The project began as a collaboration between Austria’s federal statistical office (Statistik Austria) and several Austrian universities, led by the University of Vienna, with €250,000 in funding from the Higher Education Structural Funds (HRSM) provided by the Ministry of Education, Science and Research. Shortly after its launch, the project was extended to include all public universities in Austria. The project ran from 1 August 2017 to 31 December 2021. More information on ATRACK, along with a project report, can be found here
As of 1 January 2022, ATRACK is being continued as a consortium, again led by the University of Vienna.
The data on labour market entry and career status are aggregated and presented in the form of factsheets for each degree programme (see below). The factsheets provide information on:
- Labour market status: What is the employment status of the statistical population (graduates) at different times after graduation?
- Duration until first employment
- Top five occupational fields three years after graduation
- Gross monthly income for full-time salaried employment
The consortium updates the factsheets every two years with the addition of further graduate cohorts to the dataset. The most recent reporting period for each factsheet can be found on the last page, along with explanations of the terminology used.
- The statistical population for the data consists of university graduates under 35 years of age who are not enrolled in any further education or training programme. This very specific limitation makes it easier to interpret the results (especially the starting salaries), as graduates under 35 years of age who are not pursuing further education are most likely to be career entrants.
- Some values in the factsheets are missing (n.a.). Where this occurs, it is because there are still too few data points available to report stable values. For reasons of data privacy, certain characteristics of the subjects are interchanged (target record swapping) to prevent individuals from being clearly identifiable. When these data are combined, the swapping does not affect the overall result. Stable, generalisable values are possible from a minimum of 30 persons per subcategory.