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Data from the Northern Ireland (NI) Health Survey 2014/15 (n = 2,231) were statistically analysed to examine the prevalence of food insecurity according to both indicators. Pearson's X² test for association and logistic regressions were used to examine associations between food security status and predictor variables.

Household food insecurity has been identified as a significant societal issue in both developed and developing nations, but there exists no universal indicator to approximate its prevalence. In NI, two indicators (United States Household Food Security Survey Module [HFSSM] and the European Union Statistics on Income and Living Conditions [EU-SILC] food deprivation questions) have been used. This study examines how both indicators differ in their classification of food insecurity prevalence in a population sample and also examines the relationship between various demographic and household factors and food security status.

According to the EU-SILC food deprivation questions, 8.3% (n = 185) were indicated to be food insecure, while according to the HFSSM, 6.5% (n = 146) were indicated to be food insecure. The HFSSM and EU-SILC regression models differed in the underlying variables they identified as significant predictors of food insecurity. Significant variables common to both modules were tenure, employment status, health status, anxiety/depression and receipt of benefits.

Findings can inform policy action with regards to targeting the key contributors and can inform policy decisions in NI and elsewhere with regards to choosing the most appropriate food insecurity indicator.

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 purpose of this paper is to determine the occurrence of job level, salary and job authority demotions in the workplace through the analysis of Belgian Statistics on Income and Living Conditions (SILC)-data of 2007-2011.

Three hypotheses are tested: H1: there is a gender inequality in job authority demotions. H2: the level of education and the probability of being subject to a job level, salary or job authority demotion are negatively correlated. H3: age is negatively correlated with job level, salary or job authority demotion probabilities. The cross-sectional data of the SILC cover a specific time period with data on inter alia living conditions. The longitudinal data give information on inter alia income and non-monetary variables over a period of four years. The authors estimate multivariate regression models for binary demotion variables. These analyses allow the authors to estimate the odds of being demoted. The authors discuss the demotion rates, the bivariate correlations and the regression analysis.

The data analysis result in the fact that base salary demotions are not commonly applied as literature and the Belgian law on salary protection endorses. Fringe benefits demotions, as for instance the abolition of a company car or a bonus are, however, more frequent. There is a gender gap with regard to job authority demotion. Highly educated respondents are less confronted with job authority demotions. Age is negatively correlated with base salary/fringe benefits or job authority demotion probabilities, but not with job-level demotions. H1 is thus confirmed. H2 and H3 only partly confirmed.

Several analyses were restricted because the EU-SILC did not question all dimensions of demotion in detail.

This study contributes to the scarce literature on demotion and to empirical studies on demotions regarding job level, salary and job authority.

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.

The purpose of this paper is to study the gender specific private returns on education (RE) in Europe in a comparative perspective. The authors extend the model of de la Fuente (2003) by estimating the parameters by gender and introducing maternity leaves and benefits. The paper analyses the impact of the public policy variables evaluating the elasticities with respect to unemployment benefits, marginal and average tax rates, maternity leave and childcare benefits.

The authors estimate the Mincerian coefficients, with the Heckman’ selection model, for 12 West European countries using the EU-SILC data. The authors then use them as input to calibrate the decision model.

The RE of females tend to be higher than those of males in all the Europeans countries but Germany, the Netherlands and Sweden. The gender gap can be explained mainly by the wage premia and labour income taxes which more than compensate the negative effects on females’ returns triggered by higher unemployment rates and maternity-related benefits.

The tax system has the most pronounced effect on RE. An increase in the marginal tax rates has a negative impact. An increase in the average tax rates can have a negative or positive impact, depending on the progressivity of the tax system. An increase in unemployment benefits and maternity or child-care benefits has a negative but fairly small impact.

The analysis considers just one dimension of maternity related policies: the effect on RE and differences across gender. These policies may have aims which are beyond the scope of this paper, for instance to increase fertility. From this viewpoint, the small values of the elasticities presented are reassuring in that they suggest that they can be implemented at a fairly small cost in terms of investment in human capital.

The authors compute the RE using a model which allows us to take into account and assess the significance of relevant variables: wage premium, income tax, some public transfers and benefits, costs of the investments. Moreover, the authors estimate the wage premia using relatively recent EU-SILC data. Finally, the paper compares 12 EU countries spanning quite different labour market conditions and institutions.

The authors analyse how the association between parental background and adult children's earnings changes when net rather than gross children's earnings are considered and disentangle what such changes depend on: differences between pre and after taxes earnings inequality or reranking of individuals along the earnings distribution before and after taxes.

Using data from European Union Statistics on Income and Living Conditions (EU-SILC) 2011, the authors focus on two large European countries, Italy and Poland, with comparable levels of inequality and background-related earnings premia but very different personal income tax (PIT) design and estimate – at both the mean and the deciles of the earnings distribution – the association between parents' characteristics and children's gross and net earnings.

The authors find that in Italy the PIT reduces the magnitude of the association between parental background and adult children's earnings at the top of the distribution, while no effects emerge for Poland, and the reduction is mostly due to a decrease in earnings inequality rather than to a re-ranking of children along the distribution. The findings are confirmed when the authors simulate the introduction of a “quasi flat tax” regime in Italy.

The findings suggest that the higher the tax progressivity, the higher the background-related inequality reduction and the lower the intergenerational association, signalling that the degree of progressivity amongst children may be an effective weapon to reduce intergenerational inequality.

In the literature on intergenerational inequality, the role of taxes is usually overlooked. In this paper, the authors try to fill this gap and enquire how the PIT design affects the association between parental background and adult children's earnings.

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.