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In this chapter we explore different ways to obtain decompositions of rank-dependent indices of socioeconomic inequality of health, such as the Concentration Index. Our focus is on the regression-based type of decomposition. Depending on whether the regression explains the health variable, or the socioeconomic variable, or both, a different decomposition formula is generated. We illustrate the differences using data from the Ethiopia 2011 Demographic and Health Survey (DHS).

This paper devises a new method for using the information contained in income-generating equations to “account for” or “decompose” the level of income inequality in a country and its change over time. In the levels decomposition, the shares attributed to each explanatory factor are independent of the particular inequality measure used. In the change decomposition, methods are presented to break down the contribution of each explanatory factor into a coefficients effect, a correlation effect, and a standard deviation effect. In an application to rising earnings inequality in the United States, it is found that schooling is the single most explanatory variable, only one other variable (occupation) has any appreciable role to play, and all of schooling’s effect was a coefficients effect.

This chapter examines the impact of education on income inequality in 18 Latin American countries between 2000 and 2010. This period has raised interest in the academic community because inequality has fallen across the region, after several years of consistent high levels. Employing the novel technique proposed by Firpo, Fortin, and Lemieux (2007), the author’s research provides a detailed decomposition of inequality. Three main findings emerge from the author’s results: First, the expansion of education increases inequality in six countries but reduces inequality in four countries. Second, the changes in returns to education are the driving component of the effects of education on inequality. Those countries where education contributes to a fall in inequality are those where the returns to education fell at the top of the income distribution. Third, the rise in the average years of education, considered alone, had an inequality-increasing effect in most of the countries under analysis.

In the wake of the Stiglitz Commission, we assess German economic well-being by considering income, wealth and consumption. A decomposition approach is used to test for corresponding inequality differences of these well-being dimensions. Total inequality is decomposed into within- and between-group inequality (via a normalised coefficient of variation). The decompositions are categorised into those that refer to socio-demographic characteristics (place of residence, age, household type) and those belonging to different well-being (sub-)categories (potential and net income, expenditure and wealth categories). The empirical analyses are performed for Germany using the 2008 German Sample Survey of Income and Expenditure. By decomposing German well-being inequality in great detail, we shed light on its dimensions. Our analyses illustrate that it is necessary to consider all well-being dimensions to make statements about the material well-being of private households or individuals.

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).

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.

Inequality is an essential factor for the alleviation of poverty. In Cameroon, most of the households derive their livelihoods from non-wage income and a better understanding of how different variables affect income inequality is a way to reduce those inequalities and improve social welfare. Studies carried out so far barely make out the determinants among non-wage earners. This study sets out to identify these determinants, using the regression-based decomposition technique and data obtained from the 2005 Employment and Informal Sector Survey (EISS) undertaken by the National Statistic Institute (INS) in Cameroon. Results show that the total inequality of an hourly active income ensues from the ratio of age/experience and unobserved individual heterogeneity among non-wage earners.

This paper proposes a generalized approach to the issue of decomposing inequality by population subgroups. This generalization uses the concept of Shapley value decomposition and takes into account the fact that either the between or the within groups inequality may be considered as residual terms, that the population size of the subgroups may have an impact on inequality and finally that there are various ways of ranking the individuals when defining the Gini index of inequality. The paper presents an empirical illustration based on the 1998 Israeli Income Survey where the subgroups distinguished are the male- and female-headed households.

Purpose: This paper examines for the first time inequality of opportunity for income in Africa, by analyzing large-sample surveys, all providing information on individuals’ parental background, in five comparable Sub-Saharan countries: Ivory Coast, Ghana, Guinea, Madagascar, and Uganda.

Methodology/approach: We compute inequality of opportunity indexes in keeping with the main proposals in the literature, and propose a decomposition of between-country differences that distinguishes the respective impacts of intergenerational mobility between social origins and positions, of the distribution of education and occupations, and of the earnings structure.

Findings: Among our five countries, Ghana in 1988 has by far the lowest income inequality between individuals of different social origins, while Madagascar in 1993 displays the highest. Ghana in 1998, Ivory Coast in 1985–1988, Guinea in 1994, and Uganda in 1992 stand in-between. Decompositions reveal that the two former British colonies (Ghana and Uganda) share a much higher intergenerational educational and occupational mobility than the three former French colonies. Further, Ghana distinguishes itself from the four other countries, because of the combination of widespread secondary schooling, low returns to education, and low income dualism against agriculture. Nevertheless, it displays marked regional inequality insofar as being born in the Northern part of this country produces a significant restriction of income opportunities.

Originality/value of paper: By providing the first figures for five countries of Sub-Saharan Africa, this paper allows enlarging the sample of international comparisons in the study of inequality of opportunity. It also reveals some suggestive evidence regarding the long-term origin of intergenerational mobility differences, and in particular the colonial legacy of school extension and of dualism against agriculture.