Quantile Random-Coefficient Regression with Interactive Fixed Effects: Heterogeneous Group-Level Policy Evaluation

TL;DR

This paper introduces a novel quantile random-coefficient regression model with interactive fixed effects, effectively capturing unobservable heterogeneities in group-level policy analysis, demonstrated through minimum wage impact on earnings.

Contribution

It presents the first latent factor-based approach for handling unobservable heterogeneity in quantile regression with random coefficients, along with asymptotic theory and inference methods.

Findings

Minimum wage increases have positive effects on black and female workers at the median.

The policy reduces income disparity between certain demographic groups.

Little effect of the policy on income gaps within subpopulations.

Abstract

We propose a quantile random-coefficient regression with interactive fixed effects to study the effects of group-level policies that are heterogeneous across individuals. Our approach is the first to use a latent factor structure to handle the unobservable heterogeneities in the random coefficient. The asymptotic properties and an inferential method for the policy estimators are established. The model is applied to evaluate the effect of the minimum wage policy on earnings between 1967 and 1980 in the United States. Our results suggest that the minimum wage policy has significant and persistent positive effects on black workers and female workers up to the median. Our results also indicate that the policy helps reduce income disparity up to the median between two groups: black, female workers versus white, male workers. However, the policy is shown to have little effect on narrowing the income gap between low- and high-income workers within the subpopulations.