Crettaz, Eric

2019, Crettaz, Eric, Dahinden, Janine

Transnational studies have been in vogue for the past two decades. Nevertheless, there remain important knowledge gaps concerning migrants’ transnational formations. First, most of the literature relies on qualitative case studies. The few existing quantitative studies have shown that transnationality is far from being a “lifestyle” and that factors other than individual preferences are at play. Second, most studies in this field focus on one nationally defined group, which renders impossible the elaboration of an overall model of transnationality that goes beyond description. Third, few studies have tried to link the question of transnationality simultaneously to migration and mobility. To address these gaps, we propose here an analysis of migrant transnationality based on the Migration-Mobility Survey. We define transnationality along three dimensions. We make a distinction between transnational (pre-and-post-migration) mobilities, network transnationality and transnational belonging. We use regression models and multiple correspondence analysis to identify the prevalence of transnationality and the main determinants of transnational patterns. The analysis confirms the hypothesis that transnationality can be linear – an “automatic effect” of migration – resource-dependent, but also reactive upon discrimination. Migrant transnationality can simultaneously be a sign of possessing high resources – most importantly, in terms of legal capital, education and economic resources – or of discrimination. Furthermore, our analysis brings to light five ideal-typical configurations of what we call the Migration-Mobility-Transnationality Nexus. Our analysis contributes to this book by investigating the Migration-Mobility-Nexus with respect to transnationality, going beyond the normative ideas of migration and mobility by integrating them analytically into one model.

2016, Suter, Christian, Kuhn, Ursina, Gazareth, Pascale, Crettaz, Eric, Ravazzini, Laura, Franzen, Axel, Jann, Ben, Joppke, Christian, Widmer, Eric

We compared time series of eight different data sources (HBS, SLFS, SESS, SHP, SILC, SHS, SPS, tax data) and calculated various inequality measures (Gini coefficient, Atkinson coefficient, Theil’s T, MLD, SCV, p90/p10, p80/p20, p50/p10, p90/p50) for the period from 1990 to 2012. While the level of inequality varies strongly across surveys, the results concerning the evolution over time are rather coherent. For disposable household income, inequality has remained stable, but evolves parallel to the business cycle of the Swiss economy. For individual employment income, findings across datasets are less consistent.

2013, Crettaz, Eric, Suter, Christian