New Migration Data: Challenges and Opportunities

Abstract

Migration is hard to measure due to the complexity of the phenomenon and the limitations of traditional data sources. The Digital Revolution has brought opportunities in terms of new data and new methodologies for migration research. Social scientists have started to leverage data from multiple digital data sources, which have huge potential given their timeliness and wide geographic availability. Novel digital data might help in estimating migrant stocks and flows, infer intentions to migrate, and investigate the integration and cultural assimilation of migrants. Moreover, innovative methodologies can help make sense of new and diverse streams of data. For example, Bayesian methods, natural language processing, high-intensity time series, and computational methods might be relevant to study different aspects of migration. Importantly, researchers should consider the ethical implications of using these data sources, as well as the repercussions of their results.

Nowcasting Daily Population Displacement in Ukraine through Social Media Advertising Data

“…Digital traces left behind when people use internet-based services provide an opportunity to fill these data gaps with anonymized and aggregated data on user locations in near real-time. A growing body of literature has leveraged digital traces for studying population dynamics, with a focus in particular on migration dynamics (Kashyap 2021;Rampazzo, Rango, and Weber 2023). Studies on migration have used a diverse range of digital traces, including email IP addresses (Zagheni and Weber 2012), geolocated tweets (Zagheni et al 2014), LinkedIn profiles (State et al 2014), Skype calls (Kikas, Dumas, and Saabas 2015), among others (Cesare et al 2018;Kashyap 2021).…”

Migration Processes in the Republic of Kazakhstan: Regularities, Problems, and Prospects

“…Long-term migration, on the other hand, is defined as a migration that lasts more than one year and can be for work, study, family reunification, or other reasons. It often involves settling in the destination country and can be measured using digital trace data (Rampazzo et al, 2022). It is important to note that short-term and long-term migration processes are not mutually exclusive, and some migrants may engage in both types of migration.…”

Reinforcement learning for sequential decision making in population research

“…Notably, the quantity of individual-level population data is exploding (Langedijk et al 2019;Ruggles 2014) and efforts have been made to ensure their free access for the academic community; see e.g., the IPUMS project, 7 releasing data for almost 800 million observations drawn from 300 censuses of about 100 countries (McCaa and Ruggles 2002). Big micro-data represent a new kind of source material and they are expected to provide individual-level data about entire populations spanned over multiple time-points and at high geographic resolution (Ruggles 2014;Rampazzo et al 2023). However, although the potential comprehensiveness of data continue to improve, due to privacy and security factors, there are significant access restrictions for these data.…”