Hostname: page-component-7b9c58cd5d-g9frx Total loading time: 0 Render date: 2025-03-17T06:19:49.982Z Has data issue: false hasContentIssue false
  • English
  • Français

Range effects and lottery pricing

Published online by Cambridge University Press:  14 March 2025

Pavlo R. Blavatskyy  and
Wolfgang R. Köhler
Pavlo R. Blavatskyy*
Affiliation:
Institute for Empirical Research in Economics, University of Zurich, Winterthurerstrasse 30, 8006, Zurich, Switzerland
Wolfgang R. Köhler*
Affiliation:
Institute for Empirical Research in Economics, University of Zurich, Winterthurerstrasse 30, 8006, Zurich, Switzerland
*
e-mail: pavlo.blavatskyy@iew.unizh.ch
e-mail: wolfgang.koehler@iew.unizh.ch
Get access Rights & Permissions [Opens in a new window]

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. Nonexpected 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 equivalentExperimentStochasticBecker-DeGroot-Marschak (BDM) methodElicitation procedureRange effects

JEL classification

C91: Laboratory, Individual Behavior D81: Criteria for Decision-Making under Risk and Uncertainty
Type
Research Article
Information
Experimental Economics , Volume 12 , Issue 3 , September 2009 , pp. 332 - 349
Copyright
Copyright © Economic Science Association 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

We thank seminar participants in Rotterdam and Zürich and at the ESA World Meeting in Rome for helpful comments. We are grateful to Ganna Pogrebna for her assistance with programming the experiment and to Franziska Heusi for her help in organizing the experimental session. Pavlo Blavatskyy acknowledges financial support from the Fund for Support of Academic Development at the University of Zurich. A previous version of this paper was circulated under the title “Lottery Pricing in the Becker-DeGroot-Marschak Procedure”.

References

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