Commentary: Plugging leaks and repelling boarders—where to next for the SS Income Inequality?

John Lynch1, Sam Harper1 and George Davey Smith2

1 Department of Epidemiology and Center for Social Epidemiology and Population Health, University of Michigan, USA.
2 Department of Social Medicine, University of Bristol, UK.

Correspondence: John Lynch, Center for Social Epidemiology and Population Health, University of Michigan, 1214 South University, Ann Arbor, MI 48104-2548, USA. E-mail: jwlynch{at}umich.edu

Keywords Income inequality, population health, race

Imagine that the research programme on income inequality and health is the ship ‘SS Income Inequality’. Think back to the launch ceremonies—enthusiastic passengers, a well-intentioned captain with a stout ship, on a journey full of promise. But then storms, arguments about the vessel’s sturdiness, leaks in the hull, attack by pirates, course alterations, and suggestions of sabotage by mutinous ex-crew members—you get the idea. This metaphor is used light heartedly as way of capturing some of the ‘to and fro’ within the research programme on income inequality and health and does not diminish anyone’s efforts to shed light on the important question of how income inequality might affect health.


    The current debate
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 The current debate
 Settling the dispute?
 Race/ethnic composition, income...
 How to understand race/ethnic...
 Charting a new course
 References
 
Subramanian and Kawachi’s paper is in response to the latest salvos from those claiming that ‘the emperor has no clothes’. It addresses the question of whether health effects of income inequality are merely markers for racial composition. The influential US economist Angus Deaton claimed that:

In the US, the relationship between income inequality and mortality is a mask for the effects of race; whites die younger in cities and states where there is a larger fraction of the population that is black.1

Deaton and Lubotsky explicated the argument as follows:

This divergent behavior of black and white incomes means that the income difference between blacks and whites is larger in cities with larger black populations, which is what induces the relationship between overall income inequality and racial composition. Of course, this does not [emphasis in the original] mean that racial composition and income inequality are the same thing, nor that either is an equally valid marker for the same underlying health risk.2

Subramanian and Kawachi responded that:

In other words, it has been claimed that the effect of ‘per cent black’ trumps the effects of state income inequality on health, and that the real culprit behind poor health achievement is racial heterogeneity, not income inequality per se.3

Deaton and colleagues had used individual and aggregate data for US states,4 and aggregate data for US states and cities2 to show there was no effect of income inequality on mortality risk after controlling for the proportion of African Americans living in the area. In their current paper, Subramanian and Kawachi use multilevel analysis of Census and Current Population Survey data to show that there is an effect of state-level income inequality on self-rated poor health, after adjustment for per cent black and an extensive array of other individual-level covariates including education, income, health insurance, and employment status. In other words, they find the opposite of Deaton and colleagues —racial heterogeneity (as measured by ‘per cent black’) does not trump the effects of income inequality on self-ratings of poor health. They conclude that their results ‘... at least in the case of the US, may settle some of the current disputes’.3

Subramanian and colleagues are no strangers to these skirmishes. They have previously engaged another pair of marauding economists—Jennifer Mellor and Jeffrey Milyo—who argued in several publications that there is no reliable effect of income inequality on health in the US either in time series analyses or after control for regional differences.5–7 In a recent interchange, Mellor and Milyo7 and Subramanian and colleagues8 reached opposite conclusions using the same data but employing different modelling strategies. Readers were left to adjudicate which one was correct. A case could be made that they both were, because they were asking somewhat different questions. In that case, Mellor and Milyo’s question was whether there was an effect of income inequality after controlling for unmeasured regional differences, so they used a fixed-effects model with regional dummy variables and arrived at a negative answer. Subramanian and colleague’s question was not really the same. They asked: after accounting for the geographical clustering of individuals in states and regions, was there an effect of income inequality on self-rated health? Thus, they set out to try to explain, rather than adjust for, the between-region variation. They used a fixed- and random-effects model that accounted for regional and state clustering to arrive at the opposite answer.


    Settling the dispute?
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 The current debate
 Settling the dispute?
 Race/ethnic composition, income...
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 Charting a new course
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So, does Subramanian and Kawachi’s current analysis settle this dispute with Deaton and colleagues over race/ethnic composition? There are two main issues. First, their analysis demonstrates that ‘per cent black’ does not remove the effects of income inequality on self-rated health. While this is convincing, it appears they did not adjust for mean income, as they have in previous analyses of US states.8 The income inequality effects in the current paper were of the order of odds ratio (OR) = 1.35 (after individual-level adjustments) while in a similar analysis of the same data that was adjusted for state mean income, the OR was around 1.18.8 Adjustment for some measure of average income has been standard in all studies on income inequality and health. In fact, in their analyses of self-rated health at the US metropolitan level they point out the importance of adjusting for mean income, as it confounds the association between income inequality and self-rated poor health.9 However, in the current paper they may have been concerned about using more than one or two second-level predictors, given they were analysing US states and so had only 50 second-level units. Thus, all we can conclude is that health effects of income inequality remain after adjustment for ‘per cent black’, but that this income inequality effect was unadjusted for mean income.

Second, the studies are difficult to compare because they differ in regard to the outcome, dataset, and modelling strategy—a fact Subramanian and Kawachi acknowledge in their paper. An important issue here relates to the imprecise way the word ‘health’ is used—not just in the literature related to income inequality but in the social determinants field in general. Deaton studied mortality and Subramanian and Kawachi studied self-rated health. The usual approach for justifying the use of self-rated health as a valid outcome—followed here by Subramanian and Kawachi—is to cite studies10,11 that show self-rated health is a strong predictor of mortality. Thus, the claim by Subramanian and Kawachi that their analysis settles the dispute relies on the assumption that mortality and self-rated poor health are reasonably interchangeable as outcomes.

Mortality and morbidity are both important population health indicators, but there are several issues that may raise difficulties in considering them as equivalent, especially in aetiological studies. First, social exposures associated with self-rated health may not be associated with mortality.12 For instance, while self-esteem was strongly associated with self-rated health, it did not predict mortality among a cohort of Finnish men.13 In The Netherlands, Mackenbach and colleagues showed how a range of psychosocial factors which were related to self-assessed poor health were not associated with mortality.14 Even with disease-specific measures it has been shown that morbidity and mortality do not measure the same thing. For example, in one study, self-reported cardiovascular disease morbidity was related to daily stress, whereas cardiovascular disease mortality was not.15 This suggests that a common tendency to report aspects of peoples’ lives as negative influences both the reporting of stress and the reporting of morbidity. Second, over the long term, mortality and morbidity transitions demonstrate countervailing trends, with declining mortality accompanied by increasing self-reported morbidity.16,17 This suggests that mortality and self-reported morbidity have somewhat different long-term determinants at the population level. The importance of determinants of trends has been demonstrated in regard to disentangling ischaemic and haemorrhagic stroke in relation to coronary heart disease trends. Lawlor and colleagues18 show how outcomes often considered closely related (stroke and heart disease) because they share common risk factors, have sub-components that show dramatically different long-term trends, and this implicates different determinants. Third, even contemporary short-term trends in self-rated poor health and mortality in the US do not follow the same pattern. Figure 1Go shows the association between mortality change and change in self-rated health for US states from 1993 to 1998. If anything the weak association suggests that states that experienced larger increases in self-rated poor health had larger declines in mortality. Fourth, social inequalities in mortality and morbidity can follow different trends.19 For instance, in Korea educational inequality in mortality remained constant during the 1990s, while inequality in self-rated poor health increased.20



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Figure 1 Relative changes in the all-cause age-adjusted (2000 standard) mortality rate and the proportion reporting their health as fair or poor, US states 1993–1998

 
Aetiological studies of the social determinants of health may be most informative when examining more specific rather than more general outcomes, such as all-cause mortality or self-rated health. Different causes of death have distinct aetiological pathways, so the mechanism through which a particular social factor is linked to heart disease may be different than the mechanism through which it is linked to homicide. This potentially important mechanistic specificity is masked by examining general outcomes such as all-cause mortality. What are the potential pathways to self-ratings of poor health? It is likely they are even more varied than the multiple pathways to mortality, and include a wide variety of symptomatic physical and psychosocial malaise. Understanding the complex pathways to self-reported morbidity is additionally complicated by the potential for reporting tendency to influence self-assessments of health.15,21 So the veracity of Subramanian and Kawachi’s claims about settling the dispute relies on the extent to which aetiological links between income inequality and mortality can be transposed onto links between income inequality and self-rated poor health. In other words, are the mechanisms the same, and with what time lags do they operate? These are important but unanswered questions.


    Race/ethnic composition, income inequality, and mortality
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 The current debate
 Settling the dispute?
 Race/ethnic composition, income...
 How to understand race/ethnic...
 Charting a new course
 References
 
So given the potential limitations imposed by Subramanian and Kawachi’s analysis of self-rated health, is there other evidence that income inequality and race/ethnic composition affect mortality? To date there are only five published studies that examine contextual effects of income inequality on individual mortality risk in the US. Three have largely negative findings,22–24 and Wolfson et al.’s study25 is actually a simulation that shows the potential for inequality to affect mortality over and above individual income. The best evidence for an effect of income inequality on mortality comes from the study by Lochner et al.,26 but even this is somewhat mixed. While there was an overall effect on mortality, it is odd that the strongest effects should be observed among the near poor (as opposed to the poor). Nevertheless, none of these studies examined the effect of racial composition on the link between income inequality and mortality. Thus, to date, Deaton’s unpublished study4 utilizing public-use data from the National Longitudinal Mortality Study (NLMS) is the only one that has directly examined the effect of race/ethnic composition on income inequality and individual mortality risk.

While not wishing to pre-empt publication in another journal, we can report that Backlund and colleagues have conducted the largest multilevel analysis of mortality to date.27 It uses the restricted files of the NLMS and employs innovative statistical methodology to increase power. This study sheds light on two issues of relevance. First, they do find an effect of income inequality on mortality, after adjustment for an extensive set of individual-level covariates, including income and race. However, this effect is only observed among those aged 25–64, with effects being considerably weaker among women. There is no income inequality effect among those older than 65. Second, the income inequality effect on working-age men is not explained by racial composition, although it does remove the weaker income inequality effect for women aged 25–64. It is important to note though, that race/ethnic composition of the state is associated with increased mortality risk after adjustment for individual covariates and income inequality, in all age-sex groups, although more weakly for older men. So, there is a little something for everyone here. There is an independent effect of income inequality especially among younger men (supporting Lochner et al.). This effect is not completely removed by adjustment for race/ethnic composition (supporting Subramanian and Kawachi), but nevertheless, the race/ethnic composition of the state remains important to understanding mortality risk in all race/ethnic groups (supporting Deaton and Lubotsky).


    How to understand race/ethnic composition and population health —a life-course approach
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 Settling the dispute?
 Race/ethnic composition, income...
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So what is it about the race/ethnic composition of places that could affect the health of everyone living there? That places with higher concentrations of racial/ethnic minorities, in some way, generate worse health for everyone is not a new observation.28,29 In 1950 Alfred Yankauer showed that infant mortality was higher for whites and non-whites in areas of New York with higher proportions of births to non-whites.30 In Deaton and Lubotsky’s aggregate analysis, they reject health services, education, and regional differences as explanations for the effect of race/ethnic composition. Instead they endorse a direct psychosocial explanation: that the physical presence of greater concentrations of non-white race/ethnic groups reduces trust in the community and induces stress that affects white mortality. However, the proportion of blacks in a state is correlated with many other factors. In a highly racialized society such as the US, places with more minorities tend to under-invest across a broad spectrum of infrastructure that may influence health for everyone via ‘spillover effects’ of racial discrimination. For instance, in states with higher proportions of blacks there are adverse conditions affecting whites as well, including greater overall poverty, lower average incomes, smaller monthly welfare support payments (but not welfare case loads), higher proportions of the population living in urban environments, more women without Medicaid insurance, less home ownership, less health insurance coverage, more female single-headed households, and lower educational attainment. This list is not dissimilar to the correlates of income inequality. Table 1Go shows the correlations between income inequality and a range of other characteristics of states presented by Kaplan et al.31 as potential pathways between income inequality and health. However, it is clear that such Census-derived aggregate variables can be highly collinear,32 and so more needs to be done to disentangle what this variable ‘per cent black’ actually stands for as an exposure.


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Table 1 Correlations between the proportion of household income received by the least well off 50% of households and selected characteristics of US states, adjusted for median income 1990a
 
In addition to its contemporary correlates, ‘per cent black’ also has historical correlates. The geographical distribution of African Americans is inextricably tied to more than 200 years of US history of slavery, economic and political development, and patterns of inter-state migration. Table 2Go shows that the ‘per cent black’ in US states in 1990 is correlated with their distribution across states in 1930 (r = 0.79). So whatever it may be that the variable ‘per cent black’ currently stands for, it additionally shows continuity over time, despite large inter-state migrations of blacks from the south and east, to the north and west that began after the First World War.33 Thus, historical conditions may also be relevant for understanding current geographical patterns of US mortality, because mortality statistics recorded in the 1990s are dominated by the life-course experiences of cohorts born between 1910 and the 1930. This population life-course view is analogous to an individual life-course approach, where conditions early in life may have relevance for health later in life.34,35 In this regard it is interesting that Table 2Go also shows that the current ‘per cent black’ is strongly correlated with levels of child illiteracy in 1930 (particularly for black children, r = 0.56)—a correlation as strong as with 1990 levels of education. So not only do states with higher ‘per cent black’ in 1990 have worse educational profiles in 1990, they had worse educational profiles in 1930—the time when those now appearing in mortality statistics were children. This may be partly why region of birth has been noted as a predictor of health outcomes in adulthood.36,37 Table 2Go also shows that educational conditions in 1930 are correlated with income inequality in 1990, suggesting that indicators of current socioeconomic conditions in a state are also related to historical social conditions.


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Table 2 Correlations between current and historical socioeconomic conditions, US states 1990
 
In aggregate analyses, Muller showed how adjustment for current levels of education across US states removed the effect of income inequality on mortality.38 Mackenbach pointed out that it is difficult to decide if aggregate differences in education should be considered a confounder or an intermediary.39 Kawachi et al. argued that adjustment for aggregate education in links between income inequality, social capital, and mortality may be inappropriate over-adjustment.40 On one hand this is reasonable —if income inequality is a marker for current social investments then it will affect current levels of equitable investments in education. While that is likely true, how then does income inequality affect social capital, education, and then mortality? If the current distribution of education is in the causal pathway, and that distribution reflects the differential educational experiences of multiple cohorts over time, how do we align that with the fact that mortality statistics are dominated by deaths among those aged over 65, who were educated 40 or more years earlier? The over-adjustment argument may be clearer within a life-course perspective. At the individual level, education is prior to income and the case has been made that education serves at least in part as a marker of early-life social conditions.41 At the population level, given the importance of education on earnings inequality,42 one could also argue that historical investments in education may affect subsequent distributions of income. It is interesting to note that the US regions with the highest levels of income inequality in 1990—the Northeast and the South—generally provided weaker support for higher education in the early 20th century.43,44 One could hypothesize that places that had historically higher average levels of education may be more likely, 20–50 years later, to have a more educated workforce that can generate higher average income levels, and secure more equal distributions of income, because for individuals, one of the main inputs into income generating potential is education.

So, the obvious question is whether levels of illiteracy in the 1930s help explain the effects of current income inequality and race/ethnic composition on US state mortality in 1990. They do not. There is no association between 1930 illiteracy and 1990 mortality after adjustment for current income inequality (though there is a residual effect for 1930 black illiteracy). However, we should keep in mind two things. First, illiteracy may not be the best historical indicator; even by 1930 it characterizes the educational experience of only about 10% of the population. Second, all-cause mortality may not be a specific enough outcome. Nevertheless, the enduring correlation of illiteracy with current per cent black, education, and income inequality suggests that longer-term processes affect current determinants of population health. Future research should explore a wider range of indicators of historical conditions. Additionally, being able to observe links between historical population conditions and subsequent mortality is complicated by inter- and intra-national migration that changes the composition of the population and distribution of population sub-groups over time. There is no doubt that this is the case for the US from the latter half of the 19th century into the early decades of the 20th century.45

Despite these negative results for illiteracy, we think there is value in examining historical population life-course processes. First, they have been shown to be useful in international comparisons46 where migration is less of an issue. Second, our earlier cross-national study47 does suggest an effect of income inequality on infant mortality that may be real and important, and this may point to the potential importance of life-course processes, in that the 99.5% babies who survive in high income inequality places may carry residual effects that for the other 0.5% of babies lead to death, and these residual effects may influence long-term population health. Finally, at the individual level there is increasing evidence for the importance of life-course processes.34 These individual-level life-course processes acting across successive birth cohorts are the bedrock of future patterns of population health.


    Charting a new course
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 The current debate
 Settling the dispute?
 Race/ethnic composition, income...
 How to understand race/ethnic...
 Charting a new course
 References
 
In addition to defending the US research agenda on income inequality and health, Subramanian and Kawachi advocate new ports-of-call for the SS Income Inequality. They argue8 that more work needs to be done to determine the settings in which income inequality is harmful to health and that we may have gone as far as we can with US studies. Their assessment is that income inequality does not appear to be uniformly harmful in all countries, or for all health outcomes. Studies conducted outside the US in relatively egalitarian societies do not support the income inequality hypothesis, suggesting that there may be income inequality thresholds for health effects. They echo some of our earlier ideas47–50 that health effects of income inequality are contingent, especially on the outcome, and on the extent and equitable distribution of welfare state protections for the most vulnerable members of society—social investments we termed neo-material infrastructure. We are glad that they also endorse these positions.

Subramanian and colleagues argue that we should search for new evidence, ‘particularly in parts of the world that are even more unequal than the United States’.8 However, before setting sail on this new course we should recognize that this amounts to ‘shifting the goalposts’. Such a strategy may well be informative, but the original income inequality hypothesis was intended to explain between and within country health differences among wealthy nations, where gross domestic product was not as influential for population health.50–54 So this is more than simply a course correction—in practice it turns the original logic on its head. As the US is the most unequal of the wealthy nations, the countries with more inequality than the US will necessarily be less wealthy than industrialized western nations. Thus, this new strategy effectively means that health effects of income inequality are apparently now to be understood among less, rather than more, wealthy countries. To illustrate the implications, we took data from the Luxembourg Income Study55—the most reliable international source on income inequality. This database does not cover all countries, but Table 3Go shows that of the 29 countries included, there are only two with higher levels of income inequality than the US—Russia and Mexico—and 26 with lower income inequality. According to their GDP per capita ranking, this list of 26 countries includes 16 of the world’s richest 20 nations.


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Table 3 Income inequality and GDP rank for countries contained in the Luxembourg Income Study (LIS) database
 
Do Subramanian and colleagues now believe that research on income inequality is of limited relevance to understanding population health in these 26 less unequal, predominantly wealthy countries? If so, then that is important, because it is consistent with our assessments, and those of Mackenbach,39 that evidence for the income inequality hypothesis (at least the original version) is weak, beyond its important mechanical effects on individual income.56–58 In fact, Subramanian and colleagues’ own assessment of the current state of evidence was that ‘there are somewhat more negative studies than there are positive studies’.8 Thus, given their apparent agreement on the relative paucity of evidence, we are not sure why, in their current paper, they contradict our earlier International Journal of Epidemiology commentary.58 We nevertheless stand by our conclusion, that among wealthy nations, there is little solid evidence for the negative health effects of income inequality per se, outside of a handful of US studies on self-rated health, and even this association depends on the level of aggregation of income inequality. A study by Blakely and colleagues found no effects of income inequality on self-rated health in US metropolitan areas.9

So, we are a bit confused as to where Subramanian and colleagues really stand. On one hand, in apparent defence of the SS Income Inequality, they repel attacks by pirates and back-hand sceptical mutineers. On the other hand, they give the impression that it is time to down-size operations in the US and move onto less wealthy, more unequal countries where income inequality is more likely to be expressed in population health. They may well be correct that investigations of this new version of the income inequality hypothesis will be informative. Indeed, we will be surprised if evidence cannot be found that income inequality is important in determining some health outcomes in some contexts. Nevertheless, there remain difficult issues for the research on income inequality and health. Two of the most salient will be availability of appropriate data and adequately incorporating appropriate time lags in regard to specific outcomes.48,59,60 Neither of these will be solved by looking at more unequal, less wealthy countries. Figure 2Go shows male and female mortality and income inequality in Korea (1996–2000). As we have already shown in the US61 and the UK,50 income inequality and mortality trends move in opposite directions—even in less wealthy countries such as Korea—which has comparable levels of income inequality to the US. This remains one of the most important challenges for understanding how income inequality may affect aspects of population health in rich or poor countries.



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Figure 2 Trends in male and female all-cause mortality (ages 15–74) and income inequality, Korea, 1996–2000

Data from:

Korea National Statistical Office. Household Income and Expenditure Survey. 1996–2000.

Korea National Statistical Office. The Cause of Death Statistics. 1996–2000.

 


    Acknowledgments
 
We would like to thank Professor Young-Ho Khang from Ulsan University Medical School for kindly supplying us with the income inequality and mortality data for Korea. John Lynch and George Davey Smith are supported by an Investigators Award in Health Policy from the Robert Wood Johnson Foundation. John Lynch and Sam Harper were also supported by grants from the US National Institutes of Health (RO1 HD35120–01A2; P50 HD38986–01). In addition, this paper has been facilitated by the European Science Foundation Program on Health Variations, of which John Lynch and George Davey Smith were members of the Lifecourse Working Group.


    References
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 The current debate
 Settling the dispute?
 Race/ethnic composition, income...
 How to understand race/ethnic...
 Charting a new course
 References
 
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