Office of the

Administrator

EPA 240-R-10-001

December 2010

Guidelines for Preparing

Economic

Analyses
Guidelines for Preparing Economic Analyses

Pre-publication Edition

National Center for Environmental Economics

Office of Policy 

US Environmental Protection Agency
i

Table of Contents

Acronyms and Abbreviations ...................................................................................................................vi

Glossary ......................................................................................................................................................ix

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

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

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

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

1.4 Organization of the EA Guidelines............................................................................................1-6

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‖ .......................................................................................................................2-4

2.1.6 Executive Order 13211, ―Actions Concerning Regulations that Significantly Affect 

Energy Supply, Distribution, or Use‖ ...................................................................................2-4

2.2 Statutes ......................................................................................................................................2-5

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)..................2-5

2.2.2 The Unfunded Mandates Reform Act of 1995 (P.L. 104-4) (UMRA)....................2-5

2.2.3 The Paperwork Reduction Act of 1995 (44 U.S.C. 3501) (PRA)...........................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.................................................4-3

4.1 Evaluating Environmental Policy..............................................................................................4-3

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

4.1.2 Cost Effectiveness...................................................................................................4-5

4.2 Traditional Command-and-Control or Prescriptive Regulation ................................................4-5

4.2.1 Technology or Design Standards............................................................................4-6

4.2.2 Performance Standards............................................................................................4-6

4.3 Market-Oriented Approaches....................................................................................................4-8

4.3.1 Marketable Permit Systems.....................................................................................4-8

4.3.2 Emission Taxes.....................................................................................................4-12
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4.3.3 Environmental Subsidies.......................................................................................4-13

4.3.4 Tax-Subsidy Combinations...................................................................................4-15

4.4 Other Market-Oriented or Hybrid Approaches........................................................................4-16

4.4.1 Combining Standards and Pricing Approaches.....................................................4-16

4.4.2 Information Disclosure..........................................................................................4-17

4.4.3 Liability Rules.......................................................................................................4-18

4.5 Selecting the Appropriate Market-Based Incentive or Hybrid Approach ...............................4-19

4.5.1 The Type of Market Failure ..................................................................................4-19

4.5.2 The Nature of the Environmental Problem ...........................................................4-20

4.5.3 The Type of Pollutant Information that is Available and Observable...................4-20

4.5.4 Uncertainty in Abatement Costs or Damages .......................................................4-21

4.5.5 Market Competitiveness........................................................................................4-21

4.5.6 Monitoring and Enforcement Issues .....................................................................4-21

4.5.7 Potential for Economy-Wide Distortions..............................................................4-22

4.5.8 The Goals of the Policymaker...............................................................................4-22

4.6 Non-Regulatory Approaches...................................................................................................4-22

4.6.1 How Voluntary Approaches Work........................................................................4-23

4.6.2 Economic Evaluation of Voluntary Approaches...................................................4-24

4.7 Measuring the Effectiveness of Regulatory or Non-Regulatory Approaches .........................4-26

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..............................................................................5-7

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

5.4 Compliance Rates......................................................................................................................5-9

5.4.1 Full Compliance....................................................................................................5-10

5.4.2 Under-Compliance ................................................................................................5-10

5.4.3 Over-Compliance ..................................................................................................5-11

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

5.5.1 Linked Rules.........................................................................................................5-12

5.5.2 Unlinked Rules......................................................................................................5-12

5.5.3 Indirectly Related Policies and Programs .............................................................5-13

5.6 Partial Benefits to a Threshold ................................................................................................5-13

5.7 Behavioral Responses..............................................................................................................5-15

5.7.1 Potential for Cost-Savings.....................................................................................5-16

5.7.2 Voluntary Actions.................................................................................................5-16

5.8 Conclusion...............................................................................................................................5-17

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
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6.1.3 Net Future Value .....................................................................................................6-4

6.1.4 Comparing the Methods..........................................................................................6-5

6.1.5 Sensitivity of Present Value Estimates to the Discount Rate..................................6-5

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

6.2 Background and Rationales for Social Discounting..................................................................6-7

6.2.1 Consumption Rates of Interest and Private Rates of Return...................................6-7

6.2.2 Social Rate of Time Preference...............................................................................6-8

6.2.3 Social Opportunity Cost of Capital .........................................................................6-9

6.2.4 Shadow Price of Capital Approach .......................................................................6-10

6.2.5 Evaluating the Alternatives...................................................................................6-12

6.3 ―Intergenerational‖ Social Discounting...................................................................................6-13

6.3.1 The Ramsey Framework .......................................................................................6-14

6.3.2 Key Considerations...............................................................................................6-17

6.3.3 Evaluating Alternatives.........................................................................................6-19

6.4 Recommendations and Guidance ............................................................................................6-22

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

7.1 Introduction ...............................................................................................................................7-1

7.2 The Benefits Analysis Process ..................................................................................................7-2

7.3 Economic value and types of benefits.......................................................................................7-7

7.3.1 Human health improvements ................................................................................7-11

7.3.2 Ecological benefits................................................................................................7-19

Application of economic valuation methods to ecological changes ...................................7-21

7.3.3 Other benefits........................................................................................................7-24

7.4 Economic Valuation Methods for Benefits Analysis ..............................................................7-25

7.4.1 Revealed Preference Methods...............................................................................7-26

7.4.2 Stated Preference...................................................................................................7-41

7.4.3 Combining Revealed and Stated Preference Data.................................................7-51

7.5 Benefit Transfer.......................................................................................................................7-51

7.6 Accommodating Non-monetized Benefits ..............................................................................7-57

7.6.1 Qualitative Discussions.........................................................................................7-57

7.6.2 Alternative Analyses.............................................................................................7-57

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.................................................................................8-5

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

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

8.2 A Typology of Costs .................................................................................................................8-9

8.2.1 Alternative Concepts of Cost ..................................................................................8-9

8.2.2 Additional Cost Terminology ...............................................................................8-10

8.2.3 Transitional and Distributional Costs....................................................................8-11

8.3 Measurement Issues in Estimating Social Cost.......................................................................8-12

8.3.1 Evaluating Costs Over Time .................................................................................8-12

8.3.2 Other Issues in Estimating Social Cost .................................................................8-14
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8.3.3 Use of Externally-Produced Cost Estimates .........................................................8-15

8.4 Models Used in Estimating the Costs of Environmental Regulation ......................................8-17

8.4.1 Compliance Cost Models......................................................................................8-18

8.4.2 Partial Equilibrium Models...................................................................................8-19

8.4.3 Linear Programming Models ................................................................................8-20

8.4.4 Input-Output Models.............................................................................................8-21

8.4.5 Input-Output Econometric Models........................................................................8-23

8.4.6 Computable General Equilibrium (CGE) Models.................................................8-24

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

9.1 Statutes and Policies..................................................................................................................9-2

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.................................................................................9-7

9.2.4 Detailing Impacts on Governments and Not-for-Profit Organizations .................9-13

9.2.5 Detailing Impacts on Small Entities......................................................................9-17

9.3 Approaches to Modeling in an Economic Impact Analysis ....................................................9-19

9.3.1 Direct Compliance Costs.......................................................................................9-19

9.3.2 Partial Equilibrium Models...................................................................................9-21

9.3.3 Computable General Equilibrium Models ............................................................9-21

10 Environmental Justice, Children and Other Distributional Considerations...............................10-1

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

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

11.1.1 General Principles.................................................................................................11-1

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

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

11.1.4 Presenting the Results of Economic Impact Analysis and Distributional Analyses

11-11

11.1.5 Reporting the Effects of Uncertainty on Results of Economic Analyses ...........11-12

11.2 Communicating Data, Model Choices and Assumptions, and Related Uncertainty ...........11-12

11.2.1 Data.....................................................................................................................11-13

11.2.2 Model Choices and Assumptions .......................................................................11-13

11.2.3 Addressing Uncertainty Driven by Assumptions and Model Choice .................11-14

11.3 Use of Economic Analyses..................................................................................................11-15

Appendices

A. Economic Theory ..............................................................................................................................A-1

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

A.2 Reasons for Market or Institutional Failure .........................................................................A-5

A.3 Benefit-Cost Analysis..........................................................................................................A-7

A.4 Measuring Economic Impacts..............................................................................................A-9
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A.4.1 Elasticities..............................................................................................................A-9

A.4.2 Measuring the welfare effect of a change in environmental goods......................A-12

A.4.3 Single Market, Multi-Market, and General Equilibrium Analysis.......................A-15

A.5 Optimal Level of Regulation..............................................................................................A-17

A.6 Conclusion .........................................................................................................................A-20

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

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

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

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

F. Subject Index ................................................................................................................................ F-1
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Acronyms and Abbreviations

AC annualized costs

ACN AirControlNET

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

CAAA Clean Air Act Amendments

CAIR Clean Air Interstate Rule

CAMR Clean Air Mercury Rule

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

CFR Code of Federal Regulations

CGE computable general equilibrium

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

DOE Department of Energy

DOT Department of Transportation

DWL dead weight loss

EA economic analysis

EBIT earnings before interest and taxes

EEAC Environmental Economics Advisory Committee

EIA economic impact analysis

ELG Effluent Limitation Guidelines

EO Executive Order

EPA Environmental Protection Agency

ES equivalent surplus

EV equivalent variation

EVRI Environmental Valuation Reference Inventory

FINDS Facility Index Data System

FTE full-time equivalent employment

GDP gross domestic product

GIS Geographic Information System

HCFC hydrochlorofluorocarbon

ICR Information Collection Request

I/O input-output

IPCC Intergovernmental Panel on Climate Change
vii

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

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

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

OCC opportunity cost of capital

OECD Organization for Economic Cooperation and Development

OGC Office of General Counsel

OLS ordinary least squares

OMB Office of Management and Budget

OSHA Occupational Safety and Health Administration

PACE Pollution Abatement Costs and Expenditures

PRA Paperwork Reduction Act

POTW publicly-owned (wastewater) treatment work

PVC present value of costs

QA quality assurance

QALY quality-adjusted life year

RAPIDS Rule and Policy Information Development System

RCRA Resource Conservation and Recovery Act

RDD random digit dialing

RFA Regulatory Flexibility Act

RIA regulatory impact analysis

RP revealed preference

RUM random utility model

SAB Science Advisory Board

SAM social accounting matrix

S&P Standard & Poor‘s

SBA Small Business Administration

SBREFA Small Business Regulatory Enforcement Fairness Act 

SIC Standard Industrial Classification

SISNOSE significant economic impact on a substantial number of small entities

SO2 sulfur dioxide

SP stated preference

TAMM Timber Assessment Market Model

TMDL Total Maximum Daily Loadings

TRI Toxics Release Inventory

TSLS two-stage least squares
viii

UMRA Unfunded Mandates Reform Act

UPF utility possibility frontier

USC United States Code

VSL value of statistical life

VSLY value of a statistical life-year

WTA willingness to accept

WTP willingness to pay
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Glossary

Annualized value

An annualized value is a constant stream of benefits or costs. The annualized cost is the amount one 

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 one 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 which is used for comparison with one or more 

alternative scenarios. For example, an economic analysis of a policy or regulation compares "the world 

with the policy or regulation" (the policy scenario) with "the world absent the policy or regulation" (the 

baseline scenario).

Benefit-cost analysis

A benefit-cost analysis 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 of a policy or action. Economists define benefits by focusing on 

measures of individual satisfaction or well-being, referred to as measures of welfare or utility. 

Willingness to pay is the preferred approach to valuing benefits.

Benefit/cost ratio

A benefit/cost ratio is the ratio of the net present value of benefits associated with a project or proposal, 

relative to the net present value 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

as two projects with the same benefit/cost ratio may 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. These regulations usually impose the same requirements on all 

sources, although new and existing sources 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 the 

production process by using different inputs or different mixtures of inputs, or (3) capturing the waste 

products and selling or reusing them.
x

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

Cost effectiveness analysis expresses the costs associated with 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.

Costs

Costs are the dollar values of resources needed to produce a good or service, and hence 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 the transaction. The bearers of such 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 and an 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

An economic impact analysis examines the distribution of monetized effects such as changes in 

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 demand 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 demand falls as price rises, the greater 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, 

however, 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 supply that occurs in response to a 

percentage change in price. For many goods the supply can be increased over time by locating alternative 

sources, investing in an expansion of production capacity, or developing competitive products which can 

substitute. One might therefore expect that the price elasticity of supply will be greater in the long term 
xi

than the short term for such a good, that is, that supply can adjust to price changes to a greater degree over 

a longer time.

Emission taxes

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 may 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

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 (e.g., 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 

involved. 

Flow pollutants

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 a flow pollutant.

Hotspot

A hotspot is a geographic area with a high level of pollution/contamination within a larger 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 

 

1 Definition taken from http://www.epa.gov/compliance/basics/ejbackground.html (Accessed April 17, 2008).
xii

amount the gainers are prepared to accept as a bribe to forgo the change. In other words, the Hicks 

compensation test is from the losers' point of view, while the Kaldor compensation test is from the 

gainers' point of view. The Kaldor-Hicks criterion is widely applied in welfare economics and 

managerial economics. For example, it forms an underlying rationale for cost-benefit analysis.

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

Displacement of pollution from one location to another as a result of the imposition of tighter pollution 

controls. Under tradable permits, leakages occur when pollution is displaced to an area not affected by 

the cap.

Marginal benefit

The benefit received from a small 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 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 benefit received by the producer of a good (marginal private benefit) plus the marginal 

benefit received by other members of society (external benefit).

Marginal social cost

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., 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 system under which sources are required to have emissions permits matching their actual emissions, 

with each permit specifying how much the firm is allowed to emit and transferable.

Market-based incentives

Market-based incentives include a wide variety of methods for environmental protection. For example, 

taxes, fees, charges, and subsidies generally "price" pollution and leave decisions about the level of 

emissions to each source. Another example, is the marketable permit approach which sets the total 

quantity of emissions and then allows trading among firms.

Meta-analysis

Meta-analysis is a statistical method of combining data from a set of comparable studies of a problem in 

order to provide a larger sample size for evaluation and to produce a stronger conclusion than can be 

provided by any single study. Meta-analysis yields a quantitative summary of the combined results. 
xiii

Net benefits

Net benefits are calculated by subtracting total costs from total benefits.

Net future value

Net future value is similar to net present value, 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 the policy's effects.

Net present value

The net present value 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 individuals may attach to the mere knowledge of the existence of a good 

or resource, as opposed to enjoying its direct use. It can be motivated for a variety of reasons, including 

bequest values for future generations, existence values and values of paternalistic altruism for others' 

enjoyment of the resource.

Opportunity cost

Opportunity cost is the value of the second best alternative to a particular activity or resource. 

Opportunity cost need not be assessed in monetary terms, but rather can be assessed in terms of anything 

which is of value to the person or persons doing the assessing. For example, using a grove of trees to 

produce paper may have as a second best alternative 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 may 

produce the illusion that its 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)

QALYs are a composite measure used to convert different types of health effects to a common, integrated 

unit, incorporating both the quality and quantity of life lived in different health states. These metrics are 

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 may be defined as the sum of all opportunity 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. 
xiv

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, is 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, in order to 

encourage certain behavior. For example, polluters may be paid 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 may 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

The sum of all costs associated with a given activity.

Use value

An economic value based on the tangible human use of some environmental or natural resource.

Value of a Statistical Life

The value of a statistical life (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 identifiable life is valued 

at this amount, but rather that the aggregate value of reducing a collection of small individual risks is 

worth $5 million in this case.

Value of a Statistical Life Year

The value of a statistical life year (VSLY) is the estimated dollar value for a year of statistical life. In 

practice, it 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

Willingness to accept (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, willingness to accept is the 

least amount of money an individual would accept to forego the improvement (or endure the decrement).
xv

Willingness to Pay

Willingness to pay (WTP) is the largest amount of money that an individual or group could pay, along 

with a change in policy, 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 the environmental effects of concern.
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1 Introduction

1.1 Background to the Guidelines for Performing Economic Analyses

The Guidelines for Preparing Economic Analyses are part of a continuing effort by the U.S. 

Environmental Protection Agency (EPA) to 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 (US EPA 1983) and later revised in the late 1990s. In September 

of 2000, the EPA issued its Guidelines for Preparing Economic Analyses (US EPA 2000) (EA 

Guidelines), 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, 

the EPA committed to periodically revise the EA 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 EA Guidelines, describes new Executive Orders and related 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. Key changes from the 2000 EA Guidelines include:

 a more detailed treatment of defining baseline conditions

 an expanded discussion of defining and valuing ecological benefits

 a revised and updated discussion of approaches to intergenerational discounting

 an extensively rewritten chapter on social costs as defined and used in EPA analyses

 more direction on appropriate ways to account for employment effects of regulation

 greater emphasis on presenting the results of benefit-cost analysis, including effects that 

cannot be quantified and/or monetized.

The focus of this document is on the conduct of economic analysis to support policy decisions and the

fulfillment of requirements described by relevant statutes, Executive Orders and other related guidance 

materials. With a few exceptions, the collection of Executive Orders and statutes that govern the conduct 

of economic analysis and distributional analysis has remained largely unchanged since 2000. Executive 

Order 12866, directing federal agencies to perform a benefit-cost analysis for economically significant 

rules (those with an economic impact of $100 million or more), still provides the primary impetus for 

much of the formal benefit-cost analysis within the Agency.2 However, new guidance documents and 

handbooks on how to comply with a number of executive orders and statutes have been issued both 

within and outside the Agency in the last several years. The Office of Management and Budget (OMB), 

for instance, released its Circular A-4 in 2003 to replace both its ―Best Practices‖ document (OMB 1996) 

 

1EO 13422, a 2007 amendment to EO 12866, contributed to the formal 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.
1-2

and its “OMB Guidelines‖ (OMB 2000). Circular A-4 provides recommendations to federal agencies on 

the development of economic analyses supporting regulatory actions. As such, it greatly influences the 

conduct of economic analysis and the development of new analytic tools and approaches within the 

Agency. The OMB recommendations as well as other guidance documents are referenced in the revised 

EA Guidelines where appropriate.

As a result of these modifications and updates, the new, revised EA Guidelines will ensure that the EPA‘s 

economic analyses are prepared to inform the policy-making processes and satisfy OMB‘s requirements 

for regulatory review. The new EA Guidelines also seek 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 for economic efficiency, but also for 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 may provide useful insights.

It is important to note that economic analysis is but one component in the decision-making process and 

under some statutes 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 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 EA Guidelines

The scope of the EA Guidelines is on economic analysis typically conducted for environmental policies 

using regulatory or non-regulatory management strategies. Other 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 EA 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. Thus, to fully understand and apply the approaches and recommendations presented in 

the EA 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. 
1-3

The EA 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 EA 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 EA Guidelines recommend adhering to the following general principles as stated by OMB 

(OMB 1996):

 ̳―Analysis of the risks, benefits, and costs associated with regulation must be guided 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.‖‘

Thus, 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. Care must also be taken 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 policymaking, however, the norm is 

to limit standing to the national level. This decision is based on the fact that authority to regulate only 
1-4

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 to a new state of equilibrium with the regulation in effect. The 

differences between the old and new states measured as changes in prices, quantities produced and 

consumed, income and other economic quantities 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 efficiency and distribution. These issues often 

take the form of three distinct questions:

 Is it theoretically possible for the ―gainers‖ from the policy to fully compensate the ―losers‖ and 

still remain better off?

 Who are the gainers and losers from the policy and associated economic changes?

 And 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 benefit-cost analysis, 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.3

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 to adopt a 

 

3 Appendix A gives a conceptual overview of this discussion. See in particular, section A.3 on Benefit-Cost 

Analysis. 
1-5

general equilibrium approach.4 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 weight the need for additional precision against the cost of employing general 

equilibrium models over other methods and in doing so should consider the size, impact, and complexity 

of the question at hand. In general, the more detailed methods are justified by larger and more complex 

questions. This question is considered in each of the chapters on specific models.

The EA Guidelines follow more traditional practices and adopt 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 sub-populations, 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 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 benefit-cost analysis, an economic impacts 

analysis and an equity assessment. 

Benefit-cost analysis evaluates the favorable effects of policy actions and the associated opportunity 

costs of those actions. The favorable effects are defined as benefits and the 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 even organize the analysis of benefits differently from the 

analysis of costs, but they should be aware of the conceptual relationship between the two. Even so, 

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. 

However, as the problems we seek to tackle become increasingly complex (e.g., climate change), so in 

turn will the models needed to track the various processes to describe and capture policy effects. 

Computable General Equilibrium models for these types of policies will become increasingly important. 

 

4 The general equilibrium framework will at least capture all ―market‖ benefits and costs, but may not include nonmarket benefits, such as those associated with existence value. In practice, models of general equilibrium may 

also be unable to analyze relatively small sectors of the economy. For more on general equilibrium analysis see 

Chapter 8, section 4.5.
1-6

Once the change in pollution levels resulting from a policy is predicted, these are 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 the 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, constrain and limit the estimation 

techniques used to examine gainers and losers in an economic impact analysis or to examine impacts on 

disadvantaged sub-populations in an equity assessment. To estimate these two categories of impacts we 

rely on a multiplicity of estimation techniques. The constraints faced by these analyses as well as details 

regarding estimation techniques are given by Chapters 9 and 10.

Note that none of these three types of analyses address the cost-effectiveness of a policy option. Cost 

effectiveness analyses report the estimated costs needed to achieve a specific goal or an additional unit of 

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

of cost effectiveness measures. When comparisons are made across policies, cost effectiveness analyses 

may also be used to help identify the least costly approach to achieving a specific goal.5 

1.4 Organization of the EA Guidelines

The remainder of this document is organized into nine 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;

 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 it;

 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. 

 

5 Note that cost-effectiveness analysis is not covered extensively in this document. Additional sources for details on 

cost effectiveness analysis include IOM (2006) and Boardman et al. (2006).
1-7

 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 Social Costs presents the basic theoretical approach for assessing the social 

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 and Other Distributional Considerations (under 

development) 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 EA Guidelines

with suggestions for presenting the quantified and unquantified results of the various economic 

analyses to policy makers.
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2 Statutory and Executive Order Requirements for 

Conducting Economic Analyses

Various statutes and executive orders direct agencies to conduct specific types of economic analyses.5

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.6

The scope of requirements for economic analysis can vary substantially. In some cases, the statute or 

executive order may contain language that limits its applicability to only those regulatory actions that fall 

above a threshold in significance or impact. Economic analysis may be useful in determining if a 

regulatory action exceeds a significance or impact threshold, i.e., if it is in the class of regulatory actions 

the statute or executive order is targeting. For example, provisions of Executive Order 12866 apply to 

rules that have an annual effect on the economy of $100 million or more or are otherwise considered 

―economically significant.‖7 If a regulatory action must comply with the requirements of a given statute 

or executive order, additional economic analysis (e.g., Executive Order 12866 requires an analysis of 

benefits and costs), procedural steps (e.g., Executive Order 13132 may require consultation with affected 

State and local governments), or a combination of economic analysis and procedural steps may be 

required. This chapter describes the general requirements for economic analysis contained in each statute 

and executive order, and identifies thresholds beyond which a regulatory action must follow additional 

economic analysis requirements.8 Requirements of the statutes and executive orders that do not

necessitate economic analysis are not covered in this chapter.

Guidance for the development of regulatory economic analysis is provided in the Office of Management 

and Budget‘s (OMB) recent Circular A-4 (OMB 2003), which replaces its 1996 Best Practices document 

and its 2000 OMB Guidelines. These guidance documents have helped to shape EPA‘s methodology for 

analytical and empirical economic analysis as described in EPA‘s own guidance documents. For each 

executive order 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 your program's Office of General Counsel (OGC) attorney.9

 

5

 For the text of each statute and executive order appearing in this chapter and guidance specific to them, or for 

more information on their implications for EPA rule development generally, 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 executive orders can easily be found using 

http://usasearch.gov/.

6

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.

7 Note that the threshold is defined in terms of annual costs or annual benefits of the option that is proposed or 

finalized and applies regardless of whether the rule is regulatory or deregulatory in nature. See Section 2.1.1 for 

a more complete definition.

8Note that for some statutes and executive orders, requirements for proposed regulatory actions may vary slightly 

from the requirements for final regulatory actions.

9

See US EPA (2005b) for more information.
2-2

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.

While any one of the four criteria 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 option that is 

proposed or finalized. So, if one option poses costs or benefits in excess of $100 million, but the 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 added 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 executive 

order 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) is required. The requirements for benefit-cost 

analysis 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 Executive Order 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)).10 

Guidance: Chapters 3 through 8 of this document provide guidance for meeting these requirements. 

OMB‘s Circular A-4 provides guidance to Federal agencies on the development of regulatory analysis of 

 

10 Executive Order 13422, amended E.O. 12866 and 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 benefit-cost analyses requirement to ―significant‖ guidance documents. Although E.O. 13497 

issued in January 2009 revoked E.O. 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 E.O. indicated that ―significant policy and guidance documents... 

remain subject to OIRA‘s review.‖
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economically significant rules as required by Executive Order 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 economic impact analyses while Chapter 

10 addresses how to assess environmental justice implications.

11

 

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

Populations and Low-Income Populations” 

Threshold: Programs, policies, and activities that have disproportionately high and adverse human 

health or environmental effects on minority populations, including Native American populations, and/or 

on low-income populations. 

Requirements contingent on threshold: No specific requirements for additional economic analysis; 

rather, certain procedural steps are required.

Guidance: 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.12 These materials provide guidance on key terms in the Executive Order. 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 Executive Order 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 Executive Order 13045 (U.S. 

EPA 1998b). EPA has also prepared the Children’s Health Valuation Handbook (US EPA 2003b), which 

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, unless the costs 

 

11In its Statement of Regulatory Philosophy, EO 12866 states that agencies should consider the distributional and 

equity effects of a rule (Section 1(a)).

12For more information see U.S. EPA (1998a) and CEQ (1997).
2-4

are expressly required by statute or there are federal funds available to cover the State or local 

governments‘ compliance costs. 

Requirements contingent on the threshold: Submission to OMB of a Federalism Summary Impact 

Statement and consultation with elected officials of affected State and local governments.

Guidance: Guidance on Executive Order 13132: Federalism, November 2008.

13

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.‖

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 direct compliance costs of the tribal governments 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.14 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 Executive Order 12866 and that are likely 

to have significant adverse effects on the supply, distribution, or use of energy. 

Requirements contingent on threshold: Agencies must submit a Statement of Energy Effects to OMB. 

The Statement of Energy Effects addresses the magnitude of expected adverse effects and describes 

reasonable alternatives to the action and the expected effects of such alternatives on energy supply, 

distribution, and use.

Guidance: EPA has prepared guidance on what effects might be considered significant. OMB has 

guidance for implementing Executive Order 13211 as well.15

 

13This document is located at http://intranet.epa.gov/adplibrary/documents/federalismguide11-00-08.pdf (accessed 

March 4, 2010, internal EPA document).

14Please check the ―Action Development Process Library‖ on EPA‘s intranet, http://intranet.epa.gov/adplibrary 

(accessed April 8, 2010, internal EPA document) for the status of this guidance.

15U.S. Environmental Protection Agency, 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 Executive Order 13211 is 

located at http://www.whitehouse.gov/omb/memoranda/m01_27.html (accessed July 8, 2008).
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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: The EPA must prepare a regulatory flexibility analysis, and 

comply 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.16

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

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.‖17

Requirements contingent on threshold one: Section 202 of UMRA requires preparation of a written 

statement that includes the legal authority for the action; a benefit-cost analysis; a distributional analysis; 

estimates of macroeconomic impacts; and a description of the Agency‘s consultation with elected 

representatives of the affected State, local, or tribal governments. Section 205 of UMRA requires the 

Agency to consider a reasonable number of regulatory alternatives and select the least costly, most costeffective, 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 written interim guidance and OMB has provided general guidance on complying 

with UMRA.

18

 

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

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

disclosure requirements or other information collection activities calling for answers to identical questions 

imposed on or posed to ten or more persons other than federal agencies or employees.

 

16U.S. Environmental Protection Agency, EPA Final Guidance for EPA Rulewriters: Regulatory Flexibility Act as 

amended by the Small Business Regulatory Enforcement Fairness Act, November 2006. Available at 

http://intranet.epa.gov/adplibrary (accessed May 1, 2008, internal EPA document). 

17 Note that it is the threshold in this case that is ―adjusted annually for inflation.‖

18See http://intranet.epa.gov/adplibrary (accessed April 14, 2004).
2-6

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 (i.e., fewer than 

10 persons would be surveyed) in a specific situation, the agency may proceed with the collection without 

seeking OMB approval. Agencies must 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 can be found on EPA‘s intranet site, ―ICR Center.‖19

 

19See http://intranet.epa.gov/icrintra/ (accessed April 14, 2004, internal EPA document).
3-1

3 Statement of Need for Policy Action

A clear statement of the need for policy action should be included in economic analyses of environmental 

policy prepared for economically significant rules.

20 This chapter discusses the key elements that should 

comprise this statement, which include:

 A definition of the environmental problem to be addressed (Section 3.2);

 An analysis of the reasons existing legal and other institutions have failed to correct the problem 

(Section 3.3); and

 A justification of the need for Federal intervention instead of other alternatives (Section 3.4). 

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 this document should be applied selectively to be 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;

 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. 

 

20 Executive order 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 12866 to guidance 

documents, but has since been revoked.
3-2

OMB‘s Circular A-4 discusses three categories of ―market failure‖ including externalities, market power, 

and inadequate or asymmetric information.21 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 Executive Order 12866, the statement of need should also 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 in which other factors are 

responsible for the failure of the market or public and private sector institutions to adequately address 

environmental problems.

3.3 Need for Federal Action 

The final component of the statement of need for the 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.22 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 executive order 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 the 

policy action should also 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.

 

21 For further discussion of market failure, see Perman et al. (2003), Hanley et al. (2001), and Nicholson (1995).

22 See Executive Order 13132 on ―Federalism‖ for introductory statements regarding principles of federalism, and a 

section describing the special requirements for preemption.
4-3

4 Regulatory and Non-Regulatory Approaches to 

Pollution Control

Once Federal action is deemed necessary to address an environmental problem, policymakers have a 

number of options at their disposal to influence pollution levels. In deciding which approach to 

implement, policymakers must be cognizant of constraints and limitations of each approach in addressing 

specific environmental problems. It is also important to account for how political and information 

constraints, imperfect competition, or pre-existing market distortions interact with various policy options. 

Finally, 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 existing regulations. 

This chapter briefly describes several regulatory and non-regulatory approaches used in environmental 

policymaking. The goals of this chapter are to introduce several important terms and concepts to analysts, 

describe the conceptual foundations of each approach, and provide additional references for those 

interested in a more in-depth discussion.23 Specifically, this chapter discusses the following four general 

approaches to environmental policymaking: command-and-control regulation, market-based incentives, 

hybrid approaches, and voluntary initiatives. While command-and-control regulations have been a

commonly used method of environmental regulation in the U.S., the three other approaches are also

employed by EPA. Market-based incentives and hybrid approaches offer the regulated community an 

opportunity to meet standards with increased flexibility and lower costs compared to many commandand-control regulations, while voluntary initiatives may allow environmental improvements in areas not 

traditionally regulated by EPA.

The remainder of this chapter is organized as follows:

 Section 4.1 introduces the concepts of economic efficiency and cost-effectiveness;

 Section 4.2 discusses the traditional command-and-control approach to environmental regulation;

 Section 4.3 examines four classic market-oriented approaches to environmental regulation:

marketable permits, emission taxes, subsidies, and tax-subsidy combinations;

 Section 4.4 examines three other market-oriented but often hybrid approaches: standards-andpricing, information disclosure, and liability rules;

 Section 4.5 highlights potentially relevant factors in the selection of appropriate market-based or 

hybrid approaches; and

 Section 4.6 discusses non-regulatory or voluntary approaches to environmental regulation. 

The chapter concludes with a discussion of criteria used to evaluate the effectiveness of regulatory and 

non-regulatory approaches to pollution control.

4.1 Evaluating Environmental Policy

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 

 

23 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.
4-4

for framing the discussion and comparison of the regulatory options presented in the remaining sections 

of this chapter.

4.1.1 Efficiency

Economic efficiency can be defined as the maximization of social welfare. In other words, an efficient 

market is one that allows society to maximize the difference between a stream of social benefits and 

social costs over time, or the net present value of benefits. The efficient level of production is also 

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 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 social 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, reductions in emissions continue to occur 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).24 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), we achieve an efficient aggregate 

level of emissions in which 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 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 also 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.

 

24 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.
4-5

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 cost effectiveness 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, the cost of abating the last unit of 

pollution should be the same for all polluters. 

4.2 Traditional Command-and-Control or Prescriptive Regulation

Many environmental regulations in the U.S. are prescriptive in nature (often referred to as command-andcontrol regulation).

25 26

 A prescriptive regulation can be defined as a policy that prescribes how much 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 cost-effective methods for regulating emissions, this type of regulation is 

still commonly used and sometimes statutorily required. It is also 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. In other words, 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 non-prescriptive approach), a polluter facing relatively high 

abatement costs would continue to emit at its current level but 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 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. 

 

25 A number of notable exceptions do exist, however. These include the Lead phase-down 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 US EPA (2001b).

26 Goulder and Parry (2008) refer to these as ―direct regulatory instruments‖ because they feel that ―command-andcontrol‖ has a ―somewhat negative connotation.‖ Ellerman (2003) refers to them as prescriptive regulations. 

We follow that convention here.
4-6

Thus, while pollution may be reduced to the desired level, it is often accomplished at a higher cost under 

a prescriptive approach.

27

It is also common to ―grandfather‖ or exempt older polluters from new prescriptive regulations. This 

means that they are effectively subject 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.28 As a result, grandfathered older facilities with higher emission rates tend to 

remain active longer than they would if the 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.

29

Two types of prescriptive regulations exist: technology or design standards and performance standards. 

4.2.1 Technology or Design Standards

A technology or design standard, mandates the specific control technologies or production 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 

cost-effective methods of abatement or to explore new and innovative abatement strategies or production 

processes that are not permitted by regulation.

4.2.2 Performance Standards 

A performance-based standard also requires that polluters meet a source-level emissions standard, but 

allows the polluters to choose among available methods – at times, that also meet additional criteria 

specified in the regulation – to comply with the standard. Performance-based standards that are 

technology-based 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.30

 

27 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.

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

29

 For cases where the optimal level of pollution is at or near zero, the literature also indicates that market-based 

incentives may sometimes be useful as a transition instrument for the phasing-out of a particular chemical or 

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

30 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 taking into account a variety of 

facility-specific costs and impacts on air quality. EPA has been restrictive in its definition of technologies 

meeting this requirement and eliminates those that are not commercially available (see Swift (2000)).
4-7

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 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.

31

 In other words, 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.32

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 several 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. Assymetric information may 

also hinder the ability of one or more party to come to an agreement. Going back to our example, if there 

are many farms in the watershed using the pesticide on their crops, clearly the ability of the homeowners 

to negotiate with every farm is more difficult than negotiating with one farm.

 

31 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).

32 See Swift (2000) and US EPA (1991) for a detailed discussion of how emission rate-based standards hinder 

technological innovation.
4-8

4.3 Market-Oriented Approaches

Market-oriented 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.33 Market-oriented approaches may 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 minimize polluters‘ abatement costs, 

an objective that often is not achieved under command-and-control based approaches. Because marketbased 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. In general, environmental economists

favor market-based policies because they tend to be least costly, place lower information burden on the 

regulator, and 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. 34

While operationally different, these market-based 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 market-based approaches in turn. 

4.3.1 Marketable Permit Systems

Several forms of emissions trading exist including cap and trade systems, project-based trading systems 

and emissions rate trading systems. The common element across these programs is that sources are able 

to trade credits or allowances so that those with opportunities to reduce emissions at lower costs have an 

incentive to do so. Each of these systems is discussed in turn below.35

4.3.1.1 Cap and Trade Systems

 

33 The incentive to innovate means that the marginal abatement cost curve shifts downward over time as cheaper 

abatement options are introduced. 

34 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, 2000) compile information on both the theory and 

empirical use of economic incentives. Newell and Stavins (2003) generate rules-of-thumb designed to make it 

easy for policymakers to determine when market-based incentives may result in cost savings over commandand-control regulations. Harrington, Morgenstern, and Sterner (2004) compare the costs and outcomes of 

command-and-control and incentives-based regulatory approaches to the same environmental problem in the 

U.S. and Europe. Additional sources include Sterner (2003), Stavins (2003), Tietenberg (1999, 2002), U.S. 

EPA (2004a, 2001b), OECD (1994a, 1994b), and proceedings published under the ―Project 88" forum, Stavins 

(1988, 1991).

35 For a more detailed discussion of the various systems and how to design them, see US EPA (2003c).
4-9

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 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 that 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 aggregate emissions 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, which 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. 36

 

36 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 the EPA‘s Acid Rain Website at http://www.epa.gov/acidrain (accessed 04/05/2004).
4-10

Text Box 4.2- Acid Rain Trading Program for Sulfur Dioxide

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 sulfur dioxide 

in 1995 were initially limited to 8.7 million tons for those facilities. Of the plants that participated, most were coalfired 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 sulfur dioxide generating units 

into the system. Additional emissions restrictions will occur beginning in 2010.

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 Acid Rain Program‘s benefits (including SO2 trading and 

direct NOx controls) reaching upwards of $120 billion annually in 2010 with annual costs around $3 billion (in 

2000$) for 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.

Source: USEPA 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 USEPA (2007a).

Generally, allowances initially sold at auction represent income transfers from the purchasers to the 

government in the amount of the price bid for the allowances. The collection of revenue through this 

method of allowance allocation gives the government the opportunity to reduce pre-existing market 

inefficiencies, reduce distributional consequences of the policy, or invest in other social priorities.

Allowances may also be allocated to polluters according to a specified rule which represent a transfer 

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

1990199519961997199819992000200120022003200420052006

Percent Change Relative to 1990

Electricity Retail Price NOx Emissions

SO2 Emissions Electricity Generation
4-11

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), who receives them and 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 policymaker should also consider potential barrier-to-entry 

effects. Overall, the treatment of 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 

U.S. experience suggests that a market characterized as having 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 may be significantly 

hindered by burdensome requirements that increase the transaction costs associated with each trade.37 

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.‖

However, two recent reviews of the literature (Burtraw et al. 2005 and Harrington et al. 2004) have found 

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 in some cases. These results come 

primarily from studies of the SO2 and NOx trading programs and the results may not transfer to other 

pollutants which have more localized effects if the market-based policy is not carefully designed. 

Banking introduces increased flexibility into a trading system by allowing polluters to bank unused 

permits for future use. For example, 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 monitored emission levels, it is in noncompliance and the source must provide 

 

37 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 U.S. and the main reasons for its failure.
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allowances to cover its environmental obligation. In addition, the source must pay a penalty 

automatically levied per each ton of excess emissions.38

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-and-control 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, which is 

then used to accommodate the emissions from the latter. 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 have been used mostly to control air pollution in 

non-attainment areas, they have historically been 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 (US EPA 2001b).

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 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 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 control mobile source emissions (US EPA 2003c). 

4.3.2 Emission Taxes

Emission taxes are exacted per unit of pollution emitted and induce a polluter to take into account the 

external cost of its emissions. Under an emission 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 emission 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, which may include 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 individually decide 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, reduce other taxes, or redistribute to finance other 

 

38 Notably, the U.S. Acid Rain Trading Program has nearly 100 percent compliance and requires a limited number of 

staff to administer. Specifically, the program only requires about 50 EPA staff.
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public services.39 While difficult to implement in cases where there is temporal and/or spatial variation in 

emissions, policymakers may more closely approximate the ambient impact of emissions by incorporating 

adjustment factors for 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 U.S.40 This is because taxing emissions directly may not be 

feasible when emissions are difficult to measure or accurately estimate, 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.41 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 the distribution of economic rents that result from these programs. 

User or product charges are a variation on emission taxes that are occasionally utilized in the U.S. These 

charges may be imposed directly on users of publicly operated facilities or on intermediate or final 

products whose use or disposal harms the environment, and they 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 nonhazardous 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 usually are 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.

Taxes and charges facilitate environmental improvements similar to those that result from marketable 

permits 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 their per unit reduction in pollution create 

the same abatement incentives as emission taxes or charges. For example, 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 may 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 

 

39 For more information on how the government may use revenues from taxes to offset distortions created by other 

taxes, see Goulder (1995) and Goulder, Parry, and Burtraw (1997).

40 These taxes are called ―Pigovian‖ after the economist, Arthur Pigou, who first formalized them. See Pigou (1932).

41 See Fullerton (1996) for a discussion of the advantages and disadvantages of emission taxes.
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firms under a subsidy, the net result may be a decrease in pollution emissions from individual polluters 

but an increase in the overall amount.42 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 outlay, and analysts should consider the opportunity costs associated with 

using public funds. 

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 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. 43

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. However, 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 emission 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 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.44 Buy-back programs also exist to promote the scrapping of old, highemission vehicles.

Environmental subsidies in the U.S. 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 cost-sharing 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.

 

42 See Sterner (2003) for a more in-depth discussion of how subsidies work and for numerous examples of subsidy 

programs in the U.S. and other countries.

43 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 really is 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.

44 For more information on the Office of Air‘s Small Engine Buy-back Program see US EPA (2006c).
4-15

4.3.4 Tax-Subsidy Combinations

Emissions taxes and environmental subsidies can also be combined to achieve the same level of 

abatement as when 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 deposit-refund is used to encourage firms to 

use a cleaner input, the deposit on output induces the firm to 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 function 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.45 

The most common type of tax-subsidy combination is the deposit-refund system, most of which are 

designed to encourage consumers to reduce litter and increase the recycling of certain components of 

municipal solid waste.46 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 also have been discussed in the literature as a means of controlling non-point 

source water pollution, cadmium, mercury, and the removal of carbon from the atmosphere.47

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. Because firms have access to better information than government, they may 

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). Policymakers 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. sulfur dioxide from power

plants) are easily and accurately monitored, other market incentives may be more appropriate. If a 

 

45

 See Sterner (2003) for a more detailed description of this and other examples of tax-subsidy combinations.

46 For example, Arnold (1995) analyses the merits of a deposit-refund system in a case study focusing on enhancing 

used-oil recycling, and Sigman (1995) reviews policy options to address lead recycling.

47 See U.S EPA (2004a), Fisher et al. (1995), and O‘Connor (1994).
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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.48 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 up-front funds to a pool that 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 been required in extraction 

industries such as mining, timber, coal and oil.49

4.4 Other Market-Oriented or Hybrid Approaches

In addition to the four classic market-based instruments discussed above, several other market-oriented 

approaches are often discussed in the literature and increasingly used in practice. Often, these approaches 

combine aspects of command-and-control and market-based incentive policies. As such, they are not 

always the most economically efficient approach because either the level of abatement or the cost of the 

policy is greater than what would be achieved through the use of a pure market-based incentive approach. 

Nevertheless, such approaches are appealing to policymakers 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, however, 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 that combines standards 

with pricing mechanisms.50 In the case of a standard and tax combination, the same emissions standard is 

imposed on all polluters and all polluters are then subject to a unit tax for emissions in excess of the 

standard. 

 

48 The main advantages and disadvantages of deposit-refund systems are discussed in U.S. GAO (1990), Palmer, 

Sigman and Walls (1997), and Fullerton and Wolverton (2001, 2005).

49 For more information on the use of financial assurance or performance bonds, see Boyd (2002).

50 See Roberts and Spence (1976) and Spence and Weitzman (1978). 
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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 also has 

several other attractive features. First, if the standard is set properly, proper protection of health and the 

environment will be assured. This feature of the policy maintains the great advantage of standards: 

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 marketbased 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 increasingly been 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.51 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 the government or the public. By 

collecting and making such information publicly available, firms, government agencies, and consumers 

may be 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 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. In other cases, a 

community with information on a nearby firm‘s pollution activities may exert pressure on the firm to 

reduce emissions, even if formal regulations or monitoring and enforcement are weak or nonexistent.52

Requirements for information disclosure need not be tied explicitly to an emissions standard; however, 

they 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. In addition, as is the case with market-based instruments, polluters still have the 

flexibility to reduce emissions in response to community pressure in the cheapest way possible.

The use of information disclosure or labeling rules has several other advantages. First, 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 may 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. Otherwise, information disclosure may 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 

 

51 See OMB 2010 for guidance issued to regulatory agencies on the use of information disclosure and simplification 

in the regulatory process.

52 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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costless: U.S. firms report spending approximately $346 million per year to monitor and report releases.53

Any required investments in pollution control are in addition to this amount. Second, the amount of 

pressure a community exerts on a plant may be related to socioeconomic status. Poorer, less educated 

populations tend to exert far less pressure than communities with richer, well-educated populations.54 

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 and not on possible ecological or 

aggregate societal effects, or do not understand how to properly interpret information released in terms of 

the health risks associated with exposure to particular pollutants.

EPA-led information disclosure efforts include the Toxics Release Inventory (TRI); the reporting of green 

house gases. The TRI and greenhouse gas reporting rule both 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 on the risks of particular toxic substances, the level 

of contamination in drinking water; the dangers of pesticides; and air quality index forecasts for over 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 emissions are released to 

the public (Hamilton (1995) finds a stock price return of -0.03 percent due to TRI release). Firms that 

experienced the largest drop in their stock prices also reacted by reducing their reported emissions most in 

subsequent years.55

4.4.3 Liability Rules

Liability rules are legal tools of environmental policy that can be used by victims (or the government) to 

force polluters to pay for environmental damages after they occur. These instruments serve two main 

purposes: 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 to compensate 

victims when careful planning does not occur. These rules are used to guide courts in their compensation 

decisions when the court rules in favor of the victim. Liability rules may serve as an incentive to polluters 

- to the extent that polluters are aware that they will be held liable before the polluting event occurs - to 

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. If damages depend on more than just polluter actions alone, 

then the liability rule should be designed to provide adequate incentives in multiple arenas. For instance, 

it is possible to take into account whether victims take action to reduce or avoid damages when 

establishing a liability rule. In this case, if victims do not demonstrate some standard of care in an 

attempt to avoid damages, the polluter may not be liable for the full amount.

While a liability rule can be constructed to mimic an efficient market solution in certain cases, there are 

reasons to expect that this will not be the case. First, uncertainty exists as to the magnitude of payment

because how much polluters are required to pay after damages have occurred is dependent on the legal 

system and may be limited by the inability to prove the full extent of damages or the ability of the firm to 

pay. Second, liability rules can generate relatively large costs, both in terms of assessing the 

 

53 See O‘Connor (1996) for information on the costs of monitoring and reporting environmental information. In 

addition, see World Bank (2000) for a discussion of the main advantages and disadvantages of information 

disclosure as a policy tool.

54 See Hamilton (1993) and Arora and Cason (1999).

55 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.
4-19

environmental damage caused, and the amount due.56 Thus, liability rules are most useful in cases where

damages requiring compensation are expected to be stochastic (e.g., accidental releases), and monitoring 

firm compliance with regulatory procedures is 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 damages whereas under negligence polluters are liable only if they 

do not exhibit ―due standard of care.‖ Regulations that impose strict liability on polluters for the health 

and environmental damage caused by their pollution 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 CERCLA (or Superfund) 

and the Corrective Action provisions of 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

The selection of the most appropriate market-based incentive or hybrid regulatory approach depends on a 

wide variety of factors, including: 57

 The type of market failure being addressed;

 The specific nature of the environmental problem;

 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 market-based 

or hybrid instruments. The first is the failure of firms or consumers to integrate into their decisionmaking the impact of their own production or consumption decisions on entities external to themselves. 

The second type of market failure is the inability of firms or consumers to make optimal decisions due to 

lack of information on available abatement technologies, emission levels, or associated risks. Marketbased or hybrid instruments that incorporate the marginal external damages of a unit of pollution into a 

 

56 See Segerson (1995) and Alberini and Austin (2001) for discussions of the various types of liability rules, the 

efficiency properties of each type of rule, and an extensive bibliography.

57 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).
4-20

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 because 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 non-point 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 sources?

58 Point sources, which emit at identifiable and 

specific locations, are much easier to identify and control than diffuse and often numerous non-point 

sources, and therefore are often amenable to the use of a wide variety of market instruments. Although 

non-point sources are not regulated under EPA, the pollution emitted from a non-point source is. This 

makes the monitoring and control of non-point source emissions a challenge. In instances where both 

point and non-point sources contribute to a pollution problem, a good case can be made for a tax-subsidy

combination or a tradable permits system. Under such a system, emissions from point sources might be 

taxed while non-point source controls are subsidized. 

Flow pollutants tend to dissipate quickly, while stock pollutants persist in the environment and tend to 

accumulate over time. While it is possible to rely on a wide variety of market and hybrid instruments for 

the control of flow pollutants, stock pollutants may require strict limits to prevent bioaccumulation or 

detrimental health effects at small doses, making direct regulation potentially more appealing. If the limit 

is not close to zero, then 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 

potentially practical options. These same instruments are appealing when pollutants are not uniformly 

mixed across space. In this case, it is important to account for differences in baseline pollution levels, 

and 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 may vary by time of day or season. For 

example, health impacts associated with vehicle traffic are primarily 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 type of market-oriented approach used may depend on the data available. 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 certain technology 

known to reduce pollutants by a certain amount. The pollutant levels may be imputed based on regulation 

placed on the input or the technology used. 

 

58 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), and Sterner (2003).
4-21

The link between pollution and heterogeneous sources is often difficult and costly to make. And these 

costs may increase if the pollutant levels vary over time. For simplicity, uniform policies are often used. 

However, information technologies such as continuous emissions monitoring equipment (CEMs) or 

geographical information systems (GIS) may be used to link sources to pollutant levels. In these cases, 

policies that make use of this new information may be used, often reducing costs. As technology 

improves or more data become available, analysts may want to reassess the design of the regulation.59 

4.5.4 Uncertainty in Abatement Costs or Damages

The choice between price-based instruments (e.g. taxes or charges) and quantity-based instruments (e.g. 

marketable permits) also 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 costs of abatement exists but 

damages do not change much with additional pollution, then policymakers can effectively limit costs 

without having much effect on the benefits of the policy by using a price instrument. If, on the other 

hand, there is more uncertainty associated with the benefits of controlling pollution and policymakers 

wish to guard against high environmental damages, they can limit these damages by using a quantity 

instrument.60 In this way, the policymaker can avoid potentially costly or damaging mistakes. The 

policymaker also should be aware of any discontinuities or threshold values above which sudden large 

changes in damages or costs could occur due to a small increase in the level of abatement required.

4.5.5 Market Competitiveness

Market power is a type of market failure in and of itself, resulting 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 in which firms have some amount of 

market power. A combination of market-based instruments may work more effectively than a single 

instrument in this instance. In addition, to the extent that cost burdens are differentiated, the use of 

certain market-based instruments may cause a change in market structure to favor existing firms, creating 

barriers of entry and allowing these firms a certain degree of control over price. Permit systems that set 

aside a certain number of permits for new firms, for instance, may guard against such barriers.

4.5.6 Monitoring and Enforcement Issues

Market-oriented instruments differ in the degree of difficulty required to monitor and enforce them. For 

example, subsidies, deposit-refund systems, and information disclosure shift the burden of proof to 

demonstrate compliance from government to regulated entities. Because firms generally are in a better

position than government to monitor and report their own emissions, they may do so at a potentially 

lower cost. This feature makes these approaches attractive when monitoring is difficult or emissions must 

be estimated (e.g. when there are non-point 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. 

 

59 For more information, see Xabadia, Goetz, and Zilberman (2008).

60 See Weitzman (1974) for the classic paper on the ways in which uncertainty (also referred to as lack of 

information) affects instrument choice. See also Chapter 10 of these Guidelines for more information on the 

treatment of uncertainty in analyses.
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4.5.7 Potential for Economy-Wide Distortions

Analysts should also consider the potential distortionary effects of any policy option considered. Even 

policies that are deemed relatively efficient interact with pre-existing environmental, economic, or 

agricultural policy (e.g., product standards, non-environmental subsidies, trade barriers) in non-intuitive 

ways that may 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 the reduction of market inefficiencies.61 See 

Chapter 8 and the Appendix A for a more detailed discussion of economy-wide distortions.

4.5.8 The Goals of the Policymaker

Finally, the goals of policymakers may influence the instrument selected to regulate pollution. Each 

instrument considered may have different distributional and equity implications for both costs and 

benefits that should be accounted for when deciding among instruments. For example, policymakers may 

wish to ensure clean-up of future pollution by firms. In this case, insurance and financial assurance 

mechanisms may be useful instruments 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 level at which policymakers allow the market to 

determine exact outcomes may influence the instrument chosen. Marketable permits, for example, set the 

total level of pollution control, but the market determines which polluters reduce emissions. On the other 

hand, taxes let the market determine 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. Others have been developed to improve environmental quality in areas 

that policymakers expect may be regulated in the future but are currently not regulated, such as 

greenhouse gas emissions and non-point source water pollution.62

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;

 

61 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). 

62 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.
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 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.63 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 about 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.64 The 

voluntary programs in which these groups participate tend to either have broad environmental objectives 

and target a variety of firms from different industries, or focus on more specific environmental problems 

that are often relevant to a single industrial sector. In the U.S., nearly one third of all multi-sector federal 

voluntary programs focus on energy efficiency and climate change issues, with general pollution 

prevention efforts being the next most popular issue. 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 attempt to provide targeted and effective 

technological expertise and assistance to participating firms.65

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. 

Goal setting is a very common method used in the design of voluntary programs. Implementation-based 

goals are typically EPA-specified program-wide targets designed to provide a consistent objective across 

firms. Target-based goals are usually qualitative and process-oriented so that firms may individually set 

an unique target. The EPA‘s 33/50 program, which set a goal of a 33% reduction of toxic emissions by 

firms in the chemical industry by 1992 and a 50% reduction by 1995 (relative to a 1988 baseline), is an 

example of a voluntary program with an implementation-based goal. The EPA‘s WasteWise and Climate 

Challenge programs are examples of programs with target-based goals.

Programs designed to promote environmental awareness and encouraging process change within firms 

often involve putting a system in place to evaluate firms‘ ongoing operations and to provide information 

 

63 Because these programs are voluntary there is no need for formal public comment. However, industry often is 

consulted during the design phase.

64 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 11/03/2010). 

65

 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 policymaking.
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on newly available technologies. Examples of this type of approach are SmartWay program, which 

encouraged firms to adopt energy efficient changes that also yield fuel savings for freight trucking 

companies, and the Green Suppliers Network program, which provides technical reviews and suggestions 

to firms 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 

their competitions‘ products.66 This is the intent of the Green Power Partnership and the WasteWise 

program.

Finally, product labeling can be used on either the intermediate inputs in a production process or on the 

final good. Labels on intermediate goods encourage firms to purchase environmentally-responsible 

inputs. Labels on final goods allow consumers to differentiate between goods produced using a relatively 

clean production process and those that do not. For example, products deemed energy efficient may 

apply for the use of 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 

approach. 

Several factors contribute to difficulties in evaluating voluntary approaches. Many programs target 

general environmental objectives and thus lack a measurable environmental output. Alternatively, a 

measurable output may exist, but there may be a lack of data on the firm‘s environmental outputs. 

Additionally, a reasonable baseline from which to make a comparison must be established, which will 

require an extensive analysis comparing the actions of participants to non-participants in the program.67 

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 obstacles may be overcome 

if voluntary approaches use more defined and detailed goal setting and require more complete data 

collection and reporting from the outset.68

 

66 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.

67 See Chapter 5 for a discussion of baselines and specifically section 5.7 for a discussion of behavioral responses.

68 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).
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Text Box 4.3 - Water Quality Trading of Non-Point Sources

In 2003, EPA issued a ―Water Quality Trading Policy‖ (US 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, the 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 non-point sources. Point sources are 

regulated by the EPA and, as such, required to hold National Pollutant Discharge Elimination System 

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

pollution from unregulated, non-point sources. Nutrients and sediment from urban and agricultural runoff 

have led to water quality problems that limit recreational uses of rivers, lakes, and streams, create hypoxia 

in the Gulf of Mexico, and decrease fish populations in the Chesapeake Bay. The impetus for allowing 

effluent trading between point and non-point sources is to lower nutrient and sediment loadings and 

improve or preserve water quality. 

To ensure that the reduction resulting from the trade has the same effect on the water quality than 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 buyer and seller 

 uncertainty about nonpoint source reductions 

 equivalency of different forms of the same pollutant discharged from the trading partners 

 additional water quality improvements above and beyond those required.

The idea behind trading is to allow point sources to meet the discharge limit but at a lower cost that allows 

continued growth and expansion of production, while giving non-point sources an incentive to reduce 

pollution through participation in the market. To the extent that it is cheaper for a non-point source to 

reduce pollution than to forgo revenues earned from the sale of any unused credits to point sources, the 

non-point 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 (US 

EPA 2007f). Trading has been limited for several reasons. First, there is no aggregate ―cap‖ on 

discharges that applies to both point and non-point sources within a watershed. Reductions by non-point 

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 then complete a trade. 

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

from a non-point 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 non-point source difficult. Fourth, encouraging non-point 

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 non-point sources. 

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 that they were 

successful in reducing participant emissions. However, that fails 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 other voluntary programs have led to greater emission reductions than 
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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 is weak, abatement levels are

likely to be lower than under regulation. 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

There are several policy criteria that should be considered when evaluating the success of regulatory or 

non-regulatory approaches. These include environmental effectiveness; economic efficiency; savings in 

administrative, monitoring and enforcement costs; inducement of innovation; and increased 

environmental awareness. In many cases, the answers to these questions will make evident the particular 

advantages of one or more market-based incentive approach 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; 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.

Several key questions regarding each policy objective must be considered in determining the overall 

effectiveness of the approach:

 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 program?

 Economic Efficiency. How close does the approach come to the most efficient outcome? Does 

the policy instrument reach a given 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 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 

may 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 abatement 

techniques that make the cost of compliance with environmental regulations decrease over time?

To address a number of these key evaluation criteria, Chapters 8 and 9 of these guidelines offer 

instruction on how to measure social costs and how to address equity issues, respectively. 
5-1

5 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 potential benefits and costs of a 

proposed regulation. Because the 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 analysis 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 of 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 (e.g., judicial and statutory deadlines, 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.69 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, 

however, 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, and behavioral 

responses by firms and the public to the proposed rule.

On occasion a regulatory program may be set to expire or dramatically change, however, 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, 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 (i.e., the baseline 

scenario) to the expected state of the world with the proposed policy or regulation in effect (i.e., the 

policy scenario). Economic and other impacts of policies or regulations are measured as the differences 

between these two scenarios.

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 

 

69 A policy action includes both regulations and the issuance of Best Management Practices or guidance documents 

that do not carry the same force as a regulation, but do affect the decisions of firms and consumers.
5-2

scenario and allows for an unequivocal measure of the benefits, costs, and other consequences of the rule. 

There are, however, 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. If, for instance, 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 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 may 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 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

To assist analysts in baseline specification, several guiding principles are listed and discussed below. 

Though they exhibit a common-sense approach to the issue, 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.

Guiding Principles for a Baseline Analysis

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.
5-3

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 

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 parties affected. There should also be a 

discussion of why a particular regulatory approach was chosen (e.g., best available technology (BAT), 

performance measures, market incentives, or non-regulatory approaches). 

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 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 may 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 demand for other goods may decrease. Where these trends are highly 
5-4

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 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 is first published in a Notice 

of Proposed Rule Making, 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 

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, then, 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 emissions. 

In the baseline scenario, the level of greenhouse gases in the atmosphere would steadily increase over 

time, with a corresponding increase in expected impacts on human health and welfare and ecological 

outcomes. A benefit-cost analysis limited to the first decade after initiation of the policy 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.
5-5

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 also 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.

Alternatively, 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 costeffective analysis 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.70

 

70 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.
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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 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 also 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 their levels and 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, 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. Therefore, 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 rapidly moving overseas, this 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 
5-7

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 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, cross-price, and income elasticities allowing changes in consumer behavior to be estimated over 

time and for the baseline and policy scenarios.71

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 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 change, however, 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 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 

 

71

 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.
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regulation. For example, the cost of controlling chlorofluorocarbons 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 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 – a sometimes difficult 

distinction to make. 

Additionally, 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 their cost of compliance. In cases where small changes in technology could dramatically affect 

the costs and benefits, or where technological change is reasonably anticipated, the analyst should 

consider exploring these effects in a sensitivity analysis. This might include probabilities associated with 

specific technological changes or adoption rates of a new technology, or it may be an analysis of the rate 

required to alter the policy decision. Such an analysis should show the policy significance of emerging 

technologies that have already been accepted, or, at a minimum, are in development or reasonably 

anticipated.

In some cases it may be possible to make the case that learning effects will lead to lower costs over 

time.72 Estimated rates of learning effects often indicate that costs decline by approximately 5 percent to 

10 percent for every doubling of cumulative production. If learning effects are to be included in an 

analysis, the analyst should carefully examine the existing data for relevance to the problem at hand 

because estimated learning effects can vary according to many factors, including across industries and by 

the length of the time period considered. Also, because estimates of learning effects are based on 

doubling of cumulative production, inclusion of learning effects will have a greater influence on rules 

with longer time periods and may have little effect on rules with short time periods.

 

72 See US EPA (1997b, 2007b).
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Text Box 5.1, Technological Change, Induced Innovation, and the Porter Hypothesis.

5.4 Compliance Rates

One aspect of baseline specification that is particularly complex, and for which assumptions are typically 

necessary, is the setting of compliance rates. The treatment of compliance in the baseline scenario can 

significantly affect the results of the 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.73 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 

 

73

 An exception would be if the proposed regulation were designed to correct the under-compliance from the 

previous rule. This is discussed in the section below on under-compliance.

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; 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 does 

not predict the magnitude of these innovations or the cost savings from them. 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. by using economic incentives) will induce more innovation than inflexible regulation and vice 

versa. There is evidence that this is the case (Kerr and Newell 2003; 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 

minimize 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; Brannlud 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.
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basis of their social costs and benefits, the effect of compliance assumptions on the estimated economic 

impacts should be described as well as 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, care should also be taken 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, analysts should develop baseline and policy scenarios that assume full compliance

with existing and newly enacted (but not yet implemented) regulations for analyses of 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 counting benefits and costs captured by 

analyses performed for other rules. 

Assuming full compliance with all previous regulations may pose some challenges to the analyst, 

however, when current observed or reported economic behavior indicate otherwise. 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 the 

EPA.

As a practical matter, before rejecting assumptions of "full compliance" 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 ―current practice‖ baseline. For a deregulatory rule (e.g., a rule 

designed to address potential changes in or clarify definitions of regulatory performance that frees entities 

from enforceable requirements contained in an existing rule), for example, 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 may 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 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 (US 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 under-compliance 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 
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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 may 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 under-compliance 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 have been 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.

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 
5-12

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., OSHA, DOT, 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 agency, 

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 should always be 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 rules together that affect the same industrial category. This was true of the pulp and 

paper effluent guidelines and NESHAP rules (EPA 1997). 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. ELG and NPDES work together to achieve one objective so 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 may 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 benefit-cost analysis. 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 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 
5-13

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 non-arbitrary 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 may 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 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, the EPA‘s Office of Water has calculated the benefits associated with improving river miles 

for various designated uses (e.g., swimming, fishing, 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 
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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 there a few illustrative cases.

Including final rules that have not yet taken effect: This is the most straight-forward 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 assuming firms will comply with these

promulgated rules. For example, on March 15, 2005, EPA issued the Clean Air Mercury Rule 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 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 which are 

reasonably expected with a high degree of certainty in the absence of EPA action. 
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the public and allows the Agency to justify incremental progress to a threshold.74 Note, however, that 

once partial gains to a threshold have been claimed, there is a danger of double counting when evaluating 

the potential benefits of future rules. If partial gains have been valued in one rule, then subsequent rules 

cannot claim full credit for crossing the threshold. In effect, some of the benefits have already been used 

to justify the previous incremental rules and therefore claiming full credit in future rules would double 

count those benefits.

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 less-direct 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-of-pipe 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 

 

74 It should be recognized that sometimes calculating partial benefits to a threshold may not be a satisfactory 

solution, either because the progress to a threshold is uncertain due to multiple limiting factors (e.g., in some 

ecological improvements) or because it does not comport with the economic values (e.g., the value of avoiding 

the extinction of a species). In this case, a rule making incremental 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.
5-16

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 scenario 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 required investment. In general, affected parties are assumed to choose the option 

that minimizes their costs. 

In some cases, however, 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 cost saving measures are not undertaken in the baseline. It 

is not always obvious why firms would actively choose not to 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 cost saving 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 achieve compliance with other rules 

that are implemented or proposed. In this latter case, the 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 may be 

implemented through a formal, government-sponsored 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 provided for 

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 firm‘s 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. 
5-17

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 will meet the new standard without over complying. This is because 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 

firm‘s 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 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 benefitcost analysis and option selection. However, developing a baseline is not a straightforward process, and 

many decisions must be made 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.
6-1

6 Discounting Future Benefits and Costs 

Discounting renders costs and benefits 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 nonmarket goods and services) that will be caused by a policy by 

a discount factor. At a summary level, discounting reflects the fact that people prefer consumption today 

over consumption in the future, and the fact 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.

At a more technical level, as detailed later in this chapter, 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. It is important to 

recognize 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, and most of the discussion in 

this chapter focuses on real discount rates and values.

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. Private discounting, on the 

other hand, is discounting from the specific, limited perspective of private individuals or firms. 

Implementing this distinction in practice can be complex, as detailed in this chapter, but it is an important 

distinction to maintain because using a given private discount rate instead of a social discount rate may 

bias results as part of a benefit-cost analysis. 

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 those contexts 

that may well 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 because 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 leading to different recommendations across these two scenarios.

Several sensitive issues surround the choice of discount rate and 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 (e.g., sections 6.2.2.2 and 6.3.1)

 ―prescriptive‖ approaches vs. ―descriptive‖ approaches to selecting a discount rate (section 6.3.1), 

and

 uncertainty about future economic growth and other conditions (e.g., sections 6.3.2.1 and 6.3.2.2).
6-2

The chapter begins with a description of the mechanics of discounting, followed by overviews of the 

background and rationale for discounting, and key considerations for discounting in the intergenerational 

context. The chapter concludes with recommendations and guidance for discounting in EPA benefit-cost 

analyses.

75

 

6.1 The Mechanics of Summarizing Present and Future Costs and 

Benefits

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

The net present value (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  NB  d NB  d NB   dnNBn... 0 1 1 2 2

 (1)

where NBtis the net difference between benefits and costs (Bt - Ct) that accrue at the end of period t. The 

discounting weights, dt, are given by:

t t

r

d

(1 )

1



 (2)

where r is the discount rate. The final period of the policy‘s future effects is designated as time n.

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, any policy could be 

rejected by making the opposite choices.

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 may 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 

 

75 This chapter is intended to summarizes some key aspects from the core literature on social discounting;, it is not 

a detailed review of the vast and varied social discounting literature on the topic. Excellent sources for 

additional information are Lind (1982a, b; 1990; 1994), Lyon (1990, 1994), Kolb 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).
6-3

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.76 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 (i through iv) 

as shown in the bottom 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 almost always extremely small in the 

scope of an entire economic analysis.

 

Figure 6.1

Time →

Year t 0 1

$ NB0i NB0ii NB0iii NB0iv

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.77

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).

 

76 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.

77

 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 2002). [6th Edition, EPA/452/B02-001, January 2002, OAQPS]. 
6-4

Annualizing costs when there is no initial cost at t=0 is estimated using the following equation:

(1 ) 1

*(1 )

*

 





n

n

r

r r

AC PVC (3)

where

AC = annualized cost accrued at the end of each of n periods;

PVC = present value of costs (estimated as in 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:

(1 ) 1

*(1 )

*

( 1)

 





n

n

r

r r

AC PVC

. (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 costeffectiveness 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 their 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   dn1NBn1  NBn

 (5)

NBtis the net difference between benefits and costs (Bt - Ct) that accrue in year t and the accumulation 

weights, dt, are given by

( )

(1 )

n t

t d r



  (6)

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: NPV = NFV/ (1+r)n
6-5

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. Net present value represents the 

present value of all costs and benefits, annualization represents the value as spread smoothly through 

time, and net future value 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 they 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 net present value 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 net present value is positive does not depend on the discount rate, although the 

discount rate may affect the relative present value if a policy is compared to another policy).

Discounting can, however, substantially affect the net present value 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 net present value. 
6-6

Text Box 6.1 - Potential Impact of Discounting

Suppose the benefits of a given program occur 30 years in the future and is valued (in real terms) at $5 

billion at that time. The rate at which we discount the $5 billion future benefits, however, can 

dramatically alter the economic assessment of the policy: $5 billion 30 years in the future discounted at 

1% is $3.71 billion, at 3% it is worth $2.06 billion, at 7% it is worth $657 million, and at 10% 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 net present value (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.

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, 

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 net present value 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 benefit-cost analysis.

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 benefit-cost analysis reflect the welfare 

outcomes of those affected by the policy. Another way to view this is to consider that the time horizon T

where 



of an analysis should be chosen such that 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 
6-7

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.

 The extent to which benefits and costs are separated by generations.

The choice should be explained and well-documented. 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: in other words, the tradeoff 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 tradeoffs 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 uses a variety of terms and frameworks, often to describe identical or very 

similar key concepts. General themes throughout this literature, however, 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 benefit-cost analysis, 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.
6-8

Text Box 6.2 - Social Rate and Consumption Rates of Interest

Suppose the market rate of interest, net of inflation, is 5%, and that taxes on capital income amount to 40 

percent of the net return. In this case, private investments will yield 5%, of which 2% is paid in taxes to 

the government, with individuals receiving the remaining 3%. From a social perspective, consumption 

can be traded from the present to the future at a rate of 5%. But individuals effectively trade consumption 

through time at a rate of 3% because they owe taxes on investment earnings. As a result, the consumption 

rate of interest is 3%, which is substantially less than the 5% social rate of return on private sector 

investments (also known as the social opportunity cost of private capital).

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 benefit-cost analysis. 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).

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 tradeoffs. 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, have different levels of 

assets, and 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. 
6-9

The historical rate of return, post-tax and after inflation, is a useful measure because it is relatively riskfree, and benefit-cost analysis 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 that later. These factors are 

combined in the equation:

r g    

 (7) 

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 may be derived from data, the third is unobservable and must be 

determined.78 (A more detailed discussion of the Ramsey equation can be found in the intergenerational 

discounting section of this chapter.)

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 pretax 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. 79 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 may 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 

 

78

 The 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. (US EPA 2004c).

79

 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 that it offers substantial present value net benefits.
6-10

corporate debt, equity (e.g., returns to stocks) or some combination of the two. In practice, we typically 

observe average returns which are likely to be higher than marginal return 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. 

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 which 

represents how society trades and values consumption over time. 80 The adjustment factor is referred to as 

the "shadow price of capital."81 Many sources recognize this method as the preferred analytic approach to 

social discounting for public projects and policies. 82

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.

Text Box 6.3 - Shadow Price of Capital

Suppose that the consumption rate of interest is 3%, the pre-tax rate of return on private investments is 

5%, 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% 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.

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 capital adjustments would treat costs and 

benefits symmetrically in this sense. 

 

80

 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 we use more 

general terminology. Discounting based on the shadow price of capital is referred to as a ―supply side‖ 

approach by EPA‘s SAB Council (US EPA, 2004c).

81 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.

82 See OMB Circular A-4 (2003), Freeman (2003), and the report of EPA‘s Advisory Council on Clean Air 

Compliance Analysis (US EPA 2004c).
6-11

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 6.4.83

Text Box 6.4 - Shadow Price of Capital Approach

Suppose that the pre-tax rate of return from private investments is 5%, and the post-tax rate is 3%, with 

the difference attributable to taxation of capital income. Assume as well 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% with government debt and 25% 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% 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, to which is added 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% consumption rate of 

interest ($1.5328) minus the $1.5025 social cost.

6.2.4.1 Estimating the Shadow Price of Capital

The shadow price of capital approach is data intensive, requiring, among other things, estimates of the 

social rate of time preference, the social opportunity cost of capital, as well as 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.84

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 considered 

relative to the flow of investment capital from abroad.85

 

83

 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 net present value results substantially different from the shadow price of 

capital approach. (For an example of these potential differences see Spackman 2004.)

84 Depending on the magnitudes of the various factors, shadow prices from close to 1 to 3, 20, 100, and infinity 

can result. 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.

85 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. 
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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.86 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) and, 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 dollar-for-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, providing little detailed empirical evidence as to the 

relationship between the nature and size of projects and capital displacement. Thus, while the approach is 

often recognized as being technically superior to simpler methods, it is difficult to implement in practice. 

Text Box 6.5: Alternative Social Discounting Perspectives

Some of the social discounting 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 some aspects of the future 

differently from other consequences. 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 

also indicates that discount rates appear to be lower the larger the magnitude of the underlying effect being valued 

is, higher for gains than for losses, and tend 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. Additional studies have attempted to address the 

time-consistency problems that emerge from hyperbolic discounting. 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. 87

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. For 

this, historical rates of return, post-tax and after inflation, on "safe" assets, such as U.S. Treasury 

securities, are normally used, although some may use the return to private savings. Recent studies and 

reports have generally found government borrowing rates in the range of around 2-4%.88 Some studies 

 

86 See Lind (1990) for this revision of the shadow price of capital approach.

87 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. 

88 OMB (2003) cites evidence of a 3.1% pre-tax rate for 10-year Treasury notes. According to the US 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 10-year Treasury notes. Newell and Pizer (2003) find rates 

slightly less than 4% for 30-year Treasury securities. Nordhaus (2008) reports a real rate of return of 2.7% for 

20-year Treasury securities. The Congressional Budget Office estimates the cost of government borrowing to 

be 2%, a value used as the social discount rate in their analyses (US CBO 1998).
6-13

have expanded this portfolio to include other bonds, stocks, and even housing, and 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 time periods selected and the classes of assets considered.89 

Studies of the social discount rate for the United Kingdom place the consumption rate of interest at 

approximately 2% to 4%, with the balance of the evidence pointing toward the lower end of the range.

90

 

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), for example, estimate an intragenerational rate to be 

3.5%. 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 we address in the next section.

The social opportunity cost of capital represents a situation where investment is crowded out dollar-fordollar by the costs of environmental policies. This is an unlikely outcome, but can be useful for 

sensitivity analysis and special cases. Estimates of the social opportunity costs of capital are typically in 

the 4.5% to 7% range depending upon the type of data used.91

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 and capital markets fit an ―open 

economy‖ model, then there is probably little displaced investment.92 Because 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.93

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 

 

89 Ibbotson and Sinquefield (1984 and annual updates) provide historical rates of return for various assets and for 

different holding periods.

90 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.

91 OMB (2003) recommends a real, pre-tax opportunity cost of capital of 7% and refers to Circular A-94 (1992) 

as the basis for this conclusion. Moore, et al. (2004) estimate a rate of 4.5% 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 

(US EPA 2004c; US EPA 2007b) recommends using a single central rate of 5% as intermediate between, 3% 

and 7% rates based generally on the consumption rate of interest and the cost of capital, respectively.

92

 Lind (1990) first suggested this.

93

 See, in particular, Lesser and Zerbe (1994) and Moore et al. (2004).
6-14

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. 

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 or not the estimated rates are measured in private capital or consumption terms. 

Policies with very long time horizons often 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 impacts into decision-making. However, there is less 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. 

As a starting point, the section first lays out 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 that 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 the social rate of time preference. In particular, the fundamental choice of what moral 

perspective should guide intergenerational social discounting - a social planner who weighs the utilities of 

present and future generations, the 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, systematic consideration of uncertainty, and 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 net present 

value of the flow of utility from consumption over time. 94 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 it typically excludes numerous real-world departures from the idealized assumptions 

 

94 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).
6-15

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 

also be equal to the social rate of return on private investments and the consumption rate of interest. 

It is worth noting, however, that the optimal growth literature is only one strand of the substantial body of 

research and writing on inter-temporal 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 an 

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    

.

The first term 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 (e.g., 

Valdés 1999)—then future generations will be richer than the current generation and therefore increments 

to consumption will be valued less highly in the future than today due to the diminishing marginal utility 

of consumption. Thus, 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 in 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 normative, 

approach. The descriptive approach attempts to derive likely estimates of the underlying parameters in 

the Ramsey equation, based on the argument 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 may be estimated, allowing 



to be inferred. 

Other economists take what is referred to as a prescriptive approach and assign parameters to the Ramsey 

equation to match what they believe to be ethically correct.95 For instance, there has been a long debate 

on whether the pure rate of time preference should be greater than zero (starting with Ramsey himself). 

The main responses to the prescriptive approach are (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). 

 

95 Arrow et al. (1996a).
6-16

There is also an argument that a very low discount rate advocated by some adherents to the prescriptive 

approach leads to unethical shortchanging of current and close generations.
6-17

Text Box 6.6 Applying these approaches to the Ramsey Equation

The Ramsey approach has been most widely debated in the context of climate change, and 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 DICE model, 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 estimate the initial return on capital 

(and social discount) to be 6 percent, the rate of time preference to be 2, 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.

Other analyses have adopted at least aspects of a prescriptive approach. For example, the Stern Review (see Text 

Box 6.7) 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.

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 (1996) 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. Hence, 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 policymaking, 

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 distributed asymmetrically over vast expanses of time. 

Here 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. Social 
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discounting in these cases can no longer be thought of as a process of consulting the preferences of all 

affected parties concerning their valuation today of effects they will experience in different 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 far into the future has 

essentially zero present value when discounted at even a low rate. But a modest sum invested today at the 

same low interest rate can 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. 96

For example, in the climate change context, Pearce et al. (2003) show that decreasing the discount rate 

from a constant 6% to a constant 4% nearly doubles the estimate of the marginal benefits from CO2 

emission reductions. Weitzman (2001) shows that moving from a constant 4% 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% to 95% with an initial rate of 7%, and 440% to 700% with an 

initial rate of 4%, 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 rates97- 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; Portney and Weyant 1999). 

Intergenerational Transfers

The notion of Pareto compensation attempts to identify the appropriate social discount rate in an 

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 U.S. 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.98

 

96 OMB‘s Circular A-4 (OMB 2003) requires the use of constant three and seven percent for both intra- and 

intergenerational discounting for benefit-cost estimation of economically significant rules but allows for lower, 

positive consumption discount rates, perhaps in the 1 to 3 percent range, if there are important intergenerational 

benefits/costs. 

97

 Another issue is that there are no market rates for intergenerational time periods.

98 See Lind (1990) and a summary by Freeman (2003).
6-19

Equal Treatment Across Generations

Environmental policies that affect distant future generations can be considered to be altruistic acts.99 As 

such, some argue that they should be valued by current generations in exactly the same way as other acts 

of altruism are valued. For this reason, the relevant discount rate is not that applied to an individual's 

consumption, but instead that applicable for an individual's valuation of the consumption or welfare of 

someone else, identified by the analyst through either revealed or stated preference.

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, and decline as the time horizon involved lengthens.

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 what that 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; Groom et al. 2007).

100 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 we can infer a constant, certainty-equivalent discount rate that yields the discount factor (for any 

given distribution of r). Several methods for accounting for uncertainty into intergenerational discounting

are discussed in more detail in the next section.

6.3.3 Evaluating Alternatives

There are a wide range of options available to the analyst for discounting intergenerational costs and 

benefits. We describe several of these 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, we encourage the analyst to explore a variety of approaches.

 

99 Schelling (1995) and Birdsall and Steer (1993) are good references for these arguments.

100 Gollier and Zeckhauser (2005) reach a similar result using a model with decreasing absolute risk aversion.
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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 U.K., the Treasury recommends the use of a 3.5% discount rate 

for the first 30 years followed by a declining rate over future time periods until it reaches 1% for 301+ 

years.101 . 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; Spackman 

2004).

6.3.3.3 Declining or Non-constant Discount Rate

Using a constant discount rate in benefit-cost analysis (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 

 

101

 The guidance also requires a lower schedule of rates, starting with 3% for 0-30 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.
6-21

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.102 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 

account for this uncertainty in the calculation of the expected net present value of the policy. Over the 

long time horizons 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 then dominate the expected net present value calculations for benefits and cost far in the future 

(Weitzman 1999).

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 

seven percent could undervalue net present benefits by between 21 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 ignore the effects of uncertainty in economic growth over time. Thus, this 

approach is not subject to the time inconsistency problems of some of these 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 under net present value (NPV) versus net future value (NFV).

103

 Because the choice between NPV and NFV is arbitrary, such an outcome would be problematic for 

applied policy analysis. More recent work, however, has appeared to resolve this seeming inconsistency, 

confirming the original findings and providing sound conceptual rationale for the approach.104 

 

102 E.g., Nordhaus 2008.

103

 See Gollier (2004) for a technical characterization of this concern, and Hepburn and Groom (2007) for additional 

exploration of the issues.

104

 See Gollier and Weitzman (2009) provide a concise and clear treatment. Freeman (2009) and Gollier (2009) 

also propose solutions.
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Text Box 6.7: What’s the Big Deal with the Stern Report?

In autumn 2006, the UK government released a detailed report titled The Economics of Climate Change: The Stern 

Review, headed by Nobel Laureate Sir Nicholas Stern (2006). The report drew mainly on published studies to 

estimate that damages from climate change could result in a 5% to 20% decline in global output by 2100, while 

costs to mitigate these impacts were significantly less (about 1% 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 Review recommended that policies aimed towards sharp reduction in

greenhouse-gas emissions should be enacted immediately. 

While generally lauded for its thoroughness and use of current climate science, the Review drew significant 

criticism and discussion of how future benefits were calculated, namely Stern‘s assumptions about the discount rate 

(Tol and Yohe 2006; Nordhaus 2008). The Review used the Ramsey discounting equation (see section 6.3.1), 

applying rates of 0.1% for the annual pure rate of time preference, 1.3% 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%, 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% or 7% 

commonly observed in market rates. Furthermore, Stern‘s relatively low values of ρ and η imply that the current 

generation should operate at a higher savings rate than what is observed, thus implying that we should save more 

today to compensate losses incurred by future generations.

Why did Stern use these particular parameter values? First, he argues that we have 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 and Guidance

As summed up by Freeman (2003 p. 206), ―economists have not yet reached a consensus on the 

appropriate answers‖ to all of the issues surrounding intergenerational discounting. And while there may 

be more agreement on matters of principle for discounting in the context of intragenerational policies, 

there is still some disagreement on the magnitude of capital displacement and therefore the importance of 

accounting for the opportunity costs of capital in practice. Thus, the recommendations provided here are 

intended as practical and plausible default assumptions rather than comprehensive and precise estimates 

of social discount rates that must be applied without adjustment in all situations. That is, these 

recommendations should be used as a starting point for benefit-cost analyses, but if the 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.
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 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:

o 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%. 

o 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 Office of Management and Budget estimates a rate of 7% for 

the opportunity cost of private capital.

o 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 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 time-declining schedule of discount factors (e.g., Newell and Pizer 2003; Groom et al. 

2007; Hepburn et al. 2009). This approach accounts for discount rate uncertainty and variability, 

which are known to have potentially large effects on net present value 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 carbon dioxide 

emission reductions includes treatment with certainty-equivalent constant discount rates of 2.5, 3 

and 5 percent. (See Text Box 7-XX 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.

When implementing any discounting approach the following principles should be kept in mind:
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 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, with the monetary benefits from the expected future 

impacts--be they changes in human health, environmental conditions, ecosystem services, etc.--

discounted at the same rate as other benefits and costs in the analysis.
7-1

7 Analyzing Benefits

7.1 Introduction

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. In addition, 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. 

While this chapter focuses on monetized benefits analysis, it is important to note that there are other 

methods for evaluating policies. One example is cost-effectiveness analysis, which does not require 

monetization of benefits but rather divides the costs of a policy by a particular effect (e.g., number of 

lives saved). Cost-effectiveness analysis can be used to compare proposed policy changes on an effectby-effect basis, but, unlike benefit-cost analysis, it 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.

 

Many EPA benefits analyses face several major obstacles. First, a given policy may produce multiple 

environmental effects, but it is seldom possible to analyze all effects simultaneously in an integrated 

fashion. In most cases, analysts will have to address each effect individually, and then attempt to 

aggregate the individual values to generate an estimate of the total benefits of a policy. Although there 

are exceptions to this ―effect-by-effect‖ approach to benefits analysis (e.g., efforts to estimate the social 

benefits of reducing carbon dioxide emissions), much of the discussion in this chapter assumes that 

analysts will need to adopt this approach. A constant challenge in employing an effect-by-effect approach

is to balance potential tradeoffs between inclusion and redundancy. 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 nonuse values, but 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. 

A second obstacle analysts often face is the difficulty of conducting original valuation research in support 

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 7.5, but much of this chapter 

is written with benefit transfer in mind. In particular, the descriptions of revealed and stated preference 
7-2

valuation methods in sections 7.4.1 and 7.4.2 include recommendations for evaluating the quality and 

suitability of published studies for use in benefit transfer.

A third major obstacle sometimes faced in benefits analysis arises from the lack of appropriate analytical 

tools and/or data with which to apply them. Even though the theory and practice of benefits analysis 

continue to improve, an off-the-shelf model and data set usually are not available. For this reason, 

analysts often must either adapt existing tools to the situation using their best professional judgment or 

simply leave some benefit categories non-monetized. 

The rest of the chapter is organized as follows: 

 Section 7.2 describes the main steps in the benefits analysis process;

 Section 7.3 discusses the economic definition of ―value,‖ the major categories of benefits relevant 

for environmental policies, and some important considerations associated with valuing benefits in 

each category;

 Section 7.4 focuses on the economic valuation of environmental changes;

 Section 7.5 discusses the practice of benefit transfer; and

 Section 7.6 describes the presentation of non-monetized benefits.

The goals of these sections are to familiarize the reader with the available methods, to provide key 

references for more detailed information, and to highlight important considerations for judging the quality 

of studies that use different valuation methods. These considerations will apply whether the study is a 

new one conducted specifically for the policy being analyzed or a previous study being considered for use 

in benefit transfer. 

7.2 The Benefits Analysis Process

This section discusses the main steps in the benefits analysis process. Although there are exceptions to 

this approach (see Text box 7.1), the discussion here is framed in terms of the general ―effect-by-effect‖ 

approach to benefits analysis described above.

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-
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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.

Text Box 7.1 Estimating benefits from reducing carbon dioxide emissions: The Social Cost of 

Carbon (SCC)

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 cost-benefit analyses. One 

way to accomplish this is through the estimation of the ―social cost of carbon.‖ The social cost of carbon (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, 

including 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, 3, and 

2.5 percent.1 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). 

From the model runs the workgroup selected four SCC estimates 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 socio-economic and emissions scenarios for the 

5, 3, 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 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 group 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: 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.
7-4

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 7.3. 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 

policymakers. 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 may have detrimental effects on natural resources (e.g., affecting 

oxygen availability in surface water or reducing crop yields) as well as direct or indirect effects on human 

health (e.g., 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 evaluation of how the pathways 

differ in the ―post-policy‖ 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)? 

An assessment of how the physical effects of each policy option will differ and how each physical 

effect will impact each benefit category should be conducted. 

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.105

 

105 This criterion relates to distributional considerations detailed in Chapter 10.
7-5

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 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 a 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. 

The principal role of the economist at this stage is to ensure that the information provided is useful for the 

subsequent economic valuation models that may be used later in the benefits analysis. The analyst should 

give special care to ensuring that the endpoints evaluated are appropriate for use in benefits estimation. 

Effects that are described too broadly or that cannot be linked to human well-being limit the ability of the 

analysis to capture the full range of a policy‘s benefits. Text Box 7.2 provides examples and a more 

detailed discussion. 

Another important role for economists at this stage is to provide insights, information, and analysis on 

behavioral changes that can affect the results of the risk assessment as needed. Changes in behavior due 

to changes in environmental 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. 
7-6

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. As a result, traditional 

measures of risk often are difficult or impossible to incorporate into benefits analyses. For example, traditional 

measures of risk often are based on endpoints not directly related to health outcomes or ecological services that 

can be valued using economic methods. In addition, these measures often are 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. 

However, 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 economist‘s 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, i.e., endpoints 

that can 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 (US EPA 1997e), and the 1997 Guiding 

Principles for Monte Carlo Analysis (US EPA 1997d).

Step 3: Estimate the Values of the Effects

The next step is to estimate 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. That is, we calculate 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 may be applied can be found in section 7.4. 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 
7-7

applying more than one method, however, and any potential overlap should be noted when presenting the 

results. The discussion of benefit transfer in section 7.4.3 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 

also include a quantitative uncertainty assessment when possible. Guiding principles for presenting 

uncertainty are addressed in Chapter 11.

7.3 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, and 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 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 of one and a little less of the other to achieve a higher level of utility. Thus, market prices can 

be used to measure the value of market goods and services directly, and 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 benefit of an environmental improvement is shown graphically in Figure 7.1. Reducing emissions 

from e0 to e1 produces benefits equal to the shaded area under the marginal damages curve. Because 

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 
7-8

Figure 7.1

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 7.4 

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.106 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 benefit-cost analysis 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). 

 

106 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, Hueth, and Schmitz (2005), and Appendix A of 

these Guidelines.

e1 e0
7-9

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 non-market. However, there may be instances in which income and 

substitution effects are important.107 To simplify the presentation, the term WTP is used throughout the 

remainder of this chapter to refer 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 benefit-cost analysis (Boadway and Bruce 

1984; Just et al. 1982; Freeman 2003). These tests can be carried out by summing the WTP for all 

affected individuals and comparing them 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.108

 

107 For more information see Appendix A and Hanemann (1991).

108 See section A.3 of the Appendix for further explanation of Kaldor-Hicks conditions.
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Table 7.1 - Types of Benefits Associated With Environmental Policies: Categories, Examples, and CommonlyUsed 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

Nonuse 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.
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The types of benefits that may arise from environmental policies can be classified in multiple ways (e.g., 

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.109 The sections below discuss each of the benefit categories listed in Table 7.1 in more detail. 

7.3.1 Human health improvements

Human health improvements from environmental policies include effects such as reduced mortality rates, 

decreased incidence of nonfatal 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.

.

7.3.1.1 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 $7.9 million (2008$) 

to value reduced mortality for all programs and policies.110 This value is based on a distribution fitted 

to twenty-six 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 (2003) provides some information on this topic, including key benefit transfer 

issues when using adult-based studies. OMB 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 benefit-cost analysis, 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. 

Methods for valuing mortality risk changes 

 

109 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.

110

 This value is adjusted from the base value reported in U.S. EPA 2000 ($4.8 million in 1990 dollars) using the 

Consumer Price Index. The value is not adjusted for income growth over time. 
7-12

Because individuals appear to make risk-income tradeoffs in a variety of ways, the value of mortality risk 

changes are estimated using three primary methods. The most common method used is the hedonic wage, 

or wage-risk, method in which value is inferred from the income-risk tradeoffs made by workers for onthe-job risks. Averting behavior studies have also been used to value risk changes by examining 

purchases of goods that may affect mortality risk (e.g., bicycle helmets). Finally, stated preference 

studies have been increasingly used to estimate willingness to pay 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., workrelated vs. environmental), results from any single study may not be directly applicable to a typical 

environmental policy case.

Additionally, other methods 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 complexity of the additional steps and the 

indirect nature of the resulting estimates must be considered and evaluated. 

At one time, reduced mortality risk was valued under a human capital approach that equated the value of 

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 willingness to pay 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, Kramer, and Hubbell (2006). EPA 

applications of VSL are numerous, and include the Clean Air Interstate Rule, the Non-Road Diesel Rule, 

and the Stage 2 Disinfection By-products Rule (DBP).

111

Important Considerations:

The analyst should keep three important considerations in mind when estimating mortality benefits: 

 

111 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); 

Stage 2 DBP (http://www.epa.gov/safewater/disinfection/stage2/pdfs/anaylsis_stage2_ecconomic_main.pdf) 
7-13

 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 

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 1989) and is typically 

expressed as ―statistical life years.‖ Looking again at the policy described above, suppose the risks were 

spread over a population who each had 20 years of remaining life expectancy. The policy would then 

save 200 statistical life years (10 statistical lives * 20 life years each). In practice, estimating statistical 

life years saved requires risk information for specific subpopulations (e.g., age groups, health status). It is 

typical to use statistical life years saved in cost-effectiveness analysis, but valuing a statistical life year 

remains a subject of debate in the economics literature. Theoretical models show that the relationship 

between willingness to pay and 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. man-made), and the causative event (e.g., 

cancer vs. accidents). Population characteristics include those generally expected to influence WTP for 

any good (e.g., income, 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 (e.g., 

Jones-Lee et al. 1993), but others find this effect to be small if it exists at all (e.g., Alberini et al. 2004). 

Still others suggest older populations have higher WTP (e.g., Kniesner, Viscusi, and Ziliak 2006). Smith 

et al. (2004) and Viscusi and Aldy (2007) 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 may cause immediate or delayed health effects. If individuals typically 

prefer health improvements earlier in time than later, all else equal, then the willingness to pay 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. 
7-14

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, US EPA 2007). 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 willingness to pay for contemporaneous risk reductions, then 

calculating the benefits of a policy with delayed risk reductions requires three steps: (i) estimating the 

time path of future mortality risk reductions, (ii) estimating the annual WTPs in all future years, and (iii) 

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. 

7.3.1.2 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. Non-fatal 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. 

Willingness to pay to reduce the risk of experiencing an illness is the preferred measure of value for 

morbidity effects (see section 7.2). 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 estimates generally only capture mitigating and indirect costs, omitting averting 

expenditures and lost utility associated with pain and suffering. 112

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 may provide 

useful data, but that data must be interpreted carefully if it is to inform 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. 

 

112

 This is why cost-of-illness 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 cost of illness even for 

minor acute respiratory illnesses (e.g., Alberini and Krupnick 2000). Still, there is not any broadly applicable 

―scaling factor‖ that relates COI to WTP generally.
7-15

The three primary methods used most often to value morbidity in an environmental context are stated 

preference (section 7.4.2), averting behavior (section 7.4.1.4), and cost-of-illness (COI) (section 7.4.1.5). 

Hedonic methods (section 7.4.1.3) have been 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 tradeoffs, for example, do not directly estimate dollar values for risk reductions, but 

rather, provide rankings of relative risks based on consumer preferences. Risk-risk tradeoffs may be 

linked to WTP estimates for related risks.113 

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, however, cannot be directly related to WTP estimates as these 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 benefit cost analyses (Hammitt 2003; 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.

Previous studies

A comprehensive summary of existing studies of morbidity values is beyond the scope of these 

guidelines. Here we provide 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 can be found in Hammitt and Haninger (2007) on food-related 

illnesses and Chestnut et al. (2006) on 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 1999b) 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 (USEPA 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; U.S. EPA 1999a) 

draw upon a number of existing studies to obtain values for reductions of a variety of health effects, 

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 vary 

with characteristics of the condition, the affected population, and the approach to valuation. 

Vassanadumrondee 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 over 230 WTP estimates from 

seventeen 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 

 

113

 EPA analyses have, for example, used risk-risk tradeoffs 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 2005)
7-16

studies may be available through the Environmental Valuation Reference Inventory (EVRI) maintained 

by Environment Canada and containing over 1,100 studies that can be referenced according to medium, 

resource, stressor, method, and country.

114

 

Text Box 7.3 - Non-WTP Measures

Economic measures of value calculate willingness to pay (WTP) for environmental changes. WTP is defined as 

that amount of money which, 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. We consider some examples 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, however, 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, affected people might be compensated simply by giving them enough money to buy substitutes for the 

fish they caught 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. This number is, however, 

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 (e.g., Wackernagel and Rees 1996). Dasgupta (2002) 

interprets these approaches as single-factor 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. Health effects are often proxied by the ―cost of illness‖ (COI), which are the total costs of 

treatment and time lost due to illness. Although COI is discussed in greater detail in Section 7.4.1.5, we 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 are the awards made by 

juries. 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, then, 

―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. 

Guilty defendants are made examples of to deter others. 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. Therefore, these estimates are not appropriate for application to ex ante evaluation of 

the value associated with a statistical probability. 

 

114 See www.evri.ca for more information.
7-17
7-18

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.‖115 Severity may also be described in terms of health state indices that combine multiple 

health dimensions into a single measure.116 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 frequency of effects 

also can vary widely across illnesses. Some effects are one-time events, such as a gastrointestinal illness, 

that are unlikely to recur. Other effects do recur or can be aggravated regularly (e.g. asthma), 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 may 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 (e.g., Cameron and DeShazo, 2008). (Refer to section 7.4.1.5 

and Text Box 7.3 on the use of COI versus WTP measures of value.) 

 

115

 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. 

116

 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 ―healthrelated quality of life‖ index values and WTP measures for improved health outcomes (e.g., Hammitt 2002). 

Still the inclusion of some aspects of these indices may prove useful in valuation studies (e.g., 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).
7-19

Incomplete estimates of WTP

The widespread availability of health insurance and paid sick leave shift part 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).

7.3.2 Ecological benefits

In addition to human health benefits, many EPA policies also 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; NRC 2005; 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, the same general approach used to estimate human health benefits can be used to estimate 

ecological benefits: identify the pollutants (or other environmental stressors) whose emissions will be 

altered by the regulation, 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 

willingness to pay for changes in the endpoints using revealed or stated preference valuation methods. In 

the case of ecological benefits estimation, however, the relevant endpoints will include measures of 

ecosystem health rather than human health, and the methods and data required to estimate the doseresponse functions and willingness to pay will differ accordingly. As in the human health case, the 

estimation of dose-response relationships between pollutants and endpoints will fall mainly to natural 

scientists, although collaboration between scientists and economists often will be 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]). However, 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 p 457-460). 

We explore some of these complications 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 
7-20

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 Banzhaff 2007, Wallace 2007, 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 nonconsumptive outdoor activities. Identifying relevant endpoints for nonuse values, on the other hand, may 

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 nonuse 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, and 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 (e.g., Freeman 1991, Barbier et al. 

2002, Smith 2007, 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. However, 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; Phaneuf et al. 

2008). Furthermore, if a significant portion of the benefits for ecological changes are thought to consist 

of nonuse values rather than use values, 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 7.4.2), 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;

U.S. EPA 2009b). 
7-21

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 Science Advisory Board 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 we give examples of studies that have applied 

traditional valuation methods (discussed more generally in the following sections of this chapter) to 

ecosystem goods and services, and we highlight some of the special complications that can arise in such 

studies.

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 (e.g., Polasky et al. 2008a, 2008b, Boyd and Banzhaf 2007, U.S. EPA

2009b). Some simple examples have long been known: commercially valuable species ―produce‖ 

themselves, and such early work 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 (e.g., Bell 1997, Ellis and 

Fischer 1987, Massey et al. 2006). However, 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, surface water flow characteristics, etc. Hence the degradation of supporting 

ecological resources should be reflected in diminished outputs of these commodities. However, direct 

application of production function approaches has often been hampered by data and methodological 

limitations. Specifically, it can be difficult to measure the flow of nonmarket ecosystem services that a 

particular production process receives, as well as to statistically control for the effects of unobserved 

characteristics of climate and topography statistically. 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
7-22

Econometricians have generally 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 

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; Willis and Garrod 1991), wetlands (Lupi et al. 1991; 

Mahan et al. 2000; Woodward and Wui 2001; Bin and Polasky 2005, Costanza et al. 2008), or other 

varieties of ―open space‖ (Geoghegan et al. 1997; Benson et al. 1998; Irwin and Bockstael 2002; Irwin 

2002; Thorsnes 2002). 

Travel cost models

A large number of studies have used 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 focused on 

one of twenty-five 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, 

Adamowicz et al. 1997), beach use (Bockstael et al. 1987a, Parsons and Massey 2003), backcountry 

recreation (Boxall et al. 1996), rock-climbing (Shaw and Jakus 1996), and kayaking (Phaneuf and 

Siderelis 2003).

Stated preference methods

Revealed preference methods cannot capture nonuse 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 1995) in their 

enjoyment; in such cases only stated preference methods can provide estimates of willingness to pay or 

accept (Freeman 2003). More generally, stated preference methods may be employed when researchers 

want to identify the widest possible spectrum of values, both use and nonuse (e.g., 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
7-23

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 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 wellknown 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 valued ecological endpoints that were 

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; 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 (e.g., 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. 

For example, if the policy leads to spatially heterogeneous environmental quality improvements, then it 
7-24

may lead to a resorting not only of recreators but also of the target species among the recreation sites. 

Ignoring this spatial resorting effect can give biased welfare estimates (Newbold and Massey 2009). 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, care must be taken to avoid doublecounting. 

It also will be important to identify and discuss any omitted benefit categories that are thought to be 

important but that could not 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, analysts may be able to identify 

the most cost-effective approach among different alternatives, or to present natural science information 

that may convey the biophysical impact of a policy even if it does not quantify the willingness to pay or 

accept for such a policy. It is better to acknowledge gaps in information by discussing them qualitatively 

or by reporting physical measures (if available) than by employing conceptually flawed methods of 

monetization. In particular, analysts should avoid the use of replacement cost, embodied energy-based 

evaluation methods, or ecological footprint analysis to derive estimates of willingness to pay or accept.

7.3.3 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. 

For example, effects from changes in air quality on the provision of the service flows from physical 
7-25

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 will 

typically begin 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 will be 

used to relate local concentrations of pollutants to corrosion, soiling, or other physical damages that will 

affect the production (inputs) or consumption (output) of the material service flows. The market response 

to these impacts then 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).

7.4 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 nonmarket inputs, which are, by definition, not traded in markets.117 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. We also discuss cost of illness methods in this section, which are sometimes used to value 

human health effects when estimates of willingness to pay 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 behavior observed in actual 

 

117 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. Prices in such markets are determined by regulationinduced scarcity, however, not by considerations of marginal utilities or marginal products. 
7-26

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.

7.4.1 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.

118

7.4.1.1 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.

119

 Marginal changes, however, may be 

evaluated by considering the production side of the market alone. 

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 utility obtained from the consumption good in whose production 

the input is employed by the marginal product of the input. 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, however, 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; 

 

118 Although not a revealed preference method as it does not measure WTP, we discuss COI methods in this section 

since estimates are based on observable data.

119 See Appendix A for more detail.
7-27

 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 (e.g., Varian 1992, 

for an explication of the relationships among these functions). 

The value of a marginal change in some environmental condition can, then, 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.120 It should be noted, however, that problems of data availability and 

reliability often arise. These problems may motivate the choice among these conceptually equivalent 

approaches, or in favor of another.

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 would 

need to know the price of output and the marginal product of the environmental input. To derive the 

equivalent measure using a cost function approach, the analyst would need 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.121

In the statements above it has been emphasized 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 

will require consideration of changes in both producer and consumer surplus. The latter will necessitate 

application of techniques (e.g., travel cost, hedonics, and stated preference) discussed elsewhere in this 

chapter. 

Before moving on to those topics, there is 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), and its purchase price 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 7.4.1.3. It is introduced now to 

show that production, cost, or profit function approaches are generally equivalent to hedonic approaches.

 

120 For a good review of statistical procedures used for estimating production, cost, and profit functions see Berndt 

(1991). 

121

 Derivation of marginal values often involves an application of the ―envelope theorem‖: the principle that effects 

from variables which are already optimized are negligible. So, for example, 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 approach, the analyst would determine how 

pqdq CQ e

Q

,

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.
7-28

―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, we might also say that 

developers ―produce‖ housing services when they build residences. Therefore, hedonic pricing 

approaches may 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.122 

Production, cost, or profit functions have also found interesting applications to the estimation of some 

ecological benefits.

123

 Probabilistic models of new product discovery from among diverse collections of 

natural organisms can also be regarded as a type of ―production.‖

124

 Finally, work in ecology also points 

to ―productive‖ relationships among natural systems that may yield insights to economists as well.125

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, as data on prices are often more complete than are data on quantities, and 

because 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 may 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.

 

122 Refer to Adams et al. (1986), Kopp and Krupnick (1987), Ellis and Fisher (1987), Taylor (1993), and U.S. EPA 

(1997a) for examples. 

123 See, for example, Acharya and Barbier (2002) on groundwater recharge and Pattanayak and Kramer (2001) on 

water supply. 

124 For example, see Weitzman (1992), Simpson et al. (1996), and Rausser and Small (2000).

125 For example, see e.g., Tilman, Lehman and Polasky 2005.
7-29

Market imperfections. Most analyses assume perfectly competitive behavior on the part of producers 

and input suppliers, and 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 that originated with Buchanan (1969), analysts 

should exercise caution and restraint in attempting to correct for departures from competitive behavior. 

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.

7.4.1.2 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. For example, 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 a traveler makes for recreational purposes. In most cases, 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.126 A variety of approaches 

have been used in the literature to define the opportunity cost of time. Most commonly, researchers have 

 

126 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 

methods for allowing time onsite to vary across trip and alternatives. 
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used a fixed fraction of a person‘s hourly wage ranging from one third to one, 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 also may 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 and 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 on site 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 (e.g., parking, restrooms, boat ramps), participant 

characteristics (e.g., age, income, experience, 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.127

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 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 willingness to pay 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 (i.e., the addition or increase of 

an admission fee) is another common application of the model. 

 

127 For a comprehensive treatment of the theoretical and econometric properties of recreation demand models see 

Phaneuf and Smith (2005).
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A weakness of the single site model is its inability to deal with large numbers of substitute sites. If, for 

example, as is often the case, a policy affects several recreation sites in a region, 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 may 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.128

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 over a fixed period of time. Compared to the single site 

model, the strength of multiple site models are 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 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. 129 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 also is often included 

as a potential alternative.130 From the researcher‘s perspective, the observable components of utility enter 

the recreator‘s assumed utility function. The unobservable portions of utility are captured by an error 

term whose assumed distribution gives rise to different model structures. Assuming that error terms are 

distributed type 1 extreme values 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 willingness to pay for a given change in sites quality or availability. 

 

128Researchers 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 Kuhn-Tucker 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. 

129 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. 

130In 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 nonparticipation 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.
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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 choosing a given 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.131

The Kuhn-Tucker Model. The KT model is a seasonal demand model that estimates recreators‘ choice 

of which sites to visit (like a multi-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 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 trip taken subject to a 

budget and and non-negative trip constraint yields the ―Kuhn-Tucker‖ 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 Kuhn-Tucker 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) and for more advanced treatments of the modes 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) because 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). 

 

131 See Train (1998) and Train (2003) for detailed descriptions of the nested and mixed logit models.
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In addition to the definition of what constitutes a site, the number of sites included in a recreator‘s choice 

set also 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.132 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 (e.g., Peters et al 1995 and Haab and Hicks 1997) or 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.

133

 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, 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 bullet). 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 multiday 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. 

7.4.1.3 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 their attributes (e.g., risk

levels, supervisory nature, etc.) that should be reflected in wages. Hedonic pricing models are commonly 

used to value the characteristics of properties or jobs using variations in property prices or wages. 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. 

 

132 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. 

133 Excluding any type or class of trip (like multi-site or multi-purpose) 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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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, for example, a cleaner neighborhood, are 

observable, then that price difference can be interpreted as the willingness to pay 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.134 

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 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 (e.g. Leggett and Bockstael 2000), natural amenities (e.g. Thorsnes 2002), land contamination 

(e.g., 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 ―value of a statistical life.‖ (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 tradeoffs 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 

 

134

 Michaels and Smith (1990) offer guidance for defining the extent of the market. 
7-35

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. In addition, 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 (e.g., Viscusi 

2004; Viscusi and Aldy 2007; Aldy and Viscusi 2008; Kniesner, Viscusi and Ziliak 2006; Leeth and 

Ruser 2003; Viscusi 2003; 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 other 

characteristics of the workers involved (e.g., age, gender, race) are also captured.135 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,

136 hedonic wage studies have been used most frequently in benefits assessments to 

estimate the value of fatal risk reductions. That is, 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 7.2.1 and Appendix C.

137

 The value of a statistical life determined by a hedonic wage study, for 

example, typically relates willingness to accept higher wages in exchange for the increased likelihood of 

accidental death during a person‘s working years. However, care should be taken 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 have typically been conducted using residential housing data, but they 

have also been applied to commercial and industrial property, agricultural land, and vacant land.138 Bartik 

(1988) and Palmquist (1988;1991) provide detailed discussions of benefits assessment using hedonic 

methods. Property value studies require large amounts of disaggregated data. Market transaction prices 

on individual parcels or housing units are preferred to aggregate data such as census tract information on 

average housing units to avoid aggregation problems. Problems may 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 

 

135 More information on the CFOI data is available at: http://www.bls.gov/iif/oshfat1.htm.

136 For example, EPA‘s Science Advisory Board 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 2000, EPASAB-EEAC-00-013)

137 As part of the revision of this document, EPA is revisiting the VSL estimate used in policy analysis; further 

guidance will be forthcoming. 

138 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.
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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 to determine the tradeoffs between 

automobile price and safety features to infer the value of a statistical life.

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. If in relating the wage premium paid on dangerous jobs to the statistics 

on premature mortality we fail to distinguish between causes of death—between on-the-job accidents and 

environmentally induced conditions acquired at home, for example—analysts might underestimate the 

wage premium demanded on the job. Conversely, if the same job poses multiple risks – say the risk of 

both accidental death and serious, but non-fatal injury were higher on a particular job – the wage premium 

the job offers would overstate willingness to pay 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 vs. 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 of the hedonic 

price function can be interpreted as willingness to pay 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.
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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 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 recently as the capability for assessing such locational 

relationships within hedonic property data has improved, 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, 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).

7.4.1.4 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 may 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;

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 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).
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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; 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 (e.g., 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, 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 (e.g., age, health status, chronic conditions).

Separability of joint benefits. Analysts should exercise caution in interpreting the results of studies that 

focus on goods in which there may be significant joint benefits (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 
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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.

7.4.1.5 Cost of Illness

A frequently encountered alternative to willingness-to-pay 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 illnessrelated 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 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 cost-of-illness estimates in support of a specific policy. 

Economic Foundations of Cost of Illness 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 (e.g., 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 cost-of-illness 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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General Application by Type of Cost of Illness 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 willingness-to-pay 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 physiciandiagnosed 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. 

Incidence-based 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 cost-of-illness 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. The 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 Cost-of-Illness 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 cost-ofillness 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 
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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 included in most cost-of-illness studies. (See Text Box 7.5 for 

a discussion of the value of time).

7.4.2 Stated Preference 

The distinguishing feature of stated preference (SP) methods compared to revealed preference (RP) 

methods is that SP methods rely on data drawn from people‘s responses to hypothetical questions while 

RP methods rely on observations of actual choices. SP 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 SP methods include their ability to estimate nonuse values and to incorporate 

hypothetical scenarios that closely correspond to a policy case. The main disadvantage of SP methods is 

that they may be subject to systematic biases that are difficult to test for and correct.

The Report of the NOAA Panel on Contingent Valuation often cited as a primary source of information on 

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. 

7.4.2.1 Economic Foundation of Stated Preference Methods 

The responses elicited from SP surveys, if truthful, are either direct expressions of willingness to pay or 

can be used to estimate willingness to pay 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 SP methods as useful and reliable if conducted properly, a 

non-trivial fraction of economists are more skeptical of the results elicited from SP surveys. Due to this 

skepticism, it is important to employ validity and reliability tests of SP results when applying them to 

policy decisions. 

If the analyst decides to conduct an SP survey or use SP results in a benefit transfer exercise, then a 

number of survey design issues should be considered. SP researchers have attempted to develop methods 

to make individuals‘ choices in SP 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 

SP value estimates. Three components of market transactions need to be constructed in SP surveys: the 

commodity, the payment, and the scenario (Fischoff and Furby 1988).

SP studies thus need to carefully define the commodity to be valued, including the 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 
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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 who 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, OMB guidance (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. 

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.139

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.

 

139 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 will need to 

be balanced against the ultimate goals of the survey. Regardless the commodity will need to be specified with 

enough detail to make the scenario credible.
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The hypothetical scenario(s) should also be described so as to minimize potential strategic behavior such 

as ―free-riding‖ or ―overpledging.‖ In the former case, 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 latter case, respondents pledge amounts greater than their true WTP with the 

expectation that they will not be made to pay for the good but believe 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 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.

7.4.2.2 General Application by Type of Stated Preference Study

Two main types of SP survey format are currently used: direct WTP questions and stated choice 

questions. Stated choice questions can be either dichotomous choice questions or multi-attribute choice 

questions. Following a general discussion of survey format, each of the SP survey formats is described in 

detail below.

Goals that should guide selection of the survey format include the minimization of survey costs, 

nonresponse, unexplained variance, and complications associated with WTP estimation. For example, 

open-ended questions are simpler to analyze than other methods of asking the valuation question and 

require smaller sample sizes. These advantages could lead to significant cost reductions. However, these 

advantages may be mitigated by higher nonresponse 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 WTP Questions 

Direct/open-ended WTP questions ask respondents their maximum WTP for the good or service that has 

been described to them, including specific quantity or quality changes. An important advantage of openended SP questions is that the answers provide direct, individual-specific estimates of WTP. Although 

this is the measure that economists want to estimate, early SP 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 SP 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 
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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; 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 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. (Thus, when analyzing the data the 

dependent variable will be continuous for open-ended WTP formats and discrete for stated choice 

formats.)140 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, ―live in area A or area B); two alternatives with an 

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 / 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?‖ 

 

140 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 we use the term 

―stated preference‖ to refer to all valuation studies based on hypothetical choices (including open-ended WTP 

and stated choice formats), as distinguished from ―revealed preference.‖ 
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However, these questions are not always posed as a vote decision and are now commonly called 

dichotomous choice questions.

In recent years, SP 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, SP 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 dichotomous choice questions to obtain the same degree of statistical efficiency in the sample 

means as direct/open ended responses that reveal point-values for WTP (Cameron and James 1987).

To increase the estimation efficiency of dichotomous choice questions, recent applications have 

commonly used what is called a double-bounded 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.

141

 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; Kanninen 1995). 

Multi-Attribute Choice Questions. In multi-attribute choice questions, respondents are presented with 

alternative choices that are characterized by different combinations of goods and services attributes and 

prices. Multi-attribute 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 (e.g., Johnson et al. 1995; 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 it would be paid need to be explained clearly 

and plausible, as with any payment mechanism in a SP study. Boyle and Ozdemir (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. 

 

141 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.
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There are many desirable aspects of multi-attribute 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.142 Asking repeated choices also 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.

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).

7.4.2.3 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 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 door-to-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 answer questions 

over the phone. The same may 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 U.S. 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 non-response 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. 

 

142 Yea-saying refers to the behavior of respondents when they overstate their true willingness to pay in order to 

show support for a situation described in survey questions. 
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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 also 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 willingness to pay 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; Balistreri et al. 2001). It has not been determined 

whether yea-saying may 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‖ 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. SP 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 SP 

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 SP 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 meta-analysis to evaluate eight studies conducted to measure 

values for groundwater protection. (Also see Section 7.5.)
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Validity tests seek to assess whether WTP estimates from SP 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 willingness to 

pay 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).

 Criterion Validity. Criterion validity assesses whether SP results relate to other measures that 

are considered to be closer to the concept being assessed (WTP). Ideally, one would compare 

results from a SP 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 an SP survey. Another 

approach would be to estimate a sample of individuals‘ WTP for a commodity using an SP 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; 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; Schlapfer 

2006). Neither test is necessary or sufficient to establish criterion validity (Heberlein et al. 2006) 

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 willingness to pay for partial changes and relating them to 

the income elasticity of willingness to pay. Other researchers, however, argue that the Diamond 

test may not be practicable or even necessarily correct (Carson et al. 2001).
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 Convergent Validity. Convergent validity examines the relationship between different measures 

of a concept.143 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 their 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 SP results are found to be larger than RP results for the same good, it is 

often presumed that the difference is the result of hypothetical bias because RP results are based 

on actual behavior. However, there can be many other sources of bias and error in both SP and 

RP results that cause them to differ from one another and ―true‖ WTP. 

Empirical convergent validity tests use comparisons of SP results with RP or experimental results 

that are thought to be free of hypothetical bias.144 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 7.4.3 for a 

discussion on combining RP and SP data.

Hypothetical bias occurs when the responses to hypothetical stated preference questions are 

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; Murphy and Allen 2005), 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; List 2001); calibrating hypothetical values (List and 

Shogren 1998; 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; 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 (e.g. Cummings et al 1995; 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 (e.g. 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 willingness to pay. In rare 

instances comparisons have been made between actual referenda for public good provision and 

 

143 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.

144 Some analysts include the comparisons of SP 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.
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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 non-respondents 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 by those who refuse to take the survey when their 

responses are systematically different from 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.145 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 

most common is to examine the relevant measurable characteristics of the respondent group, such 

as income, resource use, gender, age, etc., and 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 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).

 

145 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‖ (page 60-70).
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7.4.3 Combining Revealed and Stated Preference Data

Instead of looking at revealed preference (RP) and stated preference (SP) 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 RP and SP data include:

 Helping to ground the hypothetical SP data with real world behavior potentially decreasing any 

hypothetical bias.

 Providing the ability to test the validity of both data sources.146

 Increasing the range of historical SP data to include conditions not observed in the past and 

thereby reducing the need to make out of sample predictions.

 Increasing the sample size

 Extending the size of the market or population to include larger segments than captured by either 

method alone.

 Exploiting the flexibility of SP experimental design to overcome RP data‘s potential 

multicollinearity and endogeneity problems (von Haefen and Phaneuf 2008)

The different strategies for combining RP and SP data can be roughly grouped into three main methods. 

The first two methods rely on joint estimation. If the RP and SP 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, Louviere, and Williams 1994; Boxall, Englin, and 

Adamowicz 2003; Morgan, Massey, and Huth 2009). If the RP and SP 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;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; Schlapfer et al. 2004).

7.5 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 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 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 

 

146 Herriges, Kling, and Phaneuf (2004) point out the RP may not always be valid for estimating WTP for quality 

changes when weak complementarity cannot be assured.
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is one of the most common approaches for completing a benefit-cost analysis 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.147 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.148 

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 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, older adults across the U.S.) if possible. 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 peer-reviewed 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.‖149 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 are of an expected sign or 

magnitude).

150

 Online searchable databases summarizing valuation research may be especially helpful at 

this stage.151 

 

147 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.

148

 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. 

149 Peer review of benefit transfer studies using gray literature is highly advisable.

150 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 
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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 also 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 are focused on a ―good‖ with unique attributes or where the 

magnitude of the change or improvement across the two cases differs substantially (OMB 2003).152 

The analyst should determine whether adjustments should and can be made for important differences 

between each study and policy case studies. For example, some case studies will report Marshallian 

demand while others may report Hicksian demand.153 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 metaanalysis. 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 

 

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.

151 For example, the Environmental Valuation Reference Inventory (EVRI) is maintained by Environment Canada 

and managed by a working group that also 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. 

152 In some cases the transfer method itself may inform the choice of study cases to include. For example, metaanalysis approaches (discussed below) can facilitate some forms of statistical validity testing (e.g. Hunter and 

Schmidt 1990; Stanley 2001), so some otherwise suitable studies may be rejected as ―outliers.‖ 

153 See Desvousges et al. (1992), Brouwer (2000), Florax et al. (2002), Bergstrom and Taylor (2006), 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 1999b), 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. 
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general rule, the more related case study estimates involved in a benefit transfer are the more reliable the 

estimate. 

Unit value transfers are the simplest of the benefit transfer 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 may be useful for developing preliminary, 

order-of-magnitude estimates of benefits, but it should be possible to base final benefit estimates on more 

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 may yield inaccurate results. In addition, 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 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.
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Text Box 7.6 Benefits Transfer: Water Quality Benefits in the Combined Animal Feeding Operations Rule.

 

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 

values transfers should only be used if the case and policy studies are evaluating very similar 

environmental goods, change in environmental levels, and affected populations.

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 (e.g., 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 US EPA (2006e) for 

more information.154 More recently Bergstrom and Taylor (2006) discuss the theory and practice 

 

154 The last reference contains a detailed discussion of the protocols for conduction a meta-analsysis.

There are two prominent water quality benefit transfer applications in the 2002 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 

(2002b)). 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 (2002b) 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 Mitchells 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 (2002b) 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 (2002b)).

The second major benefit transfer application in the CAFO rule involves the valuation of reduced eutrophication in 

estuaries (Section 9 of U.S. EPA (2002b)). 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 (2002b)). 
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Text Box 7.7: The Benefits and Costs of the Clean Air Act 1990 to 2010: Reduced Acidification in Freshwater 

Adirondack Lakes

underlying meta-analysis for benefit-transfer, discussing three major necessary steps: theory, data 

collection and analysis. In general, in 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 USEPA (2006e) for a detailed discussion of meta-analysis as 

applied to value of statistical life (VSL) estimates. USEPA (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 structural 

transfer functions are that they can accommodate different types of economic value measures (e.g., WTP, 

WTA, 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 which is then 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.

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 then 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. 
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Text Box 7.8 Structural Benefit Transfer with an Application to Visibility

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 

also be quantified and reported when possible. (See Chapter 11 on Presentation of Analysis and Results). 

7.6 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.

7.6.1 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 decisionmaking. Chapter 11 discusses how to describe benefit categories that are quantified in physical terms but 

not monetized.

7.6.2 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, may provide 

decision-makers with some useful information; however, analysts should remember that because these 

U.S. EPA (2006b) employs a structural benefit transfer to derive values for visibility improvements associated with 

the PM 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, 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 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.
7-58

alternatives do not estimate the net benefits of a policy or regulation, they fall short of benefit-cost 

analysis in their ability to identify an economically efficient policy. This and other short-comings should 

be discussed when presenting results from these analyses to decision-makers.

7.6.2.1 Break-even Analysis

Break-even analysis is one alternative that can be used when either risk data or valuation data are 

lacking.155 Analysts who have per unit estimates of economic value but lack risk estimates, cannot 

quantify net benefits. They may, 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.156 For 

example, 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 willingness to pay 

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 can then 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 may be performed when analysts lack valuation estimates, producing a breakeven 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 estimate of willingness to pay to avoid a case of this endpoint. 

In this case, the policy can be considered to ―break even‖ if willingness to pay is at least $200.

One way to assess the credibility of economic break-even 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 willingness to pay to avoid a case of a more serious effect was only $100, the 

above ―break-even‖ 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.

7.6.2.2 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 

 

155 Boardman et al. (1996) describes determining breakeven points under the general subject of sensitivity analysis 

and includes empirical examples.

156 Circular A-4 (OMB 2003) refers to these values as ―switch points‖ in its discussion of sensitivity analysis.
7-59

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.
8-1

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. 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 benefit-cost analysis, social cost is the 

correct measure to use. Social cost represents the total burden that a regulation will impose on the 

economy, and is defined as the sum of all opportunity costs incurred as a result of the regulation. 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 the 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 a large number of 

sectors are expected to be impacted and the effects will be more broadly spread throughout the economy. 

The third challenge is choosing one or more models to use in an analysis. Factors that may be considered 

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. 

In the next section, social cost and its underlying economic theory are discussed. In the third section, 

several alternative concepts of cost are presented. The fourth section discusses several additional issues 

in cost estimation. The final section presents a number of the models that may be employed in the 

estimation and analysis of costs. 

8.1 The Economics of Social Cost

The most comprehensive measure of the costs of a regulation – and thus the appropriate measure to use in 

a benefit-cost analysis – is ―social cost.‖ Social cost represents the total burden a regulation will impose 

on the economy; it may be defined as the sum of all opportunity 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.157 

 

157 This section discusses the prospective estimation of social cost, i.e. 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). 
8-2

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.158 

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 other broad measure of economic activity, that may result from its 

imposition. While 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 may be 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 will be more 

appropriate to use general equilibrium analysis to estimate social cost. The use of these two analytical 

paradigms is explored in the following sections. 

8.1.1 Partial Equilibrium Analysis

When the effects of a regulation are expected to be confined primarily to a single market or a small 

number of markets, the estimation of social cost can be performed using partial equilibrium analysis. 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. In this 

section, some simple diagrams are presented 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 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.159 

In this market, the imposition of a new environmental regulation raises firm‘s production costs. Each unit 

of output is now more costly to produce because of expenditures made 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 

 

158 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. 

159 It should be noted that total welfare as depicted ignores the negative pollution externality arising in this market

that the environmental regulation is designed to correct. The appendix presents a graphical description of how 

to account for this externality, the reduction of which 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 

the appendix. 
8-3

equilibrium output to Q1, holding all else constant. As can be seen by comparing Figures 8.1 and 8.2, the 

overall effect on welfare is a decline in both producer and consumer surplus.

160

 

Compliance costs in this market are equal to the area between the old and new supply curves, bounded by 

the new equilibrium output, Q1.

161

 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, it can be seen that when consumers are price 

sensitive – as reflected in the downward sloping demand curve – a higher price causes them to reduce 

their consumption of the good. If costs are estimated ex ante and this price sensitive behavior is not taken 

into account – so that the estimate is based on the original level of output, Q0 – compliance costs will be 

overstated. This can be seen by extending the vertical dotted line in Figure 8.2 from the original 

equilibrium to the new supply curve, S1.

162

 

Figure 8.1: Competitive Market Before Regulation

 

160 The figure depicts an equal distribution of welfare between consumers and producers, in both the old and new 

equilibria, but this need not be the case depending on the elasticities of supply and demand. These elasticities 

will determine the magnitude of the price and quantity changes induced by the cost increase. 

161 Here we abstract from distinctions between the fixed and variable costs of abatement and assume that all of the 

costs are represented in the movement of the supply curve. See Tietenberg (2002).

162 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. 

Consumer Surplus

Producer Surplus

Q

P

0

D

S0

Q0

Q0

P0
8-4

Figure 8.2: Competitive Market After Regulation

`

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‖ shown in Figure 8.2 is an additional, real cost arising 

from the regulation, reflecting the foregone net benefit due to the reduction in output.163 Moreover, unlike 

many one-time compliance costs, it 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 benefit-cost analysis 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 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 

 

163 Typically, in a market already distorted with pollution externalities, the deadweight loss triangle shown in Figure 

8.2 will serve to offset (at least in part) the existing deadweight loss 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. 

P0

P1

Consumer Surplus

Producer Surplus

Compliance Costs

Deadweight Loss

Q

P

0

D

S0

Q0

Q0

S1

Q1
8-5

can alter the estimate of social cost.

164

 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 may still be possible to use a partial equilibrium framework to estimate 

social cost. Multi-market analysis extends a single-market, partial equilibrium analysis of the directly 

regulated sector to include closely related markets, such as 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. These 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 may be analyzed in a similar fashion.165 

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 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 many 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, can also be 

calculated in the model. 

 

164 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. 

165 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. 
8-6

The previous section showed how the social cost of a regulation can be estimated in a single market using 

partial equilibrium analysis. The regulation caused a deadweight loss in that market, reflecting a decline 

in economic welfare as measured by consumer and producer surplus. In reality, deadweight losses 

already exist 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 has caused the net, after-tax wage 

(

n W0

) to be lower than the gross, pre-tax wage (W

g

) by the amount of the tax. With this tax distortion, 

the quantity of labor supplied is L0and there is a deadweight loss. 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 – with an upward sloping labor supply curve – the amount of labor supplied.166 This is shown in the 

figure as a decrease in the net wage to 

n W1

and 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 deadweight loss.

167

 Similar interactions are 

likely to occur in other markets with pre-existing distortions. In cases where they are likely to have a 

significant impact, these distortions should be incorporated into models used to estimate social cost.

168

 

 

166 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. 

167 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). 

168 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 pre-existing 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 Mooij 

(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). 
8-7

Figure 8.3: Labor Market with Pre-Existing Distortions

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 may 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.169 

Even in a general equilibrium analysis, care must be taken 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.170 

 

169 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 1997). 

170 Final demand consists of household purchases, investment, government spending, and net exports (exports minus 

imports). 

Additional

Deadweight Loss

Pre-Existing

Deadweight Loss

S

L

g W

n W0

n W1

tax

Hours

Wage

0

D

L

L1L0

Q0
8-8

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 and compensating variations. 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 equivalent and compensating 

variation 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. 

When conducting a benefit-cost analysis in which the intertemporal effects of a regulation are expected 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, an 

estimation of social cost can be made 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 longterm impacts of changes in labor supply, savings, factor accumulation, and factor productivity on the 

process of economic growth.171 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 benefit-cost analysis as these are typically 

categorized as transitional job losses.

172

 Benefit-cost analysis requires monetized values of both the social 

benefits and costs associated with the regulation. The social cost of a regulation already includes the 

value 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 (see Chapter 9 for more details).

 

171 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. 

172 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.
8-9

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 benefit-cost analysis. 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. Measures that define temporary costs or define how costs are 

distributed across different entities are also discussed. 

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. However, an important distinction is that while social cost is 

a measure derived from economic theory, these three concepts are, in general, only descriptive.173 

Consideration of these alternative concepts may 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. In addition, several executive and legislative mandates require that a 

number of different types of costs be included in a Regulatory Impact Analysis (RIA).174 

8.2.1.1 Explicit and Implicit Costs

The total costs of a regulation may include both explicit and implicit costs.

175

 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 would normally include the costs of purchase and 

operation of pollution control equipment. This would include 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 would be included in explicit costs. Implicit costs, on the other hand, are costs for 

which monetary values do not readily exist and which are thus likely to be 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. 

 

173 In certain cases, a single component, such as direct cost, may provide a reasonable estimate of social cost. 

174 Executive Order 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 Unfunded Mandates Reform Act of 1995 requires that 

cost estimates take into account both indirect and implicit costs on state and local governments. 

175 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. 
8-10

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 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 may 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 care must be taken 

to avoid double counting.176 

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. 

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 and operating 

costs. Compliance costs may 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. 

 

176 References which provide definitions of cost terminology include Congressional Budget Office (1988) and 

Callan and Thomas (1999). 
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 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 benefit-cost analysis, 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 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 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 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.177 

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

 

177 Government entities may themselves be polluters and therefore subject to regulation. Compliance costs under 

this scenario would be captured as such. 
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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 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 benefit-cost analysis 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 benefitcost analysis. 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: those that arise when estimating costs over time and 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 by their imposition. When measuring costs over time, assumptions 

related to the time horizon of the analysis, the use of a static vs. 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, great care must be taken 

to ensure that the year(s) selected are truly representative, that no important transitional costs have been 

effectively dismissed by assumption, and that no one-time costs have been 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 contrast, in the long term, 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.
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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, all of which 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.178 

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). 

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), it has been borne out by experiences in many sectors. Indeed, OMB now requires cost 

analyses to consider possible learning effects among the cost-saving innovations.

179

 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). 

 

178 In a cost-effectiveness analysis, it is equally important to properly discount cost estimates of different regulatory 

approaches to facilitate valid comparisons. 

179 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 that should be considered include those 

resulting from a shift to regulatory performance standards and incentive based policies.
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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 over 100 studies including some 

dealing with the energy and manufacturing sectors. It should be noted 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).180 

8.3.2 Other Issues in Estimating Social Cost

Difficulties in measuring social cost generally fall into two categories: difficulties in developing a 

numeric value for some social cost categories; and, for social cost categories where numeric values have 

been successfully developed, accounting for uncertainty in these values. 

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 benefit-cost analysis. Some alternative techniques for measuring 

and presenting these effects to policymakers 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. Therefore, the conclusions drawn in the social cost analysis will be 

sensitive to the degree of uncertainty regarding the assumptions that were made. 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 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 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 

 

180 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. 
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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, 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.
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Text Box 8.1 - The SO2 Cap and Trade Program – A Case Study181

Under Title IV of the 1990 Clean Air Act Amendments (CAAA), coal fired power plants are required to hold 

one 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, uncertainties) can make to actual cost estimates. 

Estimates of Title IV‘s compliance cots have declined over time, particularly 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; Burtraw and Palmer 2004). Furthermore 

Popp (2003) concluded that Title IV induced R&D led to technological innovations which 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 Ellerman et al., 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 

(Burtraw and Palmer 2004).‖

 

181 This example is taken from Burtraw and Palmer (2004).
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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, while others may be complex systems of thousands of 

equations that require highly specialized software.182 

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, while in 

other cases, the use of more than one model will be required. For example, a compliance cost model may 

be used to estimate the direct costs of a regulation in the affected sector. These direct cost estimates may 

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. 

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

Compliance 

Cost

Partial 

Equilibrium

Linear 

Programming Input-Output

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   

Can capture feedback and interaction effects 

Selecting the most appropriate model (or models) to use in an analysis can be difficult. However, a 

number of factors can be identified that may be helpful in making a choice:183 

 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. 

 

182 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. 

183 The following draws on Industrial Economics, Inc. (2005). Proprietary models discussed in this section are 

examples only and no endorsement by EPA is given or implied. 
8-18

 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 the direct costs to an industry 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 are often 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 may be used as inputs into other models in estimating 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. 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, VOC, PM10, PM2.5, NH3) as well as 

CO and Hg from point (utility and non-utility), area, nonroad, and mobile sources as provided in EPA's 
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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 in its operation. 

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 

can then be used to estimate the change in market price and output. Changes in producer and consumer 

surplus reflect the social cost of the regulation. In addition, the relative changes between producer and 

consumer surplus provides 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 either be estimated from 

industry and consumer data or taken from previous studies.184 

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; 

 

184 Because of their widespread use, the Air Benefit and Cost (ABC) Group in EPA‘s Office of Air and Radiation 

maintains an elasticity database. 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. An online submittal form allows you 

to provide elasticity estimates for consideration as part of this databank. Available online at 

http://www.epa.gov/ttn/ecas/Elasticity.htm. 
8-20

 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. 

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 may be employed in a variety of applications, their use in the 

analysis of EPA regulations occurs most frequently in the estimation of compliance costs.

185

 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 endof-pipe controls, fuel 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 also 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 

 

185 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.) 
8-21

electrical generating units are mapped to approximately 1,700 representative plants and the 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 mercury. 

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 and this permits an existing model to be used in a range of applications. 

Limitations:

 Linear programming models do not normally estimate costs beyond a single sector and are thus 

unable to estimate indirect or distributional costs. 

 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 inputoutput 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.186 

An input-output model is based on an input-output table. The input-output table assembles data in a 

tabular format that describes the interrelated flows of goods and factors of production over the course of a 

year. An input-output table may consist of hundreds of sectors or may be aggregated into as few as two 

or three. 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. 

 

186 Miller and Blair (1985) is a standard reference on input-output analysis. 
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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 10-sector table.)

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 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.187 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. 

 

187 An off-the-shelf input-output model often used in the analysis of the impacts of environmental regulation is 

IMPLAN. IMPLAN is based on data for the United States which covers over 500 sectors and can be 

disaggregated down to the county level. 
8-23

 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, input-output 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 economy-wide 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 input-output 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 relationships that more accurately account for these 

conditions. However, unlike standard macroeconometric 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, making input-output 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 REMI 

Policy Insight.188 The standard REMI model includes 70 production sectors and 25 final demand sectors 

and can provide output on changes in income and consumption for over 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. 

 

188 REMI stands for Regional Economic Models, Inc. 
8-24

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 (CGE) 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, the impacts of domestic and international 

policies for greenhouse gas 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 may 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 

may 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 then 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 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 benefit-cost analysis of the Clean Air 

Act (CAA), which used a dynamic CGE model to compute the costs of CAA compliance over the period 

1970 to 1990 (US 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 these long-term capital and operating costs from the industries that incurred them. A 

comparison was then made between the simulated path of the economy without these abatement 

expenditures and the actual path of the economy, which included them. Changes in both long-run GDP 

and equivalent variation were computed, as well as impacts on investment, household consumption, and 

sectoral prices, output, and employment. 
8-25

Text Box 8.2 - The Pollution Abatement Costs and Expenditures (PACE) 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 the 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, except 1999. 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. 

CGE models have also been used extensively in estimating the costs of greenhouse gas 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 carbon taxes or emissions trading. Other studies have expanded 

the analysis by examining other greenhouse gases 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 also begun to include nonmarket goods into CGE models (e.g. Smith et al. (2004) and Carbone and Smith (2008)). 

Given the large number of parameters in a typical CGE model, great care should be taken in insuring 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. 
8-26

Advantages:

 CGE models are best suited for estimating the cost of policies that will have large economy-wide 

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.
9-1

9 Economic Impact Analyses

The detailed study of regulatory consequences allows policymakers 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: first, the regulation‘s 

efficiency, and second, its distributional effects. In principle, both of these consequences could be 

estimated simultaneously by a general equilibrium model. In practice, however, they are usually 

estimated separately, for the reasons discussed in Section 1.3.

This chapter discusses how the distributional effects of environmental regulations can be examined 

through economic impact analyses (EIA). An EIA focuses on traditional classifications of affected 

entities, such as industrial sector classifications, governments, not-for-profit institutions, and small 

entities.189 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. These estimates are derived from a study of the economic changes 

that occur across broadly defined economic sectors of society, including industry, government, and notfor-profit organizations. An EIA also examines more narrowly defined sectors within these broad 

categories, such as the solid waste industry or even an individual solid waste company. Therefore, EIAs 

may measure a broad variety of impacts, such as direct impacts on private business - including individual 

plants, whole firms, and industrial sectors - and indirect impacts on consumers and suppliers. The term 

―impacts‖ includes changes in profitability, employment, prices, government revenues or expenditures, 

and trade balances. 

For any regulation, it is essential to ensure consistency between the EIA and the benefit-cost 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.

The remainder of this chapter is organized as follows: Section 9.2. reviews the statutes and policies that 

that address economic impact analyses, Section 9.3 discusses the components of an economic impact 

analysis, including details regarding screening, data sources, and relevant dimensions to consider. 

Sections 9.4 discusses potential modeling approaches and frameworks to consider.

 

189

 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 

Unfunded Mandates Reform Act (UMRA) and Executive Order 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. See Chapter 8 for more details. 
9-2

9.1 Statutes and Policies

The following major statutes and executive orders, all described in Chapter 2, directly address impact 

analyses:190

 Regulatory Flexibility Act of 1980 (RFA), as amended by the Small Business Regulatory 

Enforcement Fairness Act of 1996 (SBREFA); 

 Unfunded Mandates Reform Act of 1995 (UMRA); 

 E.O. 13132, ―Federalism‖;

 E.O. 13175, ―Consultation and Coordination with Indian Tribal Governments‖; and 

 E.O. 13211, ―Actions Concerning Regulations That Significantly Affect Energy Supply, 

Distribution, or Use.‖

Table 9.1 - Potentially Relevant Dimensions to Economic Impact Analyses191

Dimension

Statute, Order, or 

Directive Entity Subpopulation

Sector UMRA; E.O. 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-forprofit 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 E.O. 13211 Entities that use, 

distribute, or generate 

energy

Energy sector

9.2 Conducting an Economic Impact Analysis

There are three important distinctions between BCA and EIA to keep in mind when conducting an EIA.

192

 

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. 

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 subcategories 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 

 

190

 EPA‘s Regulatory Management Division‘s Action Development Process Library 

(http://intranet.epa.gov/adplibrary) is a resource for those who wish to access relevant statutes, executive orders, 

or Agency policy and guidance documents in their entirety. 

191

 Some environmental statutes may also identify subpopulations that merit additional consideration. This 

document is limited to those statutes with broad coverage.

192

 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. 
9-3

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 we discuss 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 benefit-cost analyses. Results may be 

presented for specific counties or other geographic units or types of entities, as appropriate, placing heavy 

demands on the modeling framework.

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 may be thought of as a ―mini-EIA‖ 

consisting of a rough examination of the data to identify sectors that may warrant further analysis. 193 

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. 

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.194 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. 

 

193

 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). 

194

 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.
9-4

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 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.195

 Industry summary statistics. Summary statistics of total employment, revenue, number of 

establishments, number of firms, and size of firms are available from U.S. Department of 

Commerce Economic Census or the Small Business Administration.196 

 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. In 

addition, 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, notfor-profit organizations, or households.

197

 For example, air pollution regulations 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; and 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. 

 

195

 For more information see www.census.gov/epcd/www/naics.html, which includes a NAICS/SIC 

correspondence.

196

 See www.sba.gov/advo/stats/data.html for more information. 

197

 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-forprofit hospitals, colleges, universities, and research institutions.
9-5

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. Socio-economic factors influence demands on state or local government resources; a 

high proportion of children means more educational resources, for example. 

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. For example, 

most county websites provide information on property values and private companies, such as Standard 

and Poor‘s or Fitch‘s, provide community credit ratings. 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.198

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/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.

 

198 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). 
9-6

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 Census Bureau.

199

 Relevant literature can be useful in characterizing 

industry activities and markets as well as regulations that already affect the industry and can usually be 

efficiently identified through a computerized search using on-line services such as Dialog, BRS/Search 

Services, Dow Jones News/Retrieval, or EconLit. These on-line services contain over 800 databases 

covering business, economic, and scientific topic areas. Table 9.2 describes some commonly used data 

sources for retrieving quantitative data.200

The industry profile may also identify situations where insufficient data are available through standard 

sources. These situations often arise when the affected industry is one of many product lines or activities 

of the identified facilities. In addition, for some industries, identification of the appropriate NAICS code 

for all the firms or facilities included in the industry may be difficult 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 either a statistical sample or a census of affected facilities may be required to provide 

sufficient data for analysis.

 

199

 Academic literature may or may not contain quantitative data. 

200 The Thomas Registry (www.thomasnet.com) is a source of qualitative information on manufacturing companies 

in the U.S. In addition, Lavin (1992) provides sources of business information. 
9-7

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/outloo

k/ 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

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 
9-8

continuing production, lost profits on products or services that are no longer produced, or some 

combination of the two. In addition, 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.201 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 directly-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. Also, there could be backward shifting of regulatory costs (e.g., lowering wages of 

workers), which is another economic impact to consider. 

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. The methods used 

to conduct the analysis of the directly-affected markets depend on the availability of appropriate estimates 

of supply and demand elasticities.

202

 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. For example, 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 pass through 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.203 According to 

the 2005 Pollution Abatement and Costs and Expenditure (PACE) Survey, the U.S. manufacturing sector 

spent approximately $20.7 billion dollars on pollution abatement operating costs -- less than 1 percent of 

their total revenue, which is similar to this sector‘s 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. 

 

201

 For example, regulations limiting sulfur 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. 

202

 See Appendix A for a more complete discussion of elasticity.

203 More detail on The Pollution Abatement Costs and Expenditures (PACE) Survey is available at 

http://yosemite.epa.gov/ee/epa/eed.nsf/pages/pace2005.html].
9-9

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

Abatement Costs in 2005

Source: Pollution Abatement Costs and Expenditures: 2005

Figure 9.2 Pollution Abatement Costs are a very Small Percentage of Total Manufacturing Costs

Source: U.S. Census Bureau, Pollution Abatement Costs and Expenditures: 2005

U.S. Census Bureau, Annual Survey of Manufacturers: 2005

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Chemicals Petroleum Primary Metal Paper Leather

Industry

%

Average 

= 0.44
9-10

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 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 benefit-cost analysis. For most situations, employment impacts 

should not be included in the formal benefit-cost analysis.204 However, if desired, the analyst can, as part

of an economic impact analysis, assess the employment impacts of a regulation. 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 discusses in more detail the pitfalls often encountered when 

performing such an analysis as well as 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:

 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;

 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 

 

204 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.
9-11

spending leads to a statistically positive, yet quite small, net increase in jobs due to the substitution of

labor for other inputs and relatively inelastic estimated demand for their output.205

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 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 

also 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 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. For example, to build scrubbers, the environmental protection industry will order more 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 also important to consider less obvious 

but potentially significant links, such as basic suppliers like electricity generators.

 

205 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.
9-12

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 gross domestic 

product, 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.206 

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 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.207 

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.208 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, 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 

 

206 Chapter 8 discusses the use of regional modeling. 

207

 See Jaffe, et al. (1995) for an overview. 

208

 Sunk costs are sometimes referred to as exit barriers. 
9-13

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

Executive Order 13211 requires agencies to prepare a Statement of Energy for ―significant energy 

actions,‖209 which are defined as significant regulatory actions (under Executive Order 12866) that also 

are ―likely to have a significant adverse effect on the supply, distribution, or use of energy.‖ 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 one percent; 

 Increases in the cost of energy distribution in excess of one 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.

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 may affect governments and not-for-profit organizations in at least three significant 

ways. First, they may directly impose requirements on the entity, such as water pollution requirements 

for publicly-owned wastewater treatment works or air pollution restrictions that affect municipal bus 

systems or power plants. Second, they may impose costs on government agencies associated with 

implementing and enforcing regulations. Finally, they may impose indirect costs, such as increased 

 

209 See section 2.1.6 for EPA and OMB‘s guidance on EO 13211.
9-14

unemployment (and thus less tax revenues) in a community because a regulation has resulted in reduced 

production (or even closure) at a plant in the 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 various 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 the 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 one 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 private-sector 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 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).210 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, however, 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 

this needs to be taken into account and a more narrow measure may be appropriate. In addition, 

if some of the costs are borne by local firms, then that portion of the costs should be handled 

separately. 

 

210 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 over 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. 
9-15

There are two commonly used impact measures for not-for-profit entities: annualized compliance costs as 

a percentage of annual operating costs, and 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 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.211 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.212 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. Also, 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 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 

 

211

 The indicators and benchmark values in Table 9.3 are drawn from a document, ―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 1997). These are general benchmarks that 

may prove useful in assessing financial stability in an EIA. 

212 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.
9-16

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.213 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. 

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

Source: U.S. EPA 1997b.

 

 

213

 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. 

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%
9-17

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 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 familiar 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 
9-18

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.214 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. The impacts on small businesses are generally assessed by 

estimating their 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 discussed in section 9.3.3 (specifically in relation to small businesses) may 

provide additional information for decision-makers.215

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 

if required, the 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 factors indicating 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.216 See Section 9.3.4 for information on measures of impacts to governments in general.

9.2.5.3 Small Not-for-Profit Organizations

 

214

 The current version of SBA‘s size standards can be found at http://www.sba.gov/size. 

215

 See Agency guidance (U.S. EPA 1999) for details on complying with the RFA.

216

 Guidance on complying with Section 203 of UMRA, ―Interim Small Government Agency Plan,‖ is available on 

EPA=s intranet site, ―Action Development Process Library‖ at http://intranet.epa.gov/adplibrary/statutes.htm. 
9-19

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 in-depth 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 entities. 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, but 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 regions. Second, the costs for each response need to be examined to 

determine if all elements are included and the costs are consistent within a given base year. To ensure 

consistency across years, either a general inflation factor, such as the Gross Domestic Product (GDP) 

implicit price deflator, or various cost indices specific to the type of project should be used.217 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 (e.g., 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 registrants‘ 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, 

particular 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: 

 

217

 The GDP implicit price deflator is reported by the U.S. Department of Commerce, Bureau of Economic Analysis 

in its Survey of Current Business (www.bea.gov/bea/pubs.htm). The annual Economic Report of the President, 

Executive Office of the President, is another convenient source for the GDP deflator 

(www.gpoaccess.gov/eop/). 
9-20

 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 are generally fully deductible as expenses in the year incurred. 

Capital investments associated with compliance must generally be depreciated.218 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.219

 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.220 

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.221 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. 

 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 

 

218 Current federal and state income tax rates can be obtained from the Federation of Tax Administrators, State Tax 

Rates & Structure, available from http://www.taxadmin.org/fta/rate/default.html. 

219

 These transfers cancel out in a BCA. In an EIA the distribution of results is important, therefore the transfers are 

included. 

220

 The present value of costs may then be annualized to provide an annual equivalent of the uneven compliance 

cost stream. Annualized costs are also discussed in Chapter 6. 

221

 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. 
9-21

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.222 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 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 computable general equilibrium models). These models are 

best used when potential impacts on related markets might be considerable, but more complete modeling 

using a computable general equilibrium (CGE) framework may not be available or practical. Partial 

equilibrium models may also be more appropriate for regionally-based or resource specific regulations 

which are too specific for more aggregated CGE models.223 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 costs increases due to a regulation are passed on from the upstream to 

the downstream businesses then care should be taken not to include impacts on both sets of entities for 

this would be double-counting results. 

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. For example, as noted in Chapter 8, 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. The 

distribution of the changes in production and consumption that result can be assessed by a CGE model. 

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 

 

222

 Economies of scale characterize costs that decline on a per unit basis as the scale of the operation increases. 

223

 See the discussion of multi-market modeling in Chapter 8 and Just, Hueth, and Schmitz (1982). 
9-22

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 may 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 may 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 

may 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 may be solved dynamically over a long time horizon, 

incorporating inter-temporal 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.224 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. 

 

224

 For a discussion of CGE analysis see Chapter 8 and Dixon, et al. (1992). 
10-1

10 Environmental Justice, Children and Other 

Distributional Considerations

Distributional analyses address the impact of a regulation on various subpopulations. Minority, lowincome and tribal populations may be of particular concern and are typically addressed in an 

environmental justice (EJ) analysis. Children and other groups may also be of concern and warrant 

special attention in a regulatory impact analysis. With increasing attention on the distributional 

consequences of rulemaking activities, the Agency is developing tools, methods, and policies regarding 

when and how to incorporate these considerations into the decision-making process. 

Additional guidance on how to conduct distributional analyses will be added here as it becomes available.
11-1

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 policymakers.

Economic analysis is often motivated by a desire to find an optimal outcome—e.g., 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 (e.g. best available 

control technology, lowest achievable emission rate, etc.). Policy makers rely on quantitative analysis to 

promulgate these approaches, in particular 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 policymaking 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 which cannot be quantified 

and/or put into dollar terms. The chapter then discusses the components, or inputs, of an economic 

analysis, and how their affect on the economic analysis can best be communicated 

11.1 Presenting Results of Economic Analyses

11.1.1 General Principles

The presentation of the results of an economic analysis should be thorough and transparent. The reader 

should be able to understand:

 What the primary conclusions of the economic analysis are;

 How the benefits and costs were estimated;

 What the important non-quantified or non-monetized effects are;

 What key assumptions were made for the analysis;

 What the primary sources of uncertainty are in the analysis; and

 How those sources of uncertainty affect the 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.

Whenever possible, benefits and costs should be reported in monetary terms. 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 
11-2

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, below, 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. These Guidelines for Preparing 

Economic Analyses and Circular A-4 should be relied upon for developing these estimates. Circular A-4

describes the accounting statement on pages 44-46 and also contains a suggested format for this 

accounting statement.225

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 2010, p. 51). EPA‘s guidance for satisfying this 

criteria is described more fully in section 11.1.2, below, as part of the Agency‘s general guidance on 

reporting the results of benefit-cost analysis.

The results of economic analyses of environmental policies should generally be presented in three 

sections.

 Results from Benefit-Cost Analysis. 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 in the presentation and described.

 Results from Cost-Effectiveness Analysis. Under OMB Circular A-4, cost-effectiveness

analysis 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.226

 Results from Economic Impact Analysis and Distributional Assessment.s Results of the 

economic impact analysis should be reported, including predicted effects on prices, profits, plant 

closures, employment, and 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.

11.1.2 Presenting the Results of Benefit-Cost Analyses

When presenting the results of a benefit-cost analysis, the expected benefits and costs of the preferred 

regulatory option should be reported, together with the expected benefits and costs of alternative 

 

225 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.

226 The Institute of Medicine (IOM 2006) recently issued recommendations to regulatory agencies on how to 

perform health-based cost effectiveness analyses. Recent examples of cost effectiveness analyses 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) and the Ground Water Rule (see Appendix H listed at 

http://www.epa.gov/safewater/disinfection/gwr/regulation.html).
11-3

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 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, 

however, not to exclude an important benefit or cost category from benefit-cost analysis 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 nonmonetized 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 subpopulations, the various options‘ incremental impacts on these subpopulations/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.227 In this case, the cost side of the equation includes general equilibrium 

feedback effects while the benefit side does not and 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 this table, 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 are:

 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.

 

227 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, Jena et al. 2008)
11-4

 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 cost-effectiveness analysis because the goal here 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. 

These guidelines recommend four tables for presenting the results of a benefit cost analysis, each of 

which is described further here, along with a template. Simpler analyses may not 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 ―over 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.

11.1.3 Presenting the Results of Cost-Effectiveness Analyses

When benefit-cost analysis is not possible, cost-effectiveness analysis may be the best available option. 

The cost-effectiveness of a policy 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 Regulatory Impact Analyses, or other analyses of specific regulatory or policy options, 

cost-effectiveness analysis 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.
11-5

Table 11.1: Template for Regulatory Benefits Check-list

These templates are to be used when producing tables to present the results of a benefit cost analysis. The benefit 

categories in this template (e.g., improved human health, improved environment, and other benefits,) will need to be 

revised to reflect the benefits categories for the rule under consideration. 

Not all of the benefits of a rule can be quantified and then put in dollar terms (monetized) for benefit-cost 

analysis. In order to provide a more complete characterization of benefits this table is in three parts: (A) 

overview of benefits, (B) quantified benefits, and (C) monetized benefits.

Part A: 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
11-6

Table 11.2: Template for Quantified Regulatory Benefits

Part B: Quantified Benefits

Benefits

Quantified 

Benefits

(conf. 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.
11-7

Table 11.3: Template for Dollar-Valued Regulatory Benefits

Part C: 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.
11-8

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 

other key 

notes

Number $ Millions Number $ Millions Number $ Millions 

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
11-9

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.

Costs

2006 dollars annualized at 3% discount rate

Best estimate, with range

Option 1 Proposed Option Option 3 Source, 

limitations, or 

other key 

notes

$ Millions $ Millions $ Millions

 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)
11-10

TOTAL Net Benefits that can be 

monetized

(annualized, millions $2006)

$ estimate

(range)

$ estimate

(range)

$ estimate

(range)
11-11

The non-monetary measure of benefits used in a cost-effectiveness analysis 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 cost-effectiveness analysis will be. Consider the 

following steps that a typical environmental economic assessment follows:

 Changes in emissions are estimated (e.g., tons of emissions), then 

 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 cost effectiveness analyses.

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 

the value of the analysis to decision makers (and the public).

When presenting the results of a cost effectiveness analysis, the rationale for the selection of the nonmonetary 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.

Cost-effectiveness analysis 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 cost-effectiveness analysis. 

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 costeffectiveness.) (U.S. EPA 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, costeffectiveness analysis can indicate which control measures or policies are inferior options.

11.1.4 Presenting the Results of Economic Impact Analysis and Distributional Analyses

Economic impact analyses 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.
11-12

The results of the economic impact analyses 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, non-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.

11.1.5 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 the 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.
11-13

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 2000) 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 an Agency Guidance, Guidance for Quality Assurance Project Plans,

that includes procedures for documenting secondary data (U.S. EPA 2002.)

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. In addition, 

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 also 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.
11-14

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 the key assumptions and the potential magnitude and direction of likely 

errors in assumptions in the summary of results. 

11.2.3 Addressing Uncertainty Driven by Assumptions and Model Choice

The 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 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, to allow 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 benefit cost analysis 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 making this choice 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.
11-15

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, 

however, 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. Thus, even if the criterion of 

economic efficiency were the sole guide to 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 tradeoffs among them. While ultimately 

statutory requirements may dictate if and how the analytic results are used in standard setting, these 

results, nevertheless, serve as important inputs for this broader policy-making process along with other 

analyses and considerations.
A-1

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.228 The usefulness of benefit-cost analysis 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, and 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

229

– of their choices, given their own resource constraints. 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.

Figure .1

 

228 EPA‘s mandates frequently rely on criteria other than economic efficiency as well, so policies are sometimes 

adopted that are not justified by the lack of efficiency.

229 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. 
A-2

The demand curve for a single individual shows the quantity of a good or service that the individual will 

purchase at any given price (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 

curve 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 

afforded by the previous unit so 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 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 willingness to pay is one of the fundamental concepts used in economic 

analyses, and it is important to distinguish between total and marginal willingness to pay (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 gives the marginal WTP for the Qd -th unit. The total WTP is equal to the sum of 

the marginal WTP for each unit up to Qd – i.e., the shaded area under the demand curve from the origin 

up to Qd. 

Figure A.2

An individual producer‘s supply curve shows the quantity of a good or service that an individual or firm 

is willing to sell (Qs) at a given price. As 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, and so the supply curve is assumed to slope upward. In Figure 

A.2, the marginal cost of producing the Qs-th unit of the good is given by the height of the supply curve at 

Qs. The total cost of producing Qs units is equal to the shaded area under the supply curve from the origin 
A-3

to the quantity Qs.

230

 The market supply curve is simply the horizontal summation of the individual 

producers‘ marginal cost curves for the good or service in question. 

Figure A.3

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 willingness to pay 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 price Pm and quantity Qm. 

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 Qsis greater 

than Qd (i.e., there is excess supply). As producers discover that they cannot sell off their inventories, 

some may 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 decreases). This 

adjustment continues until Qd equals Qs. The reverse situation occurs if the price becomes 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 Qs. Prices will continue to rise until Qs 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 Pm for the purchase of quantity Qm. However, the demand curve

shows that there are consumers willing to pay more than price Pm for all units prior to Qm. 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 

 

230 This is actually the long run total cost. In the short run there would be fixed costs as well. 
A-4

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.231 

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, Pm. 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 willingness to pay 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 + producer surplus) is 

maximized and 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 occurs 

automatically.232 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.

233

 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 one person without decreasing the 

 

231 Section A.4.2 provides a more technical discussion of how consumer surplus serves as a measure of benefits. 

232 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.)

233 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.
A-5

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.234 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.235

Figure A.4

A.2 Reasons for Market or Institutional Failure

If the market supply and demand curves reflect society‘s true marginal social cost and willingness-to-pay, 

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.236 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, machinery, energy –

and 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 

 

234 Note that efficiency could be obtained by moving along the vertical line V, which keeps utility of person 1 

constant while increasing utility of person 2, 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.

235 Note that outcomes on the frontier such as C and D, although efficient, may not be desired on equity, or fairness, 

grounds. 

236 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-6

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.237 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.238 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. These additional social costs are not included in firms‘ profit maximization 

decisions and so are not considered when firms decide how much pollution to emit. Thus, the lack of a 

market for clean air causes problems and provides the impetus for government intervention in markets 

involving polluting industries. 

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, but 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:

 D is the market demand (marginal benefit) curve for the product;

 MPC is the firm‘s marginal private real-resource 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).

 

237 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).) 

238 Often these are goods that exhibit public good characteristics. Pure public goods are those which are nonrivalrous 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.
A-7

Figure A. 5

In an incomplete market, producers pay no attention to external costs, and production occurs where 

market demand 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 willingness to pay minus total social costs) is equal to 

the area of the triangle p0p1X less the area of triangle XYZ.

239

 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.240 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, p0p1X 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.241 The shaded triangle (XYZ), 

referred to as the deadweight loss, 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.242 Figure A.5 

 

239 Recall from Section A.1 that total willingness to pay 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 marginal social cost

curve (MSC) from the origin to the point of production (0p0YQm).

240 When conducting benefit-cost analysis related to resource stocks, the marginal social damage 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 marginal social cost of resource use. 

241 Similarly, the private market will undersupply goods for which there are positive externalities, such as parks and 

open space.

242 Chapter 4 discusses the various regulatory techniques and some non-regulatory means of achieving pollution 

control. 
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neatly summarized 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 Benefit-Cost Analysis (BCA).

BCA can be thought of as an accounting framework of the overall social welfare of a program, which 

illuminates the tradeoffs 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. 

Benefit-cost analysis 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 willingness to pay for 

the environmental commodity. As with private markets, the efficient outcome is the option that 

maximizes net benefits. 

As mentioned above, the key to performing BCA lies in the ability to measure both benefits and costs in 

monetary terms so that they are comparable. The 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 the 

regulation often occur outside of markets. For example, the primary benefits of drinking water 

regulations are improvements in human health. Once the expected reduction in illness and premature 

mortality associated with the regulation has been calculated, economists use a number of techniques to 

estimate the value that society places on these health improvements.243 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 KaldorHicks 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). 

 

243 Chapter 7 discusses a variety of methods economists use to value environmental improvements.
A-9

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 Kaldor-Hicks 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 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.244 There are also practical limitations to BCA. Most importantly, it requires assigning 

monetized values to non-market benefits and costs. In practice, it may be very difficult or impossible to 

quantify gains and losses in monetary terms (e.g., the loss of a species, intangible effects).245 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 decision-making 

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 responsive 

producers and consumers‘ decisions are to a change in price. Economists 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 one 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% increase in the price of 

tuna results in a 1% 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% increase in price would 

reduce quantity demanded by 2.5% (from 8 lbs to 7.8 lbs). At a price of $4 per pound, a 10% increase in 

price would result in a 40% 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. Therefore, when 

calculating elasticities it is important to state the price or quantity of the good demanded (or supplied).

 

244 Chapter 9 addresses equity assessment and describes the methods available for examining the distributional 

effects of a regulation. 

245 Kelman (1981) argues that it is even unethical to try to assign quantitative values to non-marketed benefits.
A-10

Figure A. 6

Elasticities are important in measuring economic impacts because they determine how much of a 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).246 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. Supply characteristics in the industries affected by a 

regulation can be as important as demand characteristics in determining the economic impacts of a rule. 

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, 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 

 

246 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‖.
A-11

(e.g., salmon) will have more elastic demands than broadly defined markets (e.g., food) since there are 

more substitutes for narrow goods.

Whether the affected product represents a substantial or necessary portion of customers‘ costs or budgets 

is another factor that affects demand elasticities. 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 than for basic requirements. If the good is a necessity item, the quantity 

demanded is unlikely to change drastically for a given change in price and 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, as customers begin to find substitutes or as 

new substitutes are developed. However, temporary price changes may affect consumers‘ decisions 

differently than permanent ones. The response of quantity demanded during a 1-day sale, for example, 

will be much greater than the response of quantity demanded when prices are expected to decrease 

permanently. Finally, it also 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 costs per unit 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 also 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.247 When 

 

247 Another useful source of elasticity estimates is the recently developed EPA Elasticity Databank. 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 
A-12

such information is unavailable, as is often the case for intermediate goods, elasticities may be 

quantitatively or qualitatively assessed.248 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 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.249 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 benefit-cost 

analysis. 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 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 

movements along the Hicksian demand function can be used to determine the monetary change that 

would compensate the consumer for the price change, considering the substitution effect alone. 

 

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.

 

elasticities, and trade elasticities) and economic sectors/product markets. The database is populated with EPA 

generated estimates used in Environmental Impact Assessment (EIA) studies conducted by the Agency since 

1990 as well as estimates found in the economics literature. It may be accessed from the Technology Transfer 

Network Economics & Cost Analysis Support website: http://www.epa.gov/ttnecas1/Elasticity.htm.

248 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). 

249 See Perman et al. (2003), Just et al. (2005), or any graduate level text for a more thorough exposition of this 

issue.
A-13

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.

250

 This 

can be shown with indifference curves and a budget line, as seen in Figure A.7. 

Figure A. 7

Assume that the consumer is considering the tradeoff 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 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.251 A utility-maximizing consumer will choose 

quantities C1′ and C2′, the point where the indifference curve is tangent to the budget constraint.252 

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 the welfare change253 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. 

 

250 The notation and discussion in this section follow Chapter 12 of Perman et al. (2003).

251 In Figure A.7, C2 is considered the numeraire good (i.e., prices are adjusted so that P2′ is equal to 1).

252 For a review of the utility maximizing behavior of consumers, see any general microeconomics textbook.

253 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. 
A-14

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.254 Notice that in the case of a price decrease, the CV measures how much the consumer would be 

willing to pay (WTP) to receive the price reduction and EV measures how much the consumer would be 

willing 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.

Figure A.8

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 (WTP) 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 (WTA) to not experience the gain in 

 

254 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. 
A-15

environmental quality. If valuing an environmental degradation, then CS measures the WTA and ES 

measures WTP.

Figure A.9

Whereas statements can be made about the relative size of CV, EV, and MCS for price changes of normal 

goods,255 it is not possible to make similar statements about CS, ES, and MCS for a change in 

environmental quality (Bockstael and McConnell, 1993). 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 can be illustrated in Figure A.9 where the market 

 

255 Willig (1976) shows that ordinary, or Marshallian, demand curves may 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% (see Perman et al., 2003).
A-16

produces the quantity Qm in equilibrium without intervention. The external costs of production are shown 

by the marginal external costs (MEC) curve without any abatement. Total external costs are given by the 

area under the MEC curve up to the market 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 Q1. The social cost of the requirement is the resulting change in consumer 

and supplier surplus, shown here as the total observed abatement costs (parallelogram P0P1AC) 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 also 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 

was 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.256 

In some cases, a regulation may have such a significant impact on the economy that a general equilibrium

modeling framework is required.257 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 preexisting 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 

 

256 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 (EPA, 1989).

257 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 pre-shock values to determine the expected impacts of the policy or change in 

exogenous variables. 
A-17

linkages between all sectors of the economy and all feedback effects, and can measure total costs 

comprehensively.258

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. 

Note that this is not necessarily the point at which:

 Benefits are maximized, 

 Costs are minimized,

 Total benefits = total costs (i.e., benefits/costs ratio = 1), 

 Benefits/costs ratio is the largest, or

 The policy is most cost-effective. 

Figure A.10

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 benefits/costs 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 benefits/costs ratio is not optimal either. Two policy options 

could yield equivalent benefits/costs 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. 259 Finally, finding the most cost-effective policy has similar 

 

258 Chapter 8 provides a more detailed discussion of partial equilibrium, multi-market, and general equilibrium

analysis.

259 However, 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 
A-18

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)

260 is 

equal to the cost of abating an additional unit (i.e., marginal abatement cost).261 If the marginal 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 it provides in benefits, and will make it 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.262

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 

 

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 would 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 would 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 would yield $1,000 in total net benefits, while choosing options by the benefit-cost ratio 

criterion would yield $500 in total net benefits. In most cases, choosing options in decreasing order of benefitcost 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 (e.g., 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 cost-effectiveness analysis (e.g., Hyman and Leibowitz (2000)), while the 

net-benefit criterion is not. 

260

 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.

261 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 

willingness to pay for pollution equals the marginal social costs of polluting.)

262

 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.
A-19

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 of abating one more unit of pollution is 

equal across all polluters.263

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. 264 Emissions are drawn on the horizontal axis and increase 

from left to right.. The damages from emissions are represented by the marginal damage curve (MD). 

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

 

263 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.)

264 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)).
A-20

The total damages associated with emissions level E

*

are represented by the area of the triangle AE0E

*

, 

while the total abatement costs are represented by area AE1E

*

. The total burden on 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, E0AE1.

Now assume that emissions are something other than E

*

. For example, suppose emissions were EX, which 

is greater than E*. In this case, total damages for this level of emissions are equal to the area of the 

triangle BE0Ex, while total costs of abatement to this level is equal to the area CExE1. 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 marginal damage (MD) curve) and the marginal abatement cost (MAC) 

curve intersect – is economically efficient because it imposes the least net cost on (i.e., yields the highest 

net benefits for) society. That is, the triangle E0AE1 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 emissions based policies 

emphasized in the Guidelines may be less feasible for addressing nonpoint source pollution, such as

agriculture, that 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 (2000) and Portney and Stavins (2000). Finally, general microeconomics textbooks 

(Mankiw (2004), Varian (2005) at the undergraduate level, and Mas-Colell et al. (1995), Kreps (1990), 

Varian (2002) at the graduate level), and applied welfare economics textbooks (Just et al., 2005) are 

useful references as well.
B-1

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 benefit-cost analyses, 

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‖ 

to express these benefits in monetary terms.265, 266 

The risk reductions themselves can generally be classified according to the characteristics of the risk in 

question (e.g., voluntariness, controllability) and the characteristics of the affected population (e.g., age, 

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 4.3 

and will not be repeated here.

Central Estimate of VSL

Table B-1 contains the VSL estimates that currently form the basis of the Agency‘s recommended central 

VSL estimate. Fitting a Weibull distribution to these estimates yields a central estimate (mean) of $7.4 

 

265 Suppose a policy affects 100,000 people and reduces the risk of premature death by 1 in 10,000 for each 

individual. Summing these individual risks across the entire affected population results in 10 statistical lives 

―saved‖ if the policy is implemented. Suppose, too, that individuals are willing to pay $500 each for the risk 

reduction. Aggregating the individual values to get the value of $500 statistical life would yield a VSL of $5 

million (=$500* 1/(risk reduction)). The total mortality benefits for the policy would be $50 million.

266 It is important to emphasize that the Value of Statistical life does not represent the value of an identifiable person. 

Rather it is the value associated with a small change in the probability of dying, aggregated up to the value of a 

statistical life.
B-2

Table B-1: VALUE OF STATISTICAL LIFE ESTIMATES 

(mean values in millions of 2006 dollars)

Study Method Value of Statistical Life

Kneisner 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

Kneisner 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, 1979) Labor Market $6.07

R.S. Smith (1976) Labor Market $6.80

V.K. Smith (1976) Labor Market $6.92

Olson (1981) Labor Market $7.65

Viscusi (1981) Labor Market $9.60

RS.Smith (1974) Labor Market $10.57

Moore and Viscusi (1988) Labor Market $10.69

Kneisner 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 (1997) and Viscusi (1992); Updated to 2006$ with GDP deflator.
B-3

million ($2006) with a standard deviation of $4.7 million.267 268 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 

Economic Analyses were drafted.269 It remains EPA‘s default guidance for valuing mortality risk changes 

although the Agency has considered and presented alternatives.270 

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 value of statistical life (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 metaanalyses 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) meta-analysis of 43 studies. The 

mean estimate of $5.5 million (2000$)1 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 rule-makings 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, 

including commissioning 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 Science Advisory Board Environmental Economics Advisory Committee 

 

267 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 USEPA 1997a for more details.

268 This VSL estimate was produced using the Gross Domestic Product (GDP) Deflator inflation index. Some 

economists prefer using the Consumer Price Index (CPI) in some applications. The key issue for EPA analysts 

is that the chosen index be used consistently throughout the analysis.

269 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; 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.

270 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 

USEPA 2007g for recent SAB recommendations).
B-4

(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 (i.e., wagerisk and stated preference) (USEPA 2007).

Until updated guidance is available, the Agency determined that a single, peer-reviewed estimate applied 

consistently best reflects the SAB-EEAC advice it has received to date. Therefore, the VSL that was 

vetted and endorsed by the SAB described above should be applied in relevant analyses while the Agency 

continues its efforts to update its guidance on this issue.

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.

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:271

 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.272 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. 

 

271 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 Fischhoff et al. (1978).

272 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-5

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 

Science Advisory Board (SAB) reviewed an Agency summary of the available empirical literature on the 

effects of risk and population characteristics on willingness to pay (WTP) for mortality risk reductions 

(USEPA 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 willingness-to-pay estimates to account for higher future income levels, though not for crosssectional differences in income; (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 cross-sectional 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, 1990-2010.273 Recent EPA analyses have assumed a 

triangular distribution from these values and used the results in a probabilistic assessment of 

benefits.274 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.

 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.275 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.

 

273 For details see Kleckner, N. and J. Neuman. 2000. Update to Recommended Approach to Adjusting WTP 

Estimates to Reflect Changes in Real Income. Industrial Economics, Incorporated. Memorandum to Jim 

DeMocker. U.S. Environmental Protection Agency. September 30, 2000.

274 See, for example, page 6-84 of the Final Economic Analysis for the Stage 2 DBPR,(USEPA,2005).

275 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.
B-6

 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 Environmental Economics Advisory Committee on issues 

related to valuing changes in life expectancy for which age and baseline health status are close 

correlates.276 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 willingness to pay 

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 

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.277 In 

general this literature has shown that the relationship between age and willingness to pay for 

mortality risk changes is ambiguous, requiring strong assumptions to even sign the 

relationship.278 Empirical evidence is also mixed. A number of empirical studies (mostly 

hedonic wage studies) suggest that the value of a statistical life follows a consistent "inverted-U"

life-cycle, peaking in the region of mean age.279 Others find no such statistically significant 

relationship and still others show willingness to pay increasing with age.280 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.281

In spite of the ambiguous relationship between age and WTP, two alternative adjustment 

techniques have been derived from this literature. The first, 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 value of statistical life 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 

 

276 USEPA (2006d) summarizes much of the literature related to the effects of age and health status on willingness 

to pay for changes in mortality risk and includes the charge questions put to the SAB-EEAC on these issues.

277 See, for example, Shepard and Zeckhauser (1982), Rosen (1988), Cropper and Sussman (1988, 1990) and 

Johannson (2002).

278 See Evans and Smith (2006) for a recent summary.

279 See Jones-Lee et al. (1985), Aldy and Viscusi (2008) and Viscusi and Aldy (2007a and b), Kniesner, Viscusi and 

Ziliak (2006). 

280 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.

281 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 a 20 percent reduction for those 

aged 70 and over; however, this difference was not statistically significant. 
B-7

prepared for EPA's retrospective and prospective studies of the costs and benefits of the Clean Air 

Act (US EPA 1997a and US EPA 1999). 

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.282

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, 

however, especially when children are found to be a significant portion of the affected 

population.283 Although the literature on the valuation of children‘s health risks is growing, there 

is still not enough information currently to derive age-specific 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 studies 

showing no statistically significant effects (Krupnick et al. 2002).284

Application of existing value-of-statistical-life-year approaches implicitly assumes a linear relationship in 

which each discounted life year is valued equally. As OMB (1996) notes, although ―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 

 

282 This second approach was illustrated in one EPA study (US EPA, 2002) for valuation of air pollution mortality 

risks, drawing upon adjustments measured in Jones-Lee et al. (1985).

283 See USEPA (2003) 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.

284 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).
B-8

techniques, is beginning to help establish a basis for valuation in cases where the mortality risk reduction 

involves relatively short extensions of life.285

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 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 caseby-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, 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 Science Advisory Board as guidance recommendations are revised.

 

285 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 implies support for variation in protection from 

environmental risks. Another consideration is that existing economic methods may not capture social 

willingness to pay to reduce health risks. Chapter 9 details how some these considerations may be informed by 

a separate assessment of equity. 
C-1

C. Accounting for Unemployed Labor in Benefit Cost 

Analysis

In 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 RIAs under this scenario. 
D-1

D. References

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5 U.S.C 601-612: The Regulatory Flexibility Act of 1980 (P.L. 96-354), as amended by the Small 

Business Enforcement Fairness Act (P.L. 104-121), 1996. http://www.archives.gov/federalregister/laws/regulatory-flexibility/

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Adamowicz, W.L., J. Louviere, and J. Swait. 1998b. Introduction to Attribute-Based Stated Choice 

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E. Author Index

Under development
F-1

F. Subject Index

Under development