Optimal Experimental Design for Nonmarket Choice ExperimentsEPA Grant Number: R827987
Title: Optimal Experimental Design for Nonmarket Choice Experiments
Investigators: Kanninen, Barbara J.
Institution: Hubert H. Humphrey Institute of Public Affairs
EPA Project Officer: Lee, Sonja
Project Period: January 3, 2000 through January 2, 2003
Project Amount: $61,014
RFA: Decision-Making and Valuation for Environmental Policy (1999) RFA Text | Recipients Lists
Research Category: Economics and Decision Sciences
To assess the total value, including use and nonuse values, of nonmarket goods such as environmental amenities, researchers often apply survey techniques that allow them to explore public preferences for hypothetical goods or services. Until recently, the standard survey technique for this purpose has been the contingent valuation method. By now, a similar but more complex approach to choice experiments, conjoint analysis, has been used in several environmental contexts. Conjoint analysis is a marketing technique that can be used to assess values for attributes of market or nonmarket goods based on survey respondents' willingness to trade-off different bundles of these attributes.
The goal of this study is to extend the research being completed by the principal investigator (PI) on optimal design for conjoint analysis. In that research, the PI derived optimal designs for "main effects" binary and multinomial choice experiments. Although this represents an important extension to the current literature on experimental design for conjoint analysis, there are still several issues that must be addressed to fill out the literature on optimal survey design, especially for the nonmarket valuation context.
Specifically, the current conjoint design research has not addressed models that include significant substitution effects. Such models have been shown to be more appropriate for environmental amenities than models that ignore interactive effects. The nonlinearities that occur with these models present a new complexity to the experimental design process.
Further, the optimal design criterion used in the PI's current research, "D-optimality," is not necessarily the most appropriate for nonmarket valuation problems. D-optimality focuses on jointly obtaining the most efficient model parameter estimates. Environmental valuation problems often are more focused, however, on obtaining the best estimates possible for willingness to pay, a nonlinear function of the model parameters, or in cases of resource compensation, the best estimates for a marginal rate of substitution, a ratio of two specific model parameters. A more appropriate design criterion for nonmarket valuation would focus on one of these statistical measures. This project is to address the issues of including substitution effects and developing more appropriate design criteria for binary and multinomial nonmarket choice experiments. Objectives/Hypothesis:
To derive optimal designs for choice experiments when substitution effects are significant. To make the current literature and research of the PI more relevant to environmental valuation by using optimal design criteria that are directly linked to the goals of improving estimation of willingness to pay or marginal rates of substitution. To compare the statistical gains or losses associated with multinomial versus binary choice experiments and with the inclusion or omission of substitution effects. To generate rules of thumb that can be easily communicated to experimental choice researchers to improve their survey designs in practice.
The design problem will be set up by specifying the utility-theoretic choice model and analytically deriving the optimal design criteria. In this study, the criteria will be the minimization of the variances of willingness to pay and a marginal rate of substitution. Analytical solutions will be derived and specific numerical solutions will be calculated from the analytical solutions using the Mathematica software program. Once numerical solutions are obtained, rules of thumb can be developed that can be easily communicated to conjoint researchers to improve their study designs.
The proposed research will provide explicit expressions and rules of thumb for the design of conjoint analysis questions, allowing researchers to estimate willingness to pay with greater statistical precision using lower sample sizes than they otherwise would need. This ability will, in turn, benefit governmental policy-makers who sponsor valuation studies by either reducing the costs of survey administration or increasing the reliability of the study conclusions.