Working Paper: NBER ID: w20342
Authors: Charles E. Gibbons; Juan Carlos Suárez Serrato; Michael B. Urbancic
Abstract: We replicate eight influential papers to provide empirical evidence that, in the presence of heterogeneous treatment effects, OLS with fixed effects (FE) is generally not a consistent estimator of the average treatment effect (ATE). We propose two alternative estimators that recover the ATE in the presence of group-specific heterogeneity. We document that heterogeneous treatment effects are common and the ATE is often statistically and economically different from the FE estimate. In all but one of our replications, there is statistically significant treatment effect heterogeneity and, in six, the ATEs are either economically or statistically different from the FE estimates.
Keywords: Fixed Effects; Average Treatment Effect; Heterogeneous Treatment Effects
JEL Codes: C18; C21
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| fixed effects estimator (FE) (C23) | average treatment effect (ATE) (C22) |
| heterogeneous treatment effects (C21) | fixed effects estimator (FE) (C23) |
| fixed effects estimator (FE) (C23) | biased estimates of the average treatment effect (ATE) (C51) |
| interaction-weighted estimator (IWE) and regression-weighted estimator (RWE) (C51) | average treatment effect (ATE) (C22) |
| average treatment effect (ATE) differs from fixed effects estimator (FE) estimates (C23) | statistical significance (C12) |