Arriving Young, Leaving Old(er): Age-Structured International Migration on Subnational Scale in Austria

TL;DR

This paper presents a diaspora-based model that jointly estimates arrivals and exits from Austria by linking flow intensities to diaspora size and pull/push rates, then allocates these flows across more than 2,000 municipalities. The approach, formulated as Poisson processes with arrivals $A_i(t)
ext{Pois}(\lambda_i t)$ and exits $E_i(t)
ext{Pois}(\\mu_i t)$, uses a Skellam distribution for net migration and a multinomial allocation $_{ij}(t)
ext{Pois}(\\pi_{ij}\\lambda t)$, where $\\pi_{ij} = R_{ij}/R_i$. Results show substantial, predictable exit flows across nationalities and ages, a clear age-dynamics pattern with young adults driving most movement, and urban concentration of arrivals for younger cohorts, implying a recurrent rejuvenation of Austria’s population. The study highlights the value of modeling both directions of migration and accounting for space, age, and origin in demographic projections and policy planning. Overall, the diaspora-flows framework offers a transparent, transferable tool for understanding short-term migration dynamics at the subnational level and informing integration and labor-market policies.

Abstract

Modelling migration is complicated, as people move for many reasons. Some leave their country for the first time, others return to places they once called home, or move on to new destinations. However, most models focus only on who arrives, missing the full picture of how migrant populations evolve. We introduce a model for diaspora flows that estimates both arrivals and exits using daily migration flow rates, disaggregated by age and nationality. Drawing on high-resolution administrative data from Austria covering over 1.8 million foreign nationals, the model allocates these movements across more than 2,000 municipalities based on the size of local diaspora communities. We find that exits are not exceptions but a consistent and predictable feature across all groups. Migration rejuvenates Austria's population, as both arriving and departing migrants are younger than the average resident. This effect has distinct age-geography patterns: younger migrants are drawn to cities, while older migrants are more evenly distributed in the country. By capturing both arrivals and exits simultaneously, our approach provides a more comprehensive and interpretable picture of migration dynamics, how populations change over time, and how they are influenced by space, age, and national origins.

Figures (11)

• Figure 1: Dynamics of Arrival and Exit Flows to and from Austria.(A) Cumulative arrivals (blue) and exits (red), showing a strong match between observed data and modelled flows. Arrivals consistently exceed exits across all groups, indicating stable net immigration. (B) Daily arrival and exit rates by citizenship, based on the model for diaspora flows. Most groups lie below the 45-degree line, reflecting net immigration. Ukrainian and Syrian nationals show unusually high arrival rates, likely due to conflict-related migration. Iraq is the only diaspora among the 25 largest in Austria with net emigration.
• Figure 2: Diaspora Half-Lifes, or the time it takes for half of the pre-existing population size to leave Austria without accounting for any further entry, varies by citizenship, age, and location. Ukrainians leave fastest, while Germans and Serbians stay longer. Younger migrants and those in rural areas move on sooner, whereas older groups and city residents, especially in Vienna, remain longer.
• Figure 3: Age-specific arrival and exit dynamics: Daily migration flows are shown for each age from 2 to 100, with arrivals (blue dashed) and exits (red dashed) indicating movement rates across the life course. The black line represents net migration, categorised into five age groups: children (2–17), young adults (18–34), middle-aged adults (35–54), older adults (55–69), and seniors (70+), marked by shaded segments. Migration is highest among young adults, who drive the majority of the net population growth. In contrast, net migration declines steadily with age, approaching zero or becoming negative among older groups. The pattern mirrors the age schedule of migration rogers_model_1981, while extending the perspective to include exit flows alongside arrivals.
• Figure 4: Age-specific migration flows by degree of urbanisation: Daily arrivals (blue dashed), exits (red dashed), and net migration (black line) are shown by age for urban, intermediate, and rural areas. Urban centres see the highest inflows, especially among young adults. In contrast, net migration for middle-aged adults, older adults, and seniors is more evenly distributed across intermediate and rural areas, suggesting a greater likelihood of settling outside cities at older ages.
• Figure 5: Arrival and exit rates by age-cohort, Austrian state, and degree of urbanisation:(A) Daily arrival and exit rates by Austrian state and age group, disaggregated by urbanisation level. Cells show flow intensities with $\lambda_i$ for arrivals and $\mu_i$ for exits. Each cell is shaded by the balance of arrivals and exits (blue = net immigration, red = net emigration). Young adults account for the highest migration volumes, especially in urban centres like Vienna and Upper Austria. Older cohorts show lower flows, which are more balanced or negative, and are spatially more evenly distributed. (B) Daily arrival rates for young adults, concentrated in urban regions such as Vienna, Upper Austria, and Styria. (C) Arrival rates for older adults are more evenly spread, with balanced flows across urban, intermediate, and rural areas, particularly in Lower Austria, Upper Austria, and Tyrol.