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The dynamics of school attainment of England’s ethnic minorities


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.

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  1. Web Appendix available at; longer version of this paper available at

  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

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

  5. For more information see

  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.


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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.

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Correspondence to Deborah Wilson.

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Responsible editor: Klaus F. Zimmermann

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Wilson, D., Burgess, S. & Briggs, A. The dynamics of school attainment of England’s ethnic minorities. J Popul Econ 24, 681–700 (2011).

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  • Ethnic test score gap
  • School attainment
  • Education

JEL Classification

  • I20
  • J15