Ziteng Lei | Searcharxiv

arXiv2024

Randomly Assigned First Differences?

We consider a first-difference regression of an outcome evolution $\Delta Y$ on a treatment evolution $\Delta D$. If the treatment effect changes over time, the regression residual is a function of the period-one treatment $D_{1}$. Then, researchers should test if $\Delta D$ and $D_{1}$ are correlated: if they are, the regression may suffer from an omitted variable bias. To solve it, researchers may control for $E(\Delta D|D_{1})$. We revisit Acemoglu et al (2016), who study the effect of imports from China on US employment. $\Delta D$ and $D_{1}$ are correlated. $\Delta D$'s coefficient is less negative when controlling for $E(\Delta D|D_{1})$.

econ.EM↗

arXiv2023

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

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.

econ.EM↗