THE INEQUALITY OF EMPLOYMENT AND SELF-EMPLOYMENT INCOMES: A DECOMPOSITION ANALYSIS FOR THE U.K.

U.K. employment and self-employment income inequality are analysed over 1979–94/95. Robust inequality decompositions reveal occupation to be a relatively important and hitherto neglected determinant of earnings inequality. In contrast, self-employment income inequality is harder to explain, although occupation is also the most important single factor in the mid-1990s. The paper also provides a novel implementation of a decomposition of changes in Kolm's inequality index.     

中国农村地区间居民收入差距及构成变化:1995—2008年——基于基尼系数的分解

本文采用分省的面板数据对农村地区间基尼系数分解结果表明,1995—2008年,中国农村地区间收入差距总体上仍然呈现上升趋势,基尼系数上升了0.0639,但增幅出现明显减弱趋势。基尼系数变化及农村居民工资性收入对基尼系数的贡献变化显示出与经济发展阶段变化一致的含义;结构性效应已不再是农村地区间收入差异的唯一构成来源,收入集中效应对基尼系数起了明显的增加作用,工资性收入的集中效应超过了其他收入来源。这些构成来源的变化蕴含着中国面临调节地区间农村收入差距的政策机遇。     

Approximation and decomposition of Gini, Pietra–Ricci and Theil inequality measures

Two related problems are dealt with in this article, concerning some popular inequality indices proposed by Gini, Pietra?CRicci and Theil: (1) the calculation of the index when only a frequency distribution is available, thus needing some kind of approximation; and (2) a reasonable decomposition of the index calculated for a mixture, with components related to ??within?? and ??between?? inequalities, and possibly to the separate contributions of each group to the overall inequality. Beside the proposals arising from the specific structure of each inequality index, a general approach for identifying the within component is utilized, which is based on the fixation of a given number of fictitious individuals (called aggregate units), common to every group. Regarding the Gini index, a general expression is obtained for the approximation problem, while the within inequality is more easily managed by the recourse to aggregate units. The decomposition of the Pietra?CRicci index displays three components, clearly ascribable to within inequality, to a mixture effect and to a mean effect. Regarding the Theil index, some simple and very accurate approximation formulae are obtained. An application of all the indices and their decompositions has been made for the 2004 income distribution for Italy (Bank of Italy Survey).     

A Note on α‐Gini Measures

This note provides a characterization of α‐Gini inequality measures. These measures generalize the standard Gini index by including one sensitivity parameter α, which captures different value judgments. The α‐Gini measures are shown to be weakly decomposable and unit consistent. Weak decomposition provides within‐group and between‐group inequalities. Unit consistency keeps unchanged the ranking of two income distributions when the income units vary. It is shown that the α‐Gini measures are relevant with either “leftist” or“rightist” views.     

Decomposition of the Gini Index by Sources of Income

The Gini concentration coefficient is considered to be the best synthetic inequality measure and is widely used in economic research. In this paper, we present its decomposition by factor components with an application to income distributions in Poland. Income inequality measures proposed by Gini, Zenga and Bonferroni are calculated for different socio-economic groups based on their exclusive or primary source of maintenance. For theoretical income distribution, the Dagum type-I model was used. The basis for the calculations was the individual data coming from the Household Budgets Survey conducted quarterly by the Polish Central Statistical Office. Using the decomposition of inequality by source, we were able to examine how changes in particular income components affected overall inequality.      

Industrialization and spatial income inequality in rural China, 1986-921

Rural industrialization has been a dominant factor responsible for China's rapid economic growth over the last 18 years, but it has been accompanied by increased inter-regional income inequality. Using the most recent rural income data and two alternative Gini coefficient decomposition methods, this study finds that income inequality, especially inter-regional income inequality increased significantly in the period 1986-92. More than half of national income inequality was due to its inter-provincial component, and three quarters of inter-provincial inequality was due to its inter-zonal component. Uneven development of township and village enterprises has been a major cause of increased regional income inequality.     

Income inequality among minority farmers in China: Does social capital have a role?

We used recently available household panel data collected by China's National Bureau of Statistics to examine the effects of natural, human, material, and social capital on income inequality among minority farmers in China's Xinjiang Province between 2011 and 2012. Results obtained from panel quantile regression and correlated random effects’ models show that income inequality and the poverty of ethnic farmers have been decreasing to some extent. Results also indicated that human capital exacerbates income inequality among the minorities, whereas the effect of natural capital on income inequality is not evident. Social and material capital reduce income inequality among ethnic minorities. Results from the decomposition of the Gini coefficient indicate that material and social capital contribute to a substantial majority of the income inequality in the region.     

Income distribution and economic development: Insights from machine learning

We draw upon recent advances that combine causal inferences with machine learning, to show that poverty is the key income distribution measure that matters for development outcomes. In a predictive framework, we first show that LASSO chooses only the headcount measure of poverty from 37 income distribution measures in predicting schooling, institutional quality, and per capita income. Next, causal inferences with post‐LASSO models indicate that poverty matters more strongly for development outcomes than does the Gini coefficient. Finally, instrumental variable estimates in conjunction with post‐LASSO models show that compared to Gini, poverty is more strongly causally associated with schooling and per capita income, but not institutional quality. Our results question the literature's overwhelming focus on the Gini coefficient. At the least, our results imply that the causal link from inequality (as measured by Gini) to development outcomes is tenuous.     

Decomposing Global Inequality

This paper provides an intuitive additive decomposition of the global income Gini coefficient with respect to differences within and between countries. In 2005, nearly half the total global income inequality is due to income differences between Europeans and North Americans on the one side and inhabitants of Asia on the other, with the China‐USA income differences alone accounting for six percent of global inequality. Historically, income differences between Asia and Europe have driven a large part of global inequality, but the quantitative importance of within‐Asia income inequality has increased substantially since 1950.     

MULTILEVEL DECOMPOSITION METHODS FOR INCOME INEQUALITY MEASURES*

This paper studies the multilevel decomposability of the respective income inequality measures proposed by Theil, Rao and Bahattacharya–Maharanobis. All the methods can be decomposed into multilevels if and only if each lower level subgroup belongs to only one particular higher level group. We found not only analytically but also empirically that the residual in the decomposed Bahattacharya–Maharanobis measure tends to increase when the decomposition levels increase. We conclude that Theil's and Rao's decompositions have advantages in empirical analysis and that the choice of the decomposition methods depends on the purpose of the analysis.     

Calculation and decomposition of income inequality in low- and middle-income countries: a survey data analysis

This article estimates income inequality in a sample of four low- and middle-income (LMI) countries namely; Albania, Nepal, Tajikistan and Tanzania using the household survey data – Nepal Living Standard Measurement Survey Second. First, we estimate the income generation function for each country and calculate the income inequality using Gini index (GI). Second, we decompose the income Gini into the determinants of income generation functions. Based on the decomposition result, socio-economic factors are the most important determinants of income inequality followed by geographic factors. Demographic factors have the least effect on income inequality in all four countries. Third, we propose a new method to quantify the effect of change in each covariate of income generation function on income Gini. That allows us to quantify the effects of change in specific policy such as increase in investment in schooling or public health to specific group of the population in society on income inequality. A carefully chosen, integrated policy can significantly reduce inequality in all four countries under study.     

Wealth inequality: Theory,measurement and decomposition

This paper reviews the basic principles of inequality measurement, underlining the advantages and shortcomings of alternative measures from a theoretical standpoint and in the context of the study of the distribution of wealth. Adopting the two most popular measures, the Gini index and the P‐shares, the paper documents wealth inequality in Canada using the 1999, 2005 and 2012 Survey of Financial Security (SFS). It carries out several decompositions with covariates, featuring DFL‐type reweighting methods and Gini and P‐shares RIF regressions. The latter parallel decompositions deepen our understanding of how changes in socio‐demographic characteristics, including the compensating role of family formation and human capital, impact wealth inequality.     

A matrix approach to the Gini index decomposition by subgroup and by income source

The literature offers two main ways of decomposing the Gini index: decomposition by population subgroup and by income source. This article proposes merging the two decomposition dimensions by suggesting a matrix formula for the Gini index which permits the simultaneous decomposition by subgroup and by income source. Using this multi‐decomposition, one can investigate the role of the interaction between the subgroup and the source components in determining the overall inequality. We apply the methodology to sample data on Italian household incomes collected in 2008.     

POVERTY AND INCOME INEQUALITY IN PUERTO RICO, 1969–89: TRENDS AND SOURCES

The paper examines Puerto Rican trends in income distribution during the 1970s and 1980s, then attempts to ascertain the source of the changes in income inequality through a decomposition of the Gini coefficient by factor income source. The study finds that poverty and inequality declined unambiguously during the twenty-year period. The source of the fall in income concentration was found to have been tied, not to changes in the distribution or share of earned income, but to that of unearned income, particularly transfers. These appear to have also accounted for the decline in poverty.     

BETWEEN GROUP INEQUALITY AND TARGETED TRANSFERS

This paper provides two extensions to the group decomposition of the Gini index by Yitzhaki and Lerman. First, within group, stratification, and between group inequality are analyzed along several dimensions at once. This provides for a better understanding of the determinants of inequality. Second, the impact on the Gini of marginal changes in income or consumption by group is derived. This can be used to evaluate targeted redistributive policies or assess the impact of exogenous shocks by group. The analysis is applied to data from Bangladesh with a focus on the impact of land ownership, education, and occupation on inequality.     

Inequalities in Income Distribution in Slovenia

This article presents an empirical analysis of income distribution based on income tax data for Slovenia in 1991-2000. It presents evidence of rising inequalities in income distribution (gross income, gross wages and pensions). These results are supported by coefficients of variation, Gini coefficients and by the Lorenz curves. Inequalities increased rapidly in the 1991 to 1993 period. After a significant decrease in 1994 and a steady increase from 1995 to 2000, the peak value from 1993 was not surpassed. Atkinson's requirements for dominance comparisons are not violated. Inequalities are also present in the distribution of the benefits of economic growth among income groups.     

USING THE P90/P10 INDEX TO MEASURE U.S. INEQUALITY TRENDS WITH CURRENT POPULATION SURVEY DATA: A VIEW FROM INSIDE THE CENSUS BUREAU VAULTS

The March Current Population Survey (CPS) is the primary data source for estimation of levels and trends in U.S. earnings and income inequality. However, time-inconsistency problems related to top coding lead many CPS users to measure inequality via the ratio of the 90th to the 10th percentile (P90/P10) rather than by more traditional summary measures. With access to public use and restricted-access internal CPS data, and by applying bounding methods, we show that using P90/P10 does not completely obviate time-inconsistency problems, especially in capturing household income inequality trends. Using internal data, we create consistent cell mean values for all top-coded public use values that, when used with public use data, closely track inequality trends in earnings and household income using internal data. But estimates of longer-term inequality trends with these corrected data based on P90/P10 differ from those based on the Gini coefficient. The choice of inequality measure still matters.     

Distributional national accounts for the Netherlands and a comparison with 12 other countries

Most inequality studies rely on micro data that do not capture a substantial share of income identified in the national accounts. In the Netherlands, almost one fifth of household disposable income is missed by current inequality statistics. In this paper, we present inequality statistics for the Netherlands that capture all of household income, so-called distributional national accounts. Compared to the current inequality statistics, the Gini coefficient for disposable income increases substantially from 0.289 to 0.337. Cross-country comparisons show that such a change between Gini coefficients based on micro-data versus Gini coefficients based on distributional national accounts does not apply to all countries. The difference between both Gini coefficients varies not only between countries in the size, but also in the sign of the difference.     

On the Limitations of Some Current Usages of the Gini Index

This note constructs a simple two class example in which the Gini index is held constant while the size of the rich and poor populations change, in order to illustrate how very different societies can have the same Gini index and produce very similar estimates of standard inequality averse Social Welfare Functions. The rich/poor income ratio can vary by a factor of over 12, and the income share of the top one per cent can vary by a factor of over 16, with exactly the same Gini index. Focussing solely on the Gini index can thus obscure perceptions—e.g. of important market income trends or large changes in the redistributive impact of the tax and transfer system. Hence, analysts should supplement the use of an aggregate summary index of inequality with direct examination of the segments of the income distribution which they think are of greatest importance.     

Estimation of Concentration Measures and Their Standard Errors for Income Distributions in Poland

Measures of concentration (inequality) are often used in the analysis of income and wage size distributions. Among, them the Gini and Zenga coefficients are of greatest importance. It is well known that income inequality in Poland increased significantly in the period of transformation from a centrally planned economy to a market economy. High income inequality can be a source of serious problems, such as increasing poverty, social stratification, and polarization. Therefore, it seems especially important to present reliable estimates of income inequality measures for a population of households in Poland in different divisions. In this paper, some estimation methods for Gini and Zenga concentration measures are presented together with their application to the analysis of income distributions in Poland by socio-economic groups. The basis for the calculations was individual data coming from the Polish Household Budget Survey conducted by the Central Statistical Office. The standard errors of Gini and Zenga coefficients were estimated by means of the bootstrap and the parametric approach based on the Dagum model.