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Using novel longitudinal data, this paper studies the short- and medium-term effects of Russia's invasion of Ukraine on February 24, 2022 on social trust of adolescents in Germany. Comparing adolescents who responded to our survey shortly before the start of the war with those who responded shortly after the conflict began and applying difference-in-differences (DiD) models over time, we find a significant decline in the outcome after the war started. These findings provide new evidence on how armed conflicts influence social trust and well-being among young people in a country not directly involved in the war.
Residential segregation is a wide-spread phenomenon that can be observed in almost every major city.
In these urban areas residents with different racial or socioeconomic background tend to form homogeneous clusters.
Schelling's famous agent-based model for residential segregation explains how such clusters can form even if all agents are tolerant, i.e., if they agree to live in mixed neighborhoods.
For segregation to occur, all it needs is a slight bias towards agents preferring similar neighbors.
Very recently, Schelling's model has been investigated from a game-theoretic point of view with selfish agents that strategically select their residential location.
In these games, agents can improve on their current location by performing a location swap with another agent who is willing to swap.
We significantly deepen these investigations by studying the influence of the underlying topology modeling the residential area on the existence of equilibria, the Price of Anarchy and on the dynamic properties of the resulting strategic multi-agent system. Moreover, as a new conceptual contribution, we also consider the influence of locality, i.e., if the location swaps are restricted to swaps of neighboring agents.
We give improved almost tight bounds on the Price of Anarchy for arbitrary underlying graphs and we present (almost) tight bounds for regular graphs, paths and cycles. Moreover, we give almost tight bounds for grids, which are commonly used in empirical studies.
For grids we also show that locality has a severe impact on the game dynamics.
German and European migration policy operates in permanent crisis mode. Sudden increases in irregular immigration create a sense of loss of control, which is instrumentalised by populist forces. This has generated great interest in quantitative migration predictions. High expectations are placed in the AI-based tools currently under devel­op­ment for forecasting irregular migration. The potential applications of these tools are manifold. They range from managing and strengthening the EU's reception capacity and border protections to configuring humanitarian aid provision and longer-term planning of development programmes. There is a significant gap between the expectations placed in the new instruments and their practical utility. Technical limits exist, medium-term forecasts are methodologically implausible, and channels for feeding the results into political decision-making processes are lacking. The great demand for predictions is driven by the political functions of migration prediction, which include its uses in political communication, funding acquisition and legitimisation of political decisions. Investment in the quality of the underlying data will be more productive than developing a succession of new prediction tools. Funding for applications in emergency relief and development cooperation should be prioritised. Crisis early warning and risk analysis should also be strengthened and their networking improved.
In 2022, there were 4.62 billion social media users worldwide. Social media generates a wealth of data which migration scholars have recently started to explore in pursuit of a variety of methodological and thematic research questions. Scholars use social media data to estimate migration stocks, forecast migration flows, or recruit migrants for targeted online surveys. Social media has also been used to understand how migrants get information about their planned journeys and destination countries, how they organize and mobilize online, how migration issues are politicized online, and how migrants integrate culturally into destination countries by sharing common interests. While social media data drives innovative research, it also poses severe challenges regarding data privacy, data protection, and methodological questions relating to external validity. In this chapter, I briefly introduce various strands of migration research using social media data and discuss the advantages, disadvantages, and opportunities.
In 2015, German Chancellor Angela Merkel decided to allow over a million asylum seekers to cross the border into Germany. One key concern was that her decision would signal an open-door policy to aspiring migrants worldwide – thus further increasing migration to Germany and making the country permanently more attractive to irregular and humanitarian migrants. This ‘pull-effect’ hypothesis has been a mainstay of policy discussions ever since. With the continued global rise in forced displacement, not appearing welcoming to migrants has become a guiding principle for the asylum policy of many large receiving countries. In this article, we exploit the unique case study that Merkel's 2015 decision provides for answering the fundamental question of whether welcoming migration policies have sustained effects on migration towards destination countries. We analyze an extensive range of data on migration inflows, migration aspirations and online search interest between 2000 and 2020. The results reject the ‘pull effect’ hypothesis while reaffirming states’ capacity to adapt to changing contexts and regulate migration.
Background
Many high-income countries are grappling with severe labour shortages in the healthcare sector. Refugees and recent migrants present a potential pool for staff recruitment due to their higher unemployment rates, younger age, and lower average educational attainment compared to the host society's labour force. Despite this, refugees and recent migrants, often possessing limited language skills in the destination country, are frequently excluded from traditional recruitment campaigns conducted solely in the host country’s language. Even those with intermediate language skills may feel excluded, as destination-country language advertisements are perceived as targeting only native speakers. This study experimentally assesses the effectiveness of a recruitment campaign for nursing positions in a German care facility, specifically targeting Arabic and Ukrainian speakers through Facebook advertisements.
Methods
We employ an experimental design (AB test) approximating a randomized controlled trial, utilizing Facebook as the delivery platform. We compare job advertisements for nursing positions in the native languages of Arabic and Ukrainian speakers (treatment) with the same advertisements displayed in German (control) for the same target group in the context of a real recruitment campaign for nursing jobs in Berlin, Germany. Our evaluation includes comparing link click rates, visits to the recruitment website, initiated applications, and completed applications, along with the unit cost of these indicators. We assess statistical significance in group differences using the Chi-squared test.
Results
We find that recruitment efforts in the origin language were 5.6 times (Arabic speakers) and 1.9 times (Ukrainian speakers) more effective in initiating nursing job applications compared to the standard model of German-only advertisements among recent migrants and refugees. Overall, targeting refugees and recent migrants was 2.4 (Ukrainians) and 10.8 (Arabic) times cheaper than targeting the reference group of German speakers indicating higher interest among these groups.
Conclusions
The results underscore the substantial benefits for employers in utilizing targeted recruitment via social media aimed at foreign-language communities within the country. This strategy, which is low-cost and low effort compared to recruiting abroad or investing in digitalization, has the potential for broad applicability in numerous high-income countries with sizable migrant communities. Increased employment rates among underemployed refugee and migrant communities, in turn, contribute to reducing poverty, social exclusion, public expenditure, and foster greater acceptance of newcomers within the receiving society.
‘Modern talking’
(2024)
Despite growing interest, we lack a clear understanding of how the arguably ambiguous phenomenon of agile is perceived in government practice. This study aims to alleviate this puzzle by investigating how managers and employees in German public sector organisations make sense of agile as a spreading management fashion in the form of narratives. This is important because narratives function as innovation carriers that ultimately influence the manifestations of the concept in organisations. Based on a multi-case study of 31 interviews and 24 responses to a qualitative online survey conducted in 2021 and 2022, we provide insights into what public sector managers, employees and consultants understand (and, more importantly, do not understand) as agile and how they weave it into their existing reality of bureaucratic organisations. We uncover three meta-narratives of agile government, which we label ‘renew’, ‘complement’ and ‘integrate’. In particular, the meta-narratives differ in their positioning of how agile interacts with the characteristics of bureaucratic organisations. Importantly, we also show that agile as a management fad serves as a projection surface for what actors want from a modern and digital organisation. Thus, the vocabulary of agile government within the narratives is inherently linked to other diffusing phenomena such as new work or digitalisation.
We examine how the gender of business owners is related to the wages paid to female relative to male employees working in their firms. Using Finnish register data and employing firm fixed effects, we find that the gender pay gap is—starting from a gender pay gap of 11 to 12%—two to three percentage points lower for hourly wages in female-owned firms than in male-owned firms. Results are robust to how the wage is measured, as well as to various further robustness checks. More importantly, we find substantial differences between industries. While, for instance, in the manufacturing sector, the gender of the owner plays no role in the gender pay gap, in several service sector industries, like ICT or business services, no or a negligible gender pay gap can be found, but only when firms are led by female business owners. Businesses with male ownership maintain a gender pay gap of around 10% also in the latter industries. With increasing firm size, the influence of the gender of the owner, however, fades. In large firms, it seems that others—firm managers—determine wages and no differences in the pay gap are observed between male- and female-owned firms.
Money matters!
(2024)
This paper examines the context dependency of attitudes toward maternal employment. We test three sets of factors that may affect these attitudes—economic benefits, normative obligations, and child-related consequences—by analyzing data from a unique survey experimental design implemented in a large-scale household panel survey in Germany (17,388 observations from 3,494 respondents). Our results show that the economic benefits associated with maternal employment are the most important predictor of attitudes supporting maternal employment. Moreover, we find that attitudes toward maternal employment vary by individual, household, and contextual characteristics (in particular, childcare quality). We interpret this variation as an indication that negative attitudes toward maternal employment do not necessarily reflect gender essentialism; rather, gender role attitudes are contingent upon the frames individuals have in mind.