Skip to main content

The dynamics of school attainment of England’s ethnic minorities

Abstract

We exploit a universe dataset of state school students in England with linked test score records to document the evolution of attainment through school for different ethnic groups. The analysis yields a number of striking findings. First, we show that, controlling for personal characteristics, all minority groups make greater progress than White students over secondary schooling. Second, much of this improvement occurs in the high-stakes exams at the end of compulsory schooling. Third, we show that for most ethnic groups, this gain is pervasive, happening in almost all schools in which these students are found.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

Notes

  1. Web Appendix available at http://www.bris.ac.uk/cmpo/people/researchers/wilson; longer version of this paper available at http://www.bris.ac.uk/cmpo/workingpapers/wp130.pdf.

  2. These are the old ethnic categorisation tables used in PLASC. We are unable to use the finer new ethnic categorisation tables, as it was optional which classification schools used in 2001/2002 and the majority chose the old categories. Clearly, some of these groups are quite heterogeneous. For example, Bhattacharya et al. (2003) note that the compulsory introduction of new ethnicity codes in 2003 (which included ‘Mixed’ categories such as White and Black Caribbean or White and Asian) resulted in fewer pupils being classified in the ‘Other’ group. Some further information from DfES (2005) shows that Black African pupils are principally from Nigeria, Somalia and Ghana. The catch-all Other ethnicity category includes principally students with Vietnamese, Arab and Latin American ethnicity, and pupils with mixed heritage.

  3. For more information see http://www.odpm.gov.uk/stellent/groups/odpm_urbanpolicy/documents/page/odpm_urbpol_608140.hcsp.

  4. A ward is a small geographical unit, containing on average around 12,000 people.

  5. For more information see http://www.experian.co.uk/business/products/data/113.html.

  6. We discuss in detail the differences in scores between the balanced panel and each individual cross section in the Web Appendix that accompanies this paper. For most ethnic groups, these are negligible and do not have a material impact on the findings below. For Black African students, the impact is non-negligible, but even for this group it does not change the thrust of our story.

  7. The line for White pupils is very close to zero and very flat; this is the result of our use of standardised scores and the fact that White pupils are the overwhelming majority. The age 11 test scores for the more recent cohort (C1) are higher than the age 11 test scores for the earlier cohort (C2) for all but the White groups. This implies that the overall progress through time from 1997 to 2002 had a greater impact on raising KS2 test scores for minorities.

  8. Choice of neighbourhood is also endogenous, but does not have a one-for-one impact on school assignment—half of all these students do not attend their nearest school (Burgess et al. 2006). The results we present include specifications with and without neighbourhood controls for comparison.

  9. In a table available in the Web Appendix, we illustrate the effect of adding these variables sequentially for KS4. A regression with only ethnicity dummies explains only 0.8% of the variation in KS4 scores. Adding individual controls such as poverty status raise the explanatory power to 0.25%. The coefficients on ethnic identities also change considerably. Controlling for these personal characteristics, the coefficients on all but Indian and Chinese groups increase. For Black African, Bangladeshi and Pakistani students, this switches the sign to positive—that is, they score more highly than equivalent whites. For Black Caribbean and Black Other students, the coefficient is less negative (by 0.3 SDs for Black Caribbean students). For students with Indian and Chinese ethnicity, a high positive coefficient falls slightly. Given that these groups are not much different to whites in terms of poverty, this is as expected.

  10. One point to deal with here is that while we are running these regressions on the different Key Stages (and therefore different dates), we only know the values of FSM, SEN and neighbourhood at one date, 2002. In the regressions for the earlier tests, the value of the coefficient on, say, FSM, reflects two things: that disadvantage may impact differently on attainment at different ages and that FSM2002 is only proxying FSM2000 and FSM1997. Thus, the fact that FSM is increasing in importance through both cohorts could reflect a real phenomenon, or it could simply be that the lower coefficients from the earlier ages result from the same impact on attainment, attenuated by measurement error.

  11. This is only partly because we do not have time-varying poverty, neighbourhood or SEN data. If we imposed poverty to have the same effect at all ages, then with a single time observation on these the pattern would be exactly the same, though shifted up or down. But we do allow these factors to have changing effects and this in principle allows for a very different pattern.

  12. We are grateful to Paul Gregg for this comment.

  13. Again we have explored the effects of adding these groups of variables sequentially, available in the Web Appendix. The coefficients are generally stable across the specifications, though less so for students with Black Caribbean or Black Other heritage. This is rather different from the marked changes across the equivalent table for KS4 scores (discussed above and also in the Web Appendix). This arises because value added measures change, and these other controls appear to have a lesser impact on the change in test scores than they do on the score level at age 16. Similarly, the R 2 values are a lot lower, confirming that we can explain much less of change than of level. Again ethnicity per se only explains 2% of variation in pupil progress.

  14. This seems at odds with the comparison of the repeated cross-sections on the standardised z-scores. But they are very different metrics, so a direct comparison is misleading.

  15. Two of the issues are as follows. First, there is no reason to suppose that the uni-dimensional measure of school quality provided by a fixed effect will have the same impact on progress for all ethnic groups. Second, the sorting of ethnic minority children into schools is certainly not random, and so we cannot interpret the impact of the school effect as causal rather than selection.

References

  • Bali V, Alvarez R (2004) The race gap in student achievement scores: longitudinal evidence from a racially diverse school district. Policy Stud J 32(3):393–415

    Article  Google Scholar 

  • Bhattacharya G, Ison L, Blair M (2003) Minority ethnic attainment and participation in education and training: the evidence. DfES Research Topic Paper RTP01-03. DfES, London

    Google Scholar 

  • Blackaby D, Leslie D, Murphy P et al (2002) White/ethnic minority earnings and employment differentials in Britain: evidence from the LFS. Oxf Econ Pap 54:270–297

    Article  Google Scholar 

  • Bradley S, Taylor J (2004) Ethnicity, educational attainment and the transition from school. Manch Sch 72(3):317–346

    Article  Google Scholar 

  • Burgess S, Briggs A, McConnell B et al (2006) School choice in England: background facts. CMPO DP 06/159. University of Bristol, Bristol

    Google Scholar 

  • Clark K, Drinkwater S (2007) Ethnic minorities in the labour market: dynamics and diversity. The Policy Press, Bristol

    Google Scholar 

  • Clotfelter C, Ladd H, Vigdor J (2004) Teacher quality and minority achievement gaps. Terry Sanford Institute of Public Policy working paper series san04-04. Duke University, Durham

    Google Scholar 

  • Cook M, Evans W (2000) Families or schools? Explaining the convergence in white and black academic performance. J Labor Econ 18(4):729–754

    Article  Google Scholar 

  • DfES (2005) Ethnicity and education: the evidence on minority ethnic pupils. DfES Research Topic Paper RTP01-05. DfES, London

    Google Scholar 

  • Ehrenberg R, Goldhaber D, Brewer D (1995) Do teachers’ race, gender, and ethnicity matter? Evidence from the national educational longitudinal study of 1988. Ind Labor Relat Rev 48(3):547–561

    Article  Google Scholar 

  • Fryer R, Levitt S (2004) Understanding the black–white test score gap in the first two years of school. Rev Econ Stat 86(2):447–464

    Article  Google Scholar 

  • Fryer R, Levitt S (2005) The black–white test score gap through third grade. NBER working paper 11049. NBER, Cambridge

    Google Scholar 

  • Jencks C, Phillips M (1998) The black–white test score gap (eds) The Brookings Institute, Washington DC

  • Leslie D (2005) Why people from the UK’s minority ethnic communities achieve weaker degree results than whites. Appl Econ 37:619–632

    Article  Google Scholar 

  • Leslie D, Drinkwater S (1999) Staying on in full-time education: reasons for higher participation rates among ethnic minority males and females. Economica 66:63–77

    Article  Google Scholar 

  • Modood T (2003) Ethnic differentials in educational performance. In: Mason D (ed) Explaining ethnic differences: changing patterns of disadvantage in Britain. The Policy Press, Bristol

    Google Scholar 

  • Modood T (2005) The educational attainments of ethnic minorities in Britain. In: Loury GC, Modood T, Teles SM (eds) Ethnicity, social mobility and public policy. CUP, Cambridge

    Google Scholar 

  • Neal D (2006) Why has black–white skill convergence stopped? In: Hanushek E, Welch F (eds) Handbook of the economics of education, vol 1. North-Holland, Amsterdam

    Google Scholar 

  • Phillips M, Crouse J, Ralph J (1998) Does the black–white test score gap widen after children enter school? In: Jencks C, Phillips M (eds) The black–white test score gap. The Brookings Institute, Washington DC

    Google Scholar 

  • Platt L (2007) Making education count: the effects of ethnicity and qualifications on intergenerational social class mobility. Sociol Rev 55(3):485–508

    Article  Google Scholar 

  • Tomlinson S (2001) Some success, could do better: education and race 1976–2000. In: Phillips R, Furlong J (eds) Education, reform and the state: twenty -five years of politics, policy and practice. Routledge, London

    Google Scholar 

Download references

Acknowledgements

Thanks to the Leverhulme Trust for funding this research through CMPO. Thanks also for comments to Howard Glennerster, Paul Gregg, Carol Propper, Leon Tikly and three anonymous referees. All errors are the responsibility of the authors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Deborah Wilson.

Additional information

Responsible editor: Klaus F. Zimmermann

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Wilson, D., Burgess, S. & Briggs, A. The dynamics of school attainment of England’s ethnic minorities. J Popul Econ 24, 681–700 (2011). https://doi.org/10.1007/s00148-009-0269-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00148-009-0269-0

Keywords

  • Ethnic test score gap
  • School attainment
  • Education

JEL Classification

  • I20
  • J15