Beyond BMI: The Value of More Accurate Measures of Fatness and Obesity in Social Science Research

51 Pages Posted: 3 Aug 2006 Last revised: 17 Jan 2014

See all articles by John Cawley

John Cawley

Cornell University - College of Human Ecology, Department of Policy Analysis & Management (PAM); Cornell University - College of Arts & Sciences, Department of Economics; The University of Sydney - School of Economics; National University of Ireland, Galway (NUIG) - J.E. Cairnes School of Business & Economics; NBER; IZA

Richard V. Burkhauser

Cornell University - Department of Policy Analysis & Management (PAM); University of Melbourne, Melbourne Institute

Date Written: June 2006

Abstract

Virtually all social science research related to obesity uses body mass index (BMI), usually calculated using self-reported values of weight and height, or clinical weight classifications based on BMI. Yet there is wide agreement in the medical literature that such measures are seriously flawed because they do not distinguish fat from fat-free mass such as muscle and bone. Here we evaluate more accurate measures of fatness (total body fat, percent body fat, and waist circumference) that have greater theoretical support in the medical literature. We provide conversion formulas based on NHANES data so that researchers can calculate the estimated values of these more accurate measures of fatness using the self-reported weight and height available in many social science datasets.To demonstrate the benefits of these alternative measures of fatness, we show that using them significantly impacts who is classified as obese. For example, when the more accurate measures of fatness are used, the gap in obesity between white and African American men increases substantially, with white men significantly more likely to be obese. In addition, the gap in obesity between African American and white women is cut in half (with African American women still significantly more likely to be obese). As an example of the value of fatness in predicting social science outcomes, we show that while BMI is positively correlated with the probability of employment disability in the PSID, when body mass is divided into its components, fatness is positively correlated with disability while fat-free mass (such as muscle) is negatively correlated with disability.

Suggested Citation

Cawley, John and Burkhauser, Richard V., Beyond BMI: The Value of More Accurate Measures of Fatness and Obesity in Social Science Research (June 2006). NBER Working Paper No. w12291. Available at SSRN: https://ssrn.com/abstract=908530

John Cawley (Contact Author)

Cornell University - College of Human Ecology, Department of Policy Analysis & Management (PAM) ( email )

3M24 MVR Hall
Ithaca, NY 14853
United States

Cornell University - College of Arts & Sciences, Department of Economics ( email )

414 Uris Hall
Ithaca, NY 14853-7601
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The University of Sydney - School of Economics ( email )

Rm 370 Merewether (H04)
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Australia

National University of Ireland, Galway (NUIG) - J.E. Cairnes School of Business & Economics ( email )

Galway
Ireland

NBER

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Cambridge, MA 02138
United States

IZA ( email )

P.O. Box 7240
Bonn, D-53072
Germany

Richard V. Burkhauser

Cornell University - Department of Policy Analysis & Management (PAM) ( email )

120 Martha Van Rensselaer Hall
Ithaca, NY 14853
United States

University of Melbourne, Melbourne Institute ( email )

Level 5, FBE Building, 111 Barry Street
161 Barry Street
Carlton, VIC 3053
Australia

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