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Social evaluation functions used in policy impact analysis can be viewed as real-valued functionals of the underlying outcome distributions. Influence functions may be used to identify the sources of variation in social outcomes in terms of individual or household characteristics. This chapter sets forth in clear terms the definition of the influence function and recentered influence function, and catalogs these functions for a wide range of distributional statistics, including measures of central tendency, inequality, and poverty and also measures of the degree of pro-poorness of a shock- or policy-induced change in income levels.

This paper introduces a new quinquennial dataset of educational inequality disaggregated by age group for 146 countries, from 1950 to 2010, by using the Gini index of education as a measure of the distribution of years of schooling. Based on recent estimates of average years of schooling from Barro and Lee (2010), our calculations take into consideration, for the first time, the changes over time in the duration of educational stages, in each country and for each age group. The downward trends in educational inequality observed during the last decades depend on age group, gender, and development level.

This paper defines local segregation measures that are sensitive to status differences among organizational units. So far as we know, this is the first time that status-sensitive segregation measures have been offered in a multigroup context with a cardinal measure of status. These measures allow researchers to aggregate employment gaps of a target group by penalizing its concentration in low-status occupations. They are intended to complement rather than substitute for previous local segregation measures. The usefulness of these tools is illustrated in the case of occupational segregation by race and ethnicity in the United States.

It is our pleasure as editors to dedicate Research on Economic Inequality, Volume 20 to Professor Jacques Silber. Jacques is a long-time friend of the series and has kindly functioned as a mentor and advisor to us.

In a recent paper entitled “On Lateral Thinking,” Atkinson (2011) argued that Economics has benefited not only from borrowing ideas from other disciplines such as physics (e.g., Samuelson's Foundations of Economic Analysis, 1947) or psychology (e.g., the growing importance of behavioral economics) but also from applying ideas that appeared in one subfield of Economics to another domain of Economics. As examples of such a cross-fertilization, Atkinson cites duality theory where cost functions were applied to consumer theory or Harberger's (1962) model of tax incidence that was borrowed from international trade theory. Atkinson in fact cited a sentence from his famous 1970 (Atkinson, 1970) article: “My interest in the question of measuring inequality was originally stimulated by reading an early version of the paper by Rotschild and Stiglitz (1970, 1971)” The same parallelism between uncertainty and inequality had been drawn previously by Serge Kolm in his well-known presentation at the meeting of the International Economic Association in Biarritz, France (see Kolm, 1969), which was inspired by his previous work on uncertainty (Kolm, 1966). Atkinson, however, stressed also the need for care in drawing parallels.

The economic reality of the 1990s in Europe forced the labor markets to become more flexible. Using a consistent comparative dataset for 14 countries, the European Community Household Panel (ECHP), we explore the degree of earnings mobility and inequality across Europe, and the role of labor market institutions in understanding the cross-national differences in earnings mobility. We study the degree of rank mobility and the degree of mobility as equalizer of long-term earnings. The country ranking in long-term earnings inequality is similar with the country ranking in annual inequality, which is a sign of limited long-term equalizing mobility within countries with higher levels of annual inequality. In long-term earnings inequality, Denmark renders the most mobile earnings distribution with the second highest equalizing effect. The only disequalizing mobility in a lifetime perspective is found in Portugal. With respect to the relationship between earnings mobility and earnings inequality, we find a significant negative association both in the short and the long run. Based on the rankings in long-term Fields mobility and long-term inequality, Denmark is expected to have the lowest lifetime earnings inequality in Europe, followed by Finland, Austria, and Belgium. The Mediterranean countries (Spain and Portugal) are expected to have the highest long-term inequality. With respect to the institutional factors that may be related to earnings mobility, we bring evidence that the deregulation in the labor and product markets, the degree of unionization, the degree of corporatism and the spending on ALMPs are positively associated with earnings mobility.

Models of race-based segregation establish that individual characteristics or housing market attributes are complementary causes of the observed level of races’ concentration inside an urban space. The goal of this work is to establish which variables, and in which order of magnitude, among individual characteristics, housing features, and local amenities correlate with immigrants’ segregation, in the case of consistent within-city immigrants’ mobility. We capture the degree of segregation for different immigration groups by a local concentration statistics that is directly obtained from segregation curves, and we use data on the Verona Municipality as a case study. We find strong evidence in favor of the role of the housing market and housing ownership distribution across city areas.