TY  - JOUR
AU  - Cawley,John
AU  - Burkhauser,Richard V.
TI  - Beyond BMI: The Value of More Accurate Measures of Fatness and Obesity in Social Science Research
JF  - National Bureau of Economic Research Working Paper Series
VL  - No. 12291
PY  - 2006
Y2  - June 2006
UR  - http://www.nber.org/papers/w12291
L1  - http://www.nber.org/papers/w12291.pdf
N1  - Author contact info:
John Cawley
3M24 MVR Hall
Department of Policy Analysis and Management
and Department of Economics
Cornell University
Ithaca, NY  14853
Tel: 607/255-0952
Fax: 607/255-4071
E-Mail: jhc38@cornell.edu

Richard V. Burkhauser
Cornell University
Department of Policy Analysis & Management
259 MVR Hall
Ithaca, NY 14853-4401
Tel: 607/255-2097
Fax: 607/255-4071
E-Mail: rvb1@cornell.edu
AB  - 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.
ER  -