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The purpose of this paper is to first develop indicators for how total inequality, measured through the ANalysis Of GIni (ANOGI) framework, is mapped onto each group – i.e. indicators for each group's share of total inequality. Second, to develop indicators for the sensitivity of total inequality and its structure to changes in the composition of the population. Specifically, to develop indicators for how the Gini index and its ANOGI components react to (1) changes in the population-share of each group, (2) migration between groups, (3) changes in group incomes and (4) income transfers between groups.

First, the expressions for these indicators are derived analytically. Following this, the indicators are applied to labour-market data from Brazil, contrasting the results to others available in the literature.

The indicators described above are presented and their characteristics discussed. Empirically, it is illustrated how labour formalisation in Brazil was an inequality-reducing process between 2002 and 2011, contrary to previous incorrect measurements of the phenomenon based on income-source decompositions for Latin American countries.

Indicators for how total inequality reacts to changes in group sizes and income were unavailable for the ANOGI framework, which this article provides. The empirical illustration shows how this leads to a reassessment of important inequality dynamics, using the example of labour formalisation in Brazil. Contrary to the existing literature, it is shown how this was a progressive development, with key implications for social and labour-market policy. This framework can be used to assess the impact of diverse processes in the ANOGI methodology.

This paper considers a parametric model for the joint distribution of income and wealth. The model is used to analyze income and wealth inequality in five OECD countries using comparable household-level survey data. We focus on the dependence parameter between the two variables and study whether accounting for wealth and income jointly reveals a different pattern of social inequality than the traditional “income only” approach. We find that cross-country variations in the dependence parameter effectively account only for a small fraction of cross-country differences in a bivariate measure of inequality. The index appears primarily driven by differences in inequality in the wealth distribution.

The paper examines several measures of multidimensional inequality, analysing their properties and majorisation criteria. Moreover, it presents a new measure which generalises Bourguignon (1999) and includes Tsui measures (1999), while preserving the advantages of Maasoumi's method (1986) of explicitly acknowledging the role of parameters relevant to multivariate settings. Finally, an application to Argentine data is provided in order to illustrate the decisions involved in the process of applying these measures and the usefulness of having appropriate criteria when making those decisions.

Mobility implies initial and final distributions and a transition process linking the observations of these two distributions. An inequality index describes properties of the intitial or final distribution. A mobility index describes the transition. In most cases, mobility indices have been developed using properties of transition matrices independently of the concepts of inequality and equity that may also be used in the analysis. This paper presents a new tool – the Gini index of mobility – that provides an overall consistent framework for the analysis of mobility, inequality, and horizontal equity. The theoretical concepts are illustrated empirically using panel data from rural Mexico.

The data employed to measure income inequality usually come from household surveys, which commonly suffer from atypical observations such as outliers and contamination points. This is of importance since a single atypical observation can make classical inequality indices totally uninformative. To deal with this problem, robust univariate parametric or ad hoc procedures are commonly used; however, neither is fully satisfactory. The purpose of this paper is to propose a methodology to deal with this problem.

The author propose two robust procedures to estimate inequality indices that can use all the information from a data set, and neither of them rely on a parametric distributional assumption. The methodology performs well irrespectively of the size and quality of the data set.

Applying these methods to household data for UK (1979) and Mexico (2006 and 2011), the author find that for UK data the Gini, Coefficient of Variation and Theil Inequality Indices are over estimated by between 0.02 and 0.04, while in the case of Mexico the same indices are over estimated more deeply, between 0.1 and almost 0.4. The relevance of including atypical observations that follow the linear pattern of the data are shown using the data from Mexico (2011).

The methodology has two main limitations: the procedures are not able to identify a bad leverage outlier from a contamination point; and in the case that the data has no atypical observations, the procedures will tag as atypical a very small fraction of observations.

A reduction in the estimate of inequality has important consequences from a policy maker perspective. First, ceteris paribus, the optimal amount of resources destinated to directly address inequality/poverty. Those “extra” resources can be destinated to promote growth. Notice that this is a direct consequence of having a more egalitarian economy than previously thought, this is due to the fact that poor people will actually enjoy a bigger share of any national income increment. This also implies that, in order to reduce poverty, public policies should focus more on economic growth.

To the knowledge, in the inequality literature this is the first methodology that is able to identify outliers and contamination points in more than one direction. That is, not only at the tails of the distribution, but on the whole marginal distribution of income. This is possible via the use of other variables related to income.

In large parts of the world, income inequality has been rising in recent decades. Other regions have experienced declining trends in income inequality. This raises the question of which mechanisms underlie contrasting observed trends in income inequality around the globe. To address this research question in an empirical analysis at the aggregate level, we examine a global sample of 73 countries between 1981 and 2010, studying a broad set of drivers to investigate their interaction and influence on income inequality. Within this broad approach, we are interested in the heterogeneity of income inequality determinants across world regions and along the income distribution. Our findings indicate the existence of a small set of systematic drivers across the global sample of countries. Declining labour income shares and increasing imports from high-income countries significantly contribute to increasing income inequality, while taxation and imports from low-income countries exert countervailing effects. Our study reveals the region-specific impacts of technological change, financial globalisation, domestic financial deepening and public social spending. Most importantly, we do not find systematic evidence of education’s equalising effect across high- and low-income countries. Our results are largely robust to changing the underlying sources of income Ginis, but looking at different segments of income distribution reveals heterogeneous effects.

The main purpose of this paper is to construct a poverty profile for Sri Lanka, and examine the micro‐level determinants and correlates of poverty.

The study is based on the latest Sri Lanka Integrated Survey commissioned by the World Bank. The unconditional poverty profile was constructed using three different poverty measures (poverty headcount, average poverty gap and squared poverty gap), nested in the Foster‐Greer‐Thorbecke index. The conditional poverty profile was constructed on the basis of a multivariate analysis of poverty correlates. Partial correlates of poverty are computed using two comparable methodologies. First, a logistic regression was estimated, with the probability of a household being in poverty as the dependent variable and a set of economic and demographic variables as correlates. Second, the quantile regression approach was utilized to examine the correlates of per capita consumption at different points on the distribution.

The empirical findings are broadly encouraging. The estimation results show that the education of the household head, being salary employed and being engaged in business have a significant positive effect on the standard of living. The probability of being poor increases with the household size, household head being female, living in a rural area, and being a casual wage earner. These findings indicate the importance of a set of policies which are super pro‐poor, namely increasing school enrolment and achievement, effective family planning programs to reduce the birth rate and dependency load within households, and granting priorities for specific cohorts (children‐, elderly‐, rural‐ and female‐headed households) in targeted interventions.

This is the first study that examines the probable determinants and correlates of Sri Lankan poverty in a multivariate framework employing both logit and quantile regressions.

This chapter studies trends in income distributions and inequality in the European Union using data from the European Union Statistics on Income and Living Conditions. The author models the income distribution for each country under a Dagum distribution assumption and using maximum likelihood techniques. The author uses parameter estimates to form distributions for regions defined as finite mixtures of the country distributions. Specifically, the author studies the groups of ‘new’ and ‘old’ countries depending on the year they joined the European Union. The author provides formulae and estimates for the regional Gini coefficients and Lorenz curves and their decomposition for all the survey years from 2007 through 2011. The estimates of this study show that the ‘new’ European Union countries have become richer and less unequal over the observed years, while the ‘old’ ones have undergone a slight increase in inequality which is however not significant at conventional levels.

Adults raised in poor households tend to be more prone to live in poverty than the rest, ceteris paribus. This holds true even in the presence of observed income transmission channels such as education attainment. We identify this differential poverty risk as intergenerational transmission of economic disadvantage (ITED). This chapter contributes to the literature on cross-country differences in the intensity of ITED in the EU by explicitly testing how macro-economic/institutional features shape the phenomenon. Working on a sample of 30- to 39-year-old interviewees from the EU-SILC 2011 module on Intergenerational transmission of disadvantages, the authors find that, first, past income inequality is positively correlated with current ITED intensity; second, past efforts on inequality reduction via social protection for families with children and unemployment benefits are negatively correlated with later ITED levels; finally, educational expansion correlates with lower ITED, pointing to the relevance of public investments in education as a way to fight inequality of opportunity.

The aim of this paper is to extend a theoretical model due to Ljungqvist and data from a sample of 19 developing economies to empirically test it.

Data for all variables are from the 2005 Human Development Report and the 2006 World Development Report. The author applies the least‐squares estimation technique in a multivariate linear regression.

Based on data from the World Bank and the United Nations Development Programme, the paper uses a sample of 19 developing economies and finds that cross‐country variations in income/consumption inequality may be explained by inequality of investment in human capital as measured by inequalities in child health as well as inequality in education and by inequality in the distribution of land as measured by the land Gini index.

Assuming a population consisting of skilled laborers, unskilled laborers, educators/health care personnel, and farmers, the paper shows that starting from an initial distribution of assets and in the absence of a perfect capital market along with human capital exhibiting increasing returns it is possible to have persistent inequality in the distribution of income or consumption. Regression results also are consistent with the theoretical implication of the model as the extent of inequality in land distribution and in access to education as well as inequalities in child health do linearly influence income or consumption inequality as measured by the ratio of the share of income or consumption accounted for by the richest quintile to that of the poorest quintile. As a result, if governments in developing countries aim to reduce inequality, they need to implement programs designed to reduce inequalities in child health by allowing children from the poorest of the poor to get fully immunized, which in turn would lead to a reduction in infant and child mortality and in education by providing low‐income families with means so that their children have better access to education. Government land policies, on the other hand, that succeed in reducing inequality in land distribution in developing countries, may be beneficial in terms of lessening income/expenditure inequality. Finally, while the present model does not test for the impact that improving capital markets would have, it stands to reason that improving capital markets could also have an impact on decreasing inequality.

In this paper the author uses a model due to Ljungqvist to show that individuals are relatively wealthy because they either own a fixed input such as land or they are able to invest in human capital, which in turn allow them to earn sufficient rent or labor income to remain wealthy. On the other hand, poor people either do not own land or are not capable of investing in human capital, and, as a result, earn low incomes and remain poor. This joint causation of factor endowment or human capital investment and income helps explain income distribution. Using data from the United Nations Development Programme and the World Bank for a sample of 19 developing economies, it is found that cross‐country variations in income/consumption inequality may be explained by inequality of investment in human capital as measured by inequalities in child health as well as well as inequality in education and by inequality in the distribution of land as measured by the land Gini index. These results will help governments in developing countries identify areas that need to be improved upon in order to reduce income/consumption inequality.