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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.

Suggested Citation

  • John Jerrim & John Micklewright, 2012. "Socioeconomic gradients in children's cognitive skills: Are cross-country comparisons robust to who reports family background?," DoQSS Working Papers 12-06, Quantitative Social Science - UCL Social Research Institute, University College London.
  • Handle: RePEc:qss:dqsswp:1206
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    Cited by:

    1. Ludger Woessmann, 2016. "The Importance of School Systems: Evidence from International Differences in Student Achievement," Journal of Economic Perspectives, American Economic Association, vol. 30(3), pages 3-32, Summer.
    2. Per Engzell, 2021. "What Do Books in the Home Proxy For? A Cautionary Tale," Sociological Methods & Research, , vol. 50(4), pages 1487-1514, November.
    3. Álvaro Choi & María Gil & Mauro Mediavilla & Javier Valbuena, 2018. "The Evolution of Educational Inequalities in Spain: Dynamic Evidence from Repeated Cross-Sections," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(3), pages 853-872, August.
    4. Ã lvaro Choi & John Jerrim, 2015. "The use (and misuse) of PISA in guiding policy reform: the case of Spain?," DoQSS Working Papers 15-04, Quantitative Social Science - UCL Social Research Institute, University College London.
    5. Catherine Haeck & Pierre Lefebvre, 2020. "The Evolution of Cognitive Skills Inequalities by Socioeconomic Status across Canada," Working Papers 20-04, Research Group on Human Capital, University of Quebec in Montreal's School of Management.
    6. Álvaro Choi & John Jerrim, 2015. "The use (and misuse) of Pisa in guiding policy reform: the case of Spain," Working Papers 2015/6, Institut d'Economia de Barcelona (IEB).
    7. Eva Six & Matthias Schnetzer, 2022. "Highbrow heritage: the effects of early childhood cultural capital on wealth," Working Paper Reihe der AK Wien - Materialien zu Wirtschaft und Gesellschaft 240, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik.
    8. Álvaro Choi & María Gil & Mauro Mediavilla & Javier Valbuena, 2016. "The evolution of educational inequalities in Spain: dynamic evidence from repeated cross-sections," Working Papers 2016/25, Institut d'Economia de Barcelona (IEB).
    9. Keller, Tamás, 2016. "Ha a jegyek nem elég jók... Az önértékelés szerepe a felsőoktatásba való jelentkezésben [Self-assessment and its effects on applications for tertiary education]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(1), pages 62-78.
    10. Aparicio, Juan & Cordero, Jose M. & Ortiz, Lidia, 2019. "Measuring efficiency in education: The influence of imprecision and variability in data on DEA estimates," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    11. Engzell, Per, 2017. "What Do Books in the Home Proxy For? A Cautionary Tale," Working Paper Series 1/2016, Stockholm University, Swedish Institute for Social Research.
    12. Gabriel Gutiérrez & John Jerrim & Rodrigo Torres, 2020. "School Segregation Across the World: Has Any Progress Been Made in Reducing the Separation of the Rich from the Poor?," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(2), pages 157-179, June.
    13. Pierre Lefebvre & Claude Felteau, 2023. "Can universal preschool education intensities counterbalance parental socioeconomic gradients? Repeated international evidence from Fourth graders skills achievement," Working Papers 23-01, Research Group on Human Capital, University of Quebec in Montreal's School of Management.
    14. Lim, Youngshin & Park, Hyunjoon, 2022. "Who have fallen behind? The educational reform toward differentiated learning opportunities and growing educational inequality in South Korea," International Journal of Educational Development, Elsevier, vol. 92(C).
    15. Silvan Has & Jake Anders & Nikki Shure, 2020. "Monetary and time investments in children's education: how do they differ in workless households?," CEPEO Working Paper Series 20-10, UCL Centre for Education Policy and Equalising Opportunities, revised Apr 2020.
    16. Vardardottir, Arna, 2015. "The impact of classroom peers in a streaming system," Economics of Education Review, Elsevier, vol. 49(C), pages 110-128.

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    More about this item

    Keywords

    Educational inequality; social mobility; measurement error; PISA; PIRLS;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality

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