Measuring and Controlling for the Compromise Effect When Estimating Risk Preference Parameters.

B49 D03 D14 D83 G11 compromise effect cumulative prospect theory loss aversion risk preferences

Journal

Experimental economics
ISSN: 1386-4157
Titre abrégé: Exp Econ
Pays: United States
ID NLM: 101697134

Informations de publication

Date de publication:
Dec 2020
Historique:
entrez: 21 12 2020
pubmed: 22 12 2020
medline: 22 12 2020
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 33343223
doi: 10.1007/s10683-019-09640-z
pmc: PMC7747750
mid: NIHMS1547361
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1069-1099

Subventions

Organisme : NIA NIH HHS
ID : P01 AG005842
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG021650
Pays : United States
Organisme : NIA NIH HHS
ID : R21 AG037741
Pays : United States

Références

Science. 1974 Sep 27;185(4157):1124-31
pubmed: 17835457
J Econ Perspect. 2018;32(2):115-34
pubmed: 30203932
J Eur Econ Assoc. 2013 Dec 1;11(6):1231-1255
pubmed: 30546272

Auteurs

Jonathan P Beauchamp (JP)

George Mason University.

Daniel J Benjamin (DJ)

University of Southern California and NBER.

David I Laibson (DI)

Harvard University and NBER.

Christopher F Chabris (CF)

Geisinger Health Systems.

Classifications MeSH