Dean R. Lillard 1,3 , Richard V. Burkhauser 2,3,4 , Markus H. Hahn 4 and Roger Wilkins 4

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

www.fbe.unimelb.edu.au

Does Early-Life Income Inequality Predict Later-Life Self-Reported Health?

Evidence from Three Countries

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)

Introduction – What’s the link between inequality and health? (And why does it matter?)


(+) Infant mortality

(–) Life expectancy

(–) Average age at death

(+) Mortality risk

(–) Self-reported health

Empirical evidence – Earlier studies (mostly 1980s and 1990s)


• 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

Shortcomings of older literature


• 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 inequalityIncluding data from tax records (Leigh and Jencks 2007)

• Examining lagged effects (Blakely et al.,2000; Mellor and Milyo, 2003; Karlsson et al. 2010)

More recent studies


Combine:1. Panel data on self-rated health from three

countriesAustralia, 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.

Our contribution


• US Panel Study of Income Dynamics (PSID)• British Household Panel Study (BHPS)• Household, Income and Labour Dynamics in

Australia Survey (HILDA)

Sample selectionPSID: 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: 1908 US: 1913Australia : 1921

Data (other than for early-life inequality)


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)

Health measure


Health measure distribution (%)Males

US GB AU

Excellent (Excellent) 25.5 26.7 10.3

Very good (Good) 33.0 45.1 35.1

Good (Fair) 26.7 19.8 36.9

Fair (Poor) 10.6 6.6 14.4

Poor (Very poor) 4.2 1.8 3.3

FemalesUS GB AU

Excellent (Excellent) 19.7 22.7 10.5

Very good (Good) 31.5 44.9 36.3

Good (Fair) 31.1 21.6 35.6

Fair (Poor) 13.0 8.5 14.5Poor (Very poor) 4.8 2.4 3.2(GB categories in parentheses)

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

Inequality data


• Pre-tax income

• Sensitive to the personal income tax base

• Tax unit differs across countries and time:

Australia – individualGB – family until 1989, individual afterUS – 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)

Inequality data – Limitations



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

5

10

15

20

25

Average income share of the top 1% in the first 20 years of life, by birth year

UKUSAAustralia

Birth year

%

Estimate ordered probit modelsStart with parsimonious model and progressively add controls

M1: Early life inequality and time/period controls onlyM2: M1 + age controlsM3: M2 + permanent household incomeM4: 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

Empirical strategy


Results (coefficient estimates) – Men


M1 M2 M3 M4 Sample size

US

Early-life inequality-

0.1196***

-0.0394**

*

-0.0329**

*- 101,743

Britain


0.0553***

-0.0171** -0.0072 -0.0064 78,419

Australia


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

Results (coefficient estimates) – Women


M1 M2 M3 M4 Sample size

US


0.1162***

-0.0456**

*

-0.0280**

*- 124,806

Britain


0.0526***

-0.0121** 0.0015 0.0018 91,184

Australia


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

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***

• 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.

Robustness checks and caveats


Focus on current inequality and current health theoretically weakWe find evidence that early-life inequality matters in the USPermanent 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)

Discussion


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Dean R. Lillard 1,3 , Richard V. Burkhauser 2,3,4 , Markus H. Hahn 4 and Roger Wilkins 4

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