Rising inequality in life expectancy by socioeconomic status. (English) Zbl 1461.91259

Summary: Inequality in life expectancy is growing in the United States, but evidence is mixed regarding how much it has grown. Some studies have found that life expectancies have decreased for those with the lowest socioeconomic status (SES). Other studies have found that while inequality is rising, there have been life expectancy gains across the board. A primary difference in these studies is how SES is measured. Some studies use an absolute measure, such as years of school completed, while others use relative measures, such as a person’s ranking of years of school completed compared to others born at the same time. This study uses regression analysis to assign people a relative education ranking and, in doing so, attempts to isolate the changing relationship between SES and mortality from the fact that certain education-based groups, especially high school dropouts, actually have a lower SES level today than in the past. The study finds that when SES is defined in this way – relatively – inequality in mortality by SES is increasing but life expectancies have also increased across SES groups. The study also finds that white women in the bottom of the education distribution have experienced the least improvement of any group and that rectangularization of the mortality distribution has occurred much more in the top of the income distribution than at the bottom.


91G05 Actuarial mathematics
91D20 Mathematical geography and demography
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[1] Aaron, H. J.; Burtless., G., Closing the deficit: How much can later retirement help (2013), Washington, DC: The Brookings Institution Press, Washington, DC
[2] Andreev, K.; Vaupel, J., Patterns of mortality improvement over age and time in developed counties: Estimation, presentation, and implications for mortality forecasting. Paper Presented at Population Association of America Annual Meeting (2005), Philadelphia, PA
[3] Bell, F. C.; Miller., M. L., Life tables for the United States Social Security area 1900-2100. Actuarial Study No. 120 (2005), Washington, DC: U.S. Social Security Administration, Washington, DC
[4] Bound, J.; Geronimus, A.; Rodriguez, J.; Waidmann, T., The implications of differential trends in mortality for Social Security policy. Paper Presented at 16th Annual Meeting of the Retirement Research Consortium (2014), Washington, DC
[5] Brown, J.; Liebman, J. B.; Pollet., J.; Feldstein, M.; Liebman, J. B., The distributional aspects of Social Security and Social Security reform, Appendix: Estimating life tables that reflect socioeconomic differences in mortality, 447-57 (2002), Chicago, IL: University of Chicago Press, Chicago, IL
[6] Cristia, J. P., Rising mortality and life expectancy differentials by lifetime earnings in the United States, Journal of Health Economics, 28, 5, 984-95 (2009)
[7] Cutler, D. M. (2009), Cambridge, MA: National Bureau of Economic Research, Cambridge, MA
[8] Cutler, D. M.; Lange, F.; Meara, E.; Richards-Shubik, S.; Ruhm., C. J., Rising educational gradients in mortality: The role of behavioral risk factors, Journal of Health Economics, 30, 6, 1174-87 (2011)
[9] Gavrilov, L. A.; Gavriloca., N. S., Mortality measurement at advanced ages: A study of the Social Security Administration death master file, North American Actuarial Journal, 15, 3, 432-47 (2011)
[10] Goldin, C., America’s graduation from high school: The evolution and spread of secondary schooling in the twentieth century, Journal of Economic History, 58, 2, 345-74 (1998)
[11] Gompertz, B., On the nature of the function expressive of the law of human mortality and on a new mode of determining the value of life contingencies, Philosophical Transactions of the Royal Society of London, 115, 513-85 (1825)
[12] Haverstick, K.; Sapozhnikov, M.; Triest, R. K.; Zhivan, N. A. (2007), Chestnut Hill, MA: Center for Retirement Research at Boston College, Chestnut Hill, MA
[13] Jemal, A.; Ma, J.; Ward, E. M.; Siegel., R. L., Temporal trends in mortality in the United States 1969-2013, Journal of the American Medical Association, 314, 16, 1731-39 (2015)
[14] Johnson, N. E., The racial crossover in comorbidity, disability, and mortality, Demography, 37, 3, 267-83 (2000)
[15] Kitagawa, E. M.; Hauser, P. M., Differential mortality in the United States: A study in socioeconomic epidemiology (1973), Cambridge, MA: Harvard University Press, Cambridge, MA
[16] Lee, R. D.; Carter., L. R., Modeling and forecasting U.S. mortality, Journal of American Statistical Association, 87, 419, 659-71 (1992) · Zbl 1351.62186
[17] Leonesio, M. V.; Vaughn, D. R.; Wixon., B., Increasing the early retirement age under Social Security: Health, work, and financial resources. Health and Income Security for an Aging Workforce No. 7 (2003), Washington, DC: National Academy of Social Insurance, Washington, DC
[18] Lu, J.; Wong., W. (2011), Orlando, FL
[19] Lynch, S. M.; Brown, J. S.; Harmsen., K. G., Black-white differences in mortality compression and deceleration and the mortality crossover reconsidered, Research on Aging, 25, 3, 456-83 (2003)
[20] Monk, C.; Turner, J. A.; Zhivan, N. A. (2010), Chestnut Hill, MA: Center for Retirement Research at Boston College, Chestnut Hill, MA
[21] Munnell, A. H., The average retirement age—An update. Issue in Brief 15-4 (2015), Chestnut Hill, MA: Center for Retirement Research at Boston College, Chestnut Hill, MA
[22] Munnell, A. H.; Libby, J., Will people be healthy enough to work longer? Issue in Brief 7-3 (2007), Chestnut Hill, MA: Center for Retirement Research at Boston College, Chestnut Hill, MA
[23] Olshansky, S. J.; Antonucci, T.; Berkman, L.; Binstock, R. H.; Boersch-Supan, A.; Cacioppo, J. T.; Carnes, B. A.; Carstensen, L. L.; Fried, L. P.; Goldman, D. P.; Jackson, J.; Kohli, M.; Rother, J.; Zheng, Y.; Rowe., J., Differences in life expectancy due to race and educational differences are widening, and many may not catch up, Health Affairs, 31, 8, 1803-13 (2012)
[24] Panis, C.; Hurd, M.; Loughran, D.; Zissimopoulos, J.; Haider, S.; St. Clair., P., The effects of changing Social Security Administration’s early entitlement age and the normal retirement age. DRU-2903-SSA (2002), Santa Monica, CA: RAND, Santa Monica, CA
[25] Pappas, G.; Queen, S.; Hadden, W.; Fisher., G., The increasing disparity in mortality between socioeconomic groups in the United States, 1960 and 1986, New England Journal of Medicine, 329, 2, 103-9 (1993)
[26] Rogot, E.; Sorlie, P. D.; Johnson, N. J., Life expectancy by employment status, income, and education in the national longitudinal mortality study, Public Health Reports, 107, 4, 457-61 (1992)
[27] Sasson, I., Trends in life expectancy and lifespan variation by educational attainment: United States, 1990-2010, Demography, 53, 2, 269-93 (2016)
[28] Turner, J. A. (2007), Chestnut Hill, MA: Center for Retirement Research at Boston College, Chestnut Hill, MA
[29] U. S. Social Security Administration, The long-range demographic assumptions for the 2015 trustees report (2015), Washington, DC
[30] Waldron, H., Trends in mortality differentials and life expectancy for male Social Security-covered workers, by socioeconomic status, Social Security Bulletin, 67, 3, 1-28 (2007)
[31] Waldron, H., Mortality differentials by lifetime earnings decile: Implications for evaluations of proposed Social Security law changes, Social Security Bulletin, 73, 1, 1-37 (2013)
[32] Wang, H.; Schumacher, A. E.; Levitz, C. E.; Mokdad, A. H.; Murray., C. J. L., Left behind: Widening disparities for males and females in U.S. county life expectancy, 1985-2010, Population Health Metrics, 11, 8, 1-15 (2013)
[33] Weller, C. E., Raising the retirement age for Social Security: Implications for low wage, minority, and female workers. CAP Economic Policy Report (2005), Washington, DC: Center for American Progress, Washington, DC
[34] Yao, L.; Robert., S. A., Examining the racial crossover in mortality between african american and white older adults: A multilevel survival analysis of race, individual socioeconomic status, and neighborhood socioeconomic context, Journal of Aging Research, 1-8 (2011)
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