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This timely volume introduces a new social class schema, the European Socio-economic Classification (ESeC), which has been specifically developed and tested for use in EU comparative research. Social Class in Europe aims to introduce researchers to the new classification and its research potential. Since socio-economic classifications are so widely used in official and academic research, this collection is essential reading for all users of both government and academic social classifications. While primarily aimed at researchers who will be using the ESeC, the book's contents will also have a wider appeal as it is suitable for students taking substantive courses in European studies or as a supplementary text for undergraduates studying the EU, Sociology and Economics. Because of its inherent methodological interest, the book should prove a valuable tool for undergraduate and graduate courses that discuss how social scientists construct and validate basic measures. It will also be required reading for policy makers and analysts concerned with social inequality and social exclusion across Europe.

Using hourly traffic and readership data from a major news website, and taking advantage of a global Facebook outage, we study the relationship between social networks and online news consumption. More specifically, we test if online social networks compete with content providers or instead play a complementary role by promoting and attracting traffic to external websites. During the outage, consistent with a promotional effect, we observe a significant decrease in traffic and unique visitors to the news website lasting beyond the outage hours. We further find that direct referrals from Facebook links grossly underestimated the actual impact of Facebook in generating traffic. Instead, during the outage, we observe a more significant reduction in visitors arriving at the news website from search engines or directly typing the website URL or using bookmarks. Additionally, readership of articles and types of pages viewed also changed during the outage. Although we observe a drop in news consumption during the outage hours for all news categories, the subsequent news consumption differs across categories. Time sensitive categories like sports and local news see an increase in consumption, whereas news on women issues or health topics see a decrease. Analysis of individual-level visit and readership behavior during the outage also reveals that Facebook not only introduces selectivity bias by attracting shallower readers but also changes readership patterns (in the absence of Facebook, visitors engage in more in-depth reading). To test the generalizability of our results, we study the impact of the outage on referrals from other social media outlets, on other news sites, and on other content and e-commerce sites. We find similar effects on other news providers, whereas data from nonnews sites, including e-commerce, show no major outage effects. Overall, our results have important managerial implications. We highlight how our results unearth the importance of search engine optimization and of strong branding for news websites, if providers want to harness fully the power of their social media presence. This paper was accepted by Chris Forman, information systems.

Despite growing understanding of resilience as a process associated with both individual capacities and physical and relational resources located in social ecologies, most instruments designed to measure resilience overemphasize individual characteristics without adequately addressing the contextual resources that support resilience processes. Additionally, most resilience studies have focused on children and youth, without significant attention to social ecological factors that promote post-risk adaptation for adults and how this is measured. Consequently, a key issue in the continued study of adult resilience is measurement instrument development. This article details adaptation of the Child and Youth Resilience Measure for use with an adult population. The article draws on data from a mixed methods study exploring the resilience processes of Irish survivors of clerical institutional abuse. The sample included 105 adult survivors (aged 50–99) who completed the RRC-ARM and the Warwick–Edinburgh Mental Well-being Scale (WEMWBS) during the first phase of the study. Exploratory factor analysis, Cronbach Alpha and MANOVA were conducted on the data. EFA identified five factors; social/community inclusion, family attachment and supports, spirituality, national and cultural identity, and personal competencies. The RRC-ARM shows good internal reliability and convergent validity with the WEMWBS, with significant differences on scale scores for men and women, as well as place of residence. This exploratory adaptation supports the potential of the RRC-ARM as a measure of social ecological resilience resources for adult populations and may have particular applications with vulnerable communities. Further validation is required in other contexts and specifically with larger samples.

Hanging up our coat, tidying our desk, classifying our books, what meanings do these mundane practices convey? Extending Mary Douglas’s work, this article investigates tidiness from the angle of symbolic pollution. Based on photo-elicitation, it shows that, similarly to symbolic pollution described at a macro-social level, tidiness depends on two conditions, namely, a set of classifications and the dangerous transgression of these classifications. However, at a micro-social level, individuals negotiate boundaries between classifications in order to cope with symbolic pollution. Consumers define their domestic classifications through a juxtaposition of micro-practices, which does not necessarily create a hierarchically ordered system but which enables these consumers to avoid anomalies and transgressions. Furthermore, respondents are willing to break tidiness rules on specific occasions because the danger-beliefs associated with transgression are context-dependent. This analysis of tidiness gives new insights into materiality, emphasizing the cultural meaning of ordering one’s possessions.

Modelling relationships between individuals is a classical question in social sciences and clustering individuals according to the observed patterns of interactions allows us to uncover a latent structure in the data. The stochastic block model is a popular approach for grouping individuals with respect to their social comportment. When several relationships of various types can occur jointly between individuals, the data are represented by multiplex networks where more than one edge can exist between the nodes. We extend stochastic block models to multiplex networks to obtain a clustering based on more than one kind of relationship. We propose to estimate the parameters—such as the marginal probabilities of assignment to groups (blocks) and the matrix of probabilities of connections between groups—through a variational expectation-maximization procedure. Consistency of the estimates is studied. The number of groups is chosen by using the integrated completed likelihood criterion, which is a penalized likelihood criterion. Multiplex stochastic block models arise in many situations but our applied example is motivated by a network of French cancer researchers. The two possible links (edges) between researchers are a direct connection or a connection through their laboratories. Our results show strong interactions between these two kinds of connection and the groups that are obtained are discussed to emphasize the common features of researchers grouped together.

We identify a variety of R&D alliance modes in a knowledge-intensive industry (e.g., Pharmaceuticals), and classify them into four ordered categories which go beyond the traditional binary equity vs non-equity alliance classification. This enriches our understanding of alliance governance structures and broadens the application of alliance modes in what is today a more complicated international R&D collaboration setting. We then explore national, industry and firm factors that determine the selection of an appropriate R&D alliance governance mode, using a sample of 237 international alliance deals. The likelihood of using a more-integrated alliance governance mode decreases as the difference or \"distance\" between nations of the partner firms increases in terms of human capital and cultural distance. On the other hand, a greater geographic and institutional difference is positively associated with the selection of more integrated alliance governance modes. Furthermore, firms in the research stage are more likely to use a more-integrated governance mode, as opposed to firms in the development stage. These findings advance research on alliance governance structure. They reveal the factors affecting the R&D alliance governance mode choice.

The recent influx of migrants and refugees into Europe and elsewhere raises questions as to whether migrant behavior reflects cultural predispositions and whether assimilation through exposure to host institutions can be expected. The paper focuses on financial behavior and uses high-quality administrative data on migrants and refugees to Sweden. It uncovers differences across cultural groups in how behavior relates to household characteristics, and shows that differences diminish with exposure to host country institutions, even for large cultural distances. Interestingly, robust cultural classification of European countries based on genetic distance or on Hofstede’s cultural dimensions fails to identify a single “southern” culture but points to a “northern” culture. Our results also have implications for the potential of European institutional harmonization, exogenously imposed during the fiscal crisis, to alleviate cultural differences in financial behavior. This paper was accepted by Lauren Cohen, finance .

Although there are a number of approaches to constructing a measure of multidimensional social exclusion in later life, theoretical and methodological challenges exist around the aggregation and weighting of constituent indicators. This is in addition to a reliance on secondary data sources that were not designed to collect information on social exclusion. In this paper, we address these challenges by comparing a range of existing and novel approaches to constructing a composite measure and assess their performance in explaining social exclusion in later life. We focus on three widely used approaches (sum-of-scores with an applied threshold; principal component analysis; normalisation with linear aggregation), and three novel supervised machine-learning based approaches (least absolute shrinkage and selection operator; classification and regression tree; random forest). Using an older age social exclusion conceptual framework, these approaches are applied empirically with data from Wave 1 of The Irish Longitudinal Study on Ageing (TILDA). The performances of the approaches are assessed using variables that are causally related to social exclusion.

This paper focuses on the prevalence and measurement of old-age social exclusion. Currently there is limited knowledge of the prevalence of old-age social exclusion in Belgium. Although studies have already shown that older adults can experience exclusion in more than one dimension, the multidimensional nature of social exclusion is often lost when constructing a scale. Consequently, this paper’s aim is twofold. First, it examines the prevalence of different dimensions of old-age social exclusion in Flanders and Brussels and seeks to demonstrate the influence of applying different measurement thresholds. Second, this study develops an old-age social exclusion measure that preserves its multidimensionality. Descriptive and Latent Class Analysis were performed on the Belgian Ageing Studies data (2008–2014), a survey among home-dwelling older adults (60 + years) (N = 20,275; 80 municipalities). Findings revealed that older adults are mainly digitally excluded and excluded from the neighbourhood, civic participation, and social relations. More than 60% older adults experience exclusion in two or more dimensions. The use of different thresholds, however, leads to different interpretations concerning the prevalence of social exclusion. Results of the Latent Class Analysis revealed four categories of old-age exclusion: those at “low risk”, “the non-participating financially excluded”, “the environmentally excluded” and the “severely excluded”. The discussion emphasizes the importance of preserving a multidimensional perspective when studying social exclusion. When addressing old-age exclusion, policy should be sensitive to the diverse categories and realize that one-size-fits-all policies and interventions are no solution.

The main objective of the article is twofold. On the one hand, it aims to offer a critical analysis of the different operationalizations of the concept of social exclusion at the international level, including reflection on widely used methods such as the “At risk of poverty or social exclusion” rate. On the other hand, it offers an empirically tested proposal of indicator aggregation for the measurement of social exclusion. The debate regarding the measurement of social exclusion has been widely addressed, but there are hardly any proposals that test different systems of indicator aggregation. The multiple correspondence analysis allows the implementation of a new approach for measuring the weights of the indicators, based on the distance to the integration point, which is understood as the absence of problems. The proposed new system shows an important potential to be extrapolated to the comparative measurement of social exclusion, also allowing the comparison of different social groups. The empirical reference used for the analysis is the Survey on Social Needs and Social Integration of the FOESSA Foundation for Spain 2018.