ENVIRONMENTAL VALUATION: STATED PREFERENCE METHODS

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1 The Economics of the Environment and Natural Resources R. Quentin Grafton, Wiktor Adamowicz, et al. Copyright 2004 by R. Quentin Grafton, Wiktor Adamowicz, et al. CHAPTER NINE ENVIRONMENTAL VALUATION: STATED PREFERENCE METHODS 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. (A. K. Sen (1973), Behavior and the Concept of Preference, Economica 40: p. 258) 9.1 INTRODUCTION The most common objective of environmental valuation is to determine the value of a change in environmental quality or the provision of some public good, as measured by compensating or equivalent variation. In an ideal economic world there would be no need to measure these values because a set of institutional arrangements would exist that would reveal their value. In a somewhat less ideal world it might be possible to identify the values of environmental quality changes through market transactions (the topic of chapter 10). But in many cases there are no markets to rely on to provide information on the value of environmental goods. In these cases we must rely on stated preference methods or methods that reveal values through non-market or political behavior. The values may be revealed through voting or referenda in which constituents agree to increase their own taxes to provide a public good. Or they may be revealed through mechanisms that currently do not exist in the actual market, but which could be accurately described to individuals using surveys. In these cases economists resort to methods that involve conversations with individuals in an attempt to elicit their trade-offs or values. These methods have been called conversational, direct, expressed preference, or stated preference. While there are several variants of these methods they all share the basic traits of developing scenarios and asking individuals to provide a response that indicates their willingness to trade off money against other goods/services/environmental conditions.

2 250 ENVIRONMENTAL VALUATION There are several examples of cases that require the use of stated preference methods. Any attempt to assess passive use value, or the values that are not associated with any observable behavior (visiting a wilderness area, etc.), requires stated preference methods. By definition there is no market behavior that can be used to identify the trade-off involved. The amount that the public is willing to pay to protect coastal wilderness areas from oil spills is an example of such value. Stated preference methods may also be used in cases where revealed preference methods could operate, but for a variety of reasons may not accurately reflect preferences. In assessing the value of reduced risks of mortality, examinations of wages across jobs with different levels of mortality risk can reveal the implicit value of risk reduction. However, this assumes that workers accurately perceive the risk levels and are informed about the risk levels. Such analysis also requires a significant degree of variation in the risks. Finally, wage-risk studies are useful for identifying the risk trade-offs made by individuals in the labor force, but they may not be accurate in identifying the risk trade-offs for retired individuals or children. In these cases, stated preference methods can be used to elicit the value of risk reductions. Stated preference valuation involves a blending of economic theory, econometrics, survey design (psychology, social-psychology), and other disciplines. It involves issues that economists do not typically deal with including administering focus groups, designing and administering surveys, and employing experimental designs to elicit trade-offs. To deal with these issues effectively requires an entire volume of its own (see Further reading for some suggestions). In this chapter we examine the basic economic and econometric issues associated with stated preference methods and we focus on two major stated preference variants: contingent valuation and choice experiments. Figure 9.1 illustrates the variety of stated preference methods. There is a considerable amount of confusion regarding the names of these approaches. In figure 9.1 the approaches are categorized according to whether they involve rating, ranking, or choice, and then within choice whether the approach involves a choice Stated preference Rating Ranking Stated choice Discrete choice / referendum contingent valuation Other choice methods Attribute based stated choice Figure 9.1 Stated preference methods Source: Adamowicz, Louviere, and Swait (1998)

3 ENVIRONMENTAL VALUATION: STATED PREFERENCE METHODS 251 of yes or no between scenarios (as in a referendum) or a choice between options described by attributes (attribute based methods). Simple open-ended contingent valuation approaches approaches that ask individuals to state how much they would be willing to pay for a good/service or quality change, can be thought of as a form of rating approach. However, the rating scale used is not a numerical score but a very specific form of preference indicator money. 9.2 THE CONTINGENT VALUATION METHOD The contingent valuation method is the most common stated preference valuation method. It involves careful structuring of a scenario in which an individual is offered a good (improved environmental quality, new public good, etc.) and is requested to identify whether he/she would be willing to trade off money for the offered public good. The value of the good is elicited contingent on there being a market for the good (where the market is described by the researcher). A contingent valuation survey typically involves a series of 6 steps (based on Carson, 2000). These are 1 identification of the issue in general; 2 specific description of the current situation and the proposed alternative regarding the public good or environmental quality change in question; 3 description of the payment mechanism including the institutions surrounding the payment (e.g. annual property taxes and government provisions); 4 elicitation of the willingness to pay or choice decision in a discrete choice framework; 5 a set of debriefing questions to identify how certain the individual is about their choice, why they chose a particular option and elicit other information about the individual s reaction to the valuation task; and 6 elicitation of demographic characteristics and attitudinal information from the individual. Step 1 provides a warm-up for the respondent and begins framing the issue for them to consider. Step 2 typically contains a considerable amount of descriptive material, often biological in nature, regarding the current situation and the proposal for the improvement. This component often requires considerable effort with focus groups to develop descriptions that are understandable and objective yet not burdensome. Step 3 outlines the mechanism by which the improvement will be funded. This step also requires the development of credible realistic payment institutions. Step 4 is relatively straightforward if the open-ended approach is used. However, within the discrete choice approach there are several alternatives, each with its own merits and difficulties. These are discussed below. Step 5 is important for analysis of the responses to the valuation question and allows the researcher to assess the degree to which the respondent understood the task and their confidence in their responses. Step 6 is a relatively standard component of survey design and development although it will be fine-tuned to the specific application.

4 252 ENVIRONMENTAL VALUATION Table 9.1 outlines the major variants of the actual valuation question or the variants of step 4. These include open-ended approaches in which the individual is asked to state the amount that they would be willing to pay (a single question) or auctions in which the individual participates in an auction for the good. Individuals may also be presented with discrete choice questions that identify if their true value is less than or more than the bid that is presented to them. Note that these discrete choice approaches do not identify the actual willingness to pay, rather, they bound the willingness to pay. The simplest form of discrete choice response is a single presented offer and a request for a Yes (pay the amount and receive the public good) or No response. This can also be framed as a referendum in which the program will only be approved if 50 percent of the population votes Yes in which case all households/ individuals will be required to pay. The latter is a useful framing in that it identifies what obligations other members of the community will have in funding the program and in many cases makes the situation more realistic. Such a question is also often incentive compatible (discussed below). In order to increase efficiency in the elicitation of the willingness to pay amount, the respondent could be asked repeated questions. If they voted Yes the respondent could be asked if they would also vote Yes if the price was higher. If they voted No they could be asked if they would vote Yes for a lower amount. This is a double-bounded approach, which can be generalized to a N-bounded approach with a series of N questions (see Hanemann and Kanninen, 1999). A variant on double-bounded questions is the spike model in which individuals are given an option to say if they would pay anything (CV 0) and then they are asked to vote yes or no to specific bid amounts. This helps identify those who would pay positive amounts (but not necessarily pay the amount requested of them) from those who would not pay anything (Hanemann and Kanninen, 1989). Finally, in response to some challenges arising in the double-bounded approaches the one and a half bounded approach has been suggested (Cooper et al., 2002; Hanemann and Kanninen, 1999). Double-bounded approaches often generate irrational responses to the second bid. Individuals may perceive the good offered in the second bid to be somehow different from the good offered in the first bid (perhaps Table 9.1 Forms of contingent valuation questions (based on Mitchell and Carson, 1989) Actual WTP Discrete choice Single question Open ended Referendum Payment card Take-it-or-leave-it Sealed bid Iterated or series Bidding game Take-it-or-leave-it with follow up of questions Auctions Double-bounded CVM N-bounded CVM Spike CVM 1 1 bounded CVM 2

5 ENVIRONMENTAL VALUATION: STATED PREFERENCE METHODS 253 because price is perceived as an indicator of quality). They may also imagine that the situation has changed into a bargaining game. For these reasons the one and a half bounded approach has been suggested. In this case the individual is first told that two bids have been prepared because uncertainties about the situation preclude the development of single bid. The respondent is then offered the choice with the first bid (randomly selected from the two). The second bid is presented to the respondent if it is consistent with the respondent s answer. For example, if the respondent voted Yes to the first bid, and the remaining bid is higher than the first bid, the interviewer would ask about the second bid. If the remaining bid is lower no further questions are asked, as it is clear that the respondent would pay at least that amount (Cooper et al., 2002). Discrete choice contingent valuation 1 In this section the basic structure of random utility theory and the fundamental versions of the discrete choice contingent valuation approach are presented. Contingent valuation involves a combination of economic theory associated with the structure of the utility function, and econometric theory associated with the way that randomness enters into the process. This means that economic theory is not separable from econometrics for these types of models (thus this chapter and chapter 10 contain a significant amount of econometric presentation). The choice of distribution for the random component will affect the structure of the utility function. In principle, respondents are assumed to know their preferences but researchers only observe a portion of the elements that explain these preferences. The researcher can only explain in probabilistic terms the decision that the respondent makes. Let us begin with a simple case of utility arising from a yes or no response to a referendum contingent valuation question. In this case utility is assumed to arise from income (M), the presence or absence of the public program. This is an indirect utility function since utility is a function of income and not goods. Also, it is often referred to as a conditional indirect utility function as the utility received is conditional on the choice of Yes or No. For the moment we will ignore demographic factors and other elements in the utility function. Utility is made up of a systematic component (V) and a random component (ε). The subscript i indexes the alternatives Yes and No (i 1(Yes), 0 (No) ). If the individual votes Yes they receive the program or public good and their income is reduced by the amount of the bid $B. If they vote No they do not receive the program and their income is not reduced. The second argument in V i indicates the presence or absence of the program. U i V i ε i (1) V 1 V(M $B, 1) (2) V 0 V(M, 0) (3)

6 254 ENVIRONMENTAL VALUATION The probability that the individual says Yes to the bid amount (the probability that the utility of yes is greater than the utility of no) is: Rearranging terms this becomes: Pr{Yes} Pr{V 1 ε 1 V 0 ε 0 } (4) Pr{Yes} Pr{ε 0 ε 1 V 1 V 0 } (5) Equation (5) is a cumulative distribution function where the left-hand side of the inequality is a random variable and the right-hand side is the utility difference (a function of observable elements). We can make assumptions about the errors in this expression and statistically derive estimates of the parameters of the indirect utility functions. To understand the decision to say Yes we need to examine the utility difference between the Yes and No states. This utility difference will depend on the bid amount and the utility from the program being voted on. This logic forms the basis of random utility models and discrete choice analysis. The analyst is examining the probability of a certain response (Yes or No) as a function of the differences in the utilities in the options. Let us examine this issue in a slightly more general fashion. Rather than examine the probability of saying Yes in terms of utility differences, we can also think of the probability that the individual s actual willingness to pay is greater than the amount they are presented as the bid. Consider a version of the indirect utility function that includes the random element associated with choice (elements not observed by the researcher). In addition to the notation above, let Q 1 be the quality level associated with the program or public good and Q 0 the quality level without the program. Let M represent income and B the bid or price presented to the respondent. The probability of saying Yes is: Pr(Yes) Pr(V(Q 1, M B, ε) V(Q 0, M, ε) ) (6) C defines compensating variation in the expression: V(Q 1, M C, ε) V(Q 0, M, ε) (7) A variation function (Hanemann and Kanninen, 1999) that describes the compensating variation for any chosen quality difference is defined as: C(Q 1, Q 0, M, ε) (8) An alternate way to express the probability of saying Yes is based on the compensation function, or the notion that the respondent will say Yes if the willingness to pay (defined by compensating variation) is greater than the bid, or; Pr(Yes) Pr(C(Q 1, Q 0, M, ε) B) 1 Pr(C(Q 1, Q 0, M, ε) B) (9)

7 ENVIRONMENTAL VALUATION: STATED PREFERENCE METHODS 255 Define F as the investigator s assumption of a cumulative distribution function. The probability of saying Yes can now be stated as a function of the bid B, or: Pr(Yes) 1 F(B) (10) Suppose the indirect utility function V is linear. An expression for compensating variation can be formed as follows where the information on the quality change (Q 0 to Q 1 ) is captured in the elements superscripted by 1 and 0 (the intercept and error components): or, after re-arranging terms, V 1 1 (M C) ε 1 0 (M) ε 0 V 0 (11) C ( 1 0 ) ( 1 0 ) (12) The compensation function in this case is the utility difference set equal to zero and solved for C. Note that in this case compensating variation depends on the random terms and therefore is itself a random variable. Given assumptions about the distribution of ε i we can estimate the parameters i and. Quality in equation (11) is reflected in the parameter i. The compensating variation depends on the difference in the i parameters (with the program versus without), however, we will normalize this difference by assigning the utility without the program a value of zero (since only relative utility matters) or we will redefine the difference in i values as simply. Define delta V, or the utility difference as: V V 0 V 1 0 (M) ( 1 (M B) ) (13) and normalize the values to a single. The change in utility between having the program and not having the program is V V 0 V 1 (B) (14) When set equal to zero this provides a measure of the bid B that would make the individual indifferent between having the program and not having the program, or it forms an expression of the compensating variation function (7). The probability of saying Yes can be defined based on equation (7) and (9) for this case of a linear utility function. The probability of saying Yes is the probability that the willingness to pay or compensation function (the utility difference expression) is greater than or equal to the bid amount B. This is also 1 minus the probability that the willingness to pay is less than or equal to the bid amount B, one minus the cdf of the compensation function (Hanemann and Kanninen, 1999).

8 256 ENVIRONMENTAL VALUATION Since F reflects our assumption of the form of the cdf, the probability of saying Yes becomes: Pr(Yes) 1 F( B) (15) If we assume a type I extreme value distribution for the error terms, or a logistic distribution for the difference in the error terms, the following closed form expression results for the probability of saying Yes. B) e( Pr(Yes) 1 1 e 1 ( B) 1 e ( B) (16) If a normal distribution is assumed, the probability of saying Yes becomes Pr(Yes) 1 ( B) (17) where is the cdf of the normal distribution. One can make other assumptions about the shape of the utility function and corresponding assumptions about the shape of the cdf, including a logarithmic, log-normal, Weibull (see Hanemann and Kanninen, 1999). The parameters of these indirect utility functions are typically estimated by maximum likelihood where the likelihood function is the product across respondents of the probability of their response, or N L Pr n (Yes) 1 Pr n (No) (18) n 1 where the probability of saying No is simply 1 minus the probability of saying yes, and is an indicator variable that equals 1 for those who voted Yes. Note that this simple model assumes that everyone has the same preferences. There has been considerable advance on the representation of heterogeneity in random utility models (see Haab and McConnell, 2002, and the discussion below). Welfare measures in the contingent valuation model Given the definition of the probability of Yes the expected value of the random variable compensating variation is (Hanemann and Kanninen, 1999): C 0 F(B) db 0 (1 F(B)) db A graphical depiction is presented in figure 9.2. The measure in equation (19) assumes that willingness to pay for this program can be either positive or negative. (19)

9 ENVIRONMENTAL VALUATION: STATED PREFERENCE METHODS 257 In some cases researchers limit the measure of willingness to pay to include only the positive component, ruling out negative willingness to pay for programs that are welfare improving. This results in a measure of expected compensating variation of: C 0 (1 F(B)) db (20) Graphically this is illustrated in figure 9.3. In addition, one can also define the median welfare measure by the value of B that solves the expression (Hanemann and Kanninen, 1999): 1 F(B) 0.5 (21) C M Figure 9.2 Welfare measures in a discrete choice model Pr (Yes) 1.0 $A Figure 9.3 Expected value of willingness to pay assuming no non-positive WTP

10 258 ENVIRONMENTAL VALUATION Pr(YES) Median WTP $A Figure 9.4 Median willingness to pay or the value that makes the probability of saying Yes equal 0.5. The median is illustrated in figure 9.4 and in figure 9.2 as C m. Double-bounded contingent valuation In the case of double-bounded contingent valuation the respondent is asked two questions. They are first asked if they would be willing to pay a specified amount (B). If they say Yes they are asked if they would pay a higher amount (B U ). If they say no they are asked if they would pay a lower amount (B L ). Four discrete outcomes are possible, {Yes, Yes}, {Yes, No}, {No, Yes}, and {No, No}. Using the notation above, the probability expressions for each of the four cases are: Pr{Yes, Yes} P YY 1 F(B U ) Pr{Yes, No} P YN F(B U ) F(B) (22) Pr{No, Yes} P NY F(B) F(B L ) Pr{No, No} P NN F(B L ) The logic behind equation (22) can be illustrated using a distribution of bids as presented in figure 9.5. The initial bid, upper bid, and lower bid are illustrated on the graph. The areas corresponding to the probabilities are also illustrated. For example, a person who answers Yes and Yes falls into the right hand tail of the distribution and thus the probability of saying Yes is identified by examining the distribution relative to B U or Bid upper.

11 ENVIRONMENTAL VALUATION: STATED PREFERENCE METHODS 259 NN NY YN YY Bid lower Bid Bid upper Figure 9.5 Illustration of double-bounded contingent valuation These probabilities are employed in a likelihood function of the form: L Π n {I YY Pr n YY * I YN Pr n YN * I NY Pr n NY * I NN Pr n NN } (23) where I jk is an indicator of the response (Y or N) to the first (j) and the second (k) questions and n indexes individuals. For example, I YY equals one if the person answered Yes, Yes, and 0 otherwise (Hanemann and Kanninen, 1999). Design issues in contingent valuation While considerable effort has been spent on the economic and econometric structure of contingent valuation, there are several issues that the practitioner or reviewer must be aware of. The first of these is the issue of Strategic Behavior, or the potential that the respondent is behaving strategically when responding. Strategic behavior depends on two elements, the perceived payment obligation and the expectations about the actual provision of the program. For example, if an individual perceives that they will never have to actually pay the amount they offer, but they expect that high reported bids will result in provision of the program, they will report a high (untruthful) value. Conversely, if they think that they may actually have to pay the amount they report, but they don t think that the amount that they report will affect the actual provision of the good, they will under-report their value. This suggests that in order to accurately elicit truthful responses a contingent valuation question must be credible in the sense that the respondent should believe that they will actually have to pay and they should expect that the amounts they report will affect the provision of the public good. In other words they should believe that the contingent valuation task is consequential or will have consequences regarding the provision of the program and the collection of funds. There is still considerable debate in the literature

12 260 ENVIRONMENTAL VALUATION regarding strategic behavior, but it appears that binary choice, referendum contingent valuation questions have the potential to be incentive compatible (or elicit a truthful response) when they are consequential. Conversely, open-ended contingent valuation questions or questions that elicit payments as donations or as voluntary contributions (not binding referenda) will probably not be incentive compatible (see Carson, Groves, and Machina, 2000). Further discussion of preference revelation mechanisms as a way of avoiding strategic behavior can be found in Johansson (1991) A second and somewhat related issue is the degree to which there is a difference between hypothetical and real valuations. A number of experiments have been conducted comparing hypothetical valuations and real payments. Some of these have been conducted by comparing actual sales with hypothetical sales. Other experiments have been in the form of hypothetical and real auctions. It appears that individuals can provide values in contingent valuation tasks that approximate values in actual transactions when they are dealing with familiar goods or transactions they have had some experience with. There is also some evidence that adding cheap talk scripts aids in obtaining accurate valuations (Cummings and Taylor, 1999; List, 2001). Cheap talk is a statement that explains to the respondent that this is a hypothetical valuation question, and that individuals often don t treat hypothetical valuations as they would an actual transaction. The script then encourages the respondent to choose as if this was an actual transaction. These relatively innocuous scripts appear to have a significant impact on valuation elicitation. A further addition to surveys that can improve elicitation is to ask the respondent how certain they are of their decision (or value). This information helps identify those individuals who are unsure of their valuation and reflects a high variance in the valuation response. A phenomenon discovered by contingent valuation practitioners referred to as scoping and sequencing or by some as the embedding effect (Kahneman and Knetsch, 1992) is best illustrated by an example. Imagine that three groups of respondents, A, B, and C are asked contingent valuation questions. The first group is asked to value program 1 as well as a program that is a subset of program 1, called program 2. They are also asked to value program 3, a subset of program 2. The value of program 1 should exceed program 2 which should exceed program 3. The second respondent group is only asked to value programs 2 and 3 and the third respondent group is asked to value program 3. Results of the following form have been discovered (Kahneman and Knetsch, 1992). Respondent groups Program A B C 1 $150 2 $30 $150 3 $15 $40 $140

13 ENVIRONMENTAL VALUATION: STATED PREFERENCE METHODS 261 The values expressed by the groups on their first choice indicate that across the samples the respondents are not sensitive to the scope of the program. This illustrates a lack of sensitivity to scope (see Carson, 2000; Carson and Mitchell, 1995). Within a respondent group the valuation declines with program as one would expect. However, in this case it appears that question order makes a difference in the valuation. If the survey began with program 2, it would be worth $150 to group B. However, it would only appear to be worth $30 to group A. Placing the program lower in the list of items to be valued appears to reduce the value of the good. This is referred to as the sequencing effect (Carson and Mitchell, 1995; Carson, 2000). Of these two issues scope has received the most attention. That is because a good survey instrument should show sensitivity to scope. In most cases contingent valuation practitioners now include tests of scope within their survey designs. The sequencing effect presents a different problem. Economic theory suggests that compensating variation elicited in a sequence will result in decreasing values because of income and substitution effects (Carson and Mitchell, 1995). However, the amount of the decrease is an empirical question and will not in general be resolved in a general sense. This is a kind of context effect and the degree of difference within the sequence depends on the specifics of the case in question. A phenomenon known as warm glow arises in contingent valuation responses (Kahneman and Knetsch, 1992; Andreoni, 1990). The respondent appears to be voting Yes (or providing a large willingness to pay amount) because of the general cause associated with the program rather than the specifics of the program itself. The respondent appears to be purchasing moral satisfaction rather than a specific public good. In environmental cases this occurs when respondents wish to pay for anything that is good for the environment, rather than considering the specific circumstances of the program being offered and the other available uses for their funds. This phenomenon can be identified to a certain degree using debriefing questions and by probing individuals during interviews. It is often also confounded with scoping and sequencing effects. Another way to view the issue of bias in response to contingent valuation or stated preference questions is to assess the validity of the response (Mitchell and Carson, 1989). There are several forms of validity. Construct validity assesses the degree to which the responses conform to predictions from theory. Contingent valuation responses, for example, should be sensitive to changes in income and should vary with the price or bid. Content validity examines whether the survey as presented accurately captures the description of the environmental change and other associated components of the valuation context. Criterion validity examines whether the response corresponds to other similar measures derived using different approaches. For example, a contingent valuation case may be comparable to an experimental auction providing some support for the results on the basis of criterion validity. A challenging issue arising in valuation is the difference between willingness to pay and willingness to accept compensation (Knetsch, 1989; Kahneman et al.,

14 262 ENVIRONMENTAL VALUATION 1990; Mitchell and Carson, 1989). Almost all contingent valuation tasks are structured as willingness to pay questions, implying a property rights situation with the respondent not the current owner of the good/service. Willingness to accept questions are much more difficult to frame and ask because of the implied property right. Furthermore, these values will not, in general, be the same. The difference between willingness to pay and willingness to accept can be substantial and has been found in experiments with market goods as well as hypothetical experiments. One should expect these values to be different if there are limited substitutes for the good being valued (Hanemann, 1991). This suggests that in many cases contingent valuation researchers are eliciting willingness to pay values while they should be eliciting willingness to accept values. In a case of determining compensation for environmental damage, for example, the willingness to pay valuation results will underestimate the true value. The difference between willingness to pay and willingness to accept has substantial ramifications for valuation researchers, as well as for economists in general. Figure 9.6 illustrates the willingness to pay versus willingness to pay difference. This figure examines preferences for income versus a single public good. The initial position is at income level M0 and public good X0. Suppose the public good level was increased to X1. The individual would move to a higher indifference curve. We can examine the amount of money that the individual would give up to get to this utility level (U1) by determining the amount of money it would take to move from U0 to U1 at public good level X1. This is illustrated on the right-hand side of figure 9.6 as the WTP. Conversely, if the amount of the public good dropped to X2, utility would decrease and be reflected by indifference curve U2. The amount of money required to compensate the individual for this loss is illustrated by WTA on the graph. Notice the significant difference between WTP and WTA. M($) WTA WTP M0 U1 U2 U0 X2 X0 X1 X Figure 9.6 Illustration of the difference between willingness to pay and willingness to accept

15 ENVIRONMENTAL VALUATION: STATED PREFERENCE METHODS 263 Table 9.2 NOAA panel recommendations: a selected shortlist 1 Use unbiased/probability sampling 2 Minimize non-response 3 Employ personal interviews 4 Pre-test for interviewer effects 5 Report: the sampling scheme, non-response rates, item non-response rates, the actual questions 6 Pre-test the CV question 7 Employ a conservative design 8 Use a willingness to pay format 9 Use a referendum format 10 Pre-test the photographs/description. 11 Remind respondents of substitutes 12 Allow for adequate time lapse from the incident 13 Average responses from several time periods 14 Include a no-answer (don t know) option 15 Include debriefing questions 16 Present simple crosstabulations 17 Include checks on the respondent s understanding 18 Remind respondents of alternative expenditure possibilities 19 Reduce the warm glow effect 20 Burden of proof on survey designers Summary Contingent valuation has evolved significantly since the 1970s. It has been rigorously tested on a number of different dimensions and substantial improvements in the protocols and methods have been developed. The current standards for a contingent valuation task are outlined in the NOAA Panels Recommendations (Arrow et al., 1993). These are summarized in table 9.2. There is some debate about specific elements in the NOAA recommendations, but in general they provide sound guidelines on the collection of values. 9.3 ATTRIBUTE BASED STATED CHOICE METHODS An alternative to contingent valuation that has emerged over the past few years is Attribute Based Stated Choice Methods (ABSCM). These methods present a set of alternatives (not just two as in the discrete choice contingent valuation case) where the alternatives are defined by attributes (including the price or payment). The choice sets or sets of alternatives are constructed from specific experimental designs that allow the attributes to be uncorrelated and thereby yield un-confounded estimates of the parameters of the conditional indirect utility function. Most applications of ABSCMs also elicit several responses from each individual.

16 264 ENVIRONMENTAL VALUATION ABSCMs will not be applicable to all valuation cases. However, ABSCMs will be useful for cases in which the investigator is interested in the valuation of the attributes of the situation, or cases in which the decision lends itself to a case of respondents choosing from a set of alternatives. ABSCMs arose from the marketing and transportation literature where they were used to measure the demands for market goods or services, especially new goods and services. The technique also has its roots in conjoint analysis ( consider jointly ) in which individuals are asked to provide ratings of products with different profiles. Design and analysis of modern ABSCMs is based on random utility theory and thus is consistent with the theoretical underpinnings of contingent valuation. ABSCMs can be used to identify values in passive use cases or for use values. These methods can also be used to provide data to mix with data from actual markets and help identify preference parameters. Most practitioners in the field recognize the advantages of ABSCM as: (1) the control of the stimuli is in the experimenter s hand, as opposed to the low level of control generally afforded by observing the real marketplace; (2) the control of the design matrix yields greater statistical efficiency and eliminates collinearity (unless explicitly built into the design); (3) the development of more robust models because wider attribute ranges can be applied than are found in real markets; and (4) the introduction and/or removal of products and services is straightforwardly accomplished, as is the introduction of new attributes (see Adamowicz, Louviere, and Swait, 1998; Holmes and Adamowicz, 2003). Applications of ABSCMs generally follow the seven steps outlined below (see Adamowicz, Louviere, and Swait, 1998; Holmes and Adamowicz, 2003): (1) Characterization of the decision problem: This involves the identification of the problem at hand (change in environmental quality affecting recreation behavior, change in provision of public goods that requires a social choice mechanism to be specified for this issue, etc.). The researcher may decide to frame the decision problem as a referendum with multiple alternatives, or as a choice of a set of hypothetical recreation sites, depending on the context. (2) Attribute-level selection: The number of attributes and value of the levels for each attribute is defined in this stage, as appropriate for the decision problem at hand. The attributes of the situation are generally determined by the research problem (definitions of the program or public good) and the interpretation of the respondents. The attributes must be presented in a fashion that is understandable to the respondent and meaningful in terms of the policy problem. Some examples of ABSCMs employ large numbers of attributes (6 or more) while more tend to simplify the problem to 4 or 5 attributes, each with 3 or 4 levels. An example is presented in figure 9.7. The attributes considered in this case were the population levels of important wildlife species, the size of wilderness area, the degree to which recreation was restricted, the employment status of the forest industry and the tax paid by the household. Each of these attributes had four levels that spanned the historical range of the attributes.

17 ENVIRONMENTAL VALUATION: STATED PREFERENCE METHODS 265 (3) Experimental design development: Once attributes and levels have been determined, experimental design procedures are used to construct the choice tasks, alternatives, or profiles that will be presented to the respondents. There is a large literature on experimental design that provides many options for designing choice tasks. The main problem is that the universe of all possible combinations of attributes and levels is usually very large. In the example in figure 9.7 there are 5 attributes and each has 4 levels. The number of combinations of these attributes and levels is 4 5. If two alternatives are presented at a time (two different combinations of attributes) the number of possible combinations (the full-factorial) is or over 1 million combinations. There are several ways to generate combinations of attributes that are useful in statistical analysis and provide a set of alternatives that will elicit trade-offs from respondents. The first is to randomly sample from the universe of all possible combinations of attributes and levels. In the limit this random sample will be orthogonal (no correlation between attributes) and will allow for estimation of the utility parameters. However, it is not clear how large the sample should be to be satisfactory and one cannot ask Utility Choice experiment model parameters Utility of Caribou Caribou Linear Effects Quadratic Recreation restrictions Utility Restriction category Figure 9.7 Attribute based stated preference methods: an application to woodland caribou conservation in Alberta

18 266 ENVIRONMENTAL VALUATION respondents an unlimited number of questions. An alternative is to use experimental design principles. A main effects fraction of the full factorial can be employed. This is a fraction or subset of the full factorial that allows for the estimation of main effects of attributes (but not interactions between attributes). In the case presented in figure 9.7 the main effects orthogonal design generated 32 choice pairs to be presented to respondents (see Adamowicz, Louviere, and Swait, 1998; Holmes and Adamowicz, 2003; Louviere, Hensher, and Swait, 2000). (4) Questionnaire development: The questionnaire can vary from paper and pencil tasks to computer aided surveys. As in any survey-based research, pre-testing of the questionnaire is a necessary component of the research program. The issues raised in the discussion of contingent valuation also apply here focus groups and pre-tests are necessary elements of a good ABSCM application. (5) Sample size and data collection: The usual considerations of desired accuracy levels versus data collection costs must guide definition of sample sizes. (6) Model estimation: ABSCMs are based on random utility theory. The most common estimation approach has been the use of multinomial logit (MNL), and the most common estimation method has been maximum likelihood, although the most appropriate method will depend on the issues being examined. These methods are extensions of the methods presented for contingent valuation. Random utility theory poses the notion that individual consumers choose alternatives that provide them with the greatest utility. Therefore, the probability of selecting an alternative increases as the utility associated with it increases. The utility that an individual derives from an alternative is considered to be associated with the attributes of the alternative, and her utility function is composed of a deterministic component (V) and an unobservable or stochastic component (ε): U V ε (24) where V is the indirect utility function in which the attributes are arguments. Therefore, V can be characterized as: V i k X i (25) where X is a vector of k attributes associated with alternative i and is a coefficient vector. If the stochastic component or error term, is distributed as a type-i extreme value random variable, McFadden (1981) shows that the conditional choice probability of selecting alternative i is: Pr ob(i) e ( kx i ) / j c e ( kx j ) (26)

19 ENVIRONMENTAL VALUATION: STATED PREFERENCE METHODS 267 where is a scale parameter and C is the choice set. Note, however, that is confounded with the parameter vector and cannot be identified. Normally, is set equal to 1.0 and the parameters are estimated using maximum likelihood methods. An individual application of the method involves the generation of a number of bundles of attributes, and these are presented to respondents in series of choice tasks. Thus, the attributes of each alternative offered in a task comprise the X vector and the sets of alternatives in each task comprise C, the choice set. If a respondent was required to answer eight choice tasks, each consisting of three alternatives, the common method of analysis would consider this information as eight individual choices from a trinary universe. The econometric analysis (maximization of the likelihood employing the probabilities derived in (26) ) provides the estimates of the marginal utilities associated with the attributes and allows for their use in welfare measures. (7) Policy Analysis and Decision Support System (DSS) development: Most ABSCM applications are targeted to generating welfare measures, or predictions of behavior, or both. Thus, the models are used to simulate outcomes that can be used in policy analysis or as components of decision support tools. In a linear form of the conditional indirect utility function V, the coefficients are the marginal utilities of the attributes. The ratio of the coefficients provides a measure of the marginal rate of substitution between the attributes. The ratio of any attribute and the price parameter provides a measure of the marginal value of an attribute. For example, if the coefficient estimated for the tax or price attribute was 0.01 and the coefficient for an attribute describing the number of caribou present (as in figure 9.7) was 0.05, then the implicit value of an additional caribou would be $5.00. It is important to note that these ratios of coefficients are not welfare measures like compensating variation but are only marginal value measures. There are two main categories of welfare measures that arise from the use of ABSCMs. The first is the state of the world approach in which one compares the utility in the base case with the utility in a changed case. In these state of the world models the welfare comparison is between two states of the world even though the choice task may ask respondents to choose from several states. In contrast, in ABSCMs that involve choice from several alternatives that can all exist simultaneously (such as multiple brands for products, or multiple recreation sites), the welfare measure must take into account the probability of choosing each alternative when developing the estimate. In these latter cases, the base situation contains multiple alternatives and so also does the improved situation, thus the welfare measure must examine the utilities with and without the improvements, as well as the probabilities of choosing each alternative. If there is an improvement at a site that has little chance of being chosen, then the welfare impact will be small.

20 268 ENVIRONMENTAL VALUATION Welfare measures in state of the world models Assessment of economic welfare involves an investigation of the difference between the well-being (or utility) achieved by the individual under the status quo (or constant base) alternative and some other alternative. It is therefore a matter of considering the value of a change away from the status quo. Let V 0 be the base situation and V 1 be the improved situation. Compensating variation can be expressed as CV (1/ $ )(V 1 V 0 ) (27) Where $ is an estimate of the marginal utility of money. More complex expressions will result from non-linear functional forms of the utility function but the concept of evaluating the amount of money it will take to make V 1 equal V 0 remains. Models with multiple alternatives If there are multiple alternatives available, as in the case of recreation sites or product choice, the welfare measure involves the expected value of the maximum of utility (or utility for each alternative and the probability of choosing each alternative) arising from the multiple alternatives. The expected value of the base case is compared to the expected value of the changed case and again, in the case of linear models, the difference is multiplied by 1 over the marginal utility of income to convert the utility difference into monetary values. For multinomial logit models (MNL) with no income effects, the expected value across the alternatives can be expressed as the log-sum or ln exp(v i ) where ln indicates natural logarithm, exp is the mathematical constant e, the summation is over all of the alternatives, and V i is the conditional indirect utility associated with alternative i. The expression for welfare in these cases is: 1 $ ln C e V i 1 ln 0 C e V i i 1 i 1 (28) where the superscript 0 indicates the base situation and the superscript 1 indicates the changed situation. The alternatives are indexed by i 1,, C. Note that instead of quality changes this measure can also be used to assess the impact of the addition of a new alternative as: 1 $ C 1 ln e V i 1 ln C i 1 i 1 e V i 0 (29) or the removal of an alternative, for example the removal of alternative 1: 1 $ ln C e V i 1 ln 0 C e V i i 2 i 1 (30)

21 ENVIRONMENTAL VALUATION: STATED PREFERENCE METHODS 269 Design issues in attribute based stated choice methods Practitioners employing ABSCMs must be concerned with many of the same issues as those using contingent valuation. Is the scenario accurately specified? Do the respondents understand the choices and the payment vehicles? Is the task consequential or does it appear to be entirely hypothetical? Issues of strategic behavior in response apply to choice experiments just as they do to contingent valuation (Carson et al., 2000) although the more complex nature of choice tasks may limit this behavior. Issues of scope are addressed internally by ABSCMs (via changing attribute levels) but there remain questions about attribute range a form of scope effect. ABSCMs have also elicited differences in willingness to pay and willingness to accept, just as other mechanisms have. However, a number of other design issues arise in ABSCMs. First the researcher must decide on the number of attributes, the number of levels and the number of replications that each respondent will face. This is a difficult task involving a combination of judgment arising from focus groups and pre-testing to considerations of fatigue and learning. It is now relatively commonplace to provide one or two warm-up tasks for respondents. These tasks familiarize the respondent with the process and result in reduced noise throughout the remainder of the tasks. Most practitioners suggest using no more than eight replications per person, although this depends on the context being considered. Econometric considerations also apply in ABSCMs. There is less debate regarding the choice of error distribution as most researchers have adopted the logit framework. However, there are concerns regarding the limitations on preferences arising from the simple logit specification. For example, the simple logit structure implies that the independence of irrelevant alternative assumptions holds. In terms of the probabilities of choice this implies that the ratio of probabilities of any two alternatives is independent of any other alternative (equation (31)). P r (i) P r (j) e(v(i)) e (V(j)) (31) This also implies that the cross-elasticities for the alternatives are all identical. This is a relatively severe restriction of preferences. As an alternative some have proposed the use of nested logit models that involve a type of grouping of alternatives into similar classes (or groups with positive correlations in the unobserved component). For example, the utility of choosing alternative jm, where j indicates the elemental alternative and m indicates a non-status quo choice, can be decomposed into the utility of making a non-status quo choice (U m ) and the utility of choosing alternative j, conditional on making a non-status quo choice. U jm U j m U m V j m V m e j m e m (32) This type of model results in the estimation of utility parameters as well as the estimation of the degree of similarity between alternatives in a group.