Global variations in health and mortality:
evaluating the relative income hypothesis
(also see video version)
http://www.cmm.bristol.ac.uk/learningtraining/videos/index.shtml
Min-Hua Jen and
Kelvyn Jones School of Geographical Sciences
LEMMA, University of Bristol
Structure of talk
• The changing patterns of global life expectancy,
1970-2000?
• The relative income hypothesis as an
explanation, but the problem of aggregate
analysis
• Evaluating the relative income hypothesis using
the World Value Survey: a combined micro and
macro analysis
Latent group trajectory models
Key question: are there groups of countries with a similar
underlying and distinctive trajectory?
•
•
•
•
INPUT (simplest possible mode)
Yij
Life expectancy for time i in place j
Tij
Time for occasion i in place j:1970 to 2002
How many latent groups? Fit sequence 1, 2…
The order of the polynomial for trends; usually LE 3
OUTPUT
• Polynomial trends for each group with a distinctive trajectory
• BIC: a goodness of fit measure, penalized by complexity of
model; using here in a highly exploratory mode!
One and two group solutions
One trajectory
Life expectancy
80
70
60
50
40
1970
1980
1990
2000
Year
80
G1
Life expectancy
G2
70
Two trajectories
60
50
40
1970
1980
1990
Year
2000
Latent trajectory models: global mortality
Life expectancy
80
G1
G2
G3
G4
G5
G6
G7
G8
G9
70
60
50
G10
40
1970
1980
1990
Year
2000
Ten group solution
So Far
• Substantial differences between countries
• Evidence of growing differences between countries in recent years
• Strong ‘macro geography’
eg
continuing improvement in W Europe and N America;
major improvement in North Africa and the Middle East;
stagnation in the western former satellite Soviet states;
major decline in the former Soviet Union.
• In search of an explanation for differences in advanced
economies…………The relative income hypothesis
Relative income hypothesis
Development of the argument by Richard Wilkinson in
‘Unhealthy societies’ (1996) ‘Mind the gap’ (2000)
• Very highly summarised here …
1 Income and health: within-country relations
Age adjusted mortality of 300k white
American men by median family
income;
marked negative relationship; poor
die young
Relative income (continued)
2: GNP and health: between-country relations
Life expectancy and GNP per capita in OECD
countries, 1993
In advanced economies: no relation
3 Income inequality and Health
Life expectancy and income
distribution in developed countries
Most egalitarian: live longer
Relative income Summary of the argument
• In the developed world, it is not the richest countries which have the best
health, but the most egalitarian (US, 1996,3)
• ‘where income is related to social status, as it is within countries, it
is also related to health. Where income differences mean little or
nothing for people’s position in the social hierarchy (such as those
between countries) income makes little difference to health. This
strongly implies that psychosocial pathways are important
(MTG,2000, 10-11)
•‘Income distribution is linked to social cohesion which is turn is linked
to mortality’ (US, 1996,ix)
Relative income (continued)
Underlying psycho-social model
Income
inequality
Material
Circumstances
Dominance
Hierarchy
Health
Inequality
Gaps in
Social Status
Social Cohesion
Social Capital
Societal
Structures
Anxiety
Stress
Violence
Life
Expectancy
Psycho-social Outcomes
Pathways
But: The problem of aggregate analysis
Artificial relation between
mortality and inequality
Simulated non-linear relation
between income and mortality
0.38
Mean probability of death
Probability of death
0.6
0.5
0.4
0.3
0.37
0.36
0.35
0.34
0.2
500
1000
Individual Income
1500
Multilevel analysis
0.33
0
10
20
30
Coefficient of variation
NO between
country
Variation
40
50
Evaluating the Relative income hypothesis
• Requires
micro and macro analysis simultaneously
• Are there effects for income inequality (macro) after
taking account of individual income (micro)
• Last ten years, dozens of studies have done this with
mixed results
•But none done at the scale at which Wilkinson made the
original argument: Countries
•The World Value Survey…………..have to use self-rated
health; no mortality data with individual income exists on a
global basis
The World Values Survey
• Response: “All in all, how would you describe your state of health
these days? (Good, Fair and Poor) with Good as the base.
•Structure: respondents with 4 waves (1981, 1990, 1995, 2001) in 69
countries. a representative national sample of at least 1,000 people,
(not every country is included at each wave)
•Predictors
-Micro: household income, age, sex and marital status
-Macro: World Bank country-level income data on GDP per capita (in
purchase power parity for 2004 US dollar) on an annual basis; income
inequality data comes from UTIP-UNIDO project ( University of Texas)
-Modelling : 3- level (170k respondents, 4 waves, 69 countries)
multilevel multinomial model fitted in MLwiN
The World Values Survey Some results
• Differences between countries (after taking account of age and sex)
•13 fold difference between
countries with the most and
least poor health (Ukraine
versus Switzerland)
Effects for Individual variables
• Differences between countries
• Individual Income: nonlinear ‘dose response’
Effect of inequality
• After taking account of
individual income
• Above threshold:
Unequal countries have
lower odds of reporting
poor cf good health!
Essentially flat relation
• for no income group is
the relation as posited by
Wilkinson
Conclusions
• Substantial differences between countries in self-reported health
after taking account of age and sex
• Individual income has clear effect: poorer people report worse
health
•Income inequality does not have the hypothesized pattern of
egalitarian societies reporting better health
• There remains substantial differences between countries even
after taking account of micro and macro variables; in particular the
Former Communist report high levels of poor health
• Problem of data quality…European Household Panel Survey