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More countries, similar results. A nonlinear programming approach to normalising test scores needed for growth regressions

Author

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  • Martin Gustafsson

    (Department of Economics, University of Stellenbosch)

Abstract

Analysts such as Hanushek and Woessman have brought to the fore the deceptiveness of education enrolments, or years of schooling, in growth regressions and the need to consider educational quality. In this paper, a nonlinear programming solution is proposed as a way of normalising to a single scale country average test scores from various international testing programmes. This method, though less transparent and more dependent on certain subjective choices than the existing approach put forward by Hanushek and Woessman, allows for the inclusion of more countries, in particular more African and developing countries, into a growth regression. The regression produces the results one would expect, namely a strong conditional correlation between growth and educational quality. The utility of growth regressions with an educational quality variable for the education policymaker is discussed. A method for arriving at feasible annual improvements in educational quality and hence feasible country targets is presented

Suggested Citation

  • Martin Gustafsson, 2012. "More countries, similar results. A nonlinear programming approach to normalising test scores needed for growth regressions," Working Papers 12/2012, Stellenbosch University, Department of Economics.
    • Handle: RePEc:sza:wpaper:wpapers164
    as

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    File URL: https://www.ekon.sun.ac.za/wpapers/2012/wp122012/wp-12-2012.pdf
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    References listed on IDEAS

    as
    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. R Burger & S du Plessis, 2011. "Examining the Robustness of Competing Explanations of Slow Growth in African Countries," Studies in Economics and Econometrics, Taylor & Francis Journals, vol. 35(3), pages 21-47, December.
    3. Robert J. Barro & Xavier Sala-i-Martin, 2003. "Economic Growth, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262025531, December.
    4. Christopher F Baum, 2006. "An Introduction to Modern Econometrics using Stata," Stata Press books, StataCorp LP, number imeus, March.
    5. N. Gregory Mankiw & David Romer & David N. Weil, 1992. "A Contribution to the Empirics of Economic Growth," The Quarterly Journal of Economics, Oxford University Press, vol. 107(2), pages 407-437.
    6. Eric A. Hanushek & Ludger Woessmann, 2007. "The Role of School Improvement in Economic Development," NBER Working Papers 12832, National Bureau of Economic Research, Inc.
    7. Robert J. Barro & Rachel McCleary, 2003. "Religion and Economic Growth," NBER Working Papers 9682, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Adaiah Lilenstein, 2018. "Integrating Indicators of Education Quantity and Quality in Six Francophone African Countries," Working Papers 09/2018, Stellenbosch University, Department of Economics.

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    More about this item

    Keywords

    human capital; cross-country growth model; test score data; nonlinear programming; education policy; PISA; SACMEQ; SERCE;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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