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- Fachgruppe Soziologie (14) (remove)
Web scraping, a technique for extracting data from web pages, has been in use for decades, yet its utilization in the field of migration, mobility, and migrant integration studies has been limited. The field faces notorious limitations regarding data access and availability, particularly in low-income settings. Web scraping has the potential to provide new datasets for further qualitative and quantitative analysis. Web scraping requires no financial resources, is agnostic to epistemic divides in the field, reduces researcher bias, and increases transparency and replicability of data collection. As large providers of digital data such as Facebook or Twitter increasingly restrict access to their data for researchers, web scraping will become more important in the future and deserves its place in the toolbox of migration and mobility scholars. This short and nontechnical methods note introduces the fundamental concepts of web scraping, provides guidance on how to learn the technique, showcases practical applications of web scraping in the study of migrant populations, and discusses potential future use cases.
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.
Risky journeys
(2022)
In response to well-documented harms inflicted on irregular migrants attempting to travel from West Africa to Europe, various actors have scaled up information interventions to counter misinformation by smuggling networks and facilitate safe migration decisions. Many interventions include information on the potential dangers involved in migration. However, there is a striking lack of empirical evidence assessing a key assumption of campaign effectiveness, that is the relationship between risk perceptions and the decision to migrate irregularly. This study contributes an empirical account based on two independently collected surveys in Senegal and Guinea. Consistent with rational choice theories on migration decisions under uncertainty, the results suggest that higher risk perceptions are consistently and strongly associated with reduced intentions to migrate irregularly. Yet, the explanatory power of risk perceptions depends on context and is generally less important than structural and socio-economic factors.
Background: Following the rapid increase of asylum seekers arriving in the European Union in 2015/16, policymakers have invested heavily in improving their foresight and forecasting capabilities. A common method to elicit expert predictions are Delphi surveys. This approach has attracted concern in the literature, given the high uncertainty in experts’ predictions. However, there exists limited guidance on specific design choices for future-related Delphi surveys.
Objective: We test whether or not small adjustments to the Delphi survey can increase certainty (i.e., reduce variation) in expert predictions on immigration to the EU in 2030.
Methods: Based on a two-round Delphi survey with 178 migration experts, we compare variation and subjective confidence in expert predictions and assess whether additional context information (type of migration flow, sociopolitical context) promotes convergence among experts (i.e., less variation) and confidence in their own estimates.
Results: We find that additional context information does not reduce variation and does not increase confidence in expert predictions on migration.
Conclusions: The results reaffirm recent concerns regarding the limited scope for reducing uncertainty by manipulating the survey setup. Persistent uncertainty may be a result of the complexity of migration processes and limited agreement among migration experts regarding key drivers.
Contribution: We caution policymakers and academics on the use of Delphi surveys for eliciting expert predictions on immigration, even when conducted based on a large pool of experts and using specific scenarios. The potential of alternative approaches such as prediction markets should be further explored.
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.
In response to mounting evidence of harm inflicted on irregular migrants along their journeys from West Africa to Europe, international organizations, civil society organizations, and governments have scaled up campaigns as a tool for raising awareness about the risks of irregular migration. Campaigns aim to counter misinformation by smugglers and facilitate safe migration decisions. Despite the growing number of interventions, there is limited empirical evidence on the impact and effectiveness of such campaigns. Based on a difference-in-difference design, this study investigates the effect of a mobile cinema and community discussion intervention on the perceptions, knowledge, and intentions of potential irregular migrants in Northern Guinea in 2019. The results show that potential migrants who participated in events were significantly more likely to show awareness gains and less likely to report high intentions to migrate irregularly. While the relative importance of risk perceptions and their impact on migration flows remain unclear, the findings provide evidence supporting the assumption that risk awareness can be a relevant factor in the decision-making process of potential irregular migrants. While campaigns may be an effective tool in certain contexts, effect sizes highlight the need for policymakers to keep realistic expectations.
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.
Emerging evidence has highlighted the important role of local contexts for integration trajectories of asylum seekers and refugees. Germany's policy of randomly allocating asylum seekers across Germany may advantage some and disadvantage others in terms of opportunities for equal participation in society. This study explores the question whether asylum seekers that have been allocated to rural areas experience disadvantages in terms of language acquisition compared to those allocated to urban areas. We derive testable assumptions using a Directed Acyclic Graph (DAG) which are then tested using large-N survey data (IAB-BAMF-SOEP refugee survey). We find that living in a rural area has no negative total effect on language skills. Further the findings suggest that the "null effect" is the result of two processes which offset each other: while asylum seekers in rural areas have slightly lower access for formal, federally organized language courses, they have more regular exposure to German speakers.
A growing number of studies have recently postulated a so-called local turn in the study of immigrant and refugee integration policy. A fundamental, yet untested, assumption of this body of research is that local (sub-national) policies and administrations shape how migrants and refugees integrate into society. We develop and apply an analytical model using multilevel modeling techniques based on large-N, longitudinal survey data (N > 9000) with refugees (2012–2018) in a highly decentralized country (Germany) to estimate the scope for local policy effects net of individual-level and state- and district-level characteristics. We show that region and district-level variation in integration outcomes across multiple dimensions (employment, education, language, housing, social) is limited (∼5%) within 4–8 years after immigration. We find modest variation in policy indicators (∼10%), which do not appear to directly translate into outcomes. We discuss implications for the study of local policies and the potential for greater convergence between administrative and political science, interested in governance structures and policy variation, and sociology and economics, interested primarily in integration outcomes.
Studies from several countries suggest that COVID-19 vaccination rates are lower among migrants compared to the general population. Urgent calls have been made to improve vaccine outreach to migrants, however, there is limited evidence on effective approaches, especially using social media. We assessed a targeted, low-cost, Facebook campaign disseminating COVID-19 vaccine information among Arabic, Turkish and Russian speakers in Germany (N = 888,994). As part of the campaign, we conducted two randomized, online experiments to assess the impact of the advertisement (1) language and (2) depicted messenger (government authority, religious leader, doctor or family). Key outcomes included reach, click-through rates, conversion rates and cost-effectiveness. Within 29 days, the campaign reached 890 thousand Facebook users. On average, 2.3 individuals accessed the advertised COVID-19 vaccination appointment tool for every euro spent on the campaign. Migrants were 2.4 (Arabic), 1.8 (Russian) and 1.2 (Turkish) times more likely to click on advertisements translated to their native language compared to German-language advertisements. Furthermore, findings showed that government representatives can be more successful in engaging migrants online compared to other messengers, despite common claims of lower trust in government institutions among migrants. This study highlights the potential of tailored, and translated, vaccination campaigns on social media for reaching migrants who may be left out by traditional media campaigns.
Studies from several countries suggest that COVID-19 vaccination rates are lower among migrants compared to the general population. Urgent calls have been made to improve vaccine outreach to migrants, however, there is limited evidence on effective approaches, especially using social media. We assessed a targeted, low-cost, Facebook campaign disseminating COVID-19 vaccine information among Arabic, Turkish and Russian speakers in Germany (N = 888,994). As part of the campaign, we conducted two randomized, online experiments to assess the impact of the advertisement (1) language and (2) depicted messenger (government authority, religious leader, doctor or family). Key outcomes included reach, click-through rates, conversion rates and cost-effectiveness. Within 29 days, the campaign reached 890 thousand Facebook users. On average, 2.3 individuals accessed the advertised COVID-19 vaccination appointment tool for every euro spent on the campaign. Migrants were 2.4 (Arabic), 1.8 (Russian) and 1.2 (Turkish) times more likely to click on advertisements translated to their native language compared to German-language advertisements. Furthermore, findings showed that government representatives can be more successful in engaging migrants online compared to other messengers, despite common claims of lower trust in government institutions among migrants. This study highlights the potential of tailored, and translated, vaccination campaigns on social media for reaching migrants who may be left out by traditional media campaigns.
Phone surveys have increasingly become important data collection tools in developing countries, particularly in the context of sudden contact restrictions due to the COVID-19 pandemic. So far, there is limited evidence regarding the potential of the messenger service WhatsApp for remote data collection despite its large global coverage and expanding membership. WhatsApp may offer advantages in terms of reducing panel attrition and cutting survey costs. WhatsApp may offer additional benefits to migration scholars interested in cross-border migration behavior which is notoriously difficult to measure using conventional face-to-face surveys. In this field experiment, we compared the response rates between WhatsApp and interactive voice response (IVR) modes using a sample of 8446 contacts in Senegal and Guinea. At 12%, WhatsApp survey response rates were nearly eight percentage points lower than IVR survey response rates. However, WhatsApp offers higher survey completion rates, substantially lower costs and does not introduce more sample selection bias compared to IVR. We discuss the potential of WhatsApp surveys in low-income contexts and provide practical recommendations for field implementation.
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 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.