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.      

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。     

影响我国农村居民收入差距的主要因素:从收入构成角度的分解分析

This paper attempts to explore the causes behind the change of the inequality in China rural areas at the very beginning of this century by decomposing the inequality of the total per capita income into the contributions from different income components. Furthermore, we develop the decomposition method of Gini coefficients from the income components and use it not only in the static analysis but also in comparative static analysis. Namely it can be used to explore the change of the overall inequality by decomposing the change of Gini Coefficient from income components. The empirical results show that the wage from local employment, the income from agricultural household business and the incomes from non-agricultural household business are the three income components that made the largest contributions to the inequality of the total per capita income. The total contribution to the overall inequality of non-agricultural incomes was much more than that of agricultural incomes. The incomes from agricultural household business, the incomes from non-agricultural household business and the wages from migration made the positive impact on the increase of the overall inequality. The incomes donated by relatives and friends made the most important negative impact on the increase of the overall inequality.     

A Gini decomposition analysis of inequality in the Czech and Slovak Republics during the transition1

Disposable income inequality, as measured by the Gini coefficient and using the Family Budget Survey data, increased very little, and by a similar amount, from 1989 to 1993 in the Czech Republic and Slovakia. This surprising result is examined with an analysis of changes in the channels of redistribution and Gini decomposition. We find that the sizeable increase in overall inequality due to changes in the wage earnings component is mitigated by changes in the tax and transfer components in both republics. As for the relative effects of government policies, changes in the transfer component contributed more than changes in the tax component to lowering the growth of inequality in the Czech Republic, while the reverse was true for Slovakia.     

一个新的基尼系数子群分解公式-兼论中国总体基尼系数的城乡分解

基尼系数的子群分解公式有多种形式,不同分解公式的实证结论有较大差异。本文分析了群内基尼系数的权数以及群间不平等指标所应具备的基尼系数内涵,提出了一个新的群间不平等指标,从而给出了一个新的基尼系数子群分解公式。利用这个新的基尼系数子群分解公式,本文实证分析了近年来中国总体基尼系数的城乡分解。实证结果表明,农村群间不平等对总体基尼系数的贡献率最大,城镇群内不平等的贡献率不断上升,这是促使中国总体基尼系数不断上升的两个重要因素。     

DEMOGRAPHIC TRENDS AND CONSUMPTION INEQUALITY IN AUSTRALIA BETWEEN 1975 AND 1993

We examine trends in consumption inequality among Australian households using the Australian Bureau of Statistics Household Expenditure Surveys collected over the period 1975 to 1993. We find that the distribution of consumption is much more equal than that of income and that both income and consumption inequality rose by significant amounts over the period. However, consumption inequality rose by much less (the Gini coefficient for income inequality rose by 17 percent while that for nondurable consumption rose by 9 percent). We then examine the effects of demographic trends, specifically population aging and changing family structures, and find they account for only a minor fraction in the overall growth in economic inequality.     

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.     

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.     

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.     

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.     

Some Ethnic Dimensions of Income Distribution from Pre- to Post-reform New Zealand, 1984–1998*

Based on unit record data from four household surveys conducted by Statistics New Zealand for the years 1983/1984, 1991/1992, 1995/1996 and 1997/1998, this paper addresses some ethnic dimensions of income inequality among New Zealanders over the period of the surveys. It applies alternative techniques of decomposition of the Gini coefficient of inequality by subgroups of population. It then analyses how changes in the incomes of specific population subgroups affect the overall inequality. The results help quantify the economic distances among the different ethnic populations of New Zealand, and indicate how and why these distances have been changing over time.     

Inequality within generation: Evidence from France

Intra-generational inequalities focus on the distributions within age groups. On the basis of French household income surveys carried out from 1996 to 2014, the Gini coefficient and D9/D1 inter-decile ratio were calculated so as to evaluate intra-generational income inequality before and after redistribution by the tax and welfare system. Age-Cohort-Period models were then estimated in order to disentangle age and generation effects. Over a life cycle, intra-generational inequality displays a hump-shaped curve peaking at age 55–59. This inequality is significantly lower among the youngest, whichever inequality indicator is used, and among the oldest, when measured by the inter-decile ratio. Comparison of pre- and post-redistribution income reveals that the tax and welfare system particularly reduces inequality among the young. Intra-generational inequality measured by the Gini coefficient increases significantly from one generation to the next. Measured by the inter-decile ratio, the increase is considerable for the gross income of those generations born from the 1970s on. However, the tax and welfare system has compensated for this increase, because analysis of the inter-decile ratio applied to disposable income shows no significant difference between generations.     

The Changing Pattern of Sources of Income and its Impact on Inequality: The Method and its Application to Australia, 1975–94

This paper proposes a new method of explaining the temporal change in economic inequality in terms of changes in the sources of income. The method is based on the decomposition of the Gini coefficient by source of income. Using this method, the paper also examines changes of the sources of private income in Australia over the last two decades and their impact on the distribution of income. It will be seen that not only have the shares of income from different sources changed, but that there has been significant change in inequality of distribution of the components themselves.     

Why has Income Inequality in Germany Increased From 2002 to 2011? A Behavioral Microsimulation Decomposition

This paper proposes a method to decompose changes in income inequality into the contributions of policy changes, wage rate changes, and population changes while considering labor supply reactions. Using data from the Socio‐Economic Panel (SOEP), this method is applied to decompose the increase in income inequality in Germany from 2002 to 2011, a period that saw tax reductions and a controversial overhaul of the transfer system. The simulations show that tax and transfer reforms have had an inequality‐reducing effect as measured by the mean log deviation and the Gini coefficient. For the Gini, these effects are offset by labor supply reactions. In contrast, policy changes explain part of the increase in the ratio between the 90th and the 50th income percentiles. Changes in wage rates have led to a decrease in income inequality. Thus, the increase in inequality was due to changes in the population.     

How large is income inequality in China: assessment on different estimates of Gini coefficient

The rising income inequality in China has attracted social attentions, especially since SWUFE published the unbelievably high Gini coefficient in December 2012. In order to answer the question how large income inequality in current China is, this paper compares existing different Gini coefficients estimated from four different household surveys, which are the NBS household survey, the CHIP survey, the CHFS of SWUFE, and the CFPS of Peking University, and then assesses these household surveys themselves. The relevant evidences indicate that the national Gini coefficient in current China is between 0.47~0.52. The sampling defects of the CHFS are significantly major and that the national Gini coefficient of 0.61 published by SWUFE is seriously overestimated.     

A tale of two Ginis in the US, 1921–2012

Following a methodology by Jantzen and Volpert (2012), we use IRS Adjusted Gross Income data for the US (1921–2012) to estimate two Gini indices representing inequality at the bottom and the top of the income distribution, and to calculate the overall Gini as a function of the parameters underlying the two indices. A steady increase in the overall Gini since the Second World War actually hides two different periods of distributional changes. First, the increase in inequality from the mid 1940s to the late 1970s is driven by rising inequality at the bottom of the income distribution that more than offsets a decrease in inequality at the top. The implication is that middle-income earners gained relative to high-incomes, and especially relative to low-income earners. Second, the rise in the Gini after 1981 is driven by rising inequality at the top. Third, top-driven inequality follows a U-shaped trajectory consistent with Piketty and Saez (2003, 2006). Fourth, the welfare effects of the different distributional changes behind increasing inequality can be evaluated in light of the Lorenz-dominance criterion by Atkinson (1970): we argue that the rise in inequality since 1981 is much more likely to be associated with a social welfare loss net of compensating growth.     

Unemployment Shocks and Income Distribution: How did the Nordic Countries Fare during their Crises?

We study the inequality of disposable income in Denmark, Finland, Norway and Sweden during the late 1980s and early 1990s when unemployment rose dramatically in all four countries. A standard measure of inequality — the Gini coefficient – was surprisingly stable in all countries during this period. By decomposing the Gini into income components, we test hypotheses about the reasons for this stable income distribution. Our most straightforward hypothesis, that rising unemployment benefits counteracted the impact of more unequally distributed earnings, receives only limited support. More complex mechanisms seem to have been at work.
JEL classification: D 30; D 31; J 60     

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 inequality and FDI: evidence with Turkish data

This article explores how foreign direct investment (FDI) and other determinants impact income inequality in Turkey in the short- and long-run. We apply the nonlinear auto-regressive distributed lag (ARDL) modelling approach, which is suitable for small samples. The data for the study cover the years from 1970 to 2008. The empirical results indicate the existence of a co-integration relationship among the variables with asymmetric adjustment of the income distribution in the short- and long-run. The negative impact of FDI on the Gini coefficient, decreasing income inequality, is statistically significant in the short- and long-run, though with a quantitatively small impact in both cases. In the short run, GDP growth increases inequality initially, an effect that is reversed in the next period, increases in domestic gross capital formation decreases inequality, and increases in the literacy rate have very minor adverse effects on income equality. However, in the long run these variables have no statistically significant effects on the Gini coefficient. A reduction in the population growth rate reduces inequality in the short run but has no effect in the long run, whereas an increase in the rate reduces inequality in the long run but has no effect in the short run.