Grantee Research Project Results

2000 Progress Report: Optimal Experimental Design for Nonmarket Choice Experiments

EPA Grant Number: R827987
Title: Optimal Experimental Design for Nonmarket Choice Experiments
Investigators: Kanninen, Barbara J.
Institution: University of Minnesota
EPA Project Officer: Chung, Serena
Project Period: January 3, 2000 through January 2, 2003
Project Period Covered by this Report: January 3, 2000 through January 2, 2001
Project Amount: $61,014
RFA: Decision-Making and Valuation for Environmental Policy (1999) RFA Text |  Recipients Lists
Research Category: Environmental Justice

Objective:

The overall objective of this project is to derive optimal designs for stated choice experiments when substitution effects are present. Specific objectives are to:

1. Make the current literature and the investigator's research 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.
2. Compare the statistical gains or losses associated with multinomial versus binary choice experiments and with the inclusion or omission of substitution effects.
3. Generate rules of thumb that can be easily communicated to experimental choice researchers to improve their survey designs in practice.
   

Progress Summary:

In environmental economics, choice experiments are used to assess people's willingness to pay for different environmental attributes. "Optimal design" for choice experiments refers to the way attributes are assigned to choice sets so that the researcher can obtain as much information as possible about underlying preferences.

Earlier research by the principal investigator (PI) derived optimal designs for choice experiments based on the D-optimality criterion. This is a popular optimal design criterion that focuses on maximizing the statistical information that can be obtained about the full set of underlying model parameters. The first phase of the current project focused on another optimal design criterion: C-optimality. This criterion focuses on getting the best estimate possible for willingness to pay.

The solution to the C-optimal design problem is to generate choice sets that make respondents perfectly indifferent between the alternatives. In other words, there should be a zero utility difference between each alternative in a choice set. Unfortunately, in practice, this solution results in multicolinearity. Attribute levels in each alternative are exact linear combinations of the other attributes. Multicolinearity occurs because there are more variables in the problem than are necessary for estimating willingness to pay. The model falls apart and is inestimable.

Based on this result, the PI has concluded that our standard approach to estimating willingness to pay, as a ratio of estimated parameters, is inherently inefficient. From a purely statistical perspective, the C-optimal solution makes clear that the best approach is to drop extraneous variables and estimate willingness to pay directly.

The results of the current phase of the research bring us back to the PI's previous research project. Because we generally use an indirect approach to estimate willingness to pay, the PI has concluded that the D-optimality criterion, rather than C, is the most appropriate basis for designing choice experiments.

The PI also briefly investigated the question of how the number of alternatives in a choice set affect estimation efficiency. This turns out to be a complicated question that is directly tied to the specific attribute levels used in the different alternatives. In a Monte Carlo study, the PI found that adding alternatives with intermediate attribute levels can actually decrease the statistical information provided by a choice set. Exactly when and how this occurs would be an interesting research line to pursue further.

Future Activities:

The next phase of the project is to address the model with substitution effects (the first objective listed above). Substitution effects occur when an individual's willingness to pay for two attributes together is less than the sum of his or her willingness to pay for each attribute separately. Because of the results just discussed on C-optimality, the next phase will focus on D-optimality as the design criterion. The model with substitution effects will have a nonlinear utility expression, which greatly complicates derivation of the optimal designs. It is not clear whether it will be possible to derive a general solution. Instead, it might be that solutions are dependent on the specific parameter values. If this is the case, solutions will be provided in a series of tables. The project currently is on schedule and no changes are anticipated.

Journal Articles on this Report : 1 Displayed | Download in RIS Format

Publications Views

Other project views:	All 5 publications	2 publications in selected types	All 1 journal articles

Publications

Type	Citation	Project	Document Sources
Journal Article	Kanninen BJ. Optimal design for multinomial choice experiments. Journal of Marketing Research 2002;39(2):214-227.	R827987 (2000) R827987 (Final)	Abstract: Journal of Marketing Research Abstract Exit

Supplemental Keywords:

conjoint analysis, nonmarket valuation, contingent valuation, survey, willingness-to-pay, multinomial logit, D-optimality, C-optimality., RFA, Economic, Social, & Behavioral Science Research Program, Scientific Discipline, Economics, decision-making, Ecology and Ecosystems, Ecological Risk Assessment, Economics & Decision Making, Social Science, contingent valuation, multi-objective decision making, policy analysis, surveys, optimal experimentation, dichotomous-choice, decision analysis, nonmarket choice, market valuation models, non-market valuation, standards of value, D-optimality, public policy, willingness to pay, multinominal nonmarket choice experiments, conjoint analysis

Progress and Final Reports:

Original Abstract

2001

Final Report