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In large parts of the world, income inequality has been rising in recent decades. Other regions have experienced declining trends in income inequality. This raises the question of which mechanisms underlie contrasting observed trends in income inequality around the globe. To address this research question in an empirical analysis at the aggregate level, we examine a global sample of 73 countries between 1981 and 2010, studying a broad set of drivers to investigate their interaction and influence on income inequality. Within this broad approach, we are interested in the heterogeneity of income inequality determinants across world regions and along the income distribution. Our findings indicate the existence of a small set of systematic drivers across the global sample of countries. Declining labour income shares and increasing imports from high-income countries significantly contribute to increasing income inequality, while taxation and imports from low-income countries exert countervailing effects. Our study reveals the region-specific impacts of technological change, financial globalisation, domestic financial deepening and public social spending. Most importantly, we do not find systematic evidence of education’s equalising effect across high- and low-income countries. Our results are largely robust to changing the underlying sources of income Ginis, but looking at different segments of income distribution reveals heterogeneous effects.

This paper examines associations of mass media and information and communications technologies (ICTs) with inequality and poverty. It has been found that newspaper circulation has a robust negative association with inequality. Radios and TVs also have a negative association with poverty. ICT expenditures (as a percentage of GDP) have a negative association with poverty. An ICT index is constructed which also has a negative association with poverty. An instrumental variable analysis confirms the robust negative association between newspaper circulation and inequality.

Brazil’s regional inequality is an important topic due to the large and persistent differences in development between states and the high levels of inequality in the country. These variations in development can potentially render survey data inaccurate since the significance of capital income varies across the states. Besides, previous studies incorporating tax and national accounts data globally have mainly focused on measuring the income distribution at the country-level. This approach can limit the understanding of inequality, especially when considering large countries such as Brazil.

The methodology used to construct these estimates follows the guidelines of the Distributional National Accounts, whose core goal is to provide income distribution measures consistent with macroeconomic aggregates and harmonized across countries and time. The procedure has three main steps: first, it corrects the survey’s underrepresentation of top incomes using tax data. Then, it accounts for national income items not included in the survey or tax data, such as imputed rents and undistributed profits. Finally, it ensures that all components match the national income.

Compared to survey-based estimations, the results reveal a new angle on the state-level inequality. This study indicates that Amazonas, Rio de Janeiro and São Paulo have a more concentrated income distribution. The top 1\% of earners in these states receives around 28\% of total pre-tax income, while the top 10\% receive nearly 60\%. On the other end, Amapá (AP), Acre (AC), Rondônia (RO) and Santa Catarina (SC) are the states where the income distribution is less concentrated. There were no significant changes in the income distribution across the states during the period analyzed.

This study combines survey, tax and national accounts data to construct new estimates of Brazil’s state-level income distribution from 2006 to 2019. Previous results only considered income captured in surveys, which usually misses a significant part of capital incomes. This limitation may bias comparisons as capital income has different importance across the states. The new estimates represent the income of top groups more accurately, account for the entire national income and enable to compare regional inequality levels consistently with other countries.

Faced with contradictory outcomes in empirical studies on the relation between democracy and income inequality, this paper attempts to provide empirical relations between democracy and income inequality. In particular, the authors seek to find if any curvilinear relation exists as in the Kuznets hypothesis.

Given elusiveness in empirical relations, the authors will consider several specifications using different estimation methods such as ordinary least squares (OLS), panel data estimation and performing statistical tests to determine the best specification for the relation between income inequality and democracy. Once the authors choose the specification, then the authors will apply this specification to the different groups of data to find any meaningful implications.

Using the unbalanced panel of 136 countries spanning from 1980 to 2018, the authors found an inverse U-shaped relation, called a political Kuznets curve – income inequality increases first and then decreases later as more democracy is achieved. By quantifying the curve, the authors find that the direct impact of democracy on income inequality is small and that the incremental impact of democracy on income inequality is smaller in a semi-democracy while relatively larger in a full democracy and autocracy.

From the study’s findings, the following policy implications can be considered. First, any change in income inequality caused by democratization should not be concerning as the impact of democracy on income inequality is measured to be very small. Second, the largest factor reducing income inequality is real GDP per capita. Third, the authors find that an impact of government expenditure on income inequality is also inversely U-shaped.

The purpose of this study is to firstly, find out does the relationship between finance, trade and income inequality provides matching evidence using the macro- and firm-level data? Secondly, whether a causal relationship from the firm-specific data can be established.

The authors employed a panel data fixed effect regression and two-stage least square (2sls) using key instruments. Their analysis is conducted based on a sub-sample analysis that emphasizes Latin America Region (LAR), Africa (AFR) and Asia by merging South Asia, East Asia and Pacific Regions (SEAR). These areas are characterized by an unequal society represented by a mounting gini index (Robilliard, 2020; De Rosa et al. 2020). Accordingly, the authors estimated a comparable model to unfold the impact of finance and trade. Besides, the authors exploited the interaction between their predictors with labor productivity and ownership structure to claim the difference between the results using the two data sets. The effort to establish a causal relationship from the combined firm-level data challenges the current literature that leans towards either macro or micro perspectives.

The result obtained from macro-level data shows that while financial development widens income inequality, the impact of trade on income inequality is negative. However, the estimation result from the combined firm-level data portrays that the proportion of investment financed by banks and trade widens income inequality. Given the contrasting result concerning trade, the authors test whether the firm-level evidence is causal following different identification strategies. The exercise shows that the correlation presented in the paper is causal. That challenges the current literature that falls short of providing firm-specific evidence.

The authors adds to a growing body of literature on finance, trade and income inequality by paying due emphasis on firms from 2006 to 2020. The authors show the private sector development effect on income inequality by linking topics from the firm and country-level data.

The authors extend the macro-level discussion and contribute to the existing literature by offering firm-specific evidence on the relationship between finance, trade and income inequality.

This paper focuses on three (marginal?) questions surrounding the analysis of economic convergence and uses Spanish provinces as a means of illustration. The three questions in hand are as follows. Given that the geographical units of analysis are usually quite different in economic size, is the weighting of economic units relevant in convergence analysis? The average per capita income of a given region, or country, is the first moment in the distribution of income, but what about the second moment, inequality, have we converged in inequality? An aggregate welfare index must take into account, at least, the evolution of the first two moments of the distribution of income, and so does the adjustment for inequality make important differences in the evolution of average per capita income? The answer to the first two questions is yes, but to the third it is clearly no.

The purpose of this paper is to empirically examine the relationship between fiscal consolidations and changes in income distribution.

Looking at a sample of 27 emerging market economies between 1980 and 2014, the authors resort to both static panel techniques as well as dynamic impulse response function analysis using local projection methods to uncover the direct impact of adjustments on inequality.

The authors find that fiscal consolidations tend to lead to an increase in income inequality and reduce the redistributive role of fiscal policy. Spending-based consolidations are more detrimental to income distribution than tax based ones and fiscal retrenchment during bad times raises inequality. In times of fiscal expansion inequality seems to rise in the medium term and this effect is larger if the economy is booming.

The distributional effects of consolidation, i.e. whether consolidation can confer benefits, must be balanced against the potential longer term benefits. It should be recognized that there is scope for improving the targeting and efficiency of public programs and that fiscal adjustments would not unavoidably run into such an efficiency vs equity trade-off.

The paper, applying a consistent methodology, documents the set of fiscal episodes emerging market economies experienced over time. The authors empirically examine both the static and dynamic links between fiscal consolidation and inequality. Since composition matters, the authors explore how spending and tax-based fiscal consolidations affect income distribution. The authors conduct several robustness checks including the use of alternative income distribution proxies and state-contingent estimations on the phase of the business cycle.

This study aims to determine how taxes can be used to bridge income inequality gap in sub-Saharan Africa (SSA).

A panel data set of 36 SSA countries was analysed using generalised method of moments.

The results suggest that an increase in direct taxes relative to indirect taxes has a positive significant impact on income inequality. This is mostly due to the progressive nature of direct taxes as compared to indirect taxes.

This research contributes to the scant literature on how specific tax components affect income inequality, especially in developing countries.

China's increasing income inequality might cause a series of problems, such as the slowdown of economic growth, social and economic tension, the decline of the ecological environment quality and the threat to citizens' health. Consequently, income inequality will inevitably affect the ecological well-being performance (EWP) level of China's provinces through the above aspects. Analyzing the impact of income inequality on EWP and its spatial spillover effects are conducive to improving the level of EWP in China. Therefore, the research purpose of this paper is to use China's provincial data from 2001 to 2017 to analyze the impact of income inequality on EWP and the spatial spillover effect based on the evaluation of the EWP value of each province.

At first, this study utilizes the super efficiency slacks-based measure model (Super-SBM model) to calculate the EWP values of 30 provinces in China, which can evaluate and rank the effective decision units in the SBM model and make up for the defect that the effective decision units cannot be distinguished. Then this study applies the spatial Durbin model and Tobit regression model (SDM-Tobit model) to explore the impact of income inequality and other influencing factors on EWP and the spatial spillover effects in adjacent areas.

Firstly, the average EWP in China fluctuated slightly and showed a downward trend from 2001 to 2017. In addition, the EWP values of the provinces in the western region are usually weaker than those in the eastern and central regions. Moreover, income inequality is negatively correlated with EWP, and the EWP has a spatial spillover effect, which means the EWP level in a region is affected by EWP values in the adjacent regions. Furthermore, the industrial structure and urbanization level are both negatively related to EWP, while technology level, investment openness, trade openness and education level are positively related to EWP.

Compared with the existing research, the possible contribution of this research is that it takes income inequality as one of the important influencing factors of EWP and adopts the SDM-Tobit model to analyze the impact mechanism of income inequality on EWP from the perspective of time and space, providing new ideas for improving the EWP of various provinces in China.