This chapter analyses the main policy options available to public authorities in OECD countries to allocate core operating grants to public and publicly funded higher education institutions, recognising the importance of such grants for institutions’ financial sustainability. Drawing on detailed examples from the six systems analysed in depth in the OECD Resourcing Higher Education Project, the chapter focuses on the mechanisms that authorities can use to make funding allocations transparent and predictable and to promote efficient, effective and accountable use of public funds.
The Financial Sustainability of Higher Education
Insights from Policy in OECD Countries
Abstract
Most higher education funding systems in OECD countries provide at least some public funding to HEIs as direct grants. The extent to which policies aim for core public funding to cover the costs of institutional activity in individual systems depends largely on the level of tuition fees and expectations about the ability of institutions to raise funds from third-party sources.
Australia, England and Ireland are arguably the OECD systems where public authorities have most explicitly coordinated policy on tuition fees and funding of HEIs as part of reforms to implement different models of cost sharing between students and government.
Many OECD systems provide public direct-grant funding to universities explicitly for research, but this practice is not universal. While research contributes to the quality of the learning environment for students, high rates of cross-subsidy of research from teaching grants or student fees may be difficult to justify on these grounds and raise legitimate questions about who should fund university research.
Three main variables inform the design of funding allocation models: a) the proportions of fixed (historical) and variable (formula-driven) funding and, where formulas are used, b) the indicators deployed in the calculation; and c) whether and which weightings are used to provide differentiated payments for specific inputs or outputs (such as students in different fields, students from different backgrounds or students who complete their studies on time).
Variable funding linked to student or graduate numbers, is more responsive to increases in enrolment, allowing funding to keep pace with changes in expanding systems and institutions but can create financial instability for institutions when student numbers decline. Funding models use fixed funding components, averaged historical data in formulas and limits to annual fluctuations in budget allocations to counter such instability.
Denmark and Finland are the two systems analysed in the Resourcing Higher Education Project that allocate the highest share of institutional core funding based on output and outcome variables. More generally, available evidence points to only modest effects from performance-based formula funding on targeted outputs and highlights risks of unintended effects if indicators incentivise quantity of research or educational outputs over quality and creativity.
Institutional performance agreements between government and individual HEIs have had a positive effect on institutional strategy and dialogue between institutions and public authorities in the multiple OECD systems where they have now been implemented. Such systems offer a useful tool to tailor performance objectives to individual institutions in small and medium-sized higher education systems.
Public subsidies to HEIs form part of a broader higher education funding ecosystem
This chapter analyses how public authorities allocate core public funding to individual higher education providers. The focus is on the publicly subsidised institutions for which governments have greatest direct responsibility and which, as highlighted earlier in this report, account for most tertiary enrolment in nearly all OECD countries. The discussion in the following sections centres on the allocation of core public operating grants, which usually aim to cover at least part of the cost of teaching and associated overheads and may also explicitly fund research. These core grants typically represent the largest share of the public funding received by public and government-dependent higher education institutions and make a substantial – and often fundamental – contribution to these institutions’ financial sustainability in virtually all OECD countries1. Nevertheless, direct grants to institutions – represented in the central pyramid in Figure 4.1 overleaf – are always anchored in a broader funding and regulatory ecosystem for higher education. While the different components of this funding ecosystem are usually present in OECD higher education systems, their scope and exact configuration vary substantially between countries.
Note: Adapted from Figure 3.1 in OECD (2022[1]) Resourcing Higher Education in Portugal, https://doi.org/10.1787/a91a175e-en.
Direct grants to higher education providers from public authorities in OECD countries nearly always take the form of block grants, which institutions can use to cover different costs with substantial autonomy (Golden, Troy and Weko, 2021[2]). As discussed below, these grants may be calculated based on historical allocations or using indicators of institutional activity or performance. Core funding to cover the day-to-day operations of HEIs may be complemented within block grants by additional public funding for general institutional and system development goals – as in Finland (OECD, 2023[3]) – or funds earmarked for specific projects or activity types, such as capital investment or student services, as in Ireland (OECD, 2022[4]) or the Flemish Community of Belgium (OECD, 2021[5]).
Alongside direct grants to institutions, public authorities frequently allocate public funding through competitive mechanisms, whereby individual academics or institutions submit project applications and compete with others for a share of available funding. A large proportion of funding allocated by research councils or similar public research-funding bodies operates in this way, although project-based funding may also be used to target investments to other areas – such as digitalisation, widening access initiatives or capital projects – through specific calls for proposals. From an institutional perspective, competitive funding from public sources may be classified and viewed as a form of third-party funding in a similar way to grants from non-profit organisations or contracts with private businesses. From a government perspective, however, competitive and project-based funding provides a mechanism to steer institutional activity by defining specific goals for which available funding should be used. Research-funding bodies, for example, can vary the degree of specificity in thematic calls for proposals, defining broad objectives to facilitate a wide range of curiosity-driven research projects or formulating precise objectives where funds are intended to support pre-identified goals.
As illustrated in Figure 4.1, different forms of sectoral regulation often influence the level of funding that higher education institutions receive and the way they can use this funding. The size of core funding allocations may be influenced directly by student-number caps – sometimes referred to as numerus clausus – used by governments to regulate the total number of study places or the number of publicly subsidised places offered across a system or by individual HEIs. Equally, in some higher education systems, the conditions under which higher education institutions employ academic and non-academic staff may be regulated by rules – adopted by government or through collective agreements and distinct from general employment law – that are specific to the higher education sector. As discussed in Chapter 2, rules that govern the remuneration and conditions of the academic and non-academic workforce substantially affect the total cost of undertaking labour-intensive activities such as teaching and research in higher education.
In many higher education systems, public funding to students – usually in the form of non-repayable grants and publicly backed, repayable loans – and public funding to higher education providers are largely treated as distinct areas of higher education policy. Student support systems have been analysed in previous OECD reports (OECD, 2020[6]; Golden, Troy and Weko, 2021[2]) and, given their complexity and importance, will be a focus area in a separate OECD thematic report.
In systems with student fees, student support payments may be calibrated with the level of the tuition fees that students pay to institutions and may even be viewed as a form of indirect institutional funding. In Portugal, for example, the level of the means-tested student grant for students from low-income backgrounds is set to ensure this covers more than the cost of regulated tuition fees in public institutions (OECD, 2022[1]). In the United States, the level of the federal Pell grant accounts for – although by no means generally covers – the total cost of attending higher education, including tuition fees (OECD, 2020[7]).
In Australia, the level of tuition fees for students in publicly subsidised study places and the corresponding level of grant that institutions receive from the Commonwealth Grant Scheme (CGS) vary by study field, with students able to access income-contingent loans though the Higher Education Loan Program (HELP) to cover the cost of their contribution (Australian Government, 2025[8]). In England (United Kingdom) the income-contingent student loan system also provides loans to undergraduate students to cover the cost of regulated fees. Australia and England – along with Ireland, where undergraduate students pay a EUR 3 000 annual “student contribution” – are arguably the OECD systems where public authorities have most explicitly coordinated policy on tuition fees and institutional funding, as they have implemented different approaches to cost sharing between students and government.
A final area of the funding ecosystem for higher education institutions illustrated in Figure 4.1 is public funding for non-financial support to students, such as subsidised housing, catering, sports facilities, medical care or transport. Here a distinction can be made between targeted or earmarked subsidies to higher education institutions to provide services such as housing or catering and public subsidies to other service providers to offer services to students, such as funding for discounted or free public transport or public funding for student housing offered by non-HEI providers. As previously discussed, whether student services are provided in-house by higher education institutions or by third-party providers influences the overall cost of running higher education institutions and standard funding metrics, such as per-student expenditure on institutions.
Policy on direct-grant funding to institutions and tuition fees are closely intertwined
Chapter 3 illustrated the effects on institutional revenue of policy choices around tuition fees and direct government funding to public and government-dependent higher education institutions. In basic terms, in systems where governments decide to permit substantial tuition fees for domestic and international students, direct government subsidy to institutions accounts for a lower share of institutional income and vice versa. In systems where tuition fees represent a high share of institutional income – such as the state-systems in the United States and provincial systems in Canada – changes to the design of public direct-grant funding to higher education institutions might be expected to have less impact on institutional finances than would be the case in systems where institutions are more dependent on this funding source (OECD, 2020[7]). Nevertheless, as noted, governments typically co-ordinate policies on direct-grant funding and tuition fees closely and the experience of implementing indicator-based funding models in several OECD jurisdictions shows that financial incentives or signals created by direct-grant funding models can influence institutional behaviour, even when such grants make up a comparatively low share of total institutional revenue (see discussion of performance funding later in this chapter).
Example countries included based on OECD Higher Education Policy Survey findings and complementary research
Note: (1) For core public funding, the categorisation of systems is based on UOE data on source of expenditure on tertiary education institutions and ETER data (2022) for European systems where total public expenditure exceeds 50% of total expenditure on public and government-dependent tertiary education institutions. The data on tuition fees is drawn from Education at a Glance 2025 [add reference].
(2) In Austria, universities of applied science may charge fees.
(3) In Lithuania, state-funded places and non-state-funded places co-exist.
As summarised in Table 4.1, it is possible to classify OECD higher education systems broadly into four main categories regarding their tuition-fee and institutional-grant policies:
1. Systems where governments choose to fully subsidise the education of full-time, domestic bachelor’s students (meaning institutions charge no tuition fees for this group) and core public direct-grant funding accounts for over 50% of institutional revenue on average. This group is exemplified by the Nordic countries and the German federal states.
2. Systems where public and government-dependent higher education institutions charge moderate, government-regulated fees of less than USD 3 000 (EUR 2 600) a year for full-time, domestic bachelor’s students but core public direct-grant funding still accounts for over half of institutional revenue on average. Many continental European systems fall into this category.
3. Systems where system-wide, government-regulated tuition fees charged to full-time, domestic bachelor’s students in public and government-dependent institutions are higher – over USD 3 000 (EUR 2 600) a year on average, core public direct-grant funding accounts for less than half of institutional income on average and fee and institutional funding policy is closely coordinated. Australia and England (United Kingdom) are example systems.
4. Systems where core public direct-grant funding accounts for less than half of institutional income on average in public and government-dependent institutions but institutions have greater freedom to set tuition fees – meaning fees vary substantially between institutions – and tuition-fee and institutional-grant policies are less explicitly coordinated than in type 3 systems. Canada and the United States are examples of this approach.
Not all higher education funding models fit neatly into the categories in Table 4.1. Several countries in Central and Eastern Europe, for example, have maintained dual tuition-fee systems, whereby domestic students eligible for state-funded places based on their past academic performance pay no or low tuition fees, while non-eligible students pay higher fees. However, a combination of policy reform and demographic change is reducing the differences between these systems and those in other OECD countries. Estonia, for example, has moved to model where all eligible domestic students are state funded (Eurydice, 2025[9]). In Lithuania, a declining youth cohort means that most students who apply to enter higher education are now able to access a state-funded place (OECD, 2023[10]) – a pattern increasingly observed in other Central and Eastern European countries.
As core public direct-grant funding and tuition fee revenue from domestic students are the most important revenue streams for higher education institutions in most OECD countries, government policy on these funding streams substantially influences the financial sustainability of higher education institutions. The question of the financial sustainability of the higher education sector, alongside access and equality of opportunity, features prominently in contemporary debates on tuition-fee and public-funding policies in countries with comparatively high fees, such as the United Kingdom and Australia (Lewis and Bolton, 2025[11]; Department of Education, 2024[12]), and in OECD systems where institutions are more dependent on direct public funding (Bennetot Pruvot, Estermann and Popkhadze, 2025[13]).
Approaches to public funding of research impact the design of core funding to HEIs
Differing policy choices on tuition fees are arguably the most fundamental – and visible – way in which higher education funding systems vary across countries. However, systems also vary substantially in the way in which they provide public funding for research in higher education institutions. A first key difference lies in whether funding models provide public direct-grant funding to institutions explicitly for research, rather than channelling dedicated research funding through competitive or project-based allocation mechanisms. A second difference, among those systems that do allocate direct-grant funding to institutions for research, is whether these allocations are integrated into a single, main block grant or are paid as a separate institutional research grant.
The 2020 OECD Higher Education Policy Survey (HEPS) found that four of the 27 responding jurisdictions (Canada, Chile, Portugal and Slovenia) at the time provided no explicit direct-grant funding for research to HEIs, with public funding for research allocated through competitive means (Golden, Troy and Weko, 2021[2])2. States in the United States, which did not participate in the 2020 HEPS, also follow this pattern, providing direct-grant funding to public universities for instruction, without explicit allocations for research (OECD, 2020[7]). In practice, across all these systems, core public funding is used to contribute to the salaries of academics who undertake research, so the absence of explicit direct-grant funding for research does not imply core public funding to HEIs does not contribute to the costs of research activity.
The 23 other systems responding to the 2020 HEPS provide some form of direct-grant funding to institutions for research, with the precise allocation modalities varying between systems. In Finland, for example, the core research grant is integrated into the main block grant to institutions. An explicit share of available public funding for direct grants to universities (34%) and universities of applied sciences (19%) is designated for research and development, with funds allocated to individual institutions based on research metrics, combined with other direct-grant funding allocated for teaching and strategic investments, and paid to institutions as a single block grant each year (OECD, 2023[3]). The Flemish Community of Belgium also incorporates funding for research into the core operating grant for its five universities but also complements this with a separate institutional research grant (the Special Research Funds – BOF) allocated based on a specific formula containing a series of bibliometric and other research metrics. Flemish university colleges receive a smaller funding allocation for practice-oriented research integrated into their core operating grant (OECD, 2021[5]).
In Denmark, universities receive a separate direct grant for research and development, distinct from the teaching grant, with non-university institutions receiving a smaller allocation for practice-oriented research (OECD, 2021[14]). The main block-grant funding for research delivered by Research England - quality-related research (QR) funding, which accounted for around 5% of university revenue in 2022/23 (UKRI, 2024[15]) – is allocated to universities based on universities’ results in the Research Excellence Framework (REF), numbers of postgraduate research students and third-party research funding, with additional recurrent funding provided for capital investment and innovation activities (Research England, 2022[16]).
In all the systems with direct-grant funding for research, higher education institutions have autonomy to use the research grants largely as they see fit, provided funds are used on research or research-related activities. In general, such funds can be used to pay for research activities initiated internally within institutions by individual academics or research groups and to cover excess costs incurred in undertaking externally funded research, where the corresponding grant or contract does not cover the full-economic costs of activities.
In systems with comparatively high levels of funding for higher education research, such as Denmark, core research grants and competitive research funding appear to go a long way towards funding the full costs of research activity (OECD, 2021[5]). In some other OECD higher education systems, comparatively limited – or a total absence of – core funding for research, combined with funding rates by third-party research funders that do not cover the full-economic costs of research activities, lead to cross-subsidy of research with revenue from other income streams. In the United Kingdom, for example, income streams intended explicitly for research cover around 69% of the costs related to research identified by the Transparent Approach to Costing (TRAC) accounting system, with the remaining costs cross-subsidised primarily by international student fees (UKRI, 2024[15]). While research contributes to the quality of the learning environment for students, high and sustained rates of cross-subsidy from student fees may become difficult to justify purely on these grounds and raise legitimate questions about the place of research in higher education and who should pay for it.
In broad terms, policy makers need to make three main decisions in designing and implementing core funding systems for higher education institutions:
1. What core funding is intended to finance.
2. How much money should be allocated through core funding, and
3. How the available funds should be allocated to individual institutions.
The goals of direct-grant funding to HEIs may be reflected in distinct funding allocations
Core public funding for public and government-dependent higher education institutions invariably aims to support teaching and day-to-day operational expenditure. As outlined above, although universities are expected to undertake research in all OECD systems and non-university institutions are often expected to engage in practice-oriented research and development, not all funding systems make explicit direct-grant allocations for research. The extent to which policies intend core funding to cover the costs of institutional activity – overall and in specific areas - depends largely on the level of student tuition fees and expectations about the ability of institutions to raise funds for third-party sources.
The objectives of core funding are reflected in the design of core-funding models, although core-funding models with similar objectives can be structured differently for a range of historical reasons.
Note: (1) In Ireland, as part of the Recurrent Grant Allocation Model (RGAM),10% of the allocation to each institution is deducted (top-sliced) and redistributed among universities and technological universities based on research-related metrics, but there is no distinct core-funding allocation for research.
(2) In Portugal, public research units to which academics are affiliated, and which are established as separate legal entities, receive core funding from the Foundation for Science and Technology (FCT).
Table 4.2 highlights that, among the higher education systems reviewed in system-specific analyses as part of the OECD Resourcing Higher Education Project, all provide core funding for teaching. Ireland operates a mix model of core funding through which the state pays a proportion of a regulated tuition fee for each eligible student and additionally allocates a budget envelope for teaching grants through the Recurrent Grant Allocation Model (RGAM), which is itself driven primarily by student numbers. This situation reflects a decision by the Irish government in the mid-1990s to cover a substantial proportion of the cost of tuition fees for undergraduate students in Irish higher education.
Finland, the Flemish Community of Belgium and Lithuania integrate explicit allocations for research into their core block grants for institutions, while Denmark and the Flemish Community provide separate direct grants for research to universities – in addition to the research component of the core grant in the case of the Flemish Community. While core research funding for non-university institutions is limited to under 5% of total core public funding in Denmark and the Flemish Community, the research allocation to universities of applied sciences in Finland represents almost one-fifth (19%) of core funding (OECD, 2023[3]).
Finland’s core-funding models for universities and universities of applied sciences are also distinctive in including an explicit allocation for strategic development as part of the integrated block grants to institutions. For universities, this third pillar of the funding model – alongside allocations for teaching and research – accounts for 15% of total core funding, while the equivalent proportion for universities of applied science is 5% (OECD, 2023[3]).
The Flemish Community of Belgium and Ireland provide recurrent capital grants to higher education institutions notionally to cover costs related to the maintenance of existing physical infrastructure and new construction. However, evidence from the Flemish Community of Belgium and elsewhere has shown that these grants – in addition to being a frequent target in government spending reductions – are typically insufficient to cover real needs for capital investment, meaning that institutions need to cover such expenses from other funding sources in addition. Furthermore, it is difficult for government to establish the level of capital investment needed at system level, when individual investment plans and decisions are made locally in individual HEIs. This explains why many systems – including the others shown in Table 4.2 – expect institutions to cover capital investment from their core income from public and private sources and, in some cases, dedicated, time-limited and project-based capital investment programmes.
The size of the budget envelope for HEIs is sometimes informed by the allocation model
The level of public resources available to fund higher education institutions ultimately depends on a range of factors, of which the three most important are arguably the state of the economy and related tax revenues, the extent of competing pressures for public funds and political choices about investment in higher education and research. Historically, governments in most OECD countries have sought to ensure public funding – alone or in combination with regulated tuition fees – covers all or a majority of the core operating expenses of public and government-dependent higher education institutions. They have generally done this by considering the existing – historical – costs of running higher education institutions or current and projected student numbers (or a combination of these) when determining the available budget envelope for higher education. At specific moments over time, many governments may also make commitments to expand funding to higher education institutions, perhaps to support increased enrolment in periods of expansion or to invest in research as part of wider efforts to increase national research and innovation capacity.
In systems where public direct-grant funding to higher education institutions is based on incremental, historical allocations, the budget envelope for the previous year(s) is the only reference point for setting the budget for the next financial year. In higher education systems that use formulas based on enrolment or graduate numbers to allocate a least a proportion of funds to higher education institutions (see below), budget proposals for approval by government and parliament ideally need to consider projected enrolment or graduation rates. Broadly, there are three main models of managing budgetary planning in systems with student-related funding formulas:
1. Systems can theoretically fix guaranteed levels of per-student unit payments in advance – i.e. a specific sum of money for each student or graduate of a particular category – and allow open student recruitment by institutions. The only notable recent example of such a system in an OECD jurisdiction with institutional block grants was Australia’s demand-driven system for undergraduate enrolment, used from 2012 to 2017. Such a system requires careful projections of student enrolment and latitude in the total budget envelope to cope with demand for funding generated by student enrolment. Despite the success of the system in increasing enrolment, the budgetary uncertainty and expanding costs led the Australian government to re-introduce student-enrolment limits in 2017 (Norton, 2020[17]). England (United Kingdom) also abolished student enrolment limits for most fields of study in 2015. As undergraduate study for domestic students is funded largely though regulated tuition fees for which students can receive income-contingent loans, the implications for public funding of a demand-driven system are less direct than in a system that provides direct grants to institutions for each student. The English authorities nevertheless need to make assessments of student demand to calculate the cost of the public loan system for the exchequer, particularly as recent analysis shows that this creates a net loss for government even when loans are fully repaid (Ogdenn, Ridpath and Waltmannn, 2024[18]).
1. Some systems fix guaranteed per-student unit payments in advance, but place limits on recruitment for funded students. This is the system used in Scotland, for example. In such cases, it is possible to work with closed budget envelopes and adjust student recruitment caps depending on the level of resource available. The Danish funding model is somewhere between this system and a demand-driven system (type 1), as it uses pre-determined unit payments per student in its formula, which are calculated based on predicted enrolment, combined with enrolment limits in some – expensive – fields such as medicine (OECD, 2021[14]).
2. Other systems combine work with a closed or nearly closed budget envelope but allow per-student unit payments to vary depending on the available budget envelope and actual enrolment or graduation rates. This purely distributive model is used in the Flemish Community of Belgium, Ireland (for the RGAM model) and in Portugal’s new funding formula. Such systems may, as in both the Flemish Community and the new Portuguese formula, seek to achieve broad stability in per-student funding over time by basing allocations on historical enrolment and credit acquisition data from multiple years, but do not construct their budgets using guaranteed unit costs per-student. In such systems, if the available budget envelope does not increase in line with enrolment or graduation, the level of funding per student will ultimately fall.
Note: (1) The Flemish Community of Belgium uses a unique mechanism in its funding model (the “click” system) that (theoretically) automatically triggers an increase or decrease of up to 2% in the total budget envelope for the core public funding allocation to HEIs in a given financial year when enrolment in a given sub-sector (universities, university colleges, schools of arts) increases or decreases by more than 2% between two reference periods. In practice, governments do not always apply this principle (OECD, 2021[5])
(2) In Ireland, tuition-fee payments by public authorities (“free fees”) use fixed unit costs. The Recurrent Grant Allocation Model (RGAM) is used to distribute public funding left after the cost of the “free fees” has been deducted.
The results of the 2020 OECD Higher Education Policy Survey and available literature show little evidence of governments systematically using underlying information about the cost of provision in particular subject fields to determine the level of unit payments per student (Golden, Troy and Weko, 2021[2]). Evidence from the TRAC accounting system in the UK has informed the level of residual teaching grants for medical, science and engineering studies in England and for teaching grants in other UK nations (OECD, 2022[4]). In most other systems, detailed cost data are not available. As higher education institutions are free to allocate block-grant funding internally as they please, even in UK nations, policy only seeks to achieve a broad alignment between payments and costs in different subject areas.
Three main variables inform the design of funding allocation models
When it comes to the mechanisms for distributing available public resources to individual higher education institutions, policy makers need to consider three main design variables:
1. The proportion of funding allocated to institutions based on previous (historical) allocations – or as an incremental fixed sum – and the proportion allocated on a variable basis using some form of formula.
2. Where a formula is used, which indicators will be used in the calculation and whether the system will work with fixed unit payments for specific inputs or outputs or purely as a “distributive” tool to share out the total available budget.
3. Whether and which weightings will be built into the formula to provide differentiated amounts for different inputs or outputs, such as different levels of funding for students in different fields or levels of study and for students with different levels of need.
Where used, formula funding is often combined with historical funding
In recent decades, an increasing number of OECD jurisdictions have adopted allocation models which award at least a proportion of core funding to higher education institutions based on some form of variable-driven formula. The 2020 Higher Education Policy Survey found that 24 of the 27 responding OECD jurisdictions used formulas to allocate at least a proportion of core public funding to higher education institutions (Golden, Troy and Weko, 2021[2]). Eight of the responding jurisdictions, including the Flemish Community of Belgium, Ireland and Lithuania, reported that all core funding for public and government-dependent institutions in their systems was allocated based on a formula. Ireland and Lithuania both use two types of variable allocation method: respectively fee payments by the state in Ireland and student vouchers in Lithuania, and, in both cases, distributive formulas using different variables. 12 other jurisdictions responding to the survey, including Denmark, combine formula funding and historical (fixed) funding, while Finland combines formula funding for most of the allocation with negotiations every four years related to the allocation of specific funds for strategic development.
Table 4.4 summarises the methods used to allocate direct-grant funding to higher educations in the six case-study jurisdictions analysed in depth in the OECD Resourcing Higher Education Project, including the new funding model introduced in 2024 in Portugal (see Box 4.1). In Denmark, since a new funding model was introduced in 2019, universities and non-university institutions receive a fixed payment, which was originally set at a level equivalent to 25% of their core funding for education in 2017. This “basic grant” (grundtilskud) is reviewed every four years, with continued payment of 10% of the basic grant (i.e. 2.5% of total core funding for education) in the next four-year period dependent on individual institutions’ performance against two indicators – a measure of graduate employment and the results of a student feedback survey (OECD, 2021[14]). This means that institutions that perform poorly against these indicators receive a lower basic grant in the following four-year period. In Portugal, the new funding model, when fully operational, will also include a fixed component equivalent to 20% of institutions’ average allocation over the previous three years (Government of Portugal, 2024[19]).
Note: (1) The Flemish Community of Belgium also uses a base component (sokkel) and a variable component in its allocation model, but the base component is also driven by student numbers, so is not strictly a “fixed” component.
(2) The education component of funding to universities and colleges in Lithuania is linked primarily to student enrolment (an input) but direct-grant funding for research is awarded taking into the results of the period research assessment exercise (reflecting research outputs).
(3) Portugal introduced a new funding model in 2024, with a transition period (see below). The intention is to include performance-related indicators (outputs and/or outcomes) from 2027.
Table 4.4 also illustrates the main types of indicators used in the variable, formula-driven components of the direct-grant allocation models for education and research in these six systems. Four of the six systems use input indicators related to student enrolment, four use output indicators related primarily to credit and degree acquisition and only Denmark and Finland incorporate outcome indicators, related to graduate employment outcomes and student feedback. As discussed below, Denmark and Finland stand out among these six systems – and among OECD higher education systems more generally – because of the output and outcome orientation of their core-funding models (Golden, Troy and Weko, 2021[2]; OECD, 2023[3]).
The appropriate balance between fixed and variable funding has been a subject of discussion in funding policy design in several OECD jurisdictions (de Zwart et al., 2021[20]). In broad terms, variable funding – usually based on student enrolment, progression or graduation – makes it possible to link funding to real levels of activity and outputs in a transparent and equitable manner, which is widely acknowledged as a desirable characteristic for sound allocation models. Variable funding linked to student or graduate numbers, even when based on data for past years, is also more responsive to increases in enrolment, allowing funding to keep pace with changes in expanding systems and institutions. As discussed below, it also permits funding levels to be adjusted between fields of study or student profiles meaning that policy can take better account of differential costs and direct resources more precisely to fields or populations – engineering, arts or under-represented groups, for example – that are considered important for policy objectives.
Nevertheless, variable, student-linked funding can have downsides. It creates incentives for institutions to maximise enrolment or graduation rates, which can potentially be to the detriment of quality standards if other safeguards, such as institutional and system-level quality assurance, are not effective. In the increasing number of OECD systems where the population of traditional student age is declining, this incentive to maximise enrolment typically becomes even stronger, increasing competition between institutions at a time when policy makers may seek to promote greater cooperation and consolidation in public higher education (OECD, 2022[4]). More generally, variable funding can create greater financial uncertainty and instability for higher education institutions when student numbers decline. As institutions have high fixed costs for staff and infrastructure, a rapid decline in student-related funding can be challenging to manage.
To reduce the instability of variable funding – particularly where student numbers are declining in all or parts of the system – policy makers tend to adopt three main mechanisms. The first is to use average data values for the last two or three years in the calculation of budget allocation for the next financial year. This is the system used in the Flemish Community of Belgium and in the new model in Portugal, for example (OECD, 2021[5]; Government of Portugal, 2024[19]). Secondly, use of historical data may be combined with explicit limits in percentage terms on the year-on-year fluctuations in funding allocations for individual institutions – a technique also applied in the Flemish system. Finally, policy makers may decide to increase the stability of the funding allocations provided by their model by combining variable allocations with a historical or fixed proportion of funding, as described above. As noted, both Denmark and Portugal apply this method, with Denmark having made an explicit choice to introduce a fixed funding element (the “basic grant”) into its allocation model in 2019 after the previous system based largely on student enrolment (the taximeter system) was judged to lead to excessive funding instability for institutions (OECD, 2021[14]).
In Portugal, legislation from 2003 calls for the public funding made available for higher education institutions in each annual state budget to be allocated using a formula based on objective criteria. Allocation formulas were designed and adopted in 2003 and again in 2006. However, in the wake of the financial crisis of 2008, the 2006 formula was effectively suspended, with funds allocated to each institution based on historical allocations and cross-sectoral agreements between the government and institutions from 2009 to 2023. This situation led to core funding to institutions becoming increasingly misaligned with actual levels of enrolment (OECD, 2022[1]).
Considering the recommendations of an OECD report published in 2022 as part of the Resourcing Higher Education Project, a new formula system was designed and adopted in March 2024 in time for the 2024 annual budget exercise. The new system is being introduced progressively over a period of four years up to 2027, during which 70% of increases in core funding will be allocated based on a new formula and 30% allocated to HEIs whose historical allocation was lower than it should be, based on real enrolment levels. An increase in the overall budget envelope for higher education meant that this has been possible without cutting the core-funding allocation to any institution.
The new funding model will ultimately comprise three components. 20% of the budget envelope will be allocated as a “stabilisation component”, shared among institutions based on their average share of total funds in the previous three years. An activity component, accounting for the majority of funding will be allocated based on the number of enrolled students, weighted by subject field. It is intended that a final, performance component, allocated based on output and outcome indicators, will be developed by an expert group during the initial transition period and implemented from 2028 onwards.
Other changes implemented in the new model compared to the 2006 model include a reduction in the number of subject-field weightings for both universities and polytechnic universities and explicit provision for the formula to fund short-cycle Higher Professional Technical Courses (CTeSP) in future, when current funding arrangements using European Union resources come to an end.
Source: Government of Portugal (2024[19]) Portaria n.º 101/2024/1 Procede à aprovação da fórmula de cálculo do orçamento de referência das instituições de ensino superior (Ordinance 101/2024/1 approving the formula for calculating the reference budget for higher education institutions), https://diariodarepublica.pt/dr/detalhe/portaria/101-2024-855215460 (accessed on 4 August 2025).
Most systems base formulas on simple student-related variables
Among OECD jurisdictions that use formula-based approaches for allocating core funding to HEIs, a majority link all or most of this funding to student-related variables. The most common variables are enrolment (an input variable), the number of degrees awarded, or the number of study credits successfully passed (output variables). As summarised in Table 4.5, systems such as Ireland, Lithuania and Portugal allocate all or most of their core funding to institutions for education based on student enrolment. The Flemish Community of Belgium allocates around half of its core funding for education to university colleges and one-third of its core funding for education to universities based on the number of credits for which students enrol in a system designed to permit part-time study and ensure institutions are compensated for this (OECD, 2021[5]).
Indicators used in the allocation model for the education component of the public direct grant to HEIs
Note: This is an updated table based on OECD (2023[3]) The future of Finland’s funding model for higher education institutions, https://doi.org/10.1787/3d256b59-en. It incorporates information on the new models used in Finland and Portugal.
(1) Finland uses coefficients (weightings) to reward timely degree completion, applying a coefficient of 1.8 for degrees completed in the theoretical duration and 1.2 for degrees completed no later than 12 months after the targeted time frame. The system also applies a coefficient of 0.5 applied to degrees completed by individuals who already have a degree at the same level.
(2) Lithuania’s core-funding model combines a payment per student (voucher) and institutional grants driven primarily by the results of the periodic research assessment exercise (not represented in this table).
Three of the six systems shown in Table 4.5 use output indicators in the core funding model for education. The Flemish Community links the remaining core funding for education to HEIs to credits and degrees successfully completed. Denmark and Finland link a majority of core funding to student outputs, with Denmark using credits successfully passed as it driving indicator (the taximeter system) and Finland linking two-thirds of the education component of core funding for universities and universities of applied sciences to degrees awarded. and gained). Denmark attaches around 4% of core funding for education in HEIs to a measure of study duration to incentivise timely progression and completion of studies. Rather than using a separate indicator in the formula, Finland also uses multipliers (coefficients) to provide higher funding rates for degrees completed within the theoretical programme duration (1.8) or within 12 months of the theoretical duration (1.3) (Ministry of Education and Culture, 2025[21]).
Of the six systems shown in Table 4.5, only Denmark and Finland incorporate outcome indicators into their funding models for education in HEIs and these two systems are among very few in the OECD to do so (Golden, Troy and Weko, 2021[2]). Both Denmark and Finland tie between 8% and 10% of core funding for education to the shares of graduates in employment, while Finland goes further to include the share of graduates in graduate-level jobs in the calculation, with data collected through graduate surveys. Both countries also link some education funding to the results of student feedback surveys. The proportion of funding linked to student feedback varies from around 6% of the core education budget for universities in Finland to less than 2% in Denmark, where student feedback is taken into account every four years in evaluating the size of future fixed grant allocations (the “basic grant”).
Five of the six systems analysed here use formulas to distribute the available funding envelope, allowing unit payments per input or output to vary over time in function of available funds and enrolment and graduation levels (see Table 4.3). Denmark manages to implement a system combining a formula and fixed unit payments (with three cost categories) per 60 study credits passed with a nominally “open” budget envelope and without a universal system of study-place regulate and on. It does so by using detailed projections of student numbers to calculate the annual budget envelope, alongside student caps in medical programmes and restrictions of the number of study places in fields from which graduates have persistently higher-than-average levels of unemployment (OECD, 2021[14]).
Subject weightings are used to align payments more closely to costs – other weightings are rarer.
Alongside the selection of variables to be used in funding allocation formulas comes the question of whether to weight – in other words, multiply – the variables to provide higher or lower levels of funding for specific inputs or outputs. The most common weighting systems used in formula-driven higher education funding allocation models adjust funding allocations for students, credits or degrees in different subject fields, in an attempt to recognise differences in the cost of delivering programmes in these fields.
As shown in Table 4.6, the multipliers used for different fields of study are broadly similar in the OECD higher education systems analysed in depth in the OECD Resourcing Higher Education Project. The systems in Finland and Denmark systems use a smaller span of values than is the case in the other systems. These weighting systems mirror to some extent the cost differentials revealed in analyses of the full-economic costs of education in different fields, discussed in Chapter 2. In the case of Denmark, the low value of the weighting for medical studies is partly explained by the way funding of medical studies and university hospitals is organised in the country. The new model in Portugal uses five cost categories for polytechnic universities and four for universities, while Denmark and Finland use three categories across the whole system.
By linking payments to notional costs – even if very broadly - the use of subject-field weightings creates an incentive for institutions to provide study programmes in more expensive fields. This is particularly significant for medical disciplines, laboratory-based subjects, the natural sciences and engineering and in the performing and visual arts, where a combination of higher investment in equipment, greater use of floor space per student and necessarily lower staff-to-student ratios make provision substantially more expensive than for classroom-based subjects in areas such as maths, business, the humanities or social sciences. Adequately compensating institutions for providing study places in the Science, Technology, Engineering and Maths (STEM) is an important component in strategies to achieve an adequate supply of skills in these areas.
Weighting factors for undergraduate students used in funding allocation formula in selected OECD jurisdictions
Notes: (1) Since 2017, university programmes in medicine in the Flemish Community have been funded through a ring-fenced budget with variable component of the teaching grant.
(2) Lithuania uses a voucher system whereby institutions receive a standard allocation for each student that enrols.
(2) For Portugal, the weightings have been recalibrated to start at 1 by dividing the weightings by 1.5 (the lowest weighting in the relevant ordinance).
While many systems that use student-related formula for allocate core funding to higher education institutions use subject-field weightings, a smaller number of systems use weightings to create additional incentives for institutions. From the systems analysed in depth for the resourcing higher education project, both Finland and Ireland use weightings for this purpose. Finland not only applies multipliers of 1.8 and 1.3 for degrees completed, respectively, within the theoretical duration and within the theoretical duration plus 12 months but also applies a multiplier of 0.5 for degrees awarded to individuals who already hold a degree at this level. This latter mechanism was adopted to counter a widespread tendency in Finland for people who already hold a degree to pursue additional degrees, at a time when overall tertiary-education attainment rates are stagnating. Ireland uses a multiplier of 1.33 in its Recurrent Grant Allocation Model for each student from a designated “access group”, defined as those from under-represented socio-economic groups; first-time mature students; disabled students and those from a traveller background (OECD, 2022[4]).
The potential of financial incentives to influence the behaviour of higher education institutions and academics has motivated governments in many OECD countries to use public funding to steer the direction and increase the performance of higher education systems (OECD, 2020[6]). In broad terms, public authorities have used three main mechanisms to encourage or coerce higher education institutions to deliver outputs and outcomes that have been prioritised in public policy:
1. The inclusion of output and outcome variables in the design of allocation formulas for core institutional funding for teaching and research, discussed in the previous section.
2. The institution of performance agreements between public authorities and individual higher education institutions, sometimes with funding attached.
3. Directing an increasing share of public research funding through competitive funding mechanisms rather than through direct grants to institutions.
The first and second of these two mechanisms are typically classed together as “performance-based funding”, even though, as discussed below, performance agreements are in all cases more than a funding mechanism and, in many cases, not automatically linked to payments or financial consequences for institutions.
Evidence on the impact of output and outcome-based formulas is inconclusive
Among the 27 OECD jurisdictions responding to the 2020 OECD Higher Education Policy Survey, 13 reported using bibliometric indicators in the allocation of core funding for research to HEIs and 13 that their allocation formulas for teaching were at least partly driven by degrees completed or credits successively passed (Golden, Troy and Weko, 2021[2]). A 2023 review of the higher education funding models in all 27 European Union member states found that more than half of systems (15) based core-funding allocations for teaching on graduation rates, degrees awarded or credits passed, while the most common output indicators related to research activity were doctoral degrees awarded and publications, in respectively 11 and seven EU systems (Jongbloed et al., 2023[22]). A recent review of higher education in Canada highlighted that two of the ten provinces, Ontario and Alberta, use performance funding based on outputs (Usher and Balfour, 2024[23]), while in the United States, 10 of the 50 states were found to use performance-based funding to allocate 10% of more of state appropriations to public HEIs in the most recent survey by the State Higher Education Executive Officers Association (SHEEO, 2025[24]).
Although an increasing number of OECD member countries have introduced output and outcome-related funding models, robust research into the effects of such systems has been limited. State governments in the United States were among the first in the OECD to embrace output-based funding, initially in the 1980s and 1990s, and subsequently in another wave of reforms in the 2000s. As a result of this early experimentation, as well as the capacity of the US scientific community, most available studies into the effects of performance funding are from the United States. A significant number of these studies deployed robust quasi-experimental research designs. An analysis of the results of these studies (see Box 4.2) found only limited evidence of positive effects from performance-based funding systems on target variables, such as student progression and completion rates. The analysis also found widespread examples of unintended and undesirable consequences (Ortagus et al., 2020[25]).
Fewer studies have investigated the impact of output and outcome funding in European higher education systems, although the evidence that does exist suggests a similarly limited impact. A study in Denmark found the completion-oriented “taximeter” system to have had a mixed influence on completion rates in Danish higher education institutions. At the Copenhagen Business School, for example, the implementation of the taximeter was followed by an increase in completion rates at the bachelor’s level, but a reduction in rates at the master’s level (Claeys-Kulik and Estermann, 2015[26]). Likewise, an evaluation of different performance-based funding formulas used in German federal states between 2000 and 2008 found that their introduction was rarely followed by significant changes in the outputs they sought to influence, casting doubt on their efficacy, particularly given the cost of their implementation (Dohmen, 2016[27]).
In the United States, 41 of the 50 states have linked state appropriations for higher education institutions to outputs or outcomes in the last 20 years. These systems have typically used credit hours earned, degrees awarded and attainment among historically under-represented groups as variables. The proportion of state appropriations now tied to output and outcome indicators varies from 3% in Arkansas to 100% in Ohio. State appropriations typically account for less than 50% of total income in public universities in US states, although can account for a higher proportion of revenue in public community colleges, which generally charge substantially lower fees (OECD, 2020[7]).
In meta-analysis, Ortagus et al. examine evidence from research studies with strong causal inference designs examining the effects of these performance-based funding (PBF) systems in the United States. The evidence review focuses on 23 studies with quasi-experimental designs and a further 15 studies using robust difference-in-difference techniques.
They find that the introduction of PBF systems is associated with no or only minor positive effects on retention and graduation (completion). Modest positive effects have been established for a limited number of longstanding PBF programmes and for elements of PBF models that provide bonuses for degrees achieved in specific fields (notably targeting additional institutional funds to boost uptake of STEM subjects). Moreover, there is some evidence that institutions took steps to improve academic and student support services in response to PBF systems focused on progression and completion. However, the evidence review also found that the introduction of PBF systems frequently has unintended consequences:
Selective institutions tend to become more selective, disadvantaging under-represented groups, who face the greatest challenges in accessing and completing higher education.
PBF systems tend to exacerbate funding disparities between institutions, with lower-resourced institutions losing out on funding that could potentially be used to improve performance. This is a particular concern for Historically Black Colleges and Universities (HBCU).
The authors conclude that it is challenging, in higher education, to apply and implement performance-based funding systems that focus on a narrow set of outputs (or a single output) given the wide range of desirable outputs generated by universities and colleges. Furthermore, they question whether the principal-agent approach inherent in most PBF systems is an appropriate means to regulate relations between government and autonomous higher education institutions, particularly given a more general shift away from top-down accountability mechanisms in education in recent years.
Source: Ortagus et al. (2020[25]) Performance-Based Funding in American Higher Education: A Systematic Synthesis of the Intended and Unintended Consequences, http://dx.doi.org/10.3102/0162373720953128.
A more recent review of the reported effects of performance-based funding in European higher education systems also highlighted that available meta-reviews that synthesise the existing research on the impact of performance-based funding have often had difficulty drawing firm conclusions (Jongbloed et al., 2023[22]). Based on qualitative evidence collected on eight European funding systems, notably through interviews and expert meetings, Jongbloed et al. conclude that performance-based funding system can incentivise the performance-orientation of HEIs and support strategic dialogue between HEIs and their funding authorities. However, the authors stop short of identifying clear effects on target variables from performance funding and highlight the risk of unintended consequences. Among these are the tendency of bibliometric indicators to modify the publication behaviour of researchers, increased competition between HEIs and the potential for one-size-fits all indicator-based models to unintentionally disadvantage certain types of institution due to their subject specialisation, size or location (Jongbloed et al., 2023[22]).
Some international evidence shows that students’ initial momentum in their studies has a determining impact on their chances of success. For example, students in Flemish higher education who enrol for higher numbers of credits in the early stages of their higher education career are found to have higher chances of passing credits and obtaining qualifications than students who take lower numbers of credits (Werkgroep "Studievoortgangbewaking", 2014[28]). This finding is supported by a body of work in the United States focusing on study “momentum”, which also finds that students with higher study intensity at the start of their higher education career are significantly more likely to complete a degree (Attewell, Heil and Reisel, 2012[29]; Clovis and Chang, 2019[30]). A question for policy makers is whether to encourage HEIs to focus on supporting students to progress and complete their studies, particularly in the initial stages of the academic pathways, through the core funding allocation model or through other mechanisms.
Institutional performance agreements offer a promising option in smaller systems
Alongside – or instead of – performance-based core funding allocation models, an increasing number of OECD jurisdictions have implemented systems of various forms of institutional performance agreements in recent years. They involve public authorities and individual institutions agreeing on strategic goals and targets that the institution should deliver, typically over a three to six-year period. These agreements generally outline institutions’ specific profiles and missions, as well as establishing objectives and targets. In some cases, public funding is provided explicitly to support delivery of the objectives set out in the agreements, while in other systems the agreements form a complementary steering tool alongside the core funding model. Although some systems have experimented with making funding linked to performance agreements conditional on achievement of objectives in the agreements, this has been found to be complex to implement and has in at least two known cases been discontinued.
In Europe, Denmark was one of the first higher education institutions to introduce performance agreements as a profiling and steering tool and retains “strategic framework contracts”, with achievement of goals linked to a small proportion of total funding, in its current governance and funding model (OECD, 2021[14]). Finland introduced performance agreements in the university sector in 1995. Its Nordic neighbour, Norway, implemented a new system of “development agreements” (utviklingsavtalene) as the primary steering and performance-related component in its funding model for the period 2023-26 (Ministry of Education and Research, 2023[31]).
As illustrated in Table 4.7, in the “quality agreement” system used in the Netherlands until 2024, a proportion of public funding allocated to institutions was theoretically at risk if institutions did not meet the goals in their institutional agreements at the end of the six-year implementation period. In practice, however, this money was not withheld. Ireland also experimented with making funding conditional on achievement of the goals in its performance agreements, but there to, funding was never withheld, despite remediation plans having been agreed in a limited number of cases. In the more recent iterations of the performance agreement system in Ireland, the Higher Education Authority introduced additional “bonus” payments awarded on a competitive basis to institutions that demonstrate, through case studies, that they have achieved results in relation to a goal in their performance agreement (HEA, 2023[32]).
The evidence on the effects of institutional agreements is broadly positive. A study in Germany by Dohmen (2016[27]) found that performance agreements (Zielvereinbarungen) were associated with more positive effects. Notwithstanding the challenges of proving causality, effects reported by institutions and stakeholders included increases in third-party funding and improved graduation rates in universities of applied sciences. Perhaps more significantly, the introduction of performance agreements in German federal states was found to have led to an increased focus on results and more strategic, evidence-based decision-making in higher education institutions.
Note: (1) the Dutch system of quality agreements has been discontinued.
Another study, in North-Rhine Westphalia (Germany), reported in de Boer et al. (2015[33]), also found that performance agreements provided a basis for better internal decision-making in higher education institutions. A similar pattern was found in Ireland in relation to the first system of institutional compacts, which appears to have had limited direct effect on the behaviour of institutional staff and observed outputs, but to have improved institutional strategy and dialogue between the institutions and public authorities (O Shea and O Hara, 2020[34]).
The systematic evaluation of the first generation of Dutch performance agreements also concluded that the agreements had generated positive effects on the organisation and strategic focus of higher education institutions (Reviewcommissie Hoger Onderwijs en Onderzoek, 2017[35]). In particular, the review commission responsible for the evaluation argued that the process of developing, negotiating and monitoring the agreements had helped higher education institutions to refine their institutional strategies, tailor their educational offerings and, in universities, sharpen their research profiles. The evaluation also noted that pass rates and on-time completion rates in universities increased during the implementation period for the performance agreements, but that on-time completion rates in bachelor’s programmes in universities of applied sciences actually decreased (from 70% to 67% overall), particularly in large institutions. The review team acknowledged that the inherently challenging task of establishing causal relationships between the performance agreement system and outputs – such as pass rates – was made even harder by an accumulation of other policy changes that were implemented in parallel.
The Dutch review commission concluded that a new generation of agreements should avoid the strong focus on centrally determined quantitative indicators and adopt a more qualitative approach, albeit with measurable indicators of progress at institutional level:
The committee recognises the limitations of working with indicators: not everything that is valuable can be measured. It is therefore important that in the assessment of and accountability for the agreements there is room for the context and the underlying story of the institution. Performance agreements offer the possibility of a strategic dialogue with the institution. The risk of strategic behaviour and perverse effects is greater if performance indicators are part of a mechanically applied formula in the funding model. The committee recognises the importance of qualitative goals, but is of the opinion that there must also be demonstrable efforts and results.[OECD translation] (Reviewcommissie Hoger Onderwijs en Onderzoek, 2017, p. 73[35])
The balance of evidence internationally therefore suggests that performance agreements can have positive effects on system governance and institutional strategy even where their impact on core output variables may be limited. It seems likely that institutional agreements function most effectively as accountability, transparency and strategic planning tools and that these should be their primary objective. To function as accountability tools, to take up the phrase of the Dutch review commission, there must be “demonstrable efforts and results”, but the use of one-size-fits-all indicators is ineffective as it masks complex realities. Using tailored institutional agreements with a limited number of targets that can be assessed through qualitative and quantitative methods could be a promising approach. The performance agreement systems used in Denmark, Finland and Ireland already align with this model and it will be important to continue to monitor their effects as they enter and conclude new agreement cycles.
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← 1. England (United Kingdom) is arguably an exception to this given the low share of institutional revenue that comes from direct-grant funding.
← 2. Slovenia adopted a new Scientific Research and Innovation Activity Act in 2022, which introduced a new model for public funding of scientific research, which provides core public funding as direct grants to research performing organisations, including higher education institutions.