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Accounting for Non-Response Bias using Participation Incentives and Survey Design

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  • Mark McGovern
  • David Canning
  • Till Bärnighausen

Abstract

Standard corrections for missing data rely on the strong and generally untestable assumption of missing at random. Heckman selection models relax this assumption, but have been criticized because in practice they typically require a selection variable which predicts non-response but not the outcome of interest, and can impose bivariate normality. Using a copula methodology which does not rely on this assumption, we implement the selection model approach in data on HIV testing at a demographic surveillance site in rural South Africa which are affected by non-response. Randomized incentives are the ideal selection variable, particularly when implemented ex ante to deal with potential missing data. However, elements of survey design may also provide a credible method of correcting for non-response bias ex post. For example, although not explicitly randomized, allocation of food gift vouchers during our survey was plausibly exogenous and substantially raised participation, as did effective survey interviewers. Based on models with receipt of a voucher and interviewer identity as selection variables, our results imply that 37% of women in the population are HIV positive, compared to imputation-based estimates of 28%. For men, confidence intervals are too wide to reject the absence of non-response bias. Consistent results obtained when comparing different selection variables and error structures strengthen these conclusions.

Suggested Citation

  • Mark McGovern & David Canning & Till Bärnighausen, 2018. "Accounting for Non-Response Bias using Participation Incentives and Survey Design," CHaRMS Working Papers 18-02, Centre for HeAlth Research at the Management School (CHaRMS).
  • Handle: RePEc:qub:charms:1802
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    More about this item

    Keywords

    Participation Incentives; Survey Design; Selection Bias; Non-Ignorable Missing Data; Selection Models; HIV;

    JEL classification:

    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • I13 - Health, Education, and Welfare - - Health - - - Health Insurance, Public and Private
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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