Title:Randomly Assigned First Differences?

Abstract:I consider treatment-effect estimation with a two-periods panel, using a first-difference regression of the outcome evolution $\Delta Y_g$ on the treatment evolution $\Delta D_g$. To justify this regression, one may assume that $\Delta D_g$ is as good as randomly assigned, namely uncorrelated to the residual of the first-differenced model and to the treatment's effect. This note shows that if one posits a causal model in levels between the treatment and the outcome, then the residual of the first-differenced model is a function of $D_{g,1}$, so $\Delta D_g$ uncorrelated to that residual essentially implies that $\Delta D_g$ is uncorrelated to $D_{g,1}$. This is a strong, testable condition. If $\Delta D_g$ is correlated to $D_{g,1}$, assuming that $\Delta D_g$ is uncorrelated to the treatment effect and to the remaining terms of the residual may not be sufficient to have that the first-difference regression identifies a convex combination of treatment effects. I use these results to revisit Acemoglu et al (2016).