Combining stated and revealed preferences

TL;DR

This paper introduces a method that combines stated and revealed preferences to identify unobserved heterogeneity and correct for endogeneity in demand estimation, providing bounds and bootstrap inference for causal effects.

Contribution

It proposes a novel approach to use both stated and actual choices to identify unobserved heterogeneity and derive bounds on causal effects, including unmatched data scenarios.

Findings

Bounds on causal effects are derived for unmatched data.

Bootstrap inference performs well in simulations.

Method effectively corrects for endogeneity using combined data.

Abstract

Can stated preferences inform counterfactual analyses of actual choice? This research proposes a novel approach to researchers who have access to both stated choices in hypothetical scenarios and actual choices, matched or unmatched. The key idea is to use stated choices to identify the distribution of individual unobserved heterogeneity. If this unobserved heterogeneity is the source of endogeneity, the researcher can correct for its influence in a demand function estimation using actual choices and recover causal effects. Bounds on causal effects are derived in the case, where stated choice and actual choices are observed in unmatched data sets. These data combination bounds are of independent interest. We derive bootstrap inference for the bounds and show its good performance in a simulation experiment.