Multiscale patterns of migration flows in Austria: regionalization, administrative barriers, and urban-rural divides

TL;DR

This study analyzes 20 years of internal migration in Austria, revealing persistent regionalization and urban-rural divides that challenge traditional gravity models, using network-based methods on administrative data.

Contribution

The paper introduces a novel network-based methodology to analyze internal migration patterns, highlighting systematic deviations from gravity law predictions and uncovering persistent regional and urban-rural divides.

Findings

Migration shows significant regionalization at multiple scales.

Internal migration biases reduce boundary-crossing movements.

Gravity models systematically underestimate local migration patterns.

Abstract

Migration is central in various societal problems related to socioeconomic development. While much of the existing research has focused on international migration, migration patterns within a single country remain relatively unexplored. In this work we study internal migration patterns in Austria for a period of over 20 years, obtained from open and high-granularity administrative records. We employ inferential network methods to characterize the flows between municipalities and extract their clustering according to similar target and destination rates. Our methodology reveals significant deviations from commonly assumed relocation patterns modeled by the gravity law. 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 division between urban and rural areas. These patterns appear to be remarkably persistent across decades of migration data, demonstrating systematic limitations of conventionally used gravity models in migration studies. Our approach presents a robust methodology that can be used to improve such evaluations, and can reveal new phenomena in migration networks.