American Economic Review· 2020Q1
Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects
- 4,646citations
- Q1SCImago
- 2020year
Short summary
Standard linear regressions with period and group fixed effects can produce misleading treatment effect estimates, even showing negative coefficients when all underlying effects are positive.
AI-generated from the title and abstract; the full text is not read.
Key points
- Standard fixed effects regressions estimate a weighted sum of treatment effects, not a simple average.
- Weights in these regressions can be negative, leading to counterintuitive results (e.g., negative overall effect with all positive subgroup effects).
- A new estimator is proposed to address this weighting issue.
- The new estimator produced significantly different results in two empirical examples.
AI-generated from the title and abstract; the full text is not read.
Abstract
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)
The authors' abstract, as published at the source. American Economic Review, 2020 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics