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Estimating Obesity Rates in the Presence of Measurement Error

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  • O'Neill, Donal

    (National University of Ireland, Maynooth)

  • Sweetman, Olive

    (National University of Ireland, Maynooth)

Abstract

Reliable measures of obesity are essential in order to develop effective policies to tackle the costs of obesity. In this paper we examine what, if anything, we can learn about obesity rates using self-reported BMI once we allow for possible measurement error. We combine self-reported data on BMI with estimated misclassification rates obtained from auxiliary data to derive upper and lower bounds for the population obesity rate for ten European countries. For men it is possible to obtain meaningful comparisons across countries even after accounting for measurement error. In particular the self-reported data identifies a set of low obesity countries consisting of Denmark, Ireland, Italy, Greece and Portugal and a set of high obesity countries consisting of Spain and Finland. However, it is more difficult to rank countries by female obesity rates. Meaningful rankings only emerge when the misclassification rate is bounded at a level that is much lower than that observed in auxiliary data. A similar limit on misclassification rates is also needed before we can begin to observe meaningful gender differences in obesity rates within countries.

Suggested Citation

  • O'Neill, Donal & Sweetman, Olive, 2013. "Estimating Obesity Rates in the Presence of Measurement Error," IZA Discussion Papers 7288, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp7288
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    Cited by:

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    3. Fernihough, Alan & McGovern, Mark E., 2015. "Physical stature decline and the health status of the elderly population in England," Economics & Human Biology, Elsevier, vol. 16(C), pages 30-44.
    4. Åsa Ljungvall & Ulf Gerdtham & Ulf Lindblad, 2015. "Misreporting and misclassification: implications for socioeconomic disparities in body-mass index and obesity," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(1), pages 5-20, January.

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    More about this item

    Keywords

    obesity; measurement error; bounds;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • I14 - Health, Education, and Welfare - - Health - - - Health and Inequality

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