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iii

Valuing the Health Benefits of Food Safety

A conference held September 14-15, 2000,

at the University of Maryland, College Park

Many Federal agencies conduct cost-benefit analyses of regulations that are intended to increase

the safety of foods. The agencies that implement programs and regulations to reduce the risks

associated with food consumption include:

• The Food Safety and Inspection Service (FSIS), Department of Agriculture

• Economic Research Service (ERS), Department of Agriculture

• The Food and Drug Administration’s Center for Food Safety and Applied Nutrition

(CFSAN), Department of Health and Human Services

• Centers for Disease Control and Prevention (CDC), Department of Health and Human

Services

• The Environmental Protection Agency (EPA).

The different agencies use many different methods to estimate the reductions in risk associated

with their policies; they also use different methods to assign dollar values to the human health

benefits from the reduced risk. Because each agency uses a different method, it is difficult to

compare programs across agencies. If there were a consensus approach to assigning values to

risk reductions and each agency used it, risk managers could compare the effects of different

agencies’ programs. Those comparisons would provide risk managers with information to help

them choose food safety programs that reduce both food-borne illness and public costs.

The differences across agencies—and in some instances within agencies—in approaches to

measuring health benefits make it difficult to compare the estimated benefits of different health

and safety regulations. If there were a consensus approach to assigning values to risk reductions

and each agency used it, risk managers could compare the health benefits of different regulations

and programs. Those comparisons would provide risk managers with information to help them

choose food safety (and other) programs that reduce both illness and public costs. One purpose

of bringing together different agencies is to investigate methods that can be used by all agencies

concerned with public health. What should economists be trying to measure when they assign

values to reducing morbidity and postponing deaths? What methods should economists use to

assign those values?

As a first step toward generating a common approach, several agencies sponsored a conference

on valuing the health benefits of food safety. The conference was a first step toward forging a

consensus across agencies on how to value improvements in food safety. Most of the issues

discussed applied to general health and safety issues in addition to food safety. The topics

covered included:

(1) valuing statistical lives;

(2) valuing risk reductions for different hazards;

(3) valuing risk reductions using different methods; and

(4) valuing pain and suffering and lost productivity.
iv

Conference sessions included formal presentations, comments, and both general and small-group

discussions on current best practices for valuing the prevention of food-borne illnesses and death.

Although the general sessions and the breakout sessions identified many areas of disagreement

on valuing health benefits, the sessions also gave economists from different agencies an

opportunity to see how other agencies estimated health benefits. Participants from the Federal

agencies, academia, and private organizations agreed that getting FSIS, CDC, ERS, EPA, and

CFSAN together was a major step toward reaching consensus and improving the methods used

by the various agencies. Conference participants also agreed that economists from the different

agencies should continue to meet and work toward consensus on methods of valuing food safety.

This report includes the major papers presented at the conference, comments of discussants who

provided written remarks, and summaries of discussion sessions.

Conference Agenda

Opening Remarks

• David R. Lineback, Director, Joint Institute for Food Safety & Applied Nutrition

• Richard Williams, Food and Drug Administration, Center for Food Safety and Applied

Nutrition

• Al McGartland, U.S. Environmental Protection Agency, National Center for Environmental

Economics

• Steve Crutchfield, U.S. Department of Agriculture, Economic Research Service

Valuing Statistical Lives--Moderated by Nicole Owens and Nathalie Simon, U.S. Environmental

Protection Agency, National Center for Environmental Economics

• “Using Estimates of the Value of Statistical Life in Evaluating Regulatory Effects” by

Donald Kenkel, Cornell University

• Comments by Maureen Cropper, World Bank and University of Maryland

• Comments by Chris Dockins, U.S. Environmental Protection Agency, National Center for

Environmental Economics

Valuing Pain and Suffering and Lost Productivity--Moderated by Clark Nardinelli, Food and

Drug Administration, Center for Food Safety and Applied Nutrition

• “Measuring the Pain, Suffering, and Functional Disability Associated with Foodborne

Illness” by Josephine Mauskopf, Research Triangle Institute

• Comments by James Hammitt, Harvard School of Public Health

• Comments by Amber Jessup, Food and Drug Administration, Center for Food Safety and

Applied Nutrition

Valuing Risk Reductions Using Different Valuation Methods--Moderated by Elise Golan, U.S.

Department of Agriculture, Economic Research Service

• “Valuing Pathogenic Risk: Methods, Skill & Rationality” by Jason Shogren, University of

Wyoming
v

• Comments by V. Kerry Smith, North Carolina State University

• Comments by Fred Kuchler, U.S. Department of Agriculture, Economic Research Service

Valuing Risk Reductions for Different Hazards--Moderated by Tammy Riggs, U.S. Department

of Health and Human Services, Centers for Disease Control and Prevention

• “Benefits Transfer and the Value of Food safety” by Alan J. Krupnick, Resources for the

Future

• Comments by Ian Savage, Northwestern University

• Comments by Carol Scotton, U.S. Department of Health and Human Services, Centers for

Disease Control and Prevention

Reports from breakout sessions

• Report from breakout session on valuing statistical lives--David Widawsky, U.S.

Environmental Protection Agency, Office of Prevention, Pesticides, and Toxic Substances

• Report from breakout session on valuing pain and suffering and lost productivity--Clark

Nardinelli, Food and Drug Administration, Center for Food Safety and Applied Nutrition

• Report from breakout session on valuing risk reductions using different valuation methods--

Jean Buzby, U.S. Department of Agriculture, Economic Research Service

• Report from breakout session on valuing risk reductions for different hazards--Tammy Riggs,

U.S. Department of Health and Human Services, Centers for Disease Control and Prevention

Conference Summary

Clark Nardinelli, Food and Drug Administration, Center for Food Safety and Applied Nutrition
Session I: Valuing Statistical Lives 1

SESSION I

VALUING STATISTICAL LIVES

Moderators’ Notes

Moderators: Nathalie Simon and Nicole Owens (Environmental Protection Agency)

Currently, in order to value mortality risks associated with foodborne illness, analysts in various

Federal agencies rely on the existing economic literature in this area, comprised primarily of

hedonic labor market studies. The resulting values from these compensating wage studies have a

number of shortcomings in the context of valuing food safety risks. First, the risks addressed by

the labor market studies generally befall prime-aged males, whereas the populations most

susceptible to foodborne mortality risks are children and the elderly. Second, the mortality

events in the labor market studies are accidental and are therefore preceded by short, if not nonexistent, morbidity periods. Foodborne mortality events in contrast can sometimes follow

extended and very painful morbidity periods that vary in length from one illness to another. The

discussion in the breakout session focused on the shortcomings of existing statistical measures

and the need for more appropriate measures (either pulled from a re-examination of the existing

literature or new VSL studies). Before embarking on a quest for new estimates on the value of a

statistical life, however, several issues need to be addressed:

1. How should the agencies define “high-quality” VSL studies? Is there a set of characteristics

that should be present for a study to be characterized as such? If so, what features are

included in this set?

2. Which foodborne illnesses pose the greatest mortality risk to the population and what are the

characteristics (symptoms, population at risk, length of morbidity period, etc.) of these

illnesses?

3. Should VSL estimates be developed for deaths resulting from specific illnesses? If so, for

which illnesses should specific values be estimated? 

4. Should the mortality event be treated separately from the morbidity period that often precedes

a death due to foodborne illness? Are there circumstances in which it is appropriate to value

the illness as a whole (morbidity + mortality)? If so, what are these circumstances?

5. Should new VSL studies focus on risk reductions for specific illnesses or is it more

appropriate to focus on risk reductions resulting from a particular program? That is, should

we be valuing health endpoints or programs?

6. How can analysts more adequately transfer VSL estimates derived from studies in which the

magnitude of the risk reduction is stated explicitly to situations in which the magnitude of the

risk is very uncertain.
2 Session I: Valuing Statistical Lives

Using Estimates of the Value of a Statistical Life

in Evaluating Regulatory Effects

by Donald Kenkel

Basic Concepts and Current Practice

The problem of allocating scarce societal resources to life-saving activities arises when

evaluating a wide variety of regulations and government programs. Most economists and policy

analysts agree on the general principle that the life-saving benefits of public sector activities

should somehow be compared to the costs of the activities. The agreement fades, however, when

this general principle is put into practice to evaluate specific regulations. Several different

conceptual approaches that have been proposed over the years continue to influence the current

practice of economic evaluation of the life-saving benefits of regulations. This section briefly

reviews selected evaluations conducted by the U.S. Environmental Protection Agency (EPA), the

Economic Research Service of the U.S. Department of Agriculture (USDA), and the U.S. Food

and Drug Administration (FDA). The review of current practice also allows some basic concepts

to be defined and illustrated.1

 The following sections of the paper propose a common-sense rule

for improving current practice: The same life-saving benefit should always be given the same

value, but different life-saving benefits should be given different values.

The standard approach to placing a dollar value on the life-saving benefits of regulations is based

on societal willingness to pay (WTP) for mortality risk reductions. Unlike an emergency rescue

operation, the specific people whose lives are saved by regulations cannot be identified. Instead,

regulations reduce mortality risks in the population affected by the regulation. As a hypothetical

example, suppose a new food safety regulation reduces the annual risk of dying of a

foodborne illness by 0.00001. In a population of 100,000, the regulation is expected, in a

statistical sense, to result in 1 fewer death from foodborne illness each year. Using this

reasoning, regulations are sometimes said to save “statistical lives” as opposed to identified lives.

If each person in that population of 100,000 is willing to pay $20 a year for the reduction

The author is with the Department of Policy Analysis & Management, Cornell University, Ithaca, NY

14853 (607-255-2594; dsk10@cornell.edu). Comments welcome. The author thanks Maureen Cropper,

Chris Dockins, and other participants at the conference for useful comments. All views expressed are

those of the author and do not necessarily reflect the views of the U.S. Environmental Protection Agency,

the Economic Research Service of the U.S. Department of Agriculture, the U.S. Food and Drug

Administration, or any other Federal agency.

1

The phrase “current practice” is used loosely and refers to the selected published evaluations conducted

by the EPA, USDA, and FDA. These past evaluations may no longer be representative of “current

practice.” In addition, it is simplistic to describe the methods used by an agency as a single, current

practice; in reality, each agency may use a variety of methods depending upon the circumstances of the

regulatory evaluation.
Session I: Valuing Statistical Lives 3

in mortality risks, the total WTP is $2 million for an annual risk reduction that can be expected in

the statistical sense to save one life. In this case, $2 million is said to be the value of a statistical

life (VSL). The VSL should be thought of as a convenient way to summarize the value of small

reductions in mortality risks. It is not meant to be applied to the value of saving the life of an

identified person (i.e., the value of changing the risk of mortality from one to zero).

Based on an extensive review of the research literature, the U.S. EPA (1997) suggests that a

reasonable estimate of the VSL has a mean of $4.8 million with a confidence interval of plus or

minus $3.2 million (in 1990 dollars). The suggested range for the VSL suggested by the EPA is

consistent with other reviews including Fisher, Chestnut and Violette (1989), Viscusi (1992,

1993), and the meta-analysis by Desvousges et al. (1998). The EPA’s review identified 26

studies that were judged to reflect “sound and defensible” methods. Five of these studies used

the contingent valuation method, where survey respondents are directly asked about their WTP

for mortality risk reductions. The remaining 21 studies estimated the value of risk reductions

based on workers’ willingness to accept riskier jobs in return for higher wages. The conceptual

foundation of both empirical approaches to estimating the VSL is that societal WTP for risk

reductions should reflect individuals’ risk valuations, whether elicited directly through surveys or

revealed in their labor market decisions.

The WTP approach to valuing the lifesaving benefits of regulatory effects is consistent with

Haveman and Weisbrod’s (1983, p. 82) more general argument that “cost-benefit analysis can be

viewed as an attempt to develop a public-sector analogue for private market decision-making.”

In their private-market decisionmaking, consumers do not demonstrate an infinite WTP for

safety. Instead, they often demonstrate their willingness to trade off safety for other desirables,

such as time and money. Evaluating regulatory effects based on estimates of consumers’ WTP

allows public-sector safety decisions to reflect that same willingness to make tradeoffs involving

safety. However, the relevance of private-market decisionmaking will be limited when there are

questions about the competence of individual decisionmaking under uncertainty. For example,

evidence shows that people tend to underestimate risks of common causes of death, while they

tend to overestimate the risks of rare causes of death (Slovic, Fischhoff, and Lichenstein, 1985).

Ideally, public-sector safety decisions should reflect people’s preferences but not reflect their

mistakes.

Several recent evaluations of EPA regulations use the $4.8 million VSL estimate, with the only

adjustment being for inflation to express benefits in current dollars. As part of its estimates of

the benefits of drinking water regulations related to disinfectants and disinfection byproducts, the

EPA (1998) uses a value per statistical life saved for fatal bladder cancers represented by a

distribution with a mean of $5.6 million (1998 dollars). In a Regulatory Impact Analysis of a

proposed rule on surface water treatment, improvements in drinking water filtration are

estimated to reduce mortality from Cryptosporidium illnesses (EPA, 2000a). The benefits of

these mortality reductions are based on a mean value of $5.7 million (January 1999 dollars) per

statistical life saved. In a Regulatory Impact Analysis for a proposed ground water rule, the EPA

(2000b) estimates the monetized benefit from viral deaths avoided using a VSL of $6.3 million

(1999 dollars).
4 Session I: Valuing Statistical Lives

In several of its evaluations conducted several years ago, the Economic Research Service of the

USDA supplemented WTP-based estimates of the VSL with estimates based on the human

capital/WTP hybrid approach (Landefeld and Seskin, 1982). Estimated this way, the VSL

ranges from roughly $15,000 to $2,037,000 (1996 dollars), depending on the age group

benefiting from the mortality risk reduction (Buzby and Roberts, 1997). The conceptual

foundation of this empirical approach to estimating the VSL reflects two schools of thought. In

the human capital or cost of illness approach, life-saving benefits are estimated by the impact of

mortality reductions on the measured productivity of the economy. In the standard human

capital approach, the VSL is therefore equal to the discounted present value of lifetime earnings

lost due to the premature mortality. This approach has been criticized on a number of grounds,

with the most fundamental objection being that an individual’s WTP to reduce mortality risks

has no necessary relationship to his or her discounted lifetime earnings.2 Landefeld and Seskin

(1982) develop an adjusted process to calculate forgone earnings, allowing for the individual’s

perspective in that earnings are computed net of tax, nonlabor income is included, an individual

discount rate is used (as opposed to the social discount rate), and a risk-aversion factor is applied.

The USDA’s analyses also include the value of housekeeping services as a component of

lifetime earnings. Compared to the methodology of the original human capital approach, this

hybrid approach yields VSL estimates that are closer theoretically and empirically to individual

WTP-based estimates of the VSL, such as those reviewed by the EPA (1997).

The USDA used estimates from the hybrid human capital/ WTP approach in two conceptually

distinct types of analyses. The first type are cost-of-illness studies that quantify or account for

the impact of specific conditions on the economy (Kuchler and Golan, 1999, p. 53). Cost-ofillness studies include medical expenditures as the direct costs of treating illness and productivity

losses, including the present value of future earnings forgone due to premature mortality, as the

indirect costs of illness. Cost-of-illness studies use a standardized accounting framework and

methodology, enhancing the comparability of studies of different illnesses and conditions

(Hodgson and Meiners, 1982). The National Institutes of Health [NIH] (1998) presents over 50

disease-specific estimates of the direct and/or indirect costs of illness. USDA analyses include a

study of the economic costs of congenital toxoplasmosis, which can result from handling raw

meat or eating undercooked pork and other meats (Roberts and Frenkel, 1990). The present

value of an infant's lifetime earnings, estimated at $983,000 (1989 dollars), is one component of

the indirect costs of toxopolasmosis. Similarly, Roberts and Pinner (1990) use an estimate that

the present value of forgone lifetime earnings is $1.1 million per infant to estimate the economic

costs of disease caused by Listeria monocytogenes. Buzby, Roberts, Lin, and MacDonald (1996)

include forgone lifetime earnings in their estimate that foodborne bacteria impose between $2.9

and $6.7 billion of economic costs. In an update, Buzby and Roberts (1997) include estimates of

the costs of Guillain-Barré‚ syndrome related to Campylobacter jejuni infection.

2

In one response to the criticisms of this approach, Robinson (1986) argues that it is based on assumptions

that are similar to the propositions of the material welfare school of thought, dominant in English

economics between 1880 and 1940. Robinson agrees with the critics that the approach is not consistent

with the approach of modern welfare economics that provides the conceptual foundation for cost-benefit

analysis.
Session I: Valuing Statistical Lives 5

The USDA also used estimates from the hybrid human capital/WTP approach as an alternative

approach to estimating the value of the life-saving benefits of specific interventions. Compared

with the WTP-based estimates such as those reviewed by the EPA (1997), the hybrid human

capital/WTP approach generally yields lower estimates of the VSL, so this approach is

sometimes seen as a more conservative approach to estimating the value of life-saving benefits.

For example, Roberts, Buzby, and Ollinger (1996) and Crutchfield et al. (1997) use the hybrid

approach to estimate the value of the life-saving benefits of a food safety regulation the Hazard

Analysis and Critical Control Point regulation for meat and poultry plants. The VSL estimates

ranged from $12,000 to $1,585,000 (1993 dollars), or $15,000 to $1,979,000 (1995 dollars),

depending upon age. Crutchfield et al. (1997) explicitly acknowledge that their VSL estimates

are low compared with WTP-based estimates around $5 million used in other agencies’

evaluations of regulatory effects. In these studies, as well as in the review by Kuchler and Golan

(1999), WTP is described as the conceptually correct approach, with the VSL estimates based on

the hybrid human capital/ WTP approach presented as more conservative.

Several evaluations conducted by the FDA measure the life-saving benefits based on the value of

a life year (FDA 1999a, 1999b). For example, the FDA (1999b) values each life year saved by a

nutrition labeling regulation at $100,000. Similar values for a life year are suggested by Zarkin

et al. (1993), Tolley, Kenkel, and Fabian (1994), and Cutler and Richardson (1997). Placing a

dollar value on a life year begins to build a bridge between two different conceptual approaches

to economic evaluation: cost-benefit analysis and cost-effectiveness analysis. Cost-effectiveness

analysis avoids placing a monetary value on health. Instead, the analysis compares the

incremental cost of the intervention to the incremental health effect achieved. The qualityadjusted life year, or QALY, has emerged as a standard measure of effectiveness, and the cost

per QALY saved has been estimated for a wide range of health interventions (Gold et al., 1996).

The QALY approach not only incorporates the quantity of life or years of life extension from an

intervention, but also the quality of life, based on individuals’ preferences over different health

states. For example, a year of life with a serious illness might be weighted as being as valuable

as 0.7 of a year of life with perfect health. Placing a monetary value on a QALY allows the

health effects to be monetized, converting any cost-effectiveness analysis into a cost-benefit

analysis.

The use of the life year approach is illustrated in the FDA’s (1999b) analysis of a proposed rule

about food labeling related to trans fatty acids, nutrient content claims, and health claims. By

encouraging more healthful dietary choices, changes in food labeling regulations have the

potential to reduce mortality from coronary heart disease. In the FDA’s analysis, the cost of a

fatal event is the discounted years of life lost multiplied by the dollar value of a quality-adjusted

life year. FDA estimates that the average victim of coronary heart disease loses 13 years of life,

which discounted at 7 percent becomes 8.4 discounted years. Valuing each life year at $100,000,

the average value per fatal case, i.e., the analogue to the VSL, is about $840,000.

The Same Life-Saving Benefits Should Be Given the Same Value

Although there is general agreement on the principle of valuing the life-saving benefits of

regulations, the review of recent practice suggests that the EPA, the USDA, and the FDA rely on

somewhat different VSL estimates. A hypothetical example provides an extreme case of the
6 Session I: Valuing Statistical Lives

extent of disagreement. Suppose that each agency were considering the life-saving benefits of a

regulation that reduced mortality risks for a group of 50-year-olds. The EPA’s (1997) review of

WTP estimates suggests the benefits should be based on a VSL of $4.8 million. The hybrid

human capital/ WTP approach used by the USDA suggests a VSL of $721,418. The value of a

life year approach used by the FDA implies that a VSL of $1.2 million should be used.3 This

range shows substantial disagreement; for example, both the USDA and FDA estimates fall

outside the confidence interval around the EPA’s estimate of the mean VSL.

If different regulatory agencies’ efforts are guided by an inconsistent set of VSL's, the result will

be an inefficient set of regulations. Consider a stylized regulatory decisionmaking process,

where each agency uses cost-benefit analysis to decide whether to enact possible life-saving

regulations. Given their different areas of regulatory responsibility and authority, assume each

agency faces a schedule showing that it can save additional lives through more regulations, but

only at an increasing marginal cost imposed on the economy. Each agency chooses to regulate

until the marginal benefits of life saving, as measured by its preferred estimate of the VSL, just

equal the marginal costs. When decisionmakers in the different agencies use inconsistent

estimates of the VSL, the result is inefficient, in the sense that it is possible to re-allocate

regulatory efforts to reach an outcome that all decisionmakers would agree is an unambiguous

improvement.4

 Using the different VSL's from the hypothetical example, suppose the EPA

marginally reduced its regulatory efforts and saved two fewer lives, while at the same time the

USDA and the FDA marginally increased their regulatory efforts to save one more life each.

The net result would be the same number of lives saved, but the regulatory costs would fall by

$7.7 million.

Tengs and Graham (1996) provide a detailed analysis of the inefficiency that results from

inconsistent, or in their perhaps more appropriate term, “haphazard,” public investments in lifesaving activities. They consider 185 life-saving interventions, which included but were not

limited to regulatory efforts like those of the EPA, USDA, and FDA. In total, the interventions

currently implemented are estimated to cost the economy approximately $21.4 billion and save

approximately 56,700 lives. By choosing interventions to minimize costs, Tengs and Graham

estimate that it is possible instead to save the same number of lives and save about $31.1 billion

of costs. This surprising result is because Tengs and Graham identify many untapped

interventions that save both lives and money. Implementing these interventions makes it

possible not only to save the $21.4 billion currently being spent, but to save another $10 billion

as well. Alternatively, holding the cost constant at the current level of $21.4 billion but choosing

interventions to maximize lives saved, Tengs and Graham estimate that it would be possible to

save about twice as many lives 117,000 annually than results from the current set of regulations.

The analysis of Tengs and Graham raises an important question: Are decisions about life-saving

regulations based on inconsistent estimates of the VSL, or simply made haphazardly with little

3

Following the FDA (1999b), the value of a year of life is assumed to be $100,000, and a discount rate of

7 percent is applied. Assuming an individual at age 50 has a life expectancy of 29 years, discounting at 7

percent yields 12.3 years.

4

This explanation draws on Sugden and Williams (1986, pp. 187-190) general discussion of the

importance of consistency in decisionmakers’ valuations of costs and benefits.
Session I: Valuing Statistical Lives 7

described above, where cost-benefit analysis drives every regulatory decision, is not a literal

description of how decisions are made. Several reviews provide a mixed picture of the role of

consideration of the benefits and costs? Clearly, the stylized regulatory decisionmaking process

cost-benefit analysis in regulatory efforts. Based on a review of the record of the 1980's, Viscusi

(1996) suggests that different agencies’ reliance on cost-benefit analysis makes a systematic

difference in their regulatory efforts. Using a VSL of $5 million as the cutoff for efficient

regulations, Viscusi lists 13 regulations that pass a cost-benefit test per life saved, and 20

regulations that fail a cost-benefit test. Similarly, Hahn’s (1996) review of 92 final and proposed

rules for 1990 to mid-1995 finds that, using the agencies’ estimates of benefits and costs, only 17

would pass a cost-benefit test. However, Viscusi (1996) notes an interesting pattern in the earlier

record. He argues that the fact that all of the listed regulations that were issued by the U.S.

Department of Transportation (DOT) pass a cost-benefit test is no accident: The DOT relies

heavily on cost-benefit analysis, using $3 million for the VSL in its evaluations.

Aside from regulatory decisions of the DOT, it is hard to escape the conclusion that few

decisions about life-saving regulations are being made primarily based on benefits and costs.

These so-called “haphazard” decisions may reflect other systematic influences, such as political

pressure. Whether these influences are a legitimate part of the regulatory decisionmaking

process is a much broader question. For the narrower question considered here, using a

consistent VSL estimate in evaluations of life-saving regulations by different agencies is clearly

an important step toward the more limited goal of improving the economic efficiency of

regulatory efforts.

Different Life Saving Benefits Should Be Given Different Values

The last section emphasized the desirability of different regulatory agencies using the same VSL

when they are evaluating the same life-saving benefits. However, different regulations often

result in fundamentally different types of life-saving benefits. The selected evaluations reviewed

in section 1 address the life-saving benefits of reducing mortality from bladder cancer,

cryptosporidium illnesses, viral illnesses, congenital toxoplasmosis, Guillain-Barré‚ syndrome,

and coronary heart disease. This section discusses when it is desirable to use different estimates

of the VSL for these different risk reductions. Two sources of heterogeneity in the VSL are

discussed. First, there is heterogeneity in the willingness to pay across different health risks for

the same individual. Second, there is heterogeneity across individuals in their willingness to pay

for risk reductions.

Individual WTP for a given reduction in mortality risks probably differs depending upon the

cause of death. The VSL typically estimated applies most directly to WTP to reduce the risks of

unforeseen instant death, such as a workplace or traffic accident. However, people may be

willing to pay substantially more to reduce risks where there is a lengthy period of morbidity

preceding death, both because of the value of morbidity avoided and the psychic costs of

imminent death. Empirical evidence on WTP for different types of mortality risks appears to be

fairly limited. Jones-Lee, Hammerton, and Philips (1985) report that, given a choice between

preventing 100 deaths from cancer, heart disease, or motor vehicle accidents, most respondents

preferred to prevent deaths from cancer. The means of responses indicate that preventing 100

deaths from heart disease is worth almost twice as much preventing 100 deaths from motor
8 Session I: Valuing Statistical Lives

vehicle accidents, while preventing 100 cancer deaths is valued at about three times the value of

preventing accidental deaths. Based on this and other evidence, Tolley, Kenkel, and Fabian

(1994) argue that the appropriate VSL for lung cancer mortality may be twice the size of the

VSL appropriate for mortality due to unforeseen, instant death. Similarly, it may be appropriate

to use a higher VSL to evaluate the benefits of reducing mortality from bladder cancer due to

water pollution and a lower VSL to evaluate the benefits of reducing mortality from acute

infection from foodborne illness.

While the same individual may have a different WTP to reduce the risks of different causes of

death, different individuals may also have different WTP’s to reduce the same risk. Just as for

other commodities, people may value health and safety differently due to idiosyncratic

differences in tastes and preferences, income levels, and so on. Just as for schooling human

capital, people may value their health and safety capital differently at different points in the life

cycle (Cropper, 1977). In addition to variation over the life cycle in WTP to reduce risks to

adults, altruism in societal WTP means that there may be a special premium on reducing

mortality risks for children.

The various sources of heterogeneity in WTP lead to the somewhat controversial conclusion that

different VSL's should be used to evaluate the life-saving benefits of regulations that affect

different groups of people. The EPA (1997, p. 77) notes that the population most affected by

reductions in air pollution-related mortality risks is likely to be older than average,

disproportionately drawn from those aged 65 and over. Improvements in drinking water

filtration to prevent infection by cryptosporidium will reduce risks especially for sensitive

populations, including children, especially the very young, the elderly, pregnant women, and the

immunocompromised (EPA, 2000a). Regulations to prevent exposure to Toxoplasma gondii in

food will reduce risks for infants (Roberts and Frenkel, 1990). Ideally, the benchmark VSL of

$4.8 million suggested by the EPA (1997) should be adjusted to account for these differences.

For example, because of the methods used the standard estimate of the VSL mainly reflects the

preferences of workers with an average age around 40. The correct VSL to evaluate an air

pollution regulation might be somewhat lower, while a higher VSL should be used when

evaluating food-handling regulations that affect the risks to infants. The conceptual justification

for the use of different VSL’s is that, in private decisions, individuals display different WTP to

reduce risks depending upon their age and other characteristics, so public decisions should reflect

the same preferences.

Limited evidence suggests that WTP to reduce mortality risks varies systematically over the life

cycle of working age adults. In a theoretical analysis, Shepard and Zeckhauser (1982) predict

that the relationship between the VSL and age will show an inverted U-shape, with a peak

around the age of 40 years, dropping to about 50 to 70 percent of the peak by the age of 60. This

pattern is roughly consistent with the empirical results of Jones-Less et al. (1985), but the

magnitude of the changes in VSL over the life cycle are estimated to be smaller than predicted

by theory. For example, the VSL at age 65 is estimated to be still about 90 percent of the peak

VSL from age 40. These patterns are also similar to the life cycle patterns in the VSL estimated

by the hybrid human capital/WTP approach.
Session I: Valuing Statistical Lives 9

Moore and Viscusi (1988) extend the standard wage-risk study to explore if workers of different

ages reveal different WTP for job risks. Using data from the 1977 Quality of Employment

Survey, their results imply that the value of a life year averaged more than $170,000 (1986

dollars), and that the VSL is about $6 million. The results imply that different types of lifesaving activities will have much different values depending upon the age of the affected

individuals and the timing of the risk reduction. For example, to a worker who expects to live

for 35 more years, a 1-year life extension is estimated to be worth only $11,000 now. But a oneyear life extension is estimated to be worth about $400,000 for an older worker with a life

expectancy of 5 years.

The wage-risk approach does not provide information on the value of life-saving activities that

affect children, the elderly, and other groups who are not in the labor force. Studies that examine

revealed preferences for risks in other market contexts are beginning to provide preliminary

estimates of the appropriate VSL for these groups. Blomquist et al. (1996) analyze decisions

about seat belt use for children, and estimate that WTP to reduce risks to children is equal to or

larger than WTP to reduce risks to adults. Mount et al. (2000) use data on automobile purchases

to estimate how much single-car families and families of different composition spend on safety.

This approach allows them to estimate the VSL for children, adults, and the elderly. For

example, one set of estimates suggests the VSL for adults is $6.34 million, while the VSL for

children is $4.28 million and the VSL for the elderly is $4.59 million. This suggests the same

inverted U-shape predicted by the theoretical analysis of Shepard and Zeckhauser (1982).

However, the estimated VSL for children is sensitive to certain assumptions made in the

analysis. Under some sets of assumptions, the VSL for children exceeds that of adults and the

estimated VSL steadily declines over the life cycle.

Another approach to estimating WTP for life-saving activities that affect different age groups is

to conduct surveys that directly ask about preferences for hypothetical life-saving programs.

Cropper, Aydede, and Portney (1994) report the results of surveys of over 3,000 respondents

given choices between various pairs of life-saving activities. For the median respondent, saving

one 20-year-old is equivalent to saving seven 60-year-olds, while saving the lives of 20-year-olds

and 40-year-olds are viewed similarly. This suggests a somewhat different pattern for the VSL

over the life cycle, with the VSL being roughly constant until the age of 40 but sharply dropping

at older ages. However, in these surveys respondents were put in the role of social

decisionmaker. Asking people about how they think societal decisions should be made is

different that asking them about their willingness to pay to reduce their own risks. Because of

this difference, it could be argued that these survey responses are not that relevant to the

empirical pattern of VSL over the life cycle.

In addition to the role of age, other individual characteristics may affect WTP for risk reductions.

The role of income is another controversial example. If regulatory efforts are judged solely on

the basis of economic efficiency, the principles of cost-benefit analysis imply that the VSL

should also depend on the average income of the population experiencing the risk reduction.

Many analysts object to this implication on the grounds of equity or social justice, and in fact it

is often cited as a reason to prefer cost-effectiveness analysis over cost-benefit analysis when

evaluating health interventions. As Pauly (1995) notes, this objection is often a red herring

because many health and safety interventions do not have a wealth bias. Kenkel (1997) argues
10 Session I: Valuing Statistical Lives

further that concern about the unequal distribution of benefits and costs is not new or unique to

the analysis of health interventions. He points out that there are several methods, including the

distributional weights approach and the basic needs approach, to bring distributional concerns

into cost-benefit analysis in a systematic way. Taking this approach suggests the use of incomeadjusted VSL's for strictly efficiency-based cost-benefit analysis of life-saving regulations, to be

supplemented with additional analysis that account for the distribution of costs and life-saving

benefits.

Conclusions

Another way to summarize the arguments made in the preceding sections is that the evaluations

of the life-saving benefits of regulations should use consistent and specific estimates of the VSL.

When different agencies reduce similar health risks for similar populations, they should use

consistent estimates of the VSL; but each agency should use VSL estimates that are specific to

the health risk and population affected by its regulations. This presents a challenge for both the

research community that generates VSL estimates and the policymaking community that uses

them.

One way to develop a catalogue of consistent and specific VSL estimates is the “monetized

QALY approach” such as that used by the FDA (1999a, 1999b). Achieving consistency would

be straightforward: different agencies could use consistent dollar value per QALY when

evaluating all regulatory efforts. The monetized QALY approach would also yield specific

VSL’s that depend on age, pre-existing health state, and cause of death. Whether the monetized

QALY approach yields specific VSL’s that are good estimates of societal WTP is more

problematic. For example, the monetized QALY approach might understate societal WTP for

regulations that save the lives of children, because there is some evidence that people are willing

to pay a special premium for such risk reductions. Empirical evidence also suggests that the

VSL declines more slowly with age than that implied by the monetized QALY approach.

The conceptual foundation for monetizing QALY’s needs to be examined closely and linked to

the VSL literature. QALY weights are based on individual preferences over health states, as

revealed in surveys or experiments. These methods deserve at least the same level of scrutiny as

the contingent valuation method. A noted limitation is that many methods used to develop

QALY weights rely on asking respondents about health states that they have not experienced.

As a validity check, market behavior should be analyzed to see if it is consistent with QALY

approach and weights. This might also provide a revealed-preference approach to monetizing

QALY’s. Until a number of important issues along these lines are resolved, it is premature to

view the monetized QALY approach as meeting the need for a set of consistent and specific VSL

estimates.

References

Buzby, Jean C., and Tanya Roberts. 1997. “Guillain-Barré Syndrome Increases Foodborne

Disease Costs.” FoodReview 20 (3): 36-42.
Session I: Valuing Statistical Lives 11

Buzby, Jean C., Tanya Roberts, C.-T. Jordan Lin, and James M. MacDonald. 1996. Bacterial

Foodborne Disease: Medical Costs and Productivity Losses. U.S. Department of Agriculture,

Economic Research Service, AER-741.

Crutchfield, Steve, Jean C. Buzby, Tanya Roberts, Michael Ollinger, C.-T. Jordan Lin. 1997. An

Economic Assessment of Food Safety Regulations: The New Approach to Meat and Poultry

Inspection. U.S. Department of Agriculture, Economic Research Service, AER-755.

Cutler, David M., and Elizabeth Richardson. 1997. “Measuring the Health Benefits of the U.S.

Population.” Brookings Papers on Economic Activity: Microeconomics.

Desvousges, William H., F. Reed Johnson, and H. Spencer Banzhaf. 1998. Environmental

Policy Analysis with Limited Information, Principles and Applications of the Transfer Method.

Edward Elgar Publishing Limited: Northhampton.

Fisher, Ann, Lauraine G. Chestnut, and Daniel M. Violette. 1989. “The Value of Reducing

Risks of Death: A Note on New Evidence,” Journal of Policy Analysis and Management 8 (1):

88-100.

Haveman, Robert H., and Burton Weisbrod. 1983. “Defining Benefits of Public Programs: Some

Guidance for Policy Analysis.” In Robert H. Haveman and Julius Margolis, editors, Public

Expenditure and Policy Analysis, 3rd Edition. Boston: Houghton Mifflin Company, pp. 80-104.

Jones-Lee, M. W., M. Hammerton, and P.R. Philips. 1985. “The Value of Safety: Results of a

National Survey,” Economic Journal 95: 49-72.

Kenkel, Don. 1997. “On Valuing Morbidity, Cost-Effectiveness Analysis, and Being Rude,”

Editorial, Journal of Health Economics 16 (6): 749-757.

Kuchler, Fred, and Elise Golan. 1999. Assigning Values to Life: Comparing Methods for

Valuing Health Risks. U.S. Department of Agriculture, Economic Research Service, AER-784.

Landefeld, J.S., and E.P. Seskin. 1982. “The Economic Value of Life: Linking Theory to

Practice,” American Journal of Public Health 6: 555-566.

Moore, Michael J., and W. Kip Viscusi. 1988. “The Quantity-Adjusted Value of Life,”

Economic Inquiry 26: 369-388.

Mount, Timothy, Weifeng Weng, William Schulze, and Laurie Chestnut. 2000. “Automobile

Safety and the Value of Statistical Life in the Family: Valuing Reduced Risk for Children,

Adults and the Elderly.” Working Paper, Cornell University.

Pauly, Mark V. 1995. “Valuing Health Benefits in Money Terms,” in Frank A. Sloan, editor.

Valuing Health Care: Costs, Benefits, and Effectiveness of Pharmaceuticals and Other Medical

Technologies. Cambridge: Cambridge University Press, 99-124.
12 Session I: Valuing Statistical Lives

Roberts, Tanya, and J. K. Frenkel. 1990. “Estimating Income Losses and Other Preventable

Costs Caused by Congenital Toxoplasmosis in People in the United States.” JAVMA 196 (2):

249 - 256.

Roberts, Tanya, Jean Buzby, Jordan Lin, Peggy Nunnery, Paul Mead, and Phillip I. Tarr. 1998.

“Economic Aspects of E. coli 0157:H7: Disease Outcome Trees, Risk, Uncertainty, and the

Social Cost of Disease Estimates,” in B. Greenwood and K. De Cock, editors, New and

Resurgent Infections: Prediction, Detection and Management of Tomorrow’s Epidemics. John

Wiley & Sons, Ltd., New York.

Slovic, Paul, Barry Fischhoff, and Sarah Lichenstein. 1985. “Regulation of Risk: A

Psychological Perspective,” in Regulatory Policy and the Social Sciences (ed. Roger Noll), pp.

241-278. California Series on Social Choice and Political Economy. Berkeley, California, and

London: University of California Press.

Sugden, Robert, and Alan Williams. 1986. The Principles of Practical Cost-Benefit Analysis.

Oxford: Oxford University Press.

Tengs, Tammy O., and John D. Graham. 1996. “The Opportunity Costs of Haphazard Social

Investments in Life-Saving,” in Robert W. Hahn, editor, Risks, Costs, and Lives Saved: Getting

Better Results from Regulation. New York and Oxford: Oxford University Press; Washington,

DC: The AEI Press.

Tolley, George S., Donald S. Kenkel, and Robert Fabian. 1994. Valuing Health for Policy: An

Economic Approach. Chicago: University of Chicago Press.

U.S. Environmental Protection Agency. 1997. The Benefits and Costs of the Clean Air Act:

1970-1990. 410-R-97-002.

U.S. Environmental Protection Agency. 1998. “National Primary Drinking Water Regulations:

Disinfectants and Disinfection Byproducts; Final Rule,” Federal Register, December 16, 1998.

U.S. Environmental Protection Agency. 2000a. “Regulatory Impact Analysis for the Proposed

Long Term 1 Enhanced Surface Water Treatment and Filter Backwash Rule,” Federal Register,

February 15, 2000.

U.S. Environmental Protection Agency. 2000b. “Regulatory Impact Analysis for the Proposed

Ground Water Rule,” Federal Register, April 5, 2000.

U.S. Food and Drug Administration. 1995. “Lead-Soldered Food Cans,” Federal Register, June

27, 1995.

U.S. Food and Drug Administration. 1999a. “Preliminary Regulatory Impact Analysis and

Initial Regulatory Flexibility Analysis of the Proposed Rule to Require Refrigeration of Shell

Eggs at Retail and Safe Handling Labels,” Federal Register, July 6, 1999
Session I: Valuing Statistical Lives 13

U.S. Food and Drug Administration. 1999b. “Food Labeling: Trans Fatty Acids in Nutrition

Labeling, Nutrient Content Claims, and Health Claims; Proposed Rule,” Federal Register,

November 17, 1999.

Viscusi, W. Kip. 1992. Fatal Tradeoffs: Public & Private Responsibilities for Risk. New York:

Oxford University Press.

Viscusi, W. Kip. 1993. “The Value of Risks to Life and Health,” Journal of Economic

Literature. 31 (4): 1912-1946.

Viscusi, W. Kip. 1996. “Economic Foundations of the Current Regulatory Reform Efforts,”

Journal of Economic Perspectives 10 (3): 119-134.

Zarkin, Gary A., N. Dean, J.S. Mauskopf, and R. Williams. 1993. “Potential Health Benefits of

Nutrition Label Changes,” American Journal of Public Health 83: 717-724.
14 Session I : Valuing Statistical Lives

Comments by Chris Dockins on

“Using Estimates of the Value of a Statistical Life

in Evaluating Regulatory Effects”

(by Donald Kenkel)

My goal here is not to recapitulate the state of the literature or to outline a detailed conceptual

framework for discussing the subject of health risk valuation in the context of food safety.

Rather, I’d like to give you the perspective of someone inside a Federal agency facing these

challenges in applied work. Dr. Kenkel provided an appropriate starting point for our

discussions over the next few days, and the two fundamental propositions he submitted are

sensible: the value of similar risks should be similar; the value of different risks should differ. I

hesitate to use his exact words “the same risk should be given the same value” because in my

capacity I have found it valuable to always be conscious of the language we use to discuss

economics with non-economists. It is certainly a convenient shorthand to say that we “give

values” to risk. It is shorthand that we use frequently to mean, “we expect to observe individuals

to value similar risks similarly.” However, when we say that we should “give” values to risk

some might infer that these values are ours to give. In fact, they are ours only to observe and to

estimate, if we can.

In any case, the most pressing question for an economist inside a regulatory agency is not

whether we should follow through on these propositions, but whether we can, in practice, do so.

I submit to you that this is not as simple as it may appear.

The subject of consistency in valuation of health risk reductions has come to the fore in EPA as

we have undertaken efforts to revise our Guidelines for economic analyses. As part of this

process we established an Economic Consistency Workgroup to assess how we can approach

these questions more consistently than we may have done in the past. The conversations of the

workgroup made it clear that different offices within EPA had approached these questions in

different manners, in no small part because of the variety of statutes and requirements under

which they operate. For example, the 1996 amendments to the Safe Drinking Water Act allow,

for the first time, the consideration of benefits and costs in setting maximum contaminant limits.

The amendment even specifies that benefits are to be measured in terms of willingness to pay.

The Clean Air Act, on the other hand, does not allow for considerations of benefits and costs in

setting ambient air quality standards. These are familiar examples to many of you.

These varying requirements affect the decision processes in the program offices, including the

types of information seen and considered in decisionmaking. I don’t know the details of the

statutes under which FDA and other Federal agencies operate, but if such diversity exists within

___________________

Chris Dockins is with the National Center for Environmental Economics, U.S. Environmental Protection

Agency. Any opinions, findings, and conclusions or recommendations expressed here are those of the

author and do not necessarily reflect the views of the Environmental Protection Agency.
Session I : Valuing Statistical Lives 15

a single agency, a more thorough review of statutory requirements across the Federal

Government would surely reveal even greater variation.

This diversity is reflected in the decisions made by Federal agencies. A relatively recent review

of the cost-effectiveness of these regulations labels these investments as “haphazard.” This

struck me as curious so I turned to my Webster’s II New Riverside University Dictionary, the one

given to me when I came to work for EPA and with “Property U.S. Government” stamped in

gold across the cover, and looked up the word. There is only one definition, “dependent upon or

marked by chance.” I don’t fault the authors for their choice of title because it obviously served

its purpose, getting me among others to read the article and think about it afterward. But the

decisions analyzed are by no means haphazard. Instead the implications of regulatory actions are

carefully considered before a final decision is made. I would like to see an estimate of the mean

number of hours that go into analysis and consideration of EPA regulations. My guess is that it

would be in the thousands. No, the investments are not haphazard. One may argue, however,

and this is what the authors do, that the individual investments are not efficient, and that, taken

together, they are not the most cost-effective use of resources. That is, they are not economic.

I note this only because it serves to underscore the importance of considering the context in

which such regulatory decisions are made. Those of us who would like to see economics play a

larger role in these decisions do ourselves no favors by downplaying these contexts. In fact, a

better understanding allows us to make our case more convincingly and credibly.

Nor does the lack of economic considerations in some regulatory decisions let us “off the hook”

in our economic analyses. Actually, I believe that it does just the opposite, placing an even

greater burden on us to demonstrate that applied economic work, especially the analysis of health

benefits, can lead to better decisions. But to do this we must demonstrate that we can do

economics well – or, at least, that our analyses are consistent with one another and with

theoretical expectations. We must demonstrate that we can produce reliable, sensible estimates

for health benefits.

Can we do this for a variety of types of health risks? Can we produce analyses that value

different risks differently?

Recently, EPA raised this issue to its Science Advisory Board for their consideration.

Specifically the issue was how we may value reduced cancer fatalities given that we don’t know

much directly about how people value these kinds of risks.

The EPA produced a white paper that intentionally extrapolated beyond the existing literature on

the subject. The white paper drew on speculative conclusions drawn from Revesz (1999) and

other studies that have pushed this particular envelope in the field of valuing health risks. In

comparing cancer risks (which we know little about) and accidental fatality risks (which we

know much more about) the white paper enumerated many differences in risk characteristics

(e.g., dread, voluntariness and controllability, the timing between exposure and death, morbidity

periods prior to death) and population characteristics (e.g., age, income, health status).
16 Session I : Valuing Statistical Lives

The question put to the Committee was what could we do empirically in a benefit cost analysis to

account for these factors? In the opinion of the SAB the only thing we can do empirically to date

is to adjust for a latency period through discounting. Some of the other factors were identified as

potentially or theoretically important, but lacking the empirical research necessary to be included

into the valuation of reduced cancer risks.

I don’t necessarily disagree with the conclusions of the Science Advisory Board. In most cases

there is precious little empirical research to rely upon in accounting for these factors, despite the

fact that psychometric factors that affect the perception—and perhaps the value—of risk have

been studied for over 20 years in the cognitive psychology literature. The conclusion of the

Science Advisory Board highlights the fact that we have a great deal of work to do before we are

able to systematically and appropriately value different risks differently.

We should not give up hope on this, but should instead accept this conclusion as a call for

additional research. I know that several empirical studies that may provide some insight are

underway even now and I hope that many of the questions will be much clearer when the results

are considered. But perhaps this also tells us that, on a practical level within Federal agencies,

we should focus on the first proposition of valuing similar risks similarly. My experience

suggests that even this seemingly simple task is not without its challenges. However, we have

the ability to do this now—or at least to make a credible start. We must move down this path if

we wish to make a sincere case that benefits analysis play a serious and substantive role in policy

decisions.

References

Revesz, Richard L. 1999. “Environmental Regulation, Cost-Benefit Analysis, and the

Discounting of Human Lives,” Columbia Law Review. 99(4):941-1017.

Tengs, Tammy O., and John D. Graham. 1996. “The Opportunity Costs of Haphazard Social

Investments in Life-Saving,” in Robert W. Hahn, ed., Risks, Costs and Lives Saved: Getting

Better Results from Regulation. Oxford University Press, New York.

U.S. Environmental Protection Agency. 1999. SAB Report on EPA’s White Paper ‘Valuing the

Benefits of Fatal Cancer Risk Reduction,’ EPA-SAB-EEAC-00-013.

U.S. Environmental Protection Agency. 1999. “Valuing the Benefits of Fatal Cancer Risk

Reduction,” draft produced for the Science Advisory Board, Environmental Economics Advisory

Committee.

U.S. Environmental Protection Agency. 2000. Guidelines for Preparing Economic Analyses,

National Center for Environmental Economics, EPA-240-R-00-003.
Session I : Valuing Statistical Lives 17

Rappporteurs’ Notes for

Valuing Statistical Lives

Rapporteurs: Linda Chappell, Nicole Owens, David Widawsky, and Nathalie Simon,

all with the U.S. Environmental Protection Agency

The Value of a Statistical Life discussion centered around four primary themes: limitations of the

current VSL approaches used by the various agencies, ideal measures of VSL, applications in a

regulatory setting and future research needs.

The breakout session was attended by 18 participants, some of whom were not necessarily

familiar with foodborne illnesses and the nature of these illnesses. The session therefore began

with a brief discussion of the types of foodborne risks, the nature of the ensuing illnesses, and the

populations with greatest mortality risk. This was followed by a recap of the approaches taken by

the various agencies in valuing fatal risks. Generally, EPA separates the morbidity period from

the mortality endpoint and values each separately. Premature mortality is valued at $5.8 million

(1997 dollars) regardless of age, health status, etc., while the morbidity period is generally

valued using cost-of-illness numbers in the absence of willingness-to-pay values. The VSL

figure used by EPA is based on a summary of 26 studies that estimate VSL (primarily labor

market studies). FDA uses a value of $5.0 million, based on similar studies.

The use of a single value for mortality risk valuation was recognized as one of the most

significant limitations of the VSL numbers currently in use by the agencies. These values are not

illness specific and, since they are based primarily on labor market studies as noted above, are

based on values for reductions in risks of accidental (relatively immediate) deaths. Deaths

associated with foodborne illness on the other hand are sometimes preceded by extended and

often painful periods of morbidity. While some participants argued that, in many cases, analysts

could value the morbidity period separately from the mortality endpoint, there was general

concensus that cancers should be treated as a separate class of illness. It was recognized that

cancers generally have an lengthy morbidity period, are often fatal, and have a large “dread”

factor associated with them that in all likelihood would affect willingness to pay to reduce cancer

risk.

Another serious limitation of the current values used by the agencies is that they are derived from

studies of prime-aged males with an average age of 40 years. In the case of foodborne illness,

however, the exposed population most susceptible to mortality risks are children and the elderly.

It is not clear that the VSL values currently in use are appropriate to value risk reductions to

these subpopulations with such disparate ages.

Yet another concern identified by the group involved benefit transfer. When VSL values are

derived from survey results or even hedonic wage studies, the risks are treated as certain and of a

given magnitude. The resulting VSL numbers are transferred to scenarios where the magnitude
18 Session I : Valuing Statistical Lives

of the risk is often very uncertain given the nature of the risk assessment. This uncertainty could

have serious implications for VSL.

Given the limitations of the current values, the group agreed that ideally specific values should

be developed; however, there was some discussion as to whether it was most appropriate to value

health endpoints or specific reductions in exposure, involving specific populations and ranges of

risk levels. It was also recognized that specific values, regardless of whether they were for

specific illnesses or reductions in specific contaminants, would be very costly to obtain. A

concensus was eventually reached that specific values for reductions in cancer risk should be

developed.

The remainder of the discussion centered around identifying priorities for future research in the

short, medium, and long term. In order to avoid duplicated efforts, it was noted that agencies

sponsoring or engaging in mortality risk valuation research should at the very least make

abstracts of funded research projects available to the public. The agencies involved should also

consider jointly funding some of projects as well as working with the EPA/NSF grant program.

A number of projects were identified that could make better use of existing information as well

as expanding the information available for economic analyses of food safety regulations.

Research goals are categorized below by the time necessary to realize results.

Short term (1 to 2 years):

1. Determine which foodborne illnesses pose the greatest mortality risk

In order to focus future mortality risk valuation studies, it will be important to identify which

foodborne illnesses create the greatest mortality risk to the population. It will also be

important to identify the characteristics of the illnesses in question, such as length of

morbidity period, symptoms, age distribution of those affected, etc.

2. Revisit mortality risk valuation literature

EPA and other agencies rely on various combinations of 26 VSL studies, including examples

of hedonic wage and contingent valuation approaches, identified by Viscusi (1992) as Ahigh

quality.@ Since the publication of Viscusi=s review article however, other studies may have

been completed that could be relevant to mortality risk valuation from a food safety context,

while others may since have been deemed inappropriate for various reasons. The mortality

risk valuation literature should be revisited with the aim of identifying the most relevant

high-quality studies for food safety risk valuation. Prior to completing this task, a general

understanding should be reached regarding the definition and desirable characteristics of

Ahigh-quality@ studies.

3. Rework existing data

Economists should follow the lead of epidemiologists and allow other researchers to attempt

to replicate results using available data. Some of the heavily cited VSL studies were

completed 5 to 10 years ago. While many of the mortality risk valuation studies were

completed using publicly available data, the datasets from which these data were compiled

are large and cumbersome to use. Researchers should attempt to obtain access to previously

utilized data to reanalyze them and to determine whether VSL figures vary according to

sample characteristics, sub-sample of focus, etc.
Session I : Valuing Statistical Lives 19

4. Explore more refined ways to aggregate studies

In addition to reworking the existing data, researchers should explore more refined ways of

aggregating results from existing studies through meta-analysis and other techniques. In

order to accomplish more meaningful aggregations, it will be necessary to obtain more

detailed information on the age distribution of individuals included in each study as well as

other socio-demographic characteristics, details regarding estimation approaches and datacleaning techniques.

5. Pilot Experimental Studies for Calibration of VSL

Using a relatively small sample of respondents, calibration factors for VSL figures can be

developed by observing risk-averting behaviors in some detail. Using the observed

behaviors, researchers can calculate an implicit VSL and accompanying calibration factors

for extrapolation to a larger population.

Medium Term (2 to 3 years):

1. Expanded Experimental Studies

Conditional on the relative success of the pilot studies, expanded experimental studies should

be considered for calibration of VSL values. These expanded studies may combine market

research data (brands purchased, quantity purchased, etc.) with experimental approaches.

2. Revisit Benefit-Cost Analysis of Selected Regulations

Using the improved VSL figures derived in studies described above, the economic analysis

for several regulations should be revisited in order to determine the effect of the updated

estimates on the outcomes of the analyses.

Long term (more than 3 years):

More VSL studies need to be conducted with the aim of answering a number of specific

questions:

1. Does the contingent valuation method yield similar results to those from a revealed

preference study?

A paired CVM and revealed preference study should be conducted in which the same good is

the focus of each. The results should from the two approaches should then be compared.

2. How does VSL vary by cause of death?

It is generally thought that VSL may be affected by various factors associated with the illness

considered. For instance, the higher the amount of dread associated with the illness, the

higher the willingness to pay to avoid the illness. Cancer, for instance, is one category of

fatal illness thought to carry a high dread level. Other illnesses may be similarly positioned

and other factors may have similar effects on willingness to pay. Researchers should conduct

studies that focus on mortality by specific causes to gauge the effect of illness specific

mortality on VSL.

3. How does VSL vary across socio-demographic characteristics?
20 Session I : Valuing Statistical Lives

In cases where a specific sub-sample of the population is more susceptible to the risk

in question than the rest of the population, it is important to gauge how VSL varies by

specific characteristics of the population. Age is a potentially important characteristic

in this regard as is income. In the context of foodborne illnesses, children and the

elderly are more susceptible to mortality risks than middle-aged and young adults.

While some analysts advocate adjusting VSLs according to the number of life-years

remaining for specific sub-groups of the population, there is no empirical evidence to

date indicating that the value of a statistical life year is constant across the life span of

an individual. Furthermore, almost no information exists on the value of a statistical

child=s life.

4. How does VSL vary across different risk sources?

As mentioned above, most of the VSL studies used by agencies to value health risks

are labor market studies. Researchers should determine whether the source of the risk

(such as foodborne risks versus labor market risks) affects willingness to pay.

5. How does the nature of the risk affect valuation?

Even if the vehicle of exposure is the same, other aspects of the risk may affect

willingness to pay such as whether the risk is manmade or whether it occurs naturally

in the environment.

6. How does altruism affect VSL?
Session II: Valuing Pain and Suffering and Lost Productivity 21

SESSION II

VALUING PAIN AND SUFFERING

AND LOST PRODUCTIVITY

Measuring the Pain, Suffering, and Functional Disability

Associated with Foodborne Illness

Josephine Mauskopf, Ph.D.

Roberta A. Morales, D.V.M., Ph.D.

Abstract

The annual impacts of foodborne illness on the U.S. population can be estimated by multiplying

the number of cases of illness at different levels of severity each year by the impact on pain,

suffering, and functional disability associated with each case. In this paper, we categorize the

methods that have been used to estimate the number of cases of illness into ‘top-down’ and

‘bottom-up’ approaches, depending on whether or not they start with observed cases or use dose

response relationships to estimate cases. We describe these methods and identify the strengths

and weaknesses of each. We then describe a methodology to estimate the impact on pain,

suffering, and functional disability of a case of foodborne illness. This method first estimates the

number of deaths, number of hospital days, number of restricted-activity days (bed-days or

house-days), and types of symptoms associated with each foodborne illness at each level of

severity. These health outcomes are then combined into a single measure of disease burden, the

quality-adjusted life years (QALY’s) lost by associating the disease outcomes with utility

weights and duration. The paper concludes by demonstrating how these estimates of the

QALY’s lost can be used to estimate the impact of food safety regulations on the pain, suffering,

and functional disability of the U.S. population.

For more information contact Josephine Mauskopf, Ph.D., Center for Economics Research, Research

Triangle Institute, 3040 Cornwallis Road, Research Triangle Park, NC 27709.

Phone 919-541-6996; Fax 919-541-6683; E-mail JOM@RTI.ORG
22 Session II: Valuing Pain and Suffering and Lost Productivity

Introduction

Foods produced or imported for human consumption in the United States are regulated for safety

and quality under as many as 35 different laws implemented by 12 different Federal agencies

(GAO, 1990). Some of these agencies have major roles in implementing the food safety laws:

• the Food and Drug Administration (FDA), which is part of the U.S. Department of Health

and Human Services;

• the U.S. Department of Agriculture (USDA) Agricultural Marketing Service (AMS),

Federal Grain Inspection Service (FGIS), and Food Safety and Inspection Service (FSIS);

• The Environmental Protection Agency (EPA); and

• the National Marine Fisheries Service (NMFS), which is part of the U.S Department of

Commerce (GAO 1990).

One of the chief goals of U.S. food safety laws is to reduce the presence of contaminants and

adulterants in domestic and imported foods. Consuming foods that contain illegal contaminants

or adulterants (e.g., pesticide residues, illegal food dyes, microorganisms) increases an

individual’s risk of foodborne illness.

The probability that a violation of the food safety laws will adversely affect a consumer’s health

depends on the type of violation, the level of contamination, the food processing pathway before

consumption, and the typical portion size for the contaminated product. For example, a food

product contaminated with salmonella is more likely to adversely affect a consumer if the level

of contamination is high, the product is eaten without further cooking, and the portion size is

large. In addition, excess risk of cancer from exposure to pesticides is likely to be related to both

duration and intensity of exposure. Biologists and toxicologists have studied these relationships

for many years, but it is still difficult to predict accurately the association between ingesting a

particular product that violates the U.S. food safety laws and the probability and severity of a

particular adverse health effect.

The impact of a particular adverse health effect on the patient depends on the expected

symptoms, duration, and effect on functional status. Clearly, the impacts are greater for adverse

health effects that have serious symptoms, require extensive treatment, last for a long period of

time, and/or cause premature death.

In this paper, we describe methods that are currently used to measure the impacts of foodborne

illness on the U.S. population. The focus is on non-monetary measures of these impacts;

measures of pain, suffering, and functional disability. These measures include the number of

deaths, number of hospital days, number of restricted-activity days (bed-days or house-days),

types of symptoms, and the number of disability-adjusted or quality-adjusted life years lost. The

annual impacts of foodborne illness on the U.S. population are estimated by multiplying the

number of cases of illness at different levels of severity each year by the impact on pain,

suffering, and functional disability associated with each case. We also describe how estimates of

the population impacts of foodborne illness can be combined with information on the impacts of

Federal regulation on violation rates and level of contamination to estimate the benefits of these

regulations.
Session II: Valuing Pain and Suffering and Lost Productivity 23

Measuring the Annual Number of Cases

of Foodborne Illness

The non-monetary impacts of foodborne illness depend critically on the number of cases of each

illness at each level of severity that occur each year. There are two main methods that have been

proposed for estimating the number of cases occurring each year in the United States—a “topdown” method and a “bottom-up” method. The “top-down” method is based on the number of

cases reported each year to the Centers for Disease Control (CDC) or the number of cases

observed in other national surveys. The “bottom-up” method combines estimates of number of

exposures to the violative food and of the level of contamination with dose-response

relationships.

Buzby et al. (1999) identify several sources of data that can be used for estimating the number of

new cases of different illnesses attributable to the food supply each year in the United States.

These include data from large national surveys such as the National Ambulatory Care Survey or

the Hospital Discharge Survey or the Healthcare Cost and Utilitization Project that give

estimates of annual US physician visits or hospital stays by diagnosis. A second data source is

the CDC, including annual reporting data and epidemiologic data, such as outbreak investigation

and surveillance data. A third data source is case studies of individual cases or groups of cases

from the same causative organism that are published in the medical literature. Other data sources

include risk models that combine information on the prevalence of pathogens in food and on the

size of the infectious dose (i.e., exposure assessments) and dose response relationships to obtain

estimates of the distribution of disease severity (i.e., hazard characterization). The first three

data sources are generally used to derive “top-down” estimates and risk models are used to

derive “bottom-up” estimates.

CDC has recently proposed a methodology that should be used for estimating the number of

cases of foodborne illness by severity using a “top-down” approach. Their approach uses an

inverted pyramid (see figure 1). In developing this model, CDC augmented the data on reported

cases from the Foodborne Diseases Active Surveillance Network (FoodNet) with laboratory,

physician, and population surveys from the eight sentinel sites. This top-down approach enabled

CDC to sequentially develop several estimates:

• culture-confirmed cases, culture/testing of submitted specimens from the laboratory

surveys;

• specimens collected as part of treatment regime from the physician surveys; and

• individuals becoming ill and proportion seeking medical care from the population

surveys.

They used these estimates to arrive at an overall estimate of the number of cases of foodborne

illness (CDC, 1999). Mead et al. (1999) expands this approach to develop estimates of

foodborne illnesses and deaths in the United States.

The CDC approach has been used by Buzby et al. (1999) to estimate the number of cases of

several bacterial foodborne diseases. For example, for salmonellosis cases, they use an

extrapolation method based on CDC’s surveillance system and outbreak data. CDC’s

surveillance system for salmonellosis includes reporting from the State health departments based
24 Session II: Valuing Pain and Suffering and Lost Productivity

on physician reports as well as reports from State health department laboratories. Since both

surveillance data and outbreak data are underestimates of the total number of cases, these data

are adjusted upwards.

The upward adjustments in the Buzby study were calculated using multipliers derived by

Chalker and Blaser (1988) to account for the underreporting inherent in passive surveillance

systems. Three methods were employed by Chalker and Blaser to derive these multipliers. In

the first approach, information on carriage rate and median duration of excretion were obtained

from the literature and used to calculate an annual incidence of infection for a particular

pathogen. The second method uses the passive surveillance system as a starting point then,

through a literature review of the sequential steps required for a case to be reported, identifies or

develops corresponding estimates of those sequential artifacts resulting in underreporting. The

sequential steps that are required for a foodborne illness to be reported to the CDC as defined by

Chalker and Blaser (1988) are:

1) The patient must be infected with the organism;

2) The patient must be ill;

3) The patient must consult a doctor;

4) The doctor must obtain a culture;

5) The culture must be positive;

6) The laboratory must report the isolation to the county or State health department;

7) The State health department must report the isolation to the CDC.

The resulting underreporting estimate is multiplied by the number of reported cases to obtain an

annual incidence rate. The third approach involves a review of outbreak investigation data (case

definition is the presence of acute gastroenteritis) and the ratio of cases initially reported to those

cases identified upon completion of the outbreak investigation provides an overall estimate of

underreporting. This adjustment is applied to the number of cases reported to the CDC to give

the total number of salmonellosis cases in a year. The estimates of annual incidence obtained for

Salmonella using each of these approaches were 3.7 million, 1.6 million, and 800,000,

respectively and this range of estimates was used in the Buzby study.

An alternative data source was used for estimating the number of people and days with

symptoms of acute foodborne illness by Golan et al. (2000) in their study of the distributional

consequences of improvements in food safety. They used estimates of total days with symptoms

of foodborne illness that caused at least half a day of restricted activity or required a physician

visit as reported in the National Health Interview Survey. This is a good measure of the total

impact of acute foodborne illness on the U.S. population, but the impact of specific diseases

cannot be measured and so it would be hard to estimate the impact of food safety regulations

targeted at specific diseases on these restricted-activity days. In addition, the estimates from the

NHIS may not include the mildest cases of illness, may not include foodborne illnesses with

primarily neurological symptoms, and may include illness from non-foodborne causes with

similar symptoms. Finally, the NHIS does not include the institutionalized population.

To estimate the losses associated with these cases of salmonellosis (either in terms of cost of

illness or in measures of pain, suffering, and functional disability), it is necessary to subdivide

them by degree of severity. In the Buzby study, salmonellosis cases are subdivided into four
Session II: Valuing Pain and Suffering and Lost Productivity 25

categories, those who do not seek medical attention, those who visit a physician, those who are

hospitalized, and those who die prematurely.

Death rates for salmonellosis in the Buzby study were estimated based on a 1984-85 survey by

the CDC that estimated the death rate for reported cases. This was applied to the estimated

number of salmonellosis cases. The proportion of people who were hospitalized or who visited

a physician were estimated using survey data from the largest recent U.S. outbreak of

salmonellosis (Chicago, 1985). This survey provided estimates of the percent hospitalized

among those who had symptoms and the percent who visited a physician among those who had

symptoms. These percentages were applied to the total number of cases. The number of people

who had symptoms but received no medical attention and did not die was then calculated as the

difference between the total number of cases and those who received medical attention and/or

died.

The main strength of the “top-down” approach is that it is based on observed data. Its main

weakness is that no explicit relationship is derived between the number of exposures to the

pathogen and the level of contamination and the resulting disease incidence by severity. Thus,

using this approach, it is not immediately obvious how to estimate the impact on the number of

cases of foodborne illness of regulations that have as their goal a reduction in the number or

intensity of exposures to different contaminants. Another weakness of this approach is that it can

be used to estimate only the number of cases of acute or chronic illness that can readily be

associated with a foodborne cause and thus form part of the CDC surveillance system. Cancer

cases from ingested pesticides would not be included, nor would chronic conditions unless they

were reported as sequelae to diagnosed acute illness.

An alternative method that has been proposed and is now being used for the estimation of cases

of foodborne illness is the “bottom-up” approach. In this approach, the number of cases of

foodborne illness of different degrees of severity are estimated based on the:

• probability of foods having a specific violation of the food safety laws and the level of

contamination;

• number of people eating these violative foods and their estimated level of exposure to the

violation; and

• probability distribution of different levels of severity of illness that result from this

exposure.

These three estimation steps will be described below and illustrated using estimates of the

number of cases of foodborne illness associated with imported foods and estimates of the number

of cases of foodborne illness associated with shell eggs.

The probability of foods’ having a specific violation of the food safety laws and the level of

contamination will depend on multiple factors including the:

• likely association of the violation with different types of food; and

• conditions of growth and/or preparation, handling, and distribution of the food before it

reaches the consumer.

For example, imported foods may have different probabilities of being in violation of the food

safety laws both because of different natural growth conditions as well as different local
26 Session II: Valuing Pain and Suffering and Lost Productivity

regulations about pesticide use and food processing. Foods from different parts of the United

States may also vary in their likelihood of being violative because of different growing

conditions. In a study of imported foods, Martin et al. (1993) assumed that the probability of a

given imported foodlot’s having a specific violation of the FD&C Act was dependent on the

history of violations in similar foodlots and import alerts for that foodlot issued by FDA’s Office

of Compliance. The results from government testing laboratories were used in the Martin study

to estimate the levels of contamination likely to be observed.

 A USDA/FDA risk assessment for Salmonella Enteritidis (SE) in shell eggs and egg products

(USDA, 1998) simulated the probability of contamination of eggs with Salmonella Enteritidis

based on the total number of egg-producing flocks, the prevalence of SE in these flocks, flock

size, and frequency of molting. This resulted in an estimated total number of contaminated eggs

produced annually by the U.S. egg industry. For example, using U.S. Agriculture Census data

from 1992, a simulation estimated a total of 5,028 flocks, stratified according to flock size (<

20,000, 20,000-49,999, 50,000-99,999, and 100,000 or more), had either high or low SE

prevalence. An estimated 22 percent of flocks producing eggs on any given day were assumed

to have molted previously, and an increased frequency of SE-positive eggs for 70 days post-molt

was assumed. This simulation resulted in estimates that on average, 37 percent of egg-laying

flocks were SE-positive, 11 percent of which were high-prevalence flocks, and a mean frequency

of 1 in 20,000 SE-positive eggs were produced per year.

The level of exposure to the violation for people eating violative foods depends on the level of

contamination, whether or not the contamination is detected before the food is eaten, serving

size, handling, and preparation before eating. Whether or not the contamination is detected

before the food is eaten will depend on government inspections as well as the consumer’s

inspection. Martin et al. (1993) estimated the probability that the violative food reached the

consumer based on whether or not the food is inspected and/or tested and the accuracy of the

instrument used to test the sample. Another relevant factor is the likelihood that a violative

substance would not be in the actual sample taken for analysis, even though the food lot did

include the violation. Some violations are detected by the consumer before use. An example of

this type of violation is filth. Standard serving sizes are used to estimate the amount of the

violative food ingested per person. Final exposures to the contaminant are then estimated based

on common food preparation practices and the persistence characteristics for the contaminant.

The exposure assessment of the USDA/FDA SE risk assessment models identifies two key

factors that affect the growth of the organism in eggs—cumulative ambient storage temperatures

and length of storage as the eggs move from production to processing, transportation, and

distribution (see figure 2). For example the relationship between the number of SE at time of use

of a shell egg and the number of SE at laying, can be illustrated with the following simplified

equation:

Number SE at use (e.g., 10,000) = Number of SE at laying (e.g., 100) x (Weeks to use (e.g., 13)

– Weeks to yolk membrane breakdown (e.g., 1)) x Average growth rate per week (e.g., 8.33)

The values for the independent variables in this equation depend on processing time and

temperature.
Session II: Valuing Pain and Suffering and Lost Productivity 27

Other factors influencing final human exposure, such as pooling of eggs, institutional versus

home use, and the effect of "bacterial kill steps" including common preparation and cooking

methods, on levels of contamination are also incorporated in modeling the associated probability

and level of contamination in consumed eggs (see figure 3). For example, for an undercooked

egg eaten by a single consumer, the relationship between the number of SE in the consumed egg

and the number of SE at time of use can be illustrated with the following simplified equation:

Number of SE consumed (e.g., 100) = Number of SE at use (e.g., 10,000) x Proportion of SE

remaining in undercooked egg (e.g., 0.01) x Proportion of egg eaten (e.g., 1)

The proportion of SE remaining in the undercooked egg depends on the cooking type and time.

The probability distribution of different levels of severity of illness that result from specific

exposures to a violative food product is the final piece of information that is needed in the

“bottom-up” approach for estimation of the number of people experiencing foodborne illness.

There is likely to be a dose-response relationship between the level of the contaminant and the

likelihood that the person exposed will experience different levels of severity of illness. In

addition, this dose-response relationship will likely be different for different population

subgroups. For example, susceptible populations such as those with conditions that adversely

affect the immune system such as HIV/AIDS, diabetes, chemotherapy for cancer, solid organ and

other transplants, pregnant women, and children or the elderly, are likely to experience more

serious disease at lower exposure levels.

Martin et al. (1993) assumed that the population was a mix of susceptible and non-susceptible

people and obtained estimates for the total population only. Potency factors estimated by the

EPA for pesticide residues were used to estimate dose-response relationships for exposures to

carcinogens. Dose response relationships for the microbial violations were developed through an

expert elicitation procedure similar to procedures that have been used in the past by EPA for

estimating the health impacts of lead exposure and ozone exposure (Whitfield and Wallsten,

1989; Whitfield et al., 1991). These elicitation procedures were based on a fact sheet for each

microbial agent. These fact sheets included all the available data on the dose-response

relationship derived from both animal and human studies. The elicitation procedure resulted in

estimates of the likelihood of observing both acute and chronic sequelae from the microbial

violations at different levels of severity (mild, moderate, severe) for different exposure ranges of

the contaminants.

The hazard characterization of the USDA/FDA risk assessment linked exposure to contaminated

eggs with a dose-response relationship to model public health outcomes of varying severity. The

morbidity and mortality outcomes of concern that were modeled included acute gastroenteritis

followed by uneventful recovery without medical care, recovery following a physician visit,

recovery following hospitalization, death, and chronic reactive arthritis. These outcomes were

estimated for both susceptible and normal populations, and resulting simulations demonstrated

more severe outcomes in susceptible populations including a disproportionate contribution to

deaths. To demonstrate the differing outcomes in susceptible and normal populations, a

simulation was conducted for two hypothetical populations of 100,000 persons, each exposed to

1,000 cfu of the bacteria, one population with normal susceptibility and one with high
28 Session II: Valuing Pain and Suffering and Lost Productivity

susceptibility. The results of the simulation are presented in table 1. Of particular relevance are

the differences in case fatality rates between these two populations with mean estimates of 20

and 98 deaths in the normal and highly susceptible populations, respectively.

One of the main strengths of the “bottom-up” approach is that it can be used to estimate the

number of chronic sequelae from acute illness and the number of cancer cases attributable to

pesticide residues as well as the number of cases of acute illness. This approach also explicitly

relates contaminant exposure levels to the number of cases of illness at different levels of

severity. Because of this, the health impacts of regulations designed to reduce contaminant

exposures, for example through more targeted inspections or through required changes in food

processing techniques, can readily be estimated. The main weakness of this approach is that it is

not based directly on observed cases of foodborne illness. However, the dose-response

relationships are generally derived from observed outbreak data. In addition, estimates of the

number of cases derived using the “bottom-up” approach can be validated using surveillance or

outbreak data.

Measuring the Pain, Suffering, and Functional Disability

from a Case of Foodborne Illness

Most foodborne illness can be subdivided into three categories:

• Acute illness, which occurs with a very short latency period after exposure, has a welldefined duration, and ends in death, complete cure, or transitions into a chronic illness.

• Chronic illness, which has a short or long latency period after either a short or prolonged

exposure, may be a sequel to an acute illness, has a prolonged duration, and ends in either

premature or natural death.

• Cancer (a subcategory of chronic disease), which has a long latency period after either a

short or prolonged exposure, has a short or prolonged duration, and ends in premature death,

natural death, or complete cure.

The amount of pain, suffering, and functional disability is very different for these different types

of illness. However, it is possible to use a uniform set of measures to describe the patient

impacts.

In general, foodborne illness can occur at various levels of severity. Each level of severity

affects a person to a different extent. To simplify the analysis, we can consider three levels of

severity for all acute and chronic illnesses, mild, moderate, and severe. The severity levels vary

in severity and types of symptoms, functional disability, and mortality risk. Cancers can be

categorized in terms of extent of spread at first diagnosis in terms of local, regional, and distant.

For each severity level of the illness, we can describe the effect of the illness in terms of typical

symptoms, mortality rate, duration of treatment and recovery, frequently used medical treatments

including time spent in the hospital, and functional status of the patients during treatment and

recovery. This information can be derived from the medical literature (e.g., Bryan, 1982; the
Session II: Valuing Pain and Suffering and Lost Productivity 29

Merck Manual). This information can be used to compare the impact of different diseases and

levels of severity on proxy measures for pain and suffering (e.g., deaths, symptom days, hospital

days) and functional status (e.g., restricted-activity days). For example, based on Bryan (1982)

and Cohen (1978) and Mauskopf et al. (1988), we estimated the following patient impacts for

salmonella:

• Mild disease—1 bed day and 1 house day with nausea/vomiting, diarrhea, abdominal

pain, anorexia, and weakness and a zero risk of mortality;

• Moderate disease—4 house bed days and 3 house days with the same symptoms as for

mild plus fever, headache, and prostration and a zero risk of mortality; and

• Severe disease—5-14 hospital days, 3 house bed days, 3 house days, with the same

symptoms as for moderate disease plus enteric bacteremia or septicemia and a mortality rate

of 13 percent.

To determine the relative burden on patients of different illnesses, it is useful to have a single

measure that combines the mortality and various morbidity effects. One measure that is

currently used by the World Health Organization (WHO) for estimating the combined impact of

mortality and morbidity associated with a specific disease is the disability-adjusted life-year

(DALY) (Murray, 1994). The DALY is the product of four factors:

• A disability weight (ranging between 0 and 1 depending on productivity levels with the

illness);

• Age at onset and duration of the illness;

• An age weighting function to reflect social value for each year lost (ranging from 0 at

birth peaking to 1.5 at age 25 and then declining slowly with age); and

• A discounting function.

The DALY thus measures the impact of an illness in terms of the loss in productivity. It does not

include any weighting for pain and suffering.

Another unit frequently used in economic evaluations of health care interventions for measuring

the total impact of a disease on the patient in a single unit of measure is the quality-adjusted lifeyear (QALY). A QALY has been defined by Zeckhauser (1985) as 1 year in perfect health or its

equivalent. QALY’s lost because of illness are computed as follows: assume the illness lasts for

2 years, during which time the patient is in a health state that is less than perfect and has, for

example a utility weight of 0.35. Thus, during the 2 years, the patient experiences only 0.70

QALY. In this case, the QALY loss relative to perfect health is 1.3 QALY’s. Gold et al. (1996)

suggest for a “reference case” cost- effectiveness analysis that the utility weights used to

compute the QALY’s lost be derived from community-based surveys and reflect both the

financial (e.g., productivity losses) and health status impact (e.g., pain, suffering, and functional

disability) of morbidity. The surveys should be designed so that the utility weights reflect

people’s overall well-being in different health states.

In the most general sense, a health status index converts the concepts of healthy and unhealthy

into quantifiable measures of an individual’s perception of the impact of their health status on

their general sense of well-being. For example, Bush, Chen et al. (1972) classified all possible

health states based on the degree of restriction of mobility, social interaction, and physical

activity, and the pain or other symptoms a person may experience. The utility weights for these

health states were derived using a survey of 867 subjects in California. The subjects were asked
30 Session II: Valuing Pain and Suffering and Lost Productivity

to indicate the relative disutility of different health states, assuming that each health state lasted

for only 1 day, that zero was as bad as dying, and that 1 was perfect health. Another set of health

states was constructed by Rosser and Kind (1978) using a two-dimensional description of health,

based on disability and distress level. There are eight disability levels based on ability to work,

social interactions, and mobility. There are four levels of distress. The weights were derived

from 70 subjects, and they were told to assume that the health states were permanent and that

zero represented death and 1 perfect health. The resulting indexes are very different for the two

studies, possibly because of the different time horizons assumed for the surveys. One possible

strategy is to use the Bush, Chen et al. (1972) utility weights for estimating the loss in patient

well-being for acute illness and cancer and the Rosser and Kind (1978) utility weights for

estimating the utility losses for chronic illness.

Based on the disease symptoms and estimates of their duration, any specific adverse health effect

can be translated into the time spent in different health states. These health states can have utility

weights assigned to them such as those estimated by Bush, Chen et al. (1972) or Rosser and Kind

(1978). The health utilities index (HUI) (ref) is a more recent set of health states for which

utility weights have been estimated. In order to take the duration estimates for each health state

into estimate of the QALY’s lost with each illness, assumptions also need to be made about some

or all of the following:

• Age at onset (e.g., 30 years);

• Remaining life expectancy (e.g., 46 years);

• Quality of life in the absence of the foodborne illness (e.g., 1);

• Discount rate (e.g., 3 percent);

• Number of different functional states (e.g., hospital days, bed days, and house days);

• Morbidity for those dying from acute conditions (e.g., none);

• Pattern of utility loss for chronic conditions and cancer (e.g., constant utility loss); and

• Life expectancy loss for chronic conditions (e.g., no life expectancy loss).

The adverse health effects for each illness can then be assigned utility weights and durations.

For example, a moderately severe case of salmonellosis is estimated to result in an adverse health

effect consisting of gastrointestinal symptoms as well as fever and headache that results in about

7 days lost from work. Using the Bush, Chen et al. (1972) measures, we assumed:

• 4 days in bed, in the house, needing help with self care and with symptoms of vomiting,

diarrhea, fever, headache, and spells of feeling hot, nervous, or shaky (utility weight =

0.4407); and

• 3 days in the house, no problem walking and performing self care with symptoms of

diarrhea and spells of feeling hot, shaky, or nervous (utility weight = 0.6533).

Based on these utility weights and duration, the QALY’s lost associated with a moderate case of

salmonellosis can be estimated (0.00932).

In a recent regulatory impact analysis of food labeling for trans fatty acids (Federal Register,

1999), FDA used an estimate of the utility weight for a survivor of coronary heart disease (CHD)

of 0.71 (Cutler and Richardson). Their estimate of remaining life expectancy after onset of CHD

was 13 years or 8.4 discounted life years (7 percent discount rate). Thus, the loss attributable to

a nonfatal case of CHD was estimated to be 0.29 x 8.4 or 2.436 QALY’s (Federal Register,

1999). The loss attributable to a fatal case of CHD was 8.4 QALY’s.
Session II: Valuing Pain and Suffering and Lost Productivity 31

Hypothetical Estimate of the Impact of Federal Regulations

The benefits of different government inspection programs, measured as the reduction in pain,

suffering, and functional losses, depends critically on:

• The ability of the inspection program to prevent products that violate food safety laws

from reaching the consumer;

• The probability that violations would cause foodborne illness; and

• The impact of the foodborne illness on the patient.

A hypothetical example will illustrate this. For example, assume that there is a choice between

inspecting product A for C. botulinum toxin or product B for pesticide residue. Second, assume

that the proportion of products like A that are contaminated with C. botulinum toxin is 0.001 and

the probability of product B being contaminated with the pesticide residue is 0.001. Further, we

assume that if product A contains C. botulinum toxin, there is a 0.25 probability that the person

eating it will get a severe case of botulism (QALY loss = 5.96), a 0.25 probability they will get a

moderate case (QALY loss = 0.03), and a 0.25 probability they will get a mild case (QALY loss

= 0.005), and a 0.25 probability that they will not get ill (QALY loss = 0). For a person eating

product B contaminated with the pesticide residue, we assume that there is only a 0.000001

excess probability of getting liver cancer (QALY loss = 10.16) if they eat one serving of the

contaminated product. The expected benefits for preventing product A from being consumed are

given by:

EBA = (0.001 x .25 x 5.96) + (0.001 x .25 x 0.03) + (0.001 x .25 x 0.005)

 = 0.0015 QALY’s

The expected benefits for preventing product B from being consumed are given by:

EBB = (0.001 x 0.000001 x 10.16)

 = 0.00000001 QALY’s

Thus the expected benefits of preventing product B from being consumed are much lower than

those of preventing product A from being consumed, even though the value of the utility losses

associated with a case of liver cancer is higher than that associated with a case of botulism.

However, in this calculation we did not consider that, if the product with C. botulinum toxin is

consumed, the contamination will be detected and other similar products prevented from

reaching the consumer. By contrast, if the product with the pesticide residue is consumed, that

contamination will not be detected and more exposures may take place so that value of

inspecting for pesticide residues is, in fact, higher than our calculations indicate.

Picking up on the egg example again, the USDA/FDA Salmonella enteritidis (SE) risk

assessment model was constructed to allow simulation of the effects of different mitigations on

total human illnesses due to SE from egg consumption. Sensitivity analysis was conducted as a

first step in identifying the possible strategies for reducing the foodborne illnesses associated

with SE in eggs. Then selected mitigation strategies chosen and modeled either singly or in
32 Session II: Valuing Pain and Suffering and Lost Productivity

combination to demonstrate their comparative effect on public health outcomes of interest. For

example, simulating the reduction of either storage time or ambient temperature by 25 percent at

the home, institutional, or retail setting resulted in an estimated reduction in mean number of SE

cases by 13 percent and 11.6 percent respectively; while a reduction in both time and

temperature resulted in a 21-percent reduction. Reducing prevalence rates of SE in large flocks

(> 100,000) by 25 percent resulted in an estimated 15.2-percent reduction in mean illnesses. A

combination of mitigations which included 25% reduction in storage time in homes, institutions,

and retail coupled with an equivalent reduction in prevalence rates in large flocks resulted in an

estimated 32% reduction in illnesses. In the simulated examples, combining several mitigation

strategies aimed across the continuum resulted in a greater reduction in mean numbers of cases

than any single mitigation strategy alone, implying that broad-based policies may be more

effective in achieving a desired improvement in public health.

When estimates of the number of cases of foodborne illness avoided due to a regulation are

derived using the “top-down” approach, the reduction in number of cases of illness is generally

assumed to be equal to the reduction in pathogen levels attributable to the regulation. For

example, Roberts et al. (1996) estimated that a HACCP program for inspection of meat and

poultry would reduce levels of pathogens between 10 and 100 percent and would reduce the

incidence of the relevant foodborne illnesses by the same percentage amounts.

Conclusions

In estimating the pain, suffering, and productivity impacts of foodborne illness, it is critical to be

able to measure both the annual number of cases subdivided by disease severity as well as the

impact of each case. There are two methodological approaches for estimating the number of

annual cases, the top-down and bottom-up approaches. The advantage of the top-down approach

is that it is based on observed cases. The advantage of the bottom-up approach is that one can

estimate the number of chronic conditions and cancers attributable to food causes and one can

more readily estimate the impact of new regulations on the number of cases. Both methods are

currently used by the U.S. Government for evaluations of food safety regulations.

The impact on a patient of each case of foodborne illness in terms of pain, suffering, and

productivity loss can be measured by proxies for pain and suffering such as deaths, hospital days,

and restricted-activity days and by direct measures of productivity loss such as work loss days.

Alternatively, a single measure can be derived that combines these factors into a single measure,

either the DALY or the QALY. The QALY is frequently used in the United States in the

economic evaluation of programs designed to improve health outcomes. Utility weights

representing general well being in different health states are combined with estimates of the

duration of those health states to compute the QALY’s lost attributable to specific foodborne

illnesses.

There are many uncertainties in the measurement of the number of cases of illness and in the

measurement of the impact of that illness whatever methods are used. Thus the results of such

measurement should be viewed with caution and subject to sensitivity or threshold analyses.

Threshold analyses can be especially useful when decisions have to be made despite uncertain
Session II: Valuing Pain and Suffering and Lost Productivity 33

data. Threshold values can be chosen that would result in different decisions, and then scenarios

identified that give the threshold values.

If the impact of foodborne illness on pain, suffering, and productivity is measured in QALY’s,

then the benefits of new regulations can be measured in QALY’s gained. These QALY’s gained

can be compared with the net costs of the regulation (the cost of implementing the regulation

minus any direct medical care cost savings because of the reduction in foodborne illness). This

comparison can occur by computing a ratio of the incremental cost per QALY gained (Gold et al.

1996). Alternatively, Stinnett and Mullahy (1998) have proposed a method for estimating net

benefits from new health care programs by assigned a value to a QALY equal to the benchmark

value for cost effectiveness in a society—say $50,000 per QALY. Each QALY gained can be

converted into dollars by multiplying it by $50,000. The net costs are then subtracted from these

dollar benefits to give an estimate of the net benefits. If the net benefits are positive, then the

intervention is acceptable based on the $50,000 per QALY threshold.

Another method for applying a dollar value to a QALY (Mauskopf and French, 1991) is to take

an estimate of the value of life and convert it into an implied value for a QALY depending on the

average remaining life expectancy for the person in the value-of life study. For example, if the

value of a statistical life is $5,000,000 and the discounted life expectancy for the population from

which the estimate was derived is 22.5 years, then the value of a QALY is $222,222 ($5

million/22.5). This value can then be used to convert the QALY’s into dollar estimates that can

be compared to the net costs of the regulation. This method was used by the FDA in its

evaluation of nutrition labeling for trans fatty acids.

The measures that we have described in this paper are designed to measure the impact on the

patient of foodborne illness in terms of pain, suffering, and lost productivity. Although the

impact of foodborne illness on productivity is frequently estimated directly as part of the cost-ofillness approach, pain and suffering have not generally been measured. In this paper, pain and

suffering are measured either by proxy with death rates, hospital days, and restricted activity

days or directly through the utility weight for different health states. It is critical that these

benefits of regulations to reduce foodborne illness be captured in regulatory impact analyses.

References

Bryan F. 1992. Diseases Transmitted by Foods (A Classification and Summary). U.S.

Department of Health and Human Services Public Health Services. Centers for Disease Control.

Center for Professional Development and Training. HHS Publication No. (CDC) 83-8237.

Bush J., S. Fanshel, and M. Chen. 1972. “Analysis of a Tuberculin Testing Program Using a

Health Status Index,” Socio-economic Planning Sciences 6: 49-68.

Buzby J., T. Roberts, C. Lin, J. MacDonald. 1996. Bacterial Foodborne Disease: Medical Costs

and Prodcutivity Losses. Food and Consumer Economics Division, Economic Research Service,

U.S. Department of Agriculture. AER-741.
34 Session II: Valuing Pain and Suffering and Lost Productivity

Centers for Disease Control and Prevention. 1999. FoodNet 1998 Annual Report --

CDC/USDA/FDA Foodborne Diseases Active Surveillance Network. Atlanta, GA. November

1999. Also available at: http://www.cdc.gov/ncidod/dbmd/foodnet/ANNUAL/98_surv.htm.

Chalker, R.B., and M.J. Blaser. 1988. “A Review of Human Salmonellosis: III. Magnitude of

Salmonella Infection in the United States,” Reviews of Infectious Diseases 10(1):111-124.

Cutler, D., and E. Richardson. 1997. Measuring the Health of the U.S. Population. Brookings

Papers on Economic Activity: Microeconomics. Washington, DC.

Golan E., K. Ralston, P. Frenzen, S. Vogel. 2000. “The Costs, Benefits and Distributional

Consequences of Improvements in Food Safety: The Case of HACCP.” In The Economics of

HACCP: Costs and Benefits, L. Unnevehr, editor. Eagen Press, St. Paul MN.

Gold, M., D. Patrick, G. Torrance, et al. 1996. “Identifying and Valuing Outcomes.” Chapter 4

in Cost-Effectiveness in Health and Medicine, M. Gold, J. Siegel, L. Russell, and M. Weinstein,

editors. Oxford University Press: New York. Pp. 82-134.

Martin, S., N. Dean, J. Mauskopf, et al. 1993. “A Sampling Aid for Implementing Risk-Based

Import Inspection.” Prepared for Dr. Richard Williams, Center for Food Safety and Applied

Nutrition, Washington, DC.

Mauskopf, J., and M. French. “Estimating the Value of Avoiding Morbidity and Mortality from

Foodborne Illness,” Risk Analysis 11: 619-632.

Mead, P.S., L. Slutsker, V. Dietz, L.F. McCaig, J.S. Bresee, C. Shapiro, P.M. Griffin, and R.V.

Tauxe. 1999. “Food-Related Illness and Death in the United States,” Emerging Infectious

Diseases 5(5): 607-625.

Murray, C. 1994. “Quantifying the Burden of Disease: the Technical Basis for DisabilityAdjusted Life Years,” in Global Comparative Assessments in the Health Sector, World Health

Organization, Geneva, Switzerland.

Roberts, T., J. Buzby, and M. Ollinger. “Using Benefit and Cost Information to Evaluate a Food

Safety Regulation: HACCP for Meat and Poultry,” Amer J of Agr Econ 78: 1297-1301.

Rosser, R. and P. Kind. 1978. “A Scale of Valuations of States of Illness: Is There a Social

Consensus?” International Journal of Epidemiology 7 (4): 347-358.

Stinnett, A.A., and J. Mullahy. 1998. “Net health benefits: a new framework for the analysis of

uncertainty in cost-effectiveness analysis.” Med Decis Making Apr-Jun;18(2 Suppl):S68-80.

U.S. Department of Agriculture, Food Safety and Inspection Service. 1998. Salmonella

Enteritidis Risk Assessment: Shell Eggs and Egg Products. Final Report. Also available at:

http://www.fsis.usda.gov/OPHS/risk/index.htm.
Session II: Valuing Pain and Suffering and Lost Productivity 35

U.S. Department of Health and Human Services. 1999. “Food Labeling: Trans Fatty Acids in

Nutrition Labeling, Nutrient Content Claims, and Health Claims; Proposed Rule.” Federal

Register, November 17; 62746-62797.

U.S. General Accounting Office. 1990. Food Safety and Quality: Who Does What in the

Federal Government? GAO/RCED-91-19A.

Whitfield, R., and T. Wallsten. 1989. “A Risk Assessment for Selected Lead-Induced Health

Effects: An Example of a General Methodology,” Risk Analysis 9 (2): 197-207

Whitfield, R., T. Wallsten, H. Winkler, et al. 1991. Assessing the Risk of Chronic Lung Injury

Attributable to Long-Term Ozone Exposure. Argonne National Laboratory. ANL/EAIS-2.
36 Session II: Valuing Pain and Suffering and Lost Productivity

Table 1. “Bottom-Up” Approach Example, Part IV: Severity of Illness Caused by

Salmonella Enteritidis in Eggs in 100,000 Normal and 100,000 Susceptible Persons

Each Exposed to 1,000 SE Organisms Using Dose-Response Functions

Outcomes Normal population Susceptible population

Total number ill 65,000 82,000

Recover, no treatment 61,607 76,121

Physician visit, recover 3,146 5,150

Physician visit, hospital, recover 227 631

Die 20 98

Reactive arthritis 1,949 2,458

Percent of U.S. population 78% 22%

Figure 1: CDC Pyramid for “Top-Down” Estimates

of the Annual Number of Cases of Foodborne Illness

Illness in the general population

Person becomes ill

Person seeks care

Specimen obtained

Lab tests for organism

Population survey

Physician survey

Laboratory survey

Active surveillance

Report to Health Dept./CDC

Culture-confirmed case
Session II: Valuing Pain and Suffering and Lost Productivity 37

Figure 2: Number of Salmonella Enteritidis in Each Egg

at Time of Use Using “Bottom-Up” Estimation Model

#SE when

Egg Laid

Final Sale

Transport

before

Processing

Processing

Transport

after

Processing

#SE at Use

Time &

Temperature

Figure 3: Number of Salmonella Consumed in Undercooked Egg

Using “Bottom-Up” Estimation Model

#SE

at Use

Preparation

of Egg

Serving

Size

#SE

Consumed
38 Session II: Valuing Pain and Suffering and Lost Productivity

Comments by Amber Jessup on

“Measuring the Pain, Suffering, and Functional Disability

Associated with Foodborne Illness”

(by Josephine Mauskopf and Roberta Morales)

1. I enjoyed the paper.

2. This paper dealt with a number of difficult problems. First, how to “count” the number of

foodborne illnesses. Second, how to assess the impact of regulatory actions on the number of

foodborne illnesses. Third, how to quantify pain, suffering, and disability associated with

foodborne illness. Fourth, how to use quality-adjusted life years (QALY’s) to value pain,

suffering, and disability.

3. The paper identifies two primary methods for counting the number of foodborne illnesses, a

“top-down” and a “bottom-up” approach. The top-down approach takes the number of

reported cases and multiplies it by a surveillance multiplier that accounts for non-reported

cases; the bottoms-up approach takes exposure to the hazard and multiplies it by the

probability of illness, given exposure. The top-down approach is limited by uncertainty

surrounding the correct value of the surveillance multiplier. The multiplier should vary with

how the case is observed, whether outbreak, passive, or active surveillance and the severity

of the observed cases. The bottom-up approach is limited by its specificity. For example, a

Salmonella Enteritidis risk assessment for eggs is only applicable for eggs and Salmonella

Enteritidis. Each bottom-up analysis is a very difficult undertaking, which is limited by

uncertainty about the prevalence of the bacteria, how the bacteria grows before consumption,

the dose-response relationship, the susceptibility of different populations, food preparation

practices, and the quantity of tainted food consumed.

4. For analysis of the economic impact of regulatory actions, the bottom-up approach can

potentially be more useful. It allows the analyst to directly link illness to a food source and,

ideally, each regulatory requirement can be linked to a reduction in illness. The top-down

approach is helpful for assessing baseline levels of foodborne illness, but does not link illness

to a particular food source. However, although often associated with the top-down approach,

outbreak data can helpful for tying illness to specific food sources.

5. There are a number of methods commonly used to assess QALY’s.

• Rating Scale—The rating scale asks consumers to rate the level of utility associated with

condition A along a line between 1 and 100. The QALY associated with condition A is

calculated by a linear transformation.

• Time Trade-Off—Time trade-off asks an individual what number of years at full health is

equivalent to some other number of years with condition A. The QALY associated with

condition A is the number of years in full health divided by the number of years with

condition A.
Session II: Valuing Pain and Suffering and Lost Productivity 39

• Standard Gamble—Standard gamble offers two alternatives to an individual, a number of

years in full health with some probability of death, or the same number of years with

condition A. They are then asked what probability of death would make them indifferent

between full health with a chance of death and the same number of years with condition

A. The QALY is the acceptable probability of death.

• Expert Opinion—Health care professionals offer their opinion on the QALY loss

associated with condition A.

• Multivariate Analysis—Multivariate analyses of large data sets can be used to estimate

the marginal effect of a given condition on a health status. The results of these analyses

can then be adjusted to represent a QALY value. A fully specified model requires data

on self-perceived health status, activity limitations, other illnesses, and basic

demographics. This method corrects a number of shortcomings of the other methods.

For example, responses may be colored by individual characteristics, be based on indirect

knowledge of the condition, be strategic responses, and reflect embedding in the other

methods. Using large surveys, such as NHIS, it is possible to control for individual

characteristics and other conditions. However, this method shares some of the limitations

of other approaches. The primary limitation is that the interpretation of self-perceived

health status varies by individual. For example, blind persons may perceive themselves

as in full health.
40 Session II: Valuing Pain and Suffering and Lost Productivity

Rapporteurs’ Notes for

Valuing Pain and Suffering and Lost Productivity

Rapporteurs: Clark Nardinelli and Cristina McLaughlin

We talked about many issues associated with measuring the benefits of preventing food-borne

illness and the session’s specific topic, which was pain and suffering and lost productivity. The

conversation dealt with these three general questions:

1. What should be included in the measure of lost welfare associated with foodborne (or

other) illness? We discussed what we should be trying to measure when we estimate

the benefits of increasing food safety.

2. How should we measure the value of preventing foodborne (or other) illness? We

mostly talked about the relative merits of quality-adjusted life years (QALY’s) and

contingent valuation (CV) measures of willingness to pay.

3. What should we do next? We agreed that the goal remains consensus, but we need to

find a way to get there.

1. What should be included in the measure of lost welfare associated with foodborne (or other)

illness?

The session began with a general discussion of whether pain and suffering should be counted as

a cost of illness. Many regulatory agencies do not include the value of pain and suffering in costbenefit analyses of health and safety regulations. Some agencies only count lives saved, rather

than pain and suffering avoided, because lives saved dominates the measure. One discussant

pointed out, however, that for chronic conditions and for end-of-life issues, pain and suffering

can give you big numbers. An example is post-bacterial reactive arthritis.

Most of us agreed that in principle pain and suffering belonged in a measure of the losses

associated with illness. We then discussed some of the problems involved in measuring it. If you

use individual valuations, you can weigh the suffering of some people more than the suffering of

others. Third-party valuations of pain and suffering also have drawbacks; physicians apparently

do not do a good job valuing pain and suffering.

Timing is another issue. If we use individual valuations, should we use the valuations of people

who have not had the illness (and must imagine how it would feel) or of those who have had it

(and can draw on experience)? For some conditions, the difference is large.

The talk about individual valuations of various health conditions led us to the question of dread.

Some risks inspire more dread than others. We discussed the reasons for and against including

dread as a cost of illness. Dread tends to vary with education, income, information, risk aversion,

and other variables not directly associated with the hazard. Dread can be based on inaccurate
Session II: Valuing Pain and Suffering and Lost Productivity 41

information or unknown risks. People also tend to overvalue small, unknown risks. Perhaps a

focus group approach, in which people were given information and a chance to discuss their

fears, might isolate the “pure” dread factor. We discussed whether public health policy should be

made on the basis of dread; Superfund, for example, may be a response to dread.

The government economists and policy analysts all agreed that a major reason to include all of

the benefits of preventing illness in our measures is that the Office of Management and Budget

(OMB) asks that all benefits be included. OMB wants full estimates both as a way to value the

overall effects of health and safety regulations and as a way to compare regulations.

2. How should we measure the value of preventing foodborne (or other) illness?

By far the greater part of the breakout session was devoted to the relative merits of contingent

valuation (CV) and quality-adjusted life years (QALY’s) as measures of the value of preventing

illness. The group failed to reach consensus on the larger issue of which approach is better, but

many smaller issues were resolved.

Several participants described the advantages of quality-adjusted life years as follows:

• QALY’s are bounded, running between zero for death and 1 for perfect health.

• QALY’s are relatively easy to compute.

• QALY’s can be used in cost-effectiveness analyses.

• There is a large medical literature on QALY’s.

Many discussants questioned these advantages of QALY’s. If some health states are worse than

death, then QALY’s are not necessarily bounded. The ease of computing QALY’s may hide their

inaccuracy. The use of QALY’s for cost-effective or medical intervention purposes may not be

easily transformed into use for cost-benefit analysis of public health policies. Medical

interventions convey a very large benefit on a very small number of people, whereas public

health policies convey very small benefits on a very large number of people.

We discussed methods of measuring QALY’s: expert opinion, surveys, and questionnaires. We

agreed that functional disability is much easier to measure than pain, suffering, dread, and other

subjective losses.

QALY’s alone are not sufficient to value reductions in illness. Most of us agreed that the biggest

problem in using QALY’s for cost-benefit analysis was transforming them into dollars. One way

to do so would be to ask what people are willing to pay to avoid the loss of QALY’s (or some

other characterization of the hazard). Another discussant suggested that we get monetary values

by asking people how they would allocate public funds across different risk reduction programs.

One discussant said that in many instances the values placed on QALY’s are arbitrary. In

response, a discussant summarized how the speaker from the first day of the conference found a

non-arbitrary value for QALY’s. The method starts with the value of a statistical life and works

down to the value of a life-year, which can then be multiplied by QALY’s to get the value of

preventing the loss of QALY’s. The dollars per life-year measure derived from the value of a

statistical life, however, falls linearly with life expectancy, a result that contradicts the direct
42 Session II: Valuing Pain and Suffering and Lost Productivity

empirical evidence on the value of statistical life for people of different ages. We discussed other

factors that influence value of a statistical life year (in addition to age), including attitudes

toward risk, time preference, other activities, and capital market imperfections. Some people

questioned whether we could get good enough measures of the value of statistical life-years to

make valuing QALY’s worthwhile.

According to some discussants, QALY measures of the loss from illnesses tend to be biased

upward. One reason may be that we measure the lost QALY’s associated with an illness or

condition as the difference between the QALY in the reduced Health State and the QALY for

perfect health (1.0). If the average QALY is when health is less than one, then our measure

overstates the QALY loss from illness.

We talked about surveys is general, and about the following approaches to getting responses on

health states:

• Place a mark on a visual scale (works well)

• Standard gamble—live the rest of your life in state x, or risky operation

• Trade-off: 40 years in impaired health state vs. x years in perfect health

The session participants who disliked the use of QALY’s generally supported using direct

willingness to pay, based on contingent valuation (CV) studies. The main advantage of CV is

that it directly measures what we are interested in—the person’s willingness to pay to avoid

illness. An advantage of straight CV questions—as opposed to standard gamble or time trade-off

questions—is that they usually represent a real choice people could make.

One objection raised to direct surveys is that it is difficult to keep psychological factors out of

people’s responses. Another potential problem is strategic behavior from respondents. We

discussed whether strategic behavior might be more of a problem for health conditions than for

environmental issues.

We discussed the problems of misperceptions and attitudes toward risk. For example, should we

take a paternalistic attitude toward 20-year-olds? Economists generally say no; if young people

have a low willingness to pay for risk reduction, so be it. It seems inconsistent for regulators to

say that people have good enough information to value risk, but that their information is so

imperfect that we have to regulate risk. Better information might remove the need to regulate, if

20-year-olds simply miscalculate risk. Is the problem attitude or information? We may be

regulating attitudes.

Another problem with direct measures of willingness to pay is the private versus social question.

Surveys asking for private willingness to pay might give us different answers than surveys

asking for social willingness to pay.

The main objection to CV voiced during the session was that the surveys have great practical

problems. We discussed how sensitive CV studies are to how the question is posed, for example.

Another question was how stated willingness-to-pay deals with sick leave and health insurance.

Some participants pointed out that QALY studies are also based on questionnaires and are thus
Session II: Valuing Pain and Suffering and Lost Productivity 43

sensitive to how questions are posed, as well as being sensitive to many of the same issues that

affect CV studies.

One participant described how to do a contingent valuation approach to health states associated

with various illnesses. We could start with stated willingness-to-pay to not have the illness, and

then place implicit values on the various symptoms associated with the illness. For example,

diary studies give the data needed to estimate willingness-to-pay as a function of symptoms,

severity, frequency, length illness lasted, and other confounding factors. In a manner similar to

hedonic housing studies, a well-designed CV study would allow us to compute the marginal

willingness to pay for avoiding the different symptoms (of a given severity and length)

associated with an episode.

The session’s participants appeared to agree on the theory but disagree on what works best. Most

participants agreed that a direct measure (such as CV) of willingness to pay was theoretically

preferable. We failed to reach consensus on whether the practical difficulties were greater for

actual CV studies or for QALY plus value of statistical life-year studies. We did agree that the

ideal QALY study and ideal CV study should include the same list of independent variables.

3. What Should We Do Next?

The goal is for the responsible agencies to meet and establish common guidelines for regulating

food safety. The guidelines should start with OMB guidelines. How can the different agencies

work together to standardize the values placed on symptoms and illnesses?

We discussed the lack of standardization within agencies, much less across agencies. It is

probably better to standardize across agencies first, and within-agency differences will take care

of themselves. We then discussed the desirability of adopting standards to allow flexibility, given

limits imposed by data, time, and political constraints. Standardization must be designed to allow

evolution and improvement. Standardization should not be numbers, but should be methods,

criteria, ways of thinking, and level of acceptable evidence. We also need some benchmarks to

make comparisons.

We discussed the benefits of jointly sponsored studies that would compare CV and QALY

approaches. The two studies we identified as desirable were: (1) an empirical study comparing

the two approaches, and (2) a conceptual comparison of the two approaches. Both studies should

be strongly oriented toward policy applications.
44 Session III: Valuing Risk Reductions Using Different Valuation Methods

SESSION III

 VALUING RISK REDUCTIONS USING DIFFERENT

VALUATION METHODS

Valuing Pathogenic Risk: Methods, Skill, & Rationality

Jason F. Shogren, Tommy Stamland,

Todd L. Cherry, Thomas D. Crocker

Introduction

Constrained budgets and increased fiscal accountability prevent a policymaker from reducing all

foodborne risk to all individuals. Deciding which risks to reduce and by how much requires

evaluation of each new or revised regulation. Comparability of value across all sectors of the

economy requires that policymakers rank regulatory alternatives in terms of a common unit.

Arguably, the most common denominator is money, or monetary equivalence. Risk valuation

systematically evaluates each regulation by estimating the monetary value—both benefits and

costs—of a reduction in risk from unsafe food.

Here, we explore some issues in how rational people might value a reduction in risk from

foodborne pathogens. Valuing the costs and benefits of reduced risk is formidable and

controversial. While measuring the cost to control risk is more straightforward, the benefits are a

challenge to quantify. Problems arise because goods associated with reduced risk—death and

injury—are often not bought and sold on the auction block. These goods rarely if ever enter a

private market, and remain unpriced by collective agency action. Stores and restaurants often

do not like to market “safer food” because to do so would suggest that their food might otherwise

be “unsafe.”

Valuing risk reductions requires that we value death and illness. These efforts give rise to the

loaded term: "the value of life." The idea of a monetary value of life, or more correctly the value

of reduced mortality risk, raises more than a few eyebrows (see Schelling, 1968; Viscusi, 1992).

Ethical and moral beliefs often force a person to balk at the idea. But our everyday choices put

a value on life, whether we explicitly quantify it or not. Whenever a policy change is enacted or

whenever the status quo remains, life and limb are implicitly valued. For example, a North

Carolina hospital once refused to spend $150 per health care worker for an inoculation against

The authors are with the Department of Economics and Finance, University of Wyoming, Laramie, WY 82071-

3985. The authors gratefully acknowledge the support of the Economic Research Service, U.S. Department of

Agriculture.
Session III: Valuing Risk Reductions Using Different Valuation Methods 45

hepatitis B. Given the workers odds of catching the disease, the hospital had implicitly placed a

relatively low value on life. Nothing is lost by explicitly examining the value of reduced

statistical risk.

How do we value a reduction in risk? One straightforward answer:

The value of risk reduction = Willingness to pay for risk reduction

Change in risk

Rational risk policy says that a person’s value for a risk reduction equals his or her maximum

willingness to pay to increase the chances to stay healthy, conditional of his previous private

actions to reduce risk. For example, suppose a person was willing to pay $6 to reduce the risk of

death to 1 life in 1,000,000 from 4 lives in 1,000,000—a 3 in 1m risk reduction. The value of

life is then $2,000,000 (= $6 /[3/1,000,000]). If the person was willing to pay $0.60, the implied

value of life would be $200,000. This willingness to pay is called the option price. The option

price is the maximum a person is willing to pay that keeps him indifferent between the gamble

and the next best alternative.

What methods exist to actually measure the value of risk reduction? The literature on rational

risk valuation has developed two general approaches to measuring the economic benefits of

reduced risk: the human capital and willingness-to-pay approaches. The human capital

approach values risk reductions by examining a person’s lifetime earnings and activities. The

value of a risk reduction is the gain in future earning and consumption. The value of saving a

life is often calculated as what the individual contributes to society through the net present value

of future earnings and consumption. The human capital approach has an advantage in that it is

actuarial, i.e., it uses full age-specific accounting to evaluate risk reductions. A major drawback

of the approach is that it assigns lower values to the lives of women and minorities, and zero

value to retired individuals. The approach also lacks justification based on traditional economic

welfare theory. For this reason, economists have downplayed the human capital method in favor

of the willingness-to-pay approach (see, for example, Buzby et al., 1999).

Economists have advocated the willingness-to-pay approach since it is based on the theory of

welfare economics. Welfare economics lays the foundation for estimating the value of risk

reduction. People value risk reduction if it leads to a greater level of utility or welfare. The

welfare change is measured by the maximum that the average person would be willing to pay to

reduce risk or the minimum compensation he would be willing to accept for an increase in risk.

Economists then use this willingness to pay or accept to estimate the implied value of life and

limb. And although far from perfect, economists argue that the willingness-to-pay approach is

preferable to the alternative—many believe it is better to have a rough estimate of a wellgrounded theory than a precise estimate of a questionable one (see, for instance, Kuchler and

Golan, 1999). One can reveal this value indirectly by teasing out the implied willingness-to-pay

values from real choices within market settings or one can directly estimate values by asking

people what they would be willing to pay for a change in risk. See Freeman (1993) for a good

general overview on non-market valuation, and see Caswell (1995) for specific case studies

using standard valuation methods for food safety work.
46 Session III: Valuing Risk Reductions Using Different Valuation Methods

In what follows, we quickly review the key methods used to value risk. We concentrate mainly

on two key behavioral underpinnings on the value of food safety—the hidden skill and debatable

rationality of the people who confront pathogenic risks. We explore how skill affects the value

of reduced risk in indirect methods, and we consider how presumptions of rationality in direct

methods compare to and can be modified by exposure to active exchange institutions.

Indirect Methods: Risk and Skill

Valuing risk indirectly is often done in two ways—exploring the wage-risk tradeoffs people

make, and exploring the expenditures made on averting behavior, or self-protection. Wage-risk

tradeoffs are based on the theory of hedonic prices. Hedonic price theory captures the idea that a

person’s wage rate depends on skill, education, occupation, location, environment of work, and

job safety or risk. A worker will accept a higher wage for more risk, holding all other job

attributes constant. More risk, higher wages. And a worker selects his job to equate the

incremental willingness-to-pay for each attribute to the incremental contribution of each attribute

to the wage rate. The value of risk reduction is the incremental willingness-to-pay for the

attribute "job safety." Workers then compare their risk-wage tradeoffs to the rate that the market

is willing to trade risk for wages. The market equilibrium between workers and employers then

determines the risk premium—the extra compensation for risky jobs. The wage-risk tradeoff is

thus determined, other job attributes held constant. A review of the early (1974-1983) empirical

results of the hedonic wage-risk model indicates that value of statistical life estimates fall in two

ranges—$450,000-$720,000 and $4-$10 million (in 1990 dollars). Wage-risk studies set the

value of a statistical life between $900,000 and $6,800,000 (see Viscusi, 1993). But note that

these values can be challenged. Critics question the presumptions that workers know all the risks

in the job, and can change jobs costlessly. Also they point out the weak correlation between job

safety and environmental hazards. They also stress that hedonic models consider only a segment

of the population—people with a job; children and seniors are under-represented.

The averting behavior method estimates willingness to pay for risk based on what people

actually pay to protect their families and themselves. People reveal their preferences for lower

risk through the market for self-protection such as smoke detectors, seat belts, medicine, bottled

water, and water filters. The current estimates of the value of life range from $0.46 million to

$0.61 million (in 1986 dollars) (see Fisher et al., 1987; Viscusi, 1993).

The idea that people can use private markets to reduce risk themselves raises an important issue

in the value of life and limb. The value of life or limb is usually defined as the cost of an

unidentified single death or injury weighted by a probability of death or injury that is uniform

across people. The willingness-to-pay approach captures this cost by revealing the previously

unobserved preferences for risk reduction. But here is the rub. These estimates actually contain

more than just unobserved preferences—they capture preferences for risk reduction conditional

on each person’s unobserved ability to reduce risk privately.
Session III: Valuing Risk Reductions Using Different Valuation Methods 47

Consider an example. Suppose people have identical preferences for risk reduction from

contaminated food supplies but they differ in their ability to access private risk reduction

markets. And now say each person is asked to reveal his or her value for a collective program to

reduce risk. Each person’s value for this collective risk reduction is conditional on his or her

private actions (see Ehrlich and Becker, 1972). Following the standard procedures to value life,

one might assume that people with a low value for collective risk reduction are willing to tolerate

greater risk. But in fact it just might be that they have access to effective private risk reduction

and have reduced the risk themselves.

But why does this matter? This matters because the key to estimating the benefit side of rational

risk policy is the value of a statistical life (VSL). Of concern is whether the use of the value of

statistical life estimate overestimates the actual value of reduced mortality risk. We know that

health, safety, and environmental concerns drive most new regulations promoted in Washington,

DC. By far, the most critical category of benefits that economists can quantify and monetize is

the VSL. The greater the value for reduced mortality risk, the greater the odds the benefits of

any given regulation will justify the extra costs. Recent reviews suggest that the VSL is

somewhere between $2 million and $8 million, from the overall range of $100,000 to $10

million (Viscusi, 1993). From this range of estimates, the VSL currently used in the Federal

Government, first by the Environmental Protection Agency (EPA) and now by other agencies, is

$5.9 million (1997 dollars).

But is this value of reduced mortality risk potentially misleading for foodborne pathogens? In

discussing how wage-risk tradeoffs are estimated by the wage differential between jobs with

different risks, researchers have suggested that worker heterogeneity can affect the value of

reduced mortality risk. The marginal worker sets the wage differential and hence the inferred

value of risk reduction. And if this marginal worker’s unobserved risk preference differs from

that of other workers, this local tradeoff can be a misleading index of the required wage

premium. The same can be said about protection from foodborne pathogens—different people

have different skill to avoid risks from food.

Consider now why worker heterogeneity might matter more to the value of statistical life than

many people think (Shogren and Crocker, 1991, 1999). Let workers be heterogeneous in two

respects: they have unique risk preferences (i.e., they put different values on life and health) and

they have unique skill to protect themselves so that they encounter different risks even if their

occupations and job activities are identical. Workers select occupations of different inherent

risks based on both their skill to protect themselves and their risk preference. This means that

the occupation selection is unlikely to reveal perfectly both personal characteristics (Stamland,

1999). When a choice is made based upon two pieces of private information, the choice is

unlikely to perfectly reveal either piece although it conveys some information about both.

Hence, one would expect workers in a more risky occupation to be more skilled or more tolerant

to risk or both. They need not be equally skilled or equally tolerant to risk due to selfprotection, self-insurance, job stickiness, switching costs, irreversibility, imperfect mobility

across occupations, life cycle in skills, experience, education, and safety.
48 Session III: Valuing Risk Reductions Using Different Valuation Methods

One can show that the VSL is likely to be systematically biased upward once one accounts for

worker heterogeneity in both skill and risk preference (Shogren and Stamland, 2000a). A

worker’s unobserved skill to privately reduce his own risk affects the value of risk reduction.

The reason for this is now the marginal worker is not randomly selected. Rather he is the person

among those in the occupation who demands the highest compensation for his risk in the job.

Relative to other workers, the marginal person has either higher risk or lower tolerance to risk or

both. This implies that when the marginal worker’s wage differential is divided by the

statistical risk in the occupation, which measures the average risk of all the workers in the

occupation, the resulting VSL estimate is biased. The VSL estimate is most likely upwardly

biased because the highest required wage differential among the workers is divided by their

average risk. The result holds even if one allows workers to self-select between risky and safe

occupations.

These results support those who have argued that currently used VSL estimates could

overestimate the benefits of new major regulatory decisions. An EPA Science Advisory Board’s

Advisory Council in its evaluation of the health and ecosystem effects of the Clean Air Act

Amendments (CAAA) between 1970 to 1990 worried that the values were biased upward.

Additional observers have pointed out that EPA’s best CAAA benefit estimate of $22 trillion is

nearly the value of total U.S. households and nonprofit organization assets in 1990 ($22.8

trillion), and actually exceeds the gain in the stock market from 1970 to 1990 ($1.2 trillion).

The idea that skill matters does not contradict the general concern that the operative value of

reduced mortality risk used in public policy is suspiciously high.

Consider workers who differ in two respects: they are unequally skilled and they disagree on the

value of reduced mortality risk. Different skill levels imply that workers do not face the same

probability of a fatal accident in the same job; different risk preferences means that they have

different tradeoffs between job wages and the on-the-job risk of a fatal accident. For simplicity,

assume there are two types of jobs, safe and dangerous jobs, and two types of workers, highly

skilled (H) and low skilled (L).

In the safe job, both types of workers face a probability p ≥ 0 of a fatal accident. In the dangerous

job, the likelihood of an accident decreases as the worker’s skill increases. Assume the lowskilled worker faces a probability q > p of a fatal accident in this job, whereas the high-skilled

worker accident probability remains at p. The safe job pays a compensation ws, and the

dangerous job pays wd. The difference in the wages, wd - ws, is endogenously given so that the

dangerous job is able to attract low-skilled workers, i.e., we are considering an equilibrium in the

labor market so that the dangerous job employs workers of different skills. This equilibrium

means that the difference in wages, wd - ws, is just sufficient to make the low-skilled, high-risk

worker indifferent between the dangerous and the safe jobs. In general, with two types of jobs,

it must be the case in equilibrium that the wage difference is just sufficient to compensate the

worker in the dangerous job who requires the highest compensation.

Let π(t) denote the fraction of type t workers in the dangerous job. We denote the workers’ utility

functions by u(t,P,W) where t ∈ {L,H} is the worker’s type, P ∈ {p, q} is the worker’s fatality
Session III: Valuing Risk Reductions Using Different Valuation Methods 49

risk in his job, and W ∈ {ws,wd} is the worker’s wage. For now, assume the worker’s utility

function takes this simple form:

 (1) u(t,P,W) = W - P⋅VOLt

where VOLt is the monetary equivalent of type t’s opportunity cost associated with premature

death in the current period; i.e., VOLt is type t’s value of life, or more precisely, the value of

reduced mortality risk. Allowing VOLt to depend upon the type allows different types of

workers to have different life expectancies and different non-wage utility of life. For instance,

the low-skilled workers may be young, inexperienced workers who have longer life expectancies

(outside work) than the high-skilled workers.

The wage difference is set so that

 (2) u(L,q,wd) = u(L,p,ws)

which, given equation 1, yields the following solution for the wage of the safe job, ws, in terms

of the wage of the dangerous job, wd:

 (3) wd = ws + (q-p) VOLL

If we do not observe VOLt directly, we can infer from the wages and the risks that:

 (4)

q - p

w - w VOL = d s

L ,

if a type’s risk is observable. We shortly consider the case in which it is unobservable. The

overall probability that a randomly selected worker will have a fatal accident in the safe job is Ps

≡ p because all types of workers have the same risk, p, in this job. The corresponding

probability in the dangerous job is Pd ≡ pπ(H) + qπ(L) and, rearranging, we have that

 (5) Pd = p + (q-p)π(L)

The true probabilities of an accident, p or q, are likely to be unobservable. If the wages and the

statistical probabilities of an accident are used to infer the value of a statistical life, VSL, as

follows:

 (6)

P - P

w - w VSL = d s

d s

then we have

(q - p) (L)

w - w = P - Pw - w VSL = d s

d s

d s

π

and comparing with equation 4, we have
50 Session III: Valuing Risk Reductions Using Different Valuation Methods

(7) VOL (L)

1 VSL = L

π

For example, if we plug VOLL = $1,000,000 into equation 7, this yields a VSL of $4.8 million

when π(L)=0.2083. Currently, many Federal agencies including the EPA use a VSL of $4.8

million (1990 dollars).

We now present two useful results about the VSL that follow directly from expression 7. To

facilitate our discussion, define VOLL < VOLH as the case in which a low-skilled worker

undervalues reduced mortality risk relative to the high-skilled worker. First, unless the lowskilled worker undervalues reduced mortality risk, the VSL overestimates the average value of

mortality risk reduction. Second, unless the low-skilled worker undervalues reduced mortality

risk, the less the proportion of low-skilled workers in the dangerous job, the more VSL

overestimates the value of mortality risk reduction.

The third result follows from the observation that high-skill/low-risk workers earn a rent which

equals the difference between their wage and their reservation wage, (q-p)VOLL. The highskill/low-risk worker earns a skill rent, (q-p)VOLL, that is higher,

1) the larger the difference in skill as measured by the risk difference q - p.

2) the higher the low-skilled worker’s value of reduced mortality risk, VOLL.

For the VSL to be an unbiased estimator of the average value of reduced mortality risk, we must

have:

(1-π (L)) VOLH +π (L) VOLL = VSL

which is equivalent to:

 (8) VOL ( ) > VOL

(L)- (L)

1- (L) VOL = 2 L L

2

H

π π

π

These three results hold as long as VOLH is lower than the critical value identified in expression

8. And on examination one sees that it would be a coincidence if expression 8 holds; it would

have to satisfy rather stringent constraints. Noting also that VOLH may be higher than the

critical value given by expression 8, we obtain the fourth result: VSL is an unbiased estimator of

the value of mortality risk reduction in certain cases, but this occurs on a null set. VSL may

underestimate the value of reduced mortality risk if the high-skilled workers value mortality risk

reduction much more highly than the low-skilled workers.

These four results suggest that value of lives saved is likely to be systematically biased upward

once we account for worker heterogeneity in both skill and risk preference. The reason for this

systematic bias is that the marginal worker is the person who demands the highest compensation

for his risk in the job. That means that this marginal person faces relatively higher risk because

of less skill or has lower tolerance to risk or both relative to other workers. And if his wage

differential is divided by the statistical risk in the occupation, which measures the average risk of

all the workers, the estimate of the workers' value of reduced mortality risk is biased upward
Session III: Valuing Risk Reductions Using Different Valuation Methods 51

because the highest required wage differential among the workers is divided by their average

risk. Our results are robust as one allows for more than two types and more general utility

functions (see Shogren and Stamland, 2000a).

We now take this perspective on skill and valuation and apply it directly to the question of food

safety and the VOL (Shogren and Stamland, 2000b). Consider a consumer who chooses a

consumption vector of N available goods and services, some of which reduce health risk and

some of which are foods. Consumers may derive different levels of enjoyment and different

levels of health and fatality risk from the same food. Risks differ for physiological reasons such

as allergies and pre-existing health issues whereas enjoyment levels differ due to taste. Let xt

denote individual t’s consumption vector. This vector may include - in addition to the

consumption all possible foods, all possible ways of dining, etc. - also all other consumption

goods and services. But for our purposes, we focus on the consumption of food and other goods

or services that impact health risks caused by food.

Let βt be a N-vector and At a positive definite (and, without loss of generality, symmetric) NxN

matrix so that βt’x - x’Atx denotes the enjoyment that individual t derives from the consumption

vector x. The first term denotes the utility gain from small levels of consumption and the second

term captures satiation effects in the consumption of single foods and the interaction effect

between the consumption of different foods. Both βt and At depend upon the consumer, t, due to

differences in taste, nutritional needs, etc. We have not included any threshold consumption

necessary for survival because we assume that at any optimal choice the individual might make,

this ‘survival constraint’ is non-binding. But the food consumption may involve other health

issues such as the possibility for food poisoning and the potential consumption of types of food

or quantities of food that engender health risk. The level of risk induced by the food consumption

depends upon the individual due to individual risk characteristics such as age, health, etc. We

represent this risk induced by the consumption vector by the scalar product ρt’x where ρt is the

vector of the individual’s risks per unit of consumption. Finally, we assume there is an

(opportunity) cost induced by increased health risk, VOLt, that also depends upon the individual

so that the consumer’s utility function can be represented as

(9) t t t t u(t,x )=β ' x− x' A x−ρ ' xVOL

We denote the consumer’s wealth by Wt and the vector of food prices by P so that the consumer

seeks to maximize u(t,x) subject to P’x = Wt. We assume that the budget constraint is binding,

meaning that the individual would change the consumption choice if he or she became wealthier.

The first-order conditions for the maximization of u(t,x) with respect to x subject to the budget

constraint form a set of linear equations. The first N first-order conditions (equating to zero the

partials, with respect to x, of individual t’s Lagrangian, L(t,x,λt) ≡ u(t,x) - λt (P’x – Wt) ) yield the

following solution for x, in terms of the budget constraint’s shadow price, λt:

(10) t

1 * At x = α −

where
52 Session III: Valuing Risk Reductions Using Different Valuation Methods

(11) ( ) VOL P

2

1

αt = βt − t ρt −λt

The second-order conditions hold because At is positive definite so the Hessian is negative

definite. When this is plugged into the budget constraint, we obtain the following solution for λt:

(12) P' A P

P' A 2W 1

t

t t

1

t

t −

− γ − λ =

where

(13) t = βt − VOLt ρt γ .

From the first-order conditions for food i and j, we obtain the following result: assuming there

are no interaction effects in food consumption, the individual’s opportunity cost of health risk

satisfies the following relationship:

(14)

j ti i tj

j ti tii i i tj tjj j

t

P P

P ( 2a x ) P( 2a x ) VOL

ρ − ρ

β − − β − =

To prove this note that when there are no interaction effects At is a diagonal matrix so that the

first-order condition obtained by taking the ith partial of the Lagrangian is as follows:

(15) βti −2atii xi −VOLt ρti −λtPi =0

where βti is the ith element of βt, atii is the ith element along the diagonal of At, and so forth. By

solving this equation for λt, plugging the result into the jth first-order condition, and rearranging,

we obtain equation 14. 

Researchers have identified some pitfalls in estimating the opportunity cost of health or fatality

risk by using variables that are determined by the equilibrium behavior of a group of

heterogeneous individuals. The problem is that an estimator based upon this group behavior may

be biased by a sorting effect. This sorting determines the equilibrium’s marginal individual who

winds up determining the equilibrium numbers that enter into the estimator. Since this marginal

individual was not randomly selected, but rather according to a particular sorting of the

individuals, this individual is almost never representative of the whole group. Thus arises the

bias.

Here we take a completely different approach. Rather than looking at the equilibrium behavior of

a group of people, we look at the utility-maximizing choices made by each individual. The

estimator we obtain in equation 14 is therefore the estimator of one particular person’s

opportunity cost of health and fatality risks. This therefore explicitly accounts for the

heterogeneity among the individuals. Furthermore, equation 14 defines not one but rather N(N1)/2 estimators of VOLt per person where N is the number of health risk relevant consumption

choices that are sampled. With the expansion of the model in the next subsection, we obtain an

additional N estimates for a total of N(N+1)/2 estimates per person. If there were no noise in the

estimation process, all these estimates would of course be the same. But in the presence of noise,

such collections of many estimates of VOLt for each of many persons in a sample may provide
Session III: Valuing Risk Reductions Using Different Valuation Methods 53

the possibility of bringing the estimation of the opportunity cost of health risks to a new level of

accuracy.

If we want to use equation 14 to estimate VOLt correctly, we need to know the relevant six terms

from βt, At, and ρt, as well as the chosen quantities, xi and xi, and the prevailing prices, Pi and Pj.

Most likely, these parameters need themselves to be estimated so that the resulting VOLt

estimates are noisy. In the next subsection we look at an expanded and more complex model that,

far from yielding a more complicated estimator for VOLt, actually yields estimators that require

fewer consumer specific variables to be estimated. The present model therefore provides a menu

of estimators between which one may select.

Now let’s expand the model. Define the indirect utility function u*(t,ρt) as the maximum of

u(t,x) over x subject to the budget constraint. This indirect utility is, as denoted, a function of the

risk levels, ρt, that the individual faces from the food consumption vector, x. Given the above, we

have that:

(16) t

1

t t t At u* (t,ρ )=(α +λ P )' α−

We assume that as well as having wealth that can be spent in markets to buy goods and services,

some of which reduce health risks, the individual may exert effort in order to reduce the health

risk engendered by consumption, ρt. One example of such an effort would be physical exercise.

The benefits obtained from exercise are not necessarily (or only) obtained through the purchase

of a service in a market, but is perhaps primarily a service one does oneself at a personal

opportunity cost. Other similar “personal” services include choice of leisure activities, lifestyle,

etc. To capture this, we assume that the individual chooses his or her optimal risk reduction

efforts by maximizing the indirect utility function less opportunity costs that, in net, takes the

following form:

(17) U(t, ) u* (t, ) c ln( ) ti ti

N

i 1

ρt = ρt + ti ρ − ρ =

where ρti might be thought of as a physiologically defined minimum risk, which very well could

be zero (or even negative if the good or service in question is a risk reduction activity), from a

given consumption good.

We have then the following implicit solution to the first-order condition for maximizing U(t,ρt)

with respect to ρti:

1

(18)

u *

c

t i

i

ti ti

− ρ

ρ =ρ +

where

(19) t

1

t i t

ti

ti

t i VOL 1' A u* (t, ) u * = − α

∂ρ

∂ ρ ≡ −

ρ

and 1i is a N-vector with 1 in its ith element and zeroes elsewhere.

 

1 The second-order conditions for this maximization problem are checked in each instance in the numerical

analysis below because we cannot determine whether they are satisfied in general.
54 Session III: Valuing Risk Reductions Using Different Valuation Methods

One issue, which we return to in the connection with numerical examples below, is that we are

unable to verify that the second order conditions for the implicit solution in equation 18 do hold.

Numerical examples verify that the second order conditions do not hold always since we are in

some cases unable to generate a solution using equation 18 iteratively. However, we encountered

no difficulties in finding instances where iterations of equation 18 converge and in most of these

instances the second-order conditions do hold.

Plugging equation 11 into equation 19, we obtain

(20) u * VOL x * tρi ≡ − t i

and so we can rewrite equation 18 as

(21) VOL x *

c *

t i

i ρti =ρti + .

Solving equation 21 for VOLt, we obtain the following result: assuming the individual spends

effort to reduce health risks and the second-order conditions hold, the individual’s opportunity

cost of health risk satisfies the following relationship:

(22) ( * )x *

c VOL

ti ti i

ti

t

ρ − ρ = .

Thus we obtain an additional estimator of VOLt for each health risk relevant consumption

choice. Furthermore, the estimator in equation 22 is simpler than that in equation 14 since it

relies on estimating only three variables for each consumption choice, it does not require price

information, and it does not require comparisons across consumption goods. It may be difficult

to obtain good estimates of some of the parameters in equation 22, perhaps especially cti.

Accounting for the heterogeneity between individuals necessarily makes it more complicated to

infer these individuals’ opportunity costs of health risk. We hope that this model, by providing a

potentially large number of estimates for each individual, takes a step toward resolving these

complications. Clearly, to assess the usefulness of the estimators we propose, one must employ

them empirically and assess the resulting insights.

An additional benefit our model may provide is the fundament for a deeper statistical analysis of

the uncertainty with which we estimate the opportunity cost of health risks. Our framework holds

the possibility that one may obtain many estimates of this health risk for each single person in a

sample. This may provide an opportunity to analyze the errors we make in estimating the

opportunity cost of health risk. Are the different estimates for a single person’s opportunity cost

typically narrowly, or widely, spread around the mean estimate? The answer to this question

should be of considerable interest.

Direct Methods: Risk and Rationality

Direct methods to estimate the value of reduced risk can be grouped into two categories—stated

preferences and experimental auction methods. Among others, key differences between the two

is whether the choice is actually binding when made, and the context of information that can be

provided.
Session III: Valuing Risk Reductions Using Different Valuation Methods 55

Stated preferences methods (e.g., contingent valuation) directly ask people, through a survey or

interview, how much they would be willing to pay to reduce risk. The approach constructs a

hypothetical market, in which a person buys or sells safety. The method attempts to reveal a

person’s willingness to pay for a risk reduction. The challenge is to make these hypothetical

markets realistic and relevant to people. The judgmental best estimate of the value of a statistical

life was approximately $0.1-$15.0 million for both studies (in 1990 dollars). The range of values

is consistent with the high-range estimates of the hedonic wage-risk model, thereby dampening

the complaints of its critics. Although a carefully designed survey can add information on

tradeoffs between safety and income, the method has its critics. A major complaint is that people

are asked to answer a hypothetical valuation question that neither puts their money on the line

nor enforces a budget constraint.

Experimental auction markets are a relatively recent approach to directly value reductions in risk.

Experimental auctions use the laboratory to sell real goods to real people within a stylized

setting. Laboratory experiments can isolate and control how different auctions and market

settings affect values in a setting of replication and repetition. Experiments with repeated market

experience provide a well-defined incentive structure that allows a person to learn that honest

revelation of his or her true preferences is his or her best strategy. With demand-revealing

auctions (e.g., the second-price, sealed-bid auction mechanism), subjects participate in an auction

market that allows for learning as participants realize the actual monetary consequences of their

bidding. The non-hypothetical auctions with repeated market experience can improve the

precision of risk valuation.

Hayes et al. designed a set of experimental auctions to explore the ex ante willingness to pay for

safer food.this question. They constructed an experimental auction to elicit both the option price

and compensation measures of value for five different foodborne pathogens. They also used

additional treatments to evaluate how subjects respond to changes in the risk of illness for a

given pathogen, Salmonella, and to explore if pathogen-specific values act as surrogate measures

of general food safety preferences. All experiments used real money, real food, repeated

opportunities to participate in the auction market, and full information on the probability and

severity of the food-borne pathogen. The design also used a Vickrey second-price auction to

provide incentive to reveal preferences for risk reduction truthfully.

Four results emerge from their experiments. First, people underestimated the objective risk of

foodborne pathogens. Second, values across foodborne pathogens were not robust to changes in

the relative probabilities and severity, suggesting that people place more weight on their own

prior perceptions than on new information on the odds of illness. Third, marginal willingness to

pay an option price decreases as risk increases, again suggesting that the people weighed their

prior beliefs more than new information. Fourth, they found support that values for specific

pathogens might act as surrogates for general food safety preferences.

Overall, the results suggest that the average subject in our experimental environment was willing

to pay approximately $0.70 per meal for safer food. The Salmonella treatments under alternative

risk levels indicate that the average person would pay about $0.30 per meal to reduce risk of
56 Session III: Valuing Risk Reductions Using Different Valuation Methods

foodborne pathogens by a fraction of 10. If one could transfer these values to the U.S.

population, the value of food safety could be at least three times the largest previously available

estimates.

Use of the lab to elicit value raises several questions of method. Consider three. First, does the

unique lab environment inflate values? The observed premium paid in the Hayes et al.

experiment exceeded some expectations of what people would pay in a real retail market. One

explanation might be the novelty of the experimental experience. These auctions are usually a

one-time experience, and the concern is that people might experiment with their bids, bidding

high because the costs of doing so are low. Theory suggests an alternative explanation for the

high price premia—the novelty of the good. Many bidders have never experienced the goods up

for auction, e.g., irradiated meat. In this case, theory says that a bid will reflect two elements of

valuethe consumption value of the good and the information value of learning how the good

fits into his or her preference set. Preference learning would exist if people bid large amounts

for a good because they wanted to learn about an unfamiliar good they had not previously

consumed, because it was unique, or because it was unavailable in local stores. Shogren et al.

(2000a) tested these competing explanations by auctioning off three goods that varied in

familiarity—candy bars, mangos, and irradiated pork, in four consecutive experimental auctions

over two weeks. Their results suggest that preference learning seems to explain the high price

premia. No statistical change in bids was measured for candy bars and mangos, whereas the price

premia for irradiated pork dropped by 50 percent over the four sessions. These findings suggest

that people benefit from the information they gain about how an unfamiliar good fits into their

preference ordering.

Second, how do posted prices affect bidding behavior? Lab valuation exercises use multiple

trials with posted market prices to provide experience to bidders who walk into these auctions

cold. The information sent by a posted market price helps bidders learn about the market

mechanism and the upper support of the valuation distribution. Concern exists that market

experience will contaminate bids as posted prices turn independent private values into affiliated

private values, especially if people are unfamiliar with the good up for sale (Harrison et al.,

1995).2

 List and Shogren (1998) explore this possibility by examining panel data from over 40

second-price auctions with repeated trials. Three results emerge. First, the market price affects

bidding behavior for unfamiliar products, as implied by affiliated private values. Second, the

price effect dissipates when bidders receive non-price information about the good or are familiar

with the product before entering the lab. Third, evidence of strategic behavior independent of any

price signal still exists; buyers start bidding low and sellers start offers high, and then bids

quickly stabilize after one or two trials. These results suggest posted prices can influence

bidding behavior for unfamiliar products, but the effect dissipates when people have non-price

information about the good or are familiar with the good.3

 

2

 Affiliation exists when one bidder who values the good highly increases the chance that other bidders will

also put a high value on the good.

3

 The results have two pragmatic implications for lab valuation research. First, the affiliation of private

values can be reduced, if not removed, by providing product information prior to bidding. Second, a few

trials help people learn about the market mechanism. Some people might need the experience since it

appears that they did not fully comprehend the strategic implications of the second-price auctions.
Session III: Valuing Risk Reductions Using Different Valuation Methods 57

Third, how does direct lab valuation for food safety within the lab and using surveys match up

with an actual retail market behavior? Lab experiments introduce new price and non-price

information and then observe the subsequent changes in bidding behavior. People still know that

they are being monitored, however, and the range of alternative purchases is more limited than in

a retail setting. Valuation surveys usually use hypothetical questions, and no guarantee exists

that hypothetical answers would match those that occur under more realistic circumstances.

Respondents know they are not accountable for their choices. Since these institutional

differences send unique information flows to consumers, it is possible that distinct decision

processes are involved that may cause a person to respond differently to the same choice.

Shogren, Fox, and Hayes (1999) explore the similarity of these choices for risk reduction via

irradiation, and how they match up with actual choices in a retail setting. All subjects came

from the same small college town, and made choices between typical chicken breasts versus

chicken breasts irradiated to reduce the risk of foodborne pathogens. Figure 1 shows that the

results from both the survey and experimental market suggested significantly higher levels of

acceptability of irradiated chicken than were shown in the retail trial at an equal or discounted

price for irradiation. Consumer choices were more similar across market settings at a price

premium for irradiation.

But all this work rests on the idea that the foundation of the economic theory of choice lies in the

expression of values through repeated give and take with others in an exchange institution. The

institution defines incentives and articulates knowledge and beliefs about relevant laws of nature

and of humans. It relates a person’s choice to the choices of others and to the consequences

these aggregated choices produce. Moreover, it conserves resources and goods by redistributing

them in accord with desires. The exchange institution is therefore a collective habit. When it is

absent, a person must draw more intensely upon his or her personal resources.

But since exchange institutions often, even usually, do not exist for environmental assets, a person

can act as if his value expressions will go uncontested; he is asocial, and need not be accountable to

others. Unless one presumes he is a complete image of a nonstrategic, anonymous, competitive

market that is broad in scope, the person may lack the incentives to act in accordance with the

utility maximization paradigm and the economic theory of choice that follows from it. Economic

and psychological evidence is abundant that, absent the disciplines and the protections of exchange

institutions, people often depart from the axiomatic foundation of the economic theory of choice.

When no exchange institution provides the gravity to hold his rationality together, the unsocialized

person commonly engages in anomalous behaviors (naïve expectations or sucker behaviors)

inconsistent with the paradigm. Unsocialized individuals fail to exploit existing gains from trade

and often engage in behaviors that allow others to exploit these gains. Thaler (1992) presents a

lengthy catalogue of these violations, including endowment effects, framing effects, and preference

reversal effects.

Because exchange institutions—especially markets—are thin or nonexistent for environmental

commodities, individual irrationalities plausibly exercise great influence over the allocation of

these commodities and thus the values derived from their presence and use. Delivery of a means

to allocate these commodities rationally might be viewed as a major purpose of benefit-cost
58 Session III: Valuing Risk Reductions Using Different Valuation Methods

analysis, the kit of tools that economists use to infer the values people would attach to these

commodities if market discipline were in place. The delivery may be less than the acme of

perfection however, if only because applications of the kit usually require the introduction of

strong auxiliary conditions linking a person to the market. The auxiliary conditions can be used

to make what appears to be an ill-structured problem even to the researcher appear as a wellstructured problem to that same researcher (Simon, 1973).

But risks from foodborne pathogens can conjure up images of either a secure chain of food

inspections that guarantee our food arrives in sanitized, air-tight receptacles, or a risky adventure

every time one goes out to eat. The two images induce vividly different perceptions of risk to

public health. Yet such a range of public risk perceptions can exist simultaneously in a

community, causing considerable disagreement as to whether a risk is acceptable or not.

Determining whether the risk needs to be regulated depends on how people are willing to trade

off risks for the benefits they can generate to society. Their willingness to surrender benefits

for reduced risk represents the value they place on risk reduction. Estimating this value for risk

reduction is a critical component of risk-benefit analysis, now commonly used in policymaking

on environmental risk.

This value of reduced risk depends in part on the rationality we are willing to presume when

people confront risk. People deathly afraid of the risks they see around every corner are likely

to value risk reduction more than those who live to take risks. This statement seems

straightforward enough, and the logic behind it guides most economists who address

environmental risk. Those at most risk who are most afraid of risk and who have the most

income should consistently value risk reduction the most.

Economists who work with risk most often use the expected utility framework, which presumes

people have well-defined preferences for risk and can logically form rational perceptions of risk.

The working presumption is that people have a solid foundation that drives their choices, such

that when they confront a risk, new or old, they are able to evaluate the odds and consequences

in a systematic and predictable way. A person’s stated value for risk reduction is based on a

logical foundation of choice—welfare economics, and thus economics, is able to judge the

overall economic efficiency of some policy decision. Without well-grounded preferences and

perceptions, there is a crack in the foundation of the rational theory of choice on which the

economist’s risk-benefit analysis rests.

But cracks exist. Psychologists and some economists have documented numerous exceptions to

the idea of a rational theory of choice (see Machina, 1987; Camerer, 1995). These behavioral

researchers have shown how people use rules of thumb, or heuristics, to simplify their reasoning

about risk. Using these rules, people often react to risk in broader patterns than predicted by

expected utility theory. This suggests that the standard model used to guide risk-benefit

decisions is “too thin”—the model does not predict systematic aspects of behavior under risk

regularly observed in many situations. In fact, the evidence suggests that risk preference and

perceptions seem to be systematically influenced by the context of choice. People who make

judgments about risk use heuristics, or rules of thumb, that the popular expected utility
Session III: Valuing Risk Reductions Using Different Valuation Methods 59

framework fails to capture. There is a long list of behavioral anomalies and paradoxes

uncovered by cognitive researchers.

In the case of food safety, one key bias in judgment is when people overestimate low-probability

risks and underestimate high-probability risks. Imagine a 45o line that represents the case in

which the general public’s subjective risk equals objective risk as defined by expert opinion.

Now imagine a slightly flatter line intersecting the 45o line from above reflects the evidence

from different experiments and surveys examining how people actual rank the threats posed by

different risk. People seem to inflate low risks that they have little to no control over (e.g.,

nuclear power) and deflate high risks that they can control to some degree (e.g., driving to work).

They tend to worry more about how and where a risk arises than its magnitude, e.g., synthetic

versus natural carcinogens. This poor calibration between experts' objective opinions and the

lay person’s perceptions can lead to rejection of potentially beneficial technologies, e.g.,

commercial nuclear power.

Such irrational reactions can affect the values collected with stated preference methods. The

isolated individual, in response to a researcher request to do so, is presumed to be able to imagine

an exchange institution, visualize the details of his and others' participation in it, and then to state

his one-time value for a nonmarketed environmental commodity. Though contingent valuation

employs a data gathering tool that psychologists and sociologists apply to study the cognitive

processes that generate a person’s choice, its economist practitioners do not often address the

general reluctance of these other disciplines to use the utility maximization paradigm to explain the

isolated behaviors of individuals. These non-economic disciplines prefer to downplay the

quantitative features of a decision process to focus on the framing of the problem, learning about it,

clarifying options, etc. These are the functions an effective exchange institution performs on a

person. And though the verdict is still out, enough empirical evidence exists to fuel skeptics who

wonder whether asking an isolated person to visualize active participation in a repeated give and

take situation is sufficient to cause him to behave for one time in accordance with the utility

maximization paradigm. A polite request to visualize a nonexistent market does not obviously

cause the rationality of the real market to rub off on him.

We posit that a Coasean corollary exists for nonmarket valuation—if information processing costs

are zero, the researcher will have enough understanding to provide identically perceived

information such that the beliefs of respondents will be complete, and, consequently, elicited

values would be identical to the market price, if it existed. But if information costs really are zero,

respondents will endogenously generate their own information frames that will be identical to the

exogenously provided information frames, i.e., elaborate information packages in survey research

would be redundant as respondents would select the same frame endogenously. Information costs

are not zero, obviously, thereby implying that the rules and exchange institutions implied by a

researcher’s exogenous frame impact a respondent’s elicited value. Therefore, artificially

restricting the range of rules and exchange institutions will result in revealed values that

underestimate the true value of the resource. Allowing for the endogenous choice of exchange

institution in nonmarket valuation is needed to permit incomplete beliefs to become more complete

such that the respondent has the opportunity to participate in the selection of both what goods will
60 Session III: Valuing Risk Reductions Using Different Valuation Methods

be produced and how they will be provided. Additionally, some form of arbitrage, albeit

possibly mock, should be used to make beliefs even more complete.

Maximum extraction of potential surplus is performed at the level of the exchange institution rather

than at the level of the individual (Becker, 1962; Smith, 1991; Grether, 1994; Plott, 1996).

Consider the odds that you will suffer a dreaded environmental disease by a given date. You will

be able to specify a lower probability value such that odds less than this value are incredibly low

and another, higher value such that odds greater than this value are incredibly high. You will insist

or try to placate someone who insists that you pick a single, unique value. de Finetti (1974) shows

how the selected lower value, upper value, and single-value odds can be interpreted as your

greatest buying price, your lowest selling price, and your no-arbitrage price. Your beliefs are

complete at the no-arbitrage price (Nau and McCardle, 1991).

If a person has incomplete beliefs, they can be made more complete if he chooses to participate in

one or more exchange institutions. It is exchange institutions that pressure the individual to behave

in accordance with the utility maximization paradigm (see for example Chu and Chu, 1991; Gode

and Sunder, 1993). However the manner in which these pressures induce the individual to submit

his beliefs and preferences may differ considerably across institutions and a person may well have

preferences over the manner of submission. For example, a person may prefer the anonymity of

the nonstrategic, competitive market over a public good club that compels him to divulge more

than he would like in interminable meetings for which he does not care. The incompleteness of

his beliefs about and thus the value he attaches to a lottery for the environmental commodity will

therefore differ with the exchange institutions in which he chooses to participate and with the

intensity of his participation.

As we stated, experimental evidence suggests that an isolated person often acts outside the ropes

of economic theory. We discussed how he commonly reverses his preferences, makes different

bids and offers for the same good, and puts too much weight on his initial endowments. But

people usually return to the ring when they interact with other intelligent self-interested people in

an active exchange institution. These institutions arbitrage the irrational decisions of people by

rewarding those acting rationally or learning to act rationally. As such, economists often

question the importance of isolated anomalistic behavior to explain behavior in thick, wellfunctioning markets and economic systems.

But markets for several key goods and services are thin or non-existent; they lack sufficient

arbitrage opportunities that can induce rational economic behavior. Most environmental assets,

for instance, lack well-defined exchange institutions, and as a consequence, behavior in the

allocation and valuation of environmental goods is more likely to be irrational. This observation

calls into question the reliability of nonmarket valuation surveys that have emerged to

understand behavior for these goods. The typical survey asks the unsocialized person to imagine

an exchange institution, visualize the details of his and others’ participation in it, and then to

state his one-time value for a non-marketed environmental good. While these surveys generate

numbers, the hypothetical institutions are perilously thin and provide the undisciplined and

uncontested values that raise fears that irrational behavior is the rule not the exception.
Session III: Valuing Risk Reductions Using Different Valuation Methods 61

But most people participate in both thick and thin markets simultaneously. The key question is

whether the rationality induced from arbitrage in a thick market could spill over to behavior in a

thin market. If so, non-market valuation surveys might be improved by a explicit connection to

an active market with arbitrage. Cherry et al. (2000) provide experimental evidence of such

rationality spillovers—induced rationality in an arbitraged market can spill over to a second nonarbitraged market that would otherwise consist of irrational behavior in the case of preference

reversals. Only arbitrage and socialization appear to stop the phenomena (Chu and Chu, 1990).

But will the rationality induced in a market with arbitrage spill over to a second market without

arbitrage?

Here we extend the account of induced rationality in Crocker et al. (1998) to rationality

spillovers. To reduce notational clutter, we consider one representative agent who makes his

choices in one time period and takes the state of nature as fixed. A lottery is sequentially

available to the agent in two settings, first an arbitrage, market-like setting, MK, and then an

isolated, nonmarket setting, NM. Let NM θ be the agent’s effort to overcome any irrationalities

that his cognitive and computational limitations cause him to have in the isolated setting.

Represent the agent’s irrationalities by ∆ ,k MK,NM k = , the inconsistency between his choice

and the fair price for a lottery ticket. Any such gap, k ∆ , invites efforts to close it – like a gap in

potential that, when sufficiently great, is crossed by electric energy as a spark. But this need not

mean that the agent’s shock is great enough to elicit enough effort to close the gap and thus

maximize extracted surplus.

The gap MK ∆ affects the agent’s rationality in the isolated setting parametrically. Assume this

effect is independent of the agent’s wealth. With a result, MK ∆ , the agent’s surplus extraction

problem in the isolated setting is to choose effort to maximize

(23) [ ( )( ) ( )] NM NM NM MK NM NM MK

θ

NM Max W 1 θ ∆ θ ,∆ C θ ,∆ NM

= − − − NM Z , 

where NM Z is the maximum surplus the agent could extract if he were fully rational in the

isolated state. This surplus corresponds to that which would be generated in a world of nonstrategic, anonymous, competitive markets for a lottery ticket. Let NM θ be continuous. It can be

characterized as lying in the unit interval [0,1], in which the upper bound implies that the agent

expends enough effort to override his endowed cognitive and computational limitations

completely and the lower bound implies no overriding whatsoever. Getting more education or a

new set of eyeglasses might be examples of effort expenditures. Expenditures like these are

costly in terms of time and resources. Expression 23 makes both the agent’s residual irrationality

or unextracted surplus, NM ∆ , and his costs of extracting surplus, NM C , in the isolated setting

functions of his irrationality, MK ∆ , in the arbitrage setting and his effort expenditures, NM θ , in

the isolated setting. His irrationality in the isolated setting is determined by what he learned in

the arbitrage setting and his willingness to apply that learning in the isolated setting.
62 Session III: Valuing Risk Reductions Using Different Valuation Methods

Assume

0

∆

C

0; θ

C

0; ∆

∆

0; θ

∆

MK

NM

NM

NM

MK

NM

NM

NM

≥

∂

∂

>

∂

∂ = ∂∂

≤

∂

∂ ,

and

( ) ( ) 0.

θ ∆

C

0;

∆

C

0; ( θ )

C

0;

θ

∆

NM MK

2 NM

MK 2

2 NM

NM 2

2 NM

NM 2

2 NM

>

∂ ∂

∂

>

∂

∂

>

∂

∂

<

∂

∂

We discuss the sign of NM MK

2 NM

θ ∆

∆

∂ ∂

∂

 subsequently. Assume MK

NM

∆

∆

∂

∂

equals zero because, by

definition, the isolated agent can be more rational only by trying to be so, i.e., by drawing upon

his internal resources to make lessons from his prior arbitrage experiences.

In the isolated setting, the agent’s optimal residual irrationality is

(24) ( ) 0

θ

C ∆

θ

∆ 1 θ

θ

W ∆ : NM

NM

NM

NM

NM

NM

NM

NM

NM = ∂

∂

+ − ∂

∂ = − − ∂∂

(25) ( ) ( ) ( ) 0

θ

C

θ

∆

( θ )

∆ 1 θ

( θ )

W ∆ :

NM 2

2 NM

NM

NM

NM 2

2 NM

NM

NM 2

2 NM

NM 2

<

∂

∂ − ∂∂

+

∂

∂ = − − ∂∂

Analogous conditions exist in the arbitrage setting which produced MK ∆ if the extraction of

surplus in that setting involves agent efforts to grasp the implications of and to implement

transactions, coordination, and negotiation.

Now differentiate the first-order equilibrium conditions for the arbitrage and the isolated settings

with respect to a parametric shift in the agent’s residual irrationality in the arbitrage setting.

Better arbitrageurs than the agent previously had to face might cause this shift. They force the

agent to get smarter. This differentiation yields

(26) ( ) , NM 2

NM MK

MK

NM

∆

∆ ∆

d∆

d∆ = − 

where MK ∆ is the agent’s optimal residual irrationality in the arbitrage setting alone. The

NM MK ∆ ∆ term on the right-hand-side of expression 26 is

(27) ( ) . θ ∆

C

∆

∆

θ ∆

∆ ∆ ∆ : 1 θ NM MK

2 NM

MK

NM

NM MK

2 NM

NM MK NM

∂ ∂

∂ − ∂∂

+

∂ ∂

∂ − −

If the agent does not carry over his residual irrationality in the arbitrage setting to the isolated

setting, then expression 27 will be zero, implying from expression 26 that the agent does not

choose to use whatever he learned in the arbitrage setting to improve the quality of his decisions

in the isolated setting. For expression 26 to be positive such that a rationality spillover occurs,

isolated effort and reductions in arbitrage irrationality must be complements. Reductions in
Session III: Valuing Risk Reductions Using Different Valuation Methods 63

arbitrage irrationality must increase the marginal product, 0

θ ∆

∆

NM MK

2 NM

<











∂ ∂

∂ , and reduce the

marginal costs, 0

θ ∆

C

NM MK

2 NM

>











∂ ∂

∂ , of the agent’s efforts in the isolated setting. Our null

hypothesis is that the net effect of these two cross-partials causes expression 26 to be zero or

negative in sign, which means respectively that arbitrage experiences do not impact the quality

of the agent’s unarbitraged decisions or that these experiences are dysfunctional for these

decisions.4

 The alternative hypothesis is that expression 26 will be positive. To state our

hypotheses in the text formally, first consider arbitrage and rationality spillovers.

For a person in the kth institution, (k= MK, NM) let wk

 denote his initial wealth level; Ak, Bk , and

ψk indicate his holding of lottery A, lottery B and no lottery; and WTP(A)k and WTP(B)k is his

maximum willingness to pay for lotteries A and B. Preference and indifference are indicated by

(≻) and (~). By definition of WTP(A)k

, and WTP(B)k, the following holds for the kth institution:

[wk

+ WTP(A)k,ψk] ~ [wk, Ak] and [wk + WTP(B)k,ψk] ~ [wk, Bk].

Preference for lottery A over lottery B implies [wk

, Ak] ≻ [wk, Bk] and by transitivity

[wk

 + WTP(A)k,ψk] ≻ [wk + WTP(B)k,ψk].

Given wealth provides positive utility, WTP(A)k

 > WTP(B)k follows from the initial preference

of lottery A over lottery B. Rational behavior leaves no arbitrage opportunities, i.e., surplus on

the table available for others to capture.

In reality, isolated people often contradict this theoretical result. They reverse their preferences

by either indicating

[wk

, Ak] ≻ [wk, Bk] and [WTP(A)k] < [WTP(B)k].

or

[wk

, Ak] ≺ [wk, Bk] and [WTP(A)k] > [WTP(B)k].

These inconsistent preferences create opportunities for others to extract gains through exchange.

Surplus left on the table from inconsistent preferences may arise from errors in judging

preferences or valuations or both. Errors may be reduced or eliminated when the surplus is

captured through the arbitrage provided within an exchange institution. Let the preference of

lottery A over lottery B be denoted as

[Ak

+ ε(A)k] ≻ [Bk + ε(B) k],

 

4

 By treating arbitrage rationality levels tomorrow as positive functions of isolate rationality levels today, this

framework could readily be adapted to a multiperiod, sequential process in which irrationality declines with each

passing period. This learning-by-doing process need not presume dynamic elemental rationality in the sense that the

agent weighs the value of behaving today in terms of what such behavior will do for the temporal conjunction of his

irrationalities in all tomorrows. Unintentional rationality spillovers can, in principle, occur over time, events, states,

institutional settings, or any other economic dimension.
64 Session III: Valuing Risk Reductions Using Different Valuation Methods

where k ε ( ) is the error in judgement for lottery in institution k ( = A, B and k = MK,

NM). Summing judgment errors yields the total error in preference ordering for institution k, Ε k

= ε(A)k + ε(B)k. The valuations for lotteries A and B for the preference- reversing individual are

given by

[WTP(B)k

 + φ(A)k] > [WTP(A)k + φ(B)k]

where φ ( )k

 is the error in valuing lottery in institution k ( = A, B and k = MK, NM). Total

error in valuation for institution k is given by θ k

 = φ(A) k + φ(B) k. The total error in institution

k is the sum of the error in preference ordering and valuation, k ∆ = Ε k + θ k.

Let total error within the kth institution with and without arbitrage be k (1) ∆ and k (0) ∆ , in which

k(1) and k(0) represent arbitrage and no arbitrage. Direct rationality effects from arbitrage imply

that total error is reduced or eliminated in the presence of arbitrage such that k (1) ∆ < k (0) ∆ .

Accordingly the direct rationality hypothesis refers to the rationality effects of arbitrage within a

single institution,

HO : k (1) ∆ = k (0) ∆ ; Ha: k (1) ∆ < k (0) ∆ . k = MK, NM

Plentiful theoretical and experimental evidence supports direct rationality effects from arbitrage.

Now consider the indirect or rationality spillover effects of arbitrage in the jth institution, j ≠ k.

“Rationality spillover” simply assumes that the individual’s total error in the kth institution is

reduced after he is subjected to arbitrage in the jth institution. The rationality spillover

hypothesis is thus

H ∆ = ∆ H ∆ < ∆ j ≠ k = k

j

k

a j

k

j

k

O j

(0)

(0)

(0)

(1)

(0)

(0)

(0)

(1) : ; : MK, NM

where (0)

(1)

k ∆ j is the individual’s total error in the kth institution subsequent to his participation in

arbitrage in the jth institution.

For a given institution, do his preference orderings or do his valuations register the effect that

arbitrage has on the person’s rationality? Arbitrage may induce him to correct errors in his

preference ordering alone such that dE k(1) < 0 and dθ k(1) =0, in his valuations alone dE k(1) = 0

and dθ k(1) < 0, or both dE k(1) < 0 and dθ k(1) < 0. Further, rationality effects may occur in only a

single lottery type as in 0 ( ) (1) < k dE and 0 ( ) (1) < k dθ or pairs as in 0 ( ) (1) < k dE ,

( ) 0 (1) < k dθ , = A,B, k= MK, NM. Three hypotheses follow:

• Preference adjustment hypothesis – total errors are reduced by correcting preference

orderings alone, (0)

(0)

(0)

(1)

(0)

(0)

(0)

(1) : ( ) ( ) ; : ( ) ( )

k

j

k

A j

k

j

k HO E j = E H E < E

• Valuation adjustment hypothesis for low-risk lotteries – total errors are reduced by

adjusting valuations of lottery A, (0)

(1)

(0)

(1)

(0)

(0)

(0)

(1) : ( ) ( ) ; : ( ) ( )

k

j

k

a j

k

j

k HO θ A j = θ A H θ A <θ A

• Valuation adjustment hypothesis for high-risk lotteries – total errors are reduced by

adjusting valuations of lottery B, (0)

(1)

(0)

(1)

(0)

(0)

(0)

(1) : ( ) ( ) ; : ( ) ( )

k

j

k

a j

k

j

k HO θ B j =θ B H θ B <θ B

Cherry et al. (2000) designed an experiment to address this question using a computer program

to simulate two choices—market and non-market. Treatment 1 was the no-arbitrage baseline—
Session III: Valuing Risk Reductions Using Different Valuation Methods 65

both choices had real money lotteries and no arbitrage. In treatments 2, 3, and 4, the market

choice was held constant—real money lotteries with arbitrage (after round 5). The non-market

choice varied across the treatments: (a) real money lotteries without arbitrage in treatment 2; (b)

hypothetical money lotteries without arbitrage in treatment 3; and (c) hypothetical environmental

lotteries without arbitrage in treatment 4.

In an arbitraged treatment, all possible rents from subjects reversing their preferences were

extracted in three steps. The market (1) sells the least preferred and most valued lottery to the

subject; (2) trades the least preferred lottery for the most preferred lottery; and (3) buys the most

preferred and least valued lottery from the subject. The subject is left with no lotteries and a

monetary loss equaling the difference between the indicated values of the lotteries. Note that the

arbitrage mechanism was not active until after the fifth round. Under a non-arbitraged treatment,

reversals were left unchecked for all rounds.

Figures 2-4 summarize the key results. First, the results suggest that arbitrage directly impacts

individual rationality. The non-arbitraged reversal rate is about 33 percent and persists over the

15 rounds. Second, rationality spillovers exist. Once arbitrage is introduced in the market, the

rate of reversals in the non-market choice decreased too. Reversal rates were about 20 percent

after 11 trials, and 10 percent after 15 trials. Rationality spillovers were also strong in the

hypothetical treatment, and weaker in the environmental treatment. Third, and of key

importance, people adjusted valuations rather than preferences, which indicates the potential for

rationality spillovers to improve non-market valuation. Although isolated individuals often fail

to behave in accordance with the classic economic paradigm of utility maximization, these

results suggest a case in which such irrationality can be overcome if people receive information

and discipline from an active exchange institution.

Concluding Remarks

Valuing food safety from collective action is complicated by the fact that people have private

information on both their risk preferences and their skill to avoid risk privately. They also make

many decisions on low-odds risks in non-market situations in which their rationality can be

called into question. Herein we explore how hidden skill and shaky rationality can affect both

the theory and methods used to define and estimate value of reduced pathogenic risk. The results

suggest that both skill and rationality matter, and as such both factors are worthy of attention in

future research efforts.

Pathogen risk to children is one factor we have not discussed in this paper, but it deserves more

attention. Children can face disproportionately greater risk from foodborne pathogens because

they are kids—smaller bodies, faster metabolisms, shorter attentions spans, less knowledge, and

fewer resources. Food safety programs that reduce risks to children produce benefits to society

that should be adequately represented so policymakers have more information to help them

decide which policies are the most worthwhile relative to their costs. The open question is just

how exactly to value these reductions in risks to children, which can either arise from a direct

effect on their health or an indirect effect on their life chances due to illness in other family

members or the degradation of the environment. Do standard benefits estimation adequately
66 Session III: Valuing Risk Reductions Using Different Valuation Methods

capture the indirect effects on healthy children? In some cases, risks to children might be

accounted for in revealed and stated values, and estimating these effects could imply double

counting of benefits. But if policymakers fear that caregivers face choice without complete

information or experience, the benefits of reduced risks to children might be understated. It also

seems constructive to devote resources to explore the link between adults, children, and the value

of safer food.
Session III: Valuing Risk Reductions Using Different Valuation Methods 67

References

Becker, G., S. 1962. "Irrational Behavior and Economic Theory," Journal of Political

Economy 70, 1-13.

Buzby, J., T. Roberts, C.T. J. Lin, and J. MacDonald. 1999. Bacterial Foodborne

Disease: Medical Costs and Productivity Losses, AER-741. Economic Research

Service, U.S. Department of Agriculture.

Camerer, C. 1995. “Individual Decision Making.” The Handbook of Experimental

Economics (J. Kagel and A. Roth, eds.). Princeton University Press. Princeton,

New Jersey.

Caswell, J., ed. 1995. Valuing Food Safety and Nutrition. Boulder, CO: Westview Press.

Cherry, T., T. Crocker, and J. Shogren. 2000. “Rationality Spillovers,” Working paper,

University of Wyoming.

Chu, Y.-P., and R.-L. Chu. 1990. "The Subsidence of Preference Reversals in Simplified

and Marketlike Experimental Settings," American Economic Review 80, 902-911.

Crocker, T. D., J. Shogren, and P. Turner. 1998. "Incomplete Beliefs and Nonmarket

Valuation," Resources and Energy Economics

de Finetti, B. 1974. Theory of Probability, Vol. I, New York, NY: John Wiley & Sons.

Ehrlich, I. and G. Becker. 1972. “Market Insurance, Self-insurance, and Self-protection,”

Journal of Political Economy 80, 623-648.

Fisher, A., L. Chestnut, and D. Violette. 1989. “The Value of Reducing Risks of Death:

A Note on New Evidence,” Journal of Policy Analysis and Management 8, 88-

100.

Freeman, A.M. III. 1993. The Measurement of Environmental and Resource Values:

Theory and Methods. Washington, D.C., Resources for the Future.

Gode, D. and S. Sunder. 1993. "Allocative Efficiency of Markets with Zero Intelligence

Traders: Markets as a Partial Substitute for Individual Rationality," Journal of

Political Economy 70, 1-13.

Grether, D. 1994. "Individual Behavior and Market Performance" American Journal of

Agricultural Economics 76, 1079-1083.

Hanemann, W. M. 1994. "Valuing the Environment through Contingent Valuation,"

Journal of Economic Perspectives 8, 45-64.
68 Session III: Valuing Risk Reductions Using Different Valuation Methods

Hayes, D., J. Shogren, S. Shin, and J. Kliebenstein. 1995. ”Valuing Food Safety in

Experimental Auction Markets,” American Journal of Agricultural Economics 77,

40-53.

Kuchler, F., and E. Golan. 1999. “Assigning Values to Life: Comparing Methods for

Valuing Health Risks,” AER-748. Economic Research Service, U.S. Department

of Agriculture.

Machina, M. 1987. “Choice Under Uncertainty: Problems Solved and Unsolved,”

Journal of Economic Perspectives 1, 121-154.

Nau, R., and K. McCardle. 1991 "Arbitrage, Rationality, and Equilibrium, " Theory and

Decision 31, 199-240.

Plott, C. 1996. “Rational Individual Behavior in Markets and Social Choice Processes,”

The Rational Foundations of Economic Behavior. London: Macmillan and New

York: St. Martin Press.

Schelling, T. 1968 (1984). The Life You Save May be Your Own. Choice and

Consequence: Perspectives of an Errant Economist. Cambridge, MA: Harvard

University Press, 113-146.

Shapiro, C. and J. Stiglitz. 1984. “Equilibrium Unemployment as a Worker Discipline

Device,” American Economic Review 74: 433-444.

Shogren, J. and T. Crocker. 1991. “Risk, Self-protection, and Ex Ante Economic

Valuation,” Journal of Environmental Economics and Management 21: 1-15.

Shogren, J. and T. Crocker. 1999. “Risk and its Consequences,” Journal of

Environmental Economics and Management 37, 44-51.

Shogren, J., J. Fox, and D. Hayes. 1999. “Observing Food Safety Choices in Retail,

Survey, and Auction Markets,” American Journal of Agricultural Economics 81,

1192-1199.

Shogren, J., J. List and D. Hayes. 2000. “Preference Learning in Consecutive

Experimental Auctions,” American Journal of Agricultural Economics

(forthcoming).

Shogren, J. and T. Stamland. 2000a. “Skill and the Value of Life,” Working paper,

University of Wyoming.

Shogren, J. and T. Stamland. 2000b. “Risk and Pleasure in the Consumption of Food,”

Working paper, University of Wyoming.
Session III: Valuing Risk Reductions Using Different Valuation Methods 69

Simon, H. 1973. "The Structure of Ill-Structured Problems," Artificial Intelligence 4,

181-201.

Smith V. L. 1991. "Rational Choice: The Contrast between Economics and Psychology,"

Journal of Political Economy 99, 877-897.

Stamland, T. 1999. “Partially Informative Signaling,” Journal of Economic Theory 89,

148-161.

Thaler, R. 1992. The Winner’s Curse. New York, Free Press.

Viscusi, W. K. 1992. Fatal Tradeoffs: Public and Private Responsibilities for Risk. New

York: Oxford University Press.

Viscusi, W. K. 1993. The Value of Risks to Life and Health. Journal of Economic

Literature 31: 1912-1946.
70 Session III: Valuing Risk Reductions Using Different Valuation Methods

Figure 1 — Ratio of consumers who prefer irradiated meat, 

given relative price

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

10% discount same price 10% premium 20% premium

Relative Price

Ratio

survey

experiment

retail
Session III: Valuing Risk Reductions Using Different Valuation Methods 71

 Figure 2 – Preference reversal rates in the market setting

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Round

Reversal Rate

Real money lotteries

with no arbitrage (T1)

Real money lotteries with arbitrage (T3)

Real money lotteries with arbitrage (T2)

Real money lotteries with arbitrage (T4)

Arbitrage begins in Round 6
72 Session III: Valuing Risk Reductions Using Different Valuation Methods

Figure 3 – Preference reversal rates in the nonmarket setting

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Round

Reversal Rate

Real money lotteries 

with no arbitrage (T1)

Hypothetical money lotteries with no arbitrage (T3)

Real money lotteries with no arbitrage (T2)

Hypothetical wildlife lotteries with no arbitrage (T4)

Arbitrage begins in Round 6
Session III: Valuing Risk Reductions Using Different Valuation Methods 73

 Figure 4 – Mean values for high-risk lotteries: Real versus hypothetical

1

2

3

4

5

6

7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Round

Price

Arbitrage begins in Round 6

No arbitrage baseline (T1)

 Real money (T2)

 Hypothetical money (T3)

 Hypothetical Environmental (T4)
74 Session III: Valuing Risk Reductions Using Different Valuation Methods

Comments by Fred Kuchler (ERS) on

“Valuing Pathogenic Risk: Methods, Skill, & Rationality”

(by Jason Shogren, Tommy Stamland,

Todd Cherry, and Thomas Crocker)

I’m very happy to be able to comment on the paper by Shogren and others. The paper is very

provocative. In a few pages, it sweeps away the most important developments in estimating

value of statistical life (VSL), the values we Federal bureaucrats have used routinely for valuing

reductions in fatal risks. Shogren et al. (henceforth denoted Shogren) argue that the potential for

bias in values we have been using is enormous. The likely direction of bias is that the values we

have used are grossly inflated. By implication, that means that the policy guidance economists

have offered on health and safety issues for at least the last 17 years has been misleading from a

welfare or efficiency perspective.1 That is, if he is right, we have sometimes said net benefits of

proposed regulations are positive when they are decidedly negative.

I would like to confine my comments to practical issues of regulatory cost-benefit analysis rather

than theory, but the two are not totally divorced. My comments will be structured as answers to

three questions.

1. Does the proposed explanation of the relation between behavior toward risk and VSL make

more sense than the explanation we rely on now?

2. Does the proposed explanation lead to something that we can estimate or is it simply a good

theory?

3. If we can make this estimate, what will happen if we adopt Shogren’s ideas in regulatory cost

benefit analyses?

Answering the first question is easy. Our current explanation of behavior and prices doesn’t hold

up, especially for valuing reductions in foodborne risks. The paper by Shogren is a very

different approach and is internally consistent.

Almost every agency involved in health and safety regulations either uses the $5 million value

Viscusi (Viscusi, 1993) recommended or did roughly the same exercise he did to come up with a

value of statistical lives. Viscusi arrayed the empirical estimates of VSL and put them on a

common footing regarding assumptions. Then, he looked for a measure of central tendency.

Most of these studies were wage-risk studies. EPA did an exercise similar in spirit to Viscusi’s

and came up with $4.8 million.

 

1 Viscusi (1998) describes the transition in regulatory cost-benefit analyses from cost-of-illness to

willingness-to-pay measures as occurring in 1983. The issue was a hazard communication regulation

proposed by U.S. Occupational Safety and Health Administration. Initially, the regulation failed a costbenefit test. But when benefits were valued using willingness-to-pay estimates, net benefits were

positive, and the regulation was approved.
Session III: Valuing Risk Reductions Using Different Valuation Methods 75

The estimates that are driven by results from wage-risk studies are very likely inappropriate ways

to value foodborne risks. Principally, the people who are really at risk from the foods they eat

are different and are likely to have different risk preferences than the people who make job

choices based on wages and risks. Wage-risk studies reflect mostly preferences of middle-aged

men in robust good health. That is, they are working and not home sick. Foodborne risks are

primarily borne by the very young, the very old, and the immune compromised. The people who

die by getting run over by a truck at work are not the same as those who die from foodborne

illness.

Just to underline this point, I prepared a histogram of deaths in the United States from

Salmonella. The histogram arrays deaths by age at death. These are deaths for which

Salmonella was listed as the primary cause of death on death certificates. The 1987-92 data

came from the National Center for Health Statistics (more recent data are not yet available).

Although it is not shown on the histogram, the highest bars at the oldest ages are almost entirely

women. At age 40, there are relatively few deaths.

So, transferring VSL estimates from wage-risk studies amounts to saying that middle-aged men’s

preferences reflect the way we value lives of children and elderly women. Viscusi, Magat, and

Huber (1987) have studied values placed on reducing risks to children. They found much higher

values for kids than adults. Cropper, Aydede, and Portney (1994) have studied values placed on

reducing risks to the elderly. They found lower values for the elderly than for adults.

In effect, it makes little sense to think that one VSL will fit all at-risk populations and all risks

(Golan and Kuchler, 1999).

Shogren has taken an entirely different approach. Instead of trying to transfer risk-dollar

estimates from one set of individuals and risks to another set of individuals and another risk, he

argues for starting with the individual and the full complement of risks each faces. Shogren

argues it is both differences in risk preferences and differences in skill in mitigating health

hazards that matters for value-of-life estimates.

Let’s talk briefly about the bias in current estimates that Shogren identified. Current methods

calculate VSL by dividing the marginal worker’s wage differential by the statistical risk

Salmonella Deaths, 1987-1992

0

20

40

60

80

100

<1

5

15

25

35

45

55

65

75

85+

Age

Number of deaths
76 Session III: Valuing Risk Reductions Using Different Valuation Methods

differential. The latter is an average. The former is the highest wage differential among all

workers. It is the worker who demands the highest compensation for his risk in the job. It is

probably the worker who is least able to manage risk and the worker who is most concerned with

risk. So, if workers differ in skill in managing risk and differ in risk tolerance, then VSL

estimates overestimate benefits of risk reduction. Shogren’s illustrative calculation suggests a

factor of 5.

Reducing VSL estimates by a factor of 5 would have a clear impact on Federal cost-benefit

analyses. Virtually none would pass a benefit-cost test.

Shogren shows in a formal way that we need individual value-of-life estimates for each risk.

And he shows how to derive, in theory, those values. So, whether we should or shouldn’t follow

his suggestion, we have to pay attention to it.

My answer to the second question, raising practical estimation questions, is less certain.

Shogren’s analysis comes down to the following equation.

Here, he calculates value of life as an individual’s willingness to pay to avoid risk of death. He

divides individuals into classes, t. Each class is defined by ability to manage risk and risk

tolerance. The denominator has two terms. The parenthetical is the difference in risk per unit of

consumption between what the individual chooses and the lowest that is physically possible.

This is like the risk of E. coli O157:H7 infection per hamburger, depending on how well you

cook and clean. Microbiologists tell us that if we cook hamburgers to an internal temperature of

160 degrees and we use lots of chlorine bleach to reduce cross-contamination in the kitchen, we

can drive the risk of infection to zero. Clearly, these averting behaviors are choices. The risk

per unit is then multiplied by the number of units of risk, or hamburgers consumed, some

proportion of which harbor E. coli.

Empirically getting at the denominator will be difficult, but is not necessarily impossible.

Suppose that the epidemiologists and physicians come up with dose-response functions, relating

exposure to pathogens to disease, and can do so for different types of people, distinguishing

likelihood of infection by type of person (good health, elderly, child, able to control crosscontamination in the kitchen). Food consumption surveys might also tell us about xi*.

The impossible part to measure is the numerator. This is simply defined as the opportunity cost

of activities that reduce risk. Some economists argue that opportunity cost is not something that

an external observer can measure. I have copied a paragraph from James Buchanan’s book, Cost

and Choice (1969). He argues that opportunity cost is inherently unknowable.

You face a choice. You must now decide whether to read this Preface, to read

something else, to think silent thoughts, or perhaps to write a bit for yourself. The

value that you place on the most attractive of these several alternatives is the cost

* *

it it i

it

t

x

c Vol  



 −

=

ρ ρ
Session III: Valuing Risk Reductions Using Different Valuation Methods 77

that you must pay if you choose to read this Preface now. This value is and must

remain wholly speculative; it represents what you now think the other opportunity

might offer. Once you have chosen to read this Preface, any chance of realizing

the alternative and, hence, measuring its value, has vanished forever. Only at the

moment or instant of choice is cost able to modify behavior. (p. vii)

That is, opportunity cost is whatever a person thinks he is giving up by choosing a particular

course of action. The result of a choice is one course that precludes all others. Thus, the other

cannot exist. It was known to the person who did the choosing, but vanishes without a trace after

the choice is made. Until we have a way of peering into the heads of people as they are making

choices, the opportunity cost of their actions is necessarily unobservable. We certainly cannot

directly estimate the values Shogren recommends.

Question 3—if we really could estimate individual values of life, that would raise some difficult

ethical questions for economics. Suppose that we could accurately measure opportunity cost.

Then our trouble would really start. We would have different values for different people. Some

of the variation would be systematic. For instance, the opportunity cost rich people face would

likely be much greater than that faced by poorer people, that is those with fewer opportunities.

Chances are we would have relatively large values for the rich and small values for the poor.

The policy guidance offered by cost-benefit analyses would then depend on who was at risk. We

would see relatively large benefits from putting in stop signs in rich neighborhoods and relatively

small benefits in poor neighborhoods. This is a straightforward aspect of demand analysis--

quantity demanded depends on relative prices and income. In our case, willingness to pay

depends a lot on ability to pay. Some will say, “of course willingness to pay depends on ability

to pay--it is worth more to the rich to be safe.” Others will find this troubling, and will be really

disturbed by policy guidance that gives greater attention to small risks in rich communities than

to large risks in poor communities. I think economists would be better off recognizing this

minefield before we walk into it.

References

Buchanan, J.M. 1969. Cost and Choice--An Inquiry in Economic Theory.

Chicago: Markham Publishing Company.

Cropper, M., S. Aydede, and P. Portney. 1994. “Preferences for Life Saving Programs: How

the Public Discounts Time and Age,” Journal of Risk and Uncertainty 8:243-265.

Golan, E., and F. Kuchler. 1999. “Willingness to Pay for Food Safety: Costs and Benefits of

Accurate Measures,” American Journal of Agricultural Economics 81(5):1185-1191.

Viscusi, W.K. 1998. Rational Risk Policy. New York: Oxford University Press.

Viscusi, W.K. 1993. “The Value of Risks to Life and Health,” Journal of Economic Literature

31(Dec.):1912-1946.
78 Session III: Valuing Risk Reductions Using Different Valuation Methods

Viscusi, W.K., W.A. Magat, and J. Huber. 1987. “An Investigation of the Rationality of

Consumer Valuations of Multiple Health Risks.” Rand Journal of Economics 18(Winter):465-

479.
Session III: Valuing Risk Reductions Using Different Valuation Methods 79

Rapporteur’s Notes for

Valuing Risk Reductions Using Different Valuation Methods

Rapporteur: Jean C. Buzby

As we have seen throughout this conference, economists have developed several measurement

techniques for valuing risk and nonmarket goods. Our breakout session discussed the general

advantages and disadvantages of six techniques and their appropriateness for estimating the

monetary benefits of policies or actions that reduce the risks of unsafe food. Benefit estimates

from these techniques can be used as input in cost-benefit analyses for policies that reduce food

safety risks.

In particular, we discussed six measurement techniques: cost-of-illness, contingent valuation,

experimental auction markets, hedonic pricing, cost-effectiveness analysis, and health-health

analysis techniques. The first four techniques provide estimates that are measured in dollar

values, three of which (contingent valuation, experimental auction markets, and hedonic pricing)

provide willingness-to-pay (WTP) estimates. Policymakers may feel more comfortable using

benefit estimates in dollars instead of estimates of the number of lives lost or saved computed

from techniques such as health-health analysis, but this is not always the case.

Cost-of-Illness Analysis

There was little enthusiasm in our group for the cost-of-illness (COI) method. The COI method

tallies the dollars spent on medical expenses and the dollars of employment compensation that

are forgone as a result of illnesses, accidents, or premature deaths (Kuchler and Golan, 1999). 

For example, the annual costs of a specific health risk such as Salmonella in food can be

estimated by adding together all of the estimated annual medical costs and productivity losses

and other illness-specific costs, such as special education and residential-care costs, for all

patients stricken with foodborne salmonellosis in a given year. Examples of COI studies for food

safety include Buzby et al. (1996) for bacterial foodborne illness and Frenzen et al. (1999) for

foodborne salmonellosis.

One advantage of the COI method is that it measures the economic impacts of a particular illness,

and this accounting is useful to policymakers interested in the economic flows of public health

regulations. COI is also meaningful to the health industry as it is interested in data associated

with particular illnesses (e.g., the number of days of hospitalization for an average salmonellosis

patient). One disadvantage is that the COI analysis may not reveal the severity of illness. The

analysis may also be complicated by our modern system of payment for health care where prices

for medical care vary depending on how they are paid. Co-payments by ill individuals tend to be

lower than total costs billed to insurance companies. This suggests that insurance co-payments

are a far cry from the WTP notion and so the rationale that COI is a lower bound disappears (see

appendix in Kuchler and Golan, 1999).
80 Session III: Valuing Risk Reductions Using Different Valuation Methods

Contingent Valuation

The second technique that we discussed was contingent valuation (CV). CV surveys or

interviews elicit consumers' WTP for a particular nonmarket good "contingent" on a given

hypothetical scenario. There have been over 1,600 publications on contingent valuation and CV

surveys have been increasingly used to measure consumers' WTP for food safety risk reductions.

For example, CV surveys have elicited consumers’ WTP for reduced risk from toxins in shellfish

(Lin and Milon, 1995), nitrates in drinking water (Crutchfield et al., 1997), Salmonella in

chicken and eggs (Henson, 1996), and pesticide residues in food (Buzby et al., 1995 and 1998).

One advantage of the CV method is that it is grounded in the theory of welfare economics

(people will pay more if they get greater utility or welfare). Another strength is that a

hypothetical scenario can be built for any market of interest. 

On the other hand, V.K. Smith discussed how CV studies may fail to be successful in eliciting

true WTP in dealing with conditional probabilities, a concept that many consumers find difficult

to understand. This concept is very relevant to valuing the benefits of a food safety risk

reduction. For example, in describing a risk reduction for human illness from Salmonella in

eggs, the CV survey must make it clear to respondents that there is both the probability of having

an egg contaminated with Salmonella serotype Enteritidis (SE) and the probability of getting sick

given that the egg was contaminated with SE.

Because of the hypothetical nature of the CV technique and because no money is exchanged,

there are various biases that may be associated with CV estimates (see Mitchell and Carson,

1989). The hypothetical scenario in the CV surveys may not come across as relevant or realistic.

And, because health and safety are normal goods, predicted changes in WTP values can be

explained by changes in income, ceteris paribus.

Another weakness is that it may be difficult to ascertain the validity and implications of the

results. Despite the detailed guidance for implementation of the CV technique provided by the

1990 National Oceanic and Atmospheric Association (NOAA) Blue Ribbon Panel, there is no

standard which should be used to judge whether results are believable. We can however be more

confident with the results if the prescribed sequence of events such as focus groups, pretesting,

and so forth is followed. Of the techniques that we discussed, CV has the most detailed guidance

for implementation because of this NOAA panel and because of the relatively extensive use of

this technique. In general, researchers want and will use guidelines for applying a valuation

technique if the guidelines are from a reputable source or consensus.

Conjoint Analysis

At this point, we went off on a brief tangent and discussed conjoint analysis which is another

hypothetical market tool. Essentially, in the CV technique, the attributes are embedded whereas

in conjoint analysis, the attributes are distilled. Our group generally felt that this technique

should be explored further. Two advantages are that there is more design control with this
Session III: Valuing Risk Reductions Using Different Valuation Methods 81

technique and that it is cheaper in the sense that the researcher can ask more questions for a given

budget. One downside is that the resulting observations are dependent.

Experimental Market Techniques

The third technique that we discussed was the experimental market technique (or "experimental

auction technique"), where individual choices made in constructed market situations reveal

preferences for a good that usually cannot be directly purchased in the market. Food safety is a

non-market good primarily because of high information costs and/or information asymmetry. 

Contingent valuation of food safety overcomes the information problem by providing objective

assessments of health risk. Valuation of food safety in experimental markets attempts to go one

step further – eliminating the informational deficiency and placing the good in something akin to

a market situation where money actually changes hands. Examples of experimental market

studies for food safety issues include studies on reductions in pesticide risk (Roosen et al., 1997),

bovine growth hormone in milk (Fox, 1995), and a series of studies on irradiated pork by

Shogren, Fox, and others.

Valuation in the lab offers some advantages in valuing food safety risk reductions. First,

although the choice situation is artificially created, the choices are real not hypothetical and force

respondents to consider their budget constraints. Second, revelation of truthful values is

encouraged through the requirement that the winner of the auction eats one of the “risky” food

products being valued and through the use of an incentive-compatible auction mechanism. In

one common auction mechanism, the Vickrey second-price auction, the person with the highest

bid buys the good at the second highest price. This is incentive compatible because the bidders’

best strategy is to bid their true WTP value. Third, Shogren states in his conference paper that

because of the laboratory nature of this technique, researchers can replicate or repeat the

experiments to isolate, control for, and understand the ramifications of a wide range of different

auction or market settings. Fourth, Kuchler and Golan (1999) point out that as with the CV

technique, the experimental auction technique incorporates the recognition that individual

preferences are unique and that individual demands for risk reduction vary. Despite the

advantages of this technique, according to Shogren, the results from experimental markets do not

always fare better than results from contingent valuation studies, and there is clearly a great deal

more to learn.

Hedonic Price Techniques

The fourth technique that we discussed was hedonic pricing. This technique estimates the value

of each attribute of a good or service, including health-influencing attributes. The dominant

application calculates compensating wage differentials or a “risk premium” revealed in labor

markets through the higher wages employers must offer to induce workers to take riskier jobs. 

Some hedonic studies have focused on nutrition and other food-related issues such as fat content

in milk, fiber in cereal, and organic versus non-organic. 1 Shogren mentioned one study that he

 

1 Other studies have covered goods and issues such as seat belts, property values, smoke detectors, and

highway speed.
82 Session III: Valuing Risk Reductions Using Different Valuation Methods

did on the marbling, coloring, and size of pork chops.

In his conference paper, Shogren pointed out that some of the disadvantages of hedonic wagerisk studies include the presumption that workers know all the risks in the job and that they can

change jobs costlessly. Another common criticism is that these studies focus on those who have

a job and underrepresent other segments of our population such as seniors and children. One

challenge is to fully understand the market and its consumers, with only price and quantity data.

Cost-Effectiveness Analysis

The fifth technique that we discussed was cost-effectiveness analysis, which is essentially a

comparison of costs with the number of physical benefits. The ratio of dollar costs to physical

benefits is the cost per physical benefit, and the program with the lowest cost per benefit is the

most cost-effective (Kuchler and Golan, 2000). One advantage of this technique is that it uses

real cost and outcome data. There are numerous Centers for Disease Control and Prevention

(CDC) studies and studies by other health professionals that use this technique. However, to

date, it has not been applied to food safety issues.

The cost-effectiveness method is becoming closer to the traditional cost-benefit analysis because

of the trend toward monetization of quality-adjusted life-years (QALY) as we have seen in

Josephine Mauskopf's presentation. Also, note that it is a short step from choosing a number for

a QALY to having to choose a value of a statistical life for a study. On the downside, unless we

measure the benefits in QALY’s, the questions that can be answered with this method are fairly

limited, and unless we monetize health benefits, the net benefits cannot be determined. Instead,

only projects with identical outcomes can be ranked by their cost per physical benefit. This

limitation may be fine if the research stays within one narrowly defined health outcome.

Health-Health Analysis

The sixth technique that we discussed was health-health analysis. In essence, health-health

analysis evaluates policies by comparing a count of deaths prevented with a count of deaths

induced by transferring income from individuals to the government in order to finance health and

safety programs. One advantage of this technique is that it uses real markets and real tradeoffs. 

The premise is that if people pay for a policy that reduces a particular health or safety risk, their

disposable income goes down, in effect reducing the amount that they can spend to protect

themselves in other ways. This means that there could be an increase or decrease in their overall

level of safety. One food safety example is the study by Kuchler et al. (1999) on oysters.

In cases where agencies are really averse to assigning dollars to a life, this is an alternate route

that still maintains the spirit of cost-benefit analysis in that a net benefits figure can be calculated.

It also provides some insight into income-risk tradeoffs. However, this approach acknowledges

the general equilibrium nature of tradeoffs but then focuses on partial equilibrium tradeoffs.
Session III: Valuing Risk Reductions Using Different Valuation Methods 83

Where Do We Go From Here?

The selection of a valuation technique for a particular research project depends largely on the

goals of the project. And even when a particular valuation technique is selected, researchers will

tailor the approach as necessary for the project. As previously mentioned, the 1989 NOAA panel

provided guidelines and recommendations for contingent valuation. However, some of the other

techniques are more broadly defined than contingent valuation and do not have similar

guidelines. 

The consensus of the breakout group was that we would like to see a panel discuss issues and

come up with guidelines or recommendations about the different methodologies and about how

to interpret the results. These guidelines could also cover the scope, role, and implementation of

benefit-cost analysis. Perhaps a second panel could look at specific issues related to food safety

and other similar health outcomes. We would like to see guidance for food safety but in a way

that makes sense such as by identifying a class of risks. However, as a precursor to any panel,

there needs to be a great effort made in organizing what is known about the methodologies to

provide background for the panel to make their guidelines or recommendations. Also, the panel

would benefit from a list of issues that need to be addressed.

References

Buzby, Jean C., John A. Fox, Richard C. Ready, and Stephen R. Crutchfield. 1998. "Measuring

Consumer Benefits of Food Safety Risk Reductions," Journal of Agricultural and Applied

Economics, 30,1:69-82.

Buzby, Jean C., Tanya Roberts, C. -T. Jordan Lin, and James M. MacDonald. 1996. Bacterial

Foodborne Disease: Medical Costs and Productivity Losses. U.S. Dept. of Agr., Econ. Res.

Serv., AER-741.

Buzby, J.C., R.C. Ready, and J.R. Skees. 1995. "Contingent Valuation in Food Policy Analysis:

A Case Study of a Pesticide-Residue Risk Reduction," Journal of Agricultural and Applied

Economics 27,2:613-25.

Crutchfield, S.R, J. Cooper, and D. Hellerstein. 1997. Benefits of Safer Drinking Water: The

Value of Nitrate Reduction. U.S. Dept. Agr., Econ. Res. Serv., AER-752.

Department of Commerce (DOC), National Oceanic and Atmospheric Administration’s (NOAA)

Blue Ribbon Panel. Federal Register Vol. 58, No. 10. Proposed Rules, DOC/NOAA, 15 CFR

Chapter IX. Natural Resource Damage Assessments Under the Oil Pollution Act of 1990, 58 FR

4601-4614.

Fox, J.A. 1995. "Determinants of Consumer Acceptability of Bovine Somatotropin," Review of

Agricultural Economics 17:51-62.
84 Session III: Valuing Risk Reductions Using Different Valuation Methods

Frenzen, P., T. Riggs, J. Buzby, T. Breuer, T. Roberts, D. Voetsch, S. Reddy, and the FoodNet

Working Group. 1999. “Salmonella Cost Estimate Update Using FoodNet Data.” U.S. Dept. of

Agr., Econ. Res. Serv., FoodReview, 22,2:10-15.

Henson, S. 1996. “Consumer Willingness to Pay for Reductions in the Risk of Food Poisoning in

the UK,” Journal of Agricultural Economics 47,3:403-20.

Kuchler, F., and E. Golan. 1999. Assigning Values to Life: Comparing Methods for Valuing

Health Risks. U.S. Dept. Agr., Econ. Res. Serv., AER-784.

Fred Kuchler, Jackqueline L. Teague, Richard A. Williams, and Don W. Anderson. 1999.

"Health Transfers: An Application of Health-Health Analysis to Assess Food Safety

Regulations," Risk: Health, Safety, & Environment, 10,4:315-332.

Lin, J., and W. Milon. 1995. “Consumer Valuation of Health Risk on Food Demand and the

Implications for Regulation.” Chapter 5 in J.A. Caswell, ed., Valuing Food Safety and Nutrition,

Boulder: Westview Press.

Mitchell, Robert Cameron, and Richard T. Carson. 1989. Using Surveys to Value Public Goods.

Washington, DC: Resources for the Future.

Roosen, J., D.A. Hennessy, J.A. Fox, and Alan Schreiber. 1997. “Measuring Consumers’

Willingness to Pay for a Partial Banning of Pesticides on Apples.” Working Paper. Dept. of

Econ., Iowa State Univ.
Session IV: Valuing Risk Reductions for Different Hazards 85

SESSION IV

VALUING RISK REDUCTIONS FOR DIFFERENT HAZARDS

Benefits Transfer and the Value of Food Safety

Alan Krupnick

Resources for the Future

Introduction

We take it as given that cost-benefit analysis (CBA) is a useful tool for choosing among

alternative courses of action according to the criterion of social efficiency. It does this by

providing a single, money metric that under certain assumptions indexes social welfare. Other

things being the same, policies that would increase welfare as indicated by the metric would be

preferred to policies that would reduce welfare, and policies that would increase welfare more

would be preferred to policies that would increase welfare less.

The application of CBA in the food safety area is challenging for many reasons. Of most

importance here, is the fact that benefits of food safety regulations or other interventions are

primarily registered in improved health status. One can perform useful policy analyses by

comparing the costs of various interventions per unit health improvements across the set of

available interventions (a cost-effectiveness analysis), but to be able to aggregate across various

types of health effects, and to be able to say something normative about the regulations or

interventions being contemplated, requires estimating health benefits, i.e., placing monetary

values on these health improvements.

But to successfully apply CBA in the food safety area means valuing an enormous range of

possible diseases related to food safety – a task far beyond what the literature on health valuation

in a food safety context can currently support. There are only three solutions to this problem.

We can wait until the valuation literature catches up – not a very satisfying option; we can

“retreat to defensible borders,” in the sense of using medical cost information as a placeholder

and lower bound for the more appropriate “willingness to pay” measures of value; or we can use

techniques to modify the existing, non-food safety valuation literature to make it more

appropriate for use in a food safety context. This last option is called benefits transfer.

In fact, there is a reasonably robust literature for valuing the respiratory effects of air pollution

reductions, containing a significant and reasonably accepted number of studies valuing

respiratory-related symptom-days and chronic respiratory disease cases. There is also a very

large number of studies yielding values of statistical life (VSL) that, while not defining a policy

context appropriate to deaths associated with air pollution (a discussion we will take up below),

are typically used in CBA studies associated with air pollution regulation performed by the

government and others. The question for this paper is: Should and can this literature be used to
86 Session IV: Valuing Risk Reductions for Different Hazards

help value food safety outcomes? A supplementary question is: What research needs are

suggested by this analysis?

What is Benefits Transfer?

To be more formal about it, benefits transfer is the application of valuation results from a study

performed for one policy context to another policy context (see Desvousges, et al., 1992, for a

complete discussion in the context of recreation demand; see AERE, 1992, for many papers on

this topic).

There are two types of results that can be used for benefits transfer—unit values and valuation

functions. The first is more easily and more widely used. It involves the application of a value,

say in terms of the willingness to pay (WTP) for a reduction in a health “unit,” such as a

symptom-day of a particular kind, being applied, after any needed adjustments, to

epidemiological estimates of symptom-day reductions as a result of a food safety intervention.

The second approach allows the analyst to adjust the values for the policy context at the cost of

placing a greater burden on the food safety analyst. There is an ad hoc approach to do this and

one relying on statistical results. The ad hoc approach involves developing from information in

the literature or the analysts’ own judgment, various factors to adjust the WTP estimates from the

study context. The statistical approach involves, first, finding an original study where statistical

analyses explain variation in the WTP for, in this case, avoiding a symptom-day using a set of

explanatory variables, such as severity of the symptom, income, sex, health status, etc. The

outcome is a regression equation describing the contribution of each factor to WTP. The food

safety analyst then takes the resulting regression equation and substitutes in the values for the

factors in the regression that are appropriate to the food safety case. For example, suppose the

regression shows that every year of life over age 40 results in a $1 reduction in the WTP to avoid

a symptom-day. If the age of those affected by a food safety intervention averages 75 years, then

this factor would drop WTP estimated from the other factors by $35.

Why Use Benefits Transfer?

Of course, if simple adjustments like those for age and income were all it took to perform a

credible benefit transfer, they would be used much more frequently and with much less

controversy. After all, a benefits transfer is far cheaper than performing an original research

study. For instance, the cost of an in-person contingent valuation survey is at least $100 per

completed survey, with a sample of about 1,000 fairly standard. Add to this the cost of

developing the survey and analyzing it, and such a study, on one or at most a few health

endpoints, could cost over $200,000. Moreover, a benefits transfer from an established set of

unit values or functions carries with it instant professional and institutional legitimacy. The unit

values appearing in EPA’s recent study of the Costs and Benefits of the Clean Air Act

Amendments of 1990 (USEPA, 1999), for instance, have been reviewed by a special panel of

economists and undergone review both inside and outside the Agency.
Session IV: Valuing Risk Reductions for Different Hazards 87

Unfortunately, the needed adjustments are not simple. The food safety context differs markedly

from the air pollution context in several areas, some of which have so far not been very well

addressed by researchers.

Available Unit Values

Above, we noted that the health unit values for the air pollution context are drawn from the most

robust literature. Table 1 provides a small sample of the midpoint values typically used by

practitioners of health benefits analyses in the air quality area, as well as ranges of these values.

We picked the unit values for health endpoints chosen by four major studies or models in the

United States, Canada, and Europe, ordered from highest to lowest based on the first of these

studies--the U.S. study on the Costs and Benefits of the 1990 Clean Air Act Amendments

(USEPA, 1999) --and put them in common currency and constant dollars.

The willingness to pay for reducing risks of mortality and chronic morbidity is expressed, for

convenience, in terms of the value of a statistical life (VSL) and the value of a statistical case of chronic

disease (VSC).1

 The table shows quite close agreement on the size of the best or midpoint VSL’s and

VSC’s. The differences that do exist may be explained partly by currency conversions and partly by

researchers’ not always adjusting such values over time for inflation. Also, the rank ordering of values is

found to be very similar across the studies, although not every study considers the same set of health

endpoints. The low VSL’s for the Tracking and Analysis Framework (TAF) (Bloyd, et al., 1996, which

came from Lee et al., 1995) and the Air Quality Valuation Model (AQVM) (Stratus Consulting, 1999)

result from adjustments to the VSL for the older age of those affected by air pollution relative to those

included in the studies underlying the VSL estimates. ExternE (ExternE, 1999, originally from ExternE,

1996) makes a similar adjustment by converting the VSL to a value of a life-year for subsequent

analysis. In other analyses, EPA (USEPA, 1999) and TAF have done the same thing.2 These efforts

have yielded values ranging from $50,000 to $300,000 per life year.3

In our judgment, this close agreement among the studies is the result of several factors, including

replicability of findings in original studies in different locations (i.e., independent choices made

 

1 It is important to note that these terms are merely a shorthand for the WTP for a given risk reduction

divided by that risk reduction. This relationship is convenient because the VSL’s or VSC’s can be

multiplied by estimates of the "lives saved" or "chronic cases saved" to obtain benefits.

2 Other adjustments to VSL’s have been made (or suggested) for latency (ExternE, 1999), for health status

(basically the “harvesting” issue) (UKDH, 1999) and for a range of attributes, such as dread and

voluntariness (USEPA, 2000).

3 The ranges around these estimates are all somewhat different, seemingly without pattern. This result

perhaps could be expected since there is no treatment of uncertainty that is universally accepted. The

EPA mortality results are based on one standard deviation from the distribution (the Weibull) that best fits

the mean WTP estimates from 26 studies. The Canada results are based on a representation of uncertainty

as a three-point probability distribution, which includes expert judgment. The TAF distributions are

Monte Carlo-based, assuming, unless otherwise indicated by the original studies, that errors about mean

estimates are normally distributed, with variances given in the concentration-response and valuation

studies relied upon for the underlying estimates. Bounds are defined as 5th and 95th percentile. Error

bounds in the latest ExternE report (1999) are established as one-half (low) and twice (high) the

geometric mean.
88 Session IV: Valuing Risk Reductions for Different Hazards

by different research teams), and the consensus reached by research teams on a common pool of

studies, results, and interpretations. We believe that the social cost of electricity studies in the

United States and the ExternE effort in Europe have something to do with this commonality. In

addition, the Canadian studies have been informed by the AQVM model developed by Hagler

Bailly (now Stratus Consulting) and others who have been active participants in the U.S. debate

over the social cost of electricity as well (Hagler Bailly, 1995). Many U.S. studies pre-date and

presage these efforts.

It is worth noting that the endpoints being valued are not all comparable to one another. The unit

values for mortality risk, chronic lung disease risk, and acute symptoms all are derived from a

willingness-to-pay approach that may be thought of as capturing, however imperfectly, the full

value to the individual of reducing the risk or the symptom. The other values are only partial,

mainly relying on cost of illness techniques. They are meant to capture the more severe

manifestations of either acute events or chronic states and may, without proper adjustments,

double-count WTP benefits or provide significant underestimates of the WTP to reduce such

effects. Indeed, it is fairly common practice to adjust such cost of illness (COI) estimates by a

factor to bring them up to a WTP estimate, so as to eliminate such underestimation. AQVM, for

instance, recommends using a factor of 2-3 to make this adjustment. The evidentiary basis for

the generality of this adjustment across endpoints is weak.

Example of Ad Hoc Adjustment of Unit Values

A recent attempt to adjust the VSL used by EPA in its cost-benefit analyses for the context of

death from cancer (Revesz, 1999) is illustrative of an ad hoc approach to benefits transfer and the

critique it received by the EPA’s own Science Advisory Board is illustrative of its problems.

Revesz sought to develop a VSL for cancer for use in addressing alternative policies to control

emissions of carcinogenic pollutants. He proposed to adjust the standard VSL used by EPA

($4.8 million in 1996 dollars, based on hedonic wage studies) for involuntariness of exposure,

the lack of controllability of that exposure, and for the dread cancer causes in those facing it.

Relying on some studies in the literature, Revesz suggested that the first two factors would lead

to a doubling of the VSL, while the third would lead to another doubling.

The EPS’s Science Advisory Board (2000) carefully reviewed the paper and concluded that the

literature relied upon was not robust enough to justify adjusting the standard VSL.

Elements of the Policy Context

It is helpful to develop a taxonomy of the elements of the policy context. If a function were

available that related each of these elements to WTP, then one could do a confident benefits

transfer. Even though such a function is not available, we can use the taxonomy to compare the

study context to the policy context to make judgments about how credible a benefits transfer

would be.

There is no universally accepted list of all the elements of a policy context one needs to address

for a conceptually complete benefits transfer. Table 2 gives one such list, listing four elements

and various features for each element. This list does not attempt to distinguish what elements or
Session IV: Valuing Risk Reductions for Different Hazards 89

features should affect values, only those that have been shown or speculated to affect values.

The nature of the risk refers to whether the risk refers to death or morbidity (either chronic or

acute), the features of morbidity that might be relevant, and the qualities of both mortality and

morbidity that might be relevant, such as dread and controllability (Slovic, 1987). While it might

seem strange that morbidity and mortality are valued separately, in fact the health valuation

literature is bifurcated in this way, probably more due to the difficulties of obtaining values on

this more complex combined commodity than any other reason.

Referring back to table 2, the risk change element has three features. One is baseline risk, which

refers to the risk of death or morbidity faced by the individual in the absence of a policy

intervention. The timing refers to whether the intervention will have an immediate effect on risk

or a latent effect. Size is the size of the risk change.

The only other element that needs clarification is “Other.” Whether the good is provided

privately, through markets or through an individual’s actions, or publicly, say through the

government to an entire class of individuals, may affect valuations directly. In addition,

particularly in the public good setting, individuals may hold values for health improvements to

other individuals, i.e., they may be altruistic. Whether altruistic WTP should be counted in

addition to individual WTP depends on the type of altruism individuals hold. Paternalistic

altruism is defined as one person’s caring about another’s health, to the exclusion of caring about

other facets of the person’s life, facets that might be affected by their wealth. In this case, there

is reasonable agreement that the altruistic portion of WTP can be counted. If the altruism is nonpaternalistic, however, in the sense that one individual cares about another's utility, then the cost

of the policy to the other person should enter into the altruistic person’s WTP. In this case,

altruism would not be counted.

Avoidance behavior means the ability of at-risk individuals to reduce their health risks. A health

intervention may reduce the need for avoidance behavior taken by an individual, without

necessarily changing health risks. While this benefit is not strictly speaking a health benefit, it

should be counted in a benefits analysis, and individuals would count such improvements as a

benefit of an intervention. Looked at in another way, individuals may not be willing to pay

much for an intervention that would reduce their chance of a headache where taking an aspirin

(the averting behavior) costs 2 cents and it works immediately and without side effects. As a

food safety example, WTP to reduce the risk of health effects from an outbreak of giardiasis

would be limited by the cost of avoiding the contaminated water. Where avoidance behavior

options are limited, this factor would not have to (or need to) be taken into account.

Finally, some researchers have speculated that the willingness to pay to reduce risks may be

affected by the agent causing the health effect, e.g., that the WTP to reduce risks of a named

disease (e.g., listeriosis) may be different than the WTP for avoiding a disease solely described

by its symptoms. This effect may overlap with the disease quality feature discussed above.

Mortality Risk Valuation and Benefits Transfer

Table 3 provides the elements of the study context for the main literature used to value mortality

risk reductions in governmental environmental cost-benefit analyses and compares them to the

elements for the policy context for air pollution and health.
90 Session IV: Valuing Risk Reductions for Different Hazards

We have identified five approaches to estimating preferences for reducing mortality risks and

expressing these preferences in monetary terms: the human capital approach, various revealed

preference approaches (most importantly the hedonic labor market approach, but also the

consumer products approach), and stated preference techniques that address health and those that

do not.

The human capital approach is not a WTP measure. It seeks to estimate the productivity lost when a

person dies prematurely. This approach is utilitized as part of the cost-of-illness methodology employed

by ERS, for instance (Buzby et al., 1996). Without special adjustments, it ignores losses to society of

nonworkers.

The hedonic labor market studies statistically relate wage differentials to mortality or morbidity risk

differences across occupations and industrial/commercial sectors, under the theory that in competitive

labor markets, workers in risky jobs should receive wage premiums equal to the value they place on

avoiding increased mortality or morbidity risks. One study asks workers their perception of the death

risks they face in order to address the issue of whether their behavior would be consistent with perceived

risks rather than actual risks and that these two types of risks might diverge. These studies are numerous

and form the foundation for most VSL estimates.

The stated preference approaches, of which contingent valuation and conjoint analysis are the two most

prominent, are survey approaches that set up choice situations. These methods ask individuals to chose

among various hypothetical choices. For instance, they ask whether individuals are willing to pay some

amount, or to vote yes on a referendum, or to prefer one package of attributes over another, in order to

acquire reductions in mortality risk. The ability of conjoint analysis to recover preferences is a matter of

debate. Also, both of these approaches may suffer from a variety of their own biases, and their results

have been shown to be very sensitive to question wording and ordering. They are capable of being

molded to whatever population and context are appropriate, however. And respondents can be tested for

their cognition and understanding of the issues being examined in the survey (see Hammitt (2000) for a

detailed discussion of the CV-mortality risk valuation literature). Some of the best known CV studies

for mortality risks (Jones-Lee et al., 1985; Hammitt and Graham, 1999) look at traffic fatalities.

The comparison between the study and air pollution policy context reveals that there is already a

benefits transfer underlying valuation of mortality in the air pollution context and that there is a

serious disconnect between the study context and this policy context. The main body of literature

is the hedonic labor market studies and contingent valuation studies based on accidental fatality

risk in mainly a transportation context. This literature focuses primarily on prime age, healthy

individuals (mostly men) and involves “private” labor market or transportation choices, while the

air pollution context involves a very different at-risk population and affects more the nature of a

public good. The sizes of the risk reductions match fairly well, although some air pollutants may

have latent effects on health, which the literature has not captured.

Is a benefits transfer from this literature to the food safety context any more credible than its use

in an air pollution context? To answer this question, we first must define the "food safety

context" For the purpose of this paper, we focus simply on the top four foodborne diseases
Session IV: Valuing Risk Reductions for Different Hazards 91

identified by the ERS/USDA/CDC: salmonellosis, campylobacteriosis, Escherichia coli diseases,

and listeriosis. Box 1 provides summarizes key features of these diseases.

Turning to the last column of table 3 for food safety, we find that the diseases have a number of

characteristics that are distinct from one another, making them difficult to classify according to

table 2’s taxonomy. Nevertheless, they do have in common that the unconditional mortality risks

are very small, on the order of 1 in 100,000 or lower. And, they have conditional death risks that

are considerably larger than those in the study context—anywhere from 1 in 1,000 to 1 in 5

annually. The latter is for listeriosis—a disease hard to contract but remarkably easy to die from.

As estimated VSL’s appear to be very sensitive to the size of the risk change (larger for smaller

risk changes) and communicating conditional risk continues to be problematic in the literature,

these differences are serious.

In addition, as in the air pollution context, but unlike the study context, effects are concentrated

on the ill. Somewhat unique for the food safety context is that children, particularly very young

children, are a significant fraction of the population at risk and that averting behavior after an

outbreak is announced is a significant component of the behavioral response to risk. The upshot

of this comparison is that benefits transfers for mortality valuation are probably more

problematic for the food safety context than for the air pollution context.

Theory and Empirical Evidence for VSL

How important are the distinctions being made in the above section? If the differences in context do not

amount to much empirically, perhaps such differences can be ignored. In fact, some differences matter

more than others.

Table 4 (based partly on Hammitt, 1999) attempts to summarize both the theoretical and empirical

literature to answer this question. Neoclassical welfare economics, in particular the life-cycle utility

model, lies at the heart of the theoretical modeling. Its predictions about the effects of various factors on

WTP are provided in the first row of the table. WTP should clearly increase with the size of the risk

change; indeed, subject to some minor caveats, the life cycle model predicts a proportional relationship.

This implies that the VSL would be constant for any risk change. The model also implies that the

further in time any risk change begins, the lower WTP should be. The effect on WTP of baseline risk

was studied by Pratt and Zeckhauser (1996), who show that those facing higher baseline risks should be

willing to pay more for a given risk reduction (the “dead anyway” effect). Higher incomes or wealth

should be related to higher WTP. The age effect varies depending on whether the individual can borrow

against future earnings. With borrowing, the predicted relationship is an inverted U-shape, peaking,

according to these studies, at around 40. Finally, the models do not make a prediction on health status.

These predictions have not always been matched by the model results. Table 4 provides some

information on findings of empirical studies and rates the robustness of the results, ranging from “high,”

which means there is a body of literature in basic agreement, to “low,” which means either a very thin

literature, and/or a new literature, or a literature in disagreement.

Beginning with responsiveness to risk change, a perfectly responsive finding, where WTP moves

proportionally with the size of the change, would imply a constant VSL. Rarely have most studies even
92 Session IV: Valuing Risk Reductions for Different Hazards

tested for sensitivity of the WTP for different risk changes provided to separate samples (the external

scope test), let alone passed them. The recent CV study by Krupnick et al (2000) passes this test but

fails the more stringent proportionality test, showing less than proportional responsiveness, and therefore

a VSL that is higher for small risk changes. It is worth noting that the VSL’s ranged from $1-$3 million

(1999 dollars), still far lower than EPA’s choice of $6 million, drawn from labor market studies.

The same study (as noted above) is the only one to test for the WTP for future risk reductions in a

survey also testing the WTP for contemporaneous reductions. This study found the former—for average

futurity of 15 years, and with an average perceived probability of making it to 70 (when the risk

reduction would begin) of about 75 percent—to be about 40 percent of the latter.

The empirical studies have all found income effects as expected but have had difficulty separating

baseline risk from age, because the two move together. Also, all studies have had very limited

participation of older individuals. An exception is Krupnick et al. (2000) which had one-third of their

sample over 60 years old (up to 75), finding no statistical difference in VSL across ages until age 70 and

above, the latter VSL being lower than that for the 40-year-olds by a fraction similar to that of Jones-Lee

at al. (1985) (about 33 percent) but at a much lower initial VSL. Indeed the VSL for those over 70 was

only about $650,000 for a 5-in-10,000 annual risk change. Finally, only the Krupnick et al. study

explicitly addresses the effect of health status on WTP, finding no effect of health status on WTP, except

for those with cancer, who were WTP about 45 percent more to reduce their annual risks by 5 in 10,000

than their counterparts with the same characteristics who did not have cancer.
Session IV: Valuing Risk Reductions for Different Hazards 93

Summary of Four Foodborne Bacterial Diseases

The largest rate of food poisonings is from Salmonella.

1

 Its symptoms range from mild

abdominal discomfort to dehydration and vomiting and can cause secondary chronic illnesses,

such as septicemia, which can lead to death. The most vulnerable populations are the very

young (below age 5), the old (over age 70) and the immunocompromised. The incidence rate

has been estimated to be between 1.7 percent and 8.3 percent, with 2 percent of cases resulting

in hospitalization and death rates around 6 percent of this or about 1 in 10,000 in the

population. Salmonella contamination occurs in undercooked poultry, raw eggs and beef, and

other dairy products, such as ice cream, and even fruits and vegetables. Avoidance options are

easily available in some cases, but not in others. Cost-of-illness estimates, which include

medical costs and productivity losses (including losses of human capital from premature

death), range from $0.60-$3.5 billion annually.

Campylobacter infections occur through the consumption of poultry and pork, as well as clams,

raw milk, and untreated water. The symptoms are mostly diarrhea and at times very strong

abdominal pain, and can last from a day to weeks—the latter in about 20 percent of the cases.

Chronic conditions can follow in from 2-10 percent of cases, including meningitis,

inflammation of the gall bladder, and appendicitis. The very young and people in their

twenties seem to have the most cases. Case rates are about 1 percent, with 0.6 percent of these

hospitalized and with 2-6 percent of these resulting in death, or about 1 to 4 in a million in the

population. The costs of foodborne campylobacteriosis are $0.6 to $1 billion annually, using

the cost-of-illness approach. Improved food handling could reduce cases, particularly in

supermarkets.

Escherichia coli infections can be caused by a variety of foods, some cross-contaminated by

bovine products. The bacteria can produce symptoms from mild diarrhea to death; some

infections can cause hemorrhagic colitis, and some of these cases can result in hemolytic

uremic syndrome (HUS), which can lead to death through kidney failure and other

complications. There are a small number of cases throughout the U.S., only a rate of 0.004-

0.008 percent. Of these, 18 percent are hospitalized, 20 percent of these turn into HUS, and 33-

40 percent of these die, or from 5 per 100,000 to 1 in 10,000. The very young are most

susceptible, followed by those over age 60. Cost estimates range from $0.2-$0.6 billion

Infection from Listeria monocytogenes (listeriosis) has a “bimodal distribution of severity, with

most cases either mild or severe.” Even the “mild” cases are characterized by fever, severe

headache, and vomiting. Severe cases, which are experienced in utero, by infants, and some

immunocompromised or pregnant adults can involve delirium, coma, and death. It survives

refrigeration and can be present in high doses on underheated hot dogs, cheese, and pate. The

reported number of cases is tiny, only 1,800 in 250 million. But all are hospitalized and of

these about a quarter die, or 2 per million. The severity of this disease pushes costs to $0.2-

$0.3 billion annually.

1

 All the information in this box is taken from Buzby et al. (1996).
94 Session IV: Valuing Risk Reductions for Different Hazards

Morbidity Valuation and Benefits Transfer

Table 5 takes the same format as table 3, but describes the study, air pollution, and food safety contexts

for morbidity. The key studies here vary for whether the morbidity is chronic or acute. In general, the

cost of illness approach breaks down the consequences of illness into its component parts and attempts

to place values on each part. The ideal WTP measures would capture all the medical costs, pain and

suffering, time loss, productivity loss and fear of an illness (see Harrington and Portney (1987) for the

basic model). This approach has a welfare theoretic basis but is basically a stop-gap to use when other

approaches fail. For instance, Hartunian (1985) uses a model of the progress of cancer and (separately)

of respiratory disease to estimate the medical costs of these diseases over one’s lifetime. Cropper and

Krupnick (2000) have estimated the consequences of various chronic diseases on wages and labor force

participation. Ultimately, such measures founder on their inability to capture the pain and suffering that

are likely to arise from chronic illness. The approach has fared better with acute illness.

Considering the valuation of chronic illness, conjoint analysis has been used by two studies to value the

WTP to reduce risks of developing chronic respiratory disease, and several studies have addressed the

WTP to reduce cancer morbidity risks. Viscusi, Magat, and Huber (1991) and Krupnick and Cropper

(1992) used conjoint analysis to examine the WTP to reduce the risks of chronic respiratory disease.

Considering acute health valuation, three contingent-valuation studies (Loehman et al., 1979; Tolley et

al., 1986; Dickie et al., 1987) are the original studies of this type. They used bidding procedures to elicit

estimated values for respiratory-symptom days, with average estimates ranging from $5 to $25,

depending on the symptom, its severity, and whether a complex of symptoms is experienced.

All those studies have drawbacks, related mainly to their methodology--the CV studies were performed

before many of the most important advances in CV techniques. But they offer consistent ranges of

estimates for WTP to avoid a particular type of symptom.

Differences in the study and air pollution contexts are not very large because many studies have

been designed with the air pollution context in mind. However, differences between these and

the food safety context are more pronounced than those for mortality risks. Many foodborne

illnesses have strange and serious symptoms, making reliance on studies of respiratory disease

for benefits transfer questionable. In addition, some of these symptoms may be unfamiliar and

dreaded, adding additional dimensions to the WTP. Furthermore, baseline risks are different.

The foodborne disease risks may be in the 1 per 100 range—very large compared to the chronic

disease in the air pollution context and smaller than respiratory symptom probabilities. And

again, the ill and very young are at risk, and avoidance behavior is perhaps a major element of

the behavioral response.

Conclusions and Recommendations

In comparing the study and policy contexts for valuing food safety, the clear conclusion emerges

that reliable health values for food safety are not going to be found in the health valuation

literature used in air pollution benefit-cost analyses. That literature has its own problems of

credibility and reliability. But, more important, at least in the case of valuing mortality risks of

air pollution, there is a serious disconnect between the study and policy contexts. While far from
Session IV: Valuing Risk Reductions for Different Hazards 95

a consensus has been reached, agreement is growing that there should not be one VSL for use in

all cases. Where different groups are affected in different contexts, whether they differ by age,

sex, race, health status, or other characteristics, it is their WTP that matters for valuation. A

priori, there is no reason that each of these groups should hold the same willingness to pay for an

equivalent risk, although Krupnick et al.’s findings on the lack of sensitivity of WTP to age

(below 70) and physical health suggest that not all these characteristics may matter. In the

context of food safety, where some diseases affect children, those with AIDS, and the elderly,

these considerations suggest not only that benefit transfers would be unreliable but that VSL’s

will differ in different food safety contexts.

Moreover, as empirical evidence mounts that WTP changes less than proportionally with risk

reductions (this in spite of the conceptual support for proportionality from the lifecycle model

literature), the possibility grows that different VSL’s would be used for different size risk

reductions experienced by the same groups. Again, in the food safety context, risk reductions

are either relatively smaller or relatively larger than the air pollution or labor market contexts,

suggesting, again, that transfers are unlikely to be reliable and that more than one VSL may be

appropriate, depending on the food safety context.

For morbidity valuation, the case against useful benefit transfers is clearer still. The symptoms

associated with the major foodborne illnesses are specialized and varied far beyond the simple

respiratory effects or diarrheal effects that have been valued in the health literature. Also, the

high prevalence of foodborne diarrheal disease among children means that WTP estimates

appropriate for this group are necessary; however, the state of the literature for deriving these

values is embryonic.

The food safety context also has some unique elements affecting valuation relative to that of the

air pollution context. Most important, for many foodborne illnesses there is a pre-outbreak

period where damage is being done before people are aware of the cause and the need for

averting action. This is followed by a post-outbreak period where averting activities can be

taken. Values are needed that are associated with both periods, because WTP in the second

period is conditioned on the cost of averting behavior while WTP for the first period is not.

Implications

If the conclusions of this paper are accepted, then, aside from the thin literature directly

addressing valuation of foodborne health effects, there is not much of a literature available to use

in estimating the benefits of food safety policy. What should the agencies with regulatory

responsibilities in this area do in the mean time?

The first option is to do what the ERS in particular has been doing—use cost-of-illness estimates

instead of WTP estimates. This may be viewed as a “retreat to defensible borders” because the

COI approach has a long history in food safety, is transparent, and can be thought of as a lower

bound on WTP. Another option is to use cost-effectiveness analysis rather than attempting to

value health effects. Here the key decision is the health measure. Where one type of physical

effect dominates, a measure of the reduction in this effect could be used—something like a

health index measure. However, if the analysis is meant to compare cost-effectiveness measures
96 Session IV: Valuing Risk Reductions for Different Hazards

across diseases, there probably will not be a single “index” symptom to use. This obvious

problem has led to the creation of various indexes for physical symptoms—the Quality of Well

Being scale (Torrance), the Quality-Adjusted Life Year (QALY) (Neumann et al., 1997), and

others.

This paper is not the place for an examination of the problems of these various measures as

compared with one another and as compared with WTP measures. Suffice it so say that the

assumptions needed for their equivalency as measures of utility in alternative health states are

many, the empirical literature testing these assumptions shows that they are unrealistic, and the

empirical literature shows a fairly consistent lack of association between these measures

(O’Brien, 2000) within the same sample of individuals.

A third option is for the longer run and requires a sustained research effort: develop a literature

based on food safety valuation and suitable to be used in benefit transfer exercises across the

myriad food safety contexts. The idea would be to develop functional relationships applicable

to a wide variety of food safety contexts. Such an approach would be less expensive than

developing a catalogue of values for different risks and different populations, as suggested by

Kuchler and Golan (1999), although it would be less accurate as well.

Beyond this broad agenda for research, our discussion of the food safety context brings out some

more specific research priorities. These include:

• WTP applicable to children and household WTP. Research is beginning on building

conceptual and testing empirical models appropriate to the valuation of children’s health

changes. None of the conventional approaches to valuation are applicable because (very

young) children are not capable of valuing their own health in hypothetical situations and are

not in situations to reveal it. Parent WTP is obviously a key element here. The prominence

of children as a sensitive group to foodborne disease makes such research imperative.

Equally important is research on the WTP of the household as a unit. Altruism within the

household is a fact of human existence and economic decisions are often made as a

household unit. Most valuation literature looks at individual WTP and tries hard to eliminate

considerations of family. In the air pollution context, where averting behavior opportunities

are minimal and contagion is not a serious issue, the individual valuation paradigm may be

sensible. But for food safety, the above concerns loom far larger.

• WTP for combined improvements in mortality risk and morbidity. The health valuation

literature is almost entirely bifurcated into studies of the WTP for mortality risks and the

WTP for reductions in morbidity. This split is probably not defensible for the air pollution

context any more than it is for food safety. Yet the problems in valuing lifetime, integrated

health risk changes are so formidable that progress will be slow.

• Improving communication about small probabilities/conditional probabilities. The mortality

risk valuation literature continues to struggle with how to communicate small probabilities

effectively and to obtain sensitivity to scope. A smaller literature (Smith and Desvousges,

1987) has also tried to communicate conditional probabilities. In the food safety context,
Session IV: Valuing Risk Reductions for Different Hazards 97

where unconditional probabilities are very small (1 in 100,000 or less) but conditional

probabilities can be startlingly large (1 in 5 for listeriosis!), progress will be needed.

• Altruism and WTP. The conceptual literature is reasonably in agreement about the

conditions under which altruism (outside the family) can be counted and alternatively, results

in double-counting. What is not known is the prevalence of these conditions and when one

set or the other might apply. There is also only the smallest literature on how large altruistic

values might be and no information on such values in a food safety context, where private

actions can do so much to limit exposure that people may not hold significant altruistic

values.

• Qualitative risk attributes and WTP. The ERS and CDC funded projects recently to examine

whether attributes, such as the cause of the health effect (bacteria vs. virus vs. something

else), the type of carrier (e.g., foodborne), the name of the disease, and dread, voluntariness,

controllability, etc., have any independent effect on WTP for reduced health risks. More of

this type of research will be needed if initial results show such sensitivity. Perhaps the

results might be appropriately transferred to the air quality valuation literature, where

virtually no information of this nature is available!

References

AERE. 1992. “Benefit Transfer: Procedures, Problems, and Research Needs,” Proceedings of a

1992 Association of Environmental and Resource Economists Workshop, Snowbird, Utah, June

3-5.

Bloyd, Cary, et al. 1996. Tracking and Analysis Framework (TAF) Model Documentation and

User’s Guide, ANL/DIS/TM-36, Argonne National Laboratory, December.

Buzby, Jean C., Tanya Roberts, C.-T. Jordan Lin, and James M. MacDonald. 1996. Bacterial

Foodborne Disease: Medical Costs and Productivity Losses, Economic Research Service, U.S.

Department of agriculture, AER-741.

Cropper, Maureen L., and Alan J. Krupnick. 2000. "The Social Costs of Chronic Heart and

Lung Disease," in Valuing Environmental Benefits: Selected Essays of Maureen Cropper,

Maureen Cropper, ed. (Edward Elgar; United Kingdom). See also, Resources for the Future

Discussion Paper QE89-16-REV (Washington, DC: Resources for the Future).

Desvousges, et al. 1992 “Benefit Transfer: Conceptual Problems in Estimating Water Quality

Benefits Using Existing Studies,” Water Resources Research 28 (3) 675-683.

Dickie, M., et al. 1987. "Reconciling Averting Behavior and Contingent Valuation Benefit

Estimates of Reducing Symptoms of Ozone Exposure (draft)," in Improving Accuracy and

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ExternE. 1999. Externalities of Energy, Volume 7: Methodology 1998 Update. Brussels:

European Commission.
98 Session IV: Valuing Risk Reductions for Different Hazards

ExternE. 1996. Externalities of Energy, prepared by ETSU, UK and Metroeconomica, LTD. UK

for European Commission, DG XII EUR 16521 EN.

Hagler Bailly Consulting, Inc. 1995. The New York State Externalities Cost Study. Oceana

Publications, Dobbs Ferry, NY.

Hammitt, James K. 2000. “Valuing Mortality Risk: Theory and Practice.” Environmental

Science and Technology 34(8) 1396-1400.

Hammitt, James K., and John D. Graham. 1999. “Willingness to Pay for Health Protection:

Inadequate Sensitivity to Probability,” Journal of Risk and Uncertainty 18(1, April): 33-62.

Harrington, W. and P. Portney. 1987. “Valuing the Benefits of Health and Safety Regulations,”

Journal of Urban Economics 22, 101-112

Hartunian, Nelson S., and others. 1981. The incidence and economic costs of major health

impairments : a comparative analysis of cancer, motor vehicle injuries, coronary heart disease,

and stroke. Lexington, MA: Heath.

Jones-Lee, M. W., M. Hammerton, and P. R. Philips. 1985. "The Value of Safety: Results of a

National Sample Survey," The Economic Journal 95, 49-72.

Krupnick, Alan J., Maureen Cropper, Anna Alberini, Nathalie Simon, Bernie O’Brien, Ronald

Goeree, and Martin Heintzleman. 2000. “Age, Health and the Willingness to Pay for Mortality

Risk Reductions: A Contingent Valuation Survey of Ontario Residents.” Resources for the

Future Discussion Paper 00-37, Washington, DC.

Krupnick, Alan J., and Maureen Cropper. 1992. "The Effect of Information on Health Risk

Valuation," Journal of Risk and Uncertainty, 5, 29-48.

Kuchler, Fred, and Elise Golan. 1999. Assigning Values to Life: Comparing Methods for

Valuing Health Risks. Food and Rural Economics Division, Economic Research Service, U.S.

Department of Agriculture, AER-784.

Lee et al. 1995. Estimating Externalities of Electricity Fuel Cycles. Oak Ridge National

Laboratories/Resources for the Future, 1994-1995, seven volumes, McGraw-Hill/Utility Data

Institute, Washington, DC.

Loehman, E. T., et al. 1979. "Distributional Analysis of Regional Benefits and Cost of Air

Quality Control," Journal of Environmental and Economic Management 6, 222-243.

Neumann, P., D. Zinner, et al. 1997. “Are Methods for Estimating QALYs in Cost-Effectiveness

Analyses Improving?” Medical Decision Making 17(4): 402-408.
Session IV: Valuing Risk Reductions for Different Hazards 99

O’Brien, Bernie. 2000. “Incorporating Quality of Life into Economic Evaluation: Cost-Utility

Analysis,” HRM-737. Power Point Presentation, MacMaster University, Ontario.

Pratt, J.W., and R.J. Zeckhauser. 1996. “Willingness to Pay and the Distribution of Risk and

Wealth,” Journal of Political Economy 104, 747-763.

Revesz, Richard. 1999. “Environmental Regulation, Cost-Benefit Analysis and the Discounting

of Human Lives,” Columbia Law Review 99(4) 941-1017.

Science Advisory Board, 2000. Personal Communication from Maureen Cropper, SAB

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Slovic, Paul. 1987. “Perception of Risk,” Science 236, 280-285.

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U.S. Environmental Protection Agency. 1999. The Benefits and Costs of the Clean Air Act 1990

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Scientific Advisory Board Draft.

Viscusi, W. K., W. A. Magat, and J. Huber. 1991. "Pricing Environmental Health Risks: Survey

Assessments of Risk-Risk and Risk-Dollar Trade-Offs for Chronic Bronchitis," Journal of

Environmental Economics and Management, 21(1) (July).
unit values used in several major studies or models

US

EPA1

US

TAF2

Canada

AQVM3

Europe

ExternE4

Low Central High Low Central High Low Central High Central

1990 dollars

1560000 4800000 8040000 1584000 3100000 6148000 1680000 2870000 5740000 3031000

- 260000 - 59400 260000 523100 122500 186200 325500 102700

ns - 9500 - - 9300 - 2940 5880 8820 7696

- 6900 - - 6647 - 2310 4620 6860 7696

144 194 269 - 188 - 203 399 602 218

- 83 - - - - - - - -

13 45 77 - - - - - - -

16 38 61 - 54 - 26 51 77 73

5 15 33 - 12 - 5 11 15 7

0 5.3 10.60 - - - - - - 7

12 32 54 - 33 - 12 32 53 36

- - - - 45 - 105 217 322 -

f the Clean Air Act Amendments of 1990. Low and high estimates are estimated to be 1 standard deviation below and above the -mean

 for mortality. For other health outcomes they are the minimums and maximums of a judgmental uniform distribution. 

amework, developed by a consortium of U.S. institutions, including RFF. Low and high estimates are the 5% and 95% tails of the

del Documentation, Stratus Consulting for Health Canada. Low, central, and high estimates are given respective probabilities of 33%,

certainty bounds are set by dividing (low) and multiplying (high) the mean by the geometric standard deviation (2).
f the Policy Context

Features

Mortality/morbidity

For morbidity: Symptom type Severity Frequency JointnessDisease qualities Dread Controllability Etc.

Baseline

TimingSizeHealth statusAgeIncomeEducationRace

Public vs. private good

AltruismAvoidance possibilitiesCausal agent
y Valuation and Benefit Transfers

Primary studies

(hedonic wage/CV accident)

Conventional air pollution

context

Food safety context

Familiar, voluntary (?) Familiar, involuntary Varies

Contemporaneous Contemporaneous and future Contemporaneous and future

X in 10,000 X in 10,000 X in 100,000 or smaller*

Healthy (males); no young,

few old

Ill, very old,

very young (?)

Ill, very young, other

Private Mostly public Mostly private

Minor Major

y; conditional ~ 1 in 1,000 (salmonellosis); 1 in 4-5 (listeriosis)

nd Empirical Evidence for WTP for Mortality Risk Reduction

Risk change Latency Baseline

risk

Income Age Health

status

Dread

+

proportional.

_ + + -,

+ then -

? +

+ _ + + then -

+; not prop. _ varies + varies 0,

cancer +

+

(1999) and author

Table 5. Morbidity Valuation and Benefit Transfers

ents Primary studies

(primarily CV; context free)

Conventional air pollution context Food safety context

sk Acute: Mostly respiratory

Chronic: respiratory;

Familiar

Acute: Respiratory and cardiovascular

(CA),

Chronic: respiratory and CA;

Familiar

Wide variety (GI), complex mixture

of symptoms;

Unfamiliar/dread

Contemporaneous Contemporaneous and future Contemporaneous and future

Acute: Certain

Chronic: x in 10,000

Acute: high

Chronic: small

Varies: e.g.,

~1 in 100

lation Adults Adults, asthmatics Ill, very young

ate Private Public Private

avior Few Minor Major
104 Session IV: Valuing Risk Reductions for Different Hazards

Rapporteur’s Notes for

Valuing Risk Reductions for Different Hazards

Rapporteur: Tammy Riggs, U.S. Dept. of Health and Human Services

The discussion began with the question, “Can we standardize methodologies across hazards?”

This, however, led to the question, “What are the standard methodologies being employed by the

different agencies?” The general consensus from this discussion was that the different agencies

did employ different methods and that these differences needed to be identified before being able

to conclude whether or not standardization was possible. Most people felt that, if possible, a

single approach should be used by all the agencies to enable comparisons between policies.

Next, the group discussed how to begin this standardization process. It was noted that the OMB

guidelines on cost-benefit analysis could be used as a starting point but that a needs assessment

should be conducted to assess the differences in approaches between the agencies. This

assessment could begin with the work that has already been done for this conference. This

assessment would identify differences in approaches between agencies as well as the rationale

for those differences. Then, an expert review panel would be beneficial to help resolve these

differences and to make recommendations on the final methodology. It was suggested that

epidemiologists also be involved in this process. Once the methodology was agreed upon, a

multi-agency project to use this new methodology to actually measure consumers’ willingness to

pay to reduce certain risks would be beneficial to all the agencies, since no good measures seem

to be currently available.

The final discussion went back to the first question about whether or not one would be able to

standardize, given the way consumers view different types of hazards. Agreement was reached

that willingness to pay (WTP) was the best approach to valuing these risk reductions. One

suggestion was to create a single WTP function with a set of standard variables. For example,

WTP = f(Dread, Unknown, Severity, Duration, Cost, Risk, etc.), but with a precise definition of

each of the included variables and interpretation of terminology to reduce confusion. Some of

these variables would be zero depending on the hazard. This seemed feasible but led to a

discussion of what consumers were actually going to be asked to value. Should they be asked to

value risk reductions in terms of the symptoms and sequellae caused by the hazards or in terms

of the hazards themselves? There was some thought that certain hazards would have different

WTP values than the sum of the WTP values of their associated symptoms. While a difference

in the values would be easy enough to measure, which value would be the best value to use in a

practical application? Moreover, if the difference was caused by misinformation, would it be

best to better inform survey respondents before they answered any survey questions? Or are we

then undervaluing the true societal benefits? If there is misinformation, could society simply

benefit by better informing consumers in a general information campaign? These last few

questions were left unresolved.