1. Higher Education Statistics – collection and processing Discussion on the Swedish Case Anna Gärdqvist and Mats Haglund Statistics Sweden Reflection on the Austrian Case Michaela Schaffhauser-Linzatti University of Vienna May 11, 2006, Graz

2. Presentation Sweden: students Austria: Overall Information Reporting on performance Monitoring of performance Monitoring of data Presentation Paper Data flow Organisational responsibilities General aspects of reporting on Higher Education Data flow Organisational responsibilities Selected repor- ting tools Possible answers from the Austrian experience

3. Data Flow Public Universities Vocational Universities (FH) Private Universities Theological Universities Others Federal Ministry for Education, Science and Culture FH-Council Statistics Austria University Report Report on Higher Education A: Swedish National Board of Student Aid; Ministry of Education, Research and Culture; Statistics Sweden S: Register of Higher Education

4. From Basic Data to Statistics (Example: University of Vienna) Administrative register Register-statisti- cal processing Statistical register - Editing and coding - Handling of missing objects and values - Matching and selections - Processing of time references - Creating derived objects and variables i3v Le Salaire SAP R/3 ETL Data Warehouse Reports Federal Ministry Sweden: Statistics Sweden Austria: Universities

5. Internal reportsExternal reports To the public To political decision-makers Income and expenditure accounting Others Evaluation report Performance Report Intellectual Capital Statement Financial Statement Others University Report Report of the University Council Others External Reports (Public Universities) according to UG 2002 further: f.e. UniStEVO, BiDokV-Univ

6. Performance Report Aims –Report on the qualitative and quantitative development of the university –Forecast on the future performance and financial situation –Basis for the university report Definition –Information on the performance agreement –Published annually by the rectorate –Including past two years, forecast for the following year

7. Performance Agreement Aims –Budgeting –Performance based controlling instrument Definition –Performance agreement by public law between each university and the Federal Ministry –According to the aims of the university –Regulates budget allocation by the Federal Ministry on basis of the performance of each university –For periods of 3 years –Indicators and details regulated by the order “Formelbudget- Verordnung ”

8. Intellectual Capital Report (I) Aims –Measurement, evaluation und publication of Intellectual assets Knowledge based processes Knowledge based results –Instrument for Communication between university, Federal Ministry and the House of Parliament Control for the university by separate bodies and through responsible bodies of the state owner Marketing of the university

9. Intellectual Capital Report (II) Definition –Demonstration of The university’s activities, social activities, and self-imposed objectives and strategies Intellectual capital, broken down into human, structural and relationship capital The processes set out in the performance agreement, including their outputs and impacts –Published annually –Indicators and details regulated by the order “Wissensbilanz- Verordnung ”

10. Intellectual Capital Report Model Visions, objectives, strategies + Narratives Additional topics Intellectual Capital Core processes Resumee and perspectives Narratives Indicators Narratives + Outcome and results of core processesIndicators

11. Contents Basis of data acquisition –Personalised –Aggregated Dimension of indicators –Qualitative indicators –Quantitative indicators Monetary Non-monetary Number of indicators –53 “global” indicators –Refer to 5 different reporting periods / key dates –Structured into Fields of Study Curricula

12. Pros and Cons of the New Austrian Reporting System on Public Universities Pros –New reporting system with international archetypes –Transparency –Communication External Internal –Management orientation Comprehensive data acquisition Planning basis Aid for decision making Cons –Overlappings of reports –Volume –Standardisation –Data validity –Costs of implementation, preparation and auditing

13. Possible Consequences Performance orientation Competition between the universities Competition within the universities New emphases of universities New allocation of resources

14. More information Federal Ministry forwww.bmbwk.gv.at Education, Sciencewww.weltklasse.at and Culture Fachhochschulrat www.fhr.ac.at Statistik Austria www.statistik.at