Why Lifespans Are More Variable Among Blacks Than Among Whites in the United States

Firebaugh, Glenn; Acciai, Francesco; Noah, Aggie J.; Prather, Christopher; Nau, Claudia

doi:10.1007/s13524-014-0345-2

Cited by 42 publications

(37 citation statements)

References 31 publications

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“…In addition to presenting total cause-specific spread, allocation, timing, and joint effects, we also present effects separately by sex. Edwards and Tuljapurkar (2005) and Nau and Firebaugh (2012) found similar variability patterns for women and men, whereas Firebaugh et al (2014) found that effects (particularly for spread) are larger for women than for men.…”

Section: Methodsmentioning

confidence: 79%

“…The decomposition process is developed and described in detail elsewhere (Nau and Firebaugh 2012; see also Firebaugh et al 2014). Briefly, this methodology decomposes differences in lifespan variance between two populations into the contributions of differences in (1) cause-specific variability in age at death (spread effect), (2) number of deaths attributable to each cause (allocation effect), and (3) variability in cause-specific mean age at death (timing effect).…”

Section: Methodsmentioning

confidence: 99%

“…In contrast, variability remained stagnant among whites. Most recently, Firebaugh et al (2014) examined why lifespan variance is greater among blacks than among whites in the United States. They found that much of the variance difference between the two groups is due to greater within-cause variance among blacks relative to whites.…”

Section: Introductionmentioning

confidence: 99%

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Hispanic-White Differences in Lifespan Variability in the United States

et al. 2015

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This study is the first to investigate whether and, if so, why Hispanics and non-Hispanic whites in the United States differ in the variability of their lifespans. Although Hispanics enjoy higher life expectancy than whites, very little is known about how lifespan variability—and thus uncertainty about length of life—differs by race/ethnicity. We use 2010 U.S. National Vital Statistics System data to calculate lifespan variance at ages 10 and older for Hispanics and whites, and then decompose the Hispanic-white variance difference into cause-specific spread, allocation, and timing effects. In addition to their higher life expectancy relative to whites, Hispanics also exhibit 7 % lower lifespan variability, with a larger gap among women than men. Differences in cause-specific incidence (allocation effects) explain nearly two-thirds of Hispanics’ lower lifespan variability, mainly because of the higher mortality from suicide, accidental poisoning, and lung cancer among whites. Most of the remaining Hispanic-white variance difference is due to greater age dispersion (spread effects) in mortality from heart disease and residual causes among whites than Hispanics. Thus, the Hispanic paradox—that a socioeconomically disadvantaged population (Hispanics) enjoys a mortality advantage over a socioeconomically advantaged population (whites)—pertains to lifespan variability as well as to life expectancy. Efforts to reduce U.S. lifespan variability and simultaneously increase life expectancy, especially for whites, should target premature, young adult causes of death—in particular, suicide, accidental poisoning, and homicide. We conclude by discussing how the analysis of Hispanic-white differences in lifespan variability contributes to our understanding of the Hispanic paradox.

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Section: Methodsmentioning

confidence: 79%

Section: Methodsmentioning

confidence: 99%

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Hispanic-White Differences in Lifespan Variability in the United States

et al. 2015

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“…However, cause-specific contributions to variance have only recently been explored. A study of the difference between blacks and whites found that causes of death contributing the most to differences in V were distinct from causes contributing most to the difference in e 0 (15). In particular, whereas previous work showed that about one-half the gap in e 0 was because of differences in CVD, cancer, and diabetesrelated mortality (16), the gap in V was largely because of accidents, violence, and HIV/AIDS.…”

mentioning

confidence: 94%

“…Also, do causes that contribute substantially to increase in e 0 also contribute in a major way to declines in V? The work by Firebaugh et al (15) shows that this need not be true.…”

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confidence: 99%

Equity and length of lifespan are not the same

Seligman

Greenberg

Tuljapurkar

2016

Proc. Natl. Acad. Sci. U.S.A.

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Efforts to understand the dramatic declines in mortality over the past century have focused on life expectancy. However, understanding changes in disparity in age of death is important to understanding mechanisms of mortality improvement and devising policy to promote health equity. We derive a novel decomposition of variance in age of death, a measure of inequality, and apply it to cause-specific contributions to the change in variance among the G7 countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States) from 1950 to 2010. We find that the causes of death that contributed most to declines in the variance are different from those that contributed most to increase in life expectancy; in particular, they affect mortality at younger ages. We also find that, for two leading causes of death [cancers and cardiovascular disease (CVD)], there are no consistent relationships between changes in life expectancy and variance either within countries over time or between countries. These results show that promoting health at younger ages is critical for health equity and that policies to control cancer and CVD may have differing implications for equity.health equity | age of death variance | life expectancy | inequality | mortality T he 20th century was an epoch of unprecedented declines in mortality rates in most countries. Causes of death shifted from infections, which primarily killed children, to cardiovascular disease (CVD) and cancer, which primarily killed adults, in the epidemiological transition (1, 2). Understanding mortality change largely focused on understanding changes in the average age of death, the life expectancy (e 0 ). In recent years, attention has shifted to disparities in mortality, and health equity is now recognized as an important policy goal. Here, we develop and use a new method to show how changes in inequality, as measured by the variance V, are driven by changes in mortality caused by specific causes, accounting for the interdependence of changes in the different causes of death. We find differences between the magnitudes of cause-specific contributions to e 0 and V and unexpectedly inconsistent relationships between the two over time and across countries, with important scientific and policy implications.Work to date on understanding change in e 0 has found that, among wealthy nations, much of the gain in the second half of the 20th century was because of declines in CVD mortality (3-5). The male-female difference in e 0 , which widened and then, narrowed over the course of the century, has also been driven by CVD mortality as a consequence of smoking behavior (6-8). Disparities across countries in e 0 have been studied most notably by the Global Burden of Disease Project, which has noted gains to life expectancy across countries and declines in global disparity for certain age-specific mortality rates (9, 10). Related work has shown how changes in different causes of death would have different effects on the health of global poor vs. rich (11).Analyses of al...

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Discrimination at the Intersection of Age, Race, and Gender: Evidence from an Eye‐Tracking Experiment

Lahey¹,

Oxley²

2021

J Policy Anal Manage

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This paper uses a laboratory experiment with eye‐tracking and about 6,000 unique randomized resumes to explore the effects of race on employment discrimination over the life cycle for experienced applicants in their mid 30s to 70s. We find striking evidence of race discrimination against prime‐age Black job applicants that diminishes into middle age before re‐emerging for older applicants. Screeners in professional programs mechanically process Black and White resumes similarly, but spend less time on resumes for the younger Black applicants. We find evidence of levels‐based statistical discrimination, suggesting that screeners believe younger Black applicants to have worse computer skills and more gaps in their job histories. We also find evidence for variance‐based statistical discrimination against Black applicants of all ages, suggesting that screeners perceive the job history signal to be stronger for White applicants than for Black, disproportionately affecting older Black applicants. Evidence from a smaller companion study using Human Resources (HR) managers and from the Current Population Survey (CPS) supports the external validity of our experiment, particularly for female job applicants. Results are robust to a number of different controls and specification choices.

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Why Lifespans Are More Variable Among Blacks Than Among Whites in the United States

Cited by 42 publications

References 31 publications

Hispanic-White Differences in Lifespan Variability in the United States

Hispanic-White Differences in Lifespan Variability in the United States

Equity and length of lifespan are not the same

Discrimination at the Intersection of Age, Race, and Gender: Evidence from an Eye‐Tracking Experiment

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