A discrete choice model implementing gist-based categorization of alternatives, with applications to patient preferences for cancer screening and treatment

J. Swait*, E. W. de Bekker-Grob

*Corresponding author for this work

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Abstract

The rational microeconomic decision model is hard-coded into usual econometric specifications such as the Multinomial Logit and Probit models, inter alia. There is a very tight link between utility maximization and the apparatus of welfare theory that underlies economic policy analysis, which creates a tension around the possibility of representing other decision rules. We propose a less restrictive model of choice, built on the concept of gist-based categorization judgments that are assumed to precede (thus, condition) the maximization-driven selection process in decision making. This categorization facilitates decision making by allowing adoption of certain simpler decision rules under appropriate conditions, the drivers of which are endogenously determined. We demonstrate that the proposed model provides better fit than traditional choice models, using cancer screening and treatment choice data from two discrete choice experiments. In addition, we show that the model provides a deeper, more nuanced and insightful perspective on (healthcare) decision making.

Original languageEnglish
Article number102674
JournalJournal of Health Economics
Volume85
DOIs
Publication statusPublished - Sep 2022

Bibliographical note

Funding Information:
J.D. Swait was supported by the Erasmus University via the Erasmus Initiative “Smarter Choices for Better Health”. E.W. de Bekker-Grob was supported by The Netherlands Organization for Scientific Research under NWO-Talent-Scheme-Vidi-Grant No. 09,150,171,910,002, and the DCE studies by the same grantor through NWO-Talent-Scheme-Veni-Grant No. 451–15–039 . The funders had no role in the study design, in the collection, analysis, and interpretation of data, in the writing of the report, or in the decision to submit the manuscript for publication.

Publisher Copyright:
© 2022 The Author(s)

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