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Using data augmentation to correct for non‐ignorable non‐response when surrogate data are available: an application to the distribution of hourly pay

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  • Gabriele B. Durrant
  • Chris Skinner

Abstract

Summary. The paper develops a data augmentation method to estimate the distribution function of a variable, which is partially observed, under a non‐ignorable missing data mechanism, and where surrogate data are available. An application to the estimation of hourly pay distributions using UK Labour Force Survey data provides the main motivation. In addition to considering a standard parametric data augmentation method, we consider the use of hot deck imputation methods as part of the data augmentation procedure to improve the robustness of the method. The method proposed is compared with standard methods that are based on an ignorable missing data mechanism, both in a simulation study and in the Labour Force Survey application. The focus is on reducing bias in point estimation, but variance estimation using multiple imputation is also considered briefly.

Suggested Citation

  • Gabriele B. Durrant & Chris Skinner, 2006. "Using data augmentation to correct for non‐ignorable non‐response when surrogate data are available: an application to the distribution of hourly pay," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 605-623, July.
    • Handle: RePEc:bla:jorssa:v:169:y:2006:i:3:p:605-623
    DOI: 10.1111/j.1467-985X.2006.00398.x
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    References listed on IDEAS

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    1. Mark B. Stewart & Joanna K. Swaffield, 2002. "Using the BHPS Wave 9 Additional Questions to Evaluate the Impact of the National Minimum Wage," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(supplemen), pages 633-652, December.
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    3. 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.
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    5. Daniel F. Heitjan & Roderick J. A. Little, 1991. "Multiple Imputation for the Fatal Accident Reporting System," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(1), pages 13-29, March.
    6. repec:bla:obuest:v:64:y:2002:i:s1:p:633-652 is not listed on IDEAS
    7. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    8. repec:bla:obuest:v:64:y:2002:i:0:p:633-52 is not listed on IDEAS
    9. 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.
    10. Chris Skinner & Nigel Stuttard & Gabriele Beissel‐Durrant & James Jenkins, 2002. "The Measurement of Low Pay in the UK Labour Force Survey," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(supplemen), pages 653-676, December.
    11. repec:bla:obuest:v:64:y:2002:i:0:p:653-76 is not listed on IDEAS
    12. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
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    Cited by:

    1. Christopher R. Bollinger & Barry T. Hirsch, 2013. "Is Earnings Nonresponse Ignorable?," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 407-416, May.
    2. Rebecca Riley, 2013. "Modelling Demand for Low Skilled/Low Paid Labour: Exploring the Employment Trade-Offs of a Living Wage," National Institute of Economic and Social Research (NIESR) Discussion Papers 404, National Institute of Economic and Social Research.
    3. Paolo Lucchino & Dr Justin van de Ven, 2013. "Modelling the dynamic effects of transfer policy: the LINDA policy analysis tool," National Institute of Economic and Social Research (NIESR) Discussion Papers 405, National Institute of Economic and Social Research.
    4. Shonosuke Sugasawa & Kosuke Morikawa & Keisuke Takahata, 2022. "Bayesian semiparametric modeling of response mechanism for nonignorable missing data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 101-117, March.
    5. Rebecca Riley, 2013. "Modelling Demand for Low Skilled/Low Paid Labour: Exploring the Employment Trade-Offs of a Living Wage," National Institute of Economic and Social Research (NIESR) Discussion Papers 404, National Institute of Economic and Social Research.
    6. Christopher R. Bollinger & Barry T. Hirsch, 2010. "GDP & Beyond – die europäische Perspektive," RatSWD Working Papers 165, German Data Forum (RatSWD).

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