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Education, income inequality, and mortality: a multiple regression analysis

BMJ 2002; 324 doi: (Published 05 January 2002) Cite this as: BMJ 2002;324:23

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Dr. Muller’s response to electronic comments

Makenbach correctly points out that the causal order of income
inequality and educational attainment is debatable in my cross-sectional
analysis. He writes that educational attainment can be viewed as a
“confounder, or an intermediary between income inequality and mortality.”
To shed some light on this issue, I related age-adjusted mortality rates
(1989-90) to Gini coefficients for families in 1969. The lagged income
inequality measure accounts for 10.7% of the variation in age-adjusted
mortality rates. The addition of the educational attainment measure
increased the value to 54.7%. The 44-percentage point increase in
accounted variance suggests that education makes an independent
contribution and appears to be a strong confounder.

Parker raises the question whether educational inequality is
considered in the regression analysis. The answer is: yes in a simple
way. States with low high school completion rates also tend to have low
completion rates for advanced degrees. In an earlier draft of the paper I
included a second educational attainment measure, the percent of persons
18 years of age and older with associate and higher educational degrees.
The variable had the expected negative effect on mortality, however the
measure was statistically insignificant when the analysis was weighted by
population size. Dropping the second education variable did not alter the
main finding of the study.

Jerret cautions that the disappearance of the income inequality
effect may be due to spatial autocorrelation bias. I examined this
possibility by looking at contiguous and non-contiguous states (AL, AR,
IA, KS, KT, ME, MI, MD, NV, NM, NY, ND, SC, WA, WY). A regression model
allowing for different slopes showed no statistically significant
differences between state types for the income inequality, the educational
attainment, and the per capita income measure. This suggests that spatial
autocorrelation may not be a problem, although the issue deserves further

Howard suggests an unexpected interpretation of the strong education-
mortality effect reported in my paper. He argues that differences in
testosterone levels among racial groups may account for the effect.
Several findings do not agree with his interpretation. A substantial
education-mortality effect is present among females (b=7.05, se=1.42) who
produce substantially lower amounts of testosterone than men. A further
analysis by race indicates that the education-mortality effect is quite
similar for the Caucasian (b=6.47; se=1.11) and the African American
population (b=7.51; se=1.67). The similarity in size of the regression
coefficients suggests that the education-mortality effect is unlikely to
reflect racial differences in testosterone levels.

According to Wilkinson’s comments, the substantial education-
mortality effect represents contextual effects of social status and
dominance that may result in higher rates of violence, and perhaps other
forms of social dysfunction. This prediction may be correct, but will
need to be empirically tested. We should not ignore potentially simpler
explanations of the same phenomenon. Part of the education-mortality
effect might simply be a reflection of conditions typically associated
with low levels of education: unstable employment in low wage jobs,
employment in riskier jobs, inadequate access to health care, smoking,
unhealthy diet, living in areas lacking health promoting resources.
Exposure to these conditions over a lifetime will deplete the stock of
health and shorten life span. There is a need for more detailed analyses
that sort out the relative importance of what may turn out to be different
aspects of a broader explanation.

Competing interests: No competing interests

17 February 2002
Andreas Muller
Department of Health Services Administration
University of Arkansas at Little Rock