Working Paper: NBER ID: w17346
Authors: Itai Sher; Jeremy T. Fox; Kyoo Il Kim; Patrick Bajari
Abstract: We study a variant of a random utility model that takes a probability distribution over preference relations as its primitive. We do not model products using a space of observed characteristics. The distribution of preferences is only partially identified using cross-sectional data on varying budget sets. Imposing monotonicity in product characteristics does not restore full identification. Using a linear programming approach to partial identification, we show how to obtain bounds on probabilities of any ordering relation. We also do constructively point identify the proportion of consumers who prefer one budget set over one or two others. This result is useful for welfare. Panel data and special regressors are two ways to gain full point identification.
Keywords: Discrete Choice Models; Random Utility Models; Preference Orderings; Partial Identification
JEL Codes: C25; L0
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| observed choice probabilities across different budget sets (D10) | distribution of preferences over discrete choices (C25) |
| three or fewer choices (C25) | distribution of preferences over discrete choices identifiable (C25) |
| four or more choices (C35) | distribution of preferences over discrete choices underidentified (C25) |
| linear programming (C61) | identification of bounds on probabilities of various preference orderings (D81) |
| observed characteristics and functional form assumptions (C51) | aid in identification (Y90) |
| proportion of consumers preferring one budget set over another (D11) | point identified (C23) |