Unconditional Quantile Treatment Effects Under Endogeneity

By Markus Frölich, Blaise Melly. Social Science Research Network, 2007

287 citations recorded by the source corpus.

This article develops estimators for unconditional quantile treatment effects when the treatment selection is endogenous. We use an instrumental variable (IV) to solve for the endogeneity of the binary treatment variable. Identification is based on a monotonicity assumption in the treatment choice equation and is achieved without any functional form restriction. We propose a weighting estimator that is extremely simple to implement. This estimator is root n consistent, asymptotically normally distributed, and its variance attains the semiparametric efficiency bound. We also show that including covariates in the estimation is not only necessary for consistency when the IV is itself confounded but also for efficiency when the instrument is valid unconditionally. An application of the suggested methods to the effects of fertility on the family income distribution illustrates their usefulness. Supplementary materials for this article are available online.

Subfields: Economics and Econometrics, Gender Studies, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3d8be7359d710f539dd8aacb78f4fe4ab2f9cdba

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Citations in the corpus

A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, methodology): “(23) This approach to identifying quantile treatment effects was used by Cattaneo (2010), Frandsen, Frölich, and Melly (2012), Chernozhukov et al. (2013), Frölich and Melly (2013), Donald and Hsu (2014), and Sant’Anna (2016), among others.”
The Welfare Impacts of Rural Electrification in Bangladesh (2011, background): “Fro¨lich and Melly (2007) proposed the following estimator for estimating : s D where and W ij is deﬁned by Details of these derivations are provided in Fro¨lich and Melly (2007).”
Semiparametric Counterfactual Density Estimation (2021, background): “…study the entire counterfactual distribution [Abadie, 2002, Chernozhukov and Hansen, 2005, Chernozhukov et al., 2013, Díaz, 2017, Firpo, 2007, Fortin et al., 2011, Frölich and Melly, 2013, Machado and Mata, 2005, Melly, 2005, Rothe, 2010, Wang et al., 2018, Wang and Qin, 2010, Zhang et al., 2012].”
Caregiving to Elderly Parents and Employment Status of European Mature Women (2014, unknown): “The aggregate or population LATE parameter is β = ∫ β(X)dF(X | compliers) = ∫ [E(LP | PH = 1, X) − E(LP | PH = 0, X)]dF(X)∫ [E(IC | PH = 1, X) − E(IC | X = 0, X)]dF(X) , (5) where the first equality integrates the Angrist and Imbens (1994) result, and the second is shown in Frölich and Melly (2007).”
Econometric Methods for Program Evaluation (2018, background): “Abadie (2002); Abadie et al. (2002); Frölich and Melly (2013) propose estimators of distributional effects for compliers.”
Gendered parenthood penalties and premiums across the earnings distribution in Australia, the United Kingdom, and the United States (2014, methodology): “5 Economists have developed procedures using instrumental variables to yield unconditional effects (i.e., Frölich and Melly, 2008).”
Including Covariates in the Regression Discontinuity Design (2018, methodology): “Similarly, we could identify quantile treatment effects by combining the previous derivations with the reasoning in Frölich and Melly (2013) and Frandsen, Frölich, and Melly (2012).”
Corporate Governance, Incentives, and Tax Avoidance (2015, methodology): “First we considered quantile regression with instrumental variables (e.g., Frolich and Melly, 2012; Kaplan and Sun, 2013; Powell, 2013).”
Beyond The Average Effects: The Distributional Impacts of Export Promotion Programs in Developing Countries (2010, unknown): “Athey and Imbens (2006); Firpo (2007a,b), and Frölich and Melly (2008).”