Quantile and interquantile regression models for returns to education by economic sector and vulnerable population in Colombia

Jacobo Campo-Robledo; Cristian Castillo-Robayo; Julimar da silva Bichara; Jacobo Campo-Robledo; Cristian Castillo-Robayo; Julimar da silva Bichara

doi:10.3934/math.20241669

AIMS Mathematics

2024, Volume 9, Issue 12: 35091-35124. doi: 10.3934/math.20241669

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Quantile and interquantile regression models for returns to education by economic sector and vulnerable population in Colombia

1.
Estudiante. Doctorado en Economía y Empresa, Departamento de Estructura Económica y Economía del Desarrollo, Universidad Autónoma de Madrid, Madrid, España
2.
Desh Consultores, Facultad de Ciencias Económicas y Administrativas, Universidad Pedagógica y Tecnológica de Colombia, Tunja, Colombia
3.
Departamento de Estructura Económica y Economía del Desarrollo, Universidad Autónoma de Madrid, Madrid, España
4.
Facultad de Ciencias Económicas y Administrativas, Universidad Católica de Colombia, Bogotá, Colombia

Received: 30 April 2024 Revised: 16 September 2024 Accepted: 04 November 2024 Published: 16 December 2024
MSC : 62G08, 62P20, 91B39

We investigated the returns to education by economic sector in Colombia, focusing on the relationship between educational levels (degree of highest educational level) and wages in different labor areas (economic sectors), as well as vulnerable populations such as women and migrants. Quantile and interquantile regressions were employed, correcting for selection bias through the inverse Mills ratio and using monthly data from Colombia's Great Integrated Household Survey (GEIH) for 2019, to explore how the effect of education varies at different points of the income distribution and between these points. Using quantile regression provided a more comprehensive view of this relationship than traditional statistical regression approaches. Traditional Mincerian socioeconomic variables such as gender, experience, hours worked, marital status, relationship with the head of the household, and social security affiliation, were controlled for. Results show that while there is a positive effect between educational level and income in all economic sectors studied, this relationship varies in magnitude and form along the wage distribution.
- education,
- academic degrees,
- economic sector,
- quantile regression,
- Colombia
Citation: Jacobo Campo-Robledo, Cristian Castillo-Robayo, Julimar da silva Bichara. Quantile and interquantile regression models for returns to education by economic sector and vulnerable population in Colombia[J]. AIMS Mathematics, 2024, 9(12): 35091-35124. doi: 10.3934/math.20241669

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Abstract

We investigated the returns to education by economic sector in Colombia, focusing on the relationship between educational levels (degree of highest educational level) and wages in different labor areas (economic sectors), as well as vulnerable populations such as women and migrants. Quantile and interquantile regressions were employed, correcting for selection bias through the inverse Mills ratio and using monthly data from Colombia's Great Integrated Household Survey (GEIH) for 2019, to explore how the effect of education varies at different points of the income distribution and between these points. Using quantile regression provided a more comprehensive view of this relationship than traditional statistical regression approaches. Traditional Mincerian socioeconomic variables such as gender, experience, hours worked, marital status, relationship with the head of the household, and social security affiliation, were controlled for. Results show that while there is a positive effect between educational level and income in all economic sectors studied, this relationship varies in magnitude and form along the wage distribution.

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