IDEAS home Printed from https://ideas.repec.org/p/qss/dqsswp/1101.html
   My bibliography  Save this paper

The use (and misuse) of statistics in understanding social mobility: regression to the mean and the cognitive development of high ability children from disadvantaged homes

Author

Listed:
  • John Jerrim

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

  • Anna Vignoles

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

Abstract

Social mobility has emerged as one of the key academic and political topics in Britain over the last decade. Although economists and sociologists disagree on whether mobility has increased or decreased, and if this is a bigger issue in the UK than other developed countries, both groups recognise that education and skill plays a key role in explaining intergenerational persistence. This has led academics from various disciplines to investigate how rates of cognitive development may vary between children from rich and poor backgrounds. A number of key studies have definitively shown that a gap in cognitive skill between richer and poorer children is evident from a very early age. Some have also suggested that highly able children from disadvantaged homes are overtaken by their rich (but less able) peers before the age of 10 in terms of their cognitive skill. It is this last conclusion that we focus on in this paper, as it has become a widely cited “fact†within the academic literature on social mobility and child development, and has had a major influence on public policy and political debate. We investigate whether this latter finding is due to a spurious statistical artefact known as regression to the mean (RTM). Our analysis suggests that there are serious methodological problems plaguing the existing literature and that, after applying some simple adjustments for RTM, we obtain dramatically different results.

Suggested Citation

  • John Jerrim & Anna Vignoles, 2011. "The use (and misuse) of statistics in understanding social mobility: regression to the mean and the cognitive development of high ability children from disadvantaged homes," DoQSS Working Papers 11-01, Quantitative Social Science - UCL Social Research Institute, University College London, revised 20 Apr 2011.
  • Handle: RePEc:qss:dqsswp:1101
    as

    Download full text from publisher

    File URL: https://repec.ucl.ac.uk/REPEc/pdf/qsswp1101.pdf
    Download Restriction: no
    ---><---

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. The rise and fall of a killer chart
      by Ben Baumberg in inequalities on 2011-06-16 12:54:38

    Citations

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


    Cited by:

    1. John Jerrim & Anna Vignoles, 2013. "Social mobility, regression to the mean and the cognitive development of high ability children from disadvantaged homes," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(4), pages 887-906, October.
    2. Rolleston, Caine & Iyer, Padmini, 2019. "Beyond the basics: Access and equity in the expansion of post-compulsory schooling in Vietnam," International Journal of Educational Development, Elsevier, vol. 66(C), pages 223-233.
    3. Katie Bates & Laura Lane & Anne Power & Nicola Serle, 2013. "CASE Annual Report 2012," CASE Reports casereport76, Centre for Analysis of Social Exclusion, LSE.
    4. Sushmita Nalini Das, 2014. "Do "Child-Friendly" Practices affect Learning? Evidence from Rural India," DoQSS Working Papers 14-03, Quantitative Social Science - UCL Social Research Institute, University College London.

    More about this item

    Keywords

    Educational mobility; socio-economic gap; disadvantaged children; regression to the mean;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • I2 - Health, Education, and Welfare - - Education
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy

    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:1101. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.