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The Impact of Early Schooling on Subsequent Literacy and Numeracy Performance - Estimates from a Policy Induced 'Natural' Experiment

This paper exploits a policy-induced natural experiment that occurred in South Australia in the mid-1980s to generate a 'causal' estimate of the effect of schooling on the literacy and numeracy performance of school students in their middle years of secondary school (in Year 9 for most students). The Early Years of School policy changed the way that an identifiable subset of students progressed through junior primary school, causing them to obtain an additional year of schooling for any completed Grade or level compared with their predecessors. The impact of the policy change on the age-grade structure of student cohorts in South Australia is captured between two waves of longitudinal data. Based on the analysis of the impact of this policy change, it appears that an additional year of junior primary school increased the numeracy and literacy performance in mid-secondary school significantly, by around one half of a standard deviation. These effects were the same for boys and girls and were similar across the distribution of ability - they were the same for low school achievers as high ones.

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Paper provided by Centre for Economic Policy Research, Research School of Economics, Australian National University in its series CEPR Discussion Papers with number 470.

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Length: 36 pages
Date of creation: Jan 2004
Date of revision:
Handle: RePEc:auu:dpaper:470
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  1. Joseph E. Stiglitz, 1973. "The Theory of 'Screening', Education, and the Distribution of Income," Cowles Foundation Discussion Papers 354, Cowles Foundation for Research in Economics, Yale University.
  2. Meyer, Bruce D, 1995. "Natural and Quasi-experiments in Economics," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(2), pages 151-61, April.
  3. Janet Currie, 2001. "Early Childhood Education Programs," Journal of Economic Perspectives, American Economic Association, vol. 15(2), pages 213-238, Spring.
  4. Ichino, Andrea & Winter-Ebmer, Rudolf, 1999. "Lower and upper bounds of returns to schooling: An exercise in IV estimation with different instruments," European Economic Review, Elsevier, vol. 43(4-6), pages 889-901, April.
  5. Jesse Levin, 2001. "For whom the reductions count: A quantile regression analysis of class size and peer effects on scholastic achievement," Empirical Economics, Springer, vol. 26(1), pages 221-246.
  6. Thomas, D. & Currie, J., 1993. "Does Head Start Make a Difference?," Papers 694, Yale - Economic Growth Center.
  7. Alan Krueger, 2000. "Economic Considerations and Class Size," Working Papers 826, Princeton University, Department of Economics, Industrial Relations Section..
  8. Colm Harmon & Ian Walker, 1995. "Estimates of the economic return to schooling for the United Kingdom," Open Access publications 10197/647, School of Economics, University College Dublin.
  9. J. A. Hausman, 1976. "Specification Tests in Econometrics," Working papers 185, Massachusetts Institute of Technology (MIT), Department of Economics.
  10. Angrist, Joshua D & Krueger, Alan B, 1991. "Does Compulsory School Attendance Affect Schooling and Earnings?," The Quarterly Journal of Economics, MIT Press, vol. 106(4), pages 979-1014, November.
  11. Omar Arias & Walter Sosa-Escudero & Kevin F. Hallock, 2001. "Individual heterogeneity in the returns to schooling: instrumental variables quantile regression using twins data," Empirical Economics, Springer, vol. 26(1), pages 7-40.
  12. Spence, A Michael, 1973. "Job Market Signaling," The Quarterly Journal of Economics, MIT Press, vol. 87(3), pages 355-74, August.
  13. 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.
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