Linear regressions with time and group fixed effects are widely used to estimate the effect of a treatment on an outcome. If the effect of the treatment is constant across groups and over time, such regressions identify the treatment effect under the standard "common trends" assumption. But these regressions have not been analyzed yet allowing for treatment effect heterogeneity. We study two commonly used two-way fixed effectsregressions. We start by showing that without assuming constant treatment effects, those regressions identify weighted sums of the average treatment effects in each group and period, where some weights may be negative. The weights can be estimated, and can help researchers assess whether their results are robust to heterogeneous treatment effects across groups and periods. When some weights are negative, their estimates may not even have the same sign as the true average treatment effect if treatment effects are heterogeneous. We then propose another estimator that does not rely on any treatment effect homogeneity assumption. We revisit two empirical articles that have estimated two-way fixed effectsregressions. In both cases, around half of the weights attached to those regressions are negative. In one application, our new estimator is of a different sign than the first two-way fixed effects estimatorwe study, and significantly different from the second one .
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 .
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the effect of a treatment on an outcome. If the effect of the treatment is constant across groups and over time, such regressions identify the treatment effect under the standard "common trends" assumption. But these regressions have not been analyzed yet allowing for treatment effect heterogeneity. We study two commonly used two-way fixed effectsregressions. We start by showing that without assuming constant treatment effects, those regressions
treatment effects. We show that they
R
where some weights
with weights that can be estimated, and that
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The weights can be estimated, and can help researchers assess whether their results are
In two articles that have used those regressions, half of the weights are negative, thus suggesting that those regressions are often not
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periods. When some weights are negative, their estimates may not even have the same sign as the true average treatment effect if treatment effects are heterogeneous. We then
over time. We
R
does not rely on any treatment effect homogeneity assumption. We revisit two empirical articles that have estimated two-way fixed effectsregressions. In both cases, around half of the weights attached to those regressions are negative. In one application, our new estimator
is robust to heterogeneous treatment effects. In an application, it
R
first two-way fixed effects estimatorwe study, and significantly different from the second one
linear regression estimator