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.     

Correction of China's income inequality for missing top incomes

After accounting for top incomes missing from the Chinese Household Income Project, this paper examines the income inequality trend in China during the 21st century. Our analysis, which involves fitting the upper tail of the income distribution to a power-law model, reveals that the authoritative income survey data miss 0.68% of the population and 6.19% of aggregate income in 2018. Despite the most recent survey data providing better coverage of top incomes, our correction is still crucial. The raw survey data indicate a consistently increasing Gini coefficient between 2002 and 2018, but the corrected index starts to decline from 2013. Meanwhile, the revised top 1% income share increases from 7.01% in 2002 to 7.89% in 2013 and then slightly decreases to 7.64% in 2018, while the revised top 10% income share stabilises at around 33% throughout the period. Notably, China's revised top 10% and top 50% income shares in 2018 are close to those of the United Kingdom but are considerably lower than those of the United States.     

A new approach to the decomposition of the Gini income inequality ratio

The purpose of this research is to introduce a new approach to the decomposition of the Gini ratio into three components, supporting them with economic and statistical rigorous interpretations. The three components are: (i) the Gini inequalitywithin subpopulations; (ii) thenet contribution of the extended Gini inequalitybetween subpopulations; and (iii) the contribution of theintensity of transvariation between subpopulations. These components are weighted by the product of the population shares times the income shares of the corresponding subpopulations where the weights add to one.The decomposition introduced in this research is applied to the 1990 U.S. family income distribution. The population of families is disaggregated by types into the following subpopulations: (a) married-couple families; (b) male householder, no wife present; and (c) female householder, no husband present.     

A refinement of the relationship between economic growth and income inequality

There is mixed evidence in the literature of a clear relationship between income inequality and economic growth. Most of that work has focused almost exclusively on developed economies. In what we believe to be a first effort, our emphasis is solely on developing economics, which we classify as high-income and low-income developing countries (HIDC and LIDC). We make such distinction on theoretical and empirical grounds. Empirically, the World Bank has classified developing economies in this manner since 1978. The data in our sample are also supportive of such classifications. We provide theoretical scaffolding that uses asymmetric credit constraints as a premise for separating developing economies in such a way. We find strong evidence of a negative relationship between income inequality and economic growth in LIDC to be in stark contrast with a positive inequality–growth relationship for HIDC. Both correlations are statistically significant across multiple econometric specifications. Using international data from 1960 to 2010, this article explores the effect of income inequality on economic growth using dynamic panel technique, such as system generalized method of moments (GMM) that is believed to mitigate endogenous problem. These results are strikingly contrasting to the previous estimation results of Forbes (2000) displaying significant positive correlation between two variables in the short to medium term.     

Empirical likelihood confidence intervals for the Gini measure of income inequality

Gini coefficient is among the most popular and widely used measures of income inequality in economic studies, with various extensions and applications in finance and other related areas. This paper studies confidence intervals on the Gini coefficient for simple random samples, using normal approximation, bootstrap percentile, bootstrap-t and the empirical likelihood method. Through both theory and simulation studies it is shown that the intervals based on normal or bootstrap approximation are less satisfactory for samples of small or moderate size than the bootstrap-calibrated empirical likelihood ratio confidence intervals which perform well for all sample sizes. Results for stratified random sampling are also presented.     

Cointegration with structural changes between per capita product and income inequality in Mexico

The long-run relationship between per capita product and income inequality in Mexico is assessed for the period 1963–2010. The methodology consists of stochastic unit root techniques with structural changes. The integration and cointegration tests suggest that it is not possible to understand this link if the possibility of structural changes is not taken into account. Causality, running from per capita product to income inequality, and a negative and significant overall effect are estimated. However, after the regime shifts – mainly estimated to occur in the 1980s – the connection is no longer meaningful. This result seems to be linked to the slow growth of the Mexican economy after these structural breaks, affecting, in turn, the reductions in income inequality.     

The limitations of negative incomes in the Gini coefficient decomposition by source

Lerman and Yitzhaki (1985) developed a decomposition of the Gini coefficient by income source that has been extensively used in the literature. This method has strong limitations in the presence of negative incomes, which were not discussed by the original authors and have been widely overlooked in successive studies. Through theoretical argumentation and practical examples, this article shows that, when using negative incomes, (1) the original decomposition formulae become inappropriate, (2) the marginal effects analysis may yield erroneous results and (3) the Pigou–Dalton ‘principle of transfers’ is not always met. This has critical implications for policy development, given that strategies based upon incorrect analyses could actually result in undesired greater income inequalities. The Gini source decomposition should be carefully applied by researchers and policymakers, especially in rural developing areas, where negative incomes are common due to financial losses from agricultural activities.     

Regional income mobility in large cities throughout China

We investigated the regional income mobility in China for the year 1994–2016. Using the data collected from 185 prefecture-level cities, we find evidence that the regional income mobility was increasing over past two decades. By connecting income mobility with inequality index, we could draw a graph indicating an inverse U-shape relation between the two variables. We also provide the regional income mobility of the world for comparison.     

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.     

财政分权对中国收入差距的影响——基于居民收入基尼系数的实证分析

文章在财政分权理论的框架下,利用21个省市区1995～2004年间的面板数据,实证检验了财政分权对中国收入不平等的影响。结果发现：财政分权对中国居民收入差距的整体扩大起到推动作用,但对西部地区的整体和农村的收入差距具有抑制作用;经济发展水平、人口浓度、人力资本和城市化等控制变量对收入差距也有不同的影响。     

公平性指标:从基尼系数到收入满足度差距

本文通过对财富分配、生活方式和价格水平、货币边际效用递减、评价生活质量标准以及指标构造缺陷等方面的分析,揭示了基尼系数作为公平性指标的局限性.本文认为,公平是公众认为合理的、愿意接受的规范和准则,其结果是公众的满意和社会的和谐.根据这一观点,我们可以用收入满足度来反映公众的满意程度,从而建立能反映社会公平与和谐程度的指标--收入满足度差距指标,并用德国的经验数据加以验证.     

ICT and income inequality: a cross-national perspective

The importance of information and communications technology (ICT) for economic growth and development is widely researched and seemingly well understood, but the effect of such investments on income inequality is less well documented. On the one hand, improvements in infrastructure are expected to expand economic opportunities for previously underserved populations. On the other hand, ICT growth may exacerbate inequality due to differential access and skill premiums. We use panel data from 109 countries during the period 2001–2014 to examine the empirical connection between ICT and income inequality in a cross-national context. Our results suggest that the effect of ICT on income inequality depends both on the specific type of ICT and on the measure of income inequality. In addition, the magnitude of ICT’s effect on income inequality is comparable to that of more traditional forms of economic infrastructure. Finally, we find that the association between ICT and income inequality is conditional on other economic and political characteristics.     

Income inequality in China: causes and policy responses

The phenomenal economic growth in China has been accompanied by a rapid increase in income inequality. This paper reviews the historical trends and patterns of income inequality in China, discusses the potential causes underlying rising income inequality, and applies the functional distribution of income approach in understanding China’s income inequality. This analytical approach highlights how rising return to capital relative to wage incomes can be an important source for increasing income inequality in China. The paper provides the evidence which shows that the rapid economic growth in China has been relying on a model that pays high returns to various kinds of capital including financial capital and real estate, while the ownership of capital is very unequal. This finding prompts us to rethink about the causes of China’s income inequality and to formulate appropriate policies based on the new way of understanding this pressing issue of income distribution in China.     

中国城乡金融发展与基尼系数的灰色关联分析

用6个指标反映中国城乡金融发展水平,并利用灰色关联分析模型,分析2003—2011年中国城乡金融发展对基尼系数的影响。研究结果显示:金融结构、金融中介效率、固定资产投入产出比和金融行业劳动力对基尼系数的影响最大。最后提出缩短城乡居民收入差距的金融对策。     

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.     

Persistence and determinants of income inequality: The Brazilian case

Latin America, which is a region known for its high and persistent income inequality levels, experienced a significant decline in income inequality since the second half of the 1990s. Brazil is a particularly interesting case in Latin America. While the country presented notable economic growth and improvements in income distribution in the early 2000s, Brazil continues to experience high levels of income inequality in comparison with other Latin American or advanced economies. This research contributes to the literature by examining the key drivers of income distribution and the degree of persistence of income inequality among Brazilian states. This research also improves upon previous works by using more recent and comprehensive data and addressing concerns regarding heterogeneity and endogeneity by using the system GMM estimation method. Our findings show that income inequality is highly persistent across Brazilian states and that government policies including income transfer programs made important contributions to reduce income inequality in Brazil. This study also shows that the decline in labor income ratios between different ethnic groups and the increase of the share of formal jobs in the labor market contributed to reduce income inequality.     

Long term trends in fair and unfair inequality in the United States

This article analyses the microeconomic sources of wage inequality in the United States from 1967–2012. Decomposing inequality into factors categorized by degree of personal responsibility, education explains over twice as much of inequality today as 45 years ago. However, neither hours worked nor education, industry, marital status, or geographical location can explain the rise in income inequality. In fact, ‘unfair’ inequality (income disparity derived from non-responsibility factors) has risen faster than ‘fair’ inequality (income disparity derived from responsibility factors), regardless of the set of variables chosen as fair sources of inequality. We further examine income inequalities within gender and racial groups, finding substantial heterogeneity. Overall, using micro data to understand the sources of inequality and how these changes over time can provide better information for policymakers motivated to combat rising inequality.     

Measuring cross-country material wellbeing and inequality using consumer durables

We measure the mean and inequality in country material wellbeing based on households’ consumer durables, using household-level data from OECD’s PISA surveys for 40 countries over 2000–2012. Our consumption-based measures capture aspects of material wellbeing not captured fully by income-based measures. For 2012, tests show that the consumption-based metrics are more closely associated with objective mortality-related outcomes than are income-based measures; in 2000 (and over 2000–2012) each set of measures adds information relative to the other. The consumption-based measures may be particularly useful in revealing where income-based measures provide inaccurate indications of the mean and/or inequality in household living standards.     

The effects of transition on the distribution of income in China

Using two large samples for 1988 and 1995 we decompose the Gini coefficient of household income according to type of income with the purpose of analyzing reasons for the rapid increase of inequality. The results show that the change in relative size of money income and its changed profile are found to be the major processes behind the rapid increase of income inequality in rural China. Changes in housing allocation and an increased number of retirees in combination with higher benefits have made inequality increase in urban China and in China as a whole. JEL classification: D31, P27.