Estimating the Returns to Education Using a Machine Learning Approach -- Evidence for Different Regions

Autor/in	Kamdjou, Herve D. Teguim
Titel	Estimating the Returns to Education Using a Machine Learning Approach -- Evidence for Different Regions
Quelle	In: Open Education Studies, 5 (2023) 1, Artikel 20220201 (22 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
DOI	10.1515/edu-2022-0201
Schlagwörter	Outcomes of Education; Artificial Intelligence; Human Capital; Regression (Statistics); Algorithms; Least Squares Statistics; Geographic Regions; Foreign Countries; Higher Education; Elementary Education; Gender Differences; Accuracy; Prediction; Expenditures; Models; Income; Africa; Europe; Asia; United States; Australia; India; South Korea; Switzerland + Suchen Sie Ihr Suchwort? Lernleistung; Schulerfolg; Künstliche Intelligenz; Humankapital; Regression; Regressionsanalyse; Algorithm; Algorithmus; Ausland; Hochschulbildung; Hochschulsystem; Hochschulwesen; Elementarunterricht; Geschlechterkonflikt; Vorhersage; Ausgaben; Analogiemodell; Einkommen; Afrika; Europa; Asien; USA; Australien; Indien; Korea; Republik; Schweiz
Abstract	This article revisits the Mincer earnings function and presents comparable estimates of the average monetary returns associated with an additional year of education across different regions worldwide. In contrast to the traditional Ordinary Least Squares (OLS) method commonly employed in the literature, this study applied a cutting-edge approach known as Support Vector Regression (SVR), which belongs to the family of machine learning (ML) algorithms. SVR is specifically chosen to address the bias arising from underfitting inherent in OLS. The analysis focuses on recent data spanning from 2010 to 2018, ensuring temporal homogeneity across the examined regions. The findings reveal that each additional year of education, on average, yields a private rate of returns of 10.4%. Notably, Sub-Saharan Africa exhibits the highest returns to education at 17.8%, while Europe demonstrates the lowest returns at 7.2%. Moreover, higher education is associated with the highest returns across the regions, with a rate of 12%, whereas primary education yields returns of 10%. Interestingly, women generally experience higher returns than men, with rates of 10.6 and 10.1%, respectively. Over time, the returns to education exhibit a modest decline, decreasing at a rate of approximately 0.1% per year, while the average duration of education demonstrates an increase of 0.16 years per year (1% per year). The application of the state-of-the-art ML technique, SVR, not only improves the accuracy of estimates but also enhances predictive performance measures such as the coefficient of determination (R[superscript 2]) and Root Mean Square Error (RMSE) when compared to the OLS method. The implications drawn from these findings emphasize the need for expanding university education, as well as investments in primary education, along with significant attention toward promoting girls' education. These findings hold considerable importance for policymakers who are tasked with making informed decisions regarding education expenditure and the implementation of education financing programs. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01