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Estimation of the Distribution of Hourly Pay from Household Survey Data: The Use of Missing Data Methods to Handle Measurement Error

Author

Listed:
  • Gabriele Beissel-Durrant

    (Institute for Fiscal Studies)

  • Chris Skinner

    (Institute for Fiscal Studies and London School of Economics)

Abstract

Measurement errors in survey data on hourly pay may lead to serious upward bias in low pay estimates. We consider how to correct for this bias when auxiliary accurately measured data are available for a subsample. An application to the UK Labour Force Survey is described. The use of fractional imputation, nearest neighbour imputation, predictive mean matching and propensity score weighting are considered. Properties of point estimators are compared both theoretically and by simulation. A fractional predictive mean matching imputation approach is advocated. It performs similarly to propensity score weighting, but displays slight advantages of robustness and efficiency.

Suggested Citation

  • Gabriele Beissel-Durrant & Chris Skinner, 2003. "Estimation of the Distribution of Hourly Pay from Household Survey Data: The Use of Missing Data Methods to Handle Measurement Error," CeMMAP working papers CWP12/03, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:12/03
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    File URL: http://cemmap.ifs.org.uk/wps/cwp0312.pdf
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    References listed on IDEAS

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    1. Richard Dickens & Alan Manning, 2004. "Has the national minimum wage reduced UK wage inequality?," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(4), pages 613-626, November.
    2. Chen J. & Shao J., 2001. "Jackknife Variance Estimation for Nearest-Neighbor Imputation," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 260-269, March.
    3. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
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