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Title: Earnings Distribution of Cuban Immigrants in the USA from a Flexible Quantile Regression Perspective
Citation Type: Miscellaneous
Publication Year: 2017
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Abstract: This paper analyzes the conditional distribution of salaries of Cuban immigrants to the United States of America based on a structured additive quantile (STAQ) model. In accordance with Fahrmeir et al. (2004), Kneib et al. (2009) and Fenske et al. (2012), we used a boosting algorithm in the estimation of the model specified. STAQ models are an extension of the linear quantile regression of the so-called generalized structured additive regression (STAR) models proposed by Fahrmeir et al. (2004) and Brezger and Lang (2006) This is the first attempt in the migration literature to use quantile regression in a flexible context. The data used come from the US American Community Survey. The results show that increments in earnings associated with the socioeconomic characteristics usually included in labor studies vary between the different quantiles considered. The main conclusions are that a decline in returns from education may be a sign that a high level of education no longer provides a competitive advantage and that being a black person is associated with substantially lower earnings, regardless of the individuals’ position in the earnings distribution.
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Authors: Cobas-Valdés, Aleida; Fernández-Macho, Javier; Fernández-Sainz, Ana
Publisher: Dpt. of Econometrics & Statistics
Data Collections: IPUMS USA
Topics: Migration and Immigration
Countries: United States