Laboratory
Migration and Mobility
At a Glance
Projects
Publications
Team
Project
Temporal and Undercounting Effects in Migration Measurement
Maciej Danko, Emilio Zagheni, Arkadiusz Wisniowski, Domantas Jasilionis, Dmitri A. Jdanov; in Collaboration with Guy Abel (Shanghai University, China), Lee Fiorio (University of Washington, Seattle, USA)
Detailed Description
Migration data are scarce compared to data on other fundamental demographic processes like fertility and mortality. What is more, the migration data that do exist often measure migration using different temporal definitions, making the data difficult to compare even in the same research context. These differences in time scales often reflect the different needs of the various institutions that collect migration data, as well as the methods used to generate them. For example, a receiving country might classify a newcomer as an immigrant after 12 months of residency, whereas a sending country might classify the same person as an emigrant just three months after their departure. Similarly, an annual survey might use a one-year retrospective interval to estimate the number of migrants, whereas a decennial census might use a five-year retrospective interval.
The challenges that arise in reconciling migration data stem from the rich complexity of population movement. Unlike fertility, migration is not necessarily characterized by a singular event. Migrants might begin as temporary visitors, or they might split their time between multiple locations before ultimately settling in a new location. Furthermore, unlike mortality, migration offers no guarantee of permanence. Even a person who migrated long ago might someday return or move to some other place. The migration literature has long recognized the fuzzy boundary between short-term mobility and long-term migration, as well as the measurement difficulties created by return and onward flows.
To address these longstanding issues in migration scholarship, this project leverages the unique spatial and temporal granularity of georeferenced digital trace data to measure population flows at multiple time scales. Georeferenced digital trace data have become increasingly ubiquitous, as they are cultivated and captured as metadata when individuals make calls and send texts, or when they interact with web and smartphone applications. The structure of these data is relatively standardized, with each record consisting of a tuple (containing the user ID, a time stamp, and the location of the user), and their size allows for the estimation of many different measures of migration under many different temporal specifications. In a first paper (Fiorio et al. 2021), by systematically varying the temporal specification, we analyze changes in migration estimates along a quasi-continuous time scale, analogous to a survival function, to evaluate data quality, assess migration patterns, and map the relationship between different kinds of estimates.
A second research line within this project proposes a novel, data-driven approach that incorporates year-specific and duration of stay-adjusted classifications to assess undercounting in migration statistics. The proposed methodological solution relies on comparisons of flows in the same direction reported by a given country with high-quality data, and by another set of countries. We use bilateral migration data provided by Eurostat, the United Nations, and selected national statistical institutes. Duration of stay correction coefficients are derived through an optimization model or borrowed from the literature. Metadata and expert opinion scores are also integrated to classify undercounting. The result is a dynamic classification of undercounting for 32 European countries (2002–21), made accessible through an online Shiny application, that offers flexibility and adaptability. The findings provide evidence of significant undercounting in new European Union member states, particularly Bulgaria, Latvia, and Romania. Interestingly, other European countries, including those presumed to maintain reliable population statistics, also exhibit notable periods of undercounting. As we continue to refine estimates of migration flows, this project ultimately aims to lay the groundwork for producing the highest-quality data on migration, which should, in turn, enable more robust empirical assessments of migration flows and improve migration theory.
Data and Surveys, Migration
Publications
Dańko, M. J.; Wiśniowski, A.; Jasilionis, D.; Jdanov, D. A.; Zagheni, E.:
Migration Studies 12:2, 1–21. (2024)
Dańko, M. J.; Wiśniowski, A.; Jasilionis, D.; Jdanov, D. A.; Zagheni, E.:
MPIDR Working Paper WP-2023-026. (2023)
Fiorio, L.; Zagheni, E.; Abel, G. J.; Hill, J.; Pestre, G.; Letouzé, E.; Cai, J.:
Demography 58:1, 51–74. (2021)
Fiorio, L.; Zagheni, E.; Abel, G. J.; Hill, J.; Pestre, G.; Letouzé, E.; Cai, J.:
MPIDR Working Paper WP-2020-024. (2020)