Quantile Structural Treatment Effect: Application to Smoking Wage Penalty and its Determinants

@inproceedings{Hsu2018QuantileST,
  title={Quantile Structural Treatment Effect: Application to Smoking Wage Penalty and its Determinants},
  author={Yu-Chin Hsu and Kamhon Kan and Tsung-Chih Lai},
  year={2018}
}
This paper proposes the quantile structural treatment effect (QSTE) to partial out different values of covariates from the quantile treatment effect which allows us to distinguish between observed and unobserved treatment heterogeneity. We show the QSTE is identified under the unconfoundedness assumption and propose an inverse probability weighted estimator which converges weakly to a Gaussian process at √ n rate. A multiplier bootstrap is proposed for uniform confidence bands. Using data from… CONTINUE READING

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