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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.

Globalization is a process of interaction and integration among the countries, their people, their businesses, and their governments. It is a change driven by international trade, implemented by various policies and aided by modern technology. It has impacts on the environment, culture, political systems, economic development and prosperity, and human physical well-being in societies around the world. But there can be some negative impacts as well. One possible outcome of globalization of all sorts can be the income inequalities. Objective of this chapter is to search for any such connection. Gini coefficient, unemployment rate, Below Poverty Line (BPL) is taken to understand the extent of inequalities in different countries. Outcome shows some countries do not show any association between globalization and income inequalities; some do. Therefore, there are some other variables which influence the above relationship. This chapter tries to identify all such background factors. It reveals that factors like level of development, demographic structure, urbanization, adult and tertiary level of education and government expenditure share in higher education play important roles. All these have different magnitudes of impacts on change in income distribution due to the initial process of globalization.

Traditional indices of bi-dimensional inequality and polarization were developed for cardinal variables and cannot be used to quantify dispersion in ordinal measures of socioeconomic status and health. This chapter develops two approaches to the measurement of inequality and bi-polarization using only ordinal information. An empirical illustration is given for 24 European Union countries in 2004–2006 and 2011. Results suggest that inequalities and bi-polarization in income and health are especially large in Estonia and Portugal, and that inequalities have significantly increased in recent years in Austria, Belgium, Finland, Germany, and the Netherlands, whereas bi-polarization significantly decreased in France, Portugal, and the United Kingdom.

Purpose – We propose the Information Theory of Segregation, which holds that all measures of segregation and of inequality are united within a single conceptual framework. Accepting this framework implies that all measures of inequality can also be used to measure segregation and that all measures of segregation are fundamentally based on measures of inequality.

Methodology – We state several propositions that follow from the information theory perspective, and show mathematically that many common measures of inequality and segregation satisfy the propositions.

Findings – We show that all common measures of inequality can be used to form measures of segregation and that the resulting measures can be applied to binary, polytomous, and continuous variables. Further, we develop several new measures, including a Gini Segregation Index (GS) for continuous variables and Income Dissimilarity Index (ID), a version of the Index of Dissimilarity suitable for measuring economic segregation. We show that segregation measures can easily be adapted to handle persons of mixed race, and describe the Non-Exclusive Index of Dissimilarity (NED) and the Non-Exclusive Entropy Index of Segregation (NEH). We also develop a correction for structural constraints on the value of segregation measures, comparable to capacity constraints in a communications channel, which prevent them from reaching their theoretical maximum or minimum value.

Originality – Placing inequality and segregation measures in a common framework is useful for several reasons. It highlights a common mathematical structure shared by many different segregation measures, and it suggests certain useful variants of these measures that have not been recognized previously.

This paper examines the notion of intermediate inequality and its measurement. Specifically, we investigate whether the intermediateness of an intermediate measure can be preserved through repeated (affine) inequality-neutral income transformation. For all existent intermediate measures of inequality, we show that the intermediateness cannot be preserved through the transformation; each intermediate measure tends to either a relative measure or an absolute measure. This observation is then generalized to the class of unit-consistent inequality measures. An inequality measure is unit-consistent if inequality rankings by the measure are not affected by the measuring units in which incomes are expressed. We show that the unit-consistent class of intermediate measure of inequality consists of generalizations of an existent intermediate measure and, hence, the intermediateness also cannot be retained in the limit through transformations.

Scholars and activists working both within and outside the massive health-related machinery of government and the private sector and within and outside communities of color address the same fundamental questions: Why do health disparities exist? Why have they persisted over such a long time? What can be done to significantly reduce or eliminate them?

This chapter is aimed at reflecting technologies as the key resources of modern regions, identifying the essence and modelling the digital inequality of Russian regions, as well as forming the methodological foundation for the consistent resolution of conflicts in the regional economy of Russia. Through the example of the regional economy of modern Russia, the method of the variation analysis is used for the analysis of differences between regions that are individually investigated in relation to the level of digitalization, and their regression dependence on the level of technological development is determined. As a result, it has been found that Russian regions are characterized by a number of conflicts due to their multi-aspect inequality; these include innovation conflicts, investment conflicts and quality of life conflicts. The abovementioned conflicts are mainly caused by differences in the provision of technological resources to regions (in their digitalization). The novelty and fundamental significance of this chapter consist in the clarification of the cause of spatial inequality through differences in the provision of technological resources to regions. The unique character of this chapter consists in justification of the technology factor of the emergence of inequality and conflicts of regions. This chapter proves that technological inequality exacerbates other aspects of inequality and conflicts of regions. Originality and practical relevance of this chapter consist in the evaluation of the prospects and development of recommendations for conflict management in Russian regions through overcoming differences in their technological support and accelerating the pace of their digitalization.

European Union (EU) central and eastern economies have gone through a process of structural change since 1989, when the post-communist transition started. This process was afterwards reinforced by the three EU enlargement waves that took place in 2004, 2007 and 2013. Though exhibiting low levels of aggregate productivity, this group of countries joined the EU with higher levels of human capital than the southern member states, an advantage that should have accelerated real convergence towards the EU15. However, evidence to date suggests that the convergence process came to a halt in 2007–2008 when massive capital inflows stopped, highlighting the fragilities of the growth strategies implemented so far. In these peripheral countries, structural change has been characterised by an expanding services sector alongside growing income inequality. The two strands of literature on these issues highlight that: (a) an expanding services sector may not be detrimental for growth, quite the opposite, depending on services composition and on the capacity of services sub-sectors to incorporate information and communication technologies (ICTs); and (b) inequality is negatively related to growth through the fiscal policy, socio-political instability, borrowing constraints to investment in education and endogenous fertility channels and positively through the savings channel and incentives. We analyse the nexus between structural change, inequality and growth in this group of countries highlighting income inequality as a potential mechanism that connects the other two variables. We provide a descriptive quantitative analysis of the profiles of structural change and income inequality in our sample and apply dynamic panel methods to investigate the existence of causality among services sector expansion, inequality and aggregate productivity considering a maximum period between 1980 and 2010.

When individual or household incomes are collected for administrative or scientific surveys, the accounting period is sometimes a month, sometimes a quarter, and sometimes a year. The accounting period likely affects the shape of the income distribution and the level of measured inequality. The present study systematically explores the sensitivity of inter-temporal and inter-regional inequality comparisons to the length of the accounting period.