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We investigate the reasons why income inequality is so high in Spain in the EU context. We first show that the differential in inequality with Germany and other countries is driven by inequality among households who participate in the labor market. Then, we conduct an analysis of different household income aggregates. We also decompose the inter-country gap in inequality into characteristics and coefficients effects using regressions of the Recentered Influence Function for the Gini index. Our results show that the higher inequality observed in Spain is largely associated with lower employment rates, higher incidence of self-employment, lower attained education, as well as the recent increase in the immigration of economically active households. However, the prevalence of extended families in Spain contributes to reducing inequality by diversifying income sources, with retirement pensions playing an important role. Finally, by comparing the situations in 2008 and 2012, we separate the direct effects of the Great Recession on employment and unemployment benefits, from other more permanent factors (such as the weak redistributive effect of taxes and family or housing allowances, or the roles of education and the extended family).

This paper applies a recently developed method of ranking socioeconomic inequality in health to ranking U.S. happiness from 1994 to 2012 using the GSS data. We also compare happiness between subgroups as decomposed by gender, race, and age. We establish and test a monotone condition of happiness – a richer person is likely to be happier. Under the monotone condition, standard tools of welfare and inequality ranking can be applied straightforwardly.

Various indicators of income inequality and social welfare can be obtained simply by using the Gini index and the mean income of the population. This paper reviews existing indicators and presents several new indicators of this kind. While contemporary researchers seem to be preoccupied with relative inequality, this paper advocates for using intermediate inequality views and supplementing inequality rankings of countries with rankings based on social welfare. Empirical analysis, performed for 36 European countries, demonstrates such an approach’s advantages.

This chapter provides a detailed analysis of the recent evolution (1993–2007) of Brazilian income inequality. Particularly, we assess the contribution of different income sources to inequality, using three different decomposition techniques: Shorrocks (1982), Lerman and Yitzhaki (1985), and Gini decomposition. We exploit a recent dataset (PNAD, 2004) that allows the identification of different governmental transfer programs (Bolsa-Família, PETI, and BPC) and their impacts into inequality. While informal labor income and self-employment income reduces inequality in almost every measure, the opposite is true for public sector wages. Private formal labor income is becoming less important in explaining Brazilian inequality over time, but its behavior is still important, as it represents more than 40% of the total income. We find that social transfer programs have a limited, but positive impact on equality. On the other hand, dynamics of pensions attenuate the recent path of decreasing inequality in Brazil.

The catch word “Globalization” has been defended by advocates for lifting people out of poverty and the inequality in the world. But it has been criticized by opponents for failing to solve the problem of poverty, inequality, and for increasingly creating wealth disparity. This raises the question. The fact is that the contemporary world exhibits very high levels of inequality of income and wealth both between countries and within countries. Wealth inequality is more pronounced than that of income inequality across the globe and within-countries. Evidence suggests that rising inequality and wealth disparity arising out of globalization drive is choking off the potential benefits to the poor. In this backdrop, a composite assessment has been made in the present chapter to answer the question “whether globalization with its particular ideology, the market fundamentalism has benefited many and whether the performance on the distributional front has really been impressive.” From facts and evidence, the study finds that inequalities in income and wealth, also in wages have widened in many developed, developing developed, and developing countries. Technological change and globalization are their main sources.

When health is measured by a bounded variable, differences in health can be presented as levels of attainment or shortfall. Measurement of heath inequality then usually involves the choice of either the attainment or the shortfall distribution, and this choice may affect comparisons of inequality across populations. A number of indices have been introduced to overcome this problem. This chapter proposes a framework in which attainment and shortfall distributions can be jointly analyzed. Joint distributions of attainments and shortfalls are defined from points of view consistent with concerns for relative, absolute or intermediate inequality. Inequality measures invariant according to the corresponding ethical criterion are then applied. A dominance criterion that guarantees unanimous rankings of the joint distributions is also proposed.

Purpose – Our ultimate goal is to characterize three methodological issues. First, compare the relative performance of alternative estimation methods for long time series, second, estimate the degree of correlation between effort and circumstances, and, third, decompose total inequality into inequality of opportunity and inequality of effort according to an ideal tree.

Methodology – We estimate parametrically and nonparametrically the ex-ante inequality of opportunity in the United States between 1969 and 2007. The degree of correlation between effort and circumstances is computed following the proposal in Björklund et al. (2011). In addition, we decompose total inequality based on an ideal tree with three levels of disaggregation by applying the natural decomposition of the squared coefficient of variation and the Nested Shapley value.

Findings – We find significant differences between the nonparametric and parametric approaches. In particular, our results reveal that considering cross-effects between circumstances may be relevant. Moreover, the degree of correlation between effort and circumstances which has significantly increased over the period 1969 and 2007 in the United States, explains between 5% and 20% of total IO. In addition, race is the main circumstance during the 1970s and 1980s, accounting for more than 50% of the direct IO, while parental education take the lead in the last two decades.

Originality – We modify the parametric specification by considering cross-effects between circumstances. We estimate the degree of correlation between effort and circumstances for long time series. We decompose total inequality according to a three-level hierarchical model.

Existing research shows that popular income inequality measures fail to reflect their respondents’ perceptions of income inequality. However, most of the current literature focuses on what is negative, telling us what individuals do not perceive. This paper presents an alternative methodology to help uncover actual perceptions of inequality, how people perceive inequality instead of how they don’t. Multidimensional scaling, a statistical tool for visualizing dissimilarity data as a low-dimensional map, is used on results of a simple grouping task with a given distribution set. The outcome is a perception map that presents respondents’ answers spatially, which enables additional insight into respondents’ thinking. The map created by the respondents’ replies, presented in this paper, indicates that their decisions are driven by two factors: what the biggest gap in incomes of a given distribution is and whether some groups have equal incomes. The result additionally validates multidimensional scaling as a tool for measuring income inequality perception and opens new ways of improving inequality perception questionnaires.

In this paper, we use the distributions of order statistics to define functions with the appropriate properties to represent social preferences regarding income distributions. Following the approach of Yaari (1987, 1988), this allows constructing a set of social welfare functions from which the corresponding inequality indices are derived. The obtained measures incorporate diverse normative criteria, with different degrees of preference for equality. The generalized Gini coefficients and the family of indices proposed by Aaberge (2000) are obtained as particular cases. This approach allows interpreting each inequality measure in terms of the statistics computed from a randomly selected sample and the identification of unbiased estimators of the Social Welfare Functions. It also shows that each of the families of inequality indices are obtained from the moments of the order statistics and, therefore, each of the families characterizes any income distribution with finite mean. This characterization is very useful in the case of distributions with heavy tail and pronounced positive skew that shows only a few potential moments.