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The inequality footprints of nations: a novel approach to quantitative accounting of income inequality.

ABSTRACT: In this study we use economic input-output analysis to calculate the inequality footprint of nations. An inequality footprint shows the link that each country's domestic economic activity has to income distribution elsewhere in the world. To this end we use employment and household income accounts for 187 countries and an historical time series dating back to 1990. Our results show that in 2010, most developed countries had an inequality footprint that was higher than their within-country inequality, meaning that in order to support domestic lifestyles, these countries source imports from more unequal economies. Amongst exceptions are the United States and United Kingdom, which placed them on a par with many developing countries. Russia has a high within-country inequality nevertheless it has the lowest inequality footprint in the world, which is because of its trade connections with the Commonwealth of Independent States and Europe. Our findings show that the commodities that are inequality-intensive, such as electronic components, chemicals, fertilizers, minerals, and agricultural products often originate in developing countries characterized by high levels of inequality. Consumption of these commodities may implicate within-country inequality in both developing and developed countries.

SUBMITTER: Alsamawi A

PROVIDER: S-EPMC4212986 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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The inequality footprints of nations: a novel approach to quantitative accounting of income inequality.

Alsamawi Ali A Murray Joy J Lenzen Manfred M Moran Daniel D Kanemoto Keiichiro K

PloS one 20141029 10

In this study we use economic input-output analysis to calculate the inequality footprint of nations. An inequality footprint shows the link that each country's domestic economic activity has to income distribution elsewhere in the world. To this end we use employment and household income accounts for 187 countries and an historical time series dating back to 1990. Our results show that in 2010, most developed countries had an inequality footprint that was higher than their within-country inequa ...[more]

PMID: 25353333

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Project description:This data article contains information on the distribution of household incomes in the five most populous European countries as surveyed in 2012, with data released in 2014 and published here aggregated and so further anonymized in 2015. The underlying source data is the already anonymized EU Statistics on Income and Living Conditions (EUSILC) Microdata. The data include the annual household income required in each country to fall within the best-off 1% in that country, median and mean incomes, average (mean) incomes of the best off 1%, 0.1% and estimates for the 0.01%, 0.001% and so on for the UK, and of the 90% and worse-off 10%, the best-off 10% and best-off 1% of households for all countries. Average income from the state is also calculated by these income categories and the number of people working in finance and receiving over €1,000,000 a year in income is reported from other sources (the European Banking Authority). Finally income distribution data is provided from the USA and the rest of Europe in order to allow comparisons to be made. The data revealed the gross household (simple unweighted) median incomes in 2012 to have been (in order from best-off country by median to worse-off): France €39,000, Germany: €33,400, UK: €36,300, Italy €33,400 and Spain €27,000. However the medians, once households are weighted to reflect the nation populations do differ although they are in the same order: France €36,000, Germany: €33,400, UK: €31,300, Italy €31,000 and Spain €23,700. Thus weighting to increase representativeness of the medians reduces each by €3000, €0, €5000, €3300 and €3300 respectively. In short, the middle (weighted median) French household is €4700 a year better off than the middle UK family, and that is before housing costs are considered. This Data in Brief article accompanies Dorling, D. (2015) Income Inequality in the UK: Comparisons with five large Western European countries and the USA [1].

Project description:BackgroundGeneral practitioners (GPs) are among the most important resources of healthcare system and public health is considerably influenced by the function of this group. Income inequality among GPs considerably affects the motivation and performance of this group. The present study aims to examine the income inequality among Iranian GPs in order to provide the necessary evidence for health human resource policy.MethodsIn this cross-sectional study, the distribution of income and wage inequality among GPs was investigated using income quintiles. We also used the Dagum's model to analyze the inequality between different groups of GPs through the decomposition of the Gini coefficient. Moreover, a regression model was used to determine the effective factors on GPs' income.ResultsThe results of this study indicated that income and wages of GPs in the highest quintile were eight times more than those of doctors at the lowest quintile. Regression estimates showed that factors such as gender, practice setting, and activity as the family physician (P < 0.001) were effective on income of GPs; and also male and self-employed GPs had significantly more wage (P < 0.001). Total Gini coefficient of GPs' income and wage were estimated at 0.403 and 0.412, respectively. Highest monthly income was found in GPs with 16-20 years practice experience ($8358) based on Purchasing Power Parity (PPP), male ($8339 PPP), and self-employed GPs ($8134 PPP) subgroup. However, the female ($5389 PPP) and single ($5438 PPP) GPs had the lowest income. Population share; income/wage share; income/wage mean; Gini coefficient; and within, between and overlap decomposed components of Gini coefficient are also reported for each GPs subgroups.ConclusionsWe found significant inequalities in income and wages among Iranian GPs. Adjustment of income based on working hours indicated that one of the most common causes of income inequality among GPs in Iran was different workloads among different groups. Since the motivation and function of physicians can be influenced by income inequality, policymakers in the health system should consider factors increasing such inequalities.

Project description:BACKGROUND:Several studies have confirmed the existence of a significant positive relationship between income and health. Conventional regression techniques such as Ordinary Least Squares only help identify the effect of the covariates on the mean of the health variable. In this way, important information of the income-health relationship could be overlooked. As an alternative, we apply and compare unconventional regression techniques. METHODS:We adopt a distributional approach because we want to allow the effect of income on health to vary according to people's health status. We start by analysing the income-health relationship using a distributional regression model that falls into the GAMLSS (Generalized Additive Models for Location, Scale and Shape) framework. We assume a gamma distribution to model the health variable and specify the parameters of this distribution as linear functions of a set of explanatory variables. For comparison, we also adopt a quantile regression analysis. Based on predicted health quantiles, we use both a parametric and a non-parametric approach to estimate the lower tail of the health distribution. RESULTS:Our data come from Wave 13 of the Household, Income and Labour Dynamics in Australia (HILDA) survey, collected in 2013-2014. According to GAMLSS, we find that the risk of ending up in poor, fair or average health is lower for those who have relatively high incomes ($80,000) than for those who have relatively low incomes ($20,000), for both smokers and non-smokers. In relative terms, the risk-lowering effect of income appears to be the largest for those who are in poor health, again for both smokers and non-smokers. The results obtained on the basis of quantile regression are to a large extent comparable to those obtained by means of GAMLSS regression. CONCLUSIONS:Both distributional regression techniques point in the direction of a non-uniform effect of income on health, and are therefore promising complements to conventional regression techniques as far as the analysis of the income-health relationship is concerned.

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.

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The inequality footprints of nations: a novel approach to quantitative accounting of income inequality.

Publications

The inequality footprints of nations: a novel approach to quantitative accounting of income inequality.

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