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.     

Decomposing World Income Distribution: Does The World Have A Middle Class?

Using the national income/expenditure distribution data from 111 countries, we decompose total inequality between the individuals in the world, by continents and regions. We use Yitzhaki's Gini decomposition which allows for an exact breakdown of the Gini. We find that Asia is the most heterogeneous continent; between-country inequality is much more important than inequality in incomes within countries. At the other extreme is Latin America where differences between the countries are small, but inequalities within the countries are large. Western Europe/North America is fairly homogeneous both in terms of countries' mean incomes and income differences between individuals. If we divide the world population into three groups: the rich (those with incomes greater than Italy's mean income), the poor (those with incomes less than Western countries' poverty line), and the middle class, we find that there are only 11 percent of people who are "world middle class"; 78 percent are poor, and 11 percent are rich.     

On the impact of innovation and inequality in economic growth

We present robust results on the empirical relationship among income inequality, innovation, and economic growth for a panel dataset of 74 countries over the period 1996–2014. We estimate pairwise causality tests to show that there is bidirectional causality between GDP per capita and R&D, while R&D causes the Gini index of income inequality, and it causes human capital. Allowing coefficients to be different across cross-sections of countries, we get in any case a pairwise bi-directionality. By dynamic panel data estimations, when regressing R&D on GDP per capita, we obtain a threshold value of 0.16% of R&D such that for values above it there is economic growth. While regressing R&D on the Gini index, we get a threshold of 0.10% of R&D above which, the income distribution begins to improve. Finally, we estimate a growth equation that depends on R&D, income inequality, and physical capital. We obtain two thresholds, one of 38.79 for the Gini (above which the economic growth decreases), and one of 0.06% for R&D such that above it, economic growth is rising.     

ECONOMIC FREEDOM & HAPPINESS INEQUALITY: FRIENDS OR FOES?

This article examines the relationship between economic freedom and happiness inequality for a large sample of countries. We find that economic freedom is negatively associated with happiness inequality and robust to several alternative measures of happiness inequality, including the standard deviation, mean absolute difference, coefficient of variation, and Gini coefficient. Among the economic freedom areas, legal system and sound money are negatively correlated with happiness inequality. Drawing on the Engerman‐Sokoloff hypothesis, we use a measure of factor endowments as an instrument for economic freedom to provide a further robustness test, finding a negative association between economic freedom and happiness inequality. (JEL D63, I31, P16)     

EVIDENCE ON RELATIONSHIPS AMONG ALTERNATIVE MEASURES OF CONCENTRATION: A TOOL FOR ANALYSIS OF LDC INEQUALITY

The evidence on economic inequality in nearly all developing countries is both seriously incomplete and of moderate to poor quality. In addition, information often corresponds to distributions which appear to be less revealing and useful than other ones; thus it can be argued that the frequently available distribution of income among households ranked by household income is less helpful than the seldom found distribution of consumption among persons ranked by per capita household consumption. Whether one's objective is to assess inequality in some absolute sense or (especially) to make comparisons across countries or evaluate trends over time, it is useful to know whether systematic relationships exist among various measures of inequality, in particular between those most commonly available and those conceptually most interesting. Illustrative comparisons of a variety of inequality indicators are presented. They suggest that in developing countries the concentration of income among persons (assuming equal distribution within the family) does not differ much from the concentration among households. They also suggest that the concentration of consumption is somewhat less unequal than that of income, the ratio of the respective Gini coefficients tending to cluster around 0.85 to 0.90.     

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 ambiguous economic freedom–inequality relationship

Previous research on the relationship between economic freedom and income inequality has produced mixed results. We provide a short survey of this literature, identifying potential causes for this empirical heterogeneity. Next, we replicate the results from two significant studies using six alternative measures of income inequality for an updated dataset of up to 112 countries over the period 1970–2010. Notably, we use the latest release of the Standardized World Income Inequality Dataset, which allows us to account for the uncertainty of the estimated Gini coefficients. We find that the results of previous studies are sensitive to the choice of country sample, time period and/or inequality measure used. We conclude with suggestions for future research in the area.     

Revisiting the redistribution hypothesis with perceived inequality and redistributive preferences

It is a long-standing puzzle whether or not changes in economic inequality lead to changes in redistribution. However, there has been a lack of conclusive evidence about this relationship. Moreover, redistributive preferences as an intervening factor between inequality and redistribution, which are taken for granted implicitly or explicitly in redistribution theories, have been largely overlooked in the existing analyses. Besides, recent comparative studies of inequality and redistribution have started paying attention to inequality perceptions that deviate from actual inequality. Thus, this inquiry aims to reconstruct the classical redistribution theory by employing perceived inequality and preferences for redistribution and to test the reformulated redistribution hypotheses. One of the most challenging efforts for the analysis is to develop a country-level measure of perceived inequality. To this end, the Gini coefficient of perceived social position (perceived Gini) was first created by using data from 16 rounds of the International Social Survey Programme (ISSP 1987 to 2014), covering 34 OECD countries. The empirical results show robust evidence that perceived inequality, not actual inequality, is significantly associated with redistributive preferences, while preferences for redistribution do not translate into any type of redistribution.     

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.     

GROWTH, POVERTY, AND INEQUALITY IN LATIN AMERICA: A CAUSAL ANALYSIS, 1970–94

Taking advantage of consistent poverty and income inequality data for 12 Latin American countries between 1970 and 1994, we analyze the determinants of changes in the incidence of urban and rural poverty and in Gini coefficients over spells of years, stressing in particular the role of aggregate income growth. We find that income growth reduces urban and rural poverty but not inequality. We also find that income growth is more effective in reducing urban poverty if the levels of inequality and poverty are lower, and the levels of secondary education higher. We show that there is an asymmetry in the impact of growth on poverty and inequality, with recession having strong negative effects on both poverty and inequality. Since growth does not reduce inequality, economic cycles create ratchet effects on the level of inequality. However, post-structural adjustment growth is quite effective at reducing poverty, particularly if inequality is low.     

INEQUALITY OF OPPORTUNITIES VS. INEQUALITY OF OUTCOMES: ARE WESTERN SOCIETIES ALL ALIKE?

This paper analyzes the relationship between income inequality and inequality of opportunities for income acquisition in nine developed countries during the 1990s. Equality of opportunity is defined as the situation where income distributions conditional on social origin cannot be ranked according to stochastic dominance criteria. We measure social origin by parental education and occupation and use the database built by Roemer et al. (2003 ). Stochastic dominance is assessed using nonparametric statistical tests. Our results indicate strong disparities in the degree of equality of opportunity across countries and a strong correlation between inequality of outcomes and inequality of opportunity. The U.S. and Italy show up as the most unequal countries in terms of both outcome and opportunity. At the opposite extreme, income distributions conditional on social origin are almost the same in Scandinavian countries even before any redistributive policy. We complement the ordinal comparison by resorting to an original scalar “Gini” index of opportunities, which can be decomposed into a risk and a return component. In our sample, inequality of opportunity is mostly driven by differences in mean income conditional on social origin, and differences in risk compensate the return element in most countries.     

我国居民收入不平等的主要原因：市场还是政府政策？

市场因素和政府收入再分配政策是决定一国居民收入不平等程度的两个重要因素。本文采用中国家庭收入调查（China Household Income Project ,CHIP）住户数据,计算出我国居民市场收入基尼系数与可支配收入基尼系数,并同发达国家进行比较,借此探讨目前我国居民收入不平等是由市场力量造成的,还是政府收入再分配政策力度不足的结果。研究发现,从市场收入基尼系数来看,我国与发达国家之间的差距并不大。由此认为,政府收入再分配政策效果不明显是导致我国居民收入分配不平等状况较发达国家严重的主要原因。加大转移支付等再分配政策力度是缓解和改善目前我国居民收入不平等的主要途径。     

Inequality in Pre-Income Survey Times: A Methodological Proposal

We propose different alternatives of inequality estimation for economies with a big agricultural sector where land is a decisive factor in income generation and where we do not have enough information about personal earnings. To this end, we use the Uruguayan case to test our methodology. We propose six analytical exercises where Gini indexes are calculated, and as reference we choose the estimation that better adjusts to some theoretical and empirical conditions. Finally, we check the historical accuracy of the series by looking at income distribution explicative variables and the shape of the Inequality possibility frontier. Our results are consistent with the economic and social events of the period (1870–1912) and with previous estimates which reveal worsening trends in income distribution. However, our annual data allow capturing the dynamics of the process where breaks in the series are observed and improvements and declines alternate in the evolution of income distribution.     

Inflation and income distribution in Central America,Mexico, Panama,and the Dominican Republic

Analyses of the impact of inflation on income distribution typically only consider the general inflation rate. However, when the consumption structure of households is shaped by its income level and inflation varies across goods and services, they are affected differently by inflation. The aim of this study is to contribute to the analysis of this effect in Central American countries (Guatemala, El Salvador, Honduras, Nicaragua, and Costa Rica), Panama, Mexico, and the Dominican Republic (CAPMDR) for the period 2007–2018. According to our findings, there have been significant differences in the inflation rates faced by different income groups. By employing these percentile‐specific inflation rates, we have computed an “inflation‐corrected Gini index.” This adjustment is important because although inequality has been decreasing during past years in the countries in our sample, on average, about half of the gains observed using the standard Gini index are lost once the Gini indices are corrected for inflation, a clearly nonnegligible magnitude.     

Wealth inequality in Central and Eastern Europe: Evidence from household survey and rich lists’ data combined*

We study how the problem of the ‘missing rich’, the underrepresentation of the wealthiest in household surveys, affects wealth inequality estimates for the post‐socialist countries of Central and Eastern Europe (CEE). The survey data from the second wave of the Household Finance and Consumption Survey (HFCS) are joined with the data from the national rich lists for Estonia, Hungary, Latvia, Poland and Slovakia. Pareto distribution is fitted to the joined survey and rich lists’ data to impute the missing observations for the largest wealth values. We provide the first estimates of the top‐corrected wealth inequality for the CEE region in 2013/2014. Despite a short period of wealth accumulation during the post‐1989 market economy period, our adjustment procedure reveals that wealth inequality in the Baltic countries is comparable to that of Germany (one of the most wealth‐unequal countries in Europe), while in Poland and Hungary it has reached levels observed in France or Spain. We discuss possible explanations of these findings with reference to the speed and range of privatization processes, extent of income inequality, and the role of inheritances and wealth taxes in the region.     

Corruption, Income Distribution, and Growth

This paper uses an encompassing framework developed by Murphy et al. (1991, 1993) to study corruption and how it affects income distribution and growth. We find that (1) corruption affects income distribution in an inverted U-shaped way, (2) corruption alone also explains a large proportion of the Gini differential across developing and industrial countries, and (3) after correcting for measurement errors, corruption seems to retard economic growth. But the effect is far less pronounced than the one found in Mauro (1995). Moreover, corruption alone explains little of the continental growth differentials. In countries where the asset distribution is less equal, corruption is associated with a smaller increase in income inequality and a larger drop in growth rates.     

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.     

Regional migration and wage inequality in the West African economic and monetary union

We investigate the impact of intra-regional migration on wage inequality in the West African Economic and Monetary Union (UEMOA). We exploit unique data from a unified labour force household survey which covers natives and migrants in the seven economic capital cities of that region. We first estimate the counterfactual wage distributions of UEMOA migrants in absence of migration to evaluate the compositional effect of migration (i.e., when wages are treated as exogenous). We find that regional migration increases average wages by 1.8% and generates a decrease in inequality that ranges between −1.5% (for the Gini index) and −4.5% (for the interquartile ratio). This is essentially driven by a reduction in inequality between capital cities, while the effect of migration on within-capital cities inequality is heterogeneous across countries and remains small overall. Second, when accounting for possible general equilibrium effects of migration on stayers’ wages (i.e., when wages are treated as endogenous), we find similar to stronger effects on inequality, albeit with a smaller increase in the average wage.     

Aggregation bias-correcting approach to the health–income relationship: Life expectancy and GDP per capita in 148 countries, 1970–2010

A large amount of data consisting of 148 countries for the years 1970 to 2010 is analysed in the context of the health–income relationship. The literature suggests that the biased income–health effect obtained with macro data can be a result of the aggregation of individual concave income functions on average health. This aggregation problem is analysed in detail, and a bias-correcting method is proposed to overcome it. The results with new model alternatives show that they correct the income effects on average health in the right direction; that is, they produce smaller parameter estimates than biased models. Augmenting the results with the quantile regression approach, which is sensitive to health differences between countries, indicates that the poorest countries’ income gradient is still much larger than that of rich countries. However, the median life expectancy effect of the log of GDP per capita across the countries decreased during the sample decennials. The results for income inequality measured with the Gini coefficient indicate that the effects of inequality on health are still significant in the poorest countries but non-significant among rich countries after the year 2000. We argue that the proposed bias-correcting method retains the interest in macro health modelling and offers new model alternatives in other contexts.     

TRENDS IN AMERICAN LIVING STANDARDS AND INEQUALITY, 1959–2007

We analyze the trends from 1959 to 2007 using an expanded measure of income called the Levy Institute Measure of Economic Well‐Being (LIMEW). LIMEW is different in scope from the official U.S. Census Bureau measure of gross money income (MI) in that our measure includes non‐cash transfers, public consumption, imputed income from wealth, and household production and nets out personal taxes. While the annual growth rates of median LIMEW and MI are very close over the whole period (0.67 and 0.63 percent), median LIMEW grew much faster than median MI after 1982 and much slower before. The Gini coefficient of MI is uniformly higher than that of LIMEW but both show about the same change from 1959 to 2007. Decomposition analysis shows that changes in inequality are driven to a large extent by non‐home wealth in LIMEW and earnings in MI. While the racial gap in MI declined somewhat over the 1990s and 2000s, the racial gap in LIMEW actually widened a bit. Over the same years, while there was little change in the gap in MI between the elderly and non‐elderly, the LIMEW of the elderly actually overtook that of the non‐elderly.