Modelling health,income and income inequality: the impact of income inequality on health and health inequality

A framework is developed to analyse the impact of the distribution of income on individual health and health inequality, with individual health modelled as a function of income and the distribution of income. It is demonstrated that the impact of income inequality can generate non-concave health production functions resulting in a non-concave health production possibility frontier. In this context, the impact of different health policies are considered and it is argued that if the distribution of income affects individual health, any policy aimed at equalising health, which does not account for income inequality, will lead to unequal distributions of health. This is an important development given current UK government attention to reducing health inequality.     

Peripherality, income inequality, and life expectancy: revisiting the income inequality hypothesis

BACKGROUND: Recent criticisms of the income inequality and health hypothesis have stressed the lack of consistent significant evidence for the stronger effects of income inequality among rich countries. Despite such criticisms, little attention has been devoted to the income-based criteria underlying the stratification of countries into rich/poor groups and whether trade patterns and world-system role provide an alternative means of stratifying groups. METHODS: To compare income-based and trade-based criteria, 107 countries were grouped into four typologies: (I) high/low income, (II) OECD membership/non-membership, (III) core/non-core, and (IV) non-periphery/periphery. Each typology was tested separately for significant differences in the effects of income inequality between groups. Separate group comparison tests and regression analyses were conducted for each typology using Rodgers (1979) specification of income, income inequality, and life expectancy. Interaction terms were introduced into Rodgers specification to test whether group classification moderated the effects of income inequality on health. RESULTS: Results show that the effects of income inequality are stronger in the periphery than non-periphery (IV) (-0.76 vs -0.23; P < 0.05). An incremental F-test confirmed significant differences in the coefficient subsets between the two groups (F(2,101) = 6.31; P < 0.01). CONCLUSIONS: Cross-national analyses of income inequality and population health have assumed (i) income differences between countries best capture global stratification and (ii) the negative effects of income inequality are stronger in high-income countries. However, present findings emphasize (i) the importance of measuring global stratification according to trading patterns and (ii) the strong, negative effects of income inequality on life expectancy among peripheral populations.     

Ecological analysis of the health effects of income inequality in Argentina

OBJECTIVES: Despite a large body of empirical literature, a consensus has not been reached concerning the health effects of income inequality. This study contributes to ongoing debates by examining the robustness of the income inequality-population health relationship in Argentina, using five different income inequality indexes (each sensitive to inequalities in differing parts of the income spectrum) and five measures of population health. STUDY DESIGN: Cross-sectional, ecological study. METHODS: Income and self-reported morbidity data from Argentina's 2001 Encuesta de Condiciones de Vida (Survey of living conditions) were analysed at the provincial level. Provincial rates of male/female life expectancy and infant mortality were drawn from the Instituto Nacional de Estadistica y Censos database. RESULTS: Life expectancy was correlated in the expected direction with provincial-level income inequality (operationalized as the Gini coefficient) for both males (r=-0.55, P<0.01) and females (r=-0.61, P<0.01), but this association was not robust for all five income inequality indexes. In contrast, infant mortality, self-reported poor health and self-reported activity limitation were not correlated with any of the income inequality indexes. CONCLUSIONS: This study adds further complexity to the literature on the health effects of income inequality by highlighting the important effects of operational definitions. Mortality and morbidity data cannot be used as reasonably interchangeable variables (a common practice in this literature), and the choice of income inequality indicator may influence the results.     

Beefing up with the Chans: evidence for the effects of relative income and income inequality on health from the China Health and Nutrition Survey

A great deal of research has examined the hypothesis that the well-being of individuals is shaped not just by the absolute level of resources available to them but also the level of resources available to them relative to others in their cohort or community. Several causal pathways have been hypothesized to explain associations between relative social position and health. For example, greater community income could increase the overall availability of health care in a community or decrease the availability for people for any given level of individual income. Relative social position could also create stress, resulting in adverse health outcomes through increased hypertension and other pathways. We explore yet another pathway by which relative social position may affect health. Specifically, to the extent that norms about physical appearance might be shaped by one's observations of others, we examine whether obesity might constitute another physiologic pathway by which community attributes could influence aspects of individual health, such as hypertension. We examine this hypothesis in rural China, where income often limits food intake so that, if community norms are an important determinant of individual obesity, higher community income could increase the obesity rate in a community and therefore change norms about obesity. These norms, in turn, could increase individuals' chances of being obese given their income. To test this hypothesis, we use multilevel linear probability models to examine the relationship between ecologic factors, i.e., relative income and income inequality, and health risk factors, i.e., obesity and hypertension among a sample of Chinese adults interviewed in four waves over 9 years. The results suggest that, among rural Chinese residents, increasing community average income and income inequality are positively associated with both obesity and hypertension. However, the effect of relative income on hypertension is not accounted for by increases in obesity. We did not find a strong relationship between socioeconomic conditions and the health risk factors among urban residents, where norms might be likely to be less strongly influenced by local attributes. Hence, the present study provides evidence supporting the hypothesis that relative income and income inequality affect obesity and hypertension, but no evidence that the effects on hypertension operated through effects on obesity.     

Healthcare investment and income inequality

This paper examines how the relative shares of public and private health expenditures impact income inequality. We study a two period overlapping generation's growth model in which longevity is determined by both private and public health expenditure and human capital is the engine of growth. Increased investment in health, reduces mortality, raises return to education and affects income inequality. In such a framework we show that the cross-section earnings inequality is non-decreasing in the private share of health expenditure.We test this prediction empirically using a variable that proxies for the relative intensity of investments (private versus public) using vaccination data from the National Sample Survey Organization for 76 regions in India in the year 1986–87. We link this with region-specific expenditure inequality data for the period 1987–2012. Our empirical findings, though focused on a specific health investment (vaccines), suggest that an increase in the share of the privately provided health care results in higher inequality.     

The impact of income inequality on individual and societal health: absolute income, relative income and statistical artefacts

The relative income hypothesis, that relative income has a direct effect on individual health, has become an important part of the literature on health inequalities. This paper presents a four-quadrant diagram, which shows the effect of income, relative income and aggregation bias on individual and societal health. The model predicts that increased income inequality reduces average health regardless of whether relative income affects individual health. If relative income does have a direct effect then societal health will decrease further.     

Contextual effect of income inequality on birth outcomes

BACKGROUND: Though associations between income inequality and birth outcome have been suggested, mechanisms underlying this relationship are not known. In this analysis, we examined the relationship between income inequality and preterm birth (PTB) and post-neonatal mortality (PNM) to explore two potential mechanisms-the proposed psychosocial stress and neo-material pathways. METHODS: Data on singleton births from 1998 to 2000 were obtained from the CDC's National Center for Health Statistics' Linked Birth and Infant Death files. The Gini Index was utilized to measure income inequality and was divided into tertiles representing high, medium, and low county-level inequality. To determine the association between the birth outcomes and county income inequality and to account for clustering within counties, we employed generalized estimating equation (GEE) modelling. RESULTS: PTB increased from 8.3% in counties with low income inequality to 10.0% in counties with high inequality. The Gini Index remained modestly associated with PTB after adjusting for individual level variables and mean county-level per capita income within the total population (AOR: 1.06; 95% CI 1.03-1.09) as well as within most of the racial/ethnic groups. PNM increased from 1.15 deaths per 1000 live births in low inequality counties to 1.32 in high-inequality counties. However, after adjustment, income inequality was only associated with PNM within the non-Hispanic black population (AOR: 1.20; 95% CI 1.03-1.39). CONCLUSIONS: These findings may provide some support for the association between income inequality and PTB. Further research is required to elucidate the biological mechanisms of income inequality.     

Changes in income inequality and the health of immigrants

Research suggests that income inequality is inversely associated with health. This association has been documented in studies that utilize variation in income inequality across countries or across time from a single country. The primary criticism of these approaches is their inability to account for potential confounders that are associated with income inequality. This paper uses variation in individual experiences of income inequality among immigrants within the United States (U.S.) to evaluate whether individuals who moved from countries with greater income inequality than the U.S. have better health than those who migrated from countries with less income in equality than the U.S. Utilizing individual-level (March Current Population Survey) and country-level data (the United Nations Human Development Reports), we show that among immigrants who have resided in the U.S. between 6 and 20 years, self-reported health is more favorable for the immigrants in the former category (i.e., greater income inequality) than those in the latter (i.e., lower income inequality). Results also show that self-reported health is better among immigrants from more developed countries and those who have more years of education, are male, and are married.     

The effects of US state income inequality and alcohol policies on symptoms of depression and alcohol dependence

Mental health is likely to be influenced by contextual variables that emerge only at the level of the group. We studied the effect of two such group-level variables, within-state income inequality and alcohol tax policy, on symptoms of current depression and alcohol dependence in a US national sample, controlling for state-level and individual characteristics. A cross-sectional US national probability sample provided the individual-level data. State income data were obtained from the 1990 US census. The Gini coefficient (raw and adjusted) indicated income inequality. Outcome measures included current symptoms of depression and alcohol dependence. Controlling for individual-level variables and state median income, the odds of depressive symptoms was not positively associated with state income inequality. Controlling for individual-level variables, state median income and alcohol distribution method, a weak negative association between Gini and alcohol dependence was observed in women, but this association disappeared after additional adjustment for beer tax. No association was observed in men. Higher state beer tax was significantly associated with lower prevalence of alcohol dependence symptoms for both men and women. The results suggest that state income inequality does not increase the experience of alcohol dependence or depression symptoms. However, evidence was found for a protective effect of increased beer taxation against alcohol dependence symptoms, suggesting the need to further consider the impact of alcohol policies on alcohol use disorders.     

A multilevel analysis of the effects of neighbourhood income inequality on individual self-rated health in Hong Kong

We examined the effect on self-rated health of neighbourhood-level income inequality in Hong Kong, which has a high and growing Gini coefficient. Data were derived from two population household surveys in 2002 and 2005 of 25,623 and 24,610 non-institutional residents aged 15 or over. We estimated neighbourhood-level Gini coefficients in each of 287 Government Planning Department Tertiary Planning Units. We used multilevel regression analysis to assess the association of neighbourhood income inequality with individual self-perceived health status. After adjustment for both individual- and household-level predictors, there was no association between neighbourhood income inequality, median household income or household-level income and self-rated health. We tested for but did not find any statistical interaction between these three income-related exposures. These findings suggest that neighbourhood income inequality is not an important predictor of individual health status in Hong Kong.     

Social context and depression after a disaster: the role of income inequality

STUDY OBJECTIVE: To examine the association between neighbourhood income inequality and depression, both overall and among those with different levels of income, in the post-disaster context. DESIGN: A representative cross sectional random digit dial telephone survey was conducted. SETTING: New York City (NYC) six months after September 11, 2001. PARTICIPANTS: 1570 respondents were interviewed, of whom 1355 provided residence information permitting their inclusion in this analysis. Past six month depression was assessed using a lay administered instrument consistent with DSM-IV criteria. Income inequality was measured with the Gini coefficient. MAIN RESULTS: The sample was demographically representative of NYC (56.2% female, 35.7% white, 6.3% Asian 24.2% African American, 29.7% Hispanic, and 4.2% other race or ethnicity) and the prevalence of past six month depression was 12.4%. In a final adjusted model, neighbourhood level income inequality was positively associated with depression but this association was not significant (beta = 7.58, p = 0.1). However, among those with low individual income (< 20,000 US dollars) there was a strong significant association between income inequality and depression (beta = 35.02, p<0.01), while there was no association among those with higher income. CONCLUSIONS: In the post-disaster context, neighbourhood level income inequality was associated with depression among persons with lower income; this group may be more socially or economically marginalized and dependent on local resources. Future research should examine potential mechanisms through which income inequality and other features of the social context may affect mental health in the post-disaster context.     

Associations between income inequality and mortality among US states: the importance of time period and source of income data

OBJECTIVES: We used census data to examine associations between income inequality and mortality among US states for each decade from 1949 to 1999 and tax return income data to estimate associations for 1989. METHODS: Cross-sectional correlation analyses were used to assess income inequality-mortality relationships. RESULTS: Census income analyses revealed little association between income inequality and mortality for 1949, 1959, or 1969. An association emerged for 1979 and strengthened for 1989 but weakened for 1999. When income inequality was based on tax return data, associations were weaker for both 1989 and 1999. CONCLUSIONS: The strong association between income inequality and mortality observed among US states for 1989 was not observed for other periods from 1949 through 1999. In addition, when tax return rather than census data were used, the association was weaker for 1989 and 1999. The potential for distal social determinants of population health (e.g., income inequality) to affect mortality is contingent on how such determinants influence levels of proximal risk factors and the time lags between exposure to those risk factors and effects on specific health outcomes.     

Measuring income related inequality in health: standardisation and the partial concentration index

The partial concentration index (PCI) is commonly used as a measure of income related inequality in health after removing the effects of standardising variables such as age and gender which affect health, are correlated with income, but not amenable to policy. Both direct and indirect standardisation have been used to remove the effects of standardising variables. The paper shows that with individual level data direct standardisation is possible using the coefficients from a linear regression of health on income and the standardising variables and yields a consistent estimate of the PCI. Indirect standardisation estimates the effects of the standardising variables on health from a health regression which excludes income. The coefficients on the standardising variables include some of the effects of income on health if income is correlated with the standardising variables. Using these coefficients to remove the effects of the standardising variables also removes some of the effect of income on health and leads to an inconsistent estimate of the PCI. Indirect standardisation underestimates the PCI irrespective of the signs of the correlations of standardising variables and income with each other and with health. An adaptation of the PCI when the marginal effect of income on health depends on the standardising variables is also proposed.     

Beyond the income inequality hypothesis: class, neo-liberalism, and health inequalities

This paper describes and critiques the income inequality approach to health inequalities. It then presents an alternative class-based model through a focus on the causes and not only the consequences of income inequalities. In this model, the relationship between income inequality and health appears as a special case within a broader causal chain. It is argued that global and national socio-political-economic trends have increased the power of business classes and lowered that of working classes. The neo-liberal policies accompanying these trends led to increased income inequality but also poverty and unequal access to many other health-relevant resources. But international pressures towards neo-liberal doctrines and policies are differentially resisted by various nations because of historically embedded variation in class and institutional structures. Data presented indicates that neo-liberalism is associated with greater poverty and income inequalities, and greater health inequalities within nations. Furthermore, countries with Social Democratic forms of welfare regimes (i.e., those that are less neo-liberal) have better health than do those that are more neo-liberal. The paper concludes with discussion of what further steps are needed to "go beyond" the income inequality hypothesis towards consideration of a broader set of the social determinants of health.     

我国居民与收入相关的健康不平等实证研究

本文利用中国健康与营养调查（CHNS）2006年的调查数据，从定量的角度对我国居民与收入相关的健康不平等进行了分析。本文利用有序Probit模型获得了自评健康数据背后的实际健康得分，在此基础上计算健康集中指数衡量我国居民与收入相关的健康不平等程度。结果表明，我国居民的健康不平等问题较为严重，健康不平等问题在城乡之间和不同经济发展水平地区之间存在着较大的差别。     

An exploratory multilevel analysis of income,income inequality and self-rated health of the elderly in China

In the last three decades, China has experienced rapid economic development and growing economic inequality, such that economic disparities between rural and urban areas, as well as coastal and interior areas have deepened. Since the late 1990s China has also experienced an ageing population which has attracted attention to the wellbeing of the rapidly growing number of elderly. This research aims to characterise province differences in health and to explore the effects of individual income and economic disparity in the form of income inequality on health outcomes of the elderly. The study is based on the Chinese Longitudinal Healthy Longevity Survey data collected in 2008 for 23 provinces. Multilevel logistic models are employed to investigate the relationship between income, income inequality and self-rated health for the elderly using both individual and province-level variables. Results are presented as relative odds ratios, and for province differentials as Median Odds Ratios. The analysis is deliberately exploratory so as to find evidence of income effects if they exist and particular attention is placed on how province-level inequality (contemporaneous and lagged) may moderate individual relationships. The results show that the health of the elderly is not only affected by individual income (the odds of poor health are 3 times greater for the elderly with the lowest income compared to those at the upper quartile) but also by a small main effect for province-level income inequality (odds ratio of 1.019). There are significant cross-level interactions such that where inequality is high there are greater differences between those with and without formal education, and between men and women with the latter experiencing poorer health.