Laboratory
Migration and Mobility
At a Glance
Projects
Publications
Team
Project
The Human Migration Database (HMigD)
Conducted by Maciej Danko, Emilio Zagheni; Domantas Jasilionis, Emanuele Del Fava, Dmitri A. Jdanov, Aysha Basheer, Boris Barron; in Collaboration with Arkadiusz Wiśniowski (The University of Manchester, United Kingdom), Jakub Bijak (University of Southampton, United Kingdom), Paweł Kaczmarczyk (University of Warsaw, Centre of Migration Research, Poland), Francisco Rowe (University of Liverpool, United Kingdom), Agnieszka Fihel (French National Institute for Demographic Studies, Paris, France / University of Warsaw, Centre of Migration Research, Poland), Weronika Kloc-Nowak (University of Warsaw, Centre of Migration Research, Poland)
Detailed Description
The Human Migration Database (HMigD) provides reliable evidence on international migration flows. The database follows four main guiding principles that were first formulated in the Human Mortality Database (HMD): comparability, flexibility, accessibility, and reproducibility. Thus, the HMigD fully adheres to the concept of open data. To ensure full reproducibility of the results, we provide the original input data (where possible), exhaustive country-specific metadata and documentation, and the scripts used for calculations. However, unlike the HMD, the HMigD is a synthetic database, i.e., the main output data are produced by statistical modeling.
Data quality is one of the key aspects to consider in migration models. In general, the quality depends on the ability of governmental agencies to trace migration flows (including the legal incentives for registering the migration event and the methodology used to measure migration). Moreover, approaches to measuring migration are usually not consistent across countries and statistical offices. Migration estimates produced by national statistical institutes and other sources (e.g., labor force surveys) are thus not directly comparable. The major problems in migration data quality can be classified into four groups: (i) accuracy issues related to random rather than systematic errors made in the data collection process; (ii) undercounting, reflecting a nonsystematic bias in migration estimates; (ii) coverage issues related to the systematic exclusion or undercounting of certain population segments, such as nationals who are return migrants or foreigners who are not counted in the official immigration and emigration figures; and (iv) inconsistencies in the definition of international migrant due to deviations of national migration criteria (minimum duration of stay) from international (UN/Eurostat) standards.
First, we systematically evaluate and classify data quality across sources, which is an important task for creating a reliable evidence base for further stages of the project. Ignoring potential systematic errors and misinterpreting problematic data can result in misleading conclusions or estimates. The quality of migration data is assessed using available metadata, expert opinion, and data-driven methods.
Second, we integrate the available data within a Bayesian modeling framework. We extend previous work on estimating international migration (Raymer et al. 2013) by developing a hierarchical Bayesian model that integrates and harmonizes different migration data sources, and by considering differences in data quality and definitions used.
Third, we develop methods to reconcile potential discrepancies between migration flows and net migration (obtained indirectly via the residual method), while also taking into account uncertainty in the data sources. A key aim is to generate new and reliable estimates of migration flows between pairs of countries, and, over time, estimates that are consistent with measures of births, deaths, and population counts in the Human Mortality Database and Human Fertility Database. The broader goal is to provide high-quality estimates that advance our understanding of the causes and consequences of migration, and to improve our predictive capacity.
HMigD serves as an overarching framework that links and coordinates multiple methodological and data-focused projects. While we initially concentrate on EU countries, we plan to expand the database and include more countries in the future. As we develop our methods further, we also expect to incorporate into the HMigD advancements in the use of digital trace data for migration estimation and nowcasting.
Data and Surveys, Migration
Publications
Dańko, M. J.; Del Fava, E.; Wiśniowski, A.; Zagheni, E.:
MPIDR Working Paper WP-2026-020. (2026)
Dańko, M. J.; Rampazzo, F.; Donzowa, J.; Kim, J.; Zagheni, E.:
MPIDR Technical Report TR-2026-001. (2026)
Dańko, M. J.:
Software. https://github.com/MaciejDanko/UndercountMigScores: GitHub. (2023)
Dańko, M. J.:
Software. https://github.com/MaciejDanko/HMigD_Shiny_App_I: GitHub. (2023)
Dańko, M. J.; Wiśniowski, A.; Jasilionis, D.; Jdanov, D. A.; Zagheni, E.:
MPIDR Working Paper WP-2023-026. (2023)
Mooyaart, J.; Dańko, M. J.; Costa, R.; Boissonneault, M.:
The Hague. (2021)