Experimental Economics

, Volume 12, Issue 3, pp 332–349 | Cite as

Range effects and lottery pricing

Article

Abstract

A standard method to elicit certainty equivalents is the Becker-DeGroot-Marschak (BDM) procedure. We compare the standard BDM procedure and a BDM procedure with a restricted range of minimum selling prices that an individual can state. We find that elicited prices are systematically affected by the range of feasible minimum selling prices. Expected utility theory cannot explain these results. Non-expected utility theories can only explain the results if subjects consider compound lotteries generated by the BDM procedure. We present an alternative explanation where subjects sequentially compare the lottery to monetary amounts in order to determine their minimum selling price. The model offers a formal explanation for range effects and for the underweighting of small and the overweighting of large probabilities.

Keywords

Certainty equivalent Experiment Stochastic Becker-DeGroot-Marschak (BDM) method Elicitation procedure Range effects 

JEL Classification

C91 D81 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ballinger, T. P., & Wilcox, N. T. (1997). Decisions, error and heterogeneity. Economic Journal, 107, 1090–1105. CrossRefGoogle Scholar
  2. Bateman, I., Day, B., Loomes, G., & Sugden, R. (2007). Can ranking techniques elicit robust values?. Journal of Risk and Uncertainty, 34, 49–66. CrossRefGoogle Scholar
  3. Becker, G. M., DeGroot, M. H., & Marschak, J. (1964). Measuring utility by a single-response sequential method. Behavioral Science, 9, 226–232. CrossRefGoogle Scholar
  4. Blavatskyy, P., & Köhler, W. (2007). Lottery pricing under time pressure. IEW working paper. Google Scholar
  5. Fischbacher, U. (2007). z-tree: Zurich toolbox for ready-made economic experiments. Experimental Economics, 10(2), 171–178. CrossRefGoogle Scholar
  6. Harbaugh, W. T., Krause, K., & Vesterlund, L. (2003). Prospect theory in choice and Pricing tasks. Working paper. Google Scholar
  7. Hey, J., & Orme, C. (1994). Investigating generalisations of expected utility theory using experimental data. Econometrica, 62, 1291–1326. CrossRefGoogle Scholar
  8. Johnson, J. G., & Busemeyer, J. R. (2005). A dynamic, stochastic, computational model of preference reversal phenomena. Psychological Review, 112, 841–861. CrossRefGoogle Scholar
  9. Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47, 263–291. CrossRefGoogle Scholar
  10. Karni, E., & Safra, Z. (1987). Preference reversal” and the observability of preferences by experimental methods. Econometrica, 55, 675–685. CrossRefGoogle Scholar
  11. Quiggin, J. (1981). Risk perception and risk aversion among Australian farmers. Australian Journal of Agricultural Recourse Economics, 25, 160–169. Google Scholar
  12. Starmer, C., & Sugden, R. (1991). Does the random-lottery incentive system elicit true preferences? An experimental investigation. American Economic Review, 81, 971–978. Google Scholar
  13. Tversky, A., Slovic, P., & Kahneman, D. (1990). The causes of preference reversal. American Economic Review, 80, 204–217. Google Scholar
  14. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5, 297–323. CrossRefGoogle Scholar
  15. Wilcox, N. (1994). On a lottery pricing anomaly: time tells the tale. Journal of Risk and Uncertainty, 8, 311–324. Google Scholar

Copyright information

© Economic Science Association 2009

Authors and Affiliations

  1. 1.Institute for Empirical Research in EconomicsUniversity of ZurichZurichSwitzerland

Personalised recommendations