EDUCATION, EXPERIENCE, AND GENDER: EVIDENCE ON THE DETERMINANTS OF EARNINGS IN THE BRAZILIAN FORMAL LABOR MARKET

This article estimates the determinants of earnings and the gender wage gap in Brazil’s formal labor market, combining insights from human capital theory and feminist economics. Using 2022 microdata from the Relação Anual de Informações Sociais (RAIS), we estimate a semilog Mincer wage equation with controls for schooling, tenure, age, gender, and region. A stratified sample of 100,000 formal employment records was drawn to preserve geographic representativeness, and educational levels were standardized. Quadratic terms for age and tenure capture diminishing returns, and a progressive modeling strategy assessed individual and joint effects. Inference relies on heteroskedasticity-consistent standard errors. Results indicate a persistent wage penalty for women, with hourly earnings about 20 percent lower than those of men with similar profiles after controls. Higher education is associated with higher pay, although a negative differential appears for master’s degree holders, suggesting institutional specificities. Regional disparities are notable, with lower wages in the North and Northeast. Overall, the evidence points to entrenched gender and regional inequalities not fully explained by productivity, reinforcing the need for policies that promote gender equity and the valuation of human capital across Brazil.

Authors

Publication Details

Journal
Derecho y cambio social.
Published
2025-09-29
DOI
https://doi.org/10.54899/dcs.v22i82.3387
Primary Topic
Labor market dynamics and wage inequality
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

EDUCATION, EXPERIENCE, AND GENDER: EVIDENCE ON THE DETERMINANTS OF EARNINGS IN THE BRAZILIAN FORMAL LABOR MARKET

Max Richard Coelho Verginio, Dimas de Oliveira Estevam, Liara Darabas Ronzani, Caroline Jacques
Derecho y cambio social.
Labor market dynamics and wage inequality
article

EDUCATION, EXPERIENCE, AND GENDER: EVIDENCE ON THE DETERMINANTS OF EARNINGS IN THE BRAZILIAN FORMAL LABOR MARKET

Max Richard Coelho Verginio, Dimas de Oliveira Estevam, Liara Darabas Ronzani, Caroline Jacques
article en

Abstract

This article estimates the determinants of earnings and the gender wage gap in Brazil’s formal labor market, combining insights from human capital theory and feminist economics. Using 2022 microdata from the Relação Anual de Informações Sociais (RAIS), we estimate a semilog Mincer wage equation with controls for schooling, tenure, age, gender, and region. A stratified sample of 100,000 formal employment records was drawn to preserve geographic representativeness, and educational levels were standardized. Quadratic terms for age and tenure capture diminishing returns, and a progressive modeling strategy assessed individual and joint effects. Inference relies on heteroskedasticity-consistent standard errors. Results indicate a persistent wage penalty for women, with hourly earnings about 20 percent lower than those of men with similar profiles after controls. Higher education is associated with higher pay, although a negative differential appears for master’s degree holders, suggesting institutional specificities. Regional disparities are notable, with lower wages in the North and Northeast. Overall, the evidence points to entrenched gender and regional inequalities not fully explained by productivity, reinforcing the need for policies that promote gender equity and the valuation of human capital across Brazil.

Derecho y cambio social.Vol. 22(82)
Openalex Percentile: Top 65%
Labor market dynamics and wage inequality
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.