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Missing data and small area estimation in the UK Labour Force Survey

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  • Nicholas T. Longford

Abstract

Summary. We apply multivariate shrinkage to estimate local area rates of unemployment and economic inactivity by using UK Labour Force Survey data. The method exploits the similarity of the rates of claiming unemployment benefit and the unemployment rates as defined by the International Labour Organisation. This is done without any distributional assumptions, merely relying on the high correlation of the two rates. The estimation is integrated with a multiple‐imputation procedure for missing employment status of subjects in the database (item non‐response). The hot deck method that is used in the imputations is adapted to reflect the uncertainty in the model for non‐response. The method is motivated as a development (improvement) of the current operational procedure in which the imputed value is a non‐stochastic function of the data. An extension of the procedure to subjects who are absent from the database (unit non‐response) is proposed.

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  • Nicholas T. Longford, 2004. "Missing data and small area estimation in the UK Labour Force Survey," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 341-373, May.
    • Handle: RePEc:bla:jorssa:v:167:y:2004:i:2:p:341-373
    DOI: 10.1046/j.1467-985X.2003.00728.x
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    References listed on IDEAS

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    1. Andrew Harvey & Chia‐Hui Chung, 2000. "Estimating the underlying change in unemployment in the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 163(3), pages 303-309.
    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. Harvey Goldstein & David J. Spiegelhalter, 1996. "League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 385-409, May.
    4. Nan M. Laird & Thomas A. Louis, 1989. "Empirical Bayes Ranking Methods," Journal of Educational and Behavioral Statistics, , vol. 14(1), pages 29-46, March.
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    1. Longford, Nicholas T., 2010. "Small area estimation with spatial similarity," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 1151-1166, April.
    2. Jan Pablo Burgard & Domingo Morales & Anna-Lena Wölwer, 2022. "Small area estimation of socioeconomic indicators for sampled and unsampled domains," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(2), pages 287-314, June.
    3. Nicholas T. Longford & P. Tyrer & U. A. M. Nur & H. Seivewright, 2006. "Analysis of a long‐term study of neurotic disorder, with insights into the process of non‐response," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 507-523, July.
    4. Albert Satorra & Eva Ventura & Alex Costa, 2006. "Improving small area estimation by combining surveys: new perspectives in regional statistics," Economics Working Papers 969, Department of Economics and Business, Universitat Pompeu Fabra.
    5. Nicholas Longford, 2008. "Small-area estimation with spatial similarity," Economics Working Papers 1105, Department of Economics and Business, Universitat Pompeu Fabra, revised Sep 2009.

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