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Black-White Disparities in Life Expectancy: How Much Can the Standard SES Variables Explain?

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Demography

Abstract

This article quantifies the extent to which socioeconomic and demographic characteristics can account for black-white disparities in life expectancy in the United States. Although many studies have investigated the linkages between race, socioeconomic status, and mortality, this article is the first to measure how much of the life expectancy gap remains after differences in mortality are purged of the compositional differences in socioeconomic characteristics between blacks and whites. The decomposition is facilitated by a reweighting technique that creates counterfactual estimation samples in which the distribution of income, education, employment and occupation, marital status, and other theoretically relevant variables among blacks is made to match the distribution of these variables among whites. For males, 80% of the black-white gap in life expectancy at age 1 can be accounted for by differences in socioeconomic and demographic characteristics. For females, 70% percent of the gap is accounted for. Labor force participation, occupation, and (among women only) marital status have almost no additional power to explain the black-white disparity in life expectancy after precise measures for income and education are controlled for.

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Notes

  1. Potter (1991) regressed racial differences in life expectancy across metropolitan statistical areas (MSAs) on MSA characteristics. Importantly, whereas Potter explored the effect of area-level socioeconomic characteristics, such as the local employment and poverty rates, the data and technique used here permit attribution of life expectancy differentials to individual-level characteristics, including own employment status and own income.

  2. The sex difference in current marital status is due entirely to older respondents. Because women live longer than men, they are more likely to be (unmarried) widows and skew the sex balance of marriage rates among both whites and blacks.

  3. Because the NLMS excludes institutionalized persons, it is somewhat surprising that the NLMS estimates of e 1 for whites fall slightly below the NCHS figures. For most ages, age-specific mortality is indeed lower in the NLMS. The lower life expectancy among whites in the NLMS is driven entirely by the age ranges 60 to 85 for men and 65 to 85 for women, over which the white NLMS cohorts experience higher mortality compared with those in the NCHS.

  4. The reverse scenario, of no support in the white density function but positive support in the black density function, poses no technical problem. It results in a reweighting term equal to 0.

  5. To the extent that marriage provides benefits to women primarily through income, but to men through other channels such as social ties and relations, it is sensible that the residual predictive power of marital status conditional on income (but with no control for social networks) is much smaller for women than for men.

  6. A similar mechanism drives the results (among women) when white-collar status is included in the reweight after conditioning on income and education.

  7. This feature is shared with other decomposition and standardization techniques. As Kitagawa (1955:1148) explained, “The difference between two crude rates is not the equivalent of a concept like total variance of a dependent variable in regression analysis, for example, which will be increasingly ‘explained’ as more independent variables are added to the regression equation.”

  8. Percentiles are approximate.

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Acknowledgments

I would like to thank Anne Case, Angus Deaton, Thomas Espenshade, Scott Lynch, John Papp, and seminar participants at Princeton’s Center for Health and Wellbeing for helpful comments. I also thank the referees for many useful suggestions. This article uses data supplied by the National Heart, Lung, and Blood Institute; NIH; and DHHS from the National Longitudinal Mortality Study. The views expressed in this article are those of the author and do not necessarily reflect the views of the National Heart, Lung, and Blood Institute; the Census Bureau; or the National Center for Health Statistics.

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  1. Department of Economics, Princeton University, 348 Wallace Hall, Princeton, NJ, 08544, USA

    Michael Geruso

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  1. Michael Geruso
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Correspondence to Michael Geruso.

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Appendix

Appendix

Table 6 Decomposition of e 1 differences by SES and demographics: Results for males, reweighting the white sample
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Table 7 Decomposition of e 1 differences by SES and demographics: Results for females, reweighting the white sample
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Geruso, M. Black-White Disparities in Life Expectancy: How Much Can the Standard SES Variables Explain?. Demography 49, 553–574 (2012). https://doi.org/10.1007/s13524-011-0089-1

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