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Effectiveness of Private and Public High Schools: Evidence from Finland

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  • Mika Kortelainen
  • Kalle Manninen

Abstract

A number of papers have compared the effectiveness of private and public schools in different institutional settings. However, most of these studies are observational and do not utilize experimental or quasi-experimental design to evaluate the value-added or the effectiveness of private schools in comparison to public schools. This study focuses on private and public high schools in Helsinki, the capital city of Finland. We use two different methods to compare private and public schools, value-added estimation and regression discontinuity design (RDD). Although based on somewhat different assumptions, both methods allow us to evaluate the causal effect of private schools on the exit exam results in high school. We find that private schools perform marginally better than public schools, but the difference in performance is very small and statistically insignificant according to both methods. Various robustness and validity checks strengthen our RDD results and the validity of the discontinuity design.

Suggested Citation

  • Mika Kortelainen & Kalle Manninen, 2019. "Effectiveness of Private and Public High Schools: Evidence from Finland," CESifo Economic Studies, CESifo Group, vol. 65(4), pages 424-445.
  • Handle: RePEc:oup:cesifo:v:65:y:2019:i:4:p:424-445.
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    File URL: http://hdl.handle.net/10.1093/cesifo/ifz014
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    References listed on IDEAS

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    1. Kirjavainen, Tanja & Loikkanent, Heikki A., 1998. "Efficiency differences of finnish senior secondary schools: An application of DEA and Tobit analysis," Economics of Education Review, Elsevier, vol. 17(4), pages 377-394, October.
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    5. Atila Abdulkadiroğlu & Parag A. Pathak & Christopher R. Walters, 2018. "Free to Choose: Can School Choice Reduce Student Achievement?," American Economic Journal: Applied Economics, American Economic Association, vol. 10(1), pages 175-206, January.
    6. Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell & Roc ́ıo Titiunik, 2017. "rdrobust: Software for regression-discontinuity designs," Stata Journal, StataCorp LP, vol. 17(2), pages 372-404, June.
    7. Stephen W. Raudenbush & JDouglas Willms, 1995. "The Estimation of School Effects," Journal of Educational and Behavioral Statistics, , vol. 20(4), pages 307-335, December.
    8. Hakkinen, Iida & Kirjavainen, Tanja & Uusitalo, Roope, 2003. "School resources and student achievement revisited: new evidence from panel data," Economics of Education Review, Elsevier, vol. 22(3), pages 329-335, June.
    9. Sean F. Reardon & Stephen W. Raudenbush, 2009. "Assumptions of Value-Added Models for Estimating School Effects," Education Finance and Policy, MIT Press, vol. 4(4), pages 492-519, October.
    10. Mark Berends & R. Joseph Waddington, 2018. "School Choice in Indianapolis: Effects of Charter, Magnet, Private, and Traditional Public Schools," Education Finance and Policy, MIT Press, vol. 13(2), pages 227-255, Spring.
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    Cited by:

    1. Edmark, Karin & Persson, Lovisa, 2021. "The impact of attending an independent upper secondary school: Evidence from Sweden using school ranking data," Economics of Education Review, Elsevier, vol. 84(C).

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    More about this item

    Keywords

    education; public sector outsourcing; regression discontinuity; school choice; value-added;
    All these keywords.

    JEL classification:

    • H44 - Public Economics - - Publicly Provided Goods - - - Publicly Provided Goods: Mixed Markets
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I26 - Health, Education, and Welfare - - Education - - - Returns to Education
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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