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Reporting error in weight and its implications for bias in economic models

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  • Cawley, John
  • Maclean, Johanna Catherine
  • Hammer, Mette
  • Wintfeld, Neil

Abstract

Most research on the economic consequences of obesity uses data on self-reported weight, which contains reporting error that has the potential to bias coefficient estimates in economic models. The purpose of this paper is to measure the extent and characteristics of reporting error in weight, and to examine its impact on regression coefficients in models of the healthcare consequences of obesity.

Suggested Citation

  • Cawley, John & Maclean, Johanna Catherine & Hammer, Mette & Wintfeld, Neil, 2015. "Reporting error in weight and its implications for bias in economic models," Economics & Human Biology, Elsevier, vol. 19(C), pages 27-44.
    • Handle: RePEc:eee:ehbiol:v:19:y:2015:i:c:p:27-44
    DOI: 10.1016/j.ehb.2015.07.001
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    References listed on IDEAS

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    1. Courtemanche, Charles & Pinkston, Joshua C. & Stewart, Jay, 2015. "Adjusting body mass for measurement error with invalid validation data," Economics & Human Biology, Elsevier, vol. 19(C), pages 275-293.
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    7. Cawley, John & Meyerhoefer, Chad, 2012. "The medical care costs of obesity: An instrumental variables approach," Journal of Health Economics, Elsevier, vol. 31(1), pages 219-230.
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    9. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
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    11. John Cawley & Chad Meyerhoefer & Adam Biener & Mette Hammer & Neil Wintfeld, 2015. "Savings in Medical Expenditures Associated with Reductions in Body Mass Index Among US Adults with Obesity, by Diabetes Status," PharmacoEconomics, Springer, vol. 33(7), pages 707-722, July.
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