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Ethnic parity in labour market outcomes for benefit claimants in Great Britain

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  • Claire Crawford

    ()
    (Institute for Fiscal Studies, 7 Ridgmount Street, London, WC1E 7AE; Institute of Education, University of London, 20 Bedford Way, London WC1H 0AL, UK.)

  • Lorraine Dearden

    ()
    (Institute for Fiscal Studies, 7 Ridgmount Street, London, WC1E 7AE; Institute of Education, University of London, 20 Bedford Way, London WC1H 0AL, UK.)

  • Alice Mesnard

    ()
    (Institute for Fiscal Studies, 7 Ridgmount Street, London, WC1E 7AE)

  • Barbara Sianesi

    ()
    (Institute for Fiscal Studies, 7 Ridgmount Street, London, WC1E 7AE)

  • Jonathan Shaw

    ()
    (Institute for Fiscal Studies, 7 Ridgmount Street, London, WC1E 7AE)

Abstract

Accurate estimates of the extent of ethnic parity amongst benefit claimants are very important for policymakers who provide interventions for these groups. We use new administrative data on benefit claimants in Great Britain to document differences in labour market outcomes between Ethnic Minority and White claimants, both before and after controlling for rich observable characteristics. We do so using a variety of methods, from OLS to propensity score matching to difference-in-differences. We find that, in many cases, Minorities and Whites are simply too different for satisfactory estimates to be calculated, and that results are sensitive to the methodology used. This calls into question previous results based on simple regression techniques, which may hide the fact that observationally different ethnic groups are being compared by parametric extrapolation. For Income Support and Incapacity Benefit claimants, however, we could calculate satisfactory results. For these groups, large and significant raw penalties almost always disappear once we appropriately control for pre-inflow characteristics.

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Bibliographic Info

Paper provided by Department of Quantitative Social Science - Institute of Education, University of London in its series DoQSS Working Papers with number 10-07.

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Length: 30 pages
Date of creation: 18 Jun 2010
Date of revision:
Handle: RePEc:qss:dqsswp:1007

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Keywords: Non-response; bias; school survey; data linkage; PISA;

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