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

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