Events

Date: 5 February 2026   Time: 13:00 - 14:00     

Location: MB-503

Migration plays a crucial role in urbanization, segregation, gentrification, and numerous phenomena related to socioeconomic development. Understanding the diverse drivers of mobility not only provides a deeper comprehension of the underlying social dynamics but also provides essential insights for the design of effective policies.

A major challenge in migration research, particularly when using data-driven approaches, is the limited availability of high-resolution, high-quality data. Traditional sources, such as surveys or digital records, are often non-uniformly sampled and may fail to accurately capture the migrant population, thereby introducing distortions into migration estimates. Moreover, these data are typically aggregated and characterized by restricted coverage, posing substantial obstacles for a comprehensive quantitative understanding of migration.

To overcome these limitations, we draw on newly available nationwide administrative data provided by Austria's Federal Statistical Office. This dataset includes individual-level records on migration based on address registrations, as well as detailed information on income tax, employment, and demographic attributes, offering near-complete population coverage between 2002 and 2022.

In this talk, I will present the WWTF-funded project "Multiscale network modeling of migration flows in Austria (MOMA)", which leverages these unique datasets to uncover the multiscale, hierarchical structure of internal migration flows in Austria over more than two decades, while also identifying their underlying social and economic determinants.

I will further discuss preliminary results obtained by employing inferential network methods to characterize municipality-level flows. Our analyses reveal significant deviations from commonly assumed relocation patterns described by the gravity law models. At the same time, we observe unexpected biases of internal migrations that leads to less frequent movements across boundaries at both district and state levels than predictions suggest. This leads to significant regionalization of migration at multiple geographical scales and augmented distinction between urban and rural areas. These patterns appear to be remarkably persistent across two decades of migration data, demonstrating systematic limitations of conventionally used gravity models in migration studies.