# Discrete Choice under Risk with Limited Consideration

## TL;DR

This paper develops a semi-nonparametric discrete choice model to identify decision makers' preferences and consideration sets under risk, with an application to property insurance data.

## Contribution

It introduces a novel model accounting for unobserved heterogeneity in consideration and risk aversion, with conditions for semi-nonparametric identification.

## Key findings

- Model successfully identifies preferences and consideration sets.
- Estimator is computationally efficient and applicable to large choice sets.
- Empirical application demonstrates practical utility in insurance markets.

## Abstract

This paper is concerned with learning decision makers' preferences using data on observed choices from a finite set of risky alternatives. We propose a discrete choice model with unobserved heterogeneity in consideration sets and in standard risk aversion. We obtain sufficient conditions for the model's semi-nonparametric point identification, including in cases where consideration depends on preferences and on some of the exogenous variables. Our method yields an estimator that is easy to compute and is applicable in markets with large choice sets. We illustrate its properties using a dataset on property insurance purchases.