More Robust Estimators for Instrumental-Variable Panel Designs, With An Application to the Effect of Imports from China on US Employment

TL;DR

This paper introduces a new estimator for instrumental-variable panel data that is more robust to heterogeneous effects, addressing biases in traditional methods, and applies it to assess the impact of Chinese imports on US employment.

Contribution

The paper develops an IV-CRC estimator that improves robustness to heterogeneity and demonstrates its application to real-world economic data.

Findings

Traditional first-difference IV estimates may be biased under heterogeneity.

The new IV-CRC estimator yields smaller, insignificant effects.

Results differ significantly from standard IV estimates.

Abstract

We show that first-difference two-stages-least-squares regressions identify non-convex combinations of location-and-period-specific treatment effects. Thus, those regressions could be biased if effects are heterogeneous. We propose an alternative instrumental-variable correlated-random-coefficient (IV-CRC) estimator, that is more robust to heterogeneous effects. We revisit Autor et al. (2013), who use a first-difference two-stages-least-squares regression to estimate the effect of imports from China on US manufacturing employment. Their regression estimates a highly non-convex combination of effects. Our more robust IV-CRC estimator is small and insignificant. Though its confidence interval is wide, it significantly differs from the first-difference two-stages-least-squares estimator.