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Explaining the Female Black-White Obesity Gap: A Decomposition Analysis of Proximal Causes

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Demography

Abstract

There exist remarkably large differences in body weights and obesity prevalence between black and white women in the United States; and crucially, these differences are a significant contributor to black-white inequalities in health. In this article, we investigate the most proximal explanations for the weight gap: namely, differences in diet and exercise. More specifically, we decompose black-white differences in body mass index and waist-to-height ratio into components reflecting black-white differences in energy intake and energy expenditure. The analysis indicates that overconsumption is much more important than a lack of exercise in explaining the weight gap, which suggests that diet interventions will have to play a fundamental role if the weight gap between black and white women is to decline.

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Notes

  1. The Centers for Disease Control (CDC) and the World Health Organization (WHO) define “overweight” and “obesity” as a body mass index (BMI) value of more than 25 and more than 30, respectively, where BMI is the ratio of weight measured in kilograms, to squared height measured in meters.

  2. Studies using the DFL decomposition include Bell and Pitt (1998), Butcher and DiNardo (2002), Cobb-Clark and Hildebrand (2006), Daly and Valletta (2006), and Hyslop and Mare (2005).

  3. The BO decomposition also involves a reweighting but of a very different nature. In a basic BO decomposition, group differences in the independent variables are weighted by regression coefficients in order to determine the part that is explained by the independent variables. When one uses coefficients from the pooled model over both groups as the reference coefficients, the pooled coefficients can be expressed as \( b = {\mathbf{W}}{b^W} + \left( {{1} - {\mathbf{W}}} \right){b^B} \), where W is a weighting matrix given by W = diag(b – b B) diag(b W – b B)–1; and b, b B, and b W denote the coefficients from a pooled model, the white model, and the black model, respectively.

  4. This presentation draws on Cobb-Clark and Hildebrand (2006).

  5. To account for unequal probabilities of selection resulting from the complex survey design in NHANES, relevant sampling weights provided by NHANES were also used in our estimation.

  6. For example, a female aged 30–39 would be considered to be reporting plausible caloric intake information if she reports consuming daily calories that are between 1.18 to 2.18 times her BMR.

  7. The formula is \( {\text{BMR}} = \left( {{9}.{99} \times {\text{weight in kg}}} \right) + \left( {{6}.{25} \times {\text{height in cm}}} \right) - \left( {{4}.{92} \times {\text{age}}} \right) + \left( {{166} \times {\text{male}}} \right) - {161} \).

  8. This estimate is based on the calorie counter on the Mayo Clinic website (http://www.mayoclinic.com/health/calorie-calculator/NU00598) assuming an inactive lifestyle. It is essentially based on a BMR equation with an allowance for different levels of physical activity.

  9. NHANES questionnaires and a comprehensive listing of laboratory and examination components are posted on the NHANES website (http://www.cdc.gov/nchs/nhanes.htm).

  10. Because NHANES collects nationally representative data that use actual height and weight measurements, rather than self-reported data, it is often referred to as the “gold standard” for studies that focus on obesity in the United States.

  11. SAS code is available from the NIH for analyzing ActiGraph 7164 Physical Activity Monitor (PAM) data from the 2003–2004 NHANES. These programs are written to import and analyze accelerometer data downloaded from the National Center for Health Statistics. For this article, we adapt this code to similarly analyze PAM data from the 2005–2006 NHANES.

  12. The ActiGraph AM-7164 device was programmed to detect and record the magnitude of acceleration or “intensity” of movement; acceleration data in NHANES were stored in memory according to a one-minute interval. The intensity count is the intensity value recorded by the device, and each minute has an intensity value. The intensity files were reviewed for outliers and unreasonable values. The criteria used for reasonable ranges of activity count data were based on published literature and expert judgment.

  13. The averages and propensities reported in Table 1 for the analysis sample with nonmissing values of EI and EE variables are very similar to those for the entire sample. This suggests that our analysis sample does not constitute an unusual group of respondents.

  14. Common support is imposed in our analysis by dropping all observations whose propensity score is smaller than the minimum and larger than the maximum in the opposite group. Implementing the common support condition ensures that any combination of characteristics observed for blacks can also be observed among whites, and that we are not comparing the incomparable.

  15. We also performed the BO procedure using an equation that included interaction terms in the intake category and expenditure category and found that the results were very similar to the results presented in Table 2.

  16. It is common to also decompose differences at the extremes of the distribution—for example, at the 10th and 90th percentiles. We do not follow this approach here because of a low density of observations at the extremes of the distribution, which will result in estimates that are too imprecise.

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Acknowledgments

We thank Richard Burkhauser and Deborah Cobb-Clark for valuable comments and discussions. We are also grateful to two anonymous referees for useful suggestions that helped improve the paper and also to participants at the 2009 Australian Health Economics Conference for comments. All errors are our own.

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Authors and Affiliations

  1. Centre for Health Economics, Monash University, Clayton, Victoria, 3800, Australia

    David W. Johnston

  2. School of Economics, Finance and Marketing, RMIT University, Level 12, 239 Bourke Street, Victoria, 3000, Australia

    Wang-Sheng Lee

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Correspondence to Wang-Sheng Lee.

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Johnston, D.W., Lee, WS. Explaining the Female Black-White Obesity Gap: A Decomposition Analysis of Proximal Causes. Demography 48, 1429–1450 (2011). https://doi.org/10.1007/s13524-011-0064-x

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