Fachgruppe Soziologie
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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.
Die deutsche und europäische Migrationspolitik befindet sich im permanenten Krisenmodus. Plötzliche Anstiege ungeregelter Zuwanderung nähren ein Gefühl von Kontrollverlust, das wiederum von populistischen Kräften instrumentalisiert wird. Daher hat die Politik großes Interesse an quantitativen Migrationsprognosen. Besondere Erwartungen wecken KI-gestützte Instrumente zur Vorhersage ungeregelter Wanderungsbewegungen, wie sie zurzeit entwickelt werden. Die Anwendungsfelder dieser Instrumente sind vielfältig. Sie reichen von einer Stärkung der Aufnahmekapazitäten in der EU über die präventive Verschärfung von Grenzschutzmaßnahmen und eine bedarfsgerechte Bereitstellung von Ressourcen in humanitären Krisen bis zur längerfristigen entwicklungspolitischen Programmplanung. Allerdings besteht eine deutliche Kluft zwischen den Erwartungen an die neuen Instrumente und ihrem praktischen Mehrwert. Zum einen sind die technischen Möglichkeiten begrenzt, und mittelfristige Vorhersagen zu ungeregelten Wanderungen sind methodisch kaum möglich. Zum anderen mangelt es an Verfahren, um die Ergebnisse in politische Entscheidungsprozesse einfließen zu lassen. Die hohe Nachfrage nach Prognosen erklärt sich aus den politischen Funktionen quantitativer Migrationsvorhersage - beispielsweise ihrem Potential für die politische Kommunikation, die Mitteleinwerbung und die Legitimierung politischer Entscheidungen. Investitionen in die Qualität der den Prognosen zugrunde liegenden Daten sind sinnvoller als die Entwicklung immer neuer Instrumente. Bei der Mittelvergabe für Prognosen sollten Anwendungen in der Nothilfe und der Entwicklungszusammenarbeit priorisiert werden. Zudem sollten die Krisenfrüherkennung und die Risikoanalyse gestärkt werden, und die beteiligten Akteure sollten sich besser vernetzen.
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.
Zuhause
(2024)
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.
Wir nehmen eine vergleichende Untersuchung der Nominierten und Preisträger:innen von sieben Buchpreisen im deutschsprachigen Raum vor, die mit einer vorab veröffentlichten Long- und/oder Shortlist arbeiten. Dazu vergleichen wir die Preise in Bezug auf soziodemographische Faktoren der Autor:innen (Geschlecht, Alter und Muttersprache), deren Bekanntheit zum Zeitpunkt der Nominierung (Aufrufe auf Wikipedia), die Anzahl vorheriger Nominierungen der Autor:innen für den gleichen Buchpreis, die ›Qualität‹ der ausgezeichneten Bücher (Anzahl der Rezensionen des nominierten Buches, positive bzw. negative Beurteilung in Rezensionen sowie die Einigkeit der Rezensent:innen darüber), das Ansehen der Verlage und die Geschlechterzusammensetzung der Jurys. Der Analysezeitraum umfasst 15 Jahre. Unser Datensatz beinhaltet Informationen zu 428 Autor:innen mit insgesamt 627 zwischen den Jahren 2005 und 2020 nominierten Büchern und 2.469 Rezensionen zu diesen Büchern. Der Datensatz wurde mittels mehrerer Methoden (z. B. Web-Scraping, Hand-Kodierung, Expert:innenbewertungen) aus verschiedenen Quellen (z. B. Web-Daten, Bibliothekskataloge, Expert:innenbewertungen) zusammengestellt. Auf diese Weise können wir unter anderem zeigen, dass für alle untersuchten Preise überwiegend deutsche Muttersprachler:innen mit gut rezensierten Büchern aus renommierten Verlagen nominiert werden und die Preise gewinnen.
Organizational commitments to equality change how people view women’s and men’s professional success
(2024)
To address women’s underrepresentation in high-status positions, many organizations have committed to gender equality. But is women’s professional success viewed less positively when organizations commit to women’s advancement? Do equality commitments have positive effects on evaluations of successful men? We fielded a survey experiment with a national probability sample in Germany (N = 3229) that varied employees’ gender and their organization’s commitment to equality. Respondents read about a recently promoted employee and rated how decisive of a role they thought intelligence and effort played in getting the employee promoted from 1 “Not at all decisive” to 7 “Very decisive” and the fairness of the promotion from 1 “Very unfair” to 7 “Very fair.” When organizations committed to women’s advancement rather than uniform performance standards, people believed intelligence and effort were less decisive in women’s promotions, but that intelligence was more decisive in men’s promotions. People viewed women’s promotions as least fair and men’s as most fair in organizations committed to women’s advancement. However, women’s promotions were still viewed more positively than men’s in all conditions and on all outcomes, suggesting people believed that organizations had double standards for success that required women to be smarter and work harder to be promoted, especially in organizations that did not make equality commitments.
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.