Design efficiency for non-market valuation with choice modelling: how to measure it, what to report and why
AbstractWe review the basic principles for the evaluation of design efficiency in discrete choice modelling with a focus on efficiency of WTP estimates from the multinomial logit model. The discussion is developed under the realistic assumption that researchers can plausibly define a prior belief on the range of values for the utility coefficients. D-,A-,B-,Sand Cerrors are compared as measures of design performance in applied studies and their rationale is discussed. An empirical example based on the generation and comparison of fifteen separate designs from a common set of assumptions illustrates the relevant considerations to the context of non-market valuation, with particular emphasis placed on Cefficiency. Conclusions are drawn for the practice of reporting in non-market valuation and for future work on design research
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Bibliographic InfoArticle provided by Australian Agricultural and Resource Economics Society in its journal Australian Journal of Agricultural and Resource Economics.
Volume (Year): 52 (2008)
Issue (Month): 3 (September)
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Other versions of this item:
- John M. Rose & Riccardo Scarpa, 2007. "Designs Efficiency for Non-market Valuation with Choice Modelling: How to Measure It, What to Report and Why," Working Papers in Economics 07/21, University of Waikato, Department of Economics.
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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- Train,Kenneth E., 2009.
"Discrete Choice Methods with Simulation,"
Cambridge University Press, number 9780521747387, December.
- Riccardo Scarpa & Danny Campbell & W. George Hutchinson, 2007. "Benefit Estimates for Landscape Improvements: Sequential Bayesian Design and Respondents’ Rationality in a Choice Experiment," Land Economics, University of Wisconsin Press, vol. 83(4), pages 617-634.
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- DeShazo, J. R. & Fermo, German, 2002. "Designing Choice Sets for Stated Preference Methods: The Effects of Complexity on Choice Consistency," Journal of Environmental Economics and Management, Elsevier, vol. 44(1), pages 123-143, July.
- Jayson L. Lusk & F. Bailey Norwood, 2005. "Effect of Experimental Design on Choice-Based Conjoint Valuation Estimates," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(3), pages 771-785.
- Ferrini, Silvia & Scarpa, Riccardo, 2007. "Designs with a priori information for nonmarket valuation with choice experiments: A Monte Carlo study," Journal of Environmental Economics and Management, Elsevier, vol. 53(3), pages 342-363, May.
- Hensher, David A. & Stopher, Peter R. & Louviere, Jordan J., 2001. "An exploratory analysis of the effect of numbers of choice sets in designed choice experiments: an airline choice application," Journal of Air Transport Management, Elsevier, vol. 7(6), pages 373-379.
- Arentze, Theo & Borgers, Aloys & Timmermans, Harry & DelMistro, Romano, 2003. "Transport stated choice responses: effects of task complexity, presentation format and literacy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 39(3), pages 229-244, May.
- David A. Hensher, 2006. "How do respondents process stated choice experiments? Attribute consideration under varying information load," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 861-878.
- Barbara J. Kanninen, 1993. "Optimal Experimental Design for Double-Bounded Dichotomous Choice Contingent Valuation," Land Economics, University of Wisconsin Press, vol. 69(2), pages 138-146.
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