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2017 | OriginalPaper | Buchkapitel

5. Choice Experiments

verfasst von : Thomas P. Holmes, Wiktor L. Adamowicz, Fredrik Carlsson

Erschienen in: A Primer on Nonmarket Valuation

Verlag: Springer Netherlands

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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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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).

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).

See also subsequent work by Manski (1977) and Yellot (1977).

A useful graphical tool for visualizing the role of price on choice in a multiattribute context is described by Sur et al. (2007).

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.

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.

Attribute level balance leads to larger experimental designs when the number of attribute levels differs across attributes.

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.

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.

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.

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).

The variance-covariance matrix is the inverse of the Fisher information matrix and is based on the second derivative of the log-likelihood function.

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.

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.

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 \(x_{jn} = z_{jn} - \frac{1}{{J_{n} }}\mathop \sum \nolimits_{i = 1}^{{J_{n} }} z_{jn}\).

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.

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.

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).

Although the covariances equal zero in both designs, the efficiency of the fold-over design is gained by the minimal overlap property.

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).

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.

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.

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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Titel: Choice Experiments
verfasst von: Thomas P. Holmes
Wiktor L. Adamowicz
Fredrik Carlsson
Verlag: Springer Netherlands
Buch: A Primer on Nonmarket Valuation
Print ISBN: 978-94-007-7103-1

Electronic ISBN: 978-94-007-7104-8

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-94-007-7104-8_5

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