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Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging
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Body composition and wages.

Roy Wada1, Erdal Tekin

  • 1UCLA School of Public Health, Box 951772, Los Angeles, CA 90095-1772, USA. roywada@ucla.edu

Economics and Human Biology
|April 6, 2010
PubMed
Summary

Body fat (BF) is linked to lower wages, while fat-free mass (FFM) is associated with higher earnings in the U.S. These findings offer a more nuanced view than BMI for understanding body composition

Area of Science:

  • Economics
  • Health Economics
  • Anthropometry

Background:

  • Traditional obesity measures like Body Mass Index (BMI) do not differentiate between body fat and fat-free mass.
  • Concerns exist regarding the accuracy of BMI in controlling for internal body composition variations.
  • Existing research often relies on less precise body size metrics.

Purpose of the Study:

  • To investigate the relationship between specific body composition measures and wages in the U.S.
  • To introduce and utilize refined measures of body fat (BF) and fat-free mass (FFM) as alternatives to BMI.
  • To determine if body composition impacts wages independently of unobserved individual characteristics.

Main Methods:

  • Utilized data from the National Health and Nutrition Examination Survey III (NHANES III) for bioelectrical impedance analysis (BIA).

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  • Developed precise measures for body fat (BF) and fat-free mass (FFM).
  • Estimated wage models using data from the National Longitudinal Survey of Youth 1979 (NLSY79).

    • Main Results:

    • Body fat (BF) showed a significant negative association with wages for both males and females across racial groups.
    • Fat-free mass (FFM) demonstrated a significant positive association with wages.
    • Statistical analyses confirmed that these associations were not due to unobserved heterogeneity.

    Conclusions:

    • Body composition, specifically BF and FFM, has a discernible impact on wages.
    • Refined body composition measures offer a superior alternative to BMI for economic and health analyses.
    • The findings are robust and validated through multiple specification checks and alternative BIA equations.