Mapping the European Higher Education landscape. New empirical insights from the EUMIDA project

1. Creation of Human Capital and Spillovers from Research. New Evidence from the EUMIDA Census of European Higher EducationAndrea BonaccorsiUniversity of PisaDIME Pecs, March 31, 2011

2. Mapping the European Higher Education landscape. New empirical insights from the EUMIDA project Andrea Bonaccorsi 1, Benedetto Lepori 2, Tasso Brandt3, Daniela De Filippo2, Andreas Niederl4, Ulrich Schmoch3, Torben Schubert3, Stig Slipersaeter5 1University of Pisa, Italy 2 University of Lugano, Switzerland 3 Fraunhofer Institute, Karslruhe, Germany 4 Joanneum Research, Graz, Austria 5 NIFU STEP, Oslo, Norway

3. Outline • Part 1 • Building a new dataset of microdata on universities across all European countries • an interesting case of “vertical integration” between production of raw data, theory and empirical research • recognition of the role of universities in regional growth but aggregate estimates (e.g. human capital based on % of population with tertiary degree) • importance of subject mix for spillover • spatial econometrics at regional and sub-regional level • Part 2 • A new technique for the measurement of spillover

4. Background • Role of higher education in the formation of human capital and the creation of spillovers • But: Large heterogeneity of institutions of higher education in Europe • Emergence of the notion of mapping and multi-dimensional ranking (U-Map) • Existing studies based on comparative qualitative analyses, or on judgmental sampling + aggregate statistics • Official statistics on education are produced by EUROSTAT only at aggregate level • based on attendance to courses (ISCED 6, ISCED 5° and 5b), not on institutions • Lack of an official delineation of the perimeter (list of institutions) • Lack of a census of all institutions based on microdata

5. The EUMIDA project • Tender by European Commission (DG Research) in collaboration with of DG Education and EUROSTAT in 2008 • EUMIDA Consortium • University of Pisa (coordinator) • Fraunhofer ISI Karlsruhe • University of Lugano (USI) • Joanneum Research • NIFU STEP • Pioneering work with AQUAMETH project (2004-2008) (Bonaccorsi and Daraio, 2007) • Network of national experts in 27 EU countries + Switzerland and Norway • Start June 2009- End October 2010

6. Goals of the project • Work directly with National Statistical Authorities (NSA) • Establish a validated perimeter covering: • all institutions delivering ISCED 6, ISCED 5 a + those delivering 5b with a stable organization • the subset of “research active” institutions • Develop a Handbook of statistical definitions • Collect microdata on an experimental basis at national level • Examine issues of • data availability • confidentiality • comparability • Suggest an architecture for a regular data collection to be carried out in the future by NSA and EUROSTAT

7. Perimeter delineation • Tertiary education institutions are defined as entities which are to some extent recognisable as distinct organisations and whose main activity is providing education at the tertiary level (ISCED 5 and/or 6), as well as R&D. • Recognisable means that the perimeter of these institutions can be identified rather unambiguously, they have an internal organisational structure and, at least in principle, their own budget. • Typical cases of tertiary education institutions in most countries are universities and universities of applied sciences, but also institutions delivering professional education curricula classified at level ISCED 5B.

8. List of Core set of variables/ 1 Data collection 1 (core set of data) Identifiers These data are meant to identify unambiguously the institution; they include a unique identifier. the official institutional name both in the national language and translated in English. Institutional descriptors These discrete descriptors provide some general information on the HEI considered. Moreover, we include in this set the simplest possible indicator on institutional size, namely staff employed by the institution.

9. Data collection 1 (core set of data) • List of Core set of variables 2 Educational activities These indicators provide a rather simple but complete few of the main dimensions of educational activities, without particular burden for data collection. Research activities The first is a subjective evaluation which is however needed to decide if to integrate an institution in the full data collection. It is also required for this dimension in order to broadly distinguish institutions which don’t award a PhD, but perform research, from teaching-only institutions. The second is the simplest indicator on research activities in the PhD awarding sector Knowledge exchange refers broadly to the transfer of activities to economy, society and culture. Most indicators in this area are problematic in terms both of methodology and data collection, and thus no indicators is included for the moment being. International attractiveness International orientation is a very relevant and emerging dimension for most higher education institutions and is very likely to provide relevant input for classification purposes, distinguishing between more local and regional oriented institutions and those more open internationally. Most indicators in this area based on shares of students and staff from abroad, as well as on international publication. For the purposes of the core set of data, the first indicator referring to education, the second to research Regional engagement Presence and contribution to the social and economic development in their region would be considered as a very important dimension of higher education, especially for the institution in the non-university sector. This would need detailed data collection and, secondly, breakdown of data by region and not at level of whole institution.

10. Research active institutions • Among the whole population of tertiary education institutions, we distinguish the research-active ones, i.e. those having a sizeable and institutionalised research activities. This distinction is relevant because of the specific functions and organizations of these institutions. • The definition of research active does not imply a specific level of research intensity and care should be taken in distinguishing between research active and research intensive institutions (exceeding some threshold, like the one used in the Carnegie classification). However, it implies that research is considered as constitutive part of institutional activities and it is organised institutionally and with a durable perspective. • Criteria for inclusion are then the following: • the existence of research units institutionally recognised • the existence of an official research mandate • regular PhD programs • consideration of research in institutions strategic objectives and plans • regular funding for research projects either from public agencies or from private companies.

11. Data collection 2 (extended set of data)

12. Extended set of data Expenditures This variable measure the overall level of expenditures of higher education institutions; the chosen perimeter corresponds to expenditures inside educational institutions as defined in the UOE manual covering all types of goods and services, namely both educational goods and services, R&D and non-instructional services • Breakdown by categories • current expenditures, i.e. expenditures for goods and services consumed within the current year, current expenditures should be further broken down as follows: • personnel expenditures; • non-personnel expenditures. • capital expenditures. • capital outlays must be recorded in the years where they are made, while the costs of depreciation of capital assets is not included. Total expenditures include expenditures for educational services, R&D and other services; no breakdown between these categories is required. R&D expenditure (where available)

13. Extended set of data Revenues measures the overall revenues of the considered institution in a calendar year (based on effective payments). Thus, it measures the overall level of financial resources for the whole institution; in general, it is not expected that it corresponds exactly to total expenditures, since in some cases HEI are allowed to set aside reserves. • Total revenues • Direct public expenditures on educational institutions. • Fees from households and students • Direct expenditures of other private entitites (than households) on educational institutions • - Direct foreing payments to educational institutions • Breakdown by categories • core budget. • third-party funding. • fees.

14. Extended set of data • Personnel • These variables provide information on the staff employed by higher education institutions conforming to UOE manual definitions and practices. These data are extremely relevant since they provide much information on resources available to institutions; also, cross-country comparisons are considered to be more reliable when using personnel data than financial data. • Total personnel • Includes data on: • Total staff (persons employed in tertiary education institutions) • Academic staff (includes all teaching and research personnel employed by the institution, including teaching/research assistants) • -Non academic staff (management, quality control and administrationstaff.Maintenance and operational personnel). • PhD students (in some countries PhD students are financed by national grants and thus they might not be included in personnel statistics, while in other countries their level of employment might vary depending if the time devoted to the dissertation is included in the working contract) Breakdown by categories - personnel categories - field - nationality All data in FTE

15. Extended set of data • Educational production • Indicators on educational production cover both students enrolled and graduations. Breakdown by categories - field of education - programme level Students at ISCED 5 and 6 Students at ISCED 5 and 6 are defined as any individuals participating in educational programs classified either at level ISCED 5 or 6 of the UOE manual enrolled in the considered institution. Graduations at ISCED 5 level Graduations refer to the number of qualifications obtained during the reference year at ISCED 5 level; similar definitions apply to graduations at ISCED 6 level Number of graduations could be larger than number of graduates, since in principle it is possible that during a refernece year a person gets more than one qualification Data on students and degrees should be broken down between national and international students.

16. Extended set of data • Research and technology production • These set of variables measures the production of research results, of technological results, as well as of so-called thrid-mission of activities to transfer knowledge and research results towards economy and society Number of ISCED 6 graduations Number of graduations delivered at ISCED 6 qualification; excluding intermediate stage ISCED 6 programs like DEA in France and ISCED 6 post-doctorate qualifications (like the German habilitation). In most countries, this will correspond to the definition of doctorate or PhD degree. Definitions and data collection should comply with UOE manual. Following UOE manual graduations should be based on the calendar year. Breakdown by categories Breakdown is requested by fields of education at 1-st digit level.

17. Coverage of universe The institutions registered in the EUMIDA census enrol 89.8% of the total number of Eurostat students. We loose 10,2% of the Eurostat total. The difference is largely explained by the lack of inclusion of very small providers of vocational training in three large countries (Germany, Poland, Spain). Very good coverage of the universe.

18. Census

19. Segmentation by degree The higher education landscape is composed by three groups that are almost equivalent in number, delivering degrees at bachelor, master or PhD level. The system is not apparently organized as a pyramid, with a large base of institutions covering lower level curricula, but as a cylinder, or perhaps a clepsydra. However, it turns out that 11.418.082 students, or 78,3% of the total, are enrolled into institutions that can deliver up to the doctorate degree. Although the distribution of institutions in the three groups is balanced, almost 80% of European students go to a university-like institution.

20. Segmentation by research activity Among research active institutions there are institutions that deliver only the bachelor (n= 226) or up to the master degree (n= 317). This group includes several Fachhoschule and Universities of Applied Sciences, as well as many specialised institutions. It accounts for 39% of research active institutions.

21. Model-based cluster analysis • Model-based clustering technique (Dasgupta and Raftery, 1998, Fraley and Raftery, 1998, 1999, 2002) • Model selection criteria (Schwartz-Bayes; Akaike-Information Criterion)) • Variables in the model • legal status (private yes/no) • size (number of ISCED 5 and ISCED 6 students, in log) • teaching intensity (number of ISCED5 students per staff) • graduate-teaching intensity (number of ISCED 6 students per staff) • internationalisation (share of international ISCED 5 and ISCED 6 students) • number of subjects covered (simple count over 9 distinct subjects) • indicator for research-activity (research-active yes/no).

22. Model-based cluster analysis • Main results • The optimal Gaussian mixture distribution is an ellipsoidal model with equal shape • There are only two clusters or different types of HEIs • (a) traditional university model • (b) college model (undergraduate education) • By adding variables related to research (Phd students/total number of students; % foreign Phd students) only a small cluster of specialised institutions emerge (mainly private) • There is no “research university” cluster

23. Model-based cluster analysis Graduate teaching intensity Undergraduate teaching intensity Share of international Phd students

24. Model-based cluster analysis Legal status (public/private) Number of fields covered Research activity (yes/no)

25. Characterizing the models

26. Country profiles

27. Census vs rankings

28. Some areas of utilization • Country patterns • Statistical basis for sampling and multi-dimensional ranking • Regional studies • - data on universities as covariates in models of regional growth (at NUTS 2 and NUTS 3 level) • impact of creation of universities on regional growth • spillover • entrepreneurship • spatial contiguity and human capital spillover • intentional vs spatial vs webometrics • Subject mix analysis • publication patterns, cost per student, student/staff ratios are largely variable across fields • Efficiency analysis

32. 2008

33. Extensions • Bibliometric profile of all higher education institutions in Europe • feasibility study for a small sample of non-ranked universities on Scopus data (Fraunhofer ISI) • standardization of query format and estimate of cost of a large scale exercise • Webometric analysis • linking web adresses to an official list of institutions with officially validated name (CSIC) • Academic patents • matching of names of authors in bibliometric databases with names of inventors and integration with the affiliations

34. Publication of data • EUROSTAT • placed Register of universities in the Work program 2011 • will develop the methodology in 2011 based on the EUMIDA Handbook and experience with NSA • will launch regular data collection in 2012 • EUROPEAN COMMISSION • will ask permission to all NSA to publish the microdata collected by EUMIDA • possible levels of authorization: (a) all variables with names of institutions; (b) all variables without names of institutions; (c) mixed (e.g. expenditure data not published, or private sector not published); (d) none • will publish the dataset whatever the level of coverage (principle of variable geometry) making a final decision in Spring 2011

35. Part 2 A new method for the estimation of knowledge spillovers Andrea Bonaccorsi- Cinzia Daraio To be presented at European Workshop on Efficiency and Productivity Analysis- EWEPA June 2011 • Theoretical issues in the identification and measurement of spillover effects • A crucial theoretical role (literature review) • Econometric issues (production functions, cost functions) • Conditional robust measures • A robust measure of spillover • An application to the Italian system • Extensions: applications to general input-output models

36. Data description ULA = number of employees in manufacturing at province level (units) IP = stock of private fixed capital (€ 000) DPM = stock of public infrastructure (€ 000) VA_IND = manufacturing value added (€)

37. Data description Brev9597 = number of patent applications EPO 1995-1997/ population (000) Pub_TOT9597 = number of publications 1995-1997/ population (000) Pub_engtech = number of publications in Engineering and technology 1995-1997/ population (000)

38. MOD1: X1=ULA IND, X2=DPM, Y=VA IND; Z1=brev9597+0.01Z2=pubtot9597+0.01

39. MOD1: X1=ULA IND, X2=DPM, Y=VA IND; Z1=brev9597+0.01Z2=pubtot9597+0.01 m=45, alpha=0.975

40. MOD1’: X1=ULA IND, X2=IP (Picci), Y=VA IND; Z1=brev9597+0.01Z2=pubtot9597+0.01

41. MOD1’: X1=ULA IND, X2=IP, Y=VA IND; Z1=brev9597+0.01Z2=pubtot9597+0.01 m=45, alpha=0.975

42. MOD1’: X1=ULA IND, X2=IP, Y=VA IND; Z=brev9597+0.01 m=45, alpha=0.975 Milano

43. MOD1’: X1=ULA IND, X2=IP, Y=VA IND; Z=brev9597+0.01 m=45, alpha=0.975 A zoom Torino Bologna

44. MOD1’: X1=ULA IND, X2=IP, Y=VA IND; Z=pubtot9597+0.01 m=45, alpha=0.975 Milano Roma

45. MOD1’: X1=ULA IND, X2=IP, Y=VA IND; Z=pubtot9597+0.01 m=45, alpha=0.975 A zoom Torino

46. MOD2: X1=ULA IND, X2=IP, Y=VA IND; Z1=brev9597/pop*1000+0.01Z2=pubtot9597/pop*1000+0.01 m=45, alpha=0.975 Pop in in thousands Hence intensity refers to brev (or pub) per million population

47. MOD2: X1=ULA IND, X2=IP, Y=VA IND; Z1=brev9597/pop*1000+0.01Z2=pubtot9597/pop*1000+0.01 m=45, alpha=0.975

48. MOD2: X1=ULA IND, X2=IP, Y=VA IND; Z1=brev9597/pop*1000+0.01Z2=pubtot9597/pop*1000+0.01 m=45, alpha=0.975

49. MOD2: X1=ULA IND, X2=IP, Y=VA IND; Z1=brev9597/pop*1000+0.01Z2=pubtot9597/pop*1000+0.01 m=45, alpha=0.975

50. MOD3: X1=ULA IND, X2=IP, Y=VA IND; Z1=brev9597/pop*1000+0.01Z2=pubengtech9597/pop*1000+0.01 m=45, alpha=0.975