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

Abstract

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.