Linear regressions with time and group fixed effects are widely used to estimate treatment effects. We show that they identify weighted sums of the average treatment effects  in each group and period, with weights that can be estimated, and that may be negative. In two articles that have used those regressions, half of the weights are negative , thus suggesting that those regressions are often not robust to heterogeneous treatment effects across groups and over time . We propose another estimator that is robust to heterogeneous treatment effects. In an application , it is of a different sign than the linear regression estimator.
Linear regressions with period and group fixed effects are widely used to estimate treatment effects. We show that they estimate weighted sums of the average treatment effects (ATE) in each group and period, with weights that may be negative. Due to the negative weights, the linear regression coefficient may for instance be negative while all the ATEs are positive . We propose another estimator that solves this issue. In the two applications we revisit , it is significantly different from the linear regression estimator.
R
time
period
R
identify
estimate
A
(ATE)
D
can be estimated, and that
R
In two articles that have used those regressions, half of the weights are negative , thus suggesting that those regressions are often not robust to heterogeneous treatment effects across groups and over time
Due to the negative weights, the linear regression coefficient may for instance be negative while all the ATEs are positive
R
is robust to heterogeneous treatment effects. In an application
solves this issue. In the two applications we revisit
R
of a different sign than
significantly different from