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The case for small area microdata

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  • Mark Tranmer
  • Andrew Pickles
  • Ed Fieldhouse
  • Mark Elliot
  • Angela Dale
  • Mark Brown
  • David Martin
  • David Steel
  • Chris Gardiner

Abstract

Summary. Census data are available in aggregate form for local areas and, through the samples of anonymized records (SARs), as samples of microdata for households and individuals. In 1991 there were two SAR files: a household file and an individual file. These have a high degree of detail on the census variables but little geographical detail, a situation that will be exacerbated for the 2001 SAR owing to the loss of district level geography on the individual SAR. The paper puts forward the case for an additional sample of microdata, also drawn from the census, that has much greater geographical detail. Small area microdata (SAM) are individual level records with local area identifiers and, to maintain confidentiality, reduced detail on the census variables. Population data from seven local authorities, including rural and urban areas, are used to define prototype samples of SAM. The rationale for SAM is given, with examples that demonstrate the role of local area information in the analysis of census data. Since there is a trade‐off between the extent of local detail and the extent of detail on variables that can be made available, the confidentiality risk of SAM is assessed empirically. An indicative specification of the SAM is given, having taken into account the results of the confidentiality analysis.

Suggested Citation

  • Mark Tranmer & Andrew Pickles & Ed Fieldhouse & Mark Elliot & Angela Dale & Mark Brown & David Martin & David Steel & Chris Gardiner, 2005. "The case for small area microdata," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 29-49, January.
    • Handle: RePEc:bla:jorssa:v:168:y:2005:i:1:p:29-49
    DOI: 10.1111/j.1467-985X.2004.00334.x
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    References listed on IDEAS

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    1. Edward A. Fieldhouse, 1999. "Ethnic Minority Unemployment and Spatial Mismatch: The Case of London," Urban Studies, Urban Studies Journal Limited, vol. 36(9), pages 1569-1596, August.
    2. N. T. Longford, 1999. "Multivariate shrinkage estimation of small area means and proportions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(2), pages 227-245.
    3. Macintyre, Sally & Ellaway, Anne & Cummins, Steven, 2002. "Place effects on health: how can we conceptualise, operationalise and measure them?," Social Science & Medicine, Elsevier, vol. 55(1), pages 125-139, July.
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    Cited by:

    1. Joseph W. Sakshaug & Trivellore E. Raghunathan, 2014. "Generating synthetic microdata to estimate small area statistics in the American Community Survey," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(3), pages 341-368, June.
    2. Stefan Jestl & Mathias Moser & Anna Katharina Raggl, 2021. "Cannot keep up with the Joneses: how relative deprivation pushes internal migration in Austria," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 49(2), pages 210-231, November.
    3. Azizur Rahman & Ann Harding & Robert Tanton & Shuangzhe Liu, 2010. "Methodological Issues in Spatial Microsimulation Modelling for Small Area Estimation," International Journal of Microsimulation, International Microsimulation Association, vol. 3(2), pages 3-22.
    4. Gillian A. Lancaster & Mick Green & Steven Lane, 2006. "Reducing bias in ecological studies: an evaluation of different methodologies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 681-700, October.
    5. Ben Phillips & S.F. Chin & Ann Harding, 2007. "Housing Stress Today: Estimates for Statistical Local Areas in 2005," NATSEM Working Paper Series 2006 019, University of Canberra, National Centre for Social and Economic Modelling.
    6. Christopher Jackson & And Nicky Best & Sylvia Richardson, 2008. "Hierarchical related regression for combining aggregate and individual data in studies of socio‐economic disease risk factors," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 159-178, January.
    7. Sophia Rabe-Hesketh & Anders Skrondal, 2007. "Multilevel and Latent Variable Modeling with Composite Links and Exploded Likelihoods," Psychometrika, Springer;The Psychometric Society, vol. 72(2), pages 123-140, June.
    8. Jackson, Christopher H. & Richardson, Sylvia & Best, Nicky G., 2008. "Studying place effects on health by synthesising individual and area-level outcomes," Social Science & Medicine, Elsevier, vol. 67(12), pages 1995-2006, December.

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