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Socioeconomic gradients in children's cognitive skills: Are cross-country comparisons robust to who reports family background?

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  • John Jerrim

    ()
    (Department of Quantitative Social Science, Institute of Education, University of London. 20 Bedford Way, London WC1H 0AL, UK.)

  • John Micklewright

    ()
    (Department of Quantitative Social Science, Institute of Education, University of London. 20 Bedford Way, London WC1H 0AL, UK.)

Abstract

The international surveys of pupil achievement – PISA, TIMSS, and PIRLS – have been widely used to compare socioeconomic gradients in children’s cognitive abilities across countries. Socioeconomic status is typically measured drawing on children’s reports of family or home characteristics rather than information provided by their parents. There is a well established literature based on other survey sources on the measurement error that may result from child reports. But there has been very little work on the implications for the estimation of socioeconomic gradients in test scores in the international surveys, and especially their variation across countries. We investigate this issue drawing on data from PISA and PIRLS, focusing on three socioeconomic indicators for which both child and parental reports are present for some countries: father’s occupation, parental education, and the number of books in the family home. Our results suggest that children’s reports of their father’s occupation provide a reliable basis on which to base comparisons across countries in socioeconomic gradients in reading test scores. The same is not true, however, for children’s reports of the number of books in the home – a measure commonly used – while results for parental education are rather mixed.

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Bibliographic Info

Paper provided by Department of Quantitative Social Science - Institute of Education, University of London in its series DoQSS Working Papers with number 12-06.

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Date of creation: 09 Oct 2012
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Handle: RePEc:qss:dqsswp:1206

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Keywords: Educational inequality; social mobility; measurement error; PISA; PIRLS;

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  1. Hermann, Z. & Horn, D., 2011. "How are inequality of opportunity and mean student performance related? A quantile regression approach using PISA data," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, Euro-American Association of Economic Development, vol. 11(3).
  2. John Hendrickx, 2002. "ISKO: Stata module to recode 4 digit ISCO-88 occupational codes," Statistical Software Components S425802, Boston College Department of Economics, revised 20 Oct 2004.
  3. Schütz, Gabriela & Ursprung, Heinrich W. & Woessmann, Ludger, 2005. "Education Policy and Equality of Opportunity," IZA Discussion Papers 1906, Institute for the Study of Labor (IZA).
  4. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053, 9.
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