American Economic Review · 2020 · 4.5K citations · 10 references
Public HealthBiostatisticsEconomicsCausal InferenceEconometricsMeta-analysisCausal Effect EstimationEffect Size EstimationFixed-effect ModelsHeterogeneous Treatment EffectsTreatment Effect HeterogeneityTreatment EffectsAverage Treatment EffectsFixed Effects EstimationFixed-effect EstimatorsHeterogeneous Treatment Effect EstimationLinear RegressionTreatment Effect EstimationTreatment Effects EstimationTwo-way Fixed Effects Models
Linear regressions with period and group fixed effects are widely used to estimate treatment effects. The authors aim to develop an estimator that addresses the bias introduced by negative weights in such regressions. They propose a new estimator that corrects for these negative weights, thereby providing unbiased treatment effect estimates. They demonstrate that the standard fixed‑effects estimator yields weighted sums of ATEs with potentially negative weights, which can produce misleading signs, and that their new estimator differs significantly from the standard one in two empirical applications. JEL codes: C21, C23, D72, J31, J51, L82.
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. (JEL C21, C23, D72, J31, J51, L82)
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The Effect of Unions on the Structure of Wages: A Longitudinal Analysis
David Card · Econometrica · 1996 · 698 citations