Empirical Economics

, Volume 32, Issue 2–3, pp 433–464 | Cite as

What accounts for international differences in student performance? A re-examination using PISA data

Original Paper

Abstract

We use the PISA student-level achievement database to estimate international education production functions. Student characteristics, family backgrounds, home inputs, resources, teachers and institutions are all significantly associated with math, science and reading achievement. Our models account for more than 85% of the between-country performance variation, with roughly 25% accruing to institutional variation. Student performance is higher with external exams and budget formulation, but also with school autonomy in textbook choice, hiring teachers and within-school budget allocations. Autonomy is more positively associated with performance in systems that have external exit exams. Students perform better in privately operated schools, but private funding is not decisive.

Keywords

Education production function PISA International variation in student performance Institutional effects in schooling 

JEL classification

I28 J24 H52 L33 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adams R, Wu M (eds) (2002) PISA 2000 technical report. Organisation for Economic Co-operation and Development (OECD), ParisGoogle Scholar
  2. Akerhielm K (1995) Does class size matter? Econ Educ Rev 14:229–241CrossRefGoogle Scholar
  3. Betts JR (1998) The impact of educational standards on the level and distribution of earnings. Am Econ Rev 88:266–275Google Scholar
  4. Bishop JH (1997) The effect of national standards and curriculum-based exams on achievement. Am Econ Rev 87:260–264Google Scholar
  5. Bishop JH (2006) Drinking from the fountain of knowledge: student incentive to study and learn. In: Hanushek EA, Welch F (eds) Handbook of the economics of education. (forthcoming) North-Holland, AmsterdamGoogle Scholar
  6. Bishop JH, Wößmann L (2004) Institutional effects in a simple model of educational production. Educ Econ 12:17–38CrossRefGoogle Scholar
  7. Costrell RM (1994) A simple model of educational standards. Am Econ Rev 84:956–971Google Scholar
  8. Dronkers J, Robert P (2003) The effectiveness of public and private schools from a comparative perspective. EUI Working Paper SPS 2003–13. European University Institute, FlorenceGoogle Scholar
  9. DuMouchel WH, Duncan GJ (1983) Using sample survey weights in multiple regression analyses of stratified samples. J Am Statist Assoc 78:535–543CrossRefGoogle Scholar
  10. Epple D, Romano RE (1998) Competition between private and public schools, vouchers, and peer-group effects. Am Econ Rev 88:33–62Google Scholar
  11. Fertig M (2003a) Who’s to blame? The determinants of German students’ achievement in the PISA 2000 study. IZA Discussion Paper 739. Institute for the Study of Labor, BonnGoogle Scholar
  12. Fertig M (2003b) Educational production, endogenous peer group formation and class composition: evidence from the PISA 2000 study. IZA Discussion Paper 714. Institute for the Study of Labor, BonnGoogle Scholar
  13. Fertig M, Schmidt CM (2002) The role of background factors for reading literacy: straight national scores in the PISA 2000 study. IZA Discussion Paper 545. Institute for the Study of Labor, BonnGoogle Scholar
  14. Fuchs T, Wößmann L (2004a) What accounts for international differences in student performance? A re-examination using PISA data. CESifo Working Paper 1235. CESifo, MunichGoogle Scholar
  15. Fuchs T, Wößmann L (2004b) Computers and student learning: Bivariate and multivariate evidence on the availability and use of computers at home and at school. Brussels Econ Rev 47:359–385Google Scholar
  16. Gundlach E, Wößmann L, Gmelin J (2001) The decline of schooling productivity in OECD countries. Econ J 111:C135–C147CrossRefGoogle Scholar
  17. Hanushek EA (2002) Publicly provided education. In: Auerbach AJ, Feldstein M (eds) Handbook of public economics, Vol 4. North Holland, Amsterdam, pp 2045–2141Google Scholar
  18. Hanushek EA et al (1994) Making schools work: improving performance and controlling costs. Brookings Institution Press, WashingtonGoogle Scholar
  19. Hoxby CM (1999) The productivity of schools and other local public goods producers. J Public Econ 74:1–30CrossRefGoogle Scholar
  20. Hoxby CM (2001) All school finance equalizations are not created equal. Q J Econ 116:1189–1231CrossRefGoogle Scholar
  21. Jürges H, Schneider K, Büchel F (2005) The effect of central exit examinations on student achievement: quasi-experimental evidence from TIMSS Germany. J Eur Econ Assoc 3:1134–1155CrossRefGoogle Scholar
  22. Little RJA, Rubin DB (1987) Statistical analysis with missing data. Wiley, New YorkGoogle Scholar
  23. Moulton BR (1986) Random group effects and the precision of regression estimates. J Econ 32:385–397Google Scholar
  24. Nechyba TJ (2000) Mobility, targeting, and private-school vouchers. Am Econ Rev 90:130–146CrossRefGoogle Scholar
  25. Nechyba TJ (2003) Centralization, fiscal federalism, and private school attendance. Int Econ Rev 44:179–204CrossRefGoogle Scholar
  26. Organisation for Economic Co-operation and Development (OECD) (2000) Measuring student knowledge and skills: the PISA 2000 assessment of reading, mathematical and scientific literacy. OECD, ParisGoogle Scholar
  27. Organisation for Economic Co-operation and Development (OECD) (2001) Knowledge and skills for life: first results from the OECD Programme for International Student Assessment (PISA) 2000. OECD, ParisGoogle Scholar
  28. Organisation for Economic Co-operation and Development (OECD) (2002) Manual for the PISA 2000 database. OECD, ParisGoogle Scholar
  29. Organisation for Economic Co-operation and Development (OECD) (2003) Education at a glance: OECD indicators 2003. OECD, ParisGoogle Scholar
  30. Rubin DB (1987) Multiple imputation for nonresponse in surveys. Wiley, New YorkGoogle Scholar
  31. Schafer JL, Schenker N (1997) Inference with imputed conditional means. Pennsylvania State University, Department of Statistics, Technical Report #97–05 (available at http://www.stat.psu.edu/reports/1997/tr9705.pdf)Google Scholar
  32. Schafer JL, Schenker N (2000) Inference with imputed conditional means. J Am Statist Assoc 95:144–154CrossRefGoogle Scholar
  33. Shleifer A (1998) State versus private ownership. J Econ Perspect 12:133–150Google Scholar
  34. Todd PE, Wolpin KI (2003) On the specification and estimation of the production function for cognitive achievement. Econ J 113:F3–F33CrossRefGoogle Scholar
  35. West MR, Wößmann L (2006) Which school systems sort weaker students into smaller classes? International evidence. Eur J Politi Econ (forthcoming) (available as CESifo Working Paper 1054, CESifo, Munich)Google Scholar
  36. Wolter SC, Coradi Vellacott M (2003) Sibling rivalry for parental resources: a problem for equity in education? A six-country comparison with PISA data. Swiss J Sociol 29:377–398Google Scholar
  37. Wooldridge JM (2001) Asymptotic properties of weighted m-estimators for standard stratified samples. Econ Theory 17: 451–470CrossRefGoogle Scholar
  38. World Bank (2003) World development indicators CD-Rom. World Bank, WashingtonGoogle Scholar
  39. Wößmann L (2003a) Schooling resources, educational institutions and student performance: the international evidence. Oxford Bull Econ Statist 65:117–170CrossRefGoogle Scholar
  40. Wößmann L (2003b). Central exit exams and student achievement: international evidence. In: Peterson PE, West MR (eds). No child left behind? The politics and practice of school accountability. Brookings Institution Press, Washington, pp. 292–323Google Scholar
  41. Wößmann L (2003c) Central exams as the “currency” of school systems: international evidence on the complementarity of school autonomy and central exams. DICE Report – J Inst Comp 1:46–56Google Scholar
  42. Wößmann L (2005) Educational production in Europe. Econ Policy 20:445–504CrossRefGoogle Scholar
  43. Wößmann L, West MR (2006) Class-size effects in school systems around the world: Evidence from between-grade variation in TIMSS. Eur Econ Rev 50:695–736CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Ifo Institute for Economic ResearchMunichGermany
  2. 2.Ifo Institute for Economic ResearchUniversity of Munich and CESifoMunichGermany

Personalised recommendations