Identifying the Distribution of Welfare from Discrete Choice
Empirical welfare analyses often impose stringent parametric assumptions on individuals' preferences and neglect unobserved preference heterogeneity. We develop a framework to conduct individual and social welfare analysis for discrete choice that does not suffer from these drawbacks. We first adapt the class of individual welfare measures introduced by Fleurbaey (2009) to settings where individual choice is discrete. Allowing for unrestricted, unobserved preference heterogeneity, these measures become random variables. We then demonstrate that their distribution can be derived from choice probabilities, which can be estimated nonparametrically from cross-sectional data. Additionally, we derive nonparametric results for the joint distribution of welfare and welfare differences, and for social welfare. The former is an important tool in determining whether the winners of a price change belong disproportionately to those groups who were initially well-off.