Abstract
In this paper, we propose a new criterion for selecting efficient conjoint choice designs when the interest is in quantifying willingness to pay (WTP). The new criterion, which we call the WTP-optimality criterion, is based on the c-optimality criterion which is often used in the optimal experimental design literature. We use a simulation study to evaluate the designs generated using the WTP-optimality criterion and discuss the design of a real-life conjoint experiment from the literature. The results show that the new criterion leads to designs that yield more precise estimates of the WTP than Bayesian D-optimal conjoint choice designs, which are increasingly being seen as the state-of-the-art designs for conjoint choice studies, and to a substantial reduction in the occurrence of unrealistically high WTP estimates.
Article PDF
Similar content being viewed by others
References
Adamowicz W, Boxall P, Williams M, Louviere J (1998) Stated preference approaches for measuring passive use values: choice experiments and contingent valuation. Am J Agric Econ 80: 64–75
Alberini A (1995) Optimal designs for discrete choice contingent valuation surveys: single-bound, double-bound, and bivariate models. J Environ Econ Manage 28: 287–306
Atkinson AC, Donev AN (1992) Optimum experimental designs. Clarendon Press, Oxford
Atkinson AC, Haines LM (1996) Designs for nonlinear and generalized linear models. In: Ghosh S, Rao CR (eds) Handbook of statistics 13: design and analysis of experiments. Elsevier, Leiden, pp 437–475
Baiocchi G (2005) Monte carlo methods in environmental economics. In: Scarpa R, Alberini A (eds) Applications of simulation methods in environmental and resource economics, chapter 16. Springer, Dordrecht, pp 317–340
Banfi S, Farsi M, Filippini M, Jacob M (2008) Willingness to pay for energy-saving measures in residential buildings. Energy Econ 30: 503–516
Bliemer MCJ, Rose JM (2010) Construction of experimental designs for mixed logit models allowing for correlation across choice observations. Trans Res B 44: 720–734
Boxall PC, Adamowicz WL (2002) Understanding heterogeneous preferences in random utility models: a latent class approach. Environ Resource Econ 23: 421–446
Brau R, Cao D (2006) Uncovering the macrostructure of tourists’ preferences. A choice experiment analysis of tourism demand of Sardinia. Note di Lavoro della Fondazione Eni Enrico Mattei
Carlsson F, Martinsson P (2001) Do hypothetical and actual marginal WTP differ in choice experiments?. J Environ Econ Manage 41: 179–192
Carson R, Louviere J, Wasi N (2009) A cautionary note on designing discrete choice experiments: a comment on Lusk and Norwood’s “Effect on experiment design on choice-based conjoint valuation estimates”. Am J Agric Econ 91: 1056–1063
Ferrini S, Scarpa R (2007) Designs with a-priori information for nonmarket valuation with choice experiments: a Monte Carlo study. J Environ Econ Manage 53: 342–363
Hearne R, Salinas Z (2002) The use of choice experiments in the analysis of tourist preferences for ecotourism development in Costa Rica. J Environ Manage 65: 153–163
Hensher D, Sullivan C (2003) Willingness to pay for road curviness and road type. Trans Res D 8: 139–155
Hole AR (2007) A comparison of approaches to estimating confidence intervals for willingness to pay measures. Health Econ 16: 827–840
Huber J, Zwerina K (1996) The importance of utility balance in efficient choice designs. J Market Res 33: 307–317
Kanninen B (1995) Bias in discrete response contingent valuation. J Environ Econ Manage 28: 114–125
Kanninen BJ (1993) Optimal experimental design for double-bounded dichotomous choice contingent valuation. Land Econ 69: 138–146
Kessels R, Goos P, Vandebroek M (2006) A comparison of criteria to design efficient choice experiments. J Market Res 43: 409–419
Kessels R, Jones B, Goos P, Vandebroek M (2008) Recommendations on the use of Bayesian optimal designs for choice experiments. Qual Reliab Eng Int 24: 737–744
Kessels R, Jones B, Goos P, Vandebroek M (2009) An efficient algorithm for constructing Bayesian optimal choice designs. J Bus Econ Stat 27: 279–291
Kimenju S, Morawetz U, Groote HD (2005) Comparing contingent valuation methods, choice experiments and experimental auctions in soliciting consumer preference for maize in Western Kenya: preliminary results, Paper prepared for presentation at the African Econometric Society 10th annual conference on econometric modeling in Africa, Nairobi, Kenya
Kuhfeld W, Tobias R, Garratt M (1994) Efficient experimental designs with marketing applications. J Market Res 31: 545–557
Louviere J, Eagle T (2006) Confound it! That pesky little scale constant messes up our convenient assumptions! in Sawtooth Software Conference Proceedings; Sequem, Washington: Sawtooth Software, pp 211–228
Louviere JJ, Street D, Carson R, Ainslie A, DeShazo J, Cameron T, Hensher D, Kohn R, Marley A (2002) Dissecting the random component of utility. Market Lett 13: 177–193
Lusk JL, Norwood FB (2005) Effect of experimental design on choice-based conjont valuation estimates. Am J Agric Econ 87: 771–785
Lusk JL, Norwood FB (2009) A cautionary note on the design of discrete choice experiments: reply. Am J Agric Econ 91: 1064–1066
Lusk JL, Roosen J, Fox JA (2003) Demand for beef from cattle administered growth hormones or fed genetically modified corn: a comparison of consumers in France, Germany, the U.K. and the U.S. Am J Agric Econ 85: 16–29
Morey E, Rossmann K (2003) Using stated-preference questions to investigate variations in willingness to pay for preserving marble monuments: classic heterogeneity, random parameters, and mixture models. J Cultural Econ 27: 215–229
Mtimet N, Albisu L (2006) Spanish wine consumer behavior: a choice experiment approach. Agribusiness 22: 343–362
Nyquist H (1992) Optimal designs of discrete response experiments in contingent valuation studies. Rev Econ Stat 74: 559–563
Ruto E, Garrod G, Scarpa R (2008) Valuing animal genetic resources: a choice modeling application to indigenous cattle in Kenya. Agric Econ 38: 89–98
Ryan M (2004) A comparison of stated preference methods for estimating monetary values. Health Econ 13: 291–296
Sammer K, Wüstenhagen R (2006) The influence of eco-labelling on consumer behaviour—results of a discrete choice analysis for washing machines. Bus Strategy Environ 15: 185–199
Sándor Z, Wedel M (2001) Designing conjoint choice experiments using managers’ prior beliefs. J Market Res 38: 430–444
Scarpa R, Campbell D, Hutchinson WG (2007) Benefit estimates for landscape improvements: sequential Bayesian design and respondents’ rationality in a choice experiment. Land Econ 83: 617–634
Scarpa R, Rose J (2008) Design efficiency for non-market valuation with choice modelling: how to measure it what to report and why. Aust J Agric Resource Econ 52: 253–282
Scarpa R, Thiene M, Train K (2008) Utility in WTP space: a tool to address confounding random scale effects in destination choice to the Alps. Am J Agric Econ 90: 994–1010
Sonnier G, Ainslie A, Otter T (2007) Heterogeneity distributions of willingness-to-pay in choice models. Quant Market Econ 5(3): 313–331
Swait J, Adamowicz W (2001) The influence of task complexity on consumer choice: a latent class model of decision strategy switching. J Consumer Res 28: 135–148
Swait J, Louviere J (1993) The role of the scale parameter in the estimation and comparison of multinomial logit models. J Market Res 30: 305–314
Train K (2003) Discrete choice methods with simulation. Cambridge University Press, New York
Train K, Weeks M (2005) Discrete choice models in preference space and willingness-to-pay space. In: Scarpa R, Alberini A (eds) Applications of simulation methods in environmental and resource economics. Springer, Dordrecht
Vermeulen B, Goos P, Scarpa R, Vandebroek M (2009) Design criteria to develop choice experiments to measure the WTP accurately. Research Report KBI_0816, Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, 29 pp
Yu J, Goos P, Vandebroek M (2009a) Efficient conjoint choice designs in the presence of respondent heterogeneity. Market Sci 28: 122–135
Yu J, Goos P, Vandebroek M (2009b) Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity. Research Report KBI_0902, Department of Decision Sciences and Information Management, Katholieke Universiteit Leuven, 15 pp
Acknowledgments
The first author’s research was funded by the project G.0611.05 of the Fund for Scientific Research Flanders.
Open Access
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
About this article
Cite this article
Vermeulen, B., Goos, P., Scarpa, R. et al. Bayesian Conjoint Choice Designs for Measuring Willingness to Pay. Environ Resource Econ 48, 129–149 (2011). https://doi.org/10.1007/s10640-010-9401-6
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10640-010-9401-6