Title: Valuing the environment through contingent valuation.

Subject(s): NATURAL resources -- Valuation; VALUATION

Source: Journal of Economic Perspectives, Fall94, Vol. 8

Issue 4, p19, 25p

Author(s): Hanemann, W. Michael

Abstract: Focuses on the use of contingent valuation to

measure people's values for environmental resources.

Conducting reliable surveys; Objections to surveys;

Contingent valuation and economic theory.

AN: 9501060641

ISSN: 0895-3309

Full Text Word Count: 13108

Database: Academic Search Elite

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VALUING THE ENVIRONMENT THROUGH CONTINGENT VALUATION

 

The ability to place a monetary value on the consequences of pollution

discharges is a cornerstone of the economic approach to the environment. If

this cannot be done, it undercuts the use of economic principles, whether to

determine the optimal level of pollution or to implement tiffs via Pigouvian

taxes or Coase-style liability rules. Sometimes, the valuation involves a

straightforward application of methods for valuing market commodities, as

when sparks from a passing train set fire to a wheat field. Often, however,

the valuation is more difficult. Outcomes such as reducing the risk of human

illness or death, maintaining populations of native fish in an estuary, or

protecting visibility at national parks are not themselves goods that are

bought and sold in a market. Yet, placing a monetary value on them can be

essential for sound policy.

 

The lack of a market to generate prices for such outcomes is no accident.

Markets are often missing in such cases because of the nonexcludable or

nonrival nature of the damages: for those affected by it, pollution may be a

public good (or bad). The public good nature of the damages from pollution

has several consequences. It explains, for example, why the damages are

sometimes large--only a few people may want to own a sea otter pelt, say,

but many may want this animal protected in the wild. It also explains why

market prices are inappropriate measures of value. In the presence of

externalities, market transactions do not fully capture preferences.

Collective choice is the more relevant paradigm.

 

This is precisely what Ciriacy-Wantrup (1947) had in mind when he first

proposed the contingent valuation method. Individuals should be interviewed

and "asked how much money they are willing to pay for successive additional

quantities of a collective extra-market good." If the individual values are

aggregated, "the result corresponds to a market-demand schedule" (p. 1189).

Thus, surveys offered a way to trace the demand curve for a public good that

could not otherwise be gleaned from market data. Schelling (1968) made a

similar point in his paper on valuing health. While the price system is one

way to find out what things are worth to people, he wrote, another way is to

ask people, whether through surveys or votes. Answering surveys may be

hypothetical, but no more than buying unfamiliar or infrequent commodities.

"In any case, relying exclusively on market valuations and denying the value

of direct enquiry in the determination of government programs would depend

on there being for every potential government service, a close substitute

available in the market at a comparable price. It would be hard to deduce

from first principles that this is bound to be the case" (pp. 143-4).

 

Schelling's point was not that indirect methods using market transactions

have no role, but rather that they cannot always be counted on to provide a

complete measure of value. Analysts can often capture some effects of a

change in air quality or a change in risk to human health through a hedonic

analysis that looks for evidence to property values or wage rates (Rosen,

1974). But people may also value those items in ways not reflected in wages

or property values. Similarly with averting expenditures and household

production models (Freeman, 1993), which rely on the demand for market

commodities that are complements to, or surrogates for, the nonmarket good.

If people value that good at least partly for reasons unrelated to their

consumption of the complementary private goods, those methods capture just

part of people's value--what is called the "use value" component, following

Krutilla (1967).[1] They fail to measure the "non-use value" or "existence

value" value component, which contingent valuation can capture.

 

An alternative is to turn to the political system, for example using

collective choice models to estimate demands for local public goods (Oates,

1994). However, Cropper (1994) suggests this is unlikely to be useful for

the environment because, in the United States, there are few cases where

local governments actually set environmental quality. Moreover, as Chase

(1968) noted, the method contains an element of circularity: a major reason

for the spread of benefit-cost analysis is legislators' desire to obtain

information on the public's value for government programs. While it may

sometimes be desirable to leave the assessment of value to the legislative

process, it is not obvious that this is always so. Measuring liability for

damages from pollution is an example. In some cases one wants to ascertain

how the public values something, and contingent valuation may be the only

way to measure this short of a plebiscite.

 

Ciriacy-Wantrup (1947) recognized that surveys are not foolproof. The degree

of success depends on the skill with which the survey is designed and

implemented. But it was time, he felt, that economics took advantage of

developments in social psychology and the newly emerging academic field of

survey research: "Welfare economics could be put on a more realistic

foundation if a closer cooperation between economics and certain young

branches of applied psychology could be established" (p. 1190). This finally

occurred in the 1980s, and contingent valuation came of age. Two landmarks

were an EPA conference in 1984 that brought together leading practitioners,

other economists, and psychologists to assess the state-of-the-art (Cummings

et al., 1986), and the publication of what has become the standard reference

on contingent valuation, Mitchell and Carson (1989), which puts it in a

broader context involving elements from economics, psychology, sociology,

political science, and market research.

 

Contingent valuation is now used around the world (Navrud, 1992; Bateman and

Willis, forthcoming), both by governments agencies and the World Bank for

assessing a variety of investments. A recent bibliography lists 1600 studies

and papers from over 40 countries on many topics, including transportation,

sanitation, health, the arts and education, as well as the environment

(Carson et al., 1994c). Some notable examples are Randall, Ives and Eastman

(1974) on air quality in the Four Corners area, the first major non-use

value study; Brookshire et al. (1982) on air pollution in Southern

California; Carson and Mitchell (1993) on national water quality benefits

from the Clean Water Act; Smith and Desvousges (1986) on cleaning up the

Monongahela River, Jones-Lee, Hammerton and Phillips (1985) on highway

safety; Boyle, Welsh and Bishop (1993) on rafting in the Grand Canyon;

Briscoe et al. (1990) on drinking water supply in Brazil; and the study on

the Exxon Valdez oil spill I helped conduct for the State of Alaska (Carson

et al., 1992).

 

This paper focuses generally on the use of contingent valuation to measure

people's values for environmental resources, rather than specifically on

natural resource damages. It will describe how researchers go about

conducting reliable surveys. It then addresses some common objections to

surveys and, lastly, considers the compatibility between contingent

valuation and economic theory.

 

Conducting Reliable Surveys

 

In all research, details matter. How a contingent valuation survey is

conducted is crucial. While there is no panacea, various procedures have

been developed in recent years that enhance the credibility of a survey and

make it more likely to produce reliable results. These touch all aspects,

including sampling, instrument development, formulation of the valuation

scenario, questionnaire structure, and data analysis. The main ways of

assuring reliability are summarized here.

 

Suppose one approached people in a shopping mall, made them put their bags

down for a moment, and asked them what was the most they would be willing to

pay for a sea otter in Alaska or an expanse of wilderness in Montana. This

is how the President of American Petroleum Institute and other critics have

characterized contingent valuation (DiBona, 1992). The essence of their

argument is summarized in titles such as "Ask a Silly Question" and "Pick a

Number" (Anon., 1991; Bate, 1994). It does not require any unusual

perspicacity to see that this approach is unlikely to produce reliable

results. For precisely this reason, it is not what good contingent valuation

researchers do, and it is not what was recommended by the NOAA Panel on

Contingent Valuation (Arrow et al., 1993) described in Portney's paper in

this issue.

 

Serious surveys of the general public avoid convenience sampling, such as

stopping people in the street; they employ statistically based probability

sampling.[2] They also avoid self-administered surveys, such as mail surveys

or questionnaires handed out in a mall, because of the lack of control over

the interview process. For a major study, the NOAA Panel recommended

in-person interviews for their superior reliability. Furthermore, interviews

should occur in a setting that permits respondents to reflect and give a

considered opinion, such as their home. Unless the study deals with consumer

products, shopping malls are a poor choice. Indeed, the only contingent

valuation study where people were stopped for a few minutes in a mall was

one performed for Exxon (Desvousges et al., 1992).

 

The crux is how one elicits value. The two key developments have been to

confront subjects with a specific and realistic situation rather than an

abstraction, and to use a closed-ended question which frames the valuation

as voting in a referendum.

 

A common temptation is to characterize the object of valuation in rather

general terms: "What would you pay for environmental safety?" "What would

you pay for wilderness?" The problem is that these are abstractions.

People's preferences are not measured in the abstract but in terms of

specific items. "Paying for wilderness" is meaningless; what is meaningful

is paying higher taxes or prices to finance particular actions by somebody

to protect a particular wilderness in some particular manner. Therefore, one

wants to confront respondents with something concrete. Moreover, one should

try to avoid using counter factuals. "What would you pay not to have had the

Exxon Valdez oil spill?" is utterly hypothetical because one cannot undo the

past. By contrast, "What would you pay for this new program that will limit

damage from any future oil spills in Prince William Sound?" offers something

that is tangible.

 

The goal in designing a contingent valuation survey is to formulate it

around a specific commodity that captures what one seeks to value, yet is

plausible and meaningful. The scenario for providing the commodity may be

real; if not, the key is to make it seem real to respondents. They are not

actually making a payment during the interview, but they are expressing

their intention to pay. The vaguer and less specific the commodity and

payment mechanism, the more likely respondents are to treat the valuation as

symbolic. To make the payment plausible, one needs to specify the details

and tie them to provision of the commodity so this cannot occur without

payment. There should be a clear sense of commitment; for example, if the

program is approved, firms will raise prices, or the government taxes, so

there is no avoiding payment once a decision is made.[3]

 

Until the mid-1980s, most contingent valuation surveys used some version of

an open-ended question, like "What is the most you would be willing to pay

for . . . ?" Since then, most major contingent valuation studies have used

closed-ended questions like "If it cost $x, would you be willing to pay this

amount?" or "If it cost $x, would you vote for this?" Different people are

confronted with different dollar amounts. Plotting the proportion of "yes"

responses against the dollar amount traces out the cumulative distribution

function of willingness-to-pay.[4]

 

Of course, if people carried utility functions engraved in their brains, the

question format would not matter. But they don't, and it does matter. In

this country, posted prices are the norm rather than bargaining. In market

transactions people usually face discrete choices: here is an item, it costs

$x, will you take it? Similarly in voting. Moreover, there is abundant

evidence that respondents find the open-ended willingness-to-pay question

much more difficult to answer than the closed-ended one; for market and

nonmarket goods alike, people can generally tell you whether they would pay

some particular amount, but they find it much harder to know what is the

most that they would possibly pay. Indeed, the experience with open-ended

willingness-to-pay questions for market goods is that people are more likely

to tell you what the good costs than what it is worth to them. In addition

to being less realistic and harder to answer, the open-ended format creates

incentives which are different from those in the closed-ended format. With

the open-ended format, as with an oral auction, there are strategic reasons

for stating less than one's full value--a theoretical result strongly

supported by experimental evidence. This is not so with a closed-ended

format; there, the NOAA Panel held, there is no strategic reason for the

respondent to do other than answer truthfully.[5]

 

For these reasons, the NOAA Panel considered the closed-ended format

combined, where possible, with a voting context the most desirable for

contingent valuation: "The simplest way to approach the valuation problem,"

it held, "is to consider a contingent valuation survey as essentially a

self-contained referendum in which respondents vote to tax themselves for a

particular purpose" (p. 20). This is a rather different conception of

contingent valuation from asking silly questions of passers-by.

 

In his introduction to this symposium, Portney describes other ways to make

a contingent valuation questionnaire more reliable: providing adequate and

accurate information; making the survey balanced and impartial; insulating

it from any general dislike of big business; reminding respondents of the

availability of substitutes, and of their budget constraint; facilitating

"don't know" responses; allowing respondents to reconsider at the end of the

interview. Several steps can be taken to eliminate any perception of

interviewer pressure. At the outset, the interviewer can assure respondents

that there are no "right" answers. Before asking the voting question, to

legitimate a negative response, the interviewer could say something like:

"We have found that some people vote for the program and others vote

against. Both have good reasons for voting that way," and then list some

reasons for saying "no."[6] Another possibility is if the interviewer does

not actually see the respondents' votes, for example by having them write on

a ballot placed in a sealed box.

 

A recent innovation, considered essential by the NOAA Panel, is a

"debriefing" section at the end of the survey. This checks respondents'

understanding and acceptance of key parts of the contingent valuation

scenario. For example, was the damage as bad as described? Did you think the

program would work? Did you think you really would have to pay higher taxes

if the program went through? This also probes the motives for their answer

to the willingness-to-pay question. What was it about the program that made

you decide to vote for it? Why did you vote no? Moreover, throughout the

survey, all spontaneous remarks by the respondent are recorded verbatim as

they occur. After the survey, the interviewer is debriefed and asked about

the circumstances of the interview, how attentive the respondent was,

whether the respondent seemed to understand the questions and appeared

confident in his responses. In this way, one creates a rich portrait of the

interview. This information can be exploited in the data analysis. One can

monitor for the misunderstandings, measure statistically how they affected

respondents' willingness-to-pay, and adjust accordingly. For example, if a

subject who voted "yes" appeared to be valuing something different than the

survey intended, this case can be dropped or the "yes" converted to a "no."

 

With any data, different statistical procedures can produce different

results. The closed-ended format raises several statistical issue, for

example, one might summarize the willingness-to-pay distribution by using

its mean, or its median, or another quantile. The mean is extremely

sensitive to the right tail of the distribution; that is, to the responses

of the higher bidders. For this reason, if the mean is to be used, a

nonparametric or bounded influence approach is highly recommended for

fitting the willingness-to-pay distribution. The median, by contrast, is

usually very robust (Hanemann, 1984). Another issue is that the choice of

dollar bids affects the precision with which the parameters of the

willingness-to-pay distribution are estimated; significant improvements can

be achieved by using optimal experimental designs (Kanninen, 1993).

Statistical techniques can also be used to probe for yea-saying or other

response effects, and correct for them if they are present (Hanemann and

Kanninen, forthcoming).

 

While none of these alone is decisive, taken together they are likely to

produce a reliable measure of value. Apart from the expense of in-person

interviews, they are all eminently feasible.[7] Other essential ingredients

are relentless attention to detail and rigorous testing of the instrument,

usually in collaboration with survey experts, so that the researcher

understands exactly how it works in the field and is sure it communicates

what was intended.

 

It is no coincidence that the handful of studies that Diamond and Hausman

select from the contingent valuation literature in their companion paper in

this issue violate most of these precepts, as do the Exxon surveys reported

in Hausman (1993). None uses in-person interviews. Many are

self-administered. Most use open-ended questions. None is cast as voting.[8]

Many ask questions with a remarkable lack of detail.[9] Several seem

designed to highlight the symbolic aspects of valuation at the expense of

substance.[10] The Exxon surveys were designed and fielded in great haste,

with little pretesting, just at a time when federal agencies were gearing up

for natural resource damage regulations.[11] The only way to justify this is

to make the tacit assumption that, if contingent valuation is valid, details

of its implementation should not matter. This is fundamentally wrong:

measurement results are not invariant with respect to measurement practice

in any science.

 

Objections to Surveys

 

McCloskey (1985, p. 181) observes that economists generally dislike surveys:

"Economists are so impressed by the confusions that might possibly arise

from questionnaires that they have turned away from them entirely, and

prefer the confusions resulting from external observation." In this section,

I discuss four common objections to surveys.

 

Surveys are Vulnerable to Response Effects

 

Small changes in question wording or order sometimes cause significant

changes in survey responses (Schuman and Presser, 1981). Since virtually all

data used in economics come from surveys (including experiments, which are a

form of survey), and all surveys are vulnerable to response effects, it is

important to understand why these arise and how they can be controlled. A

consensus is beginning to emerge based on insights from psychology and

linguistics. Answering survey questions requires some effort, usually for no

apparent reward. Respondents must interpret the meaning of the question,

search their memory for pertinent information, integrate this into a

judgment, and communicate the judgment to the interviewer. Although some are

motivated to make the effort, others may become impatient, disinterested, or

tired. Instead of searching for an accurate and comprehensive answer, they

satisfice, just aiming for some response that will be accepted. Furthermore,

interviews are interactions governed by social and linguistic norms that

shape assumptions and expectations. Viewing respondents as satisficing

agents following norms of conversation has proved helpful in interpreting

survey data, explaining response effects, and designing more effective

surveys (Groves, 1989; Krosnick, 1991).

 

Not all response phenomena are equally intractable. Some, such as order

effects (for example, bias towards the first item in a list), can be

detected and controlled, either by choosing the sequence that produces a

conservative result or by randomizing the order of items across interviews.

 

A second type of effect is where there is a shift in meaning. This is

substance, not noise. For example, similar words turn out to mean different

things: "allow" is not the same as "not forbid," nor "higher prices" the

same as "higher taxes."[12] Or there are framing effects, where subjects

respond differently to situations the researcher saw as equivalent. It has

been shown through debriefings that the subjects perceived the situations as

substantively different, because either the researcher induced an unintended

change in meaning or context, or the subjects made inferences that went

beyond the information given (Frisch, 1993).[13] In each case, the shift in

meaning is a source of error only if the researcher is unaware of it.

Through rigorous testing with cognitive techniques, the researcher can come

to understand exactly what the instrument means to people, and what they

mean in response.[14]

 

A third phenomenon arises from the inherent difficulty of the task assigned

the respondent. In recalling past events or behavior, for example,

respondents resort to rounding, telescoping (time compression) and other

inferential strategies that yield inaccurate reports of magnitudes and

frequencies.[15] Bradburn et al. (1987) emphasize that factual and

attitudinal surveys share many similar cognitive processes and errors. There

is no easy solution for recall errors. This continues to be a problem for

many data used by economists,[16] though not for contingent valuation data

since there is no recall.

 

One cannot avoid the fact that surveys, like all communication, are

sensitive to nuance and context and are bound by constraints of human

cognition. One tries to detect discrepancies and repair them, but they

cannot be entirely ruled out. It is important to keep a sense of proportion.

As far as I know, nobody has stopped using data from the Current Population

Survey, Consumer Expenditure Survey, Monthly Labor Survey, or Panel Study on

Income Dynamics because there are response effects in such surveys. The same

should apply to contingent valuation surveys.

 

The Survey Process Creates the Values

 

It has been asserted that contingent valuation respondents have no real

value for the item, but just make one up during the course of the interview:

the process creates the values that it seeks to measure. Debriefings can

identify whether subjects were inattentive or unfocused and offered hasty or

ill-considered responses, and these can be discarded if desired. But, the

issue raised here is more fundamental. Diamond and Hausman feel they know

real preferences when they see them, and they do not see them in contingent

valuation. Based on the debriefing statements in Schkade and Payne (1993)

that show most subjects, faced with an open-ended willingness-to-pay

question, think about either what the item could cost or what they have

spent on something remotely similar, Diamond and Hausman conclude that these

people are just making up their answer rather than evincing "true economic

preferences." But, what are "true economic preferences"? If a subject

responds thoughtfully to a question about voting to raise taxes for a public

good, by what criterion is that not a valid preference?

 

It is true that economists often assume consumer choice reflects an

individual's global evaluation of alternatives, a "top-down" or

"stored-rule" decision process. The stored-rule notion traces back to Hobbes

and the English empiricists who conceived of cognition in terms of storing

and retrieving "slightly faded copies of sensory experiences" (Neisser,

1967). Wilson and Hodges (1992) call this the "filing cabinet" concept of

the mind. It long dominated not only economics but also psychology. But it

is now being abandoned in the face of accumulating evidence from the

neurosciences (Rose, 1992) and elsewhere that all cognition is a

constructive process--people construct their memories, their attitudes, and

their judgments. The manner of construction varies with the person, the

item, and the context. A general principle is that people are cognitive

misers: they tend to resolve problems of reasoning and choice in the

simplest way possible. This is the emerging consensus not only in survey

research, but also in social psychology, political psychology, and market

research (Martin and Tesser, 1992; Sniderman, Brody and Tetlock, 1991;

Payne, Bettman and Johnson, 1988).

 

For non-habituated and complex consumer choices, people often make

"bottom-up" decisions; that is, they make up a decision rule at the moment

they need to use it (Bettman, 1988). Olshavsky and Granbois (1979, p. 98)

found that "for many purchases a decision process never exists, not even on

first purchase." Bettman and Zins (1977) found that grocery shoppers

construct a choice heuristic "on the spot" about 25 percent of the time;

bottom-up construction of preferences occurred especially for meat and

produce "as might be expected, since consumers cannot really rely on brand

name for most choices of this type," less often for beverages and dairy

products "where either strong taste preferences may exist or only a limited

number of brands are available" (p. 81). This calls to mind a remark by

Robert Solow that the debriefings in Schkade and Payne "sound an awful lot

like Bob Solow in the grocery store." I suppose critics of contingent

valuation would consider that Solow does not have true economic preferences,

or that he has true economic preferences when buying milk but not meat.

 

The real issue is not whether preferences are a construct but whether they

are a stable construct. While this surely varies with circumstances, the

evidence for contingent valuation is quite strong. There is now a number of

test-retest studies in the contingent valuation literature, and these show

both consistency in value over time and a high correlation at the individual

level (Carson et al., 1994b). These levels of consistency are comparable to

the most stable social attitudes such as political party identification.

 

Ordinary People are Ill-Trained For Valuing the Environment

 

If, as the NOAA Panel suggests, the goal of a contingent valuation survey is

to elicit people's preferences as if they were voting in a referendum, then

prior experience or training are irrelevant. These are not a criterion for

voting.[17] Nor is their absence an argument against contingent valuation

per se. Through direct questioning, one can readily identify which

respondents knew of the issue before the interview, or before the oil spill,

and determine whether they hold different values from those who did not. How

one proceeds in calculating aggregate willingness-to-pay is something that

can be decided separately from the survey. Who has standing, and whose

values should count, are questions that we as economists have no special

competence to judge.

 

Survey Responses Can't Be Verified

 

There are three ways to validate contingent valuation results: replication,

comparison with estimates from other sources, and comparison with actual

behavior where this is possible. Replication is useful even on a small scale

both to see if results hold up and to check whether the instrument is

communicating as intended. This is the single best way for a researcher to

determine whether somebody's survey instrument works as claimed.

 

When contingent valuation measures direct use values, it may be possible to

make a comparison with estimates obtained through indirect methods. Knetsch

and Davis (1966) conducted the first test, comparing contingent valuation

and travel demand estimates (a method described in Portney's paper) of

willingness-to-pay for recreation in the Maine woods. The difference was

less than 3 percent. There are now over 80 studies, offering several hundred

comparisons between contingent valuation and indirect methods. The results

are often fairly close; overall, the contingent valuation estimates are

slightly lower than the revealed preference estimates and highly correlated

with them (Carson et al., 1994a).

 

The ideal is direct testing of contingent valuation predictions against

actual behavior. There are about ten such tests in the literature. Diamond

and Hausman mention only five of these. The ones not mentioned yield results

quite favorable to contingent valuation.

 

Bohm (1972) conducted the first test, where subjects in Stockholm were asked

their willingness-to-pay to see a new TV program. In five treatments, the

program was shown if the group raised 500 Kr, with actual payment based in

various ways on stated willingness-to-pay. A sixth treatment asked subjects

what was the highest amount they would have given if they had been asked to

pay an individual admission fee. The mean response was 10.2 Kr (about $2)

when the group was asked a hypothetical question, versus an overall average

of 8.1 Kr when the group actually paid. The difference between contingent

valuation and non-contingent valuation means was not statistically

significant in four of the five cases.

 

Bishop and Heberlein (1990) conducted a series of experiments with hunters

who had applied for a deer-hunting permit in a favored game preserve run by

the state of Wisconsin. The most relevant for current practice is an

experiment in which they wrote to two groups of hunters offering to sell

them a permit at a specified price. In one case, this was a real offer; in

the other, it was asked as a hypothetical question. Estimated

willingness-to-pay was $31 in the real sale versus $35 in the hypothetical

sale, a statistically insignificant difference.

 

Dickie, Fisher and Gerking (1987) offered boxes of strawberries door-to-door

at different prices. One treatment was a real offer--the household could buy

any number of boxes at this price. The other asked how many boxes they would

buy if these were offered at the given price. The resulting two demand

curves were not significantly different. The parameter estimates were

actually more robust over alternative model specifications for the

hypothetical than the actual data (Smith, 1994).

 

Carson, Hanemann and Mitchell (1986) tested the accuracy of voting

intentions in a water quality bond election in California in 1985.

Closed-ended contingent valuation questions were placed on the Field

California Poll a month before the vote, using different figures for the

household cost. Adjusted for "don't know" responses, the predicted

proportion of yes votes at the actual cost was 70-75 percent. The ballot

vote in favor was 73 percent.

 

Cummings, Harrison and Rutstrom (1993) offered subjects small commodities at

various prices. For one group, it was a real sale. A second group was first

asked a hypothetical contingent valuation question--this item is not

actually for sale but, if it were, would you buy it now? The experimenter

then announced that, after all, she would sell the item, but they should

feel free to revise their answer. When juicers were the item, 11 percent

actually bought them in the real sale; with the second treatment, 41 percent

said they would buy it if it were on sale, but then only 16 percent did. The

41 percent and 11 percent are significantly different. With calculators, 21

percent would buy in the hypothetical sale, versus 8 percent in the real

sale. One wonders whether some respondents interpreted the question as "if

you needed a juicer, would you buy this one?" Smith (1994) shows that the

calculator responses do not generate a downward sloping demand curve for

either the actual or hypothetical data. The experimental procedure contained

nothing to emphasize commitment or counteract yea-saying in the hypothetical

treatment. Cummings and his colleagues have recently added wording like the

"reasons to say no" mentioned earlier. In one case, this reduced the

hypothetical yes for calculators from 21 percent to 10 percent, not

significantly different from the real 8 percent; in another there was no

effect (Cummings, 1994).

 

Other contingent valuation tests have used open-ended payment questions,

with predictable difficulties. Boyce et al. (1989) measured

willingness-to-pay and willingness-to-accept for a house plant, with mixed

results; Neill et al. (1994) measured willingness-to-pay for a map and a

picture, with negative results. Both confound the issue by comparing

contingent valuation responses to an experimental auction, begging the

question of whether auction behavior understates willingness-to-pay.

Duffield and Patterson (1991) and Seip and Strand (1992) compare actual and

hypothetical contributions to an environmental cause. Diamond and Hausman

focus on these studies because they showed a significant difference. But,

soliciting an intention to make a charitable donation is a poor test of

contingent valuation, because it invites less commitment than soliciting an

intention to vote for higher taxes. To make things worse, Seip and Strand

used members of the environmental group as the interviewers in their

hypothetical treatment, thus increasing pressures for compliance. They

compared hypothetical phone responses with responses to an actual mail

solicitation. Duffield and Patterson compared hypothetical mail

solicitations from the University of Montana with actual mail solicitations

from the Nature Conservancy. In both studies, the difference in survey

administration introduces a confounding factor which undermines the

comparison. [18]

 

A cleaner test is provided by Sinden (1988) who conducted a series of 17

parallel experiments soliciting actual and hypothetical monetary donations

to a fund for assisting soil conservation or controlling eucalypt dieback.

In all 17 cases, there was no statistical difference between actual and

hypothetical willingness-to-pay.

 

Thus, there is some substantial evidence for the validity of contingent

valuation survey responses, although more studies are certainly needed. Many

existing studies do not incorporate the refinements in contingent valuation

method, described earlier, that emphasize realism and commitment. In this

respect, the test by Carson, Hanemann and Mitchell (1986) points in the

right direction because it deals directly with expression of voting

intentions. The positive results in that study are consistent with other

evidence showing that polls in this country reliably indicate public

sentiment at the time they are taken, and polls close to an election are

generally accurate predictors of the outcome. [19] Kelley and Mirer (1974)

found voting intentions correctly predicted the actual vote in four

presidential elections for 83 percent of those respondents who voted.[20]

Surveys of purchase intentions in market research may not be accurate

predictors of subsequent purchase behavior, but surveys of voting intentions

are.[21]

 

Contingent Valuation and Economic Theory

 

Critics of contingent valuation like Diamond and Hausman, and their

coauthors in Hausman (1993), reject contingent valuation as a method of

economic valuation because the results of contingent valuation studies are

inconsistent with economic theory as they see it. These assertions have

become quite widely known. However, careful examination shows that in some

cases the claims are not supported by the findings in the contingent

valuation literature, and in others they rest on unusual notions about what

economic theory does or does not prescribe. I briefly review these issues

here, leaving a more detailed treatment to Hanemann (1994a).

 

Diamond and Hausman, and Milgrom (1993), make a number of statements about

what is a permissible argument in a utility function. They argue that people

should care about outcomes, not about the process whereby these are

generated. People should not care whether animals are killed by man or die

naturally. They should not care about details of provision or payment for a

commodity, only price. Above all, they should value things for purely

selfish motives. In their accompanying piece, Diamond and Hausman phrase

this argument by saying that respondents should not contemplate "what they

think is good for the country," because that reflects "warm glow" rather

than "true economic preferences."[22] From this perspective, contingent

valuation is unacceptable because it picks up existence values; for those to

be allowed in a benefit-cost analysis, Milgrom (1993, p. 431) argues, "it

would be necessary for people's individual existence values to reflect only

their own personal economic motives and not altruistic motives, or sense of

duty, or moral obligation."[23]

 

This criticism hardly comports with the standard view in economics that

decisions about what people value should be left up to them. For example,

Kenneth Arrow (1963, p. 17) wrote: "It need not be assumed here that an

individual's attitude toward different social states is determined

exclusively by the commodity bundles which accrue to his lot under each. The

individual may order all social states by standards he deems relevant." Or

as Gary Becker (1993, p. 386) writes: "[I]ndividuals maximize welfare as

they conceive it, whether they be selfish, altruistic, loyal, spiteful, or

masochistic." When estimating demand functions for fish prior to Vatican II,

no economist ever proposed removing Catholics because they were eating fish

out of a sense of duty. Nor, when estimating collective choice models, do we

exclude childless couples who vote for school bonds because they lack a

personal economic motive.

 

A more substantive matter is how willingness-to-pay varies with factors that

could reasonably be expected to influence it. This has been raised in

connection with the embedding effect and the income elasticity of

willingness-to-pay. Regarding the latter, Diamond and Hausman assert in this

issue that the income effects measured in typical contingent valuation

surveys are lower than would be expected if true preferences are measured.

McFadden and Leonard (1993, p. 185) make the more specific claim that an

income elasticity of willingness-to-pay less than unity constitutes grounds

for doubting the validity of the contingent valuation method. There is no

basis for either assertion. In the literature on the demand for state and

local government services in the United States, the income elasticities

generally fall in the range 0.3 to 0.6 (Cutler, Elmendorf and Zeckhauser,

1993). With charitable giving by individuals, the income elasticities

generally fall in the range of 0.4 to 0.8. (Clotfelter, 1985). The income

elasticities in the contingent valuation literature vary with the item being

valued, but are generally in this same range (Kristrom and Riera, 1994).

 

The term "embedding effect," introduced by Kahneman and Knetsch (1992), has

come to mean several different things. The general notion is captured in the

(mis)conception that, with contingent valuation, you get the same

willingness-to-pay if you value one lake, two lakes, or ten lakes.[24] This

combines three distinct notions. One assertion, which arises when the object

of preference is thought to be simply the number of lakes, is that

willingness-to-pay varies inadequately with changes in the scale or scope of

the item being valued. This is a scope effect. Alternatively, if each lake

is seen as a separate argument in the utility function, then the assertion

is that a given lake has quite different value if it is first, second or

tenth in a set of items to be valued--it gets a high value when the first,

but it adds little or nothing to total value when second or tenth. This is a

sequencing effect. Thirdly, with either preference structure, the

willingness-to-pay for a composite change in a group of public goods may be

less than the sum of the willingnesses-to-pay for the individual changes

separately. This is a sub-adaptivity effect.

 

The question of how willingness-to-pay varies with the scale or scope of the

item being valued in a contingent valuation survey has long been considered,

starting with Cicchetti and Smith (1973)who elicited hiker's values for

trips in a Montana wilderness area and found that the willingness-to-pay for

trips where other hikers were encountered on two nights was 34 percent lower

than the willingness-to-pay for trips with no encounters. Many other studies

have since reported comparable findings using both internal (within-subject)

and external (split-sample) scope tests, including meta-analyses by Walsh,

Johnson and McKean (1992) covering over 100 contingent valuation studies of

outdoor recreation, and Smith and Osborne (1994) on 10 contingent valuation

studies of air quality. Carson (1994) reviews 27 papers with split-sample

tests of scope and finds a statistically significant effect of scope on

willingness-to-pay in 25 of them.

 

The two exceptions are Kahneman and Knetsch (1992) and Desvousges et al.

(1992). Critics of contingent valuation rely heavily on these two studies

when asserting the absence of scope effects in contingent valuation.[25]

Some of the problems with these two studies have already been noted,

including their failure to use a closed-ended voting format, the

after-the-fact provision of information in Kahneman and Knetsch's "top-down"

procedure, and he use of brief shopping mall intercepts by Desvousges et

al.[26] The latter elicited people's willingness-to-pay for preventing the

deaths of migratory waterfowl. Three separate versions of the questionnaire

said that 2,000, 20,000, and 200,000 out of 85 million birds die each year

from exposure to waste-oil holding ponds that could be sealed under a new

program. Respondents were told that the deaths amounted to much less than 1

percent of the bird population, to less than 1 percent, and to about 2

percent. If respondents focused on the relative impact on the population, it

is hard to believe that they would have perceived any real difference among

these percentages. The results of the scope test depend crucially on how

much one trims the data to remove what are clearly outliers. With a 10

percent trim, one obtains a highly significant scope effect.[27] At any

rate, even if one regards these two studies as highly credible evidence that

respondents were insensitive to scope, they certainly do not represent the

majority finding in the contingent valuation literature regarding the

variation of willingness-to-pay with scope.

 

How much should willingness-to-pay vary with scope? Diamond (1993) asserts

that economic theory requires it to increase more than proportionately with

the number of bird deaths. The variables in his model are the number of

birds originally in the population, q[sub O], the number at risk of dying,

q[sub R], and the number of those that are saved, q[sub S]. Let q[sub F] is

equivalent or equal to q[sub O] - q[sub R] + q[sub s]. Diamond assumes that

people should care only about q[sub F], the ultimate number of birds, not

how many were alive initially, at risk, or saved. He also assumes

preferences are quasiconcave in q[sub O]. The two assumptions together imply

quasiconvexity in q[sub R], which is what makes the elasticity of

willingness-to-pay with respect to q[sub R] greater than unity. The

conclusion depends critically on the assumption of perfect substitution

between q[sub O], q[sub S] and -q[sub R]. When contingent valuation data

disconfirm this, Diamond dismisses the method. Others might be more inclined

to believe the data and drop the assumption.[28]

 

With regard to sequencing and sub-adaptivity effects, these effects are

certainly present in contingent valuation responses, but one expects them to

occur, and they can be explained in terms of substitution effects and

diminishing marginal rates of substitution. When the quality of one lake

improves, you value an improvement in a second lake less if the lakes are

what Madden (1991) calls R-substitutes, and more if they are R-complements.

Far from being inconsistent with economic preferences (Diamond et al., 1993,

pp. 48-49), sub-adaptivity is likely to be the norm: while all goods cannot

be R-complements, Madden shows they can all be R-substitutes.[29] Similarly,

R-substitution explains sequence effects: if the lakes are R-substitutes,

the willingness-to-pay for an improvement in one lake is lower when it comes

at the end of a sequence of changes in lake improvements than at the

beginning while the willingness-to-accept for the change in the lake is

higher when it comes later in a sequence (Carson, Flores and Hanemann,

1992).[30] It should come as no surprise that the value of one commodity

changes when the quantity of another varies: in other words, that

willingness-to-pay depends on economic context.[31]

 

For many economists, the ultimate argument against contingent valuation is

that it violates the habitual commitment of the profession to revealed

preference. Three points should be noted. First, one must distinguish

between private market goods and public goods. Revealed preference is harder

to apply to the latter, especially when they are national rather than local

public goods (Cropper, 1994). Second, revealed preference is not foolproof,

either. It involves an extrapolation from observation of particular choices

to general conclusions about preference. One relies on various auxiliary

assumptions to rule out factors that might invalidate the extrapolation.

Those assumptions are not themselves verifiable if one is restricted to

observed behavior. This can sometimes make revealed preference a relatively

hypothetical undertaking.[32] Third, there is no reason why observing

people's behavior and asking them about behavioral intentions and motives

should be mutually exclusive. Fathoming human behavior is never easy; one

should utilize every possible source of information.

 

Above all, one should take a balanced view of the difficulties with each

approach. As Sen (1973, p. 258) wrote, "we have been too prone, on the one

hand, to overstate the difficulties of introspection and communication and,

on the other, to underestimate the problems of studying preferences revealed

by observed behavior." In the debate on contingent valuation, critics have

shown a tendency to employ simplistic dichotomies. Surveys of attitudes are

fallible and subject to the vagaries of context and interpretation; surveys

of behavior are unerring. In the market place, people are well informed,

deliberate, and rational. Outside it, they are ignorant, confused, and

illogical. As consumers, people can be taken seriously; as voters, they

cannot. In particular instances, these assertions may be correct. As

generalizations, however, they are a caricature.

 

Conclusions

 

When cost-benefit analysis started in the United States in the 1930s,

economic valuation was generally perceived in terms of market prices. To

value something, one ascertained an appropriate market price, adjusted for

market imperfections if necessary, and then used this to multiply some

quantity. Two things changed this. The first was the recognition, prompted

by the "new welfare economics" of the 1940s and especially Hotelling's paper

on public utility pricing, that the appropriate welfare criterion is

maximization of aggregate consumers' plus producers' surplus. While market

prices can safely be used to value marginal changes for market commodities,

the impact of non-marginal changes is measured by the change in areas under

demand and supply curves. The second development was Samuelson's theory of

public goods and his finding that their valuation must be based on vertical

aggregation of individual demand curves.

 

Together, these developments led to an important paradigm shift--one that

contributed directly to the emergence of nonmarket valuation and is still

evident in the current debate on contingent valuation.[33] This shift

changed the focus of valuation away from market prices towards demand and

supply functions as the underlying repositories of value. These functions

are behavioral relations, and the implication of the paradigm shift was that

economics is not just the study of markets, but more generally the study of

human preferences and behavior.

 

The conceptual link to nonmarket valuation is the recognition that, while a

demand curve is not observable if there is no market for a commodity, there

still exists a latent demand curve that perhaps can be teased out through

other means. Indirect methods are one approach to doing this, and contingent

valuation is another. In both cases, the details of implementation have a

large impact on the quality of the results.

 

Faced with the assertion that contingent valuation surveys can never be a

reliable source of information either for benefit cost analysis or for

damage assessment, the NOAA Panel rejected this as unwarranted. Two years

later, there is now even more evidence from recent studies and literature

analyses to support the Panel's conclusion. However, it would be misleading

for me to suggest that contingent valuation surveys can be made to work well

in all circumstances. I am sure situations could exist where a contingent

valuation researcher might be unable to devise a plausible scenario for the

item of interest. Nor would I wish to argue that all contingent valuation

surveys are of high quality. The method, though simple in its directness, is

in fact difficult to implement without falling into various types of design

problems that require effort, skill and imagination to resolve. Each

particular study needs to be scrutinized carefully. But the same is true of

any empirical study.

 

While I believe in the feasibility of using contingent valuation to measure

people's value for the environment, I do not mean to advocate a narrow

benefit-cost analysis for all environmental policy decisions, nor to suggest

that everything can or should be quantified. There will be cases where the

information is inadequate, the uncertainties too great, or the consequences

too profound or too complex to be reduced to a single number. I am well

aware of the fallacy of misplaced precision. But this cuts both ways. It

also applies to those who suggest that it is better not to measure nonuse

values at all than to measure them through contingent valuation. I reply to

such critics by quoting Douglass North: "The price you pay for precision is

an inability to deal with real-world issues" (Wall Street Journal, 7/29/94).

 

Is expert judgment an alternative to contingent valuation? Experts clearly

play the leading role in determining the physical injuries to the

environment and in assessing the costs of clean-up and restoration.

Assessing what things are worth is different. How the experts know the value

that the public places on an uninjured environment, without resort to

measurement involving some sort of survey, is unclear. When that public

valuation is the object of measurement, a well-designed contingent valuation

survey is one way of consulting the relevant experts--the public ITSELF.

 

* I want to thank Richard Carson, Jon Krosnick, Robert Mitchell, Stanley

Presser and Kerry Smith for their helpful comments, and Nicholas Flores and

Sandra Hoffmann for excellent assistance. I also thank the editors, without

whom this paper would be far longer.

 

1 For a formal definition, see Hanemann (1994a).

 

2 DiBona's scenario actually was the practice in the 1930s when most surveys

were "brief encounters" on the street or in stores (Smith, 1987). The 1940s

saw the adoption of probability sampling, standardized survey techniques,

longer and more complex survey instruments, and in-depth focused interviews

(Merton and Kendall, 1946).

 

3 To underscore this, the interviewer may tell respondents that the

government uses surveys like this to find out whether taxpayers are willing

to pay for new programs it is considering.

 

4 The methodology here is to assume a random utility model for individual

preferences. This can be estimated using standard techniques for binary

choices. Bishop and Heberlein (1979) were the first to use this format; the

link with utility theory was developed in Hanemann (1984).

 

5 With auctions, it is well documented that formal matters and that oral

auctions generate lower prices than posted-price auctions. Why the surprise

when the same holds true for open versus closed-ended payment questions?

 

6 For example, the interviewer might note that some people prefer to spend

the money on other social or environmental problems instead, or they find

the cost is more than they can afford or than the program is worth, or they

cannot support the program because it would benefit only one area (Carson et

al., 1992).

 

7 Is there an acceptable alternative to in-person surveys? The NOAA Panel

felt mail surveys have significant problems rendering them unsuitable.

Telephone surveys avoid these problems, but preclude the use of visual aids

and need to be short. The most promising alternative is a mail/telephone

combination in which an information package is mailed to respondents who are

then interviewed by phone (Hanemann, Loomis and Kanninen, 1991). This

permits an extensive phone interview which seems to provide many of the

benefits of an in-person survey at much lower cost.

 

8 Two studies Diamond and Hausman cite as showing a lack of commitment in

contingent valuation, Seip and Strand (1992) and Duffield and Patterson

(1991), used open-ended questions about payment to an environmental charity.

Most of Seip and Strand's subjects who were followed up afterwards said that

they had been expressing their willingness-to-pay for environmental problems

generally, rather than the particular environmental group. Careful

pretesting would have discovered this beforehand.

 

9 This is notably a problem in Diamond et al. (1993).

 

10 Including Kahneman and Ritov (1993), Kahneman and Knetsch (1992), and

Kemp and Maxwell (1993). The last two employ a "top-down" procedure in which

respondents are given details of the item only after they value it. They are

first confronted with something broad, like "preparedness for disasters."

After stating their willingness-to-pay for the broad category, they are told

what it comprises and asked their willingness-to-pay for one of those

components. Then, they are told what this comprises, and so on. The change

in the quantity of any item is never specified.

 

11 Hanemann (1994a, b) critiques these studies.

 

12 And different words can mean the same thing, as in the movie Annie Hall

where Woody Allen and Diane Keaton are asked by their psychiatrists how

often they have sex. He says: "Hardly ever, maybe three times a week." She

says: "Constantly, I'd say three times a week." With consumer expenditure

surveys, Miller and Guin (1990) attest that life imitates art.

 

13 When there is incomplete information in a survey, respondents may go

ahead and make their own assumptions. Consequently, the researcher loses

control over his instrument. Diamond et al. (1993) is a contingent valuation

example.

 

14 On testing by federal survey agencies, see Tanur (1992). Lack of adequate

testing can explain some notable violations of procedural

invariance--respondents saw cues or meaning which the researcher didn't

intend and failed to detect. An example is the base rate fallacy where "when

no specific information was given, prior probabilities are properly

utilized; when worthless evidence is given prior probabilities are ignored"

(Tversky and Kahneman, 1974). A norm of conversation is to present

information one believes relevant. That this was the expectation of subjects

could have been detected through debriefings. On violations of

conversational norms in base-rate experiments, see Krosnick, Li and Lehman

(1990).

 

15 Some pronounced telescoping errors are to be found in the Alaska

recreation survey conducted by Hausman, Leonard and McFadden (1993).

 

16 Juster and Stafford (1991) and Mathiowetz and Duncan (1988) discuss

biases in labor supply estimates due to problems with bunching and

misreporting in Current Population Survey data. Atkinson and Micklewright

(1983) discuss errors in Family Expenditure Survey reports of income and its

components. Other inconsistencies between micro- and macro-data sets for the

household sector are discussed in Maki and Nishiyama (1995).

 

17 Voter ignorance is a constant refrain for Diamond and Hausman. They use

it to form a syllogism: voters are ill-informed, contingent valuation is

like a referendum, therefore contingent valuation respondents are

ill-informed. Both parts are false. Contingent valuation researchers take

pains to ensure their samples are representative and their questionnaires

intelligible, informative, and impartial, thus avoiding the vagaries of

turnout and biased advertising in election campaigns. This is why political

scientists are becoming interested in "deliberative polling"--in effect,

extended contingent valuation surveys (Fishkin, 1991). Many analysts see a

substantial core of rationality in voter behavior. Cronin (1989) finds

Magleby's (1984) assessment of voter ignorance in referenda overblown.

Fiorina (1981) and McKelvey and Ordeshook (1986) emphasize how campaign

protagonists use signals to inform voters. Lupia (1993) analyzes the

insurance reform battle in the 1988 California ballot and finds that

informational "short cuts" enabled poorly informed voters to act as though

they were well informed. What Sniderman (1993) calls "the new look in public

opinion research" stresses how ordinary citizens use the information at hand

to make sense of politics.

 

18 The problem with mail surveys is that people may think the survey is junk

mail and throw it out unopened. Duffield and Patterson made no allowance for

the difference in sponsor identity on the envelope, which could explain the

difference in response rates (Schuman, 1992). Response rates apart, the

pattern of contributions was similar in the two treatments. Seip and Strand

made no allowance for the fact that phone and mail solicitations generally

have different response rates. Infosino (1986) found a sales rate three

times higher with telephone than mail in an AT&T marketing effort.

 

19 Diamond and Hausman seem troubled that voters change their minds during

the course of an election campaign. They cite a 1976 electricity rate

proposition in Massachusetts where support went from 71 percent in February

to 25 percent in the November ballot. They fail to mention the reasons.

Magleby (1984, p. 147) identifies opposition spending as the chief cause of

such opinion reversals, and that certainly occurred in 1976--opponents

outspent supporters more than threefold. In May, the Dukakis administration

came out against it, as eventually did businesses, the unions, hospitals,

colleges, and major newspapers.

 

20 Ajzen and Fishbein (1980) offer some reasons to expect a high level of

attitude-behavior correspondence for voting in terms of their theory of

reasoned action.

 

21 One reason for the difference is timing: unlike elections, people

generally control the timing of their market purchases. The result is they

may end up buying the commodity, but later than they said (Juster, 1964).

This is especially likely for durables, the focus of much literature, since

their durability permits delay in replacement. This is consistent with

findings that purchase intentions are significantly more accurate for

nondurables than durables (Ferber and Piskie, 1965); intentions not to

purchase durables are highly accurate (Theil and Kosobud, 1968); and

predictions of the brand selected when the purchase does occur tend to be

highly accurate (Ajzen and Fishbein, 1980; Warshaw, 1980).

 

22 "Warm glow" is simply a red herring. I have seen no empirical evidence

that people get a warm glow from voting to raise their own taxes, whether in

real life or in a contingent valuation study.

 

23 Milgrom (1993) also asserts that using contingent valuation to measure

altruistic preferences creates double counting. His analysis has three

flaws. First, it depends on the particular specification of the utility

function, as Johansson (1992) notes; if the argument of the utility function

is another's consumption rather than his utility, there is no double

counting. Second, it derives its force from the auxiliary assumption that

the respondent does not realize that the other people for whom he cares will

have to pay, too; this is not a problem in a referendum format. Third, in

many contingent valuation studies the object of the altruism is often

wildlife--sea otters, for example. Since those creatures are not surveyed,

the issue of double counting is moot.

 

24 Though widely believed, this is a myth. It may be traced to Kahneman

(1986), which is usually cited as showing that respondents were willing to

pay the same amount to clean up fishing lakes in one region of Ontario as in

all of Ontario. His data actually show a 50 percent difference. Moreover,

the survey involved a brief telephone interview using an open-ended

willingness-to-pay question. It provided no detail on how and when the

cleanup would occur. Respondents may not have seen cleaning up all the lakes

as something likely to happen soon.

 

25 Also, in their contingent valuation survey, Diamond et al. (1993, pp.

45-46) mention that, using a Kruskal-Wallis test, they found no difference

in willingness-to-pay for three wilderness areas ranging in size from

700,000 to 1.3 million acres. If they had run a simple regression of

willingness-to-pay on acreage, they would have found a significant scope

effect.

 

26 Other questions about Kahneman and Knetsch are raised by Harrison (1992)

and Smith (1992).

 

27 How the survey was administered clearly affected the results. Schkade and

Payne (1993) used the same questionnaire as Desvouges et al., but slowed

respondents down and made them think about their answer. Their data show a

different pattern of willingness-to-pay responses, and a significant

relationship between willingness-to-pay and the percentage of birds killed

(Haneman, 1994b).

 

28 Some, while not sharing Diamond's extreme position on the elasticity of

willingness-to-pay, still hold that contingent valuation responses vary

inadequately with scale. People's perceptions undoubtedly differ from

objective measures of attributes. But this is not just a feature of

contingent valuation. In psychophysics, it has been known since the 1880s

that there is a general tendency for judgments of magnitude to vary

inadequately. Observers standing at a distance overestimate the height of

short posts, and underestimate that of tall ones; people reaching quickly

for an object overestimate small distances and angles, and underestimate

large ones; subjects matching loudness of a tone to a duration overestimate

the loudness of short tones, and underestimate the loudness of long ones;

people overestimate infrequent causes of death, and underestimate frequent

ones; small probabilities are overestimated, large ones underestimated

(Poulton, 1989). This "response contraction bias" in judgment or rating is

an authentic feature of how people perceive the world, not an artifact of

contingent valuation.

 

29 If the intention of the Diamond et al. (1993) contingent valuation survey

was to test the adding-up of willingness-to-pay, it was strangely designed

for the purpose. The survey stated that there were 57 federal wilderness

areas in the Rocky Mountain states, without identifying them, and said that

there now was a proposal to open these to commercial development. In one

version, respondents were told that seven unidentified areas had already

been earmarked for development, and were asked their willingness-to-pay to

protect an eighth area, identified as the Selway Bitterroot Wilderness. In

another, respondents were told that eight unnamed areas had been earmarked

for development and asked their willingness-to-pay to protect a ninth area,

identified as the Washakie Wilderness. In a third version, respondents were

told that seven unnamed areas had been earmarked for development and asked

their willingness-to-pay to protect two areas identified as Selway and

Washakie. In all three cases, respondents were not told the identity or fate

of the other 48 or 49 areas. Given that respondents were not indifferent

among wilderness areas, as evidenced by the regression mentioned in note 25,

I leave it to the reader to decide whether the surveys constitute a sensible

basis for testing the adding up of willingness-to-pay.

 

30 In natural resource damages, where willingness-to-accept is the relevant

welfare measure, this implies that the usual practice of taking the inured

resource as the first item in any possible valuation sequence is a

conservative procedure.

 

31 The practical implications are that, when one values a program, it be

placed in whatever sequence applies under the circumstances, and that one

take care when extrapolating results in a benefits transfer exercise because

the values might change with the difference in circumstances (Hoehn and

Randall, 1989).

 

32 Revealed preference estimates are sensitive to the measurement of price,

which is often uncertain and precarious for disaggregated commodities

(Pratt, Wise and Zeckhauser, 1979; Randall, 1994). The price at which demand

falls to zero, needed to estimate consumer's surplus, may lie outside the

range of the observed data and be estimated inaccurately (for example. one

knows travel cost only for participants, or one believes that participants

and nonparticipants have different preferences). This can cause revealed

preference to produce a less reliable estimate of use value than contingent

valuation (Hanemann, Chapman and Kanninen, 1993). With other variables there

may be inadequate variation in the data (for example, attributes are

correlated across brands). Hence, revealed preference data alone may yield a

less reliable estimate of demand functions than contingent valuation choice

data, and one may need to combine both types of data for best results

(Adamowicz, Louviere and Williams, 1994).

 

33 For an account of the development of nonmarket valuation generally, see

Hanemann (1992).

 

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~~~~~~~~

 

By W. Michael Hanemann

 

W. Michael Hanemann is Associate Professor of Agricultural and Resource

Economics, University of California, Berkeley, California.

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