Consequences of Private Tutoring for Educational Attainment: The Case of South Korea

Thursday, 14 July 2016: 16:45
Location: Hörsaal 47 (Main Building)
Oral Presentation
Inhoe KU, Seoul National Universty, South Korea
Jung-Eun KIM, Seoul National University, South Korea
Hyerim LEE, Seoul National University, South Korea
Private tutoring, defined as fee-based tutoring that provides supplementary instruction to children in academic subjects outside a formal educational system, is a widespread phenomenon across the world. South Korea is one of the countries where private tutoring is the most prevalent and the most entrenched. Spending by households on private tutoring reaches 2.9 percent of GDP, an amount nearly equivalent to public expenditure on education. Private tutoring has become a public concern since it imposes high financial burden on families and is perceived as a main culprit for the rising educational gap between children from rich and poor families in Korea. However, previous studies provide conflicting findings on the effectiveness of private tutoring with some showing positive effects on educational performance and others showing no effects. Disagreements among those studies may be due to different outcomes measured, different datasets analyzed, and/or different statistical techniques applied.

 This study attempts to reconcile different findings from previous studies by estimating the effect of private tutoring on various educational outcomes based on the same dataset. Data come from Korean Educational Longitudinal Study 2005-2014, which collected information on educational progress of children from middle school years to their early twenties. We estimate ordered logit models for GPA-based ranks, OLS regression models for college SAT scores, multinomial logit models for college enrollment results. However, the standard regression approach does not consider that parents and children who take private tutoring may be different from others in unobserved as well as observed characteristics. We use fixed effect approaches and instrumental variables techniques to address the endogeneity bias. Results from different analytic methods are compared. Implications for educational inequality and social mobility are discussed.