Pandemics and income inequality: a historical review
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ABSTRACT: Supplementary Information
The online version contains supplementary material available at 10.1007/s43546-021-00059-4.
SUBMITTER: Sayed A
PROVIDER: S-EPMC7970770 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The novel coronavirus is part of a series of infectious disease outbreaks that include: Ebola, Avian influenza, Middle East respiratory syndrome coronavirus, and Influenza A. This paper addresses the question of how do these epidemics and pandemics affect income inequality in countries around the world during the first two decades of this century. To achieve its objective, the paper develops a model that indicates a positive association between these health crises and income inequality. To empirically test our theoretical predictions, the paper explores the effect on the Gini coefficient of a dummy variable that indicates the occurrence of an epidemic or a pandemic in a country in a given year and the number of deaths per 100,000. To properly address potential endogeneity, we implement a Three-Stage-Least Squares technique. The estimation shows that the number of deaths per 100,000 population variable has a statistically significant positive effect on the Gini coefficient, especially when we incorporate COVID-19 data. This suggests that not only the occurrence, but also the health consequences of COVID-19 have a significant and economically important effect on income inequality.BackgroundThe purpose of the study is to examine the effect of epidemics and pandemics on income inequality. This has important implications as the outcome of this study can guide policymakers into implementing policies that can mitigate the economic consequences of these health crises.MethodsThe study is a cross country analysis using fixed effects estimation. To address potential endogeneity and determine causality, the paper uses the Three-Stage-Least-Squares estimation.ResultsThe paper finds that the number of epidemic deaths per 100,000 population variable has a statistically significant positive effect on the Gini coefficient, especially when we incorporate COVID-19 data.ConclusionsThe paper finds that it is not only the occurrence of an epidemic, captured by the epidemics dummy variable, but also the health consequences, captured by the number of deaths per 100,000 population, that have a significant effect on income inequality. This is especially the case when we incorporate COVID-19 in our analysis.Trial registrationNot Applicable.
| S-EPMC8765494 | biostudies-literature
Project description:BackgroundIncome inequality is associated with numerous negative health outcomes. There is evidence that ecological-level socio-environmental factors may increase risk for schizophrenia.AimsThe aim was to investigate whether measures of income inequality are associated with incidence of schizophrenia at the country level.MethodWe conducted a systematic review of incidence rates for schizophrenia, reported between 1975 and 2011. For each country, national measures of income inequality (Gini coefficient) along with covariate risk factors for schizophrenia were obtained. Multi-level mixed-effects Poisson regression was performed to investigate the relationship between Gini coefficients and incidence rates of schizophrenia controlling for covariates.ResultsOne hundred and seven incidence rates (from 26 countries) were included. Mean incidence of schizophrenia was 18.50 per 100,000 (SD = 11.9; range = 1.7-67). There was a significant positive relationship between incidence rate of schizophrenia and Gini coefficient (β = 1.02; Z = 2.28; p = .02; 95% CI = 1.00, 1.03).ConclusionsCountries characterized by a large rich-poor gap may be at increased risk of schizophrenia. We suggest that income inequality impacts negatively on social cohesion, eroding social capital, and that chronic stress associated with living in highly disparate societies places individuals at risk of schizophrenia.
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Project description:Using multiple data sources, we establish that China's income inequality since 2005 has reached very high levels, with the Gini coefficient in the range of 0.53-0.55. Analyzing comparable survey data collected in 2010 in China and the United States, we examine social determinants that help explain China's high income inequality. Our results indicate that a substantial part of China's high income inequality is due to regional disparities and the rural-urban gap. The contributions of these two structural forces are particularly strong in China, but they play a negligible role in generating the overall income inequality in the United States, where individual-level and family-level income determinants, such as family structure and race/ethnicity, play a much larger role.
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Project description:BACKGROUND:Reducing income inequality is one possible approach to boost subjective well-being (SWB). Nevertheless, previous studies have reported positive, null and negative associations between income inequality and SWB. OBJECTIVES:This study reports the first systematic review and meta-analysis of the relationship between income inequality and SWB, and seeks to understand the heterogeneity in the literature. METHODS:This systematic review was conducted according to guidance (PRISMA and Cochrane Handbook) and searches (between January 1980 and October 2017) were carried out using Web of Science, Medline, Embase and PsycINFO databases. RESULTS:Thirty-nine studies were included in the review, but poor data reporting meant that only 24 studies were included in the meta-analysis. The narrative analysis of 39 studies found negative, positive and null associations between income inequality and SWB. The meta-analysis confirmed these findings. The overall association between income inequality and SWB was almost zero and not statistically significant (pooled r?=?-?0.01, 95% CI -?0.08 to 0.06; Q?=?563.10, I 2?=?95.74%, p?<?0.001), suggesting no association between income inequality and SWB. Subgroup analyses showed that the association between income inequality and SWB was moderated by the country economic development (i.e. developed countries: r?=?-?0.06, 95% CI -0.10 to -0.02 versus developing countries: r?=?0.16, 95% CI 0.09-0.23). The association between income inequality and SWB was not influenced by: (a) the measure used to assess SWB, (b) geographic region, or (c) the way in which income inequality was operationalised. CONCLUSIONS:The association between income inequality and SWB is weak, complex and moderated by the country economic development.
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Project description:Is there a relationship between family income inequality and income mobility across generations in the United States? As family income inequality rose in the United States, parental resources available for improving children's health, education, and care diverged. The amount and rate of divergence also varied across US states. Researchers and policy analysts have expressed concern that relatively high inequality might be accompanied by relatively low mobility, tightening the connection between individuals' incomes during childhood and adulthood. Using data from the Panel Study of Income Dynamics, the National Longitudinal Survey of Youth, and various government sources, this paper exploits state and cohort variation to estimate the relationship between inequality and mobility. Results provide very little support for the hypothesis that inequality shapes mobility in the United States. The inequality children experienced during youth had no robust association with their economic mobility as adults. Formal analysis reveals that offsetting effects could underlie this result. In theory, mobility-enhancing forces may counterbalance mobility-reducing effects. In practice, the results suggest that in the US context, the intergenerational transmission of income may not be very responsive to changes in inequality.
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Project description:Mass shootings are becoming a more common occurrence in the United States. Data show that mass shootings increased steadily over the past nearly 50 years. Crucial is that the wide-ranging adverse effects of mass shootings generate negative mental health outcomes on millions of Americans, including fear, anxiety, and ailments related to such afflictions. This study extends previous research that finds a strong positive relationship between income inequality and mass shootings by examining the effect of household income as well as the interaction between inequality and income. To conduct our analyses, we compile a panel dataset with information across 3,144 counties during the years 1990 to 2015. Mass shootings was measured using a broad definition of three or more victim injuries. Income inequality was calculated using the post-tax version of the Gini coefficient. Our results suggest that while inequality and income alone are both predictors of mass shootings, their impacts on mass shootings are stronger when combined via interaction. Specifically, the results indicate areas with the highest number of mass shootings are those that combine both high levels of inequality and high levels of income. Additionally, robustness checks incorporating various measures of mass shootings and alternative regression techniques had analogous results. Our findings suggest that to address the mass shootings epidemic at its core, it is essential to understand how to stem rising income inequality and the unstable environments that we argue are created by such inequality.
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