Johannes Giesecke

The notion that jobs should be awarded on applicants’ qualifications and not on the basis of gender, ethnicity, religion, or family background is a key component of modern liberal societies. Drawing on a novel public opinion survey covering 26 countries from all regions of the world, we analyze the degree to which citizens support the idea that job recruitment should be based on applicants’ qualifications. To understand the variation in citizens’ attitudes toward nondiscrimination, we derive our hypotheses from two broader sociological theories: world society theory and modernization theory. We find strong support for the idea of nondiscrimination in almost all countries surveyed, and at the same time significant variation across the 26 countries, with support being highest in Chile, Latvia, and Sweden and lowest in India, Indonesia, Nigeria, and Türkiye. Modernization theory as well as world society theory contribute to better understanding of citizens’ attitudes, albeit to different degrees and mediated through different channels. Citizens living in countries more deeply embedded in world society are more likely to support the principle of nondiscrimination. On the individual level, it turns out that people’s level of education and their general commitment to the norms of a global culture are associated with support for nondiscrimination. With regard to modernization theory, we find that a country’s level of modernization does not directly impact nondiscrimination attitudes, but many of the individual features derived from modernization theory do because individuals who are better educated and more secularized and those holding postmaterial values are more likely to support the nondiscrimination norm.

In this article, we describe the package twostep, a bundle of programs to perform analyses of hierarchical data applying the two-step approach. We consider a two-level data setup in which “microlevel” units are nested within “macrolevel” units. One-step models (which can be fit using, for example, mixed) are the most common approach to modeling two-level data. The two-step approach is an alternative in which parameters associated with microlevel and macrolevel predictors are estimated separately for each level. It can be used as an alternative to one-step models if the estimand is a cross-level interaction. We also show how the two-step approach usefully complements one-step approaches by providing exploratory data analysis, descriptive graphs, and regression diagnostics.

Objective: This chapter introduces the reader to the Special Issue "Female Employment and Migration in European Countries". Background: While there is a large body of research on the labour market performance of male migrants, women’s employment behaviour after migration has only recently moved into the focus of attention. Method: This Special Issue draws on various research methods and data sources, including register, census, and survey data. Some of the studies focus on specific national contexts, such as the German, Spanish, Dutch, and Belgian situations. Other studies compare female migrants across European countries and between origin and destination countries. Results: The contributions in this Special Issue help to disentangle the complex interplay of socio-economic factors, family and fertility behaviour, gender role attitudes, and institutional constraints and policies that shape the employment behaviour of migrant women after they migrate. Conclusion: In many European countries, the employment rates of first-generation female migrants, and particularly those of women from non-EU countries of origin, lag behind the employment rates of native women. While prior research has often reported that socio-economic and cultural factors play a role in shaping the employment behaviour of female migrants, the contributions in this volume also emphasise the strong relevance of institutional factors in the receiving country, including migration, family, and labour market policies.