Working Paper: NBER ID: w29873
Authors: Clément de Chaisemartin; Xavier Dhaultfoeuille
Abstract: We study treatment-effect estimation using panel data. The treatment may be nonbinary, non-absorbing, and the outcome may be affected by the treatment lags. We make parallel-trends assumptions, but do not restrict treatment effect heterogeneity, unlike commonly-used two-way-fixed-effects regressions. We propose reduced-form event-study estimators of the effect of being exposed to a weakly higher treatment dose for ℓ periods. We also propose normalized event-study estimators, that estimate a weighted average of the effects of the current treatment and its lags. Finally, we show that the reduced-form estimators can be combined into an economically interpretable cost-benefit ratio.
Keywords: treatment effects; panel data; event-study estimators
JEL Codes: C21; C23
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| exposure to a weakly higher treatment dose (C90) | treatment effects (C22) |
| treatment effects (C22) | lagged treatment effects (C22) |
| treatment effects (C22) | outcomes (P47) |
| treatment assignments (C90) | potential outcomes (D79) |
| treatment changes (C22) | cost-benefit ratio (H43) |
| banking deregulations (G28) | persistent effects (C41) |