Doutorado em Economia dos Negócios

URI permanente para esta coleçãohttps://repositorio.insper.edu.br/handle/11224/3243

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    Unconditional Quantile Selection Models with a Gender Wage Gap Application
    (2023) Sunao, Stefanie Sayuri
    We propose constructing a consistent estimator that addresses the problem of sample selection in unconditional quantile models. The proposed approach is based on three steps: (i) estimation of a control function using a logistic distribution regression; (ii) construction of a counterfactual distribution of the latent dependent variable conditional on the previously estimated control function; (iii) application of the recentered influence function (RIF) on the estimated counterfactual distribution and, finally, we run an ordinary least square regression. Besides this theoretical contribution, we propose an empirical application to measure the impact of maternity on the gender wage gap using Danish data.