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When and why do initially high attaining poor children fall behind?

Author

Listed:
  • Claire Crawford

    (Department of Economics, University of Warwick and Institute for Fiscal Studies)

  • Lindsey Macmillan

    (Department of Quantitative Social Science, Institute of Education, University College London)

  • Anna Vignoles

    (Faculty of Education, University of Cambridge)

Abstract

The role of education as a potential driver of social mobility has been well established and it is critical that we understand how children from different socio-economic backgrounds fare in the education system. In this paper, we examine the trajectories of initially high- and low-achieving children from lower and higher socio-economic status families from age 7 through to the end of compulsory education (age 16) in England for the first time. This enables us to provide new insights into when initially high attaining poor children fall behind their better-off peers. We show that there are substantial differences in educational attainment by socio-economic background at age 7, and that these differences increase as children move through the education system. Our results indicate that pupils from poor backgrounds who score highly in primary school fall behind their better-off but lower achieving peers during secondary school. These findings are not caused by ''regression to the mean'' (where a child with 'high' or 'low' achievement on any given day may have over- or under-performed relative to their 'true' attainment, meaning that the next time they are tested they will look more like the average individual). This suggests that secondary school may be a critical period to intervene to ensure poor children do not fall behind their better-off peers. We also provide suggestive evidence on the extent to which these patterns can be explained by the types of schools that pupils from different backgrounds attend, and by the differing attitudes and aspirations of the pupils and their families. Our analysis suggests that there is less convergence amongst pupils who attend the same schools. And if all pupils had the attitudes and aspirations of the average pupil, there would be more convergence. While we remain cautious about the implications of these findings, they provide suggestive evidence that schools (or the sorting of pupils into schools) and the attitudes and aspirations held by children from different backgrounds may contribute to the convergence in attainment that we see.

Suggested Citation

  • Claire Crawford & Lindsey Macmillan & Anna Vignoles, 2015. "When and why do initially high attaining poor children fall behind?," DoQSS Working Papers 15-08, Quantitative Social Science - UCL Social Research Institute, University College London.
  • Handle: RePEc:qss:dqsswp:1508
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    References listed on IDEAS

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    1. Paul Clarke & Claire Crawford & Fiona Steele & Anna Vignoles, 2010. "The Choice between fixed and random effects models: some considerations for educational research," The Centre for Market and Public Organisation 10/240, The Centre for Market and Public Organisation, University of Bristol, UK.
    2. Haroon Chowdry & Claire Crawford & Lorraine Dearden & Alissa Goodman & Anna Vignoles, 2013. "Widening participation in higher education: analysis using linked administrative data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(2), pages 431-457, February.
    3. Jo Blanden, 2004. "Family Income and Educational Attainment: A Review of Approaches and Evidence for Britain," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 20(2), pages 245-263, Summer.
    4. Gary S. Becker & Nigel Tomes, 1994. "Human Capital and the Rise and Fall of Families," NBER Chapters, in: Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education, Third Edition, pages 257-298, National Bureau of Economic Research, Inc.
    5. Jo Blanden & Paul Gregg & Lindsey Macmillan, 2007. "Accounting for Intergenerational Income Persistence: Noncognitive Skills, Ability and Education," Economic Journal, Royal Economic Society, vol. 117(519), pages 43-60, March.
    6. Jo Blanden & Lindsey Macmillan, 2014. "Education and Intergenerational Mobility: Help or Hindrance?," CASE Papers case179, Centre for Analysis of Social Exclusion, LSE.
    7. Rebecca Allen & Simon Burgess & Tomas Key, 2010. "Choosing secondary school by moving house: school quality and the formation of neighbourhoods," The Centre for Market and Public Organisation 10/238, The Centre for Market and Public Organisation, University of Bristol, UK.
    8. Gibbons, Steve & Machin, Stephen, 2003. "Valuing English primary schools," Journal of Urban Economics, Elsevier, vol. 53(2), pages 197-219, March.
    9. A. B. Atkinson & S. P. Jenkins, 1984. "The Steady-State Assumption and the Estimation of Distributional and Related Models," Journal of Human Resources, University of Wisconsin Press, vol. 19(3), pages 358-376.
    10. Cunha, Flavio & Heckman, James J. & Lochner, Lance, 2006. "Interpreting the Evidence on Life Cycle Skill Formation," Handbook of the Economics of Education, in: Erik Hanushek & F. Welch (ed.), Handbook of the Economics of Education, edition 1, volume 1, chapter 12, pages 697-812, Elsevier.
    11. Rebecca Allen, 2007. "Allocating Pupils to Their Nearest Secondary School: The Consequences for Social and Ability Stratification," Urban Studies, Urban Studies Journal Limited, vol. 44(4), pages 751-770, April.
    12. Jo Blanden & Lindsey Macmillan, 2014. "Education and Intergenerational Mobility: Help or Hindrance?," CASE Papers case179, Centre for Analysis of Social Exclusion, LSE.
    13. Leon Feinstein, 2004. "Mobility in Pupils' Cognitive Attainment During School Life," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 20(2), pages 213-229, Summer.
    14. repec:cep:sticas:/179 is not listed on IDEAS
    15. 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.
    16. Leon Feinstein, 2003. "Inequality in the Early Cognitive Development of British Children in the 1970 Cohort," Economica, London School of Economics and Political Science, vol. 70(277), pages 73-97, February.
    17. Rebecca Allen & Anna Vignoles, 2006. "What Should an Index of School Segregation Measure?," CEE Discussion Papers 0060, Centre for the Economics of Education, LSE.
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    Cited by:

    1. Gwyther Rees, 2018. "The Association of Childhood Factors with Children’s Subjective Well-Being and Emotional and Behavioural Difficulties at 11 years old," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 11(4), pages 1107-1129, August.

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    More about this item

    Keywords

    Social Mobility; Education Achievement; Regression to the mean;
    All these keywords.

    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • I24 - Health, Education, and Welfare - - Education - - - Education and Inequality
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth

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