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We present a comparison of coverage and values for five inequality data sets that have worldwide or major international coverage and independent measurements that are intended to present consistent coefficients that can be compared directly across countries and time. The comparison data sets are those published by the Luxembourg Income Studies (LIS), the OECD, the European Union’s Statistics on Incomes and Living Conditions (EU-SILC), and the World Bank’s World Development Indicators (WDI). The baseline comparison is with our own Estimated Household Income Inequality (EHII) data set of the University of Texas Inequality Project. The comparison shows the historical depth and range of EHII and its broad compatibility with LIS, OECD, and EU-SILC, as well as problems with using the WDI for any cross-country comparative purpose. The comparison excludes the large World Incomes Inequality Database (WIID) of UNU-WIDER and the Standardized World Income Inequality Database (SWIID) of Frederick Solt; the former is a bibliographic collection and the latter is based on imputations drawn, in part, from EHII and the other sources used here.

This chapter studies the distribution of labour earnings among employees within the EU using data from Wave 2007-1 of the EU-SILC. The ranking of countries by median full-time equivalent monthly gross earnings shows Eastern European nations at the bottom and Luxembourg at the top; earnings differences are sizeable, both across and within countries. Taking the euro area and the EU-25 as a whole, inequality is higher when earnings are measured in euro at market exchange rates than at purchasing power parities. Unsurprisingly, the wage distribution is narrower in the euro area than in the EU-25, which includes the poorer Eastern European countries joining the Union in 2004. The higher inequality observed for the EU-25 is largely attributable to between-country differences, which in turn reflect differences in returns to individual attributes more than in workforce composition.

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).

The secondary analysis of the European Union Statistics on Income and Living Conditions (EU-SILC) is used to examine inequality in the United Kingdom compared with other European Union (EU) countries and to analyse how inequality has changed over the period from the start of the great financial crisis in 2008–2015. The analysis compares inequality in market income, gross income and disposable incomes, and measured inequality using the Gini coefficient, 80/20 and 90/10 ratios. It includes an analysis of the impact of cash benefits and direct taxes on market income and how the composition of households in different parts of the income distribution has changed over time. In addition, inequality within the EU is explored. The chapter concludes with a discussion of what contribution the EU itself through its own institutions and policies plays in mitigating market inequalities. We find that the distribution of market income in the United Kingdom is comparatively unequal, but the UK’s relative position on disposable income is greatly improved, due to an effective system of direct taxes and transfers. The conclusions remain broadly similar for all the inequality indices that are considered. There is evidence that households with children have moved down the distribution between 2008 and 2014 and aged households have moved up the distribution in most EU countries including the United Kingdom. The chapter concludes that EU policies have relatively little impact on inequality and that inequalities can really only be tackled using national redistributive policies.

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.

Recent development of internet applications and increase in application of various information technology have supported growing demand for new skills related to ICT and internet use, computer literacy and technical digital skills. New technologies have changed many aspects of life and have led to significant differences in digital skills, computer literacy, ICT and internet use along the usual dimensions of social inequality. Purpose of the study is to analyze main challenges and problems of digital inequalities in households in Latvia by regions, territories and income and level of education. Design/Methodology/Approach: This chapter analyzes scientific publication and previous conducted research results and data of Digital Economy and Society Index, Central Statistical Bureau of Latvia (2019) data and Eurostat. Data are analyzed using indicators of descriptive statistics (indicators of central tendency or location – arithmetic mean, mode and median), indicators of variability (indicators of dispersion – range, standard deviation and standard error of mean), cross-tabulations by region, territories, income and level of education and analysis of variance are used. Findings: The results of analysis indicated that there are differences between rural and urban Internet access, socioeconomic differences between people with different income and education that affects their ability to access the Internet and digital skills. Practical implications: Latvia would benefit from motivating life-long learning, investing on digital technology and raise awareness of the importance of digitization. Originality/value: The results of this chapter can provide valuable pointers for decision-makers how to improve digital skills and digitalization process in regions of Latvia and how digital inequality can be reduced.

To investigate the extent to which disability discourages an individual from going on the job market, using data from an Italian survey.

We use an extended definition of labour force participation based on being employed or currently seeking work even if the persons declare themselves as housewives, students, retired or in any other condition otherwise. We use probit, sequential and multinomial logit models for analysing labour force participation and outcomes. We distinguish between the impact of disability in its strict sense and chronic illness explaining the difference.

In all variants we find that chronic illness is a stronger deterrent for labour force participation than disability. Women are more discouraged compared to men. Intellectual disability is the strongest barrier and hearing the least influential. In a sequential decision-making process, we find that disability affects both labour force participation decision and the ability to be employed but not so much the choice between part-time and full-time.

Policies providing tailored solutions for improved access to education and health care for disabled persons will enhance their work opportunities.

Data set is cross-sectional and characterised by attrition. It would be interesting to compare results with a longitudinal and more representative data set.

We have a unique data set from a survey which was specifically targeted at people who were identified as disabled in a previous survey. The Italian context is also special due to its high legal employment quotas and noncompliance sanctions.

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.

This study investigates the formal/informal employment earnings gap in Turkey. We focus on the earnings differentials that can be explained by observable characteristics and unobservable time-invariant individual heterogeneity. We first, estimate the standard Mincer earnings equations using ordinary least squares (OLS), controlling for individual, household, and job characteristics. Next we use, panel data and the quantile regression (QR) techniques in order to account for unobserved factors which might affect the earnings and the intrinsic heterogeneity within formal and informal sectors. OLS results confirm the existence of an informal sector penalty almost half of which is explained by observable variables. We find that formal-salaried workers are paid significantly higher than their informal counterparts and of the self-employed confirming the heterogeneity within the informal employment. QR results show that pay differentials are not uniform along the earnings distribution. In contrast to the mainstream literature which views informal self-employment as the upper-tier and wage-employment as the lower-tier, we find that self-employment corresponds to the lower-tier in the Turkish labor market. Finally, fixed effects estimation indicates that unobserved individual characteristics combined with controls for observable characteristics explain the pay differentials between formal and informal employment entirely in the total and the female sample. However, informal sector penalty persists in the male sample.