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The present study aims to determine the existence of simultaneous relationship between economic growth, income inequality, fiscal policy, and total trade of the 13 emerging market economies as a group for the period 1980–2010. After establishing the existence of simultaneity between the above relationships, a simultaneous panel model has been formulated and estimated incorporating the nonlinearity among the variables as suggested by the existing literature. An inverted U-shape relationship is evident between (1) economic growth, income inequality, and total trade in economic growth equation, (2) income inequality, economic growth, and per capita income in income inequality equation, and (3) total trade and economic growth in total trade equation. Thus, the existence of a two-way nonlinear relationship is highlighted between economic growth, income inequality, and total trade. Apart from these nonlinear relationships, positive and significant effect of (1) gross capital formation, inflation, population growth, human capital, fiscal policy, monetary policy, and domestic credit to private sector on economic growth; (2) civil liabilities on income inequality; (3) gross capital formation and inflation on total trade; (4) total trade, population growth of those aged 65 years and above, political system on fiscal policy is highlighted. Also, negative and significant effect of (1) fiscal policy on income inequality and (2) income inequality on fiscal policy is revealed.

Using decomposable measures of inequality, the implications of household structure are investigated by examining inequality between and within household groups based on the number of exemptions, which correlates with household size, and the filing status, which correlates with the common forms of household structure, i.e. married, single, head of household. Detailed household income data are used to measure income inequality for both pre-tax/transfer and post-tax/transfer definitions of income. These decompositions provide information about the degree of inequality, both before and after taxes and transfers, which is due to household size and filing status. The bootstrap is employed to construct standard errors for the inequality measures and their decompositions, and hypothesis tests are conducted to determine whether the observed changes in the distribution of income are statistically significant.

The size distribution of income, or income inequality, has long been a concern to scholars in many disciplines tor different reasons. Statisticians have approached the distribution of income among individuals as a stochastic process. Economists have sought to explain income distribution by means, of economic and institutional factors. More recently, economists have been interested in the effects of economic growth and government policies on income distribution. Sociologists and political scientists have thought of income inequality as a major source of social revolt or political violence.

This paper investigates the effect of income inequality on health status. A model of health status was specified in which the main variables were income level, income inequality, the level of savings and the level of education. The model was estimated using a panel data set for 44 countries covering six time periods. The results indicate that income inequality (measured by the Gini coefficient) has a significant effect on health status when we control for the levels of income, savings and education. The relationship is consistent regardless of the specification of health status and income. Thus, the study results provide some empirical support for the income inequality hypothesis.

The purpose of this paper is to extend the existing literature on cross‐country disparities by providing measures of cross‐country inequality in human development index (HDI) and real income per capita over the 30‐year period 1975‐2004.

A well‐recommended inequality index is applied to the data.

Ten points are noted: first, HDI inequality declined over the period; second, the pace of decline slowed somewhat since 1990; third, magnitude of HDI inequality has been quite small; fourth, inequality in gross domestic product per capita also shows a declining pattern over the period; fifth, there is very high correlation between HDI and per capita income; sixth, despite the high correlation, magnitudes of inequalities in the two variables are dramatically different; seventh, therefore, even very high correlation may not be interpreted as implying similar inequalities in the variables; eighth, cross‐country inequalities in various regions show huge differences; ninth, negative trend in inequalities over the period shows high statistical significance; and tenth, t‐tests for equality of means do not pick up well even huge differences in regional inequalities, suggesting need for considerable caution in the use of such tests.

The primary scientific significance of the work lies in providing the measures of cross‐country inequality in HDI over the 30‐year period; showing dramatically different inequalities in HDI and income despite very high correlation between the two variables; and indicating cross‐country inequalities in eight different regional groups and also across regions.

The financial crisis of 2008 and ensuing recession led to falls in earnings in the United Kingdom, not seen since the Great Depression of the 1930s, and it was only in 2014 that overall household income returned to its pre-crisis levels. At the same time, according to one official measure, income inequality has actually fallen, although different data indicate no change. This situation follows from several factors, notably the continued growth in pensions, higher earnings of lower-income households as these have worked more since the recovery in 2013, and the continued stagnation of earnings in higher income households (even if very high incomes have continued to pull away from the rest of the population). Incomes of younger workers also remain below their pre-crisis peak. This chapter shows, however, that the picture of poverty and inequality in the United Kingdom is far more complex than suggested by the main measure of income inequality. To this end, it begins by reviewing the definitions of poverty and inequality, in order to provide a broader overview of these pressing but complex social problems. The chapter goes on to examine wealth inequalities, the impact of housing costs on inequality and poverty, and it concludes by presenting recent studies suggesting that Brexit may well lead to future rises in inequality, as higher inflation could well hit lower-income households most.

In the United States, there is little difference in annual income inequality and income mobility between the rural and urban sectors of the economy. This forms a sharp contrast with China where income inequality is greater and income mobility lower among rural households than among urban households. When incomes are averaged over three years and when adjustments are made for the size and composition of households, income inequality among all households differs little between China and the United States in the 1990s. Moreover when pooling rural households and urban households and when measuring annual income inequality and income mobility of the pooled households, the mobility of incomes of households in the United States differs little from that in China. Social welfare functions are posited that allow for a trade-off between increases in income and increases in income inequality. These suggest strong increases in well-being for urban households in China. The corresponding changes in rural China and in the United States are smaller. Four sets of data on households are drawn on to document these findings.

This paper produces a comprehensive assessment of income redistribution to the working-age population, covering OECD countries over the last two decades. Redistribution is quantified as the relative reduction in market income inequality achieved by personal income taxes (PIT), employees’ social security contributions, and cash transfers, based on household-level micro-data. A detailed decomposition analysis uncovers the respective roles of size, tax progressivity, and transfer targeting for overall redistribution, the respective role of various categories of transfers for transfer redistribution; as well as redistribution for various income groups. The paper shows a widespread decline in redistribution across the OECD, both on average and in the majority of countries for which data going back to the mid-1990s are available. This was primarily associated with a decline in cash transfer redistribution while PIT played a less important and more heterogeneous role across countries. In turn, the decline in the redistributive effect of cash transfers reflected a decline in their size and in particular by less redistributive insurance transfers. In some countries, this was mitigated by more redistributive assistance transfers but the resulting increase in the targeting of total transfers was not sufficient to prevent transfer redistribution from declining.

The study examines the dynamic relationship among globalisation, income inequality and poverty in Mexico, Indonesia, Nigeria and Turkey (MINT countries) between 1980 and 2018.

A Bayesian vector autoregressive (BVAR) approach is used as a technique of estimation hanging on the fact that the method uses prior distribution for the estimated parameters.

The results show that globalisation is a strong predictor of itself in all the MINT countries only in the short run. In the long run, income inequality and poverty strongly influence globalisation, respectively, in Indonesia and Turkey, while globalisation still has more impact on itself in Nigeria. Income inequality has a strong endogenous impact on itself in Mexico and Indonesia over the time horizon, whereas globalisation and poverty are strong predictors of income inequality in the long run in Nigeria and Turkey, respectively. Also, poverty strongly influences itself in all the MINT countries in all the periods, meaning that poverty begets itself in all the MINT countries, except for Indonesia in the long run.

The study suggests that all the MINT countries should ensure political stability and a strong institutional framework to gain from the process of globalisation and to experience reductions in the levels of income inequality and poverty.

This study is distinct from other studies in the sense that an overall globalisation index (GBI) as used by Dreher et al. (2008) is used for the globalisation variable, and the Multidimensional Poverty Index (MPI) is used to capture poverty in all the MINT countries. Also, the research paper uses a BVAR approach as against the classical VAR, and this helps in solving over-fitting problems.

The objectives of this study are threefold: firstly, to measure the impact of educational inequality on income inequality, and per capita income; secondly, to measure the impact of gender inequality in education on income inequality, per capita income and educational inequality; and lastly, to test the Kuznets inverted U-shape hypothesis between inequality in education and average year of schooling.

The study has adopted the Marin and Psacharopoulos (1976) model of human capital in which income earned by an individual can be estimated as a function of number of year spent in schooling or education. Gini coefficient is used as a measure of income inequality, while inequality in education is measured by Gini index of educational inequality. Gender inequality in education is measured by the difference between male and female enrolment ratios as a proportion of male enrolment. The study utilizes the data of six South Asian countries, i.e. Bangladesh, India, Maldives, Nepal, Pakistan and Sri Lanka from 1980 to 2010 at five-year average and employs fixed effect model (FEM) and random effect model (REM) for estimation.

Result suggests that educational inequality and average year of schooling have positive and significant impact on income inequality. Primary (basic) education and tertiary (higher) education reduce income inequality, while secondary education widens income inequality. Negative relationship exists between educational inequality and per capita income. Unequal distribution of education among boys and girls at primary level increases income inequality, while reduces income inequality at tertiary level. Gender inequality in secondary and tertiary level of education reduces per capita income, while unequal distribution of education among boys and girls further increases the educational inequality. Kuznets inverted U-shape hypothesis does not hold between education expansion and educational inequality, while weak U-shape relationship exists in South Asian countries.

Government has to provide free education in poor regions and makes employment programs to reduce the income and educational inequality respectively, while to remove gender inequality in education it is necessary to build more schools especially for girls. Government has to launch different online education programs for expansion in education at all levels.

This study adds to the literature by analyzing whether the inequality in income increases (decreases) due to increase (decrease) in educational and gender inequality in South Asian countries. This study contributes in the existing literature by developing a measure of educational and gender inequality in education in South Asian countries.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2020-0226.