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Educational scores: How does Russia fare?

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  • Amini, Chiara
  • Commander, Simon

Abstract

This paper uses large multi-country datasets on educational scores – namely PISA and TIMSS – to examine the factors associated with educational outcomes. In particular, it distinguishes between individual and family background factors and those emanating from the school or institutional environment. Using pooled data as well as cross sectional evidence we look at the variation across countries before looking at within country variation in Russia. We find that both in the benchmark cross-country estimates, as also those using just Russia data, a number of individual and family variables are robustly associated with better educational outcomes. Institutional variables also matter – notably student–teacher ratios and indicators of school autonomy – but there are also some clear particularities in the Russian case.

Suggested Citation

  • Amini, Chiara & Commander, Simon, 2012. "Educational scores: How does Russia fare?," Journal of Comparative Economics, Elsevier, vol. 40(3), pages 508-527.
  • Handle: RePEc:eee:jcecon:v:40:y:2012:i:3:p:508-527
    DOI: 10.1016/j.jce.2012.02.006
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    1. Eric Hanushek & Ludger Woessmann, 2012. "Do better schools lead to more growth? Cognitive skills, economic outcomes, and causation," Journal of Economic Growth, Springer, vol. 17(4), pages 267-321, December.
    2. Daniel Münich & Jan Svejnar & Katherine Terrell, 2005. "Returns to Human Capital Under The Communist Wage Grid and During the Transition to a Market Economy," The Review of Economics and Statistics, MIT Press, vol. 87(1), pages 100-123, February.
    3. Hanushek, Eric A. & Woessmann, Ludger, 2007. "The role of education quality for economic growth," Policy Research Working Paper Series 4122, The World Bank.
    4. Sophia Rabe‐Hesketh & Anders Skrondal, 2006. "Multilevel modelling of complex survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 805-827, October.
    5. Abhijit V. Banerjee & Shawn Cole & Esther Duflo & Leigh Linden, 2007. "Remedying Education: Evidence from Two Randomized Experiments in India," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 122(3), pages 1235-1264.
    6. Hanushek, Eric A. & Woessmann, Ludger, 2011. "The Economics of International Differences in Educational Achievement," Handbook of the Economics of Education, in: Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), Handbook of the Economics of Education, edition 1, volume 3, chapter 2, pages 89-200, Elsevier.
    7. Sims,Christopher A. (ed.), 1994. "Advances in Econometrics," Cambridge Books, Cambridge University Press, number 9780521444606, November.
    8. Shahrukh Rafi Khan & David Kiefer, 2007. "Educational Production Functions for Rural Pakistan: A Comparative Institutional Analysis," Education Economics, Taylor & Francis Journals, vol. 15(3), pages 327-342.
    9. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    10. Rosalind Levacic & Stephen Machin & David Reynolds & Anna Vignoles & James Walker, 2000. "The Relationship between Resource Allocation and Pupil Attainment: A Review," CEE Discussion Papers 0002, Centre for the Economics of Education, LSE.
    11. Gyimah-Brempong, Kwabena & Gyapong, Anthony O., 1991. "Characteristics of education production functions: An application of canonical regression analysis," Economics of Education Review, Elsevier, vol. 10(1), pages 7-17, March.
    12. Andreas Ammermueller, 2007. "PISA: What makes the difference?," Empirical Economics, Springer, vol. 33(2), pages 263-287, September.
    13. Dan D. Goldhaber & Dominic J. Brewer, 1997. "Why Don't Schools and Teachers Seem to Matter? Assessing the Impact of Unobservables on Educational Productivity," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 505-523.
    14. Sims,Christopher A. (ed.), 1994. "Advances in Econometrics," Cambridge Books, Cambridge University Press, number 9780521444590, November.
    15. Richard B. Freeman & Stephen Machin & Martina Viarengo, 2010. "Variation in Educational Outcomes and Policies across Countries and of Schools within Countries," NBER Working Papers 16293, National Bureau of Economic Research, Inc.
    16. Giorgio Brunello & Elena Crivellaro & Lorenzo Rocco, 2012. "Lost in transition?," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 20(4), pages 637-676, October.
    17. Eide, Eric & Showalter, Mark H., 1998. "The effect of school quality on student performance: A quantile regression approach," Economics Letters, Elsevier, vol. 58(3), pages 345-350, March.
    18. Petra E. Todd & Kenneth I. Wolpin, 2003. "On The Specification and Estimation of The Production Function for Cognitive Achievement," Economic Journal, Royal Economic Society, vol. 113(485), pages 3-33, February.
    19. Cooper, Samuel T. & Cohn, Elchanan, 1997. "Estimation of a frontier production function for the South Carolina educational process," Economics of Education Review, Elsevier, vol. 16(3), pages 313-327, June.
    20. Robert Mislevy, 1991. "Randomization-based inference about latent variables from complex samples," Psychometrika, Springer;The Psychometric Society, vol. 56(2), pages 177-196, June.
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    Cited by:

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    2. Lucia Mangiavacchi, 2016. "Family structure and children’s educational attainment in transition economies," IZA World of Labor, Institute of Labor Economics (IZA), pages 303-303, October.
    3. Riccardo Crescenzi & Alexander Jaax, 2017. "Innovation in Russia: The Territorial Dimension," Economic Geography, Taylor & Francis Journals, vol. 93(1), pages 66-88, January.
    4. Dilmaghani, Maryam, 2021. "The gender gap in competitive chess across countries: Commanding queens in command economies," Journal of Comparative Economics, Elsevier, vol. 49(2), pages 425-441.
    5. Cheti Nicoletti & Birgitta Rabe, 2013. "School inputs and skills: Complementarity and self-productivity," Discussion Papers 13/30, Department of Economics, University of York.
    6. Quentin Lippmann & Claudia Senik, 2018. "Math, Girls and Socialism," Working Papers halshs-01387272, HAL.
    7. Amini, Chiara & Nivorozhkin, Eugene, 2015. "The urban–rural divide in educational outcomes: Evidence from Russia," International Journal of Educational Development, Elsevier, vol. 44(C), pages 118-133.

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    More about this item

    Keywords

    Education; Russia; PISA; TIMSS;
    All these keywords.

    JEL classification:

    • H5 - Public Economics - - National Government Expenditures and Related Policies
    • I21 - Health, Education, and Welfare - - Education - - - Analysis of Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration
    • P5 - Political Economy and Comparative Economic Systems - - Comparative Economic Systems

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