Returns to education in Argentina: a regional analysis

• Autores: Quiroga Martínez, Facundo; Fernández-Vázquez, Esteban; Alberto, Catalina Lucía
• Año de publicación: 2016
• Idioma: inglés
• Tipo de recurso: parte de libro
• Materia: Returns to human capital; Wage gap decomposition; Mincer equations; Argentina
• Institución: Universidad Nacional de Córdoba

Fil: Quiroga Martínez, Facundo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.
Fil: Fernández-Vázquez, Esteban. Universidad de Oviedo. Departamento de Economía Aplicada; España.
Fil: Alberto, Catalina Lucía. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.
The study of regional differences in Argentina in terms of returns to education is highly relevant, primarily due to the existence of deep inequalities in the configuration of regional structures in this country. The main differences are based on several economic and demographic characteristics across regions that have an impact on the social dynamics of such regions.This issue has been acknowledged in the literature, in order to get a full understanding of the dynamics that might explain those dissimilarities, especially in the educational and labour fields. However, empirical literature or the case of Argentina is still limited.The analysis of regional differences in human capital, as well as their impact on private returns to education - i.e. income levels of individuals - has been addressed in a number of studies, such as Winters (2012), Lopez-Bazo and Motellón (2012), and Ciccone et al. (2004), for the cases of the United States, Spain, and Italy, respectively. For the Argentinean case, no attention has been paid to differences in the returns to education by region. Only a few recent studies, such as Giovagnoli et al. (2005), have approached this issue by using Mincerian equations as the methodological strategy to estimate the returns to education, combined with a quantile regression analysis to detect differences in the returns across the distribution of wages. However, the regional perspective was not incorporated in this study, even when wage differences between regions are remarkable.On the other hand López Bóo (2010) quantify the returns to education in Argentina according different macroeconomic shocks from 1992 to 2003 but not including the regional perspective neither.Several literature across the Latin-American countries quantifies the rates of the returns to education using - most of them - quintile regression or time series, such as: Psacharopoulos & Velez (1992) who estimate the returns of education in Colombia for a ten years period; López-Acevedo (2004) that analyse the contribution of educational inequality as a key variable for understanding earnings inequality in Mexico and Patrinos & Sakellariou (2010) who study the relation between the returns to education and the effect of the swings in economic activity on the demand and supply of education and skills in Venezuela for the period 1992 to 2002. Nevertheless none of them use the regional perspective in their analysis, even when the different levels of returns to education can be explained through regional characteristics as a determinant of those differences.It is surprising that being this issue greatly relevant in order to understand the heterogeneity among geographic regions in Argentina, no previous studies have considered the role played by human capital in order to explain the substantive regional differences within the national labour market. This paper seeks to contribute to the study of regional labour markets in terms of their returns to education in Argentina. For that aim we firstly quantify the returns to education for every region using a typical Mincerian equation and then analyse the wage gap through the Oaxaca-Blinder decomposition.The paper is organized in five sections, as follow: the next one describe the regional structure in Argentina, as well as the main characteristics of the labour market and the endowment of human capital. Then we explain the methodological strategy so as to estimate the empirical wage model in the second section. A description and summary of the data set is briefly presented in third section. Before that we present the results for different specifications of the model by region in section fourth. Finally, in the last section the conclusions and future extensions.
https://rdu.unc.edu.ar/handle/11086/4528
Fil: Quiroga Martínez, Facundo. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.
Fil: Fernández-Vázquez, Esteban. Universidad de Oviedo. Departamento de Economía Aplicada; España.
Fil: Alberto, Catalina Lucía. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.
Matemática Aplicada