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All work and no play? The effects of ability sorting on students’ non-school inputs, time use, and grade anxiety

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  • Wang, Liang Choon

Abstract

How students’ non-school inputs respond to ability grouping may explain the currently mixed findings in the literature about the impacts of tracking. Using data from South Korea, where students are randomized into middle schools under the country's equalization policy, but sorted into different high schools on the basis of achievement in some non-equalization policy areas, I find that under ability sorting, students’ demand for private tutoring, self-study time, and grade anxiety levels are lower, and their hours of sleep and leisure are higher. The effects on private tutoring are particularly significant for high achievers, while the effects on self-study and leisure hours are strong for low achievers. The results potentially help reconcile the mixed findings in the ability grouping and tracking literature.

Suggested Citation

  • Wang, Liang Choon, 2015. "All work and no play? The effects of ability sorting on students’ non-school inputs, time use, and grade anxiety," Economics of Education Review, Elsevier, vol. 44(C), pages 29-41.
    • Handle: RePEc:eee:ecoedu:v:44:y:2015:i:c:p:29-41
    DOI: 10.1016/j.econedurev.2014.10.008
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    References listed on IDEAS

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    1. Changhui Kang & Cheolsung Park & Myoung-Jae Lee, 2007. "Effects of ability mixing in high school on adulthood earnings: quasiexperimental evidence from South Korea," Journal of Population Economics, Springer;European Society for Population Economics, vol. 20(2), pages 269-297, April.
    2. Hahn, Youjin & Wang, Liang Choon & Yang, Hee-Seung, 2018. "Does greater school autonomy make a difference? Evidence from a randomized natural experiment in South Korea," Journal of Public Economics, Elsevier, vol. 161(C), pages 15-30.
    3. Esther Duflo & Pascaline Dupas & Michael Kremer, 2011. "Peer Effects, Teacher Incentives, and the Impact of Tracking: Evidence from a Randomized Evaluation in Kenya," American Economic Review, American Economic Association, vol. 101(5), pages 1739-1774, August.
    4. Sunwoong Kim & Ju-Ho Lee, 2010. "Private Tutoring and Demand for Education in South Korea," Economic Development and Cultural Change, University of Chicago Press, vol. 58(2), pages 259-296, January.
    5. Gibbons, Stephen & Silva, Olmo, 2011. "School quality, child wellbeing and parents' satisfaction," Economics of Education Review, Elsevier, vol. 30(2), pages 312-331, April.
    6. Jishnu Das & Stefan Dercon & James Habyarimana & Pramila Krishnan & Karthik Muralidharan & Venkatesh Sundararaman, 2013. "School Inputs, Household Substitution, and Test Scores," American Economic Journal: Applied Economics, American Economic Association, vol. 5(2), pages 29-57, April.
    7. Hyunjoon Park & Jere Behrman & Jaesung Choi, 2013. "Causal Effects of Single-Sex Schools on College Entrance Exams and College Attendance: Random Assignment in Seoul High Schools," Demography, Springer;Population Association of America (PAA), vol. 50(2), pages 447-469, April.
    8. Figlio, David N. & Page, Marianne E., 2002. "School Choice and the Distributional Effects of Ability Tracking: Does Separation Increase Inequality?," Journal of Urban Economics, Elsevier, vol. 51(3), pages 497-514, May.
    9. Liang Choon Wang, 2013. "The deadly effect of high-stakes testing on teenagers with reference-dependent preferences," Monash Economics Working Papers 40-13, Monash University, Department of Economics.
    10. Hai-Anh Dang & F. Halsey Rogers, 2008. "The Growing Phenomenon of Private Tutoring: Does It Deepen Human Capital, Widen Inequalities, or Waste Resources?," The World Bank Research Observer, World Bank, vol. 23(2), pages 161-200, April.
    11. Kim, Taejong & Lee, Ju-Ho & Lee, Young, 2008. "Mixing versus sorting in schooling: Evidence from the equalization policy in South Korea," Economics of Education Review, Elsevier, vol. 27(6), pages 697-711, December.
    12. Hahn, Sunku & Sung, Taeyoon & Baek, Jisun, 2008. "Mixing versus sorting: Entering top universities," Economics Letters, Elsevier, vol. 100(1), pages 43-46, July.
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    Cited by:

    1. Pan, Zheng & Lien, Donald & Wang, Hao, 2022. "Peer effects and shadow education," Economic Modelling, Elsevier, vol. 111(C).
    2. Liang Choon Wang, 2016. "The effect of high-stakes testing on suicidal ideation of teenagers with reference-dependent preferences," Journal of Population Economics, Springer;European Society for Population Economics, vol. 29(2), pages 345-364, April.
    3. Hahn, Youjin & Wang, Liang Choon & Yang, Hee-Seung, 2018. "Does greater school autonomy make a difference? Evidence from a randomized natural experiment in South Korea," Journal of Public Economics, Elsevier, vol. 161(C), pages 15-30.
    4. Ahn, Taehyun & Goh, Young-Geun, 2021. "The long-term influences of ability mixing on soft skills," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 367-386.
    5. Högberg, Björn & Strandh, Mattias & Petersen, Solveig & Johansson, Klara, 2019. "Education system stratification and health complaints among school-aged children," Social Science & Medicine, Elsevier, vol. 220(C), pages 159-166.

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    More about this item

    Keywords

    Tracking; Shadow education; Leisure; Time use; Anxiety; Korea;
    All these keywords.

    JEL classification:

    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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