Organizational diversity practices are widely implemented to support the workplace integration of international migrants (IMs), yet their outcomes remain inconsistent. Little is known about why IMs respond differently to the same initiatives offered by their organizations or why these practices sometimes fail or backfire. Drawing on signaling and attribution theory, we argue that IMs interpret diversity practices through the lens of two key disadvantages: unfamiliarity with the destination country and exposure to discrimination within this context. We develop a dual disadvantage typology of IMs and theorize how resource and nondiscrimination practices differentially affect organizational commitment depending on migrants’ disadvantage configurations. This typology refines existing diversity frameworks by introducing a configurational view of migrant diversity and explaining why identical practices may produce divergent outcomes. Using survey data from 394 IMs working in Germany, we find that while nondiscrimination practices are consistently positively associated with commitment, the effects of resource practices vary: some IMs view them as empowering, while others interpret them as stigmatizing or misaligned with their needs. In particular, training initiatives can backfire for IMs who are already familiar with the labor market and face discrimination. Our findings demonstrate the importance of tailoring initiatives to different migrant groups. We contribute a theory-driven framework that explains how diversity signals are differentially interpreted across migrant disadvantage configurations and when diversity practices produce intended versus unintended effects.
Testing for dependence in time series is essential for understanding complex dynamics and assessing model adequacy. Nonparametric methods are particularly valuable as they do not rely on restrictive distributional assumptions. Novel and nonparametric Box-Ljung-type test statistics are introduced, and their asymptotic distributions are derived. Based on ordinal patterns, the tests offer a flexible, robust, and distribution-free framework for capturing serial or spatial dependence structures beyond linear correlations. Like classical Box-Ljung tests, the proposed methods allow the user to set a maximum number of potential delay parameters. Evidence from simulations and real-world applications demonstrates the effectiveness of the proposed methods as useful complements to classical approaches for detecting nonlinear dynamics in time series or regular grid data.
This study analyzes interregional migration patterns associated with eight different non-overlapping life course events. Count-data regression models are applied to analyze the response of migration flows across life-event group to meso-regional push and pull factors, and results are used for migration profiling and as input for regional policy and planning. While interregional migration flow data related to life-events are generally not publicly available from statistical offices, we present a way to construct origin–destination migration flow matrices from Danish register data on migration, economic and social events at the household level. Our findings corroborate theoretical model predictions and prior evidence that migration across life-event groups responds heterogeneously to regional labor market conditions, with interregional migrants in the transition from job-qualifying education to work responding most strongly to local economic signals. Over the life cycle, interregional migration after family transitions such as forming a legally recognized partnership, childbearing, and the “empty nest” phase are comparably stronger influenced by regional housing market conditions, local population structures and public service provision or place-based amenities. We also show that estimates for mixed migration groups experiencing multiple simultaneous events typically fail to detect associations between migration and regional context conditions, which highlights the methodological advantage of utilizing non-overlapping event group migration data for theory testing, demographic modeling and informing regional policies.
Early childhood education and care (ECEC) supports children's holistic development, which encompasses cognitive, social, emotional, and moral growth. Although this comprehensive vision is widely recognized among ECEC professionals, the empirical research that examines pedagogical interactions in ECEC centers has largely emphasized children's acquisition of knowledge and skills. This article does not aim to challenge the importance of teaching and learning, nor the research approaches that have deepened our understanding of these processes. Rather, it seeks to enrich this field by examining another, often implicit, dimension: directive education, the pedagogical practices in which ECEC professionals impart orientations to children, meaning ways of acting, perceiving, and thinking that children are expected to incorporate. Although these orientations can support learning, they also may address broader educational aims. This article focuses on such processes of directive education for which it draws on a qualitative inquiry based on 63 hours of videography recorded in three ECEC centers in Germany and interpreted using the Documentary Method. The findings make it possible to differentiate directive education into three forms: reactive, proactive, and catalyzing education. Furthermore, the inquiry explores how educational interactions address children beyond the individual, distinguishing between pars pro toto and ad omnibus education.
This study examines the direct, indirect, and total effects of climate change mitigation technologies from developed countries - transferred via foreign direct investment (FDI) - on greenhouse gas (GHG) emissions in developing countries. Using the G7 countries as sources of green technology spillovers and analyzing a panel of 64 developing countries from 2009 to 2019, we find that, for our full sample, FDI-related spillovers of climate change mitigation technologies have a direct negative effect on GHG emissions in developing countries and an indirect positive effect through GDP growth. The magnitude of these effects depends on the share of FDI from G7 countries in a developing country's GDP. At low FDI shares, the total effect is positive but statistically insignificant, whereas at high FDI shares it becomes significantly negative. Our results suggest that FDI-related spillovers of climate change mitigation technologies offer a promising pathway to reconciling economic growth with climate protection.
