Eğitimde Etkinliğin Ölçülmesi: PISA Verileri İle Bootstrap Veri Zarflama Analizi Tahmini

Öz

Bu çalışmanın amacı Türk eğitim sisteminin performansını değerlendirmek ve belirleyenlerini ortaya koymaktır. Bu kapsamda, Ekonomik Kalkınma ve İşbirliği Örgütü-OECD tarafından ölçülen güncel veri olan 2015 yılı Uluslararası Öğrenci Başarılarını Değerlendirme Projesi-PISA verileri iki aşamalı bir yaklaşım izlenerek mikro düzeyde değerlendirilmektedir. Çalışmanın ilk aşamasında, Türkiye’deki her bir okul için etkinlik skorları Bootstrap Veri Zarflama Analizi ile hesaplanmaktadır. İkinci aşamada ise, parçalı Probit modelleri kullanılarak okulların etkinliğine etki eden faktörler araştırılmaktadır. Elde edilen bulgular,  eğitim sürecinde kullanılan girdiler sabit kalmak koşuluyla, başarı puanlarında yaklaşık % 22 oranında potansiyel bir iyileşme yapılabileceğini göstermektedir. Marjinal etkiler sonucuna göre, sertifikalı öğretmen sayısı etkinliği artırırken, okullardaki öğretmen açığı etkinliği azaltmaktadır.   

Anahtar Kelimeler

• eğitim iktisadı,etkinlik,bootstrap veri zarflama analizi

Measurement of Efficiency in Education: Estimation of Bootstrap Data Envelopment Analysis with PISA Data

Öz

Kaynakça

1. Afonso, A., Aubyn, M.S. 2006. “Cross-country efficiency of secondary education provision: a semi-parametric analysis with non-discretionary inputs.”, Economic Modeling, 23, 476-491
2. Agasisti, T., 2011a, ‘The effect of competition on schools’ performance: preliminaryevidence from Italy through OECD-PISA data’,European Journal of Education, 46(4),549–565.
3. Agasisti,T., 2011b, ‘How competition affects schools’ performances: does specificationmatter?’Economics Letters, 110(3), 259–261.
4. Agasisti, T,, & Zoido, P. 2018. “Comparing the efficiency of schools through international benchmarking: results from an empirical analysis of OECD PISA 2012 data.” Educational Researcher, 47, 352-362.
5. Aristovnik, A. 2013. “Relative efficiency of education expenditures in Eastern Europe: A non-parametric approach.” Journal of Knowledge Management, Economics and Information Technology, 3(3), 1-4.
6. Arias, J.C., & Garcia, A.T. 2017. "Economic efficiency of public secondary education expenditure: how different are developed and developing countries?," Documentos de Trabajo CIEF 015919, Universidad EAFIT.
7. Banker, D.R., Charnes, A., & Cooper, WW. 1984. “Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis.” Management Science, 30, 1078-1092.
8. Cooper, W.W., Seiford, LM., & Tone, K. 2007. “Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References And DEA-Solver Software.” Second Edition: Springer, doi: 10.1007/978-0-387-45283-8

9. De Witte, K., & Lopez-Torres, L. 2015. “Efficiency in education: A review of literature and a way forward.” Journal of the Operational Research Society, 68(4), 1-33.
10. EACEA(2009), National Testing of Pupils in Europe: Objectives, Organisation and Use of Results, Brussels: Education, Audiovisual and Culture Executive Agency, European Commission, http://www.eurydice.org.
11. Johnes, J., Portela, M., & Thanassoulis, E. 2017. “Efficiency in education.” Journal of the Operational Research Society ,68(4), 331-338.
12. Lorcu, F., & Bolat, BA. 2015. “Comparison of Secondary Educatıon Pisa Results in European Member States and Turkey Via Dea and Sem”, Journal of WEI Business and Economics, 4(3), 7-17.
13. Mancebón, M.J., Choi, A.C., & Ximénez-de-Embún, D.P. 2012. “The efficiency of public and publicly subsidized high schools in Spain: Evidence from PISA-2006”, Journal of the Operational Research Society, 63(11), 1516-1533.
14. MEB, PISA 2015 Ulusal Raporu, Ankara, 2016.
15. OECD, PISA 2015 Results Excellence and Equility In Education (Volume 1), OECD Publishing, Paris, 2016.
16. Ponzo, M., 2011, ‘The effects of school competition on the achievement of Italian stu-dents’,Managerial and Decision Economics, 32(1), 53–61.
17. Santín, D.& Sicilia, G. 2015. "Measuring the efficiency of public schools in Uruguay: main drivers and policy implications," Latin American Economic Review, 24(1), 1-28.
18. Simar, L., & Wilson, PW. 1998. “Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Non-parametric Frontier Models.” Management Science, 44, 49-61.
19. Simar, L., & Wilson, PW. 2000. “A general methodology for bootstrapping in non-parametric frontier models.” Journal of Applied Statistics, 27, 779 -802.
20. Simar, L., & Wilson, PW. 2008. “Statistical Interference in Nonparametric Frontier Models: Recent Developments and Perspectives.” In: Fried H, Lovell CAK, Schmidt S (eds) The Measurement of Productive Efficiency and Productivity Change, Oxford University Press, New York.
21. Thanassoulis, E., Portela, M.S.C., & Despic, O. 2008. “Data Envelopment Analysis: The Mathematical Programming Approach to Efficiency Analysis.” In H. O. Fried, C. A. Knox Lovell, & S. S. Schmidt (Eds.), The Measurement of Productive Efficiency and Productivity Change, Oxford University Press, New York.
22. Wilson, PW. 2005. “Efficiency in education production among PISA Countries, with emphasis on transitioning economies.” World Bank Working Paper.
23. Yalçın, S., & Tavşancıl, E. 2014. The Comparison of Turkish Students’ PISA Achievement Levels by Year via Data Envelopment Analysis, Educational Sciences: Theory & Practice, 14(3), 961-698.