Sources of Increasing Differential Mortality among the Aged by Socioeconomic Status

Sources of Increasing Differential Mortality among the Aged by Socioeconomic Status

Barry Bosworth, Gary Burtless and Kan ZhangTHE BROOKINGS INSTITUTION

16th Annual Joint Conference of the Retirement Research ConsortiumAugust 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, 1979-1985

$0 $10,000 $20,000 $30,000 $40,000 $50,000 $60,000 $70,000 $80,00070

74

78

82

86

Life expectancy(in years)

Nominal family income in 1980 $

Median family in-come in 1980

Women

Men

Source: Rogot, Sorlie, and Johnson (1992).

Health & Retirement Study Cohorts spanning birth years 1890-1965

We examine deaths occurring in 1992-2010

Sample includes almost 32,000 aged & near-aged Americans Of whom more than 11,500 died between

1992-2010 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.

50 55 60 65 70 75 80 85 900%

2%

4%

6%

8%

10%

12%Mortality rates: Males

Age

Age-specific mortality rates observed in RHS sample: 1992-2010

Born in 1925

Born in 1935

Born in 1945

Source: Tabulations of HRS mortality data.

50 55 60 65 70 75 80 85 900%

2%

4%

6%

8%

10%

12%Mortality rates: Males

Age

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).

50 55 60 65 70 75 80 85 900%

2%

4%

6%

8%

10%Mortality rates: Women

Age

Age-specific mortality rates observed in RHS sample: 1992-2010

Born in 1925

Born in 1935

Born in 1945


50 55 60 65 70 75 80 85 900%

2%

4%

6%

8%

10%Mortality rates: Women

Age

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

55 60 65 70 75 80 850%

2%

4%

6%

8%

10%

12%

Predicted probability of death: Males born in 1920

Age

Predicted low earner

Predicted high earner


Estimated mortality rates of low and high predicted earners, by age

55 60 65 70 75 80 850%

2%

4%

6%

8%

10%

12%

Predicted probability of death: Males born in 1920 and 1935

Age

Predicted low earner

Predicted high earner

High earner born in 1935


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 1915-

1930 “Later cohorts” = Born between 1931-

1942 Restrict sample to respondents born in 1915-1942

Restrict sample to observations for these respondents when they were between ages 68-79

What is impact of predicted income in top half of income distribution in “Early” vs. “Later” cohorts?

66 68 70 72 74 76 78 800%

2%

4%

6%

8%

10%

Mortality rate by age: Males born before 1931

Age

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


66 68 70 72 74 76 78 800%

2%

4%

6%

8%

10%

Mortality rate by age: Males born before & after 1931

Age

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


66 68 70 72 74 76 78 800%

2%

4%

6%

0.80

1.20

1.60

2.00

Mortality differential by age: Males born before 1931

Age

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 (%)


Difference Ratio

66 68 70 72 74 76 78 800%

2%

4%

6%

0.80

1.20

1.60

2.00

Mortality differential by age: Males born before & after 1931

Age

Mortality rate differential of low and high predicted earners, by age in “Early” & “Later” cohorts


born before 1931



Mortality rate difference for

those born after 1930 (%)


Difference Ratio

66 68 70 72 74 76 78 800%

2%

4%

6%

0.80

1.20

1.60

2.00

Mortality differential by age: Women born before 1931

Age

Mortality rate differential of low and high predicted earners, by age in “Early” cohorts


born before 1931




Difference Ratio

66 68 70 72 74 76 78 800%

2%

4%

6%

0.80

1.20

1.60

2.00

Mortality differential by age: Women born before & after 1931

Age

Mortality rate differential of low and high predicted earners, by age in “Early” & “Later” cohorts


born before 1931



Mortality rate difference for

those born after 1930 (%)


Difference Ratio

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

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Sources of Increasing Differential Mortality among the Aged by Socioeconomic Status