The Impact of the Homicide Decline on Life Expectancy of African American Males

Abstract

Homicide is a leading cause of death for young people in the United States aged 15–34, but it has a disproportionate impact on one subset of the population: African American males. The national decline in homicide mortality that occurred from 1991 to 2014 thus provides an opportunity to generate evidence on a unique question—How do population health and health inequality change when the prevalence of one of the leading causes of death is cut in half? In this article, we estimate the impact of the decline in homicide mortality on life expectancy at birth as well as years of potential life lost for African American and white males and females, respectively. Estimates are generated using national mortality data by age, gender, race, and education level. Counterfactual estimates are constructed under the assumption of no change in mortality due to homicide from 1991 (the year when the national homicide rate reached its latest peak) to 2014 (the year when the homicide rate reached its trough). We estimate that the decline in homicides led to a 0.80-year increase in life expectancy at birth for African American males, and reduced years of potential life lost by 1,156 years for every 100,000 African American males. Results suggest that the drop in homicide represents a public health breakthrough for African American males, accounting for 17 % of the reduction in the life expectancy gap between white and African American males.

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Fig. 1

Notes

  1. 1.

    The national homicide rate rose to 4.9 murders per 100,000 residents in 2015 (FBI 2016).

  2. 2.

    Risk of death for each age (x) is calculated as

    qx = 1 – exp(–mx),

    where qx is risk of death, and mx is the mortality rate (Phillips n.d.).

  3. 3.

    These proportions were calculated using estimates of the 2005 U.S. population by single years of age, obtained from https://wonder.cdc.gov/bridged-race-population.html.

  4. 4.

    This value is obtained by multiplying the rate of mortality from homicide at age x by the population at age x.

  5. 5.

    Life expectancy at age x is calculated by dividing the total number of years that survivors to age x live (after reaching age x) by the number of such survivors from among the 100,000 live births assumed when constructing a life table.

  6. 6.

    This value is assumed to equal 0.50 years for victims aged 1–84 and 0.48 years (calculated based on CDC data on the timing of infant homicide deaths) for deaths before age 1.

  7. 7.

    We obtain the number of excess deaths (in the counterfactual vs. the actual scenario) before age 20 in each educational attainment group by multiplying the total number of such excessive deaths before age 20 by the proportion of homicide victims aged 20–74 in that group.

  8. 8.

    See https://wonder.cdc.gov/bridged-race-population.html for these estimates.

  9. 9.

    In its life tables, the CDC adjusts mortality statistics for the oldest ages using models that combine data from the CDC’s vital statistics, the U.S. Census, and Medicare. For further details, see Arias (2012).

  10. 10.

    For the years 2001–2013, we obtained mortality statistics regarding those aged 75 or older from the CDC’s annual life tables. For 1991–2000, we obtained these mortality statistics by applying linear interpolation to estimates from the U.S. Decennial life tables for 1989–1991 (treated as applying to 1990) and the U.S. Decennial life tables for 1999–2001 (treated as applying to 2000). Because annual life tables for 2014 were not available when we conducted our analysis, we applied 2013 mortality statistics regarding those aged 75 or older to this year.

  11. 11.

    The CDC obtains death records from states. Some states report a single race per subject, while others report one or more races. When multiple races are reported, the CDC uses a bridging procedure to determine a single racial classification. See CDC (2004) for a summary of this procedure. For consistency, we use population counts that were bridged by the CDC using the same procedure, when we calculate mortality rates.

  12. 12.

    Our data source for population counts (the CDC’s online bridged-race population estimates) makes available a single estimate of the population aged 85 and over (of each race/gender), and a separate estimate for each year of age from 0 to 84.

  13. 13.

    See https://www.census.gov/topics/education/educational-attainment/data/tables.html for these data.

  14. 14.

    We tested our results’ sensitivity to this means of handling missing educational data by running our models with two alternative approaches to handling missing data: (1) excluding deaths with missing educational attainment data from the calculation of mortality rates and reducing population counts (used in calculating these rates) by the percentage of all deaths that were so excluded; and (2) assigning all deaths with missing educational attainment data to the “less than high school” educational attainment category. (The latter approach is conservative because it increases homicide deaths under the counterfactual scenario among the population’s least-educated segment, which has the shortest lifespan.) Using these alternative approaches, we obtained results similar to those in our model (see Table 1, panel B). For the first alternative approach, the difference between actual and counterfactual LE for black males was 0.715 years (95 % confidence interval = 0.360 to 1.070); for the second alternative approach, this difference was 0.705 years (95 % confidence interval = 0.353 to 1.058).

  15. 15.

    Firebaugh et al. (2014) found that declines in homicide from 2000 to 2010 had little impact on the black–white gap in life expectancy. Consistent with this finding, Fig. 1 shows that most of the effect of declining homicide rates on this gap occurred between 1993 and 2000.

  16. 16.

    These estimates pertain to LE at age 25.

  17. 17.

    We should be clear that both the effect of incarceration on violence and the overall effect of rising incarceration on population health are not entirely settled. The rise of incarceration through the 1980s likely had some negative impact on violent crime, although the magnitude is not clear. The continuing increase in incarceration in the 1990s is thought not to have had a large impact on crime (see National Research Council 2014). Incarceration has been found to have short-term positive effects on the health of inmates but may have long-term negative effects on individuals who have experienced incarceration as well as on their family members and friends, and particularly on their children (see Wildeman and Wang 2017).

  18. 18.

    We tested our results’ sensitivity to the assumption that LE of potential homicide victims is equivalent to that of others in the same race/gender/educational attainment groups in two ways. First, we assumed that unmeasured characteristics of homicide victims would lead to their having lifespans that were, on average, five years shorter than other individuals in the same age/race/gender/education groups. Second, we assumed the difference between LE of homicide victims and LE of others in the same age/race/gender/education groups would be equivalent to the difference in LE between the highest and lowest educational attainment categories for individuals of the same race/gender. Using these alternate assumptions, our results were similar to those in our original models (see Table 1, panel B). Under the first alternative assumption, the counterfactual LE of black males was 71.763 years (95 % confidence interval = 71.515 to 72.012), resulting in a gap between actual and counterfactual LE of 0.745 years (95 % confidence interval = 0.397 to 1.093). Under the second alternative assumption, African American males’ counterfactual LE was 71.806 years (95 % confidence interval = 71.555 to 72.056), resulting in a gap between actual and counterfactual LE of 0.702 years (95 % confidence interval = 0.352 to 1.052).

  19. 19.

    There were a total of 1,494 more murder victims, and 906 more African American male murder victims, nationally in 2015 than in 2014 (FBI 2016).

References

  1. Aizer, A., & Currie, J. (2017). Lead and juvenile delinquency: New evidence from linked birth, school and juvenile detention records (NBER Working Paper No. 23392). Cambridge, MA: National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w23392

  2. Aragón, T. J., Lichtensztajn, D. Y., Katcher, B. S., Reiter, R., & Katz, M. H. (2008). Calculating expected years of life lost for assessing local ethnic disparities in causes of premature death. BMC Public Health, 8(116). https://doi.org/10.1186/1471-2458-8-116

  3. Arias, E. (2012). United States life tables, 2008 (National Vital Statistics Reports Vol. 61, No. 3). Hyattsville, MD: National Center for Health Statistics. Retrieved from https://www.cdc.gov/nchs/data/nvsr/nvsr61/nvsr61_03.pdf

  4. Branas, C. C., Cheney, R. A., MacDonald, J. M., Tam, V. W., Jackson, T. D., & Ten Have, T. R. (2011). A difference-in-differences analysis of health, safety, and greening vacant urban space. American Journal of Epidemiology, 174, 1296–1306.

    Article  Google Scholar 

  5. Carey, J. R. (1989). The multiple decrement life table: A unifying framework for cause-of-death analysis in ecology. Oecologia, 78, 131–137.

    Article  Google Scholar 

  6. Centers for Disease Control and Prevention (CDC). (2001). Influence of homicide on racial disparity in life expectancy—United States, 1998. Morbidity and Mortality Weekly Report, 50, 780–783. Retrieved from https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5036a2.htm

  7. Centers for Disease Control and Prevention (CDC). (2004). NCHS procedures for multiple-race and Hispanic origin data: Collection, coding, editing, and transmitting. Atlanta, GA: Division of Vital Statistics, National Center for Health Statistics, CDC. Retrieved from https://www.cdc.gov/nchs/data/dvs/Multiple_race_documentation_5-10-04.pdf

  8. Centers for Disease Control and Prevention (CDC). (2017). Leading causes of death in males and femalesUnited States, 2014. Atlanta, GA: CDC. Retrieved from https://www.cdc.gov/healthequity/lcod/index.htm

  9. Centers for Disease Control and Prevention (CDC), National Center for Injury Prevention and Control. (n.d.). 10 leading causes of death by age group, United States2013. Atlanta, GA: National Vital Statistics System, National Center for Health Statistics, CDC. Retrieved from https://www.cdc.gov/injury/wisqars/pdf/leading_causes_of_death_by_age_group_2013-a.pdf

  10. Chalfin, A., & McCrary, J. (2013). The effect of police on crime: New evidence from U.S. cities, 1960–2010 (NBER Working Paper No. 18815). Cambridge, MA: National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w18815.pdf

  11. Coker, A. L., Davis, K. E., Arias, I., Desai, S., Sanderson, M., Brandt, H. M., & Smith, P. H. (2002). Physical and mental health effects of intimate partner violence for men and women. American Journal of Preventive Medicine, 23, 260–268.

    Article  Google Scholar 

  12. Cook, P. J., Dodge, K., Farkas, G., Fryer, R. G., Guryan, J., Ludwig, J., . . . Steinberg, L. (2015). Not too late: Improving academic outcomes for disadvantaged youth (Working Paper No. 15-01). Evanston, IL: Northwestern University, Institute for Policy Research. Retrieved from https://www.ipr.northwestern.edu/publications/docs/workingpapers/2015/IPR-WP-15-01.pdf

  13. Cook, P. J., & MacDonald, J. (2011). Public safety through private action: An economic assessment of BIDS. Economic Journal, 121, 445–462.

    Article  Google Scholar 

  14. Dowell, D., Arias, E., Kochanek, K., Anderson, R., Guy, G. P., Jr., Losby, J. L., & Baldwin, G. (2017). Contribution of opioid-involved poisoning to the change in life expectancy in the United States, 2000–2015. JAMA, 318, 1065–1067.

  15. Farrell, G., Tilley, N., & Tseloni, A. (2014). Why the crime drop? Crime and Justice, 43, 421–490.

    Article  Google Scholar 

  16. Federal Bureau of Investigation (FBI), Uniform Crime Reporting Program. (2015). Crime in the United States 2014. Washington, DC: FBI. Retrieved from https://ucr.fbi.gov/crime-in-the-u.s/2014/crime-in-the-u.s.-2014

  17. Federal Bureau of Investigation (FBI), Uniform Crime Reporting Program. (2016). Crime in the United States 2015. Washington, DC: FBI. Retrieved from https://ucr.fbi.gov/crime-in-the-u.s/2015/crime-in-the-u.s.-2015

  18. Federal Bureau of Investigation (FBI), Uniform Crime Reporting Program. (2017). UCR data tool [Data set]. Retrieved from https://www.ucrdatatool.gov

  19. Firebaugh, G., Acciai, F., Noah, A., Prather, C. J., & Nau, C. (2014). Why the racial gap in life expectancy is declining in the United States. Demographic Research, 31, 975–1006. https://doi.org/10.4054/DemRes.2014.31.32.

    Article  Google Scholar 

  20. Fowler, P. J., Tompsett, C. J., Braciszewski, J. M., Jacques-Tiura, A. J., & Baltes, B. B. (2009). Community violence: A meta-analysis on the effect of exposure and mental health outcomes of children and adolescents. Development and Psychopathology, 21, 227–259.

    Article  Google Scholar 

  21. Fox, J. A., & Zawitz, M. W. (2017). Homicide trends in the United States. Washington, DC: U.S. Department of Justice, Office of Justice Programs, Bureau of Justice Statistics. Retrieved from https://www.bjs.gov/content/pub/pdf/htius.pdf

  22. Friedson, M., & Sharkey, P. (2015). Violence and neighborhood disadvantage after the crime decline. ANNALS of the American Academy of Political and Social Science, 660, 341–358.

    Article  Google Scholar 

  23. Fuchs, V. R. (2016). Black gains in life expectancy. JAMA, 316, 1869–1870.

    Article  Google Scholar 

  24. Gardner, J. W., & Sanborn, J. S. (1990). Years of potential life lost (YPLL)—What does it measure? Epidemiology, 1, 322–329.

    Article  Google Scholar 

  25. Geronimus, A. T., Bound, J., & Colen, C. G. (2011). Excess black mortality in the United States and in selected black and white high-poverty areas, 1980–2000. American Journal of Public Health, 101, 720–729.

    Article  Google Scholar 

  26. Harper, S., Lynch, J., Burris, S., & Davey Smith, G. (2007). Trends in the black-white life expectancy gap in the United States, 1983–2003. JAMA, 297, 1224–1232.

    Article  Google Scholar 

  27. Harper, S., Rushani, D., & Kaufman, J. S. (2012). Trends in the black-white life expectancy gap, 2003–2008. JAMA, 307, 2257–2259.

    Article  Google Scholar 

  28. Heller, S. B. (2014). Summer jobs reduce violence among disadvantaged youth. Science, 346, 1219–1223.

    Article  Google Scholar 

  29. Keith, V. M., & Smith, D. P. (1988). The current differential in black and white life expectancy. Demography, 25, 625–632.

    Article  Google Scholar 

  30. Kochanek, K. D., Arias, E., & Anderson, R. N. (2013). How did cause of death contribute to racial differences in life expectancy in the United States in 2010? (NCHS Data Brief No. 125). Atlanta, GA: National Center for Health Statistics. Retrieved from https://www.cdc.gov/nchs/data/databriefs/db125.pdf

  31. Lai, D., & Hardy, R. J. (1999). Potential gains in life expectancy or years of potential life lost: Impact of competing risks of death. International Journal of Epidemiology, 28, 894–898.

    Article  Google Scholar 

  32. Levine, R. S., Foster, J. E., Fullilove, R. E., Fullilove, M. T., Briggs, N. C., Hull, P. C., . . . Hennekens, C. H. (2001). Black-white inequalities in mortality and life expectancy, 1933–1999: Implications for healthy people 2010. Public Health Reports, 116, 474–483.

  33. Levitt, S. D. (2004). Understanding why crime fell in the 1990s: Four factors that explain the decline and six that do not. Journal of Economic Perspectives, 18(1), 163–190.

    Article  Google Scholar 

  34. McEwen, B. S. (2000). The neurobiology of stress: From serendipity to clinical relevance. Brain Research, 886, 172–189.

    Article  Google Scholar 

  35. Meara, E. R., Richards, S., & Cutler, D. M. (2008). The gap gets bigger: Changes in mortality and life expectancy, 1981–2000. Health Affairs, 27, 350–360.

    Article  Google Scholar 

  36. Mooney, C. Z. (1997). Monte Carlo simulation (Sage University Paper Series on Quantitative Applications in the Social Sciences, Series No. 07-116). Thousand Oaks, CA: Sage.

    Google Scholar 

  37. National Institutes of Health. (2017). Estimates of funding for various research, condition, and disease categories (RCDC). Bethesda, MD: National Institutes of Health. Retrieved from https://report.nih.gov/categorical_spending.aspx

  38. National Research Council. (2014). The growth of incarceration in the United States: Exploring causes and consequences. Committee on Causes and Consequences of High Rates of Incarceration, J. Travis, B. Western, & S. Redburn (Eds.). Committee on Law and Justice, Division of Behavioral and Social Sciences and Education. Washington, DC: National Academies Press.

  39. Olshansky, S. J., Antonucci, T., Berkman, L., Binstock, R. H., Boersch-Supan, A., Cacioppo, J. T., . . . Rowe, J. (2014). Differences in life expectancy due to race and educational differences are widening, and many may not catch up. Health Affairs, 31, 1803–1813.

  40. Olshansky, S. J., Passaro, D. J., Hershow, R. C., Layden, J., Carnes, B. A., Brody, J., . . . Ludwig, D. S. (2005). A potential decline in life expectancy in the United States in the 21st century. New England Journal of Medicine, 352, 1138–1145.

  41. Phillips, D. P. (n.d.). The life table. San Diego, CA: University of California, San Diego. Retrieved from http://pages.ucsd.edu/~dphillips/lifetable_detailed_construction.txt

  42. Pridemore, W. A. (2003). Recognizing homicide as a public health threat: Toward an integration of sociological and public health perspectives in the study of violence. Homicide Studies, 7, 182–205.

    Article  Google Scholar 

  43. Redelings, M., Lieb, L., & Sorvillo, F. (2010). Years off your life? The effects of homicide on life expectancy by neighborhood and race/ethnicity in Los Angeles County. Journal of Urban Health, 87, 670–676.

    Article  Google Scholar 

  44. Reyes, J. W. (2007). Environmental policy as social policy? The impact of childhood lead exposure on crime. B. E. Journal of Economic Analysis & Policy, 7(1). https://doi.org/10.2202/1935-1682.1796

  45. Roeder, O., Eisen, L. B., & Bowling, J. (2015). What caused the crime decline? New York, NY: Brennan Center for Justice, New York University School of Law. Retrieved from https://www.brennancenter.org/publication/what-caused-crime-decline

  46. Sharkey, P. (2010). The acute effect of local homicides on children’s cognitive performance. Proceedings of the National Academy of Sciences, 107, 11733–11738.

    Article  Google Scholar 

  47. Sharkey, P., Torrats-Espinosa, G., & Takyar, D. (2017). Community and the crime decline: The causal effect of local nonprofits on violent crime. American Sociological Review, 82, 1214–1240.

    Article  Google Scholar 

  48. Stewart, S. T., & Cutler, D. M. (2014). The contribution of behavior change and public health to improved U.S. population health (NBER Working Paper No. 20631). Cambridge, MA: National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w20631.pdf

  49. U.S. Census Bureau. (2017). American fact finder [Data set]. Retrieved from https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml

  50. Wildeman, C., & Wang, E. A. (2017). Mass incarceration, public health, and widening inequality in the USA. Lancet, 389, 1464–1474.

    Article  Google Scholar 

  51. Wong, M. D., Shapiro, M. F., Boscardin, W. J., & Ettner, S. L. (2002). Contribution of major diseases to disparities in mortality. New England Journal of Medicine, 347, 585–592.

    Article  Google Scholar 

  52. Zimring, F. E. (2006). The great American crime decline. New York, NY: Oxford University Press.

    Google Scholar 

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Acknowledgments

We thank Amar Hamoudi for taking the time to review and talk through our methods and code in detail. His feedback and guidance were enormously valuable. Thanks also to Glenn Firebaugh, Robert Sampson, and Larry Wu for helpful feedback on the project.

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Correspondence to Patrick Sharkey.

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Sharkey, P., Friedson, M. The Impact of the Homicide Decline on Life Expectancy of African American Males. Demography 56, 645–663 (2019). https://doi.org/10.1007/s13524-019-00768-4

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Keywords

  • Life expectancy
  • Homicide
  • Mortality
  • Public health
  • Race