IDEAS home Printed from https://ideas.repec.org/p/qss/dqsswp/1206.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://repec.ucl.ac.uk/REPEc/pdf/qsswp1206.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), 2011. "Handbook of the Economics of Education," Handbook of the Economics of Education, Elsevier, edition 1, volume 4, number 4, June.
    2. 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, vol. 11(3).
    3. Robert Haveman & Barbara Wolfe, 1995. "The Determinants of Children's Attainments: A Review of Methods and Findings," Journal of Economic Literature, American Economic Association, vol. 33(4), pages 1829-1878, December.
    4. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053, October.
    5. Peter Dolton & Rita Asplund & Erling Barth (ed.), 2009. "Education and Inequality Across Europe," Books, Edward Elgar Publishing, number 12921.
    6. Gabriela Schütz & Heinrich W. Ursprung & Ludger Wößmann, 2008. "Education Policy and Equality of Opportunity," Kyklos, Wiley Blackwell, vol. 61(2), pages 279-308, May.
    7. John Jerrim, 2012. "The Socio‐Economic Gradient in Teenagers' Reading Skills: How Does England Compare with Other Countries?," Fiscal Studies, Institute for Fiscal Studies, vol. 33(2), pages 159-184, June.
    8. Brunello, Giorgio & Weber, Guglielmo & Weiss, Christoph T., 2012. "Books Are Forever: Early Life Conditions, Education and Lifetime Income," IZA Discussion Papers 6386, Institute of Labor Economics (IZA).
    9. Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), 2011. "Handbook of the Economics of Education," Handbook of the Economics of Education, Elsevier, edition 1, volume 3, number 3, June.
    10. John Jerrim, 2012. "The socio-economic gradient in teenagers' literacy skills: how does England compare to other countries?," DoQSS Working Papers 12-04, Quantitative Social Science - UCL Social Research Institute, University College London.
    11. Zoltan Hermann & Daniel Horn, 2011. "How inequality of opportunity and mean student performance are related? - A quantile regression approach using PISA data," CERS-IE WORKING PAPERS 1124, Institute of Economics, Centre for Economic and Regional Studies.
    12. John Hendrickx, 2002. "ISCO: Stata module to recode 4 digit ISCO-68 occupational codes," Statistical Software Components S425801, Boston College Department of Economics.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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).
    12. 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.
    13. 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.
    14. 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).
    15. 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.
    16. Vardardottir, Arna, 2015. "The impact of classroom peers in a streaming system," Economics of Education Review, Elsevier, vol. 49(C), pages 110-128.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jerrim, J. & John Micklewright, 2013. "GINI DP 65: Socioeconomic gradients in children’s cognitive skills: are cross-country comparisons robust to who reports family background?," GINI Discussion Papers 65, AIAS, Amsterdam Institute for Advanced Labour Studies.
    2. John Jerrim & Álvaro Choi, 2013. "The mathematics skills of school children: how does England compare to the high performing east Asian jurisdictions?," Working Papers 2013/12, Institut d'Economia de Barcelona (IEB).
    3. John Jerrim & Álvaro Choi, 2013. "The mathematics skills of school children: how does England compare to the high performing east Asian jurisdictions?," Working Papers 2013/12, Institut d'Economia de Barcelona (IEB).
    4. John Jerrim & Alvaro Choi, 2013. "The mathematics skills of school children: How does England compare to the high performing East Asian jurisdictions?," DoQSS Working Papers 13-03, Quantitative Social Science - UCL Social Research Institute, University College London.
    5. Zlata Bruckauf & UNICEF Innocenti Research Centre, 2016. "Falling Behind: Socio-demographic profiles of educationally disadvantaged youth. Evidence from PISA 2000-2012," Papers inwopa837, Innocenti Working Papers.
    6. 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.
    7. Tommaso Agasisti & Francesco Avvisati & Francesca Borgonovi & Sergio Longobardi, 2021. "What School Factors are Associated with the Success of Socio-Economically Disadvantaged Students? An Empirical Investigation Using PISA Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 157(2), pages 749-781, September.
    8. José Manuel Cordero Ferrera & Manuel Muñiz Pérez & Rosa Simancas Rodríguez, 2015. "The influence of socioeconomic factors on cognitive and non-cognitive educational outcomes," Investigaciones de Economía de la Educación volume 10, in: Marta Rahona López & Jennifer Graves (ed.), Investigaciones de Economía de la Educación 10, edition 1, volume 10, chapter 21, pages 413-438, Asociación de Economía de la Educación.
    9. Bernhard C. Dannemann, 2020. "Peer Effects in Secondary Education: Evidence from the 2015 Trends in Mathematics and Science Study Based on Homophily," Working Papers V-428-20, University of Oldenburg, Department of Economics, revised Feb 2020.
    10. Gradstein, Mark & Brückner, Markus, 2013. "Income and schooling," CEPR Discussion Papers 9365, C.E.P.R. Discussion Papers.
    11. Bönke Timm & Neidhöfer Guido, 2018. "Parental Background Matters: Intergenerational Mobility and Assimilation of Italian Immigrants in Germany," German Economic Review, De Gruyter, vol. 19(1), pages 1-31, February.
    12. Elke Lüdemann, 2011. "Schooling and the Formation of Cognitive and Non-cognitive Outcomes," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 39.
    13. Marchionni, Mariana & Pinto, Florencia & Vazquez, Emmanuel, 2013. "Determinantes de la desigualdad en el desempeño educativo en la Argentina [Determinants of the inequality in PISA test scores in Argentina]," MPRA Paper 56421, University Library of Munich, Germany.
    14. Deborah A. Cobb-Clark & Mathias Sinning & Steven Stillman, 2012. "Migrant Youths’ Educational Achievement," The ANNALS of the American Academy of Political and Social Science, , vol. 643(1), pages 18-45, September.
    15. Nathalie Picard & François-Charles Wolff, 2014. "Les inégalités intrafamiliales d'éducation en France," Revue économique, Presses de Sciences-Po, vol. 65(6), pages 813-840.
    16. Deborah A. Cobb-Clarke & Mathias Sinning & Steven Stillman, 2011. "Migrant Youths' Educational Achievement: The Role of Institutions," ANU Working Papers in Economics and Econometrics 2011-565, Australian National University, College of Business and Economics, School of Economics.
    17. Bergbauer, Annika B., 2019. "How did EU membership of Eastern Europe affect student achievement?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 27(6), pages 624-644.
    18. Giannelli, Gianna Claudia & Rapallini, Chiara, 2019. "Parental occupation and children’s school outcomes in math," Research in Economics, Elsevier, vol. 73(4), pages 293-303.
    19. Contini, Dalit, 2014. "Cross-sectional learning assessments: comparability of regression coefficients and validity of difference-in-difference estimation to evaluate institutional effects," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201431, University of Turin.
    20. Contini, Dalit & Cugnata, Federica, 2018. "How do institutions affect learning inequalities? Revisiting difference-in-difference models with international assessments," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201817, University of Turin.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:qss:dqsswp:1206. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr Neus Bover Fonts (email available below). General contact details of provider: https://edirc.repec.org/data/dqioeuk.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.