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Measuring and controlling for the compromise effect when estimating risk preference parameters

Published online by Cambridge University Press:  14 March 2025

Jonathan P. Beauchamp Open the ORCID record for Jonathan P. Beauchamp [Opens in a new window] ,
Daniel J. Benjamin ,
David I. Laibson  and
Christopher F. Chabris
Jonathan P. Beauchamp*
Affiliation:
George Mason University, Arlington, USA
Daniel J. Benjamin
Affiliation:
University of Southern California, Los Angeles, USA NBER, Cambridge, USA
David I. Laibson
Affiliation:
NBER, Cambridge, USA Harvard University, Cambridge, USA
Christopher F. Chabris
Affiliation:
Geisinger Health Systems, Lewisburg, USA
*
[email protected]
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Abstract

The compromise effect arises when being close to the “middle” of a choice set makes an option more appealing. The compromise effect poses conceptual and practical problems for economic research: by influencing choices, it can bias researchers’ inferences about preference parameters. To study this bias, we conduct an experiment with 550 participants who made choices over lotteries from multiple price lists (MPLs). Following prior work, we manipulate the compromise effect to influence choices by varying the middle options of each MPL. We then estimate risk preferences using a discrete-choice model without a compromise effect embedded in the model. As anticipated, the resulting risk preference parameter estimates are not robust, changing as the compromise effect is manipulated. To disentangle risk preference parameters from the compromise effect and to measure the strength of the compromise effect, we augment our discrete-choice model with additional parameters that represent a rising penalty for expressing an indifference point further from the middle of the ordered MPL. Using this method, we estimate an economically significant magnitude for the compromise effect and generate robust estimates of risk preference parameters that are no longer sensitive to compromise-effect manipulations.

Keywords

Compromise effectCumulative prospect theoryLoss aversionRisk preferences

JEL classification

D14: Household Saving • Personal Finance G11: Portfolio Choice • Investment Decisions B49: Other D83: Search • Learning • Information and Knowledge • Communication • Belief • Unawareness
Type
Original Paper
Information
Experimental Economics , Volume 23 , Issue 4 , December 2020 , pp. 1069 - 1099
Copyright
Copyright © 2019 Economic Science Association

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Footnotes

Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10683-019-09640-z) contains supplementary material, which is available to authorized users.

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