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Small-area measures of income poverty

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  • Alex Fenton
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    Abstract

    This paper considers techniques for measuring the prevalence of income poverty within small areas, or "neighbourhoods", in Britain. The ultimate purpose is applying such statistics to investigating how the micro-spatial distribution of poverty within cities and regions changes over time as a consequence of political decisions and economic events. In the paper, some general criteria for small-area poverty measures are first set out, and two broad methods, poverty proxies and modelled income estimates, are identified. Empirical analyses of the validity and coverage of poverty proxies derived from UK administrative data, such as social security benefit claims, are presented. The concluding section assesses a new poverty proxy that will be used within a wider programme of analysis of the spatial distributional effects of tax and welfare changes and of economic trends in Britain from 2000 to 2014. Particular attention is paid to the relationship between the proxy values and other local poverty measures in different kinds of places. These suggest that the proxy is an adequate, albeit imperfect,tool for investigating changes in intra-urban distributions of poverty.

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    File URL: http://sticerd.lse.ac.uk/dps/case/cp/casepaper173.pdf
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    Bibliographic Info

    Paper provided by Centre for Analysis of Social Exclusion, LSE in its series CASE Papers with number /173.

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    Date of creation: May 2013
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    Handle: RePEc:cep:sticas:/173

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    Web page: http://sticerd.lse.ac.uk/case/_new/publications/default.asp

    Related research

    Keywords: Small-area poverty estimates; small-area estimation; poverty proxies; poor neighbourhoods; deprivation indices;

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    1. Robert Tanton & Yogi Vidyattama & Binod Nepal & Justine McNamara, 2011. "Small area estimation using a reweighting algorithm," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(4), pages 931-951, October.
    2. Nikos Tzavidis & Nicola Salvati & Monica Pratesi & Ray Chambers, 2008. "M-quantile models with application to poverty mapping," Statistical Methods and Applications, Springer, vol. 17(3), pages 393-411, July.
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