A Non-Parametric Approach to Heterogeneity Analysis

TL;DR

This paper introduces a non-parametric method to analyze consumer preference heterogeneity by clustering individuals based on revealed preferences, enabling testing of demographic explanations for unobserved differences.

Contribution

It develops a novel non-parametric framework that estimates latent preference similarities and tests demographic effects on heterogeneity, advancing consumer choice analysis.

Findings

The similarity matrix accurately estimates shared utility functions.

Hypothesis tests effectively identify demographic influences.

Simulation results validate the methodology.

Abstract

We develop a non-parametric methodology to quantify preference heterogeneity in consumer choices. By repeatedly sampling individual observations and partitioning agents into groups consistent with the Generalized Axiom of Revealed Preferences (GARP), we construct a similarity matrix capturing latent preference structures. Under mild assumptions, this matrix consistently and asymptotically normally estimates the probability that any pair of agents share a common utility function. Leveraging this, we develop hypothesis tests to assess whether demographic characteristics systematically explain unobserved heterogeneity. Simulations confirm the test's validity, and we apply the method to a standard grocery expenditure dataset.