Estimating Obesity Rates in Europe in the Presence of Self-Reporting Errors
AbstractReliable measures of obesity are essential in order to develop effective policies to tackle the costs of obesity. We examine what, if anything, we can learn about obesity rates using self-reported BMI once we allow for possible measurement error. Existing approaches that correct for self-reporting errors often require strong assumptions. In this paper 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 using minimal assumptions on the error process. 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Department of Economics, Finance and Accounting, National University of Ireland - Maynooth in its series Economics, Finance and Accounting Department Working Paper Series with number n236-13.pdf.
Length: 20 pages
Date of creation: 2013
Date of revision:
Obesity; Self-Reporting Errors; Bounds;
Find related papers by JEL classification:
- I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
- C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Factor Analysis
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Nicoletti, Cheti & Peracchi, Franco & Foliano, Francesca, 2011.
"Estimating Income Poverty in the Presence of Missing Data and Measurement Error,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 29(1), pages 61-72.
- Cheti Nicoletti & Franco Peracchi & Francesca Foliano, 2009. "Estimating Income Poverty in the Presence of Missing Data and Measurement Error," SOEPpapers on Multidisciplinary Panel Data Research 252, DIW Berlin, The German Socio-Economic Panel (SOEP).
- Cheti Nicoletti & Franco Peracchi & Francesca Foliano, 2009. "Estimating Income Poverty in the Presence of Missing Data and Measurement Error," CEIS Research Paper 145, Tor Vergata University, CEIS, revised 30 Sep 2009.
- Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
- Toni Mora, 2010. "BMI and Spanish labour status: evidence by gender from the city of Barcelona," The European Journal of Health Economics, Springer, vol. 11(3), pages 239-253, June.
- Molinari, Francesca, 2008.
"Partial identification of probability distributions with misclassified data,"
Journal of Econometrics,
Elsevier, vol. 144(1), pages 81-117, May.
- Molinari, Francesca, 2005. "Partial Identification of Probability Distributions with Misclassified Data," Working Papers 05-10, Cornell University, Center for Analytic Economics.
- Donal O’Neill & Olive Sweetman, 2013. "The consequences of measurement error when estimating the impact of obesity on income," IZA Journal of Labor Economics, Springer, vol. 2(1), pages 1-20, December.
- Paul P. Biemer & Christopher Wiesen, 2002. "Measurement error evaluation of self-reported drug use: a latent class analysis of the US National Household Survey on Drug Abuse," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 97-119.
- John Cawley, 2004. "The Impact of Obesity on Wages," Journal of Human Resources, University of Wisconsin Press, vol. 39(2).
- A. Konnopka & M. Bödemann & H.-H. König, 2011. "Health burden and costs of obesity and overweight in Germany," The European Journal of Health Economics, Springer, vol. 12(4), pages 345-352, August.
- John Cawley & Richard V. Burkhauser, 2006.
"Beyond BMI: The Value of More Accurate Measures of Fatness and Obesity in Social Science Research,"
NBER Working Papers
12291, National Bureau of Economic Research, Inc.
- Burkhauser, Richard V. & Cawley, John, 2008. "Beyond BMI: The value of more accurate measures of fatness and obesity in social science research," Journal of Health Economics, Elsevier, vol. 27(2), pages 519-529, March.
- Fernando Antonanzas & Roberto Rodríguez, 2010. "Feeding the economics of obesity in the EU in a healthy way," The European Journal of Health Economics, Springer, vol. 11(4), pages 351-353, August.
- Richard Breen & Pasi Moisio, 2004. "Poverty dynamics corrected for measurement error," Journal of Economic Inequality, Springer, vol. 2(3), pages 171-191, July.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.