  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.
