Income inequality and mental health--empirical evidence from Australia

The causal association between absolute income and health is well-established; however, the relationship between income inequality and health is not. The conclusions from the received studies vary across the region or country studied and/or the methodology employed. Using the Household, Income and Labour Dynamics in Australia panel survey, this paper investigates the relationship between mental health and inequality in Australia. A variety of income inequality indices are calculated to test both the income inequality and relative deprivation hypotheses. We find that mental health is only adversely affected by the presence of relative deprivation to a very small degree. In addition, we do not find support for the income inequality hypothesis. Importantly, our results are robust to a number of sensitivity analyses.     

Income inequality and mortality in Italy

BACKGROUND: The relationship between income inequality and health remains controversial in terms of whether or not it exists and, if so, its extent and the mechanisms involved. This study examines the relationship between income inequality, as indicated by the Gini coefficient, and mortality in Italy. METHODS: Cross-sectional ecological study on the 57,138,489 inhabitants living in the 95 provinces existing in Italy in 1994. Multivariate weighted regression analysis of total and age-specific mortality, income inequality, gender, and interaction between income inequality and median income or geographical area. RESULTS: A positive association between income inequality and total mortality was observed for both genders in provinces with a low per capita income and in Southern and Central Italy. The effect was present for infants and for persons over 24 years of age; it was marked for the elderly, particularly women. A negative association with mortality was observed for males living in the North-west. Interactions between income inequality and median income, and between income inequality and geographical area were found. CONCLUSION: In Italy, the relationship between income inequality and health is mixed and not universal, in so far as a positive association was observed only in provinces with lower absolute income. Elderly persons living in Southern Italy represent the population subgroup most vulnerable to unequal income distribution. Income inequality can, in part, explain the historically higher mortality among women in Southern Italy compared to women in the North. These results indicate that income inequality affects the health of population subgroups differentially.     

Income inequality and mortality in England.

BACKGROUND: Despite the increasing evidence that income inequality causes reductions in life expectancy in developed countries, this relationship has not been explored in the United Kingdom, where local income data are not routinely available. We have surmounted this problem by employing an ecological design which applies national income data to local mortality and occupational data. METHODS: This ecological, cross-sectional study used 1991 mortality and Census data on the 366 English local government districts, and 1991 New Earnings Survey data for England, to determine the independent effect of income inequalities within English local authorities on the variation in all cause mortality between them. The subjects were all men and women recorded as economically active in the 1991 Census. We carried out linear regression analyses between all cause, all ages standardized mortality ratios, income inequality indexes and mean income levels of the local government districts. Results Both income inequality and mean income were independently associated with mortality. CONCLUSIONS: It is likely that income inequality makes an independent contribution to life expectancy in English local authorities. This finding adds further to the international evidence supporting the potentially positive health impact of increasing the scale of redistributive fiscal policies.     

Income inequality and socioeconomic gradients in mortality

Objectives. We investigated whether the processes underlying the association between income inequality and population health are related to those responsible for the socioeconomic gradient in health and whether health disparities are smaller when income differences are narrower.Methods. We used multilevel models in a regression analysis of 10 age- and cause-specific US county mortality rates on county median household incomes and on state income inequality. We assessed whether mortality rates more closely related to county income were also more closely related to state income inequality. We also compared mortality gradients in more- and less-equal states.Results. Mortality rates more strongly associated with county income were more strongly associated with state income inequality: across all mortality rates, r= −0.81; P=.004. The effect of state income inequality on the socioeconomic gradient in health varied by cause of death, but greater equality usually benefited both wealthier and poorer counties.Conclusions. Although mortality rates with steep socioeconomic gradients were more sensitive to income distribution than were rates with flatter gradients, narrower income differences benefit people in both wealthy and poor areas and may, paradoxically, do little to reduce health disparities.The nature of the association between income distribution and population health is of crucial importance to public health. The United States has wider income differences and a lower life expectancy than other developed countries, which appears to fit the international correlation between the 2.1 Likewise, life expectancy among the 50 states also appears to be correlated with state income distribution.2 However, despite a growing body of research and supportive findings from a large majority of published papers, disagreement remains about whether income inequality is a determinant of population health,3^,4 the nature of the processes through which it might influence population health, and how those processes might be related to the causes of health inequalities. A deeper understanding of these issues may pave the way for policymakers to improve population health and reduce health inequalities.An initially plausible hypothesis is that both the socioeconomic gradient in health and the association between health and income distribution reflect the effects of socioeconomic disadvantage on health; if narrower income differences reduce disadvantage, they might improve average health by reducing health disparities. However, it is also possible that any effects of income distribution could reflect quite separate causal processes from those responsible for the socioeconomic gradient in health. Although health disparities are sometimes attributed primarily to material and behavioral factors such as smoking, diet, bad housing, and lack of health care,5^–7 we and others have suggested that income inequality is more likely to influence health through processes of social comparison.8^–10 It is even possible that there are 2 completely separate domains: health inequalities may reflect the direct effects of material living standards, and income inequality may reflect the psychosocially mediated effects of social comparisons.Determining whether income inequalities work through the same or different processes from those responsible for health disparities is complicated by our lack of precise knowledge of the causal processes for either. Therefore, it would be informative to examine whether mortality rates that have steep socioeconomic gradients are also those most strongly related to income inequality and whether mortality rates that have little or no socioeconomic gradient have little or no relation to income inequality. Are mortality rates that are sensitive to the causes of health disparities also sensitive to income inequality?     

Income inequality and child mortality in wealthy nations

BACKGROUND: Relationships between income inequality and various health indicators have been the subject of much study and some controversy. We investigated associations between child mortality and income inequality amongst the wealthier OECD countries as well as changes in their relative child mortality rankings over time. METHODS: Data were drawn from the 2003-2006 'State of the World's Children' reports published by UNICEF; Gini coefficients on income inequality were also used. Pearson correlation coefficients were calculated to investigate associations. Longitudinal child mortality data was used to compare rankings. RESULTS: We discovered very strong associations between child mortality and income inequality. In contrast to earlier results, these associations persist when the USA is excluded from the analysis. The countries with the worst child mortality figures were previously singled out in a 1993 UNICEF study on child neglect in rich nations. We also report their worsening child mortality rankings, since 1960, relative to the other OECD countries. CONCLUSIONS: The results strengthen the existing evidence linking child mortality with income inequality in wealthy nations, and add to the evidence that sociopolitical factors are important in this regard.     

Income inequality and infant mortality in New York City

A series of studies have demonstrated that people who live in regions where there are disparities in income have poorer average health status than people who live in more economically homogeneous regions. To test whether such disparities might explain health variations within urban areas, we examined the possible association between income inequality and infant mortality for zip code regions within New York City using data from the 1990 census and the New York City Department of Health. Both infant mortality and income inequality (percentage of income received by the poorest 50% of households) varied widely across these regions (range in infant mortality: 0.6–29.6/1,000 live births: range in income inequality: 12.7–27.3). An increase of one standard deviation in income inequality was associated with an increase of 0.80 deaths/1,000 live births (P<.001), controlling for other socioeconomic factors. This finding has important implications for public health practice and social epidemiological research in large urban areas, which face significant disparities both in health and in social and economic conditions. Data collection and initial analyses for this project were supported in part by an Investigator Award in Health Policy Research from The Robert Wood Johnson Foundation. The views expressed are those of the authors and do not imply endorsement by The Robert Wood Johnson Foundation.     

Income, inequality and mortality in 14 developed countries

The tendency for more egalitarian societies to have lower mortality rates has been identified in international data and subsequently confirmed in analyses of areas within countries, particularly within the USA. However, recent reports using data on OECD countries from the Luxembourg Income Study (LIS) suggest this relation no longer exists. We investigated whether the shift in relative poverty from elderly people (with high death rates) to young families (low death rates) may have affected the associations. Using age- and sex-specific mortality among 14 OECD countries in relation to income inequality, median income and absolute and relative poverty, we found that wider income distribution is related to higher premature mortality, and higher age-specific mortality rates below, but not above, age 65 years. Absolute income levels showed no consistent relation to mortality. The changing age distribution of relative poverty may have affected the way income inequality impacts on mortality measured across all ages.     

Income inequality and mortality in U.S. cities: Weighing the evidence. A response to Ash

Deaton and Lubotsky (2003) found that the robust positive relationship across American cities between mortality and income inequality became small, insignificant, and/or non-robust once they controlled for the fraction of each city's population that is black. Ash and Robinson (Ash, M., & Robinson D. Inequality, race, and mortality in US cities: a political and econometric review. Social Science and Medicine, 2009) consider alternative weighting schemes and show that in one of our specifications, in one data period, and with one of their alternative weighting schemes, income inequality is estimated to be a risk factor. All of our other specifications, as well as their own preferred specification, replicate our original result, which is supported by the weight of the evidence. Conditional on fraction black, there is no evidence for an effect of income inequality on mortality.     

Income inequality and population health: a review and explanation of the evidence

Whether or not the scale of a society's income inequality is a determinant of population health is still regarded as a controversial issue. We decided to review the evidence and see if we could find a consistent interpretation of both the positive and negative findings. We identified 168 analyses in 155 papers reporting research findings on the association between income distribution and population health, and classified them according to how far their findings supported the hypothesis that greater income differences are associated with lower standards of population health. Analyses in which all adjusted associations between greater income equality and higher standards of population health were statistically significant and positive were classified as "wholly supportive"; if none were significant and positive they were classified as "unsupportive"; and if some but not all were significant and supportive they were classified as "partially supportive". Of those classified as either wholly supportive or unsupportive, a large majority (70 per cent) suggest that health is less good in societies where income differences are bigger. There were substantial differences in the proportion of supportive findings according to whether inequality was measured in large or small areas. We suggest that the studies of income inequality are more supportive in large areas because in that context income inequality serves as a measure of the scale of social stratification, or how hierarchical a society is. We suggest three explanations for the unsupportive findings reported by a minority of studies. First, many studies measured inequality in areas too small to reflect the scale of social class differences in a society; second, a number of studies controlled for factors which, rather than being genuine confounders, are likely either to mediate between class and health or to be other reflections of the scale of social stratification; and third, the international relationship was temporarily lost (in all but the youngest age groups) during the decade from the mid-1980s when income differences were widening particularly rapidly in a number of countries. We finish by discussing possible objections to our interpretation of the findings.     

Income inequality and health. Coastal communities in British Columbia,Canada

OBJECTIVE: An imbalance in the distribution of economic resources, i.e., income inequality, is a characteristic of a community that may influence the aggregate health of the population. In North America, income inequality seems to be strongly related to mortality rates among American communities such as states and metropolitan areas but largely irrelevant for health at similar levels of geopolitical aggregation in Canada. This article summarizes relevant international and North American evidence and then explores relationships between income inequality and mortality rates among coastal communities in the province of British Columbia, Canada. METHODS: Cross-sectional analysis was conducted among twenty-four coastal communities in British Columbia, utilizing four measures based on the 1996 Census to measure income inequality and crude, age-standardized and age- and gender-specific mortality rates averaged over the five-year period 1994-98 to measure health. RESULTS: The three valid measures of income inequality were positively and significantly related to the crude mortality rate but were not significantly related to the age-standardized mortality rate. Two of the inequality measures were related to mortality rates for males aged 0-44 and for males aged 45-64 before but not after controlling for mean household income. DISCUSSION: Health researchers have yet to report a meaningful relationship between income inequality and population health within Canada. At the risk of committing the ecological fallacy, these findings provisionally support a psycho-social interpretation of the individual-level relationship between income and health wherein members of these communities compare themselves to an encompassing community, e.g., all Canadians.     

Income inequality and adult nutritional status: anthropometric evidence from a pre-industrial society in the Bolivian Amazon

Evidence has been accumulated about the adverse effects of income inequality on individual health in industrial nations, but we know less about its effect in small-scale, pre-industrial rural societies. Income inequality should have modest effects on individual health. First, norms of sharing and reciprocity should reduce the adverse effects of income inequality on individual health. Second, with sharing and reciprocity, personal income will spill over to the rest of the community, attenuating the protective role of individual income on individual health found in industrial nations. We test these ideas with data from Tsimane' Amerindians, a foraging and farming society in the Bolivian Amazon. Subjects included 479 household heads (13+ years of age) from 58 villages. Dependent variables included anthropometric indices of short-run nutritional status (body-mass index (BMI), and age- and sex-standardized z-scores of mid-arm muscle area and skinfolds). Proxies for income included area deforested per person the previous year and earnings per person in the last 2 weeks. Village income inequality was measured with the Gini coefficient. Income inequality did not correlate with anthropometric indices, most likely because of negative indirect effects from the omission of social-capital variables, which would lower the estimated impact of income inequality on health. The link between BMI and income and between skinfolds and income resembled a U and an inverted U; income did not correlate with mid-arm muscle area. The use of an experimental research design might allow for better estimates of how income inequality affects social capital and individual health.     

Income inequality, poverty, and population health: evidence from recent data for the United States

In this study, state-level US data for the years 2000 and 1990 are used to provide additional evidence on the roles of income inequality and poverty in population health. Five main points are noted. First, contrary to the suggestion made in several recent studies, the income inequality parameter is observed to be quite robust and carries statistical significance in mortality equations estimated from several observation sets and a fairly wide variety of specificational choices. Second, the evidence does not indicate that significance of income inequality is lost when education variables are included. Third, similarly, the income inequality parameter shows significance when a race variable is added, and also when both race and urbanization terms are entered. Fourth, while poverty is seen to have some mortality-increasing consequence, the role of income inequality appears stronger. Fifth, income inequality retains statistical significance when a quadratic income term is added and also if the log-log version of a fairly inclusive model is estimated. I therefore suggest that the recent skepticism articulated by several scholars in regard to the robustness of the income inequality parameters in mortality equations estimated from the US data should be reconsidered.     

Income inequality and mortality in metropolitan areas of the United States.

OBJECTIVES: This study examined associations between income inequality and mortality in 282 US metropolitan areas. METHODS: Income inequality measures were calculated from the 1990 US Census. Mortality was calculated from National Center for Health Statistics data and modeled with weighted linear regressions of the log age-adjusted rate. RESULTS: Excess mortality between metropolitan areas with high and low income inequality ranged from 64.7 to 95.8 deaths per 100,000 depending on the inequality measure. In age-specific analyses, income inequality was most evident for infant mortality and for mortality between ages 15 and 64. CONCLUSIONS: Higher income inequality is associated with increased mortality at all per capita income levels. Areas with high income inequality and low average income had excess mortality of 139.8 deaths per 100,000 compared with areas with low inequality and high income. The magnitude of this mortality difference is comparable to the combined loss of life from lung cancer, diabetes, motor vehicle crashes, human immunodeficiency virus (HIV) infection, suicide, and homicide in 1995. Given the mortality burden associated with income inequality, public and private sector initiatives to reduce economic inequalities should be a high priority.     

Income inequality,household income,and health status in Canada: a prospective cohort study

OBJECTIVES: This study sought to determine whether income inequality, household income, and their interaction are associated with health status. METHODS: Income inequality and area income measures were linked to data on household income and individual characteristics from the 1994 Canadian National Population Health Survey and to data on self-reported health status from the 1994, 1996, and 1998 survey waves. RESULTS: Income inequality was not associated with health status. Low household income was consistently associated with poor health. The combination of low household income and residence in a metropolitan area with less income inequality was associated with poorer health status than was residence in an area with more income inequality. CONCLUSIONS: Household income, but not income inequality, appears to explain some of the differences in health status among Canadians.