Laboratory
Migration and Mobility
At a Glance
Projects
Publications
Team
Project
The Human Migration Database (HMigD)
Maciej Danko, Domantas Jasilionis, Emanuele Del Fava, Dmitri A. Jdanov, Emilio Zagheni; in Collaboration with Arkadiusz Wiśniowski (The University of Manchester, United Kingdom), Jakub Bijak (University of Southampton, United Kingdom), Weronika Kloc-Nowak (University of Warsaw, Centre of Migration Research, Poland), Agnieszka Fihel (French National Institute for Demographic Studies, Paris / Warsaw University, 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. This way, the HMigD fully adheres to the concept of Open Data. To ensure full reproducibility of the results, we will 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.
One of the key aspects of migration data to consider in migration models is their quality. 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 measure 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 non-systematic 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 not being counted in the official immigration and emigration counts; 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 lead to 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 and, in some circumstances, socioeconomic and demographic information.
Third, we plan to carry out an exhaustive data quality check, including a plausibility check, where the estimated flows will be assessed by experts and will be tested for consistency, using the balancing demographic equation, together with census data as well as birth and death records. A key goal is to generate new and reliable estimates of migration flows between pairs of countries and over time in order to improve our understanding of the causes and consequences of migration. Initially, the focus is on EU countries, but we plan to extend the database and include more countries in the future.
Data and Surveys, Migration
Publications
Dańko, M. J.:
Software. https://github.com/MaciejDanko/UndercountMigScores: GitHub. unpublished. (2023)
Dańko, M. J.:
Software. https://github.com/MaciejDanko/HMigD_Shiny_App_I: GitHub. unpublished. (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)