HOW DO EUROPEANS EVALUATE INCOME DISTRIBUTIONS? AN ASSESSMENT BASED ON HAPPINESS SURVEYS

The purpose of this paper is to measure the evaluation of income inequality by European citizens. Starting from the concept of a social welfare function defined on income distributions the paper estimates the degree and nature of inequality aversion of Europeans. It uses subjective well-being (SWB) as an empirical measure of welfare and estimates how SWB is related to average income and measures of income inequality (from an appropriate class). The estimated relationship is used to determine those inequality measures which qualify as proper representations of people's inequality aversion.     

MEASURING SUB‐NATIONAL INCOME POVERTY BY USING A SMALL AREA MULTIVARIATE APPROACH

The present paper proposes a statistical strategy for the analysis of regional disparities in income poverty. For the EU countries, information on individual income has been collected until now by the European Community Household Panel survey, which only yields reliable estimates for very large regions within countries. In order to obtain reliable estimates for some of the poverty indicators suggested by the Laeken Council at the sub‐national level, we suggest the adoption of a multivariate small area estimation approach which enables us to reduce estimate variability. We concentrate on Italy, the country with the lowest degree of regional cohesion within the EU. Results show that disparity cannot be reduced to the so‐called “North–South divide,” with the “poor” South separated from the “affluent” North, as both these macro‐regions display large internal differences in terms of both poverty level and income inequality. The strategy we propose could also be adopted in order to measure poverty in other European regions, using information produced by the new EU Survey on Income and Living Conditions, which is replacing the European Community Household Panel.     

The urban–rural divide

A vast literature has related perceptions of income inequality to individuals' income: the higher the level of income, the less inequality is perceived. Here, examining the perceptions of income and social inequality, we argue that rural or urban residence affects both inequality perceptions and the impact of income on these perceptions. We test the theory using survey data from 12 Central and Eastern European countries and we find that income negatively affects inequality perceptions but only in urban areas. These findings confirm the importance of accounting for urbanity to understand what drives individuals' perceptions of inequality.     

A COMPARISON OF DISTRIBUTIONS OF ANNUAL AND LIFETIME INCOME: SWEDEN AROUND 1970.

The concern with income distribution has always mainly existed because of a concern with individuals' economic welfare. In recent years, the question has arisen whether the distribution of annual income—the distribution most often studied—is the best proxy for the distribution of economic welfare. Other measures, such as lifetime income, have been proposed instead.
The paper starts with a discussion of how to define and measure the distribution of lifetime income. By using a simulation model, which partly consists of estimated functions and partly of tax functions taken directly from tax laws, distributions of lifetime income, variously defined, are then constructed. These distributions are compared with each other, and with distributions of annual income. The simulations indicate that the distribution of lifetime income is considerably less unequal than the distribution of annual income. Whether inheritances are included or not seems to be of no importance for the inequality of lifetime income. If, on the other hand, we include the value of leisure time in lifetime income, inequality increases by about 10–15 percent. Distributions of income after tax have Gini coefficients which are approximately 25 percent less than the Ginis for the before-tax distributions. We thus find that the picture of inequality we get is very much dependent on which income concept we use.     

FISCAL CONSOLIDATION PROGRAMS AND INCOME INEQUALITY

We document a strong empirical relationship between higher income inequality and stronger recessive impacts of fiscal consolidation episodes across time and space. To explain this finding, we develop a life‐cycle economy with uninsurable income risk. We calibrate our model to match key characteristics of several European economies, including inequality and fiscal structures, and study the effects of fiscal consolidation programs. In our model, higher income risk induces precautionary savings behavior, which decreases the proportion of credit‐constrained agents in the economy. These agents have less elastic labor supply responses to fiscal consolidations, which explain the correlation with inequality in the data.     

Economic inequality in Spain: the european community household panel dataset

This article uses data from the 1998 European Community Household Panel to study economic inequality in Spain. It reports data on the Spanish distributions of income, labor income, and capital income, and on related features of inequality, such as age, employment status, educational attainment, and marital status. It also reports data on the income mobility of Spanish households, and data on income inequality in other European countries and in the US. We find that income, earnings, and, especially, capital income are very unequally distributed in Spain and that economic inequality in Spain is well above the European average.     

Bivariate income distributions for assessing inequality and poverty under dependent samples

As indicators of social welfare, the incidence of inequality and poverty is of ongoing concern to policy makers and researchers alike. Of particular interest are the changes in inequality and poverty over time, which are typically assessed through the estimation of income distributions. From this, income inequality and poverty measures, along with their differences and standard errors, can be derived and compared. With panel data becoming more frequently used to make such comparisons, traditional methods which treat income distributions from different years independently and estimate them on a univariate basis, fail to capture the dependence inherent in a sample taken from a panel study. Consequently, parameter estimates are likely to be less efficient, and the standard errors for between-year differences in various inequality and poverty measures will be incorrect. This paper addresses the issue of sample dependence by suggesting a number of bivariate distributions, with Singh–Maddala or Dagum marginals, for a partially dependent sample of household income for two years. Specifically, the distributions considered are the bivariate Singh–Maddala distribution, proposed by Takahasi (1965), and bivariate distributions belonging to the copula class of multivariate distributions, which are an increasingly popular approach to modelling joint distributions. Each bivariate income distribution is estimated via full information maximum likelihood using data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey for 2001 and 2005. Parameter estimates for each bivariate income distribution are used to obtain values for mean income and modal income, the Gini inequality coefficient and the headcount ratio poverty measure, along with their differences, enabling the assessment of changes in such measures over time. In addition, the standard errors of each summary measure and their differences, which are of particular interest in this analysis, are calculated using the delta method.     

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.      

Spain is different: Falling trends of inequality

In this article we characterize the evolution of inequality in hourly wages, hours of work, labor earnings, household disposable income and household consumption for Spain between 1985 and 2000. We look at both the Encuesta Continua de Presupuestos Familiares and the European Household Community Panel. Our analysis shows that inequality in individual net labor earnings and household net disposable income has decreased substantially. The decreases in the tertiary education premium and in the unemployment rate have been key ingredients to understand this falling trend. Public transfers have played a crucial role in smoothing out the inequality arising in the labor market, but instead the Spanish family does not seem to have been an important insurance mechanism. Regarding household consumption, inequality has fallen much less than inequality in household net disposable income, with the decrease mostly concentrated in the second half of the eighties. This suggests that the reduction in income inequality has affected the sources of permanent differences between households only during the second half of the eighties. Our estimates of the earnings process for the period are consistent with this view.     

INTERSTATE INCOME INEQUALITY IN THE UNITED STATES: MEASUREMENT, MODELLING AND SOME CHARACTERISTICS

Five points are made in this study. First, using a well-recommended measure, interstate income inequality is reported for each year from 1950 through 1989, and its very small magnitude is pointed out along with the U-shaped profile. Second, it is shown that a simple quadratic-form model fits the data extremely well. Third, inequality indices for 1977 and 1988 are recomputed after adjusting for interstate price-level variations, and large reductions in the indices, and a virtual disappearance of the increase in inequality after 1978, are noted. Fourth, a simple decomposition shows that income changes account for most of the inequality change in each decade. Last, states that have contributed most to inequality are identified.     

Inadequate Bivariate Measures of Health Inequality: The Impact of Income Distribution*

Bivariate measures of health inequality are influenced by changes in two variables: health and a socioeconomic variable, such as income. For these measures, what is reported as an increase in health inequality might just as well be a reduction in income inequality. In particular, several papers have found that socioeconomic health inequalities in Nordic countries are no less than in other European countries. The correct interpretation could just be that income inequality is no higher in Nordic countries than in the rest of Europe. The problem is especially profound when the causality is running from health to income.     

The Pro‐Poorness,Growth and Inequality Nexus: Some Findings From a Simulation Study

An income growth pattern is pro‐poor if it reduces a (chosen) measure of poverty by more than if all incomes were growing equiproportionately. Inequality reduction is not sufficient for pro‐poorness. In this paper, we explore the nexus between pro‐poorness, growth, and inequality in some detail using simulations involving the displaced lognormal, Singh–Maddala, and Dagum distributions. For empirically relevant parameter estimates, distributional change preserving the functional form of each of these three‐parameter distributions is often either pro‐poor and inequality reducing, or pro‐rich and inequality exacerbating, but it is also possible for pro‐rich growth to be inequality reducing. There is some capacity for each of these distributions to show trickle effects (weak pro‐richness) along with inequality‐reducing growth, but virtually no possibility of pro‐poorness for growth which increases overall inequality. Implications are considered.     

Gini playing soccer

The level of income inequality in a European country influences the competitive balance of its major soccer leagues. We test this hypothesis using cointegration techniques for seven male professional soccer leagues (the Dutch, English, French, German, Italian, Spanish and Ukrainian soccer leagues) from the 1980/1981 season to the 2011/2012 season. Controlling for the level of income inequality using variables such as real GDP per capita, trade openness and the emigration rate, we conclude that income inequality (measured by the Gini index) causes changes in the measures of competitive balance that we employ (the Hirschman–Herfindahl index and the SD) concerning the final number of points scored by the various teams.     

Income inequality and colonialism

This paper proposes that colonialism is a major explanation behind today's differences in income inequality across countries. We argue that income inequality has been higher in the colonies where the percentage of European settlers to total population was higher, as long as Europeans remained a minority. The countries where Europeans became the majority of the population did not suffer from high inequality. These initial differences continue to hold today. The empirical evidence we provide strongly supports our thesis.     

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.     

Self‐Assessed Social Position and Poverty

In this article, we use microlevel data extracted from the 2006 Japanese General Social Surveys to analyze the relationships between self‐assessed social position and socioeconomic factors such as income and poverty. We provide the posterior results of the estimation of the Bayesian multivariate ordered probit model and propose an inequality measure for self‐assessed social position on the basis of the posterior results. We call the inequality measure “regret” and show that the distributions of regret differ for people above and below the poverty line.     

When income differences hurt or excite: The nonlinear effect of regional inequality on subjective wellbeing

Combining information from the European Union Statistics on Income and Living Conditions and the European Social Survey, we investigate the relationship between subjective well-being and income inequality using regional inequality indicators and individual data. We assume that inequality aversion and perception of social mobility affect the impact of regional inequality on subjective well-being in opposite directions. We find evidence of an inverse U-shaped effect of inequality, where inequality starts to have a positive effect on subjective well-being that becomes negative with a switch point before the average of the Gini index for the entire sample. The rationale for our nonlinear finding is that Hirschman's tunnel effect (and the positive effect of perceived social mobility) prevails for low levels of inequality, while inequality aversion and negative relative income effects are relatively stronger when inequality is higher. Robustness checks on different sample splits are consistent with the hypothesis of the two drivers.     

IRELAND's INCOME DISTRIBUTION IN COMPARATIVE PERSPECTIVE

There are concerns that the unprecedented economic boom which Ireland experienced in the second half of the 1990s has raised only some living standards and has widened income gaps. This paper analyzes Ireland's income distribution in comparative perspective, to understand how Ireland's distribution changed and how it compares to other rich countries. We begin with OECD (Organization for Economic Cooperation and Development) and the Luxembourg Income Study (LIS) data to compare Ireland's degree of well-being and inequality with other advanced countries. We also look in some detail at alternative sources of Irish income and their implications for the trends in income inequality in Ireland from 1994 to 2000. For instance, we examine the top of the distribution using data from the administration of the income tax system. We conclude that the spectacular economic growth in the past decade has seen the gap in average income between Ireland and the richer OECD countries narrow dramatically. However, this growth has not greatly affected the Irish ranking in terms of income inequality. Ireland remains an outlier among rich European nations in its high degree of income inequality, though still falling well short of the level seen in the United States. In the end, we find that Ireland's new-found prosperity provides a "social dividend," and choices about how it is used will fundamentally affect whether the current high level of income inequality persists into the future.     

CORRUPTION AND INCOME INEQUALITY IN THE UNITED STATES

In this study we analyze the effects of corruption on income inequality. Our analysis advances the existing literature in three ways. First, instead of using one of the corruption indices assembled by various investment risk services, we use an objective measure of corruption: the number of public officials convicted in a state for crimes related to corruption. Second, we minimize the problems which are likely to arise because of data incomparability by examining the differences in income inequality across the United States. Finally, we exploit both time series and cross‐sectional variation in the data. We find robust evidence that an increase in corruption increases income inequality. (JEL D31, D73, I32)     

The Transnational Challenge to the Corporate State

Income inequality is not persistent as far as the Netherlands is concerned. Dutch income inequality diminished with the rise of the welfare state. One of the explanatory factors of the development of income inequality is the corporatist model applied to socio-economic negotiations.

The Dutch case endorses the view that corporatist institutions are significant for income distribution. Corporatism also may positively influence productivity. Therefore, the integration of the successful corporatist countries into the European Union does not imply that they have to converge to the socio-economic governance structure of the other countries. It is likely that European countries, such as the Netherlands, can continue their approach to income equality.