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Health-related non-response in the British Household Panel Survey and European Community Household Panel: using inverse-probability-weighted estimators in non-linear models

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  • Andrew M. Jones
  • Xander Koolman
  • Nigel Rice

Abstract

The paper considers health-related non-response in the first 11 waves of the British Household Panel Survey and the full eight waves of the European Community Household Panel and explores its consequences for dynamic models of the association between socioeconomic status and self-assessed health. We describe the pattern of health-related non-response that is revealed by the British Household Panel Survey and European Community Household Panel data. We both test and correct for non-response in empirical models of the effect of socioeconomic status on self-assessed health. Descriptive evidence shows that there is health-related non-response in the data, with those in very poor initial health more likely to drop out, and variable addition tests provide evidence of non-response bias in the panel data models of self-reported health. Nevertheless a comparison of estimates-based on the balanced sample, the unbalanced sample and corrected for non-response by using inverse probability weights-shows that, on the whole, there are not substantive differences in the average partial effects of the variables of interest. The main differences are between unweighted and one form of inverse-probability-weighted estimates for the average partial effects of income and education in those countries that have fewer than eight waves of data. Similar findings have been reported concerning the limited influence of non-response bias in models of various labour market outcomes; we discuss possible explanations for our results. Copyright 2006 Royal Statistical Society.

Suggested Citation

  • Andrew M. Jones & Xander Koolman & Nigel Rice, 2006. "Health-related non-response in the British Household Panel Survey and European Community Household Panel: using inverse-probability-weighted estimators in non-linear models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 543-569.
  • Handle: RePEc:bla:jorssa:v:169:y:2006:i:3:p:543-569
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    References listed on IDEAS

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