consider only the effect of 

emissions on them as individuals, ignoring possible 

ecological or aggregate societal effects, or when 

they do not understand how to properly interpret 

the released information in terms of the health risks 

associated with exposure to particular pollutants. 

EPA-led information disclosure efforts include 

the Toxics Release Inventory (TRI) and the 

mandatory reporting of greenhouse gases (GHG). 

Both the TRI and the GHG reporting rule require 

firms to provide the government and public with 

information on pollution at each plant, on an 

annual basis, if emissions exceed a threshold. There 

are also consumer-based information programs 

targeting the risks of particular toxic substances, 

the level of contamination in drinking water, 

the dangers of pesticides, and air quality index 

forecasts for more than 300 cities. There is some 

evidence in the literature regarding the impact of 

TRI reporting on firm value: the most polluting 

firms experience small declines in stock prices on 

the day TRI emission reports are released to the 

public. Hamilton (1995) finds a stock price return 

of -0.03 percent due to TRI report release. Firms 

that experienced the largest drop in their stock 

prices also reduced their reported emissions by the 

greatest quantity in subsequent years.32 

4.4.3 Liability Rules 

Liability rules are legal tools of environmental 

policy that can be used by victims (or the 

30	 See O’Connor (1996) for information on the costs of monitoring and 

reporting environmental information. See World Bank (2000) for a 

discussion of the main advantages and disadvantages of information 

disclosure as a policy tool. 

31	 See Hamilton (1993), and Arora and Cason (1999). 

32	 Hamilton (1995); Konar and Cohen (1997); and Khanna, Quimio, and 

Bojilova (1998) are empirical studies that have investigated how the 

TRI has affected firm behavior and stock market valuation. 

government) to force polluters to pay for 

environmental damages after they occur. These 

instruments serve two main purposes: (1) 

to create an economic incentive for firms to 

incorporate careful environmental management 

and the potential cost of environmental damages 

into their decision-making processes; and (2) to 

compensate victims when careful planning does 

not occur. These rules are used to guide courts in 

compensation decisions when the court rules in 

favor of the victim. Liability rules can serve as an 

incentive to polluters. To the extent that polluters 

are aware that they will be held liable before 

the polluting event occurs, they may minimize 

or prevent involvement in activities that inflict 

damages on others. In designing a liability rule it 

is important to evaluate whether damages depend 

only on the amount of care taken on the part of 

the polluter or also on the level of output; and 

whether damages are only determined by polluter 

actions or are also dependent on the behavior 

of victims. For instance, if victims do not 

demonstrate some standard of care in an attempt 

to avoid damages, the polluter may not be held 

liable for the full amount. If damages depend 

on these other factors in addition to polluter 

actions, then the liability rule should be designed 

to provide adequate incentives to address these 

other factors. 

While a liability rule can be constructed to mimic 

an efficient market solution in certain cases, there 

are reasons to expect that this efficiency may not 

be achieved. First, uncertainty exists as to the 

magnitude of payment. The amount that polluters 

are required to pay after damages have occurred is 

dependent on the legal system and may be limited 

by an inability to prove the full extent of damages 

or by the ability of the firm to pay. Second, liability 

rules can generate relatively large costs, both in 

terms of assessing the environmental damage 

caused, and the damages paid.33 Thus, liability rules 

are most useful in cases where damages requiring 

compensation are expected to be stochastic 

(e.g., accidental releases), and where monitoring 

firm compliance with regulatory procedures is 

33	 See Segerson (1995), and Alberini and Austin (2001) for discussions 

of the types of liability rules, the efficiency properties of each type of 

rule, and an extensive bibliography. 
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

 

 

 

 

 

 

 

 

difficult. Depending on the likely effectiveness 

of liability rules to provide incentives to firms to 

avoid damages, they can be thought of as either 

an alternative to or as a complement to other 

regulatory approaches. 

Strict liability and negligence are two types of 

liability rules relevant to polluters. Under strict 

liability, polluters are held responsible for all 

health and environmental damage caused by their 

pollution, regardless of actions taken to prevent 

the damages. Under negligence, polluters are 

liable only if they do not exhibit “due standard 

of care.” Regulations that impose strict liability 

on polluters may reduce the transactions costs of 

legal actions brought by affected parties. This may 

induce polluters to alter their behavior and expend 

resources to reduce their probability of being 

required to reimburse other parties for pollution 

damages. For example, they may reduce pollution, 

dispose of waste products more safely, install 

pollution control devices, reduce output, or invest 

in added legal counsel. 

Liability rules have been used in the remediation 

of contaminated sites under the Comprehensive 

Environmental Response, Compensation 

and Liability Act (CERCLA), also known as 

Superfund, and under the Corrective Action 

provisions of the Resource Conservation and 

Recovery Act (RCRA). These rules have also 

been used in the redevelopment of potentially 

contaminated industrial sites, known as 

brownfields. 

4.5 Selecting the Appropriate 

Market-Based Incentive or 

Hybrid Approach 

Selection of the most appropriate market-based 

incentive or hybrid regulatory approach depends 

on a wide variety of factors, including: 34 

• The type of market failure being addressed; 

• The specific nature of the environmental 

problem; 

34 Helpful references that discuss aspects to consider when comparing 

among different approaches include Hahn and Stavins (1992), OECD 

(1994a, 1994b), Portney and Stavins (2000), and Sterner (2003). 

• The type of pollutant information that is 

available and observable; 

• The degree of uncertainty surrounding costs 

and benefits; 

• Concerns regarding market competitiveness; 

• Monitoring and enforcement issues; 

• Potential for exacerbating economy-wide 

distortions; and 

• The ultimate goals of policy makers. 

4.5.1 The Type of Market Failure 

There are two main types of market failure that are 

commonly addressed through the use of marketbased or hybrid instruments. The first, externality, 

occurs when firms or consumers fail to integrate 

into their decision making the impact of their own 

production or consumption decisions on entities 

external to themselves. The second type of market 

failure, asymmetric information, occurs when firms 

or consumers are unable to make optimal decisions 

due to lack of information on available abatement 

technologies, emission levels, or associated 

risks. Market-based or hybrid instruments that 

incorporate the marginal external damages of a 

unit of pollution into a firm or consumer’s cost 

function address the first type of market failure. 

Information disclosure or labeling are often 

suggested when the second type of market failures 

occurs. As discussed in Section 4.4.2, policy makers 

believe that private- and public-sector decision 

makers will act to address an environmental 

problem once information has been disseminated. 

4.5.2 The Nature of the 

Environmental Problem 

The use of a particular market-oriented approach 

is often directly associated with the nature of the 

environmental problem. Do emissions derive from 

a point source or a nonpoint source? Do emissions 

stem from a stock or flow pollutant? Are emissions 

uniformly mixed or do they vary by location? Does 

pollution originate from stationary or mobile 
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

sources?35 Point sources, which emit at identifiable 

and specific locations, are much easier to control 

than diffuse and often numerous nonpoint sources, 

and therefore are often responsive to a wide 

variety of market instruments. Although nonpoint 

sources are not regulated under EPA, the pollution 

emitted from a nonpoint source is. Clearly, this 

makes the monitoring and control of nonpoint 

source emissions challenging. In instances where 

both point and nonpoint sources contribute to a 

pollution problem, a good case can be made for a 

tax-subsidy combination or a marketable permit 

system. Under these alternatives, emissions from 

point sources might be taxed while nonpoint 

source controls are subsidized. 

Flow pollutants tend to dissipate quickly, and it 

is possible to rely on a wide variety of market and 

hybrid instruments for emissions control. But 

stock pollutants persist in the environment and 

tend to accumulate over time. Controlling stock 

pollutants may require strict limits to prevent 

bioaccumulation or detrimental health effects at 

small doses, making direct regulation a potentially 

more appealing approach. If these limits are not 

close to zero, then potentially practical instrument 

options include a standard-and-pricing approach 

or a marketable permit approach that defines 

particular trading ratios to ensure that emission 

standards are not violated at any given source 

are. These same instruments are appealing when 

pollutants are not uniformly mixed across space. 

In the case of non-uniformly mixed emissions, it 

is important to account for differences in baseline 

pollution levels, and differences in emissions across 

more and less polluted areas. 

Stationary sources of pollution are easier to 

identify and control through a variety of market 

instruments than are mobile sources. Highly 

mobile sources are usually numerous, each 

emitting a small amount of pollution. Emissions 

therefore vary by location and damages can vary 

by time of day or season. For example, health 

impacts associated with vehicle traffic are primarily 

35 For a detailed discussion of how the nature of the environmental 

problem affects instrument choice, see Kahn (1998), Goulder et al. 

(1999), Parry and Williams (1999), Harris (2002), Tietenberg (2002), 

a problem at rush hour when roads are congested 

and cars spend time idling or in stop-and-go 

traffic. Differential pricing of resources used by 

these mobile sources (such as higher tolls on roads 

or greater subsidies to public transportation during 

rush hour) is a potentially useful tool. 

4.5.3 The Type of Pollutant 

Information that is Available 

and Observable 

The selection of market-oriented approach 

may depend on the available data. Is the level 

of pollutant actually observable or measurable? 

Or will the level need to be imputed based on 

inputs and technology used? Are the sources 

heterogeneous? Does the pollutant vary across time 

and space? Are information technologies available 

to the analyst to improve data collection? When the 

pollutant concentration can be directly and easily 

measured then it is possible to directly regulate the 

level of the pollutant. But if monitoring costs are 

high, it may be easier to target a particular input 

or require a specific technology known to reduce 

pollutants by a certain amount. The pollutant levels 

can be imputed based on regulation placed on the 

input or the technology used. 

The link between pollution and heterogeneous 

sources is often difficult and costly to determine, 

and costs may increase if the pollutant levels 

vary over time. Uniform policies are often used 

for the sake of simplicity. However, information 

technologies such as continuous emissions 

monitoring equipment (CEMs) or geographical 

information systems (GIS) can be used to link 

sources to pollutant levels. In these cases, policies 

that make use of this new information may be 

used and often can reduce costs. As technology 

improves or more data become available, analysts 

should consider reassessing the regulation design.36 

4.5.4 Uncertainty in Abatement 

Costs or Damages 

The choice between price-based instruments 

(e.g., taxes or charges) and quantity-based 

and Sterner (2003). 36 For more information see Xabadia, Goetz, and Zilberman (2008). 
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Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

instruments (e.g., marketable permits) has been 

shown theoretically to rest on the uncertainty 

surrounding estimated benefits and costs of 

pollution control, as well as on how marginal 

benefits and costs change with the stringency 

of the pollution control target. If uncertainty 

associated with the cost of abatement exists but 

damages do not change much with additional 

pollution, then policy makers can effectively 

limit costs by using a price instrument without 

having much impact on the benefits of the policy. 

If, on the other hand, there is more uncertainty 

associated with the benefits of controlling 

pollution and policy makers wish to guard 

against high environmental damages, a quantity 

instrument is likely preferable.37 In this way, 

the policy maker can avoid potentially costly or 

damaging mistakes. The policy maker should also 

be aware of any discontinuities or threshold values 

above which sudden large changes in damages or 

costs could occur in response to a small increase in 

the required abatement level. 

4.5.5 Market Competitiveness 

Market power is a type of market failure in and of 

itself, as it may result in output that is too low and 

prices that are too high compared to what would 

occur in a competitive market. Instruments that 

cause firms to further restrict output may create 

additional inefficiencies in sectors where firms 

have some degree of market power. A combination 

of market-based instruments may work more 

effectively than a single instrument in this instance. 

To the extent that cost burdens are differentiated, 

the use of certain market-based instruments may 

cause a change in market structure that favors 

existing firms by creating barriers of entry and 

allowing existing firms a certain amount of control 

over price. Permit systems that set aside a certain 

number of permits for new firms, for instance, may 

guard against such barriers. 

37	 See Weitzman (1974) for the classic paper on the ways in which 

uncertainty (also referred to as lack of information) affects instrument 

choice. See Chapter 10 of these Guidelines for more information on the 

treatment of uncertainty in analyses. 

4.5.6 Monitoring and 

Enforcement Issues 

Market-oriented instruments differ in the degree 

of effort required to monitor and enforce the 

desired emissions level. For example, subsidies, 

deposit-refund systems, and information 

disclosure shift the burden of proof to demonstrate 

compliance from government to the regulated 

entities. Because firms are generally in a better 

position than government to monitor and report 

their own emissions, they likely can do so at a 

potentially lower cost. This feature makes such 

approaches attractive when monitoring is difficult 

or emissions must be estimated (e.g., when there 

are nonpoint sources or large numbers of small 

polluters). In these cases, attempts to prohibit or 

tax the actions of polluters are likely to fail due to 

the risk of widespread noncompliance (e.g., illegal 

dumping to avoid the tax) and costly enforcement. 

4.5.7 Potential for 

Economy-Wide Distortions 

Analysts should consider the potential 

distortionary effects of any policy option 

considered. Even if a policy is deemed relatively 

efficient on its own, it may interact with 

pre-existing environmental, economic, or 

agricultural policies (e.g., product standards, 

non-environmental subsidies, trade barriers) in 

non-intuitive ways that can exacerbate distortions 

in the economy and result in unintended 

environmental consequences. Instruments that 

include a revenue-raising component, such 

as auctioned permits or taxes, may allow for 

opportunities to direct collected resources to 

reduce other taxes and fees and the associated 

inefficiencies.38 See Chapter 8 and Appendix A 

for a more detailed discussion of economy-wide 

distortions. 

38	 For useful references on the issues concerning the uses of revenues 

from pollution charges (e.g., applying environmental tax revenues so 

as to reduce other taxes and fees in the economy) and ways to analyze 

these policies, see Bovenberg and de Moojii (1994), Goulder (1995), 

Bovenberg and Goulder (1996), Goulder et al. (1997), and Jorgenson 

(1998a, 1998b). 
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4.5.8 The Goals of the 

Policy Maker 

Finally, the goals of policy makers may influence 

the instrument selected to regulate pollution. 

Each considered instrument may have different 

distributional and equity implications for both 

costs and benefits; these implications should be 

accounted for when deciding among instruments. 

For example, policy makers may wish to ensure 

clean-up of future pollution by firms. Policy 

makers may consider using insurance and financial 

assurance mechanisms to supplement existing 

standards and rules when there is a significant 

risk that sources of future pollution might be 

incapable of financing the required pollution 

control or damage mitigation method. In addition, 

the degree to which policy makers want to allow 

the market to determine exact outcomes may 

influence the choice of instrument. The quantity 

of marketable permits issued, for example, sets the 

total level of pollution control, but the market 

determines which polluters reduce emissions. On 

the other hand, taxes let the market determine 

both the extent of control by individual polluters 

and the total level of control. 

4.6 Non-Regulatory 

Approaches 

EPA has pursued a number of non-regulatory 

approaches that rely on voluntary initiatives 

to achieve emissions reductions and improve 

management of environmental hazards. These 

programs are usually not intended as substitutes 

for formal regulation, but instead act as important 

complements to existing regulation. Many of EPA’s 

voluntary programs encourage polluting entities to 

go beyond what is mandated by existing regulation. 

Other voluntary programs have been developed to 

improve environmental quality in areas that policy 

makers expect may be regulated in the future 

but are currently not regulated, such as GHG 

emissions and nonpoint source water pollution.39 

39 While this chapter only discusses government-led voluntary initiatives 

at the federal level at EPA, other government agencies, industry, 

non-profits, and international organizations have also initiated 

and organized voluntary initiatives designed to address particular 

environmental issues. These initiatives are beyond the scope of this 

chapter, which limits itself to a brief description of policy options 

available to EPA. 

Much of the technical foundation for these 

voluntary initiatives rests on the concepts 

underlying a “pollution prevention” approach 

to environmental management choices. In the 

Pollution Prevention Act of 1990, Congress 

established a national policy that: 

• Pollution should be prevented or reduced at 

the source whenever feasible; 

• Pollution that cannot be prevented should be 

recycled in an environmentally safe manner 

whenever feasible; 

• Pollution that cannot be prevented or recycled 

should be treated in an environmentally safe 

manner whenever feasible; and 

• Disposal or other release into the 

environmental should be employed as a 

last resort and should be conducted in an 

environmentally safe manner. 

EPA typically designs its voluntary programs 

through regular consultation (but little direct 

negotiation) with affected industries or 

consumers.40 In many cases, voluntary programs 

facilitate problem solving between EPA and 

industry because information on procedures or 

practices that reduce or eliminate the generation 

of pollutants and waste at the source are shared 

through the consultative process. 

In slightly more than a decade, voluntary programs 

at EPA have increased from two programs to 

approximately 40 programs involving more than 

13,000 organizations. Partner organizations 

include small and large businesses, citizen groups, 

state and local governments, universities, and 

trade associations.41 Voluntary programs in which 

these groups participate tend to have either broad 

environmental objectives targeting a variety of 

firms from different industries, or focus on more 

specific environmental problems relevant to a 

single industrial sector. In the United States, nearly 

40 Because these programs are voluntary there is no need for formal 

public comment. However, industry often is consulted during the 

design phase. 

41 For information on EPA’s voluntary programs, see the Partners for 

the Environment List of Programs at http://www.epa.gov/partners/ 

programs/index.htm (accessed November 03, 2010) (U.S. EPA 2008e). 
Guidelines for Preparing Economic Analyses | December 2010 4-19 

Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

one third of all multi-sector federal voluntary 

programs focus on energy efficiency and climate 

change issues. General pollution prevention efforts 

represent the next most popular type of voluntary 

program. Single-sector federal voluntary programs 

tend to target environmental problems associated 

with transportation-related issues and energy 

producing sectors such as coal mining and power 

generation. These programs strive to provide 

participating firms with targeted and effective 

technological expertise and assistance.42 

4.6.1 How Voluntary 

Approaches Work 

Voluntary programs can use the following four 

general methods to achieve environmental 

improvements: (1) require firms or facilities to set 

specific environmental goals; (2) promote firm 

environmental awareness and encourage process 

change; (3) publicly recognize firm participation; 

and (4) use labeling to identify environmentally 

responsible products. These methods are not 

mutually exclusive, and most U.S. voluntary 

programs use a combination of methods. 

Goal setting is a very common method used in the 

design of voluntary programs. Implementationbased goals are typically EPA-specified, programwide targets designed to provide a consistent 

objective across firms. Target-based goals are 

usually qualitative and process-oriented so that 

firms may individually set a unique target. EPA’s 

WasteWise and Climate Challenge programs are 

examples of programs with target-based goals. 

EPA’s 33/50 program, which set a goal of a 33 

percent reduction of toxic emissions by firms in 

the chemical industry by 1992, and a 50 percent 

reduction by 1995 (relative to a 1988 baseline), 

is an example of a voluntary program with an 

implementation-based goal. 

Programs designed to promote environmental 

awareness and to encourage process change within 

firms often involve implementing a system to 

42 See Khanna (2001); OECD (1999, 2003); U.S. EPA (2002a); and 

Brouhle, Griffiths, and Wolverton (2005) for discussions of how 

voluntary programs work and how they are used in U.S. environmental 

policy making. 

evaluate firms’ ongoing operations and to provide 

information on newly available technologies. 

Examples of this type of approach include the 

SmartWay program, which encourages firms to 

adopt energy efficient changes that also yield 

fuel savings for freight trucking companies, and 

the Green Suppliers Network program, which 

provides partner firms with technical reviews 

and suggestions on how to eliminate waste from 

production processes. 

Voluntary programs that publicly recognize 

firm participation are designed to provide 

green consumers and investors with new 

information that may alter their consumption 

and investment patterns in favor of cleaner 

firms. Firms may also use their environmental 

achievements to differentiate their products 

from competitors’ products.43 These 

information and firm differentiation effects are 

the intent of the Green Power Partnership and 

the WasteWise program. 

Finally, product labeling can be applied to either 

intermediate inputs in a production process or 

to a final good. Labels on intermediate goods 

encourage firms to purchase environmentally 

responsible inputs. Labels on final goods allow 

consumers to identify goods produced using a 

relatively clean production process. For example, 

products deemed by EPA to be energy efficient 

may be eligible for the Energy Star or Design for 

the Environment labels. 

4.6.2 Economic Evaluation of 

Voluntary Approaches 

A formal economic analysis is not required for the 

selection and implementation of a non-regulatory 

or voluntary approach to pollution reduction. 

Several factors contribute to the difficulty of 

evaluating voluntary approaches. Many programs 

target general environmental objectives and thus 

43 See Arora and Cason (1995); Arora and Gangopadhyay (1995); Konar 

and Cohen (1997, 2001); Videras and Alberini (2000); Brouhle, 

Griffiths, and Wolverton (2005); and Morgenstern and Pizer (2007) for 

more information on the main arguments for why firms participate in 

voluntary programs. 
4-20 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

 

 

 

 

 

Text Box 4.3 - Water Quality Trading of Nonpoint Sources 

In 2003, EPA issued a “Water Quality Trading Policy” (U.S. EPA 2003d) that encourages states and tribes to develop 

and implement voluntary water-quality trading to control nutrients and sediments in areas where it is possible to 

achieve these reductions at lower costs. Under the Clean Water Act, EPA is required to establish Total Maximum 

Daily Loadings (TMDL) of pollutants for impaired water bodies. The TMDL does not establish an aggregate cap 

on discharges to the watershed, but it does provide a method for allocating pollutant discharges among point and 

nonpoint sources. Point sources are regulated by EPA and, as such, are required to hold National Pollutant Discharge 

Elimination System (NPDES) permits that limit discharges. However, many water bodies are still threatened by 

pollution from unregulated, nonpoint sources. Nutrients and sediment from urban and agricultural runoff have led 

to water quality problems that limit recreational uses of rivers, lakes, and streams; that create hypoxia in the Gulf of 

Mexico; and that decrease fish populations in the Chesapeake Bay. The impetus for allowing effluent trading between 

point and nonpoint sources is to lower nutrient and sediment loadings and to improve or preserve water quality. 

To ensure that the reduction resulting from the trade has the same effect on the water quality as the reduction that 

would be required without the trade, trading ratios are often applied. These ratios attempt to control for the differential 

effects resulting from a variety of factors, which may include: 

• location of the sources in the watershed relative to the downstream area of concern; 

• distance between the permit buyer and seller; 

• uncertainty about nonpoint source reductions; 

• equivalency of different forms of the same pollutant discharged by the trading partners; and 

• additional water quality improvements above and beyond those required by regulation. 

The idea behind trading is to allow point sources to meet the discharge limit at a lower cost. This allows continued 

growth and expansion of production, while giving nonpoint sources an incentive to reduce pollution through 

participation in the market. To the extent that it is cheaper for a nonpoint source to reduce pollution than to forgo 

revenues earned from the sale of any unused credits to point sources, the nonpoint source is predicted to choose to 

emit less pollution. 

As of March 2007, 98 NPDES permits, covering 363 dischargers, included provisions for trading. However, only 

about a third of the dischargers had carried out one or more trades under these permits (U.S. EPA 2007f). Trading 

has been limited for several reasons. First, there is no aggregate “cap” on discharges that applies to both point 

and nonpoint sources within a watershed. Reductions by nonpoint sources are essentially voluntary. Point-source 

dischargers often explore trading as a way to expand production while meeting the requirements of their individual 

permits, but there is no general signal in the market to do so. Second, these are often thin markets. The way in which 

the market is designed or trading ratios are established can make it difficult or expensive for an entity to identify and 

complete a trade. Third, while Best Management Practices (BMPs) are typically used to define a pollution reduction 

credit from a nonpoint source, uncertain or changing climatic conditions, river flow, and stream conditions make 

it difficult to measure the effect of a BMP on water quality. Such uncertainty also makes measuring and enforcing a 

pollution reduction from a nonpoint source difficult. Fourth, encouraging nonpoint source involvement in trading, 

given the agriculture industry’s distrust of regulators, is challenging. Finally, it is difficult to define appropriate trading 

ratios between point and nonpoint sources. 

lack a measurable environmental outcome. Even a reasonable baseline from which to make a 

if a measurable output exists, there may be a lack comparison must be established. This requires 

of data on a firm’s or industry’s environmental an extensive analysis comparing the actions of 

outputs. In order to perform an evaluation, participants to non-participants in the program; 
Guidelines for Preparing Economic Analyses | December 2010 4-21 

Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

 

 

 

 

 

such data is likely difficult and costly to obtain.44 

Any economic evaluation of voluntary programs 

should net out pollution abatement activities 

that would have occurred even if the voluntary 

program were not in place. Some of these 

evaluation obstacles can be overcome if voluntary 

approaches use more defined and detailed goal 

setting and require more complete data collection 

and reporting from the outset.45 

The economic literature evaluating the efficacy 

of voluntary programs is decidedly mixed. The 

vast majority of existing empirical studies focus 

on a few large, multi-sector voluntary programs 

such as 33/50, Green Lights, and Energy Star. 

For these programs, there is some evidence 

of success in reducing participant emissions. 

However, studies generally fail to account for 

non-program factors such as the ability to count 

reductions that occurred prior to the start of 

the program; to compare reductions relative to 

a baseline counterfactual may overstate these 

reductions. Researchers have been less successful 

in demonstrating that voluntary programs have 

led to greater emission reductions than would 

have occurred without the program in place. One 

thread of literature points to the positive impact 

of a regulatory threat on voluntary program 

effectiveness. When the threat of regulation is 

weak, abatement levels are likely to be lower. 

However, when the threat of regulation is strong, 

levels achieved are closer to those under optimal 

regulatory action. 

4.7 Measuring the 

Effectiveness of Regulatory or 

Non-Regulatory Approaches 

Several policy criteria should be considered 

when evaluating the success of regulatory or nonregulatory approaches. These include environmental 

effectiveness; economic efficiency; savings in 

administrative, monitoring and enforcement 

costs; inducement of innovation; and increased 

44 See Chapter 5 for a discussion of baselines and specifically Section 

5.7 for a discussion of behavioral responses. 

45 See Segerson and Miceli (1998); Khanna and Damon (1999); National 

Research Council (2002); Segerson and Wu (2006); Morgenstern and 

Pizer (2007); and Brouhle, Griffiths, and Wolverton (2009). 

environmental awareness. In many cases, analysis 

of these factors will make evident the particular 

advantages of one or more market-based incentive 

approaches over command-and-control regulation. 

While a formal analysis may not be required when 

considering the implementation of a non-regulatory 

approach, these factors are still important to 

consider. According to recent reviews (Harrington 

et al. 2004, and Goulder and Parry 2008) it is 

unlikely that any one policy will dominate on all of 

these factors. However, in many areas an incentive 

policy, if available, can be more cost-effective than a 

competing command-and-control policy.

 In determining the effectiveness of a policy 

approach, policy makers should consider the 

following factors and questions: 

• Environmental Effectiveness: Does the 

policy instrument accomplish a measurable 

environmental goal? Does the policy 

instrument result in general environmental 

improvements or emission reductions? Does 

the approach induce firms to reduce emissions 

by greater amounts than they would have in 

the absence of the policy? 

• Economic Efficiency: How closely does 

the approach approximate the most efficient 

outcome? Does the policy instrument reach 

the environmental goal at the lowest possible 

cost to firms and consumers? 

• Reductions in Administrative, Monitoring, 

and Enforcement Costs: Does the 

government benefit from reductions in costs? 

How large are these cost savings compared to 

those afforded by other forms of regulation? 

• Environmental Awareness and Attitudinal 

Changes: In the course of meeting particular 

goals, are firms educating themselves on the 

nature of the environmental problem and 

ways in which it can be mitigated? Does 

the promotion of firm participation or 

compliance affect consumers’ environmental 

awareness or priorities and result in a demand 

for greater emissions reductions? 

• Inducement of Innovation: Does the 


policy instrument lead to innovation in 

4-22 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 4 Regulatory and Non-Regulatory Approaches to Pollution Control

abatement techniques that decrease the cost 

of compliance with environmental regulations 

over time? 

To address a number of these key evaluation 

criteria, Guidelines Chapters 8 and 9 offer 

instruction on how to measure social costs and 

how to address equity issues, respectively. 
Chapter 5 

Baseline
 

The baseline of an economic analysis is a reference point that reflects the world 

without the proposed regulation. It is the starting point for conducting an 

economic analysis of the potential benefits and costs of a proposed regulation. 

Because an economic analysis considers the impact of a policy or regulation 

in relation to this baseline, its specification can have a profound influence on 

the outcome of the economic analysis. A careful and correct baseline specification assures the 

accuracy of benefit and cost estimates. The baseline specification can vary in terms of sources 

analyzed (e.g., facilities, industries, sectors of the economy), geographic resolution (e.g., 

census blocks, GIS grid cells, counties, state, regions), environmental objectives (e.g., effluents 

and emissions versus pollutant concentrations), and years covered. Because the level of detail 

presented in the baseline specification is an important determinant of the kinds of analysis 

that can be conducted on proposed regulatory options, careful thought in specifying the 

baseline is crucial. 

The drive for a thorough, rigorous baseline analysis should be balanced against other 

competing objectives such as judicial and statutory deadlines, and legal requirements. The 

analyst is responsible for raising questions about baseline definitions early in the regulatory 

development process to ensure that the analysis is as comprehensive as possible. Doing so will 

facilitate analysis of regulatory changes to the baseline regulation. 

5.1 Baseline Definition 

A baseline is defined as the best assessment of the 

world absent the proposed regulation or policy 

action.1

 This “no action” baseline is modeled 

assuming no change in the regulatory program 

under consideration. This does not necessarily 

mean that no change in current conditions will 

take place, since the economy will change even in 

the absence of regulation. A proper baseline should 

incorporate assumptions about exogenous changes 

in the economy that may affect relevant benefits 

and costs (e.g., changes in demographics, economic 

activity, consumer preferences, and technology), 

industry compliance rates, other regulations 

promulgated by EPA or other government entities, 

A policy action includes both regulations and the issuance of Best 

Management Practices (BMPs) or guidance documents, which do not carry 

the same force as a regulation, but do affect the decisions of firms and 

consumers. 

and behavioral responses to the proposed rule by 

firms and the public. 

On occasion a regulatory program may be set to 

expire or dramatically change, even in the absence 

of the proposed action. In this case, the baseline 

specification might consider a state of the world 

different from current conditions. This situation, 

however, is less common. 

The baseline serves as a primary point of comparison 

for an analysis of a proposed policy action. An 

economic analysis of a policy or regulation 

compares the current state of the world, the baseline 

scenario, to the expected state of the world with the 

proposed policy or regulation in effect, the policy 

scenario. Economic and other impacts of policies or 

regulations are measured as the differences between 

these two scenarios. 

Guidelines for Preparing Economic Analyses | December 2010 5-1 

1 
Chapter 5 Baseline

 

 

 

 

 

 

 

In most cases, a single, well-defined description 

of the world in the absence of the regulation is 

generally all that is needed as a baseline. A single 

baseline produces a clear point of comparison with 

the policy scenario and allows for an unequivocal 

measure of the benefits, costs, and other 

consequences of the rule. There are a few cases in 

which more than one baseline may be necessary. 

Multiple baseline scenarios are needed, for 

example, when it is impossible to make a 

reasonable unique description of the world in 

the absence of the proposed regulation. For 

instance, if the current level of compliance with 

existing regulations is not known, then it may 

be necessary to compare the policy scenario to 

both a full compliance baseline and a partial 

compliance baseline. Further, if the impact 

of other rules currently under consideration 

fundamentally affects the economic analysis of 

the rule being analyzed, then multiple scenarios, 

with and without these rules in the baseline, may 

be necessary. 

The decision to include multiple baselines 

should not be taken lightly as a complex set of 

modeling choices and analytic findings may result. 

These must be interpreted and communicated 

to decision makers, increasing the possibility 

of erroneous comparisons of costs and benefits 

across different baselines. When more than one 

baseline is required, analysts should endeavor to 

construct scenarios that can provide benchmarks 

for policy analysis. The number of baselines 

should be limited to as few as possible that cover 

the key dimensions of the economic analysis and 

any phenomena in the baseline about which there 

is uncertainty. 

In some cases, probabilistic analysis can be used 

to avoid the need for multiple baselines and still 

provide an appropriate benchmark for policy 

analysis. A probabilistic analysis is a form of 

uncertainty analysis in which a single modeling 

framework is generally specified, but statistical 

distributions are assigned to the uncertain input 

parameters. The policy scenario is then compared 

to a continuum of baselines, with a probability for 

any given outcome, rather than being compared to 

a single baseline. The benefit-cost analysis (BCA) 

would then report the probability that a policy 

intervention produces net benefits rather than 

reporting the net benefits compared to one (or 

more) deterministic baseline(s). 

Analysts are advised to seek clear direction from 

management about baseline definitions early on in 

the development of a rule. Each baseline-to-policy 

comparison should be internally consistent in its 

definition and use of baseline assumptions. 

5.2 Guiding Principles of 

Baseline Specification 

In specifying the baseline, analysts should employ 

the following guiding principles each of which is 

discussed more fully below: 

1. Clearly specify the current and future 

state of relevant economic variables, the 

environmental problem that the regulation 

addresses and the regulatory approach being 

considered; 

2. Identify all required parameters for the 


analysis;
 

3. Determine the appropriate level of effort for 

baseline specification; 

4. Clearly identify all assumptions made in 

specifying the baseline conditions; 

5. Specify the “starting point” of the baseline 

and policy scenario; 

6. Specify the “ending point” of the baseline 

and policy scenario; 

7. Detail all aspects of the baseline specification 

that are uncertain; and 

8. Use the baseline assumptions consistently for 

all analyses for this regulation. 

Though these principles exhibit a general 

common-sense approach to baseline specification, 

the analyst is advised to provide her own explicit 

statements on each point. Failure to do so may 

result in a confusing presentation, inefficient use 

of time and resources, and misinterpretation of the 

economic results. 

5-2 Guidelines for Preparing Economic Analyses | December 2010 
Chapter 5 Baseline

Clearly specify the current and future state of 

relevant economic variables, the environmental 

problem that the regulation addresses and the 

regulatory approach being considered. A clear 

written statement about the current state of the 

relevant economic variables (see Chapter 8 in 

particular to determine what variables are relevant) 

and environment will help decision makers and 

the general public to understand both the positive 

and negative consequences of a regulation. The 

statement should include a description of: (1) the 

pollution problem being addressed; (2) the current 

regulatory environment; (3) the method by which 

the problem will be addressed; and (4) the affected 

parties. There should also be a discussion of why a 

particular approach [e.g., best available technology 

(BAT), performance measures, market incentives, 

or non-regulatory approaches] was chosen. 

In general, the most appropriate baseline will 

be the “no change” or “reality in the absence 

of the regulation” scenario; but in some cases, 

a baseline of some other regulatory approach 

may be considered. For example, if an industry 

is certain to be regulated (e.g., by court order 

or congressional mandate) but that regulation 

has not yet been implemented, then a baseline 

including this regulation should be used. To ensure 

that provisions contained in statutes or policies 

preceding the regulatory action in question 

are appropriately addressed and measured, it is 

common practice to assume full compliance with 

regulatory requirements, although sensitivity 

analyses assuming less-than-full compliance may 

be considered. However, analysts should consult 

with their management and the Office of General 

Counsel (OGC) before doing so. 

Identify all required parameters for the 

analysis. To ensure that the baseline scenario 

can be compared to the policy scenario, there 

should be a clear understanding of the path from 

environmental damage to adverse impact on 

humans. The models and parameters required for 

the baseline analysis should be chosen so that the 

baseline assumptions can feed into all subsequent 

analyses. Measured differences between the 

baseline and policy scenario can include changes in 

usage or production of toxic substances, changes in 

pollutant emissions and ambient concentrations, 

and incidence rates for adverse health effects 

associated with exposure to pollutants. This 

does not mean that the analyst must identify all 

parameters that could possibly change, but the 

analyst should recognize all relevant parameters 

needed to compare the baseline scenario to the 

policy scenario. As a general rule of thumb, at 

a minimum, the analyst should identify the 

parameters that are expected to vary by option, the 

parameters that are expected to have the largest 

impact on cost and benefit differences, and the 

parameters that are anticipated to come under 

close public scrutiny. 

Determine the appropriate level of effort 

for baseline specification. The analyst 

should concentrate analytic efforts on those 

components (e.g., assumptions, data, models) 

of the baseline that are most important to the 

analysis, taking into consideration factors such 

as the time given to complete the analysis, the 

person-hours available, the cost of the analysis, 

and the available models and data. If several 

components of the baseline are uncertain, the 

analyst should concentrate limited resources on 

refining the estimates of those components that 

have the greatest effect on the interpretation of 

the results. Analysts should pay special attention 

to the components that will be used to calculate 

costs and benefits and those that are important 

determinants of the policy option selected. 

Clearly identify all assumptions made in 

specifying the baseline conditions. Whether 

variables are modeled or set by fixed assumptions, 

the analyst should explain the assumptions and 

uncertainties about the parameters in detail. 

Assumptions should include changes in behavior 

and business trends, and how these trends may 

be affected by regulatory management options. 

Analysts may observe trends in economic activity 

or pollution control technologies that occur 

for reasons other than direct environmental 

regulations. For example, as the purchasing 

power of consumer income increases over time, 

demand for different commodities may change. 

Demand for some commodities may grow at rates 

faster than the rate of change in income, while 

Guidelines for Preparing Economic Analyses | December 2010 5-3 
Chapter 5 Baseline

demand for other goods may decrease. Where 

these trends are highly uncertain or are expected 

to have significant influence on the evaluation of 

regulatory alternatives (including a “no-regulatory 

control” alternative), the analyst should clearly 

explain and identify the assumptions used in 

the analysis, with the goal of laying out the 

assumptions clearly enough so that other analysts 

(with access to the appropriate models) would be 

able to replicate the baseline specification. 

Specify the “starting point” of the baseline and 

policy scenario. A starting point of an analysis 

is the point in time at which the comparison 

between the baseline and policy scenarios begins. 

This is conceptually the point in time at which the 

two scenarios diverge. For example, one approach 

is to organize the analysis assuming that the policy 

scenario conditions diverge from those in the 

baseline at the time an enforceable requirement 

becomes effective. Another convenient approach 

is to set the starting point as the promulgation 

of the final rule. These dates may be appropriate 

to use because they are clearly defined under 

administrative procedures or because they 

represent specific deadlines. 

However, where behavioral changes are motivated 

by the expected outcome of the regulatory process, 

the actual timing of the formal issuance of an 

enforceable requirement may not be the most 

appropriate starting point to define differences 

between the baseline and policy scenarios. Earlier 

starting points, such as the date when authorizing 

legislation was signed into law, the date the 

rule was first published in a Notice of Proposed 

Rulemaking, or other regulatory development 

process milestones, may be justified when 

divergence from the baseline occurs due to the 

anticipation of promulgation. 

Specify the “ending point” of the baseline and 

policy scenario. The ending point of an analysis 

is the point in time at which the comparison 

between the baseline and policy scenarios ends. 

Generally, the duration of important effects of 

a policy determines the period chosen for the 

analysis and baseline. However, other analytical 

considerations, such as the relative uncertainty 

in projecting out-year conditions, may also need 

to be weighed. To compare the benefits and costs 

of a proposed policy, the analyst should estimate 

the present discounted values of the total costs 

and benefits attributable to the policy over the 

period of the study. How one defines the ending 

point of the baseline is particularly important 

in situations where the accrual of costs and/or 

benefits do not coincide due to lagged effects, 

or where they occur over an extended period of 

time. For example, the human health benefits 

of a policy that reduces leachate from landfills 

may not manifest themselves for many years if 

groundwater contamination occurs decades after 

closure of a landfill. In theory, the longer the time 

frame, the more likely the analysis will capture 

all of the major benefits and costs of the policy. 

Naturally, the forecasts of economic, demographic, 

and technological trends that are necessary for 

baseline specification should also span the entire 

period of the analysis. However, because forecasts 

of the distant future are less reliable than forecasts 

of the near future, the analyst should balance the 

advantages of structuring the analysis to include a 

longer time span against the disadvantages of the 

decreasing reliability of the forecasts for the future. 

In some cases, the benefits of a policy are expected 

to increase over time. When this occurs, analysts 

should extend the analysis far enough into the 

future to ensure that benefits are not substantially 

underestimated. For example, suppose a proposed 

policy would greatly reduce greenhouse gas 

(GHG) emissions. In the baseline scenario, the 

level of GHG in the atmosphere would steadily 

increase over time, with a corresponding increase 

in expected impacts on human health and welfare 

and ecological outcomes. A BCA limited to the 

first decade after policy initiation would likely 

distort the relationship of benefits and costs 

associated with the policy. In this case, the conflict 

between the need to consider a long time frame 

and the decreasing reliability of forecasting far 

into the future may be substantial. In most cases, 

primary considerations in determining the time 

horizon of the analysis will be the time span of the 

physical effects that drive the benefits estimates 

and capital investment cycles associated with 

environmental expenditures. 

5-4 Guidelines for Preparing Economic Analyses | December 2010 
Chapter 5 Baseline

In some circumstances, it may make sense to model 

the annual flow of benefits and costs rather than 

model them over time. For example, if the benefits 

and costs remain constant (in real terms) over 

time, then an estimate for a single year is all that is 

necessary. The duration of the policy will not affect 

whether there are net benefits nor will it affect the 

choice of the most economically efficient option, 

although it will obviously still affect the magnitude 

of net benefits. In this case, an “ending point” may 

not be needed and a present discounted value 

of the net benefits may be unnecessary as well. 

However, the absence of these values should be 

explicit in the analysis. An alternative to providing 

no present discounted value is to conduct a single 

year estimate of costs and benefits, but calculate a 

present discounted value of net benefits assuming 

an infinite time period. 

Detail all aspects of the baseline specification 

that are uncertain. Because the analyst does 

not have perfect foresight, the appropriate 

baseline conditions cannot be characterized with 

certainty. Future values always have some level 

of uncertainty associated with them, and current 

values often do as well. To the extent possible, 

estimates of current values should be based 

on actual data, and estimates of future values 

should be based on clearly specified models and 

assumptions. Where reliable projections of future 

economic activity and demographics are available, 

this information should be adequately referenced. 

In general, uncertainties underlying the baseline 

conditions should be treated in the same way as 

other types of uncertainties in the analysis. All 

assumptions should be clearly stated and, where 

possible, all models should be independently 

reproducible. 

It is important to detail information that was 

not included in the analysis due to scientific 

uncertainty. For example, a health or ecological 

effect may be related to the regulated pollutant, 

but the science behind this connection may be too 

uncertain to include the effect in the quantitative 

analysis. In this case, the effect should not be 

included in the baseline, but a discussion of why the 

effect was excluded should be added — especially 

if the magnitude is such that it could significantly 

affect the net benefit calculation. A similar 

recommendation can be made for model choice 

or even the choice of parameter values; known 

aspects of the analysis, which are not included in 

the baseline due to scientific uncertainty, should be 

included in the uncertainty section. 

Large uncertainty in significant variables may 

require the construction of alternative baselines 

or policy scenarios. This leads to numerous 

complications in policy analysis, especially in costeffectiveness analysis (CEA) and the calculation of 

net benefits. While sensitivity analysis is usually a 

better choice, multiple scenarios may be beneficial 

in selecting policy options, especially if there is a 

significant probability of irreversible consequences 

or catastrophic events. 

Use the baseline assumptions consistently for 

all analyses for this regulation. The models, 

assumptions, and estimated parameters used in 

the baseline should be carried through for all 

components of the analysis. For example, the 

calculation of both costs and benefits should 

draw upon estimates derived using the same 

underlying assumptions of current and future 

economic conditions. If the benefits and costs are 

derived from two different models, then the initial 

baseline conditions of costs and benefits should be 

compared to ensure that they are making identical 

assumptions. Likewise, when comparing and 

ranking alternative regulatory options, comparison 

to the same baseline should be used for all options 

under consideration.2 

In some cases, an analysis may not have been 

anticipated during the baseline specification. 

For example, a sector might be singled out for 

more detailed analysis, or a follow-on analysis 

might be needed to assess impacts on a particular 

low-income or minority group. In this case, a 

complete baseline specification that would make 

this secondary analysis fully consistent with the 

primary analyses may not be available. Even in 

2 In the less common case in which more than one baseline scenario 

is modeled, the analyst must avoid the mistake of combining analytic 

results obtained from different baseline scenarios. To limit confusion 

on this point, if multiple baseline scenarios are included in an analysis, 

the presentation of economic information should clearly describe and 

refer to the specific baseline scenario being used. 

Guidelines for Preparing Economic Analyses | December 2010 5-5 
Chapter 5 Baseline

this case, however, some type of baseline will have 

to be produced in order to conduct the analysis. 

While it may not be identical to the baseline 

used to analyze the benefits and costs, the analyst 

should endeavor to make it as similar as possible. 

The analyst should explicitly state the differences 

between the two baselines or any uncertainty 

associated with the secondary baseline. 

5.3 Changes in Basic Variables 

Certain variables are very important for modeling 

both the baseline scenario and the policy scenario. 

Some of these variables, such as population and 

economic activity, are commonly modeled by other 

government agencies and are available for use in 

economic analyses. The values of these variables 

will change over the period of study and, as a result 

of the policy, may differ significantly between the 

two scenarios. Even when they are the same across 

scenarios, these values can have a substantial impact 

on the overall benefits and costs and should be 

explicitly reported over time. Other variables, such 

as consumer spending patterns and technological 

growth in an industry, are also important for 

modeling, but are more difficult to estimate. In 

these cases, the analyst should specify the variable 

levels and report whether these variables changed 

during the period of the study. When they are 

assumed to change, both over time and between 

scenarios, the analyst should explicitly state the 

assumptions of how and why they change. 

5.3.1 Demographic Change 

Changes in the size and distribution of the 

population can affect the impact of EPA programs 

and, as a consequence, can be important in 

economic analyses. For example, risk assessments 

of air toxics standards require assumptions about 

the number of individuals exposed. Therefore, 

assumptions about future population distributions 

are important for measuring potential future 

incidence reductions and for estimating the 

maximum individual risk or exposures. Another 

example is when population growth affects the 

level of vehicle emissions due to an increased 

number of cars and greater highway congestion. 

For most analyses, U.S. Census Bureau projections 

of future population growth and distribution 

can be used. In some cases, however, behavioral 

models may be required if the population growth 

or distribution changes as a consequence of the 

regulation. For example, demographic trends 

in an area may change as a result of cleaning up 

hazardous waste sites. EPA analyses should reflect 

the consequences of population growth and 

migration, especially if these factors influence the 

regulatory costs and benefits. 

5.3.2 Future Economic Activity 

Future economic activity can have a significant 

effect on regulatory costs and benefits because it 

is correlated with emissions and, in some cases, 

can influence the feasibility or cost-effectiveness 

of particular control strategies. Even small changes 

in the rate of economic growth may, over time, 

result in considerable differences in emissions 

and control costs. Assuming no change in the 

economic activity of the regulated sector, or in 

the nation as a whole, will likely lead to incorrect 

results. For example, if the regulated industry is in 

significant decline, or is rapidly moving overseas, 

this information should be accounted for in the 

baseline. In such a case, incremental costs to the 

regulated community (and corresponding benefits 

from the regulation) are likely to be less than if the 

targeted industry were growing. 

Official government estimates of future economic 

growth are the most appropriate values to use. In 

many cases, however, the future economic activity 

of the particular sectors under regulation will 

have to be modeled. In both cases, the models and 

assumptions used should be made as explicit as 

possible. When economic growth is a significant 

determinant of the relative merits of regulatory 

alternatives or when there are significant 

differences between official and private growth 

estimates, then sensitivity analyses using alternative 

growth estimates should be included. 

5.3.3 Changes in 

Consumer Behavior 

The bundle of economic goods purchased by 

consumers can affect the benefits and costs of a 

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Chapter 5 Baseline

rule. An increase in the price and decrease in the 

quantity of goods from the regulated sector should 

be included as part of the cost of the regulation. 

Likewise, a reduction in the number of goods (e.g., 

bottled water) that were previously purchased 

to reduce health effects caused by the regulated 

pollutant will result in economic benefits to 

the public. Thus, changes in consumer behavior 

are important in the overall economic analysis. 

Changes in consumer purchasing behavior should 

be supported by estimates of demand, crossprice, and income elasticities allowing changes in 

consumer behavior to be estimated over time and 

for the baseline and policy scenarios.3 

One controversial extension involves the income 

elasticity for environmental protection. There is 

some evidence that the demand for environmental 

quality rises with income (Baumol and Oates 

1988). However, this does not necessarily 

justify adjusting the benefit of environmental 

improvements upward as income rises. This 

is because the willingness to pay (WTP) for a 

marginal improvement in the environmental 

amenity, the appropriate measure of the benefits 

of environmental protection, may not necessarily 

have a positive income elasticity (Flores and 

Carson 1997). It is appropriate to account for 

income growth over time where there are empirical 

estimates of income elasticity for a particular 

commodity associated with environmental 

improvements (e.g., for reduced mortality risk). 

In the absence of specific estimates, it would 

be appropriate to acknowledge and explain the 

potential increase in demand for environmental 

amenities, as incomes rise. 

5.3.4 Technological Change 

Future changes in production techniques 

or pollution control may influence both the 

baseline and the costs and benefits of regulatory 

alternatives. Estimating the future technological 

Demand elasticities show how the quantity of a product purchased 

changes as its prices changes, all else equal. Cross-price elasticities 

show how a change in the price of one good can result in a change in 

the price of another good (either a substitute or a complement), thereby 

altering the quantity purchased. Income elasticity allows a modeler 

to forecast how much more of a good consumers will buy when their 

income increases. See Appendix A for more information on elasticity. 

change is quite difficult and often controversial. 

Technological change can be thought of as having 

at least two components: true technological 

innovation, such as a new pollution control 

method; and learning effects, in which experience 

leads to cost savings through improvements in 

operations, experience, or similar factors. It is not 

advisable to assume a constant, generic rate of 

technological progress, even if the rate is small, 

simply because the continuous compounding of 

this rate over time can lead to implausible rates of 

technological innovation. However, in some cases 

learning effects may be included in analyses. 

Undiscovered technological innovation is often 

considered to be one reason why regulatory costs 

are overstated (Harrington et al. 1999). Because 

of the difficulty and controversy associated with 

estimating technological change in an economic 

analysis, analysts should be careful to avoid 

the perception of bias when introducing it. If 

technological change is introduced in the cost 

analysis, then it should be introduced in the 

benefits analysis as well. While technological 

innovation in the regulated sector can reduce 

the cost of compliance, technological innovation 

in other sectors can reduce the benefits of the 

regulation. For example, the cost of controlling 

CFCs has declined over time due to technological 

improvements. However, innovation in mitigating 

factors, such as improvements in skin cancer 

treatments and efficacy of sunscreen lotions — both 

of which decrease the benefits of the regulation 

— have also occurred. Further, the analysis 

should include the costs associated with research 

and development (R&D) for the innovations 

to correctly value cost-reducing technological 

innovation, but only if the costs are policy-induced 

and do not arise from planned R&D budgets. This 

distinction is sometimes difficult to make. 

If technological innovation is included in the 

policy scenario, then it should be included in 

the baseline as well (see Text Box 5.1). While 

accepting that innovation will occur in the baseline 

and policy scenarios, rates across scenarios may 

differ because regulation may cause firms to 

innovate more to reduce the cost of compliance. 

In cases where small changes in technology could 

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Chapter 5 Baseline

Text Box 5.1 - Technological Change, Induced Innovation, and the 

Porter Hypothesis 

There are many proposed mechanisms by which environmental regulation could cause technological change. One 

mechanism is by induced innovation: the induced innovation hypothesis states that as the relative prices of factors 

of production change, the relative rate of innovation for the more expensive factor will also increase. This idea is well 

accepted; for example, Newell et al. (1999) found that a considerable amount of the increase in energy efficiency over 

the last few decades has been caused by the increase in the relative price of energy over that time. 

A similar idea has also been described (somewhat less formally) as the “Porter Hypothesis” (Porter and van der Linde 

1995, and Heyes and Liston-Heyes 1999). Jaffe and Palmer (1997) delineate three versions of the hypothesis: weak, 

narrow, and strong. 

The weak version of the hypothesis assumes that an environmental regulation will stimulate innovation but it does 

not predict the magnitude of these innovations or the resulting cost savings. This version of the hypothesis is very 

similar to the induced innovation hypothesis. The narrow version of the hypothesis predicts that flexible regulation 

(e.g., incentive-based) will induce more innovation than inflexible regulation and vice versa. There is empirical 

evidence that this is the case (Kerr and Newell 2003, and Popp 2003). Analysts may be able to estimate the rate of 

change of innovation under the weak or narrow version of the hypothesis, or under induced innovation. However, this 

innovation may crowd out other forms of innovation. 

The strong version predicts cost savings from environmental regulation under the assumption that firms do not 

maximize cost saving without pressure to do so. While anecdotal evidence of this phenomenon may exist, the 

available economic literature has found no statistical evidence supporting it as a general claim (Jaffe et al. 1995; 

Palmer, Oates, and Portney 1995; Jaffe and Palmer 1997; and Brännlund and Lundgren 2009). The strong version 

of the Porter Hypothesis may be true in some cases, but it requires special assumptions and an environmental 

regulation combined with other market imperfections (such as bounded rationality) that are difficult to generalize. 

Analysts should not assume cost savings from a regulation based on the strong version of the Porter Hypothesis. 

dramatically affect the costs and benefits, or where production. If learning effects are to be included in 

technological change is reasonably anticipated, the an analysis, the analyst should carefully examine the 

analyst should consider exploring these effects in a existing data for relevance to the problem at hand. 

sensitivity analysis. This might include probabilities Estimated learning effects can vary according to 

associated with specific technological changes or many factors, including across industries and by the 

adoption rates of a new technology, or it may be length of the time period considered. Also, because 

an analysis of the rate required to alter the policy estimates of learning effects are based on doubling of 

decision. Such an analysis should show the policy cumulative production, inclusion of learning effects 

significance of emerging technologies that have will have a greater influence on rules with longer 

already been accepted, or are, at a minimum, in time periods and may have little effect on rules with 

development or reasonably anticipated. short time periods. 

In some cases it may be possible to make the case that 

learning effects will lead to lower costs over time.4 5.4 Compliance Rates 

Estimated rates of learning effects often indicate One aspect of baseline specification that is 

that costs decline by approximately 5 percent particularly complex, and for which assumptions 

to 10 percent for every doubling of cumulative are typically necessary, is the setting of compliance 

rates. The treatment of compliance in the baseline 

4 See U.S. EPA (1997b, 2007b). scenario can significantly affect the results of the 

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Chapter 5 Baseline

analysis. It is important to separate the changes 

associated with a new regulation from actions 

taken to meet existing requirements. If a proposed 

regulation is expected to increase compliance with 

a previous rule, the correct measure of the costs 

and benefits generally excludes impacts associated 

with the increased compliance.5

 This is because 

the costs and benefits of the previous rule were 

presumably estimated in the economic analysis 

for that rule, and should not be counted again for 

the proposed rule. This is of particular importance 

if compliance and enforcement actions taken to 

meet existing requirements are coincident with, 

but not caused by, changes introduced by the new 

regulation. 

Assumptions about compliance behavior for 

current and new requirements should be clearly 

presented in the description of the analytic 

approach used for the analysis. When comparing 

regulatory options on the basis of their social costs 

and benefits, the effect of compliance assumptions 

on the estimated economic impacts should be 

described, along with the sensitivity of the results 

to these assumptions. 

In most cases, a full compliance scenario should 

be analyzed. If a baseline is used that assumes a 

scenario other than full compliance, the analyst 

should take care to explain the compliance 

assumption for the current regulation under 

consideration. The Agency is unlikely to propose 

a rule that it believes will not be followed, but if 

there is widespread non-compliance with previous 

rules then this suggests a persistent problem. 

5.4.1 Full Compliance 

As a general rule, when preparing analyses of 

regulations analysts should develop baseline and 

policy scenarios that assume full compliance 

with existing and newly enacted (but not 

yet implemented) regulations. Assuming full 

compliance with existing regulations enables the 

analysis to focus on the incremental economic 

effects of the new rule or policy without double 

An exception would be if the proposed regulation were designed to 

correct the under-compliance from the previous rule. This is discussed 

in Section 5.4.2. 

counting benefits and costs captured by analyses 

performed for other rules. 

Assuming full compliance with all previous 

regulations when current observed or reported 

economic behavior indicate otherwise may pose 

some challenges to the analyst. For example, 

it is possible to observe over-compliance by 

regulated entities with enforceable standards. 

One can find industries whose current effluent 

discharge concentrations for regulated 

pollutants are measured below concentrations 

legally required by existing effluent guideline 

regulations. On the other hand, evidence for 

under-compliance is apparent in the convictions 

of violators and negotiated settlements 

conducted by EPA. 

As a practical matter, before rejecting full 

compliance assumptions for existing policies, the 

emissions from noncompliant firms should be 

known, estimable, and occurring at a rate that can 

affect the evaluation of policy options. In some 

cases, two baselines may have to be assumed: one 

assuming full compliance with existing regulation 

and a separate “current practice” baseline. In the 

case of a deregulatory rule, which is designed to 

address potential changes in or clarify definitions 

of regulatory performance that frees entities from 

enforceable requirements contained in an existing 

rule, it may make sense to perform the analysis 

using both baselines. A full-compliance scenario in 

this instance introduces some added complications 

to the analysis, but it may be important to report 

on the economic effects of failing to take the 

deregulatory action. 

5.4.2 Under-Compliance 

When compliance issues are important and there is 

sufficient monitoring data to support the analysis, 

a “current practice” baseline can be used. A 

“current practice” baseline is established using the 

actual degree of compliance rather than assumed 

full compliance. Current practice baselines are 

useful for actions intended to address or “fix-up” 

compliance problems associated with existing 

policies. In these cases, assuming a full-compliance 

baseline that disregards under-compliant behavior 

Guidelines for Preparing Economic Analyses | December 2010 5-9 

5 
Chapter 5 Baseline

could obscure the value of investigating additional 

or alternative regulatory actions. This was the 

case in a review of the banning of lead from 

gasoline, which was precipitated, in part, by the 

noncompliance of consumers who put leaded 

gasoline in vehicles that required non-leaded fuel 

to protect their catalytic converters, resulting in 

increased vehicle emissions (U.S. EPA 1985). 

If under-compliance is assumed in the baseline, 

then the nature of that non-compliance becomes 

important. For example, in a case where undercompliance occurs uniformly (or at random) 

across an industry, then changing the compliance 

rate assumption will not affect the benefit-cost 

ratio nor the sign of net benefits, assuming 

the effect on ambient concentrations is also 

uniform (or random), although it will affect 

the magnitude of net benefits. In other words, a 

proposed regulation that can be justified from a 

net benefit perspective under full compliance can 

also be justified under any baseline compliance 

rate. However, if non-compliance with previous 

regulation occurs selectively when compliance 

costs are high, then the benefit-cost ratio will 

decline as higher rates of compliance are assumed, 

and net benefits could potentially switch from 

positive to negative for a proposed regulation. This 

occurs because the cost per unit of benefit will 

continue to increase as full compliance is reached. 

Analysts may elect to incorporate predicted 

differences in compliance rates within policy 

options in cases where compliance behavior is 

known to vary systematically. 

While a baseline assuming under-compliance 

can be useful in some cases, it should be executed 

carefully or the issue should be examined with a 

sensitivity analysis. A partial compliance baseline 

has the potential for double counting both benefits 

and costs. A sequence of emissions tightening rules 

could be justified by repeatedly factoring undercompliance into the baseline, while assuming that 

entities will fully comply with the new rule under 

consideration. Summing the benefits from the 

total sequence of rules would overstate benefits 

because each rule claims part of the same benefits 

each time. Additionally, while the benefits flowing 

from previous regulations may not have been 

realized due to lack of compliance, the full costs of 

their implementation may not have been realized 

either. The additional costs associated with coming 

into compliance should also be included to avoid 

producing inflated net benefits. In the case where 

an under-compliance baseline (or sensitivity 

analysis) is justified, care should be taken to 

explain these potential biases. 

5.4.3 Over-Compliance 

Over-compliance may occur due to risk aversion, 

technological lumpiness, uncertainty in pollution 

levels, or other behavioral responses. Here the benefits 

(and potentially the costs) of the previous regulation 

have been understated rather than overstated. In 

this case, as with under-compliance, true societal net 

benefits of a regulation will not be calculated correctly 

under an assumption of full compliance. 

In cases of over-compliance with existing policies, 

current practices can be used to define baseline 

conditions unless these practices are expected to 

change. For example, over-compliance may be 

the result of choices made in anticipation of more 

stringent regulations. If these stringent regulations 

are not implemented, the analyst will need to 

establish whether over-compliance will be reduced 

to meet the relatively less stringent requirements. 

If the regulated entities are expected to continue 

to over-comply despite the absence of the more 

stringent regulation, then the costs and benefits 

attributable to this behavior are not related to the 

policy under consideration. In this case, it would 

be appropriate to account for the over-compliance 

in the baseline scenario that describes the “world 

without the regulation.” However, if the regulated 

entities are expected to relax their pollution 

control practices to meet relatively less stringent 

requirements, then the costs and benefits of the 

over-compliance behavior should be attributed 

to the new policy scenario, and over-compliance 

should not be included in the baseline. In these 

situations, it may be useful to consider performing 

a sensitivity analysis to demonstrate the potential 

economic consequences of different assumptions 

associated with the expected changes in behavior. 

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Chapter 5 Baseline

5.5 Multiple Rules 

Although regulations that have been finalized 

clearly belong in the baseline of a proposed rule, 

the baseline specification may be complicated if 

other regulations in addition to the one being 

implemented are under consideration or nearing 

completion. In this case it becomes difficult to 

determine which regulations are responsible for 

the environmental improvements and can “take 

credit” for reductions in risks. It is also necessary 

to determine how these other regulations affect 

market conditions that directly influence the costs 

or the benefits associated with the policy of interest. 

This is true not only for multiple rules promulgated 

by EPA, but also for rules passed by other federal, 

state, and local agencies. In addition to agencies that 

regulate environmental behavior, other agencies 

that regulate consumer and industrial behavior [e.g., 

Occupational Safety and Health Administration 

(OSHA), Department of Transportation (DOT), 

and Department of Energy (DOE)] develop rules 

that may overlap with upcoming EPA regulations. 

Even the potential implementation of another such 

rule may affect the benefits and costs of an EPA 

regulation being analyzed, due to the strategic 

behavior of regulated entities. Therefore, it is 

important to consider the impact of other rules 

when establishing a baseline. If another federal, 

state, or local agency is legally required to impose 

a regulation but is still in the process of finalizing 

that regulation, then a baseline which includes this 

impending regulation should be considered. The 

intent of the baseline is always to characterize the 

world in the absence of regulation being analyzed. 

5.5.1 Linked Rules 

In some cases it is possible to consider multiple 

rules together as a set. For example, some regulatory 

actions have linked together rules that affect the 

same industrial category. This was true of the 

pulp and paper effluent guidelines and National 

Emissions Standards for Hazardous Air Pollutants 

(NESHAP) rules (U.S. EPA 1997c). In other 

cases, multiple rules may not necessarily be a set of 

similar policies associated with the same industry, 

but rather are a set of different policies that are all 

necessary to achieve a policy objective. For example, 

EPA may issue effluent limitation guidelines 

(ELG) to provide technical requirements for a 

type of pollution discharge, and may then issue 

a complementary National Pollution Discharge 

Elimination System (NPDES) rule, providing 

details of the permitting system. Since ELG and 

NPDES work together to achieve one objective it 

would not make sense to analyze them separately. 

The optimal solution in both of the cases described 

above is to include all of the rules in the same 

economic analysis. In this case, the multiple rules 

are analyzed as if they were one rule and the 

baseline specification simplifies to one with none 

of the rules included. While statutory requirements 

and judicial deadlines can inhibit promulgating 

multiple rules as one, coordination between 

rulemaking groups is still possible. The sharing 

of data, models, and joint decisions on analytic 

approaches may make a unified baseline possible so 

that the total costs and benefits resulting from the 

package of policies can be assessed. 

5.5.2 Unlinked Rules 

In some cases, it is simply not feasible to analyze a 

collection of overlapping rules together in a single 

economic analysis with a single baseline. This may 

be true for rules originating from different program 

offices or different regulatory agencies, or when 

the timing of the various rules is not clear. In this 

case, each rule should be analyzed separately with 

its own baseline, but the order in which the rules 

are analyzed may have a substantial effect on the 

outcome of a BCA. For example, in 2005, EPA 

promulgated both the Clean Air Interstate Rule 

(CAIR) and the Clean Air Mercury Rule (CAMR) 

to reduce pollution from coal fired power plants. 

While the primary purpose of CAIR was to reduce 

sulfur dioxide (SO2

) and nitrogen oxides (NOx), 

the control technologies necessary to achieve 

this also reduced mercury emissions. Because the 

CAMR analysis assumed that CAIR had been 

implemented and was, therefore, in the baseline, 

the estimated incremental reduction in mercury 

from CAMR was much smaller than if CAIR 

had not been included in the baseline. In a similar 

fashion, if some of the costs of fully complying 

with the second rule are incurred in the process of 

complying with the first rule, then these costs are 

Guidelines for Preparing Economic Analyses | December 2010 5-11 
Chapter 5 Baseline

part of the baseline and are not considered as costs 

of the second rule. In general, only the incremental 

benefits and costs of the second rule should be 

included if the first rule is in the baseline. 

The practical assumption commonly made when 

rules cannot be linked together is to consider the 

actual or statutory timing of the promulgation 

and/or implementation of the policies, and use 

this to establish a sequence with which to analyze 

related rules. However, this may not always be 

possible. For example, a rule may be phased in over 

time, complicating the analysis of a new rule going 

into effect during that same period. In that case, the 

baseline for the new rule should include the timing 

of each stage of the phased rule and its resulting 

environmental, health, and economic changes. 

In the absence of some orderly sequence of events 

that allows the attribution of changes in behavior 

to a unique regulatory source, there is no nonarbitrary way to allocate the costs and benefits of a 

package of overlapping policies to each individual 

policy. That is, there is no theoretically correct 

order for conducting a sequential analysis of 

multiple overlapping policies that are promulgated 

simultaneously. The only solution in this case 

is to make a reasonable assumption and clearly 

explain it, detailing which rules are included in the 

baseline (see Text Box 5.2). If the costs and benefits 

from these rules are small, then this may be all 

that is necessary. It may not be worth additional 

time and resources to reconcile the overlapping 

rules. On the other hand, for major rules or if 

the number of overlapping rules is small, then 

a sensitivity analyses can be included to test for 

the implications of including or omitting other 

regulations. Under this sensitivity analysis, it may 

also be possible to use the overlapping nature of the 

regulations to allow for some regulatory flexibility 

in compliance dates and regulatory requirements. 

5.5.3 Indirectly Related Policies 

and Programs 

In some instances, less directly related 

environmental policies or programs can influence 

the baseline. For example, potential changes in 

farm subsidy programs may significantly influence 

future patterns of pesticide use. In an ideal analysis, 

all of the potential direct and indirect influences on 

baseline conditions (and on the costs and benefits 

of regulatory alternatives) would be examined and 

estimated. In other words, this situation can be 

handled in the same way as unlinked overlapping 

rules described above. Practically speaking, however, 

it is up to the analyst to determine if these indirect 

influences are important enough to incorporate 

into the regulatory analysis. If indirect influences 

are known but are not considered to be significant 

enough to be included in the quantitative analysis, 

they can be discussed qualitatively. 

5.6 Partial Benefits to a Threshold 

Some benefits only occur after a threshold has 

been reached. For example, the benefits associated 

with improving a stream to allow for recreational 

swimming are realized only when all of the pollutants 

have been reduced enough to allow for primary 

contact and an enjoyable swimming experience. 

Likewise, valued species populations may only 

recover when multiple limiting factors are addressed. 

However, a particular benefits threshold may not 

be met with a single rule. In such cases, associating 

the benefits only with the rule that actually passes 

the threshold could make it impossible to justify 

the incremental progress (via previous rules). It is 

generally reasonable to account for the benefits of 

making progress toward a goal, even if the threshold 

is not met in the rule under consideration. 

For example, EPA’s Office of Water has calculated 

the benefits associated with improving river miles 

for various designated uses (e.g., swimming, 

fishing, and boating) in a number of rules. In 

each case, some river miles were improved for the 

designated use, while other miles were improved, 

but not enough to change their designated use. 

Earlier rules claimed benefits only if a river mile 

actually changed its designation, implicitly giving 

a value of zero to partially improved river miles. 

More recent regulation claims partial benefit for 

incremental improvements toward the threshold. 

Neither approach is necessarily correct, but 

accounting for the benefits of partial gains provides 

better information to decision makers and the 

public and allows the Agency to justify incremental 

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Chapter 5 Baseline

Text Box 5.2 - Sequencing Unlinked Rules 

It is impossible to identify all of the possible scenarios one might need to consider when determining which rules to 

include in a baseline, but a few illustrative cases are provided below. 

Including final rules that have not yet taken effect: This is the most straightforward case. All final rules 

promulgated prior to the rule under consideration should be included in the baseline. The costs and benefits of 

the regulation under consideration must be evaluated against a baseline that assumes firms will comply with these 

promulgated rules. For example, on March 15, 2005, EPA issued the Clean Air Mercury Rule (CAMR) to reduce 

mercury emissions from coal-fired power plants. Five days earlier, on March 10, 2005, EPA finalized the Clean Air 

Interstate Rule (CAIR) to reduce sulfur dioxide (SO2) and nitrogen oxides (NOx) emissions from coal-fired power 

plants. Because the control technology assumed under CAIR included some mercury reductions, the baseline used 

for CAMR included the actions that firms would need to take to comply with CAIR. 

Including rules anticipated to occur after a regulation is promulgated but before it takes effect: 

This is a more difficult case and only applies to regulations that have a long lag between the date on which they are 

issued and the date when they take effect. The longer the difference between these two dates, the more important it is 

to include rules that can be expected in the interim. For example, National Ambient Air Quality Standards (NAAQS) 

can have a number of years between the date on which a standard is announced and the date on which designations 

of attainment or nonattainment are made. In this case, if another rule is imminent and will take effect prior to the 

effective date of the new NAAQS, then it should be included in the baseline for the NAAQS. It is important, however, 

that the analyst not simply speculate that another rule will be implemented. Any other rule included in the baseline, 

other than those already promulgated, should be imminent or reasonably anticipated with a high degree of certainty. 

In addition, the analyst should be clear as to what assumptions have been made. 

Including state rules that are legally required but not yet implemented: This is probably the most difficult 

case. Actions by state (and even local) governments can affect the costs and benefits of federal rules, particularly if they 

are regulating the same sector or pollutant. As with the case above, any state regulation that has been finalized should be 

included in the baseline. The more difficult case occurs when the state has a legal obligation to implement a regulation 

but either has not done so or is in the process of doing so. In this case, the analyst must use professional judgment to 

determine what would happen in the absence of EPA action. If the state would implement the regulation in the absence of 

EPA action, then a reasonable case can be made that this state regulation should be included in the baseline. 

Two of the most important things to remember when sequencing multiple unlinked rules are transparency and 

objective reasoning. Transparency requires that the analyst clearly state all assumptions. Objective reasoning requires 

that the analyst not engage in speculation. If there is uncertainty about the anticipated rules, then two baselines, one 

with anticipated rules and one without, should be considered. If resources are constrained and only one baseline can 

be considered, then it should be constructed using only final rules and those that are reasonably expected with a high 

degree of certainty in the absence of EPA action. 

progress to a threshold.6 Note that once partial	 danger of double counting when evaluating the 

gains to a threshold have been claimed, there is a	 potential benefits of future rules. If partial gains 

have been valued in one rule, then subsequent rules 

Sometimes calculating partial benefits to a threshold may not be a cannot claim full credit for crossing the threshold. satisfactory solution, either because the progress to a threshold is uncertain 

due to multiple limiting factors (e.g., in some ecological improvements) or In effect, some of the benefits have already been 

because it does not comport with the economic values (e.g., the value of used to justify the previous incremental rules and avoiding the extinction of a species). In this case, a rulemaking incremental therefore claiming full credit in future rules would progress to the threshold might have to be justified on something other than
 

a benefit-cost test. This, however, does not affect the choice of a baseline. double count those benefits.
 

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Chapter 5 Baseline

While the actual valuation of incremental 

progress is a benefits issue, the specification 

of that portion of the benefits that have been 

claimed in previous rules is a baseline issue. If 

previous rules have claimed partial benefits, the 

benefits available for the current rule should be 

clearly identified in the baseline specification. 

In the simplest case, this means calculating 

benefits in the same way as previous rules. 

However, this approach is not always possible, 

or even reasonable. New valuation studies or 

new models of ambient pollution may make 

the previous benefits estimates obsolete. In this 

more complicated case, the baseline specification 

should be developed so that the current benefits 

estimates can be compared with the previous 

estimates while avoiding double counting. 

5.7 Behavioral Responses 

To measure a policy’s costs and benefits, it is 

important to clearly characterize the behavior of 

firms and individuals in both the baseline and 

the policy scenarios. Behavior is contrasted with 

the baseline and is often anticipated to change in 

response to the policy options. Some policies are 

prescriptive in specifying what actions are required 

— for example, mandating the use of a specific type 

of pollution control equipment. Responses to lessdirect performance standards, such as bans on the 

production or use of certain products or processes 

or market-based incentive programs are somewhat 

more difficult to predict and commonly require 

some underlying model of economic behavior. 

Estimating responses is often difficult for pollution 

prevention policies because these options are more 

site- and process-specific when compared to end-ofpipe control technologies. Predicting the costs and 

environmental effects of these rules may require 

detailed information on industrial processes. 

Parties anticipating the outcome of a regulatory 

initiative may change their economic behavior, 

including spending resources to meet expected 

emission or hazard reductions prior to the 

compliance deadline set by enforceable 

requirements. The same issues arise in the 

treatment of non-regulatory programs, in which 

voluntary or negotiated environmental goals may 

be established, leading parties to take steps to 

achieve these goals at rates different from those 

expected in the absence of the program. In these 

cases, it may be appropriate to include the costs 

and benefits of changed behavior in the analysis 

of the policy action, and not subsume them into 

the baseline scenario. Nevertheless, the dynamic 

aspects of market and consumer behavior, and the 

many motivations leading to change, can make it 

difficult to attribute economic costs and benefits 

to specific regulatory actions. Where behavioral 

changes are uncertain, an uncertainty analysis using 

various behavioral assumptions can provide insight 

into how important these assumptions may be. 

Behavioral responses are usually characterized as 

reactions to proposed policy options. However, 

the behavioral assumptions used in the baseline, 

when no regulatory action is taken, are also very 

important. Individuals may attempt to mitigate 

the affect of pollution (e.g., by buying bottled 

water, using masks, or purchasing medication), or 

prevent their exposure altogether through some 

type of averting behavior (e.g., keeping windows 

closed or relocating). Careful consideration of 

this behavior is important to correctly measure 

the costs and benefits of regulation. Analysts 

should make explicit all assumptions about firm 

and individual behavioral in both the baseline 

and policy scenarios so that a proper comparison 

between the two can be made. 

5.7.1 Potential for Cost Savings 

Predicting firm-level responses begins with a 

comprehensive list of possible response options. In 

addition to the possible compliance technologies 

(if the technology is not specified by the policy 

itself ) or waste management methods, less obvious 

firm-level responses should be considered. These 

include changes in operations (e.g., input mixtures, 

re-use or recycling, and developing new markets 

for waste products) to avoid or reduce the need 

for new controls or the use of restricted materials, 

shutting down a production line or plant to avoid 

the investments required to achieve compliance, 

relocation of the firm, or even exiting the industry. 

The possibility of noncompliance should also be 

explored, including the use of lawsuits to delay the 

5-14 Guidelines for Preparing Economic Analyses | December 2010 
Chapter 5 Baseline

required investment. In general, affected parties 

are assumed to choose the option that minimizes 

their costs. 

In some cases, compliance implies a reduction in 

costs from the baseline. In other words, choosing 

the least costly regulatory solution would 

provide cost savings to the firms. In this case, it is 

important to provide an analysis of why these costsaving measures are not undertaken in the baseline. 

It is not always obvious why firms would actively 

choose to not undertake a change that results in 

cost savings. If firms will eventually voluntarily 

undertake these changes without the regulation, 

then the regulatory intervention cannot be 

credited with the cost savings. 

One possibility is that firms may not adopt costsaving measures because of market failures (e.g., 

informational asymmetries or transactions costs) 

and other circumstances. In these cases, regulation 

can motivate economically beneficial actions, but 

there should be a reasonable description of the 

market failure or circumstances that the regulation 

is correcting. A second possibility is that firms are 

actively choosing a higher cost option in order to 

reduce legal liabilities or to achieve compliance 

with other implemented or proposed rules. In this 

latter case, firms will continue to choose the higher 

cost solution in both the baseline and the policy 

scenario and the costs savings can only be achieved 

by relaxing the legal liability or eliminating the 

other rule. In other words, the additional costs of 

compliance in excess of a least-cost strategy would 

be attributed to these other causes, but the rule 

itself will not achieve the cost savings. 

5.7.2 Voluntary Actions 

Occasionally, polluting industries adopt voluntary 

measures to reduce emissions. This can be 

implemented through a formal, governmentsponsored voluntary program or a firm or 

sector may independently adopt measures. Such 

voluntary measures are adopted for a variety of 

reasons, including public relations considerations, 

to avoid regulatory controls, or to gain access 

to incentives associated with joining a formal 

program. When this is the case, it is important to 

account for these voluntary actions in the baseline 

and to be explicit about the assumptions of firms’ 

future actions. 

Typically, the economic baseline should reflect 

current circumstances, which means that voluntary 

reductions in emissions should be included in the 

baseline assumptions. This is not always possible, 

however, as voluntary actions are often difficult to 

measure (Brouhle, Griffiths, and Wolverton 2005). 

In the case of data or resource limitations, analysts 

may be compelled to adopt a “current regulations” 

baseline, which effectively ignores these emission 

reductions. 

For the policy scenario, analysts should generally 

not assume that the current trends in voluntary 

reductions will persist. If firms are required to 

reduce emissions below their current level, then 

it should be assumed that the firms would meet 

the new standard without over-complying. While 

firms that go beyond compliance are often “good 

actors” who will continue to make reductions 

beyond the regulatory threshold, there is no a 

priori reason to expect this without a formal model 

explaining the firms’ motivation. If the regulatory 

threshold is set above the emissions of these “good 

actions” then it is important to hypothesize why 

the voluntary actions were taken in the first place. 

If firms were making voluntary reductions in 

anticipation of the regulation or to dissuade the 

Agency from passing the regulation, then the firm 

can probably be expected to increase emissions 

to the regulatory level. On the other hand, if 

firms were making the reduction for some other 

incentive that continues to be present after the 

regulation is passed, then the voluntary emissions 

level may remain unchanged. 

In some cases, it may be appropriate to 

demonstrate the significance of voluntary actions 

in a sensitivity analysis. This might involve 

analyzing competing assumptions of voluntary 

behavior. In all cases, the potential impact of 

the regulation on formal voluntary programs 

should be discussed. If participation in voluntary 

programs was motivated by the threat of the 

proposed regulation, then that voluntary program 

will likely be affected. In the extreme case, the 

Guidelines for Preparing Economic Analyses | December 2010 5-15 
Chapter 5 Baseline

voluntary program may be curtailed or eliminated 

as a consequence of the regulation. These potential 

implications should be included in the economic 

analysis. 

5.8 Conclusion 

Developing a baseline plays a critical role in 

analyzing policy scenarios, because it is the 

basis for BCA and option selection. However, 

developing a baseline is not a straightforward 

process, and analysts must make many decisions on 

the basis of professional judgment. 

As stated in this chapter, a well-specified baseline 

should address exogenous changes in the economy, 

industry compliance rates, other concurrent 

regulations, and behavioral responses. The 

assumptions used in the baseline will be derived 

from models, published literature, or government 

agencies and should be clearly referenced. In 

cases where the data are uncertain, or not easily 

quantified, but may have a significant influence 

on the results, the analyst should describe the 

weaknesses in the data and assumptions, and 

include some type of sensitivity analysis. In some 

cases, multiple baselines or alternative scenarios 

may be required. 

5-16 Guidelines for Preparing Economic Analyses | December 2010 
Chapter 6 

Discounting Future Benefits 


and Costs
 

Discounting renders benefits and costs that occur in different time periods 

comparable by expressing their values in present terms. In practice, it is 

accomplished by multiplying the changes in future consumption (broadly 

defined, including market and non-market goods and services) caused 

by a policy by a discount factor. At a summary level, discounting reflects 

that people prefer consumption today to future consumption, and that invested capital is 

productive and provides greater consumption in the future. Properly applied, discounting can 

tell us how much future benefits and costs are worth today. 

Social discounting, the type of discounting discussed in this chapter, is discounting from 

the broad society-as-a-whole point of view that is embodied in benefit-cost analysis (BCA). 

Private discounting, on the other hand, is discounting from the specific, limited perspective 

of private individuals or firms. Implementing this distinction can be complex but it is an 

important distinction to maintain because using a given private discount rate instead of a 

social discount rate can bias results as part of a BCA. 

This chapter addresses discounting over the relatively short term, what has become known 

as intragenerational discounting, as well as discounting over much longer time horizons, or 

intergenerational discounting. Intragenerational, or conventional, discounting applies to 

contexts that may have decades-long time frames, but do not explicitly confront impacts on 

unborn generations that may be beyond the private planning horizon of the current ones. 

Intergenerational discounting, by contrast, addresses extremely long time horizons and the 

impacts and preferences of generations to come. To some extent this distinction is a convenience 

as there is no discrete point at which one moves from one context to another. However, the 

relative importance of various issues can change as the time horizon lengthens. 

Several sensitive issues surround the choice of discount rate. This chapter attempts to address 

those most important for applied policy analysis. In addition to the sensitivity of the discount 

rate to the choice of discounting approach, a topic discussed throughout this chapter, 

these issues include: the distinction and potential confounding of efficiency and equity 

considerations (Section 6.3.2.1); the difference between consumption and utility discount 

rates (Sections 6.2.2.2 and 6.3.1); “prescriptive” vs. “descriptive” approaches to discount 

rate selection (Section 6.3.1); and uncertainty about future economic growth and other 

conditions (Sections 6.3.2.1 and 6.3.2.2). 

Guidelines for Preparing Economic Analyses | December 2010 6-1 
6-2 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 6 Discounting Future Benefits and Costs

6.1 The Mechanics of 

Summarizing Present and 

Future Costs and Benefits 

Discounting reflects: (1) the amount of time 

between the present and the point at which these 

changes occur; (2) the rate at which consumption 

is expected to change over time in the absence 

of the policy; (3) the rate at which the marginal 

value of consumption diminishes with increased 

consumption; and (4) the rate at which the future 

utility from consumption is discounted with time. 

Changes in these components or uncertainty 

about them can lead to a discount rate that 

changes over time, but for many analyses it may 

be sufficient to apply a fixed discount rate or rates 

without explicit consideration of the constituent 

components or uncertainty.1 

There are several methods for discounting future 

values to the present, the most common of 

which involve estimating net present values and 

annualized values. An alternative is to estimate a 

net future value. 

6.1.1 Net Present Value (NPV) 

The NPV of a projected stream of current and 

future benefits and costs relative to the analytic 

baseline is estimated by multiplying the benefits 

and costs in each year by a time-dependent weight, 

or discount factor, d, and adding all of the weighted 

values as shown in the following equation: 

NPV = NB0

 + d1NB1+ d2NB2 +

 ... + d NBn–1 + d NB (1) n–1 n n 

where NBt

 is the net difference between benefits 

and costs (Bt

 - Ct) that accrue at the end of period 

t. The discounting weights, dt

, are given by: 

1 dt

 = (2) (1+ r)t 

where r is the discount rate. The final period of the 

policy’s future effects is designated as time n. 

Note that accounting for changes in these components through 

discounting is distinct from accounting for inflation, although observed 

market rates reflect expected inflation. Both values (i.e., benefits and 

costs) and the discount rate should be adjusted for inflation; therefore 

most of the discussion in this chapter focuses on real discount rates 

and values. 

The NPV can be estimated using real or nominal 

benefits, costs, and discount rates. The analyst can 

estimate the present value of costs and benefits 

separately and then compare them to arrive at net 

present value. 

It is important that the same discount rate be used 

for both benefits and costs because nearly any 

policy can be justified by choosing a sufficiently 

low discount rate for benefits, by choosing 

sufficiently high discount rates for costs, or by 

choosing a sufficiently long time horizon. Likewise, 

making sufficiently extreme opposite choices could 

result in any policy being rejected. 

When estimating the NPV, it is also important to 

explicitly state how time periods are designated 

and when, within each time period, costs and 

benefits accrue. Typically time periods are years, 

but alternative time periods can be justified if 

costs or benefits accrue at irregular or non-annual 

intervals. The preceding formula assumes that 

t=0 designates the beginning of the first period. 

Therefore, the net benefits at time zero (NB0

) 

include a C0

 term that captures startup or one-time 

costs such as capital costs that occur immediately 

upon implementation of the policy. The formula 

further assumes that no additional costs are 

incurred until the end of the first year of regulatory 

compliance.2

 Any benefits also accrue at the end of 

each time period. 

Figure 6.1 illustrates how net benefits (measured 

in dollars) are distributed over time. NB1

 is the 

sum of benefits and costs that may have been 

spread evenly across the four quarters of the first 

year (NB through NB ) as shown in the bottom 0i 0iv

part of the figure. There may be a loss of precision 

by “rounding” a policy’s effects in a given year to 

the end or beginning of that year, but this is almost 

always extremely small in the scope of an entire 

economic analysis. 

2 See U.S. EPA (1995c) for an example in which operating and monitoring 

costs are assumed to be spread out evenly throughout each year of 

compliance. While the exponential function in equation (2) is the most 

accurate way of modeling the relationship between the present value 

and a continuous stream of benefits and costs, simple adjustments to 

the equations above can sometimes adapt them for use under alternative 

assumptions about the distribution of monetary flows over time. 

1 
Chapter 6 Discounting Future Benefits and Costs

 

 

 

 

 

 

 

 

 

Figure 6.1 - Distribution of Net Benefits 

over Time 

Year t 0 1 2 3 4 n ... 

$ NB ... 0 NB1 NB2 NB3 NB4 NBn

TIME �

Year t 0 1 

$ NB0i NB0ii NB0iii NB0iv

TIME �

6.1.2 Annualized Values 

An annualized value is the amount one would have 

to pay at the end of each time period t so that the 

sum of all payments in present value terms equals 

the original stream of values. Producing annualized 

values of costs and benefits is useful because it 

converts the time varying stream of values to a 

constant stream. Comparing annualized costs to 

annualized benefits is equivalent to comparing 

the present values of costs and benefits. Costs and 

benefits each may be annualized separately by 

using a two-step procedure. While the formulas 

below illustrate the estimation of annualized costs, 

the formulas are identical for benefits.3 

To annualize costs, the present value of costs 

is calculated using the above formula for net 

benefits, except the stream of costs alone, not the 

net benefits, is used in the calculation. The exact 

equation for annualizing depends on whether or 

not there are any costs at time zero (i.e., at t=0). 

Annualizing costs when there is no initial cost at t=0 

is estimated using the following equation: 

r *

 (1 + r)n

AC = PVC * (1 + r)n

 – 1 (3) 

where 

AC = annualized cost accrued at the end of 

each of n periods; 

Variants of these formulas may be common in specific contexts. See, 

for example, the Equivalent Uniform Annual Cost approach in EPA’s 

Air Pollution Control Cost Manual (U.S. EPA 2002b). 

PVC = present value of costs (estimated as in 

equation 1, above); 

r = the discount rate per period; and 

n = the duration of the policy. 

Annualizing costs when there is initial cost at t=0 

is estimated using the following slightly different 

equation: 

r * (1 + r)n

AC = PVC * (1 + r)(n + 1) – 1 (4) 

Note that the numerator is the same in both 

equations. The only difference is the “n+1” term in 

the denominator. 

Annualization of costs is also useful when 

evaluating non-monetized benefits, such as 

reductions in emissions or reductions in health 

risks, when benefits are constant over time. 

The average cost-effectiveness of a policy or 

policy option can be calculated by dividing the 

annualized cost by the annual benefit to produce 

measures of program effectiveness, such as the cost 

per ton of emissions avoided. 

As mentioned above, the same formulas would 

apply to estimating annualized benefits. 

6.1.3 Net Future Value 

Instead of discounting all future values to the 

present, it is possible to estimate value in some 

future time period, for example, at the end of the 

last year of the policy’s effects, n. The net future 

value is estimated using the following equation: 

NFV = d0NB0

 + d1NB1+ d2NB2 

+ ... + d NBn–1 + NB (5) n–1 n 

NBt

 is the net difference between benefits 

and costs (Bt

 - Ct) that accrue in year t and the 

accumulation weights, dt

, are given by 

dt = (1 + r) (n–t) (6) 

Guidelines for Preparing Economic Analyses | December 2010 6-3 

3 
6-4 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 6 Discounting Future Benefits and Costs

 

 

 

where r is the discount rate. It should be noted 

that the net present value and net future value can 

be expressed relative to one another: 

1 NPV = (7) (1+ r)n 

6.1.4 Comparing the Methods 

Each of the methods described above uses a 

discount factor to translate values across time, so 

the methods are not different ways to determine 

the benefits and costs of a policy, but rather are 

different ways to express and compare these 

costs and benefits in a consistent manner. NPV 

represents the present value of all costs and 

benefits, annualization represents the value 

as spread smoothly through time, and NFV 

represents their future value. For a given stream of 

net benefits, the NPV will be lower with higher 

discount rates, the NFV will be higher with 

higher discount rates, and the annualized value 

may be higher or lower depending on the length 

of time over which the values are annualized. 

Still, rankings among regulatory alternatives are 

unchanged across the methods. 

Depending on the circumstances, one method 

might have certain advantages over the others. 

Discounting to the present to get a NPV is likely to 

be the most informative procedure when analyzing 

a policy that requires an immediate investment and 

offers a stream of highly variable future benefits. 

However, annualizing the costs of two machines 

with different service lives might reveal that the 

one with the higher total cost actually has a lower 

annual cost because of its longer lifetime. 

Annualized values are sensitive to the 

annualization period; for any given present value 

the annualized value will be lower the longer the 

annualization period. Analysts should be careful 

when comparing annualized values from one 

analysis to those from another. 

The analysis, discussion, and conclusions presented 

in this chapter apply to all methods of translating 

costs, benefits, and effects through time, even 

though the focus is mostly on NPV estimates. 

6.1.5 Sensitivity of Present Value 

Estimates to the Discount Rate 

The impact of discounting streams of benefits and 

costs depends on the nature and timing of benefits 

and costs. The discount rate is not likely to affect 

the present value of the benefits and costs for those 

cases in which: 

• All effects occur in the same period 

(discounting may be unnecessary or 

superfluous because net benefits are positive or 

negative regardless of the discount rate used); 

• Costs and benefits are largely constant over 

the relevant time frame (discounting costs and 

benefits will produce the same conclusion as 

comparing a single year’s costs and benefits); 

and/or 

• Costs and benefits of a policy occur 

simultaneously and their relative values do 

not change over time (whether the NPV is 

positive does not depend on the discount 

rate, although the discount rate can affect the 

relative present value if a policy is compared 

to another policy). 

Discounting can, however, substantially affect 

the NPV of costs and benefits when there is a 

significant difference in the timing of costs and 

benefits, such as with policies that require large 

initial outlays or that have long delays before 

benefits are realized. Many of EPA’s policies fit 

these profiles. Text Box 6.1 illustrates a case in 

which discounting and the choice of the discount 

rate have a significant impact on a policy’s NPV. 

6.1.6 Some Issues in Application 

There are several important analytic components 

that need to be considered when discounting: 

risk and valuation, placing effects in time, and the 

length of the analysis. 

6.1.6.1 Risk and Valuation 

There are two concepts that are often 

confounded when implementing social 

discounting, but should be treated separately. 

The first is the future value of environmental 

effects, which depends on many factors, 
Guidelines for Preparing Economic Analyses | December 2010 6-5 

Chapter 6 Discounting Future Benefits and Costs

 

 

 

 

 

 

Text Box 6.1 - Potential Effects of Discounting 

Suppose the benefits of a given program occur 30 years in the future and are valued (in real terms) at $5 billion 

at that time. The rate at which the $5 billion future benefits is discounted can dramatically alter the economic 

assessment of the policy: $5 billion 30 years in the future discounted at 1 percent is $3.71 billion, at 3 percent it 

is worth $2.06 billion, at 7 percent it is worth $657 million, and at 10 percent it is worth only $287 million. In this 

case, the range of discount rates generates over an order of magnitude of difference in the present value of benefits. 

Longer time horizons will produce even more dramatic effects on a policy’s NPV (see Section 6.3 on intergenerational 

discounting). For a given present value of costs, particularly the case where costs are incurred in the present and 

therefore not affected by the discount rate, it is easy to see that the choice of the discount rate can determine whether 

this policy is considered, on economic efficiency grounds, to offer society positive or negative net benefits. 

including the availability of substitutes and the 

level of wealth in the future. The second is the 

role of risk in valuing benefits and costs. For both 

of these components, the process of determining 

their values and then translating the values into 

present terms are two conceptually distinct 

procedures. Incorporating the riskiness of 

future benefits and costs into the social discount 

rate not only imposes specific and generally 

unwarranted assumptions, but it can also hide 

important information from decision makers. 

6.1.6.2 Placing Effects in Time 

Placing effects properly in time is essential for 

NPV calculations to characterize efficiency 

outcomes. Analyses should account for 

implementation schedules and the resulting 

changes in emissions or environmental quality, 

including possible changes in behavior between 

the announcement of policy and compliance. 

Additionally, there may be a lag time between 

changes in environmental quality and a 

corresponding change in welfare. It is the change 

in welfare that defines economic value, and 

not the change in environmental quality itself. 

Enumerating the time path of welfare changes is 

essential for proper valuation and BCA. 

6.1.6.3 Length of the Analysis 

While there is little theoretical guidance on the time 

horizon of economic analyses, a guiding principle 

is that the time span should be sufficient to capture 

major welfare effects from policy alternatives. 

This principle is consistent with the underlying 

requirement that BCA reflect the welfare outcomes 

of those affected by the policy. Another way to view 

this is to consider that the time horizon, T, of an 

analysis should be chosen such that: 

Σ (B – C )e–rt ≤ ε , (8) t t t=T 

where ε is a tolerable estimation error for the NPV 

of the policy. That is, the time horizon should be 

long enough that the net benefits for all future 

years (beyond the time horizon) are expected to 

be negligible when discounted to the present. In 

practice, however, it is not always obvious when 

this will occur because it may be unclear whether 

or when the policy will be renewed or retired 

by policy makers, whether or when the policy 

will become obsolete or “non-binding” due to 

exogenous technological changes, how long the 

capital investments or displacements caused by the 

policy will persist, etc. 

As a practical matter, reasonable alternatives for 

the time span of the analysis may be based on 

assumptions regarding: 

• The expected life of capital investments 


required by or expected from the policy;
 

• The point at which benefits and costs reach a 

steady state; 

• Statutory or other requirements for the policy 

or the analysis; and/or 

• The extent to which benefits and costs are 

separated by generations. 
6-6 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 6 Discounting Future Benefits and Costs

The choice should be explained and welldocumented. In no case should the time horizon 

be arbitrary, and the analysis should highlight 

the extent to which the sign of net benefits or the 

relative rankings of policy alternatives are sensitive 

to the choice of time horizon. 

6.2 Background and Rationales 

for Social Discounting 

The analytical and ethical foundation of the social 

discounting literature rests on the traditional test 

of a “potential” Pareto improvement in social 

welfare; that is, the trade-off between the gains 

to those who benefit and the losses to those 

who bear the costs. This framework casts the 

consequences of government policies in terms of 

individuals contemplating changes in their own 

consumption (broadly defined) over time. Tradeoffs (benefits and costs) in this context reflect the 

preferences of those affected by the policy, and the 

time dimension of those trade-offs should reflect 

the intertemporal preferences of those affected. 

Thus, social discounting should seek to mimic the 

discounting practices of the affected individuals. 

The literature on discounting often uses a variety of 

terms and frameworks to describe identical or very 

similar key concepts. General themes throughout 

this literature are the relationship between 

consumption rates of interest and the rate of 

return on private capital, the need for a social rate 

of time preference for BCA, and the importance 

of considering the opportunity cost of foregone 

capital investments. 

6.2.1 Consumption Rates of 

Interest and Private Rates 

of Return 

In a perfect capital market with no distortions, the 

return to savings (the consumption rate of interest) 

equals the return on private sector investments. 

Therefore, if the government seeks to value costs 

and benefits in present day terms in the same way 

as the affected individuals, it should also discount 

using this single market rate of interest. In this 

kind of “first best” world, the market interest rate 

would be an unambiguous choice for the social 

discount rate. 

Real-world complications, however, make the 

issue much more complex. Among other things, 

private sector returns are taxed (often at multiple 

levels), capital markets are not perfect, and capital 

investments often involve risks reflected in market 

interest rates. These factors drive a wedge between 

the social rate at which consumption can be traded 

through time (the pre-tax rate of return to private 

investments) and the rate at which individuals 

can trade consumption over time (the post-tax 

consumption rate of interest). Text Box 6.2 

illustrates how these rates can differ. 

A large body of economic literature analyzes the 

implications for social discounting of divergences 

between the social rate of return on private sector 

investment and the consumption rate of interest. 

Most of this literature is based on the evaluation of 

public projects, but many of the insights still apply 

to regulatory BCA. The dominant approaches 

in this literature are briefly outlined here. More 

complete recent reviews can be found in Spackman 

(2004) and Moore et al. (2004). 

Text Box 6.2 - Social Rate and Consumption Rates of Interest 

Suppose that the market rate of interest, net of inflation, is 5 percent, and that the taxes on capital income amount to 

40 percent of the net return. In this case, private investments will yield 5 percent, of which 2 percent is paid in taxes 

to the government, with individuals receiving the remaining 3 percent. From a social perspective, consumption can 

be traded from the present to the future at a rate of 5 percent. But individuals effectively trade consumption through 

time at a rate of 3 percent because they owe taxes on investment earnings. As a result, the consumption rate of 

interest is 3 percent, which is substantially less than the 5 percent social rate of return on private sector investments 

(also known as the social opportunity cost of private capital). 
Guidelines for Preparing Economic Analyses | December 2010 6-7 

Chapter 6 Discounting Future Benefits and Costs

6.2.2 Social Rate of 

Time Preference 

The goal of social discounting is to compare 

benefits and costs that occur at different times 

based on the rate at which society is willing to 

make such trade-offs. If costs and benefits can be 

represented as changes in consumption profiles 

over time, then discounting should be based on 

the rate at which society is willing to postpone 

consumption today for consumption in the 

future. Thus, the rate at which society is willing 

to trade current for future consumption, or the 

social rate of time preference, is the appropriate 

discounting concept. 

Generally a distinction is made between individual 

rates of time preference and that of society as 

a whole, which should inform public policy 

decisions. The individual rate of time preference 

includes factors such as the probability of death, 

whereas society can be presumed to have a longer 

planning horizon. Additionally, individuals 

routinely are observed to have several different 

types of savings, each possibly yielding different 

returns, while simultaneously borrowing at 

different rates of interest. For these and other 

reasons, the social rate of time preference is 

not directly observable and may not equal any 

particular market rate. 

6.2.2.1 Estimating a Social Rate of Time 

Preference Using Risk-Free Assets 

One common approach to estimating the social 

rate of time preference is to approximate it from 

the market rate of interest from long-term, 

risk-free assets such as government bonds. The 

rationale behind this approach is that this market 

rate reflects how individuals discount future 

consumption, and government should value 

policy-related consumption changes as individuals 

do. In other words, the social rate of discount 

should equal the consumption rate of interest (i.e., 

an individual’s marginal rate of time preference). 

In principle, estimates of the consumption rate of 

interest could be based on either after-tax lending 

or borrowing rates. Because individuals may be in 

different marginal tax brackets, may have different 

levels of assets, and may have different opportunities 

to borrow and invest, the type of interest rate that 

best reflects marginal time preference will differ 

among individuals. However, the fact that, on net, 

individuals generally accumulate assets over their 

working lives suggests that the after-tax returns 

on savings instruments generally available to the 

public will provide a reasonable estimate of the 

consumption rate of interest. 

The historical rate of return, post-tax and 

after inflation, is a useful measure because it is 

relatively risk-free, and BCA should address risk 

elsewhere in the analysis rather than through the 

interest rate. Also, because these are longer-term 

instruments, they provide more information on 

how individuals value future benefits over these 

kinds of time frames. 

6.2.2.2 Estimating a Social Rate 

of Time Preference Using the 

‘Ramsey’ Framework 

A second option is to construct the social rate 

of time preference in a framework originally 

developed by Ramsey (1928) to reflect: (1) the 

value of additional consumption as income 

changes; and (2) a “pure rate of time preference” 

that weighs utility in one period directly against 

utility in a later period. These factors are combined 

in the equation: 

r = g +  (9) 

where (r) is the market interest rate, the first term 

is the elasticity of marginal utility () times the 

consumption growth rate (g), and the second term 

is pure rate of time preference (). Estimating a 

social rate of time preference in this framework 

requires information on each of these arguments, 

and while the first two of these factors can be 

derived from data,  is unobservable and must be 

determined.4

 A more detailed discussion of the 

Ramsey equation can be found in Section 6.3: 

Intergenerational Social Discounting. 

4 The Science Advisory Board (SAB) Council defines discounting based 

on a Ramsey equation as the “demand-side” approach, noting that the 

value judgments required for the pure social rate of time preference 

make it an inherently subjective concept (U.S. EPA 2004c). 
6-8 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 6 Discounting Future Benefits and Costs

6.2.3 Social Opportunity 

Cost of Capital 

The social opportunity cost of capital approach 

recognizes that funds for government projects, or 

those required to meet government regulations, have 

an opportunity cost in terms of foregone investments 

and therefore future consumption. When a regulation 

displaces private investments society loses the total 

pre-tax returns from those foregone investments. In 

these cases, ignoring such capital displacements and 

discounting costs and benefits using a consumption 

rate of interest (the post-tax rate of interest) does not 

capture the fact that society loses the higher, social 

(pre-tax) rate of return on foregone investments. 

Private capital investments might be displaced 

if, for example, public projects are financed 

with government debt or regulated firms cannot 

pass through capital expenses, and the supply of 

investment capital is relatively fixed. The resulting 

demand pressure in the investment market will 

tend to raise interest rates and squeeze out private 

investments that would otherwise have been 

made.5

 Applicability of the social opportunity 

cost of capital depends upon full crowding out of 

private investments by environmental policies. 

The social opportunity cost of capital can be 

estimated by the pre-tax marginal rate of return on 

private investments observed in the marketplace. 

There is some debate as to whether it is best to 

use only corporate debt, only equity (e.g., returns 

to stocks) or some combination of the two. In 

practice, average returns that are likely to be higher 

than the marginal return, are typically observed, 

given that firms will make the most profitable 

investments first; it is not clear how to estimate 

marginal returns. These rates also reflect risks faced 

in the private sector, which may not be relevant for 

public sector evaluation. 

Another justification for using the social opportunity cost of capital 

argues that the government should not invest (or compel investment 

through its policies) in any project that offers a rate of return less than 

the social rate of return on private investments. While it is true that 

social welfare will be improved if the government invests in projects 

that have higher values rather than lower ones, it does not follow that 

rates of return offered by these alternative projects define the level of 

the social discount rate. If individuals discount future benefits using 

the consumption rate of interest, the correct way to describe a project 

with a rate of return greater than the consumption rate is to say that it 

offers substantial present value net benefits. 

6.2.4 Shadow Price of 

Capital Approach 

Under the shadow price of capital approach costs 

are adjusted to reflect the social costs of altered 

private investments, but discounting for time 

itself is accomplished using the social rate of 

time preference that represents how society 

trades and values consumption over time.6

 The 

adjustment factor is referred to as the “shadow 

price of capital.”7

 Many sources recognize this 

method as the preferred analytic approach to social 

discounting for public projects and policies.8 

The shadow price, or social value, of private capital 

is intended to capture the fact that a unit of 

private capital produces a stream of social returns 

at a rate greater than that at which individuals 

discount them. If the social rate of discount is the 

consumption rate of interest, then the social value 

of a $1 private sector investment will be greater 

than $1. The investment produces a rate of return 

for its owners equal to the post-tax consumption 

rate of interest, plus a stream of tax revenues 

(generally considered to be consumption) for the 

government. Text Box 6.3 illustrates this idea of 

the shadow price of capital. 

If compliance with environmental policies 

displaces private investments, the shadow price 

of capital approach suggests first adjusting the 

project or policy cost upward by the shadow 

price of capital, and then discounting all costs 

and benefits using a social rate of discount equal 

to the social rate of time preference. The most 

complete frameworks for the shadow price of 

capital also note that while the costs of regulation 

might displace private capital, the benefits could 

encourage additional private sector investments. 

In principle, a full analysis of shadow price of 

6	 Because the consumption rate of interest is often used as a proxy for 

the social rate of time preference, this method is sometimes known as 

the “consumption rate of interest – shadow price of capital” approach. 

However, as Lind (1982b) notes, what is really needed is the social rate 

of time preference, so more general terminology is used. Discounting 

based on the shadow price of capital is referred to as a “supply side” 

approach by EPA’s SAB Council (U.S. EPA 2004c). 

7	 A “shadow price” can be viewed as a good’s opportunity cost, which 

may not equal the market price. Lind (1982a) remains the seminal 

source for this approach in the social discounting literature. 

8	 See OMB Circular A-4 (2003), Freeman (2003), and the report of EPA’s 

Advisory Council on Clean Air Compliance Analysis (U.S. EPA 2004c). 

5 
Guidelines for Preparing Economic Analyses | December 2010 6-9 

Chapter 6 Discounting Future Benefits and Costs

Text Box 6.3 - Estimating and Applying the Shadow Price of Capital 

To estimate the shadow price of capital, suppose that the consumption rate of interest is 3 percent, the pre-tax rate of 

return on private investments is 5 percent, the net-of-tax earnings from these investments are consumed in each period, 

and the investment exists in perpetuity (amortization payments from the gross returns of the investment are devoted to 

preserving the value of the capital intact). A $1 private investment under these conditions will produce a stream of private 

consumption of $.03 per year, and tax revenues of $.02 per year. Discounting the private post-tax stream of consumption 

at the 3 percent consumption rate of interest yields a present value of $1. Discounting the stream of tax revenues at 

the same rate yields a present value of about $.67. The social value of this $1 private investment – the shadow price of 

capital – is thus $1.67, which is substantially greater than the $1 private value that individuals place on it. 

To apply this shadow price of capital estimate, we need additional information about debt and tax financing as well as about 

how investment and consumption are affected. Assume that increases in government debt displace private investments 

dollar-for-dollar, and that increased taxes reduce individuals’ current consumption also on a one-for-one basis. Finally, 

assume that the $1 current cost of a public project is financed 75 percent with government debt and 25 percent with current 

taxes, and that this project produces a benefit 40 years from now that is estimated to be worth $5 in the future. 

Using the shadow price of capital approach, first multiply 75 percent of the $1 current cost (which is the amount of 

displaced private investment) by the shadow price of capital (assume this is the $1.67 figure from above). This yields 

$1.2525; add to this the $.25 amount by which the project’s costs displace current consumption. The total social cost 

is therefore $1.5025. This results in a net social present value of about $.03, which is the present value of the future 

$5 benefit discounted at the 3 percent consumption rate of interest ($1.5328) minus the $1.5025 social cost. 

capital adjustments would treat costs and benefits 

symmetrically in this sense. 

The first step in applying this approach is 

determining whether private investment flows 

will be altered by a policy. Next, all of the altered 

private investment flows (positive and negative) 

are multiplied by the shadow price of capital 

to convert them into consumption-equivalent 

units. All flows of consumption and consumption 

equivalents are then discounted using the social 

rate of time preference. A simple illustration of this 

method applied to the costs of a public project and 

using the consumption rate of interest is shown in 

Text Box 6.3.9 

An alternative approach for addressing the divergence between 

the higher social rate of return on private investments and lower 

consumption rate of interest is to set the social discount rate equal to a 

weighted average of the two. The weights would equal the proportions 

of project financing that displace private investment and consumption 

respectively. This approach has enjoyed considerable popularity over 

the years, but it is technically incorrect and can produce NPV results 

substantially different from the shadow price of capital approach. (For 

an example of these potential differences see Spackman 2004.) 

6.2.4.1 Estimating the Shadow 

Price of Capital 

The shadow price of capital approach is data 

intensive. It requires, among other things, 

estimates of the social rate of time preference, the 

social opportunity cost of capital, and estimates 

of the extent to which regulatory costs displace 

private investment and benefits stimulate it. While 

the first two components can be estimated as 

described earlier, information on regulatory effects 

on capital formation is more difficult. As a result 

empirical evidence for the shadow price of capital 

is less concrete, making the approach difficult to 

implement.10 

Whether or not this adjustment is necessary 

appears to depend largely on whether the economy 

in question is assumed to be open or closed, and 

on the magnitude of the intervention or program 

10	 Depending on the magnitudes of the various factors, shadow prices 

from about 1 to infinity can result (Lyon 1990). Lyon (1990) and Moore 

et al. (2004) contain excellent reviews of how to calculate the shadow 

price of capital and possible settings for the various parameters that 

determine its magnitude. 

9 
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Chapter 6 Discounting Future Benefits and Costs

considered relative to the flow of investment 

capital from abroad.11 

Some argue that early analyses implicitly assumed 

that capital flows into the nation were either 

nonexistent or very insensitive to interest rates, 

known as the “closed economy” assumption.12 

Some empirical evidence suggests, however, that 

international capital flows are quite large and are 

sensitive to interest rate changes. In this case, the 

supply of investment funds to the U.S. equity and 

debt markets may be highly elastic (the “open 

economy” assumption), thus private capital 

displacement would be much less important than 

previously thought. 

Under this alternative view, it would be 

inappropriate to assume that financing a public 

project through borrowing would result in dollarfor-dollar crowding out of private investment. If 

there is no crowding out of private investment, 

then no adjustments using the shadow price of 

capital are necessary; benefits and costs should 

be discounted using the social rate of time 

preference alone. However, the literature to date 

is not conclusive on the degree of crowding out. 

There is little detailed empirical evidence as to 

the relationship between the nature and size of 

projects and capital displacement. While the 

approach is often recognized as being technically 

superior to simpler methods, it is difficult to 

implement in practice. 

6.2.5 Evaluating the Alternatives 

The empirical literature for choosing a social 

discount rate focuses largely on estimating the 

consumption rate of interest at which individuals 

translate consumption through time with 

reasonable certainty. Some researchers have 

explored other approaches that, while not detailed 

here, are described briefly in Text Box 6.4. 

11	 Studies suggesting that increased U.S. Government borrowing does 

not crowd out U.S. private investment generally examine the impact 

of changes in the level of government borrowing on interest rates. The 

lack of a significant positive correlation of government borrowing and 

interest rates is the foundation of this conclusion. 

12	 See Lind (1990) for this revision of the shadow price of capital 

approach. 

To estimate a consumption rate of interest that 

includes low risk, historical rates of return on “safe” 

assets (post-tax and after inflation), such as U.S. 

Treasury securities, are normally used. Some may 

use the rate of return to private savings. Recent 

studies and reports have generally found government 

borrowing rates in the range of around 2 percent to 4 

percent.13 Some studies have expanded this portfolio 

to include other bonds, stocks, and even housing. 

This generally raises the range of rates slightly. It 

should be noted that these rates are realized rates 

of return, not anticipated, and they are somewhat 

sensitive to the choice of time period and the class of 

assets considered.14 Studies of the social discount rate 

for the United Kingdom place the consumption rate 

of interest at approximately 2 percent to 4 percent, 

with the balance of the evidence pointing toward the 

lower end of the range.15 

Others have constructed a social rate of 

time preference by estimating the individual 

arguments in the Ramsey equation. These 

estimates necessarily require judgments about 

the pure rate of time preference. Moore et al. 

(2004) and Boardman et al. (2006) estimate the 

intragenerational rate to be 3.5 percent. Other 

studies base the pure rate of time preference on 

individual mortality risks in order to arrive at a 

discount rate estimate. As noted earlier, this may 

be useful for an individual, but is not generally 

appropriate from a societal standpoint. The 

Ramsey equation has been used more frequently 

in the context of intergenerational discounting, 

which is addressed in the next section. 

13	 OMB (2003) cites evidence of a 3.1 percent pre-tax rate for ten-year 

U.S. Treasury notes. According to the U.S. Congressional Budget 

Office (CBO) (2005), funds continuously reinvested in 10-year U.S. 

Treasury bonds from 1789 to the present would have earned an 

average inflation-adjusted return of slightly more than 3 percent a 

year. Boardman et al. (2006) suggest 3.71 percent as the real rate 

of return on ten-year U.S. Treasury notes. Newell and Pizer (2003) 

find rates slightly less than 4 percent for thirty-year U.S. Treasury 

securities. Nordhaus (2008) reports a real rate of return of 2.7 percent 

for twenty-year U.S. Treasury securities. The CBO estimates the cost 

of government borrowing to be 2 percent, a value used as the social 

discount rate in their analyses (U.S. CBO 1998). 

14	 Ibbotson and Sinquefield (1984 and annual updates) provide historical 

rates of return for various assets and for different holding periods. 

15	 Lind (1982b) offers some empirical estimates of the consumption 

rate of interest. Pearce and Ulph (1994) provide estimates of the 

consumption rate of interest for the United Kingdom. Lyon (1994) 

provides estimates of the shadow price of capital under a variety of 

assumptions. 
Guidelines for Preparing Economic Analyses | December 2010 6-11 

Chapter 6 Discounting Future Benefits and Costs

Text Box 6.4 - Alternative Social Discounting Perspectives 

Some of the literature questions basic premises underlying the conventional social discounting analysis. For 

example, some studies of individual financial and other decision-making contexts suggest that even a single 

individual may appear to value and discount different actions, goods, and wealth components differently. This “mental 

accounts” or “self-control” view suggests that individuals may evaluate one type of future consequence differently 

from another type of future consequence. The discount rate an individual might apply to a given future benefit or 

cost, as a result, may not be observable from market prices, interest rates, or other phenomena. This may be the case 

if the future consequences in question are not tradable commodities. Some evidence from experimental economics 

indicates that discount rates appear to be lower the larger the magnitude of the underlying effect being valued. 

Experimental results have shown higher discount rates for gains than for losses, and show a tendency for discount 

rates to decline as the length of time to the event increases. Further, individuals may have preferences about whether 

sequences of environmental outcomes are generally improving or declining. Some experimental evidence suggests 

that individuals tend to discount hyperbolically rather than exponentially, a structure that raises time-consistency 

concerns. Approaches to social discounting based on alternative perspectives and ecological structures have also 

been developed, but these have yet to be fully incorporated into the environmental economics literature.16 

The social opportunity cost of capital represents a 

situation where investment is crowded out dollarfor-dollar by the costs of environmental policies. 

This is an unlikely outcome, but it can be useful for 

sensitivity analysis and special cases. Estimates of 

the social opportunity costs of capital are typically 

in the 4.5 percent to 7 percent range depending 

upon the type of data used.17 

The utility of the shadow price of capital approach 

hinges on the magnitude of altered capital flows 

from the environmental policy. If the policy will 

substantially displace private investment then a 

shadow price of capital adjustment is necessary 

before discounting consumption and consumption 

equivalents using the social rate of time preference. 

The literature does not provide clear guidance 

on the likelihood of this displacement, but it has 

been suggested that if a policy is relatively small 

16	 See Thaler (1990) and Laibson (1998) for more information on 

mental accounts; Guyse, Keller, and Eppell (2002) on preferences for 

sequences; Gintis (2000) and Karp (2005) on hyperbolic discounting; 

and Sumaila and Waters (2005) and Voinov and Farley (2007) for 

additional treatments on discounting. 

17	 OMB (2003) recommends a real, pre-tax opportunity cost of capital 

of 7 percent and refers to Circular A-94 (1992) as the basis for this 

conclusion. Moore et al. (2004) estimate a rate of 4.5 percent based on 

AAA corporate bonds. In recent reviews of EPA’s plans to estimate the 

costs and benefits of the Clean Air Act, the SAB Advisory Council (U.S. 

EPA 2004c and U.S. EPA 2007b) recommends using a single central 

rate of 5 percent as intermediate between 3 percent and 7 percent rates, 

based generally on the consumption rate of interest and the cost of 

capital, respectively. 

and capital markets fit an “open economy” model, 

there is probably little displaced investment.18 

Changes in yearly U.S. government borrowing 

during the past several decades have been in the 

many billions of dollars. It may be reasonable to 

conclude that EPA programs and policies costing 

a fraction of these amounts are not likely to 

result in significant crowding out of U.S. private 

investments. Primarily for these reasons, some 

argue that for most environmental regulations it 

is sufficient to discount using a government bond 

rate with some sensitivity analysis.19 

6.3 Intergenerational 

Social Discounting 

Policies designed to address long-term 

environmental problems such as global climate 

change, radioactive waste disposal, groundwater 

pollution, or biodiversity will likely involve 

significant impacts on future generations. This 

section focuses on social discounting in the context 

of policies with very long time horizons involving 

multiple generations, typically referred to in the 

literature as intergenerational discounting. 

18 Lind (1990) first suggested this.
 

19 See in particular Lesser and Zerbe (1994) and Moore et al. (2004).
 
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Chapter 6 Discounting Future Benefits and Costs

Discounting over very long time horizons is 

complicated by at least three factors: (1) the 

“investment horizon” is longer than what is 

reflected in observed interest rates that are 

used to guide private discounting decisions; (2) 

future generations without a voice in the current 

policy process are affected; and (3) compared to 

intragenerational time horizons, intergenerational 

investment horizons involve greater uncertainty. 

Greater uncertainty implies rates lower than 

those observed in the marketplace, regardless 

of whether the estimated rates are measured in 

private capital or consumption terms. Policies with 

very long time horizons involve costs imposed 

mainly on the current generation to achieve 

benefits that will accrue mainly to unborn, future 

generations, making it important to consider how 

to incorporate these benefits into decision making. 

There is little agreement in the literature on the 

precise approach for discounting over very long 

time horizons. 

This section presents a discussion of the 

main issues associated with intergenerational 

social discounting, starting with the Ramsey 

discounting framework that underlies most of the 

current literature on the subject. It then discusses 

how the “conventional” discounting procedures 

described so far in this chapter might need to 

be modified when analyzing policies with very 

long (“intergenerational”) time horizons. The 

need for such modifications arises from several 

simplifying assumptions behind the conventional 

discounting procedures described above. Such 

conventional procedures will likely become less 

realistic the longer is the relevant time horizon of 

the policy. This discussion will focus on the social 

discount rate itself. Other issues such as shadow 

price of capital adjustments, while still relevant 

under certain assumptions, will be only briefly 

touched upon. 

Clearly, economics alone cannot provide 

definitive guidance for selecting the “correct” 

social welfare function or social rate of time 

preference. In particular, the fundamental 

choice of what moral perspective should guide 

intergenerational social discounting — e.g., that 

of a social planner who weighs the utilities of 

present and future generations or those preferences 

of the current generations regarding future 

generations — cannot be made on economic 

grounds alone. Nevertheless, economics can offer 

important insights concerning discounting over 

very long time horizons, the implications and 

consequences of alternative discounting methods, 

and the systematic consideration of uncertainty. 

Economics can also provide some advice on the 

appropriate and consistent use of the social welfare 

function approach as a policy evaluation tool in an 

intergenerational context. 

6.3.1 The Ramsey Framework 

A common approach to intergenerational 

discounting is based upon methods economists 

have used for many years in optimal growth 

modeling. In this framework, the economy is 

assumed to operate as if a “representative agent” 

chooses a time path of consumption and savings 

that maximizes the NPV of the flow of utility 

from consumption over time.20 Note that this 

framework can be viewed in normative terms, as 

a device to investigate how individuals should 

consume and reinvest economic output over 

time. Or it can be viewed in positive terms, as a 

description (or “first-order approximation”) of 

how the economy actually works in practice. It is 

a first order approximation only from this positive 

perspective because the framework typically 

excludes numerous real-world departures from the 

idealized assumptions of perfect competition and 

full information that are required for a competitive 

market system to produce a Pareto-optimal 

allocation of resources. If the economy worked 

exactly as described by optimal growth models — 

i.e., there were no taxes, market failures, or other 

distortions — the social discount rate as defined in 

these models would be equal to the market interest 

rate. And the market interest rate, in turn, would 

be equal to the social rate of return on private 

investments and the consumption rate of interest. 

It is worth noting that the optimal growth 

literature is only one strand of the substantial 

20 Key literature on this topic includes Arrow et al. (1996a), Lind (1994), 

Schelling (1995), Solow (1992), Manne (1994), Toth (1994), Sen 

(1982), Dasgupta (1982), and Pearce and Ulph (1994). 
Guidelines for Preparing Economic Analyses | December 2010 6-13 

Chapter 6 Discounting Future Benefits and Costs

body of research and writing on intertemporal 

social welfare. This literature extends from 

the economics and ethics of interpersonal and 

intergenerational wealth distribution to the 

more specific environment-growth issues raised 

in the “sustainability” literature, and even to the 

appropriate form of the social welfare function, 

e.g., utilitarianism, or Rawls’ maxi-min criterion. 

As noted earlier, the basic model of optimal 

economic growth, due to Ramsey (1928), implies 

equivalence between the market interest rate (r), 

and the elasticity of marginal utility () times the 

consumption growth rate (g) plus the pure rate of 

time preference (): 

r = g +  (10) 

The first term, g, reflects the fact that the 

marginal utility of consumption will change over 

time as the level of consumption changes. The 

second term, , the pure rate of time preference, 

measures the rate at which individuals discount 

their own utility over time (taking a positive 

view of the optimal growth framework) or the 

rate at which society should discount utilities 

over time (taking a normative view). Note 

that if consumption grows over time — as 

it has at a fairly steady rate at least since the 

industrial revolution (Valdés 1999) — then 

future generations will be richer than the 

current generation. Due to the diminishing 

marginal utility of consumption, increments to 

consumption will be valued less in future periods 

than they are today. In a growing economy, 

changes in future consumption would be given a 

lower weight (i.e., discounted at a positive rate) 

than changes in present consumption under this 

framework, even setting aside discounting due to 

the pure rate of time preference (). 

There are two primary approaches typically used in 

the literature to specify the individual parameters 

of the Ramsey equation: the “descriptive” 

approach and the “prescriptive,” or more explicitly, 

the normative approach. These approaches 

are illustrated in Text Box 6.5 for integrated 

assessment models of climate change. 

The descriptive approach attempts to derive 

likely estimates of the underlying parameters 

in the Ramsey equation. This approach argues 

that economic models should be based on 

actual behavior and that models should be able 

to predict this behavior. By specifying a given 

utility function and modeling the economy over 

time one can obtain empirical estimates for the 

marginal utility and for the change in growth rate. 

While the pure rate of time preference cannot be 

estimated directly, the other components of the 

Ramsey equation can be estimated, allowing  to 

be inferred. 

Other economists take the prescriptive approach 

and assign parameters to the Ramsey equation to 

match what they believe to be ethically correct.21 

For instance, there has been a long debate, starting 

with Ramsey himself, on whether the pure rate 

of time preference should be greater than zero. 

The main arguments against the prescriptive 

approach are that: (1) people (individually and 

societally) do not make decisions that match this 

approach; and (2) using this approach would 

lead to an over-investment in environmental 

protection (e.g., climate change mitigation) at the 

expense of investments that would actually make 

future generations better off (and would make 

intervening generations better off as well). There 

is also an argument that the very low discount rate 

advocated by some adherents to the prescriptive 

approach leads to unethical shortchanging of 

current and close generations. 

Other analyses have adopted at least aspects of 

a prescriptive approach. For example, the Stern 

Review (see Text Box 6.6) sets the pure rate of 

time preference at a value of 0.1 percent and 

the elasticity of marginal utility as 1.0. With an 

assumed population growth rate of 1.3 percent, 

the social discount rate is 1.4 percent. Guo et al. 

(2006) evaluate the effects of uncertainty and 

discounting on the social cost of carbon where 

the social discount rate is constructed from the 

Ramsey equation. A number of different discount 

rate schedules are estimated depending on the 

adopted parameters. 

21 Arrow et al. (1996a). 
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Chapter 6 Discounting Future Benefits and Costs

Text Box 6.5 - Applying these Approaches to the Ramsey Equation 

The Ramsey approach has been most widely debated in the context of climate change. Most climate economists 

adopt a descriptive approach to identify long-term real interest rates and likely estimates of the underlying parameters 

in the Ramsey equation. William Nordhaus argues that economic models should be based on actual behavior and 

that models should be able to predict this behavior. His Dynamic Integrated model of Climate and the Economy 

(DICE), for example, uses interest rates, growth rates, etc., to calibrate the model to match actual historic levels 

of investment, consumption, and other variables. In the most recent version of the DICE model (Nordhaus 2008), 

he specifies the current rate of productivity growth to be 5.5 percent per year, the rate of time preference to be 1.5 

percent per year, and the elasticity of marginal utility to be 2. In an earlier version (Nordhaus 1993) he estimates 

the initial return on capital (and social discount) to be 6 percent, the rate of time preference to be 2 percent, and the 

elasticity of marginal utility to be 3. Because the model predicts that economic and population growth will slow, the 

social discount rate will decline. 

While use of the Ramsey discounting framework 

is quite common and is based on an intuitive 

description of the general problem of trading 

off current and future consumption, it has some 

limitations. In particular, it ignores differences in 

income within generations (at least in the basic 

single representative agent version of the model). 

Arrow (1996a) contains detailed discussion 

of descriptive and prescriptive approaches to 

discounting over long time horizons, including 

examples of rates that emerge under various 

assumptions about components of the Ramsey 

equation. 

6.3.2 Key Considerations 

There are a number of important ways in which 

intergenerational social discounting differs from 

intragenerational social discounting, essentially 

due to the length of the time horizon. Over a very 

long time horizon it is much more difficult, if not 

impossible, for analysts to judge whether current 

generation preferences also reflect those of future 

generations and how per capita consumption will 

change over time. This section discusses efficiency 

and intergenerational equity concerns, and 

uncertainty in this context. 

6.3.2.1 Efficiency and 

Intergenerational Equity 

A principal problem with policies that span long 

time horizons is that many of the people affected 

are not yet alive. While the preferences of each 

affected individual are knowable (if perhaps 

unknown in practice) in an intragenerational 

context, the preferences of future generations 

in an intergenerational context are essentially 

unknowable. This is not always a severe problem 

for practical policy making, especially when 

policies impose relatively modest costs and 

benefits, or when the costs and benefits begin 

immediately or in the not too distant future. Most 

of the time, it suffices to assume future generations 

will have preferences much like those of present 

generations. 

The more serious challenge posed by long time 

horizon situations arises primarily when costs 

and benefits of an action or inaction are very 

large and are distributed asymmetrically over vast 

expanses of time. The crux of the problem is that 

future generations are not present to participate 

in making the relevant social choices. Instead, 

these decisions will be made only by existing 

generations. In these cases social discounting can 

no longer be thought of as a process of consulting 

the preferences of all affected parties concerning 

today’s valuation of effects they will experience in 

future time periods. 

Moreover, compounding interest over very long 

time horizons can have profound impacts on 

the intergenerational distribution of welfare. An 

extremely large benefit or cost realized far into the 

future has essentially a present value of zero, even 

when discounted at a low rate. But a modest sum 

invested today at the same low interest rate can 
Guidelines for Preparing Economic Analyses | December 2010 6-15 

Chapter 6 Discounting Future Benefits and Costs

grow to a staggering amount given enough time. 

Therefore, mechanically discounting very large 

distant future effects of a policy without thinking 

carefully about the implications is not advised. 22 

For example, in the climate change context, Pearce 

et al. (2003) show that decreasing the discount rate 

from a constant 6 percent to a constant 4 percent 

nearly doubles the estimate of the marginal benefits 

from carbon dioxide (CO2

) emission reductions. 

Weitzman (2001) shows that moving from a 

constant 4 percent discount rate to a declining 

discount rate approach nearly doubles the estimate 

again. Newell and Pizer (2003) show that constant 

discounting can substantially undervalue the 

future given uncertainty in economic growth and 

the overall investment environment. For example, 

Newell and Pizer (2003) show that a constant 

discount rate could undervalue net present benefits 

by 21 percent to 95 percent with an initial rate of 

7 percent, and 440 percent to 700 percent with an 

initial rate of 4 percent, depending upon the model 

of interest rate uncertainty. 

Using observed market interest rates for 

intergenerational discounting in the representative 

agent Ramsey framework essentially substitutes 

the pure rate of time preference exhibited by 

individuals for the weight placed on the utilities 

of future generations relative to the current 

generation (see OMB 2003 and Arrow et al. 

1996). Many argue that the discount rate should 

be below market rates — though not necessarily 

zero — to: (1) correct for market distortions and 

inefficiencies in intergenerational transfers; and 

(2) so that generations are treated equally based on 

ethical principles (Arrow et al. 1996, and Portney 

and Weyant 1999).23 

Intergenerational Transfers 

The notion of Pareto compensation attempts to 

identify the appropriate social discount rate in an 

22	 OMB’s Circular A-4 (2003) requires the use of constant 3 percent and 

7 percent for both intra- and intergenerational discounting for benefitcost estimation of economically significant rules but allows for lower, 

positive consumption discount rates, perhaps in the 1 percent to 3 

percent range, if there are important intergenerational values. 

23	 Another issue is that there are no market rates for intergenerational 

time periods. 

intergenerational context by asking whether the 

distribution of wealth across generations could 

be adjusted to compensate the losers under an 

environmental policy and still leave the winners 

better off than they would have been absent the 

policy. Whether winners could compensate losers 

across generations hinges on the rate of interest 

at which society (the United States presumably, 

or perhaps the entire world) can transfer wealth 

across hundreds of years. Some argue that in the 

U.S. context, a good candidate for this rate is the 

federal government’s borrowing rate. Some authors 

also consider the infeasibility of intergenerational 

transfers to be a fundamental problem for 

discounting across generations.24 

Equal Treatment Across Generations 

Environmental policies that affect distant future 

generations can be considered to be altruistic 

acts.25 As such, some argue that they should be 

valued by current generations in exactly the same 

way as other acts of altruism are valued. Under this 

logic, the relevant discount rate is not based on 

an individual’s own consumption, but instead on 

an individual’s valuation of the consumption (or 

welfare) of someone else. These altruistic values 

can be estimated through either revealed or stated 

preference methods. 

At least some altruism is apparent from 

international aid programs, private charitable 

giving, and bequests within overlapping 

generations of families. But the evidence suggests 

that the importance of other people’s welfare to 

an individual appears to grow weaker as temporal, 

cultural, geographic, and other measures of 

“distance” increase. The implied discount rates 

survey respondents appear to apply in trading off 

present and future lives also is relevant under this 

approach. One such survey (Cropper, Aydede, and 

Portney 1994) suggests that these rates are positive 

on average, which is consistent with the rates 

at which people discount monetary outcomes. 

The rates decline as the time horizon involved 

lengthens. 

24	 See Lind (1990) and a summary by Freeman (2003). 

25	 Schelling (1995), and Birdsall and Steer (1993) are good references for 

these arguments. 
6-16 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 6 Discounting Future Benefits and Costs

6.3.2.2 Uncertainty 

A longer time horizon in an intergenerational 

policy context also implies greater uncertainty 

about the investment environment and economic 

growth over time, and a greater potential for 

environmental feedbacks to economic growth 

(and consumption and welfare), which in turn 

further increases uncertainty when attempting to 

estimate the social discount rate. 

This additional uncertainty has been shown to 

imply effective discount rates lower than those 

based on the observed average market interest 

rates, regardless of whether or not the estimated 

investment effects are predominantly measured as 

private capital or consumption terms (Weitzman 

1998, 2001; Newell and Pizer 2003; Groom et al. 

2005; and Groom et al. 2007).26 The rationale for 

this conclusion is that consideration of uncertainty 

in the discount rate should be based on the average 

of discount factors (i.e., 1/(1+r)t

) rather than the 

standard discount rate (i.e., r). From the expected 

discount factor over any period of time a constant, 

certainty-equivalent discount rate that yields the 

discount factor (for any given distribution of r) 

can be inferred. Several methods for accounting 

for uncertainty into intergenerational discounting 

are discussed in more detail in the next section. 

6.3.3 Evaluating Alternatives 

There is a wide range of options available to 

the analyst for discounting intergenerational 

costs and benefits. Several of these are described 

below, ordered from simplest to most analytically 

complex. Which option is utilized in the analysis 

is left to expert judgment, but should be based on 

the likely consequences of undertaking a more 

complex analysis for the bottom-line estimate of 

expected net benefits. This will be a function of the 

proportion of the costs and benefits occurring far 

out on the time horizon and the separation of costs 

and benefits over the planning horizon. When 

it is unclear which method should be utilized, 

the analyst is encouraged to explore a variety of 

approaches. 

26	 Gollier and Zeckhauser (2005) reach a similar result using a model 

with decreasing absolute risk aversion. 

6.3.3.1 Constant Discount Rate 

One possible approach is to simply make no 

distinction between intergenerational and 

intragenerational social discounting. For example, 

models of infinitely-lived individuals suggest the 

consumption rate of interest as the social discount 

rate. Of course, individuals actually do not live long 

enough to experience distant future consequences 

of a policy and cannot report today the present 

values they place on those effects. However, it 

is equally sufficient to view this assumption as 

a proxy for family lineages in which the current 

generation treats the welfare of all its future 

generations identically with the current generation. 

It is not so much that the individual lives forever 

as that the family spans many generations (forever) 

and that the current generation discounts 

consumption of future generations at the same rate 

as its own future consumption. 

Models based on constant discount rates over 

multiple generations essentially ignore potential 

differences in economic growth and income and/ 

or preferences for distant future generations. Since 

economic growth is unlikely to be constant over 

long time horizons, the assumption of a constant 

discount rate is unrealistic. Interest rates are a 

function of economic growth; thus, increasing 

(declining) economic growth implies an increasing 

(decreasing) discount rate. 

A constant discount rate assumption also does not 

adequately account for uncertainty. Uncertainty 

regarding economic growth increases as one goes 

further out in time, which implies increasing 

uncertainty in the interest rate and a declining 

certainty equivalent rate of return to capital 

(Hansen 2006). 

6.3.3.2 Step Functions 

Some modelers and government analysts have 

experimented with varying the discount rate with 

the time horizon to reflect non-constant economic 

growth, intergeneration equity concerns, and/or 

heterogeneity in future preferences. For instance, 

in the United Kingdom the Treasury recommends 

the use of a 3.5 percent discount rate for the first 

30 years followed by a declining rate over future 
Guidelines for Preparing Economic Analyses | December 2010 6-17 

Chapter 6 Discounting Future Benefits and Costs

time periods until it reaches 1 percent for 301 years 

and beyond.27 This method acknowledges that a 

constant discount rate does not adequately reflect 

the reality of fluctuating and uncertain growth 

rates over long time horizons. However, application 

of this method also raises several potential analytic 

complications. First, there is no empirical evidence 

to suggest the point(s) at which the discount rate 

declines, so any year selected for a change in the 

discount rate will be necessarily ad-hoc. Second, 

this method can suffer from a time inconsistency 

problem. Time inconsistency means that an 

optimal policy today may look sub-optimal in the 

future when using a different discount rate and vice 

versa. Some have argued that time inconsistency 

is a relatively minor problem relative to other 

conditions imposed (Heal 1998, Henderson and 

Bateman 1995, and Spackman 2004). 

6.3.3.3 Declining or Non-Constant 

Discount Rate 

Using a constant discount rate in BCA is 

technically correct only if the rate of economic 

growth will remain fixed over the time horizon of 

the analysis. If economic growth is changing over 

time, then the discount rate, too, will fluctuate. 

In particular, one may assume that the growth 

rate is declining systematically over time (perhaps 

to reflect some physical resource limits), which 

will lead to a declining discount rate. This is 

the approach taken in some models of climate 

change.28 In principle, any set of known changes 

to income growth, the elasticity of marginal 

utility of consumption, or the pure rate of time 

preference will lead to a discount rate that changes 

accordingly. 

6.3.3.4 Uncertainty-Adjusted 

Discounting 

If there is uncertainty about the future growth rate, 

then the correct procedure for discounting must 

27	 The guidance also requires a lower schedule of rates, starting with 3 

percent for zero to 30 years, where the pure rate of time preference 

in the Ramsey framework (the parameter  in our formulation) is 

set to zero. For details see HM Treasury (2008) Intergenerational 

wealth transfers and social discounting: Supplementary Green Book 

Guidance. 

28	 See, for example, Nordhaus (2008). 

account for this uncertainty in the calculation of 

the expected NPV of the policy. Over the long 

time horizon, both investment uncertainty and 

risk will naturally increase, which results in a 

decline in the imputed discount rate. If the time 

horizon of the policy is very long, then eventually a 

low discount rate will dominate the expected NPV 

calculations for benefits and costs far in the future 

(Weitzman 1998). 

Newell and Pizer (2003) expand on this 

observation, using historical data on U.S. 

interest rates and assumptions regarding their 

future path to characterize uncertainty and 

compute a certainty equivalent rate. In this 

case, uncertainty in the individual components 

of the Ramsey equation is not being modeled 

explicitly. Their results illustrate that a constant 

discount rate could substantially undervalue 

net present benefits when compared to one 

that accounts for uncertainty. For instance, 

a constant discount rate of 7 percent could 

undervalue net present benefits by between 21 

percent and 95 percent depending on the way in 

which uncertainty is modeled. 

A key advantage of this treatment of the discount 

rate over the step function and simple declining 

rate discounting approaches is that the analyst is 

not required to arbitrarily designate the discount 

rate transitions over time, nor required to ignore 

the effects of uncertainty in economic growth over 

time. Thus, this approach is not subject to the time 

inconsistency problems of some other approaches. 

Another issue that has emerged about the use 

of discount rates that decline over time due to 

uncertainty is that they could generate inconsistent 

policy rankings NPV versus NFV.29 Because the 

choice between NPV and NFV is arbitrary, such 

an outcome would be problematic for applied 

policy analysis. More recent work, however, 

appears to resolve this seeming inconsistency, 

confirming the original findings and providing 

sound conceptual rationale for the approach.30 

29	 See Gollier (2004) for a technical characterization of this concern, and 

Hepburn and Groom (2007) for additional exploration of the issues. 

30	 See Gollier and Weitzman (2009) provide a concise and clear 

treatment. Freeman (2009) and Gollier (2009) also propose solutions. 
6-18 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 6 Discounting Future Benefits and Costs

Text Box 6.6 - What’s the Big Deal with The Stern Review? 

In autumn 2006, the U.K. government released a detailed report titled The Economics of Climate Change: The Stern 

Review, headed by Sir Nicholas Stern (2006). The report drew mainly on published studies and estimated that 

damages from climate change could result in a 5 percent to 20 percent decline in global output by 2100. The report 

found that costs to mitigate these impacts were significantly less (about 1 percent of GDP). Stern’s findings led him to 

say that “climate change is the greatest and widest-ranging market failure ever seen,” and that “the benefits of strong 

early action considerably outweigh the cost.” The Stern Review recommended that policies aimed towards sharp 

reduction in GHG emissions should be enacted immediately. 

While generally lauded for its thoroughness and use of current climate science, The Stern Review drew significant 

criticism and discussion of how future benefits were calculated, namely targeting Stern’s assumptions about the 

discount rate (Tol and Yohe 2006 and Nordhaus 2008). The Stern Review used the Ramsey discounting equation 

(see Section 6.3.1), applying rates of 0.1 percent for the annual pure rate of time preference, 1.3 percent for the 

annual growth rate, and a elasticity of marginal utility of consumption equal to 1. Combining these parameter values 

reveals an estimated equilibrium real interest rate of 1.4 percent, a rate arguably lower than most returns to standard 

investments, but not outside the range of values suggested in these Guidelines for intergenerational discount rates. 

So why is the issue on the value of the discount rate so contentious? Perhaps the biggest concern is that climate 

change is expected to cause significantly greater damages in the far future than it is today, and thus benefits are 

sensitive to discounting assumptions. A low social discount rate means The Stern Review places a much larger 

weight on the benefits of reducing climate change damages in 2050 or 2100 relative to the standard 3 percent or 7 

percent commonly observed in market rates. Furthermore, Stern’s relatively low values of  and  imply that the 

current generation should operate at a higher savings rate than what is observed, thus implying that society should 

save more today to compensate losses incurred by future generations. 

Why did Stern use these particular parameter values? First, he argues that the current generation has an ethical 

obligation to place similar weights on the pure rate of time for future generations. Second, a marginal elasticity of 

consumption of unity implies a relatively low inequality aversion, which reduces the transfer of benefits between the 

rich and the poor relative to a higher elasticity. Finally, there are significant risks and uncertainties associated with 

climate change, which could imply using a lower-than-market rate. Stern’s (2006) concluding remarks for using a 

relatively low discount rate are clear, “However unpleasant the damages from climate change are likely to appear in the 

future, any disregard for the future, simply because it is in the future, will suppress action to address climate change.” 

6.4 Recommendations in practice.31 The recommendations provided 

and Guidance here are intended as practical and plausible 

As summed up by Freeman (2003 p. 206), default assumptions rather than comprehensive 

“economists have not yet reached a consensus and precise estimates of social discount rates 

on the appropriate answers” to all of the issues that must be applied without adjustment in all 

surrounding intergenerational discounting. And situations. That is, these recommendations should 

while there may be more agreement on matters be used as a starting point for BCA, but if the 

of principle for discounting in the context of 

intragenerational policies, there is still some 31 This chapter summarizes some key aspects from the core literature 

on social discounting; it is not a detailed review of the vast and disagreement on the magnitude of capital varied social discounting literature. Excellent sources for additional

displacement and therefore the importance of	 information are: Lind (1982a, b; 1990; 1994), Lyon (1990, 1994), Kolb 

accounting for the opportunity costs of capital	 and Scheraga (1990), Scheraga (1990), Arrow et al. (1996), Pearce and 

Turner (1990), Pearce and Ulph (1994), Groom et al. (2005), Cairns 

(2006), Frederick et al. (2002), Moore et al. (2004), Spackman (2004), 

and Portney and Weyant (1999). 
Guidelines for Preparing Economic Analyses | December 2010 6-19 

Chapter 6 Discounting Future Benefits and Costs

 

 

 

 

 

 

analysts can develop a more realistic model and 

bring to bear more accurate empirical estimates 

of the various factors that are most relevant to 

the specific policy scenario under consideration, 

then they should do so and provide the rationale 

in the description of their methods. With this 

caveat in mind, our default recommendations for 

discounting are below. 

• Display the time paths of benefits and 

costs as they are projected to occur over 

the time horizon of the policy, i.e., without 

discounting. 

• The shadow price of capital approach 

is the analytically preferred method for 

discounting, but there is some disagreement 

on the extent to which private capital is 

displaced by EPA regulatory requirements. 

EPA will undertake additional research and 

analysis to investigate important aspects of 

this issue, including the elasticity of capital 

supply, and will update guidance accordingly. 

In the interim analysts should conduct a 

bounding exercise as follows: 

• Calculate the NPV using the consumption 

rate of interest. This is appropriate for 

situations where all costs and benefits 

occur as changes in consumption flows 

rather than changes in capital stocks, i.e., 

capital displacement effects are negligible. 

As of the date of this publication, current 

estimates of the consumption rate of 

interest, based on recent returns to 

Government-backed securities, are close to 

3 percent. 

• Also calculate the NPV using the rate of 

return to private capital. This is appropriate 

for situations where all costs and benefits 

occur as changes in capital stocks rather 

than consumption flows. The OMB 

estimates a rate of 7 percent for the 

opportunity cost of private capital. 

• EPA intends to periodically review the 

empirical basis for the consumption 

discount rate and the rate of return to 

private capital. 

In most cases the results of applying the more 

detailed “shadow price of capital” approach 

will lie somewhere between the NPV 

estimates ignoring the opportunity costs of 

capital displacements and discounting all 

costs and benefits using these two alternative 

discount rates. 

• If the policy has a long time horizon (more 

than 50 years or so) where net benefits vary 

substantially over time (e.g., most benefits 

accrue to one generation and most costs 

accrue to another) then the analysis should 

use the consumption rate of interest as well 

as additional approaches. These approaches 

include calculating the expected present 

value of net benefits using an estimated timedeclining schedule of discount factors (Newell 

and Pizer 2003, Groom et al. 2007, and 

Hepburn et al. 2009). This approach accounts 

for discount rate uncertainty and variability, 

which are known to have potentially large 

effects on NPV estimates for policies with 

long time horizons. If a time-declining 

approach cannot be implemented, it is 

possible to capture part of its empirical effect 

by discounting at a constant rate somewhat 

lower than those used in the conventional 

case. For example, the current Interagency 

guidance for valuing CO2

 emission reductions 

includes treatment with certainty-equivalent 

constant discount rates of 2.5 percent, 3 

percent, and 5 percent. (See Text Box 7.1 for 

more discussion of the Interagency guidance.) 

Other more detailed alternatives, such as 

constructing discounts rate from estimates 

of the individual parameters in the Ramsey 

equation, may merit inclusion in the analysis. 

In any case, all alternatives should be fully 

described, supported, and justified. 
6-20 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 6 Discounting Future Benefits and Costs

 

 

 

When implementing any discounting approach 

the following principles should be kept in mind: 

• In all cases social benefits and costs should 

be discounted in the same manner, although 

private discount rates may be used to predict 

behavior and to evaluate economic impacts. 

• The discount rate should reflect marginal 

rates of substitution between consumption 

in different time periods and should not be 

confounded with factors such as uncertainty 

in benefits and costs or the value of 

environmental goods or other commodities 

in the future (i.e., the “current price” in 

future years). 

• The lag time between a change in regulation 

and the resulting welfare impacts should 

be accounted for in the economic analysis. 

The monetary benefits from the expected 

future impacts should be discounted at the 

same rate as other benefits and costs in the 

analysis. This includes changes in human 

health, environmental conditions, ecosystem 

services, etc. 
Chapter 7 

Analyzing Benefits
 

The aim of an economic benefits analysis is to estimate the benefits, in monetary 

terms, of proposed policy changes in order to inform decision making. 

Estimating benefits in monetary terms allows the comparison of different types 

of benefits in the same units, and it allows the calculation of net benefits — the 

sum of all monetized benefits minus the sum of all monetized costs — so that 

proposed policy changes can be compared to each other and to the baseline scenario. 

The discussion in this chapter focuses on methods and approaches available to monetize 

benefits in the context of a “typical” EPA policy, program, or regulation that reduces 

emissions or discharges of contaminants. This is not to say that those benefits that cannot 

be monetized due to lack of available values or quantification methods are not important. 

Chapter 11 on the “Presentation of Analysis and Results” discusses how to carry forward 

information on non-monetized benefits to help inform the policy-making process. In 

addition, this chapter includes a discussion of several alternatives to monetization that 

may add some context to this category of benefits. The general monetization methods and 

principles discussed here should apply to other types of EPA polices as well, such as those that 

provide regulatory relief, encourage reuse of remediated land, or provide information to the 

public to help people avoid environmental risks.1 

7.1 The Benefits 

Analysis Process 

Ideally, benefits analyses would consist of 

comprehensive assessments of all environmental 

effects attributable to the rule in question. 

However, it is seldom possible to analyze all effects 

simultaneously in an integrated fashion. In most 

cases, analysts will need to address each effect 

individually, and then aggregate the individual 

values to generate an estimate of the total benefits 

of a policy. A constant challenge in employing an 

effect-by-effect approach is to balance potential 

trade-offs between inclusion and redundancy. 

Other methods, such as cost-effectiveness analysis (CEA), can also be 

used to evaluate policies. CEA does not require monetization of benefits but 

rather divides the costs of a policy by a particular effect (e.g., number of 

lives saved). CEA can be used to compare proposed policy changes on an 

effect-by-effect basis, but, unlike BCA, cannot be used to calculate a single, 

comprehensive measure of the net effects of a policy, nor can it compare 

proposed policy changes to the status quo. Other methods for evaluating 

policies (e.g., distributional analyses) are covered in Chapter 10. 

Ideally, each effect will be measured once and only 

once. Techniques intended to bring additional 

effects into the analysis may run the risk of double 

counting effects already measured. For example, 

stated preference methods may be the only way to 

measure non-use values, but they may double count 

use values already reflected in hedonic or travel cost 

analyses. Therefore, the analyst should be careful in 

interpreting and combining the results of different 

methods. 

There are of course exceptions to this “effect-byeffect” approach to benefits analysis (e.g., efforts 

to estimate the social benefits of reducing CO2 

emissions — see Text Box 7.1), but the remainder of 

the discussion below is framed with this approach 

in mind. 

A second challenge analysts often face is the difficulty 

of conducting original valuation research in support 

Guidelines for Preparing Economic Analyses | December 2010 7-1 

1 
7-2 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

Text Box 7.1 - Estimating Benefits from Reducing Carbon Dioxide 

Emissions: The Social Cost of Carbon 

Monetized estimates of the damages associated carbon dioxide (CO2) emissions allows the social benefits 

of regulatory actions that are expected to reduce these emissions to be incorporated into BCA. One way to 

accomplish this is through the estimation of the “social cost of carbon” (SCC). The SCC is the present value 

of the stream of future economic damages associated with an incremental increase (by convention, one metric 

ton) in CO2 emissions in a particular year. It is intended to be a comprehensive measure and includes economic 

losses due to changes in agricultural productivity, human health risks, property damages from increased flood 

frequencies, the loss of ecosystem services, etc. The SCC is a marginal value so it may not be accurate for valuing 

large changes in emissions. However, many U.S. government regulations will lead to relatively small reductions in 

cumulative global emissions, so for these regulations the SCC is the appropriate shadow value for estimating the 

economic benefits of CO2 reductions. 

Most published estimates of the SCC have been derived from “integrated assessment models” (IAMs) that combine 

climate processes, economic growth, and feedbacks between the two in a single modeling framework. These models 

include a reduced form representation of the potential economic damages from climate change. Therefore IAMs 

used to estimate the SCC are necessarily highly simplified and limited by the current state of the climate economics 

literature, which continues to expand rapidly. Despite the inherent uncertainties in models such as these, they are the 

best tools currently available for estimating the SCC. 

The Interagency SCC Workgroup. In 2009, an interagency workgroup composed of members from six federal 

agencies and various White House offices was convened to improve the accuracy and consistency in how agencies 

value reductions in CO2

 emissions in regulatory impact analyses. The resulting range of values is based on estimates 

from three integrated assessment models applied to five socioeconomic and emissions scenarios, all given equal 

weight. To reflect differing expert opinions about discounting, the present value of the time path of global damages 

in each model-scenario combination was calculated using discount rates of 5 percent, 3 percent, and 2.5 percent. 

Finally, in a step toward more formal uncertainty analysis, all model runs employed a probabilistic representation of 

climate sensitivity (in addition to other parameters in two of the models). 

The workgroup selected four SCC estimates from the model runs to reflect the global damages caused by CO2 

emissions: $5, $21, $35, and $65 for 2010 emission reductions (in 2007 U.S. dollars). The first three estimates 

are based on the average SCC across the three models and five socioeconomic and emissions scenarios for the 

5 percent, 3 percent, and 2.5 percent discount rates, respectively. The fourth value, the 95th percentile of the SCC 

distribution at a 3 percent discount rate, was chosen to represent potential higher-than-expected impacts from 

temperature change. The SCC estimates grow over time at rates endogenously determined by the models. For 

instance, with a discount rate of 3 percent, the mean SCC estimate increases to $24 per ton of CO2

 in 2015 and $26 

per ton of CO2 in 2020. 

Going Forward. The Interagency SCC Workgroup presented the SCC estimates with a clear acknowledgement of 

the many uncertainties involved and the final report outlined a number of limitations to the analysis. The Interagency 

SCC Workgroup is committed to re-visiting these estimates on a regular basis and revising them as needed to reflect 

the growing body of scientific knowledge regarding climate change impacts and the potential economic damages 

from those impacts. 

Further Reading: U.S. Interagency Working Group on Social Cost of Carbon (2010). Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866, www.epa.gov/otaq/climate/regulations/scc-tsd.pdf. 
Guidelines for Preparing Economic Analyses | December 2010 7-3 

Chapter 7 Analyzing Benefits

of specific policy actions. Because budgetary and 

time constraints often make performing original 

research infeasible, analysts regularly need to draw 

upon existing value estimates for use in benefits 

analysis. The process of applying values estimated 

in previous studies to new policy cases is called 

benefit transfer. The benefit transfer method is 

discussed in detail in Section 7.4, but much of this 

chapter is written with benefit transfer in mind. In 

particular, the descriptions of revealed and stated 

preference valuation methods in Sections 7.3.1 and 

7.3.2 include recommendations for evaluating the 

quality and suitability of published studies for use 

in benefit transfer. 

A general “effect-by-effect” approach to 

benefits analysis 

This approach consists of separately evaluating the 

major effects of a given policy, and then summing 

these individual estimates to arrive at an overall 

estimate of total benefits. The effect-by-effect 

approach for benefits analysis requires three 

fundamental steps: 

1. Identify benefit categories potentially 

affected by the policies under consideration; 

2. Quantify significant endpoints to the extent 

possible by working with managers, risk 

assessors, ecologists, physical scientists, and 

other experts; and 

3. Estimate the values of these effects using 

appropriate valuation methods for new 

studies or existing value estimates from 

previous studies that focus on the same or 

sufficiently similar endpoints. 

Each step in this approach is discussed in more 

detail below. Analysts also should consider 

whether this general framework is appropriate 

for assessing a specific policy or whether a more 

integrated approach that incorporates all of the 

relevant effects simultaneously can be applied. 

When applying the effect-by-effect approach it is 

important to avoid double counting benefits across 

effects as much as possible. Collaboration with 

appropriate experts will be necessary to execute 

these steps meaningfully. 

Step1: Identify potentially affected 

benefit categories 

The first step in a benefits analysis is to determine 

the types of benefits associated with the policy 

options under consideration. More information on 

benefits categories can be found in Section 7.2. To 

identify benefit categories, analysts should, to the 

extent feasible: 

Develop an initial understanding of policy 

options of interest by working with other analysts 

and policy makers. Initially, the range of options 

considered may be very broad. Resources should 

be focused on benefit categories that are likely to 

influence policy decisions. Collaboration between 

all parties involved in the policy analysis can help 

ensure that all potential effects are recognized and 

that the necessary and appropriate information 

and endpoints are collected and evaluated at each 

step in the process. Analysts should take care to 

think through potential secondary or indirect 

effects of the policy options as well, as these may 

prove to be important. 

Research the physical effects of the pollutants on 

human health and the environment by reviewing 

the literature and consulting with other experts. 

This step requires considering the transport of the 

pollutants through the environment along a variety 

of pathways, including movement through the air, 

surface water, and groundwater, deposition in soils, 

and ingestion or uptake by plants and animals 

(including humans). Along these pathways, the 

pollutants can have detrimental effects on natural 

resources, such as affecting oxygen availability in 

surface water or reducing crop yields. Pollutants 

can also have direct or indirect effects on human 

health, for example affecting cancer incidence 

through direct inhalation or through ingestion of 

contaminated food. 

Consider the potential change in these effects 

as a result of each policy option. If policy options 

differ only in their level of stringency then each 

option may have an impact on all identified 

physical effects. In other cases, however, some 

effects may be reduced while others are increased 

or remain unchanged. Evaluating how physical 

effects change under each policy option requires 
7-4 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

evaluation of how the pathways differ in the “postpolicy” world. 

Determine which benefit categories to include 

in the overall benefits analysis using at least the 

following three criteria: 

1. Which benefit categories are likely to 

differ across policy options, including the 

baseline option? Analysts should conduct an 

assessment of how the physical effects of each 

policy option will differ and how each physical 

effect will impact each benefit category. 

2. Which benefit categories are likely to 

account for the bulk of the total benefits of 

the policy? The cutoff point here should be 

based on an assessment of the magnitude 

and precision of the estimates of each benefit 

category, the total social costs of each policy 

option, and the costs of gathering further 

information on each benefit category. A 

benefit category should not be included 

if the cost of gathering the information 

necessary to include it is greater than the 

expected increase in the value of the policy 

owing to its inclusion. The analyst should 

make these preliminary assessments using 

the best quantitative information that is 

readily available, but as a practical matter 

these decisions may often have to be based on 

professional judgments. 

3. Which benefit categories are especially 

salient to particular stakeholders? Monetized 

benefits in this category are not necessarily 

large and so may not be captured by the first 

two criteria.2 

The outcome of this initial step in the benefits 

analysis can be summarized in a list or matrix that 

describes the physical effects of the pollutant(s), 

identifies the benefit categories associated with 

these effects, and identifies the effects that warrant 

further investigation. 

The list of physical effects under each benefit 

category may be lengthy at first, encompassing all 

of those that reasonably can be associated with 

This third criterion relates to distributional considerations detailed in 

Chapter 10. 

the policy options under consideration. Analysts 

should preserve and refine this list of physical 

effects as the analysis proceeds. Maintaining the 

full list of potential effects — even though the 

quantitative analysis will (at least initially) focus 

on a sub-set of them — will allow easy revision of 

the analysis plan if new information warrants it. 

EPA has developed extensive guidance on the 

assessment of human health and ecological risks, 

and analysts should refer to those documents 

and the offices responsible for their production 

and implementation for further information 

(U.S. EPA 2009a). No specific guidance exists for 

assessing changes in amenities or material damages. 

Analysts should consult relevant experts and 

existing literature to determine the “best practices” 

appropriate for these categories of benefits. 

Step 2: Quantify significant endpoints 

The second step is to quantify the physical 

endpoints related to each category, focusing on 

changes attributable to each policy option relative 

to the baseline. Data are usually needed on the 

extent, timing, and severity of the endpoints. For 

example, if the risk of lung cancer is an endpoint of 

concern, required information will usually include 

the change in risk associated with each option, the 

timing of the risk changes, the age distribution of 

affected populations, and fatality rates. If visibility 

is the attribute of concern, required information 

will usually include the geographical areas affected 

and the change in visibility resulting from each 

policy option. 

Analysts should keep the following issues in mind 

while quantifying significant physical effects. 

Work closely with analysts in other fields. 

Estimating physical effects is largely, but not 

completely, the domain of other experts, including 

human health and ecological risk assessors and 

other natural scientists. These experts generally 

are responsible for evaluating the likely transport 

of the pollutant through the environment and its 

potential effects on humans, ecological systems, 

and manufactured materials. 2 
Guidelines for Preparing Economic Analyses | December 2010 7-5 

Chapter 7 Analyzing Benefits

 

 

 

 

 

Text Box 7.2 - Integrating Economics and Risk Assessment 

Historically, health and ecological risk assessments have been designed not to support benefits analyses per se but 

rather to support the setting of standards or to rank the severity of different hazards. Traditional measures of risk can 

be difficult or impossible to incorporate into benefits analyses. For example, traditional measures of risk are often 

based on endpoints not directly related to health outcomes or ecological services that can be valued using economic 

methods. These measures are often based on outcomes near the tails of the risk distribution for highly sensitive 

endpoints, which would lead to biased benefits estimates if extrapolated to the general population. 

Because economists rely on risk assessment outcomes as key inputs into benefits analysis, it is important that risk 

assessments and economic valuation studies be undertaken together. Economists can contribute information and 

insights into how behavioral changes may affect realized risk changes. For example, if health outcomes in a particular 

risk assessment are such that early medical intervention could reduce the chances of illness, economists may be able 

to estimate changes in the probability that individuals will seek preventative care. Even in cases where the economists’ 

contribution to the risk characterization is not direct, economists and risk assessors should communicate frequently 

to ensure that economic analyses are complete. Specifically risk assessors and economists should: 

• Identify a set of human health and ecological endpoints that are economically meaningful. The endpoints 

should be linked to human well-being and monetized using economic valuation methods. This may require risk 

assessors to model more or different outcomes than they would if they were attempting to capture only the most 

sensitive endpoint. This also may require risk assessors and economists to convert specific human health or 

ecological endpoints measured in laboratory or epidemiological studies to other effects that can be valued in 

the economic analysis. 

• Estimate changes in the probabilities of human health or ecological outcomes rather than “safety assessment” 

measures such as reference doses and reference concentrations. 

• Work to produce expected or central estimates of risk, rather than bounding estimates as in safety assessments. 

At a minimum, any expected bias in the risk estimates should be clearly described. 

• Attempt to estimate the “cessation lag” associated with reductions in exposure. That is, the analysis should 

characterize the time profile of changes in exposures and risks. 

• Attempt to characterize the full uncertainty distribution associated with risk estimates. Not only does this 

contribute to a better understanding of potential regulatory outcomes, it also enables economists to incorporate 

risk assessment uncertainty into a broader analysis of uncertainty. Formal probabilistic assessment is required 

for some regulations by Circular A-4 (OMB 2003). Also refer to EPA’s guidance and reference documents 

on Monte Carlo methods and probabilistic risk assessment, including EPA’s Policy for Use of Probabilistic 

Analysis in Risk Assessments (U.S. EPA 1997e), and the 1997 Guiding Principles for Monte Carlo Analysis 

(U.S. EPA 1997d). 

The principal role of the economist at this stage is capture the full range of a policy’s benefits. Text 

to ensure that the information provided is useful Box 7.2 provides examples and a more detailed 

for the subsequent economic valuation models discussion. 

that may be used later in the benefits analysis. The 

analyst should give special care to ensuring that Another important role for economists at this 

the endpoints evaluated are appropriate for use stage is to provide insights, information, and 

in benefits estimation. Effects that are described analysis on behavioral changes that can affect the 

too broadly or that cannot be linked to human results of the risk assessment as needed. Changes 

well-being limit the ability of the analysis to in behavior due to changes in environmental 
7-6 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

quality (e.g., staying indoors to avoid detrimental 

effects of air pollution) can be significant and care 

should be taken to account for such responses in 

risk assessments and benefit estimations. 

Step 3: Estimate the values of the effects 

The next step is to estimate willingness to 

pay (WTP) of all affected individuals for the 

quantified benefits in each benefit category, and 

then to aggregate these to estimate the total 

social benefits of each policy option. Typically, 

a representative agent approach is used when 

deriving estimates of benefits. The analyst 

calculates WTP for an “average” individual in a 

sample of people from the relevant population and 

then multiply that average value by the number 

of individuals in the exposed population to derive 

an estimate of total benefits. As discussed earlier, 

markets do not exist for many of the types of 

benefits expected to result from environmental 

regulations. Details on the economic valuation 

methods suitable for this step and examples of how 

they can be applied can be found in Section 7.3. In 

applying these methods, analysts should: 

Consider using multiple valuation methods 

when possible. Different methods often address 

different subsets of total benefits and the use 

of multiple methods allows for comparison of 

alternative measures of value when applied to the 

same category of benefits. Double counting is a 

significant concern when applying more than one 

method. Any potential overlap should be noted 

when presenting the results. The discussion of 

benefit transfer in Section 7.4 describes many of 

the issues involved in applying value estimates 

from previous studies to new policy cases, 

including various meta-analysis techniques for 

combining estimates from multiple studies. 

Describe the source of estimates and confidence 

in those sources. Valuation estimates always 

contain a degree of uncertainty. Using them 

in a context other than the one in which they 

were initially estimated can only increase that 

uncertainty. If many high-quality studies of the 

same effect have produced comparable values, 

analysts can have more confidence in using these 

estimates in their benefits calculations. In other 

cases, analysts may have only a single study, or even 

no directly comparable study, to draw from. In all 

cases, the benefits analysis should clearly describe 

the sources of the value estimates used and provide 

a qualitative discussion of the reliability of those 

sources. The analyst should include a quantitative 

uncertainty assessment when possible. Guiding 

principles for presenting uncertainty are addressed 

in Chapter 11. 

7.2 Economic Value and Types 

of Benefits 

Economic valuation is based on the traditional 

economic theory of human behavior and 

preferences, which centers on the concept of 

“utility” (or “satisfaction” or “welfare”) that people 

realize from goods and services, both market and 

non-market. Different levels and combinations 

of goods and services afford different levels of 

utility for any one person. Because different people 

have different preferences, different sets of goods 

and services will appeal more or less to different 

people. Utility is inherently subjective and cannot 

be measured directly. Therefore, in order to give 

“value” an operational definition it must be 

expressed in a quantifiable metric. Money generally 

is used as the metric, but this choice for the unit 

of account has no special theoretical significance. 

One could use “apples,” “bananas,” or anything else 

that is widely valued and consumed by individuals. 

The crucial assumption is that a person could 

be compensated for the loss of some additional 

quantity of any good by some quantity of another 

good that is selected as the metric. Table 7.1 

summarizes the types of benefits associated with 

environmental protection policies and provides 

examples of each of the benefits types, as well as 

valuation methods commonly used to monetize 

the benefits for each type. 

When goods and services are bought and 

sold in competitive markets, the ratio of the 

marginal utility (the utility afforded by the last 

unit purchased) of any two goods that a person 

consumes must be equal to the ratio of the prices 

of those goods. If it were otherwise, that person 

could reallocate her budget to buy a little more 
Chapter 7 Analyzing Benefits

Figure 7.1 - Benefits of an Environmental 

Improvement 

$

Emissions

MAC MD

e* e1 e0

of one good and a little less of the other good to 

achieve a higher level of utility. Thus, market prices 

can be used to measure the value of market goods 

and services directly. A practical rationale for using 

money as the metric for non-market valuation is 

that money is the principal medium of exchange 

for the wide variety of market goods and services 

among which people choose on a daily basis. 

The benefits of an environmental improvement 

are shown graphically in Figure 7.1. Reducing 

emissions from e0

 to e1 produces benefits equal to 

the shaded area under the marginal damages (MD) 

curve. Many environmental goods and services, 

such as air quality and biological diversity, are 

not traded in markets. The challenge of valuing 

non-market goods that do not have prices is to 

relate them to one or more market goods that do. 

This can be done either by determining how the 

non-market good contributes to the production of 

one or more market goods (often in combination 

with other market good inputs), or by observing 

the trade-offs people make between non-market 

goods and market goods. One way or another, this 

is what each of the revealed and stated preference 

valuation methods discussed in Section 7.3 is 

designed to do. Of course, some methods will be 

more suitable than others in any particular case for 

a variety of reasons, and some will be better able 

to capture certain types of benefits than others. 

In principle, though, they are all different ways 

of measuring the same thing, which is the total 

amount of money required to make all individuals 

indifferent between the baseline and policy 

scenarios. 

The economic valuation of an environmental 

improvement is the dollar value of the private 

goods and services that individuals would 

be willing to trade for the improvement at 

prevailing market prices. The willingness to 

trade compensation for goods or services can 

be measured either as willingness to pay (WTP) 

or willingness to accept (WTA). WTP is the 

maximum amount of money an individual would 

voluntarily pay to obtain an improvement. WTA 

is the least amount of money an individual would 

accept to forego the improvement.3

 The key 

theoretical distinction between WTP and WTA 

is their respective reference utility levels. For 

environmental improvements, WTP uses the level 

of utility without the improvement as the reference 

point while WTA uses the level of utility with 

the improvement as the reference point. Because 

of their different reference points, one relevant 

factor to consider when deciding whether WTP or 

WTA is the appropriate value measure to use in a 

BCA is the property rights for the environmental 

resource(s) in question. WTP is consistent with 

individuals or firms having rights to pollute 

and the affected parties needing to pay them to 

desist. WTA is consistent with individuals being 

entitled to a clean environment and needing to be 

compensated for any infringements of that right 

(Freeman 2003). 

Economists generally expect that the difference 

between WTP and WTA will be small, provided 

the amounts in question are a relatively small 

proportion of income and the goods in question 

are not without substitutes, either market or nonmarket. However, there may be instances in which 

income and substitution effects are important.4

 To 

simplify the presentation, the term WTP is used 

throughout the remainder of this chapter to refer 

3	 For simplicity, the discussion in this section is restricted to the case 

of environmental improvements, but similar definitions hold for 

environmental damages. For a more detailed treatment of WTP and 

WTA and the closely related concepts of compensating variation, 

equivalent variation, and Hicksian and Marshallian consumer surplus, 

see Hanley and Spash (1993), Freeman (2003), Just et al. (2005), and 

Appendix A of these Guidelines. 

4	 For more information see Appendix A and Hanemann (1991). 

Guidelines for Preparing Economic Analyses | December 2010 7-7 
7-8 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

to the underlying economic principles behind 

both WTA and WTP, but the analyst should 

keep the potential differences between the two 

measures in mind. 

Based on the connection to individual welfare just 

described, estimates of WTP are needed for the 

Kaldor and Hicks potential compensation tests that 

form the basis of BCA (Boadway and Bruce 1984, 

Just et al. 1982, and Freeman 2003). To carry out these 

tests, sum the WTP for all affected individuals and 

compare the summed WTP value to the estimated 

costs of the proposed policy. Because environmental 

policy typically deals with improvements rather than 

deliberate degradation of the environment, WTP is 

generally the relevant measure.5 

The types of benefits that may arise from 

environmental policies can be classified in multiple 

ways (Freeman 2003). As shown in Table 7.1, 

these Guidelines categorize benefits as human 

health improvements, ecological improvements, 

and other types of benefits, including aesthetic 

improvements and reduced materials damages, 

and list commonly used valuation methods for 

reference. The list is not meant to be exhaustive, 

but rather to provide examples and commonly 

used methods for estimating values.6

 The sections 

below provide a more detailed discussion of each 

of the benefit categories listed in Table 7.1. 

7.2.1 Human Health Improvements 

Human health improvements from environmental 

policies include effects such as reduced mortality 

rates, decreased incidence of non-fatal cancers, 

chronic conditions and other illnesses, and 

reduced adverse reproductive or developmental 

effects. While the most appropriate approach to 

valuation would consider mortality and morbidity 

together, in practice these effects are typically 

valued separately, and are therefore discussed 

separately in these Guidelines. 

5	 See Section A.3 of Appendix A for further explanation of Kaldor-Hicks 

conditions. 

6	 In very rare cases with employment implications for the structurally 

unemployed, analysts may need to include job creation as a benefits 

category. See Appendix C for more detail. 

7.2.1.1 Mortality 

Some EPA policies will lead to decreases in human 

mortality risks due to potentially fatal health 

conditions such as cancers. In considering the 

impact of environmental policy on mortality, it 

is important to remember that environmental 

policies do not assure that particular individuals 

will not die of environmental causes. Rather, they 

lead to small changes in the probability of death 

for many people. 

EPA currently recommends a default central 

“value of statistical life” (VSL) of $7.9 million 

(in 2008 dollars) to value reduced mortality 

for all programs and policies.7

 This value is 

based on a distribution fitted to 26 published 

VSL estimates. The distribution itself can be used 

in uncertainty analysis. The underlying studies, 

the distribution parameters, and other useful 

information are available in Appendix B. 

As a general matter, the impact of risk and 

population characteristics should be addressed 

qualitatively. In some cases, the analysis may 

include a quantitative sensitivity analysis. Analysts 

should account for latency and cessation lag 

when valuing reduced mortality risks, and should 

discount appropriately. 

Valuing mortality risk changes in children is 

particularly challenging. EPA’s Handbook for 

Valuing Children’s Health Risks (2003b) provides 

some information on this topic, including key 

benefit transfer issues when using adult-based 

studies. Circular A-4 also recognizes this subject, 

specifically advising: “For rules where health gains 

are expected among both children and adults and 

you decide to perform a BCA, the monetary values 

for children should be at least as large as the values 

for adults (for the same probabilities and outcomes) 

unless there is specific and compelling evidence to 

suggest otherwise” (OMB 2003). OMB guidance 

applies to risk of mortality and of morbidity. 

7	 This value is adjusted from the base value reported in U.S. EPA 2000d 

($4.8 million in 1990 dollars) using the Consumer Price Index (CPI). 

The value is not adjusted for income growth over time. 
Chapter 7 Analyzing Benefits

Table 7.1 - Types of Benefits Associated With Environmental Policies: 

Categories, Examples, and Commonly Used Valuation Methods 

Benefit Category Examples Commonly Used Valuation Methods 

Human Health Improvements 

Mortality risk reductions Reduced risk of: 

Cancer fatality 

Acute fatality 

Averting behaviors 

Hedonics 

Stated preference 

Morbidity risk reductions Reduced risk of: 

Cancer 

Asthma 

Nausea 

Averting behaviors 

Cost of illness 

Hedonics 

Stated preference 

Ecological Improvements 

Market products Harvests or extraction of: 

Food 

Fuel 

Fiber 

Timber 

Fur and Leather 

Production function 

Recreation activities and aesthetics Wildlife viewing 

Fishing 

Boating 

Swimming 

Hiking 

Scenic views 

Production function 

Averting behaviors 

Hedonics 

Recreation demand 

Stated preference 

Valued ecosystem functions Climate moderation 

Flood moderation 

Groundwater recharge 

Sediment trapping 

Soil retention 

Nutrient cycling 

Pollination by wild species 

Biodiversity, genetic library 

Water filtration 

Soil fertilization 

Pest control 

Production function 

Averting behaviors 

Stated preference 

Non-use values Relevant species populations, 

communities, or ecosystems 

Stated preference 

Other Benefits 

Aesthetic improvements Visibility 

Taste 

Odor 

Averting behaviors 

Hedonics 

Stated preference 

Reduced materials damages Reduced soiling 

Reduced corrosion 

Averting behaviors 

Production / cost functions 

Note: “Stated preference” refers to all valuation studies based on hypothetical choices, as distinguished from 

“revealed preference,” which refers to valuation studies based on observations of actual choices. 

Guidelines for Preparing Economic Analyses | December 2010 7-9 
7-10 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

 

 

 

Methods for valuing mortality risk changes 

Because individuals appear to make risk-income 

trade-offs in a variety of ways, the value of 

mortality risk changes are estimated using three 

primary methods. The most commonly used 

method is the hedonic wage, or wage-risk, method 

in which value is inferred from the income-risk 

trade-offs made by workers for on-the-job risks. 

Averting behavior studies value risk changes by 

examining purchases of goods that can affect 

mortality risk (e.g., bicycle helmets). Finally, 

stated preference studies are increasingly used to 

estimate WTP for reduced mortality risks. Key 

considerations in all of these studies include the 

extent to which individuals know and understand 

the risks involved, and the ability of the study to 

control for aspects of the actual or hypothetical 

transaction that are not risk-related. Because the 

value of risk reduction may depend on the risk 

context (e.g., work-related vs. environmental), 

results from any single study may not be directly 

applicable to a typical environmental policy case. 

There are additional methods that can be used to 

derive information on risk trade-offs. Van Houtven 

et al. (2008) use a risk-risk trade-off model to 

examine preferences for avoiding fatal cancers. 

Carthy et al. (1999) examine trade-offs between 

fatal and non-fatal risks to indirectly estimate a 

WTP. This approach may make the task more 

manageable for the respondent, but the analyst 

should consider and evaluate the complexity of 

the additional steps and the indirect nature of the 

resulting estimates. 

At one time, reduced mortality risk was valued 

under a human capital approach that equated the 

value of a statistical life with foregone earnings. 

This has largely been rejected as an inappropriate 

measure of the value of reducing mortality risks 

because it is not based on WTP for small risk 

reductions and as such does not capture the value 

associated with avoided pain and suffering, dread, 

and other risk factors that are thought to affect 

value (Viscusi 1993). 

Previous studies 

While there are many unresolved issues in 

valuing mortality risks, the field is relatively rich 

in empirical estimates and several substantial 

reviews of the literature are available. A general 

overview of common approaches and issues in 

mortality risk valuation can be found in Hammitt 

(2003). Viscusi (1993) and Viscusi and Aldy 

(2003) provide detailed reviews of the hedonic 

wage literature. Black, Galdo, and Liu (2003) 

provide a technical review of the statistical issues 

associated with hedonic wage studies. Blomquist 

(2004) provides a review of the averting behavior 

literature. Some key issues related to stated 

preference studies are included in Alberini (2004). 

Recently, some researchers have begun to use metaanalysis to combine study results and examine the 

impact of study design. Recent examples include 

Viscusi and Aldy (2003), Mrozek and Taylor 

(2002), and Kochi et al. (2006). EPA applications 

of VSL are numerous, and include the Clean Air 

Interstate Rule (CAIR), the Non-Road Diesel 

Rule, and the Stage 2 Disinfection By-Products 

Rule (DBP).8 

Important considerations 

The analyst should keep three important 

considerations in mind when estimating mortality 

benefits: 

• Characterizing and measuring mortality 


effects; 


• Heterogeneity in risk and population 


characteristics; and 


• The timing of health risk changes. 

Characterizing and measuring 

mortality effects 

Reduced mortality risks are typically measured 

in terms of “statistical lives.” This measure is the 

8	 The economic analyses for these three rules are available electronically 

as follows (accessed May 23, 2008): 

CAIR (http://www.epa.gov/air/interstateairquality/pdfs/finaltech08.pdf); 

Non-Road Diesel (http://www.epa.gov/nonroad-diesel/2004fr.htm#ria); and 

Stage 2 DBP (http://www.epa.gov/safewater/disinfection/stage2/pdfs/ 

anaylsis_stage2_ecconomic_main.pdf). 
Guidelines for Preparing Economic Analyses | December 2010 7-11 

Chapter 7 Analyzing Benefits

aggregation of many small risks over an exposed 

population. Suppose, for example, that a policy 

affects 100,000 people and reduces the risk 

of premature mortality by one in 10,000 for 

each individual. Summing these individual risk 

reductions across the entire affected population 

shows that the policy leads to 10 premature 

fatalities averted, or 10 statistical lives “saved.” 

Alternative measures attempt to capture the 

remaining life expectancy associated with the risk 

reductions. This is sometimes referred to as the 

“quantity of life” saved (Moore and Viscusi 1988) 

and is typically expressed as “statistical life years.” 

Looking again at the policy described above, 

suppose the risks were spread over a population 

where each individual had 20 years of remaining 

life expectancy. The policy would then save 200 

statistical life years (10 statistical lives times 20 life 

years each). In practice, estimating statistical life 

years saved requires risk information for specific 

subpopulations (e.g., age groups or health status). 

It is typical to use statistical life years saved in 

CEA, but valuing a statistical life year remains 

a subject of debate in the economics literature. 

Theoretical models show that the relationship 

between WTP and factors such as age, baseline 

risk, and the presence of co-morbidities is 

ambiguous and empirical findings are generally 

mixed (U.S. EPA 2006e). 

Heterogeneity in risk and 

population characteristics 

The value of mortality risks can vary both by 

risk characteristics and by the characteristics of 

the affected population. Key risk characteristics 

include voluntariness (i.e., whether risks are 

voluntarily assumed), timing (immediate or 

delayed), risk source (e.g., natural vs. manmade), and the causative event (e.g., cancer vs. 

accidents). Population characteristics include 

those generally expected to influence WTP for 

any good (e.g., income and education) as well 

as those more closely related to mortality risks 

such as baseline risk or remaining lifespan, health 

status, risk aversion, and familiarity with the type 

of risk. The empirical and theoretical literature 

on many of these characteristics is incomplete or 

ambiguous. For example, some studies suggest that 

older populations are willing to pay less for risk 

reductions ( Jones-Lee et al. 1993), but others find 

this effect to be small if it exists at all (Alberini et 

al. 2004). Still others suggest older populations 

have higher WTP (Kniesner, Viscusi, and Ziliak 

2006). Smith et al. (2004) and Viscusi and Aldy 

(2007a) discuss the relationship between age 

and VSL in the context of hedonic wage studies. 

Appendix B contains a more complete discussion 

of risk and population characteristics and how 

they may affect WTP. 

Timing of health risk changes 

Environmental contamination can cause 

immediate or delayed health effects. If individuals 

typically prefer health improvements earlier in 

time rather than later, all else equal, then the 

WTP for reductions in exposure to environmental 

pollutants will depend on when the resulting 

health risk changes will occur. The description here 

focuses on mortality risk, but the same principles 

apply to non-fatal health risks. 

The effects of timing on the present or annualized 

value of reduced mortality risk can be considered 

in the context of a lifecycle consumption model 

with uncertain lifetime (Cropper and Sussman 

1990, Cropper and Portney 1990, and U.S. EPA 

2007g). In this framework reductions in mortality 

risk are represented as a shift in the survival curve 

— the probability an individual will survive to 

all future ages — which leads to a corresponding 

change in life expectancy and future utility. 

If the basis for benefit transfer is a marginal 

WTP for contemporaneous risk reductions, then 

calculating the benefits of a policy with delayed 

risk reductions requires three steps: (1) estimating 

the time path of future mortality risk reductions; 

(2) estimating the annual WTP in all future 

years; and (3) calculating the present value of 

these annual WTP amounts. The first step should 

account for all the factors that ultimately relate 

changes in exposure to changes in mortality risk as 

defined by shifts in the survival curve. 
7-12 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

 

 

 

 

7.2.1.2 Morbidity 

Morbidity benefits consist of reductions in the 

risk of non-fatal health effects ranging from mild 

illnesses, such as headaches and nausea, to very 

serious illnesses such as cancer (see Table 7.1). 

Non-fatal health effects also include conditions 

such as birth defects or low birth weight. Nonfatal health effects differ with respect to the 

availability of existing value estimates. Values for 

reducing the risks of some of these health effects 

have been estimated multiple times using a variety 

of different methods, while others have been the 

subject of only a few or no valuation studies. 

WTP to reduce the risk of experiencing an illness 

is the preferred measure of value for morbidity 

effects. As described in Freeman (2003), this 

measure consists of four components: 

• “Averting costs” to reduce the risk of illness; 

• “Mitigating costs” for treatments such as 

medical care and medication; 

• Indirect costs such as lost time from paid 

work, maintaining a home, and pursuing 

leisure activities; and 

• Less easily measured but equally real costs of 

discomfort, anxiety, pain, and suffering. 

Methods used to estimate WTP vary in the extent 

to which they capture these components. For 

example, cost-of-illness (COI) estimates generally 

only capture mitigating and indirect costs, 

and omit averting expenditures and lost utility 

associated with pain and suffering. 9 

Methods for valuing morbidity 

Researchers have developed a variety of methods 

to value changes in morbidity risks. Some 

methods measure the theoretically preferred 

value of individual WTP to avoid a health effect. 

Others can provide useful data, but that data 

must be interpreted carefully if it is to inform 

This is why COI estimates generally understate WTP to reduce the 

same risk or avoid a given health effect. Some studies have estimated 

that total WTP can be two to four times as large as COI even for minor 

acute respiratory illnesses (Alberini and Krupnick 2000). Still, there 

is not any broadly applicable “scaling factor” that relates COI to WTP 

generally. 

economically meaningful measures. Methods 

also differ in the perspective from which values 

are measured (e.g., before or after the incidence 

of morbidity), whether they control for the 

opportunity to mitigate the illness (e.g., before or 

after taking medication) and the degree to which 

they account for all of the components of total 

WTP. The three primary methods most often used 

to value morbidity in an environmental context 

are stated preference (Section 7.3.2), averting 

behavior (Section 7.3.1.4), and COI (Section 

7.3.1.5). Hedonic methods (Section 7.3.1.3) 

are used less frequently to value morbidity from 

environmental causes. 

Many other approaches do not estimate WTP 

and their ability to inform benefits analyses 

consequently varies. Risk-risk trade-offs, for 

example, do not directly estimate dollar values 

for risk reductions, but rather provide rankings of 

relative risks based on consumer preferences. Riskrisk trade-offs can be linked to WTP estimates for 

related risks.10 

Other methods suffer from certain 

methodological limitations and are therefore 

generally less useful for policy analysis. For 

example, health-state indices, composite metrics 

that combine information on quality and 

quantity of life lived under various scenarios, are 

often used for cost-effectiveness or cost-utility 

analyses. These methods cannot be directly 

related to WTP estimates as the indices were 

developed using very different paradigms than 

those for WTP values. As such, they should not 

be used for deriving monetary estimates for use in 

BCA [Hammitt 2003, and Institute of Medicine 

(IOM) 2006], although there is evidence that 

components of these indices may still be useful 

in a benefit-transfer context (Van Houtven et al. 

2006). Another commonly suggested alternative 

is jury awards, but these generally should not be 

used in benefits analysis, for reasons explained in 

Text Box 7.3. 

10	 EPA analyses have used risk-risk trade-offs for non-fatal cancers in 

conjunction with VSL estimates as one method to assess the benefits 

of reduced carcinogens in drinking water (U.S. EPA 2005a). 

9 
Guidelines for Preparing Economic Analyses | December 2010 7-13 

Chapter 7 Analyzing Benefits

Text Box 7.3 - Non-Willingness to Pay Measures 

Economic measures of value calculate willingness to pay (WTP) for environmental changes. WTP is defined as that 

amount of money that, if taken away from income, would make an individual exactly indifferent between experiencing 

an environmental improvement and not experiencing either the improvement or any change in income. (An analogous 

measure can also be constructed for “not experiencing degradation” rather than “experiencing an improvement”). 

WTP is a valid measure of “economic value” because it is directly useful for applying the potential compensation 

tests of Kaldor and Hicks. 

Some measures of economic value are not valid, as they do not measure WTP, and cannot be related to changes in 

utility. Others should be used only in a limited set of circumstances. Some examples are provided below. 

Replacement cost. One of the common consequences of environmental deterioration is damage to assets. Some 

analysts have suggested that the economic value of the damage is the cost of replacing the asset. This will only be 

true if: (1) damage to the asset is the only cost of the environmental deterioration; and (2) the least expensive way to 

achieve the level of satisfaction realized before the deterioration would be to replace the asset. If the first condition is 

not met, consideration of replacement costs alone might underestimate the economic consequences of environmental 

degradation. If the second condition is not met, replacement costs might overestimate the consequences. Suppose 

that water pollution kills fish in a pond. Replacing those fish with healthy, edible ones might prove extremely 

expensive: the pond might need to be dredged and restocked. However, people who are no longer able to catch 

fish in the pond might be compensated simply by giving them enough money to purchase substitutes at their local 

supermarket. 

Proxy costs. A closely related concept to replacement cost is the cost of a substitute for the damaged asset. In 

widely cited work, ecologist H.T. Odum (1996) calculated the number of barrels of petroleum that would be required 

to provide the energy to replace the services of wetland ecosystems. However, this number is economically irrelevant. 

There is no reason to suppose that people would choose to replace services of damaged wetlands with those of 

purchased oil. A similar argument can be made against the interpretation of “ecological footprints” as an estimate 

of economic consequences (Wackernagel and Rees 1996). Dasgupta (2002) interprets these approaches as singlefactor theories of value (Karl Marx’s labor theory of value is the best known example), fallacies that were disproved in 

general by Samuelson’s (1951) “non-substitution theorem.” 

Cost-of-illness (COI). Health effects are often proxied by the “cost of illness,” which are the total costs of 

treatment and time lost due to illness. Although COI is discussed in greater detail in Section 7.3.1.5, note here that: 

(1) COI does not record other expenses incurred in efforts to avoid illness; (2) health insurance may drive a wedge 

between the costs incurred to treat illness and WTP to avoid it; and (3) COI ignores factors such as discomfort and 

dread that patients would also be willing to pay to avoid. 

Jury awards. Another approach sometimes taken to measure environmental damages is derived from the awards 

made by juries. Using such awards may also prove problematic for at least two reasons. First, cases only go to trial 

if both sides prefer the risk of an adverse outcome to the certainty of a pre-trial settlement. Cases that go to juries 

are “atypical” by definition. Second, since adjudication does not always occur and can never be infallible, jury 

awards often do, and arguably should (Shavell 1979), embody “punitive” as well as “compensatory” elements. Juries 

make examples of guilty defendants in an attempt to deter others from committing similar offenses. For this reason, 

jury awards may overstate typical damages. Finally, jury awards reflect a certain outcome and not the probability 

of experiencing an adverse event and therefore include the influence of characteristics typically not included in 

statistical analysis, such as pain, suffering, and likeability. These estimates are not appropriate for application to ex 

ante evaluation of the value associated with a statistical probability. 
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Chapter 7 Analyzing Benefits

 

 

Previous studies 

A comprehensive summary of existing studies 

of morbidity values is beyond the scope of these 

Guidelines. Below is a short list of references that 

can serve as a starting point for reviewing available 

morbidity value estimates for benefit transfer or 

for designing a new study. Some recent estimates 

for particular health effects include Hammitt 

and Haninger (2007), who examine food-related 

illnesses, and Chestnut et al. (2006), who examine 

respiratory and cardiovascular effects. Tolley et al. 

(1994) and Johanneson (1995) are useful general 

references for valuing non-fatal health effects. 

EPA’s Handbook for Non-Cancer Valuation (U.S. 

EPA 2000c) provides published estimates for many 

illnesses and reproductive and developmental 

effects. Desvousges et al. (1998) assess a number 

of existing studies in the context of performing a 

benefit transfer for a benefits analysis of improved 

air quality. EPA’s Cost of Illness Handbook (U.S. 

EPA 2007c) includes estimates for many cancers, 

developmental illnesses, disabilities, and other 

conditions. EPA analyses of regulations and 

policies, including EPA’s two comprehensive 

studies of the benefits and costs of the Clean Air 

Act (U.S. EPA 1997a and U.S. EPA 1999) draw 

upon a number of existing studies to obtain values 

for reductions of a variety of health effects. These 

sources describe how the central estimates were 

derived, and attempt to quantify the uncertainty 

associated with using the estimates. 

At least two meta-analyses have attempted to 

examine how the value of non-fatal risk reductions 

varies with characteristics of the condition, 

the affected population, and the approach to 

valuation. Vassanadumrongdee et al. (2004) focus 

on air pollution-related morbidity risks and posit 

a meta-regression based benefit transfer function. 

Van Houtven et al. (2006) evaluate more than 230 

WTP estimates from 17 stated preference studies, 

finding evidence that illness severity, measured 

systematically, is a significant factor explaining 

variation in WTP. The authors also illustrate 

how a meta-regression-based function can 

facilitate benefit transfer based on duration and 

severity of acute illnesses, along with population 

characteristics. While the specific benefit-transfer 

functions in these articles might not be suitable for 

application in any particular context, the estimates 

contained in them can be helpful. Other studies 

are available through the Environmental Valuation 

Reference Inventory (EVRI). EVRI is maintained 

by Environment Canada and contains more than 

1,100 studies that can be referenced according to 

medium, resource, stressor, method, and country.11 

Important considerations 

The analyst should keep two important 

considerations in mind when estimating 

morbidity benefits: 

• Characterizing and measuring morbidity 

effects; and 

• Incomplete estimates of WTP. 

Characterizing and measuring 

morbidity effects 

The key characteristics that will influence the 

values of morbidity effects are their severity, 

frequency, duration, and symptoms. Severity 

defines the degree of impairment associated with 

the illness. Examples of how researchers have 

measured severity include “restricted activity 

days,” “bed disability days,” and “lost work 

days.”12 Severity can also be described in terms 

of health state indices that combine multiple 

health dimensions into a single measure.13 For 

duration, the primary distinction is between 

acute effects and chronic effects. Acute effects are 

discrete episodes usually lasting only a few days, 

while chronic effects last much longer and are 

generally associated with long-term illnesses. The 

11	 See www.evri.ca for more information. 

12	 As Cropper and Freeman (1991) note, these descriptions are 

essentially characterizations of a behavioral response to the illness. 

Lost workdays, for example, in some cases require a decision on an 

individual’s part not to go to work due to illness. Such a response may 

depend upon various socioeconomic factors as well as the physical 

effect of the illness. 

13	 The difference in the indices is intended to reflect the relative difference 

in disutility associated with symptoms or illnesses. There are serious 

questions about the theoretic and empirical consistency between 

these “health-related quality of life” index values and WTP measures 

for improved health outcomes (Hammitt 2002). Still the inclusion of 

some aspects of these indices may prove useful in valuation studies 

(Van Houtven et al. 2006). Examples of economic analyses that have 

employed some form of health state index include Desvousges et al. 

(1998) and Magat et al. (1996). 
Guidelines for Preparing Economic Analyses | December 2010 7-15 

Chapter 7 Analyzing Benefits

frequency of effects also can vary widely across 

illnesses. Some effects are one-time events that are 

unlikely to recur, such as a gastrointestinal illness. 

Other effects, such as asthma, do recur or can be 

aggravated regularly, causing disruptions in work, 

school, or recreational activities. 

For chronic conditions or more serious 

outcomes, morbidity effects are usually measured 

in terms of the number of expected cases of a 

particular illness. Given the risks faced by each 

individual and the number of people exposed 

to this risk, an estimate of “statistical cases” can 

be defined analogously to “statistical lives.” In 

contrast, morbidity effects that are considered 

acute or mild in nature can be estimated as 

the expected number of times a particular 

symptom associated with an illness occurs. These 

estimates of “symptom days” may be used in 

benefits analysis when appropriate estimates of 

economic value are available, although a richer 

characterization of combinations of symptoms, 

severity, duration, and episode frequency would 

be an improvement over much of the existing 

literature. Some studies have attempted to deal 

with these complexities in a more systematic 

manner, but the results have not yet been widely 

applied and interpreted for policy analysis 

(Cameron and DeShazo 2008). (Refer to Section 

7.3.1.5 and Text Box 7.3 on the use of COI 

versus WTP measures of value.) 

Incomplete estimates of WTP 

The widespread availability of health insurance 

and paid sick leave shift some of the costs of illness 

from individuals to others. While this cost-shifting 

can be addressed explicitly in COI studies, it 

may lead to problems in estimating total WTP. 

If the researcher does not adequately address 

these concerns, individuals may understate their 

WTP, assuming that some related costs would be 

borne by others. However, to the extent that these 

costs represent diversions from other uses in the 

economy, they represent real costs to society and 

should be accounted for in the analysis. 

More information on these and other issues to 

consider when conducting or evaluating morbidity 

value studies is provided in EPA’s Handbook for 

Non-Cancer Health Effects Valuation (U.S. EPA 

2000c). 

7.2.2 Ecological Benefits 

In addition to human health benefits, many 

EPA policies will produce ecological benefits by 

increasing the delivery of “ecosystem services,” 

which are the end products of ecological functions 

that are valued by people (Daily 1997, National 

Research Council 2005, and Millennium 

Ecosystem Assessment 2005). There is a large and 

growing literature on the valuation of ecosystem 

services. Fisher et al. (2009) document an 

exponentially increasing number of published 

articles on ecosystem services, growing from 

essentially none in the early 1980s to around 250 

in 2007. Much of this literature focuses on the 

impacts of habitat loss and other land use changes 

on ecosystem service flows. Because EPA has only 

limited authority over private and public land 

use decisions, analysts may find that only a subset 

of the results in these studies will be directly 

transferable to traditional EPA regulations. 

Nevertheless, this growing literature can provide 

a useful conceptual framework and potentially 

transferable methods for analyzing a wide range of 

EPA policies that may affect ecological services. 

In principle, once the pollutants (or other 

environmental stressors) whose emissions will be 

altered by the regulation have been identified, the 

same general approach used to estimate human 

health benefits can be used to estimate ecological 

benefits: identify the endpoints that are affected by 

those pollutants and that are valuable to society; 

estimate dose-response relationships between 

stressors and endpoints; and estimate people’s 

WTP for changes in the endpoints using revealed 

or stated preference valuation methods. In the 

case of ecological benefits estimation, the relevant 

endpoints will include measures of ecosystem 

health rather than human health, and the methods 

and data required to estimate the dose-response 

functions and WTP will differ accordingly. As 

in the human health case, the estimation of doseresponse relationships between pollutants and 

endpoints will fall mainly to natural scientists, 
7-16 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

although collaboration between scientists and 

economists often is needed to help focus the 

analysis on the most important endpoints. [The 

Agency’s Ecological Benefits Assessment Strategic 

Plan describes an interdisciplinary approach 

for conducting ecological benefits assessments, 

as well as research priorities for improving such 

assessments (U.S. EPA 2006a)]. Even though the 

basic approach for valuing ecological benefits is 

similar to that used to value human health benefits, 

an entirely different set of complications may arise 

when estimating ecological benefits (Freeman 

2003 pp. 457-460). Some of these complications 

are explored below. 

A hypothetical policy 

To illustrate some of the complications that can 

arise when assessing ecological benefits, consider 

a hypothetical policy that would control the 

emissions of an industrial chemical that are 

believed to decrease survival and reproductive 

rates in one or more fish species. First, compared 

to the commonly accepted individual-level 

mortality and morbidity endpoints used in 

human health benefit assessments, it may be 

more difficult to identify or define the relevant 

endpoints in an ecological benefits assessment 

(de Groot et al. 2002, Boyd and Banzhaf 2007, 

Wallace 2007, and Fisher and Turner 2008). 

Identifying endpoints for estimating use values 

may be relatively straightforward. For example, 

endpoints for this hypothetical policy would 

include the abundances and distributions of 

species that are directly or indirectly affected 

by the chemical and are harvested or targeted 

for wildlife viewing or other non-consumptive 

outdoor activities. Identifying relevant endpoints 

for non-use values, on the other hand, can be more 

complicated. Even for this simplified hypothetical 

policy, it may not be clear which among the wide 

variety of measureable ecosystem attributes — 

beyond those previously identified as relevant for 

use values — would provide an adequate basis 

for eliciting non-use values in a stated preference 

survey. Evans et al. (2008) discuss some of the 

challenges they faced in defining endpoints for 

a stated preference survey to value reductions in 

acid rain in the Adirondacks. Boyd and Krupnick 

(2009) discuss problems of identifying ecological 

endpoints more generally. 

After relevant endpoints are identified, there 

may be additional complications in modeling 

the effects of the chemical on those endpoints. 

For example, the emissions-transport-exposure 

pathway(s) — i.e., the “ecological production 

function” (U.S. EPA 2009b) — may involve 

complex food web linkages that are less direct 

or have more convoluted feedbacks than in the 

human health context. Furthermore, some of 

the important feedbacks may involve human 

responses to the changed ecological conditions. 

For example, if some of the fish species in our 

hypothetical policy scenario are harvested by 

recreational or commercial fishers, then the 

nature of the management regime in the fisheries 

may influence the response of the fish stocks to 

the policy. In an extreme case, if the commercial 

fisheries are completely unregulated and subject 

to open access conditions, then any increases in 

the stock sizes from the policy may be completely 

offset in the long run by new entrants to the 

fishery (Freeman 1991, Barbier et al. 2002, 

Smith 2007, and Newbold and Iovanna 2007). 

Therefore, an integrated bio-economic modeling 

approach may be needed to accurately project the 

bio-physical effects of the policy. Some examples 

of such an approach include Smith and Crowder 

(2006), Massey et al. (2006), and Finnoff and 

Tschirhart (2008). 

After the ecological effects of the policy are 

characterized, there may be further complications 

in valuing those effects. For this hypothetical 

policy, the main requirement for revealed 

preference valuation methods might be data on 

commercial and recreational fishing activities 

in the affected water bodies. Other recreational 

activities also might be affected, and water-related 

amenities might influence property values. As with 

human health benefits, care must be taken to avoid 

double counting when using multiple datasets 

and methods that could include overlapping 

values (McConnell 1990, and Phaneuf et al. 

2008). Furthermore, if a significant portion of 

the benefits for ecological changes are thought to 

consist of non-use values rather than use values, 
Guidelines for Preparing Economic Analyses | December 2010 7-17 

Chapter 7 Analyzing Benefits

analysts may need to rely more heavily on stated 

preference methods when estimating ecological 

benefits. Considering the challenges in conducting 

reliable stated preference valuation studies even for 

well-defined and familiar commodities (described 

in detail in Section 7.3.2), this compounds the 

extra complications already discussed. This also 

points to a larger potential role for non-monetized 

and non-quantified benefits in the overall analysis 

(U.S. EPA 2006a, and U.S. EPA 2009b). 

Application of economic valuation methods 

to ecological changes 

Extensive treatments of the valuation of ecosystem 

services can be found in recent reports from the 

National Academy of Science (NAS) (2005) 

and EPA’s SAB Committee on the Valuation 

of Ecological Systems and Services (U.S. EPA 

2009b). Analysts are referred to these reports 

for more detailed discussions on the application 

of economic valuation methods to ecological 

benefits than are provided in these Guidelines. 

In this section are examples of studies that apply 

traditional valuation methods (discussed more 

generally in the following sections of this chapter) 

to ecosystem goods and services. Some of the 

special complications that can arise in such studies 

are highlighted. 

Production functions 

A number of recent contributions to the literature 

on valuing of ecosystem services emphasize the 

importance of understanding the production 

functions relating natural systems to the provision 

of products that are valuable to people (Polasky 

et al. 2008a, 2008b; Boyd and Banzhaf 2007; 

and U.S. EPA 2009b). Some simple examples 

have long been known: commercially valuable 

species “produce” themselves. Early work such as 

Faustmann’s 1848 analysis of optimal rotations 

in forestry (see also Samuelson 1976), Clark’s 

(1990) work in fisheries, and Hammack and 

Brown’s (1974) work on wetlands and waterfowl 

have provided templates for later studies. It may 

be possible to value the effects of pollution on 

the exploitation of renewable resources when 

biological production possibilities are affected by 

environmental conditions — for example, when 

fish stocks are affected by water quality, or when 

waterfowl populations are affected by the extent 

and configuration of wetlands (Bell 1997, Ellis 

and Fischer 1987, and Massey et al. 2006). As 

discussed above, analysts should keep in mind 

that institutional features such as open access to 

renewable resources may dissipate values that 

might otherwise be realized from environmental 

improvement. 

Ecological resources also can contribute to the 

production of other useful goods and services, 

such as crop yields, groundwater quality, and 

surface water flow characteristics. Hence the 

degradation of supporting ecological resources 

should be reflected in diminished outputs of these 

commodities. Direct application of production 

function approaches often is hampered by data 

and methodological limitations. Specifically, it 

can be difficult to measure the flow of non-market 

ecosystem services that a particular production 

process receives, as well as to statistically control 

for the effects of unobserved characteristics of 

climate and topography. One approach is to 

design observational studies to mimic controlled 

experiments as closely as possible. Ricketts et 

al. (2004) use this approach in a study of the 

value of pollination services to coffee crops. In 

some cases production functions might plausibly 

be derived from first principles. For example, 

Weitzman (1992), Simpson et al. (1996), Rausser 

and Small (2000), and Costello and Ward (2006) 

use simple probability models to examine the 

role of biodiversity in the development of new 

pharmaceutical products. Further examples of 

studies relating ecological conditions to economic 

outputs through production processes include 

Acharya and Barbier (2002), who examine ground 

water recharge as a function of surrounding land 

cover, and Pattanayak and Kramer (2001), who 

examine stream flow as a function of land cover. 

Hedonic models 

Econometricians generally have favored estimating 

cost or profit functions to estimating production 

functions. This is because the prices that are the 

arguments of the former will be uncorrelated with 
7-18 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

unobserved factors, whereas input choices will not 

(see Varian 1992). While a cost or profit function 

approach could be adopted in the estimation of 

ecosystem service values, a more common, and 

theoretically equivalent, approach is to estimate 

a hedonic price function. In theory, the rental 

price of land is equal to the earnings that could be 

derived from its use, while the purchase price is 

equal to the net discounted value of the stream of 

such earnings. A number of authors have estimated 

hedonic models relating the value of residential 

properties to the proximity and attributes of 

nearby forests (Anderson and Cordell 1988, 

Tyrväinen and Miettinen 2000, and Willis and 

Garrod 1991), wetlands (Lupi et al. 1991, Mahan 

et al. 2000, Woodward and Wui 2001, Bin and 

Polasky 2005, and Costanza et al. 2008), or other 

varieties of “open space” (Geoghegan et al. 1997, 

Benson et al. 1998, Irwin and Bockstael 2002, 

Irwin 2002, and Thorsnes 2002). 

Travel cost models 

A large number of studies use travel cost models 

to value ecological endpoints. The predominant 

activity in the recreational use value literature 

has been fishing; where the ecological endpoint 

is expected fish catch (or one or more proxy 

measures thereof ) at one or more recreation 

sites. For example, 122 of 325 studies in the 

recreational use value database assembled by 

Rosenberger and Stanley (2007) focused on either 

freshwater or saltwater recreational fishing. The 

remaining studies in the database focus on one 

of 25 other categories of activities, including bird 

watching (Hay and McConnell 1979), wildlife 

hunting (Creel and Loomis 1990, Coyne and 

Adamowicz 1992, Boxall 1995, Peters et al. 1995, 

and Adamowicz et al. 1997), beach use (Bockstael 

et al. 1987a, and Parsons and Massey 2003), 

backcountry recreation (Boxall et al. 1996), rockclimbing (Shaw and Jakus 1996), and kayaking 

(Phaneuf and Siderelis 2003). 

Stated preference methods 

Revealed preference methods cannot capture 

non-use values, such as those associated with the 

existence of biological diversity. This is because it 

is not possible to use data on market transactions 

or any other observed choices to estimate the value 

of goods that leave no “behavioral trail” (Larson 

1993) in their enjoyment. In such cases only stated 

preference methods can provide estimates of 

WTP or WTA (Freeman 2003). More generally, 

stated preference methods may be employed when 

researchers want to identify the widest possible 

spectrum of values, both use and non-use (Loomis 

et al. 2000). 

Stated preference studies have been used to value 

a number of ecosystem services. Examples include 

the protection of endangered species (Brown and 

Shogren 1998), the ecological consequences of 

water quality improvements in Europe (Hanley 

et al. 2006), improved ecological conditions 

resulting from reduced air pollution in the United 

States (Banzhaf et al. 2006), and restoration 

of the Florida Everglades (Milon and Scrogin 

2006). In some instances researchers may want 

to combine results of stated preference valuation 

studies of particular ecological endpoints with 

other data on the effects of pollution, land use, 

or other factors on the production of ecosystem 

services. See Boyd and Krupnick (2009) for an 

extended discussion. 

Complications that may apply to 

all methods 

When using these valuation methods or when 

transferring the results of previous valuation 

studies to assess ecological benefits for new policy 

cases, analysts should be prepared to confront 

several complications. For example: 

For new studies, it may be difficult to identify 

and/or measure the ecological endpoints that are 

most relevant for the policy case. Without a set 

of observable measures of ecological conditions 

(or measures that can be linked to ecological 

conditions through supplemental bio-physical 

modeling) thought to be relevant for outdoor 

recreation behavior, housing decisions, etc., it will 

not be possible to use revealed preference methods 

to value ecological effects. For example, users may 

care mainly about water clarity for a certain type of 

recreational activity, while the most readily available 
Guidelines for Preparing Economic Analyses | December 2010 7-19 

Chapter 7 Analyzing Benefits

data might measure nutrient loading in the water 

bodies that would be affected by a policy change. 

Under such circumstances it may be difficult to 

relate revealed preferences regarding housing 

decisions, recreational behavior, etc., to the available 

nutrient loading data, as those data are imperfect 

proxies for water clarity. There are well-known 

statistical pitfalls associated both with specifying the 

wrong “right-hand side” variables in an econometric 

relationship, as well as with “data mining” by 

including right-hand side variables in the absence 

of theoretical justification. The best, if not always 

practicable, advice that can be given is to think as 

carefully as possible about which variables should 

motivate choices before running any regressions. 

For benefit transfers, it may be difficult to find 

existing studies that value ecological endpoints that 

are the same as, or sufficiently similar to, those of 

interest in the policy case. This problem is likely to 

be more common for ecological benefits than for 

human health benefits because the latter has a larger 

set of studies to draw from and a smaller set of 

common endpoints that have been used in multiple 

studies. The less similar are the commodities valued 

in the existing ecological benefit studies, the more 

difficult it will be to synthesize those studies in a 

meta-analysis or preference calibration exercise, and 

the less valid will be the transfer of the resulting 

value estimate or function. 

Estimation difficulties are likely to arise in many 

cases of interest. In particular, explanatory variables 

may not meet the exogeneity requirement for 

estimating their associated coefficients. For 

example, in performing hedonic regressions 

of property prices on, among other things, the 

development status of nearby properties, it is likely 

that both the price of the property in question 

and the use made of nearby properties would be 

determined by factors that cannot be observed by 

the econometrician (Irwin and Bockstael 2002, 

and Irwin 2002). Similarly, in estimating recreation 

demand models in which a recreationist’s decision 

to visit a particular site depends on, among 

other things, congestion (i.e., how many others 

decide to visit the site at the same time), it is 

likely that all recreationists’ site visit choices will 

be influenced by the same unobserved factors 

(Timmins and Murdock 2007). Similar difficulties 

arise in other areas of economics; for example 

Durlauf ’s (2004) survey of empirical approaches to 

“neighborhood effects” in urban economics. The 

solution in each instance is to identify appropriate 

instrumental variables, but this can be difficult in 

many cases. One way around such problems may 

be to identify “natural experiments.” Thorsnes 

(2002), for example, identifies instances in which 

historical accidents influenced land use patterns 

independently of the later realization of adjacent 

land value in order to conduct a hedonic study of 

the effects of open space. 

For resources subject to consumptive use, such 

as harvested fish or wildlife species, expected 

harvest levels are endogenous variables, which 

can lead to biases similar to that introduced by 

congestion effects. If the policy of interest leads 

to spatially heterogeneous environmental quality 

improvements, then it may lead to a re-sorting not 

only of recreators but also of the target species 

among the recreation sites. Ignoring this spatial 

re-sorting effect can give biased welfare estimates 

(Newbold and Massey 2010). This can complicate 

both the estimation of preference parameters and 

the transfer of the estimated preference function to 

the policy case. 

A basic goal of any benefits assessment is to count all 

categories of benefits, but to count each only once. 

This may be particularly important for ecological 

benefits assessments since stated preference studies 

employed to estimate intangible values, such as 

existence values of biodiversity, might also capture 

use values that are already covered by revealed 

preference studies such as recreation demand or 

hedonic studies. When combining values estimated 

using multiple methods, the analyst should take care 

to avoid double counting. 

It is important to identify and discuss any 

omitted benefit categories that are thought to 

be important but that cannot be monetized, 

or possibly even quantified. There may be 

circumstances in which provision of some 

additional information may be helpful, even if 

does not rise to the level of presenting an explicit 

comparison of benefits with costs. For example, 
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Chapter 7 Analyzing Benefits

analysts may be able to identify the most costeffective approach among different alternatives, 

or to present natural science information that 

can convey the biophysical impact of a policy 

even if it does not quantify the WTP or WTA 

for such a policy. It is better to acknowledge gaps 

in information by discussing them qualitatively 

or by reporting physical measures (if available) 

than to employ conceptually flawed methods of 

monetization. In particular, analysts should avoid 

the use of replacement cost, embodied energybased evaluation methods, or ecological footprint 

analysis to derive estimates of WTP or WTA. 

7.2.3 Other Benefits 

Other types of potential benefits from 

environmental policies include aesthetic 

improvements and reduced material damages. 

Aesthetic improvements include effects such as 

improved taste and odor of tap water resulting 

from water treatment requirements and enhanced 

visibility resulting from reduced air pollution. 

EPA typically considers two types of benefits 

from increased visibility due to improvements 

in air quality: residential visibility benefits and 

recreational visibility benefits. Improvements in 

residential visibility are typically assumed to only 

benefit residents living in the areas in which the 

improvements are occurring, while all households 

in the United States are usually assumed to derive 

some benefit from improvements in visibility in 

areas such as National Parks. The benefits received, 

however, are assumed to decrease with the 

distance from the recreational area in which the 

improvements occur. 

Reduced materials damages include welfare 

impacts that arise from changes in the provision 

of service flows from human-made capital assets 

such as buildings, roads, and bridges. Materials 

damages can include changes in both the quantity 

and quality of such assets. Benefits from reduced 

material damages typically involve cost savings 

from reduced maintenance or restoration of soiled 

or corroded buildings, machinery, or monuments. 

Methods and previous studies 

Changes in the stock and quality of human-made 

capital assets are assessed in a manner similar to 

their “natural capital” counterparts. Analytically, 

the valuation of reduced materials damages 

parallels the methods for valuing the tangible 

end products from managed ecosystems such as 

agriculture or forestry. Effects from changes in 

air quality on the provision of the service flows 

from physical resources are handled in a similar 

fashion to the effects from changes in air quality 

on crops or commercial timber stocks. The 

most common empirical applications involve air 

pollution damages and the soiling of structures 

and other property. 

Linking changes in environmental quality with 

the provision of service flows from materials 

can be difficult because of the limited scientific 

understanding of the physical effects, the timing 

of the effects, and the behavioral responses 

of producers and consumers. An analysis of 

reduced materials damages typically begins with 

an environmental fate and transport model to 

determine the direct effects of the policy on the 

stocks and flows of pollutants in the environment. 

Then stressor-response functions are used to relate 

local concentrations of pollutants to corrosion, 

soiling, or other physical damages that affect the 

production (inputs) or consumption (output) of 

the material service flows. The market response to 

these impacts serves as the basis for the final stage 

of the assessment, in which some type of structural 

or reduced-form economic model that relates 

averting or mitigating expenditures to pollution 

levels is used to value the physical impacts. The 

degree to which behavioral adjustments are 

considered when measuring the market response 

is important, and models that incorporate 

behavioral responses are preferred to those that 

do not. Adams and Crocker (1991) provide a 

detailed discussion of this and other features of 

materials damages benefits assessment. Also see 

EPA’s benefits analysis of household soiling for an 

example that employs a reduced-form economic 

model relating defensive expenditures to ambient 

pollution (U.S. EPA 1997f ). 
Guidelines for Preparing Economic Analyses | December 2010 7-21 

Chapter 7 Analyzing Benefits

 

 

 

 

 

7.3 Economic Valuation 

Methods for Benefits Analysis 

For goods bought and sold in undistorted markets, 

the market price indicates the marginal social value 

of an extra unit of the good. There are virtually 

no markets for environmental goods. While 

some natural products are sold in private markets, 

such as trees and fish, these are “products of the 

environment” and not the types of “environmental 

goods and services” analysts typically need to value. 

The analyst’s concern is typically with non-market 

inputs, which are, by definition, not traded in 

markets.14 To overcome this lack of market data, 

economists have developed a number of methods to 

value environmental quality changes. Most of these 

methods can be broadly categorized as either revealed 

preference or stated preference methods. 

In cases where markets for environmental goods do 

not exist, WTP can often be inferred from choices 

people make in related markets. Specifically, because 

environmental quality is often a characteristic 

or component of a private good or service, it is 

sometimes possible to disentangle the value a 

consumer places on environmental quality from 

the overall value of a good. Methods that employ 

this general approach are referred to as revealed 

preference methods because values are estimated using 

data gathered from observed choices that reveal 

the preferences of individuals. Revealed preference 

methods include production or cost functions, travel 

cost models, hedonic pricing models, and averting 

behavior models. This section also discusses COI 

methods, which are sometimes used to value human 

health effects when estimates of WTP are unavailable. 

In situations where no markets for environmental 

or related goods exist to infer WTP, economists 

sometimes rely on survey techniques to gather 

choice data from hypothetical markets. The 

methods that use this type of data are referred 

to as stated preference methods because they rely 

on choice data that are stated in response to 

hypothetical situations, rather than on choice 

14	 There are examples in which environmental goods have been traded in 

markets. The Clean Air Act Amendments of 1990, for example, initiated 

a market in sulfur dioxide (SO2). However prices in such markets are 

determined by regulation-induced scarcity, and not by considerations 

behavior observed in actual markets. Stated 

preference methods include contingent valuation, 

conjoint analysis, and contingent ranking. 

Each of these revealed and stated preference 

methods is discussed in detail below. Included 

are an overview of each method, a description of 

its general application to environmental benefits 

analysis, and a discussion of issues involved in 

interpreting and understanding valuation studies. 

The discussion concludes with a separate overview 

of benefit-transfer methods. It is important to 

keep in mind that research on all of these methods 

is ongoing. The limitations and qualifications 

described here are meant to characterize the 

state of the science at the time these Guidelines 

were written. Analysts should consult additional 

resources as they become available. 

7.3.1 Revealed 

Preference Methods 

A variety of revealed preference methods for 

valuing environmental changes have been 

developed and are widely used by economists. The 

following common types of revealed preference 

methods are discussed in this section: 

• Production or cost functions; 

• Travel cost models; 

• Hedonic models; 

• Averting behavior models; and 

• Cost of Illness (COI).15 

7.3.1.1 Production and Cost Functions 

Discrete changes in environmental circumstances 

generally cause both consumer and producer 

effects, and it is common practice to separate 

the welfare effects brought about by changes 

in environmental circumstances into consumer 

surplus and producer surplus.16 Marginal changes 

can be evaluated by considering the production 

side of the market alone. 

15	 Although not a revealed preference method (as it does not measure 

WTP) COI methods are discussed in this section since estimates are 

based on observable data. 

of marginal utilities or marginal products. 	 16 See Appendix A for more detail. 
7-22 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

 

 

 

Economic foundations of production and 

cost functions 

Inputs to production contribute to welfare 

indirectly. The marginal contribution of a 

productive input is calculated by multiplying the 

marginal product of the input by the marginal 

utility obtained from the consumption good, 

in whose production the input is employed. 

The marginal utility of a consumption good is 

recorded in its price. While marginal products 

are rarely observed, the need to observe them is 

obviated when both inputs and outputs are sold 

in private markets because prices can be observed. 

Environmental goods and services are typically not 

traded in private markets, and therefore the values of 

environmental inputs must be estimated indirectly. 

Production possibilities can be represented in 

three equivalent ways: 

• As a production function relating output to 

inputs; 

• As a cost function relating production 

expenses to output and to input prices; and 

• As a profit function relating earnings to the 

prices of both output and inputs (see Varian 

1992, for an explication of the relationships 

among these functions). 

The value of a marginal change in some 

environmental condition can be represented as 

a marginal change in the value of production, 

as a marginal change in the cost of production, 

or as a marginal change in the profitability of 

production.17 It should be noted, however, that 

problems of data availability and reliability often 

arise. Such problems may motivate the choice 

among these conceptually equivalent approaches, 

or in favor of another approach. 

Note that derivation of values on the margin 

does not require any detailed understanding 

of consumer demand conditions. To evaluate 

marginal effects via the production function 

approach, the analyst needs to know the price 

of output and the marginal product of the 

environmental input. To derive the equivalent 

17	 For a good review of statistical procedures used for estimating 

production, cost, and profit functions see Berndt (1991). 

measure using a cost function approach, the 

analyst needs to know the derivative of the cost 

function with respect to the environmental input. 

In the profit function approach, the analyst needs 

to know the derivative of the profit function with 

respect to the environmental input.18 

In the statements note the emphasis that marginal 

effects are being estimated. Estimating the net 

benefits of larger, non-marginal, changes represent 

a greater challenge to the analyst. In general this 

requires consideration of changes in both producer 

and consumer surplus. The latter necessitates 

application of techniques such as travel cost, 

hedonics, and stated preference, which are 

discussed elsewhere in this chapter. 

Before moving on to those topics, note a fourth 

equivalent way to estimate environmental effects 

on production possibilities. Such effects are 

reflected in the profitability of enterprises engaged 

in production. That profitability also can be 

related to the return on fixed assets such as land. 

The value of a parcel of land is related to the stream 

of earnings that can be achieved by employing 

it in its “highest and best use.” Its rental value is 

equal to the profits that can be earned from it over 

the period of rental (the terms “rent” and “profit” 

are often used synonymously in economics). The 

purchase price of the land parcel is equal to the 

expected discounted present value of the stream 

of earnings that can be realized from its use over 

time. Therefore, the production, cost, and profit 

function approaches described above are also 

equivalent to inferences drawn from the effects 

of environmental conditions on asset values. This 

fourth approach is known as “hedonic pricing,” 

and will be discussed in detail in Section 7.3.1.3. 

18	 Derivation of marginal values often involves an application of the 

“envelope theorem” that states that effects from variables that are 

already optimized are negligible. In determining the effect of an 

improvement in a particular environmental input on welfare arising 

from the consumption of a particular product using the cost function Q 

approach, the analyst would determine how ∫ p(q)dq – C(Q, e) 0 varies with e, the environmental variable. The integral is consumer 

surplus, i.e., the area under the demand curve, and the second term 

is the cost of producing quantity Q given environmental conditions, 

e. Differentiating with respect to e yields [p(Q) – ∂C/∂Q] dQ/de – 

∂C/∂e = – ∂C/∂e, where the last equality results because competitive 

firms set price equal to marginal cost, i.e., p(Q) = ∂C/∂Q. This is the 

basis for the general proposition that marginal values can be estimated 

by looking solely at the production side of the market. 
Guidelines for Preparing Economic Analyses | December 2010 7-23 

Chapter 7 Analyzing Benefits

It is introduced now to show that production, 

cost, or profit function approaches are generally 

equivalent to hedonic approaches. 

“Production” as a term is broad in meaning and 

application, especially with regard to hedonic 

pricing. While businesses produce goods and 

services in their industrial facilities, one might also 

say that developers “produce” housing services 

when they build residences. Therefore, hedonic 

pricing approaches can measure the value of the 

environment in “production,” whether they are 

focusing on commercial or residential properties. 

Similarly, households may “produce” their health 

status by combining inputs such as air and water 

filtration systems and medical services along with 

whatever environmental circumstances they face. 

Or they “produce” recreational opportunities by 

combining “travel services” from private vehicles, 

their own time, recreational equipment purchases, 

and the attributes of their destination. Much 

of what is discussed elsewhere in this section is 

associated with this “production” analysis. This 

is not to say that estimation of production, cost, 

or profit functions is necessarily the best way to 

approach such problems, but rather, that all of 

these approaches are conceptually consistent. 

General application of production and 

cost functions 

Empirical applications of production and cost 

function approaches are diverse. Among other 

topics, the empirical literature has addressed the 

effects of air quality changes on agriculture and 

commercial timber industries. It also has assessed 

the effects of water quality changes on water 

supply treatment costs and on the production 

costs of industry processors, irrigation operations, 

and commercial fisheries.19 Production, cost, 

or profit functions have found interesting 

applications to the estimation of some ecological 

benefits.20 Probabilistic models of new product 

discovery from among diverse collections of 

natural organisms can also be regarded as a type of 

19	 Refer to Adams et al. (1986), Kopp and Krupnick (1987), Ellis and 

Fisher (1987), Taylor (1993), and U.S. EPA (1997a) for examples. 

20	 See, for example, Acharya and Barbier (2002) on groundwater 

“production.”21 Finally, work in ecology points to 

“productive” relationships among natural systems 

that may yield insights to economists as well.22 

Considerations in evaluating 

and understanding production 

and cost functions 

The analyst should consider the following factors 

when estimating the values of environmental 

inputs into production: 

Data requirements and implications. Estimating 

production, cost, or profit functions requires data 

on all inputs and/or their prices. Omitted variable 

bias is likely to arise absent such information, and 

may motivate the choice of one form over another. 

Econometricians have typically preferred to 

estimate cost or, better yet, profit functions. Data 

on prices are often more complete than are data 

on quantities and prices are typically uncorrelated 

to unobserved conditions of production, whereas 

input quantities are not. 

The model for estimation. Standard practice 

involves the estimation of “flexible functional 

forms,” i.e., functions that can be regarded as 

second-order approximations to any production 

technology. The translog and generalized Leontief 

specifications are examples. Estimation often will be 

more efficient if a system of equations is estimated 

(e.g., simultaneous estimation of a cost function 

and its associated factor demand equations), 

although data limitations may impose constraints. 

Market imperfections. Most analyses assume 

perfectly competitive behavior on the part of 

producers and input suppliers, and assume 

an absence of other distortions. When these 

assumptions do not hold, the interpretation of 

welfare results becomes more problematic. While 

there is an extensive literature on the regulation 

of externalities under imperfect competition, 

originating with Buchanan (1969), analysts should 

exercise caution and restraint in attempting to 

correct for departures from competitive behavior. 

21	 For example, see Weitzman (1992), Simpson et al. (1996), and Rausser 

and Small (2000). 

recharge, and Pattanayak and Kramer (2001) on water supply. 22 For example, see Tilman, Lehman, and Polasky 2005. 
7-24 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

The issues can become quite complex and, as is 

the case with environmental externalities, there is 

typically no direct evidence of the magnitude of 

departures from perfectly competitive behavior. 

Moreover, in many circumstances it might 

reasonably be argued that departures from perfect 

competition are not of much practical concern 

(Oates and Strassman 1984). Perhaps a more pressing 

concern in many instances will be the wedge between 

private and social welfare consequences that arise 

with taxation. An increase in the value of production 

occasioned by environmental improvement typically 

will be split between private producers and the 

general public through tax collection. The issues 

here also can become quite complex (see Parry et 

al. 1997), with interactions among taxes leading 

to sometimes surprising implications. While it is 

difficult to give general advice, analysts may wish to 

alert policy makers to the possibility that the benefits 

of environmental improvements in production may 

accrue to different constituencies. 

7.3.1.2 Travel Costs 

Recreational values constitute a potentially large 

class of environmental use benefits. However, 

measuring these values is complicated by the fact 

that the full benefits of access to recreation activities 

are rarely reflected in admission prices. Travel 

cost models address this problem by inferring the 

value of changes in environmental quality through 

observing the trade-offs recreators make between 

environmental quality and travel costs. A common 

situation recreators may face is choosing between 

visiting a nearby lake with low water quality and 

a more distant lake with high water quality. The 

outcome of the decision of whether to incur the 

additional travel cost to visit the lake with higher 

water quality reveals information about the 

recreator’s value for water quality. Travel cost models 

are often referred to as recreation demand models 

because they are most often used to value the 

availability or quality of recreational opportunities. 

Economic foundation of travel cost models 

Travel cost models of recreation demand focus on 

the choice of the number of trips to a given site or 

set of sites that a traveler makes for recreational 

purposes. Because there is no explicit market 

or price for recreation trips, travel cost models 

are frequently based on the assumption that the 

“price” of a recreational trip is equal to the cost of 

traveling to and from the site. These costs include 

both participants’ monetary cost and opportunity 

cost of time. Monetary costs include all travel 

expenses. For example, when modeling day trips 

taken primarily in private automobiles, travel 

expenses would include roundtrip travel distance 

in miles multiplied by an estimate of the average 

cost per mile of operating a vehicle, plus any tolls, 

parking, and admission fees. 

A participant’s opportunity cost of time for a 

recreational day trip is the value of the participant’s 

time spent traveling to and from the recreation 

site plus the time spent recreating.23 A variety 

of approaches have been used in the literature 

to define the opportunity cost of time. Most 

commonly, researchers have used a fixed fraction 

ranging from one third to one whole of a person’s 

hourly wage as an estimate of participants’ hourly 

opportunity cost of time. In most cases, the 

fraction used depends on how freely individuals are 

assumed to be able to substitute labor and leisure. 

If a person can freely choose their work hours then 

their opportunity cost of time will be equal to their 

full wage rate. However, if a person cannot freely 

substitute labor for leisure (for example if they have 

a set 40 hour work week), then the opportunity 

cost of the time they have available for recreation 

is unobservable and may be less or more than the 

full wage rate. Many other factors can influence 

recreators’ opportunity cost of time, including 

the utility received from traveling, non-wage 

income, and other non-work time constraints. A 

number of researchers have developed methods 

for estimating recreators’ endogenous opportunity 

cost of time although no one method has yet been 

fully embraced in the literature. For examples, 

see McConnell and Strand (1981); Smith, 

23	 If the amount of time spent recreating or doing something else (not 

including the time spent traveling to and from the sites) is assumed 

to be the same across all alternatives then it will not be identifiable 

in estimation and therefore it is not necessary to include it in the 

estimation of the participant’s opportunity cost of time. See Smith, 

Desvousges, and McGivney (1983); and McConnell (1992) for 

discussions of the implication of and the methods for allowing time 

onsite to vary across trip and alternatives. 
Guidelines for Preparing Economic Analyses | December 2010 7-25 

Chapter 7 Analyzing Benefits

Desvousges, and McGivney (1983); Bockstael 

et al. (1987b); McConnell (1992); and Feather 

and Shaw (1999). Hourly opportunity costs are 

multiplied by round trip travel time and time onsite to calculate a person’s full opportunity cost of 

time. Total travel costs are the sum of monetary 

travel costs and full opportunity costs. Following 

the law of demand, as the cost of a trip increases 

the quantity of trips demanded generally falls, all 

else equal. This means that participants are more 

likely to visit a closer site than a site farther away. 

While travel costs are the driving force of the 

model, they do not completely determine 

a participant’s choice of sites to visit. Site 

characteristics, such as parking, restrooms, or 

boat ramps; participant characteristics, such as 

age, income, experience, and work status; and 

environmental quality also can affect demand for 

sites. The identification and specification of the 

appropriate site and participant characteristics are 

generally determined by a combination of data 

availability, statistical tests, and the researcher’s 

best judgment. Ultimately, every recreation 

demand study strikes a compromise in defining 

sites and choice sets, balancing data needs and 

availability, costs, and time.24 

General application by type of 

travel cost model 

Travel cost models can logically be divided into 

two groups: single-site models and multiple-site 

models. Apart from the number of sites they 

address, the two types of models differ in several 

ways. The basic features of both model types are 

discussed below. 

Single-site models. Single-site travel cost models 

examine recreators’ choice of how many trips to 

make to a specific site over a fixed period of time 

(generally a season or year). It is expected that the 

number of trips taken will increase as the cost of 

visiting the site decreases and/or as the benefits 

realized from visiting increase. Site, participant, 

and environmental attributes, as well as the prices 

24 For a comprehensive treatment of the theoretical and econometric 

properties of recreation demand models see Phaneuf and Smith 

(2005). 

of substitute sites, act as demand curve shifters. 

For example, sites with good water quality are 

likely to be visited more often than sites with 

poor water quality, all else equal. Most current 

single-site travel cost models are estimated using 

count data models because the dependent variable 

(number of trips taken to a site) is a non-negative 

integer. See Haab and McConnell (2003) and 

Parsons (2003a) for detailed discussions and 

examples of recreation demand count data models. 

Single-site models are most commonly used to 

estimate the value of a change in access to a site, 

particularly site closures (e.g., the closure of a lake 

due to unhealthy water quality). The lost access 

value due to a site closure is the difference between 

the participant’s WTP for the option of visiting 

the site, which is given by the area between the 

site’s estimated demand curve and the implicit 

“price” paid to visit it. Estimating the value of a 

change in the cost of a site visit, for example the 

addition or increase of an admission fee, is another 

common application of the model. 

A weakness of the single-site model is its inability 

to deal with large numbers of substitute sites. If, as 

is often the case, a policy affects several recreation 

sites in a region, then traditional single-site 

models are required for each site. In cases with 

large numbers of sites, defining the appropriate 

substitute sites for each participant and estimating 

individual models for each site can impose 

overwhelming data collection and computational 

costs. Because of these difficulties, most researchers 

have opted to refrain from using single-site models 

when examining situations with large numbers of 

substitute sites.25 

Multiple-site models. Multiple-site models 

examine a recreator’s choice of which site to visit 

from a set of available site (known as the choice set) 

on a given choice occasion and in some cases can also 

examine how many trips to make to each specific site 

25 Researchers have developed methods to extend the single-site 

travel cost model to multiple sites. These variations usually involve 

estimating a system of demand equations. One example is the KuhnTucker (KT) model discussed in the following multiple-site model 

section. See Bockstael, McConnell, and Strand (1991) and Shonkwiler 

(1999) for more discussion and other examples of extensions of the 

single-site model. 
7-26 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

over a fixed period of time. Compared to the singlesite model, the strength of multiple-site models 

lies in their ability to account for the availability 

and characteristics of substitute sites. By examining 

how recreators trade the differing levels of each 

site characteristic and travel costs when choosing 

among sites it is possible to place a per trip (or 

choice occasion) dollar value on site attributes or 

on site availability for single sites or multiple sites 

simultaneously. 

The two most common multiple-site models are 

the random utility maximization (RUM) travel 

cost model and Kuhn-Tucker (KT) system of 

demand models. Both models may be described 

by a similar utility theoretic foundation, but they 

differ in important ways. In particular, the RUM 

model is a choice occasion model while the KT 

model is a model of seasonal demand. 

Random utility maximization models. In a 

RUM model each alternative in the recreator’s 

choice set is assumed to provide the recreator with 

a given level of utility, and on any given choice 

occasion the recreator is assumed to choose the 

alternative that provides the highest level of utility 

on that choice occasion.26 The attributes of each 

of the available alternatives, such as the amenities 

available, environmental quality, and the travel 

costs, are assumed to affect the utility of choosing 

each alternative. Because people generally do 

not choose to recreate at every opportunity, a 

non-participation option is often included as 

a potential alternative.27 From the researcher’s 

perspective, the observable components of utility 

enter the recreator’s assumed utility function. The 

26	 While the standard logit recreation demand model treats each choice 

occasion as an independent event, the model can also be generalized 

to account for repeated choices by an individual. 

27	 In a standard nested logit RUM model, recreators are commonly 

assumed to first decide whether or not to take a trip, and then 

conditional on taking a trip, to next choose which site to visit. By not 

including a non-participation option, the researcher in effect assumes 

that the recreator has already decided to take a trip, or in other 

words, that the utility of taking a trip is higher than the utility of doing 

something else for that choice occasion. Another way to think of it is 

that models lacking a participation decision only estimate the recreation 

values of the segment of the population that participates in recreation 

activities (i.e., recreators), while models that allow for non-participation 

incorporate the recreation values of the whole population (i.e., 

recreators and non-recreators combined). Because of this, recreation 

demand models without participation decisions tend to predict larger 

per person welfare changes than models allowing non-participation. 

unobservable portions of utility are captured by an 

error term whose assumed distribution gives rise 

to different model structures. Assuming that error 

terms have type 1 extreme values distribution leads 

to the closed form logit probability expression 

and allows for maximum likelihood estimation of 

utility function parameters. Using these estimated 

parameters it is then possible to estimate WTP for 

a given change in sites quality or availability. 

However, because the RUM model examines 

recreation decisions on a choice occasion level, 

it is less suited for predicting the number of 

trips over a time period and measuring seasonal 

welfare changes. A number of approaches have 

been used to link the RUM model’s estimates of 

values per choice occasion to estimates of seasonal 

participation rates. See Parsons, Jakus, and Tomasi 

(1999) for a detailed discussion of methods of 

incorporating seasonal participation estimates into 

the RUM framework. 

The nested logit and mixed logit models are 

extensions of the basic logit. The nested logit 

model groups similar alternatives into nests where 

alternatives within a nest are more similar with 

each other than they are with alternatives outside 

of the nest. In very general terms, recreators are first 

assumed to choose a nest and then, conditional on 

the choice of nest, they then choose an alternative 

within that nest. Nesting similar alternatives 

allows for more realistic substitution patterns 

among sites than is possible with a basic logit. The 

mixed logit is a random parameter logit model 

that allows for even more flexible substitution 

patterns by estimating the variation in preferences 

(or correlation in errors) across the sample. If 

preferences do not vary across the sample then the 

mixed logit collapses to a basic logit.28 

The Kuhn-Tucker (KT) model. The KT model is 

a seasonal demand model that estimates recreators’ 

choice of which sites to visit (like a multiple-site 

model) and how often to visit them over a season 

(like a single-site model). The model is built on the 

theory that people maximize their seasonal utility 

subject to their budget constraint by purchasing 

28	 See Train (1998) and Train (2003) for detailed descriptions of the 

nested and mixed logit models. 
Guidelines for Preparing Economic Analyses | December 2010 7-27 

Chapter 7 Analyzing Benefits

the quantities of recreation and other goods that 

give them the greatest overall utility. Similar 

to the RUM model, the researcher begins by 

specifying the recreator’s utility function. Taking 

the derivative of this utility function with respect 

to the number of trips taken, subject to a budget 

and non-negative trip constraint, yields the “KuhnTucker” conditions. The KT conditions show 

that trips will be purchased up to the point that 

the marginal rate of substitution between trips 

and other spending is equal to the ratio of their 

prices. In cases where the price of a good exceeds 

its marginal value none will be purchased. Given 

assumptions on the form of the utility function 

and the distribution of the error term, probability 

expressions can be derived and parameter estimates 

may then be recovered. While recent applications 

have shown that the KT model is capable of 

accommodating a large number of substitute sites 

(von Haefen, Phaneuf, and Parsons 2004) the 

model is computationally intensive compared 

to traditional models. For a basic application of 

the KT model see Phaneuf and Siderelis (2003). 

For more advanced treatments of the models see 

Phaneuf, Kling, and Herriges (2000), and von 

Haefen and Phaneuf (2005). 

Considerations in evaluating 

and understanding recreation 

demand studies 

Definition of a site and the choice set. The 

definition of what constitutes a unique site 

has been shown to have a significant effect on 

estimation results. Ideally, one could estimate 

a recreation demand model in which sites are 

defined as specific points such as exact fishing 

location, campsites, etc. The more exact the site 

definition, the more exact the measure of travel 

costs and site attributes, and therefore WTP, 

that can be calculated. However, in situations 

with a large number of potential alternatives, the 

large data requirements may be cost and time 

prohibitive, estimation may be problematic, and 

aggregation may be required. The method of 

aggregation has been shown to have a significant 

effect on estimated values. The direction of the 

effect will depend on the situation being evaluated 

and the method of aggregation chosen (Parsons 

and Needleman 1992; Feather 1994; Kaoru, 

Smith, and Liu 1995; and Parsons, Plantinga, and 

Boyle 2000). 

In addition to the definition of what constitutes a 

site, the number of sites included in a recreator’s 

choice set can have a significant effect on 

estimated values. When defining choice sets, 

the most common practice in the literature has 

been to include all possible alternatives available 

to the recreator. In many cases availability has 

been defined by location with a given distance 

or travel time.29 This strategy has been criticized 

on the grounds that people may not know about 

all possible sites, or even if they do know they 

exist they may not seriously consider them as 

alternatives. In response to this, a number of 

researchers have suggested methods that either 

restrict choice sets to include only those sites that 

the recreators seriously consider visiting (Peters 

et al. 1995, and Haab and Hicks 1997) or that 

weight seriously-considered alternatives more 

heavily than less-seriously-considered alternatives 

(Parsons, Massey, and Tomasi 2000). 

Multiple-site or multipurpose trips. Recreation 

demand models assume that the particular 

recreation activity being studied is the sole 

purpose for a given trip. If a trip has more than 

one purpose, it almost certainly violates the travel 

cost model’s central assumption that the “price” 

of a visit is equal to the travel cost. The common 

strategy for dealing with multipurpose trips is 

simply to exclude them from the data used in 

estimation.30 See Mendelsohn et al. (1992) and 

Parsons (2003b) for further discussion. 

Day trips versus multi-day trips. The recreation 

demand literature has focused almost exclusively 

on single-day trip recreation choices. One main 

reason researchers have focused mostly on day trips 

is that adding the option to stay longer than one 

day adds another choice variable in estimation, 

29 Parsons and Hauber (1998) explore the implication of this strategy by 

expanding the choice set geographically and find that beyond some 

threshold the effect of additional sites is negligible. 

30 Excluding any type or class of trip (like multiple-site or multipurpose) 

will produce an underestimate of the population’s total use value of a 

site. The amount by which benefits will be underestimated will depend 

on the number and type of trips excluded. 
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Chapter 7 Analyzing Benefits

thereby greatly increasing estimation difficulty. 

A second reason is that as trip length increases 

multipurpose trips become increasingly more 

likely, again casting doubt on the assumption that 

trip’s travel costs represent the “price” of one single 

activity (see previous paragraph). A few researchers 

have estimated models that allow for varying trip 

length. The most common strategy has been to 

estimate a nested logit model in which each choice 

nest represents a different trip length option. See 

Kaoru (1995) and Shaw and Ozog (1999) for 

examples. The few multi-day trip models in the 

literature find that the per-day value of multi-day 

trips is generally less than the value of a single-day 

trip, which suggests that estimating the value of 

multi-day trips by multiplying a value estimated 

for single-day trips value by the number of days of 

will overestimate the multi-day trip value. 

7.3.1.3 Hedonics 

Hedonic pricing models use statistical methods to 

measure the contribution of a good’s characteristics 

to its price. Cars differ in size, shape, power, 

passenger capacity, and other features. Houses 

differ in size, layout, and location. Even labor 

hours can be thought of as “goods” differing in 

attributes like risk levels, and supervisory nature, 

that should be reflected in wages. Hedonic pricing 

models use variations in property prices or wages 

and are commonly used to value the characteristics 

of properties or jobs. The models are based on 

the assumption that heterogeneous goods and 

services (e.g., houses or labor) consist of “bundles” 

of attributes (e.g., size, location, environmental 

quality, or risk) that are differentiated from 

each other by the quantity and quality of 

these attributes. Environmental conditions are 

among the many attributes that differ across 

neighborhoods and job locations. 

Economic foundations of 

hedonic models 

Hedonic pricing studies estimate economic 

benefits by weighing the advantages against the 

costs of different choices. A standard assumption 

underlying hedonic pricing models is that markets 

are in equilibrium, which means that no individual 

can improve her welfare by choosing a different 

home or job. For example, if an individual changed 

location she might move to a larger house, or one 

in the midst of a cleaner environment. However, to 

receive such amenities, the individual must pay for 

a more expensive house and incur transaction costs 

to move. The more the individual spends on her 

house, the less she has to spend on food, clothing, 

transportation, and all the other things she wants 

or needs. Thus, individuals are assumed to choose 

a better available option such that the benefits 

derived from it are exactly offset by the increased 

cost. So, if the difference in prices paid to live in 

a cleaner neighborhood is observable, then that 

price difference can be interpreted as the WTP for 

a better environment. 

One key requirement in conducting a hedonic 

pricing study is that the available options differ 

in measurable ways. To see why, suppose that all 

locations in a city’s housing market were polluted 

to the same degree, or all jobs in a particular 

labor market expose workers to the same risks. 

Homeowners and workers would, of course, be 

worse off due to their exposure to pollution and job 

risks, but their losses could not be measured unless 

a comparison could be made to purchasers of more 

expensive houses in less polluted neighborhoods, 

or wages in lower-paying but safer jobs. However, 

there is also a practical limit on the heterogeneity 

of the sample. Workers in different countries 

earn very different wages and face very different 

job risks, but this does not mean it is possible to 

value the difference in job risks by reference to 

international differences in wages. This is because: 

(1) there are many other factors that differ between 

widely separated markets; and (2) people simply 

are not mobile between very disparate sites. For 

these reasons it is important to exercise care in 

defining the market in which choices are made.31 

Another aspect of the heterogeneity in locations 

required to make hedonic pricing studies work is 

that people must be able to perceive the differences 

among their options. If homeowners are unable 

to recognize differences in health outcomes, 

visibility, and other consequences of differences 

31	 Michaels and Smith (1990) offer guidance for defining the extent of the 

market. 
Guidelines for Preparing Economic Analyses | December 2010 7-29 

Chapter 7 Analyzing Benefits

in air quality at different locations, or if workers 

are unaware of differences in risks at different jobs, 

then a hedonic pricing study would not be suitable 

for estimating the values for those attributes. 

Hedonic pricing studies can be used in different 

ways in environmental economics. Some are 

intended to provide direct evidence of the value of 

environmental improvements. Hedonic housing 

price studies are good examples. House prices are 

related to environmental conditions. The most 

frequent example is probably air quality (see 

Smith and Huang 1995 for a meta-analysis of 

many studies), although water quality (Leggett 

and Bockstael 2000), natural amenities (Thorsnes 

2002), land contamination (Messer et al. 2006) 

and other examples have been studied. Other 

hedonic studies evaluate endpoints other than 

environmental conditions. A good example would 

be hedonic wage studies that are used in the 

computation of the VSL. (See Viscusi 2004 for a 

recent example.) 

General application by type of hedonic 

pricing study 

Hedonic wage studies, also known as wage-risk 

or compensating wage studies, are based on the 

premise that individuals make trade-offs between 

wages and occupational risks of death or injury. 

Most analysts assume that workers understand 

on-the-job risks, but others argue that workers 

generally underestimate them (Viscusi 1993). 

Some studies attempt to account for workers’ 

perceived risks, but the results of these studies are 

not markedly different from those that do not 

(Gerking, de Haan, and Schulze 1988). Two of the 

most frequently used data sources for hedonic wage 

studies are the National Institute of Occupational 

Safety and Health (NIOSH) and Bureau of Labor 

Statistics (BLS) Survey on Working Conditions 

(SWC) data. The NIOSH data are state-level data 

of fatalities by occupation or industry, while the 

SWC data provide a finer resolution of occupation 

or industry fatalities, but do not vary by location. 

Black and Kneiser (2003), however, question 

the ability of hedonic wage studies using these 

data sources to measure job risks accurately due 

to severe measurement error. They find that the 

measurement error in the fatality rates reported 

from these sources is correlated with covariates 

commonly used in the wage equations, making the 

consistent estimation of the coefficient on risk in 

the standard hedonic wage equation a challenge. 

More recent hedonic wage studies have used the 

BLS Census of Fatal Occupational Injuries (CFOI) 

as the source for workplace risk information 

(Viscusi 2004; Viscusi and Aldy 2007b; Aldy and 

Viscusi 2008; Kniesner, Viscusi, and Ziliak 2006; 

Leeth and Ruser 2003; Viscusi 2003; and Scotton 

and Taylor 2009). These data are considered the 

most comprehensive data on workplace fatalities 

available (Viscusi 2004), compiling detailed 

information since 1992 from all states and the 

District of Columbia. Not only are the counts 

of fatal events reported by 3-digit occupation 

and 4-digit industry classifications, but the 

circumstances of the fatal events, as well as worker 

characteristics like age, gender and race, are also 

captured.32 To ensure the veracity and completeness 

of the reported data, multiple sources, including 

death certificates, workers’ compensation reports 

and federal and state administration reports are 

consulted and cross-referenced. 

Although questions still persist about the 

applicability of hedonic wage study results to 

environmental benefits assessment, hedonic wage 

studies have been used most frequently in benefits 

assessments to estimate the value of fatal risk 

reductions.33 When a benefits assessment requires 

a VSL estimate, hedonic wage estimates are a good 

source of information. Historically, EPA has used a 

VSL estimate primarily derived from hedonic wage 

studies. For more information on the Agency’s 

VSL estimate, see Section 7.1.1 and Appendix C.34 

The VSL determined by a hedonic wage study, for 

example, typically relates WTA higher wages in 

exchange for the increased likelihood of accidental 

death during a person’s working years. However, 

32	 More information on the CFOI data is available at: http://www.bls.gov/ 

iif/oshfat1.htm. 

33	 For example, EPA’s SAB has recognized the limitations of these 

estimates for use in estimating the benefits of reduced cancer incidence 

from environmental exposure. Despite these limitations, however, the 

SAB concluded that these estimates were the best available at the time 

(U.S. EPA 2000d). 

34	 As part of the revision of this document, EPA is revisiting the VSL 

estimate used in policy analysis; further guidance will be forthcoming. 
7-30 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

analysts should take care when applying results 

from one hedonic study to a new policy case, for 

example, if there are differences in the age groups 

facing mortality risks from longer-term conditions. 

Hedonic property value studies measure the 

different contributions of various characteristics 

to the value of property. These studies are typically 

conducted using residential housing data, but 

they have also been applied to commercial and 

industrial property, agricultural land, and vacant 

land.35 Bartik (1988) and Palmquist (1988, 1991) 

provide detailed discussions of benefits assessment 

using hedonic methods. Property value studies 

require large amounts of disaggregated data. To 

avoid aggregation problems, market transaction 

prices on individual parcels or housing units 

are preferred to aggregate data such as census 

tract information on average housing units. 

Problems can arise from errors in measuring 

prices (aggregated data) and errors in measuring 

product characteristics (particularly those related 

to the neighborhood and the environment). 

There are numerous statistical issues associated 

with applying hedonic methods to property value 

studies. These include the choice of functional 

form, the definition of the extent of the market, 

identification, endogeneity, and spatial correlation. 

Refer to Palmquist (1991) for a thorough 

treatment of the main econometric issues. 

Recently, advances have been made in modeling 

spatial correlation in hedonic models (see Text Box 

7.4 on spatial correlation for more information). 

Other hedonic studies. Applicability of the 

hedonic pricing method is not limited to the 

property and labor markets. For example, hedonic 

pricing methods can be combined with travel 

cost methods to examine the implicit price 

of recreation site characteristics (Brown and 

Mendelsohn 1984). Results from other studies 

can be used to infer the value of reductions in 

mortality, cancer, or injury risks. For example, 

Dreyfus and Viscusi (1995) use a hedonic analysis 

35	 See Xu, Mittlehammer, and Barkley (1993), and Palmquist and 

Danielson (1989) for hedonic values of agricultural land; Ihlanfeldt and 

Taylor (2004) for commercial property; Dale, Murdoch, Thayer, and 

Waddell (1999), and McCluskey and Rausser (2003) for residential 

property; and Clapp (1990), and Thorsnes (2002) for vacant land. 

to determine the trade-offs between automobile 

price and safety features to infer the VSL. 

Considerations in evaluating and 

understanding hedonic pricing studies 

Unobservable factors. A concern common to 

hedonic pricing studies is that it is impossible to 

observe all factors that go into a decision. People 

will choose among different jobs or houses not 

only because they can trade off differences in 

amenities and risks against differences in prices 

or wages, but also because they have different 

preferences for risks. Idiosyncratic personal tastes 

that cannot be observed may be responsible for 

a substantial portion of differences in observed 

choices. For example, mountain climbers have 

been known to pay tens of thousands of dollars to 

undertake expeditions that substantially increase 

their likelihood of early death. 

Source of risks. Similarly, analysts need to 

be careful in distinguishing the source of the 

risks used to estimate risk premia. Consider an 

individual who both works a dangerous job and 

lives in unhealthy circumstances. Such a person 

may be at greater risk of premature death than 

someone who works a different job or lives 

elsewhere. Analysts risk underestimating the wage 

premium demanded on the job if they fail to 

distinguish between causes of death — for example 

between on-the-job accidents and environmentally 

induced conditions acquired at home — when 

relating the wage premium paid on dangerous 

jobs to the statistics on premature mortality. 

Conversely, if the same job poses multiple risks — 

say the risk of both accidental death and serious, 

but nonfatal injury were higher on a particular 

job — the wage premium the job offers would 

overstate WTP for reductions in mortality risks if 

the injury risks were not properly controlled for in 

the analysis. See Eeckhoudt and Hammitt (2001), 

and Evans and Smith (2006) for more discussion 

of competing versus specific risks. 

Marginal changes. As with many results in 

economics, hedonic pricing models are best suited 

to the valuation of small, or marginal, changes in 

attributes. Under such circumstances, the slope 
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Chapter 7 Analyzing Benefits

Text Box 7.4 - Spatial Correlation 

Real property, such as buildings and land, and their associated characteristics are spatially distributed over the 

landscape. As such, the characteristics of some of the properties may be spatially correlated. If some of these 

characteristics are unobserved or for any other reason are not incorporated into the econometric model, there may 

be dependence across the error terms of the model. Spatial econometrics is a subfield of econometrics that has 

gained more attention as the capability for assessing such locational relationships within hedonic property data has 

improved. Such improvements are primarily due to the increasing use of geographic information systems (GIS) 

technology and geographically referenced data sets. 

The nature of the correlation in the data can manifest itself so that there is either spatial heterogeneity across 

observations, or more importantly, so that the characteristic values (e.g., price of homes) are correlated with those 

of nearby observations. Standard econometric techniques can readily deal with the former, but are not well equipped 

to handle the latter case. The econometric techniques allow for testing for the presence of spatial correlation, and 

specifically modeling and correcting the correlation between observations and correcting for the biasing effect it can 

have on parameter estimates. In practice, a relationship is defined between every variable at a given location and the 

same variable at other, usually nearby, locations in the data set. In most cases this relationship is based on common 

boundaries or is some specified function based on the distances between observations. This relationship between 

observations is then accounted for in the econometric model in order to correct the error terms and obtain unbiased 

model estimates. For more details on the fundamentals of spatial statistics see Anselin (1988). 

of the hedonic price function can be interpreted 

as WTP for a small change in the attribute. 

Public policy, however, is sometimes geared to 

larger, discrete changes in attributes. When this 

is the case, calculation of benefits can become 

significantly more complicated. Hedonic price 

functions typically reflect equilibria between 

consumer demands and producer supplies for 

fixed levels of the attributes being evaluated. The 

demand and supply functions are tangent to the 

hedonic price function only in the immediate 

neighborhood of an equilibrium point. Palmquist 

(1991) describes conditions under which exact 

welfare measures can be calculated for discrete 

changes. See Freeman (2003) and Ekeland, 

Heckman, and Nesheim (2004) for recent 

treatments. 

7.3.1.4 Averting Behaviors 

The averting behavior method infers values for 

environmental quality from observations of actions 

people take to avoid or mitigate the increased 

health risks or other undesirable consequences 

of reductions in ambient environmental quality 

conditions. Examples of such defensive actions 

can include the purchase and use of air filters, 

boiling water prior to drinking it, and the purchase 

of preventative medical care or treatment. By 

analyzing the expenditures associated with these 

averting behaviors economists can attempt to 

estimate the value individuals place on small 

changes in risk (Shogren and Crocker 1991, and 

Quiggin 1992). 

Economic foundations of averting 

behavior methods 

Averting behavior methods can be best understood 

from the perspective of a household production 

framework. Households can be thought of as 

producing health outcomes by combining an 

exogenous level of environmental quality with 

inputs such as purchases of goods that involve 

protection against health and safety risks (Freeman 

2003). To the extent that averting behaviors are 

available, the model assumes that a person will 

continue to take protective action as long as the 

expected benefit exceeds the cost of doing so. 

If there is a continuous relationship between 

defensive actions and reductions in health risks, 

then the individual will continue to avert until the 

marginal cost just equals her marginal WTP for 

these reductions. Thus, the value of a small change 
7-32 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

 

 

in health risks can be estimated from two primary 

pieces of information: 

•The cost of the averting behavior or good; and 

• Its effectiveness, as perceived by the individual, 

in offsetting the loss in environmental quality. 

Blomquist (2004) provides a detailed description 

of the basic household production model of 

averting behavior. More detail on the difficulties 

inherent in applying the averting behavior model 

can be found in Cropper and Freeman (1991). 

One approach to estimation is to use observable 

expenditures on averting and mitigating activities 

to generate values that may be interpreted as a 

lower bound on WTP. Harrington and Portney 

(1987) demonstrate this by showing that WTP 

for small changes in environmental quality can 

be expressed as the sum of the values of four 

components: changes in averting expenditures, 

changes in mitigating expenditures, lost time, and 

the loss of utility from pain and suffering. The 

first three terms of this expression are observable, 

in principle, and can be approximated by 

calculating changes in these costs after a change in 

environmental quality. The resulting estimate can 

be interpreted as a lower bound on WTP that may 

be used in benefits analysis (Shogren and Crocker 

1991, and Quiggin 1992). 

General application of averting 

behavior method 

Although the first applications of the method 

were directed toward values for benefits of 

reduced soiling of materials from environmental 

quality changes (Harford 1984), recent research 

has primarily focused on health risk changes. 

Conceptually, the averting behavior method 

can provide WTP estimates for a variety of 

other environmental benefits such as damages to 

ecological systems and materials. 

Some averting behavior studies focus on behaviors 

that prevent or mitigate the impact of particular 

symptoms (e.g., shortness of breath or headaches), 

while others have examined averting expenditures 

in response to specific episodes of contamination 

(e.g., groundwater contamination). The difference 

in these endpoints is important. Because many 

contaminants can produce similar symptoms, 

studies that estimate values for symptoms may be 

more amenable to benefit transfer than those that 

are episode-specific. The latter could potentially be 

more useful, however, for assessing the benefits of 

a regulation expected to reduce the probability of 

similar contamination episodes. 

Considerations in evaluating 

and understanding averting 

behavior studies 

Perceived versus actual risks. Analysts should 

remember that consumers base their actions on 

perceived benefits from defensive behaviors. Many 

averting behavior studies explicitly acknowledge 

that their estimates rest on consistency between 

the consumer’s perception of risk reduction and 

actual risk reduction. While there is some evidence 

that consumers are rational with regard to risk 

— for example, consumer expenditures to reduce 

risk vary positively with risk increases — there is 

also evidence that there are predictable differences 

between consumers’ perceptions and actual risks. 

Thus, averting behavior studies can produce biased 

WTP estimates for a given change in objective 

risk. Surveys may be necessary to determine the 

benefits individuals perceive they are receiving 

when engaging in defensive activities. These 

perceived benefits can then be used as the object 

of the valuation estimates. For example, if surveys 

reveal that perceived risks are lower than expert 

risk estimates, then WTP can be estimated with 

the lower, perceived risk (Blomquist 2004). 

Data requirements and implications. Data 

needed for averting behavior studies include 

information detailing the severity, frequency, and 

duration of symptoms; exposure to environmental 

contaminants; actions taken to avert or mitigate 

damages; the costs of those behaviors and 

activities; and other variables that affect health 

outcomes, like age, health status, or chronic 

conditions. 

Separability of joint benefits. Analysts should 

exercise caution in interpreting the results of 
Guidelines for Preparing Economic Analyses | December 2010 7-33 

Chapter 7 Analyzing Benefits

studies that focus on goods in which there may be 

significant joint benefits (or costs). Many defensive 

behaviors not only avert or mitigate environmental 

damages, but also provide other benefits. For 

example, air conditioners obviously provide 

cooling in addition to air filtering, and bottled 

water may not only reduce health risks, but may 

also taste better. Conversely, it also is possible that 

the averting behavior may have negative effects on 

utility. For example, wearing helmets when riding 

bicycles or motorcycles may be uncomfortable. 

Failure to account for these “joint” benefits and 

costs associated with averting behaviors will result 

in biased estimates of WTP. 

Modeling assumptions. Restrictive assumptions 

are sometimes needed to make averting behavior 

models tractable. Analysts drawing upon averting 

behavior studies will need to review and assess the 

implications of these assumptions for the valuation 

estimates. 

7.3.1.5 Cost of Illness 

A frequently encountered alternative to WTP 

estimates is the avoided cost of illness (COI). The 

COI method estimates the financial burden of 

an illness based on the combined value of direct 

and indirect costs associated with the illness. 

Direct costs represent the expenditures associated 

with diagnosis, treatment, rehabilitation, and 

accommodation. Indirect costs represent the 

value of illness-related lost income, productivity, 

and leisure time. COI is better suited as a WTP 

proxy when the missing components (e.g., pain 

and suffering) are relatively small as they usually 

are in cases of in minor, acute illnesses. However, 

there are usually better medical treatment and lost 

productivity estimates for more severe illnesses. 

The COI method is straightforward to implement 

and explain to policy makers, and has a number 

of other advantages. The method has been used 

for many years and is well developed. Collecting 

data to implement it often is less expensive than 

for other methods, improving the feasibility of 

developing original COI estimates in support of a 

specific policy. 

Economic foundations of COI studies 

Two conditions must be met for the COI method 

to approximate a market value of reduced health 

risk. First, the direct costs of morbidity must 

reflect the economic value of goods and services 

used to treat illness. Second, a person’s earnings 

must reflect the economic value of lost work 

time, productivity, and leisure time. Because of 

distortions in medical and labor markets, these 

assumptions do not routinely hold. Further, COI 

estimates are not necessarily equal to WTP. The 

method generally does not attempt to measure 

the loss in utility due to pain and suffering, and 

does not account for the costs of any averting 

behaviors that individuals have taken to avoid an 

illness. When estimates of WTP are not available, 

the potential bias inherent in relying on COI 

estimates should be acknowledged and discussed. 

A second shortcoming of the COI method is that 

by focusing on ex post costs, it does not capture 

the risk attitudes associated with ex ante measures 

of reduced health risk. 

Although COI estimates do not adequately 

capture several components of WTP, COI does 

not necessarily serve as a lower bound estimate 

of WTP. This is because, for some illnesses, the 

cost of behaviors that allow one to avoid an illness 

might be far lower than the cost of the illness itself. 

Depending on the design of the research question, 

WTP could reflect the lower avoidance costs while 

COI would reflect the higher costs of treating the 

illness once it has been contracted. In addition, 

COI estimates capture medical expenses passed on 

to third parties such as health insurance companies 

and hospitals, whereas WTP estimates generally 

do not. Finally, COI estimates capture the value of 

lost productivity (see Text Box 7.4 above), whereas 

these costs may be overlooked in WTP estimates 

— especially when derived from consumers or 

employees covered by sick leave. 

Available comparisons of COI and total WTP 

estimates suggest that the difference can be large 

(Rowe et al. 1995). This difference varies greatly 

across health effects and across individuals. 
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Chapter 7 Analyzing Benefits

General application by type of 

COI study 

Prevalence-based estimates. Prevalence-based 

COI estimates are derived from the costs faced by 

all individuals who have a sickness in a specified 

time period. For example, an estimate of the 

total number of individuals who currently have 

asthma, as diagnosed by a physician, reflects the 

current prevalence of physician-diagnosed asthma. 

Prevalence-based COI estimates for asthma 

include all direct and indirect costs associated with 

asthma within a given time period, such as a year. 

Prevalence-based COI estimates are a measure of 

the full financial burden of a disease, but generally 

will be lower bound estimates of the total WTP 

for avoiding the disease altogether. They are useful 

for evaluating the financial burden of policies 

aimed at improving the effectiveness of treatment 

or at reducing the morbidity and mortality 

associated with a disease. 

Incidence-based estimates. By contrast, 

incidence-based COI estimates reflect expected 

costs for new individuals who develop a disease 

in a given time period. For example, the number 

of individuals who receive a new diagnosis of 

asthma from a physician in a year reflects the 

annual incidence of physician-diagnosed asthma. 

Incidence-based COI estimates reflect the expected 

value of direct medical expenditures and lost 

income and productivity associated with a disease 

from the time of diagnosis until recovery or death. 

Because these expenses can occur over an extended 

time period, incidence-based estimates are usually 

discounted to the year the illness is diagnosed 

and expressed in present value terms. Incidencebased COI estimates are useful for evaluating 

the financial burden of policies that are aimed at 

reducing the incidence of new cases of disease. 

Most existing COI studies estimate indirect 

costs based on the typical hours lost from a work 

schedule or home production, evaluated at an 

average hourly wage. The direct medical costs 

of illness are generally derived in one of two 

ways. The empirical approach estimates the total 

medical costs of the disease by using a database 

of actual costs incurred for patients with the 

illness. The “expert elicitation” approach uses a 

panel of physicians to develop a generic treatment 

profile for the illness. Illness costs are estimated by 

multiplying the probability of a patient receiving 

a treatment by the cost of the treatment. For any 

particular application, the preferred approach will 

depend on availability of reliable actual cost data as 

well as characteristics of the illness under study. 

COI estimates for many illnesses are readily 

available from existing studies and span a wide 

range of health effects. EPA’s Cost of Illness 

Handbook (U.S. EPA 2007c) provides estimates 

for many cancers, developmental illnesses and 

disabilities, and other illnesses. 

Considerations in evaluating and 

understanding COI studies 

Technological change. Medical treatment 

technologies and methods are constantly 

changing, and this could push the true cost 

estimate for a given illness either higher or lower. 

When using previous COI studies, the analyst 

should be sure to research whether and how the 

generally accepted treatment has changed from the 

time of the study. 

Measuring the value of lost productivity. Simply 

valuing the actual lost work time due to an illness 

may not capture the full loss of an individual’s 

productivity in the case of a long-term chronic 

illness. Chronic illness may force an individual to 

work less than a full-time schedule, take a job at a 

lower pay rate than she would otherwise qualify 

for as a healthy person, or drop out of the labor 

force altogether. A second issue is the choice of 

wage rate. Even if the direct medical costs are 

estimated using individual actual cost data, it 

is highly unlikely that the individual data will 

include wages. Therefore, the wage rate chosen 

should reflect the demographic distribution of 

the illness under study. Furthermore, the value of 

lost time should include the productivity of those 

persons not involved in paid jobs. Homemakers’ 

household upkeep and childcare services, retired 

persons’ volunteering efforts, and students’ time in 

school all directly or indirectly contribute to the 

productivity of society. Finally, the value of lost 

leisure time to an individual and her family is not 
Guidelines for Preparing Economic Analyses | December 2010 7-35 

Chapter 7 Analyzing Benefits

Text Box 7.5 - Value of Time 

Estimating the cost of an illness by examining only medical costs clearly understates the true costs experienced by 

an individual with ill health. Not only does the individual incur medical expenditures, they also miss production and 

consumption opportunities. In particular they miss opportunities to work for wages, produce household goods and 

services (e.g., laundry, home-cooked meals), and enjoy leisure activities. These latter two categories are jointly referred 

to as non-work time. The value of these lost opportunities has typically been estimated by examining the value of time. 

EPA has developed an approach for valuing time losses based on the opportunity cost of time. For paid work, the 

approach is relatively straightforward. It rests on the assumption that total compensation (wages and employment 

benefits) is equal to the employers’ valuation of the worker’s output. Therefore, if a worker is absent due to illness, society 

loses the value of the foregone output, which can be estimated by examining the worker’s wages and employment benefit 

values. To value time spent on non-market work and leisure activities, the assumption is made that an individual will 

engage in such unpaid activities only if, at the margin, the value of these activities is greater than the wages that could be 

earned in paid employment. Hence after-tax wages provide a lower bound estimate of the value of non-work time. 

The loss of work time and leisure activities due to illness need not be complete. When an illness reduces but does 

not eliminate productivity at work or enjoyment of leisure time, estimates of the value of the diminishments in these 

opportunities are legitimate components of the cost of the illness. 

Valuing time lost due to illness experienced by children and other subpopulations that do not earn wages is more 

difficult. Examples of such subpopulations include the elderly, unemployed, or individuals who are out of the work 

force. Analysts could surmise the post-tax wage if such individuals were employed; however, the situation involves 

less certainty. For example, the time loss of children who suffer illness is sometimes estimated by considering the 

effect of the illness, if any, on future earnings. For this case, however, Circular A-4 (OMB 2003) currently suggests 

that, in the absence of better data, monetary values for children should be at least be as large as the values for adults 

(for the same risk probabilities and health outcomes). 

Accounting for time losses in COI estimates comes closer to a full accounting of the losses borne by individuals 

suffering illness than simply assessing medical costs. However, a third cost category remains neglected — the value 

of pain and suffering. When an individual is sick, she not only misses opportunities to produce or relax, she also 

would be willing to pay some amount to avoid the pain or discomfort of the illness. In most economic models, these 

costs are represented as declines in utility and as such are inherently difficult to estimate. To date, there are no good 

estimates, or methods for obtaining good estimates, of the value of avoiding pain. 

included in most COI studies. (See Text Box 7.5 

for a discussion of the value of time.) 

7.3.2 Stated Preference 

The distinguishing feature of stated preference 

methods compared to revealed preference methods 

is that stated preference methods rely on data 

drawn from people’s responses to hypothetical 

questions while revealed preference methods rely 

on observations of actual choices. Stated preference 

methods use surveys that ask respondents to 

consider one or a series of hypothetical scenarios 

that describe a potential change in a non-market 

good. The advantages of stated preference methods 

include their ability to estimate non-use values and 

to incorporate hypothetical scenarios that closely 

correspond to a policy case. The main disadvantage 

of stated preference methods is that they may be 

subject to systematic biases that are difficult to test 

for and correct. 

National Oceanic and Atmospheric 

Administration’s (NOAA) The Report of the 

NOAA Panel on Contingent Valuation is often 

cited as a primary source of information on 
7-36 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

stated preference techniques. Often referred to 

as the “NOAA Blue Ribbon Panel,” this panel, 

comprised of five distinguished economists 

including two Nobel Laureates, deliberated on 

the usefulness of stated preference studies for 

policy analysis (Arrow et al. 1993). While their 

findings generally mirror the recommendations 

offered below, since the release of their report a 

number of changes in the survey administration 

“landscape” have occurred including the advent of 

internet surveys, the decline in representativeness 

of telephone surveys, and the growth in popularity 

of stated choice experiments. 

7.3.2.1 Economic Foundation of Stated 

Preference Methods 

The responses elicited from stated preference 

surveys, if truthful, are either direct expressions 

of WTP or can be used to estimate WTP for 

the good in question. However, the “if truthful” 

caveat is paramount. While many environmental 

economists believe that respondents can provide 

truthful answers to hypothetical questions and 

therefore view stated preference methods as useful 

and reliable if conducted properly, a non-trivial 

fraction of economists are more skeptical of the 

results elicited from stated preference surveys. 

Due to this skepticism, it is important to employ 

validity and reliability tests of stated preference 

results when applying them to policy decisions. 

If the analyst decides to conduct a stated preference 

survey or use stated preference results in a benefit 

transfer exercise, then a number of survey design 

issues should be considered. Stated preference 

researchers have attempted to develop methods 

to make individuals’ choices in stated preference 

studies as consistent as possible with market 

transactions. Reasonable consistency with the 

framework of market transactions is a guiding 

criterion for ensuring the validity of stated 

preference value estimates. Three components of 

market transactions need to be constructed in stated 

preference surveys: the commodity, the payment, 

and the scenario (Fischoff and Furby 1988). 

Stated preference studies need to carefully 

define the commodity to be valued, including 

characteristics of the commodity such as the 

timing of provision, certainty of provision, and 

availability of substitutes and complements. The 

definition of the commodity generally involves 

identifying and characterizing attributes of the 

commodity that are relevant to respondents. 

Commodity definition also includes defining 

or explaining baseline or current conditions, 

property rights in the baseline, and the policy 

scenarios, as well as the source of the change in the 

environmental commodity.36 

Respondents also must be informed about the 

transaction context, including the method, timing, 

and duration of payment. The transaction must 

not be coerced and the individual should be aware 

of her budget constraint. The payment vehicle 

should be described as a credible and binding 

commitment should the respondent decide to 

purchase the good. The timing and duration 

of a payment involves individuals implicitly 

discounting payments and calculating expected 

utility for future events. The transaction context 

and the commodity definition should describe and 

account for these temporal issues. 

The hypothetical scenario(s) should be described 

so as to minimize potential strategic behavior such 

as “free riding” or “overpledging.” In the case of 

free riding, respondents will underbid their true 

WTP for a good if they feel they will actually be 

made to pay for it but believe the good will be 

provided nevertheless. In the case of overpledging, 

respondents pledge amounts greater than their 

true WTP with the expectation that they will not 

be made to pay for the good, but believing that 

their response could influence whether or not 

the good will be provided. Incentive-compatible 

choice scenarios and attribute-based response 

formats have been shown to mitigate strategic 

responses. Both are discussed below. 

It is recognized in both the experimental 

economics literature and the survey methodology 

36 Depending on the scenario, the description of the commodity may 

produce strong reactions in respondents and could introduce bias. 

In these cases, the detail with which the commodity of the change is 

specified needs to be balanced against the ultimate goals of the survey. 

Regardless, the commodity needs to be specified with enough detail to 

make the scenario credible. 
Guidelines for Preparing Economic Analyses | December 2010 7-37 

Chapter 7 Analyzing Benefits

literature that different survey formats can elicit 

different responses. Changing the wording 

or order of questions also can influence the 

responses. Therefore, the researcher should 

provide a justification for her choice of 

survey format and include a discussion of the 

ramifications of that choice. 

7.3.2.2 General Application by Type of 

Stated Preference Study 

Two main types of stated preference survey format 

are currently used: direct WTP questions and 

stated choice questions. Stated choice questions can 

be either dichotomous choice questions or multiattribute choice questions. Following a general 

discussion of survey format, each of the stated 

preference survey formats is described in detail below. 

Goals that should guide selection of the survey 

format include the minimization of survey costs, of 

non-responsiveness, of unexplained variance, and 

of complications associated with WTP estimation. 

For example, open-ended questions require smaller 

sample sizes and are simpler to analyze than 

other methods of asking the valuation question. 

These advantages could lead to significant cost 

reductions. However, these advantages may be 

mitigated by higher non-response rates and large 

unexplained variance in the responses. Moreover, 

there remains a great deal of uncertainty over the 

effect of the choice mechanism (i.e., open-ended, 

dichotomous choice, etc.) on the ability and 

willingness of respondents to provide accurate and 

well-considered responses. 

Because survey formats are still evolving and 

many different approaches have been used in the 

literature, no definitive recommendations are 

offered here regarding selection of the survey 

format. Rather, the following sections describe 

some of the most commonly used formats and 

discuss some of their known and suspected 

strengths and weaknesses. Researchers should 

select a format that suits their topic, and should 

strive to use focus groups, pretests, and statistical 

validity tests to address known and suspected 

weaknesses in the selected approach. 

Direct/open-ended WTP questions 

Direct/open-ended WTP questions ask 

respondents to indicate their maximum WTP 

for the specific quantity or quality changes of a 

good or service that has been described to them. 

An important advantage of open-ended stated 

preference questions is that the answers provide 

direct, individual-specific estimates of WTP. 

Although this is the measure that economists 

want to estimate, early stated preference studies 

found that some respondents had difficulty 

answering open-ended WTP questions and nonresponse rates to such questions were high. Such 

problems are more common when the respondent 

is not familiar with the good or with the idea of 

exchanging a direct dollar payment for the good. 

An example of a stated preference study using 

open-ended questions is Brown et al. (1996). 

Various modifications of the direct/open-ended 

WTP question format have been developed in an 

effort to help respondents arrive at their maximum 

WTP estimate. In iterative bidding respondents are 

asked if they would pay some initial amount, and 

then the amount is changed up or down depending 

on whether the respondent says “yes” or “no” to the 

first amount. This continues until a maximum WTP 

is determined for that respondent. Iterative bidding 

has been shown to suffer from “starting point bias,” 

wherein respondents’ maximum WTP estimates are 

systematically related to the dollar starting point in 

the iterative bidding process (Rowe and Chestnut 

1983, Boyle et al. 1988, and Whitehead 2002). A 

payment card is a list of dollar amounts from which 

respondents can choose, allowing respondents an 

opportunity to look over a range of dollar amounts 

while they consider their maximum WTP. Mitchell 

and Carson (1989) and Rowe et al. (1996) discuss 

concerns that the range and intervals of the dollar 

amounts used in payment card methods may 

influence respondents’ WTP answers. 

Stated choice questions 

While direct/open-ended WTP questions are 

efficient in principle, researchers have generally 

turned to other stated preference techniques in 

recent years. This is largely due to the difficulties 

respondents face in answering direct WTP 
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questions and the lack of easily implemented 

procedures to mitigate these difficulties. Researchers 

also have noted that direct WTP questions with 

various forms of follow-up bidding may not be 

“incentive compatible.” That is, the respondents’ 

best strategy in answering these questions is not 

necessarily to be truthful (Freeman 2003). 

In contrast to direct/open-ended WTP questions, 

stated choice questions ask respondents to choose 

a single preferred option or to rank options from 

two or more choices. When analyzing the data the 

dependent variable will be continuous for openended WTP formats and discrete for stated choice 

formats.37 In principle, stated choice questions can 

be distinguished along three dimensions: 

• The number of alternatives each respondent can 

choose from in each choice scenario — surveys 

may offer only two alternatives (e.g., yes/no, 

or “live in area A or area B); two alternatives 

with an additional option to choose “don’t 

know” or “don’t care;” or multiple alternatives 

(e.g., “choose option A, B, or C”). 

•The number of attributes varied across alternatives 

in each choice question (other than price) — 

alternatives may be distinguished by variation in 

only a single attribute (e.g., mortality risk) or by 

variation in multiple attributes (e.g., price, water 

quality, air quality, etc.). 

• The number of choice scenarios an individual is 

asked to evaluate through the survey. 

Any particular stated choice survey design could 

combine these dimensions in any given way. 

For example, a survey may offer two options to 

choose from in each choice scenario, vary several 

attributes across the two options, and present each 

respondent with multiple choice scenarios through 

the course of the survey. Using the taxonomy 

presented in these Guidelines, a complete (though 

cumbersome) description of this format would 

be a dichotomous choice/multi-attribute/ 

37	 Some researchers use the term “contingent valuation” to refer to 

direct WTP and dichotomous choice/referendum formats and “stated 

preference” to refer to other stated choice formats. In these Guidelines 

the term “stated preference” is used to refer to all valuation studies 

based on hypothetical choices (including open-ended WTP and stated 

choice formats), as distinguished from “revealed preference.” 

multi-scenario survey. The statistical strategy for 

estimating WTP is largely determined by the 

survey format adopted, as described below. 

The earliest stated choice questions were simple 

yes/no questions. These were often called 

referendum questions because they were often 

posed as, “Would you vote for . . ., if the cost 

to you were $X?” However, these questions are 

not always posed as a vote decision and are now 

commonly called dichotomous choice questions. 

In recent years, stated preference researchers 

have been adapting a choice question approach 

used in the marketing literature called conjoint 

analysis. These are more complex choice questions 

in which the respondent is asked repeatedly to 

pick her preferred option from a list of two or 

more options. Each option represents a package 

of product attributes. By incorporating a dollar 

price or cost in each option, stated preference 

researchers are able to extract WTP estimates 

for incremental changes in the attributes of the 

good, based on the preferences expressed by the 

respondents. Holmes and Adamowicz (2003) refer 

to this as attribute-based stated choice. 

Dichotomous choice WTP questions. 

Dichotomous choice questions present 

respondents with a specified environmental change 

costing a specific dollar amount and then ask 

whether or not they would be willing to pay that 

amount for the change. The primary advantage of 

dichotomous choice WTP questions is that they 

are easier to answer than direct WTP questions, 

because the respondent is not required to 

determine her exact WTP, only whether it is above 

or below the stated amount. Sample mean and 

median WTP values can be derived from analysis 

of the frequencies of the yes/no responses to each 

dollar amount. Bishop and Heberlein (1979), 

Hanemann (1984), and Cameron and James 

(1987) describe the necessary statistical procedures 

for analyzing dichotomous choice responses using 

logit or probit models. Dichotomous choice 

responses will reveal an interval containing WTP 

and in the case of a ‘yes’ response this interval 

will be unbounded from above. As a result, 

significantly larger sample sizes are needed for 
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Chapter 7 Analyzing Benefits

dichotomous choice questions to obtain the same 

degree of statistical efficiency in the sample means 

as direct/open-ended responses that reveal pointvalues for WTP (Cameron and James 1987). 

To increase the estimation efficiency of 

dichotomous choice questions, recent applications 

have commonly used what is called a doublebounded approach. In double-bounded questions 

the respondent is asked whether she would be 

willing to pay a second amount, higher if she 

said yes to the first amount, and lower if she said 

no to the first amount.38 Sometimes multiple 

follow-up questions are used to try to narrow the 

interval around WTP even further. These begin 

to resemble iterative bidding style questions if 

many follow-up questions are asked. Similar to 

starting point bias in iterative bidding questions, 

the analyses of double-bounded dichotomous 

choice question results suggest that the second 

responses may not be independent of the first 

responses (Cameron and Quiggin 1994, 1998; and 

Kanninen 1995). 

Multi-attribute choice questions. In multiattribute choice questions, respondents are 

presented with alternative choices that are 

characterized by different combinations of 

goods and services attributes and prices. Multiattribute choice questions ask respondents to 

choose the most preferred alternative (a partial 

ranking) from multiple alternative goods (i.e., 

a choice set), in which the alternatives within a 

choice set are differentiated by their attributes 

including price ( Johnson et al. 1995 and Roe 

et al. 1996). The analysis takes advantage of the 

differences in the attribute levels across the choice 

options to determine how respondents value 

marginal changes in each of the attributes. To 

measure WTP, a price (often a tax or a measure 

of travel costs), is included in multi-attribute 

choice questions as one of the attributes of 

each alternative. This price and the mechanism 

by which the price would be paid need to be 

38	 Alberini (1995) illustrated an analysis approach for deriving WTP 

estimates from such responses and demonstrates the increased 

efficiency of double-bounded questions. The same study showed that 

the most efficient range of dollar amounts in a dichotomous choice 

study design was one that covered the mid-range of the distribution 

and did not extend very far into the tails at either end. 

explained clearly and plausibly, as with any 

payment mechanism in a stated preference study. 

Boyle and Özdemir (2009) examine the impact 

of question design choices, such as the ordering 

of attributes and the number of alternatives in a 

single question, on the mean WTP estimate. 

There are many desirable aspects of multiattribute choice questions, including the nature 

of the choice being made. To choose the most 

preferred alternative from some set of alternatives 

is a common decision experience in posted-price 

markets, especially when one of the attributes of 

the alternatives is a price. One can argue that such 

a decision encourages respondents to concentrate 

on the trade-offs between attributes rather than 

taking a position for or against an initiative or 

policy. This type of repeated decision process may 

also diffuse the strong emotions often associated 

with environmental goods, thereby reducing 

the likelihood of yea-saying or of rejecting the 

premise of having to pay for an environmental 

improvement.39 Presenting repeated choices also 

gives the respondent some practice with the 

question format, which may improve the overall 

accuracy of her responses, and gives her repeated 

opportunities to express support for a program 

without always selecting the highest price option. 

Some applications of multi-attribute survey 

formats include Opaluch et al. (1993), Adamowicz 

et al. (1994), Viscusi et al. (1991), Adamowicz 

et al. (1997), Adamowicz et al. (1998a), Layton 

and Brown (2000), Johnson and Desvousges 

(1997), Boyle et al. (2001), and Morey et al. 

(2002). Studies that investigate the effects of 

multi-attribute choice question design parameters 

include Johnson et al. (2000) and Adamowicz et 

al. (1997). 

7.3.2.3 Considerations in Evaluating 

Stated Preference Results 

Survey mode. The mode used to administer 

a survey is an important component of survey 

research design because it is the mechanism by 

39	 Yea-saying refers to the behavior of respondents when they overstate 

their true WTP in order to show support for a situation described in 

survey questions. 
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Chapter 7 Analyzing Benefits

which information is conveyed to respondents, 

and likewise determines the way in which 

individuals can provide responses for analysis. 

Until recently there were three primary survey 

modes: telephone, in-person, and mail. Telephone 

surveys are primarily conducted with a trained 

interviewer using random digit dialing (RDD) 

to contact households. In-person surveys are 

conducted in a variety of ways, including doorto-door, intercepts at public locations, and via 

telephone recruiting to a central facility. Mail 

surveys are conducted by providing written survey 

materials for respondents to self-administer. As 

technology and society has changed, so has the 

preference for one mode over the other. With 

the influx of market research and telemarketing, 

the telephone has become a less convenient way 

to administer surveys. Many people refuse to 

answer the phone, or to answer questions over 

the phone. The same can be said of mail surveys. 

People are quick to ignore unsolicited mail. In 

recent years the Internet has emerged as a possible 

mode for conducting surveys. Internet access and 

email accounts are more prevalent and computer 

literacy is high in the United States and other 

developed countries. As with all of the survey 

modes mentioned, there are inherent biases. These 

biases are generally classified as social desirability 

bias, sample frame bias, avidity bias, and nonresponse bias. See Maguire (2009), Loomis and 

King (1994), Mannesto and Loomis (1991), 

Lindberg et al. (1997), and Ethier et al. (2000) for 

a discussion of different biases in survey mode. 

Framing issues. An important issue regarding 

survey formats is whether information provided in 

the questions influences the respondents’ answers 

in one way or another. For example, Cameron 

and Huppert (1991) and Cooper and Loomis 

(1992) find that mean WTP estimates based on 

dichotomous choice questions may be sensitive 

to the ranges and intervals of dollar amounts 

included in the WTP questions. Kanninen and 

Kriström (1993) show that the sensitivity of 

mean WTP to bid values can be caused by model 

misspecification, failure to include bid values that 

cover the middle of the distribution, or inclusion 

of bids from the extreme tails of the distribution. 

Selection of payment vehicle. The payment 

vehicle in a stated preference study refers to the 

method by which individuals or households 

would pay for the good described in a particular 

survey instrument. Examples include increases 

in electricity prices, changes in cost of living, a 

one-time tax, or a donation to a special fund. It is 

imperative that the payment vehicle is incentive 

compatible and does not introduce any strategic or 

other bias. Incentive compatibility means that the 

individual is motivated to respond truthfully and 

does not use their responses to try to influence a 

particular outcome (e.g., state a WTP value that is 

higher than their true WTP to try to make sure a 

particular outcome succeeds). 

Strategic behavior. Adamowicz et al. (1998a) also 

suggests that respondents may be less likely to behave 

strategically when responding to multi-attribute 

choice experiments. Repeatedly choosing from several 

options gives the respondent some practice with 

the question format that may improve the overall 

accuracy of her responses, and gives her repeated 

opportunities to express support for a program 

without always selecting the highest price option. 

Yea-saying. As mentioned above, yea-saying refers 

to the behavior of respondents when they overstate 

their true WTP in order to show support for 

situation described in survey questions. For example, 

Kanninen (1995) finds some evidence of yea-saying 

in dichotomous choice responses through testing 

in follow-up questions. The extent of this potential 

problem is not well established, but it may provide 

an explanation for the fact that mean WTP values 

based on dichotomous choice responses tend to be 

equal to or higher than values from direct WTP 

questions for the same good (Cummings et al. 1986, 

Boyle et al. 1993, Brown et al. 1996, Ready et al. 

1996, and Balistreri et al. 2001). It has not been 

determined whether yea-saying can be reduced 

by double-bounded dichotomous choice because 

in this case the respondent has more than one 

opportunity to say yes. 

Treatment of “don’t know” or neutral responses. 

Based on recommendations from the NOAA Blue 

Ribbon panel (Arrow et al. 1993), many surveys 

now include “don’t know” or “no preference” 
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Chapter 7 Analyzing Benefits

 

 

 

options for respondents to choose from. There 

have been questions about how such responses 

should enter the empirical analysis. Examining 

referendum-style dichotomous choice questions, 

Carson et al. (1998) found that when those who 

chose not to vote were coded as “no” responses, 

the mean WTP values were the same as when the 

“would not vote” option was not offered. Offering 

the “would not vote” option did not change the 

percentage of respondents saying “yes”. Thus, they 

recommend that if a “would not vote” option 

is included, it should be coded as a “no” vote, 

a practice that has become widespread. Stated 

preference studies should always be explicit about 

how they treat “don’t know,” “would not vote,” or 

other neutral responses. 

Reliability, in general terms, means consistency or 

repeatability. If a method is used numerous times 

to measure the same commodity, then the method 

is considered more reliable the lower the variability 

in the results. 

•Test-retest approach. Possibly the most 

widely applied approach for assessing 

reliability in stated preference studies has been 

the test-retest approach. Test-retest assesses 

the variability of a measure between different 

time periods. Loomis (1989), Teisl et al. 

(1995), McConnell et al. (1998), and Hoban 

and Whitehead (1999) all provide examples 

of the test-retest method for reliability. 

• Meta-analysis of stated preference survey 

results for the same good also may provide 

evidence of reliability. Meta-analysis 

evaluates multiple studies as though each 

was constructed to measure the same 

phenomenon. Meta-analysis attempts to sort 

out the effects of differences in the valuation 

approach used in different surveys, along with 

other factors influencing the elicited value. 

For example Boyle et al. (1994) use metaanalysis to evaluate eight studies conducted to 

measure values for groundwater protection. 

(Also see Section 7.4.) 

Validity tests seek to assess whether WTP 

estimates from stated preference methods behave 

as a theoretically correct WTP should. Three types 

of validity discussed below are: content validity, 

criterion validity, and convergent validity. 

• Content validity. Content validity refers to 

the extent to which the estimate captures the 

concept being evaluated. Content validity is 

largely a subjective evaluation of whether a 

study has been designed and executed in a way 

that incorporates the essential characteristics 

of the WTP concept. In a sense, it is akin to 

asking, “On the face of it, does the estimate 

capture the concept of WTP?” (This approach 

is sometimes referred to as “face validity.”) 

To evaluate a survey instrument, analysts 

look for features that researchers should have 

incorporated into the survey scenario. First, 

the environmental change being valued should 

be clearly defined. A careful exposition of 

the conditions in the baseline case and how 

these would be expected to change over time 

if no action were taken should be included. 

Next, the action or policy change should 

be described, including an illustration of 

how and when it would affect aspects of the 

environment that people might care about. 

Boyd and Banzahf (2007), and Boyd and 

Krupnick (2009) put a finer point on this 

concept and advocate developing the valuation 

scenario based on “ecological endpoints” 

rather than intermediate goods that are less 

clearly associated with outcomes of interest. 

For example, if respondents ultimately care 

about the survival of a certain species, it 

is more sensible to structure questions to 

ask about WTP for the species’ survival 

than to ask about degradation of habitat, 

as respondents are unlikely to know the 

relationship between habitat attributes and 

species survival. Respondent attitudes about 

the provider and the implied property rights 

of the survey scenario can be used to evaluate 

the appropriateness of features related to 

the payment mechanism (Fischhoff and 

Furby 1988). Survey questions that probe for 

respondent comprehension and acceptance of 

the commodity scenario can offer important 

indications about the validity of the results 

(Bishop et al. 1997). 
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• Criterion validity. Criterion validity assesses 

whether stated preference results relate to 

other measures that are considered to be 

closer to the concept being assessed (WTP). 

Ideally, one would compare results from a 

stated preference study (the measure) with 

those from actual market data (the criterion). 

This is because market data can be used to 

estimate WTP more reliably than a stated 

preference survey. Another approach would 

be to estimate a sample of individuals’ WTP 

for a commodity using a stated preference 

survey and then later give the same sample of 

individuals or a different random sample of 

individuals drawn from the same population 

a real opportunity to buy the good. (See 

Mitchell and Carson 1989, Carson et al. 

1987a, Kealy et al. 1990, Brown et al. 1996, 

and Champ et al. 1997 for examples.) 

When unable to conduct such comparisons, 

sensitivity to scope and income has been 

used to assess criterion validity. “Scope tests” 

are concerned with how WTP responds to 

changes in the amount of the referenced good 

provided in the valuation scenario (Smith 

and Osborne 1996, Rollins and Lyke 1998, 

and Heberlien et al. 2005). If the referenced 

good is indeed a “normal good” utility theory 

implies that WTP should increase with the 

provision of the good. For the same reason 

one would expect WTP to exhibit positive 

income elasticity (McFadden 1994, and 

Schlapfer 2006). Neither test is necessary 

or sufficient to establish criterion validity 

(Heberlein et al. 2005) but can serve as useful 

proxies when an alternate measure of WTP 

for the same good is unavailable. Diamond 

(1996) suggests that stronger scope tests can 

be conducted by comparing departures from 

strict “adding up” of WTP for partial changes 

and relating them to the income elasticity of 

WTP. Other researchers, however, argue that 

the Diamond test may not be practicable or 

even necessarily correct (Carson et al. 2001). 

• Convergent validity. Convergent validity 

examines the relationship between different 

measures of a concept.40 This differs from 

criterion validity in that one of the measures 

is not taken as a criterion upon which to judge 

the other measure. The measure of interest 

and the other measure are judged together to 

assess consistency with one another. If they 

differ in a systematic way (e.g., one is usually 

larger than another for the same good), it is 

not clear which one is more correct. However, 

if stated preference results are found to be 

larger than revealed preference results for 

the same good, it is often presumed that 

the difference is the result of hypothetical 

bias because revealed preference results are 

based on actual behavior. There can be many 

other sources of bias and error in both stated 

preference and revealed preference results that 

cause them to differ from one another and 

from “true” WTP. 

Empirical convergent validity tests use 

comparisons of stated preference results with 

revealed preference or experimental results 

that are thought to be free of hypothetical 

bias.41 In some circumstances, convergent 

validity tests may be incorporated as part of 

the study design. Such a test might compare 

results of an actual market exercise with the 

results of a hypothetical market exercise in 

which the exercises are otherwise identical. 

In this case there might be evidence of an 

upward or downward bias in the hypothetical 

results as compared to the simulated market 

results. See Section 7.3.3 for a discussion on 

combining revealed preference and stated 

preference data. 

Hypothetical bias occurs when the responses 

to hypothetical stated preference questions are 

40	 Mitchell and Carson (1989) define convergent validity and theoretical 

validity as two types of construct validity. Construct validity examines 

the degree to which the measure is related to other measures as 

predicted by theory. 

41	 Some analysts include the comparisons of stated preference results 

to actual markets under convergent validity rather than criterion 

validity, as discussed in the previous section, because there is no 

actual observable measure of the theoretical construct WTP. Here, 

a distinction is made between simulated markets, as in a laboratory 

experiment in which values may be “induced” by giving subject cash 

at the end based on their choices, and actual markets in which subjects 

must pay with their own money. 
Guidelines for Preparing Economic Analyses | December 2010 7-43 

Chapter 7 Analyzing Benefits

 

 

systematically different than what individuals 

would pay if the transactions were to actually 

occur. Widely cited as one of the most common 

problems with the stated preference method (List 

and Gallet 2001, and Murphy and Allen 2005), 

and researchers have made advances in techniques 

to minimize such bias. These techniques include 

the use of “cheap talk” methods to directly tell 

respondents about the potential for hypothetical 

bias (Cummings and Taylor 1999, and List 

2001); calibrating hypothetical values (List and 

Shogren 1998, and Blomquist et al. 2009); and 

allowing respondents to express uncertainty in 

their responses and restricting the set of positive 

responses to those about which the respondent 

was most certain (Vossler et al. 2003). Several 

studies have shown that attribute-based choice 

experiments reduce hypothetical bias in the bid 

amounts and the marginal value of attributes 

relative to other elicitation methods (Carlsson and 

Martinsson 2001, Murphy and Allen 2005, and 

List et al. 2006). 

Tests for hypothetical bias often involve a 

comparison of actual payments and responses 

to hypothetical scenarios that use the same 

solicitation approach. The actual payments 

typically occur in one of three scenarios. Market 

transactions are the most common (Cummings et 

al. 1995, and List and Shogren 1998) but generally 

involve payments for private goods while most 

stated preference applications are concerned with 

public or quasi-public goods. Simulated markets 

can be used to solicit actual donations for public 

good provision (Champ et al. 1997). However, 

donation solicitations are subject to free riding, 

so while it may be possible to test for hypothetical 

bias using this approach, both the actual and 

hypothetical payment scenarios lack incentive 

compatibility and may not represent total 

WTP. In rare instances comparisons have been 

made between actual referenda for public good 

provision and hypothetical responses to the same 

scenario but the conditions for a valid comparison 

of this sort are exceedingly difficult to satisfy 

( Johnston 2006). 

Non-response bias is introduced when nonrespondents would have answered questions 

systematically differently than those who did 

answer. Non-response bias can take two forms: 

item non-response and survey non-response. 

• Item non-response bias occurs when 

respondents who agreed to take the survey 

do not answer all of the choice questions 

in the survey. Information available about 

respondents from other questions they 

answered can support an assessment of 

potential item non-response bias for the WTP 

questions that were unanswered. The key issue 

is whether there were systematic differences 

in potential WTP-related characteristics of 

those who answered the WTP questions 

and those who did not. Characteristics of 

interest include income, gender, age, expressed 

attitudes and opinions about the good or 

service, and information reported on current 

use or familiarity with the good or service. 

Statistically significant differences may 

indicate the potential for item non-response 

bias, while finding no such differences 

suggests that the chance of significant nonresponse bias is lower. However, the results of 

this comparison are only suggestive because 

respondents and non-respondents may only 

differ in their preference for the good in 

question (McClelland et al. 1991). 

• Survey non-response bias is created when 

those who refuse to take the survey have 

preferences that are systematically different 

from the preferences of those who do respond. 

Although it is generally thought that surveys 

with high response rates are less likely to 

suffer from survey non-response bias, it is not 

a guarantee.42 For survey non-respondents, 

there may be no available data to determine 

how they might systematically differ from 

those who responded to the survey. The 

42	 Note that OMB’s Guidance on Agency Survey and Statistical 

Collections (OMB 2006) has fairly strict requirements for response 

rates and their calculation for Agency-sponsored surveys, 

recommending that “ICRs for surveys with expected response rates 

of 80 percent or higher need complete descriptions of the basis of the 

estimated response rate...ICRs for surveys with expected response 

rates lower than 80 percent need complete descriptions of how the 

expected response rate was determined, a detailed description of steps 

that will be taken to maximize the response rate...and a description of 

plans to evaluate non-response bias” (pp. 60-70). 
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Chapter 7 Analyzing Benefits

 

 

 

 

 

 

most common approach is to examine the 

relevant measurable characteristics of the 

respondent group, such as income, resource 

use, gender, age, etc., and to compare them to 

the characteristics of the study population. 

Similarity in mean characteristics across the 

two groups suggests that the respondents 

are representative of the study population 

and that non-response bias is expected to be 

minimal. 

A second way to evaluate potential survey 

non-response bias is to conduct a short 

follow-up survey with non-respondents. This 

can sometimes be accomplished through 

interviews conducted during the recruiting 

phase. Such follow-ups typically ask a few 

questions about attitudes and opinions on 

the topic of the study as well as collecting 

basic socioeconomic information. Questions 

need to match those in the full survey closely 

enough to compare non-respondents to 

respondents. The follow-up must be very brief 

or response rates will be low (OMB 2006). 

7.3.3 Combining Revealed and 

Stated Preference Data 

Instead of looking at revealed preference and 

stated preference data as two separate methods 

for estimating environmental benefits, an 

increasing number of researchers are using them 

in combination. The practice has been in use 

much longer in the marketing and transportation 

literature and many of the lessons learned by 

those researchers are now being employed in 

environmental economics. In theory, the strengths 

of each data type should help overcome some 

of the weaknesses of the other. As described by 

Whitehead et al. (2008) in a recent assessment 

of the state of the science, the advantages of 

combining revealed preference and stated 

preference data include: 

• Helping to ground the hypothetical stated 

preference data with real world behavior 

potentially decreasing any hypothetical bias; 

• Providing the ability to test the validity of 

both data sources;43 

• Increasing the range of historical stated 

preference data to include conditions not 

observed in the past and thereby reducing the 

need to make predictions outside of the sample; 

• Increasing the sample size; 

• Extending the size of the market or 

population to include larger segments than 

captured by either method alone; and 

• Exploiting the flexibility of stated preference 

experimental design to overcome revealed 

preference data’s potential multicollinearity 

and endogeneity problems (von Haefen and 

Phaneuf 2008). 

The different strategies for combining revealed 

preference and stated preference data can be 

roughly grouped into three main methods. The 

first two methods rely on joint estimation. If the 

revealed preference and stated preference data have 

similar dependent and independent variables and 

the same assumed error structures, then they can 

simply be pooled together and treated as additional 

observations (Adamowicz et al. 1994; Boxall, Englin, 

and Adamowicz 2003; and Morgan, Massey, and 

Huth 2009). If the revealed preference and stated 

preference data sources cannot be pooled, it is 

sometimes possible to use them in a jointly estimated 

mixed model that relies on a utility theoretic 

specification of the underlying WTP function 

(Huang, Haab, and Whitehead 1997; Kling 1997; 

and Eom and Larson 2006). If the data cannot be 

combined in estimation, it can still be useful to 

estimate results separately and then use them to test 

for convergent validity between the two data sources 

(Carson et al. 1996, and Schlapfer et al. 2004). 

7.4 Benefit Transfer 

Benefit transfer refers to the use of estimated nonmarket values of environmental quality changes 

from one study in the evaluation of a different 

policy that is of interest to the analyst (Freeman 

2003, p. 453). The case under consideration for a 

43 Herriges, Kling, and Phaneuf (2004) point out that revealed preference 

may not always be valid for estimating WTP for quality changes when 

weak complementarity cannot be assured. 
Guidelines for Preparing Economic Analyses | December 2010 7-45 

Chapter 7 Analyzing Benefits

new policy is referred to as the “policy case.” Cases 

from which estimates are obtained are referred to 

as “study cases.” A benefit transfer study identifies 

stated preference or revealed preference study cases 

that sufficiently relate to the policy context and 

“transfers” their results to the policy case. 

Benefit transfer is necessary when it is infeasible to 

conduct an original study focused directly on the 

policy case. Original studies are time consuming 

and expensive; benefit transfer can reduce both the 

time and financial resources required to develop 

estimates of a proposed policy’s benefits. While 

benefit transfer should only be used as a last resort 

and a clear justification for using this approach over 

conducting original valuation studies should be 

provided (OMB 2003), the reality is that benefit 

transfer is one of the most common approaches 

for completing a BCA at EPA. However, the 

advantages of benefit transfer in terms of time 

and cost savings must be weighed against the 

disadvantages in terms of potential reduced 

reliability of the final benefit estimates. The 

transfer of benefits estimates from any single study 

case is unlikely to be as accurate as a primary study 

tailored specifically to the policy case, although it is 

difficult to characterize the uncertainty associated 

with transferred benefits estimates. 

The number and quality of relevant studies 

available for application to the policy case can 

limit the use of benefit-transfer methods.44 Even 

when a study case is qualitatively similar to the 

policy case, the environmental change associated 

with the policy case may be of a different scope 

or nature than the changes considered in the 

study cases. In addition, methodological advances 

and changes in demographic, economic, and 

environmental conditions over time may make 

otherwise suitable studies obsolete.45 

44	 One possible reason that a relatively limited number of value estimates 

exist in peer-reviewed literature is that researchers and editors of 

scholarly journals may be more interested in new theoretical or 

methodological advances than in studies that apply established 

valuation methods to confirm earlier findings. 

45	 A 2006 special issue of Ecological Economics (volume 60) focused 

exclusively on benefit transfer for environmental policy, covering 

diverse topics such as publication bias, theoretical motivation and 

emerging issues. Florax et al. (2002), and Navrud and Ready (2007) 

are two general references for benefit transfer studies. 

Steps for conducting benefit transfer 

While there is no universally accepted single 

approach for conducting benefit transfer there are 

some generalized steps involved in the process. 

These steps are described below. 

1. Describe the policy case. The first step in 

a benefit-transfer study is to clearly describe 

the policy case so that its characteristics and 

consequences are well understood. Are human 

health risks reduced by the policy intervention? 

Are ecological benefits expected (e.g., increases 

in populations of species of concern)? It is also 

important to identify to the extent possible 

the beneficiaries of the proposed policy and to 

describe their demographic and socioeconomic 

characteristics (e.g., users of a particular set of 

recreation sites, children living in urban areas, or 

older adults across the United States). Information 

on the affected population is generally required to 

translate per person (or per household) values to an 

aggregate benefits estimate. 

2. Select study cases. A benefit-transfer study 

is only as good as the study cases from which it 

is derived, and it is therefore crucial that studies 

be carefully selected. First, the analyst should 

identify potentially relevant studies by conducting 

a comprehensive literature search. Because peerreviewed academic journals may be more likely to 

publish work using novel approaches compared 

to established techniques, some studies of interest 

may be found in government reports, working 

papers, dissertations, unpublished research, and 

other “gray literature.”46 Including studies from the 

gray literature may also help mitigate “publication 

bias” that results from researchers being more 

likely to present and/or editors being more likely 

to publish studies that demonstrate statistically 

significant results, or results that are of an expected 

sign or magnitude.47 Online searchable databases 

46	 Peer review of benefit-transfer studies using gray literature is highly 

advisable. 

47	 There is some evidence of publication bias towards studies showing 

statistically significant results. For example, in a meta-analysis of studies 

in labor economics, Card and Krueger (1995) argue that just-significant 

results are reported more frequently than would be predicted by chance. 

Similar practices may prevail in other areas of economic research. 

Combining results from a group of studies that suffer from publication 

bias may lead to inaccurate conclusions. See Stanley (2005, 2008) for a 

discussion of methods to correct for and identify publication bias. 
7-46 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

summarizing valuation research may be especially 

helpful at this stage.48 

Next, the analyst should develop an explicit set of 

selection criteria to evaluate each of the potentially 

relevant studies for quality and applicability to 

the policy case. The quality of the value estimates 

in the study cases will in large part determine the 

quality of the benefit transfer. As a first step, the 

analyst should review studies according to the 

criteria listed for each methodology in the previous 

sections in this chapter. Results from study cases 

must be valid as well as relevant. Concerns about 

the quality of the studies, as opposed to their 

relevance, will generally hinge on the methods 

used. Valuation approaches commonly used in the 

past may now be regarded as unacceptable for use 

in benefits analysis. Studies based on inappropriate 

methods or reporting obsolete results should be 

removed from consideration. 

It is unlikely that any single study will match 

perfectly with the policy case; however each 

potential study case should inform at least 

some aspect of the policy decision. Study cases 

potentially suitable for use in benefit transfer 

should be similar to the policy case in their: (1) 

definition of the environmental commodity 

being valued (include scale and presence 

of substitutes); (2) baseline and extent of 

environmental changes; and (3) characteristics 

of affected populations. Analysts should avoid 

using benefit transfer in cases where the policy 

or study case is focused on a “good” with unique 

attributes or where the magnitude of the change 

or improvement across the two cases differs 

substantially (OMB 2003).49 

48	 For example, the EVRI is maintained by Environment Canada and 

managed by a working group that includes the U.S. EPA and members 

of the European Union. EVRI contains over 1,100 studies that can 

be referenced according to medium, resource, stressor, method, 

and country. EVRI also provides a bibliography on benefit transfer. 

See www.evri.ca for more information. Envalue, developed by the 

New South Wales EPA in 1995, is similar: Studies can be identified 

according to medium, stressor, method, country, and author. 

49	 In some cases the transfer method itself may inform the choice of study 

cases to include. For example, meta-analysis approaches (discussed 

below) can facilitate some forms of statistical validity testing (Hunter 

and Schmidt 1990, and Stanley 2001), so some otherwise suitable 

studies may be rejected as “outliers.” 

The analyst should determine whether adjustments 

should and can be made for important differences 

between each study and policy case. For example, 

some case studies will report Marshallian demand 

while others may report Hicksian demand.50 The 

ability of the analyst to make these adjustments 

will depend, in part, on both the number of value 

estimates for suitably similar study sites and the 

method used to combine these estimates. These 

methods are now discussed in turn. 

3. Transfer values. There are several approaches 

for transferring values from study cases to the 

policy case. These include unit value transfers, 

value function transfers, and non-structural or 

structural meta-analysis. Each of these approaches 

is typically used to develop per person or per 

household value estimates that are then aggregated 

over the affected population to compute a total 

benefits estimate. As a general rule, the more 

related case study estimates involved in a benefit 

transfer, the more reliable the estimate. 

Unit value transfers are the simplest of the benefittransfer approaches. They take a point estimate 

of WTP for a unit change in the environmental 

resource from a study case or cases and apply it 

directly to the policy case. The point estimate 

is commonly a single estimated value from a 

single case study, but it can also be the (otherwise 

unadjusted) average of a small number of estimates 

from a few case studies. For example, a study may 

have found a WTP of $20 per household for a 

one-unit increase on some water quality scale. A 

unit value transfer would estimate total benefits for 

the policy case by multiplying $20 by the number 

of units by which the policy is expected to increase 

water quality and by the number of households 

who will benefit from the change. This approach 

can be useful for developing preliminary, orderof-magnitude estimates of benefits, but it should 

be possible to base final benefit estimates on more 

50	 See Desvousges et al. (1992), Brouwer (2000), Florax et al. (2002), 

Bergstrom and Taylor (2006), and Navrud and Ready (2007) for 

additional information on criteria used to determine quality and 

applicability. For more information on applicability as related to 

specific benefit categories, see Desvousges et al. (1998), the draft 

Handbook for Non-Cancer Valuation (U.S. EPA 2000c), and the 

Children’s Health Valuation Handbook (U.S. EPA 2003b). It may also 

be useful for the analyst to discuss her interpretation and intended use 

of the study case with the original authors. 
Guidelines for Preparing Economic Analyses | December 2010 7-47 

Chapter 7 Analyzing Benefits

Text Box 7.6 - The Benefits and Costs of the Clean Air Act 1990 to 2010: 

Reduced Acidification in Freshwater Adirondack Lakes 

One component of the total benefits of the Clean Air Act (CAA) was determined to be improved recreational fishing due 

to reduced acidification in freshwater Adirondack lakes. To value this benefit, EPA relied on the results of Montgomery 

and Needleman’s (1997) New York State Adirondack region recreational fishing study. EPA first developed estimates of 

the percentage Adirondack of lakes affected by acidification pre and post CAA. Then, using a probit model, the likelihood 

that each individual lake would become acidified was estimated (the model relates acidity to lake characteristics such 

as elevation, surface area, watershed, and others) and the lakes were ranked from highest to lowest probability of being 

acidified. The acidification status of individual lakes in the choice set was then assigned, starting with the highest probability 

lake and proceeding down until the appropriate number of lakes affected under each scenario (i.e., the estimated percentage 

of lakes affected) was achieved. Using these lake designations and the Montgomery and Needleman model’s estimated 

coefficients, welfare was calculated for the pre and post CAA levels of lake acidification. The difference between the two 

welfare estimates was assumed to be the value of improved Adirondack freshwater recreational fishing under the CAA. 

information than a single point estimate from a 

single study. Point estimates reported in study cases 

are typically functions of several variables, and 

simply transferring a summary estimate without 

controlling for differences among these variables 

can yield inaccurate results. It is important to 

recognize that unit value transfer assumes that 

the original good, as well as the characteristics 

and tastes of the population of beneficiaries, are 

the same as the policy good. Unit values transfers 

should only be used if the case and policy studies 

are evaluating the same environmental good, the 

same change in environmental levels, and same 

affected populations. 

Function transfers also rely on a single study, 

but they use information on other factors that 

influence WTP to adjust the unit value for 

quantifiable differences between the study case 

and the policy case. This is accomplished by 

transferring the estimated function upon which 

the value estimate in the study case is based to the 

policy case. This approach implicitly assumes that 

the population of beneficiaries to which the values 

are being transferred has potentially different 

characteristics, but similar tastes, as the original 

one and allows the analyst to adjust for these 

different characteristics. Generally, benefit function 

transfers are preferable to unit value transfers as 

they incorporate information relevant to the policy 

scenario (OMB 2003). For example, suppose that 

in the hypothetical example above the $20 unit 

value was the result of averaging the results of an 

estimated WTP function over all individuals in 

the study case sample, where the WTP function 

included income, the baseline water quality level, 

and the change in the water quality level for each 

household. A function transfer would estimate 

total benefits for the policy case by: 

1. Applying the WTP function to a random 

sample of households affected in the policy 

case using each household’s observed levels 

of income, baseline water quality, and water 

quality change; 

2. Averaging the resulting WTP estimates; and 

3. Multiplying this average WTP by the total 

number of households affected in the policy case. 

See Text Boxes 7.6 and 7.7 for examples of value 

and function transfers. 

If the WTP function is nonlinear and statistics 

on average income, baseline water quality, and 

water quality changes are used in the transfer 

instead of household level values, then bias would 

result. Feather and Hellerstein (1997) provide 

an example of a function transfer that attempts 

to correct for such bias. Although unit transfers 

can adjust and compensate for small differences 

between the case and policy study populations, 

they are subject to the same basic usage rules 

governing unit value transfers. Function transfers 

should only be used if the case and policy studies 

are evaluating very similar environmental goods, 

change in environmental levels, and affected 

populations. 
7-48 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

Text Box 7.7 - Benefits Transfer: Water Quality Benefits in the Combined 

Animal Feeding Operations Rule 

There are two prominent water quality benefit-transfer applications in the 2002 Combined Animal Feeding Operations 

(CAFO) rule. The first looks at the recreational value of water quality improvements in fresh water lakes and streams (see 

Section 4 of U.S. EPA 2002c). Field pollutant loadings were modeled by the National Water Pollution Control Assessment 

Model (NWPCAM) to produce pre and post regulation water quality estimates. Predicted changes in water quality were 

then valued using the results of Carson and Mitchell’s (1993) national water quality contingent valuation survey. First, 

benefits were calculated based on estimates of willingness to pay (WTP) for water quality improvements resulting in 

discrete movements to higher “rungs” of the water quality ladder (boatable, fishable, swimmable, drinkable). Very 

simply described, Carson and Mitchell’s “in-state” WTP estimates for discrete movements up the water quality ladder 

were multiplied by the number of affected residents in every state and “out-of-state,” non-use values were multiplied 

times the remaining population. State totals were then summed up to a national total (see Appendix A-4 of U.S. EPA 

2002c for more details). Benefits were also estimated a second way based on a continuous (1 to 100) water quality index 

constructed from six water quality parameters measured in the NWPCAM model. The minimum thresholds between 

rungs on the water quality ladder were then translated into points along the continuous water quality index (i.e., boatable 

= 25, fishable = 50, swimmable = 70). Carson and Mitchell’s WTP function was then used to value changes in water 

quality as measured by the water quality index (see Appendix B-4 of U.S. EPA 2002c for more details). Benefits estimated 

by the water quality index method are larger by roughly a factor of two (Exhibits 4-12 and 4-13 of U.S. EPA 2002c). 

The second major benefit-transfer application in the CAFO rule involves the valuation of reduced eutrophication in 

estuaries (Section 9 of U.S. EPA 2002c). EPA used a case study of Albemarle and Pamlico sounds to demonstrate 

the potential importance and value of reduced eutrophication on recreational fishing in affected estuaries. Again, 

NWPCAM was used to estimate pre and post regulation water quality levels. In this case, the benefit transfer made 

use of three studies (Kaoru 1995; Kaoru, Smith, and Liu 1995; and Smith and Palmquist 1988), all of which were 

based in part on the same dataset. All “reasonable” estimates of WTP for reduced phosphorus or nitrogen from the 

studies were retained and translated into their corresponding dollar per trip per ton reduction in pollutant per year 

value. A range of total benefits was then calculated by multiplying each $/trip/ton/year estimate by the number of trips 

taken and the change in loadings (in tons) for each pollutant (see Exhibit 9-3 of U.S. EPA 2002c). 

Meta-analysis uses results from multiple valuation 

studies to estimate a new transfer function. Metaanalysis is an umbrella term for a suite of techniques 

that synthesize the summary results of empirical 

research. This could include a simple ranking of 

results to a complex regression. The advantage of 

these methods is that they are generally easier to 

estimate while controlling for a relatively large 

number of confounding variables. This approach 

has been widely used in environmental economics 

(Poe et al. 2001, Shrestha and Loomis 2003a 

and 2003b, Rosenberger and Loomis 2000, and 

Bateman and Jones 2003). 

There are a number of guidelines for meta-analyses 

that outline protocols that should be followed in 

conducting or evaluating a study. See Begg et al. 

(1996), Moher et al. (1999), and U.S. EPA (2006e) 

for more information.51 More recently Bergstrom 

and Taylor (2006) discuss the theory and practice 

underlying meta-analysis for benefit transfer, 

discussing three major necessary steps: theory, data 

collection, and analysis. In general, when reporting 

meta-analysis results, researchers should provide 

information on the background of the problem, the 

strategy for selecting studies, analytic methods, results, 

discussion, and conclusions. See U.S. EPA (2006e) 

for a detailed discussion of meta-analysis as applied 

to VSL estimates. U.S. EPA (2006e) specifically 

recommends carefully specifying the search process, 

selection criteria, and analytical methods. 

Structural benefit transfer is a relatively new 

approach to benefit transfer. The advantages of 

51	 The last reference contains a detailed discussion of the protocols for 

conduction a meta-analsysis. 
Guidelines for Preparing Economic Analyses | December 2010 7-49 

Chapter 7 Analyzing Benefits

Text Box 7.8 - Structural Benefit Transfer with an Application to Visibility 

U.S. EPA (2006b) employs a structural benefit transfer to derive values for visibility improvements associated with 

the Particulate Matter (PM) National Ambient Air Quality Standards (NAAQS). It specified a constant elasticity of 

substitution utility function for visibility in residential and Class I (national park and similar) areas. This function 

assumes that the value for Class I visibility differs in and out of region but that residential visibility is valued the same 

everywhere. EPA also assumed that in-region visibility was valued more highly than out-of-region visibility. The 

function further specified utility as a function of: (1) consumption of all goods; (2) visibility in a person’s residential 

area; (3) recreational visibility in a person’s residential region; and (4) recreational visibility outside of a person’s 

residential region. Given the utility function and a budget constraint, it was then possible to define households’ 

WTP for changes in visibility as a function of income and visibility measures. The regional preference parameters of 

the function were calibrated using existing WTP estimates for visibility in Class I areas (Chestnut and Rowe 1990, 

and Chestnut 1997) if estimates existed for a given region. If not, estimates were adjusted by visitation rate. The 

preference parameter for residential visibility was assumed to be the same in all counties and was solved for based 

on a WTP estimate presented in McClelland et al. (1991). With estimates of visibility (pre and post regulation), 

county-level income, and the required preferences parameters, nationwide estimates of the value of increased 

visibility were then computed for each of the six regions of the country. 

structural transfer functions are that they can 

accommodate different types of economic value 

measures (e.g., WTP, WTA, or consumer surplus) 

and can be constructed in such a way that certain 

theoretical consistency conditions (e.g., WTP 

bounded by income) can be satisfied. This could 

be applied to value transfer, function transfer, or 

meta-analysis; although applications to function 

transfer are the most common. Structural transfer 

functions that have been estimated have specified 

a theoretically consistent preference model that is 

calibrated according to existing benefit estimates 

from the literature (see Smith and Pattanayak 

2002; and Smith, Pattanayak, and van Houtven 

2006 for descriptions on the method). See Text 

Box 7.8 for an application to of structural benefit 

transfer to visibility benefits. 

4. Report the results. In addition to reporting 

the final benefit estimates from the transfer 

exercise, the analyst should clearly describe all key 

judgments and assumptions, including the criteria 

used to select study cases and the choice of the 

transfer approach. The uncertainty in the final 

benefit estimate should be quantified and reported 

when possible. (See Chapter 11 on Presentation of 

Analysis and Results.) 

7.5 Accommodating 

Non-Monetized Benefits 

It often will not be possible to quantify all of the 

significant physical impacts for all policy options. 

For example, animal studies may suggest that a 

contaminant causes severe illnesses in humans, 

but the available data may not be adequate to 

determine the number of expected cases associated 

with different human exposure levels. Likewise, 

it often is not possible to quantify the various 

ecosystem changes that may result from an 

environmental policy. While Chapter 11 discusses 

how to present these benefits so as to provide 

a fuller accounting of all effects, this section 

discusses what analysts can do to incorporate these 

endpoints more fully into the analysis. 

7.5.1 Qualitative Discussions 

When there are potentially important effects that 

cannot be quantified, the analyst should include 

a qualitative discussion of benefits results. The 

discussion should explain why a quantitative 

analysis was not possible and the reasons for 

believing that these non-quantified effects may 

be important for decision making. Chapter 11 

discusses how to describe benefit categories that 

are quantified in physical terms but not monetized. 
7-50 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 7 Analyzing Benefits

7.5.2 Alternative Analyses 

Alternative analyses exist that can support benefits 

valuation when robust value estimates and/or risk 

estimates are lacking. These analyses, including 

break-even analysis and bounding analysis, 

can provide decision makers with some useful 

information. However analysts should remember 

that because these alternatives do not estimate 

the net benefits of a policy or regulation, they 

fall short of BCA in their ability to identify an 

economically efficient policy. This and other shortcomings should be discussed when presenting 

results from these analyses to decision makers. 

7.5.2.1 Break-Even Analysis 

Break-even analysis is one alternative that can be used 

when either risk data or valuation data are lacking.52 

Analysts who have per unit estimates of economic value 

but lack risk estimates cannot quantify net benefits. 

They can, however, estimate the number of cases (each 

valued at the per unit value estimate) at which overall 

net benefits become positive, or where the policy action 

will break even.53 Consider a proposed policy that is 

expected to reduce the number of cases of endpoint 

X with an associated cost estimate of $1 million. 

Further, suppose that the analyst estimates that WTP 

to avoid a case of endpoint X is $200, but that because 

of limitations in risk data, it is not possible to generate 

an estimate of the number of cases of this endpoint 

reduced by the policy. In this case, the proposed 

policy would need to reduce the number of cases by 

5,000 in order to “break even.” This estimate then 

can be assessed for plausibility either quantitatively or 

qualitatively. Policy makers will need to determine if the 

break-even value is acceptable or reasonable. 

The same sort of analysis can be performed when 

analysts lack valuation estimates, producing a 

break-even value that should again be assessed for 

credibility and plausibility. Continuing with the 

example above, suppose the analyst estimates that 

the proposed policy would reduce the number of 

cases of endpoint X by 5,000 but does not have an 

52	 Boardman et al. (1996) describes determining break-even points 

under the general subject of sensitivity analysis and includes empirical 

examples. 

53	 Circular A-4 (OMB 2003) refers to these values as “switch points” in 

its discussion of sensitivity analysis. 

estimate of WTP to avoid a case of this endpoint. 

In this case, the policy can be considered to break 

even if WTP is at least $200. 

One way to assess the credibility of economic breakeven values is to compare them to risk values for 

effects that are more or less severe than the endpoint 

being evaluated. For the break-even value to be 

plausible, it should fall between the estimates for 

these more and less severe effects. For the example 

above, if the estimate of WTP to avoid a case of a 

more serious effect was only $100, the above breakeven point may not be considered plausible. 

Break-even analysis is most effective when there is 

only one missing value in the analysis. For example, 

if an analyst is missing risk estimates for two 

different endpoints (but has valuation estimates for 

both), then they will need to consider a “break-even 

frontier” that allows the number of both effects 

to vary. It is possible to construct such a frontier, 

but it is difficult to determine which points on the 

frontier are relevant for policy analysis. 

7.5.2.2 Bounding Analysis 

Bounding analysis can help when analysts lack 

value estimates for a particular endpoint. As 

suggested above, reducing the risk of health effects 

that are more severe and of longer duration should 

be valued more highly than those that are less 

severe and of shorter duration, all else equal. If 

robust valuation estimates are available for effects 

that are unambiguously “worse” and others that are 

unambiguously “not as bad,” then one can use these 

estimates as the upper and lower bounds on the 

value of the effect of concern. Presenting alternative 

benefit estimates based on each of these bounds can 

provide valuable information to policy makers. If 

the sign of the net benefit estimate is positive across 

this range then analysts can have some confidence 

that the program is welfare enhancing. Analysts 

should carefully describe judgments or assumptions 

made in selecting appropriate bounding values. 
Chapter 8 

Analyzing Costs
 

The previous chapter discussed the process of estimating the benefits of 


environmental regulations and policies. This chapter discusses the estimation 


of costs, with a primary focus on estimating costs for use in benefit-cost 


analyses (BCA). While often portrayed as being relatively straightforward — 


particularly compared to the estimation of benefits — the estimation of costs 


presents a number of challenges in its own right. 


The first challenge is to identify an appropriate measure of cost for a particular application. 


A number of concepts of cost exist, with some overlap of ideas. In conducting a BCA, 


the correct measure to use is the social cost. Social cost represents the total burden that a 


regulation will impose on the economy. It is defined as the sum of all opportunity costs 


incurred as a result of a regulation where an opportunity cost is the value lost to society of any 


goods and services that will not be produced and consumed as a result of a regulation. 


A second challenge involves choosing an economic framework for the analysis. Depending 


on the scope of the regulation or policy, either a partial or general equilibrium framework is 


employed. Partial equilibrium analysis is usually appropriate when the scope of a regulation is 


limited to a single sector, or to a small number of sectors. General equilibrium analysis may be 


more appropriate if the analyst expects a large number of sectors to be impacted and that the 


effects will be spread more broadly throughout the economy. 


The third challenge is choosing one or more models to use in an analysis. Factors to consider 


in selecting a model include the types of costs being investigated, the geographic and sectoral 


scope of the likely impacts, and the expected magnitude of the impacts. For some analyses, it 


may be necessary to use more than one model. 


This chapter discusses social cost and its underlying economic theory as well as several 


alternative concepts of cost. In addition, the chapter discusses several additional issues in cost 


estimation and presents a number of the models that can be employed in the estimation and 


analysis of costs. 


8.1 The Economics of on the economy; it can be defined as the sum of 

Social Cost all opportunity costs incurred as a result of the 

The most comprehensive measure of the costs of regulation. These opportunity costs consist of the 

a regulation — and thus the appropriate measure value lost to society of all the goods and services that 

to use in a BCA — is “social cost.” Social cost will not be produced and consumed if firms comply 

represents the total burden a regulation will impose with the regulation and reallocate resources away 

Guidelines for Preparing Economic Analyses | December 2010 8-1 
8-2 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 8 Analyzing Costs

from production activities and towards pollution 

abatement. To be complete, an estimate of social 

cost should include both the opportunity costs 

of current consumption that will be foregone as 

a result of the regulation, and the losses that may 

result if the regulation reduces capital investment 

and thus future consumption.1 

The purpose of estimating social cost is to have 

a reference point for comparing the costs of a 

regulation with the estimated benefits. Social cost 

is not a particularly meaningful concept unless it 

is used as part of a net social welfare calculation, or 

perhaps compared to other (less comprehensive) 

cost measures.2

 Conceptually, it should be noted 

that the social cost of a regulation is generally not 

the same as a change in gross domestic product 

(GDP), or another broad measure of economic 

activity, that may result from its imposition. 

Expenditures on inputs into pollution abatement, 

such as equipment, materials, and labor, are 

counted as part of social cost. All or part of their 

consumption will at the same time be included 

positively in the calculation of GDP. Thus, if a 

regulation has the effect of lowering GDP, this 

decline will in general be less than the social cost of 

the regulation. 

Two broad analytical paradigms are used in 

the analysis of social cost: partial equilibrium 

and general equilibrium. A partial equilibrium 

approach is appropriate when it is assumed 

that the effects of a regulation will primarily be 

confined to a single or small number of closely 

related markets. If this is not the case, and the 

regulation is expected to cause significant impacts 

across the economy, it is more appropriate to use 

general equilibrium analysis to estimate social 

1	 This section discusses the prospective estimation of social cost for 

regulations that have not yet been implemented. However, the same 

principles apply to estimating costs retrospectively for regulations 

already in place. Likewise, while the text refers to the social cost of “a 

regulation” the same principles apply to the estimation of the social 

cost for each alternative in a set of regulatory alternatives. For a more 

rigorous and detailed treatment of the material in this section, see Pizer 

and Kopp (2005). 

2	 For example, comparing the social cost of different regulations 

may provide some sense of the relative burden they impose on the 

economy, but this exercise alone would not indicate which, if any, of 

the regulations may be worthwhile from a public policy standpoint. 

However, the accurate measurement of social cost would be an 

essential component in attempting to make such a determination. 

cost. The use of these two analytical paradigms is 

explored in the following sections. 

8.1.1 Partial Equilibrium Analysis 

When the analyst expects that the effects of a 

regulation will be confined primarily to a single 

market or a small number of markets, partial 

equilibrium analysis is the preferred approach 

for estimation of social cost. The use of partial 

equilibrium analysis assumes that the effects 

of the regulation on all other markets will be 

minimal and can either be ignored or estimated 

without employing a model of the entire economy. 

This section presents some simple diagrams to 

show how social cost can be defined in a partial 

equilibrium framework. 

Figure 8.1 shows a competitive market before the 

imposition of an environmental regulation. The 

intersection of the supply (S0

) and demand (D) 

curves determines the equilibrium price (P0

) and 

quantity (Q0

). The shaded area below the demand 

curve and above the equilibrium price line is the 

consumer surplus. The area above the supply curve 

and below the price line is producer surplus. The 

sum of these two areas defines the total welfare 

generated in this market: the net benefits to 

society from producing and consuming the good 

or service represented in this market.3 

In this market, the imposition of a new 

environmental regulation raises firms’ production 

costs. Each unit of output is now more costly 

to produce because of expenditures incurred to 

comply with the regulation. As a result, firms will 

respond by reducing their level of output. For the 

industry, this will appear as an upward shift in 

the supply curve. This is shown in Figure 8.2 as a 

movement from S0

 to S1. The effect on the market 

of the shift in the supply curve is to increase 

the equilibrium price to P1

 and to decrease the 

equilibrium output to Q1

, holding all else constant. 

3	 It should be noted that total welfare as depicted ignores the negative 

pollution externality arising in this market, which the environmental 

regulation is designed to correct. Appendix A presents a graphical 

description of how to account for this externality. Reduction of this 

negative externality would be quantified in the benefits portion of an 

analysis. The supply curve in Figure 8.1 corresponds to the marginal 

private cost (MPC) curve described in Figure A.5 of Appendix A. 
Chapter 8 Analyzing Costs

Figure 8.1 - Competitive Market Before 

Regulation 

P 

0 

P0 

S0 

D 

Q Q 0 

Consumer Surplus 

Producer Surplus 

Figure 8.2 - Competitive Market After 

Regulation 

P 

0 

P0 

P1 

S0 

S1 

D 

Q Q Q1 0

Consumer Surplus 

Producer Surplus 

Compliance Costs 

Deadweight Loss 

As seen by comparing Figures 8.1 and 8.2, the 

overall effect on welfare is a decline in both 

producer and consumer surplus.4 

Compliance costs in this market are equal to 

the area between the old and new supply curves, 

bounded by the new equilibrium output, Q1

.

5 

Noting this, a number of useful insights about the 

total costs of the regulation can be derived from 

Figures 8.1 and 8.2. First, when consumers are 

price sensitive — as reflected in the downward 

4	 The figure depicts an equal distribution of welfare between consumers 

and producers, in both the old and new equilibria. Depending on 

the elasticities of supply and demand, this may not be the case. The 

elasticities will determine the magnitude of the price and quantity 

changes induced by the cost increase, as well as the distribution of costs. 

5	 Here distinctions between the fixed and variable costs of abatement are 

abstracted and it is assumed that all of the costs are represented in the 

movement of the supply curve. See Tietenberg (2002). 

sloping demand curve — a higher price causes 

them to reduce consumption of the good. If 

costs are estimated ex ante and this price sensitive 

behavior is not taken into account (i.e., the 

estimate is based on the original level of output 

(Q0

) compliance costs will be overstated. Extending 

the vertical dotted line in Figure 8.2 from the 

original equilibrium to the new supply curve (S1

) 

illustrates this point.6 

A second insight derived from Figures 8.1 and 8.2 

is that compliance costs are usually only part of 

the total costs of a regulation. The “deadweight 

loss” (DWL) shown in Figure 8.2 is an additional, 

real cost arising from the regulation. It reflects 

the foregone net benefit due to the reduction 

in output.7

 Moreover, unlike many one-time 

compliance costs, DWL will be a component of 

social cost in future periods. 

Under the assumption that impacts outside 

this market are not significant, then the social 

cost of the regulation is equal to the sum of 

the compliance costs and the deadweight loss 

(shown in Figure 8.2). This is exactly equal 

to the reduction in producer and consumer 

surplus from the pre-regulation equilibrium 

(shown in Figure 8.1). This estimate of social 

cost would be the appropriate measure to use 

in a BCA of the regulation. As noted above, if 

some of the compliance costs are spent on other 

goods and services or on hiring additional 

labor, any fall in GDP attributable to the 

imposition of the regulation will be less than 

the social cost. 

The preceding discussion describes the use of 

partial equilibrium analysis when the regulated 

6	 In the extreme, if the regulation raised production costs so much that 

firms decided to halt production altogether, or if an outright ban on the 

product was issued, a strict compliance cost analysis would yield zero 

cost as no direct expenditures on abatement would be made. Clearly 

this would constitute an underestimate of the loss in consumer welfare. 

7	 Typically, in a market already distorted with pollution externalities, 

the DWL triangle shown in Figure 8.2 will serve to offset (at least in 

part) the existing DWL in the market that results when the real costs of 

production (including the pollution damages) are not considered in the 

production decision. Of course, if the regulatory action is too stringent 

and “over controls” the pollution problem, the optimal outcome will not 

be achieved and additional DWL will be created. Figure 8.2 is silent on 

where the optimal solution is achieved. See Appendix A for more detail. 

Guidelines for Preparing Economic Analyses | December 2010 8-3 
8-4 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 8 Analyzing Costs

market is perfectly competitive. In many cases, 

however, some form of imperfect competition, 

such as monopolistic competition, oligopoly, or 

monopoly, may better characterize the regulated 

market. Firms in imperfectly competitive markets 

will adjust differently to the imposition of a new 

regulation and this can alter the estimate of social 

cost.8

 If the regulated market is imperfectly 

competitive, the market structure can and should 

be reflected in the analysis. 

In certain situations, when the effects of a 

regulation are expected to impact a limited 

number of markets beyond the regulated sector, 

it still may be possible to use a partial equilibrium 

framework to estimate social cost. Multimarket analysis extends a single-market, partial 

equilibrium analysis of the directly regulated 

sector to include closely related markets. These 

may include the upstream suppliers of major inputs 

to the regulated sector, downstream producers 

who use the regulated sector’s output as an input, 

and producers of substitute or complimentary 

products. Vertically or horizontally related markets 

will be affected by changes in the equilibrium 

price and quantity in the regulated sector. As a 

consequence, they will experience equilibrium 

adjustments of their own that can be analyzed in a 

similar fashion.9 

8.1.2 General Equilibrium 

Analysis 

In some cases, the imposition of an 

environmental regulation will have significant 

effects in markets beyond those that are directly 

subject to the regulation. As the number of 

affected markets grows, it becomes less and 

8 The opportunity costs of lost production from the regulation will 

be less for a monopoly than a perfectly competitive industry, even 

if they face the same market demand curve. This result may seem 

counterintuitive, but the monopolist operates on a more elastic, or 

price sensitive, portion of the demand curve. As a result, it will have 

lower profits if it tries to increase price (and lower output) by as much 

as the competitive industry. 

9 In theory, impacts in undistorted related markets are “pecuniary” and 

do not need to be included if the social costs have been correctly 

measured in the primary market, but pecuniary effects are important in 

inefficient related markets and should be considered (Boardman et al. 

2006). Just et al. (2005) provide a detailed treatment of multi-market 

analysis. Kokoski and Smith (1987) demonstrate, however, that one 

must use caution when using these methods. 

less likely that partial equilibrium analysis can 

provide an accurate estimate of social cost. 

Similarly, it may not be possible to accurately 

model a large change in a single regulated market 

using partial equilibrium analysis. In such cases, 

a general equilibrium framework, which captures 

linkages between markets across the entire 

economy, may be a more appropriate choice for 

the analysis. 

For example, the imposition of an environmental 

regulation on emissions from the electric utility 

sector may cause the price of electricity to rise. 

As electricity is an important intermediate 

input in the production of most goods, the 

prices of these products will most likely also rise. 

Households will be affected as both consumers 

of these goods and as consumers of electricity. 

The increase in prices may cause them to alter 

their relative consumption of a variety of 

goods and services. The increase in the price of 

electricity may also cause feedback effects that 

result in a reduction in the total consumption of 

electricity. 

General equilibrium analysis is built around 

the assumption that for some discrete period 

of time, an economy can be characterized 

by a set of equilibrium conditions in which 

supply equals demand in all markets. When the 

imposition of a regulation alters conditions in 

one market, a general equilibrium model will 

determine a new set of prices for all markets 

that will return the economy to equilibrium. 

These prices in turn determine the outputs 

and consumption of goods and services in the 

new equilibrium. In addition, the model will 

determine a new set of prices and demands 

for the factors of production (labor, capital, 

and land), the returns to which compose the 

income of businesses and households. Changes 

in aggregate economic activity, such as GDP, 

household consumption, and other variables, 

also can be calculated in the model. 

The previous section shows how the social 

cost of a regulation can be estimated in a single 

market using partial equilibrium analysis. The 

example demonstrates how a regulation causes 
Chapter 8 Analyzing Costs

a DWL in that market, reflecting a decline in 

economic welfare as measured by consumer 

and producer surplus. In reality, DWL already 

exists in many, if not most, markets as a result 

of taxes, regulations, and other distortions. 

When the imposition of a regulation causes a 

new distortion in one market, it may interact 

with pre-existing distortions in other markets 

and this may cause additional impacts on 

welfare. 

An important example of how a regulation can 

interact with pre-existing distortions can be found 

in the labor market, depicted in Figure 8.3. Here, 

a pre-existing tax on wages causes the net, aftertax wage (Wn

0) to be lower than the gross, pre-tax 

wage (Wg

) by the amount of the tax. With this 

tax distortion, the quantity of labor supplied is 

L0

 and there is a DWL. When a new regulation 

is imposed in another market, raising production 

costs, one of the indirect effects may be an increase 

in the price level as those costs are passed through 

the economy. This increase in the price level will 

reduce the real wage and, given an upward sloping 

labor supply curve, the amount of labor supplied.10 

This is shown in Figure 8.3 as a decrease in the net 

wage to Wn

1 and a decrease in the amount of labor 

supplied to L1

. 

The interaction between new and pre-existing 

distortions is especially pronounced in the labor 

market because pre-existing distortions there 

are large. As shown in Figure 8.3, even a small 

reduction in the amount of labor supplied will 

result in a large increase in DWL.11 Similar 

interactions are likely to occur in other markets 

with pre-existing distortions. In cases where they 

are likely to have a significant impact, analysts 

10	 In general equilibrium analysis, all prices and wages are real, i.e., they 

are measured relative to a numéraire, a specific single price or weighted 

average of prices, such as the GDP deflator. Here, the consumer price 

level rises relative to the numéraire. The result is a fall in the real wage 

— the nominal wage divided by the consumer price level. 

11	 The labor tax distortion affects individual labor supply decisions at the 

margin. Thus, a full-time worker may not change (or be able to change) 

her hours worked in response to a fall in the real wage. However, 

part-time workers, workers in households with more than one full-time 

worker, or potential retirees, may be more likely to adjust the number 

of hours they work or whether they work at all. A discussion of the 

theoretical and empirical basis for this depiction of the labor market 

can be found in Parry (2003). 

Figure 8.3 - Labor Market with Pre-Existing 

Distortions 

Wage 

0 

tax{

SL 

DL 

Wg 

Wn 

0 

Wn 

1 

L Hours 0 L1 

Pre-Existing

Deadweight Loss 

Additional 

Deadweight Loss 

should incorporate these distortions into models 

used to estimate social cost.12 

In a general equilibrium analysis, the social cost 

of a regulation is estimated using a computable 

general equilibrium (CGE) model. CGE models 

simulate the workings of a market economy and 

can include representations of the distortions 

caused by taxes and regulations. As described 

above, they are used to calculate a set of price and 

quantity variables that will return the simulated 

economy to equilibrium after the imposition of 

a regulation. The social cost of the regulation 

can then be estimated by comparing the value 

of variables in the pre-regulation, “baseline” 

equilibrium with those in the post-regulation, 

simulated equilibrium.13 

12	 Economists have long recognized these interaction effects (Ballard 

and Fullerton 1992). A more recent body of work has focused on 

them in the context of environmental regulation. In this literature, 

these interactions are known as the “tax-interaction effect.” If an 

environmental regulation raises revenue through a tax on pollution or 

other revenue raising provision, and the revenue is used to reduce preexisting distortions such as taxes on wages, the tax-interaction effect 

may be offset. This is known as the “revenue recycling effect.” The 

offset may be partial, complete, or in some cases, the overall efficiency 

of the tax system may actually be improved. The net result is an 

empirical matter, depending on the nature of the full set of interactions 

across the economy and how the revenue is raised. Some of the early 

papers in this literature include Bovenberg and de Moojii (1994), Parry 

(1995), and Bovenberg and Goulder (1996). Goulder (2000) provides 

an accessible summary of the early literature. More recent papers 

include Parry and Bento (2000); Murray, Keeler, and Thurman (2005); 

and Bento and Jacobsen (2007). 

13	 CGE models are discussed in more detail in the modeling section of 

this chapter. Applications of CGE models to the estimation of the social 

cost of environmental regulation include Hazilla and Kopp (1990) 

and Jorgenson and Wilcoxen (1990). A version of the Jorgenson and 

Wilcoxen model was used as part of EPA’s retrospective study of the 

benefits and costs of the Clean Air Act for the period 1970 to 1990 

(U.S. EPA 1997a). 

Guidelines for Preparing Economic Analyses | December 2010 8-5 
8-6 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 8 Analyzing Costs

Even in a general equilibrium analysis, analysts 

must take care in selecting an appropriate 

measure of social cost. Calculating social cost 

by adding together estimates of the costs in 

individual sectors can lead to double counting. 

For example, counting both the increased 

costs of production to firms resulting from a 

regulation and the attendant increases in prices 

paid by consumers for affected goods would 

mean counting the same costs twice, leading to 

an overestimate of social cost. Instead, focusing 

on measures of changes in final demand, so that 

intermediate goods are not counted, can avoid 

the double-counting problem.14 

While it is theoretically possible to estimate social 

cost by adding up the net change in consumer 

and producer surplus in all affected markets, 

the measures most commonly used in practice 

are consumer’s equivalent variation (EV) and 

compensating variation (CV). Both are monetary 

measures of the change in utility brought about 

by changes in prices and incomes resulting from 

the imposition of a regulation. As households 

are the ultimate beneficiaries of government and 

investment expenditures, the EV and CV measures 

focus on changes in consumer welfare, rather than 

on changes in total final demand. 

8.1.3 Dynamics 

In most cases, a regulation will continue to have 

economic impacts for a number of years after its 

initial implementation. If these intertemporal 

impacts are likely to be significant, they should 

be included in the estimation of social cost. For 

example, if a regulation requires firms in the 

electric utility sector to invest in pollution control 

equipment, they may not invest as much in electric 

generation capacity as they would have in the 

absence of the regulation. This may result in slower 

growth in electricity output and reduce the overall 

growth rate of the economy. In some cases, the 

effect of a regulation on long-term growth may 

be much more significant than the effect on the 

regulated sector alone. 

14	 Final demand consists of household purchases, investment, 

government spending, and net exports (exports minus imports). 

When conducting a BCA in which the analyst 

expects intertemporal effects of a regulation to 

be confined to the regulated sector, it may be 

appropriate to simply apply partial equilibrium 

analysis to multiple periods. Relevant conditions, 

like expected changes in market demand and 

supply over time, should be taken into account 

in the analysis. The costs in individual years can 

then be discounted back to the initial year for 

consistency. 

If the intertemporal effects of a regulation 

on non-regulated sectors are expected to be 

significant, analysts can estimate social cost 

using a dynamic CGE model. Dynamic CGE 

models can capture the effects of a regulation 

on affected sectors throughout the economy. 

They can also address the long-term impacts 

of changes in labor supply, savings, factor 

accumulation, and factor productivity on the 

process of economic growth.15 In a dynamic 

CGE model social cost is estimated by 

comparing values in the simulated baseline 

(i.e., in the simulated trajectory of the economy 

without the regulation) with values from a 

simulation with the regulation in place. 

8.1.4 Social Cost and 

Employment Effects 

At times of recession, questions arise about 

whether jobs lost as a result of a regulation 

should be counted as an additional cost of the 

regulation. However, counting the number of 

jobs lost (or gained) as a result of a regulation 

generally has no meaning in the context 

of BCA as these are typically categorized 

as transitional job losses.16 BCA requires 

monetized values of both the social benefits and 

costs associated with the regulation. The social 

cost of a regulation already includes the value 

15	 In addition to affecting the growth of the capital stock, an 

environmental regulation may also negatively affect the supply of labor 

through the interaction effects discussed above, thus increasing social 

cost. However, there may also be a positive effect on labor supply if 

improved environmental quality confers health benefits that make the 

work force more productive. 

16	 In very rare cases in which a regulation contributes additional job 

losses to a sector exhibiting structural unemployment, analysts 

should consider including job losses as a separate cost category. See 

Appendix C for more detail. 
Guidelines for Preparing Economic Analyses | December 2010 8-7 

Chapter 8 Analyzing Costs

of lost output associated with the reallocation 

of resources (including labor) away from 

production of output and towards pollution 

abatement. This does not mean, of course, that 

specific individual workers are not harmed by a 

policy if they lose their jobs. EPA estimates the 

magnitude of such losses as part of an Economic 

Impact Analysis (EIA). See Chapter 9 for more 

details on this topic. 

8.2 A Typology of Costs 

The previous section defined social cost as the 

sum of the opportunity costs incurred as the 

result of the imposition of a regulation, and 

introduced the basic economic theory used in its 

estimation. Conceptually, social cost is the most 

comprehensive measure of cost, and is thus the 

appropriate measure to use in BCA. In addition 

to social cost, a number of other concepts of cost 

exist and are often used to describe the effects 

of a regulation. This section discusses these 

alternative concepts and introduces a number 

of additional terms. This section also provides 

a discussion of measures that define temporary 

costs or define how costs are distributed across 

different entities. 

8.2.1 Alternative Concepts 

of Cost 

Three alternative concepts of cost, each of which 

is composed of two components, are: explicit and 

implicit costs, direct and indirect costs, and private 

sector and public sector costs. Like social cost, all 

of these concepts are comprehensive in nature. 

An important distinction is that while social cost 

is a measure derived from economic theory, these 

three alternative concepts are in general only 

descriptive.17 

Consideration of these alternative concepts can 

provide insights into the full range of the costs of a 

regulation. They may also be useful in determining 

the appropriate framework and modeling 

methodology for an analysis. Several executive 

and legislative mandates require that a number of 

17	 In certain cases, a single component, such as direct cost, may provide 

a reasonable estimate of social cost. 

different types of costs be included in a regulatory 

impact analysis (RIA).18 

8.2.1.1 Explicit and Implicit Costs 

The total costs of a regulation can include both 

explicit and implicit costs.19 Explicit costs are those 

costs for which an explicit monetary payment is 

made, or for which it is straightforward to infer a 

value. For firms, the explicit costs of environmental 

regulation normally include the costs of purchase 

and operation of pollution control equipment. 

This includes payments for inputs (such as 

electricity) and wages for time spent on pollution 

control activities. For households, explicit costs 

may include the costs of periodic inspections of 

pollution control equipment on vehicles. For 

government regulatory agencies, wages paid to 

employees for developing a regulation and then 

for administration, monitoring, and enforcement 

are included in explicit costs. Implicit costs are 

costs for which monetary values do not readily 

exist and are thus likely more difficult to quantify. 

Implicit costs may include the value of current 

output lost because inputs are shifted to pollution 

control activities from other uses, as well as lost 

future output due to shifts in the composition of 

capital investment. Implicit costs may also include 

the lost value of product variety as a result of 

bans on certain goods, time costs of searching for 

substitutes, and reduced flexibility of response to 

changes in market conditions. 

8.2.1.2 Direct and Indirect Costs 

Direct costs are those costs that fall directly on 

regulated entities as the result of the imposition 

of a regulation. These entities may include firms, 

households, and government agencies. Indirect 

costs are the costs incurred in related markets or 

experienced by consumers or government agencies 

18	 EO 12866 specifies that an assessment of the costs of a regulation 

should include “any adverse effects on the efficient functioning of the 

economy and private sector (including productivity, employment, and 

competitiveness)” in addition to compliance costs. The UMRA of 1995 

requires that cost estimates take into account both indirect and implicit 

costs on state and local governments. 

19	 The term “total cost” is used here when discussing alternative concepts 

of cost in order to reinforce the distinction between these concepts and 

social cost. 
8-8 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 8 Analyzing Costs

 

 

 

 

not under the direct scope of the regulation. These 

indirect costs are usually transmitted through 

changes in the prices of the goods or services 

produced in the regulated sector. Changes in these 

prices then ripple through the rest of the economy, 

causing prices in other sectors to rise or fall and 

ultimately affecting the incomes of consumers. 

Government entities can also incur indirect costs. 

For example, if the tax base changes due to the exit 

of firms from an industry, revenues from taxes or 

fees may decline. In some cases, the indirect costs 

of a regulation may be considerably greater than 

the direct costs. 

8.2.1.3 Private Sector and Public 

Sector Costs 

The total costs of a regulation can also be divided 

between private sector and public sector costs. 

Private sector costs include all of the costs of a 

regulation borne by households and firms. Public 

sector costs consist of the costs borne by various 

government entities. 

8.2.2 Additional Cost Terminology 

In addition to the conceptual categories and 

their components discussed above, a variety 

of other terms are often used in describing the 

costs of environmental regulation. A number of 

these terms are defined here. It should be noted 

that there are numerous overlaps between these 

concepts, and analysts must take care to avoid 

double counting.20 

8.2.2.1 Incremental Costs 

Incremental costs are the additional costs associated 

with a new environmental regulation or policy. 

Incremental costs are determined by subtracting 

the total costs of environmental regulations and 

policies already in place from the total costs after a 

new regulation or policy has been imposed. 

20 References that provide definitions of cost terminology include U.S. 

CBO (1988), and Callan and Thomas (1999). 

8.2.2.2 Compliance Costs 

Compliance costs (also known as abatement costs) 

are the costs firms incur to reduce or prevent 

pollution to comply with a regulation. They are 

usually composed of two main components: 

capital costs and operating costs. Compliance costs 

can be further defined to include any or all of the 

following: 

• Treatment/Capture — The cost of any 

method, technique, or process designed to 

remove pollutants, after their generation in 

the production process, from air emissions, 

water discharges, or solid waste. 

• Recycling — The cost of postproduction 

on-site or off-site processing of waste for an 

alternative use. 

• Disposal — The cost involving the final 

placement, destruction, or disposition of 

waste after pollution treatment/capture and/ 

or recycling has occurred. 

• Prevention — The cost of any method, 

technique, or process that reduces the amount 

of pollution generated during the production 

process. 

8.2.2.3 Capital Costs 

Capital costs include expenditures on installation 

or retrofit of structures or equipment with the 

primary purpose of treating, capturing, recycling, 

disposing, and/or preventing pollutants. 

These expenditures are sometimes referred to 

as “one-time costs” and include expenditures 

for equipment installation and startup. Once 

equipment is installed, capital costs generally do 

not change with the level of abatement and are 

thus functionally equivalent to “fixed costs.” In 

BCA, capital costs are usually “annualized” over 

the period of the useful life of the equipment. 

8.2.2.4 Operating and 

Maintenance Costs 

Operating and maintenance costs are annual 

expenditures on salaries and wages, energy inputs, 

materials and supplies, purchased services, and 

maintenance of equipment associated with 
Guidelines for Preparing Economic Analyses | December 2010 8-9 

Chapter 8 Analyzing Costs

pollution abatement. In general, they are directly 

related to the level of abatement. Operating costs 

are functionally equivalent to “variable costs.” 

8.2.2.5 Industry Costs 

Industry costs are the costs of a regulation to 

an industry, including the effects of actual or 

expected market reactions. They often differ from 

compliance costs because compliance costs do not 

normally account for market reactions. Market 

reactions may include plant closures, reduced 

industry output, or the passing on of some costs 

directly to consumers. 

8.2.2.6 Transactions Costs 

Transactions costs are those costs that are incurred 

in making an economic exchange beyond the 

cost of production of a good or service. They may 

include the costs of searching out a buyer or seller, 

bargaining, and enforcing contracts. Transactions 

costs may be important when setting up a new 

market, such as those markets designed to be used 

for market-based regulations. 

8.2.2.7 Government Regulatory Costs 

Government regulatory costs are those borne 

by various government entities in the course of 

researching, enacting, and enforcing a policy or 

regulation.21 

8.2.3 Transitional and 

Distributional Costs 

In addition to the concepts and terms defined 

above, several other types of cost exist. Two 

qualitatively different types of cost from those 

above are transitional and distributional costs. 

8.2.3.1 Transitional Costs 

At some point in time after the imposition of a 

new environmental regulation, the economy can 

be expected to adjust to a new equilibrium. While 

21 Government entities may themselves be polluters and therefore subject 

to regulation. Compliance costs under this scenario would be captured 

as such. 

many costs are likely to be permanent additions 

to the costs of production, others will be short 

term in nature, being incurred only during the 

adjustment to the new equilibrium. These are 

known as transitional costs. Transitional costs may 

include the costs of training workers in the use of 

new pollution control equipment. After workers 

receive their initial training, the time they spend 

on pollution control activities would be counted as 

operating costs. 

8.2.3.2 Distributional Costs 

Distributional costs are those costs that relate 

to how certain entities or societal groups are 

impacted by the imposition of a policy or 

regulation. While BCA is by definition concerned 

only with the net benefits, it is likely that most 

policies or regulations will result in winners and 

losers. In some cases, the models described later in 

this chapter can be used for distributional analysis 

as well as BCA. Distributional costs are covered in 

detail in Chapter 10. 

8.3 Measurement Issues in 

Estimating Social Cost 

A number of issues may arise when estimating the 

expected social cost of a proposed regulation, or 

when measuring costs incurred as a result of an 

existing regulation. These issues can be divided 

into two broad categories: (1) those that arise 

when estimating costs over time; and (2) those 

associated with difficulties in developing numeric 

values for estimating social cost. This section 

discusses both these issues in turn. It concludes 

with a short analysis of how estimates of Title 

IV of the Clean Air Act’s costs evolved over 

time, illustrating the importance of accurately 

accounting for these issues when estimating the 

costs of a regulation. 

8.3.1 Evaluating Costs Over Time 

Most regulations cause permanent changes in 

production and consumption activities, leading 

to permanent (ongoing) social costs. As a result, 

regulations are often phased in gradually over 

time in an effort to limit any disruptions created 
8-10 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 8 Analyzing Costs

by their imposition. When measuring costs over 

time, assumptions related to the time horizon of 

the analysis, the use of a static versus a dynamic 

framework, discounting, and technical change are 

extremely important. These assumptions are each 

discussed in more detail in the paragraphs that follow. 

8.3.1.1 Time Horizon 

Irrespective of the method used for the estimation 

of social cost, the time horizon for calculating 

producer and consumer adjustments to a new 

regulation should be considered carefully. Ideally, 

the analyst estimates the value of all future costs 

of a regulation discounted to its present value. If 

the analyst is only able to estimate a regulation’s 

costs for one or a few representative future years, 

she must take great care to ensure that the year(s) 

selected are truly representative, that no important 

transitional costs are effectively dismissed by 

assumption, and that no one-time costs are 

assumed to be on-going. 

In the short term, at least some factors of 

production are fixed. If costs are evaluated over 

a short period of time, then contractual or 

technological constraints prevent firms from 

responding quickly to increased compliance costs 

by adjusting their input mix or output decisions. In 

the long term, by contrast, all factors of production 

are variable. Firms can adjust any of their factors 

of production in response to changes in costs due 

to a new regulation. A longer time horizon affords 

greater opportunities for affected entities to 

change their production processes (for instance, to 

innovate). It is important to select a time horizon 

that captures any flexibility the regulation provides 

firms in the way they choose to comply. 

8.3.1.2 Choosing Between a Static and 

Dynamic Framework 

In many cases, costs are evaluated in a static 

framework. That is, costs are estimated at a given 

point in time or for a selection of distinct points 

in time. Such estimates provide snapshots of costs 

faced by firms, government, and households but 

do not allow for behavioral changes from one time 

period to affect responses in another time period. 

In addition to the capital-induced growth effects 

discussed in Section 8.2.3, the evaluation of costs 

in a dynamic framework may be important when a 

proposed regulation is expected to affect product 

quality, productivity, innovation, and changes in 

markets indirectly affected by the environmental 

policy.22 These may have impacts on net levels of 

measured consumer and producer surplus over time. 

8.3.1.3 Discounting 

Social discounting procedures for economic 

analyses are reviewed in considerable detail in 

Chapter 6. Benefits and costs that occur over time 

must be properly and consistently discounted 

if any comparisons between them are to be 

legitimate.23 

There is one application of discounting that is 

unique to cost analysis. When calculating firms’ 

private costs (e.g., the internal cost of capital used 

for pollution abatement), the analyst should use 

a discount rate that reflects the industry’s cost of 

capital, just as a firm would. The social cost of the 

regulation, on the other hand, would be calculated 

using the social discount rate, the same discount 

rate used for the benefits of the regulation. 

8.3.1.4 Technical Change and Learning 

Estimating the costs of a given environmental 

regulation frequently entails estimating future 

technical change. Despite its importance as a 

determinant of economic welfare, the process of 

technical change is not well understood. Different 

approaches to environmental regulation present 

widely differing incentives for technological 

innovation. As a result, the same environmental 

end may be achieved at significantly different 

costs, depending on the pace and direction of 

technical change. Recent empirical work supports 

this hypothesis. Most notably, the realized costs of 

Title IV of the 1990 Clean Air Act Amendment’s 

SO2

 Allowance Trading program are considerably 

lower than initial predictions, in part due to 

unanticipated technical change (see Text Box 8.1). 

22	 See Section 8.1.3 for a discussion of dynamics. 

23	 In a CEA, it is equally important to properly discount cost estimates of 

different regulatory approaches to facilitate valid comparisons. 
Guidelines for Preparing Economic Analyses | December 2010 8-11 

Chapter 8 Analyzing Costs

Text Box 8.1 - The Sulfur Dioxide Cap-and-Trade Program — A Case Study24 

24 This example is taken from Burtraw and Palmer (2004). 

Under Title IV of the 1990 Clean Air Act Amendments (CAAA), coal fired power plants are required to hold one sulfur dioxide 

(SO2) allowance for each ton of SO2 they emit during the year. Utilities are allowed to buy, sell and bank unused allowances to 

cover future SO2 emissions (see Chapter 4 for additional detail). Title IV was subject to intensive ex ante and ex post analysis. 

The evolution of these analyses illustrates the importance of complete and thorough estimation of social costs and highlights 

the difference some of the issues discussed above (e.g., discounting or uncertainties) can make to actual cost estimates. 

Estimates of Title IV’s compliance costs have declined over time, particularly so once the program was launched and 

researchers were able to observe the behavior of electric utilities. Title IV proved less costly than originally estimated 

due to behavior responses, indirect effects, technological improvements, market structure, and prices that changed 

over time. Table 8.1 provides a comparison of some of the program’s cost estimates over time. Rows that report ex 

ante estimates are shaded gray. 

Table 8.1 - Estimates of Compliance Costs for the SO2 Program* 

Study 

Annual Costs 

(Billions) Marginal Costs per ton SO2 Average costs per ton of SO2 

Carlson et al. (2000) $1.1 $291 $174 

Ellerman et al. (2000) 1.4 350 137 

Burtraw et al. (1998) 0.9 n/a 239 

Goulder et al. (1997) 1.09 n/a n/a 

White (1997) n/a 436 n/a 

ICF (1995) 2.3 532 252 

White et al. (1995) 1.4-2.9 543 286-334 

GAO (1994) 2.2-3.3 n/a 230-374 

Van Horn Consulting et 

al. (1993) 

2.4-3.3 520 314-405 

ICF (1990) 2.3-5.9 579-760 348-499 

*Based on Table 2-1, Burtraw and Palmer (2004); n/a — not reported. 

Most of the early estimates of Title IV’s compliance costs were based on engineering models, which do not fully 

capture the concepts of consumer and producer surplus. In addition, many of these studies relied on the data and 

methodologies used to evaluate traditional command-and-control environmental policies, adjusted to estimate 

the efficiency gains of a permit trading system. Later studies that included more extensive examinations of both 

the regulatory impacts as well as outside economic pressures on the industry came up with significantly smaller 

compliance cost estimates for the regulation. 

Several developments occurred around the time of Title IV that helped reduce the program’s ex post cost estimates. For 

example, reductions in the price of low-sulfur coal, along with technological improvements that lowered the cost of fuel 

switching, allowed utilities in the East to reduce compliance costs by using low-sulfur coal from the Powder River Basin in 

Wyoming (Carlson et al. 2000, and Burtraw and Palmer 2004). Furthermore Popp (2003) concluded that Title IV-induced 

R&D led to technological innovations that improved the efficiency of scrubbers, thereby leading to lower operating costs. 

The varying cost estimates also show the importance of accounting for changing implementation costs and 

uncertainty over time. The ability of facilities to “bank” SO2

 allowances allowed flexibility in implementation and thus 

reduced compliance costs. Cost estimates by Carlson et al. (2000) and Ellerman et al. (2000) factor in the discounted 

savings from banking. According to the latter study, costs savings are a relatively minor source of overall savings, but 

are important in developing a picture of the program’s total effectiveness. This is because firms were able to “avoid the 

much larger losses associated with meeting fixed targets in an uncertain world” (Ellerman et al. 2000, p. 285). 
8-12 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 8 Analyzing Costs

Organizations are able to learn with experience, 

which permits them to produce a given good or 

service at lower cost as their cumulative experience 

increases. While there are many different 

explanations for this phenomenon (e.g., labor 

forces learn from mistakes and learn shortcuts; ad 

hoc processes become standardized) its existence 

has been borne out by experiences in many sectors. 

Indeed, OMB now requires cost analyses to 

consider possible learning effects among the costsaving innovations.25 Recent EPA Advisory Council 

guidance recommends that default learning effects 

be applied even when sector- or process-specific 

empirical data are not available (U.S. EPA 2007b). 

The decrease in unit cost as the number of units 

produced increases is referred to as an experience 

or learning curve. A useful description of the 

calculations used to identify a learning curve can 

be found in van der Zwaan and Rabl (2004). 

Learning rates for 26 energy technologies are 

described in McDonald and Schrattenholzer 

(2001). Dutton and Thomas (1984) summarize 

more than 100 studies, including some dealing 

with the energy and manufacturing sectors. Note 

that the empirical estimates in the literature 

represent a biased sample, since they only represent 

technology that has been successfully deployed 

(Sagar and van der Zwaan 2006).26 

8.3.2 Other Issues in Estimating 

Social Cost 

Difficulties in measuring social cost generally fall 

into two categories: (1) difficulties in developing a 

numeric value for some social cost categories; and 

(2) for social cost categories where numeric values 

have been successfully developed, accounting for 

uncertainty in these values. 

25 OMB’s Circular A-4 asserts that a cost analysis should incorporate 

credible changes in technology over time, stating that “...retrospective 

studies may provide evidence that ‘learning’ will likely reduce the 

cost of regulation in future years” (OMB 2003). Other cost-saving 

innovations to consider include those resulting from a shift to 

regulatory performance standards and incentive-based policies. 

26 Note that cost decreases associated with technological change 

and learning may not always be free but may have additional costs 

associated with them such as training costs. See Section 8.2.3.1 for a 

discussion of transitional costs. 

8.3.2.1 Difficulties in Developing 

Numeric Values 

Some consequences of environmental policies are 

difficult to represent in the definitive, quantitative 

terms of conventional social cost analysis. 

Irreversible environmental impacts, substantial 

changes in economic opportunities for certain 

segments of the population, social costs that span 

very long time horizons, socioeconomic effects 

on populations, and poorly-understood effects on 

large-scale ecosystems are difficult to capture in a 

quantitative BCA. Some alternative techniques for 

measuring and presenting these effects to policy 

makers are reviewed in Section 7.6.3. The relative 

significance of social cost categories that are not 

quantified — or are quantified but not valued — 

should be described in the social cost analysis. 

8.3.2.2 Uncertainty 

The values of various costs in the social cost 

analysis can be estimated, but cannot be known 

with certainty. In fact, some data and models will 

likely introduce substantial uncertainties into 

these estimates. Numerous assumptions are made 

regarding the baseline, predictions of responses to 

policy, and the number of affected markets. The 

conclusions drawn in the social cost analysis are 

sensitive to the degree of uncertainty regarding 

these assumptions. The uncertainty associated 

with the data and methods, the assumptions made, 

and how the uncertainty and assumptions affect 

the results are all-important components of the 

presentation of social cost, and should be carefully 

reported. 

8.3.2.3 Estimating Costs Under Different 

Statutory Criteria 

Some statutes require EPA to choose a regulatory 

option that is demonstrably affordable. One way 

for a decision maker to ensure that a regulatory 

option is affordable is to estimate an upper bound 

of the compliance cost associated with the chosen 

option and then to show that it is affordable. 

However, this approach is inconsistent with the 

practice of producing the best central estimate of 

the cost of a regulation for the RIA and will cause 

the net benefits of the regulation to be biased 
Guidelines for Preparing Economic Analyses | December 2010 8-13 

Chapter 8 Analyzing Costs

 

 

 

 

 

 

Table 8.2 - Major Attributes of Models Used in the Estimation of Costs 

Compliance Partial Linear Input-Output 

Cost Equilibrium Programming Input-Output Econometric CGE 

Can be used to measure • • 

direct compliance costs 

Can be used to measure • • • • • 

transitional costs 

Can be used to measure • • • • • 

distributional impacts 

Can capture indirect effects • • •
Ê

Can capture feedback and 

interaction effects • 

downward. Furthermore, using solely an upper 

bound estimate of the cost of a regulation could 

result in artificially low levels of regulation in 

situations where EPA must determine whether or 

not the benefits of the regulation justify the costs. 

It is thus very important that analysts rely on the 

best central estimate of the cost of a regulation for 

the RIA. 

8.3.3 Use of Externally-Produced 

Cost Estimates 

At various times EPA depends on externally (e.g., 

contractor, industry association, or advocacy 

group) generated cost estimates for use in its 

internal analyses. Any cost estimate produced by 

an external source and used by EPA in its internal 

analysis should be vetted by EPA to ensure that: 

(1) the information is relevant for its intended 

use; (2) the scientific and technical procedures, 

measures, methods and/or models employed to 

generate the information are reasonable for, and 

consistent with, the intended application; and (3) 

the data, assumptions, methods, quality assurance, 

sponsoring organizations, and analyses employed 

to generate the information are well documented. 

8.4 Models Used in Estimating 

the Costs of Environmental 

Regulation 

A number of different types of models have been 

used in the estimation the costs of environmental 

regulation. They range from models that estimate 

costs in a single industry (or part of an industry), 

to models that estimate costs for the entire 

economy. In practice, implementation of some of 

the models can be simple enough to be calculated 

in a spreadsheet. Others may be complex systems 

of thousands of equations that require highly 

specialized software.27 

Table 8.2 summarizes some of the major attributes 

of the models discussed in this section. Each has 

strengths and weaknesses in analyzing different 

types of economic costs. When estimating social 

cost, there will be some cases where a single model 

is enough to provide a reasonable approximation. 

In other cases the use of more than one model 

is required. For example, a compliance cost 

model can be used to estimate the direct costs of 

a regulation in the affected sector. These direct 

cost estimates could then be used in a partial 

equilibrium model to estimate social cost. While 

most of the models discussed in this section can be 

used in some form in the estimation of social cost, 

many of them also have particular strengths in the 

estimation of transitional and/or distributional 

costs, as may be required as part of an RIA. 

Selecting the most appropriate model (or models) 

to use in an analysis can be difficult. Below are a 

number of factors that may be helpful in making a 

choice.28 

27 Data requirements for these models vary. Refer to Chapter 9 for a 

discussion of the process of conducting an industry profile and details 

on a range of public and private data sources that can be used for cost 

estimation. 

28 This list of factors is derived from Industrial Economics, Inc. (2005). 

Proprietary models discussed in this section are examples only and no 

endorsement by EPA is given or implied. 
8-14 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 8 Analyzing Costs

 

 

 

• Types of impacts being investigated. Model 

selection should take into account the types 

of impacts that are important in the analysis 

being performed because models differ in 

their abilities to estimate different types of 

costs. 

• Geographic scope of expected impacts. 

While some models may be well suited for the 

analysis of impacts on a national scale, it may 

not be possible to narrow their resolution to 

focus on regional or local impacts. Similarly, 

models that are well suited for examining 

regional or local impacts may not capture the 

full range of impacts at the national level. 

• Sectoral scope of expected impacts. Some 

models are highly aggregated, and while 

proficient at capturing major impacts and 

interactions between sectors, are not well 

suited for focusing on a single or small 

number of specialized sectors. Likewise, 

models that are highly specialized for 

capturing impacts in a particular sector 

will usually be inappropriate for examining 

impacts on a broader set of sectors. 

• Expected magnitude of impacts. A model 

that is well suited for capturing the impacts 

of a regulation that is expected to have large 

effects may have difficulty estimating the 

impacts of a regulation with relatively smaller 

expected effects, and vice versa. 

• Expected importance of indirect effects. 

For a regulation that is expected to have 

substantial indirect effects beyond the 

regulated sector it is important to choose a 

model that can capture those effects. 

Usually, some combination of the above factors 

will determine the most appropriate model for 

a particular application. Finally, it should be 

noted that advances in computing power, data 

availability, and more user-friendly software 

packages continually reduce the barriers to 

sophisticated model-based analysis. 

8.4.1 Compliance Cost Models 

Compliance cost models are used to estimate 

an industry’s direct costs of compliance with 

a regulation. Estimates by engineers and other 

experts are used to produce algorithms that 

characterize the changes in costs resulting from 

the adoption of various compliance options. The 

particular parameters are usually determined for a 

number of individual plants with varying baseline 

characteristics. To estimate the control costs of a 

regulation for an entire industry, disaggregated 

data that reflects the industry’s heterogeneity 

is input into the model. The disaggregated cost 

estimates are then aggregated to the industry level. 

Compliance cost models may include capital costs, 

operating and maintenance expenditures, and costs 

of administration. Some compliance cost models 

are designed to allow the integrated estimation of 

control costs for multiple pollutants and multiple 

regulations. Some models are able to account for 

cost changes over time, including technical change 

and learning. Compliance cost models often are 

implemented in a spreadsheet; in general, they are 

relatively easy to modify and interpret. 

While precise estimates of compliance costs are 

an important component of any analysis, it is only 

in cases where the regulation is not expected to 

significantly impact the behavior of producers 

and consumers that compliance costs can be 

considered a reasonable approximation of social 

cost. As discussed in Section 8.2.1, estimating 

social cost often requires knowledge of both supply 

and demand conditions. Compliance cost models 

focus on the supply side, and in circumstances 

where producer and consumer behavior is 

appreciably affected, these models are not able to 

provide estimates of changes in industry prices 

and output resulting from the imposition of a 

regulation. However, in these cases, estimates from 

compliance cost models can be used as inputs to 

other models that estimate social cost. 

One example of a compliance cost model or tool is 

AirControlNET (ACN). ACN is a database tool for 

conducting pollutant emissions control strategy and 

costing analysis. It overlays a detailed control measure 

database of EPA emissions inventories to compute 

source- and pollutant-specific emission reductions 

and associated costs at various geographic levels 

(national, regional, local) and for many industries. 
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Chapter 8 Analyzing Costs

 

 

 

 

 

 

 

 

ACN contains a database of control measures and 

cost information that can be used to assess the 

impact of strategies to reduce criteria pollutants [e.g., 

NOX, SO2

, volatile organic compounds (VOCs), 

PM , PM , or Ammonia (NH )] as well as carbon 10 2.5	 3

monoxide (CO) and mercury (Hg) from point 

(utility and non-utility), area, nonroad, and mobile 

sources as provided in EPA’s National Emission 

Inventory (NEI). ACN is strictly a compliance cost 

model, because it does not account for changes in the 

behavior of consumers and producers. 

Advantages: 

• Compliance cost models often contain 

significant industry detail and provide 

relatively precise estimates of the direct costs 

of a regulation. This is particularly true for 

regulations with minor cost impacts. 

• Once constructed, compliance cost models 

require a minimum of resources to implement 

and are relatively straightforward to use and 

easy to interpret. 

Limitations: 

• As they are focused exclusively on the supply 

side, compliance cost models can only provide 

estimates of social cost in certain limited cases. 

• Compliance cost models are usually limited to 

estimating costs for a single industry. 

8.4.2 Partial Equilibrium Models 

While compliance cost models may provide 

reasonable estimates of the compliance costs of 

a regulation, they do not incorporate the likely 

behavioral responses of producers and consumers. 

As shown in Section 8.2.1, if these responses are 

not taken into account, estimates of social cost are 

likely to be inaccurate. In cases where the effects 

of a regulation are confined to a single market, 

partial equilibrium models, which incorporate the 

behavioral responses of producers and consumers, 

can be used to estimate social cost. 

Inputs into an analysis employing a partial 

equilibrium model may include regulatory costs 

estimated using a compliance cost model and the 

supply and demand elasticities for the affected 

market. The model then can be used to estimate 

the change in market price and output. Changes 

in producer and consumer surplus reflect the 

social cost of the regulation. The relative changes 

between producer and consumer surplus provide 

an estimate of the distribution of regulatory costs 

between producers and consumers. 

In a partial equilibrium model, the magnitude 

of the impacts of a regulation on the price and 

quantity in the affected market depends on the 

shapes of the supply and demand curves. The 

shapes of these curves reflect the underlying 

elasticities of supply and demand. These elasticities 

can be either estimated from industry and 

consumer data or taken from previous studies.29 

If the elasticities used in an analysis are drawn from 

previous studies, they should be consistent with 

the following conditions: 

• They should reflect a similar market structure 

and level of aggregation; 

• There should be sensitivity to potential 

differences in regional elasticity estimates; 

• They should reflect current economic 


conditions; and 


• They should be for the appropriate time 


horizon (i.e., short or long run). 


In some cases, if the effects of a regulation are 

expected to spill over into adjoining markets 

(e.g., suppliers of major inputs or consumers of 

major outputs), partial equilibrium analysis can 

be extended into these additional markets as well. 

These “multi-market models” have been used in the 

analysis of a number of EPA regulations.30 

29	 Because of the widespread use of elasticity estimates, the Air Benefit 

and Cost (ABC) Group in EPA’s Office of Air and Radiation maintains 

an elasticity database. This Elasticity Databank serves as a searchable 

database of elasticity parameters across economic sectors/product 

markets and a variety of types including demand and supply elasticities, 

substitution elasticities, income elasticities, and trade elasticities. 

An online submittal form allows users to provide elasticity estimates 

for consideration as part of this databank. The Elasticity Databank is 

available online at http://www.epa.gov/ttn/ecas/Elasticity.htm (U.S. EPA 

2007d). 

30	 See, for example, U.S. EPA (1989) Regulatory Impact Analysis of 

Controls on Asbestos and Asbestos Products: Final Report. 
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Chapter 8 Analyzing Costs

 

 

 

 

 

 

 

Advantages: 

• Because they usually simulate only a single 

market, partial equilibrium models generally 

have fairly limited data requirements and are 

relatively simple to construct. 

• Partial equilibrium models are comparatively 

easy to use and interpret. 

Limitations: 

• Partial equilibrium models are limited to 

cost estimation in a single or small number 

of markets and do not capture indirect or 

feedback effects. 

• Because partial equilibrium models are 

generally data driven and specific to a 

particular application, they are usually not 

available “off-the-shelf ” for use in a variety of 

analyses. 

8.4.3 Linear Programming Models 

Although linear programming models can be 

employed in a variety of applications, their use 

in the analysis of EPA regulations occurs most 

frequently in the estimation of compliance 

costs.31 Linear programming models minimize (or 

maximize) an objective function by choosing a set 

of decision variables, subject to a set of constraints. 

In EPA’s regulatory context, the objective function 

is usually direct compliance costs, which are 

minimized. The decision variables represent the 

choices available to the regulated entities. The 

constraints may include available technologies, 

productive capacities, fuel supplies, and regulations 

on emissions. 

Although linear programming models can be 

constructed to examine multiple sectors or 

economy-wide effects, they are more commonly 

focused on a single sector. For the regulated sector, 

a linear programming model can incorporate a 

large number of technologies and compliance 

options, such as end-of-pipe controls, fuel 

31 An introduction to linear programming is provided in Chiang (1984). 

The “linear” in the name refers to the linear specification of the 

objective function and constraint equations. Similar, eponymous model 

types include non-linear, integer, and mixed integer programming 

models. 

switching, and changes in plant operations. 

Similarly, the model’s constraints can include 

multiple regulations that require simultaneous 

compliance. The objective function usually 

includes the fixed and variable costs of each 

compliance option. The program then chooses a 

set of decision variables that minimize the total 

costs of compliance. In addition to compliance 

costs, the outputs from the model may include 

other related variables, such as projected fuel use, 

output and input prices, emissions, and demand 

for new capacity in the regulated industry. 

An example of a linear programming model used 

by EPA is the Integrated Planning Model (IPM). 

The IPM is a model of the electric power sector 

in the 48 contiguous states and the District of 

Columbia. It can provide long-term (10-20 year) 

estimates of the control costs of complying with 

proposed regulations, while meeting the projected 

demand for electricity. In the model, nearly 13,000 

existing and planned electrical generating units 

are mapped to approximately 1,700 representative 

plants. Results are differentiated into 40 distinct 

demand and supply regions. IPM can be used to 

estimate the impacts on costs for policies to limit 

emissions of SO2

, NOx, CO2, and Hg. 

Advantages: 

• Compared to compliance cost models, 

linear programming models are better able 

to incorporate and systematically analyze 

a wide range of technologies and multiple 

compliance options. 

• Linear programming models allow for a 

considerable amount of flexibility in the 

specification of constraints. This permits 

an existing model to be used in a range of 

applications. 

Limitations: 

• Linear programming models normally do 

not estimate costs beyond a single sector 

and are thus unable to estimate indirect or 

distributional costs. 
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Chapter 8 Analyzing Costs

 

 

 

 

 

 

 

 

 

 

 

Table 8.3 - Input-Output Table for the United States, 1999 (bil. $) 

Total 

1 2 3 Intermediate Final Total 

Agriculture Manufacturing Services Outputs Demand Outputs 

1 Agriculture 70 150 30 250 30 280 

2 Manufacturing 50 1,930 840 2,820 2,470 5,290 

3 Services 60 1,070 2,810 3,940 6,780 10,720 

Total Intermediate Inputs 180 3,150 3,680 7,010 9,280 16,290 

Value Added 100 2,140 7,040 9,280 

Total Inputs 280 5,290 10,720 16,290 

Source: Adapted from Bureau of Economic Analysis (BEA) 10-sector table. 

• A linear programming model designed for 

estimating sectoral compliance costs will 

likely be quite complex and have heavy input 

requirements. If an existing model is not 

available, the time and effort to construct one 

may be prohibitive. 

• Linear programming models minimize 

aggregate control costs for the entire industry 

simultaneously, whereas the regulated entities 

actually do so individually. This may result in 

an underestimation of total compliance costs. 

8.4.4 Input-Output Models 

While input-output models have been used in 

many environmental applications, their primary 

use in a regulatory context is for estimating the 

distributional and short-term transitional impacts 

that may result from the implementation of a 

policy. For example, an input-output model 

could be used to estimate the regional economic 

effects of a regulation that would ban a particular 

pesticide. In this case, an input-output model 

could provide estimates of the effects on output 

and employment in the affected region. A key 

feature of input-output models is their ability to 

capture both the effects on sectors directly affected 

by a regulation and the indirect effects that occur 

through spillovers onto other sectors.32 

An input-output model is based on an inputoutput table. The input-output table assembles 

data in a tabular format that describes the 

32	 Miller and Blair (1985) is a standard reference on input-output 

analysis. 

interrelated flows of goods and factors of 

production over the course of a year. An inputoutput table may consist of hundreds of sectors 

or may be aggregated into as few as two or three 

sectors. Table 8.3 is an example of a highly 

aggregated input-output table for the United 

States for the year 1999. The columns for the 

individual sectors denote how much of each 

commodity is used in the production of that 

sector’s output. These intermediate inputs are 

combined with factors of production — labor, 

capital, and land — whose payments as wages, 

profits, and rents, compose sectoral value added. 

For the agricultural sector, total inputs consist of 

$70 billion of agricultural inputs, $50 billion of 

manufactured inputs, $60 billion of service inputs, 

and $100 billion of value added, for a total of $280 

billion in inputs. The row for each sector shows 

how that sector’s output is consumed. In the case 

of the agricultural sector, $250 billion is consumed 

as intermediate inputs, while the remainder, $30 

billion, is consumed as final demand, which is 

composed of household consumption, government 

purchases, and investment. 

An input-output table can be turned into a simple 

linear model through a series of matrix operations. 

The model relates changes in final demand to 

changes in the total amount of goods and services, 

including intermediate inputs, required to meet 

that demand. The model can also relate the change 

in final demand to changes in employment of 

factors of production, such as the demand for 

labor. In the case of the banned pesticide, if a 

separate analysis determines that there will be a 
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Chapter 8 Analyzing Costs

 

 

 

 

 

 

decline in the output of cotton, the input-output 

model could be used to determine the effect on 

those sectors that supply inputs to the cotton 

sector, as well as on industries that are users of 

cotton, such as the producers of textiles and 

clothing. Declines in the output of these industries 

will have further effects on the demand for other 

intermediate inputs, like electricity, which are also 

estimated by the model. 

Input-output models are relatively simple to use 

and interpret and are often the most accessible 

tool for analyzing the short-term impacts of 

a regulation on regional output and income.33 

However, they embody a number of assumptions 

that make them inappropriate for long-term 

analysis or the analysis of social cost. Although 

their specifications can sometimes be partially 

relaxed, input-output models embody the 

assumptions of fixed prices and technology, which 

do not allow for the substitution that normally 

occurs when goods become more or less scarce. 

Similarly, input-output models are demand driven 

and not constrained by limits on supply, which 

would normally be transmitted through increases 

in prices. While the rigidities in the models may 

be reasonable assumptions in the short run or 

for regional analysis, they limit the applicability 

of input-output models for long-run or national 

issues. Because input-output models do not 

include flexible supply-demand relationships or 

the ability to estimate changes in producer and 

consumer surpluses, they are not appropriate for 

estimating social cost. 

Advantages: 

• Particularly in a regional context, input-


output models are often well suited for 


estimating distributional and short-term 


transitional impacts. 


• Input-output models are relatively transparent 

and easy to interpret. 

33 An off-the-shelf input-output model often used in the analysis of the 

impacts of environmental regulation is Impact Analysis for Planning 

(IMPLAN). IMPLAN is based on data for the United States that covers 

more than 500 sectors and can be disaggregated down to the county level. 

• Some input-output models have a great deal 

of sectoral and regional disaggregation and 

can be readily applied to issues that require a 

high degree of resolution. 

Limitations: 

• Input-output models are not appropriate for 

estimating social cost. 

• Because of their lack of endogenous 

substitution possibilities in production, inputoutput models are not appropriate for dealing 

with long-run issues. 

• Because of their fixed prices and lack of 

realistic behavioral reactions by producers and 

consumers, input-output models are not well 

suited for dealing with issues that are likely to 

have large effects on prices. 

8.4.5 Input-Output 

Econometric Models 

Input-output econometric models are economywide models that integrate the structural detail 

of conventional input-output models with 

the forecasting properties of econometrically 

estimated macroeconomic models. Input-output 

econometric models are often constructed with a 

considerable amount of regional detail, including 

the disaggregation of regional economies at the 

state and county level. At EPA, input-output 

econometric models, like conventional inputoutput models, are often used to examine the 

regional impacts of policies and regulations. 

However, unlike conventional input-output 

models, input-output econometric models are also 

able to estimate long-run impacts. 

When used for policy simulations, a major 

limitation of conventional input-output models 

is that the policy under consideration must 

be translated into changes in final demand. 

Furthermore, because they do not include 

resource constraints, the resulting solution 

may not be consistent with the actual supplydemand conditions in the economy. Input-output 

econometric models, in contrast, are driven 

by econometrically estimated macroeconomic 
Guidelines for Preparing Economic Analyses | December 2010 8-19 

Chapter 8 Analyzing Costs

 

 

 

 

relationships that more accurately account for 

these conditions. However, unlike standard 

macro-econometric models, input-output 

econometric models integrate input-output data 

and structure into the specification of production. 

This allows them to estimate changes in the 

demand for and the production of intermediate 

goods. The macroeconomic component enables 

the models to be used for long-run forecasting, 

including accounting for business cycles and 

involuntary unemployment. This makes inputoutput econometric models particularly useful 

for estimating transitional costs arising from the 

implementation of a regulation. 

An example of an input-output econometric 

model that has been used for policy analysis at 

EPA is the Regional Economic Models, Inc. 

(REMI) Policy Insight. The standard REMI 

model includes 70 production sectors and 25 

final demand sectors and can provide output on 

changes in income and consumption for more than 

800 separate demographic groups. The model is 

both national in scope and can be specially tailored 

to individual regions. The REMI model has been 

applied to a wide range of regional environmental 

policy issues, including extensive analysis of air 

quality regulation in the greater Los Angeles area. 

Advantages: 

• Input-output econometric models can be 

used to estimate both long- and short-run 

transitional costs. 

• Input-output econometric models can be used 

to estimate distributional costs. 

Limitations: 

• Because input-output econometric models 

combine elements of both macro and micro 

theory, it may not be easy to disentangle the 

mechanisms actually driving model results. 

• Compared to standard input-output models, 

input-output econometric models may not 

have the sectoral resolution necessary to 

analyze the impact of a policy expected to 

have limited impacts. 

8.4.6 Computable General 

Equilibrium Models 

CGE models have been used in a number of 

applications in the analysis of environmental 

regulation. Examples include estimation of the 

costs of the Clean Air Act (CAA), the impacts 

of domestic and international policies for GHG 

abatement, and the potential for market-based 

mechanisms to reduce the costs of regulation. 

CGE models simulate the workings of the price 

system in a market economy. Markets exist for 

commodities and can also be specified for the 

factors of production: labor, capital, and land. In 

each market, a price adjusts to equilibrate supply 

and demand. A CGE model may contain several 

hundred sectors or only a few, and may include 

a single “representative” consumer or multiple 

household types. It may focus on a single economy 

with a simple representation of foreign trade, or 

contain multiple countries and regions linked 

through an elaborate specification of global trade 

and investment. The behavioral equations that 

govern the model allow producers to substitute 

among inputs and consumers to substitute among 

final goods as the prices of commodities and 

factors shift. The behavioral parameters can be 

econometrically estimated, calibrated, or drawn 

from the literature. In some models, agents may 

be able to make intertemporal trade-offs in their 

consumption and investment choices. 

Simulating the effects of a policy change involves 

“shocking” the model, by, for example, introducing 

a regulation, such as a tax on emissions. Prices 

in affected markets will then move up or down 

until a new equilibrium is established. Prices 

and quantities in this new equilibrium can be 

compared to those in the initial equilibrium. 

A static CGE model will be able to describe 

changes in economic welfare measures due to a 

reallocation of resources across economic sectors 

following a policy shock. In a policy simulation 

using a dynamic CGE model, a time path of 

new prices and quantities is generated. This 

time path can be compared to a baseline path of 

prices and quantities that is estimated by running 

the model without the policy shock. As some 

policies can be expected to have impacts over a 
8-20 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 8 Analyzing Costs

Text Box 8.2 - The Pollution Abatement Costs and Expenditures Survey 

The Pollution Abatement Costs and Expenditures (PACE) survey is the primary source of information on pollution 

abatement-related operating costs and capital expenditures for the U.S. manufacturing sector (U.S. Bureau of the 

Census, various years). The PACE survey collects data on costs of pollution treatment (i.e., end-of-pipe controls), 

pollution prevention (i.e., production process enhancements to prevent pollution from being produced), disposal, 

and recycling. The survey is sent to approximately 20,000 establishments (who are required by law to respond to it) 

and was conducted annually by the U.S. Census Bureau from 1973 to 1994 (except in 1987) and then again in 1999. 

EPA funded the 1999 PACE survey. However, this survey was substantially different from its predecessors, making 

direct longitudinal analysis difficult (see Becker and Shadbegian 2005 for a comprehensive description of the 

conceptual differences between the 1994 and 1999 PACE surveys). More recently, with the guidance and financial 

support of EPA, a completely revised version of the PACE survey was administered by the Census Bureau to collect 

2005 data. The 2005 PACE survey was the result of a multi-year effort to evaluate the quality of the survey instrument 

and the accuracy and reliability of the responses to the survey. The 2005 PACE data, which was released in April 

2008, is longitudinally consistent with previous PACE surveys, with the exception of the 1999 iteration. EPA has no 

current plan to collect PACE data beyond 2005, but hopes to reinstate the survey in the future to once again collect 

data on an annual basis. The annual collection of pollution abatement costs would provide EPA with information 

required for its RIAs, and would better enable researchers to answer questions of interest, particularly those that 

require longitudinal data. 

The PACE survey contains operating costs and capital expenditures disaggregated by media: air, water, and solid 

waste; and by abatement activity: pollution treatment, recycling, disposal, and pollution prevention. Total operating 

costs are further disaggregated into: salary and wages, energy costs, materials and supplies, contract work, and 

depreciation. 

The PACE survey data, both aggregate and establishment-level, have been used to analyze a wide range of policy 

questions. These include assessing the impact of pollution abatement expenditures on productivity growth, 

investment, labor demand, environmental performance, plant location decisions, and international competitiveness. 

longer time horizon, dynamic models are used to 

capture, in addition to static impacts, the welfare 

consequences of reallocating resources over time, 

such as the impact that changes in savings may 

have on capital accumulation. Forward-looking 

models can also capture the effects that future 

policies may have on current decisions. 

An example of the use of a CGE model at EPA 

is the retrospective BCA of the CAA, which 

used a dynamic CGE model to compute the 

costs of CAA compliance over the period 

1970 to 1990 (U.S. EPA 1997a). Estimates of 

pollution abatement expenditures for the U.S. 

manufacturing sector were first calculated using 

Pollution Abatement Costs and Expenditures 

(PACE) survey data (see Text Box 8.2). As the 

analysis was retrospective, the relevant policy 

simulations involved removing the long-term 

capital and operating costs from the industries that 

incurred them. The retrospective BCA compared 

the simulated path of the economy without these 

abatement expenditures and the actual path of the 

economy, which included them. EPA computed 

changes in both long-run GDP and equivalent 

variation, as well as impacts on investment, 

household consumption, and sectoral prices, 

output, and employment. 

CGE models have also been used extensively in 

estimating the costs of GHG mitigation. Here, 

the analyses have been prospective, such as efforts 

to estimate the costs of complying with the Kyoto 

Protocol and more recently, proposed climate 

change legislation. Some studies have focused 

on the control of CO2

 emissions by introducing 
Guidelines for Preparing Economic Analyses | December 2010 8-21 

Chapter 8 Analyzing Costs

 

 

 

 

 

carbon taxes or emissions trading. Other studies 

have expanded the analysis by examining 

other GHGs and incorporating the effects of 

changes in land use patterns and carbon sinks. 

Of particular concern has been the problem 

of “leakage,” in which a fall in emissions in 

participating countries is offset by an increase in 

emissions in non-participating countries, induced 

by the fall in demand, and thus the world price, 

of energy inputs. 

CGE models can be useful tools for examining 

the medium- to long-term impacts of policies 

that are expected to have relatively large, 

economy-wide effects. A growing use of 

these models has been to quantify previously 

unrecognized welfare costs that can occur when 

environmental policies interact with pre-existing 

distortions in the economy. An expanding 

body of work has begun to include non-market 

goods into CGE models (Smith et al. 2004, and 

Carbone and Smith 2008). 

Given the large number of parameters in a 

typical CGE model, analysts should take great 

care in ensuring the accuracy of a model’s data 

and specifications. Sensitivity analysis should be 

performed on critical parameters. One strategy, 

currently used in EPA’s analyses of climate 

legislation, is to use two CGE models concurrently 

to analyze the same policy scenarios. 

Advantages: 

• CGE models are best suited for estimating the 

cost of policies that will have large economywide impacts, especially when indirect 

and interaction effects are expected to be 

significant. 

• CGE models are generally most appropriate 

for analyzing the medium- or long-term 

effects of policies or regulations. 

• With the appropriate specifications 

incorporated, CGE models can be used to 

estimate the distributional impacts of policy 

shocks on household groups or industrial 

sectors. 

Limitations: 

• Because of their equilibrium assumptions, 

CGE models are generally not appropriate 

for analyzing short-run transitional costs. 

However, when appropriate specifications are 

included in a model, they may be used in this 

type of analysis. 

• CGE models are generally not well suited 

for estimating the effects of policies that 

will affect only small sectors or will impact 

a limited geographic area. Although the 

costs have been reduced in recent years, the 

effort and data required to construct a new 

CGE model or revise an existing one may be 

prohibitive for some analyses. 
8-22 Guidelines for Preparing Economic Analyses | December 2010 
 

 

 

 

 

Chapter 9 

Economic Impact Analysis
 

T

he detailed study of regulatory consequences allows policy makers to fully 

understand a regulation’s impacts, and to make an informed decision on its 

appropriateness. Economic information is necessary for the evaluation of 

at least two types of consequences of a regulatory policy: the regulation’s 

efficiency, and its distributional effects. In principle, both could be estimated 

simultaneously using a general equilibrium model. In practice however, they are usually 

estimated separately. 

The distributional effects of environmental regulations can be examined through an 

economic impact analysis (EIA). A related analysis, called an equity assessment, addresses 

the distribution of impacts across individuals and households, with particular attention to 

economically or historically disadvantaged or vulnerable groups (e.g., low-income households, 

racial or ethnic minorities, and young children). Equity assessments are sometimes referred to 

as environmental justice (EJ) analyses and are the subject of Chapter 10. 

An EIA identifies the specific entities that benefit from or are harmed by a policy, and 

then estimates the magnitude of their gains and losses including changes in profitability, 

employment, prices, government revenues or expenditures, and trade balances. These 

estimates are derived from a study of the economic changes that occur across broadly-defined 

economic sectors of society, including industry, government, and not-for-profit organizations, 

but may also include more narrowly defined sectors within these broad categories, such as the 

solid waste industry or even an individual solid waste company. EIAs can measure a broad 

variety of impacts, such as direct impacts on individual plants, whole firms, and industrial 

sectors, as well as indirect impacts on consumers and suppliers. 

9.1 Statutes and Policies • EO 13132, “Federalism”; 

The following major statutes and EOs, all described in • EO 13175, “Consultation and Coordination Chapter 2, directly address impact analyses:1 

with Indian Tribal Governments;” and 

• Regulatory Flexibility Act of 1980 (RFA), as • EO 13211, “Actions Concerning Regulations 

amended by the Small Business Regulatory That Significantly Affect Energy Supply, 

Enforcement Fairness Act of 1996 (SBREFA); Distribution, or Use.” 

•Unfunded Mandates Reform Act of 1995 (UMRA);
 

Together with OMB’s Circular A-4, they raise 


1	 EPA’s Regulatory Management Division’s Action Development Process important dimensions relevant for economic impact (ADP) Library (http://intranet.epa.gov/adplibrary) is a resource for those analyses as summarized in Table 9.1. who wish to access relevant statutes, EOs, or Agency policy and guidance 


documents in their entirety. 


Guidelines for Preparing Economic Analyses | December 2010 9-1 
9-2 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 9 Economic Impact Analyses

Table 9.1 - Potentially Relevant Dimensions to Economic Impact Analyses2 

Dimension 

Statute, Order, or 

Directive Entity Subpopulation 

Sector UMRA; EO 13132; OMB 

Circular A-4 

Industry or government Industries or state, local, or tribal 

governments 

Entity size RFA; UMRA; OMB Circular 

A-4 

Businesses, governments, or 

not-for-profit organizations 

Small businesses, small governmental 

jurisdictions, or small not-for-profit 

organizations 

Time OMB Circular A-4 Individuals or households Current or future generations 

Geography OMB Circular A-4; UMRA Region Regions, states, counties, or 

non-attainment areas 

Energy EO 13211 Entities that use, distribute, or 

generate energy 

Energy sector 

The term “affected” is used throughout this 

chapter as a general term. Analysts should be aware 

that the authorizing statute for the rule, as well 

as other applicable statutes and administrative 

orders noted in this chapter, may make more 

specific use of this term. For example, the 

Regulatory Flexibility Act includes the clause 

“subject to the requirements of the rule” when 

quantifying economic impacts, meaning that 

the analysis considers only those entities that are 

directly regulated by the rule. On the other hand, 

provisions in the UMRA and EO 12866 address 

both direct and indirect impacts, and therefore 

define the affected population more broadly. Care 

should be taken to avoid double counting when 

estimating direct and indirect impacts. 

9.2 Conducting an Economic 

Impact Analysis 

There are three important distinctions between 

BCA and EIA to keep in mind when conducting 

an EIA.

3

 First, total social benefits and total social 

costs are not of primary importance in an EIA, as 

they are in a BCA. Rather, the main focus is on 

the components and distribution of the total social 

benefits and costs. 

2 Some environmental statutes may also identify subpopulations that 

merit additional consideration. This document is limited to those 

statutes with broad coverage. 

3 Traditionally, EIAs focus on the costs of a particular rule or regulation. 

However, it is also possible to focus on the distribution of benefits or to 

calculate the net benefits for particular entities. 

Second, transfers of economic welfare from one 

group to another are no longer assumed to cancel 

each other out, as they do in a BCA. Taxpayers, 

consumers, producers, governments, and the many 

sub-categories of these groups are all considered 

separately. While a BCA relies on estimates of 

the social benefits and costs of a regulation, an 

EIA focuses on the private benefits and costs 

associated with compliance responses. The EIA 

should use the same “starting point” as the BCA 

(i.e., same engineering or direct compliance costs, 

same benefit categories, etc.) for developing 

private benefit and cost estimates. In addition, 

some adjustments to these costs may be needed, 

as discussed below. For example, the tax status of 

a required piece of equipment is considered in 

private costs, but not in social costs. 

Finally, there is a greater need for disaggregation in 

EIAs than in BCAs. Results may be presented for 

specific counties or other geographic units or types 

of entities, as appropriate, placing heavy demands 

on the modeling framework. 

For any regulation, it is essential to ensure 

consistency between the EIA and the benefitcost analysis (BCA). If a BCA is conducted, the 

corresponding EIA must be conducted within the 

same set of analytical assumptions. To the extent 

possible, adjustments to these assumptions or to 

the overall modeling framework used for the BCA 

should only be made when absolutely necessary, 

and then should be noted clearly in the text of the 

analysis. 
Guidelines for Preparing Economic Analyses | December 2010 9-3 

Chapter 9 Economic Impact Analyses

 

 

9.2.1 Screening for Potentially 

Significant Impacts 

A comprehensive analysis of all aspects of all 

economic impacts associated with a rule can 

require significant time and resources, and its 

accuracy and thoroughness depend on the quality 

and quantity of available data. Thus, screening 

analyses are often employed to determine data 

availability, the severity of a rule’s anticipated 

impacts, and the potential consequences of 

further analysis if undertaking it would require 

a delay in the regulatory schedule. A screening 

analysis can be thought of as a “mini-EIA” 

consisting of a rough examination of the data 

to identify sectors that may warrant further 

analysis.4

 Screening is effective for identifying 

the magnitude of the overall level of impacts on 

the regulated industry, but may fail to identify 

potentially large impacts on a single sector, region, 

or facility. 

There are no established definitions for what 

constitutes a large or a small impact. However, 

a screening analysis is a tiered approach that 

initially captures most of the possible impacts 

(i.e., allows for many false positives) followed by 

a more detailed analysis that can help eliminate 

unfounded impacts. In this way, the screening 

analysis will eventually balance the risk of 

identifying “false positives” and “false negatives.” 

9.2.2 Profile of Affected Entities 

Analysts should consider changes imposed by 

the rule in the regulated industry, as well as how 

related industries may be affected. Some industries 

may benefit from the regulation, while others may 

be subject to significant costs. If the regulation 

causes a firm to use different inputs or new 

technologies, then the producers of the new inputs 

will gain, while the producers of the old inputs 

will suffer. Developing a detailed industry profile 

will identify those industries that may be affected 

positively and negatively by the regulation. 

The screening analysis discussed in this section is distinct from the 

screening analysis required to comply with the Regulatory Flexibility 

Act (as referred to in Section 9.3). 

9.2.2.1 Compiling an Industry Profile 

and Projected Baseline 

To determine the impacts of a particular regulation 

the analyst must understand the underlying 

structure of the affected industry and its various 

linkages throughout the economy.5

 This includes 

an understanding of the condition of the industry 

in terms of its finances and structure in the absence 

of the rule —the baseline of the EIA. A rule may 

impose different requirements and costs on new 

versus existing entities. Such rules may affect 

industry competition, growth, and innovation 

by raising barriers to new entry or encouraging 

continued use of outdated technology. Thus, 

a substantial portion of an EIA involves 

characterizing the state of the affected firms and 

industries in the absence of the rule as a basis for 

evaluating economic impacts. 

The following are important inputs to defining an 

industry profile: 

• North American Industrial Classification 

System (NAICS) industry codes. NAICS 

has replaced the U.S. Standard Industrial 

Classification (SIC) system in the U.S. 

Department of Commerce (DOC) Economic 

Census and other official U.S. Government 

statistics. NAICS was developed to provide 

comparable statistics about business activity 

across North America. It identifies hundreds 

of new, emerging, and advanced technology 

industries and reorganizes existing industries 

into more meaningful sectors, particularly in 

the service sector.6 

• Industry summary statistics. Summary 

statistics of total employment, revenue, 

number of establishments, number of firms, 

and size of firms are available from U.S. DOC 

Economic Census or the Small Business 

Administration.7 

5	 Generally, analysts should initially assume a perfectly competitive 

market structure. One of the primary purposes of developing an 

industry profile is to confirm this assumption or discover evidence to 

the contrary. 

6	 For more information see www.census.gov/epcd/www/naics.html, 

which includes a NAICS/SIC correspondence (accessed on January 

21, 2011). 

7	 See www.sba.gov/advocacy/849 for more information (accessed on 

January 21, 2011). 

4 
9-4 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 9 Economic Impact Analyses

 

 

 

 

 

 

 

 

 

 

 

 

• Baseline industry structure. Industry-level 

impacts depend on the competitive structure 

and organization of the industry and the 

industry’s relationship to other economic 

entities. The number and size distribution 

of firms/facilities and the degree of vertical 

integration within the industry are important 

aspects of industry structure that affect the 

economic impact of regulations. 

• Baseline industry growth and financial 

condition. Industries and firms that are 

relatively profitable in the baseline will be 

better able to absorb new compliance costs or 

take advantage of potential benefits without 

experiencing financial distress. Industries that 

are enjoying strong growth may be better 

able to recover increased costs through price 

increases than they would if there were no 

demand growth. Section 9.3.3.3 provides 

suggestions for using financial ratios to assess 

the significance of economic impacts on a 

firm’s financial condition. 

• Characteristics of supply and demand. 

Assessing the likelihood of changes in 

production and prices requires information 

on the characteristics of supply and demand 

in the affected industries. The relevant 

characteristics are reflected in price elasticities 

of supply and demand, which, if available, 

allow direct quantitative analysis of changes 

in prices and production. Often, reliable 

estimates of elasticities are not available and 

the analysis of industry-level adjustments 

must rely on simplifying assumptions and 

qualitative assessments. See Appendix A for a 

discussion of elasticities. 

9.2.2.2 Profile of Government Entities 

and Not-for-Profit Organizations 

Analysts should carefully consider whether a 

particular rule will directly affect government 

entities, not-for-profit organizations, or 

households.8

 For example, air pollution regulations 

Government entities that may be affected include states, cities, 

counties, townships, water authorities, villages, Indian Tribes, special 

districts, and military bases. Not-for-profit entities that may be affected 

include not-for-profit hospitals, colleges, universities, and research 

institutions. 

that apply to power plants may affect government 

entities such as municipally-owned electric 

companies. Air regulations that apply to vehicles 

may affect municipal buses, police cars, and public 

works vehicles. Effluent guidelines for machinery 

repair activities may affect municipal garages. 

The profile of these affected entities should 

include a brief description of relevant factors or 

characteristics. 

Relevant factors for government entities 

may include: 

• Number of people living in the community; 

• Property values; 

• Household income levels (e.g, median, 


income range);
 

• Number of children; 

• Number of elderly residents; 

• Unemployment rate; 

• Revenue amounts by source; and 

• Credit or bond rating of the community. 

If property taxes are the major revenue source, 

then the assessed value of property in the 

community and the percentage of this assessed 

value represented by residential versus commercial 

and industrial property should be determined. If 

a government entity serves multiple communities, 

such as a regional water or sewer authority, then 

relevant information should be collected for all the 

communities covered by the government entity. 

Socioeconomic factors influence demands on state 

or local government resources; for example a high 

proportion of children means more educational 

resources. 

Data on community size, income, number of 

children and elderly, and unemployment levels 

are available from the U.S. Census Bureau. 

Data on property values, amount of revenue 

collected from each revenue source, and credit 

rating may be available from the community 

or state finance agencies. Most county websites 

provide information on property values. Private 

companies, such as Standard and Poor’s (S&P), 

or Fitch’s, provide community credit ratings. 

8 
Guidelines for Preparing Economic Analyses | December 2010 9-5 

Chapter 9 Economic Impact Analyses

 

 

 

 

 

Depending on the number of communities 

affected and the level of detail warranted, the 

analysis may rely on generally available aggregate 

data only. In other cases, a survey of affected 

communities may be necessary.9 

Relevant characteristics of not-for-profit 

entities include: 

• Entity size and size of community served; 

• Goods or services provided; 

• Operating costs; and 

• Amount and sources of revenue. 

If the entity is raising its revenues through user 

fees or charging a price for its goods or services 

(such as university tuition), then the income levels 

of its clientele are relevant. If the entity relies on 

contributions, then it would be helpful to know 

the financial and demographic characteristics of 

its contributors and beneficiaries. If it relies on 

government funding (such as Medicaid) then 

possible future changes in these programs should 

be identified. 

9.2.2.3 Profile of Small Entities 

Small entities include small businesses, 

small governments and small not-for-profit 

institutions. While these entities may require 

special considerations, as detailed below, the 

profiling of them should follow the same steps as 

discussed above. 

9.2.2.4 Data Sources for Profiles 

Profiles generally rely on information from 

the following sources: websites for affected 

communities, industry trade publications, and 

the U.S. Census Bureau.10 Relevant literature can 

be useful in characterizing industry activities and 

markets as well as regulations that already affect 

the industry. Relevant literature can usually be 

efficiently identified through a computerized 

9 In cases where a survey is needed, care should be taken to comply with 

the requirements of the Paperwork Reduction Act (PRA) (44 U.S.C. 

3501). 

10 Academic literature may or may not contain quantitative data. 

search using on-line services such as Dialog, BRS/ 

Search Services, Dow Jones News/Retrieval, or 

EconLit. These on-line services contain more 

than 800 databases covering business, economic, 

and scientific topic areas. Table 9.2 describes 

some commonly used data sources for retrieving 

quantitative data.11 

The industry profile may also identify situations 

where insufficient data are available from standard 

sources. This situation could potentially arise 

when the affected industry has many product 

lines or activities affected by the rule. In addition, 

for some rules it may be difficult to identify the 

appropriate NAICS industry for all the firms or 

facilities affected by the rule if the industry can be 

categorized in multiple ways. In these cases, and 

particularly if facility-level data are required to 

estimate economic impacts, a survey of affected 

facilities may be required to provide sufficient data 

for analysis. 

9.2.3 Detailing Impacts 

on Industry 

This section explains how to determine the impact 

on individual plants or businesses so as to identify 

whether a particular plant or industry is likely to 

bear a disproportionate portion of the costs or 

benefits of a regulation. 

9.2.3.1 Impacts on Prices 

Predicted impacts on prices form the basis 

for determining how compliance costs are 

distributed between the directly-affected firms, 

their customers, and other related parties in 

a typical market. At one extreme, regulated 

firms may not be able to raise prices at all, and 

would consequently bear the entire burden of 

the added costs in the form of reduced profits. 

Reduced profits may result from reduced 

earnings on continuing production, lost profits 

on products or services that are no longer 

produced, or some combination of the two. 

11 The Thomas Registry (www.thomasnet.com) is a source of qualitative 

information on manufacturing companies in the United States 

(accessed on January 21, 2011). In addition, Lavin (1992) provides 

sources of business information. 
9-6 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 9 Economic Impact Analyses

Table 9.2 - Commonly Used Profile Sources for Quantitative Data 

Source Data 

Trade Publications and Associations Market and technological trends, sales, location, regulatory 

events, ownership changes 

U.S. Department of Commerce, Economic Census 

(www.census.gov) 

Sales, receipts, value of shipments, payroll, number of 

employees, number of establishments, value added, cost 

of materials, capital expenditures by sector, household and 

community characteristics 

U.S. Department of Commerce, U.S. Industry & Trade 

Outlook 

(http://www.ita.doc.gov/td/industry/OTEA/outlook/ or 

http://outlook.gov/) 

Description of industry, trends, international 

competitiveness, regulatory events 

U.S. Department of Commerce, Pollution Abatement Costs 

and Expenditures Survey 

(www.census.gov/mcd) 

Pollution abatement costs for manufacturing facilities by 

industry, state, and region 

U.S. Department of Commerce, Census of Governments 

(www.census.gov/govs/index.html) 

Revenue, expenditures debt, employment, payroll, assets 

for counties, cities, townships, school districts 

United Nations, International Trade Statistics Yearbook Foreign trade volumes for selected commodities, major 

trading partners 

Risk Management Association, Annual Statement Studies 

(www.rmahg.org/ann_studies/asstudies.html) 

Income statement and balance sheet summaries, 

profitability, debt burden and other financial ratios, all 

expressed in quartiles and available for recent years (based 

on loan applicants only) 

Dun & Bradstreet Information Services 

(www.dnb.com/us/) 

Type of establishment, NAICS code, address, facility and 

parent firm revenues and employment 

Standard & Poors 

(www.standardandpoors.com) 

Publicly-held firms, prices, dividends, and earnings, 

line-of-business and geographic segment information, 

S&P ratings, quarterly history (10 years), income 

statement, ratio, cash flow and balance sheet analyses and 

trends 

Securities and Exchange Commission Filings and Forms 

(EDGAR System Database) 

(www.sec.gov/edgar.shtml) 

Income statement and balance sheet, working capital, cost 

of capital, employment, outlook, regulatory history, foreign 

competition, lines of business, ownership and subsidiaries, 

mergers and acquisitions 

Value Line Industry Reports Industry overviews, company descriptions and outlook, 

performance measures 

Suppliers to the directly-affected firms might 

bear part of the burden in lost earnings if the 

regulation results in a decline in demand for 

particular products.12 At the other extreme, 

firms may be able to raise prices enough to 

recover costs fully. In this case, there is no 

impact on the profitability of the directly12	 For example, regulations limiting SO2

 emissions may result in reduced 

demand for high-sulfur coal, which results in a fall in the price of such 

coal and lost profits for its producers. While there is no clear rule for 

how far down the chain of effects one needs to consider, it is important 

to address effects that are likely to be substantial. 

affected firms but their customers bear the 

burden of increased prices. Assuming perfect 

competition, the amount of price pass-through 

depends on the relative elasticity of supply and 

demand. Another economic impact to consider 

is the potential backward shifting of regulatory 

costs (e.g., lowering wages of workers). 

In general, the likelihood that price increases will 

occur can be evaluated by considering whether 

competitive conditions allow the affected 

facilities to pass their costs on to consumers. 
Guidelines for Preparing Economic Analyses | December 2010 9-7 

Chapter 9 Economic Impact Analyses

The methods used to conduct the analysis of 

the directly-affected markets depend on the 

availability of appropriate estimates of supply 

and demand elasticities.13 As noted above, in 

cases where reliable estimates of elasticities are 

not available, the analyst must rely on a more 

basic investigation of the characteristics of supply 

and demand in the affected market to reach a 

conclusion about the likelihood of full or partial 

pass-through of costs via price increases. An 

examination of the number of firms, quantity of a 

product produced, and industry size will provide 

basic information about supply and demand. 

If an industry is highly concentrated with few 

producers then firms may be able to easily pass 

costs on to households and a 100 percent passthrough assumption may be justifiable. Of course, 

an industry with many producers would mean the 

opposite assumption. 

9.2.3.2 Impacts on Production 

Abatement costs tend to be only a small fraction 

of total manufacturing revenues. As such, even 

small changes in wage rates, materials costs, or 

capital costs are likely to have a much larger effect 

on manufacturing industries than any changes 

in environmental regulation. The U.S. Census 

Bureau collects data on pollution abatement 

capital expenditures and operating costs incurred 

to comply with local, state, and federal regulations 

and on voluntary or market-driven pollution 

abatement activities.14 According to the 2005 

PACE Survey, the U.S. manufacturing sector 

spent approximately $20.7 billion dollars on 

pollution abatement operating costs. This figure 

represents less than 1 percent of the sector’s 

total revenue, which is similar to the historical 

average. Moreover, every manufacturing industry, 

including the most highly regulated ones, 

spend less than 1.2 percent of their revenues on 

pollution abatement. Figure 9.1 presents data 

for the five industries with the highest pollution 

abatement operating costs (PAOC) as a percent of 

total revenues. 

13	 See Appendix A for a more complete discussion of elasticity. 

14	 More detail on the PACE Survey is available at http://yosemite.epa.gov/ 

ee/epa/eed.nsf/pages/pace2005.html (accessed March 13, 2011). 

Figure 9.1 - Pollution Abatement Costs as a 

Percentage of Total Revenues for Industries with 

Highest Pollution Abatement Costs in 2005 

1.20 

1.00 

0.80 

0.60 

% 

0.40 

0.20 

0.00 

Primary Metal Paper Leather Industry 

Source: Pollution Abatement Costs and Expenditures: 2005 

Figure 9.2 - Pollution Abatement Costs are a very 

Small Percentage of Total Manufacturing Costs 

Pollution 

Abatement 

Profits, Taxes, 

Interests on Debt, 

Operating Costs 

0.4% Labor 

12.2% Energy 

2.0% 

and Other 

31.3% 

Depreciation 

2.4% Materials 

Source: U.S. Census Bureau, Pollution Abatement Costs and 

Expenditures: 2005 

U.S. Census Bureau, Annual Survey of Manufacturers: 2005 

Considering the historical data, it is unlikely 

that the typical pollution control regulation will 

sufficiently increase the cost of doing business 

so as to make a meaningful part of production 

unprofitable, or will significantly reduce the 

quantity of output demanded as producers raise 

their prices to maintain profitability. Figure 9.2 

shows the relative magnitude of each cost category 

for the manufacturing sector. Based on these 

relative magnitudes, reducing abatement costs by 

10 percent will only reduce the total costs faced 

by industry by less than 1 tenth of 1 percent. 

Conversely, lowering material costs by 10 percent 

will reduce total costs by just over 5 percent as 

Average 

=0.44 

Chemicals Petroleum 

51.7% 
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Chapter 9 Economic Impact Analyses

 

 

 

material costs were roughly 50 percent of revenues 

in 2005. Exceptions may be regulations banning 

the sale or manufacture of a specific product (e.g., 

a chemical ban) or when a production process 

is made obsolete. In these situations, the analyst 

should assess whether the existing plants have 

other profitable uses. 

9.2.3.3 Impacts on employment 

The chapters on benefits (Chapter 7) and costs 

(Chapter 8) point out that regulatory-induced 

employment impacts are not, in general, relevant 

for a BCA. For most situations, employment 

impacts should not be included in the formal 

BCA.15 However, if desired the analyst can assess 

the employment impacts of a regulation as part 

of an EIA. If this task is undertaken, the analyst 

needs to quantify all of the employment impacts, 

positive and negative, to present a complete 

picture of the effects. This section identifies 

pitfalls often encountered when performing an 

EIA and discusses the preferred approaches for 

conducting one. 

Many analyses only present the employment 

effect on the regulated industry as a result of 

higher regulatory compliance costs. In doing so, 

these analyses make simplifying assumptions that 

employment in a given industry is proportional to 

output, i.e., if production goes down by 1 percent, 

employment goes down by 1 percent. These 

limited assessments on employment impacts from 

regulation examine how higher manufacturing 

costs lead to fewer sales and therefore lower 

employment in that sector. However, empirical 

and theoretical modeling suggests that these 

simplified relationships are faulty and should not 

be used. 

In fact, it is not even clear that employment in 

the regulated industry goes down as a result of 

environmental regulation. Morgenstern et al. 

(2002) decompose the labor consequences in an 

industry facing increased abatement costs. They 

identify three separate components: 

15 Appendix C discusses long-term, structural employment changes 

brought on by land clean up and reuse or other policies that may have 

a benefit component to them. 

• Demand effect: Higher production costs 

raise market prices. Higher prices reduce 

consumption (and production) reducing 

demand for labor within the regulated 

industry; 

• Cost effect: As production costs increase, 

plants use more of all inputs including labor 

to produce the same level of output. For 

example, pollution abatement activities 

require additional labor services to produce 

the same level of output; and 

• Factor-shift effect: Post-regulation 

production technologies may be more or 

less labor intensive (i.e., more/less labor is 

required per dollar of output). 

Morgenstern et al. empirically estimate this model 

for four highly polluting/regulated industries 

to examine the effect of higher abatement costs 

from regulation on employment. They conclude 

that increased abatement expenditures generally 

do not cause a significant change in employment. 

Specifically, their results show that, on average 

across the industries they consider, each additional 

$1 million of spending on pollution abatement 

results in a (statistically insignificant) net increase 

of 1.5 jobs. However, they find that for two 

of their four industries (pulp and paper, and 

steel) additional abatement spending leads to a 

statistically significant, yet quite small, net increase 

in jobs due to the substitution of labor for other 

inputs and relatively inelastic estimated demand 

for their output.16 

Finally, one effect that Morgenstern et al. do 

not consider is the effect regulation has on 

employment in industries that make substitute 

products, often cleaner products. Demand for 

these products increases as consumers respond 

to changes in costs. For example, more expensive 

virgin paper will cause a shift to more recycled 

paper. The recycled paper industry will employ 

more workers as sales increase. Similarly, 

employment in industries that are complements 

16 These results are similar to Berman and Bui (2001) who find that while 

sharply increased air quality regulation in Los Angeles to reduce NOx 

emissions resulted in large abatement costs they did not result in 

substantially reduced employment. 
Guidelines for Preparing Economic Analyses | December 2010 9-9 

Chapter 9 Economic Impact Analyses

may decrease. The analyst should also take these 

effects into consideration when analyzing the 

effect of regulations on employment. 

In addition to the changes in the regulated 

industry as modeled by Morgenstern et al., the 

analyst should assess the increased employment 

in the environmental protection industry. The 

engineering analysis may provide some data on 

the labor required to design, build, install (and 

in some cases operate) the pollution control 

equipment. For example, a recent study by 

Industrial Economics Inc. shows that a $19 million 

order for a new scrubber will immediately fund 

77 to 91 new jobs for a year constructing and 

installing the new equipment. It will also create 

16 permanent jobs to operate the new equipment 

(Price et al. 2010). 

9.2.3.4 Impacts on Profitability and 

Plant Closures 

In other cases, analysts may assess the impacts 

of rules on the profitability of specific firms or 

industry segments and identify potential plant 

closures based on a financial analysis. If partial 

or full plant closures are projected, then it is 

important to consider whether the production 

lost at the affected facilities will be shifted to other 

existing plants or to new sources, or simply vanish. 

If excess industry capacity exists in the baseline 

and facilities are able to operate profitably while 

complying with the rule, then these facilities may 

expand production to meet the demand created by 

the loss of plants that are no longer able to operate 

profitably. Some surviving plants could experience 

increases in production, capacity utilization, 

and profits even though they are subjected to 

regulatory requirements, if their competitors face 

even greater cost increases. 

9.2.3.5 Impacts on Related Industries 

The economic and financial impacts of regulatory 

actions spread to industries and communities 

that are linked to the regulated industries and to 

the pollution abatement industries, resulting in 

indirect business impacts. To build scrubbers, the 

steel. If a plant produces less, it will order fewer 

raw materials. These indirect impacts may include 

employment and income gains and losses. 

Although in principle every economic entity 

can be thought of as having a connection with 

every other entity, practical considerations 

usually require an analysis of indirect impacts for 

a manageable subset of economic entities that 

are most strongly linked to the regulated entity. 

In addition to considering major customers and 

specialized suppliers of the affected industry, it is 

important to consider less obvious but potentially 

significant links, such as basic suppliers like 

electricity generators. 

For these reasons, the analysis of linkages should 

use a framework that thoroughly measures 

indirect as well as direct linkages. Whatever the 

approach, the goal of the analysis is to measure 

how employment, competitiveness, and income are 

likely to change for related entities and households 

given a certain amount of employment, 

competitiveness, and income in a regulated 

market. 

9.2.3.6 Impacts on Economic Growth 

and Technical Inefficiency 

While regulatory interventions can theoretically 

lead to macroeconomic impacts, such as growth 

and technical efficiency, such impacts may be 

impossible to observe or predict. In some cases, 

however, it may be feasible to use macroeconomic 

models to evaluate the regulatory impact on 

GDP, factor payments, inflation, and aggregate 

employment. For regulations that are expected to 

have significant impacts in a particular region, use 

of regional models, either general equilibrium or 

other regionally-based models, may be valuable.17 

Typically in regulatory impact analyses some 

macroeconomic regulatory effects go unquantified 

due to analytic constraints. For example, price 

changes induced by a regulation can lead to 

technical inefficiency because firms are not 

choosing the production techniques that minimize 

environmental protection industry will order more 17 Chapter 8 discusses the use of regional modeling. 
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Chapter 9 Economic Impact Analyses

 

 

 

 

 

 

 

 

 

 

 

the use of labor and other resources in the long 

run. However, measuring these effects can be 

difficult due to data or other analytical limitations. 

9.2.3.7 Impacts on Industry 

Competitiveness 

Regulatory actions that substantially change 

the structure or conduct of firms can produce 

indirect impacts by changing the competitiveness 

of the regulated industry, as well as that of 

linked industries.18 An analysis of impacts on 

competitiveness begins by examining barriers to 

entry and market concentration, and by answering 

the following two key questions: 

• Does the regulation erect entry barriers that 

might reduce innovation by impeding new 

entrants into the market? High sunk costs 

associated with capital costs of compliance or 

compliance determination and familiarization 

would be an entry barrier attributable to the 

regulation. Sunk costs are fixed costs that 

cannot be recovered in liquidation; they can be 

calculated by subtracting the liquidation value 

of assets from the acquisition cost of assets 

facing a new entrant, on an after-tax basis.19 

Lack of access to debt or equity markets to 

finance fixed costs of entering the market can 

also present entry barriers, even if none of the 

fixed costs are sunk costs. However, if financing 

is available and fixed costs are recoverable in 

liquidation, the magnitude of fixed costs alone 

may not be sufficient to be a barrier to entry. 

• Does the regulation tend to create or 


enhance market power and reduce the 


economic efficiency of the market? 


Important measures of competitiveness of an 

industry are degrees of horizontal and vertical 

integration (i.e., concentration) between 

both buyers and sellers in the baseline 

compared to post-compliance. If an industry 

becomes more concentrated as a result of the 

regulation then there are fewer firms within 

the industry. In this case, market power will 

be concentrated in the hands of a few entities, 

18 See Jaffe et al. (1995) for an overview. 

19 Sunk costs are sometimes referred to as exit barriers. 

which may result in a less efficient market 

than before the regulation. Closely related to 

concentration, product differentiation may 

occasionally either increase or decrease due to 

a regulatory action. A regulation may result in 

less product differentiation due to restrictions 

on production. This could mean that market 

power is more concentrated among the firms 

that manufacture the product. 

9.2.3.8 Impacts on Energy Supply, 

Distribution, or Use 

EO 13211 requires agencies to prepare a 

Statement of Energy for “significant energy 

actions,” which are defined as significant regulatory 

actions (under EO 12866) that also are “likely 

to have a significant adverse effect on the supply, 

distribution, or use of energy.”20 These significant 

adverse effects are defined as: 

• Reductions in crude oil supply in excess of 

10,000 barrels per day; 

• Reductions in fuel production in excess of 

4,000 barrels per day; 

• Reductions in coal production in excess of 5 

million tons per year; 

• Reductions in natural gas production in excess 

of 25 million mcf per year; 

• Reductions in electricity production in excess 

of 1 billion kilowatt-hours per year or in 

excess of 500 megawatts of installed capacity; 

• Increases in energy use required by the 


regulatory action that exceed any of the 


thresholds above; 


• Increases in the cost of energy production in 

excess of 1 percent; 

• Increases in the cost of energy distribution in 

excess of 1 percent; or 

• Other similarly adverse outcomes. 

For actions that may be significant under EO 

12866, particularly for those that impose 

requirements on the energy sector, analysts must be 

prepared to examine the energy effects listed above. 

20 See Section 2.1.6 for EPA and OMB’s guidance on EO 13211. 
Guidelines for Preparing Economic Analyses | December 2010 9-11 

Chapter 9 Economic Impact Analyses

 

 

 

 

 

 

 

 

 

 

 

9.2.4 Detailing Impacts on 

Governments and Not-for-Profit 

Organizations 

Section 9.3.5 discusses how to measure the impact 

of regulations and requirements on private entities, 

such as firms and manufacturing facilities. When 

dealing with private entities, an important focus 

is on measures that assess changes in profits (or 

proxy measures of profit). This section describes 

impact measures for situations where profits and 

profitability are not the focus of the analysis. Rather, 

the ultimate measure of impacts is the ability of 

the organization or its residents to pay for the 

requirements. Many of the same questions apply: 

• Which entities are affected and what are their 

characteristics? 

• To what extent does the regulation increase 

operating costs? 

• To what extent does the regulation impact 

operating procedures? 

• Does the regulation change the amount and/ 

or quality of the goods and services provided? 

• Can the entity raise the necessary capital to 

comply with the regulation? 

• Does the regulation change the entity’s ability 

to raise capital for other projects? 

EPA regulations can affect governments and notfor-profit organizations in at least three significant 

ways. First, a regulation may directly impose 

requirements on the entity, such as imposing 

water pollution requirements for publicly-owned 

wastewater treatment works, or initiating air 

pollution restrictions that affect municipal bus 

systems or power plants. Second, a regulation may 

impose implementation and enforcement costs 

on government agencies. Finally, a regulation 

may impose indirect costs. For example increased 

unemployment due to reduced production (or 

even plant closure) could result in less tax revenues 

in a community. 

9.2.4.1 Direct Impacts on Government 

and Not-for-Profit Entities 

Direct impact measures can fall into two 

categories: 

• Those that measure the impact itself in terms 

of the relative size of the costs and the burden 

it places on residents; and 

• Those that measure the economic and 

financial conditions of the entity that affect its 

ability to pay for the requirements. 

For each category, there are several types of 

measures that can be used either as alternatives or 

jointly to illuminate aspects of the direct impacts. 

Measuring the relative cost and burden of 

the regulations 

There are three commonly used approaches to 

measuring the direct burden of a rule; all involve 

calculating the annualized costs of complying with 

the regulation. For government entities the three 

approaches are: 

• Annualized compliance costs as a 

percentage of annual costs for the affected 

service. This measure defines the impact as 

narrowly as possible and measures impacts 

according to the increase in costs to the entity. 

In practice, EPA has often defined compliance 

costs that are less than 1 percent of the current 

annual costs of the activity as placing a small 

burden on the entity. 

• Annualized compliance costs as a 

percentage of annual revenues of the 

governmental unit. The second measure 

corresponds to the commonly used privatesector measure of annualized compliance 

costs as a percentage of sales. Referred to as 

the “Revenue Test,” it is one of the measures 

suggested in the RFA Guidance (U.S. EPA 

2006b). 

• Per household (or per capita) annualized 

compliance costs as a percentage of median 

household (or per capita) income. The third 

measure compares the annualized costs to the 

ability of residents to pay for the cost increase. 

The ability of residents to pay for the costs 

affects government entities because fees and 

taxes on residents fund these entities. To the 

extent that residents can (or cannot) pay for 

the cost increases, government entities will 
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Chapter 9 Economic Impact Analyses

 

be impacted. Commonly referred to as the 

“Income Test,” this measure is described in 

the RFA Guidance (U.S. EPA 2006b) and 

the EPA Office of Water Interim Economic 

Guidance for Water Quality Standards: 

Workbook (U.S. EPA 1995a).21 Costs can be 

compared to either median household or 

median per capita income. In calculating the 

per household or per capita costs, the actual 

allocation of costs needs to be considered. If 

the costs are paid entirely through property 

taxes, and the community is predominately 

residential, then an average per household 

cost is probably appropriate. If some or all of 

the costs are allocated to users (e.g., fares paid 

by bus riders or fees paid by users for sewer, 

water, or electricity supplied by municipal 

utilities), then a more narrow measure may 

be appropriate. If some of the costs are borne 

by local firms, then that portion of the costs 

should be analyzed separately. 

There are two commonly used impact measures for 

not-for-profit entities: (1) annualized compliance 

costs as a percentage of annual operating costs; and 

(2) annualized compliance costs as a percentage 

of total assets. The first is equivalent to the first 

of the impact measures described for government 

entities, measuring the percentage increase in 

costs that would result from the regulation 

being analyzed. The second is a more severe test, 

measuring the impacts if the annualized costs are 

paid out of the institution’s assets. 

Measuring the economic and 

financial health of the community 

or government entity 

The second category of direct impact measures 

examines the economic and financial health of the 

community involved, since this affects its ability 

to finance or pay for expenditures required by a 

program or rule. A given cost may place a much 

heavier burden on a poor community than on a 

21	 For example, in the water guidance and other EPA Office of Water 

analyses compliance costs are considered to have little impact if 

they are less than 1 percent of household income. Compliance costs 

greater than 2 percent are categorized as a large impact, and a range 

from 1 to 2 percent fall into a gray area and are considered to have an 

indeterminate impact. 

wealthy one of the same size. As with the impact 

measures described above, there are three categories 

of economic and financial condition measures: 

• Indicators of the community’s debt 

situation. Debt indicators are important 

because they measure both the ability of the 

community to absorb additional debt (to 

pay for any capital requirements of the rule) 

and the general financial condition of the 

community. While several debt indicators 

have been developed and used, this section 

describes two common indicators. One 

measure is the government entity’s bond 

rating. Awarded by companies such as 

Moody’s and Standard & Poor’s, bond ratings 

evaluate a community’s credit capacity and 

thus reflect the current financial conditions of 

the government body.22 A second frequently 

used measure is the ratio of overall net debt 

to the full market value of taxable property 

in the community, i.e., debt to be repaid 

by property taxes. Overall net debt should 

include the debt of overlapping districts. For 

example, a household may be part of a town, 

regional school district, and county sewer 

and water district, all of which have debt that 

the household is helping to pay.23 See Table 

9.3 for interpretations of the values for these 

measures. Debt measures are not always 

appropriate. Some communities, especially 

small ones, may not have a bond rating. This 

does not necessarily mean that they are not 

creditworthy; it may only mean that they 

have not had an occasion recently to borrow 

money in the bond market. If the government 

entity does not rely on property taxes, as 

may be the case for a state government or 

an enterprise district, then the ratio of debt 

22	 The indicators and benchmark values in Table 9.3 are drawn from 

Combined Sewer Overflows — Guidance for Financial Capability 

Assessment and Schedule Development, which discusses how to 

assess the feasibility of systems being able to comply with rules (U.S. 

EPA 1997b). These are general benchmarks that may prove useful in 

assessing financial stability in an EIA. 

23	 An alternative to the net debt as percent of full market value of taxable 

property is the net debt per capita. Commonly used benchmarks for 

this measure are: net debt per capita less than $1,000 indicates a 

strong financial condition, between $1,000 and $3,000 indicates a 

mid-range or gray area, and greater than $3,000 indicates a weak 

financial condition. 
Guidelines for Preparing Economic Analyses | December 2010 9-13 

Chapter 9 Economic Impact Analyses

 

 

 

 

Table 9.3 - Indicators of Economic and Financial Well-Being of 

Government Entities 

Indicator Weak Mid-Range Strong 

Bond rating Below BBB (S&P) 

Below Baa (Moody’s) 

BBB (S&P) 

Baa (Moody’s) 

Above BBB (S&P) 

Above Baa (Moody’s) 

Overall net debt as percent of full 

market value of taxable property 

Above 5% 2% - 5% Below 2% 

Unemployment rate More than 1 percentage 

point above national 

average 

Within 1 percentage point 

of national average 

More than 1 percentage 

point below national 

average 

Median household income More than 10% below the 

state median 

Within 10% of the state 

median 

More than 10% above 

the state median 

Property tax revenue as percent 

of full market value of taxable 

property 

Above 4% 2% - 4% Below 2% 

Property tax collection rate Less than 94% 94% - 98% More than 98% 

Source: U.S. EPA 1997b 

to full market value of taxable property 

is not relevant. Information on debt and 

assessed property values are available from 

the financial statement of each community. 

The state auditor’s office is likely to maintain 

this information for all communities within a 

state. 

• Indicators of the economic/financial 

condition of the households in the 

community. There are a wide variety 

of household economic and financial 

indicators. Commonly used measures are 

the unemployment rate, median household 

income, and foreclosure rates. Unemployment 

rates are available from the Bureau of Labor 

Statistics. Median household income is 

available from the U.S. Census Bureau. 

Benchmark values for these and other 

measures are presented in Table 9.3. 

• Financial management indicators. This 

category consists of indicators that gauge the 

general financial health of the community, as 

opposed to the general financial health of the 

residents. Because most local communities rely on 

property taxes as their major source of revenues, 

there are two ratios that provide an indicator of 

financial strength. First, property tax revenue as 

a percentage of the full market value of taxable 

property indicates the burden that property taxes 

place on the community.24 Second, the property 

tax collection rate gauges the efficiency with 

which the community’s finances are managed, 

and indirectly whether the tax burden may 

already be excessive. As the property tax burden 

on taxpayers increases, they are more likely to 

avoid paying their taxes or to pay them late. 

Measuring the financial strength of not-for-profit 

entities includes assessing: 

• The size of the entity’s reserves; 

• How much debt the entity already has and 

how its annual debt service compares to its 

annual revenues; and 

• How the entity’s fees or user charges compare 

with the fees and user charges of similar 

institutions. 

As with government entities, this analysis is meant 

to judge whether the entity is in a strong or weak 

financial position to absorb additional costs. 

9.2.4.2 Administrative, Enforcement, 

and Monitoring Burdens on 

Governments 

Many EPA programs require effort on the part of 

24	 If the state caps local property taxes (e.g., Proposition 13 in California 

or Proposition 21⁄2 in Massachusetts) then it may be relevant to 

examine the ratio of property tax to the allowed level of the taxes. 
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Chapter 9 Economic Impact Analyses

different levels of government for administration, 

enforcement, and monitoring. These costs must be 

included when estimating impacts of a regulation 

to comply with UMRA and to calculate the full 

social costs of a program or rule. See Chapter 8 for 

more information on government regulatory costs. 

9.2.4.3 Induced Impacts on 

Government Entities 

The induced impacts on government entities 

should also be considered. For example, a 

manufacturing facility may reduce or suspend 

production in response to a regulation, thus 

reducing the income levels of its employees. In 

turn, these reductions will spread through the 

economy by means of changes in household 

expenditures. These induced impacts include 

the multiplier effect, in which loss of income in 

one household results in less spending by that 

household and therefore less income in households 

and firms associated with goods previously 

purchased by the first household. 

Decreased household and business income can affect 

the government sector by reducing tax revenues and 

increasing expenditures on income security programs 

(the automatic stabilizer effect), employment 

training, food and housing subsidies, and other 

fiscal line items. Due to wide variation in these 

programs and in tax structures, estimating public 

sector impacts for a large number of government 

jurisdictions can be prohibitively difficult. 

On the other hand, compliance expenditures 

increase income for businesses and employees that 

provide compliance-related goods and services. 

These income gains also have a multiplier effect, 

offsetting some of the induced losses in tax 

revenue and increases in government expenditures 

identified above. As some linkages may be more 

localized than others, it is important to clearly 

identify where the gains and losses occur. 

9.2.5 Detailing Impacts on 

Small Entities 

The Regulatory Flexibility Act, as amended by the 

Small Business Regulatory Fairness Act of 1996 

(RFA), and Section 203 of the Unfunded Mandates 

Reform Act of 1995 (UMRA) require agencies to 

consider a proposed regulation’s economic effects 

on small entities, specifically, small businesses, small 

governmental jurisdictions, or small not-for-profit 

organizations. The definition of “small” for each 

of these entities is described below. For guidance 

on when it is necessary to examine the economic 

effects of a regulation under the RFA or UMRA, 

analysts should consult EPA guidelines on these 

administrative laws (U.S. EPA 2006b and U.S. 

EPA 1995b, respectively). In general, the Agency 

must fulfill certain procedural and/or analytical 

obligations when a rule has a “significant impact on 

a substantial number of small entities” (abbreviated 

as SISNOSE) under the RFA or when a rule 

might “significantly” or “uniquely” affect small 

governments under Section 203 of UMRA. 

9.2.5.1 Small Businesses 

The RFA requires agencies to begin with the 

definition of small business that is contained in 

the Small Business Administration’s (SBA) small 

business size standard regulations.25 The RFA 

also authorizes any agency to adopt and apply an 

alternative definition of small business “where 

appropriate to the activities of the Agency” after 

consulting with the Chief Counsel for Advocacy 

of the SBA and after opportunity for public 

comment. The agency must also publish any 

alternative definition in the Federal Register (U.S. 

EPA 2006b). 

The analytical tasks associated with complying 

with the RFA include a screening analysis for 

SISNOSE. If the screening analysis reveals that a 

rule cannot be certified as having no SISNOSE, 

then the RFA requires a regulatory flexibility 

analysis be conducted for the rule, which includes 

a description of the economic impacts on small 

entities. Impacts on small businesses are generally 

assessed by estimating the direct compliance costs 

and comparing them to sales or revenues. Because 

an estimate of direct compliance costs tends to 

be a conservatively low estimate of a regulation’s 

impact, further analysis examining the impacts 

25	 The current version of SBA’s size standards can be found at 

http://www.sba.gov/size (accessed March 13, 2011). 
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Chapter 9 Economic Impact Analyses

discussed in Section 9.3.3 (specifically in relation 

to small businesses) may provide additional 

information for decision makers.26 

9.2.5.2 Small Governmental 

Jurisdictions 

The RFA defines a small governmental jurisdiction 

as the government of a city, county, town, school 

district, or special district with a population of 

less than 50,000. Similar to the definition of small 

business, the RFA authorizes agencies to establish 

alternative definitions of small government after 

opportunity for public comment and publication 

in the Federal Register. Any alternative definition 

must be “appropriate to the activity of the Agency” 

and “based on such factors as location in rural or 

sparsely populated areas or limited revenues due 

to the population of such jurisdiction” (U.S. EPA 

2006b). Under the RFA, economic impacts on 

small governments are included in the SISNOSE 

screening analysis, and any required regulatory 

flexibility analysis for a rule. 

UMRA uses the same definition of small 

government as the RFA with the addition of tribal 

governments. Section 203 of UMRA requires 

the Agency to develop a “Small Government 

Agency Plan” for any regulatory requirement 

that might “significantly” or “uniquely” affect 

small governments. In general, “impacts that may 

significantly affect small governments include — 

but are not limited to — those that may result in 

the expenditure by them of $100 million [adjusted 

annually for inflation] or more in any one year.” 

Other indicators that small governments are 

uniquely affected may include whether they would 

incur the higher per-capita costs due to economies 

of scale, a need to hire professional staff or 

consultants for implementation, or requirements 

to purchase and operate expensive or sophisticated 

equipment.27 See Section 9.3.4 for information on 

measures of impacts to governments in general. 

26	 See Agency guidance (U.S. EPA 2006c) for details on complying with 

the RFA. 

27	 Guidance on complying with Section 203 of UMRA, “Interim Small 

Government Agency Plan,” is available on EPA’s intranet site, ADP 

Library at http://intranet.epa.gov/adplibrary/statutes/umra.htm 

(accessed March 21, 2011, internal EPA document) 

9.2.5.3 Small Not-for-Profit 

Organizations 

The RFA defines a small not-for-profit 

organization as an “enterprise which is 

independently owned and operated and is not 

dominant in its field.” Examples may include 

private hospitals or educational institutions. 

Here again, agencies are authorized to establish 

alternative definitions “appropriate to the activities 

of the Agency” after providing an opportunity for 

public comment and publication in the Federal 

Register. Under the RFA, economic impacts on 

small not-for-profit organizations are included in 

the SISNOSE screening analysis, and if required, 

the regulatory flexibility analysis for a rule. See 

Section 9.3.4 for more information on measuring 

impacts on not-for-profit organizations in general. 

9.3 Approaches to Modeling in 

an Economic Impact Analysis 

This section returns to the methods for estimating 

social costs covered in Chapter 8, adding 

more insight on their application to EIA. The 

reader should refer to Chapter 8 for a more indepth discussion. As noted above, the analytic 

assumptions used for the EIA of a particular 

regulation should be consistent with those used for 

the corresponding BCA. 

9.3.1 Direct Compliance Costs 

The simplest approach to measuring the economic 

impacts is to estimate and verify the private 

costs of compliance. This is necessary regardless 

of whether the entities affected are for-profit, 

governmental, communities, or not-for-profit. 

Direct compliance costs are considered the most 

conservative estimate of private costs and include 

annual costs (e.g., operation and maintenance of 

pollution control equipment), as well as any capital 

costs. Direct compliance costs do not include 

implicit costs. 

Verifying the compliance cost estimates entails 

two steps. First, the full range of responses to the 

rule needs to be identified, including pollution 

prevention alternatives and any differences in 

response across sub-sectors and/or geographic 
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Chapter 9 Economic Impact Analyses

 

 

 

regions. Second, the costs for each response need 

to be examined to determine if all elements are 

included and if the costs are consistent within a 

given base year. To ensure consistency across years, 

either a general inflation factor, such as the GDP 

implicit price deflator, or various cost indices 

specific to the type of project should be used.28 The 

base year and indexing procedure should be stated 

clearly. 

Implicit costs that do not represent direct outlays 

may be important. The cost estimates should 

include such elements as production lost during 

installation, training of operators, and education of 

users and citizens on programs involving recycling 

of household wastes. The cost of acquiring a 

permit includes the permit fee as well as the 

lost opportunities during the approval process. 

Likewise, the cost of having a car’s emissions 

inspected is not so much the fee as it is the value of 

a registrant’s time. 

In addition, it is important to recognize that 

these expenditures may have other benefits 

and costs. For example, they may confer tax 

breaks (complying with regulations may be a tax 

deductible expense) and the new capital may 

be more productive than the old capital. These 

“offsets” should be considered, particularly when 

they may be substantial. 

There are several issues analysts should consider 

when estimating the direct compliance costs of 

environmental polices for an EIA. These include: 

• Before- versus after-tax costs. For businesses, 

the cost of complying with regulations is 

generally deductible as an expense for income 

tax purposes. Therefore, the effective burden 

is reduced for taxable entities because they 

can reduce their taxable income by the 

amount of the compliance costs. The effect of 

a regulation on profits is therefore measured 

by after-tax compliance costs. Operating costs 

28	 The GDP implicit price deflator is reported by the U.S. DOC, BEA in 

its Survey of Current Business (http://www.bea.gov/scb/index.htm). 

The annual Economic Report of the President, Executive Office of the 

President, is another convenient source for the GDP deflator, available 

at www.gpoaccess.gov/eop/ (accessed March 13, 2011). 

are generally fully deductible as expenses 

in the year incurred. Capital investments 

associated with compliance must generally 

be depreciated.29 In most cases, communities, 

not-for-profits, and governments do not 

benefit from reduced income taxes that 

can offset compliance costs. Therefore, 

adjustments to cost estimates, annualization 

formulas, and cost of capital calculations 

required to calculate after-tax costs should 

not be used in analyses of impacts on 

governments, not-for-profits, and households. 

• Transfers. Some types of compliance 

costs incurred by the regulated parties may 

represent transfers among parties. Transfers, 

such as payments for insurance or payments 

for marketable permits, do not reflect use 

of economic resources. However, individual 

private cost estimates used in the EIA include 

such transfers.30 

• Discounting. Compliance costs often vary 

over time, perhaps requiring initial capital 

investments and then continued operating 

costs. To estimate impacts, the stream of costs 

is generally discounted to provide a present 

value of costs that reflects the time value 

of money.31 In contrast to social costs and 

benefits, which are discounted using a social 

discount rate, private costs are discounted 

using a rate that reflects the regulated entity’s 

cost of capital.32 The private discount rate used 

will generally exceed the social discount rate 

by an amount that reflects the risk associated 

with the regulated entity in question. 

For firms, the cost of capital may also be 

determined by their ability to deduct debt 

from their tax liability. 

29	 Current federal and state income tax rates can be obtained from the 

Federation of Tax Administrators, State Tax Rates & Structure, available 

at http://www.taxadmin.org/fta/rate/default.html (accessed January 31, 

2011). 

30	 These transfers cancel out in a BCA. In an EIA the distribution of 

results is important, therefore the transfers are included. 

31	 The present value of costs can then be annualized to provide an annual 

equivalent of the uneven compliance cost stream. Annualized costs are 

also discussed in Chapter 6. 

32	 While the discount rate differs, the formula used to discount private 

costs is the same as used for social costs. See Chapter 6 for details. 
Guidelines for Preparing Economic Analyses | December 2010 9-17 

Chapter 9 Economic Impact Analyses

 

 

• Annualized costs. Annualizing costs involves 

calculating the annualized equivalent of 

the stream of cash flows associated with 

compliance over the period of analysis. This 

provides a single annual cost number that 

reflects the various components of compliance 

costs incurred over this period. The annual 

value is the amount that, if incurred each year 

over the selected time period, would have 

the same present value as the actual stream of 

compliance expenditures. Annualized costs 

are therefore a convenient compliance cost 

metric that can be compared with annual 

revenues and profits. It is important to 

remember that using annualized costs masks 

the timing of actual compliance outlays. 

For some purposes, using the underlying 

compliance costs may be more appropriate. 

For example, when assessing the availability 

of financing for capital investments, it is 

important to consider the actual timing of 

capital outlays. 

• Fixed versus variable costs. Some types of 

compliance costs vary with the size of the 

regulated enterprise, such as quantity of 

production. Other components of cost may 

be fixed with respect to production or other 

size measures, such as the costs involved 

in reading and understanding regulatory 

requirements. Requirements that impose 

high fixed costs will impose a higher cost per 

unit of production on smaller firms than on 

larger firms. It is important that the effects 

of any economies of scale are reflected in the 

compliance costs used to analyze economic 

impacts.33 Using the same average annualized 

cost per unit of production for all firms may 

mask the importance of such fixed costs and 

understate impacts on small entities. 

9.3.2 Partial Equilibrium Models 

A partial equilibrium framework is an alternative 

way to examine distributional effects when impacts 

are limited to a few directly and indirectly affected 

output markets only. For example, a regulation 

may increase the costs of producing a particular 

33	 Economies of scale characterize costs that decline on a per unit basis 

as the scale of the operation increases. 

chemical. Partial equilibrium models can be used 

to examine the distribution of these changes across 

directly affected industries, and a small number 

of indirectly affected entities (e.g., upstream 

and downstream). Partial equilibrium models 

can range in size from an analysis that estimates 

compliance costs for the affected industry only 

(i.e., direct compliance costs) to multi-market 

models encompassing several directly and 

indirectly affected sectors. 

If a single-market partial equilibrium model is the 

only information source available for an analysis 

of impacts, then it may be possible to adopt 

further assumptions and acquire additional data 

to approximate impacts on other areas of concern. 

This may include deriving ratios to aggregate 

changes in order to assign these changes to 

specific regions or sectors. These new assumptions 

should be consistent with those used for the 

corresponding BCA. 

Multi-market models consider the interactions 

between a regulated market and other important 

related markets (outputs and inputs), requiring 

estimates of elasticities of demand and supply for 

these markets as well as cross-price-elasticities 

(also found in CGE models). These models are 

best used when potential impacts on related 

markets might be considerable, but more complete 

modeling using a CGE framework may not be 

available or practical. Partial equilibrium models 

may also be more appropriate for regionally based 

or resource specific regulations that are too specific 

for more aggregated CGE models.34 Care should 

be taken, however, to avoid double counting, 

particularly when both upstream and downstream 

entities are affected and included in the partial 

equilibrium analysis. If cost increases due to a 

regulation are passed on from the upstream to the 

downstream businesses then analysts should take 

care not to include impacts on both sets of entities 

to avoid double counting results. 

34 See the discussion of multi-market modeling in Chapter 8 and Just et 

al. (1982). 
9-18 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 9 Economic Impact Analyses

9.3.3 Computable General 

Equilibrium Models 

CGE models are particularly effective in 

assessing resource allocation and welfare effects. 

These effects include the allocation of resources 

across sectors (e.g., employment by sector), the 

distribution of output by sector, the distribution 

of income among factors, and the distribution of 

welfare across different consumer groups, regions, 

and countries. As noted in Chapter 8, for example, 

regulations in the electric utility sector are likely 

to cause electricity prices to increase. The price 

increase will affect all industries that use electricity 

as an input to production (i.e., most industries), 

as well as households. A CGE model can assess 

the distribution of the changes in production 

and consumption that result. By design, the basic 

capacity to describe and evaluate these sorts of 

impacts exists to some extent within every CGE 

model. More detailed impacts (e.g., affects on a 

particular facility) or impacts of a particular kind 

(e.g., affects on drinking water) will require a more 

complex and/or tailored model formulation and 

the data to support it. 

The simplest CGE models generally include a 

single representative consumer, a few production 

sectors, and a government sector, all within a 

single-country, static framework. Additional 

complexities can be specified for the model in 

a variety of ways. Consumers may be divided 

into different groups by income, occupation, or 

other socioeconomic criteria. Producers can be 

disaggregated into dozens or even hundreds of 

sectors, each producing a unique commodity. The 

government, in addition to implementing a variety 

of taxes and other policy instruments, may provide 

a public good or run a deficit. CGE models can 

be international in scope, consisting of many 

countries or regions linked by international flows 

of goods and capital. The behavioral equations 

that characterize economic decisions may take 

on simple or complex functional forms. The 

model can be solved dynamically over a long time 

horizon, incorporating intertemporal decision 

making on the part of consumers or firms. These 

choices have implications for the treatment of 

savings, investment, and the long-term profile of 

consumption and capital accumulation. 

As effective as CGE models can be for looking 

at long-term resource allocation issues, they 

have limitations for the kinds of impact analyses 

described above. CGE models assume that 

markets clear in every period and often do not 

consider short-term adjustment costs, such as 

lingering unemployment. The analyst should be 

careful to select a model that does not assume 

away the underlying issue addressed by the 

distribution analysis. Moreover, a CGE model 

may not be feasible or practical to use when data 

and resources are limited or when the scope of 

expected significant market interactions is limited 

to a subset of economic sectors. In such instances 

a partial equilibrium model can be adopted as a 

more appropriate alternative to a CGE model.35 

Finally, it is worth noting that while CGE 

modeling is complex, the effort may be worthwhile 

when data are available and the distributional 

impacts are likely to be widespread. 

35 For a discussion of CGE analysis see Chapter 8 and Dixon et al. 

(1992). 
Chapter 10 

Environmental Justice, Children’s 

Environmental Health and Other 

Distributional Considerations 

Evaluating a regulation’s distributional effects is an important complement to 

benefit-cost analysis. Rather than focusing on quantifying and monetizing total 

benefits and costs, economic impact and distributional analyses examine how 

a regulation allocates benefits, costs and other outcomes across populations or 

groups of interest. See Chapter 9 of these Guidelines for more information on 

analyzing economic impacts. This chapter considers the distribution of environmental quality 

and human health risks across several populations: those that have traditionally been the 

focus of environmental justice (EJ) (i.e., minority, low-income, or indigenous populations); 

children; and the elderly. Consideration of costs or other potential impacts may also be 

addressed in a distributional analysis using approaches discussed in this chapter. The chapter 

also briefly discusses inter-generational impacts. 

This chapter suggests approaches that EPA program 

offices can use for characterizing distributional 

effects of policy choices associated with rulemaking 

activities. Based on academic literature and EPA 

documents and policies, the chapter provides a 

variety of methodological approaches that may be 

suitable across various regulatory scenarios. A clear 

consensus does not exist, however, regarding the 

most appropriate methods. Instead, this chapter 

provides a broad overview of options for analyzing 

distributional effects in regulatory analysis. 

Information in the chapter is intended to provide 

flexibility to programs that face dissimilar data, 

resources and other constraints while introducing 

greater consistency in the way EJ is addressed in 

rulemaking activities.1 

The purpose of analyzing distributional effects in 

regulatory analysis is to examine how benefits (e.g., 

risk reductions or environmental quality) and, when 

The guidance in this chapter complements, and does not supersede, any 

subsequent EJ-related guidance released by EPA. In addition, the Office of 

Environmental Justice website (http://www.epa.gov/environmentaljustice/ 

resources/policy/index.html) provides resources on Plan EJ2014 and other 

implementation guidelines related to EJ (accessed on January 24, 2012). 

relevant and feasible, costs are distributed across 

population groups and lifestages of interest.2

 While 

the chapter is focused on EJ, children, and the elderly, 

the methods discussed could be applied to any 

population of concern. 

The chapter begins with an overview of Executive 

Orders (EOs) and policies related to distributional 

analyses. It then discusses the analysis of 

distributional impacts in the context of EJ and 

children’s health. The chapter concludes with a brief 

discussion of other distributional considerations, 

including the elderly and inter-generational impacts 

that may arise in select rules. 

10.1 Executive Orders, 

Directives, and Policies 

Consideration of distributional effects arises from a 

variety of executive orders, directives, and other 

2	 This chapter recommends examining the distribution of benefits prior to 

monetization for reasons discussed in Section 10.1. 

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1 
 

 

 

 

 

 

 

 

 

Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations 

documents with broad coverage, including:3 

• EO 12898, “Federal Actions to Address 

Environmental Justice in Minority 

Populations and Low-Income Populations” 

(1994); 

• EO 13045, “Protection of Children From 

Environmental Health Risks and Safety Risks” 

(1997); 

• EO 13166, “Improving Access to Services for 

Persons With Limited English Proficiency” 

(2000); and the subsequent EPA Order 

No.1000.32, “Compliance with Executive 

Order 13166: Improving Access to 

Services for Persons with Limited English 

Proficiency” (2011); 

• EO 13175, “Consultation and Coordination 

with Indian Tribal Governments” (2000); 

• EO 12866, “Regulatory Planning and 


Review” (1993); 


• Circular A-4, Regulatory Analysis 


(OMB 2003);
 

• National Environmental Policy Act (NEPA) 

Guidance (U.S. EPA 1998a); 

• EPA’s Interim Guidance on Considering 

Environmental Justice During the Development 

of an Action (U.S. EPA 2010a); and 

• EPA’s FY2011-2015 Strategic Plan (U.S. EPA 

2010b). 

Each of these is described below. Some 

environmental statutes may also identify 

population groups that merit additional 

consideration.4 

EO 12898, “Federal Actions to Address 

Environmental Justice in Minority Populations 

and Low-Income Populations”5

 (1994), calls on 

3 	 EPA’s Regulatory Management Division’s Action Development Process 

Library (http://intranet.epa.gov/adplibrary) is a resource for accessing 

relevant statutes, executive orders, and EPA policy and guidance 

documents in their entirety (accessed on December 1, 2011). 

4 See Plan EJ 2014 Legal Tools (U.S. EPA 2011a) for a review of legal 

authorities under the environmental and administrative statutes 

administered by EPA that may contribute to the effort to advance 

environmental justice. 

5 This chapter addresses analytical components of EO 12898, and does 

not cover other components such as ensuring proper outreach and 

meaningful involvement. 

each Federal agency to make achieving EJ part of 

its mission. It directs Federal agencies, “[t]o the 

greatest extent practicable and permitted by law,” 

to “identify[...] and address[...], as appropriate, 

disproportionately high and adverse human health 

or environmental effects” of agency programs, 

policies, and actions on minority populations 

and low-income populations. Issued by President 

Clinton in 1994, it requires that EJ be considered in 

all Agency activities, including rulemaking activities. 

The President issued a memorandum to 

accompany EO 12898 directing Federal agencies 

to analyze environmental effects, including human 

health, economic, and social effects, of Federal 

actions when such analysis is required under the 

National Environmental Policy Act (NEPA). 

The Presidential memorandum also states that 

existing civil rights statutes provide opportunities 

to address environmental hazards in minority 

communities and low-income communities.6 

EO 13045, “Protection of Children From 

Environmental Health Risks and Safety Risks” 

(1997), states that each Federal agency: (1) shall 

make it a high priority to identify and assess 

environmental health risks and safety risks that 

may disproportionately affect children; and (2) 

shall ensure that its policies, programs, activities, 

and standards address disproportionate risks to 

children that result from environmental health 

risks or safety risks. The EO also states that each 

“covered regulatory action” submitted to the 

Office of Management and Budget (OMB), unless 

prohibited by law, should be accompanied by “. . . 

an evaluation of the environmental health or safety 

effects of the planned regulation on children.”7 

6 	 “In accordance with Title VI of the Civil Rights Act of 1964, 

each Federal agency shall ensure that all programs or activities 

receiving Federal financial assistance that affect human health or 

the environment do not directly, or through contractual or other 

arrangements, use criteria, methods, or practices that discriminate on 

the basis of race, color, or national origin.” See Memorandum for the 

Heads of All Departments and Agencies: Executive Order on Federal 

Actions to Address Environmental Justice in Minority Populations and 

Low-Income Populations (White House 1994). 

7 	 A “covered regulatory action” is any substantive action in a rulemaking 

that may be economically significant (i.e., have an annual effect 

on the economy of $100 million or more or would adversely affect 

in a material way the economy, a sector of the economy, or the 

environment) and concern an environmental health risk that an agency 

has reason to believe may disproportionately affect children. 

10-2 Guidelines for Preparing Economic Analyses | May 2014 
Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations 

EO 13166, “Improving Access to Services for 

Persons With Limited English Proficiency” 

(2000), requires Federal agencies to examine the 

services they provide, identify any need for services 

to those with limited English proficiency (LEP), 

and develop and implement a system to provide 

those services so LEP persons can have meaningful 

access to them. The EO also requires Federal 

agencies work to ensure that recipients of Federal 

financial assistance provide meaningful access 

to their LEP applicants and beneficiaries. EPA’s 

Order 1000.32 “Compliance with Executive Order 

13166: Improving Access to Services for Persons 

with Limited English Proficiency”8

 requires 

that EPA ensure its programs and activities are 

meaningfully accessible to LEP persons. 

EO 13175, “Consultation and Coordination 

with Indian Tribal Governments” (2000), calls on 

Federal agencies to have “an accountable process 

to ensure meaningful and timely input by tribal 

officials in the development of regulatory policies 

that have tribal implications.” To the extent 

practicable and permitted by law, if a regulatory 

action with tribal implications is proposed and 

imposes substantial direct compliance costs on 

Indian tribal governments, and is not required by 

statute, then the agency must either provide funds 

necessary to pay direct compliance costs of tribal 

governments or consult with tribal officials early in 

the process of regulatory development and provide 

OMB a tribal summary impact statement. 

EO 12866, “Regulatory Planning and Review” 

(1993), allows agencies to consider “distributive 

impacts” and “equity” when choosing among 

alternative regulatory approaches, unless 

prohibited by statute. EO 13563, issued in January 

2011, supplements and reaffirms the provisions of 

EO 12866. 

OMB’s Circular A-4 states that regulatory 

analyses “should provide a separate description 

of distributional effects (i.e., how both benefits 

and costs are distributed among populations of 

particular concern) so that decision makers can 

properly consider them along with the effects 

EPA Order 1000.32 is available at http://www.epa.gov/civilrights/docs/ 

lep_order_1000_32.pdf (accessed on May 28, 2013). 

of economic efficiency.” It specifically calls for a 

description of “the magnitude, likelihood, and 

severity of impacts on particular groups” if the 

distributional effects are expected to be important 

(OMB 2003). 

The President’s memorandum to heads of 

departments and agencies that accompanied 

EO 12898 specifically raised the importance 

of procedures under NEPA for identifying and 

addressing environmental justice concerns (White 

House 1994). The memorandum states that “each 

Federal agency shall analyze the environmental 

effects, including human health, economic and 

social effects, of Federal actions, including effects 

on minority communities and low-income 

communities when such analysis is required 

by [NEPA].” The Council on Environmental 

Quality (CEQ) issued EJ guidance for NEPA 

in 1997 (CEQ 1997). EPA issued guidance 

in 1998 for incorporating EJ goals into EPA’s 

preparation of environmental impact statements 

and environmental assessments under NEPA (U.S. 

EPA 1998a). 

In July 2010, EPA published its Interim Guidance 

on Considering Environmental Justice During the 

Development of an Action (U.S. EPA 2010a). This 

guide is designed to help EPA staff incorporate 

EJ into the rulemaking process, from inception 

through promulgation and implementation. The 

guide also provides information on how to screen 

for EJ effects and directs rulewriters to respond to 

three basic questions throughout the rulemaking 

process: 

1. How did your public participation process 

provide transparency and meaningful 

participation for minority, low-income, 

indigenous populations, and tribes? 

2. How did you identify and address existing and 

new disproportionate environmental and public 

health impacts on minority, low-income, and 

indigenous populations during the rulemaking 

process? 

3. How did actions taken under #1 and #2 impact 

the outcome or final decision? 

Guidelines for Preparing Economic Analyses | May 2014 10-3 

8 
10-4 Guidelines for Preparing Economic Analyses | May 2014 

Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

Finally, in September 2010 EPA released its 

FY2011-2015 Strategic Plan outlining how EPA 

would achieve its mission to protect human health 

and the environment over the next five years (U.S. 

EPA 2010b). Included in the plan is a cross-cutting 

fundamental strategy to focus on “working for 

environmental justice and children’s health.” To 

implement this strategy, EPA released Plan EJ 

2014 in September 2011 that provides a roadmap 

for the Agency to incorporate environmental 

justice into policies, programs and activities. One 

of five cross-agency focus areas identified in Plan 

EJ 2014 is “Incorporating Environmental Justice 

into Rulemaking.”9 

Together these documents provide a solid 

foundation for considering distributional 

effects for population groups of concern in the 

rulemaking process. 

10.2 Environmental Justice 

EPA defines environmental justice as “the fair 

treatment and meaningful involvement of all 

people regardless of race, color, national origin, 

or income with respect to the development, 

implementation, and enforcement of 

environmental laws, regulations, and policies” 

(EPA 2010a). EO 12898 specifically states 

that Federal agencies should “...identify and 

address...disproportionately high and adverse 

human health or other environmental effects... 

on minority populations and low-income 

populations...” (EPA 2010a). 

For policies that strengthen an environmental 

standard, EPA regulatory analyses have often 

relied on a default assumption that these policies 

have no EJ concerns because they reduce overall 

environmental burdens. However, it is incorrect 

to conclude that tighter standards necessarily 

improve environmental quality for everyone. The 

nuances of a rule could result in negative effects, 

such as higher emissions in some areas, even 

though net environmental quality improves. It is 

also possible that older, more polluting facilities 

Plan EJ 2014 is available at http://www.epa.gov/compliance/ej/ 

resources/policy/plan-ej-2014/plan-ej-2011-09.pdf (accessed on May 

9, 2012). 

close as a result of a rule and new facilities open 

in different locations, changing the distribution 

of emissions across communities.10 Hence, when 

data are available, a basic analysis can support 

conclusions regarding potential distributional 

effects. In addition, while there may be no adverse 

environmental impacts, other economic impacts, 

like costs, could affect population groups of 

concern disproportionately and may warrant 

examination.11 

Distributional analysis also improves transparency 

of rulemaking and provides decision makers 

and the public with more complete information 

about a given policy’s potential effects. Such 

documentation helps EPA and the public track 

and measure progress in addressing EJ concerns. 

Analysts play a role in ensuring meaningful 

involvement by explaining distributional 

analysis in plain language, including key 

assumptions, methods, and results, and by asking 

for information from the public (e.g., asking 

for comment in the proposed rulemaking) on 

exposure pathways, end points of concern, and 

data sources that may improve the distributional 

analysis.12 Further guidance on ensuring 

meaningful engagement of environmental justice 

stakeholders in the rulemaking process can be 

found in U.S. EPA (2010a). 

10.2.1 Background Literature 

The study of economic efficiency (the focus of 

benefit-cost analysis) of regulatory approaches 

has a long history in the economics literature, 

including an established theoretical foundation 

and generally accepted empirical methodology. 

But an assessment of distributional consequences 

10	 U.S. EPA (2010a) provides additional information on how an EJ 

concern may arise in the context of a rule. 

11	 See U.S. EPA (2008a) for an example where changes in costs are 

addressed in an analysis of distributional impacts in the context of EJ. 

12 	 Meaningful involvement is defined by EPA to mean that “1) potentially 

affected community members have an appropriate opportunity to 

participate in decisions about a proposed activity that will affect their 

environment and/or health; 2) the public’s contribution can influence 

the regulatory agency’s decision; 3) the concerns of all participants 

involved will be considered in the decision-making process; and 4) 

the decision makers seek out and facilitate the involvement of those 

potentially affected” (U. S. EPA 2010a, U.S. EPA 2012a). 

9 
Guidelines for Preparing Economic Analyses | May 2014 10-5 

Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

has received relatively less attention.13 Media and 

government interest in potential environmental 

inequity arising from landfill siting decisions in 

the mid-1980s led to an increased focus in the 

economics literature on distributional issues in the 

context of race, poverty, and income.14 This section 

provides a brief overview of key studies from 

the economics and health literature. For a more 

comprehensive discussion see Ringquist (2005), 

Banzhaf (2012a), and Banzhaf (2012b). 

Studies of EJ can vary by specific pollutant, the 

proxy used for risk or exposure, geographic area, 

and time period, making it difficult to directly 

apply general findings to a particular rulemaking. 

The literature illustrates, however, that EJ is a 

potential concern with regard to plant emission 

decisions and is therefore worthy of analysis in a 

regulatory context (see, for example, Wolverton 

2009). It is important to note that the economics 

literature typically focuses on addressing the 

question of whether certain population groups are 

exposed to greater amounts of pollution. There 

is also the possibility that some populations are 

more susceptible to pollution for a given level of 

exposure and that socioeconomic factors may play 

a role. While literature addressing this issue is not 

discussed here, Section 10.2.8.5 of this chapter 

discusses various risk considerations including 

susceptibility. In addition, both the EJ literature 

and this chapter tend to focus on the distribution 

of physical aspects of environmental outcomes.15 

Evidence exists of potential disproportionate 

impacts from environmental stressors on various 

population groups using a wide variety of proxies 

13 	 For a discussion of the possible distributional effects of environmental 

policies with regard to income, see Fullerton (2009). 

14 	 The rise in concern over environmental justice is often traced to 

demonstrations in Warren County, North Carolina in 1982 over the 

siting of a polychlorinated biphenyl (PCB) landfill in a poor and 

minority community. 

15 	 Differences in exposures or health effects alone may not be 

representative of differences in total benefits and costs. As discussed 

in Serret and Johnstone (2006) and Fullerton (2011), for example, 

the full distribution of environmental policy could include differences 

in product prices, wage rates, employment effects, economic rents, 

etc. It is likely, however, that the methods used to analyze the full 

distributional effects (e.g., computable general equilibrium models) are 

beyond the scope of a typical regulatory analysis and the policy tools 

to address any resultant distributional concerns (e.g., tax policy and 

redistribution programs) are beyond the scope of environmental policy. 

for exposure. Many studies are proximity-based: 

distance to a polluting facility is a surrogate for 

exposure. These studies often find evidence that 

locally-unwanted land-uses such as landfills or 

facilities that treat, store, or dispose of hazardous 

waste are more likely to be concentrated 

in predominantly minority or low-income 

neighborhoods (for example, Bullard 1983; GAO 

1983; UCC 1987; Boer et al. 1997; and Mohai et 

al. 2009).16 

Other studies attempt to better approximate 

exposure by examining whether existing 

emission patterns are related to socio-economic 

characteristics. These studies often focus on a 

particular type of pollution and geographic area. 

They also often differ in how they define the 

relevant neighborhood and comparison group. As 

such, results with regard to race and income vary 

across studies. For example, after controlling for 

other factors, Hamilton (1993, 1995) finds that 

expansion decisions for waste sites are unrelated to 

race and finds mixed evidence for income, while 

Aurora and Cason (1998) find both race and 

poverty are positively related to toxicity-weighted 

Toxic Release Inventory (TRI) emissions, 

although the significance of these relationships 

varies by region. Gray and Shadbegian (2004) find 

poor communities are exposed to more air and 

water pollution from pulp and paper mills, but 

find the opposite for minority communities. 

Finally, other studies attempt to account for 

health risks. For example, Rosenbaum et al. (2011) 

combine information on ambient concentrations 

of diesel particulate matter in marine harbor 

areas throughout the United States with exposure 

and carcinogenic risk factors broken out by race, 

ethnicity, and income. They find that the most 

important factor in predicting higher particulate 

16 	 Others note the strength of this contemporaneous relationship but find 

that the direction and magnitude of the relationship between location 

and race or income at time of siting is less clear (see Been 1994; 

Been and Gupta 1997; and Wolverton 2009). See Shadbegian and 

Wolverton (2010) for a summary of the literature on firm location and 

environmental justice, including a discussion of whether plant location 

precedes changes in socioeconomic composition that result in higher 

percentages of non-white and poor households nearby or vice versa. 

Most of these studies examine partial correlations between pollution 

and household characteristics, using statistical techniques that control 

for other factors. 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

 

matter intake fractions (i.e., mass of a pollutant 

inhaled or ingested divided by mass emitted) 

is population density and that low-income and 

minority individuals are over-represented in marine 

harbor areas that exceed risk thresholds. Likewise, 

Morello-Frosch et al. (2001) combine estimates of 

hazardous air pollutant concentrations in southern 

California with information on lifetime cancer 

risks by socioeconomic status and race and find 

that even though lifetime cancer risks are high 

for all individuals in the study, race and ethnicity 

are positively related to lifetime cancer risk after 

controlling for economic and land use variables. 

Ringquist (2005) conducts a meta-analysis of both 

facility location and emissions across 49 studies 

published prior to 2002 and finds evidence that 

plant location and higher emissions are more likely 

to occur in communities with a higher percent 

non-white population. He finds little evidence, 

however, that this is the case in communities 

with lower income or higher poverty rates. The 

finding for race holds across a wide variety of 

environmental risks (e.g., hazardous waste sites and 

air pollution concentrations), levels of aggregation 

(e.g., zip codes, census tracts, and concentric 

circles around a facility), and controls (e.g., land 

value, population density, and percent employed 

in manufacturing). The finding for race appears 

sensitive, however, to comparison groups (e.g., all 

communities versus a subset of communities). 

A potential unintended consequence of improving 

environmental quality in some communities 

more than others is that rents may increase in the 

improved neighborhoods, making them potentially 

unaffordable for poorer households. For example, 

Grainger (2012) shows that about half of the 

increases in home prices due to the Clean Air 

Act Amendments are passed through to renters. 

Thus, the net health effect of improvements in 

environmental quality for renters depends on 

whether or not they move. Those who do not 

move experience higher rents, but also improved 

neighborhoods. For those who do move the net 

effect depends on the quality of the neighborhood 

to which they relocate. If these households receive 

far less of the health benefit predicted from a static 

model and also face transaction costs from moving, 

they could be worse off. The literature refers to this 

phenomenon as “environmental gentrification” 

(see also Banzhaf and McCormick 2012). 

Sieg et al. (2004) find that even with no moving 

costs, local households could be worse off 

because other households move into the clean 

neighborhood and bid up the rents.17 Earlier 

work by Banzhaf and Walsh (2008) shows that 

neighborhood income increases following cleanup, 

but more recent analysis (Banzhaf et al. 2012) 

shows racial characteristics in the neighborhood 

may not change. The authors postulate that richer 

minorities may move back into neighborhoods 

following cleanup. 

10.2.2 Analyzing Distributional 

Impacts in the Context of 

Regulatory Analysis 

In the context of regulatory analysis, examining 

distributional effects of health and environmental 

outcomes or costs can be accomplished, when data 

are available, by comparing effects in the baseline 

to post-regulatory scenarios for minority, lowincome, or indigenous populations.18 

When evaluating health and environmental 

outcomes, the following fundamental questions 

can guide the process of considering potential 

analytical methods for assessing EJ.19 

• What is the baseline distribution of health 

and environmental outcomes across 

population groups of concern for pollutants 

affected by the rulemaking?20 

17 	 The market dynamics associated with the relationship between 

household location decisions and pollution was first examined in a 

rigorous context in Been and Gupta (2007), and further explored by 

Banzhaf and Walsh (2008). 

18 	 OMB (2003) defines the baseline as “the best assessment of the 

way the world would look absent the proposed action.” Section 

10.2.6 describes the concept of baseline briefly. For a more detailed 

discussion on properly defining a baseline to measure the incremental 

effects of regulation, see Chapter 5 of these Guidelines. 

19 	 See Maguire and Sheriff (2011) for more detail. 

20 	 The term “outcome” is used to indicate that these questions should 

be interpreted more broadly than just applying to health effects. EPA 

Program Offices have the flexibility to adapt the wording of these 

questions to reflect the realities of the particular endpoints under 

consideration for a rulemaking. 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

 

 

• What is the distribution of health and 

environmental outcomes for the options 

under consideration for the rulemaking effort? 

• Under the options being considered, how 

do the health and environmental outcomes 

change for population groups of concern?21 

Note that these analytic questions recommend the 

analyst provide information on the distribution 

of outcomes, but do not ask for a determination 

of whether differences across population groups 

constitute disproportionate impacts.22 The term 

disproportionate is neither defined in EO 12898, 

nor does the academic literature provide clear 

guidance on what constitutes a disproportionate 

impact. The determination of whether an impact is 

disproportionate is ultimately a policy judgment. 

This chapter presents a suite of methods for 

analyzing distributional effects across a variety of 

regulatory contexts. Because the data, time, and 

resource constraints will differ across programs 

and rules, these guidelines are intended to provide 

flexibility to the analyst while introducing greater 

rigor and transparency in how EJ is considered in a 

regulatory context. 

10.2.2.1 Evaluating Changes in 

the Distribution of Health and 

Environmental Outcomes 

The analysis of EJ should ideally consider how a 

policy affects the distribution of relevant health 

and environmental outcomes (e.g., mortality 

risk from a regulated pollutant). If the outcome 

data are unavailable, distribution of ambient 

21 	 It would be useful to quantify the degree to which disparities change 

from baseline, so that one could rank in order of preference the 

relative merits of various options. Any ranking metric, however, 

would require adoption of an implicit social welfare function. Such 

approaches are analytically meaningful, but still under development 

and recommendation of a specific social welfare function is beyond the 

scope of this chapter. Text Box 10.1 provides additional discussion on 

this topic. 

22 	 The EJ guidance for NEPA (CEQ 1997) provides some guidance on the 

use of the term. A population group may be disproportionately affected 

if health effects are significant or “above generally accepted norms,” 

the risk or rate of exposure is significant or “appreciably exceeds or is 

likely to appreciably exceed the risk or rate to the general population or 

other appropriate comparison group,” or is subject to “cumulative or 

multiple adverse exposures from environmental hazards.” 

environmental quality indicators (e.g., pollutant 

concentrations) can be a useful proxy. Such 

indicators are less informative than the outcomes 

themselves if population groups of concern vary 

in vulnerability to the pollutant, for example.23 

If projecting ambient environmental quality 

is not feasible, then the analysis may examine 

the distribution of pollutants from regulated 

sources. Distribution of pollutants is less desirable 

than distributions in ambient environmental 

quality or health and environmental outcomes 

due to uncertainty regarding how a reduction 

in emissions from a given source translates into 

environmental quality and how that, in turn, 

translates into the human impacts that are the 

ultimate objective of the analysis. 

It is important to consider changes in distributions 

of health and environmental outcomes between 

baseline and various policy options, rather than 

just the distribution of changes since an unequal 

distribution of environmental improvements may 

actually help alleviate existing disparities (Maguire 

and Sheriff 2011). For example, suppose a policy 

is expected to reduce a pollutant, causing a greater 

reduction in particular adverse health outcomes 

for non-minorities than for minorities. One might 

conclude that this change in the distribution of 

outcomes could pose an EJ concern. If, however, 

the non-minority population suffered greater 

ill effects from the pollutant at baseline than 

the minority population, such a change in the 

distribution of outcomes may reduce, rather than 

increase, a pre-existing disparity in outcomes. 

The difference between these two measures 

— the distribution of change in health and 

environmental outcomes and the change in 

the distribution of health and environmental 

outcomes — has implications for the suitability 

of data for analysis. In particular, analyzing the 

distribution of monetized benefits from a benefitcost analysis can be problematic. Benefit-cost 

analyses do not estimate each affected individual’s 

monetized welfare at baseline and policy 

levels of environmental quality. Instead, they 

23	 A large epidemiological literature explores differences in health effects 

across various demographic groups. See, for example, Schwartz et al. 

(2011b). 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

estimate society’s willingness to pay for a change 

in environmental quality. Thus, although the 

distribution of this change in welfare across groups 

may be of interest in its own right, in isolation 

it does not inform the question of whether the 

policy increases or reduces pre-existing disparities. 

To address the question of how a policy 

affects disparities it is necessary to evaluate 

the distribution of environmental and health 

outcomes in the baseline and for each policy 

option. As an alternative to the change in 

willingness to pay one could examine the 

distribution of physical indicators. Such an 

evaluation is fairly straightforward if there is only 

one outcome to consider. Analysis of multiple 

outcomes (e.g., asthma risk and fatal heart attack 

risk) raises the problem of whether and how to 

aggregate these outcomes into a single measure. 

Combining several outcomes into a single 

aggregate measure may be desirable, but entails 

normative value judgments regarding the weight 

to be given to each component. For example, how 

much asthma risk is equivalent to a given risk 

of a fatal heart attack? One possible weighting 

scheme would be to use quality-adjusted life 

years (QALYs) or similar measures, but these are 

generally not consistent with willingness-to-pay 

measures and benefit-cost analysis (IoM 2006). 

Another alternative is to use the willingness-to-pay 

values from the benefit-cost analysis as weights (see 

Chapter 7 of these Guidelines for a discussion of 

willingness to pay). 

A standard benefit-cost analysis aggregates 

multiple outcomes by multiplying the number of 

cases of each outcome by its respective marginal 

willingness-to-pay. In principle one could 

use this weighting scheme in a distributional 

analysis. There is a theoretical issue, however. The 

empirical techniques used to monetize health and 

environmental benefits estimate an individual’s 

marginal willingness to pay for a change in the 

outcome. That is, they reflect the amount of 

money an individual would give up for a very 

small improvement in the outcome variable, 

evaluated at a particular level. The problem is 

that economic theory suggests that even if all 

individuals had identical preferences, the marginal 

willingness to pay to avoid a bad outcome should 

increase with the level of the outcome (e.g., an 

individual would be willing to pay more to reduce 

her probability of death from a particular disease 

from 99 percent to 98 percent, than she would 

to reduce it from 2 percent to 1 percent). As a 

practical matter, however, marginal willingnessto-pay measures typically used in benefit-cost 

analysis are constant values. The approximation 

implicit in this approach is defensible when the 

changes considered are not too large. However, it 

is not necessarily reasonable to multiply, say, the 

baseline mortality risk by the value of a statistical 

life in order to get the dollar value of eliminating 

the entire baseline risk. Yet this type of calculation 

would be necessary in order to evaluate how 

policy options would change the distribution 

of monetized environmental outcomes across 

population groups of concern. Consequently, 

if analysts use monetized values to aggregate 

across outcomes, the exposition should include 

appropriate caveats and be presented alongside 

outcome-by-outcome levels for the baseline and 

each policy option. 

10.2.2.2 Evaluating the Distribution 

of Costs 

Activities to address environmental justice often 

focus on reducing disproportionate environmental 

and health outcomes in communities. However, 

certain directives (e.g., EO 13175 and OMB 

Circular A-4) specifically identify distribution of 

economic costs as an important consideration. The 

economic literature also typically considers both 

costs and benefits when evaluating distributional 

consequences of an environmental policy in order 

to understand their net effects on welfare. For 

instance, Fullerton (2011) discusses six possible 

types of distributional effects that may result 

from an environmental policy: higher product 

prices, changes in the relative returns to factors of 

production, how scarcity rents are distributed, the 

distribution of environmental benefits, transitional 

effects of the policy, and the capitalization of 

environmental improvements into asset prices 

(e.g., land or housing values). Policy decisions 

involve trade-offs, and these may differ across 

affected groups. While health or environmental 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

 

 

 

improvements may accrue to certain population 

groups of concern, costs may be borne by others. 

As a result, some groups may experience net 

costs even if everyone is expected to receive gross 

environmental benefits. 

This chapter frames the discussion in terms of 

environmental and health outcomes (referred 

to as benefits, when monetized), but many 

of the methods can be applied to costs and 

other impacts as well. Whether or not costs are 

included in an evaluation of EJ issues associated 

with a regulation should be evaluated on a 

case-by-case basis. If regulatory costs are spread 

fairly evenly across many households (e.g., in 

the form of higher prices) and expected to be 

small on a per-household basis, further analysis 

is likely not warranted or feasible. However, 

there may be cases where the analysis of the 

distribution of costs is warranted.24 Such cases 

may include situations where costs to consumers 

may be concentrated among particular types 

of households (e.g., renters); identifiable plant 

closures or facility relocations that could adversely 

affect certain communities; or when households 

may change their behavior in response to the 

imposition of costs. 

In many cases, detailed analyses of costs may be 

challenging due to data or modeling constraints. 

For example, EPA may expect air pollution control 

costs to be passed on to electricity consumers. The 

Agency might not have information, however, 

on how costs are passed through as rate increases, 

how these increases may be broken down between 

residential and commercial customers, what 

assistance is available for low-income consumers, 

and how consumption patterns differ by race and 

income. Likewise, if air quality improvements 

associated with a regulation are unevenly 

distributed, demand for housing in particular 

neighborhoods may affect rental prices. While 

hedonic approaches (discussed in Chapter 7) 

may be useful for demonstrating how changes 

in environmental quality factor into housing 

prices, predicting the effect of such price changes 

24	 EPA’s Lead Renovation, Remodeling, and Painting Final Rule (U.S. EPA 

2008c) provides the best example to date of consideration of costs in 

the context of a rulemaking. 

on household migration by race or income may 

be infeasible.25 Absent such data, it might not 

be possible to predict the total impact of the 

rule on different populations. In these instances, 

those issues that cannot be quantified can be 

qualitatively discussed. 

10.2.3 Relevant Populations 

EO 12898 identifies a number of relevant 

population groups of concern: minority 

populations, low-income populations, Native 

American populations and tribes, and “populations 

who principally rely on fish and/or wildlife for 

subsistence.”26 It may be useful to analyze these 

categories in combination — for example, lowincome minority populations — or to include 

additional population groups of concern, but 

such analysis is not a substitute for examining 

populations explicitly mentioned in the Executive 

Order. In this section, we discuss existing Federal 

definitions for population groups of concern in 

the context of EJ. We also discuss credible options 

for defining these populations in the absence of a 

Federal definition. 

10.2.3.1 Minority and Native 

American Populations 

OMB (1997) specifies minimum standards for 

“maintaining, collecting, and presenting data 

on race and ethnicity for all Federal reporting 

purposes.... The standards have been developed 

to provide a common language for uniformity 

and comparability in the collection and use of 

data on race and ethnicity by Federal agencies.” In 

particular, it defines the following minimum race 

and ethnic categories: 

• American Indian or Alaska Native 

• Asian 

• Black or African American 

25	 See Section 8.2.5.1 of the Handbook on the Benefits, Costs and 

Impacts of Land Cleanup and Reuse (U.S. EPA 2011c) for a more 

detailed discussion of EJ in the context of the potential effects of 

environmental policy on land values and household location decisions. 

26	 EO 12898 clarifies in Section 6 that the EO applies to Native Americans 

and also Indian Tribes, as specified in 6-606, as well as populations 

who principally rely on fish and/or wildlife for subsistence as specified 

in 4-401. 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

 

 

 

• Native Hawaiian or Other Pacific Islander 

• White 

• Hispanic or Latino 

Statistical data collected by the Federal 

government, such as the U.S. Census Bureau, use 

this classification system.27 Beginning with the 

2000 Census, individuals were given the option 

of selecting more than one race, resulting in 

63 different categories. OMB (2000) provides 

guidance on how to aggregate these data in a 

way that retains the original minimum race 

categories (i.e., the first five categories listed 

above) and four double race categories that are 

most frequently reported by respondents.28 In 

addition, the U.S. Census Bureau collects data 

useful for identifying minority populations 

not completely captured by either the race or 

ethnicity categories, such as households that 

speak a language other than English at home or 

foreign-born populations. 

CEQ’s NEPA Guidance for EJ (CEQ 1997) 

provides useful direction for defining minority 

and minority population based on these Federal 

classifications. Minority is defined as “individual(s) 

who are members of the following population 

groups: American Indian or Alaskan Native; 

Asian or Pacific Islander; Black, not of Hispanic 

origin; or Hispanic.” A population is identified 

as minority if “either (a) the minority population 

of the affected area exceeds 50 percent or (b) the 

minority population percentage of the affected 

area is meaningfully greater than the minority 

population percentage in the general population or 

other appropriate unit of geographic analysis.” The 

term meaningfully greater is not defined, although 

the guidance notes that a minority population 

exists “if there is more than one minority 

group present and the minority percentage, as 

calculated by aggregating all minority persons, 

meets one of the above-stated thresholds.” 

Finally, the CEQ Guidance states that analysts 

27	 Analysts should refer to the OMB Federal Register notice for 

the specific definitions: http://www.whitehouse.gov/omb/ 

fedreg_1997standards/ (accessed on December 20, 2012). 

28	 See OMB (2000) for specific guidance on how to conduct this 

aggregation. 

“may consider as a community either a group of 

individuals living in geographic proximity to one 

another, or a geographically dispersed/transient 

set of individuals (such as migrant workers or 

Native Americans), where either type of group 

experiences common conditions of environmental 

exposure or effect.” 

10.2.3.2 Low-Income Populations 

OMB has designated the U.S. Census Bureau’s 

annual poverty measure, produced since 1964, 

as the official metric for program planning 

and analytic work by all Executive branch 

agencies in Statistical Policy Directive No. 14 

(Federal Register 1978), although it does not 

preclude the use of other measures. Many 

Federal programs use variants of this poverty 

measure for analytic or policy purposes, and the 

U.S. Census Bureau publishes data tables with 

several options. 

The U.S. Census Bureau measures poverty by using 

a set of money income thresholds that vary by 

family size and composition to determine which 

households live in poverty. If a family’s total income 

is less than the threshold, then that family and every 

individual in it is considered in poverty. The official 

poverty thresholds do not vary geographically, but 

they are updated for inflation using the national 

Consumer Price Index for All Urban Consumers 

(CPI-U). The official poverty definition uses money 

income before taxes and does not include capital 

gains or noncash benefits (such as public housing, 

Medicaid, and food stamps).29 This measure of 

poverty has remained essentially unchanged — 

apart from relatively minor alterations in 1969 and 

1981— since its inception.30 

There is considerable debate regarding this 

poverty measure’s ability to capture differences in 

29	 See “How the Census Bureau Measures Poverty” available at 

http://www.census.gov/hhes/www/poverty/about/overview/measure. 

html (accessed on November 30, 2011). 

30	 The U.S. Census Bureau produces single-year estimates of median 

household income and poverty by state and county, and poverty by 

school district as part of its Small Area Income and Poverty Estimates. 

It also provides estimates of health insurance coverage by state and 

county as part of its Small Area Health Insurance Estimates. These data 

are broken down by race at the state level and by income categories at 

the county level. 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

economic well-being. In particular, the National 

Research Council (NRC) recommended that 

the official measure be revised because “it no 

longer provides an accurate picture of the 

differences in the extent of economic poverty 

among population groups or geographic areas 

of the country, nor an accurate picture of trends 

over time” (Citro and Michael 1995). OMB 

convened an interagency group in 2009 to define 

a supplemental poverty measure based on NRC 

recommendations. The U.S. Census Bureau 

released the Supplemental Poverty Measure 

(SPM) in November 2011 (Short 2011). This 

measure uses different measurement units to 

account for “co-resident unrelated children (such 

as foster children) and any co-habitors and their 

children,” a different poverty threshold, and 

modified resource measures (to account for inkind benefits and medical expenses, for example). 

It also adjusts for differences in housing prices by 

metropolitan statistical area, as well as family size 

and composition. 

The NRC recognized that annual income is not 

necessarily the most reliable measure of relative 

poverty as it does not account for differences in 

accumulated assets across households. Neither the 

SPM nor the official U.S. poverty thresholds take 

into account differences in wealth across families. 

However, the SPM examines whether a household 

is likely to fall below a particular poverty threshold 

as a function of inflows of income and outflows of 

expenses. The U.S. Census Bureau asserts that this 

measure is therefore more likely to capture shortterm poverty since many assets are not as easily 

convertible to cash in the short run (Short 2012). 

The U.S. Census Bureau also includes several 

additional measures that may prove useful in 

characterizing low-income families. Unlike 

poverty, there is no official or standard 

definition of what constitutes “low-income,” 

though it is expected to vary similarly by 

region due to differences in cost-of-living as 

well as with family composition. It is therefore 

appropriate to examine several different lowincome categories, including families that make 

some fixed amount above the poverty threshold 

(e.g., two times the poverty threshold) but still 

below the average household income for the 

United States or for a region. 

Educational attainment or health insurance 

coverage may also be useful for characterizing 

low-income families relative to other populations, 

although we caution analysts that some measures 

may be hard to interpret and use in a regulatory 

context. It is also possible to examine the percent 

of people who are chronically poor versus those 

that experience poverty on a more episodic 

basis using the Survey of Income and Program 

Participation which provides information on 

labor force participation, income, and health 

insurance for a representative panel of households 

on a monthly basis over several years (see Iceland 

2003). Finally, cross-tabulations often are available 

between many of these poverty measures and 

other socioeconomic characteristics of interest 

such as race, ethnicity, age, sex, education, and 

work experience. 

10.2.3.3 Populations that Principally 

Subsist on Fish and Wildlife

 EO 12898 directs agencies to analyze populations 

that principally subsist on fish and wildlife. CEQ’s 

NEPA Guidance for EJ (CEQ 1997) defines 

subsistence on fish and wildlife as “dependence by 

a minority population, low-income population, 

Indian tribe or subgroup of such populations on 

indigenous fish, vegetation and/or wildlife, as the 

principal portion of their diet.” It also states that 

differential patterns of subsistence consumption 

are defined as “differences in rates and/or 

patterns of subsistence consumption by minority 

populations, low-income populations, and 

Indian tribes as compared to rates and patterns of 

consumption of the general population.” 

Neither the U.S. Census Bureau nor other Federal 

statistical agencies collect nationally representative 

information on household consumption of fish 

and/or wildlife. However, EPA has conducted 

consumption surveys in specific geographic areas. 

If fish and wildlife consumption is a substantial 

concern for a particular rulemaking, EPA’s 

guidance can provide useful information for 

collecting these data (see U.S. EPA 1998b). There 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

 

 

may also be surveys conducted by state or local 

governments. It is important to verify that any 

survey used in an analysis of distributional impacts 

in the context of EJ adheres to the parameters and 

methodology set out in U.S. EPA (1998b). 

10.2.4 Data Sources 

Many data sources can be used for conducting 

analyses of EJ issues. The U.S. Census Bureau’s 

“Quick Facts” website contains frequently 

requested Census data for all states, counties, and 

urban areas with more than 25,000 people.31 Data 

include population, percent of population by race 

and ethnicity, and income (median household 

income, per-capita income, and percent below 

poverty line). 

In 2010 the U.S. Census Bureau began to 

administer the decennial Census using a short 

form to collect basic socioeconomic information. 

More detailed socioeconomic information is now 

collected annually by the American Community 

Survey (ACS), which is sent to a smaller 

percentage of households than the decennial 

Census.32 The ACS provides annual estimates 

of socioeconomic information for geographic 

areas with more than 65,000 people, three-year 

estimates for areas with 20,000 or more people, 

and five-year estimates for all areas.33 The five-year 

estimates, which are based on the largest sample, 

are the most reliable and are available at the census 

tract and block group levels. Some of the Quick 

Facts data include estimates from the ACS. 

The U.S. Census Bureau’s American Housing 

Survey (AHS), is a housing unit survey that 

provides data on a wide range of housing 

and demographic characteristics, including 

31	 Quick Facts is available at: http://quickfacts.census.gov/qfd/index.html. 

The year associated with data from Quick Facts is important to note. 

Data are updated as new information becomes available. Therefore, not 

all data elements represent the same year. 

32	 The ACS is available at: http://www.census.gov/acs/www/index.html. 

(accessed December 1, 2011.) 

33	 Because ACS variables change over time, caution should be used 

when comparing ACS estimates across samples and years. Guidance 

for comparing ACS data can be found at: http://www.census.gov/acs/ 

www/guidance_for_data_users/comparing_data/ (accessed on April 

information on renters.34 Unlike the ACS, which 

selects a random sample every year, the AHS 

returns to the same 50,000 to 60,000 housing units 

every two years. 

10.2.5 Scope and Geographic 

Considerations 

Most EPA rules are national in scope. Therefore, 

the entire country is typically considered within 

the scope of analysis. However, there may be 

reasons to consider a rule’s distributional effects at 

a sub-national level. For example, for a regulation 

of hazardous waste sites it may be appropriate 

to conduct separate state-level analyses due to 

differences in implementation of state-level 

regulations. A rule may also affect a limited part of 

the country. The 2011 Cross-State Air Pollution 

Rule (U.S. EPA 2011b), for example affects mainly 

eastern states.35 In such cases the analyst may 

wish to evaluate the effects of the regulation at a 

regional level. Finally, for some regulations, such 

as those governing the use of a household chemical 

or as a product ingredient, geography may not 

be as relevant for determining how health and 

environmental outcomes vary across population 

groups of concern. Two main issues to consider 

when comparing impacts of a rulemaking on 

minority, low-income, or indigenous populations 

across geographic areas are: 

• Unit of analysis (e.g., facilities or aggregate 

emissions to which a population group is 

exposed within a designated geographic 

area); and 

• Geographic area of analysis used to 


characterize impacts (e.g., county or 


census tract).36
 

The unit of analysis refers to how the 

environmental harm is characterized. For 

instance, in a proximity-based analysis the unit 

of analysis could be an individual facility or the 

34	 Information on owner-occupied homes versus renters may be useful 

when exploring issues of gentrification, where renters could be worse 

off due to rising housing costs. 

35	 See http://www.epa.gov/airtransport/ for details. (accessed December 

1, 2011.) 

27, 2011).	 36 This is often referred to in the literature as geographic scale. 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

total number of facilities within a particular 

geographic area (e.g., a county or census tract). 

In an exposure-based analysis the unit of analysis 

could be emissions aggregated within a particular 

geographic area to which the population is 

exposed. The unit of analysis is often identical 

to the geographic scale used to aggregate and 

compare effects on minority, low-income, or 

indigenous populations in one area to another 

(see Section 10.2.7 regarding how to select an 

appropriate comparison group).37 The choice will 

vary depending on the nature of the pollutant 

(e.g., point sources may use a facility as the 

unit of analysis, while area sources may use a 

geographic unit). In considering various units, 

an important consideration is whether the data 

are sufficiently disaggregated to pick up potential 

variation in impacts across socioeconomic 

characteristics. More aggregated units of analysis 

(e.g., metropolitan statistical area (MSA) or 

county) may mask variation in impacts across 

socioeconomic groups compared to more 

disaggregated levels (e.g., facility or census tract). 

The geographic area of analysis is the area used 

to characterize impacts (e.g., distance around a 

facility). Outcomes are aggregated by population 

groups within geographic areas to compare 

across groups. As with unit of analysis, choice 

of options for defining the geographic area will 

vary depending on pollutant and rule. Some air 

pollutants, for example, may travel hundreds of 

miles away from the source, making it appropriate 

to choose a large area for measuring impacts. In 

contrast, water pollutants or waste facilities may 

affect smaller areas, making it appropriate to 

consider a smaller area for analysis. Likewise, an 

assessment of outcomes from specific industrial 

point sources may require more spatially resolved 

air quality, demographic and health data than one 

that affects regional air quality, where coarser air 

quality, demographic and health data may suffice. 

Using more than one geographic area of analysis to 

compare effects across population groups may also 

be useful since outcomes are unlikely to be neatly 

contained within geographic boundaries. The 

literature has demonstrated that results are sensitive 

37	 In Fowlie et al. (2012), for example, the scale of the analysis varies 

between 0.5, 1 and 2 miles of the facility (which is the unit of analysis). 

to the choice of the geographic area of analysis 

(Mohai and Bryant 1992; Baden et al. 2007). 

Commonly used geographic areas of 

analysis include: 

Counties: The United States has more than 

3,000 counties according to the 2007 Census of 

Governments. Although counties are well-defined 

units of local government and provide complete 

coverage of the United States, they vary in size from 

a few to thousands of square miles and population 

density ranges from less than one person per 

square mile in some Alaskan counties to over 

66,000 in New York County. In addition, spatial 

considerations associated with using counties 

present concerns for an analysis of distributional 

impacts in the context of EJ. A facility located in 

one corner of a county may have greater effects 

on neighboring counties than on residents of the 

county where the plant is located.38, 39 

Metropolitan and Micropolitan Statistical 

Areas: The U.S. Census Bureau publishes data on 

metropolitan and micropolitan statistical areas, 

as defined by OMB (OMB 2009). Metropolitan 

statistical areas include an urban core and adjacent 

counties that are highly integrated with the urban 

core. A micropolitan statistical area corresponds 

to the concept of a metropolitan statistical area 

but on a smaller scale. Metropolitan statistical 

areas have an urban core of at least 50,000 persons; 

micropolitan statistical areas have an urban core 

population between 10,000 and 50,000 persons. 

Rural areas of the United States are not covered by 

these statistical designations, though according to 

the U.S. Census Bureau, almost 94 percent of the 

U.S. population lived in a metro- or micropolitan 

statistical area in 2010. 

Zip codes: Zip codes are defined by the U.S. 

Post Office for purposes of mail delivery and 

may change over time. They also may cross state, 

county, and other more disaggregated Census 

38	 These same advantages and disadvantages can apply to other units of 

government. 

39	 For criteria pollutants, baseline health data may be available at the 

county level (e.g., baseline death rates, hospital admissions, and 

emergency department visits). 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

statistical area definitions, making them difficult 

to use for analysis. Zip code tabulation areas 

are statistical designations first developed by 

the U.S. Census Bureau in 2000 to approximate 

the zip code using available census block level 

data on population and housing characteristics. 

Data are readily available for the approximately 

33,000 U.S. zip code tabulation areas. While 

smaller than counties, they also vary greatly in 

size and population. As a result, they may often 

be less preferable than other geographic areas for 

analyzing distributional effects across population 

groups of concern. 

Census tracts/block groups/blocks: Census 

tracts are small statistical subdivisions of a 

county, typically containing from 1,500 to 8,000 

persons. The area encompassed within a census 

tract may vary widely, depending on population 

density. Census tracts in denser areas cover 

smaller geographic areas, while those in less dense 

areas cover larger geographic areas. Census tract 

boundaries were intended to remain relatively 

fixed. However, they are divided or aggregated 

to reflect changes in population growth within 

an area over time. Although they were initially 

designed to be homogeneous with respect to 

population characteristics, economic status, and 

living conditions, they may have become less so 

over time as demographics have changed. 

Analysts may also choose to use census blocks or 

block groups. A census block is a subdivision of 

a census tract and the smallest geographic unit 

for which the U.S. Census Bureau tabulates data, 

containing from 0 to 600 persons. Many blocks 

correspond to individual city blocks bounded 

by streets, but may include many square miles, 

especially in rural areas. And census blocks may have 

boundaries that are not streets, such as railroads, 

mountains or water bodies. The U.S. Census Bureau 

established blocks covering the entire nation for 

the first time in 1990. Census block groups are a 

combination of blocks that are within — and a 

subdivision of — a given census tract. Block groups 

typically contain 600 to 3,000 persons.40 

40	 Other Census statistical area definitions (e.g., public use microdata 

areas or PUMAs) are also available. 

GIS methods: Because Census-based definitions 

often reflect topographical features such as rivers, 

highways, and railroads, they may exclude affected 

populations that, although separated by some 

physical feature, receive a large portion of the 

adverse impacts being evaluated. Since Censusbased definitions vary in geographic size due to 

differences in population density, Geographic 

Information System (GIS) software and methods 

may enable the use of spatial buffers around an 

emissions source that are more uniform in size and 

easier to customize to reflect the appropriate scale 

and characteristics of emissions being analyzed for 

a given rulemaking. 

Analysts should be aware that there are a number 

of challenges typical of working with geospatial 

data. In some cases, statistical techniques rely on 

assumptions that often are violated by these types 

of data (Chakraborty and Maantay 2011). For 

instance, spatial autocorrelation — when locations 

in closer proximity are more highly correlated than 

those further away from each other — violates the 

assumption that error terms are independently 

distributed (an assumption that underlies ordinary 

least squares). 

10.2.6 Defining the Baseline 

Proper definition of the baseline is crucial for 

evaluating a rule’s distributional effects. OMB 

(2003) defines the baseline as “the best assessment 

of the way the world would look absent the 

proposed action.” The baseline allows one to 

determine how a rule’s effects are distributed 

across population groups of concern and to assess 

whether some groups may be disproportionately 

affected. Baseline assumptions used in a 

distributional analysis should be consistent with 

those used in the benefit-cost analysis. See Chapter 

5 for a more detailed discussion of baseline issues. 

10.2.7 Comparison groups 

The choice of a relevant comparison group is 

important for evaluating changes in health, risk, 

or exposure effects across population groups of 

concern relative to a baseline. Within-group 

comparisons involve comparing effects on the 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

same demographic group across different areas 

in the state, region or nation, while acrossgroup comparisons examine effects for different 

socioeconomic groups within an affected area. 

From the perspective of EO 12898, across-group 

comparisons may be most relevant. The literature 

suggests using more than one comparison group 

to analyze whether a finding of disproportionate 

impacts is sensitive to how it is defined. Bowen 

(2001) also argues that restricting the comparison 

group to alternative locations within the same 

metropolitan area may be more defensible than 

a national level comparison in some instances, 

given heterogeneity across geographic regions in 

industrial development and economic growth over 

time and inherent differences in socioeconomic 

composition (e.g., relatively more Hispanics reside 

in the Southwest). Ringquist (2005), however, 

notes that placing restrictions on comparison 

groups in this way may “reduce the power of 

statistical tests by reducing sample sizes” or bias 

results against a finding of disproportionate 

impacts because such restrictions reduce variation 

in socioeconomic variables of interest. 

10.2.8 Measuring and 

estimating impacts 

This section presents a range of potentially 

useful approaches for describing distributions 

in regulatory analysis. To the extent feasible, 

basic summary statistics of a regulation’s impacts 

on relevant endpoints by race and income 

are recommended for distributional analyses. 

Summary statistics may be straightforward to 

calculate when data are available, and providing 

such information promotes consistency across 

EPA analytical efforts. A related document, the 

Interim Guidance on Considering Environmental 

Justice During the Development of an Action (U.S. 

EPA 2010a), suggests conducting a screening 

process for determining when an action may 

require evaluation. For economically significant 

actions, it is recommended that the results of 

the screening be demonstrated through the use 

of summary statistics. Summary statistics can be 

supplemented with other approaches described 

below when a screening analysis indicates that a 

more careful evaluation is needed. 

The health effects of exposure to pollution 

may vary across populations (likewise, with 

costs). One way to capture these effects is to 

use information regarding variation in risk 

and incidence by groups, when available, to 

characterize the baseline and projected response 

to a change in exposure (for example, see Fann et 

al. 2011). However, available scientific literature 

and data (which also often requires some level 

of spatial resolution) may not allow for a full 

characterization. In these cases, it is recommended 

that the analyst qualitatively discuss conditions 

that are not adequately accounted for in the 

risk and exposure characterization used to 

assess health effects for minority populations 

or low-income populations and the key sources 

of uncertainty highlighted in the literature 

(U.S. EPA 2010a). When data are available 

to approximate risk or exposure, for instance 

location of emitting facilities, some level of 

quantitative analysis may be possible. 

Text Box 10.1 discusses the potential usefulness 

of social welfare functions and inequality indices 

for ranking distributions. While these methods 

are useful for combining efficiency and equity 

considerations into one measure, these tools 

are not sufficiently developed for application to 

regulatory analysis. For a more detailed discussion 

of the advantages and disadvantages of methods 

commonly used to rank environmental outcomes 

see Maguire and Sheriff (2011). 

10.2.8.1 Simple Summary Statistics 

Simple summary measures can characterize 

potential differences in baseline and regulatory 

options within and across populations of concern 

relative to appropriate comparison groups. Such 

statistics can be calculated, if data are available, to 

address the three questions outlined in Section 

10.2.2. It is important to note, however, that 

summary statistics alone do not necessarily provide 

a complete description of differences across groups. 

Omitted variables are one important limitation of 

examining single statistics. In addition, summary 

statistics (e.g., means) can mask important details 

about the tails of the distribution which can be 

important for identifying potential EJ concerns 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

 

 

 

 

 

Text Box 10.1 - Social Welfare Functions and Inequality Indices 

The costs, benefits, and distributional effects of a regulation can be evaluated by a single social welfare function 

(SWF). A SWF provides a way to aggregate welfare or utility across individuals into a single value, thus allowing 

simple, direct comparisons in ranking alternative allocations. Such comparisons are potentially useful in evaluating 

whether a change from the baseline to a regulatory option makes society better off. Likewise, they can also facilitate 

comparisons between possible regulatory options (see Adler 2008, 2012 for a discussion). Sen (1970), Arrow 

(1977), and Just et al. (2004) provide theoretical discussions of SWFs, and Norland and Ninassi (1998) provide an 

example of an application to energy markets. Adler (2012) addresses practical issues of incorporating both health 

and income effects in a SWF. 

Any ranking of alternative outcomes uses an implicit set of normative criteria; a SWF makes the criteria explicit 

regarding how society prefers to distribute resources across individuals. Since there is no consensus regarding those 

preferences, a universally-accepted SWF does not exist. For example, suppose an increase in exposure to a particular 

pollutant results in an average loss of 0.1 IQ points across a population of 1,000 children (100 IQ points total). It is 

not obvious how society should rank alternative distributions of this loss. Is it worse to have 250 individuals suffer a 

loss of 0.1 each, 250 suffer a 0.3 loss, and 500 suffer no loss? Or 500 individuals suffer a loss of 0.01 and 500 suffer 

a loss of 0.19? Many sensible SWFs could be specified; some may prefer the first outcome, some may prefer the 

second, and some may be indifferent between the two. 

An inequality index is a related concept used to assign a numerical value to distributions of a single “good” or “bad” 

(e.g., income or pollution), independent of the total amount produced. A distribution with a higher index value is 

less “equal” than one with a lower number. Commonly used indices are based on simple SWFs and are subject to 

the same limitations (Blackorby and Donaldson 1978, 1980). However, unlike a SWF, an index number value has 

cardinal significance, i.e., the magnitudes, not just the rankings, contain information about how much society would 

be willing to give up in exchange for the rest to be equally distributed. 

Inequality indices were originally developed for ranking “goods,” like income. In general, it is inappropriate simply 

to use positive values of a bad outcome (e.g., pollution exposure) in the formula for an index, since doing so would 

imply that the underlying SWF is increasing in pollution, i.e., it would rank scenarios with higher overall pollution 

as more desirable. Since indices cannot accommodate negative values, some commonly used income inequality 

measures, such as the Gini coefficient, and Atkinson index, are inappropriate for evaluating distributions of adverse 

outcomes. The Kolm index (Kolm 1976a, 1976b), in contrast, does not suffer from this problem (see Maguire and 

Sheriff 2011). Given that the peer-reviewed literature does not yet contain environmental applications of the Kolm 

Index, and the Atkinson Index is undefined for “bads,” we do not recommend inequality indices be used in regulatory 

analysis of distributional impacts in the context of EJ at this time. 

• Population groups of concern for the (see Gochfeld and Burger 2011). Nonetheless, regulatory action, such information can provide useful information • Geographic scale and unit of analysis, on potential differences. when relevant, 

After reviewing the available data and feasible • Primary conclusions (e.g., statistical differences), 

methods for developing information on potential • Sources of uncertainty across alternative differences, the analyst should present information results (e.g., comparison groups and in a transparent and accessible manner such that geographic scale), and the decision maker can consider: • Data quality and limitations of the results. 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

A variety of measures can be used to characterize 

an action’s distributional effects for population 

groups of concern. 

Means and quantiles 

Reporting mean outcomes by group at the 

baseline and for each regulatory option is a 

straightforward way to display information. Tests 

for statistical significance across means provide 

additional information about differences (see 

Been and Gupta 1997 and Wolverton 2009). 

However, mean estimates can mask what might 

be important information in the tails of the 

distribution. For example, the baseline outcomes 

could be uniformly distributed across the 

population but concentrated around the mean 

for the regulatory scenario. Examining differences 

around the central tendency only would not reveal 

this information. Presenting data using different 

quantiles can provide additional information 

illuminating these effects. 

Ratios 

A simple ratio can be calculated to determine 

whether certain groups are relatively more 

exposed to an environmental hazard. For instance, 

the probability that an individual is minority 

conditional on being exposed can be divided by 

the probability that an individual is not minority 

conditional on being exposed. Alternatively, one 

can also create a ratio of the probability that an 

individual is exposed to an environmental risk 

conditional on being minority divided by the 

probability that an individual is not exposed 

conditional on being in the same demographic 

group. Because ratios may mask absolute 

differences, ratios should be used in conjunction 

with other statistics. For example, a ratio may 

show a 100-fold difference between two groups’ 

exposure to an environmental hazard but the 

absolute difference could be small. Ratios may 

exaggerate the importance of differences. 

Tests for Differences 

Statistical tests can determine whether a 

significant disparity exists across demographic 

groups. One of the simplest is a t-test of the 

difference in means. However, a t-test assumes a 

normal distribution so it would be inappropriate 

for non-normal distributions. For non-normal 

distributions, nonparametric methods may 

be used. In cases where comparisons are made 

based on the difference in probabilities between 

two groups, tests such as the Kendall test and 

the Fisher Exact test (for small samples) may be 

used. These tests compare standard errors of two 

separate and independent statistics to determine 

how likely it is that the calculated distribution is 

the actual one. More sophisticated tests are needed 

when making comparisons across more than two 

groups or a more formal examination of the full 

distribution is desired. 

Correlation coefficients 

Simple pair-wise correlations between impacts 

and relevant demographic groups may be useful 

information for characterizing distributional 

effects (e.g., Brajer and Hall 2005). It is important 

to note, however, that the value of a Pearson 

correlation coefficient, for example, is a measure 

of how closely the distribution of the relationship 

between two variables (e.g., percent minority 

population and ambient pollution concentrations) 

can be represented by a straight line. It does 

not provide information regarding the slope of 

the line, apart from being positive or negative. 

Similarly, a Spearman rank correlation coefficient 

measures how closely the relationship can be 

captured by a generic monotonically increasing 

or decreasing function. Determination of what 

constitutes a “strong” or “weak” correlation 

is somewhat arbitrary, and caution should be 

used when comparing coefficients across socioeconomic variables of interest. 

Counts 

A count of geographic areas (e.g., counties) where 

the incidence of an environmental outcome 

affected by a rule, disaggregated by race/ethnicity 

and income, exceeds the overall average is a useful 

measure. For comparison, this count should be 

accompanied by a count of geographic areas where 

the incidence does not exceed the overall average. 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

These counts do not account for magnitude of 

differences, but can help identify the need for 

more detailed analysis. 

10.2.8.2 Visual Displays 

Maps, charts, graphs, and other visual displays are 

commonly used in EJ analyses (see Shadbegian et 

al. 2007, for example). With increased access to 

GIS software and built-in graphical functions in 

spreadsheet or statistical software, it is relatively 

easy to produce a variety of visual displays of 

EJ-related information. Visual displays can be 

helpful in displaying baseline levels of pollutants or 

locations of certain facilities, and the distribution, 

demographic profile and baseline health status of 

population groups of concern. 

There are several challenges with GIS analysis 

of distributional information. These include 

spatial and data deficiencies as well as geographic 

considerations that can lead to misleading 

or inaccurate results.41 It may be difficult to 

discern differences that arise between baseline 

and regulatory options, unless such differences 

are stark. While the use of visual displays in an 

analysis of distributional impacts in the context 

of EJ may be useful for helping to communicate 

the geographic distribution of impacts, this 

information may be more effective if it is 

accompanied by other analytical information. 

10.2.8.3 Proximity-Based Analysis 

Proximity- or distance-based analysis is an 

approach commonly used in the EJ literature as 

a surrogate for more direct measures of risk or 

exposure when such information is not easily 

available. This approach examines demographic 

and socioeconomic characteristics in proximity to a 

particular location, typically a waste site, permitted 

facility, or some other polluting source (for 

instance, see Baden and Coursey 2002, Cameron et 

al. 2012, and Wolverton 2009). While a simplistic 

approach is to examine the population within a 

Census-defined geographic boundary of a location, 

it is also possible to use GIS methods to draw a 

41	 See Chakraborty and Maantay (2011) for further discussion of the 

limitations of using GIS for EJ analyses. 

concentric buffer around an emission source, such 

as a one mile radius around a site to approximate 

the distance that a particular pollutant may 

travel. In some cases, it may also be possible to 

use dispersion models to select a buffer that 

approximates the effect of atmospheric conditions 

(for instance, wind direction and weather patterns) 

on exposure, though these types of models are dataintensive (Chakraborty and Maantay 2011). 

Several analytical considerations are important 

for conducting a proximity-based analysis.42 First, 

accurate information is needed for the location of 

polluting sources. Addresses or latitude/longitude 

coordinates must reflect physical locations of 

polluting facilities, and not the location of a 

headquarters building, for example. Second, a 

decision must be made regarding the appropriate 

distance from the facility to examine community 

characteristics. A solid waste facility with strict 

monitoring and safety controls is likely to have a 

limited geographic impact, whereas a permitted air 

pollution source may have the potential for a more 

widespread geographic impact. In general, Censusdefined geographic boundaries (e.g., county, MSA) 

are unlikely to provide an accurate portrayal of 

the relevant affected population because emission 

sources are often not found in the center of the 

area (i.e., they are sometimes along a boundary 

and thus mostly affect a neighboring jurisdiction) 

and pollutant exposures do not conform to these 

boundaries.43 In addition, Census-defined areas 

often vary widely in size, implying that they may 

differ in how well they proxy for actual exposure. 

Defining proximity or distance using buffer-based 

approaches (e.g., through GIS or fate and transport 

modeling) around an emissions source has the 

potential to more closely approximate actual 

risk and exposure, but the appropriate distance 

measure can vary by situation. The literature has 

demonstrated that results in proximity-based 

analyses can vary substantially with the choice 

42	 For an overview of proximity analysis, including a discussion 

of various spatial analysis techniques used in the literature see 

Chakraborty and Maantay (2011) and Mohai and Saha (2007). 

43	 Mohai and Saha (2007) refer to this as the “unit-hazard coincidence” 

approach because the analyst uses the available geographic units and 

determines whether they are coincident with an environmental hazard 

instead of first identifying the exact location of the hazard and then 

examining effects within a particular distance. 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

 

of the geographic area of analysis (see Rinquist 

2005; Mohai and Saha 2007). For this reason, 

it is recommended that the analyst explore the 

potential value of defining and applying more than 

one specification for distance or proximity.44 

When this approach is used, it is important to be 

aware of biases and limitations introduced when 

proximity or distance is used as a substitute for risk 

and exposure modeling and that these limitations 

be clearly discussed (see Chakraborty and Maantay 

2011). In particular, it may only be possible to 

make limited observations with regard to the 

possibility of disproportionate impacts based on 

proximity-based analysis alone. 

10.2.8.4 Exposure Assessment 

Spatial patterns associated with environmental 

burdens across individuals or communities are 

difficult to analyze when pollution is diffuse. Air 

and water pollution, for example, are typically 

dispersed widely and subject to atmospheric 

or geologic features. As such, identifying the 

“proximity” to the hazards via some type of GIS 

analysis, as described above, is less useful. However, 

monitoring and/or modeling data may generate 

distributional effects at a disaggregated level. 

Criteria air pollutants (i.e., carbon monoxide, lead, 

nitrogen dioxide, ozone, particulate matter and sulfur 

dioxide) are monitored nationally. EPA’s National 

Air Toxics Assessment (NATA) data provide an 

assessment of hazardous air pollutants across the 

U.S. at the census tract level.45 Data from these 

monitoring networks may potentially be combined 

with demographic data and dispersion models to 

generate baseline and regulatory distributions of 

pollutants by population groups of concern.46 

44	 The analysis of distributional impacts in the context of EJ completed 

for EPA’s proposed Definition of Solid Waste is an example of this type 

of analysis in a rule-making context. See EPA’s Draft Environmental 

Justice Methodology for the Definition of Solid Waste Final Rule, 

January 13, 2009, available at: http://www.epa.gov/epawaste/hazard/ 

dsw/ej-meth.pdf (accessed on December 1, 2011). 

45	 See Apelberg et al. (2005) for an application to Maryland and MorelloFrosch et al. (2002) for an application to southern California. 

46	 See, for example, U.S. EPA (2011b), Fann et al. (2011), and Post et al. 

(2011). 

While this approach is promising due to spatial 

detail associated with monitoring data, it is 

currently only available for certain air pollutants. 

In addition, it is important to note that monitoring 

data measure emissions, not individual exposures 

or health effects associated with the pollutant 

under consideration. As such, these data are a 

proxy for actual effects associated with a particular 

regulation. Further, all individuals within a grid cell 

are assigned the same emissions (or concentrations 

based on air quality modeling). Actual exposures 

or health effects may differ across individuals for a 

variety of reasons discussed throughout this chapter. 

10.2.8.5 Risk Considerations 

Certain factors make some populations more 

susceptible (i.e., experience a greater biological 

response to a specific exposure) to a particular 

environmental stressor (see Adler and Rehkopf 

2008, Sacks et al. 2011 and Schwartz et al. 

2011a).47, 48 These factors can be genetic or 

physiological (such as sex and age). They may also 

be acquired due to variation in factors such as 

health-care access, nutrition, fitness, stress, housing 

quality, other pollutant exposures, or drug and 

alcohol use.49 For instance, many populations face 

exposures from multiple pollutants or exposures 

that have accumulated in ways that may affect 

their susceptibility to a particular pollutant 

and introduce complex considerations when 

attempting to address EJ concerns.50 

47	 A special issue of the American Journal of Public Health (Volume 101, 

Issue S1, December 2011) provides a set of papers exploring these 

and other issues. 

48	 EPA’s Integrated Risk Information System (IRIS) defines susceptibility 

as “increased likelihood of an adverse effect, often discussed in 

terms of relationship to a factor that can be used to describe a human 

subpopulation (e.g., life stage, demographic feature, or genetic 

characteristic).” See http://www.epa.gov/iris/help_gloss.htm#s 

(accessed on December 1, 2011). 

49	 Sexton (1997) suggests that low-income families may be more 

susceptible to environmental stressors due to differences in quality 

of life and lifestyle. Centers for Disease Control data show higher 

incidences of asthma-related emergency room visits and asthmarelated deaths among African-American populations. See http:// 

minorityhealth.hhs.gov/templates/content.aspx?ID=6170 (accessed 

December 1, 2011). 

50	 EPA’s Framework for Cumulative Risk Assessment may serve as a 

useful reference when assessing how prior exposures may affect the 

impacts of emission changes from the rule being analyzed, available at 

http://oaspub.epa.gov/eims/eimscomm.getfile?p_download_id=36941 

(accessed November 2, 2010). 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

In addition, activities linked to a specific 

cultural background or socioeconomic status 

could expose populations to higher levels of 

pollution. For example, some indigenous peoples 

and immigrant populations rely on subsistence 

fishing which could result in higher mercury 

levels from consumption of fish or expose these 

populations to other forms of pollution if fishing 

occurs in contaminated waters (see Donatuto 

and Harper 2008).51 

10.3 Children’s Environmental 

Health 

Distributional analysis may shed light on 

differential effects of regulation on children, 

a lifestage-defined group characterized by a 

multitude of unique behavioral, physiological, 

and anatomical attributes. There are two sets 

of important differences between children and 

adults regarding health benefits. First, there are 

differences in exposure to pollutants and in the 

nature and magnitude of health effects resulting 

from the exposure. Children may be more 

vulnerable to environmental exposures than adults 

because their bodily systems are still developing; 

they eat, drink, and breathe more in proportion 

to their body size; their metabolism may be 

significantly different — especially shortly after 

birth; and their behavior can expose them more 

to chemicals and organisms (e.g., crawling leads 

to greater contact with contaminated surfaces 

while hand-to-mouth and object-to-mouth 

contact is much greater for toddler age children). 

Second, individuals may systematically place a 

different economic value on reducing health risks 

to children than on reducing such risks to adults 

(U.S. EPA 2003). 

EO 13045 requires that each federal agency 

address disproportionate health risks to children. 

In addition, EPA’s Children’s Health Policy 

requires the Agency “consider the risks to infants 

and children consistently and explicitly as a part 

51	 It is also worth considering conditions that reduce a community’s 

ability to participate fully in the decision-making process such as time 

and resource constraints, lack of trust, lack of information, language 

barriers, and difficulty in accessing and understanding complex 

scientific, technical, and legal resources (see Dietz and Stern 2008). 

of risk assessments generated during its decision 

making process, including the setting of standards 

to protect public health and the environment.”52 

Generally, many approaches described earlier in 

this chapter to characterize the distribution of 

impacts may be adapted to evaluate children’s 

environmental health risks.53 For example, 

when proximity-based analysis is appropriate 

for evaluating environmental justice impacts, it 

might also be used to examine whether children 

are disproportionately located near facilities 

of concern. In such a case, the considerations 

described earlier about geography, defining the 

baseline and comparison groups, and use of 

summary statistics would all apply. 

10.3.1 Childhood as a Lifestage 

Evaluating distributional impacts of regulatory 

actions on children differs in an important way 

from evaluating the same impacts on population 

groups of concern for EJ. When EPA evaluates 

disproportionate health risk impacts from 

environmental contaminants, it views childhood as 

a sequence of lifestages from conception through 

fetal development, infancy, and adolescence, rather 

than a distinct “subpopulation.” 

Use of the term “subpopulation” is ingrained in 

both EPA’s past practices as well as various laws 

that EPA administers such as the Safe Drinking 

Water Act Amendments. Prior to publication 

of revised risk assessment guidelines in 2005,54 

EPA described all groups of individuals as 

“subpopulations.” In the 2005 guidelines, 

the Agency recognizes the importance of 

distinguishing between groups that form a 

relatively fixed portion of the population, such as 

those described Section 3 of this document, and 

52	 See http://yosemite.epa.gov/ochp/ochpweb.nsf/content/ 

policy-eval_risks_children.htm (accessed on December 1, 2011). 

53	 In principle there is a potential distinction in distributional analysis 

to be made between factors that are fixed, such as race and sex, and 

those defined by lifestages. The latter raises the possibility, at least, 

of examining distribution concerns through the lens of differences in 

lifetime utility or well-being rather than focusing on a single lifestage. 

See Adler (2008) for one proposal consistent with this approach. 

54	 See http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=55907 

(accessed on December 1, 2011). 
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Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

lifestages or age groups that are dynamic groups 

drawing from the entire population. 

The term “lifestage” refers to a distinguishable 

time frame in an individual’s life characterized 

by unique and relatively stable behavioral and/ 

or physiological characteristics associated with 

development and growth. Thus, since 2005 EPA 

characterizes childhood as a sequence of lifestages.55 

10.3.2 Analytical Considerations 

Assessing distributional consequences of policies 

that affect children’s health requires considerations 

that span risk assessment, action development, 

and economic analysis. In each case there are 

existing Agency documents that can assist in the 

evaluation. 

10.3.2.1 Risk Assessment 

Effects of pollution can differ depending 

upon age of childhood exposure. Analysis of 

disproportionate impacts to children or from 

childhood lifestages begins with health risk 

assessment, but also includes exposure assessment. 

Many risk guidance and related documents address 

how to consider children and childhood lifestages 

in risk assessment. 

A general approach to considering children 

and childhood lifestages in risk assessment is 

found in A Framework for Assessing Health Risks 

of Environmental Exposures to Children (U.S. 

EPA 2006a). The framework identifies existing 

guidance, guidelines and policy papers that relate 

to children’s health risk assessment. It emphasizes 

the importance of an iterative approach between 

hazard, dose response, and exposure analyses. In 

addition, it includes a discussion of principles for 

weight of evidence consideration across life stages. 

EPA’s 2005 Cancer Guidelines (U.S. EPA 2005a) 

explicitly call for consideration of possible 

55	 The 2005 Risk Assessment Guidelines “view childhood as a sequence 

of lifestages rather than viewing children as a subpopulation, the 

distinction being that a subpopulation refers to a portion of the 

population, whereas a lifestage is inclusive of the entire population.” 

(U.S. EPA 2005, p 1-15). 

sensitive subpopulations and/or lifestages 

such as childhood. The Cancer Guidelines 

were augmented by Supplemental Guidance for 

Assessing Susceptibility from Early-Life Exposure 

to Carcinogens.

56 Recommendations from this 

supplement include calculating risks utilizing 

lifestage-specific potency adjustments in addition 

to lifestage-specific exposure values which should 

be considered for all risk assessments. 

EPA’s Child-Specific Exposures Handbook (U.S. 

EPA 2008b)57 and Highlights of the ChildSpecific Exposure Factors Handbook (U.S. 

EPA 2009a)58 help risk assessors understand 

children’s exposure to pollution. The handbook 

provides important information for answering 

questions about lifestage specific exposure 

through drinking, breathing, and eating. EPA’s 

guidance to scientists on selecting age groups to 

consider when assessing childhood exposure and 

potential dose to environmental contaminants is 

identified in Guidance on Selecting Age Groups for 

Monitoring and Assessing Childhood Exposures to 

Environmental Contaminants (U.S. EPA 2005c). 

10.3.2.2 Action Development 

Disproportionate impacts during fetal 

development and childhood are considered 

in EPA guidance on action development, 

particularly the Guide to Considering Children’s 

Health When Developing EPA Actions: 

Implementing Executive Order 13045 and EPA’s 

Policy on Evaluating Health Risks to Children 

(U.S. EPA 2006b). The guide helps determine 

whether EO 13045 and/or EPA’s Children’s 

Health Policy applies to an EPA action and, if so, 

how to implement the Executive Order and/or 

EPA’s Policy. The guide clearly integrates EPA’s 

Policy on Children’s Health with the Action 

Development Process and provides an updated 

listing of additional guidance documents. 

56	 Available at http://www.epa.gov/cancerguidelines/guidelinescarcinogen-supplement.htm (accessed on December 1, 2011). 

57	 Available at http://cfpub.epa.gov/ncea/cfm/recordisplay. 

cfm?deid=199243 (accessed on December 1, 2011). 

58	 Available at http://cfpub.epa.gov/ncea/cfm/recordisplay. 

cfm?deid=200445 (accessed on December 1, 2011). 
10-22 Guidelines for Preparing Economic Analyses | May 2014 

Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

10.3.2.3 Economic Analysis 

While these Economic Guidelines provide general 

information on benefit-cost analyses of policies 

and programs, many issues concerning valuation 

of health benefits accruing to children are not 

covered. Information provided in the Children’s 

Health Valuation Handbook (U.S. EPA 2003), 

when used in conjunction with the Guidelines, 

allows analysts to characterize benefits and impacts 

of Agency policies and programs that affect 

children. 

The Handbook is a reference tool for analysts 

conducting economic analyses of EPA policies 

when those policies are expected to affect risks 

to children’s health. A major emphasis of the 

Handbook is ensuring that a regulation or policy’s 

economic impacts on children are fully considered 

in supporting analyses. This analysis includes 

incorporating children’s health considerations 

in an assessment of efficiency, as well as in any 

distributional analysis focused on children. 

Decision makers may also find it useful to have 

information on a policy’s specific impact on 

children’s health, regardless of whether the impact 

heavily influences overall benefit-cost analysis. 

Economic factors may also play a role in other 

analyses that evaluate children’s environmental 

health impacts. For example, if a higher 

proportion of children live in poverty, the ability 

of households with children to undertake averting 

behaviors might be compromised. This type of 

information could inform the exposure assessment. 

10.3.3 Intersection Between 

Environmental Justice and 

Children’s Health 

The burden of health problems and environmental 

exposures is often borne disproportionately by 

children from low-income communities and 

minority communities (e.g., Israel et al. 2005; 

Lanphear et al. 1996; Mielke et al. 1999; Pastor et 

al. 2006). 

The challenge for EPA is to integrate both 

environmental justice and lifestage susceptibility 

considerations for children where appropriate 

when conducting distributional analysis. This 

is especially true when short-term exposure to 

environmental contaminants such as lead or 

mercury early in life can lead to life-long health 

consequences. 

10.4 Other Distributional 

Considerations 

10.4.1 Elderly 

Another important lifestage to consider 

is that of the elderly.59 While there are no 

standard procedures for including the elderly 

in a distributional analysis, EPA stresses the 

importance of addressing environmental issues 

that may adversely impact them. Most of the 

Agency’s work in this area has been related to risk 

and exposure assessment. 

Older adults may be more susceptible to adverse 

effects of environmental contaminants due to 

differential exposures arising from physiological 

and behavioral changes with age, disease status, 

drug interactions, as well as the body’s decreased 

capacity to defend against toxic stressors. These 

considerations are highlighted in EPA’s Exposure 

Factors Handbook (U.S. EPA 2011d) and have led 

EPA’s Office of Research and Development to 

consider an exposure factors handbook specifically 

for the aging (see U.S. EPA 2007). Additionally, 

the toxicokinetic and toxicodynamic impacts of 

environmental agents in older adults have been 

considered in EPA’s document entitled Aging and 

Toxic Response: Issues Relevant to Risk Assessment 

(U.S. EPA 2005b).60 

10.4.2 Intergenerational Impacts 

Concern for intergenerational impacts arises 

when those affected by a policy are not yet alive 

when the policy is developed. If a policy’s benefits, 

costs, and impacts primarily fall upon the current 

59	 There is a lack of broad agreement about the beginning of the “elderly” 

lifestage. The U.S. and other countries typically define this lifestage to 

begin at the traditional retirement age of 65, but, for example, the U.N. 

defines “elderly” to begin at age 60 (U.S. EPA 2005b). 

60	 Available at http://cfpub.epa.gov/ncea/cfm/recordisplay. 

cfm?deid=156648 (accessed on December 1, 2011). 
Guidelines for Preparing Economic Analyses | May 2014 10-23 

Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations

generation, or if policy decisions are reversible 

within this time frame, there is little need for 

explicit consideration of intergenerational impacts. 

However, in other cases, benefits and/or costs of 

the policy will be borne by future generations, 

and it is important to consider impacts on these 

generations. One such case would be policies to 

reduce greenhouse gases, which are expected to 

result in benefits related to reduced changes in 

climate for future generations. Other examples 

may relate to toxic chemical exposures. Exposures 

to parents prior to their child’s conception can 

result in adverse health effects in the child, 

including effects that may not become apparent 

until the child reaches adulthood.61 

Assessing intergenerational impacts can be 

related to the social welfare function approach, 

described in Text Box 10.1 of this chapter, and 

to social discounting. In both cases, normative 

judgments need to be made about which there 

is no consensus. Under the Ramsey approach 

to intergenerational discounting, this judgment 

is reflected in a “pure rate of time preference” 

parameter that weighs the welfare of current and 

future generations. See Section 6.3.1 for more 

information on intergenerational discounting 

and debate about the value of this parameter. 

One way to clarify distributional consequences 

if intergenerational impacts are important is to 

display time paths of benefits and costs without 

discounting, as recommended in Chapter 6 of 

these Guidelines. 

10.5 Conclusion 

This chapter provides a variety of tools, analytical 

considerations and guidance for conducting 

distributional analyses for environmental justice, 

children’s environmental health and other factors. 

Tools and methods are intended to be flexible 

enough to accommodate various data and other 

constraints associated with particular scenarios, 

while introducing consistency and rigor in the way 

regulatory analyses consider distributional effects. 

Methods for analyzing distributional impacts in 

the context of EJ, in particular, are continually 

being discussed, debated, and improved. For 

instance, EPA is in the process of developing more 

specific guidance on considering environmental 

justice concerns when planning human health risk 

assessments (U.S. EPA 2012b). Updates to this 

chapter about strengths and limitations of various 

analytical options, as well as new approaches, will 

be added when appropriate. 

61	 See U.S. EPA (2006a) and WHO (2007). The latter is available at 

http://www.who.int/ipcs/features/ehc/en/index.html (accessed on 

January 11, 2013). 
10-24 Guidelines for Preparing Economic Analyses | May 2014 

Chapter 10 Environmental Justice, Children’s Environmental Health and Other Distributional Considerations
 

 

 

 

 

 

Chapter 11 

Presentation of Analysis and Results
 

This chapter provides some general guidance for presenting analytical results 

to policy makers and others interested in environmental policy development. 

Economic analyses play an important role throughout the policy development 

process. From the initial, preliminary evaluation of potential options through 

the preparation of a final economic analysis document, economic analysts 

participate in an interactive process with policy makers. The fundamental goal of this process 

is to collect, analyze, and present information useful for policy makers. 

Economic analysis is often motivated by a desire to find an optimal outcome, such as a 

degree of stringency in a regulation, or a level of provision of a public good that yields the 

largest possible net benefits. Environmental statutes sometimes mandate criteria other than 

economic efficiency, such as best available control technology or lowest achievable emission 

rate. Policy makers rely on quantitative analysis to promulgate these approaches. In particular 

they rely on analyses that delineate the costs, benefits, or other impacts of a wide range of 

control options. 

This guidance for presenting inputs, analyses, and results applies at all stages of this process, 

not only for the final document embodying the completed economic analysis. Conveying 

uncertainty effectively and reporting critical assumptions and key unquantified effects to 

decision makers is critical at all points in the policy-making process. 

This chapter begins by providing general guidance on how to present the results of 

economic analyses, with a particular emphasis on presenting benefits and costs, including 

those that cannot be quantified and/or put into dollar terms. The chapter then discusses 

the components, or inputs, of an economic analysis, and how their effect on the economic 

analysis can best be communicated. 

11.1 Presenting Results of • What the important non-quantified or nonEconomic Analyses monetized effects are; 

The presentation of the results of an economic • What key assumptions were made for the analysis should be thorough and transparent. The analysis; reader should be able to understand: • What the primary sources of uncertainty are in 

• What the primary conclusions of the economic the analysis; and
 

analysis are;
 • How those sources of uncertainty affect the 

• How the benefits and costs were estimated; results. 

Guidelines for Preparing Economic Analyses | December 2010 11-1 
11-2 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 11 Presentation of Analysis and Results

 

 

 

An economic analysis of regulatory or policy 

options should present all identifiable costs and 

benefits that are incremental to the regulation or 

policy under consideration. These should include 

directly intended effects and associated costs, as well 

as ancillary (or co-) benefits and costs. 

Benefits and costs should be reported in monetary 

terms whenever possible. In reality, however, there 

are often effects that cannot be monetized, and the 

analysis needs to communicate the full richness of 

benefit and cost information beyond what can be 

put in dollar terms. Benefits and costs that cannot 

be monetized should, if possible, be quantified (e.g., 

expected number of adverse health effects avoided). 

Benefits and costs that cannot be quantified should 

be presented qualitatively (e.g., directional impacts 

on relevant variables). Section 11.1.2 contains more 

detailed guidance on presenting this information in 

EPA’s economic analyses. 

Agencies are also required to provide OMB with 

an accounting statement reporting benefit and cost 

estimates when sending over each economically 

significant rule. Analysts should rely upon these 

Guidelines and Circular A-4 for developing these 

estimates. Circular A-4 describes the accounting 

statement on pages 44-46 and contains a suggested 

format for this accounting statement.1 

In addition to requirements under Circular 

A-4, the 2010 OMB Annual Report to Congress 

on the Costs and Benefits of Federal Regulations 

asks agencies to provide a “simple, clear table 

of aggregated costs and benefits” of each 

economically significant rule in the regulatory 

Preamble of the Federal Register Notice and in the 

Executive Summary of the RIA (OMB 2010a, p. 

51). EPA’s guidance for satisfying these criteria is 

described more fully in Section 11.1.2 as part of 

the Agency’s general guidance on reporting the 

results of benefit-cost analysis (BCA). 

The results of economic analyses of environmental 

policies should generally be presented in three 

sections. 

The accounting statement is on page 47 of Circular A-4, available at 

www.whitehouse.gov/sites/default/files/omb/assets/omb/circulars/ 

a004/a-4.pdf (accessed on January 21, 2011). 

• Results from BCA. Estimates of the net 

social benefits should be presented based on 

the benefits and costs expressed in monetary 

terms. Non-monetized and unquantifiable 

benefits and costs should also be included and 

described in the presentation. 

• Results from cost-effectiveness analysis 

(CEA). Under OMB Circular A-4, CEA 

should generally be performed for rules in 

which the primary effect is human health or 

safety. Results of these analyses should also be 

presented when they are conducted.2 

• Results from economic impact analysis 

(EIA) and distributional assessments. 

Results of the EIA should be reported, 

including predicted effects on prices, profits, 

plant closures, employment, and any other 

effects. Distributional impacts for particular 

groups of concern, including small entities, 

governments, and environmental justice 

populations should also be presented. 

The relative importance of these three sections will 

depend on the policy and statutory context of the 

analysis. 

11.1.1 Presenting the Results of 

Benefit-Cost Analyses 

When presenting the results of a BCA, the 

expected benefits and costs of the preferred 

regulatory option should be reported, together 

with the expected benefits and costs of alternative 

approaches. OMB’s Circular A-4 requires that 

at least one alternative be more stringent and 

one less stringent than the preferred option, 

and the incremental costs and benefits would be 

reported for each increasingly stringent option. 

Separate time streams of benefits and costs should 

be reported, in constant (inflation-adjusted), 

undiscounted dollars. Per the discussion in 

2	 The Institute of Medicine (IOM) (2006) recently issued 

recommendations to regulatory agencies on how to perform healthbased CEA. Recent examples of CEA can be found in appendices of 

several recent RIAs including those for PM NAAQS [see Appendix G 

listed at http://www.epa.gov/ttn/ecas/ria.html (accessed March 13, 

2011)] and the Ground Water Rule [see Appendix H listed at 

http://www.epa.gov/safewater/disinfection/gwr/regulation.html 

(accessed March 13, 2011)]. 

1 
Guidelines for Preparing Economic Analyses | December 2010 11-3 

Chapter 11 Presentation of Analysis and Results

 

 

 

 

Chapter 6, appropriately discounted benefits and 

costs should be reported as well. 

Ideally, all benefits and costs of a regulation 

would be expressed in monetary terms, but this 

is almost never possible because of data gaps, 

unquantifiable uncertainties, and other challenges. 

It is important not to exclude an important benefit 

or cost category from BCA even if it cannot be 

placed in dollar terms. Instead, such benefits 

and costs should be expressed quantitatively if 

possible (e.g., avoided adverse health impacts). 

If important benefit or cost categories cannot 

be expressed quantitatively, they should be 

discussed qualitatively (e.g., a regulation’s effect on 

technological innovation). 

Quantifiable benefits and costs, properly 

discounted, should be compared to determine 

a regulation’s net benefits, even if important 

benefits or costs cannot be monetized. However, 

an economic analysis should assess the likelihood 

that non-monetized benefits and costs would 

materially alter the net benefit calculation for a 

given regulation. 

Incremental benefits, costs, and net benefits of 

moving from less to more stringent regulatory 

alternatives should also be presented. If a 

regulation has particularly significant impacts 

on certain groups or sub-populations, the 

various options’ incremental impacts on these 

subpopulations or source categories should be 

reported. This should include a discussion of 

incremental changes in quantified and qualitatively 

described benefits and costs. 

Given the number of potential models presented 

in Chapters 7 and 8, the analyst should take care 

to clearly indicate the correspondence between 

the benefit and cost estimates. For example, the 

cost analysis may include results from a general 

equilibrium model but the benefit analysis may 

only include partial equilibrium effects.3

 In this 

case, the cost side of the equation includes general 

equilibrium feedback effects while the benefit 

While there have been some attempts to include benefit estimates in 

general equilibrium models, these efforts are nascent (Sieg et al. 2004, 

Yang et al. 2004, and Jena et al. 2008). 

side does not. This difference should be clearly 

presented and explained. 

The tables at the end of this chapter contain 

templates for presenting information on regulatory 

benefits and costs, including those benefits that 

cannot be quantified or put into dollar terms. 

The analyst’s primary goal, using these tables, is to 

communicate the full richness of benefit and cost 

information instead of focusing narrowly on what 

can be put in dollar terms. Some guiding principles 

for constructing these tables follow. 

• All meaningful benefit and costs are included 

in all of the tables even if they cannot be 

quantified or monetized. Not only does this 

provide consistency for the reader, but it 

also maintains important information on 

the context of the quantified and monetized 

benefits. 

• The types of benefits and costs are described 

briefly in plain terms to make them clearer to 

the public and to decision makers, and they 

should be well-defined and mutually exclusive, 

to the extent possible. Benefits should be 

grouped a manner consistent with the 

categories in Table 7.1 of Chapter 7, although 

the order and specific characterization can be 

expected to vary by rule as needed. 

• The benefits are expressed first in natural 

or physical units (i.e., number) to provide 

a more complete picture of what the rule 

accomplishes. These units are not discounted 

as they would be in a CEA because the goal 

here is to describe what might be termed the 

“physical scope” of the rule’s benefits. It may 

be the case that physical or natural units are 

not relevant for presenting costs. 

• Explanatory notes accompany each benefit and 

cost entry and can be used to describe whatever 

the most salient or important points are about 

scientific uncertainty, the type of benefit or 

cost, how it is estimated, or the presentation. 

The benefit categories in these templates (e.g., 

improved human health, improved environment, 

and other benefits,) will need to be revised to 

reflect the benefits categories for the rule under 

3 
Chapter 11 Presentation of Analysis and Results

 

 

 

 

 

 

 

 

Table 11.1 - Template for Regulatory Benefits Checklist 

Overview of Benefits 

Benefits 

Effect can be 

Quantified? 

(put in numeric 

terms) 

Effect can be 

Monetized? 

(put in dollar 

terms) 

More Information 

(e.g., reference to 

section of the economic 

analysis) 

Improved Human Health 

• Reduced incidence of adult premature 

mortality from exposure to PM2.5 

  e.g., see Section 5.2 of 

the economic analysis 

• Reduced incidence of fetal loss from reduced 

exposure to disinfection byproducts  -­

Notes and reference to 

section of the economic 

analysis 

• Unquantified human health benefit with a brief 

description -­ -­ Notes and reference 

Improved Environment 

• Fewer fish killed from reduced nutrient loadings 

into waterways   Notes and reference 

• Improved timber harvest from lower tropospheric 

ozone concentrations   Notes and reference 

• Other environmental benefit with a brief description -­ -­ Notes and reference 

Other Benefits 

• Fuel savings from improved efficiency in 

automobiles and light trucks   Notes and reference 

• Other benefit with a brief description -­ -­ Notes and reference 

consideration. Simpler analyses may need only 

the overview (Table 11.1) and the final summary 

(Table 11.4). 

Table 11.1 is a quick-glance summary of regulatory 

benefits and costs, the extent to which they could 

be quantified and monetized, and a reference 

to where they are more fully characterized or 

estimated in the economic analysis. Some benefits 

may be described only qualitatively. 

Table 11.2 reports benefits in non-monetary terms 

along with the units and additional explanatory 

notes. The goal of this table is to communicate the 

physical scope of the regulation’s benefits and costs 

rather than the dollar equivalent. Benefits here do 

not need to be discounted to present value, but the 

time associated with the quantities should be made 

clear (e.g., “annual” or “more than ten years”). 

Table 11.3 reports benefits in monetary terms 

along with a total for dollar-valued benefits. Here 

it is important to specify the reference year for the 

dollars (i.e., real terms), the discount rate(s) used, 

and the unit value and/or source. 

Table 11.4 contains a template for bringing all this 

information together in summary that includes 

the type of benefit or cost, how it is measured, 

its quantity, and dollar benefits. When multiple 

regulatory options are included in this table, it 

is appropriate for including in the regulatory 

preamble as requested by OMB. 

Consistent with recommendations in these 

Guidelines for communicating uncertainty, 

quantitative entries should generally include a 

central or best estimate in addition to a range 

or confidence interval. The ability to do this, of 

course, may be limited by data availability. 

11.1.2 Presenting the Results of 

Cost-Effectiveness Analyses 

When BCA is not possible, CEA may be the best 

available option. The cost-effectiveness of a policy 

11-4 Guidelines for Preparing Economic Analyses | December 2010 
Chapter 11 Presentation of Analysis and Results

 

 

Table 11.2 - Template for Quantified Regulatory Benefits 

Quantified Benefits 

Benefits 

Quantified 

Benefits 

(confidence 

interval or range) 

Units 

More Information 

(w/possible reference to 

section of the economic 

analysis) 

Improved Human Health 

• Reduced incidence of adult premature 

mortality from exposure to PM2.5 

estimate 

(range) 

expected avoided 

expected 

premature deaths 

per year 

e.g., range represents 

confidence interval 

• Reduced incidence of fetal loss from reduced 

exposure to disinfection byproducts 

estimate 

(range) 

expected avoided 

fetal losses per 

year 

e.g., confidence interval 

cannot be estimated. 

Range based on 

alternative studies 

• Unquantified human health benefit with a 

brief description * * e.g., data do not allow 

for quantification 

Improved Environment 

• Fewer fish killed from reduced nutrient loadings 

into waterways 

estimate 

(range) 

thousands of fish 

per year 

Notes 

(reference) 

• Improved timber harvest from lower 

tropospheric ozone concentrations 

estimate 

(range) 

thousands of 

board feet per 

year 

Notes 

(reference) 

• Other environmental benefit with a brief 

description * * Notes (reference) 

Other Benefits 

• Fuel savings from improved efficiency in 

automobiles and light trucks 

estimate 

(range) 

millions of 

gallons of 

gasoline reduced 

per year 

Notes 

(reference) 

• Other benefit with a brief description * * Notes 

(reference) 

Note: * indicates the benefit cannot be quantified with available information 

option is calculated by dividing the annualized cost 

of the option by non-monetary benefit measures. 

Options for such measures range from quantities of 

pollutant emissions reduced, measured in physical 

terms, to a specific improvement in human health 

or the environment, measured in reductions in 

illnesses or changes in ecological services rendered. 

In the context of RIA, or other analyses of 

specific regulatory or policy options, CEA is 

most informative when several different options 

are analyzed. The analysis should include at least 

one option that is less stringent and at least one 

option that is more stringent than the preferred 

option. The incremental costs and non-monetary 

benefit yield of each option, in order of increasing 

stringency, should be reported. 

The non-monetary measure of benefits used in a 

CEA must be chosen with great care to facilitate 

valid comparisons across options. The closer the 

chosen measure is to the variable that directly 

impacts social welfare, the more robust a CEA 

will be. Consider the following steps that a typical 

environmental economic assessment follows: 

• Changes in emissions are estimated (e.g., tons 

of emissions); then 

Guidelines for Preparing Economic Analyses | December 2010 11-5 
Chapter 11 Presentation of Analysis and Results

 

 

Table 11.3 - Template for Dollar-Valued Regulatory Benefits 

Dollar-Valued Benefits 

Benefit Dollar Benefits 

(millions per year) Basis of Value More Information 

(w/possible reference) 

Improved Human Health 

• Reduced incidence of adult premature 

mortality from exposure to PM2.5 

$ estimate 

($ range) 

e.g., $X based on 

Agency guidance 

Notes 

(reference) 

• Reduced incidence of fetal loss from 

reduced exposure to disinfection byproducts * Not available Notes (reference) 

• Unquantified human health benefit 

with a brief description * * 

e.g., data insufficient 

to quantify 

(reference) 

Improved Environment 

• Fewer fish killed from reduced nutrient 

loadings into waterways 

$ estimate 

($ range)

 e.g., $X based on 

WTP for recreational 

fishing 

e.g., range reflects 

two different valuation 

approaches 

(reference) 

• Improved timber harvest from lower 

tropospheric ozone concentrations 

$ estimate 

($ range) 

e.g., change in 

consumer and 

producer surplus 

e.g., estimated from 

market model across 

several species 

(reference) 

• Other environmental benefit with a brief 

description * * Notes (reference) 

Other Benefits 

• Fuel savings from improved efficiency in 

automobiles and light trucks 

$ estimate 

($ range) 

e.g., $X, based on 

net-of-tax average 

per gallon price 

e.g., there is debate on 

how well fuel savings 

represent consumer 

benefits 

(reference) 

• Other benefit with a brief description * Not available Notes 

(reference) 

TOTAL Benefits that can be monetized 

($millions per year) 

$ estimate 

($ range) 

Note: * indicates the benefit cannot be quantified with available information. 

• Changes in environmental quality (e.g., 

changes in ambient concentrations of a given 

air pollutant) are estimated; then 

• Changes in human health or welfare (e.g., 

changes in illness or visibility) are estimated. 

Each successive step in this sequence yields a better 

measure for CEA. 

To illustrate, consider a typical air pollution 

scenario. Depending on where and when air 

pollutants are released into the atmosphere, a 

given ton of a particular pollutant can have widely 

divergent impacts on ambient air quality. Similarly, 

depending on when and where air quality 

changes, widely different levels of human health 

impacts may result. Particularly when different 

regulatory approaches are under consideration 

(e.g., regulation of different source categories in 

different locations), failing to standardize the 

analyses on the benefit measure that directly affects 

human health or welfare will significantly reduce 

11-6 Guidelines for Preparing Economic Analyses | December 2010 
Chapter 11 Presentation of Analysis and Results

 

Table 11.4 - Template for Summary of Benefits and Costs 

Benefits 

Notes: e.g., “annual average numbers; 2006 dollars annualized at 3% discount rate” 

Best estimate, with range 

Option 1 Proposed Option Option 3 Source, 

limitations, or 

Number $ Millions Number $ Millions Number $ Millions other key notes

Improved Human Health 

• Reduced incidence 

of adult premature 

mortality from 

exposure to PM2.5 

estimate 

(range) 

$ estimate 

(range) 

estimate 

(range) 

$ estimate 

(range) 

estimate 

(range) 

$ estimate 

(range) 

highlight most 

important points, 

as needed 

• Reduced incidence 

of fetal loss from 

reduced exposure to 

disinfection byproducts 

estimate 

(range) * estimate (range) * estimate (range) * 

e.g., no valuation 

data exist. Effects 

are sensitive to 

dose-response 

model. 

• Unquantified human 

health benefit with a 

brief description 

* * * * * * 

e.g., risk data 

insufficient for 

quantification 

Improved Environment 

• Fewer fish killed 

from reduced nutrient 

loadings into waterways 

estimate 

(range) 

$ estimate 

(range) 

estimate 

(range) 

$ estimate 

(range) 

estimate 

(range) 

$ estimate 

(range) Notes 

• Improved timber 

harvest from lower 

tropospheric ozone 

concentrations 

estimate 

(range) 

$ estimate 

(range) 

estimate 

(range) 

$ estimate 

(range) 

estimate 

(range) 

$ estimate 

(range) Notes 

• Other environmental 

benefit with a brief 

description 

* * * * * * Notes 

Other Benefits 

• Fuel savings from 

improved efficiency in 

automobiles and light 

trucks 

estimate 

(range) 

$ estimate 

(range) 

estimate 

(range) 

$ estimate 

(range) 

estimate 

(range) 

$ estimate 

(range) Notes 

• Other benefit with a 

brief description * * * * * * Notes 

TOTAL Benefits that 

can be monetized 

(annualized, millions 

$2006) 

$ estimate 

(range) 

$ estimate 

(range) 

$ estimate 

(range) 

e.g., total range 

may be overstated 

because of 

aggregation 

(See Section 

8.1 of economic 

analysis) 

Note: * indicates the benefit cannot be quantified with available information. 

Guidelines for Preparing Economic Analyses | December 2010 11-7 
11-8 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 11 Presentation of Analysis and Results

Table 11.4 - Template for Summary of Benefits and Costs (continued) 

Costs 

2006 dollars annualized at 3% discount rate 

Best estimate, with range 

Option 1 Proposed 

Option Option 3 Source, 

limitations, or 

$ Millions $ Millions $ Millions other key notes

• Initial capital costs with any brief 

description and units. 

$ estimate 

(range) 

$ estimate 

(range) 

$ estimate 

(range) 

e.g., estimated 

from engineering 

cost models 

• Type of cost with a brief description and 

units. (This could include non-monetized 

costs.) 

$ estimate 

(range) 

$ estimate 

(range) 

$ estimate 

(range) Notes 

• Type of cost with a brief description and 

units. (This could include non-monetized 

costs.) 

$ estimate 

(range) 

$ estimate 

(range) 

$ estimate 

(range) Notes 

TOTAL Costs that can be monetized 

(annualized, millions $2006) 

$ estimate 

(range) 

$ estimate 

(range) 

$ estimate 

(range) 

TOTAL Net Benefits that can be 

monetized 

(annualized, millions $2006) 

$ estimate 

(range) 

$ estimate 

(range) 

$ estimate 

(range) 

the value of the analysis to decision makers (and 

the public). 

When presenting the results of a CEA, the 

rationale for the selection of the non-monetary 

benefit measure must be described in detail. 

The presentation of results should also include 

a discussion of the limitations of the analysis, 

especially if an inferior measure, such as cost per 

ton of pollutant, must be used. 

CEA is most useful when the policy or regulation 

in question affects a single endpoint. When 

multiple endpoints are affected (e.g., cancer and 

kidney failures), combining endpoints into a 

single effectiveness measure is impossible unless 

appropriate weighting factors exist for the multiple 

endpoints. The theoretically correct weights to 

apply are the dollar values associated with each 

endpoint, but generally it is the absence of these 

values that necessitates CEA. Therefore, it is not 

possible to compare a policy or regulation that 

reduces relatively more expected cancers, but 

fewer expected cases of kidney failure, with one 

that has the opposite relative effects. When this 

occurs, the effects of each option for each endpoint 

should be reported. A single endpoint may be 

selected for calculating cost-effectiveness, while 

other endpoints can be listed as ancillary benefits 

(or, if possible, their monetary value should 

be subtracted from the option’s cost prior to 

calculating its cost-effectiveness) (OMB 2003). 

The most cost-effective option — i.e., the 

option with the lowest cost per unit of benefit 

— is not necessarily the most economically 

efficient. Moreover, other criteria, such as 

statutory requirements, enforcement problems, 

technological feasibility, or quantity and location 

of total emissions abated may preclude selecting 

the least-cost solution in a regulatory decision. 

However, where not prohibited by statute, CEA 

can indicate which control measures or policies are 

inferior options. 
Guidelines for Preparing Economic Analyses | December 2010 11-9 

Chapter 11 Presentation of Analysis and Results

 

 

11.1.3 Presenting the Results of 

EIA and Distributional Analyses 

EIA and distributional outcomes focus on 

disaggregating effects to show impacts separately 

for the groups and sectors of interest. If costs and/ 

or benefits vary significantly among the sectors 

affected by the policy, then both costs and benefits 

should be shown separately for the different 

sectors. Presenting results in disaggregated form 

will provide important information to policy 

makers that may help them tailor the rule to 

improve its efficiency and distributional outcomes. 

The results of the EIA should also be reported for 

important sectors within the affected population 

— identifying specific segments of industries, 

regions of the country, or types of firms that may 

experience significant impacts or plant closures 

and losses in employment. 

Reporting the results in distributional assessments 

may include the expected allocation of benefits, 

costs, or both for specific subpopulations including 

those highlighted in the various mandates. These 

include minorities, low-income populations, 

small businesses, governments, not-for-profit 

organizations, and sensitive and vulnerable 

populations (including children). Where these 

mandates specify requirements that depend on the 

outcomes of the distributional analyses, such as the 

Regulatory Flexibility Act, the presentation of the 

results should conform to the criteria specified by 

the mandate. 

11.1.4 Reporting the Effects 

of Uncertainty on Results of 

Economic Analyses 

Estimates of costs, benefits and other economic 

impacts should be accompanied by indications 

of the most important sources of uncertainty 

embodied in the estimates, and, if possible, a 

quantitative assessment of their importance. 

OMB requires formal quantitative analysis of 

uncertainties for rules with annual economic 

effects of $1 billion or more. 

In economic analysis, uncertainty encompasses 

two different concepts: 

• Statistical variability of key parameters; and 

• Incomplete understanding of important 


relationships.
 

Economic analyses of environmental policies 

and regulatory options will frequently have to 

accommodate both concepts. The importance 

of statistical variability is commonly assessed 

using Monte Carlo analyses (see U.S. EPA 1997). 

Delphic panels, or expert elicitation techniques, 

can help close knowledge gaps surrounding key 

relationships (see IEc 2004). 

Ideally, an economic analysis would present results 

in the form of probability distributions that reflect 

the cumulative impact of all underlying sources of 

uncertainty. When this is impossible, due to time 

or resource constraints, results should be qualified 

with descriptions of major sources of uncertainty. 

If at all possible, information about the underlying 

probability distribution should be conveyed. (A 

forthcoming section of these Guidelines will more 

fully address uncertainty issues.) 

As recommended in Chapter 6, many EPA 

analyses will employ more than one discount 

rate to reflect different underlying approaches to 

discounting. When the choice of discount rate 

affects the outcome of the analysis, analysts should 

take extra care to convey the underlying theory and 

assumptions to decision makers. See Chapter 6 for 

more information. 

11.2 Communicating 

Data, Model Choices and 

Assumptions, and Related 

Uncertainty 

An economic analysis of an environmental 

regulation should carefully describe the data 

used in the analysis, the models it relies on, 

major assumptions that were made in running 

the models, and all major areas of uncertainty in 

each of these elements. Presentations of economic 

analyses should strive for clarity and transparency. 

An analysis whose conclusions can withstand close 

scrutiny is more likely to provide policy makers 

with the information they need to develop robust 

environmental policies. 
11-10 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 11 Presentation of Analysis and Results

11.2.1 Data 

An economic analysis should clearly describe 

all important data sources and references 

used. Unless the data are confidential business 

information or some other form of private data, 

they should be available to policy makers, other 

researchers, policy analysts and the public. 

Providing documentation and access to the data 

used in an analysis is crucial to the credibility and 

reproducibility of the analysis. 

EPA Order 5360.1 A2 (U.S. EPA 2000a) and 

the applicable federal regulations established a 

mandatory quality system for EPA. As required by 

the quality system, all EPA offices have developed 

quality management plans to ensure the quality of 

their data and information products. 

Until recently, federal quality assurance (QA) 

requirements only applied to measurement and 

collection of primary environmental data. This 

meant that QA requirements often did not apply 

to economic analyses, which usually rely on the use 

of secondary data. However, this changed with the 

introduction of QA requirements regarding use of 

secondary data. In 2002 the Agency released QA 

guidelines regarding use of secondary data, and 

released Agency guidance, Guidance for Quality 

Assurance Project Plans, that includes procedures 

for documenting secondary data (U.S. EPA 

2002f ). 

In any economic analysis, there should be a 

clear presentation of how data are used and a 

concise explanation of why the data are suitable 

for the selected purpose. The data’s accuracy, 

precision, representativeness, completeness, 

and comparability should be discussed when 

applicable. When data are available from more 

than one source, a rationale for choosing the 

source of the data should be provided. 

11.2.2 Model Choices and 

Assumptions 

An economic analysis of an environmental 

regulation should carefully describe the models it 

relies on, the major assumptions made in running 

the models (to be discussed more fully below), and 

any areas of outstanding uncertainty. The analyst 

should take particular care to explain any results 

that might be viewed as counter-intuitive. In 

particular, analysts should be careful not to accept 

model output blindly. Any model that is used 

without proper thought given to both its input 

and output may become a “black box” insofar as 

nonsensical results may result from a misspecified 

scenario, a coding error, or any of a number of 

other causes. 

In the process of conducting an economic analysis, 

it is sometimes necessary to bridge an information 

gap by making an assumption. Analysts should 

not simply note the information gap, but should 

also justify the chosen assumption and provide a 

rationale for choosing one assumption over other 

plausible options. The analyst should take care not 

to overlook information gaps that are filled with 

a piece of information that is only slightly related 

to the desired information. Analysts are advised to 

keep a running list of assumptions. This will make 

it easier to identify “key assumptions” for the final 

report. The likely impact of errors in assumptions 

should be characterized both in terms of direction 

and magnitude of effect when feasible. 

Maintaining a list of assumptions can benefit the 

analysis in several ways. In the short run, a list 

can serve to focus analysts’ attention on those 

assumptions with the greatest potential to affect 

net benefits, possibly leading to new approaches 

to bridging an information gap. In the long run, 

highlighting information gaps may encourage 

EPA or others to devote attention and resources to 

generating that information. 

Whenever the likely errors in a particular 

assumption can be characterized numerically 

or statistically, the factor is a good candidate 

for sensitivity analysis or uncertainty analysis, 

respectively. In many cases, only a narrative 

description of the impact of errors in assumptions 

is possible. The analyst should include a table that 

clearly lays out all of the key assumptions and the 

potential magnitude and direction of likely errors 

in assumptions in the summary of results. 
Guidelines for Preparing Economic Analyses | December 2010 11-11 

Chapter 11 Presentation of Analysis and Results

 

 

 

11.2.3 Addressing Uncertainty 

Driven by Assumptions and 

Model Choice 

Every analysis should address uncertainties 

resulting from the choices the analyst has made. 

For example, many economic analyses performed 

at EPA include assessments of economic impacts 

expected to occur decades into the future. 

Estimates of the future costs and benefits of a 

regulation will be sensitive to assumptions about 

growth rates for populations, source categories, 

economic activity, and technological change, as 

well as many other factors. Sensitivity analyses 

on key variables in the baseline scenario should 

be performed and reported when possible. This 

allows the reader to assess the importance of the 

assumptions made for the central case. Some of 

these variables may be affected by a regulation, 

particularly the assumed rate of technological 

innovation. (Please see Chapter 5 for additional 

guidance on specifying baselines.) 

The impact of using alternative assumptions or 

alternative models can be assessed quantitatively in 

many cases. 

• Alternative analysis. An analysis of 

alternative assumptions or “alternative 

analysis” is the substitution of one of the key 

assumptions with another. In presenting the 

results, the alternative analysis is presented 

with equal weight as the primary analysis and 

is presented alongside of the primary analysis, 

even if the probability of the alternative 

assumption differs from that of the primary 

analysis. Because performing an alternative 

analysis on all the assumptions in an analysis 

is prohibitively resource intensive, the analyst 

should focus on the assumptions that have 

the largest impact on the final results of the 

particular analysis. Thus, keeping a running 

list of the “key assumptions” in an analysis is 

recommended. 

• Sensitivity analysis. A sensitivity analysis 

is used to assess how the final results or 

other aspects of the analysis change as input 

parameters change, particularly when only 

point estimates of parameters are available. 

A regulatory impact analysis benefits from 

knowing how the cost-effectiveness of a 

particular technology changes as fuel prices 

change, or how the net benefits of a BCA 

change as one of the model coefficients change. 

Typically, a sensitivity analysis measures how 

the model’s output changes as one of the input 

parameters change. Joint sensitivity analysis 

(varying more than one parameter at a time) is 

sometimes useful as well. 

• Model uncertainty. In addition to explaining 

the uncertainty in a model’s parameters, 

analysts should discuss the uncertainty 

generated by the choice of model. Multiple 

models are often available to the analyst, and 

choosing among them is similar to making an 

assumption. Implicit in the choice of a model 

are many factors. For example, one model 

may take long-run effects into account while 

another model does not. When possible, 

presenting results of an alternate model can 

inform the reader. When resource limitations 

prevent the use of an alternative model, it is 

still often possible to predict the direction 

and likely magnitude of the use of an alternate 

model, and the analyst should present this 

information to the reader. 

11.3 Use of Economic Analyses 

The primary purpose of conducting economic 

analysis is to provide policy makers and others 

with detailed information on a wide variety 

of consequences of environmental policies. 

One important element these analyses have 

traditionally provided to the policy-making 

process is estimates of social benefits and costs — 

the economic efficiency of a policy. For this reason, 

these Guidelines reflect updated information 

associated with procedures for calculating benefits 

and costs, monetizing benefits estimates, and 

selecting particular inputs and assumptions. 

Determining which regulatory options are 

best even on the restrictive terms of economic 

efficiency is often made difficult by uncertainties in 

data and by the presence of benefits and costs that 

can be quantified but not monetized, or that can 

only be qualitatively assessed. Even if the criterion 

of economic efficiency were the sole guide to 
11-12 Guidelines for Preparing Economic Analyses | December 2010 

Chapter 11 Presentation of Analysis and Results

policy decisions, social benefit and costs estimates 

alone would not be sufficient to define the best 

policies. 

A large number of social goals and statutory 

and judicial mandates motivate and shape 

environmental policy. For this and other reasons, 

these Guidelines contain information concerning 

procedures for conducting analyses of other 

consequences of environmental policies, such 

as economic impacts and equity effects. This is 

consistent with the fact that economic efficiency is 

not the sole criterion for developing good public 

policies. 

Even the most comprehensive economic analyses 

are but part of a larger policy development 

process, one in which no individual analytical 

feature or empirical finding dominates. The role 

of economic analysis is to organize information 

and comprehensively assess the economic 

consequences of alternative actions — benefits, 

costs, economic impacts, and equity effects 

— and the trade-offs among them. Ultimately 

statutory requirements dictate if and how the 

analytic results are used in standard setting. In 

any case, these results, along with other analyses 

and considerations, serve as important inputs for 

the broader policy-making process and serve as 

important resources for the public. 
 

Appendix A 

Economic Theory
 

This appendix provides a brief overview of the fundamental theory underlying 

the approaches to economic analysis discussed in Chapters 3 through 9. The 

first section summarizes the basic concepts of the forces governing a market 

economy in the absence of government intervention. Section A.2 describes 

why markets may behave inefficiently. If the preconditions for market efficiency 

are not met, government intervention can be justified.1

 The usefulness of benefit-cost analysis 

(BCA) as a tool to help policy makers determine the appropriate policy response is discussed 

in Section A.3. Sections A.4 and A.5 explain how economists measure the economic impacts 

of a policy and set the optimal level of regulation. Section A.6 concludes and provides a list of 

additional references. 

A.1 Market Economy 

The economic concept of a market is used to describe 

any situation where exchange takes place between 

consumers and producers. Economists assume that 

consumers purchase the combination of goods 

that maximizes their well-being, or “utility,” given 

market prices and subject to their household budget 

constraint. Economists also assume that producers 

(firms) act to maximize their profits. Economic 

theory posits that consumers and producers are 

rational agents who make decisions taking into 

account all of the costs — the full opportunity 

costs — of their choices, given their own resource 

constraints.2

 The purpose of economic analysis is to 

understand how the agents interact and how their 

interactions add up to determine the allocation 

of society’s resources: what is produced, how it is 

produced, for whom it is produced, and how these 

decisions are made. The simplest tool economists use 

to illustrate consumers’ and producers’ behavior is a 

market diagram with supply and demand curves. 

1	 EPA’s mandates frequently rely on criteria other than economic efficiency, 

so policies that are not justified due to a lack of efficiency are sometimes 

adopted. 

2	 Opportunity cost is the next best alternative use of a resource. The full 

opportunity cost of producing (consuming) a good or service consists 

of the maximum value of other goods and services that could have been 

produced (consumed) had one not used the limited resources to produce 

(purchase) the good or service in question. For example, the full cost of 

driving to the store includes not only the price of gas but also the value of 

the time required to make the trip. 

The demand curve for a single individual shows the 

quantity of a good or service that the individual will 

purchase at any given price. This quantity demanded 

assumes the condition of holding all else constant, 

i.e., assuming the budget constraint, information 

about the good, expected future prices, prices 

of other goods, etc. remain constant. The height 

of the demand curve in Figure A.1 indicates the 

maximum price, P, an individual with Qd

 units of a 

good or service would be willing to pay to acquire 

an additional unit of a good or service. This amount 

reflects the satisfaction (or utility) the individual 

receives from an additional unit, known as the 

marginal benefit of consuming the good. Economists 

generally assume that the marginal benefit of an 

additional unit is slightly less than that realized by 

Figure A.1 - Marginal and Total WTP 

Price

Pd-1

P

Demand

Curve

Quantity

Q Q d-1 d

Guidelines for Preparing Economic Analyses | December 2010 A-1 
Appendix A Economic Theory

the previous unit. The amount an individual is 

willing to pay for one more unit of a good is less 

than the amount she paid for the last unit; hence, 

the individual demand curve slopes downward. 

A market demand curve shows the total quantity 

that consumers are willing to purchase at different 

price levels, i.e., their collective willingness to pay 

(WTP) for the good or service. In other words, 

the market demand curve is the horizontal sum of 

all of the individual demand curves. 

The concept of an individual’s WTP is one of the 

fundamental concepts used in economic analyses, 

and it is important to distinguish between total 

and marginal WTP. Marginal WTP is the 

additional amount the individual would pay for 

one additional unit of the good. The total WTP is 

the aggregate amount the individual is willing to 

pay for the total quantity demanded (Qd

). Figure 

A.1 illustrates the difference between the marginal 

and total WTP. The height of the demand curve 

at a quantity Qd-1 gives the marginal WTP for 

the Qd-1 

th unit. The height of the demand curve 

at a quantity Qd

 gives the marginal WTP for the 

Qd 

th unit. Note that the marginal WTP is greater 

for the Qd-1 

th unit. The total WTP is equal to the 

sum of the marginal WTP for each unit up to Qd

. 

The shaded area under the demand curve from the 

origin up to Qd

 shows total WTP. 

Figure A.2 - Marginal and Total Cost 

Price

Quantity

Qs Qs+1

P

Ps+1

Supply

Curve

An individual producer’s supply curve shows the 

quantity of a good or service that an individual 

or firm is willing to sell (Q ) at a given price. As 

s a profit-maximizing agent, a producer will only 

be willing to sell another unit of the good if the 

market price is greater than or equal to the cost 

of producing that unit. The cost of producing 

the additional unit is known as the marginal cost. 

Therefore, the individual supply curve traces out 

the marginal cost of production and is also the 

marginal cost curve. Economists generally assume 

that the cost of producing one additional unit is 

greater than the cost of producing the previous 

unit because resources are scarce. Therefore the 

supply curve is assumed to slope upward. In Figure 

A.2, the marginal cost of producing the Q th unit s of the good is given by the height of the supply 

curve at Q . The marginal cost of producing the 

s Qs+1 

th unit of the good is given by the height of 

the supply curve at Qs+1

, which greater than the 

cost of producing the Q th unit, and greater than s the price, P. The total cost of producing Q units 

s is equal to the shaded area under the supply curve 

from the origin to the quantity Q . The market 

s supply curve is simply the horizontal summation 

of the individual producers’ marginal cost curves 

for the good or service in question. 

In a competitive market economy, the intersection 

of the market demand and market supply curves 

determines the equilibrium price and quantity 

of a good or service sold. The demand curve 

reflects the marginal benefit consumers receive 

from purchasing an extra unit of the good (i.e., it 

reflects their marginal WTP for an extra unit). 

The supply curve reflects the marginal cost to the 

firm of producing an extra unit. Therefore, at the 

competitive equilibrium, the price is where the 

marginal benefit equals the marginal cost. This is 

illustrated in Figure A.3, where the supply curve 

intersects the demand curve at equilibrium price 

P and equilibrium quantity Q . m m 

A counter-example illustrates why the equilibrium 

price and quantity occur at the intersection of 

the market demand and supply curves. In Figure 

A.3, consider some price greater than Pm where Qs is greater than Qd

 (i.e., there is excess supply). As 

producers discover that they cannot sell off their 

inventories, some will reduce prices slightly, hoping 

to attract more customers. At lower prices consumers 

will purchase more of the good (Qd

 increases) 

although firms will be willing to sell less (Qs 

A-2 Guidelines for Preparing Economic Analyses | December 2010 
Appendix A Economic Theory

Figure A.3 - Market Equilibrium 

Price 

Quantity 

Consumer 

Surplus 

Producer 

Surplus 

Qm (= Qd

= Qs ) 

P m 

Supply = 

Demand = 

Marginal 

Cost 

Marginal 

Benefit 

decreases). This adjustment continues until Qd equals 

Q . The reverse situation occurs if the price becomes 

s

lower than Pm. In that case, Qd

 will exceed Qs (i.e., 

there is excess demand) and consumers who cannot 

purchase as much as they would like are willing to 

pay higher prices. Therefore, firms will begin to 

increase prices, causing some reduction in the Qd

 but 

also increasing Q . Prices will continue to rise until Q s s equals Qd

. At this point no purchaser or supplier will 

have an incentive to change the price or quantity; 

hence, the market is said to be in equilibrium. 

Economists measure a consumer’s net benefit from 

consuming a good or service as the excess amount 

that she is willing to spend on the good or service 

over and above the market price. The net benefit of 

all consumers is the sum of individual consumer’s net 

benefits — i.e., what consumers are willing to spend 

on a good or service over and above that required 

by the market. This is called the consumer surplus. 

In Figure A.3, the market demands price P for the m

purchase of quantity Q . However, the demand m

curve shows that there are consumers willing to 

pay more than price P for all units prior to Q . m m 

Therefore, the consumer surplus is the area under 

the market demand (marginal benefit) curve but 

above the market price. Policies that affect market 

conditions in ways that decrease prices by decreasing 

costs of production (i.e., that shift the marginal cost 

curve to the right) will generally increase consumer 

surplus. This increase can be used to measure the 

benefits that consumers receive from the policy.3 

Section A.4.2 provides a more technical discussion of how consumer 

surplus serves as a measure of benefits. 

On the supply side, a producer can be thought 

to receive a benefit if he can sell a good or service 

for more than the cost of producing an additional 

unit — i.e., its marginal cost. Figure A.3 shows 

that there are producers willing to sell up to Qm 

units of the good for less then the market price P . m 

Hence, the net benefit to producers in this market, 

known as producer surplus, can be measured as the 

area above the market supply (marginal cost) curve 

but below the market price. Policies that increase 

prices by increasing market demand for a good 

(i.e., that shift the marginal benefit curve to the 

right) will generally increase producer surplus. This 

increase can be used to measure the benefits that 

producers receive from the policy. 

Economic efficiency is defined as the maximization 

of social welfare. In other words, the efficient 

level of production is one that allows society to 

derive the largest possible net benefit from the 

market. This condition occurs where the (positive) 

difference between the total WTP and total 

costs is the largest. In the absence of externalities 

and other market failures (explained below), this 

occurs precisely at the intersection of the market 

demand and supply curves where the marginal 

benefit equals the marginal cost. This is also the 

point where total surplus (consumer surplus plus 

producer surplus) is maximized. There is no way 

to rearrange production or reallocate goods so 

that someone is made better off without making 

someone else worse off — a condition known as 

Pareto optimality. Notice that economic efficiency 

requires only that net benefits be maximized, 

irrespective of to whom those net benefits accrue. 

It does not guarantee an “equitable” or “fair” 

distribution of these surpluses among consumers 

and producers, or between sub-groups of 

consumers or producers. 

Economists maintain that if the economic conditions 

are such that there are no market imperfections 

(as discussed in Section A.2), then this 

condition of Pareto-optimal economic efficiency 

Guidelines for Preparing Economic Analyses | December 2010 A-3 

3 
Appendix A Economic Theory

occurs automatically.4 That is, no government 

intervention is necessary to maximize the sum 

of consumer surplus and producer surplus. This 

theory is summarized in the two Fundamental 

Theorems of Welfare Economics, which originate 

with Pareto (1906) and Barone (1908): 

1. First Fundamental Welfare Theorem. Every 

competitive equilibrium is Pareto-optimal. 

2. Second Fundamental Welfare Theorem. Every 

Pareto-optimal allocation can be achieved 

as a competitive equilibrium after a suitable 

redistribution of initial endowments. 

One graphical representation of these results is 

given in Figure A.4, which shows utility (welfare) 

levels in a two-person economy.5

 The curve 

shown is the utility possibility frontier (UPF) 

curve; the area within it represents the set of all 

possible welfare outcomes. Each point on the 

negatively sloped UPF curve is Pareto optimal 

since it is not possible to increase the utility of 

Figure A.4 - Utility Possibility Frontier 

V

C

B

A

H

D

UPF

U2

U1

4	 Technically, there are two types of efficiency. Allocative efficiency 

means that resources are used for the production of goods and 

services most wanted by society. Productive efficiency implies that 

the least costly production techniques are used to produce any mix 

of goods and services. Allocative efficiency requires that there be 

productive efficiency, but productive efficiency can occur without 

allocative efficiency. Goods can be produced at the least-costly method 

without being most wanted by society. Perfectly competitive markets 

in the long run will achieve both of these conditions, producing the 

“right” goods (allocative efficiency) in the “right” way (productive 

efficiency). These two conditions imply Pareto-optimal economic 

efficiency. (See Varian 1992 or any basic economics text for a more 

detailed discussion.) 

5 Another, perhaps more commonly used, graphical tool to explain the 

First and Second Welfare Theorems is an Edgeworth box. See Varian 

(1992) or other basic economic textbook for a detailed discussion. 

one person without decreasing the utility of the 

other. If the initial allocation is at point A, then 

the set of Pareto-superior (welfare-enhancing) 

outcomes include all points in the shaded area, 

bordered by H, V, and the UPF curve.6

 If trading is 

permitted, the First Welfare Theorem applies and 

the market will move the economy to a superior, 

more efficient point such as B. Then the Second 

Welfare Theorem simply says that for any chosen 

point along the UPF curve, given a set of lump 

sum taxes and transfers, an initial allocation can be 

determined inside the UPF from which the market 

will achieve the desired outcome.7 

A.2 Reasons for Market or 

Institutional Failure 

If the market supply and demand curves reflect 

society’s true marginal social cost and WTP, 

then a laissez-faire market (i.e., one governed 

by individual decisions and not government 

authority) will produce a socially efficient result. 

However, when markets do not fully represent 

social values, the private market will not achieve the 

efficient outcome (see Mankiw 2004, or any basic 

economics text); this is known as a market failure. 

Market failure is primarily the result of externalities, 

market power, and inadequate or asymmetric 

information. Externalities are the most likely 

cause of the failure of private and public sector 

institutions to account for environmental damages. 

Externalities occur when markets do not account 

for the effect of one individual’s decisions on 

another individual’s well-being.8

 In a free market 

producers make their decisions about what and 

how much to produce, taking into account the 

cost of the required inputs — labor, raw materials, 

6	 Note that efficiency could be obtained by moving along the vertical 

line V, which keeps utility of person 1 (U1) constant while increasing 

utility of person 2 (U2), or by moving along the horizontal line H, which 

only shows improvements in utility for person 1. Moving to point B 

improves the utility for both individuals. 

7	 Note that outcomes on the frontier such as C and D, although efficient, 

may not be desired on equity, or fairness, grounds. 

8	 More formally, an externality occurs when the production or 

consumption decision of one party has an unintended negative 

(positive) impact on the profit or utility of a third party. Even if one 

party compensates the other party, an externality still exists (Perman 

et al. 2003). See Baumol and Oates (1988) or any basic economics 

textbook for similar definitions and more detailed discussion. 

A-4 Guidelines for Preparing Economic Analyses | December 2010 
Appendix A Economic Theory

 

 

 

 

 

machinery, energy. Consumers purchase goods 

and services taking into account their income and 

their own tastes and preferences. This means that 

decisions are based on the private costs and private 

benefits to market participants. If the consumption 

or production of these goods and services poses an 

external cost or benefit on those not participating 

in the market, however, then the market demand 

and supply curves no longer reflect the true 

marginal social benefit and marginal social cost. 

Hence, the market equilibrium will no longer be 

the socially (Pareto) efficient outcome. 

Externalities can arise for many reasons. 

Transactions costs or poorly defined property 

rights can make it difficult for injured parties to 

bargain or use legal means to ensure that the costs 

of the damages caused by polluters are internalized 

into their decision making.9

 Activities that pose 

environmental risks may also be difficult to link to 

the resulting damages and often occur over long 

periods of time. Externalities involve goods that 

people care about but are not sold in markets.10 

Air pollution causes ill health, ecological damage, 

and visibility impacts over a long time period, 

and the damage is often far from the source(s) 

of the pollution. The additional social costs of 

air pollution are not included in firms’ profit 

maximization decisions and so are not considered 

when firms decide how much pollution to emit. The 

lack of a market for clean air causes problems and 

provides the impetus for government intervention 

in markets involving polluting industries. 

9	 A property right can be defined as a bundle of characteristics that 

confer certain powers to the owner of the right: the exclusive right to 

the choice of use of a resource, the exclusive right to the services of a 

resource, and the right to exchange the resource at mutually agreeable 

terms. Externalities typically arise from the violation of one or more 

of the characteristics of well-defined property rights. This implies 

that the distortions resulting from an externality can be eliminated by 

appropriately establishing these rights. This insight is summarized by 

the famous “Coase theorem” which states that if property rights over an 

environmental asset are clearly defined, and bargaining among owners 

and prospective users of the asset is allowed, then externality problems 

can be corrected and the efficient outcome will result regardless of who 

was initially given the property right. The seminal paper is Coase (1960). 

10	 Often these are goods that exhibit public good characteristics. Pure 

public goods are those that are non-rivalrous in consumption and 

non-excludable. [See Perman et al. (2003) for a detailed discussion of 

these, as well as congestible and open access resources — i.e., goods 

that are neither pure public nor pure private goods.] Because exclusive 

property rights cannot be defined for these types of goods, pure private 

markets cannot provide for them efficiently. 

Figure A.5 illustrates a negative externality 

associated with the production of a good. For 

example, a firm producing some product might 

also be generating pollution as a by-product. The 

pollution may impose significant costs — in the 

form of adverse health effects, for example — on 

households living downwind or downstream of 

the firm. Because those costs are not borne by the 

firm, the firm typically does not consider them 

in its production decisions. Society considers 

the pollution a cost of production, but the firm 

typically will not. In this figure: 

Figure A.5 - Negative Externality 

Price 

0 

MSC 

Y MPC 

D (=MB) 

P* 

P m 

P0

P1

Quantity Q* Qm

Z 

X 

MSD 

}

• D is the market demand (marginal benefit) 

curve for the product; 

• MPC is the firm’s marginal private realresource cost of production, excluding the 

cost of the firm’s pollution on households; 

• MSD is the marginal social damage of 

pollution (or the marginal external cost) that 

the firm is not considering; and 

• MSC is society’s marginal social cost 

associated with production, including the cost 

of pollution (MSC = MPC + MSD). 

In an incomplete market, producers pay no 

attention to external costs, and production occurs 

where market demand (D) and the marginal 

private real-resource cost (MPC) curves intersect 

— at a price Pm and a quantity Qm. In this case, 

net social welfare (total WTP minus total social 

costs) is equal to the area of the triangle P0P1X less 

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A-6 Guidelines for Preparing Economic Analyses | December 2010 

Appendix A Economic Theory

the area of triangle XYZ.

11 If the full social cost 

of production, including the cost of pollution, is 

taken into consideration, then the marginal cost 

curve should be increased by the amount of the 

marginal social damage (MSD) of pollution.12 

Production will now occur where the demand 

and marginal social cost (MSC) curves intersect 

— at a price P* and a quantity Q*. At this point 

net social welfare (now equal to the area of the 

triangle, P0

P1X, alone) is maximized, and therefore 

the market is at the socially efficient point of 

production. This example shows that when there 

is a negative externality such as pollution, and the 

social damage (external cost) of that pollution 

is not taken into consideration, the producer 

will oversupply the polluting good.13 The shaded 

triangle (XYZ), referred to as the deadweight loss 

(DWL), represents the amount that society loses 

by producing too much of the good. 

A.3 Benefit-Cost Analysis 

If a negative externality such as pollution exists, 

an unregulated market will not account for its 

cost to society, and the result will be an inefficient 

outcome. In this case, there may be a need for 

government intervention to correct the market 

failure. A correction may take the form of dictating 

the allowable level of pollution or introducing a 

market mechanism to induce the optimal level of 

pollution.14 Figure A.5 neatly summarizes this in a 

single market diagram. To estimate the total costs 

and benefits to society of an activity or program, 

the costs and benefits in each affected market, as 

well as any non-market costs or benefits, are added 

up. This is done through BCA. 

11	 Recall from Section A.1 that total WTP is equal to the area under the 

demand curve from the origin to the point of production (0P1ZQm). 

Total costs (to society) are equal to the area under the MSC curve from 

the origin to the point of production (0P0YQm). 

12	 When conducting BCA related to resource stocks, the MSD or marginal 

external cost is the present value of future net benefits that are lost to 

due to the use of the resource at present. That is, exhaustible resources 

used today will not be available for future use. These foregone future 

benefits are called user costs in natural resource economics (see Scott 

1953, 1955). The marginal user cost is the user cost of one additional 

unit consumed in the present, and is added together with the marginal 

extraction cost to determine the MSC of resource use. 

13	 Similarly, the private market will undersupply goods for which there are 

positive externalities, such as parks and open space. 

14	 Chapter 4 discusses the various regulatory techniques and some nonregulatory means of achieving pollution control. 

BCA can be thought of as an accounting 

framework of the overall social welfare of a 

program, which illuminates the trade-offs involved 

in making different social investments (Arrow et 

al. 1996). It is used to evaluate the favorable effects 

of a policy action and the associated opportunity 

costs. The favorable effects of a regulation are the 

benefits, and the foregone opportunities or losses 

in utility are the costs. Subtracting the total costs 

from the total monetized benefits provides an 

estimate of the regulation’s net benefits to society. 

An efficient regulation is one that yields the 

maximum net benefit, assuming that the benefits 

can be measured in monetary terms. 

BCA can also be seen as a type of market test 

for environmental protection. In the private 

market, a commodity is supplied if the benefits 

that society gains from its provision, measured 

by what consumers are willing to pay, outweigh 

the private costs of producing the commodity. 

Economic efficiency is measured in a private 

market as the difference between what consumers 

are willing to pay for a good and what it costs to 

produce it. Since clean air and clean water are 

public goods, private suppliers cannot capture 

their value and sell it. The government determines 

their provision through environmental protection 

regulation. BCA quantifies the benefits and costs 

of producing this environmental protection in the 

same way as the private market, by quantifying the 

WTP for the environmental commodity. As with 

private markets, the efficient outcome is the option 

that maximizes net benefits. 

The key to performing BCA lies in the ability 

to measure both benefits and costs in monetary 

terms so that they are comparable. Consumers 

and producers in regulated industries and 

the governmental agencies responsible for 

implementing and enforcing the regulation (and 

by extension, taxpayers in general) typically 

pay the costs. The total cost of the regulation is 

found by summing the costs to these individual 

sectors. (An example of this, excluding the costs 

to the government, is given in Section A.4.3.) 

Since environmental regulation usually addresses 

some externality, the benefits of a regulation 

often occur outside of markets. For example, the 
Guidelines for Preparing Economic Analyses | December 2010 A-7 

Appendix A Economic Theory

primary benefits of drinking water regulations 

are improvements in human health. Once the 

expected reduction in illness and premature 

mortality associated with the regulation is 

calculated, economists use a number of techniques 

to estimate the value that society places on these 

health improvements.15 These monetized benefits 

can then be summed to obtain the total benefits 

from the regulation. 

Note that in BCA gains and losses are weighted 

equally regardless of to whom they accrue. 

Evaluation of the fairness, or the equity, of the 

net gains cannot be made without specifying 

a social welfare function. However there is no 

generally agreed-upon social welfare function, and 

assigning relative weights to the utility of different 

individuals is an ethical matter that economists 

strive to avoid. Given this dilemma, economists 

have tried to develop criteria for comparing 

alternative allocations where there are winners 

and losers without involving explicit reference 

to a social welfare function. According to the 

Kaldor-Hicks compensation test, named after 

its originators Nicholas Kaldor and J.R. Hicks, a 

reallocation is a welfare-enhancing improvement 

to society if: 

1. The winners could theoretically compensate the 

losers and still be better off; and 

2. The losers could not, in turn, pay the winners to 

not have this reallocation and still be as well off 

as they would have been if it did occur (Perman 

et al. 2003). 

While these conditions sound complex, they are 

met in practice by assessing the net benefits of a 

regulation through BCA. The policy that yields 

the highest positive net benefit is considered 

welfare enhancing according to the KaldorHicks criterion. Note that the compensation 

test is stated in terms of potential compensation 

and does not solve the problem of evaluating 

the fairness of the distribution of well-being in 

society. Whether and how the beneficiaries of a 

regulation should compensate the losers involves 

15	 Chapter 7 discusses a variety of methods economists use to value 

environmental improvements. 

a value judgment and is a separate decision for 

government to make. 

Finally, BCA may not provide the only 

criterion used to decide if a regulation is in 

society’s best interest. There are often other, 

overriding considerations for promulgating 

regulation. Statutory instructions, political 

concerns, institutional and technical feasibility, 

enforceability, and sustainability are all important 

considerations in environmental regulation. In 

some cases a policy may be considered desirable 

even if the benefits to society do not outweigh 

its costs, particularly if there are ethical or equity 

concerns.16 There are also practical limitations 

to BCA. Most importantly, this type of analysis 

requires assigning monetized values to nonmarket benefits and costs. In practice it can be 

very difficult or even impossible to quantify gains 

and losses in monetary terms (e.g., the loss of a 

species, intangible effects).17 In general, however, 

economists believe that BCA provides a systematic 

framework for comparing the social costs and 

benefits of proposed regulations, and that it 

contributes useful information to the decisionmaking process about how scarce resources can be 

put to the best social use. 

A.4 Measuring 

Economic Impacts 

A.4.1 Elasticities 

The net change in social welfare brought about 

by a new environmental regulation is the sum 

of the negative effects (i.e., loss of producer and 

consumer surplus) and the positive effects (or 

social benefits) of the improved environmental 

quality. This is shown graphically for a single 

market in Figure A.5 above. The use of demand 

and supply curves highlights the importance of 

assessing how individuals will respond to changes 

in market conditions. The net benefits of a policy 

will depend on how responsively producers and 

consumers react to a change in price. Economists 

16	 Chapter 9 addresses equity assessment and describes the methods 

available for examining the distributional effects of a regulation. 

17	 Kelman (1981) argues that it is even unethical to try to assign 

quantitative values to non-marketed benefits. 
Appendix A Economic Theory

 

 

 

 

 

 

measure this responsiveness by the supply and 

demand elasticities. 

The term “elasticity” refers to the sensitivity of 

one variable to changes in another variable. The 

price elasticity of demand (or supply) for a good 

or service is equal to the percentage change in 

the quantity demanded (or supplied) that would 

result from a 1 percent increase in the price of that 

good or service. For example, a price elasticity of 

demand for tuna equal to -1 means that a 1 percent 

increase in the price of tuna results in a 1 percent 

decrease in the quantity demanded. Changes 

are measured assuming all other things, such as 

incomes and tastes, remain constant. Demand and 

supply elasticities are rarely constant and often 

change depending on the quantity of the good 

consumed or produced. For example, according to 

the demand curve for tuna shown in Figure A.6, 

at a price of $1 per pound, a 10 percent increase 

in price would reduce quantity demanded by 2.5 

percent (from 8 lbs to 7.8 lbs). At a price of $4 per 

pound, a 10 percent increase in price would result 

in a 40 percent decrease in quantity demanded 

(from 2 to 1.2 lbs). This implies that the price 

elasticity of demand is -0.25 when tuna costs $1/ 

lb but -4 when the price is $4/lb. When calculating 

elasticities it is important realize where one is 

on the supply or demand curve, and the price 

or quantity should be stated when reporting an 

elasticity estimate. 

Elasticities are important in measuring economic 

impacts because they determine how much of a 

Figure A.6 - Demand Curve for Tuna 

$/lb 

0 2 4 6 8 10 

Demand 

3 

Tuna (lbs) 

2 

1 

4 

5 

price increase will be passed on to the consumer. 

For example if a pollution control policy leads to 

an increase in the price of a good, multiplying the 

price increase by current quantity sold generally 

will not provide an accurate measure of impact of 

the policy. Some of the impact will take the form 

of higher prices for the consumer, but some of 

the impact will be a decrease in the quantity sold. 

The amount of the price increase that is passed 

on to consumers is determined by the elasticity 

of demand relative to supply (as well as existing 

price controls). “Elastic” demand (or supply) 

indicates that a small percentage increase in price 

results in a larger percentage decrease (increase) 

in quantity demanded (supplied).18 All else equal, 

an industry facing a relatively elastic demand is 

less likely to pass on costs to the consumer because 

increasing prices will result in reduced revenues. 

In determining the economic impacts of a rule, 

supply characteristics in the industries affected 

by a regulation can be as important as demand 

characteristics. For highly elastic supply curves 

relative to the demand curves, it is likely that 

cost increases or decreases will be passed on to 

consumers. 

The many variables that affect the elasticity of 

demand include: 

• The cost and availability of close substitutes; 

• The percentage of income a consumer spends 

on the good; 

• How necessary the good is for the consumer; 

• The amount of time available to the consumer 

to locate substitutes; 

• The expected future price of the good; and 

• The level of aggregation used in the study to 

estimate the elasticity. 

The availability of close substitutes is one of the 

most important factors that determine demand 

elasticity. A product with close substitutes at 

similar prices tends to have an elastic demand, 

18	 Demand (or supply) is said to be “elastic” if the absolute value of the 

price elasticity of demand (supply) is greater than one and “inelastic” 

if the absolute value of the elasticity is less than one. If a percentage 

change in price leads to an equal percentage change in quantity 

demanded (supplied) (i.e., if the absolute value of elasticity equals 

one), demand (supply) is “unit elastic.” 

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Guidelines for Preparing Economic Analyses | December 2010 A-9 

Appendix A Economic Theory

 

 

 

 

 

 

because consumers can readily switch to 

substitutes rather than paying a higher price. 

Therefore, a company is less likely to be able to pass 

through costs if there are many close substitutes 

for its product. Narrowly defined markets (e.g., 

salmon) will have more elastic demands than 

broadly defined markets (e.g., food) since there are 

more substitutes for narrow goods. 

Another factor that affects demand elasticities 

is whether the affected product represents a 

substantial or necessary portion of customers’ costs 

or budgets. Goods that account for a substantial 

portion of consumers’ budgets or disposable 

income tend to be relatively price elastic. This 

is because consumers are more aware of small 

changes in the price of expensive goods compared 

to small changes in the price of inexpensive 

goods, and therefore may be more likely to seek 

alternatives. A similar issue concerns the type of 

final good involved. Reductions in demand may 

be more likely to occur when prices increase for 

“luxuries” or optional purchases. If the good is a 

necessity item, the quantity demanded is unlikely 

to change drastically for a given change in price. 

Demand will be relatively inelastic. 

Elasticities tend to increase over time, as firms and 

customers have more time to respond to changes in 

prices. Although a company may face an inelastic 

demand curve in the short run, it could experience 

greater losses in sales from a price increase in 

the long run. Over time customers begin to find 

substitutes or new substitutes are developed. 

However, temporary price changes may affect 

consumers’ decisions differently than permanent 

ones. The response of quantity demanded during 

a one-day sale, for example, will be much greater 

than the response of quantity demanded when 

prices are expected to decrease permanently. 

Finally, it is important to keep in mind that 

elasticities differ at the firm versus the industry 

level. It is not appropriate to use an industry-level 

elasticity to estimate the ability of only one firm to 

pass on compliance costs when its competitors are 

not subject to the same cost. 

Characteristics of supply in the industries affected 

by a regulation can be as important as demand 

characteristics in determining the economic 

impacts of a rule. For relatively elastic supply 

curves, it is likely that cost increases or decreases 

will be passed on to consumers. The elasticity of 

supply depends, in part, on how quickly per unit 

costs rise as firms increase their output. Among the 

many variables that influence this rise in cost are: 

• The cost and availability of close input 


substitutes; 


• The amount of time available to adjust 


production to changing conditions; 


• The degree of market concentration among 

producers; 

• The expected future price of the product; 

• The price of related inputs and related 


outputs; and 


• The speed of technological advances in 


production that can lower costs.
 

Similar to the determinants of demand elasticity, 

the factors influencing the price elasticity of supply 

all relate to a firm’s degree of flexibility in adjusting 

production decisions in response to changing 

market conditions. The more easily a firm can 

adjust production levels, find input substitutes, 

or adopt new production technologies, the more 

elastic is supply. Supply elasticities tend to increase 

over time as firms have more opportunities to 

renegotiate contracts and change production 

technologies. When production takes time, the 

quantity supplied may be more responsive to 

expected future price changes than to current price 

changes. 

Demand and supply elasticities are available for the 

aggregate output of final goods in most industries. 

They are usually published in journal articles 

on research pertaining to a particular industry.19 

19	 Another useful source of elasticity estimates is the recently developed 

EPA Elasticity Databank (U.S. EPA 2007d). In the absence of an 

encyclopedic “Book of Elasticities” the Elasticity Databank serves as a 

searchable database of elasticity parameters across a variety of types 

(i.e., demand and supply elasticities, substitution elasticities, income 

elasticities, and trade elasticities) and economic sectors/product 

markets. The database is populated with EPA-generated estimates used 

in Environmental Impact Assessment studies conducted by the Agency 

since 1990, as well as estimates found in the economics literature. It can 

be accessed from the Technology Transfer Network Economics and Cost 

Analysis Support website: http://www.epa.gov/ttnecas1/Elasticity.htm. 
A-10 Guidelines for Preparing Economic Analyses | December 2010 

Appendix A Economic Theory

When such information is unavailable, as is 

often the case for intermediate goods, elasticities 

may be quantitatively or qualitatively assessed.20 

Econometric tools are frequently used to estimate 

supply and demand equations (thereby the 

elasticities) and the factors that influence them. 

A.4.2 Measuring the Welfare 

Effect of a Change in 

Environmental Goods 

As introduced in Section A.1 changes in consumer 

surplus are measured by the trapezoidal region 

below the ordinary, or Marshallian, demand curve 

as price changes. This region reflects the benefit a 

consumer receives by being able to consume more 

of a good at a lower price. If the price of a good 

decreases, some of the consumer’s satisfaction 

comes from being able to consume more of a 

commodity when its price falls, but some of it 

comes from the fact that the lower price means that 

the consumer has more income to spend. However, 

the change in (Marshallian) consumer surplus only 

serves as a monetary measure of the welfare gain or 

loss experienced by the consumer under the strict 

assumption that the marginal utility of income is 

constant.21 This assumption is almost never true in 

reality. Luckily, there are alternative, less demanding 

monetary measures of consumer welfare that prove 

useful in treatments of BCA. Intuitively, these 

measures determine the size of payment that would 

be necessary to compensate the consumer for the 

price change. In other words, they estimate the 

consumer’s WTP for a price change. 

As mentioned above, a price decline results in two 

effects on consumption. The change in relative 

prices will increase consumption of the cheaper 

good (the substitution effect), and consumption 

will be affected by the change in overall purchasing 

power (the income effect). A Marshallian demand 

curve reflects both substitution and income 

effects. Movements along it show how the quantity 

20	 Final goods are those that are available for direct use by consumers 

and are not utilized as inputs by firms in the process of production. 

Goods that contribute to the production of a final good are called 

intermediate goods. It is of course possible for a good to be final from 

one perspective and intermediate from another (Pearce 1992). 

21	 See Perman et al. (2003), Just et al. (2005) or any graduate level text 

for a more thorough exposition of this issue. 

demanded changes as price changes (holding all 

other prices and income constant), so it reflects 

both the substitution and the income effects. The 

Hicksian (or “compensated”) demand curve, on 

the other hand, shows the relationship between 

quantity demanded of a commodity and its price, 

holding all other prices and utility (rather than 

income) constant. This is the correct measure of a 

consumer’s WTP for a price change. The Hicksian 

demand curve is constructed by adjusting income 

as the price changes so as to keep the consumer’s 

utility the same at each point on the curve. In 

this way, the income effect of a price change is 

eliminated and the substitution effect can be 

considered alone. Movements along the Hicksian 

demand function can be used to determine the 

monetary change that would compensate the 

consumer for the price change. 

Hicks (1941) developed two correct monetary 

measures of utility change associated with a price 

change: compensating variation and equivalent 

variation. Compensating variation (CV) assesses 

how much money must be taken away from 

consumers after a price decrease occurred to return 

them to the original utility level. It is equal to 

the amount of money that would ‘compensate’ 

the consumer for the price decrease. Equivalent 

variation (EV) measures how much money would 

need to be given to the consumer to bring her to 

the higher utility level instead of introducing the 

price change. In other words, it is the monetary 

change that would be ‘equivalent’ to the proposed 

price change. 

Before examining the implications of these 

measures for valuing environmental changes, it 

is useful to understand CV and EV in the case of 

a reduction in the price of some normal, private 

good, C1

.

22 This is shown with indifference curves 

and a budget line, as seen in Figure A.7. 

Assume that the consumer is considering the 

trade-off between C1

 and all other goods, denoted 

by a composite good, C2

. The indifference curve, 

U0

, depicts the different combinations of the two 

goods that yield the same level of utility. Because of 

22	 The notation and discussion in this section follow Chapter 12 of 

Perman et al. (2003). 
Guidelines for Preparing Economic Analyses | December 2010 A-11 

Appendix A Economic Theory

 

 

Figure A.7 - Indifference Curve 

C2

Y0

C ́2

U0

C ́1 Y0 C1

P ́1

diminishing marginal utility, the curve is concave, 

where increasing amounts of C1

 must be offered 

for each unit of C2

 given up to keep the consumer 

indifferent. The budget line on the graph reflects 

what the consumer is able to purchase given her 

income, Y0

, and the prices of the two goods — 

P1

’ and P2’, respectively.23 A utility-maximizing 

consumer will choose quantities C1

’ and C2’, the 

point where the indifference curve is tangent to 

the budget constraint.24 

Figure A.8 shows the change in the optimal 

consumption bundle resulting from a reduction in 

the price of C1

. If the price of C1 falls, the budget 

line shifts out on the C1

 axis because more C1 can 

be purchased for a given amount of money. The 

consumer now chooses C1

’’ and C2’’ at point b and 

moves to a new, higher utility curve, U1

. CV then 

measures how much money must be taken away at 

the new prices to return the consumer to the old 

utility level. That is, starting at point b and keeping 

the slope of the budget line fixed at the new level, 

by how much must it be shifted downward to 

make it tangent to the initial indifference curve, 

U0

? It is, therefore, the maximum amount the 

consumer would be willing to pay to have the price 

fall occur — i.e., the precise monetary measure of 

23	 In Figure A.7, C2 is considered the numeraire good (i.e., prices are 

adjusted so that P2 ' is equal to 1). 

24	 For a review of the utility maximizing behavior of consumers, see any 

general microeconomics textbook. 

Figure A.8 - Change in Optimal 

Consumption Bundle 

C2
 

Y2
 

Y0 

Y1 

C ́2 

C ́ ́2 

U0 

f 

b 

d 

a 

U1 

C ́ C* C ́ ́ 1 1 1 Y0 Y0 C1 

P ́1 P ́ ́1 

the welfare change.25 In Figure A.8, CV is simply 

given by the amount Y0

 – Y1. EV, on the other 

hand, measures how much income must be given 

to the individual at the old price set to maintain 

the same level of well-being as if the price change 

did occur. That is, keeping the slope of the budget 

line fixed at the old level, by how much must 

it be shifted upwards to make it tangent to U1

? 

EV is, then, the minimum amount of money the 

consumer would accept in lieu of the price fall. 

This too is a proper monetary measure of the 

utility change resulting from the price decrease. In 

Figure A.8 then EV is the amount Y2

 – Y0, leaving 

the individual at point f. 

CV and EV are simply measures of the distance 

between the two indifference curves. However, 

the amount of money associated with CV, EV, and 

Marshallian consumer surplus (MCS) is generally 

not the same. For a price fall, it can be shown 

that CV < MCS < EV, and for a price increase, 

CV > MCS > EV.26 Notice that in the case of a 

price decrease, the CV measures the consumer’s 

willingness to pay (WTP) to receive the price 

reduction and EV measures the consumer’s 

25	 In Figure A.8, this would result in a shift from C1 '' to C1*. This is known 

as the income effect of the price change. The shift from C1 ' to C1* is 

considered the substitution effect. 

26	 This can be seen by redrawing Figure A.8 using a graph of Marshallian 

and Hicksian demand curves. See Perman et al. (2003) for a detailed 

explanation. 
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Appendix A Economic Theory

willingness to accept (WTA) to forgo the lower 

price. If the price of C1

 were to increase, then the 

relationships between WTP/WTA and CV/ 

EV would be reversed. CV would measure the 

consumer’s WTA to suffer the price increase and 

EV would be the individual’s WTP to avoid the 

increase in price. 

In order to examine the implications of these 

measures for valuing changes in environmental 

conditions, one can think of C1

 in the above 

discussion as an environmental commodity, 

henceforth denoted by E. Then an improvement 

in environmental quality (or an increase in an 

environmental public good) resulting from some 

policy is reflected by an increase in the amount of 

E. Holding all else constant, such an increase is 

equivalent to a decrease in the price of E and can 

be depicted as a shifting outward of the budget line 

along the E axis. 

Welfare changes due to an increase in E follow 

along the lines of the previous discussion. 

However, because E is generally non-exclusive 

and non-divisible, the consumer consumption 

level cannot be adjusted. Therefore, the 

associated monetary measures of the welfare 

change are not technically CV and EV, but are 

referred to as compensating surplus (CS) and 

equivalent surplus (ES). In practice, however, the 

process is the same; a Hicksian demand curve is 

estimated for the unpriced environmental good. 

Analogous to the preceding discussion, if there 

is an environmental improvement, then CS 

measures the amount of money the consumer 

would be willing to pay for the improvement 

that would result in the pre-improvement level 

of utility. For the purposes of environmental 

valuation, this is the primary measure of 

concern when considering environmental 

improvements. ES measures how much society 

would have to pay the consumer to give him 

the same utility as if the improvement had 

occurred. In other words, this is how much he 

would be willing to accept to not experience 

the gain in environmental quality. If valuing an 

environmental degradation, then CS measures 

the WTA and ES measures WTP. 

Whereas statements can be made about the 

relative size of CV, EV, and MCS for price changes 

of normal goods, Bockstael and McConnell 

(1993) find that it is not possible to make 

similar statements about CS, ES, and MCS for 

a change in environmental quality.27 Given that 

environmental quality is generally an unpriced 

public good, ordinary Marshallian demand 

functions cannot be estimated, so it may seem 

irrelevant that one cannot say anything about 

how MCS approximates the proper measure. 

However, Bockstael and McConnell’s results are 

important in relation to indirect methods for 

environmental valuation. However, most indirect 

valuation studies are based on Marshallian demand 

functions in practice, in the hope of keeping the 

associated error small. 

A.4.3 Single Market, Multi-Market, 

and General Equilibrium Analysis 

Both supply and demand elasticities are affected 

by the availability of close complements and 

substitutes. This highlights the fact that 

regulating one industry can have an impact 

on other, non-regulated markets. However, 

this does not necessarily imply that all of these 

other markets must be modeled. Changes due 

to government regulation can be captured 

using only the equilibrium supply and demand 

curves for the affected market, assuming: (1) 

there are small, competitive adjustments in all 

other markets; and (2) there are no distortions 

in other markets. This is referred to as partial 

equilibrium analysis. 

For example, suppose a new environmental 

regulation increases per unit production costs. 

The benefits and costs of abatement in a partial 

equilibrium setting are illustrated in Figure 

A.9 where the market produces the quantity 

Qm in equilibrium without intervention. The 

external costs of production are shown by the 

marginal external costs (MEC) curve without 

27	 Willig (1976) shows that ordinary, or Marshallian, demand curves can 

provide an approximate measure of welfare changes resulting from 

a price change. In most cases the error associated with using MCS, 

with respect to CV or EV, will be less than 5 percent (see Perman et al. 

2003). 
Appendix A Economic Theory

Figure A.9 - Benefits and Costs of Abatement 

any abatement. Total external costs are given by 

the area under the MEC curve up to the market 

Price 

0 

P* 

P m 

P1 

P1 

Q Quantity Q* m

A 

B 

C 

E 

F 

D 

Supply + MAC 

MEC 

w/abatement 

Demand 

MEC w/o 

abatement 

Supply 

output, Qm, 

or the area of triangle QmE0. 

With required abatement production, costs 

are the total of supply plus marginal abatement 

costs (MAC), shown as the new, higher supply 

curve in the figure. These higher costs result 

in a new market equilibrium quantity shown 

as Q*. The social cost of the requirement is 

the resulting change in consumer and supplier 

surplus, shown here as the total observed 

abatement costs (parallelogram P0

P1AC) plus 

the area of triangle ABC, which can be described 

as deadweight loss. 

Abatement also produces benefits by shifting 

the MEC curve downward, reflecting the fact 

that each unit of production now results in less 

pollution and social costs. Additionally, the 

reduced quantity of the output good results in 

reduced external costs. The reduced external 

costs, i.e., the benefits, are given by the difference 

between triangle QmE0 and triangle Q*D0, 

represented by the shaded area in the figure. 

The net benefits of abatement are the benefits (the 

reduced external costs) minus the costs (the loss 

in consumer and producer surplus). In the figure 

this would equal the shaded area (the benefits) 

minus total abatement costs and deadweight loss 

as described above. 

While the single market analysis is theoretically 

possible, it is generally impractical for rulemaking. 

As mentioned in Section A.3, this is often because 

the gains occur outside of markets and cannot 

be linked directly to the output of the regulated 

market. Therefore BCA is frequently done as two 

separate analyses: a benefits analysis and a cost 

analysis. 

When a regulation is expected to have a large 

impact outside of the regulated market, then the 

analysis should be extended beyond that market. 

If the effects are significant but not anticipated to 

be widespread, one potential improvement is to 

use multi-market modeling in which vertically or 

horizontally integrated markets are incorporated 

into the analysis. The analysis begins with the 

relationship of input markets to output markets. 

A multi-market analysis extends the partial 

equilibrium analysis to measuring the losses in 

other related markets.28 

In some cases, a regulation can have such a 

significant impact on the economy that a general 

equilibrium modeling framework is required.29 

This may be because regulation in one industry has 

broad indirect effects on other sectors, households 

may alter their consumption patterns when they 

encounter increases in the price of a regulated 

good, or there may be interaction effects between 

the new regulation and pre-existing distortions, 

such as taxes on labor. In these cases, partial 

equilibrium analyses are likely to result in an 

inaccurate estimation of total social costs. Using 

a general equilibrium framework accounts for 

linkages between all sectors of the economy and 

all feedback effects, and can measure total costs 

comprehensively.30 

28	 An example of the use of multi-market model for environmental policy 

analysis is contained in a report prepared for EPA on the regulatory 

impact of control on asbestos and asbestos products (U.S. EPA 1989). 

29	 General equilibrium analysis is built around the assumption that, for 

some discrete period of time, an economy can be characterized by a 

set of equilibrium conditions in which supply equals demand in all 

markets. When this equilibrium is “shocked” through a change in 

policy or a change in some exogenous variable, prices and quantities 

adjust until a new equilibrium is reached. The prices and quantities 

from the post-shock equilibrium can then be compared with their preshock values to determine the expected impacts of the policy or change 

in exogenous variables. 

30	 Chapter 8 provides a more detailed discussion of partial equilibrium, 

multi-market, and general equilibrium analysis. 

Guidelines for Preparing Economic Analyses | December 2010 A-13 
Appendix A Economic Theory

 

 

 

 

 

A.5 Optimal Level of Regulation 

Following from the definition in Section A.1, the 

most economically efficient policy is the one that 

allows for society to derive the largest possible social 

benefit at the lowest social cost. This occurs when 

the net benefits to society (i.e., total benefits minus 

total costs) are maximized. In Figure A.10, this is at 

the point where the distance between the benefits 

curve and the costs curve is the largest and positive. 

Figure A.10 - Maximized Net Benefits 

$ 

Pollution 

Abatement 

Costs 

Benefits 

Note that this is not necessarily the point at which: 

• Benefits are maximized; 

• Costs are minimized; 

• Total benefits = total costs (i.e., benefit-cost 

ratio = 1); 

• Benefit-cost ratio is the largest; or 

• The policy is most cost-effective. 

If the regulation were designed to maximize 

benefits, then any policy, no matter how expensive, 

would be justified if it produced any benefit, no 

matter how small. Similarly, minimizing costs 

would, in most cases, simply justify no action at all. 

A benefit-cost ratio equal to one is equivalent to 

saying that the benefits to society would be exactly 

offset by the cost of implementing the policy. 

This implies that society is indifferent between 

no regulation and being regulated; hence, there 

would be no net benefit from adopting the policy. 

Maximizing the benefit-cost ratio is not optimal 

either. Two policy options could yield equivalent 

benefit-cost ratios but have vastly different net 

benefits. For example, a policy that cost $100 

million per year but produced $200 million in 

benefits has the same benefit-cost ratio as a policy 

that cost $100,000 but produced $200,000 in 

benefits, even though the first policy produces 

substantially more net benefit for society.31 Finally, 

finding the most cost-effective policy has similar 

problems because the cost-effectiveness ratio can 

be seen as the inverse of the benefit-cost ratio. A 

policy is cost effective if it meets a given goal at 

least cost — i.e., minimizes the cost per unit of 

benefit achieved. Cost-effectiveness analysis (CEA) 

can provide useful information to supplement 

existing BCA and may be appropriate to rank 

policy options when the benefits are fixed and 

cannot be monetized, but it provides no guidance 

in setting an environmental standard or goal. 

Conceptually, net social benefits will be maximized 

if regulation is set such that emissions are reduced up 

to the point where the benefit of abating one more 

unit of pollution (i.e., marginal social benefit)

32 

is equal to the cost of abating an additional unit 

(i.e., marginal abatement cost).33 If the marginal 

31	 Benefit-cost ratios are useful when choosing one or more policy options 

subject to a budget constraint. For example, consider a case where five 

options are available and the budget is $1,000. The first option will cost 

$1,000 and will deliver benefits of $2,000. Each of the other four will 

cost $250 and deliver benefits of $750. If options are selected according 

to the net benefits criterion, the first option will be selected, because 

its net benefits are $1,000 while the net benefits of each of the other 

options are $500. However if options are selected by the benefit-cost 

ratio criterion, the other four options will be selected, as each of their 

benefit-cost ratios equal 3, versus a benefit-cost ratio of 2 for the first 

option. In this case, choosing options by the net benefits criterion will 

yield $1,000 in total net benefits, while choosing options by the benefitcost ratio criterion will yield $500 in total net benefits. In most cases, 

choosing options in decreasing order of benefit-cost ratios will yield 

the largest possible net benefits given a fixed budget. This method will 

guarantee the optimal solution if the benefits and costs of each option 

are independent, and if each option can be infinitely subdivided: simply 

select the options in decreasing order of their benefit-cost ratios and 

once the budget is exceeded subdivide the last option selected such 

that the budget constraint is met exactly (see Dantzig 1957). Also note 

that this strategy does not require measuring benefits and costs in the 

same units, which means that it is directly useful for CEA (Hyman and 

Leibowitz 2000), while the net-benefit criterion is not. 

32	 The benefits of pollution reduction are the reduced damages from 

being exposed to pollution. Therefore, the marginal social benefit of 

abatement is measured as the additional reduction in damages from 

abating one more unit of pollution. 

33	 The idea that a given level of abatement is efficient — as opposed to 

abating until pollution is equal to zero — is based on the economic 

concept of diminishing returns. For each additional unit of abatement, 

marginal social benefits decrease while marginal social costs of that 

abatement increase. Thus, it only makes sense to continue to increase 

abatement until the point where marginal abatement benefits and 

marginal costs are just equal. Any abatement beyond that point will incur 

more additional costs than benefits. (Alternatively, one can understand 

the efficient level of abatement as the amount of regulation that achieves 

the efficient level of pollution. If one considers a market for pollution, the 

socially-efficient outcome would be the point where the marginal WTP 

for pollution equals the marginal social cost of polluting.) 

A-14 Guidelines for Preparing Economic Analyses | December 2010 
Appendix A Economic Theory

benefits are greater than the marginal costs, then 

additional reductions in pollution will offer greater 

benefits than costs, and society will be better off. If 

the marginal benefits are less than marginal costs, 

then additional reductions in pollution will cost 

society more than they provide in benefits, and will 

make society worse off. When the marginal cost 

of abatement is equal to society’s marginal benefit, 

no gains can be made from changing the level of 

pollution reduction, and an efficient aggregate level 

of emissions is achieved. In other words, a pollution 

reduction policy is at its optimal, most economically 

efficient point when the marginal benefits equal the 

marginal costs of the rule.34 

The condition that marginal benefits must 

equal marginal costs assumes that the initial 

pollution reduction produces the largest 

benefits for the lowest costs. As pollution 

reduction is increased (i.e., regulatory 

stringency is increased), the additional benefits 

decline and the additional costs rise. While 

it is not always true, a case can be made that 

the benefits of pollution reduction follow this 

behavior. The behavior of total abatement 

costs, however, will depend on how the 

pollution reduction is distributed among 

the polluters since firms may differ in their 

ability to reduce emissions. The aggregate 

marginal abatement cost function shows the 

least costly way of achieving reductions in 

emissions. It is equal to the horizontal sum 

of the marginal abatement cost curves for 

the individual polluters. Although each firm 

faces increasing costs of abatement, marginal 

cost functions still vary across sources. Some 

firms may abate pollution relatively cheaply, 

while others require great expense. To achieve 

economic efficiency, the lowest marginal cost of 

abatement must be achieved first, and then the 

next lowest. Pollution reduction is achieved at 

lowest cost only if firms are required to make 

equiproportionate cutbacks in emissions. That 

is, at the optimal level of regulation, the cost 

34	 It is important to reemphasize the word “marginal” in this statement. 

Marginal, in economic parlance, means the extra or next unit of the 

item being measured. If regulatory options could be ranked in order of 

regulatory stringency, then marginal benefits equal to marginal costs 

means that the additional benefits of increasing the regulation to the 

next degree of stringency is equal to the additional cost of that change. 

of abating one more unit of pollution is equal 

across all polluters.35 

Figure A.11 illustrates why the level of pollution 

that sets the marginal benefits and marginal costs 

of abatement equal to each other is efficient.36 

Emissions are drawn on the horizontal axis and 

increase from left to right. The damages from 

emissions are represented by the marginal damage 

(MD) curve. Damages may include the costs 

of worsened human health, reduced visibility, 

lower property values, and loss of crop yields 

or biodiversity. As emissions rise, the marginal 

damages increase. E1

 represents the amount of 

emissions in the absence of regulation on firms. 

The costs of controlling emissions are represented 

by the marginal abatement cost curve (MAC). As 

emissions are reduced below E1

, the marginal cost 

of abatement rises. 

Figure A.11 - Efficient Level of Pollution 

Costs, Damages 

C 

MD 

MAC 

E0 

D 

Emmissions 

E* EX E1 

A 

B 

The total damages associated with emissions 

level E* are represented by the area of the triangle 

AE0

E*, while the total abatement costs are 

represented by area AE1

E*. The total burden on 

35	 Thus a regulation that requires all firms to achieve the same level 

of reduction will probably result in different marginal costs for each 

firm and not be efficient. (See Field and Field 2005 or any other 

environmental economics text for a detailed explanation and example.) 

36	 Figure A.11 illustrates the simplest possible case, where the pollutant 

is a flow (i.e., it does not accumulate over time) and marginal damages 

are independent of location. When pollution levels and damages 

vary by location, then the efficient level of pollution is reached when 

marginal abatement costs adjusted by individual transfer coefficients 

are equal across all polluters. Temporal variability also implies 

an adjustment to this equilibrium condition. In the case of a stock 

pollutant, marginal abatement costs are equal across the discounted 

sum of damages from today’s emissions in all future time periods. In 

the case of a flow pollutant, this condition should be adjusted to reflect 

seasonal or daily variations (see Sterner 2003). 

Guidelines for Preparing Economic Analyses | December 2010 A-15 
A-16 Guidelines for Preparing Economic Analyses | December 2010 

Appendix A Economic Theory

society of this level is equal to the total abatement 

costs of reducing emissions from E1 to E* plus the 

total damages of the remaining emissions, E*. That 

is, the total burden is the darkly shaded triangle, 

E0

AE1. 

Now assume that emissions are something other 

than E*. For example, suppose emissions were 

EX, which is greater than E*. Total damages for 

this level of emissions are equal to the area of the 

triangle BE0

Ex, while total costs of abatement 

to this level is equal to the area CEx

E1. The total 

burden on society of this level is the sum of the 

areas of the darkly shaded and the lightly shaded 

triangles. This means that the excess social cost 

of choosing emissions EX rather than E* is equal 

to the area of the lightly shaded triangle, ABC. 

A similar analysis could be done if emissions 

levels were below level, E*. Here, the additional 

abatement costs would be greater than the decrease 

in damages, resulting in excess social costs. The 

policy that sets the emissions level at E* — at 

the point where marginal benefits of pollution 

reduction (represented by the MD curve) and the 

MAC curve intersect — is economically efficient 

because it imposes the least net cost on, and yields 

the highest net benefits for, society. That is, the 

triangle E0

AE1 is the smallest shaded region that 

can be obtained. 

This section has focused on first-best optimal 

regulation when there are no pre-existing market 

distortions. However, it is important to note that 

realizable policy outcomes will often be “second 

best” due to information constraints, political 

constraints, imperfect competition, and market 

distortions created by tax and other government 

interventions. For example, many of the emissionsbased policies emphasized in these Guidelines 

may be less feasible for addressing nonpoint 

source pollution, such as agriculture, which is less 

observable and more stochastic than emissions 

from point sources. Agriculture is also subject to 

multiple non-environmental policy distortions 

that must be considered in the measurement of the 

social benefits and costs of regulating agriculture. 

A.6 Conclusion 

The purpose of this appendix is to present a 

brief explanation of some of the fundamental 

economics relevant to Chapters 3 through 9. 

It is not intended to provide a comprehensive 

discussion of all microeconomic theory and its 

application to environmental issues. The interested 

reader can turn to undergraduate or graduate level 

textbooks for a more thorough exposition of the 

topics covered here. At the undergraduate level, 

Field and Field (2005) provide an introduction to 

the basic principles of environmental economics. 

Tietenberg’s (2002) and Perman et al.’s (2003) 

presentations are more technical but still used 

primarily for undergraduate courses. Freeman 

(2003) is the standard text for graduate courses 

in environmental economics and deals with 

the methodology of non-market valuation. 

Supplemental texts that provide a good handle 

on environmental economics with less technical 

detail include Stavins (2000a), and Portney and 

Stavins (2000). Finally, general microeconomics 

textbooks (Mankiw 2004, and Varian 2005 at the 

undergraduate level; and Mas-Colell et al. 1995, 

Kreps 1990, and Varian 2005 at the graduate 

level), and applied welfare economics textbooks 

( Just et al. 2005) are useful references as well. 
Appendix B 

Mortality Risk 

Valuation Estimates 

Some EPA policies are designed to reduce the risk of contracting a potentially 

fatal health effect such as cancer. Reducing these risks of premature death 

provides welfare increases to those individuals affected by the policy. These 

policies generally provide marginal changes in relatively small risks. That 

is, these policies do not provide assurance that an individual will not die 

prematurely from environmental exposures; rather, they marginally reduce the probability 

of such an event. For BCA, analysts generally aggregate these small risks over the affected 

population to derive the number of statistical lives saved (or the number of statistical 

deaths avoided) and then use a “value of statistical life” (VSL) to express these benefits in 

monetary terms. 

The risk reductions themselves can generally be classified according to the characteristics 

of the risk in question (e.g., voluntariness or controllability) and the characteristics of the 

affected population (e.g., age and health status). These dimensions may affect the value of 

reducing mortality risks. Ideally the VSL would account for all possible risk and demographic 

characteristics that matter. It would be derived from the preferences of the population 

affected by the policy, based on the type of risk that the policy is expected to reduce. For 

example, if a policy were designed to remove carcinogens at a suburban hazardous waste site, 

the ideal measure would represent the preferences for reduced cancer risks for the exposed 

population in the area and would reflect the changes in life expectancy that would result. 

Unfortunately, time and resource constraints make it difficult if not impossible to obtain such 

unique valuation estimates for each EPA policy. Instead, analysts need to draw from existing 

VSL estimates obtained using well-established methods (see Chapter 7). 

This appendix describes the default VSL estimate currently used by the Agency and its 

derivation, as well as how analysts should characterize and assess benefit transfer issues 

that may arise in its application. Benefit transfer considerations that are common to all 

valuation applications, including the effect of most demographic characteristics of the 

study and policy populations, are described in Chapter 7 Section 7.3 and will not be 

repeated here. 

B.1 Central Estimate of VSL VSL estimate. Fitting a Weibull distribution to these 

Table B.1 contains the VSL estimates that currently estimates yields a central estimate (mean) of $7.4 

form the basis of the Agency’s recommended central million ($2006) with a standard deviation of $4.7 

Guidelines for Preparing Economic Analyses | December 2010 B-1 
B-2 Guidelines for Preparing Economic Analyses | December 2010 

Appendix B Mortality Risk Valuation Estimates

Table B.1 - Value of Statistical Life Estimates (mean values in millions 

of 2006 dollars) 

Study Method Value of Statistical Life 

Kniesner and Leeth (1991 - US) Labor Market $0.85 

Smith and Gilbert (1984) Labor Market $0.97 

Dillingham (1985) Labor Market $1.34 

Butler (1983) Labor Market $1.58 

Miller and Guria (1991) Contingent Valuation $1.82 

Moore and Viscusi (1988) Labor Market $3.64 

Viscusi, Magat, and Huber (1991) Contingent Valuation $4.01 

Marin and Psacharopoulos (1982) Labor Market $4.13 

Gegax et al. (1985) Contingent Valuation $4.86 

Kniesner and Leeth (1991 - Australia) Labor Market $4.86 

Gerking, de Haan, and Schulze (1988) Contingent Valuation $4.98 

Cousineau, Lecroix, and Girard (1988) Labor Market $5.34 

Jones-Lee (1989) Contingent Valuation $5.59 

Dillingham (1985) Labor Market $5.71 

Viscusi (1978) Labor Market $6.07 

R.S. Smith (1976) Labor Market $6.80 

V.K. Smith (1983) Labor Market $6.92 

Olson (1981) Labor Market $7.65 

Viscusi (1981) Labor Market $9.60 

R.S. Smith (1974) Labor Market $10.57 

Moore and Viscusi (1988) Labor Market $10.69 

Kniesner and Leeth (1991 - Japan) Labor Market $11.18 

Herzog and Schlottman (1987) Labor Market $13.36 

Leigh and Folsom (1984) Labor Market $14.21 

Leigh (1987) Labor Market $15.31 

Garen (1988) Labor Market $19.80 

Derived from U.S. EPA (1997a) and Viscusi (1992). Updated to 2006$ with GDP deflator. 

million.1, 2 EPA recommends that the central 

estimate, updated to the base year of the analysis, 

be used in all benefits analyses that seek to quantify 

mortality risk reduction benefits. 

This approach was vetted and endorsed by the 

Agency when the 2000 Guidelines for Preparing 

1 The VSL was updated from the $4.8 million ($1990) estimate 

referenced in the 2000 Guidelines by adjusting the individual study 

estimates for inflation using a GDP deflator and then fitting a Weibull 

distribution to the estimates. The updated Weibull parameters are: 

location = 0, scale = 7.75, shape = 1.51 (updated from location = 0; 

scale = 5.32; shape = 1.51). The Weibull distribution was determined 

to provide the best fit for this set of estimates. See U.S. EPA 1997a for 

more details. 

2	 This VSL estimate was produced using the GDP deflator inflation 

index. Some economists prefer using the Consumer Price Index (CPI) 

in some applications. The key issue for EPA analysts is to ensure that 

the chosen index is used consistently throughout the analysis. 

Economic Analyses were drafted.3 It remains EPA’s 

default guidance for valuing mortality risk changes 

although the Agency has considered and presented 

alternatives.4 

3	 The studies listed in Table B.1 were published between 1974 and 

1991, and most are hedonic wage estimates that may be subject to 

considerable measurement error (Black et al. 2003, and Black and 

Kniesner 2003). Although these were the best available data at the time, 

they are sufficiently dated and may rely on obsolete preferences for risk 

and income. The Agency is currently considering more recent studies 

as it evaluates approaches to revise its guidance. 

4	 EPA is in the process of revisiting this guidance and has recently engaged 

the SAB-EEAC on several issues including the use of meta-analysis as a 

means of combining estimates and approaches for assessing mortality 

benefits when changes in longevity may vary widely (U.S. EPA 2006d). 

The Agency is committed to using the best available science in its analyses 

and will revise this guidance in response to SAB recommendations (see 

U.S. EPA 2007g for recent SAB recommendations). 
Guidelines for Preparing Economic Analyses | December 2010 B-3 

Appendix B Mortality Risk Valuation Estimates

 

 

 

 

 

 

 

 

B.2 Other VSL Information 

For most of mortality risk reductions EPA 

uniformly applies the VSL estimate discussed 

above. For a period of time (2004-2008), the Office 

of Air and Radiation (OAR) valued mortality 

risk reductions using a VSL estimate derived 

from a limited analysis of some of the available 

studies. OAR arrived at a VSL using a range of $1 

million to $10 million (2000$) consistent with 

two meta-analyses of the wage-risk literature. The 

$1 million value represented the lower end of the 

interquartile range from the Mrozek and Taylor 

(2002) meta-analysis of 33 studies. The $10 million 

value represented the upper end of the interquartile 

range from the Viscusi and Aldy (2003) metaanalysis of 43 studies. The mean estimate of $5.5 

million (2000$) was also consistent with the mean 

VSL of $5.4 million estimated in the Kochi et al. 

(2006) meta-analysis. However, the Agency neither 

changed its official guidance on the use of VSL in 

rulemakings nor subjected the interim estimate to 

a scientific peer-review process through the Science 

Advisory Board (SAB) or other peer-review group. 

During this time, the Agency continued work 

to update its guidance on valuing mortality risk 

reductions. EPA commissioned a report from 

meta-analytic experts to evaluate methodological 

questions raised by EPA and the SAB on 

combining estimates from the various data 

sources. In addition, the Agency consulted several 

times with the SAB Environmental Economics 

Advisory Committee (SAB-EEAC) on the issue. 

With input from the meta-analytic experts, the 

SAB-EEAC advised the Agency to update its 

guidance using specific, appropriate meta-analytic 

techniques to combine estimates from unique data 

sources and different studies, including those using 

different methodologies such as wage-risk and 

stated preference (U.S. EPA 2007g). 

Until updated guidance is available, the Agency 

determined that a single, peer-reviewed estimate 

applied consistently best reflects the SAB-EEAC 

advice received to date. Therefore, the VSL 

described above that was vetted and endorsed by 

the SAB should be applied in relevant analyses 

while the Agency continues its efforts to update its 

guidance on this issue. 

B.3 Benefit Transfer 

Considerations 

Policy analysts valuing mortality risk reductions 

should account for differences in risk and 

population characteristics between the policy and 

study scenarios and their potential effect on the 

overall results. The ultimate objective of the benefit 

transfer exercise is to account for all of the factors 

that significantly affect the value of mortality risk 

reduction in the context of the policy. Analysts 

should carefully consider the implications of 

correcting for some relevant factors, but not for 

others, recognizing that it may not be feasible to 

account for all factors. 

B.4 Adjustments Associated 

with Risk Characteristics 

Risk characteristics appear to affect the value that 

people place on risk reduction. A large body of 

work identifies eight dimensions of risk that affect 

human risk perception:5 

• voluntary/involuntary 

• ordinary/catastrophic 

• delayed/immediate 

• natural/man-made 

• old/new 

• controllable/uncontrollable 

• necessary/unnecessary 

• occasional/continuous 

Transferring VSL estimates among these categories 

may introduce bias. There have been some recent 

efforts attempting to quantitatively assess these 

sources of bias.6

 These studies generally conclude 

that voluntariness, control and responsibility 

affect individual values for safety, although there 

is no consensus on the direction and magnitude of 

these effects. 

5 A review of issues in risk perception is found in Lichtenstein and 

Slovic (2006). Other informative sources include Slovic (1987), Rowe 

(1977), Otway (1977), and Fischoff et al. (1978). 

6 Examples include Hammitt and Liu (2004), Sunstein (1997), Mendeloff 

and Kaplan (1990), McDaniels et al. (1992), Savage (1993), Jones-Lee 

and Loomes (1994, 1995, 1996), and Covey et al. (1995). 
B-4 Guidelines for Preparing Economic Analyses | December 2010 

Appendix B Mortality Risk Valuation Estimates

In addition, environmental risks may differ from 

those that form the basis of VSL estimates in many 

of these dimensions. Occupational risks, for example, 

are generally considered to be more voluntary in 

nature than are environmental risks, and may be 

more controllable. As part of the Agency’s review 

of our mortality risk guidance we are evaluating the 

literature from which the studies are drawn. 

Support for quantitative adjustments in the 

empirical literature is lacking for most of these 

factors. The SAB reviewed an Agency summary 

of the available empirical literature on the effects 

of risk and population characteristics on WTP 

for mortality risk reductions (U.S. EPA 2000d). 

The SAB review concludes that among the 

demographic and risk factors that might affect 

VSL estimates, the current literature can only 

support empirical adjustments related to the 

timing of the risk. The review supports making 

the following adjustments to primary benefits 

estimates: (1) adjusting WTP estimates to account 

for higher future income levels, though not for 

cross-sectional differences in income; and (2) 

discounting risk reductions that are brought about 

in the future by current policy initiatives (that is, 

after a cessation lag), using the same rates used 

to discount other future benefits and costs. All 

other adjustments, if made, should be relegated to 

sensitivity analyses. 

Increases in income over time. The economics 

literature shows that the income elasticity of WTP 

to reduce mortality risk is positive, based on crosssectional data. As a result, benefits estimates of 

reduced mortality risk accruing in future years may 

be adjusted to reflect anticipated income growth, 

using the range of income elasticities (0.08, 0.40 

and 1.0) employed in The Benefits and Costs of the 

Clean Air Act, 1990-2010.7 Recent EPA analyses 

have assumed a triangular distribution from 

these values and used the results in a probabilistic 

assessment of benefits.8

 At the time of this 

writing, EPA is engaged in a consultation with the 

SAB-EEAC on the appropriate range of income 

elasticities and will update this guidance as needed. 

7	 For details see Kleckner and Neuman (2000). 

8	 See, for example, pp. 6-84 of the Final Economic Analysis for the Stage 

2 DBPR (U.S. EPA 2005a). 

Timing of reduced exposure and reduced risk. 

Many environmental policies are targeted at 

reducing the risk of effects such as cancer, where 

there may be an extended period of time between 

the reduced exposure and the reduction in the risk 

of death from the disease.9

 This delay between the 

change in exposure and realization of the reduced 

risk may affect the value of that risk reduction. 

Most existing VSL estimates are based on risks 

of relatively immediate fatalities making them 

an imperfect fit for a benefits analysis of many 

environmental policies. Economic theory suggests 

that reducing the risk of a delayed health effect 

will be valued less than reducing the risk of a more 

immediate one, when controlling for other factors. 

B.5 Effects on WTP 

Associated with Demographic 

Characteristics 

Two population characteristics are particularly 

noteworthy for their potential effect on mortality 

risk valuation estimates: age and health status of 

the exposed population. In September 2006, the 

Agency requested an additional advisory from the 

SAB-EEAC on issues related to valuing changes 

in life expectancy for which age and baseline 

health status are close correlates.10 Because the 

outcome of this review is not yet available, we 

focus here on previous advice received from the 

SAB on related questions. 

Age. It has sometimes been posited that older 

individuals should have a lower WTP for changes in 

mortality risk given the fewer years of life expectancy 

remaining compared to younger individuals. This 

hypothesis may be confounded, however, by the 

finding that older persons reveal a greater demand 

for reducing mortality risks and hence have a greater 

implicit value of a life year (Ehrlich and Chuma 

1990). Several authors have attempted to explore 

9	 Although latency is defined here as the time between exposure and 

fatality from illness, alternative definitions may be used in other 

contexts. For example “latency” may refer to the time between exposure 

and the onset of symptoms. These symptoms may be experienced for 

an extended period of time before ultimately resulting in fatality. 

10	 U.S. EPA (2006d) summarizes much of the literature related to the 

effects of age and health status on WTP for changes in mortality risk 

and includes the charge questions put to the SAB-EEAC on these 

issues. 
Guidelines for Preparing Economic Analyses | December 2010 B-5 

Appendix B Mortality Risk Valuation Estimates

potential differences in mortality risk valuation 

estimates associated with differences in the average 

age of the affected population using theoretical 

models of life-cycle consumption.11 In general 

this literature has shown that the relationship 

between age and WTP for mortality risk changes 

is ambiguous, requiring strong assumptions to 

even sign the relationship.12 Empirical evidence is 

also mixed. A number of empirical studies (mostly 

hedonic wage studies) suggest that the VSL follows 

a consistent “inverted-U” life-cycle, peaking in 

the region of mean age.13 Others find no such 

statistically significant relationship and still others 

show WTP increasing with age.14 Stated preference 

results are also mixed, with some studies showing 

declining WTP for older age groups and others 

finding no statistically significant relationship 

between age and WTP.15 

In spite of the ambiguous relationship between 

age and WTP, two alternative adjustment 

techniques have been derived from this literature. 

The first technique, value of statistical life-years 

(VSLY), is derived by dividing the estimated VSL 

by expected remaining life expectancy. This is by 

far the most common approach and presumes 

that: (1) the VSL equals the sum of discounted 

values for each life year; and (2) each life year 

has the same value. This method was applied as 

an alternative case in an effort to evaluate the 

sensitivity of the benefits estimates prepared for 

EPA’s retrospective and prospective studies of the 

costs and benefits of the Clean Air Act (U.S. EPA 

1997a, and U.S. EPA 1999). 

11	 See, for example, Shepard and Zeckhauser (1982), Rosen (1988), 

Cropper and Sussman (1988, 1990), and Johannson (2002). 

12	 See Evans and Smith (2006) for a recent summary. 

13	 See Jones-Lee et al. (1985), Aldy and Viscusi (2008), Viscusi and Aldy 

(2007a and b), and Kniesner et al. (2006). 

14	 Viscusi and Aldy (2003) review more than 60 studies of mortality risk 

estimates from 10 countries and discuss eight hedonic wage studies 

that explicitly examine the age-WTP relationship. Only five of the eight 

studies found a statistically significant, negative relationship between 

age and the return to risk. Smith et al. (2004) and Kniesner et al. 

(2006) find that WTP increases with age. 

15	 Krupnick et al. (2002) report that WTP for mortality risk reductions 

changes significantly with age after age 70. Alberini et al. (2004) find 

no difference in the WTP for younger age groups and find a 20 percent 

reduction for those aged 70 and older. However this difference was not 

statistically significant. 

A second technique is to apply a distinct value 

or suite of values for mortality risk reduction 

depending on the age of incidence. However, there 

is relatively little available literature upon which to 

base such adjustments.16 

Neither approach enjoys general acceptance 

in the literature as they both require large 

assumptions to be made, some of which have 

been contradicted in empirical studies. Since 

published support is lacking, neither approach is 

recommended at this time. 

Analysts are advised to note the age distribution 

of the affected population when possible, 

especially when children are found to be a 

significant portion of the affected population.17 

Although the literature on the valuation of 

children’s health risks is growing, there is still 

not enough information currently to derive agespecific valuation estimates. 

Health status. Individual health status may also 

affect WTP for mortality risk reduction. This 

is an especially relevant factor for valuation of 

environmental risks because individuals with 

impaired health are often the most vulnerable 

to mortality risks from environmental causes. 

For example, particulate air pollution appears 

to disproportionately affect individuals in an 

already impaired state of health. Health status 

is distinct from age (a “quality versus quantity” 

distinction) but the two factors are clearly 

correlated and therefore must be addressed 

jointly when considering the need for an 

adjustment. Again, both the theoretical and 

empirical literatures on this point are mixed 

with some studies showing a declining WTP 

for increased longevity with a declining baseline 

health state (Desvousges et al. 1996) and other 

16	 This second approach was illustrated in one EPA study (U.S. EPA, 

2002d) for valuation of air pollution mortality risks, drawing upon 

adjustments measured in Jones-Lee et al. (1985). 

17	 See U.S. EPA (2003a) for more information on the valuation of 

children’s health risks. OMB’s Circular A-4 advises agencies to use 

estimates of mortality risk valuation for children that are at least as 

large as those used for adult populations (OMB 2003). 
B-6 Guidelines for Preparing Economic Analyses | December 2010 

Appendix B Mortality Risk Valuation Estimates

studies showing no statistically significant effects 

(Krupnick et al. 2002).18 

Application of existing VSLY approaches 

implicitly assumes a linear relationship in which 

each discounted life year is valued equally. As 

OMB (1996) notes “current research does not 

provide a definitive way of developing estimates of 

VSLY that are sensitive to such factors as current 

age, latency of effect, life years remaining, and 

social valuation of different risk reductions.” The 

second alternative, applying a suite of values for 

these risks, lacks broad empirical support in the 

economics literature. However, the potential 

importance of this benefit transfer factor suggests 

that analysts consider sensitivity analysis when 

risk data — essentially risk estimates for specific 

age groups — are available. An emerging literature 

on the value of life expectancy extensions, based 

primarily on stated preference techniques, is 

beginning to help establish a basis for valuation in 

cases where the mortality risk reduction involves 

relatively short extensions of life.19 

B.6 Conclusion 

Due to current limitations in the existing 

economic literature, these Guidelines conclude 

that, for the present time, the appropriate default 

approach for valuing these benefits is provided 

by the central VSL estimate described earlier. 

However, analysts should carefully present the 

limitations of this estimate. Economic analyses 

should also fully characterize the nature of the 

risk and populations affected by the policy action, 

and should confirm that these parameters are 

18	 The fields of health economics and public health often account for 

health status through the use of quality-adjusted life years (QALYs) 

or disability adjusted life years (DALYs). These measures have their 

place in evaluating the cost-effectiveness of medical interventions and 

other policy contexts, but have not been fully integrated into the welfare 

economic literature on risk valuation. More information on QALYs can 

be found in Gold et al. (1996) and additional information on DALYs can 

be found in Murray (1994). 

19	 It should be noted that many observers have expressed reservations 

over adjusting the value of mortality risk reduction on the basis of 

population characteristics such as age. One of the ethical bases 

for these reservations is a concern that adjustments for population 

characteristics imply support for variation in protection from 

environmental risks. Another consideration is that existing economic 

methods may not capture social WTP to reduce health risks. Chapter 9 

details how some these considerations may be informed by a separate 

assessment of equity. 

within the scope of the situations considered in 

these Guidelines. While a qualitative discussion 

of these issues is generally warranted in EPA 

economic analyses, analysts should also consider 

a variety of quantitative sensitivity analyses on a 

case-by-case basis as data allow. The analytical goal 

is to characterize the impact of key attributes that 

differ between the policy and study cases. These 

attributes, and the degree to which they affect the 

value of risk reduction, may vary with each benefit 

transfer exercise, but analysts should consider the 

characteristics described above (e.g., age, health 

status, voluntariness of risk, and latency) and 

values arising from altruism. 

As the economic literature in this area 

evolves, WTP estimates for mortality risk 

reductions that more closely resemble those 

from environmental hazards may support 

more precise benefit transfers. Literature on 

the specific methods available to account for 

individual benefit-transfer considerations will 

also continue to develop. In addition, EPA will 

continue to conduct periodic reviews of the 

risk valuation literature and will reconsider and 

revise the recommendations in these Guidelines 

accordingly. EPA will seek advice from the SAB 

as guidance recommendations are revised. 
Appendix C 

Accounting for Unemployed 


Labor in Benefit-Cost Analysis
 

I

n very rare cases, the implementation of a rule or policy may result in the job implications 

for the structurally unemployed. This appendix (under development) will review the 

literature on estimating the value unemployed individuals place on their time and will 

describe what estimates of the costs of labor are most appropriate for use in regulatory 

impact analysis (RIA) under this scenario. 

Guidelines for Preparing Economic Analyses | December 2010 C-1 
C-2 Guidelines for Preparing Economic Analyses | January 2011 
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References
Author Index
 

Acharya, G., 7-17, 7-23
 

Adamowicz, W. 7-18, 7-38, 7-39, 7-40, 7-44
 

Adams, R., 7-20, 7-23
 

Adler, N., 10-16, 10-19, 10-20
 

Alberini, A., 4-14, 4-19, 7-10, 7-11, 7-12, 


7-39, B-5
 

Aldy, J., 7-10, 7-11, 7-29, B-3, B-5
 

Allen, P., 7-43
 

Anderson, L., 7-18
 

Anderson, R., 4-5
 

Anselin, L., 7-31
 

Apelberg B., 10-19
 

Arnold, F., 4-11
 

Arora, S., 4-14, 4-19, 10-5
 

Arrow, K., 6-1, 6-12, 6-13, 6-14, 6-15, 6-18, 7-36, 


7-40, 10-16, A-6
 

Austin, D., 4-14
 

Aydede, S., 6-15
 

Baden, B., 10-13, 10-18
 

Balistreri, E., 7-40
 

Ballard, C., 8-5
 

Banzhaf, H., 7-18, 10-5, 10-6
 

Banzhaf, S., 7-16, 7-17, 7-41
 

Barbier, E., 7-16, 7-17, 7-23, A-4
 

Bartik, T., 7-30
 

Bateman, I., 6-17, 7-48
 

Baumol, W. 4-1, 5-7, A-4
 

Becker, R., 8-20
 

Been, V., 10-5, 10-6, 10-17
 

Begg, C., 7-48
 

Bellinger, D., 10-7
 

Bell, F., 7-17
 

Benson, E., 7-18
 

Bento, A., 8-5
 

Bergstrom, J., 7-46, 7-48
 

Berman, E., 9-8
 

Berndt, E., 7-22
 

Bin, O., 7-18
 

Birdsall, N., 6-15
 

Bishop, R., 7-38, 7-41
 

Black, D., 7-10, 7-29, B-2
 

Blackorby, C., 10-16
 

Blair, P., 8-17
 

Blomquist, G., 7-10, 7-32, 7-43
 

Boadway, R., 7-8
 

Boardman, A., 1-5, 6-10, 7-50, 8-4
 

Bockstael, N., 7-18, 7-19, 7-25, 7-29, A-12
 

Boer, T., 10-5
 

Bohi, D., 4-6
 

Guidelines for Preparing Economic Analyses | May 2014 E-1 
E-2 Guidelines for Preparing Economic Analyses | May 2014 

Author Index

 

 

Bovenberg, A., 4-17, 8-5
 

Bowen, W., 10-15
 

Boxall, P., 7-18, 7-44
 

Boyd, J., 4-12, 7-16, 7-17, 7-18, 7-41
 

Boyle, K., 7-37, 7-39, 7-40, 7-41
 

Brajer, V., 10-17
 

Brännlund, R., 5-8
 

Brouhle, K., 4-13, 4-19, 4-21, 5-15
 

Brouwer, R., 7-46
 

Brown, G., 7-30, 7-39
 

Brown, Jr., G., 7-17, 7-18
 

Brown, T., 7-37, 7-40, 7-42
 

Bruce, N., 7-8
 

Bryant, B., 10-13
 

Buchanan, Jr., J., 7-23
 

Bui, L., 9-8
 

Bullard, R., 10-5
 

Burger, J., 10-16
 

Burtraw, D., 4-6, 4-8, 8-11
 

Butler, R., B-2
 

Cairns, J., 6-18
 

Callan, S., 8-8
 

Cameron, T., 7-15, 7-38, 7-39, 7-40, 10-18
 

Carbone, J., 8-21
 

Card, D., 7-45
 

Carlson, C., 4-6, 8-11
 

Carlsson, F., 7-43
 

Carson, R., 5-7, 7-37, 7-41, 7-42, 7-44, 7-48
 

Carthy, T., 7-10
 

Cason, T., 4-14, 4-19, 10-5
 

Chakraborty, J., 10-14, 10-18, 10-19
 

Champ, P., 7-42, 7-43
 

Chestnut, L., 4-6, 4-7, 7-14, 7-37, 7-49
 

Chiang, A., 8-16
 

Chuma, H., B-4
 

Citro, C.F., 10-11
 

Clapp, J., 7-30
 

Clark, C., 7-17
 

Coase, R., 4-4, A-5
 

Cohen, M., 4-14, 4-19
 

Cooper, J., 7-40
 

Cordell, H., 7-18
 

Costanza, R., 7-18
 

Costello, C., 7-17
 

Coursey, D., 10-18
 

Cousineau, J., B-2
 

Covey, J., B-3
 

Coyne, A., 7-18
 

Creel, M., 7-18
 

Crocker, T., 7-20, 7-31, 7-32
 

Cropper, M., 6-15, 7-11, 7-14, 7-32, B-5
 

Crowder, L., 7-16 

Guidelines for Preparing Economic Analyses | May 2014 E-3 

Author Index

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Cummings, R., 7-40, 7-43
 

Daily, G., 7-15
 

Dale, L., 7-30
 

Damon, L., 4-21
 

Danielson, L., 7-30
 

Dantzig, G., A-14
 

Dasgupta, P., 6-12, 7-13
 

de Groot, R., 7-16
 

de Haan, M., 7-29, B-2
 

de Moojii, R., 4-17, 8-5
 

DeShazo, J., 7-15
 

Desvousges, W., 7-14, 7-24, 7-39, 7-46, B-5
 

Diamond, P., 7-42
 

Dietz, T., 10-20
 

Dillingham, A., B-2
 

Dixon, P., 9-18 


Donaldson, D., 10-16
 

Donatuto, J., 10-20
 

Dreyfus, M., 7-30 


Durlauf, S., 7-19 


Dutton, J., 8-12
 

Eeckhoudt, L., 7-30
 

Ehrlich, I., B-4
 

Ekeland, I., 7-31 


Ellerman, A., 4-7, 8-11
 

Ellerman, D., 4-3 


Ellis, G., 7-17, 7-23
 

Englin, J., 7-44
 

Eom, Y., 7-44
 

Ethier, R., 7-40
 

Evans, D., 7-16 


Evans, M., 7-30, B-5
 

Fann, N., 10-15, 10-19
 

Farley, J., 6-11
 

Faustmann, M., 7-17
 

Feather, P., 7-25, 7-27, 7-47
 

Field, B., 4-1, A-15, A-16
 

Field, M., 4-1, A-15, A-16
 

Finnoff, D., 7-16
 

Fischhoff, B., 7-36, 7-41
 

Fischoff, B., B-3
 

Fisher, A., 7-17, 7-23
 

Fisher, B., 4-11, 7-15, 7-16
 

Florax, R. , 7-45, 7-46
 

Flores, N., 5-7, 7-41
 

Folsom, R., B-2
 

Fowlie, M., 10-13
 

Frederick, S., 6-18
 

Freeman, A., 6-8, 6-15, 6-18, 7-7, 7-8, 7-12, 7-14, 


7-16, 7-18, 7-31, 7-32, 7-38 7-44, A-16
 

Freeman, M., 6-17
 

Fullerton, D., 4-9, 4-12, 8-5, 10-5, 10-8
 

Furby, L., 7-36, 7-41
 
E-4 Guidelines for Preparing Economic Analyses | May 2014 

Author Index

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Galdo, J., 7-10, B-2
 

Gallet, C., 7-43 


Gangopadhyay, S., 4-19
 

Garen, J., B-2
 

Garrod, G., 7-18
 

Gegax, D. , B-2
 

Geoghegan, J., 7-18
 

Gerking, S., 7-29, B-2
 

Gilbert, C., B-2
 

Gintis, H., 6-11
 

Glass, T., 10-7
 

Gochfeld, M., 10-16
 

Goetz, R., 4-16
 

Gold, M., B-6
 

Gollier C., 6-16, 6-17
 

Goulder, L., 4-3, 4-9, 4-16, 4-17, 4-21, 8-5, 8-12
 

Grainger, C., 10-6
 

Gray, W., 10-5
 

Griffiths, C., 4-19, 4-21, 5-15
 

Groom, B. 6-16, 6-17, 6-18, 6-19
 

Guo, J., 6-13
 

Gupta, F., 10-5, 10-6, 10-17
 

Guria, J., B-2
 

Guyse, J., 6-11
 

Haab, T., 7-25, 7-27, 7-44
 

Hahn, R., 4-15
 

Hall, J. 10-17
 

Hamilton, J., 4-14, 10-5
 

Hammack, J., 7-17
 

Hammitt, J., 7-10, 7-12, 7-14, 7-30, B-3
 

Hanemann, W., 7-7, 7-38 


Haninger, K., 7-14
 

Hanley, N., 3-2, 7-7, 7-18
 

Hansen, A., 6-16
 

Harford, J., 7-32 


Harper, B., 10-20
 

Harrington, W., 4-5, 4-8, 4-21, 5-7, 7-32
 

Harris, J., 4-15
 

Hauber, A., 7-27
 

Hay, M., 7-18
 

Hazilla, M., 8-5
 

Heal, G., 6-17
 

Heberlein, T., 7-38, 7-42
 

Heckman, J., 7-31
 

Helfand, G., 4-3
 

Hellerstein, D., 7-47 


Henderson, N., 6-17
 

Hepburn, C., 6-17, 6-19
 

Herriges, J., 7-44 


Herzog, H., B-2
 

Heyes, A., 5-8
 

Hicks, J., A-10
 
Guidelines for Preparing Economic Analyses | May 2014 E-5 

Author Index

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Hicks, R., 7-27
 

Hoban, J., 7-41 


Holland, S., 10-13
 

Holmes, T., 7-38 


Huang, J., 7-29, 7-44 


Hunter, J., 7-46
 

Huppert, D., 7-40
 

Hyman, J., A-14
 

Ibbotson, R., 6-10
 

Iceland, J., 10-11
 

Ihlanfeldt, K., 7-30
 

Iovanna, R., 7-16
 

Irwin, E., 7-18, 7-19
 

Israel, B., 10-22
 

Jacobsen, M., 8-5
 

Jaffe, A., 4-5, 5-8, 9-10
 

Jakus, P., 7-18, 7-26
 

James, M., 7-38, 7-39
 

Jena, A., 11-3
 

Johanneson, P., 7-14
 

Johannson, P., B-5
 

Johnson, F., 7-39 


Johnston, R., 7-43
 

Johnstone, N., 10-5
 

Jones-Lee, M., 7-11, B-2, B-3, B-5
 

Jones, A., 7-48
 

Jorgenson, D., 4-17, 8-5
 

Joskow, P., 4-7
 

Jung, C., 4-5
 

Just, R., 7-7, 7-8, 8-4, 9-17, 10-16, A-10, A-16
 

Kahn, J., 4-1, 4-3, 4-16
 

Kanninen, B., 7-39, 7-40
 

Kaoru, Y., 7-27, 7-28, 7-48
 

Kaplan, R., B-3
 

Karp, L., 6-11
 

Kealy, M., 7-42 


Keeler, A., 8-5
 

Keller, L., 6-11
 

Kelman, S., A-7
 

Kerr, S., 4-5, 5-8
 

Kerry Smith, V., 7-28
 

Khanna, M., 4-13, 4-14, 4-19, 4-21
 

King, M., 7-40 


Kleckner, N., B-4
 

Kling, C., 7-27, 7-44
 

Kniesner, T., 7-11, 7-29, B-2, B-5
 

Kochi, I., 7-10, B-3
 

Kokoski, M., 8-4
 

Kolb, J., 6-18
 

Kolm, S., 10-16
 

Kolstad, C., 4-1
 

Konar, S., 4-14, 4-19
 
E-6 Guidelines for Preparing Economic Analyses | May 2014 

Author Index

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Kopp, R., 7-23, 8-2, 8-5
 

Kramer, R., 7-17, 7-23
 

Kreps, D.M., A-16
 

Kriström, B., 7-40
 

Krueger, A., 7-45
 

Krupnick, A., 1-3, 7-12, 7-16, 7-18,7-23, 7-41, 


B-5, B-6
 

Laibson, D., 6-11
 

Israel, B., 10-22
 

Larson, D., 7-18, 7-44
 

Lavin, M., 9-5
 

Layton, D., 7-39 


Lecroix, R., B-2
 

Leeth, J., 7-29
 

Leggett, C., 7-29
 

Leibowitz, S., A-14
 

Lesser, J., 6-11, B-2
 

Lichtenstein, S., B-3
 

Lind, R., 6-8, 6-10, 6-11, 6-12, 6-15, 6-18
 

Lindberg, K., 7-40 


List, J., 7-43 


Liston-Heyes, C., 5-8
 

Liu, J., B-3
 

Lohof, A., 4-5
 

Loomes, G., B-3
 

Loomis, J., 7-18, 7-40, 7-41, 7-48 


Lundgren, T., 5-8
 

Lupi, Jr., F., 7-18
 

Lyke, A., 7-42
 

Lyon, R., 6-9, 6-10, 6-18
 

Maantay, J., 10-14, 10-18, 10-19
 

Magat, A, 7-14
 

Maguire, K., 7-40, 10-6, 10-7, 10-15, 10-16
 

Mahan, B., 7-18
 

Mankiw, N., A-4, A-16
 

Manne, A., 6-12
 

Mannesto, G., 7-40
 

Mansur, E., 10-13
 

Marin, A., B-2
 

Martinsson, P., 7-43
 

Mas-Colell, A., A-16
 

Massey, D., 7-16, 7-17, 7-18, 7-19, 7-27, 7-44
 

McClelland, G., 7-43, 7-49
 

McCluskey, J., 7-30
 

McConnell, K., 7-16, 7-18, 7-24, 7-25, 7-41, A-12
 

McCormick, E., 10-6
 

McDaniels, T., B-3
 

McDonald, A., 8-12
 

McFadden, D., 7-42
 

Mendeloff, J., B-3
 

Mendelsohn, R., 7-30, 7-27
 

Messer, K., 7-29 


Miceli, T., 4-21
 
Guidelines for Preparing Economic Analyses | May 2014 E-7 

Author Index

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Michael, R., 10-11
 

Michaels, R., 7-28
 

Mielke, H., 10-22
 

Miettinen, A., 7-18
 

Miller, R., 8-17
 

Miller, T., B-2
 

Mills, D., 4-6, 4-7
 

Milon, J., 7-18
 

Mitchell, R., 7-37, 7-42, 7-48
 

Mittlehammer, R., 7-30
 

Mohai, P., 10-5, 10-13, 10-18, 10-19
 

Moher, D., 7-48 


Montero, J., 4-5
 

Montgomery, M., 7-47
 

Moore, M., 6-6, 6-9, 6-10, 6-11, 6-18, 7-11, B-2
 

Morello-Frosch, R., 10-6, 10-19
 

Morey, E., 7-39 


Morgan, O., 7-44 


Morgenstern, R., 4-19, 4-21, 9-8, 9-9
 

Mrozek, J., 7-10, B-3
 

Murdoch, J., 7-30
 

Murdock, J., 7-19
 

Murphy, J., 7-43 


Murray, B., 8-5
 

Murray, C., B-6
 

Navrud, S., 7-45, 7-46
 

Needelman, M., 7-27, 7-47
 

Neuman, J., B-4
 

Newbold, S., 7-16, 7-19
 

Newell, R., 4-5, 5-8, 6-10, 6-15, 6-16, 6-17, 6-19
 

Nicholson, W., 3-2
 

Ninassi, K., 10-16
 

Nordhaus, W., 6-10, 6-14, 6-17, 6-18
 

Norland, D., 10-16
 

O’Connor, D., 4-11, 4-14
 

O’Neil, W., 4-7
 

Oates, W., 4-1, 5-7, 5-8, 7-24, A-4
 

Odum, H.T., 7-13
 

Olson, C.A., B2
 

Opaluch, J., 7-39
 

Osborne, L., 7-42
 

Otway, H., B-3
 

Özdemir, S., 7-39
 

Ozog, M., 7-28
 

Palmer, K., 4-12, 5-8, 8-11
 

Palmquist, R., 7-30, 7-31, 7-48
 

Pareto, V., A-4
 

Pargal, S., 4-13
 

Parry, I., 4-3, 4-16, 4-21, 7-24, 8-5
 

Parsons, G., 7-18, 7-25, 7-26, 7-27
 

Pastor, Jr., M., 10-22
 

Pattanayak, S., 7-17, 7-23, 4-49
 
E-8 Guidelines for Preparing Economic Analyses | May 2014 

Author Index

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Pearce, D., 6-10, 6-12, 6-15, 6-18, A-10
 

Perman, R., 3-2, A-4, A-5, A-7, A-10, A-11, A-12, 


A-16
 

Peters, T., 7-18, 7-27
 

Phaneuf, D., 7-16, 7-18, 7-25, 7-27, 7-44
 

Pigou, A., 4-9
 

Pizer, W., 4-19, 4-21, 6-10, 6-15, 6-16, 6-17, 6-19, 


8-2
 

Plantinga, A., 7-27
 

Poe, G., 7-48
 

Polasky, S., 7-17, 7-18, 7-23
 

Popp, D., 5-8, 8-11
 

Porter, M., 5-8
 

Post, E., 10-19
 

Portney, P., 4-15, 6-15, 6-18, 7-11, 7-32, A-16
 

Price, J., 9-9
 

Psacharopoulos, G., B-2
 

Quiggin, J., 7-31, 7-38, 7-39
 

Quimio, W., 4-14
 

Rabl, A., 8-12
 

Ramsey, F., 6-7, 6-13
 

Rausser, G., 7-17, 7-23, 7-30
 

Ready, R., 7-40, 7-45, 7-46
 

Rees, W., 7-13
 

Rehkopf, D., 10-19
 

Ricketts, T., 7-17
 

Ringquist, A., 10-5, 10-6, 10-15, 10-19
 

Roberts, M., 4-12
 

Roe, B., 7-39 


Rollins, K., 7-42
 

Rosen, S., B-5
 

Rosenbaum, A., 10-5
 

Rosenberger, R., 7-18, 7-48
 

Rowe, R., 7-33, 7-37, 7-49
 

Rowe, W., B-3
 

Ruser, J., 7-29
 

Sacks J., 10-19
 

Sagar, A., 8-12
 

Saha, R., 10-18, 10-19
 

Samuelson, P., 7-13, 7-17
 

Savage, I., B-3
 

Schelling, T., 6-12, 6-15
 

Scheraga, J., 6-18
 

Schlapfer, F., 7-42, 7-44
 

Schlottman, A., B-2
 

Schmalensee, R., 4-6
 

Schmidt, F., 7-46
 

Schrattenholzer, L., 8-12
 

Schwartz J., 10-7, 10-19
 

Scott, A., A-6
 

Scotton, C., 7-29
 

Scrogin, D., 7-18
 

Segerson, K., 4-14, 4-21
 
Guidelines for Preparing Economic Analyses | May 2014 E-9 

Author Index

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Sieg, H., 10-6
 

Sen, A., 6-12, 10-16
 

Serret, Y., 10-5
 

Sexton, K., 10-19
 

Shadbegian, R., 8-20, 10-5, 10-18
 

Shavell, S., 7-13
 

Shaw, D., 7-18
 

Shaw, W., 7-25, 7-28
 

Shepard, D., B-5
 

Sheriff, G., 10-6, 10-7, 10-15, 10-16
 

Shogren, J., 7-18, 7-31, 7-32, 7-43
 

Shonkwiler, J., 7-25
 

Short, K., 10-11
 

Shrestha, R., 7-48
 

Siderelis, C., 7-18, 7-27
 

Sidon, J., 10-6
 

Sieg, H., 11-3
 

Sigman, H., 4-11, 4-12
 

Simpson, R., 7-17, 7-23
 

Sinquefield, R., 6-10
 

Slovic, P., B-3
 

Small, A., 7-17, 7-23
 

Smith, M., 7-16
 

Smith, R., B-2
 

Smith, V., 7-11, 7-24, 7-25, 7-27, 7-29, 7-30, 7-42, 


7-48, 7-49, 8-4, 8-21, B-2, B-5
 

Solow, R., 6-12
 

Spackman, M., 6-6, 6-9, 6-17, 6-18
 

Spash, C., 7-7
 

Spence, A., 4-12
 

Stanley, T., 7-18, 7-45, 7-46 


Stavins, R., 4-5, 4-6, 4-7, 4-15, A-16
 

Steer, A., 6-15
 

Stern, N., 6-18
 

Stern, P., 10-20
 

Sterner, T., 4-1, 4-3, 4-5, 4-10, 4-11, 4-15, 4-16, 


A-15
 

Strand, I., 7-24, 7-25
 

Strassman, D., 7-24
 

Sumaila, U., 6-11
 

Sunstein, C., B-3
 

Sussman, F., 7-11, B-5
 

Swift, B., 4-4, 4-5
 

Taylor, C., 7-23
 

Taylor, L., 7-10, 7-29, 7-30, 7-43, 7-46, 7-48, B-3
 

Teisl, M., 7-41
 

Thaler, R., 6-11
 

Thayer, M., 7-14
 

Thomas, A., 8-12
 

Thomas, J., 8-8
 

Thorsnes, P., 7-18, 7-19, 7-29, 7-30
 

Tietenberg, T., 4-3, 4-5, 4-13, 4-16, 8-3 A-16
 

Tilman, D., 7-23
 

Timmins, C., 7-19
 
E-10 Guidelines for Preparing Economic Analyses | May 2014 

Author Index

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Tol, R., 6-18 

Tolley, G., 7-14
 

Toth, F., 6-12
 

Train, K., 7-26
 

Tschirhart, J., 7-16
 

Turner, R., 6-18, 7-16
 

Tyrväinen, L., 7-18
 

Ulph, D., 6-10, 6-12, 6-18
 

Valdés, B., 6-13
 

van der Linde, C., 5-8
 

Van der Zwaan, B., 8-12
 

Van Houtven, G., 7-12, 7-14
 

Van Houtven, G., 7-10
 

Varian, H., 7-18, 7-22, A-4, A-16
 

Vassanadumrongdee, S., 7-14
 

Videras, J., 4-19
 

Viscusi, W., 7-10, 7-11, 7-29, 7-30, 7-39, B-2, B-3, 


B-5
 

Voinov, A., 6-11
 

von Haefen, R.,7-27, 7-44 


von Haefen, R., 7-27 


Vossler, C., 7-43 


Wackernagel, M., 7-13
 

Wallace, K., 7-16
 

Walsh, R., 10-6
 

Walters, C., 6-11
 

Ward, M., 7-17
 

Weitzman, M., 4-12, 4-17, 6-15, 6-16, 6-17, 7-17, 


7-23
 

Weyant, J., 6-15, 6-18
 

Wheeler, D., 4-13
 

White, K., 8-11
 

Whitehead, J., 7-37, 7-44
 

Whitehead, T., 7-41 


Whittington, D., 1-3
 

Wilcoxen, P., 8-5
 

Williams, R., 4-16 


Willig, R., A-12
 

Willis, K., 7-18
 

Wolverton, A., 4-12, 10-5, 10-17, 10-18
 

Woodward, R. 7-18
 

Wu, J., 4-21
 

Wui, Y., 7-18
 

Xabadia, A., 4-16
 

Xu, F., 7-30
 

Yang, T., 11-3
 

Yohe, G., 6-18
 

Zeckhauser, R., 6-16, B-5
 

Zerbe, R., 6-11
 
Subject Index
 

Acid Rain Program, 4-6, 4-7, D-6, D-9
 

aesthetic improvements, 7-8, 7-20
 

allocative efficiency, A-4
 

annualized costs, viii, 6-3, 9-11, 9-12, 9-17
 

annualized value, ix, 6-2, 6-4, 7-11
 

baseline, ix, 1-3, 1-6, 4-8, 4-10, 4-16, 4-19, 4-20, 4-21, 


5-1, 5-2, 5-3, 5-4, 5-5, 5-6, 5-7, 5-8, 5-9, 5-10, 


5-11, 5-12, 5-13, 5-14, 5-15, 5-16, 6-2, 7-1, 7-4, 


7-7, 7-11, 7-36, 7-41, 7-46, 7-47, 8-5, 8-6, 8-12, 


8-14, 8-19, 9-3, 9-4, 9-9, 9-10, 10-6, 10-7, 10-8, 


10-14, 10-15, 10-16, 10-17, 10-18, 10-19, 10-20, 


11-11, B-4, B-5
 

behavioral responses, 4-21, 5-1, 5-10, 5-16, 7-20, 8-15
 

benefit-cost analysis, viii, xiii, 1-1, 1-4, 1-5, 2-2, 2-4, 


5-2, 5-4, 5-11, 5-16, 6-1, 6-5, 6-6, 6-7, 6-17, 6-18, 


7-1, 7-2, 7-3, 7-7, 7-8, 7-9, 7-12, 7-45, 7-49, 8-1, 


8-3, 8-6, 8-7, 8-8, 8-9, 8-12, 8-20, 9-2, 9-8, 9-15, 


9-16, 9-17, 10-1, 10-4, 10-8, 10-14, 10-22, 11-2, 


11-3, 11-4, 11-11, A-1, A-6, A-7, A-10, A-13, 


A-14, B-1
 

benefits, ix, x, xi, xii, xiii, xiv, xv, 1-2, 1-3, 1-4, 1-5, 1-6, 


2-1, 2-2, 4-1, 4-2, 4-5, 4-6, 4-15, 4-17, 4-18, 5-1, 


5-2, 5-3, 5-4, 5-5, 5-6, 5-7, 5-8, 5-9, 5-10, 5-11, 


5-12, 5-13, 5-14, 6-1, 6-2, 6-3, 6-4, 6-5, 6-6, 6-7, 


6-8, 6-9, 6-10, 6-11, 6-12, 6-14, 6-15, 6-16, 6-17, 


6-18, 6-19, 6-20, 7-1, 7-2, 7-3, 7-4, 7-5, 7-6, 7-7, 


7-8, 7-10, 7-11, 7-12, 7-13, 7-14, 7-15, 7-16, 7-17, 


7-18, 7-19, 7-20, 7-21, 7-22, 7-23, 7-24, 7-25, 


7-27, 7-28, 7-29, 7-30, 7-31, 7-32, 7-33, 7-44, 


7-45, 7-46, 7-47, 7-48, 7-49, 7-50, 8-1, 8-2, 8-5, 


8-6, 8-9, 8-10, 8-12, 8-13, 9-2, 9-4, 9-5, 9-8, 9-16, 


10-1, 10-3, 10-7, 10-8, 10-9, 10-20, 10-22, 10-23, 


11-1, 11-2, 11-3, 11-4, 11-5, 11-6, 11-8, 11-9, 11­

10, 11-11, 11-12, A-3, A-5, A-6, A-7, A-13, A-14, 


A-15, A-16, B-1, B-2, B-4, B-5, B-6, D-2, D-6, 


D-8, D-17, D-20
 

benefit-cost ratio, xi, 5-10, A-14
 

cessation lag, xiii, 7-5, 7-8, B-4
 

CGE models, 8-5, 8-6, 8-19, 8-20, 8-21, 9-17, 9-18
 

childhood, 10-20, 10-21
 

children, 1-4, 2-3, 7-8, 7-35, 7-45, 9-1, 9-4, 10-1, 


10-2, 10-4, 10-11, 10-20, 10-21, 10-22, 10-23, 11­

9, B-5
 

Circular 1-1, 1-3, 2-2, 3-2, 6-8, 6-15, 7-5, 7-8, 7-35, 


7-50, 8-12, 9-1, 9-2, 11-2, B-5, D-23
 

closures, xii, 7-25, 8-9, 9-9, 11-2, 11-9
 

Coase, 4-4, A-5, D-7
 

Coase theorem, A-5
 

command-and-control regulation, 4-1, 4-3, 4-5, 4-7, 


4-8, 4-21
 

performance-based standards, 4-3, 4-5
 

technology standard, 4-3
 

compensating surplus, viii, A-12
 

compensating variation, viii, 7-7, 8-6, A-10, A-11, 


A-12
 

competitiveness, 4-15, 8-7, 8-20, 9-6, 9-9, 9-10
 

compliance cost, xi, 8-3, 8-11, 8-12, 8-13, 9-15, 9-16, 


9-17
 

compliance cost models, 8-14, 8-15, 8-16
 

compliance costs, 2-4, 5-10, 8-3, 8-7, 8-9, 8-10, 8-11, 


8-13, 8-14, 8-15, 8-16, 8-17, 9-2, 9-4, 9-5, 9-8, 


9-11, 9-12, 9-14, 9-15, 9-16, 9-17, A-9, 10-3
 

compliance rate, 5-1, 5-8, 5-10, 5-16
 

Guidelines for Preparing Economic Analyses | May 2014 F-1 
F-2 Guidelines for Preparing Economic Analyses | May 2014 

Subject Index

computable general equilibrium (CGE) models, 


viii, 1-5, 8-5, 8-19, 9-18
 

consumer preferences, 5-1, 7-12
 

consumer surplus, 7-21, 7-22, 7-49, 8-2, 8-3, 8-15, 


8-18, A-3, A-4, A-7, A-10
 

consumption rate of interest, xi, xiv, 6-6, 6-7, 6-8, 


6-9, 6-10, 6-11, 6-12, 6-16, 6-19
 

cost-effectiveness analysis, viii, xi, 1-5, 5-5, 7-1, 


7-11, 8-10, 11-2, 11-3, 11-4, 11-5, 11-6, 11-8, 


11-14, A-14
 

cost-effectiveness, 1-5, 4-1, 4-2, 4-3, 4-5, 5-6, 6-3, 


7-12, 11-4, 11-8, 11-11, A-14, B-6, D-12
 

costs, xi, xii, xiii, xiv, xv, 1-2, 1-3, 1-4, 1-5, 1-6, 2-1, 


2-2, 2-3, 3-2, 4-1, 4-2, 4-3, 4-4, 4-5, 4-6, 4-8, 


4-9, 4-10, 4-12, 4-13, 4-14, 4-15, 4-16, 4-17, 


4-18, 4-21, 4-22, 5-1, 5-2, 5-3, 5-4, 5-5, 5-6, 5-7, 


5-8, 5-9, 5-10, 5-11, 5-12, 5-14, 5-15, 6-1, 6-2, 


6-3, 6-4, 6-5, 6-6, 6-7, 6-8, 6-9, 6-10, 6-11, 6-12, 


6-14, 6-16, 6-18, 6-19, 6-20, 7-1, 7-4, 7-8, 7-12, 


7-13, 7-14, 7-15, 7-19, 7-23, 7-24, 7-25, 7-26, 


7-27, 7-28, 7-32, 7-33, 7-34, 7-37, 7-39, 8-1, 


8-2, 8-3, 8-4, 8-5, 8-6, 8-7, 8-8, 8-9, 8-10, 8-11, 


8-12, 8-13, 8-14, 8-15, 8-16, 8-18, 8-19, 8-20, 


8-21, 9-2, 9-3, 9-4, 9-5, 9-6, 9-7, 9-8, 9-10, 9-11, 


9-12, 9-13, 9-14, 9-15, 9-16, 9-17, 10-1, 10-3, 


10-4, 10-6, 10-8, 10-9, 10-15, 10-16, 10-22, 10­

23, 11-1, 11-2, 11-3, 11-4, 11-5, 11-8, 11-11, 


11-12, A-1, A-3, A-5, A-6, A-7, A-8, A-9, A-12, 


A-13, A-14, A-15, A-16, B-4, B-5, C-1, D-6
 

cost savings, 2-2, 4-3, 4-5, 4-7, 4-21, 5-7, 5-8, 5-15, 


7-20, 7-45
 

deadweight loss, viii, 8-3, A-6, A-13
 

demand curve, 7-22, 7-24, 7-25, 8-2, 8-3, 8-4, 


8-15, A-1, A-2, A-3, A-4, A-6, A-8, A-9, A-12
 

demand elasticities, 8-15, 9-7, A-8, A-9, A-12
 

direct costs, xi, 7-33, 8-7, 8-13, 8-14, 8-15
 

discounting, xiv, 1-6, 6-1, 6-2, 6-3, 6-4, 6-6, 6-7, 


6-8, 6-11, 6-12, 6-13, 6-14, 6-15, 6-16, 6-17, 


6-18, 6-19, 6-20, 7-2, 7-36, 8-10, 8-11, 10-23, 


11-9, B-4, D-14, D-16, D-29
 

distributional analysis, 1-1, 2-4, 8-9, 10-1, 10-4, 


10-8, 10-14, 10-20, 10-22
 

distributional costs, 8-9, 8-13, 8-16, 8-19
 

distributional impacts, 8-21, 8-13, 9-18, 10-1, 10­

6, 10-12, 10-13, 10-16, 10-18, 10-20, 10-23
 

economic efficiency, xi, 1-2, 4-1, 4-5, 4-21, 6-5, 


9-10, 10-3, 10-4, 11-1, 11-11, 11-12, A-1, A-3, 


A-4, A-15
 

economic impact analysis (EIA), viii, xii, 1-4, 1-5, 


2-2, 8-7, 9-1, 9-2, 9-3, 9-8, 9-12, 9-15, 9-16, 


11-2, 11-9
 

Edgeworth box, A-4
 

effect-by-effect approach, 7-1, 7-3
 

elasticity, xii, 5-7, 6-7, 6-13, 6-14, 6-17, 6-18, 6-19, 


7-41, 7-42, 7-49, 8-15, 9-6, A-8, A-9, B-4, 


elderly, 10-1, 10-22
 

emission taxes, 4-5, 4-9, 4-10
 

product charges, 4-9
 

employment, ix, 7-8, 7-35, 8-7, 8-17, 8-20, 9-1, 


9-3, 9-6, 9-8, 9-9, 9-14, 9-18, 11-2, 11-9
 

environmental justice, 2-2, 2-3, 9-1, 10-1, 10-3, 


10-4, 10-20, 10-22, 10-23, 11-2
 

equity assessment, xii, xiii, 1-5, 9-1, A-7
 

equivalent surplus, ix, A-12
 

equivalent variation, ix, 7-7, 8-6, 8-20, A-10
 

ethnicity, 10-5, 10-6, 10-9, 10-11, 10-12, 10-17
 

excess demand, A-3
 
Guidelines for Preparing Economic Analyses | May 2014 F-3 

Subject Index

excess supply, A-2 

Executive Order 12866, 2-2, 7-2, D-9, D-10, D-35
 

Executive Order 12898, 2-3, D-9, 10-2, 10-4, 10-7, 


10-9, 10-11, 10-15
 

Executive Order 13045, 2-3, D-9, D-32, 10-2, 


10-21
 

expert elicitation, 7-34, 11-9
 

exposure, xi, xiii, 3-1, 4-14, 5-3, 5-6, 5-14, 7-5, 


7-11, 7-16, 7-28, 7-29, 7-32, 7-49, 11-4, 11-5, 


11-6, 11-7, B-1, B-4
 

externalities, xii, xiii, xiv, 1-1, 1-4, 2-2, 3-2, 4-1, 


4-2, 4-10, 7-23, 7-24, 8-3, A-3, A-4, A-6
 

fixed costs, 8-8, 9-10, 9-17
 

flow pollutants, xiii
 

full compliance, 5-2, 5-3, 5-9, 5-10
 

future economic activity, 5-5, 5-6
 

general equilibrium, 1-3, 1-4, 8-1, 8-2, 8-4, 8-5, 


8-6, 9-1, 9-9, 11-3, A-13
 

general equilibrium analysis,1-4, 8-2, 8-5, 8-6, 


A-13
 

health risks, 10-1, 10-2, 10-11, 10-20
 

Hicksian demand curve, A-10, A-11, A-12
 

households, xiii, 1-3, 7-20, 7-23, 7-39, 7-40, 7-46, 


7-47, 7-49, 8-4, 8-5, 8-6, 8-7, 8-8, 8-10, 9-1, 9-2, 


9-4, 9-7, 9-9, 9-13, 9-14, 9-16, 9-18, 10-6, 10-9, 


10-10, 10-11, 10-12, 10-22, A-5, A-13
 

income effect, A-10, A-11
 

indigenous, 10-1, 10-3, 10-12, 10-20
 

information disclosure, 4-13, 4-14, 4-17
 

input-output econometric models, 8-18, 8-19
 

input-output models, 8-17, 8-18, 8-19
 

Kaldor-Hicks, 7-8, A-7
 

Kaldor-Hicks criterion/compensation test, xiii, 


A-7
 

labeling rules, 4-13
 

latency, xi, 7-8, B-4, B-6
 

liability rules, 4-14, 4-15
 

lifestages, 10-1, 10-20, 10-21, 10-22
 

linear programming models, 8-16
 

linked rules, 5-13
 

lost productivity, 7-33, 7-34
 

low-income, 10-1, 10-2, 10-3, 10-4, 10-5, 10-6, 


10-10, 10-11, 10-12, 10-15, 10-22
 

marginal abatement cost curve, ix, 4-5, A-13, 


A-15, A-16
 

marginal benefit, xii, xiii, 4-2, 4-17, 6-15, A-1, A-2 


A-3, A-5, A-14, A-15, A-16
 

marginal cost, xii, xiii, 4-2, 4-6, 7-22, 7-31, A-2, 


A-3, A-6, A14, A-15
 

marginal social benefit, xiii, 4-2, A-5, A-14
 

marginal social cost, ix, xiii, 4-2, A-4, A-5, A-6, 


A-14
 

marginal willingness to pay, 7-11, 7-31, 10-8, A-1, 


A-2, A-14
 
F-4 Guidelines for Preparing Economic Analyses | May 2014 

Subject Index

market, ix, xi, xiii, 1-4, 2-2, 3-1, 3-2, 4-1, 4-2, 4-3, 


4-5, 4-6, 4-7, 4-8, 4-10, 4-11, 4-12, 4-13, 4-14, 


4-15, 4-16, 4-17, 4-18, 4-21, 5-3, 5-11, 5-14, 


6-1, 6-2, 6-6, 6-7, 6-8, 6-10, 6-11, 6-12, 6-13, 


6-15, 6-16, 6-18, 7-6, 7-7, 7-18, 7-20, 7-21, 


7-22, 7-24, 7-28, 7-30, 7-33, 7-35, 7-36, 7-39, 


7-40, 7-42, 7-43, 7-44, 8-2, 8-3, 8-4, 8-5, 8-6, 


8-7, 8-9, 8-10, 8-11, 8-12, 8-15, 8-16, 8-19, 


8-21, 9-3, 9-6, 9-7, 9-8, 9-9, 9-10, 9-12, 9-13, 


9-17, 9-18, A-1, A-2, A-3, A-4, A-5, A-6, A-7, 


A-9, A-12, A-13, A-14, A-16, D-4, D-17, D-19, 


D-36, D-37
 

market economy, 8-5, 8-19, A-1, A-2
 

market failure, xiii, 1-1, 2-2, 3-2, 4-1, 4-2, 4-13, 


4-15, 4-17, 5-15, 6-12, 6-18, A-3, A-4, A-6
 

market permit system, xiv
 

bubbles, 4-7, 4-8
 

grandfathering, 4-3
 

offsets, 4-7, 4-8, 9-16
 

market power, xiii, 1-1, 2-2, 3-2, 4-17, 9-10, A-4
 

market-based incentives, 4-1, 4-3, 4-5
 

Marshallian consumer surplus, 7-7, A-11, A-12
 

Marshallian demand curve, A-10
 

material damage, 7-4, 7-20
 

meta-analysis, 7-6, 7-10, 7-19, 7-29, 7-41, 7-45, 


7-46, 7-48, 7-49, 10-6, B-2, B-3
 

minority, 10-1, 10-2, 10-3, 10-4, 10-5, 10-6, 10-7, 


10-9, 10-10, 10-12, 10-15, 10-17, 10-22
 

mobile sources, 4-16, 8-15
 

monitoring and enforcement, 4-13, 4-21
 

multiple baselines, 5-2, 5-16
 

multiple rules, 5-11
 

need for policy action, 3-1, 3-2
 

net benefits, ix, xi, xv, 1-3, 1-4, 4-2, 5-2, 5-5, 5-10, 


6-2, 6-3, 6-4, 6-5, 6-6, 6-8, 6-16, 6-19, 7-1, 7-22, 


7-50, 8-2, 8-9, 8-12, 9-2, 11-1, 11-3, 11-10, 11­

11, A-3, A-6, A-7, A-13, A-14, A-16
 

net future value, ix, 6-2, 6-3, 6-4, 6-17
 

net present value, ix, xi, xiv, 4-1, 6-2, 6-4, 6-5, 6-9, 


6-12, 6-17, 6-19
 

North American Industrial Classification System 


(NAICS), ix, 9-3, 9-5, 9-6
 

open economy, 6-10, 6-11
 

opportunity cost, ix, xi, xiv, xv, 1-5, 4-7, 4-10, 6-6, 


6-8, 6-9, 6-11, 6-18, 6-19, 7-24, 7-25, 7-35, 8-1, 


8-2, 8-4, 8-7, A-1, A-6
 

optimal growth, 6-12, 6-13
 

over-compliance, 5-9, 5-10
 

PACE Survey, 9-7
 

partial equilibrium, 8-2, 8-3, 8-4, 9-17, 11-3, A-13
 

partial equilibrium analysis, 8-2, 8-3, 8-4, 8-6, 


8-15, 9-17, A-12, A-13
 

partial equilibrium models, 8-15, 8-16
 

point sources, 1-5, 4-16, 4-17, 4-20, A-16
 

Pollution Abatement Costs and Expenditures 


(PACE) Survey, x, 8-20, 9-6, 9-7, D-2
 

poor, 7-25, 6-18, 9-12, 10-11
 

price elasticity of supply/demand, xii, A-8, A-9
 

private costs, 1-4, 4-9, 4-10, 8-10, 9-2, 9-15, 9-16, 


A-5, A-6
 

producer surplus, 1-2, 7-21, 8-2, 8-5, 8-6, 8-10, 


8-11, 11-6, A-3, A-4, A-13
 

productive efficiency, A-4
 
Guidelines for Preparing Economic Analyses | May 2014 F-5 

Subject Index

profit, xv, 7-17, 7-18, 7-22, 7-23, 8-4, 8-17, 9-5, 


9-6, 9-9, 9-11, 9-16, 9-17, 11-2, A-1, A-4, A-5
 

property rights, 4-4, 7-7, 7-36, 7-41, A-5
 

race, 10-4, 10-5, 10-6, 10-9, 10-10, 10-11, 10-12, 


10-15, 10-17
 

Regulatory Flexibility Act, x, 2-4, 9-1, 9-2, 9-3, 


9-11, 9-12, 9-14, 9-15, 11-9, D-1, D-34
 

risk, xi, xiii, xv, 1-2, 1-3, 1-5, 1-6, 2-3, 3-2, 4-13, 


4-14, 4-15, 4-17, 4-18, 5-6, 5-7, 5-10, 5-11, 6-3, 


6-4, 6-5, 6-6, 6-7, 6-8, 6-10, 6-16, 6-17, 6-18, 


7-1, 7-2, 7-3, 7-4, 7-5, 7-6, 7-8, 7-10, 7-11, 7-12, 


7-13, 7-14, 7-15, 7-28, 7-29, 7-30, 7-31, 7-32, 


7-33, 7-38, 7-45, 7-50, 9-3, 9-16, A-5, B-1, B-2, 


B-3, B-4, B-5, B-6, D-17, D-19, D-27, D-32
 

risk assessment, 10-21, 10-23
 

shadow price of capital, xiv, 6-8, 6-9, 6-10, 6-11, 


6-12, 6-19
 

small entities, 2-4, 9-14, 9-17, 11-2
 

social cost, xii, xiii, xiv, xv, 1-5, 4-1, 4-10, 4-22, 


5-9, 6-8, 6-13, 7-2, 7-4, 8-1, 8-2, 8-3, 8-4, 8-5, 


8-6, 8-7, 8-9, 8-10, 8-11, 8-12, 8-13, 8-14, 8-15, 


8-18, 9-2, 9-14, 9-15, 9-16, A-5, A-6, A-7, A-13, 


A-14, A-16
 

social welfare, xii, 4-1, 4-2, 4-3, 4-4, 4-12, 6-6, 6-8, 


6-12, 7-23, 8-1, 11-5, A-3, A-5, A-6, A-7
 

social welfare function, xv, 6-12, 6-13, 10-15, 10­

16, 10-23, A-7
 

standard and pricing approach, 4-13
 

statistical life years, 7-11
 

stock pollutants, 4-16
 

sub-populations, xiii, 1-4, 1-5, 10-20, 10-21, 11-3
 

subsidies, xii, 4-5, 4-10, 4-11, 4-16, 4-17, 9-14
 

buy-back, 4-10
 

substitution effect, 7-7, A-10, A-11
 

supply curve, 8-2, 8-3, 8-5, A-2, A-3, A-5, A-7, 


A-8, A-9, A-13
 

supply elasticities, 8-15, A-8, A-9
 

taxes, xi, xiv, 4-5, 4-7, 4-9, 4-10, 4-11, 4-12, 4-13, 


4-16, 4-17, 4-18, 6-6, 6-9, 6-12, 7-24, 8-4, 8-5, 


8-8, 8-21, 9-4, 9-12, 9-13, 9-16, 9-18, A-4, A-13
 

tax-subsidy, xv, 4-11, 4-12, 4-16
 

deposit-refund, 4-11, 4-12, 4-17
 

technological change, 4-5, 5-7, 5-8, 6-5, 8-12, 


11-11
 

total cost, xiii, xiv, xv, 5-4, 5-11, 6-4, 7-13, 8-2, 8-3, 


8-7, 8-8, 8-16, 9-7, A-2, A-3, A-6, A-13, A-14, 


A-16
 

transfers, 1-5, 2-2, 4-7, 6-15, 6-17, 7-19, 7-45, 


7-46, 7-47, 9-2, 9-16, A-4, B-6, D-14
 

transitional costs, 8-9, 8-10, 8-12, 8-13, 8-19, 8-21
 

tribal, 10-3
 

uncertainty, xiii, 4-2, 4-8, 4-9, 4-14, 4-15, 4-17, 


4-20, 5-2, 5-4, 5-5, 5-6, 5-10, 5-13, 5-14, 6-1, 


6-2, 6-12, 6-13, 6-14, 6-15, 6-16, 6-17, 6-19, 


6-20, 7-2, 7-5, 7-6, 7-8, 7-14, 7-37, 7-43, 7-45, 


7-49, 8-11, 8-12, 11-1, 11-3, 11-4, 11-9, 11-10, 


11-11
 

under-compliance, 5-9, 5-10
 

Unfunded Mandates Reform Act, x, 2-3, 2-4, 8-7, 


9-1, 9-2, 9-14, 9-15, D-1, D-31
 

use value, 7-1, 7-16, 7-18, 7-19, 7-27
 

utility, x, 6-1, 6-2, 6-7, 6-11, 6-12, 6-13, 6-14, 6-17, 


6-18, 7-6, 7-7, 7-11, 7-12, 7-13, 7-22, 7-24, 


7-25, 7-26, 7-31, 7-32, 7-33, 7-35, 7-36, 7-42, 


7-44, 7-49, 8-4, 8-5, 8-6, 8-15, 9-18, A-1, A-4, 


A-6, A-7, A-10, A-11, A-12
 

utility possibility frontier, x, A-4
 
validity, 7-36, 7-37, 7-41, 7-42, 7-44, 7-46, D-13, 


D-16, D-37
 

variable costs, 8-3, 8-9, 8-16
 

voluntary actions, 5-15
 

voluntary approaches, 4-19, 4-21
 

willingness to accept, x, xv, 7-7, 7-8, 7-18, 7-20, 


7-29, 7-49, A-12
 

willingness to pay, x, xi, xv, 5-7, 7-3, 7-6, 7-7, 7-8, 


7-10, 7-11, 7-12, 7-13, 7-14, 7-15, 7-18, 7-20, 


7-21, 7-25, 7-26, 7-27, 7-28, 7-30, 7-31, 7-32, 


7-33, 7-34, 7-35, 7-36, 7-37, 7-38, 7-39, 7-40, 


7-41, 7-42, 7-43, 7-44, 7-46, 7-47, 7-48, 7-49, 


7-50, 10-8, A-1, A-2, A-3, A-4, A-6, A-10, 


A-11, A-12, A-14, B-4, B-5, B-6, D-2, D-12, 


D-17, D-35
 

F-6 Guidelines for Preparing Economic Analyses | May 2014