The impact of extracurricular education on socioeconomic mobility in Japan: an application of causal machine learning

TL;DR

This study uses causal machine learning to analyze how extracurricular private tutoring affects social mobility in Japan, revealing that economic disparities limit its overall positive impact despite potential benefits.

Contribution

It applies causal machine learning to evaluate the socioeconomic effects of private tutoring in Japan, highlighting the complex interplay of demographics and educational interventions.

Findings

Shadow education has limited overall socioeconomic benefits due to economic disparities.

Causal machine learning effectively evaluates educational interventions.

Economic inequality diminishes the positive impact of private tutoring.

Abstract

This paper explores the socioeconomic impacts of extracurricular education, specifically private tutoring, on social mobility in Japan. Using data from the 2015 National Survey on Social Stratification and Social Mobility (SSM), we employed a causal machine learning approach to evaluate this educational intervention on income, educational attainment, and occupational prestige. Our research suggests that while shadow education holds the potential for positive socioeconomic impacts, its benefits are undermined by the economic disparities among households, resulting in minimal overall improvement. This highlights the complex mechanisms between individual demographics and educational interventions, revealing promising machine learning applications in this field.