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Average and marginal returns to upper secondary schooling in Indonesia

Author

Listed:
  • Pedro Carneiro

    () (Institute for Fiscal Studies and University College London)

  • Michael Lokshin

    (Institute for Fiscal Studies)

  • Cristobal Ridao-Cano

    (Institute for Fiscal Studies)

  • Nithin Umapathi

    (Institute for Fiscal Studies and World Bank)

Abstract

This paper estimates average and marginal returns to schooling in Indonesia using a non-parametric selection model. Identification of the model is given by exogenous geographic variation in access to upper secondary schools. We find that the return to upper secondary schooling varies widely across individuals: it can be as high as 50 percent per year of schooling for those very likely to enroll in upper secondary schooling, or as low as -10 percent for those very unlikely to do so. Average returns for the student at the margin are well below those for the average student attending upper secondary schooling.

Suggested Citation

  • Pedro Carneiro & Michael Lokshin & Cristobal Ridao-Cano & Nithin Umapathi, 2011. "Average and marginal returns to upper secondary schooling in Indonesia," CeMMAP working papers CWP36/11, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  • Handle: RePEc:ifs:cemmap:36/11
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    File URL: http://cemmap.ifs.org.uk/wps/cwp3611.pdf
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    References listed on IDEAS

    as
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    3. De Groote, Olivier & Declercq, Koen, 2018. "Tracking and specialization of high schools: heterogeneous effects of school choice," TSE Working Papers 18-958, Toulouse School of Economics (TSE), revised Jun 2020.
    4. Xinxin Chen & Yaojiang Shi & Di Mo & James Chu & Prashant Loyalka & Scott Rozelle, 2013. "Impact of a Senior High School Tuition Relief Program on Poor Junior High School Students in Rural China," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 21(3), pages 80-97, May.
    5. Shuxi Zeng & Fan Li & Peng Ding, 2020. "Is being an only child harmful to psychological health?: evidence from an instrumental variable analysis of China's one‐child policy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(4), pages 1615-1635, October.
    6. Radchenko, Natalia, 2014. "Heterogeneity in Informal Salaried Employment: Evidence from the Egyptian Labor Market Survey," World Development, Elsevier, vol. 62(C), pages 169-188.
    7. Biewen, Martin & (neé Tapalaga), Madalina Thiele, 2020. "Early tracking, academic vs. vocational training, and the value of ‘second-chance’ options," Labour Economics, Elsevier, vol. 66(C).
    8. Pessino, Carola & Izquierdo, Alejandro & Vuletin, Guillermo, 2018. "Better Spending for Better Lives: How Latin America and the Caribbean Can Do More with Less," IDB Publications (Books), Inter-American Development Bank, number 9152, March.
    9. Eckhoff Andresen, Martin & Huber, Martin, 2018. "Instrument-based estimation with binarized treatments: Issues and tests for the exclusion restriction," FSES Working Papers 492, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.

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    More about this item

    JEL classification:

    • J2 - Labor and Demographic Economics - - Demand and Supply of Labor
    • J3 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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