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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.
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.
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.