1. The Dynamics of Income-Related Health Inequality Across the Life Cycle
Dennis Petrie (Centre for Health Economics, Monash University)
Linkun Chen & Philip Clarke (University of Melbourne)
Ulf Gerdtham (Lund University)
Paul Allanson (University of Dundee)
ASSA Chicago Jan 2017

2. Overview Health inequality related to socioeconomic status (by Age)?
Data (Australia - HILDA & Great Britain - BHPS)
Measuring income-related health inequality
Accounting for changes in this relationship over time
Empirical example for Australia & Great Britain over the lifecycle
Conclusions & Limitations

3. Health inequality
In countries at all levels of income, health and illness follow a social gradient: the lower the socioeconomic position, the worse the health. …. Where systematic differences in health are judged to be avoidable by reasonable action they are, quite simply, unfair. …. Social injustice is killing people on a grand scale.
[Final Report of the Commission on Social Determinants of Health, WHO, 2008]
Age plays a critical role for health and there may be difference mechanisms taking place at different ages which impact of the socioeconomic gradient in health
To evaluate policies addressing these inequalities it is critical to understand the evolution of socioeconomic status and health at different ages

4. Literature on socioeconomic health inequality over the lifecycle
It has been found that, in general, the income gradient in health becomes steeper at young ages before reaching a peak at middle ages and then weakening at old ages (Case & Deaton, 2005; Deaton & Paxson, 1998; Siegel & Mosler, 2014; van Kippersluis et al., 2010)
Kippersluis et al. (2010) find evidence to suggest that part of the strengthening relationship at younger ages is due to health-related withdrawal from the labour force
Deaton and Paxson (1998) also suggest that health shocks diminish an individual’s earning ability now and in the future

5. Literature on socioeconomic health inequality over the lifecycle
Beckett (2000) suggests that one possible reason for the weakening relationship between income and health at older ages is due to selective mortality
However, for the Netherlands, Baeten et al. (2013) conclude that while there is some evidence of selective mortality it only plays a minor role in explaining the weakening of the relationship between income and health

6. Empirical study (Australia - HILDA & GB - BHPS)
Empirical study based on Australian HILDA and UK BHPS household panel surveys
Health (based on Quality Adjusted Life Years using SF6D)
1 = full health
0 = equivalent to being dead
Individuals’ incomes are given by annual equivalised household income (income ranks are used in the inequality calculations)
We examine changes in income-related health inequality;
Australia from 2001 to 2006
Reported death and linked with death records
Great Britain from 1999 to 2004
Reported death
All uncertainty around estimates are obtained through bootstrapping at the individual level

7. Males - Average health and equivalised income
Rolling Age groups (starting year GB1999/ AUS2001)
15-30
20-35
25-40
30-45
35-50
40-55
45-60
50-65
55-70
60-75
65+
Great Britain
Mean Health 1999 -
0.84
0.83
0.82
0.81
0.80
0.79
0.78
0.77
0.74
Mean Health 2004 (survivors)
0.75
0.72
Mean Health 2004
(including dead)
0.76
0.73
0.67
0.54
Mean Income 1999
24.7
25.9
26.3
25.8
26.8
25.5
21.9
18.2
15.4
Mean Income 2004 (survivors)
31.9
31.3
31.6
31.5
31.7
29.8
26.5
22.1
19.1
17.7
Australia
Mean Health 2001
0.71
Mean Health 2006 (survivors)
0.70
Mean Health 2006
0.69
0.64
0.59
0.43
Mean Income 2001
29.6
30.3
30.2
32.4
31.4
28.3
24.0
20.3
Mean Income 2006 (survivors)
39.9
41.2
40.1
39.4
40.8
42.3
43.3
40.3
35.7
29.4
25.6

8. Income-Related Health Inequality in Great Britain 1999
And those with higher income-ranks have better health
In general those with lower income-ranks have worse health

9. Summary measures of income-related health inequality (IRHI)
Absolute measure of inequality
Generalized Concentration index (GC): CI x mean health
Erreygers index: Absolute measure normalized so that it has a value of 1 when there is maximum absolute inequality - when the richest 50% have maximum health and poorest 50% have no health
We will focus on the Erreygers index – where the same absolute increase or decrease in health for everyone would leave inequalities unchanged
A similar analysis could be conducted for either attainment-relative or shortfall-relative perspective

10. Income-related health inequality – Males Australia
Positive values suggest that the rich have more health than the poor
The black dotted lines and grey dashed lines are the bootstrapped 95% confidence intervals around the 2001 and 2006 estimates respectively.

11. Income-related health inequality – Males GB
The black dotted lines and grey dashed lines are the bootstrapped 95% confidence intervals around the 1999 and 2004 estimates respectively.

12. Longitudinal decomposition methods
While there are some differences over time these differences look small
But examining the changes in the cross-sectional inequality can miss important mechanisms and selective mortality that are happening at the individual level
Next we are going to outline the longitudinal methods used to decompose the change in inequality between a final period and an initial period

13. Longitudinal Analysis
A stable positive CI over time could imply different things;
chronic health inequalities (same individuals remain poor and sick), or
transitory health inequalities (some poor and sick people become rich and healthy and some rich and healthy people become poor and sick)
“Increasing” health inequalities (if some of the poor and sick die and are “replaced” by some of the original poor and healthy people becoming sick)
Using a cross-sectional analysis we ignore individual level changes and population dynamics!

14. Accounting for the dead & co-movements
2006
Dead
2012
Health-related income mobility
Dead
Health
Health
Change in health
Survivors
Inequality
Co-movement
Inequality
Change in income rank
Income rank
Income rank
Survivors
Survivors
Income-related health mobility
Difference

15. The maths….. Change in income-related inequality Health mobility
Co-movement
Income mobility
Where f is the final period and s is the initial period; h is health; R is the income rank

16. The maths continued….
Each term can be further split by the contribution made by the survivors and the non-survivors to the terms e.g.
……….

17. Direct and Indirect effect of deaths
In our decomposition there is a direct effect of death on income-related health mobility
But this direct effect is mostly cancelled out by the impact of the dead dropping out from the population
But there is also an indirect effect of death
Example
Consider a population with two rich and healthy people and two poor and sick people

18. Indirect effect of death
Rich & good health
Rich & v.good health
One rich person gets better health and one gets worse health
One poor person gets better health and one gets worse health
Rich & good health
Rich & fair health
Poor & fair health
Poor & good health
Poor & fair health
Poor & poor health
NO CHANGE IN INEQUALITY

19. Indirect effect of death
Rich & good health
Rich & v.good health
One rich person gets better health and one gets worse health
One poor person gets better health and one dies
If we just look at morbidity (and exclude the dead)
Income-related morbidity changes looks pro-poor
Selective mortality has an indirect effect on the appearance of morbidity changes
Rich & good health
Rich & fair health
Poor & fair health
Poor & good health
Poor & fair health
Poor & Dead

20. Longitudinal decomposition (6 components)
(income-related) Health Mobility
1a. Initial income rank is related with morbidity changes
1b. Initial income rank is related with mortality
(health-related) Income Mobility
2a. Impact of the dead dropping out of the initial income distribution on others’ income rank
2b. Initial health status is related with changes in income rank
(income and health) Co-movement
3a. How the two income and health move together
3b. Impact of the dead dropping out of the final population

21. Poor males getting sicker
Males - GB versus AUS
Measures are constructed so that positive values imply an impact of increasing inequalities
Poor males getting sicker
Age groups are based on initial year

22. Males - GB versus AUS Healthy males moving up the income distribution
Sick and poor males dying
Age groups are based on initial year

23. Males - GB versus AUS
The final alive population looks better if we ignore those that have died
Getting worse health associated with movement down the income distribution
Age groups are based on initial year

24. Conclusions
Examining cross-sectional relationships over time hide individual movements; especially if these patterns are in opposing directions
In middle ages there is some evidence of income-related morbidity changes (both GB and AUS)
Income-related mortality is a major component of changes in inequality over time and needs to be accounted for
From for middle-aged Australian men there was a stronger socioeconomic gradient in mortality than Great Britain
In Australia, health appears to matter at younger ages for moving up the income distribution (while more important in middle ages in GB)
For older male age groups in Australia the co-movement of health and income rank was stronger - might highlight weaker income protection for those who get sick?

25. Limitations
Largely a longitudinal accounting exercise – doesn’t explain the causes of changes over time just highlights where there might be interesting mechanisms

26. Further research
There is a need to further explore mechanisms which are causing these longitudinal relationships
Longitudinal relationship between income and health risk factors (smoking, alcohol consumption, obesity)
Understand the conditional uncertainty around different health and income trajectories individuals face and the extent to which this relates to socioeconomic position
Model the impact on health and health inequalities of policies and interventions

27. Thanks Acknowledgement
Discovery Early Career Researcher Award from the Australian Research Council
Thanks