Arbeitspapier

Discrete choice under risk with limited consideration

This paper is concerned with learning decision makers' (DMs) preferences using data on observed choices from a fi nite set of risky alternatives with monetary outcomes. We propose a discrete choice model with unobserved heterogeneity in consideration sets (the collection of alternatives considered by DMs) and unobserved heterogeneity in standard risk aversion. In this framework, stochastic choice is driven both by different rankings of alternatives induced by unobserved heterogeneity in risk preferences and by different sets of alternatives considered. We obtain sufficient conditions for seminonparametric point identi fication of both the distribution of unobserved heterogeneity in preferences and the distribution of consideration sets. Our method yields an estimator that is easy to compute and that can be used in markets with a large number of alternatives. We apply our method to a dataset on property insurance purchases. We fi nd that although households are on average strongly risk averse, they consider lower coverages more frequently than higher coverages. Finally, we estimate the monetary losses associated with limited consideration in our application.

Language
Englisch

Bibliographic citation
Series: cemmap working paper ; No. CWP08/19

Classification
Wirtschaft
Subject
discrete choice
limited consideration
semi-nonparametric identification

Event
Geistige Schöpfung
(who)
Barseghyan, Levon
Molinari, Francesca
Thirkettle, Matthew
Event
Veröffentlichung
(who)
Centre for Microdata Methods and Practice (cemmap)
(where)
London
(when)
2019

DOI
doi:10.1920/wp.cem.2019.0819
Handle
Last update
10.03.2025, 11:41 AM CET

Data provider

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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.

Object type

  • Arbeitspapier

Associated

  • Barseghyan, Levon
  • Molinari, Francesca
  • Thirkettle, Matthew
  • Centre for Microdata Methods and Practice (cemmap)

Time of origin

  • 2019

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