Gender Dissimilarities in Human Capital Transferability of Cuban Immigrants in the US: A Clustering Quantile Regression Coefficients Approach with Consideration of Implications for Sustainability

Female participation in the labor market has been increasing over time. Despite the fact that the level of education among women has also increased considerably, the wage gap has not narrowed to the same extent. This dichotomy presents an important challenge that the United Nations Sustainable Devel...

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Autores: Cobas Valdés, Aleida, Fernández Macho, Francisco Javier
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/54130
Acceso en línea:http://hdl.handle.net/10810/54130
Access Level:acceso abierto
Palabra clave:transferability of human capital
earnings distribution
immigrant workers
quantile regression
discrimination by gender
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spelling Gender Dissimilarities in Human Capital Transferability of Cuban Immigrants in the US: A Clustering Quantile Regression Coefficients Approach with Consideration of Implications for SustainabilityCobas Valdés, AleidaFernández Macho, Francisco Javiertransferability of human capitalearnings distributionimmigrant workersquantile regressiondiscrimination by genderFemale participation in the labor market has been increasing over time. Despite the fact that the level of education among women has also increased considerably, the wage gap has not narrowed to the same extent. This dichotomy presents an important challenge that the United Nations Sustainable Development Goals with respect to gender inequities must address. Hispanics constitute the largest minority group in the US, totaling 60.6 million people (18.5% of the total US population in 2020). Cubans make up the third largest group of Hispanic immigrants in the US, representing 5% of workers. This paper analyzes the conditional income distribution of Cuban immigrants in the US using the clustering of effects curves (CEC) technique in a quantile regression coefficients modeling (QRCM) framework to compare the transferability of human capital between women and men. The method uses a flexible quantile regression approach and hierarchical clustering to model the effect of covariates (such as years of education, English proficiency, US citizenship status, and age at time of migration) on hourly earnings. The main conclusion drawn from the QRCM estimations was that being a woman had the strongest negative impact on earnings and was associated with lower wages in all quantiles of the distribution. CEC analysis suggested that educational attainment was included in different clusters for the two groups, which may have indicated that education did not play the same role for men and women in income distribution.The research reported here has been funded by the Econometrics Research Group (Basque Government research grant IT1359-19). It has also been partially funded by the Spanish Ministry of Science and Innovation (MCIN, Spain), Agencia Estatal de Investigación (AEI/10.13039/501100011033/) and Fondo Europeo de Desarrollo Regional (FEDER) “Una manera de hacer Europa” (I+D+i research grant PID2020-112951GB-I00). The funders played no role in the design or implementation of the research reported here, and the analysis and conclusions are the authors’ own.MDPI2021202120212021info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/54130reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoInglésinfo:eu-repo/grantAgreement/MICINN/PID2020-112951GB-I00/https://www.mdpi.com/2071-1050/13/21/12004/htminfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).oai:addi.ehu.eus:10810/541302026-06-18T09:23:17Z
dc.title.none.fl_str_mv Gender Dissimilarities in Human Capital Transferability of Cuban Immigrants in the US: A Clustering Quantile Regression Coefficients Approach with Consideration of Implications for Sustainability
title Gender Dissimilarities in Human Capital Transferability of Cuban Immigrants in the US: A Clustering Quantile Regression Coefficients Approach with Consideration of Implications for Sustainability
spellingShingle Gender Dissimilarities in Human Capital Transferability of Cuban Immigrants in the US: A Clustering Quantile Regression Coefficients Approach with Consideration of Implications for Sustainability
Cobas Valdés, Aleida
transferability of human capital
earnings distribution
immigrant workers
quantile regression
discrimination by gender
title_short Gender Dissimilarities in Human Capital Transferability of Cuban Immigrants in the US: A Clustering Quantile Regression Coefficients Approach with Consideration of Implications for Sustainability
title_full Gender Dissimilarities in Human Capital Transferability of Cuban Immigrants in the US: A Clustering Quantile Regression Coefficients Approach with Consideration of Implications for Sustainability
title_fullStr Gender Dissimilarities in Human Capital Transferability of Cuban Immigrants in the US: A Clustering Quantile Regression Coefficients Approach with Consideration of Implications for Sustainability
title_full_unstemmed Gender Dissimilarities in Human Capital Transferability of Cuban Immigrants in the US: A Clustering Quantile Regression Coefficients Approach with Consideration of Implications for Sustainability
title_sort Gender Dissimilarities in Human Capital Transferability of Cuban Immigrants in the US: A Clustering Quantile Regression Coefficients Approach with Consideration of Implications for Sustainability
dc.creator.none.fl_str_mv Cobas Valdés, Aleida
Fernández Macho, Francisco Javier
author Cobas Valdés, Aleida
author_facet Cobas Valdés, Aleida
Fernández Macho, Francisco Javier
author_role author
author2 Fernández Macho, Francisco Javier
author2_role author
dc.subject.none.fl_str_mv transferability of human capital
earnings distribution
immigrant workers
quantile regression
discrimination by gender
topic transferability of human capital
earnings distribution
immigrant workers
quantile regression
discrimination by gender
description Female participation in the labor market has been increasing over time. Despite the fact that the level of education among women has also increased considerably, the wage gap has not narrowed to the same extent. This dichotomy presents an important challenge that the United Nations Sustainable Development Goals with respect to gender inequities must address. Hispanics constitute the largest minority group in the US, totaling 60.6 million people (18.5% of the total US population in 2020). Cubans make up the third largest group of Hispanic immigrants in the US, representing 5% of workers. This paper analyzes the conditional income distribution of Cuban immigrants in the US using the clustering of effects curves (CEC) technique in a quantile regression coefficients modeling (QRCM) framework to compare the transferability of human capital between women and men. The method uses a flexible quantile regression approach and hierarchical clustering to model the effect of covariates (such as years of education, English proficiency, US citizenship status, and age at time of migration) on hourly earnings. The main conclusion drawn from the QRCM estimations was that being a woman had the strongest negative impact on earnings and was associated with lower wages in all quantiles of the distribution. CEC analysis suggested that educational attainment was included in different clusters for the two groups, which may have indicated that education did not play the same role for men and women in income distribution.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
2021
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url http://hdl.handle.net/10810/54130
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/MICINN/PID2020-112951GB-I00/
https://www.mdpi.com/2071-1050/13/21/12004/htm
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
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