Abstract
There has been an explosion of interest during the past two decades in a class of nonmarket stated-preference valuation methods known as choice experiments. The overall objective of a choice experiment is to estimate economic values for characteristics (or attributes) of an environmental good that is the subject of policy analysis, where the environmental good or service comprises several characteristics. Including price as a characteristic permits a multidimensional, preference-based valuation surface to be estimated for use in benefit-cost analysis or any other application of nonmarket valuation. The chapter begins with an overview of the historical antecedents contributing to the development of contemporary choice experiments, and then each of the steps required for conducting a choice experiment are described. This is followed by detailed information covering essential topics such as choosing and implementing experimental designs, interpreting standard and more advanced random utility models, and estimating measures of willingness-to-pay. Issues in implementing and interpreting random utility models are illustrated using a choice experiment application to a contemporary environmental problem. Overall, this chapter provides readers with practical guidance on how to design and analyze a choice experiment that provides credible value estimates to support decision-making.
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- 1.
The label “choice experiment” is a source of controversy. The previous edition of this book used the phrase “attribute-based methods” (which included ratings and rankings), while others have referred to this approach as “attribute-based stated choice methods,” “choice-based conjoint analysis,” and a host of other names. Carson and Louviere (2011) recommended the term “discrete choice experiment” to reflect the fact that these methods elicit a discrete response to an experimentally designed set of choice alternatives. Their definition includes what would normally be viewed as binary contingent valuation questions , as well as other variants of elicitation processes. This chapter focuses on what they refer to as a “multinomial choice sequence” (a series of multialternative experimentally designed choice questions).
- 2.
Rating scale approaches, or traditional conjoint analysis, are based on Torgerson’s (1958) Law of Comparative Judgment. This approach presents individuals with profiles (alternatives) or bundles of attributes and asks them to provide a rating of each profile (e.g., 1 to 10, where 10 is very good, and 1 is very poor). The development of rating-based conjoint is discussed in Green and Srinivasan (1978) and Louviere (1988b).
- 3.
- 4.
A useful graphical tool for visualizing the role of price on choice in a multiattribute context is described by Sur et al. (2007).
- 5.
A main effect is the direct effect of an attribute on a response variable (choice), and it reflects the difference between the average response to each attribute level and the average response across all attributes (Louviere et al. 2000). An interaction effect occurs if the response to the level of one attribute is influenced by the level of another attribute. Interaction effects are represented by parameter estimates for the interaction (cross product) of two or more variables and can account for more complex behavioral responses to combinations of attribute levels.
- 6.
More generally, the number of possible combinations of attribute levels in a full factorial design is \(\pi_{k = 1}^{K} L_{k}\), where \(L_{k}\) is the number of attribute levels associated with attribute k.
- 7.
Attribute level balance leads to larger experimental designs when the number of attribute levels differs across attributes.
- 8.
In general, orthogonality occurs when the joint occurrence of any two attribute levels, for different attributes, appear in attribute combinations with a frequency equal to the product of their individual frequencies. In Table 5.2 for example, each attribute level (−1 or 1) for each attribute appears in one-half of the attribute combinations. Therefore, the joint combination of any two attribute levels (say, −1 and −1) must occur in ½ × ½ = ¼ of the attribute combinations for the design to be orthogonal.
- 9.
Nonlinear effects in this context should not be confused with functional forms of the variables, such as quadratic or logarithmic transformations. If the researcher is interested in whether continuous variables (such as price) are better described by nonlinear functional forms, nonlinear effects could be estimated and used to evaluate the functional form.
- 10.
If the number of attribute levels differs across attributes, then the formulas for computing the number of degrees of freedom required to estimate nonlinear effects must be adjusted. In particular, the value of (L − 1) × A must be computed for each set of attributes with a unique number of levels. Then these values must be summed before adding 1.
- 11.
As the attribute levels of the status quo alternative are held constant across choice sets, the status quo alternative is not included in N (the number of alternatives).
- 12.
The variance-covariance matrix is the inverse of the Fisher information matrix and is based on the second derivative of the log-likelihood function.
- 13.
In particular, McFadden (1974) showed that \({\text{VC}} = \left[ {\mathop \sum \nolimits_{n = 1}^{N} \mathop \sum \nolimits_{j = 1}^{{J_{n} }} x^{\prime}_{jn} P_{jn} (Z,\beta )x_{jn} } \right]^{ - 1}\), where P jn is the probability that an individual will choose Alternative j in Choice set n, which is a function of the attribute design matrix (Z) and a vector of preference parameters (β). Also, \(x_{jn} = z_{jn} - \mathop \sum \nolimits_{i = 1}^{{J_{n} }} z_{in} P_{in}\), where z jn is a row vector describing the attributes of Alternative j in Choice set n.
- 14.
Other criteria for design efficiency have been proposed in the literature. For example, the A-error minimizes the trace of the variance-covariance matrix, which is computed as the sum of the elements on the main diagonal.
- 15.
Huber and Zwerina (1996) showed that, under the assumption that β = 0, the variance-covariance matrix simplifies to \(\left[ {\mathop \sum \nolimits_{n = 1}^{N} \frac{1}{{J_{n} }}\mathop \sum \nolimits_{j = 1}^{{J_{n} }} x^{\prime}_{jn} x_{jn} } \right]^{ - 1}\), where \(x_{jn} = z_{jn} - \frac{1}{{J_{n} }}\mathop \sum \nolimits_{i = 1}^{{J_{n} }} z_{jn}\).
- 16.
This procedure, referred to as a “shifted design,” was initially proposed by Bunch et al. (1996). In general, these designs use modulo arithmetic to shift the original design columns so they take on different levels from the initial orthogonal design.
- 17.
This approach implicitly assumes that the cognitive burden imposed by making difficult trade-offs does not influence the error variance and, therefore, does not bias parameter estimates.
- 18.
To use modulo arithmetic in constructing Table 5.6, begin by recoding each of the −1 values as 0. Then, add 1 to each value in Alternative A except for attributes at the highest level (1), which are assigned the lowest value (0).
- 19.
Although the covariances equal zero in both designs, the efficiency of the fold-over design is gained by the minimal overlap property.
- 20.
One approach to developing nonzero priors is to use an orthogonal design in a pilot study to estimate the β vector, which is then used to minimize the D p -error (Bliemer and Rose 2011).
- 21.
As discussed in Sect. 5.3, when attribute levels are the same across alternatives within a choice set, they do not elicit respondent trade-offs and therefore are uninformative.
- 22.
In all of the tables, *** denotes significance at the 0.01 level, ** denotes significance at the 0.05 level, and * denotes significance at the 0.10 level. Also, standard errors (s.e.) of the coefficients are shown in parentheses below the coefficients.
- 23.
The coefficients shown in Model 1 (Table 5.8) have been rounded to three decimal places. However, the marginal WTP values shown in Table 5.8 were computed before rounding. Computation of marginal WTP values based on the coefficients shown in Model 1 will therefore result in slightly different values than reported in Table 5.8.
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Appendices
Appendix 1: Choice Experiments and Behavior
Many choice experiments that appear in the literature examine passive-use values or “total economic values” in that they ask respondents to choose between options that may affect outcomes associated with passive-use values (e.g., endangered species) or use values (recreation enjoyment, etc.). These are often framed as referenda or social choices. The case study examined in this chapter is an example of this type of choice experiment. However, choice experiments can also be based on behavioral choices alone.
In other literature, such as transportation and marketing, choice experiments are typically used to assess how behavior such as transport mode choice or choice of a product will vary with different attributes. Indeed, the earliest applications of choice experiments in economics included cases of recreation site choice or property choices. There are a variety of reasons to use choice experiments in the analysis of behavior, even if revealed preference data on such choices are available. Choice experiments can present attributes that are outside the range of the existing set of attributes (e.g., higher levels of fishing catch rates or higher levels of congestion on hiking trails) and reflect new attributes (e.g., environmental labels on consumer products), and experimental design can help in identifying parameters on attributes that are typically correlated in the real world (e.g., water quality and fish catch). Choice experiment data can also be combined with revealed preference data to help calibrate stated-preference responses or to compare stated and revealed preference information. Efforts in “data fusion” or the combination of stated and revealed preference information have included analyses of recreation, property choices, as well as the integration of perceived and objective measures of attributes. A key aspect of the use of choice experiments in behavioral contexts is that the collection of stated choice data support a model based on economic theory such as the travel cost model of recreation choice behavior (Bockstael and McConnell 2007) or the random utility approach to property choice and valuation (Phaneuf et al. 2013). To help illustrate these approaches we provide two examples of choice experiments that involve linkages to behavioral choices—recreation site choice and property choices.
In the recreation case, the respondent is asked to choose between two moose hunting sites in which the attributes include distance (the travel cost) and other characteristics of the hunting site (Fig. 5.2). The attributes are described in a way that hunters typically view them, and in a way that they can be translated to “objective” measures of wildlife populations and site descriptions. This case study, drawn from Adamowicz et al. (1997), included information on actual site choices and elicited information from hunters on their perceptions as well as actual measures of attributes. This data set has been used in other applications including the assessment of unobserved heterogeneity in stated and revealed preference data (von Haefen and Phaneuf 2008). Note that in this case, the hunting sites are described as “generic” sites (Site A and Site B). In some contexts these can be described as actual sites with “labels” such as the name of the area or the administrative label (see Boxall and Adamowicz, 2002, for an application to canoeing sites). Similar types of choice experiments have been used to elicit tradeoffs in the context of subsistence resource use (trading off distance with traditional use hunting or caloric expenditures for fuelwood collection (see Adamowicz et al., 2004).
Example of a recreational hunting site choice
The second example (Fig. 5.3) is similar to the property choice cases used in Phaneuf et al. (2013) and is based on Kim (2014). In this case, information about a respondent’s current house is elicited. This information is used as the “base” case for the choice experiment, and attributes are presented that describe changes to the house (larger area, different water quality in the adjacent lake, etc.). This choice experiment uses an experimental design referred to as a “pivot design” in that it pivots the choices around the currently held option or behavior (Hess and Rose 2009).
Example of a property choice
Appendix 2: Choice Experiments and the Value of Health Risk Reduction
A nonmarket value that is very important in policy analysis is the value of mortality risk reduction, often referred to as the value of statistical life (see Cameron, 2010, for a review of the issues and a thorough critique of the term “value of statistical life”). The value of mortality risk reductions often comprises over 80% of the monetary value of air pollution reduction policies such as the assessment of the U.S. Clean Air Act Amendments. Mortality risk values have typically been measured using hedonic wage models (see Chap. 7) wherein the impact of changing job risk characteristics are reflected in higher wages, all else held constant. Over the past few decades however stated-preference methods have been increasingly used to elicit the value of risk reductions. In a typical setting a respondent is informed about baseline risk levels and then presented with a treatment that offers a reduction in health risks, but at a cost. The tradeoff between cost and risk change provides a measure of the monetary value of risk reduction.
While contingent valuation has typically been used to measure mortality risks (e.g., Krupnick et al. 2002), choice experiments are increasingly being used to separate mortality from morbidity risks (Adamowicz et al. 2011) or to include risk context elements within the valuation tasks, such as latency (a delay in the timing of the benefits from the risk reduction), type of risk, or other elements (Alberini and Ščasný 2011).
The value of mortality risk reduction is expressed as the willingness to pay for a small reduction in the probability of mortality. For example, if individuals are willing to pay $10,000 for a 1% reduction in their risk of dying in a year, this would translate into a $1,000,000 value of statistical life (100 people valuing a 1% risk reduction would equal one “statistical life” and 100 times $10,000 is $1,000,000). A choice experiment, therefore, can be designed to elicit trade-offs between a current situation (with the mortality risk presented) and an “improved” situation with the risks reduced.
The figures that follow illustrate these choices based on the case of water risks in Adamowicz et al. (2011). The respondent faces a base or status quo set of risks (mortality and morbidity from cancer and microbial impacts) and trades them off against a set of new programs. A two-alternative case (Fig. 5.4) and a three-alternative case (Fig. 5.5) are presented to illustrate that in this context, the choice experiment can be presented like a contingent valuation referendum task as well as in the context of a multiple alternative choice experiment (see, however, Zhang and Adamowicz, 2011). Note also that the risks are presented numerically (number of illnesses and deaths) as well as graphically, using grids of points to represent the risks.
Risk communication is a particularly important aspect of the assessment of health risk reductions. The random utility model that arises from these choices provides the marginal utility of risk and the marginal utility of money, and thus the value of a change in risk can be derived. Adamowicz et al. (2011) examined risks in water, while other researchers have examined climate change risks (Ščasný and Alberini 2012), risks from nuclear versus fossil fuel based energy (Itaoka et al. 2006), and mortality risks from different risk contexts, including transport, respiratory illness, and cancer (Alberini and Ščasný 2011). One of the most sophisticated choice experiments examining the value of health risk reductions, from Cameron and DeShazo (2013), examined latency, timing of illness, type of illness, and other factors that affect health.
Example of a two-alternative choice task eliciting values of health risk reductions
Example of a three-alternative choice experiment eliciting values of health risk reduction
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Holmes, T.P., Adamowicz, W.L., Carlsson, F. (2017). Choice Experiments. In: Champ, P., Boyle, K., Brown, T. (eds) A Primer on Nonmarket Valuation. The Economics of Non-Market Goods and Resources, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7104-8_5
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