Intergenerational education mobility: A machine learning perspective

Xiang Gao (), Dongdong Li () and Weige Huang ()

World Journal of Vocational Education and Training, 2023, vol. 5, issue 1, 1-10

Abstract: By exploiting XGBoost and SHAP algorithms, this paper aims to reveal the importance of understanding the nexus among parental characteristics for intergenerational mobility in educational attainment, especially from the statistical learning perspective. Consistent with previous findings, both parents’ income and head’s education are positively correlated with child’s income. We also show strong intergenerational education mobility for low- and high-income families. However, there exists a negative relationship between head and child’s education for the middle-income families. Unlike conventional wisdom, we find that the income of highly educated parents tends to negatively associate with child’s education and the opposite happens with poorly educated parents. Moreover, for white and black children, their parents’ income will adversely affect child’s education, but this effect turns out to be positive for children of other races. Our paper hence suggests the consideration of ethnicity and family wealth in conjunction when making education effectiveness facilitation policies.

Keywords: Intergenerational education; Machine learning mobility; Random forest; SHAP; XGBoost. (search for similar items in EconPapers)
Date: 2023
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