College majors and wages in Turkey: OLS and quantile regression with sample selection correction

Abstract

Purpose: This study aims to analyze the wage differentials of the majors in college education in Turkey, which is a country implementing an ongoing expansion in college education in recent years. Design/methodology/approach: The study implements Mincreian wage regression using ordinary least squares, Heckman two-step estimation and quantile regression with sample selection correction by using household labor force surveys of TurkStat from the years 2014–2017. Findings: The findings indicate one of the highest heterogeneity, close to 0.50 log points, between majors in the literature. The within-heterogeneity created by majors is highest among the graduates of social-behavioral sciences, law, biology, physics, mathematics, statistics, computer, engineering and manufacturing, as shown by a 90–10 difference, which is almost 700% for some of these majors. This study shows that the natural science and technical majors that are expected to be more productive and to be paid more fall behind in the wage distribution. Research limitations/implications: Estimation results show that natural science majors, except for subjects allied to medicine and engineering, are paid lower than law and service-sector-related majors. This indicates that the predictions of the skill-biased technical change hypothesis are not valid in the wage profiles in Turkey and that some majors supply more than the sectoral needs. This casts doubts on the effectiveness of the ongoing higher education expansion process of the country. Originality/value: This study contributes to the literature on wage differentials of college majors, an area with limited studies. This is the first study analyzing wage differentials of the field of studies by correcting sample selection bias for the Turkish case. © 2021, Emerald Publishing Limited.