Do we see economies of scale in universities? (or: differentiate, not merge at all cost)

1. Inserire qui il logo del proprio ateneo d’appartenenza! Do we see economies of scale in universities? (or: differentiate, not merge at all cost) Andrea Bonaccorsi, University of Pisa Cinzia Daraio, University of Pisa Léopold Simar, Institute of Statistics, UCL Tarmo Raty, VATT Finland

2. Outline • Introduction • Background • Data • Methodology • Preliminary results • Further developments

3. Economies of scale Key issue • Widespread belief among policy makers that increasing returns and critical mass effects are at place in universities • Large debate on assumed European “fragmentation” in the university landscape • Arguments: (a) economies of scale (b) economies of variety (Jacob) However, empirical evidence is ambiguous • Brinkman (1981), Brinkman and Leslie (1986), Cohn et al. (1989), de Groot, McMahon and Volkwein (1991), Nelson and Hevert (1992) and Lloyd, Morgan and Williams (1993) • Verry and Layard (1975), Verry and Davies (1976) and Adams and Griliches (1998) • Narin and Hamilton (1996), Abbott, M., & Doucouliagos, C. (2003) • Bonaccorsi and Daraio (2004,2005), Bonaccorsi, Daraio and Simar (2006, 2007) Important practical policy implications • aggregation of universities (e.g. Australian government in the ‘90s; current debate in UK and other EU countries on critical mass); • aggregation of institutes in large public research organisations (e.g. CNRS in France, CNR in Italy).

4. Background • Most empirical investigations done on a country base, at university level or on specific (but limited) subjects (e.g. Brinkman, P.T,. & Leslie, L.L. (1986), Athanassopoulos, A.D., & Shale, E. (1997), Beasley, J.E. (1990, 1995), Flegg, A.T., Allen, D.O., Field, K., & Thurlow, T.W. (2004), Fandel, G. (2007) ) • Lack of systematic comparisons across countries at discipline level: • Microdata not easily available • Comparability issues are important (Bonaccorsi, Daraio, Lepori, Slipersaeter, 2007) • Multi-output production should be taken into account explicitly • Any sensible efficiency analysis should take into account the discipline-wise structure

5. Data and empirical background

6. Data Aquameth coverage July 2007 Reasonable sample, July 2007

7. Data Aquameth coverage November 2007, including France (487 universities)

8. Methodology: Robust Nonparametric efficiency analysis

9. Advantages of Robust nonparametric techniques vs. conventional production function • No need for functional specification • No assumptions on the elasticity of substitution between inputs • Capture local effects as opposed to estimation of average tendency • Inclusion of external factors in a general way

10. Introducing conditional efficiency:an illustration Qzm = Ratio between Conditional and Unconditional efficiency Region of decreasing pattern of ratios: Z has a negative influence If the ratios =1 then Z has no influence on the efficiency 1 Region of increasing pattern of ratios: Z has a positive influence external factor Z

11. Empirical analysis • 4 countries offer data by discipline: Finland, Italy, Norway and Switzerland (later UK, now also Netherlands and a subsample of Germany) • Limited time span: preliminary analysis on the year 2002 (sensitivity analysis) • Outputs: Number of enrolled students; Number of graduates; Number of publications • First take a look at simple output to input ratios and how they vary • Scatter plots of two ratios show whether they are correlated and there are country-wise patterns • Conjoint production model to measure the impact of the university size on teaching and research efficiency.

12. Engineering and Technology • In Italian Engineering schools publication and graduate intensities go hand in hand. • In other countries the relation appears opposite, but the range in graduates is too small and single units dominate the view • If the “outliers” are removed, the figure is quite unique. • Publications/academic staff • Graduates/academic staff • In Italy also contracted employees are counted

13. Medical sciences • Graduation has similar patterns in Finland, Switzerland and Norway • Research and education seems to go hand in hand Publications/academic staff Graduates/academic staff

14. Natural Sciences • No country-wise pattern • Performance gains in joint research and teaching are weak • No clear picture of the overall relation Publications/academic staff Graduates/academic staff

15. SocHum • Publication rate can be at any level, regardless of the student population • Independent of the country • ISI cover just a small portion of the peer reviewed literature in this field.

16. Preliminary Comments • Importance of systematic international analysis discipline-wise: subject mix matters • The usefulness of robust conditional measures to summarize overall effects • Too early to draw any policy conclusions • Next Steps • UK universities, first estimates and then NL and a subsample of G

17. Engineering and Technology – Whole sample (2002)

18. Social Science and Humanities –Whole sample (2002)

19. Economies of scale ENGTECH Overall positive effect of scale (number of students) But still most universities are in the region of flat conditional efficiency Some weak evidence of diminishing returns is also present Mod Conj. PUB TEACH ENR ENGTECH

20. Economies of scale ENGTECH Mod Conj. PUB TEACH TSTAFF UNI

21. Economies of scale ENGTECH - UK No evidence whatsoever of scale economies Mod Conj. PUB TEACH ENR

22. Economies of scale ENGTECH - UK Mod Conj. PUB TEACH TSTAFF UNI

23. Economies of scale ENGTECH - IT Inverted U-shaped relation Mod Conj. PUB TEACH ENR

24. Economies of scale ENGTECH - IT Mod Conj. PUB TEACH TSTAFF UNI

25. Economies of scale SOCHUM Very small Faculties are sub-optimal Mod Conj. PUB TEACH ENR

26. Economies of scale SOCHUM Mod Conj. PUB TEACH TSTAFF UNI

27. Economies of scale SOCHUM UK Mod Conj. PUB TEACH ENR

28. Economies of scale SOCHUM UK Mod Conj. PUB TEACH TSTAFF UNI

29. Economies of scale SOCHUM IT Mod Conj. PUB TEACH ENR

30. Economies of scale SOCHUM IT Mod Conj. PUB TEACH TSTAFF UNI

31. Conclusions on scale and efficiency • Economies of scale should not be examined at the level of universities at aggregate level • Differentiated pattern by discipline • Also some country-level differences emerge No empirical support for a generalized policy of pressure on universities to grow or merge Rather, each scientific/ educational field must find its own “optimal” scale Policies of concentration/ merger should be aimed at helping universities to find their own optimal configuration among disciplines, each of which follows differentiated patterns University as a strategic multi-divisional agent The key to strategic behaviour is differentiation

32. Differentiation of European universities in PhD education • PhD education crucial in knowledge society • Internationalization and mobility • Competition • Institutional adaptation • Differentiation and “division of academic labor” as response to enlargement of the market and competition • Variable observed • Number of graduate students/ Number of undergraduate students (ratio) • Institutional differentiation

33. Entropy measure h(pi) = log (1/ pi) n H =  pi log (1/ pi) Weitzmann’s diversity V(Z) = max ( V(Z\x) + d (Z\x, x)) x Z Mean sum of squared distance n n SSD =  (wi – wj/ w^)2 i=1 j=1 n n MSSD = 1/n2 (wi – wj/ w^)2 i=1 j=1

48. Conclusions • Universities must learn to compete in an international environment • To compete, you need a strategy • The name of the game is strategic differentiation • by scale and scope • by subject mix • by main type of education (undergraduate, professional master, research training) • by ambition in research (regional producer of usable knowledge; average research producer; world class research university) • by interactions with stakeholders (proximity vs international; industry vs territory/society) • by funding mix • To have a strategy you need indicators of positioning and of competitive dynamics (not only rankings)