Does Early-Life Income Inequality Predict
Later-Life Self-Reported Health?
Evidence from Three Countries
Dean R. Lillard1,3, Richard V. Burkhauser2,3,4, Markus
H. Hahn4 and Roger Wilkins4
1Ohio
State University, 2Cornell University, 3DIW-Berlin, 4Melbourne
Institute, University of Melbourne
July 2013
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Introduction – What’s the link between
inequality and health? (And why does it matter?)
Hypothesised effects
(Leigh, Jencks and Smeeding, 2009)
• Absolute income hypothesis (health concave in income)
• Relative income (or relative deprivation) hypothesis (“status
anxiety” – chronic stress from relative deprivation)
• Violent crime (including second-order effects on stress)
• Public spending (not necessarily only health-related)
• Social capital and trust (“income inequality hypothesis” of
Wilkinson (1996) – various mechanisms, including effects
on demands for public spending)
(Matters for both health policy and redistribution policy)
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Empirical evidence – Earlier studies
(mostly 1980s and 1990s)
(+) Infant mortality
(–) Life expectancy
(–) Average age at death
(+) Mortality risk
(–) Self-reported health
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Shortcomings of older literature
• Cross-sectional data
• Health usually measured by aggregate statistic for
whole country
• Not always comparable across countries
• Often for single or limited number of years
• Failure to account for substantial heterogeneity (lack
of controls)
• Weak theoretical support
• Relates current health to current inequality
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More recent studies
• Individual-level data – better controls
• Better / Alternative health measures
• Better / Alternative inequality measures
 More mixed results
These include studies:
• Using panel data on self-reported health (Weich, Lewis,
and Jenkins 2002; Lillard and Burkhauser 2005; Lorgelly and
Lindley 2008; Bechtel et al. 2012)
• Using alternative measures of inequality
Including data from tax records (Leigh and Jencks 2007)
• Examining lagged effects (Blakely et al.,2000; Mellor and
Milyo, 2003; Karlsson et al. 2010)
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Our contribution
Combine:
1. Panel data on self-rated health from three countries
Australia, Great Britain, United States
2. New long-run country-level inequality measure
from administrative tax records
Investigate whether there is a link between early-life
inequality (average in first 20 years of life) and later-life
self-reported health
What is the potential mechanism?
Public spending / immunisation etc. most important when
young – that is, health investments when young an important
determinant of health in adulthood.
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Data (other than for early-life inequality)
• US Panel Study of Income Dynamics (PSID)
• British Household Panel Study (BHPS)
• Household, Income and Labour Dynamics in Australia
Survey (HILDA)
Sample selection
PSID: 1984 to 2009
BHPS: 1991 to 2008
HILDA: 2001 to 2011
Native-born individuals aged 21 and older
Born after tax data first observed
Britain:
US:
Australia :
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1908
1913
1921
Health measure
5-point scale in all countries:
PSID: Would you say your health in general is excellent, very
good, good, fair or poor?
BHPS: Please think back over the last 12 months about how
your health has been. Compared to people of your own age,
would you say that your health has on the whole been
excellent, good, fair, poor or very poor?
HILDA: In general, would you say your health is excellent, very
good, good, fair or poor?
Limitations:
• Not entirely certain what is being measured, especially by
HILDA and PSID (time frame, reference point)
• Potential endogeneity (eg, Johnston et al., 2009)
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Health measure distribution (%)
Males
Excellent (Excellent)
Very good (Good)
Good (Fair)
Fair (Poor)
Poor (Very poor)
US
25.5
33.0
26.7
10.6
4.2
GB
26.7
45.1
19.8
6.6
1.8
AU
10.3
35.1
36.9
14.4
3.3
GB
22.7
44.9
21.6
8.5
2.4
AU
10.5
36.3
35.6
14.5
3.2
Females
Excellent (Excellent)
Very good (Good)
Good (Fair)
Fair (Poor)
Poor (Very poor)
(GB categories in parentheses)
US
19.7
31.5
31.1
13.0
4.8
Inequality data
Tax records  Income share of the top 1%
Available from early 20th century to present day
• Data for AU from Burkhauser, Hahn and Wilkins (2013)
• Data for GB and US from Top Incomes Database on the Paris
School of Economics web site
• Excludes capital gains in AU and US; some of GB series
includes some capital gains
Inequality variable: Average income share of the top
1% over the first 20 years of life
Each birth cohort has the same value. Identification comes
from temporal variation.
Age can be controlled for because we have multiple years of
data on self-reported health
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Inequality data – Limitations
• Pre-tax income
• Sensitive to the personal income tax base
• Tax unit differs across countries and time:
Australia – individual
GB – family until 1989, individual after
US – family
• Top income share is correlated with measures of
overall income inequality such as the Gini
coefficient, but it’s not the same thing (Leigh,
2007)
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Average income share of the top 1% in the first
20 years of life, by birth year
25
20
15
%
10
UK
5
USA
Australia
1908
1911
1914
1917
1920
1923
1926
1929
1932
1935
1938
1941
1944
1947
1950
1953
1956
1959
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
0
Birth year
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Empirical strategy
Estimate ordered probit models
Start with parsimonious model and progressively
add controls
M1:
M2:
M3:
M4:
Early life inequality and time/period controls only
M1 + age controls
M2 + permanent household income
M3 + Father’s education and occupation
Permanent income: Log of average equivalised income over all
years up until two years before health measured
Also control for the number of years over which permanent income
measured
Father’s education and occupation: Proxies for early-life
economic resources
Cluster on birth year
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Results (coefficient estimates) – Men
M1
M2
M3
M4
Sample
size
-0.1196***
-0.0394***
-0.0329***
-
101,743
-0.0553***
-0.0171**
-0.0072
-0.0064
78,419
-0.1393***
0.0190
0.0483***
0.0440**
38,036
Time controls
Yes
Yes
Yes
Yes
Age controls
No
Yes
Yes
Yes
Permanent income
No
No
Yes
Yes
Early-life income
No
No
No
Yes
US
Early-life inequality
Britain
Early-life inequality
Australia
Early-life inequality
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Results (coefficient estimates) – Women
M1
M2
M3
M4
Sample
size
-0.1162***
-0.0456***
-0.0280***
-
124,806
-0.0526***
-0.0121**
0.0015
0.0018
91,184
-0.1483***
0.0055
0.0108
0.0087
43,941
Time controls
Yes
Yes
Yes
Yes
Age controls
No
Yes
Yes
Yes
Permanent income
No
No
Yes
Yes
Early-life income
No
No
No
Yes
US
Early-life inequality
Britain
Early-life inequality
Australia
Early-life inequality
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Mean marginal effects of early-life
inequality – US (Model 3)
Men
Women
Excellent
-0.0096***
-0.0070***
Very good
-0.0019***
-0.0029***
Good
0.0048***
0.0036***
Fair
0.0042***
0.0040***
Poor
0.0024***
0.0023***
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Robustness checks and caveats
• Restrict to the 2001-2009 period for all countries
• Restrict to the 1991-2009 period for US and GB
• Alternative specifications of time effects
• Alternative specifications of age effects
Yet to examine whether US result robust to inclusion of
measures of early-life income.
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Discussion
Focus on current inequality and current health
theoretically weak
We find evidence that early-life inequality matters in
the US
Permanent income and early-life income also appear
to matter
Further work:
• Early-life income measure for US
• Consider differences in effects of early-life income
inequality by level of early-life income
• Consider inequality at other ages
• Explore other (objective?) measures of health (but
data limitations)
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