International experiences and lessons drawn from a science policy view

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1 Funding formulas for teaching in public universities: International experiences and lessons drawn from a science policy view Presenter: Brigitte Ecker Senior Researcher, Joanneum Research Forschungsgesellschaft mbh Authors: Brigitte Ecker, Karl-Heinz Leitner, Claudia Steindl AIT Austrian Institute of Technology GmbH Abstract University funding has significantly changed in recent years following the trend towards an extension of competitive and targeted funding of universities. Many European countries have developed funding formulas for both research and teaching with a specific funding stream for both activities. By doing so, public money is allocated through performance contracts, single indicators or integrated formulas using a set of measures and criteria. Funding of teaching is therefore based on the number of students and/or teaching outputs, which means that a university receives a certain funding rate for every student. Such funding rates are usually differentiated according to various criteria such as the field of study and level. Our paper focuses on different formulabased funding models established for teaching, studying strategies and models from five European countries, namely Switzerland, England, the Netherlands, Finland and Sweden. The aim is to give an overview of different formula-based funding models, to analyse the components and indicators used, highlight the unit costs per student place, and finally, to synthesise the lessons that can be drawn from a science policy perspective. an indicator-driven approach, also called formula-based budgeting. For this purpose, funding for research and teaching are often considered separately and managed via two, mostly independent funding schemes. Both competitive and performance-based funding schemes are seen as innovative funding mechanisms as they tend to increase transparency and ease the communication of financial needs, foster efficiency and effectiveness of the university s activities, and consequently, increase the university s output and impact. While several studies on the effectiveness of different types of research funding (e.g. core funding versus external funding ) at universities have been published (e.g. Geuna and Martin 2003, EC 2008), there is, however, rather little systematic evidence regarding formula-based funding models practiced for teaching. We aim to address this deficiency by empirically investigating the university funding models of five European countries, focusing on three key research questions: (1) what are the different formula-based funding models practiced in public universities for teaching? 1 1. Introduction In recent years, university funding has undergone remarkable changes. Indeed, most OECD countries have extended their competitive project funding in the higher education sector, while at the same time proportionately reducing the institutional core funding, the block grants (e.g. EC 2008, CHEPS 2010, OECD 2010). Above all, institutional core funding itself has increasingly been allocated following (2) what indicators and criteria are used in such formulabased funding models, and in particular, what are the unit costs per student place in each model? (3) and finally, what lessons can be drawn from a science policy view? In this paper we mainly present the results of a study conducted on behalf of the Austrian Federal Ministry of Science and Research (see Leitner et al. 2011) which

2 investigates the strategies and determining factors of higher education funding systems, primarily focusing on formulabased funding models for teaching at public universities. Our main aim is to analyse the formula-based funding models practiced in five European countries and highlight the corresponding experiences. We aim to describe the different models, approaches and criteria used, and provide a synthesis of the lessons that can be drawn from a science policy view. The countries compared are Switzerland, England, the Netherlands, Finland and Sweden. By doing so, we use qualitative data including literature findings as well as information gained from interviews with representatives of national ministries, universities and higher education councils in each country studied. access and equity in higher education, as the allocation mechanisms ensure sustainable, adequate funding of the services provided, especially in teaching. Another main characteristic of formula-based funding is that formula rules (conditions, components, indicators, criteria, etc.) are specified ex ante, which means that neither the universities nor the ministries have an immediate influence on the budget to be allocated (Darling et al. 1989). Similar to performance-based allocation mechanisms, six core uses of formula-based funding can be generally identified (Sizer et al. 1992, Kettl 2000): increasing productivity; introducing market-like incentives; fostering monitoring and evaluation processes; improving dialogue between universities and public authorities; enhancing accountability; and finally, encouraging the rationalisation of resources. 2. introduction: Incentives of formula funding Due to strong political pressures mainly driven by the new public management reforms, more and more governments tend to distribute financial grants to universities via innovative solutions (CHEPS 2010). One of the major challenges is to enable higher education systems to respond to the growing demand for tertiary education and a growing number of students within the framework of budget constraints. Public resources should therefore be allocated in a transparent way while at the same time offering specific performance incentives. Indeed, the rationale of performance-based funding is that successful (performing) institutions should receive more funds than less performing institutions. Thus, the introduction and enforcement of competitiveness in education and research aim to stimulate the less performing institutions since the competitiveness argument includes that output should be rewarded (Herbst 2007, Hicks 2010). For this purpose, the national funding allocation mechanisms have changed in recent years as they are no longer just based on traditional negotiations of budgets between governments and universities, but in many countries additionally include an increase of competitive funds and funding mechanisms that directly pay for success, often included in the basic funding formula today (Salmi and Hauptman, 2006). The explanation for the increasing attention given to formula-based funding is that resource allocation mechanisms based on objective and standardised criteria have become a common tool in managing higher education (López 2006, Hansen 2010, OECD 2010). Thus, formula-based funding in teaching is central in many university funding models, although the strategies and mechanisms vary between countries as a consequence of national conditions such as political culture, size, demography, history and role of universities, higher education budget, and relations between the government and the sector. 3. Types of formula funding practiced in teaching Estermann and Bennetot Pruvot (2011, p. 14) define the concept of funding formula as an algorithm based on standard criteria to calculate the size of public grants to higher education institutions for teaching and/or ongoing operational activity, and in certain cases, research. It is worth noting that, especially in English-speaking countries, there is no single term used to describe the formula-based funding mechanisms; on the contrary, a wide range of terms co-exist, such as funding per student number, teaching funding formula, unit cost models/formula or formula funding based on normative costs. 2 The main driving force behind formula funding is the need for the government to limit the increasing higher education expenditures and to share that capped budget in an acceptable fashion (Smith 2003). Therefore, a welldesigned formula can greatly reduce the information and monitoring demands on governments, and thereby reduce costs. In addition, it allows funding to flow to universities in proportion to the services provided. By doing so, formulabased funding may also become an essential tool to increase Most studies (e.g. Darling et al. 1989, Koelman 1998, Salmi and Hauptman 2006, Sizer et al. 1992, OECD 2010) distinguish between input- and output-based models. Thereby, input-based funding is the most elemental type of formula funding in teaching as the formula is based on input criteria, such as the number of staff or staff salaries, or sometimes more refined criteria such as for instance the number of staff holding a PhD teaching at the university. However, in recent years formula funding has continued to

3 develop and, hence, most European countries use studentrelated input criteria rather than staff-related criteria today. In some Eastern European countries, as for instance in the Czech Republic, criteria such as the qualification of teaching staff are also considered in the calculation of teachingrelated funding (Arnold et al. 2011). Altogether, inputbased formula funding generally tends to make up for a considerable share of block grants, and therefore stands for long-term funding that is more appropriate to maintain the institution s financial sustainability (Cantillon et al. 2011). However, since the late 1980s, output criteria have been increasingly built into funding formulas. In the context of new public management approaches, in particular, negotiations between governments and universities are usually based on performance agreements and therefore refer to formula funding as an innovative funding allocation mechanism. Indeed, formula funding practiced in teaching tends to use various output criteria, such as the number of credits accumulated by students, the number of degrees awarded or the relative success of graduates on the labour market, and the number of graduates in jobs related to their training (Jongbloed and Vossensteyn 2001). Regarding its impact, output-based funding has been criticised for making it difficult, on the institutional level, to achieve long-term planning objectives, while it often results in uncertainty and fluctuation for academic staff. In this vein, Cantillon et al. (2011) also find that output-based funding may even induce universities to lower their educational standards, especially in cases where there is no adequate assessment of teaching quality. Additionally, output-based funding faces severe shortcomings since it is difficult to measure the multiple dimensions of the university s output in terms of quantity as well as quality; and above all, there are still challenges linked to the assessment and measurement of the success of teaching activities in practical terms (Hansen 2010). As a consequence, it is not astonishing that international studies and reviews indicate that funding formulas in teaching mostly tend to be based on a mix of input and output criteria, whereby the most common criteria used are the number of students enrolled and the number of degree recipients (Leitner et al. 2011). respect to the implementation of a specific system. In fact, from prior studies some specificities and opportunities can be described as follows. Basically, there is an explicit distinction between a research component and a teaching component. Separate funding implies that there are different criteria, measures and weights to be used for each component, although university laws and provisions usually do not prescribe how universities have to allocate the resources internally (OECD 2010). However, some kind of balance between teaching and research (on the institutional level as well as on the level of the academic staff) should be achieved, at least in such countries where the teaching staff at universities is also the staff engaged in research (Leitner et al. 2011). In general, formula funding means that the institution s budget is the product of the number of units (e.g. students) and their prices. Traditionally, the number of students is included in the development of teaching funding formulas. Indeed, in many countries teaching funding formulas refer to the number of students enrolled, for which there are two different calculation methods: the first option is to calculate the number of students on a prospective basis, i.e. figures are based on reviews demonstrating actual allocation flows so that funding may then be adjusted. The second option is to calculate the number of students on a retrospective basis, i.e. figures are based on available data, as for instance the average number of students enrolled in the last two years. By using enrolment-based formula, systems also need to differ between full-time and part-time students. Again, there are two different ways to deal with this specificity: one is to give part-time students a different weight in calculating unit costs (prices) per student; another is to convert parttime students to full-time students based on their course load. For the second method, in particular, it is essential that policy makers together with the university sector define what course load is required for a full time status. In this context, policy makers have to decide whether the number of students to be funded should be capped. Today, many countries have limited the number of students to be funded since the limit is an important vehicle to control budgetary expenses (Salmi and Hauptmann 2006). 4. Some specificities regarding formula funding Governments use different approaches to implement formula mechanisms in order to fund teaching at public universities. Variations notably concern the criteria used, and can often be explained by the national context which has a significant impact on the development of formulas. However, we can also identify some general rules with The number of students to be funded also plays an important role depending on which mode of allocation is used. There is a distinction between distribution and price models (Guhn 2007). In a price model, the budget is calculated by multiplying the prices by the number of students; in contrast, in a distribution model, the budget is given and the prices are calculated by dividing the budget by the number of students. In the case whereby the number of students rises, the price model proportionately increases the resources for funding, too. As this makes budgetary planning rather difficult for the government, price models 3

4 4 are often combined with an ex-ante definition of the student number; i.e. in the case of increasing student numbers (regardless of entrance restrictions), the government is able to reduce the price for a student place in the next budget round. Alternatively, prices are calculated by taking the weighting factors and the overall possible amount for funding, i.e. the budget, into account. By doing so, the budget is capped in most countries, although priorities can be set by a relative rise or decrease of prices. Indeed, in some countries, formulas include priority setting since they reflect national and regional priorities such as critical labour force needs. In England, for instance, higher prices might be paid to institutions in cases where students are studying in fields that national science policy regards as high priority. Altogether, the funding formula development follows a unit cost philosophy meaning that budgets are calculated on the basis of needs (defined by prices or standard costs) required to produce a certain unit of output, for instance, offering high-quality education until the student receives their degree. In this view costs are regarded as a relevant basis, and therefore, full cost accounting has been constantly improved in countries such as Switzerland, England, the Netherlands, Finland and Sweden. In particular, rules and accountability procedures have been simplified in order to reduce bureaucracy and complexity, and further ease the identification of the cost basis in teaching. However, there are several ways to allocate funds to institutions: one way is to calculate on the basis of actual costs per student as reported by the institution; another way is to allocate funds based on system-wide average costs per student that is usually calculated from aggregate statistics on spending and enrolments. Last but not least, another possibility is to calculate with normative costs per student using optimal staff/student ratios and other standardised efficiency measures (Salmi and Hauptmann 2006). Finally, it is also worth noting that performance measures are increasingly built into funding formulas, as for instance the number of degree recipients. However, the construction of performance criteria is not an easy task either, as each higher education institution is a multi-product firm responding to multiple incentives. As a consequence, it is important to recognise that there are strong implicit links between funding mechanisms and performance measurements since criteria (even weighted) never measure outputs in a proper way or even trigger unintended consequences (Smith 2003, Liefner 2003). When formula funding is based, for instance, on the number of degrees, universities might aim to reduce their dropout rates in order to increase their overall budget. As a result, there might be negative consequences not only on teaching capacities, but also on the quality of teaching. Given this ambiguity, performance criteria also have to take the practices of quality assessment into account. 5. models and experiences from five European countries: Switzerland, England, the Netherlands, Finland and Sweden Five European countries were analysed within the study of Leitner et al. (2011) which will be briefly presented in the following section. What these countries have in common is that they have already implemented formula funding models for teaching at public universities, but offer different modalities (see table 1). 5.1 Switzerland The university funding system is characterised by a mix of core funding for universities and public research institutions on one hand, and project funding for individual research groups on the other. Core funding for the Federal Institutes of Technology (FIT) and Cantonal universities is allocated for education as well as research (Lepori 2009). By doing so, the FITs receive most of their resources in form of federal block grants; on the contrary the funding for the Cantonal universities is much more complex and fragmented, and therefore Cantonal universities receive three different public funding streams: (1) intercantonal subsidies, (2) cantonal subsidies, and (3) federal subsidies which are formula-based. The allocation of intercantonal subsidies is a specificity of Switzerland and is ruled by the Intercantonal Agreement of This agreement represents the political consensus by which all cantons share costs proportionally to the number of students studying at the Cantonal universities. Cantons (whether hosting universities or not) pay fixed prices for their students according to three different fields of study; in return, the Cantonal universities grant equal access to students from all contributing cantons. The cantonal subsidies (second source) are allocated by the home cantons of the universities and are therefore mainly determined on a historical basis. The federal subsidies (third source) consist of two components: a 70% component for education which is calculated on the basis of student numbers, and a 30% component for research which is calculated on the basis of project funding grants. Referring to the education component, the number of students enrolled is the only criterion for which different factors (ranging from factor 1 in fields such as humanities and social sciences up to factor 4.8 in medicine) in the formula are used. There is no further differentiation between levels of study, or even between full-time and part-time students.

5 Given the complexity of the governance of Swiss universities, there are also two different models of allocation: on the intercantonal level, the allocation of subsidies (in the form of constant prices) is determined according to a price model, while on the federal level the allocation of subsidies is determined according to a formula, following a distribution model. As the complexity of the Swiss governance system has been criticised again and again, Switzerland is currently developing a new higher education act, which also considers a revision of the federal subsidies allocated to the Cantonal universities. In this context, a new formula based on a full cost accounting approach in order to calculate unit costs per student is under debate. 5.2 England England was one of the first European countries which introduced a funding system for teaching based on the number of students; implementation of a formula-based system started back in the 1980s. The Higher Education Funding Council for England (HEFCE) is the largest funding agency in England responsible for funding of teaching in all public English universities. The 1992 Further and Higher Education Act defines the most recent funding scheme for teaching. The modalities of the contractual relationship between universities and HEFCE are defined in the Financial Memorandum. While the first part of this contract describes the general framework for the governance of universities, the second part, the so-called funding agreement sets out the budget and conditions for funding (number of students). Funding for teaching is organised in a clear and transparent process and is calculated on the basis of the number of (full-time) students; it takes into account the discipline (four broad groups of subjects are defined), the location of the university (i.e. universities in London and suburb districts receive more funds), and the completion rate. When distributing the budget for universities, the income universities generate from student fees is also considered for calculating the funding amount in the different price groups. The assumed fee income is subtracted from the four price categories so-called standard resources that HEFCE is using for different disciplines. It is argued that both public and private sources are contributing to finance the teaching efforts of the universities. In 2011, the government significantly increased the ceiling for tuition fees while at the same time implementing considerable cuts in public funds for teaching distributed by HEFCE. Two principles underpin the funding method: i) similar teaching activities should be funded at similar rates; ii) institutions aiming to increase their student numbers should do so through allocations of additional funded places agreed by HEFCE. The latter one is implemented by funding so-called additional student places, the number of which is agreed between HEFCE and the universities. Like in all the other countries studied, the determination of prices is a difficult process. In England, the prices were originally determined using historical data, though in recent years cost accounting information has increasingly been used to adjust the prices. HEFCE seeks to promote a transparent and manageable system despite the difficulties encountered in the design of the method: Any analysis of subject costs will reveal significant variation in the costs of different subjects, but also significant variation within a subject between different institutions. At the same time, the boundaries between subjects at higher education level are not always clear-cut. We wish to avoid getting embroiled in discussions with institutions about... a particular course... By having only four price groups, we are able to recognise the main cost variations between different subjects, while keeping any boundary issues to a minimum (HEFCE 2010, 25). 5.3 The Netherlands The Netherlands started very early to introduce a performance-based funding system which separates funding of research from funding of teaching, with individual funding and evaluation schemes. In 1983, the so-called conditional funding model and the place cost model were introduced. With the conditional funding model, research outputs and goals were defined for the first time and negotiated between the universities and the ministry. With the place cost model, teaching and research components were separated for the first time, the number of students being used for allocation of the teaching grant, and the number of PhD students being used for the research grant. Since then, the funding system was adapted and further developed through a number of reforms. The most recent reform, carried out in 2003, consisted in the introduction of the bama model which corresponded to the adoption of the Bachelor-Master study system in the context of the Bologna Process. The funding of teaching is part of the first funding stream (direct public funding) which allocates the core budget to the university (Jongbloed 2010). Through the first funding stream, each university receives a formula-based lump sum for teaching and research, 40% of which is allocated to teaching. This amount is allocated by the Ministry of Education, Science and Culture, on the basis of measures of volume (student numbers, diplomas, competitive funds from research councils), prices and historical considerations. For the teaching component of the formula, the model uses both input (i.e. new entrants) and output (i.e. number of degrees) indicators. The rationale 5

6 6 for using the number of degrees as an indicator is to set an incentive for universities to increase their completion rate. The teaching component of the formula takes into account three different groups of studies: low cost programmes, high cost programmes, and medicine. In addition, the formula uses specific weights for Bachelor and Master studies. In 2007 for instance, the budget share allocated on the basis of the number of entrants was about 13% 61% were allocated for degrees, and 26% were allocated according to strategic (and historical) considerations. In 2010, the teaching formula was adjusted again; the number of enrolled students now replaces the number of degrees. The bama model is a distribution model as it allocates a predefined total budget to the different universities, which means that the price is calculated on the basis of the number of students. Thus, theoretically, the prices may vary over the years and may fall in case the number of students is growing faster than the budget to be distributed (taking into consideration that in most fields the number of study places is not capped). However, in practice, this did not happen in the past. Indeed, for two years the calculation of the prices with the given budget would have implied declining prices, however, it was possible to convince the government to raise the budget and hence a possible decrease of prices was used to attract additional public funds. Thus, the model served as a tool for communicating additional budget demands. In addition, student fees have been increased modestly in the last years, at least in order to keep the prices constant. Although the time to complete a degree and the dropout rate also a consequence of the performance system have been falling in the last two decades, higher education policy makers still see potential to improve the efficiency and effectiveness of the system. 5.4 Finland In the framework of the New University Act (1997), performance contracts were established for the first time. Within the performance contract, separate research and teaching components are defined, the latter of which amounts to 44% of the total university budget, while the former one (research) represents 30% of that amount. Besides the teaching and research components, a basic component (about 19% so-called extent factor ) and a component for achieving societal goals (about 7%) are used. Funding for teaching is based on a unit-cost formula which was introduced gradually from 1997 (10%) up to 2003 (100%). The procedure is as follows: each university negotiates with the Ministry target values concerning the number of graduates in each field of studies. This number is then fixed within the performance agreement. These values take into account both awarded graduates in the last three years (weighted with 1/3) and planned graduates for the next year (weighted with 2/3). Hence, this formula clearly indicates a strong output orientation, incentivising universities to ensure that students complete their studies in time. In addition, the teaching funding model takes into account five different fields of study for Master degrees with a factor ranging from 1.25 (e.g. humanities) up to 5.5 (e.g. arts). The definition of the precise number of students for each university and for each study programme (Finland has a harmonised admission system) allows the university budget to be calculated in advance. Like the other countries studied here, intensive debates have taken place between higher education policy makers and universities about the definition of prices and different weights (coefficients). While a full cost accounting system was established for research in 2009, a harmonised system has not been established so far for the teaching activities. This was mostly due to the difficulties in defining and adjusting the funding formula (and a strong individual behaviour to optimise the study programme based on costcoefficient considerations). In 2010, the government started to introduce a new system which now takes into account a criterion called discipline structure. By means of this new criterion, universities should be encouraged to develop and offer strategically new study programmes. 5.5 Sweden In Sweden, higher education funding is distributed as part of a tri-annual agreement, but universities receive their state grants per calendar year (= financial year). By doing so, direct government funding is divided into a 62% share for education at first and second level, and a 38% share for education at third level and research. In particular, the funding for the undergraduate level and the Masters courses and study programmes is based on the number of full-time equivalent students and the annual performance equivalent (CHEPS 2010). In practice, the Swedish allocation system follows the price model, insofar as the universities receive grants for all the enrolled students. This is the sum of all full-time students and part-time students enrolled during the calendar year; in addition, part-time students are accumulated to full-time equivalents. Concerning the performance criteria, payment for annual performance is linked to the sum of credits the students pass, i.e. in the case where a student is enrolled for full-time studies in humanities for the calendar year and passes all the exams, the higher education institution (university) receives the total amount calculated for this discipline. However, the amount of funding depends on the field of study - the Swedish university funding system takes into consideration a wide range of fields. Indeed, there are

7 prices calculated for 15 disciplines, showing up factors from 1 in fields such as humanities up to 12.7 in media and film. Besides the funding formula for teaching there are also public service agreements in Sweden which include how funding should be divided amongst the disciplines and which funding volume should be allocated to doctoral programmes and research. A further specificity of the Swedish system is that there is a funding cap of direct state grants limiting the number of students. However, in 2009 several institutions had more students than allowed for by the funding cap; nonetheless, it was possible to educate these students as the institutions received compensation from funding caps that had not been utilised in the years before (Högskoleverket 2010). Table 1: Parameters and criteria of funding formulas/unit cost models at universities in Switzerland, England, the Netherlands, Finland and Sweden Base of university s block grants (core budget) Distinction between teaching and research Implementation of formula funding/ unit cost models for teaching Criteria used in the teaching formula Distinction between full-time and part-time students Planning of student numbers Price model versus distribution model Unit costs per student place in 2011 (Euro) Criteria of allocation considering field of study and level Switzerland England The Netherlands Finland Sweden Performance contracts Performance contracts Performance contracts Performance contracts Performance contracts Federal subsidies allocated to Cantonal universities include a 70% component for education and a 30% component for research Funding formula on the federal level just for Cantonal universities; unit cost model on the intercantonal level Component for teaching: 63% Funding formula on the national level Component for teaching: 40% Funding formula on the national level Number of students Number of students Number of students and degrees No differentiation No planning of student numbers Distribution model on the federal level, price model on the intercantonal level For calculating the federal subsidies, only the factors of the different fields of study are relevant; for calculating the intercantonal subsidies the prices per student differ between fields of study: Differentiation between three groups of disciplines: I: Hhumanities and social sciences (factor 1) II: natural and technical sciences (factor 2.4) III: Mmedicine (factor 4.8) No differentiation between Bachelor and Master Part-time students are weighted Planning via ASN (Additional Student Numbers) Component for teaching: 44% Funding formula on the national level Number of degrees (planned and finished) 62% for education at first and second level, 38% for education at third level and research Funding formula on the national level Number of students and students performance No differentiation No differentiation Part-time students are accumulated to Full-Time-Equivalents Estimations based on Planning value for each No planning of student the developments in the university and fields numbers past years of study negotiated between each university and ministry Price model Distribution model Distribution model Price model Differ between fields of study; base price = Differentiation between four groups of disciplines: A: Cclinical stages of medicine (factor 4) B: laboratory-based subjects (science etc.) (factor 1.7) C: Ssubjects with a studio, laboratory or fieldwork element (factor 1.3) D: All others (factor 1) Further differentiation between undergraduate and postgraduate students Differ between fields of study: ,100 Differentiation between three groups of disciplines: I: Low cost programmes : Arts, humanities, law, social sciences, and languages (factor 1) II: High cost programmes : Sciences, engineering, and agriculture (factor 1.5) III: Medicine (factor 5) Further differentiation between Bachelor and Master students (factor 2) Formula model where prices are not published Differentiation between five groups of disciplines (until 2010): I: theology, humanities, social sciences, economic sciences, and law (factor 1.25) II: pedagogic, psychology, and sport (factor 1.5) III: technical and natural sciences, and agriculture (factor 1.75) IV: Hhuman medicine, dentistry, veterinary medicine, and pharmacy (factor 3.25) V: Aarts and design (factor 3.75), music (factor 4.5), theatre and dance (factor 5.5) Further differentiation between Master and Doctoral students Differ between fields of study: ,545 Differentiation between 15 groups of disciplines: I: humanities etc. (factor 1) II: Sscience and technology (factor 2.3) III: Nursing (factor 2.4) IV: Oodontology (factor 2.3) V: Medicine (factor 3.2) VI: Education (factor 1.8) VII: Other (factor 1.8) VIII: Design (factor 5.6) IX: Art (factor 7.0) X: Music (factor 4.9) XI: Opera (factor 11.4) XII: theatre (factor 10.3) XIII: media and film (factor 12.7) XIV: Dance (factor 7.5) XV: physical ed. and sports (factor 3.7) Further differentiation between undergraduate level and the Masters courses 7

8 Switzerland England The Netherlands Finland Sweden Priority setting No priority setting London -factor (up to +8%), policy incentives (e.g. higher prices for science technology, engineering, and mathematics - STEM fields) Prices are used to set incentives for adjusting the studies offered No priority setting No priority setting Role of (full) cost accounting Implementation of standardised full costing systems at universities started in 1999 In 1999 implementation of trac (Transparent Approach to Costing) model; since 2005 full cost accounting, delivering information for the periodic review of prices Admission system Open, but specific admission criteria for human medicine, dentistry, and veterinary medicine Harmonised admission procedure in all fields of study Source: Leitner et al. (2011). In 2007 universities In 2009 implementation started to develop of full cost accounting rules and guidelines for research, no (autonomous) for harmonised system for full cost accounting, teaching triggered by FP 7, for the definition of prices (so far) of less importance Open, but regulations in some disciplines Admission required for all studies Model of full costing developed in 2005; supported by the Association of Swedish Higher Education; recommended to be used in 2007 Simple rules of admission 8 6. Lessons drawn from a science policy view In the context of the new public management paradigm, formula funding models have become widespread and are considered as innovative allocation mechanisms of a university funding system. All five European country studies in this contribution have already implemented formulafunding models for teaching in public universities. In some cases, as for instance in England and the Netherlands, this originally happened in response to the increasing number of students, recently reinforced by the considerable budget constraints in these countries. However, science policy makers increasingly tend to implement formula-based funding mechanisms as formulas and cost models are seen to increase transparency and objectivity, as well as the ability to plan and control. Some countries have just implemented formula funding in recent years or are even discussing such an implementation at the moment whereas other countries, such as England and the Netherlands, have extensive experience in formula-based funding models and other competitive funding mechanisms in the higher education sector. While competitive funding has led to a culture of predominantly short-term, application-oriented, externally funded projects in research, such consequences have not been yet observed regarding formula-funding models for teaching in public universities. Thus, from a science policy view, it is seen as an increasingly necessary and successful strategy to finance education per student numbers. In doing so, some strategic decisions have to be made when designing a formula funding model, as for instance defining the term student place, the capacity in the different fields of study (which also requires implementing a specific form of capacity planning), as well as measuring the output and performance of universities. Those countries which use specifically output-based components within the formula such as the Netherlands, Finland and Sweden (number of degrees and student performance respectively) have not yet experienced a negative impact on the teaching quality due to incentives to maximise the output with a minimum of input resources (grade inflation). All three countries have a strong and long culture of evaluating teaching activities which set incentives to keep the quality standards high; moreover, universities are aware that reputation is very important and they obviously do not want to lose the attractiveness for students over time. Above all, it is the kind of model price model versus distribution model which is the key characteristic of a specific university funding system: while the price model is based on constant prices (like in England and Sweden), it also requires a stringent admission system and a funding cap as well. On the contrary, the distribution model (like in the Netherlands and Finland) tends to be more variable although increasing the number of students might lead to decreasing prices per student place. Nonetheless, countries such as the Netherlands have recently tried to keep constant prices and therefore, have considerably increased other sources, such as tuition fees. Last but not least, from a science policy view (full) cost accounting has gained importance in many countries and universities (often driven by the requirement of research funding, e.g. for calculating indirect costs) and therefore, is increasingly used to adjust the prices paid by the public today. However, experiences show that there is the danger that funding formulas and unit cost models may get too complicated and become too difficult to govern. So far, cost account information has delivered input for the definition of price categories, but political negotiations and historical aspects are equally important. However, cost information is

9 used to adjust the prices over time taking into consideration changing cost structures. As already mentioned in the context of the English experience, such systems need to take into account the main cost variations between different subjects, while keeping the system rather simple and manageable. Hence, there is always the trade-off between keeping a system manageable and transparent on the one hand and creating a fair system corresponding to real costs which differ between fields of study, level and location on the other hand. With the exception of Sweden, the countries studied only distinguish between three to five disciplines. By doing so, higher education policy, especially in countries such as England and the Netherlands, clearly sets incentives and governs the university system by defining the number of students in different fields where it has the willingness to fund (student places) and pay the associated price. However, as the case of Finland shows, there is also the danger that the autonomy of universities and their ability for strategic development may be reduced as universities mainly define their study offer by cost coefficients and efficiency considerations. As a consequence, the new Finnish funding model now integrates an educational discipline structure aiming to provide incentives for universities to develop more distinct profiles. Once introduced, formula funding models have to be adapted over the years, not just to follow priorities set in science policy, but rather to correspond to universities specificities and needs. 7. Conclusions In order to achieve the science and higher education policy goal of reaching a higher level of academic outcomes, we propose that the specific historical, cultural and national context be taken into account when establishing a specific model (as for instance in Austria and France at the moment). Moreover, the implementation of a unit cost formula model for teaching needs to be complemented with adequate funding for academic research as well. In the countries analysed, Switzerland is the only country which explicitly funds a part of the research activities by means of the teaching formula, following the Humboldt ideal of science-driven education. However, the number of doctoral candidates is regularly used within research funding models, which have not been the focus of this contribution. In addition, the number of funding councils and programmes, the average length and size of granted projects, and the success rate are important criteria for assessing the sustainability of a university funding system on the whole. The countries analysed maintain different university funding models. These models are generally based on the number of students; some of the models (especially in the Nordic countries) also include performance indicators such as degrees. However, it can be argued that there is no best way to define a generally accepted practice or even a superior indicator formula. The reason is that the design of funding models always largely depends on the national context and, moreover, on the national strategies which may improve the efficiency and quality of the higher education sector. At the same time we have to consider that university autonomy has increased considerably in recent years. Hence, independently from the funding model, formula and criteria etc. used, it is up to the university itself how the budget is internally allocated and what the development of its profile looks like. Thus, from a policy view, and although some universities (in particular the universities of applied sciences) benefit from priority-setting initiatives, the influence on the outcome and impact of the university system by formula funding might, in general, remain limited. 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10 mentpdf/?cote=dsti/stp/rihr(2010)4&doclanguage=en. HEFCE, 2010, Guiding to funding, download: Herbst, M., 2007, Financing Public Universities, Higher Education Dynamics, 18, Springer. Hicks, D., 2010, Overview of Models of Performance-Based Research Funding Systems, OECD document, download: mentpdf/?cote=dsti/stp/rihr(2010)4&doclanguage=en. Högskoleverket (Swedish National Agency for Higher Education), 2010, Swedish universities & university colleges: Short version of annual report 2010, download: R-swedish-universities-2010.pdf. Jongbloed, B., Vossensteyn, H., 2001, Keeping up Performances: an international survey of performancebased funding in higher education, Journal of Higher Education Policy Managment, 23(2), pp Jongbloed, B., 2010, Governance and Funding Reform in the European Higher Education Area: National system analyses The Netherlands, in: H. De Boer, B. Jongbloed, J. Enders and J. File (Eds.): Progress in higher education reform across Europe, Governance and funding reform, volume 2: Methodology, performance data, literature survey, national system analyses and case studies, Brussels. Kettl, D.F., 2000, The Global Public Management Revolution: A report of the transference of governance, 2 nd edn. (Washington: Brookings Institution Press). Koelman J.B.J. (1998): The funding of universities in the Netherlands: Developements and trends, Higher Education, 35, Leitner, K.-H., Ecker, B., Steindl, C. (2011): Finanzierungsmodelle universitärer Lehre Internationale Beispiele, Erfahrungen und mögliche Strategien für Österreichs Universitäten, Wien. Lepori, B. (2009): Switzerland: erawatch Country Report 2009 Analysis of policy mixes to foster R&D investment and to contribute to the era, Luxembourg. Liefner, I. (2003): Funding, resource allocation, and performance in higher education systems, Higher Education, 46, López, M.J.G. (2006): Towards decentralized and goaloriented models of institutional resource allocation: The Spanish case, Higher Education, 51, OECD (2010): Performance-based Funding for Public Research in Tertiary Education Institutions, Workshop Proceedings, OECD Publishing, download: Salmi, J., Hauptman, A.M, 2006, Innovations in Tertiary Education Financing: A comparative evaluation of allocation mechanisms, Education working paper series, number 4 (Washington, The World Bank). Sizer, J., Spee, A., Bormans, R., 1992, The role of performance indicators in higher education, Higher Education, 24, pp Smith, P., 2003, Formula Funding of Public Services: An economic analysis, Oxford Review of Economic Policy, 19(2), pp Brigitte Ecker, Senior Researcher, Joanneum Research, Austria Brigitte Ecker is an economist and works as senior researcher and project manager at the Vienna office of the Centre for Economic and Innovation Research of Joanneum Research Ltd. She holds a Doctorate in Economics and Social Sciences from the Johannes Kepler University Linz and was visiting researcher at the Swiss Economic Institute of the Swiss Federal Institute of Technology Zurich and the Amsterdam Business School. Her research focus is on internationalisation of R&D and empirical innovation research, human resources, university funding and research policy. Furthermore, she was involved in international research projects funded by the European Commission and the OECD, and worked on several projects on behalf of the Austrian Federal Ministry for Science and Research. Some of her recent studies focus on university funding models in teaching as well as research practiced in European countries. She has published in various journals, including Zeitschrift für Hochschulrecht, R&D Management, Journal of Management, Critical Perspectives on International Business and Journal of Technology Transfer. the project European Universities Forum for Financial Sustainability (EUFFINS, no ) has been funded with support from the European Commission. 10 This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.