Sources of Increasing Differential Mortality among the Aged by Socioeconomic Status Barry Bosworth, Gary Burtless and Kan Zhang T HE B ROOKINGS I NSTITUTION 16th Annual Joint Conference of the Retirement Research Consortium August 7-8, 2014

Mortality differentials by social and economic status At a given age, death rates are higher for folks with low SES  SES measured by income, earnings, or education Mounting evidence mortality gap is growing Goal of study: Use HRS data to find reason  Evidence in HRS of growing SES differential?  Causes of death that explain growing gap?  Can growing differences in health-related behavior (smoking, exercise) account for the gap?

Expected age of death among white men and women attaining age 45 in the National Longitudinal Mortality Study, Women Men Source: Rogot, Sorlie, and Johnson (1992).

Health & Retirement Study Cohorts spanning birth years  We examine deaths occurring in Sample includes almost 32,000 aged & near- aged Americans  Of whom more than 11,500 died between HRS data file contains range of info on SES  Educational attainment & current income  For almost 2/3 of sample, Social Security earnings record Also: Health status, health-related behaviors, parents’ life spans

Our measures of SES Educational attainment  Less than high school diploma  College degree or more Actual average nonzero earnings (ages 41-50)  Based on reported earnings in Social Security record  Combined husband-wife earnings adj. for family size Predicted average nonzero earnings (ages 41-50)  Regression predictions explained by education, race/ethnicity, disability, marital status In current paper, we use earnings estimate to predict R’s position in income distribution: Top or bottom half.

Age-specific mortality rates observed in RHS sample: Born in 1925 Born in 1935 Born in 1945 Source: Tabulations of HRS mortality data.

Age-specific mortality rates observed in RHS sample compared with SSA estimates (2005) Born in 1925 Born in 1935 Born in 1945 SSA estimates Source: Tabulations of HRS and SSA (2005).

Age-specific mortality rates observed in RHS sample: Born in 1925 Born in 1935 Born in 1945 Source: Tabulations of HRS mortality data.

Age-specific mortality rates observed in RHS sample compared with SSA estimates (2005) Born in 1925 Born in 1935 Born in 1945 SSA estimates Source: Tabulations of HRS and SSA (2005).

Does socioeconomic status affect mortality in the HRS? To find out we use discrete-time logistic model to estimate the influence of risk factors linked to mortality:  Age  Race / ethnicity  Marital status  Alternative measures of SES All measures of SES are linked in expected way with age-specific mortality rates  Low SES boosts mortality; High SES reduces it.

Estimated mortality rates of low and high predicted earners, by age Predicted low earner Predicted high earner Source: Tabulations of HRS mortality data.

Estimated mortality rates of low and high predicted earners, by age Predicted low earner Predicted high earner High earner born in 1935 Source: Tabulations of HRS mortality data.

Does the impact of socioeconomic status on mortality grow in successive birth cohorts? Using a simple discrete-time logistic model to estimate the influence of SES in “Early” and “Later” birth cohorts:  “Early cohorts” = Born between  “Later cohorts” = Born between Restrict sample to respondents born in Restrict sample to observations for these respondents when they were between ages What is impact of predicted income in top half of income distribution in “Early” vs. “Later” cohorts?

Mortality rates of low and high predicted earners, by age in “Early” cohort Predicted low earner born before 1931 Predicted high earner born before 1931 Source: Tabulations of HRS mortality data.

Mortality rates of low and high predicted earners, by age in “Early” and “Later” cohorts Predicted low earner born before 1931 Predicted high earner born before 1931 Predicted high earner born after 1930 Source: Tabulations of HRS mortality data.

Mortality rate differential of low and high predicted earners, by age in “Early” cohorts Mortality rate ratio for those born before 1931 Mortality rate difference for those born before 1931 (%) Source: Tabulations of HRS mortality data. DifferenceRatio

Mortality rate differential of low and high predicted earners, by age in “Early” & “Later” cohorts Mortality rate ratio for those born before 1931 Mortality rate difference for those born before 1931 (%) Mortality rate difference for those born after 1930 (%) Source: Tabulations of HRS mortality data. DifferenceRatio

Mortality rate differential of low and high predicted earners, by age in “Early” cohorts Mortality rate ratio for those born before 1931 Mortality rate difference for those born before 1931 (%) Source: Tabulations of HRS mortality data. DifferenceRatio

Mortality rate differential of low and high predicted earners, by age in “Early” & “Later” cohorts Mortality rate ratio for those born before 1931 Mortality rate difference for those born before 1931 (%) Mortality rate difference for those born after 1930 (%) Source: Tabulations of HRS mortality data. DifferenceRatio

Does the impact of socioeconomic status on mortality grow in successive birth cohorts? When we use our full sample we find meaningfully large and statistically significant increases in the SES mortality differential across successive birth cohorts  Using both actual and predicted Social-Security-earnings  Using indicators of low and high educational attainment In specifications where we control for race/ethnicity, disability, and marital status:  We find worsening of mortality in low SES groups Less than high school / Bottom half of actual or predicted income  Versus generally declining mortality in high SES groups

Changing impact of socioeconomic status on mortality by specific cause of death We use discrete time logistic models to examine evolution mortality by 8 causes of death  Significant drop in age-specific mortality due to heart disease & cancer for top half of predicted income;  No significant decline in deaths due to these causes for people in bottom half of predicted income. Similar pattern findings for male deaths due to  “Allergies, hay fever, sinusitis and tonsillitis” & “ miscellaneous ” Both among low-predicted-income men & women we find significant increases in mortality due to  “ Digestive system issues”

Can behavioral differences account for widening mortality differences by SES group? To test, we added self-reported behaviors to specification:  Alcohol consumption (level)  Smoking (Sometime in past? and Currently?)  Vigorous physical activity at least once a week We also examined impact of parental longevity Finally, we tested the explanatory power and impact of self- reported health in the first HRS interview Basic idea: If the inclusion of the behavioral variables reduces the measured impact of SES on changes in mortality differentials, then changes in behavior by SES group may account for the growing difference in mortality by SES

Can behavioral differences account for widening mortality differences by SES group? Self-reported, health-related behaviors have expected and highly significant impacts on risk of mortality --  Alcohol consumption and Smoking boost age-specific mortality  Vigorous physical activity reduces mortality Inclusion of behavior variables in specification increases estimated mortality gradient We find little effect of parental longevity on mortality Inclusion of initial health status has little impact on the estimated size of change in mortality gradient

Conclusions All our measures of SES show sizeable mortality differentials by SES group All measures also show significant increases in magnitude of differentials in later cohorts compared with earlier ones We find some causes of death--heart disease, cancer, and (among men) “Allergies, hay fever, sinusitis and tonsillitis”—have declined among those with high predicted income but not among those with low SES Mortality due to “Digestive system issues” has risen among low SES but not high SES groups Inclusion of health-related behavioral variables does not reduce noticeably the estimated increase in mortality differentials by SES