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This paper develops a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects, highlighting the biases of conventional regression-based estimators. The authors propose a robust and efficient estimator that addresses these challenges and apply their method to estimate the marginal propensity to consume from tax rebates, finding significantly lower estimates than previous studies. The paper also provides tools for inference and tests for identifying a

Author

Ji Li

Language

EN