This chapter provides an overview of geographic labour market imbalances in Austria. It first compares the labour market in Vienna with those in other Austrian federal states and capital regions across the OECD and analyses the distribution of labour demand and supply within the country. It then examines Austria’s internal mobility by analysing internal migration and commuting patterns in comparison with selected OECD countries. Finally, it profiles jobseekers in Vienna, focusing on their socio-economic characteristics and their propensity to move within Vienna’s commuting zone or to other parts of Austria.
Addressing Regional Labour Market Imbalances in Austria
Abstract
Geographic labour market imbalances persist in Austria, despite strong overlap between the skills of Vienna’s large number of jobseekers and labour demand in other regions of the country
Austria experiences a strong geographic mismatch of jobseekers and vacancies between the country’s Western and Eastern federal states, with particularly high unemployment in Vienna. In 2024, the unemployment rate among individuals aged 15–64 was 9.5% in Vienna, the highest in the country, compared with 3.2% in Salzburg, the lowest, representing a difference of 6.4 percentage points. This within-country regional unemployment disparity places Austria seventh among OECD countries, where the average gap between regions is 4.3 percentage points. These regional unemployment differences reflect a pronounced imbalance between labour demand and labour supply. In 2023, Vienna offered only 0.2 vacancies per jobseeker, compared with 2.6 in Salzburg.
Jobseekers in Vienna have lower levels of education on average and more often have a migration background compared to jobseekers outside of Vienna. Vienna’s jobseeker pool, of which almost half (46.9%) finished only mandatory schooling, has a substantially higher concentration of first-generation immigrants (65.7%) than other Austrian federal states (39.9%), mostly driven by a large share of non-EU migrants1, who account for 49.6% of jobseekers in Vienna compared to 24.3% in the rest of the country.
Although the overall geographic mobility of Austria’s workforce is similar to that of other OECD countries, internal migration is not well aligned with labour market needs. Each year, about 2.3% of Austrians relocate across TL3 regions, a rate close to the 2.1% average observed in peer countries. However, these moves are only weakly directed towards regions with stronger job growth. Federal states with lower labour demand experience slightly higher net inflows than those with higher labour demand, amounting to around 0.2 percentage points of their population. Daily commuting patterns show a similar picture: Austrian workers commute for an average of 25.5 minutes, almost identical to the 25.3-minute average in peer countries. In Vienna’s Functional Urban Area (FUA), the average commute is 26 minutes, which also closely matches the 25.9-minute average in other large FUAs in peer countries.
Based on their skills and past industry experience, between one fifth and two thirds of jobseekers from Vienna could fill available vacancies in other parts of the country. Profiles of jobseekers in Vienna and vacancies in other federal states align well by industry, especially professional, scientific and technical activities, wholesale and retail trade, accommodation and food services, and construction. Furthermore, OECD estimates show that a large share of jobseekers from Vienna could be matched with suitable positions elsewhere in Austria based on their industry experience and education: this share ranges from 21% in a conservative scenario, in which all jobseekers registered with the Austrian public employment service (Arbeitsmarktservice - AMS) are first matched with suitable local positions, to 67% in an upper-bound scenario, in which jobseekers from Vienna are matched with vacancies elsewhere regardless of whether vacancies can be filled locally.
Few jobseekers from Vienna take up employment outside the city’s commuting zone, with women showing particularly low geographic mobility. In Vienna, 84% of jobseekers who find employment take up jobs within the city core, 8.5% within the commuting zone, and only 7.5% in labour markets beyond it. Women, who make up 38.8% of jobseekers finding work in the city, are under-represented among those who move for employment, accounting for 20.8% of placements in the commuting zone and 18.8% in other regions. This likely reflects care responsibilities and the more limited availability of childcare outside Vienna.
1. Throughout the report, the term non-EU nationals refers to individuals who are nationals of countries outside both the European Union (EU) and the European Free Trade Association (EFTA). References to the EU in this report are used in an extended sense and also cover the EFTA countries, i.e. Iceland, Liechtenstein, Norway and Switzerland.
Geographic mismatches between labour supply and demand can have negative consequences for the economy, workers and public finances. In regions with few jobseekers, employers often struggle to fill vacancies, resulting in labour shortages that constrain productivity and growth. Conversely, in areas with limited job opportunities, jobseekers face greater competition for jobs, resulting in longer unemployment spells, foregone earnings and increased reliance on Public Employment Services (PES). These imbalances also place pressure on public finances, as social protection systems must absorb the higher costs associated with unemployment in regions with excess labour supply.
A lack of labour mobility contributes to geographic labour market mismatches. Mobility enables jobseekers to search for employment where opportunities exist, mitigating both structural imbalances and temporary disruptions caused by local economic shocks, and improving the quality of labour market matches. Conversely, countries that are confronted with large geographic labour market imbalances often have relatively immobile workforces that do not mitigate the structural or temporary geographic mismatches in the economy.
This chapter quantifies geographic labour market imbalances in Austria and examines the extent to which labour mobility contributes to or mitigates these imbalances. It first compares labour market conditions in Vienna with those in other Austrian federal states and with capital regions in selected OECD comparison countries, and analyses how labour demand and supply are distributed within Austria. It then reviews Austria’s internal mobility by analysing internal migration and commuting patterns in comparison with selected OECD countries. Austria’s neighbouring countries, along with additional European countries, namely Sweden, Belgium, and Spain, which were selected due to their large regional labour market mismatches or strong concentration of economic activity in a single region, form the group of ten OECD comparison countries used for the in-depth analyses of labour markets and labour mobility. Finally, the chapter profiles jobseekers in Vienna, focusing on their socio-economic characteristics and their propensity to move within Vienna’s commuting zone or to other parts of Austria.
While jobseekers concentrate in Vienna, vacancies remain unfilled in the Western federal states, leading to large geographic labour market mismatches in Austria
Across the OECD, the extent of regional unemployment disparities varies greatly, indicating strong geographic labour market mismatches in some countries. Large differences in regional unemployment rates among individuals aged 15-64 occur when jobseekers and vacancies are located in distinct areas of the country, and workers are not sufficiently mobile to bridge this geographic divide. On average, there was a difference of 4.3 percentage points between the region with highest and the one with the lowest unemployment rate within OECD countries in 2024 (Figure 2.1). This regional unemployment disparity is largest in Italy (13.9 percentage points), Türkiye (10.4 percentage points), and Greece (9.2 percentage points), while it is lowest in Ireland (0.2 percentage points), Slovenia, and Australia (both 0.8 percentage points). It is higher than four percentage points in eleven out of 37 countries.1
This chapter uses different geographic levels to analyse labour markets and mobility patterns in Austria and other OECD countries. The choice of geographic level in the different analyses is motivated by Institutional and economic factors, as well as data availability. Consequently, the following geographic levels are used in this chapter:
Austrian labour market districts (Arbeitsmarktbezirke): the Austrian labour market database (Arbeitsmartkdatenbank) provides data according to the Austrian definition of labour markets, roughly coinciding with the OECD’s TL3 definition (see below).
Vienna city and Vienna commuting zone: the Austrian definition of labour market districts does not encompass municipalities that lie outside the federal state of Vienna, but within Vienna’s Functional Urban Area (FUA) according to the European Commission-OECD definition (see below). To account for these municipalities, some analyses in this chapter (namely “Characteristics linked to jobseekers’ geographic mobility”) approximate Vienna’s commuting zone by calculating the overlap between Austrian labour market districts and the municipalities contained in the FUA of Vienna. Specifically, if more than 50% of a labour market district’s surface belongs to municipalities within the FUA, the labour market district is considered part of Vienna’s commuting zone (Annex Figure 2.A.1). In these analyses, Vienna city refers to the city’s core (coinciding with the Austrian definition of Vienna’s labour market district), and Vienna commuting zone refers to all municipalities that are part of Vienna’s approximated FUA but lie outside the city’s core.
Functional Urban Areas (FUAs): the European Commission-OECD definition of FUAs accounts for the fact that labour markets often extend beyond the administrative or political boundaries of cities in traditional geographic classifications. FUAs consist of cities (local units where at least half of the population live in clusters of densely populated grid cells with at least 50 000 inhabitants) and adjacent local units with high levels of travel-to-work flows towards the cities (i.e. at least 15% commute). Local units are then matched to municipalities.
Administrative regions: the OECD’s territorial grid is based on administrative regions, which are the regional boundaries within a country as organised by governments. As such, they refer to areas that are often under the responsibility of a particular subnational government or to the scale targeted by a specific policy implemented at the national or subnational level. Regions are classified into two scales, large (Territorial Level 2, TL2) and small (Territorial Level 3, TL3), which ensures comparability across countries. In the Austrian context, TL2 and TL3 correspond to federal states (Bundesländer) and groups of districts (Gruppen von Politischen Bezirken), respectively. In the case of Vienna, the TL2 and TL3 regions coincide.
Austria shows above-average unemployment disparities between its Western and Eastern federal states. The unemployment rate among individuals aged 15-64 in Austria stood at 5.2% in 2024, slightly below the OECD average of 5.9%. However, this average hides large regional differences. Vienna, located in the east of Austria, shows the highest unemployment rate with 9.5%, while the unemployment rate is lowest in Salzburg at 3.2%.2 This amounts to a regional unemployment disparity of 6.3 percentage points, the seventh highest among OECD countries with an average gap of 4.3 percentage points. These differences in unemployment rates across Austrian federal states reflect a general pattern according to which unemployment rates decrease from the country’s Eastern to Western federal states.
Unemployment rate among individuals aged 15-64 in the region with the highest and the lowest rate, and the country average, 2024.
Source: OECD Regions and Cities databases http://oe.cd/geostats.
Among OECD member countries, Austria stands out with persistently higher unemployment rates in its capital region than at the national level. Between 2010 and 2023, most of Austria’s neighbouring countries, as well as Sweden, Belgium and Spain, display only small gaps and broadly similar trends between unemployment rates in their capital regions and national averages (Figure 2.2). Six countries stand out with significant differences, i.e., Germany, Austria, Belgium, Spain, the Czech Republic and the Slovak Republic. For the latter two, the national average unemployment rate was considerably higher than in the TL2 region of the capital city. After 2010, the national average unemployment rate converges towards that of the capital city. In Spain, the national unemployment rate remained consistently above that of the capital, with both displaying parallel trends rather than signs of convergence. In contrast, the capital region’s unemployment rate substantially exceeds the national rate in Austria, Belgium and Germany. While Vienna’s unemployment gap with the national level is roughly constant at 4.6 percentage points on average between 2010 and 2024, Berlin’s unemployment rate converges towards the national average since 2010 with an average gap of 3.6 percentage points to the national level. At the beginning of the observed period, Brussels recorded an unemployment rate about 9 percentage points higher than the national average (17.3% vs. 8.3%), but it gradually declined to 11.7% in 2024, while the national rate fell only to 5.7%, highlighting the slow convergence of Brussels towards the national average. The unemployment rate is 0.7 percentage points higher in the capital’s TL2 region than at national level in Slovenia, while it is lower in all other countries, namely in Italy, Switzerland, and Sweden (all less than 1 percentage point lower), Hungary (1.3 percentage points lower); the Czech Republic (1.9 percentage points lower), Spain (4 percentage points lower), and the Slovak Republic (5.3 percentage points lower).
Unemployment concentrates in Vienna’s core as unemployment rates are substantially lower when considering its entire functional urban area (FUA). Throughout the analysed period, the unemployment rate among individuals aged 15 and above in the TL2 region of Vienna (9.3% on average) is higher compared to the FUA of Vienna (7.6% on average). The TL2 region captures only the federal state of Vienna, whereas the FUA also captures municipalities around Vienna, in which 15% of the population commute to work in Vienna (Box 2.1). This difference between Vienna’s TL2 region and its FUA highlights that unemployment is particularly concentrated in the city and federal state of Vienna.
Unemployment rate in TL2 regions of selected capital regions in comparison to the respective national average, 2010-2025.
Note: The figure compares the unemployment rate, based on the labour force aged 15 years and above, across selected TL2 regions that include each country’s capital, and contrasts them with the respective national averages. Countries are sorted by their capital region’s unemployment rate in 2023 in ascending order.
Source: OECD Regions and Cities databases http://oe.cd/geostats.
Patterns for unemployment rate differences between capital regions and national averages also translate into long-term unemployment rates. While capital regions tend to experience lower long-term unemployment rates among individuals aged 15 and above than the country as a whole, this gap is smaller than for short-term unemployment rates in most countries (Figure 2.3). Precisely, in Italy, Sweden, Switzerland, Hungary and the Czech Republic, long-term unemployment in the capital regions is on average less than 1 percentage point lower than in the rest of the country. Spain and the Slovak Republic are notable for having significantly lower long-term unemployment in their capital region compared to their national average with an average difference of 1.7 and 4.5 percentage points, respectively. In contrast, Belgium, Germany and Austria stand out, as long-term unemployment in their capital regions is consistently higher than in the rest of the country. The latter two countries’ capital regions display on average 1.8 percentage points higher long-term unemployment between 2010 and 2024. In Germany this difference declines from 4 to 0.6 percentage points over time, whereas in Austria, the difference is rather constant. Thus, while long-term unemployment in Berlin has converged towards the national average over time, Vienna experiences persistently higher long-term unemployment than the country as a whole. Brussels represents an exception, as its long-term unemployment rate is on average 5 percentage points higher than the national average. However, similar to the short-term unemployment rate, both rates slowly converge towards the end of the period.
Long-term unemployment in TL2-regions of selected capital regions in comparison to the respective national average, 2010-2025.
Note: The figure compares the long-term unemployment rate, based on the labour force aged 15 years and above, across selected TL2 regions that include each country’s capital, and contrasts them with the respective national averages. Long-term unemployment refers to people who have been unemployed for 12 months or more. Countries are sorted by their capital region’s long-term unemployment rate in 2023 in ascending order.
Source: OECD Regions and Cities databases http://oe.cd/geostats.
Contrary to other OECD countries, labour force participation in Austria is lower in its capital region than in the country on average. For eight out of the twelve compared countries, namely the Slovak Republic, Hungary, Czech Rupublic, Italy, Sweden, Spain, and Switzerland, labour force participation in the capital is consistently higher than the national average (Figure 2.4). This difference is the largest in the Slovak Republic, where it amounts to 5.7 percentage points. In contrast, labour force participation is higher at the national level than in the capital region in only four countries. In Slovenia and Germany, it is on average 0.9 and 1 percentage points higher than in the capital region respectively. Belgium and Austria represent outliers, as labour force participation in these countries as a whole is on average 2.5 and 3 percentage points higher than in the TL2 region of their capital respectively.
While unemployment seems to concentrate in the core of Vienna’s FUA, labour force participation is higher in Vienna than in its agglomeration. Unemployment is concentrated in Vienna’s core (Figure 2.2). In contrast, the labour force participation rate in the FUA of Vienna is on average 1 percentage point lower than in the city of Vienna during the analysed period, meaning that a higher share of the population is active in the labour market in the core.
Labour force participation rate in TL2-regions of selected capital regions in comparison to the respective national average, 2010-2025.
Note: The figure compares the labour force participation rate, based on the population aged 15 to 64 years, across selected TL2 regions that include each country’s capital, and contrasts them with the respective national averages. Countries are sorted by their capital region’s labour force participation rate in 2023 in descending order.
Source: OECD Regions and Cities databases http://oe.cd/geostats.
In Austria, job vacancies concentrate in the Western federal states, while jobseekers reside in the east, leading to labour shortages in the western parts of the country. The large regional unemployment disparities in Austria are a result of geographic mismatch of vacancies and jobseekers. Labour market tightness refers to the number of vacancies per jobseeker, with a high number indicating that vacancies are harder to fill as the pool of jobseekers for each vacancy is relatively small. Therefore, it is often taken as a proxy of labour shortages. Like the geographic pattern in unemployment rates, tightness tends to be high in Austria’s central and Western federal states and low in the east (Figure 2.5). As a result, differences in tightness across Austria are large, ranging from 0.2 to 1.3 vacancies per jobseeker in Vienna and Salzburg, the states with the highest and lowest tightness level, respectively. At the level of labour market districts, tightness even reaches 2.6 vacancies per jobseeker in Tamsweg (Salzburg), highlighting localised extent of labour shortages (Figure 2.5). These statistics on tightness are based on vacancies registered with the AMS, which represent roughly 50% of all vacancies requiring less than tertiary education and 20% of those requiring tertiary education. These data correspond to the vacancies targeted by the AMS in its cross-regional placement efforts.
Tightness (vacancies per jobseeker) in Austrian labour market districts, 2023.
Note: Vacancies and jobseekers as registered with the Austrian public employment service.
Source: Based on AMS (2025[3]), Labour market database of the AMS.
Vienna stands out with particularly low labour market tightness when compared to other OECD countries, as large metropolitan and capital regions tend to experience tight labour markets. As metropolitan and capital regions are typically among a country’s most dynamic regions and concentrate a large share of a country’s innovation capacity (OECD, 2024[4]), they tend to experience relatively high levels of labour shortages (OECD, 2024[5]), which are often proxied by labour market tightness. In four out of Austria’s neighbouring countries, the capital region shows the highest level of labour market tightness. Among these countries, capital regions experience higher shortages than the country on average, with the exception of Rome (Italy), offering 36% fewer vacancies per jobseeker than the country on average (Figure 2.6). In contrast, Vienna’s labour market is relatively slack compared to other capital regions, offering only around half (53%) as many vacancies per jobseeker than Austria on average.
Labour market tightness (i.e. vacancies per unemployed) in TL2 regions in Austria and its neighbouring countries, 2023.
Notes: The regional labour market tightness estimates above are normalised to the country’s average. Thus, these regional values should not be compared in absolute terms across countries. The region with the highest value in the country is labelled.
Source: Based on job vacancies from Lightcast and unemployment from Eurostat (lfst_r_lfe2emprc).
Labour shortages remain widespread in Austria, particularly in the accommodation and food industries, for jobs requiring vocational education, and in the country’s Western federal states. Labour shortages remain at historically high levels in Austria in 2025 with 78% of employers responding that they face labour shortages to some degree, down from their peak in 2022, when 87% of employers stated the same (Dornmayr and Riepl, 2022[6]; Dornmayr and Riepl, 2025[7]). In line with the labour market tightness estimates, labour shortages are higher in the western than in the eastern parts of Austria. For example, a higher share of employers in Salzburg (80.1%) report labour shortages than in Vienna (73.8%). In terms of industries, labour shortages are strongest in the accommodation and food services (90.7%), including tourism, in manufacturing of food products (83.3%), trade and repair of motor vehicles (80.9%), and logistics (87.1%). Regarding the educational level, employers state they often struggle to fill vacancies requiring vocational training (60.9% of employers) and those requiring only mandatory education with on-the-job experience (41.8%). Consequently, vacancies for kitchen personnel (6.6%), salespersons (4.9%), drivers (4.2%), and electrical technicians (4.1%) are among those that are hardest to fill for employers that experience labour shortages.
Despite its stated labour shortages, the tourism industry has not experienced systematic wage increases between 2019 and 2023, except for Tyrol. In the presence of labour shortages, economic theory would predict that employers raise wages to attract workers and fill the vacancies. Although labour shortages are most severe in the accommodation and food services industry according to employers in Austria, its salaries in online job postings have at most marginally increased in Austrian states between 2019 and 2023 relative to all other industries, except for Tyrol, where wages in the tourism industry increased from 85% to 94% of wages in other industries (Figure 2.7). This aligns with Tyrol being Austria’s most important tourist destination, accounting for 30% of all hotel overnight stays in Austria, and with a particularly high labour market tightness (Figure 2.5). However, wages in tourism have only marginally increased in Austria’s second most important tourist destination, Salzburg, despite high labour market tightness. These statistics come with the caveat that non-office jobs are under-represented in online job postings data, potentially affecting the representativeness of the data for the accommodation and food services industry (Vermeulen and Gutierrez Amaros, 2024[8]).
Salaries in accommodation and food services in online job postings relative to all other industries, TL-2 regions, 2019 – 2023.
Note: The industries public administration; education; household activities; activities of extraterritorial organizations; are excluded from the calculations above.
Source: Based on online job postings data from Lightcast.
Low labour mobility out of Vienna reinforces Austria’s labour market imbalances
Labour mobility from West to East would be needed to mitigate labour market imbalances, thereby reducing unemployment in Vienna city and mitigating labour shortages in the West. This section analyses the mobility of jobseekers from Vienna city when taking up employment. To do so, it focuses on all jobseekers that are registered in Vienna city, where unemployment is particularly high, and analyses the share of jobseekers that find a job in the city itself, those that take up employment in Vienna FUA, and those that relocate to other regions for employment.
Labour market districts where jobseekers from Vienna took up employment in 2023 (in %).
Source: Based on AMS (2025[3]), Labour market database of the AMS.
Less than one in five (16%) jobseekers from Vienna city take up employment outside the city core, offering large potential for cross-regional job placement. Reducing the geographic labour market mismatch requires connecting jobseekers from western parts of the country, especially Vienna, with vacancies in the East. However, the vast majority of jobseekers from Vienna stays within the city core (84% in 2023) or commute to the surrounding commuting zone (8.2%) when taking up employment (Figure 2.8). In contrast, only 7.8% of jobseekers from Vienna city took up employment outside the FUA of Vienna. Job take-up in Western federal states, which arguably requires (partial) relocation, is extremely rare among jobseekers from Vienna city. In 2023, only 481 (0.4% of all) jobseekers relocated to Salzburg and 934 (0.7%) relocated to Tyrol. These numbers contrast with 67% of jobseekers in other parts of Austria taking up employment outside their labour market district on average (Figure 2.9). The low mobility among jobseekers from Vienna city reflect individual barriers to mobility, structural factors (see chapter Labour mobility-enhancing policies in Austria), and personal preferences.
Share of jobseekers that take up employment in a different labour market district by origin, 2023.
Source: Based on AMS (2025[3]), Labour market database of the AMS.
Vienna’s service-oriented labour market attracts high-skilled and foreign-born workers despite their relatively low employment prospects in the capital
As the largest metropolitan area in Austria, Vienna’s labour market differs from the rest of the country. This section compares Vienna’s labour market to those in other Austrian federal states.
Vienna attracts high-skilled and foreign-born individuals. Vienna is the region with the highest share of high-skilled residents, with 46.5% of its residents aged 25-64 having at least a tertiary education degree compared to 34.4% in other states on average in 2024 (Figure 2.10).3 Its industrial structure, with a high presence of the ICT and other high-skilled services industries (Figure 2.12), as well as higher education institutions make Vienna particularly attractive for high skilled workers. Additionally, Vienna also hosts the largest share of foreign-born residents among Austrian states, accounting for 43.9% of its population aged 15 and above compared to 19.1% in the remaining regions. Its international environment, large diasporas of important origin countries and amenities, such as its cultural offer, make Vienna attractive for foreigners.
The low employment rate in Vienna stems from both the composition of its population and lower employment prospects across population subgroups. Employment rates in Vienna are lower than in any other state both for high- and low-educated residents, standing at 81.4% and 63.9% respectively among the population aged 25-64 in Vienna, compared to 88.3% and 75.4% on average across regions (Figure 2.10). Similarly, both native and foreign-born residents aged 15-64 experience lower employment rates in Vienna (70.7% and 63.1%, respectively) than in any other state (76.4% and 74.5% on average across regions). This points to generally lower employment prospects in Vienna’s capital, in line with a slack labour market (Figure 2.5). Additionally, the high share of immigrants among Vienna’s residents contributes to its capital’s high unemployment rate through a compositional effect as only 73.2% of all immigrants are employed compared to 75.8% of all natives in Austria on average.
Similarly, a high share of asylum seekers and humanitarian migrants4 in Vienna, who may have better employment prospects in the west of Austria, contribute to the capital’s high unemployment rate. Humanitarian migrants are generally less likely to be employed than the native population (OECD, 2024[9]). Additionally, 74.5% of all humanitarian migrants in Austria that are registered with the Austrian Public Employment Service (Arbeitsmarktservice – AMS) (i.e. either unemployed or in training) reside in Vienna. This geographic concentration contributes to the high unemployment rate in the capital. The compositional effect is aggravated by the fact that humanitarian migrants have worse employment prospects in Vienna than in the West of the country. Among refugees who arrived in 2015 and moved from the Western federal states to Vienna, 59-62% were employed in 2023, while 80% were employed among those that moved from Vienna to another state (ÖIF, 2025[26]).
Sociodemographic characteristics, employment outcomes, and disposable income in Austrian TL2 regions, latest available year.
Notes: Each dot represents an Austrian federal state. The red dot represents Vienna. Tertiary education corresponds to all ISCED categories from 5 (short-cycle tertiary education) to 8 (doctor or equivalent level). The data in the left panel correspond to the following age groups: education al achievement (25-64), foreign-born population (15 and above), employment by education (25-64), employment by county of birth (15-64). Educational achievement, employment by education, foreign-born population, and employment by country of birth correspond to data from 2024. Net disposable income is based on 2022 data.
Source: OECD Regions and Cities databases http://oe.cd/geostats., Eurostat: employment by country of birth (lfst_r_lfur2gac, 2021), population by country of birth (lfst_r_lfsd2pwc); (OECD, 2024[4]).
Disposable income is lower in Vienna than in any other Austrian state, making relocations away from Vienna attractive for the average household from a purely financial standpoint. Disposable household income is relatively even across Austrian states, except for Vienna. When accounting for housing costs, disposable income ranges from roughly 24 600 EUR per year in Styria to 27 000 EUR in Lower Austria, while Vienna represents an outlier low end at about 20 900 EUR. These patterns imply that, on purely financial grounds, incentives favour moving from Vienna to other parts of the country with higher disposable income. However, these averages of regional disposable household income may vary for different household types, as for example low-income households may not be able to access higher-paying jobs in other states. Additionally, the low average disposable income in Vienna may be partially due to the capital’s comparatively high unemployment rate.
Net disposable income per equivalised household minus housing costs by TL2 region, 2022.
Note: Housing costs refer to the average cost for tenants (i.e. rent) and homeowners (i.e. mortgage payments) in each state.
Source: Based on OECD Regional Database and OECD Regions and Cities at a Glance 2024. OECD Regions and Cities databases http://oe.cd/geostats.
Vienna’s labour market is more service- and knowledge-intensive than the rest of Austria, with a much lower share of workers employed in manufacturing and agriculture. Compared with other federal states, a higher share of workers in Vienna are employed in professional, scientific and technical services (14.4% compared to 8.8% in the rest of Austria), as well as in the information and communication industry (6.8% compared to 2.8% in the rest of Austria) (Figure 2.12). In contrast, industry (i.e. manufacturing) only accounts for 7.7% of the work force in Vienna compared to 20.7% in the rest of Austria. Employees in the accommodation and food services industry, which represent the AMS’s major target group in the cross-regional placement of jobseekers, account for a similar share in Vienna (25.3%) as in the rest of the country (23.9%), although available data also include wholesale- and retail trade, and transport in the same category. These cross-regional differences in industrial structure likely also shape mobility patterns of jobseekers. For instance, focus group interview participants previously employed in IT services and scientific research stated that relatively few job offers match their profile outside of Vienna, making employment take-up in other parts of Austria less likely.
Share of employment by sector in Vienna and other TL2 regions, 2023.
Notes: Based on NACE rev. 2.
Source: Eurostat (lfst_r_lfe2en2).
The comparatively high labour market imbalances in Austria could in theory be explained by a generally less mobile labour force. For this reason, this chapter analyses the extent of (labour) mobility in Austria and puts it into international perspective by comparing it to a selection of ten OECD countries, composed of Austria’s neighbouring countries as well as Belgium, Spain, and Sweden, which were selected based on their large regional labour market mismatches or concentration of economic activity in a single region. To do so, this chapter considers both relocations of the general population, including but not restricted to employment-related moves, across TL-3 borders (groups of districts in the case of Austria; Politische Bezirke), and workers’ commuting patterns at the municipal level.
Internal mobility and commuting patterns in Austria are similar to those observed in comparison countries
When assessing whether internal mobility aligns with labour market needs, both the frequency and the direction of relocations matter. Although a high internal migration rate (i.e. frequency) indicates that a country’s population is mobile, it does not provide information on whether movers relocate to areas where job opportunities exist. Thus, it is important to also consider the direction of internal mobility, namely whether movers relocate to areas where jobs are created. This section compares internal mobility rate and the direction of relocations in Austria to those observed in other countries. Internal mobility in this section includes both employment- and non-employment-related relocations.
Share of population that moved to another TL3 region, by destination region, earliest year until 2022.
Note: Vertical line represents the start of the COVID-19 pandemic. The countries are sorted by their internal migration rate in the last available year.
Source: Based on OECD Regional Database. OECD Regions and Cities databases http://oe.cd/geostats.
The rate of internal migration in Austria steadily increased over the past two decades and is similar to that observed in comparison countries as of 2022. Internal migration is measured as the share of residents relocating across TL-3 borders, corresponding to groups of districts (Politische Bezirke) in the case of Austria. Internal migration increased by 0.8 percentage points in Austria between 2002 and 2022, corresponding to twice the increase in internal mobility (0.4 percentage points) in comparison countries over the same time period (Figure 2.13).5 In 2022, 2.3% of Austria’s residents relocated across TL-3 borders, compared to an average of 2% in comparison countries. This places Austria in the mid-range among comparison countries in terms of its current internal migration.
Migration rate across TL3 regions and the difference in unemployment rates across TL2 regions, 2022 or latest year.
Note: Data refer to 2022, except for Germany (2019). The dashed line represents the linear trend.
Source: OECD Regional Database and Eurostat.
Although Austria’s internal migration rate is on par with its comparison countries on average, it is insufficient to reduce the large labour market disparities in the Austrian labour market. Countries with lower internal migration rates experience on average higher regional unemployment disparities, i.e. the difference between the region with the highest and the lowest unemployment within a country (Figure 2.14). While Austria’s internal migration rate is comparable to that observed in comparison countries, its regional unemployment disparity is the fifth highest (6.5 percentage points in 2022) among the ten comparison countries. The countries with the highest regional unemployment disparity are Italy (15.1%) and Spain (10.2%), both of which show substantially lower migration rates than Austria. Countries with regional unemployment disparities below five percentage points, tend to show higher migration rates than Austria, except for Switzerland and the Czech Republic. The negative relationship between internal migration and unemployment disparities, supports the idea that Austria could increase its internal migration to reduce labour market imbalances. However, the fact that Austria’s current migration rate would imply a lower level of unemployment disparities on average (i.e. it lies above the dashed trend line), highlights the importance of other factors, for example the direction of internal migration (i.e. where movers relocate to).
Average annual growth rates in net migration (i.e. inflows - outflows) and in employment rates between 2005 (or earliest year) and 2022 by TL-3 regions.
Note: Growth rates are the average of annual growth rates over the entire period. Data since 2005 (Austria, Germany, Italy), 2008 (Spain, Slovenia), 2010 (Switzerland), 2013 (Sweden), and 2018 (Italy).
Source: OECD Regions and Cities databases http://oe.cd/geostats.
In most countries, internal migration does not systematically flow towards regions where job creation takes place, except for Germany and, to a lesser degree, Austria. To align with labour market needs, workers would need to relocate to areas where labour demand is high. However, when relating a region’s average annual growth in net migration to its average annual growth in the employment rate, a flat or even negative relationship emerges across most of Austria’s comparison countries (Figure 2.15). Notable exceptions are Germany and Austria, where a 1 percentage point increase in its employment rate is associated with a statistically significant increase in the net migration rate of 0.06 and 0.01 percentage points, respectively. The associations of these two variables are either not statistically significant or not positive in the remaining countries. This suggests a better alignment between internal migration and labour market needs in Germany and, to a lesser degree, in Austria than in the remaining comparison countries.
However, the potential to improve the alignment between internal migration and labour market needs remains large in Austria. Tight labour markets experience consistently lower population (and likely worker) inflows than regions with higher vacancy-to-unemployment ratios (Figure 2.16), contributing to labour shortages in regions with tight labour market. The gap in net migration rates between tight and slack labour markets has even increased between 2017 and 2022, although it is relatively small: while labour markets that experienced labour shortages lost 0.05% of their population to those where shortages were less acute in 2017, this share more than doubled to over 0.1% in 2022. In 2022, the states experiencing the highest net internal population inflow were Burgenland (0.6% of its population), Lower Austria (0.3%) and Vorarlberg (0.3%), while those experiencing the largest outflows were Salzburg (-0.7%), Carinthia (-0.4%), and Tyrol (-0.2%). Vienna experienced a small net inflow through internal migration of 0.2% of its population, despite its relatively scarce employment opportunities.
Net migration rate (i.e. inflows minus outflows) in Austrian TL2 regions by above- and below median tightness (i.e. vacancies per unemployed), 2017-2022.
Notes: The grey vertical line highlights the beginning of the COVID-19 pandemic.
Source: Data on vacancies based on AMS (2025[3]), Labour market database of the AMS. Internal mobility data from OECD Regional Database.
Commuting patterns in an international perspective
Next to relocating for employment-related reasons, commuting to work is another form of labour mobility. This section studies the extent of commuting in both Vienna and Austria as a whole, comparing these patterns to similar cities and countries. Additionally, this section analyses whether workers use commuting as a substitute to internal relocations in Austria. To answer these questions, this section draws on novel commuting time estimates based on municipal-level commuting data and information on the existing road network (Methodological details). The granularity of these estimates allows for comparisons at the level of Functional Urban Areas, which capture a labour market in its entirety.
Commuting times of workers in Vienna FUA lie around the average of other large FUAs. Workers commute for 26 minutes per day in Vienna, just above the average of 25.9 minutes in other large FUAs on average (i.e. FUAs with more than 1.5 million inhabitants.) (Figure 2.17). The most similar FUAs in terms of commuting time are Barcelona (25.5 minutes), Stuttgart (25.6 minutes), Zurich (26.4 minutes) and Brussels (26.4 minutes). Average daily commutes in Vienna are 4.2 minutes longer than in Turin, the FUA with the shortest commutes, and 5.3 minutes shorter than in Budapest, the FUA with the longest commutes among the comparison cities.
Average commuting time in minutes for large FUAs, latest available year (see notes).
Note: FUAs are considered large if their population exceeds 1 500 000. Data refer to the following years: 2017 (ITA), 2020 (CHE), 2021 (CZE, SVK), 2022 (AUT, HUN), 2023 (DEU, SVN).
Source: Based on municipality-level census data and Open Street Maps (Methodological details).
At the national level, commuting times in Austria are slightly lower than in comparison countries, though the gap between urban and rural areas is larger than in most other countries. Workers in Austria commute for 25.5 minutes per day on average, placing Austria slightly below the cross-country average of 25.8 minutes across all comparison countries (Figure 2.18). However, these national averages hide substantial variation in commuting times by the locations’ degree of urbanisation. The gap in commuting times between rural areas and cities is relatively large in Austria, where workers in cities commute 9.3 minutes longer per day on average. Across comparison countries, this gap stands at substantially lower 6.6 minutes. Austria experiences the third-largest gap among all countries analysed, following Slovakia (12.9 minutes) and Slovenia (10.4 minutes). Thus, while national-level commuting times range around the cross-country average, workers in Austrian cities have the third shortest commutes (20.8 minutes) and those in rural areas the seventh shortest commutes (30.1 minutes) relative to all ten comparison countries. Due to the limited availability of origin-destination commuting information, the years differ across countries, which may affect the precision of cross-country comparisons. For example, since data for Switzerland come from 2020, i.e. during the COVID-19 pandemic, the statistics may underestimate the extent of commuting in normal times.
Commuting time (in minutes) by the municipal degree of urbanisation, latest year available.
Note: data from latest available year, i.e. 2011 (BEL, ITA), 2020 (CHE), 2021 (CZE, SVK), 2022 (AUT, HUN, ESP), 2023 (DEU, SVN).
Source: Based on census data and Open Street Maps (Methodological details).
There is no evidence that workers substitute commuting for internal migration and vice versa. In theory, workers might tend to commute longer but relocate less frequently (and vice versa) in some countries than in others, depending on country-specific factors such as the transport infrastructure. For example, a well-developed train infrastructure likely facilitates longer commutes, making relocations less frequent. Particularly long (short) commutes in combination with low (high) internal migration rates would be indicative of a substitution of relocation through commuting (commuting through relocation). Workers in Austria do not seem to substitute internal migration through longer commutes as its commuting times and internal migration rates are comparable to those of its neighbouring countries (Figure 2.19). Across countries, those in which commutes are longer on average also tend to have higher internal mobility, suggesting that commuting and relocation are not substitutes in general. An important caveat of this analysis is that it only considers commuting times and internal migration rates at a given point in time and does therefore not account for other country-specific factors.
Migration rate across TL-3 regions (as % of population) and average commuting times, latest available year.
Note: Data refer to migration rates and commuting time estimates from 2020 (CHE), 2021 (CZE, SVK, SWE), and 2022 (AUT, ESP, HUN). For some countries the years of the two variables differ. In these cases, the migration rate is based on data from 2017 (ITA), 2021 (DEU, SVN), or 2022 (BEL), and commuting time estimates refer to 2011 (ITA, BEL), or 2023 (DEU, SVN).
Source: Commuting time estimated based on census data and Open Street Maps (Methodological details); Migration rates from OECD Regional Database. OECD Regions and Cities databases http://oe.cd/geostats.
A higher share of workers in Vienna commute from the city core to the commuting zone compared to other large FUAs on average. The share of workers commuting in and out of the city core varies across FUAs depending on the geographic distribution of jobs and residential areas within a labour market. In Vienna a higher fraction of workers that reside in the city core commute to the city’s outskirts (9.8%) than in other large FUAs (i.e. those with a population above 1.5 million; highlighted in blue) in comparison countries (6.9%). This share is particularly high in Torino, Italy (18.6%) and low in Berlin, Germany (1.9%). Similarly, the fraction of workers in Vienna that reside in the commuting zone and commute to the city’s core (39.9%) exceeds the average of all large FUAs in comparison countries (31.9%). Madrid, Spain, shows a particularly high share of workers commuting from the commuting zone to the city core (64.2%), while this fraction is lowest in Düsseldorf, Germany (2%). These results suggest that Vienna’s commuting zone and its city core are more integrated in a single labour market than in other large FUAs in comparison countries on average.
Share of workers that commute from the city centre to the commuting zone (horizontal axis) and from the commuting zone to the city centre (vertical axis) in large FUAs (i.e. those with a population above 1.5 million), latest year.
Note: Large FUAs (i.e. those with a population above 1.5 million) are highlighted in blue. FUAs with a population below 1.5 million are displayed in grey. Data from latest available year, i.e. 2011 (BEL, ITA), 2020 (CHE), 2021 (CZE, SVK), 2022 (AUT, HUN, ESP), 2023 (DEU, SVN).
Source: Commuting time estimated based on census data and Open Street Maps (Methodological details); Migration rates from OECD Regional Database. OECD Regions and Cities databases http://oe.cd/geostats.
The feasibility and attractiveness of taking up employment in another, potentially distant, labour market depends on individual factors, for example whether somebody has care responsibilities, the right skills, and financial considerations. Hence, the socio-economic composition of jobseekers impacts the potential for cross-regional job placement, as certain subgroups of the population are more likely to relocate than others.
This chapter analyses the composition of jobseekers in Vienna city and studies which subgroups of the population show a higher propensity to commute or relocate to take up employment. Based on individual-level labour market data on all jobseekers in Austria it compares the characteristics of jobseekers who take up employment in Vienna city, its surrounding commuting zone, or the rest of Austria. Additionally, the section studies the potential of cross-regional job placement by analysing the (mis)match between the profile of jobseekers in Vienna city and vacancies in the rest of the country.
While jobseekers in Vienna more often have lower levels of education and a migration background, women are particularly immobile
Jobseekers in Vienna show lower levels of education and a disproportionately high share are immigrants, including from non-EU countries. Almost half (46.9%) of jobseekers in Vienna city only completed mandatory education, above the average among jobseekers in the rest of the country (42.5%) (Table 2.1). Consequently, the share of jobseekers that hold post-secondary level degrees is lower in Vienna than in the rest of Austria, yet still substantial (31.2%).6 With 19.7%, the share of jobseekers with vocational training is substantially lower than in other states (34%), while the share of jobseekers with tertiary education is low (11.5%), yet almost twice as high than in the rest of the country (6.3%). Furthermore, the share of immigrants among jobseekers is substantially higher in Vienna (65.7%) than in the rest of the country (39.9%). Jobseekers from non-EU countries tend to concentrate in Vienna, as 49.6% jobseekers come from non-EU countries, compared to 24.3% in the rest of the country. Humanitarian migrants (i.e. recognised refugees including those receiving subsidiary protection) account for over a quarter (26.9%) of all persons registered with the AMS in Vienna as of 2025 (ÖIF, 2025[10]). Jobseekers from EU countries account for almost equal shares of jobseekers in Vienna (16.1%) and in the rest of the country (15.6%).
Note: Employment take-up beyond the boundaries of Vienna’s commuting zone are assumed to represent relocations. The number of jobseekers in columns 1 and 2 correspond to the yearly average number of jobseekers at the end of each month. Unemployment-to-employment transitions include all transitions observed during a calendar year. For example, out of all jobseekers from Vienna, women accounted for 38.8% of jobseekers finding employment in the city core, 20.8% in the commuting zone and 18.8% in other parts of Austria. Source: AMS (2025[3]), Labour market database of the AMS.
The overwhelming majority of jobseekers from Vienna city take up employment within the city itself, while few find jobs in the commuting zone or relocate to other parts of Austria. Similar to other countries, most jobseekers in Vienna search for employment within close proximity resulting in 84% of jobseekers taking up employment within the core of the city. In contrast, only 8.5% take up a job within the commuting zone around Vienna, and 7.5% do so in other parts of Austria, most likely relocating for this purpose. Thus, with only 16% of jobseekers taking up mobility outside the city’s core, the mobility of jobseekers in Vienna is substantially lower than in other labour market districts, where 67% take up employment in another district on average (Figure 2.8). Unemployment spells are shorter on average for jobseekers who relocate or commute when taking up employment than for those who stay in Vienna, suggesting that labour mobility can lead to more efficient matching.
Women, who account for two in five (42.6%) jobseekers in Vienna, are substantially less likely to move, likely due to childcare obligations. Among all jobseekers from Vienna who take-up employment in Vienna city, women represent 38.8% (Table 2.1). However, among those that take up employment in the commuting zone and in other regions of Austria, women only represent 20.8% and 18.8%, respectively. This means that women are less mobile than men as the share of women finding employment in the commuting zone or the rest of Austria would be equivalent to the ones finding employment in Vienna city (38.8%) if they were equally mobile as men. Childcare responsibilities likely explain the lower mobility among women, as the childcare responsibilities continue to fall disproportionately on women rather than men. In line with this, the availability of childcare is highest in Vienna city and particularly low in the Western federal states (see chapter Structural and institutional factors shaping geographic differences in labour market outcomes).
Although highly educated individuals in Austria are more mobile than less educated ones, the out-mobility of jobseekers from Vienna does not depend on their educational level. When considering the entire population from Vienna, residents with only mandatory education represented 27.2% among those that remained in the city (i.e. did not relocate), while they accounted for only 19.8% of those that relocated to another municipality between 2014 and 2015. In contrast, tertiary-educated individuals were overrepresented among those who left the city (26.5%) in comparison to their share among residents who stayed in Vienna (19.2%) (Statistik Austria, 2018[11]). Similarly, among young workers with a university degree in Austria (aged 15 to 34), almost one in five (18%) declared that they relocated for their current job, representing a substantially higher share than among those with mandatory education (3%) (Statistik Austria, 2017[12]). This aligns with academic evidence that (employment-related) mobility increases with the educational level, as high-skilled workers may benefit from a wage premium when relocating due to a better skills match with distant job opportunities (see chapter Labour mobility-enhancing policies in Austria). However, we do not observe systematically higher mobility among highly educated jobseekers in Vienna, relative to their less educated counterparts (Table 2.1).
Between one in five and two thirds of jobseekers from Vienna match the demanded profiles of vacancies elsewhere in Austria
Matching jobseekers with vacancies requires an alignment between the profiles of jobseekers and those of available vacancies. Cross-regional placement can only be successful if the skills and experience of jobseekers in Vienna correspond to those demanded by employers in other parts of the country. This section assesses to what degree the profiles of these jobseekers and vacancies overlap in terms of industry (experience) and educational level, providing an estimate of the number of jobseekers from Vienna that could be placed in other federal states accounting for the local supply of jobseekers. Additionally, this section analyses how German language skills, or the lack thereof, relate with employment prospects of immigrants.
The overlap in the composition of jobseekers in Vienna and vacancies elsewhere is relatively large in terms of industry (experience). The five most common industries in which jobseekers in Vienna have experience are professional, scientific and technical activities (21.3%), wholesale and retail trade (14.9%), education (12%), accommodation and food service activities (11.3%), and construction (8.4%). Simultaneously, most vacancies outside of Vienna are posted in professional, scientific and technical activities (23.5%), wholesale and retail trade (18.1%), manufacturing (13.9%), accommodation and food service activities (13.6%), and construction (8%). This implies a relatively large overlap between jobseekers and vacancies when it comes to industry (experience).
Although roughly half of jobseekers in Vienna have, and vacancies in other parts of the country require mandatory education, the educational composition of labour supply and demand is somewhat misaligned when it comes to vocational and tertiary education. Mandatory education accounts for roughly half of all jobseekers (48.3%) and vacancies (51.2%), while secondary education is more common among jobseekers (19.8%) than among vacancies (9.9%). Importantly, there is a misalignment between the demand and supply of vocational education and tertiary training: vocational education is more common among vacancies outside of Vienna (34.8%) than among jobseekers in Vienna (19.7%). The opposite is true for tertiary education (12.3% in Vienna and 4% outside of Vienna).
Open vacancies in the rest of the country that could be filled by jobseekers from Vienna, by industry and education, net of local supply of jobseekers (lower-bound scenario), 2023.
Note: Potential refers to the number of jobseekers from Vienna that could be matched with vacancies in the rest of the country based on their past industry experience and educational level, after filling all vacancies in a labour market district with jobseekers from the same district. This corresponds to the lower-bound scenario, described in more detail in the main text.
Source: Based on AMS (2025[3]), Labour market database of the AMS.
Between one in five and two thirds of all jobseekers in Vienna could be matched with vacancies in the rest of the country based on their past industry experience and educational level. To gauge the potential of cross-regional placement of jobseekers from Vienna, this exercise estimates a lower- and an upper-bound for the share of jobseekers registered with the AMS in Vienna that could fill vacancies that correspond to their educational level and industry experience elsewhere in Austria. The lower-bound scenario assumes that all jobseekers in Austria are first matched with suitable positions in their local labour market district. In other words, vacancies in all labour market districts are filled with locally available jobseekers before matching jobseekers from Vienna with vacancies in other parts of the country. This corresponds to a conservative scenario, since it is unlikely that all vacancies are filled with locally available jobseekers (i.e. some would remain unfilled for reasons other than their education requirements and industry). The upper-bound scenario reflects the opposite extreme case, in which jobseekers from Vienna are matched with vacancies elsewhere regardless of whether vacancies can be filled with locally available jobseekers. The two scenarios estimate that the AMS could place between 21.1% and 66.9% of jobseekers from Vienna in other parts of the country.
Jobseekers with experience in the wholesale and retail trade, the accommodation and food services activities, and professional, scientific and technical activities, mostly requiring mandatory education, show the largest potential for cross-regional job placement. Table 2.2 reports the five jobseeker profiles that hold the largest potential for cross-regional matching of jobseekers from Vienna under the conservative lower-bound scenario. Profiles with mandatory education and experience in the wholesale and retail trade industry (3 733 jobseekers) or accommodation and food service activities (3 439 jobseekers) hold the largest potential. In the professional, scientific and technical activities industry, 2 819 and 2 758 jobseekers from Vienna with vocational and mandatory education could be matched with vacancies in the rest of Austria, respectively. Additionally, 1 178 jobseekers from Vienna could fill manufacturing vacancies requiring mandatory education. This exercise abstracts from the fact that vacancies can be filled with jobseekers with experience in other industries and makes the simplifying assumption that all vacancies are filled locally if their education-industry profile matches the jobseekers’ profile.
Immigrants that lack German language skills are significantly more likely to be unemployed. Relative to foreign-born individuals with native-level German skills, immigrants that do not speak any German are 12 percentage points more likely to be unemployed (Figure 2.21). This difference is slightly smaller (9 percentage points) when comparing individuals with differing language skills but the same sex, educational level, and age. Even among immigrants that have a proficient or advanced level of German, unemployment is approximately 3-4 percentage points higher than among native-level speakers. Indeed, focus group interview participants from non-German speaking countries state that their lack of German language skills complicates navigating administrative procedures, including interactions with the AMS and job applications. This clear link between German language skills and employment outcomes is particularly relevant in the context of Vienna’s labour market where over 60% of jobseekers come from non-EU countries and many have deficiencies in their knowledge of the German language. Furthermore, the association between German skills and employment outcomes is similar in urban and rural places.
Probability of being unemployed for the foreign-born population by German skills, relative to the foreign-born population with native-level German, 2021.
Note: The figure above represents the coefficients of two linear regressions that regress a dummy for unemployment on a categorical variable for German language skills. Unconditional refers to a regression model without control variables. Conditional refers to a regression model with control variables for sex, age, and gender. Robust standard errors are used.
Source: EU-LFS module 2021 on “labour market situation of migrants and their immediate descendants”.
The Austrian definition of labour market districts (Arbeitsmarktbezirke) does not encompass municipalities that lie outside the federal state of Vienna, but within Vienna’s Functional Urban Area according to the European Commission-OECD definition (Dijkstra, Poelman and Veneri, 2019[1]). To account for these municipalities, some analyses in this chapter approximate Vienna’s commuting zone by calculating the overlap between Austrian labour market districts and the municipalities contained in the FUA of Vienna. Specifically, if more than 50% of a labour market district’s surface belongs to municipalities within the FUA, the labour market district is considered part of Vienna’s commuting zone (Annex Figure 2.A.1). In these analyses, Vienna city refers to the city’s core (coinciding with the Austrian definition of Vienna’s labour market district), and Vienna commuting zone refers to all municipalities that are part of Vienna’s approximated FUA but lie outside the city’s core.
Share of the surface of Vienna’s surrounding labour market districts that fall into the EU-OECD definition of Vienna’s FUA.
To estimate average commuting time at the municipal level (Annex Figure 2.A.2), this chapter combines open-source routing data with official statistics from Eurostat and National Statistical Offices (NSOs). The methodology integrates travel time estimates from OpenStreetMap (OSM) using the r5py (Fink C., et al., 2025) routing engine with NSO commuting flow matrices and Eurostat commuting time statistics at the degree of urbanisation (DEGURBA) level. The methodology follows these steps:
1. Estimating inter-municipal driving times: Average commuting time between the population-weighted centroid of each pair of municipalities were first computed using OSM road network and r5py travel time algorithm.
2. Estimating intra-municipal average driving time: At the grid level (using the Eurostat GISCO 2021 population grid), commuting times between each pair of grid cells within the same municipality were estimated. These were then aggregated to the municipal level as population-weighted averages based on grid-level population data.
3. Estimating the theoretical average driving time by municipality: The commuting flow matrix from NSOs was used to weight the inter- and intra-municipal commuting times, producing a theoretical average commuting time for each municipality.
4. Recalibrating commuting time estimates using Eurostat official statistics: Because r5py does not account for traffic congestion, commuting times in urban areas tend to be underestimated, and congestion levels vary across countries. To correct these biases, we recalibrated the r5py-derived estimates using Eurostat statistics on employed persons by commuting time, educational attainment, and degree of urbanisation (Eurostat, 2022). As Eurostat statistics show average travel time by degree of urbanisation based on place of work, we first aggregated the r5py municipal-level commuting time estimates by place of work and we then computed recalibration factors by country and by degree of urbanisation. We then applied these recalibration factors to the inter-municipal commuting time using the degree of urbanisation classification of the place of work.
Note: The black boundaries correspond to Functional Urban Areas (FUAs) in Austria.
Source: Eurostat (2022), Employed persons by commuting time, educational attainment level and degree of urbanisation (2019), https://ec.europa.eu/eurostat/databrowser/view/LFSO_19PLWK28/bookmark/table?lang=en&bookmarkId=96ad2e59-267b-404c-97e2-133b7fa05022; OpenStreetMap contributors (2025), Planet dump retrieved from https://planet.osm.org, https://www.openstreetmap.org; Fink, C., Klumpenhouwer, W., Saraiva, M., Pereira, R., & Tenkanen, H. (2022). r5py: Rapid Realistic Routing with R5 in Python (0.0.4). Zenodo. https://doi.org/10.5281/zenodo.7060438
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← 1. These statistics exclude Luxembourg which consists of a single territorial unit in the OECD’s TL classification.
← 2. The Austrian Federal Statistical Office reports unemployment rates for the population aged 15 and above of 9.4% and 3.4% in Vienna and Salzburg, respectively. This results in a regional unemployment disparity of 6 percentage points across Austrian federal states based on the Austrian census following the ILO definition of the employment status. In contrast, the unemployment statistics cited in this report refer to the population aged 15-64, explaining the small differences.
← 3. The statistics on the population’s educational attainment in Vienna are based on the European Labour Force survey, which differ from estimates based on the Austria census. Estimates based on the latter report a share of tertiary educated people among the population aged 25-64 of 39.4% in Vienna in 2023.
← 4. The term “humanitarian migrants” refers to recognised refugees, beneficiaries of subsidiary protection, and sponsored or resettled refugees and is used throughout this report (OECD, 2023[13]).
← 5. These statistics exclude Italy and Slovenia due to insufficient data availability.
← 6. Post-secondary education refers to vocational and tertiary education, the latter consisting of universities, universities of applied sciences (Fachhochschule), and vocational schools with higher education entrance qualification (Berufsbildende Höhere Schule).