Increasingly polarized? Inequality, prosperity, and perceived socioeconomic conflict in advanced economies (1987–2019)

South Africa has historically been ranked as one of the most unequal societies in the world and, while the country has experienced sustained positive economic growth since 1994, the impact of this growth on poverty, and particularly inequality, has been disappointing. Analysis using data from the 1995 and 2000 Income and Expenditure Surveys has found, for example, a significant increase in income inequality over the period and, further, that this increase in inequality eroded any significant poverty-reduction gains from higher economic growth. The release of the Income and Expenditure Survey 2005 enables us to examine changes in inequality over the decade between 1995 and 2005. Some preliminary analysis, however, shows a further increase in inequality over the second half of the period. This new result would possibly suggest that South Africa is now the most consistently unequal economy in the world. Critically, the persistent and increasing levels of inequality have been acting as a constraint ensuring that South Africa’s economic growth results in significant declines in household poverty levels. This study has two main objectives. Firstly, the study aims to identify the drivers of the reproduction of inequality in post-apartheid South Africa. The second objective is to examine what policy levers are available to help mitigate the impact of increased inequality in South Africa. Based on the identification of what is driving the increasing levels of inequality, appropriate policy interventions, including assessing the impact and sustainability of existing policies such as the increased provision of social grants, will be evaluated. We find that not only has income inequality remained high for the period under review, but it has also increased significantly between 1995 and 2005. Throughout the time period wage inequality has been the main contributor to the growing income inequality. For a more holistic representation of inequality, we consider the effect of increased public and private assets on non-income inequality. We find that there has been a universal decrease in non-income inequality in South Africa. We also find that the effect of income inequality has been to dampen growth, specifically pro-poor growth. While we found that social transfers have little effect on income inequality when we decomposed the various sources of income, when grant income is excluded as a source of income from total income we find that it is an extremely important supportive source of income and without it many households would experience negative income growth.

South Africa has historically been ranked as one of the most unequal societies in the world and, while the country has experienced sustained positive economic growth since 1994, the impact of this growth on poverty, and particularly inequality, has been disappointing. Analysis using data from the 1995 and 2000 Income and Expenditure Surveys has found, for example, a significant increase in income inequality over the period and, further, that this increase in inequality eroded any significant poverty-reduction gains from higher economic growth. The release of the Income and Expenditure Survey 2005 enables us to examine changes in inequality over the decade between 1995 and 2005. Some preliminary analysis, however, shows a further increase in inequality over the second half of the period. This new result would possibly suggest that South Africa is now the most consistently unequal economy in the world. Critically, the persistent and increasing levels of inequality have been acting as a constraint ensuring that South Africa’s economic growth results in significant declines in household poverty levels. This study has two main objectives. Firstly, the study aims to identify the drivers of the reproduction of inequality in post-apartheid South Africa. The second objective is to examine what policy levers are available to help mitigate the impact of increased inequality in South Africa. Based on the identification of what is driving the increasing levels of inequality, appropriate policy interventions, including assessing the impact and sustainability of existing policies such as the increased provision of social grants, will be evaluated. We find that not only has income inequality remained high for the period under review, but it has also increased significantly between 1995 and 2005. Throughout the time period wage inequality has been the main contributor to the growing income inequality. For a more holistic representation of inequality, we consider the effect of increased public and private assets on non-income inequality. We find that there has been a universal decrease in non-income inequality in South Africa. We also find that the effect of income inequality has been to dampen growth, specifically pro-poor growth. While we found that social transfers have little effect on income inequality when we decomposed the various sources of income, when grant income is excluded as a source of income from total income we find that it is an extremely important supportive source of income and without it many households would experience negative income growth.

What effect does rising income inequality have on longevity in advanced developed economies? This paper focuses on the effect of income inequality on mortality rates for men and women in a subset of OECD countries over nearly six decades from 1950–2008. Using adult mortality rates at aged sixty-five as the outcome measure of mortality, the latest available data on inverted Pareto-Lorenz coefficient as a measure of income inequality, the authors conduct a range of analysis to investigate the relationship. The findings show that income inequality has a negative effect on mortality rates for both men and women, that is, an increase in income inequality at the top of the distribution does not appear to have a detrimental effect on adult mortality rates in the population of advanced developed countries. For every one unit increase in income inequality, female mortality rates decreased by 0.024 percentage points (p<=0.001) and male mortality rates decreased by 0.052 percentage points (p<=0.001). Dynamic OLS results show that for every one unit increase in income inequality, female mortality rates decreased by 0.032 percentage points (p<=0.01) and male mortality rates decreased by 0.067 percentage points (p<=0.001). The findings remain robust to changes in methodology and the inclusion of control variables including GDP, population and the health capital index.

Evidence from the U.S., UK, and Europe documented that income inequality has modest effects on the social gradient of health. However, few studies investigate how the effect of income inequality on the social gradient of health evolves over the life course. This study aims to examine to what extent, and in what ways, does the effect of income on later-life health trajectory vary across more unequal and less unequal societies. This study used data from the Standardized World Income Inequality Database (SWIID, 2004-2020) and the Survey of Health, Ageing and Retirement in Europe (SHARE, 2004-2010). We used national-level Gini index to measure income inequality. We used life satisfaction as the health outcome. We performed multilevel modelling to analyze the data in which observations (level 1) are nested in participants (level 2), country-years (level 3), and countries (level 4). Our analysis revealed three findings. First, the effect of income on life satisfaction among older adults is more pronounced in unequal countries. Second, the effect of income inequality on life satisfaction weakens as individuals age. Third, the importance of income on life satisfaction across societies of varying levels of income inequality decreases over the life course of an individual. Findings from this study show that contextual-level income inequality can modify the effects of individual income on health as individuals age. This can help the policy makers and practitioners to target the vulnerable population and offer more effective intervention.

Income inequality has attracted increasing attention of social scientists in the past few decades. Many previous and ongoing efforts have focused on the antecedents and consequences of income inequality at different levels of analysis. To extend our conceptual and empirical understanding of income inequality, we offer five papers in this symposium that examine the causes and consequences of macro-level income inequality (e.g., at the firm level or the country level), highlighting how organizations, along with their practices, serve as a primary mechanism through which income inequality can be generated and the consequences these have for individuals, organizations, and societies.

To examine the health consequences of exposure to income inequality. Secondary analysis employing data from several publicly available sources. Measures of individual health status and other individual characteristics are obtained from the March Current Population Survey (CPS). State-level income inequality is measured by the Gini coefficient based on family income, as reported by the U.S. Census Bureau and Al-Samarrie and Miller (1967). State-level mortality rates are from the Vital Statistics of the United States, other state-level characteristics are from U.S. census data as reported in the Statistical Abstract of the United States. We examine the effects of state-level income inequality lagged from 5 to 29 years on individual health by estimating probit models of poor/fair health status for samples of adults aged 25-74 in the 1995 through 1999 March CPS. We control for several individual characteristics, including educational attainment and household income, as well as regional fixed effects. We use multivariate regression to estimate the effects of income inequality lagged 10 and 20 years on state-level mortality rates for 1990, 1980, 1970, and 1960. Lagged income inequality is not significantly associated with individual health status after controlling for regional fixed effects. Lagged income inequality is not associated with all cause mortality, but associated with reduced mortality from cardiovascular disease and malignant neoplasms, after controlling for state fixed-effects. In contrast to previous studies that fail to control for regional variations in health outcomes, we find little support for the contention that exposure to income inequality is detrimental to either individual or population health.

Income inequality and subjective wellbeing: Panel data evidence from China

This study assessed whether income inequality and primary care physician supply have a different effect on mortality among Blacks compared with Whites. We conducted a multivariate ecologic analysis of 1990 data from 273 US metropolitan areas. Both income inequality and primary care physician supply were significantly associated with White mortality (P < .01). After the inclusion of the socioeconomic status covariates, the effect of income inequality on Black mortality remained significant (P < .01), but the effect of primary care physician supply was no longer significant (P > .10), particularly in areas with high income inequality. Improvement in population health requires addressing socioeconomic determinants of health, including income inequality and primary care availability and access.

The impact of income inequality on self-rated general health: Evidence from a cross-national study

Findings from numerous studies indicate that individuals living in more unequal societies are at greater risk for a variety of health problems. However, questions remain about the possible pathways that link health outcomes and income inequality. In general, the debate about how income inequality affects individual health centers around two issues: 1) whether the relationship is representative of the level of social cohesion within a given area, and/or 2) whether it is more indicative of the level of local investment in public health infrastructure. Each of these theories, then, represents a potential mediating mechanism through which income inequality impacts individual health. The purpose of this dissertation is to examine the social cohesion and local investment mechanisms through which income inequality may impact individual-level health outcomes. By examining variation in levels of social welfare spending and civic engagement, I investigate which of these competing variables has a stronger mediating effect in the relationship between income inequality and individual health outcomes. To address this research question, I use data from the Integrated Health Interview Series (IHIS)—a collection of microdata based on the public use files of the U.S National Health Interview Survey (NHIS) which is linked to the National Death Index (NDI). Using multi-level modeling techniques, I simultaneously examine the role of environmental-level effects (i.e. degree of local investment/social cohesion) and individual-level effects (e.g. income) on the likelihood of individual mortality in metropolitan areas. The findings presented in this dissertation contradict previous claims about the Income Inequality Hypothesis, which suggests that income inequality is detrimental to individual health. In addition, findings do not support the Social Cohesion or Local Investment Mechanisms as mediating pathways through which income inequality impacts individual health. These results raise questions about the causal effects of income inequality, and the sensitivity of this relationship to level of aggregation and to what factors research choose to control.

The relationship between income inequality and health across US states has been challenged recently on grounds that this relationship may be confounded by the effect of racial composition, measured as the proportion of the state's population who are black. Using multilevel statistical models, we examined the association between state income inequality and poor self-rated health. The analysis was based on the pooled 1995 and 1997 Current Population Surveys, comprising 201 221 adults nested within 50 US states. Controlling for the individual effects of age, sex, race, marital status, education, income, health insurance coverage, and employment status, we found a significant effect of state income inequality on poor self-rated health. For every 0.05-increase in the Gini coefficient, the odds ratio (OR) of reporting poor health increased by 1.39 (95% CI: 1.26, 1.51). Additionally controlling for the proportion of the state population who are black did not explain away the effect of income inequality (OR = 1.30; 95% CI: 1.15, 1.45). While being black at the individual level was associated with poorer self-rated health, no significant relationship was found between poor self-rated health and the proportion of black residents in a state. Our finding demonstrates that neither race, at the individual level, nor racial composition, as measured at the state level, explain away the previously reported association between income inequality and poorer health status in the US.

Shifting from the agriculture sector to the service sector has made Indonesia's economy rapidly grow and therefore a degradation in environmental quality. The degradation of the Environmental Quality Index (EQI) in 2019 is mostly caused by an increasing water pollution level. Income inequality and poverty in Indonesia is a problem that also causing the EQI degradation. There are 2 purposes of this article which are (1) to find out the effect of income inequality, poverty, and economic growth toward EQI (2) to find if there is causality between economic growth and EQI. The approach used in this article is a quantitative approach with a type of data is secondary data. This article used panel regression with Random Effect Model (REM) by combining cross-section data from 33 provinces in Indonesia and time-series data from 2014 – 2019. The causality between economic growth and EQI is calculated by using Granger Causality. The result shows that (1) economic growth and income inequality have a negative significant relationship toward EQI, but poverty hasn’t affect to EQI (2) There is no causality between economic growth and EQI because EQI can not affect economic growth.

Shifting from the agriculture sector to the service sector has made Indonesia's economy rapidly grow and therefore a degradation in environmental quality. The degradation of the Environmental Quality Index (EQI) in 2019 is mostly caused by an increasing water pollution level. Income inequality and poverty in Indonesia is a problem that also causing the EQI degradation. There are 2 purposes of this article which are (1) to find out the effect of income inequality, poverty, and economic growth toward EQI (2) to find if there is causality between economic growth and EQI. The approach used in this article is a quantitative approach with a type of data is secondary data. This article used panel regression with Random Effect Model (REM) by combining cross-section data from 33 provinces in Indonesia and time-series data from 2014 – 2019. The causality between economic growth and EQI is calculated by using Granger Causality. The result shows that (1) economic growth and income inequality have a negative significant relationship toward EQI, but poverty hasn’t affect to EQI (2) There is no causality between economic growth and EQI because EQI can not affect economic growth.

Does higher income inequality adversely influence infant mortality rates? Reconciling descriptive patterns and recent research findings

There are growing concerns about income inequality and health, while little is known about the relationship between income inequality and nutritional outcomes, especially in a transition economy like China. To fill this gap, the aim of this study is to explore the effect of income inequality on the nutritional outcomes of Chinese farmers, including body mass index (BMI), underweight, overweight and obesity statuses. Methodologically this study relies on the theoretical propositions of both income hypothesis and agricultural economics. Specifically, this study compares the literature examining income inequality, then analyses the possible effects of income inequality on the nutritional outcomes of Chinese farmers, and finally tests the results of the analysis using econometric models. Using data from the China Health and Nutrition Survey (CHNS) from 2015, we found that the relationship between income and BMI shifted from positive to negative with rapid growth in per capita household incomes and that higher income inequality can significantly increase the risk of being overweight or obese among low-income groups. In particular, the effect of income inequality on overweight and obesity is higher for males, while its effect tends to be negligible for females. The findings in this study are proved to be robust. Therefore, several policy implications for meeting the challenges concerning income inequality and improving nutritional outcomes for Chinese farmers are also discussed.