OCDE - Better skills data for smarter financing of education and training

Policy Paper Better skills data for smarter financing of education and training2 

BETTER SKILLS DATA FOR SMARTER FINANCING OF EDUCATION AND TRAINING © OECD 2026

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OECD 2026

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Any dispute arising under this licence shall be settled by arbitration in accordance with the Permanent Court of Arbitration (PCA) Arbitratio n Rules 2012. The seat of arbitration shall be Paris (France). The number of arbitrators shall be one. Disclaimers 3

BETTER SKILLS DATA FOR SMARTER FINANCING OF EDUCATION AND TRAINING © OECD 2026

Robust and integrated skills data are essential for effective policymaking, particularly in improving the allocation of public resources across education and training. Yet many governments face significant information gaps. Fragmented or underdeveloped data systems constrain the ability to assess programme effectiveness, prioritise spending, and target investments towards high-impact interventions. This paper examines how stronger skills data can support more evidence-based financing decisions. It introduces a framework grouping the main obstacles into four categories: institutional, governance and financin g barriers; human-capital and analytical-capacity gaps; legal and regulatory constraints; and technical and interoperability challenges. By analysing these barriers and highlighting promising country practices, the paper provides a basis for identifying priorities and guiding reforms to strengthen skills data systems and improve investment in skills. Contact Andrew BELL ( Andrew.BELL@oecd.org) Ricardo ESPINOZA ( Ricardo.ESPINOZA@oecd.org) Abstract4 

BETTER SKILLS DATA FOR SMARTER FINANCING OF EDUCATION AND TRAINING © OECD 2026

Table of contents Disclaimers 2

Abstract 3 Executive summary 6 1 Introduction 7 1.1. Why better data are essential 7 1.2. Purpose and scope 8 2 Current data gaps and their consequences 10 2.1. Fragmentation of data systems 10 2.2. Lack of longitudinal linkages 11 2.3. Coverage gaps across the skills system 12 2.4. Inconsistent definitions and quality issues 13 2.5. Timeliness and accessibility 13

2.1. Fragmentation of data systems 10 2.2. Lack of longitudinal linkages 11 2.3. Coverage gaps across the skills system 12 2.4. Inconsistent definitions and quality issues 13 2.5. Timeliness and accessibility 13 2.6. Granularity: Equity and regional blind spots 14 3 Types of data sources for decision making, and what they can and cannot tell policymakers 15 4 What is at stake: Potential gains from closing the gaps 18 4.1. Sharper financial decision making: Reallocating to high-return programmes, avoiding lowyield spend, managing fiscal risk 18 4.2. Economic and social returns over the short and long term 19 5 Barriers to building integrated skills data systems 20 5.1. Institutional and governance barriers 20 5.2. Human-capital and analytical-capacity gaps 22 5.3. Legal and regulatory constraints 24 5.4. Technical and interoperability challenges 26 5

BETTER SKILLS DATA FOR SMARTER FINANCING OF EDUCATION AND TRAINING © OECD 2026

6 Conclusion 30 Annex A. Types of data sources for decision making 31 References 39

FIGURES Figure 1. A framework for understanding barriers to integrated skills data systems 9 Figure 2. Analytical strengths of data sources for education and skills policymaking 17

TABLE Table 1. Summary table: Data sources for decision making 166 

BETTER SKILLS DATA FOR SMARTER FINANCING OF EDUCATION AND TRAINING © OECD 2026

Robust and integrated skills data are essential for effective policymaking, particularly in improving the allocation of public resources across education and training. Yet many govern ments continue to face significant information gaps that constrain their ability to assess programme effec tiveness, prioritise spending and target investments towards interventions that yield the strongest outcomes. This paper examines how stronger skills data can support more evidence-bas ed and efficient financing

allocation of public resources across education and training. Yet many govern ments continue to face significant information gaps that constrain their ability to assess programme effec tiveness, prioritise spending and target investments towards interventions that yield the strongest outcomes. This paper examines how stronger skills data can support more evidence-bas ed and efficient financing decisions in education and training systems. It begins by identifying the m ain data gaps that currently hinder strategic decision making, including fragmentation of data systems acr oss ministries and levels of government, limited longitudinal linkages between education and labour market records, uneven coverage across the skills system – particularly in early childhood and adult learning – and persistent issues with timeliness, accessibility, definitional consistency and data quality. These gaps leave policymakers without a consolidated view of how public resources translate into results, limiting their capacity to respond to evolving labour market needs. The paper then reviews ten data sources relevant for education, employment and skills policy, outlining for each their practical applications, strengths and limitations, and the types of conc lusions that can legitimately be drawn. This helps decision makers select appropriate evidence for different policy questions and avoid common misinterpretation. The paper also examines what is at stake. Better data can enable sharper fina ncial decision making – supporting cost-benefit analysis, revealing fiscal inefficiencies and improving the targeting of resources towards high-return programmes – while reinforcing the already well-established economic and social returns to skills investment. At the same time, governments are not the sole actor s in skills development: employers, private providers and individuals invest significantly in trai ning, and better public data should complement rather than substitute for these contributions. To understand what prevents progress, the paper introduces a framework grouping the main obs tacles into four categories: institutional, governance and financing barriers (such as fra gmented mandates and unstable funding for data infrastructure); human-capital and analytical-capacity ga ps (shortages of specialised data talent and limited data literacy among decision makers); lega l and regulatory constraints (particularly around data protection and cross-agency sharing); and technical and interop erability challenges (legacy systems, inconsistent standards and cybersecurity risks) . Examples from across the

specialised data talent and limited data literacy among decision makers); lega l and regulatory constraints (particularly around data protection and cross-agency sharing); and technical and interop erability challenges (legacy systems, inconsistent standards and cybersecurity risks) . Examples from across the OECD illustrate how governments are addressing these barriers through integrated data system s, centralised analytical units and legal reforms that enable secure data linkage. The paper concludes that improving data systems is not primarily about coll ecting more information. In many countries, substantial relevant data already exist but remain underutil ised because they are fragmented, stored in incompatible systems or inaccessible to decision makers. The priority is to unlock and integrate existing data assets through better co-ordination, stronger analytical capacity and governance arrangements that encourage evidence use. While the costs of modernising data infrastructure are real, the potential gains – in more effective resource allocation, improved policy outcomes and stronger public trust – are considerable. Digital technologies, including artificial intelligence, c an act as multipliers of these returns, but only when supported by strong data governance and high-quality underlying data. Executive summary 7

BETTER SKILLS DATA FOR SMARTER FINANCING OF EDUCATION AND TRAINING © OECD 2026

Public investment in skills – used here broadly to encompass the knowledge, competencies and qualifications acquired through education and training, from early childhood t o adult learning – is a cornerstone of economic and social development. Higher levels of education and training are consistently associated with better employment outcomes, higher earnings, and stronger social cohes ion, though returns vary across pathways, population groups and context. Governments thus see sk ills spending not merely as a recurrent cost but as an investment in future prosperity. Approaches to financing skills development vary considerably across countri es and stages of the life course. Each system combines a particular mix of financial instruments – ranging from supply-side funding of institutions to demand-side mechanisms such as subsidies or vouchers – shaped by national priorities and institutional contexts. The effectiveness of these arrangements depends on their alignment with policy goals and on the quality of the underlying evidence base. Efficient resource allocation has become

course. Each system combines a particular mix of financial instruments – ranging from supply-side funding of institutions to demand-side mechanisms such as subsidies or vouchers – shaped by national priorities and institutional contexts. The effectiveness of these arrangements depends on their alignment with policy goals and on the quality of the underlying evidence base. Efficient resource allocation has become increasingly important, not only in terms of how much is spent but in ensuring that funds are directed to interventions with proven impact. At the same time, governments operate under growing fiscal constraints. Ageing populations, rising health and social care costs, climate adaptation, and new security pressures continue to exert pressure on public budgets. In this constrained environment, skills investment must compete with other critical priorities. Redirecting resources from inefficient or low-impact programmes proves politically c hallenging, as entrenched interests and institutional inertia resist change. However, tighter fiscal c onditions make evidence-based decision making indispensable. It enables governments to jus tify, sustain and, where appropriate, expand investment in skills development by demonstrating clear value for money and measurable outcomes. 1.1. Why better data are essential Across the OECD, the lack of timely, reliable and integrated data is a major barri er to strategic decision making in skills investment. Governments often lack comprehensive information on the eff ectiveness of education and training programmes, the needs of different population groups, and the long-term labour market outcomes of participants. While relevant data sources are often available – from administrative records and employer surveys to tax and employment registers – their coverage, quality and accessibility vary significantly across countries. In many cases, data remain fragmented, incom plete, or difficult to access and use for policy purposes. OECD work on the data-driven public sector shows that these challenges are not uniqu e to skills policy. Governments across policy domains face similar difficulties, including uneven data quality, limited internal data sharing, and barriers to interoperability that prevent data from being used as a strategic asset for policymaking and service improvement (OECD, 2019[1]). However, these challenges are particularly acute in skills policy. Skills systems span multiple ministries, level s of government and stakeholders, from

data sharing, and barriers to interoperability that prevent data from being used as a strategic asset for policymaking and service improvement (OECD, 2019[1]). However, these challenges are particularly acute in skills policy. Skills systems span multiple ministries, level s of government and stakeholders, from education and training providers to employers and social partners. This inst itutional complexity makes it more difficult to assemble the information needed to assess outcomes across the life course and to link spending with results. OECD analysis also highlights the breadth and diversity of stakeholder engagement 1 Introduction8 

BETTER SKILLS DATA FOR SMARTER FINANCING OF EDUCATION AND TRAINING © OECD 2026

arrangements in skills systems, further underscoring the co-ordination challenges involved (Reznikova, Labanino and McKee Mathews, 2024 [2]). As a result, gaps in data integration and use have more direct consequences for the allocation and effectiveness of public investment in skills. Without quality data, governments cannot determine whether skills investments are gener ating the intended outcomes or how to target resources for maximum effect. This leaves them vulne rable to inefficiencies and missed opportunities . Funds may flow to programmes with weak results – even where this reflects deliberate policy choices to support disadvantaged group s – while high-impact initiatives remain underfunded. Insufficient data have thus become a critical bottleneck, preventing the alignment of skills policies with economic and social needs. It is important to recognise that governments are not the sole actors in developing a nd deploying skills. Employers, private providers and individuals invest significantly in training , often responding more rapidly to evolving labour market demands than public systems can. Better public data should complement – not substitute for – these private contributions. Better data would transform policymaking. Consider the questions governments currently struggle to answer. Which education and training pathways deliver lasting employment gains? Which sectors and regions generate the highest returns from upskilling? How do different forms of financing affect participation and outcomes across the life course and across different population groups, particul arly those at risk of

Better data would transform policymaking. Consider the questions governments currently struggle to answer. Which education and training pathways deliver lasting employment gains? Which sectors and regions generate the highest returns from upskilling? How do different forms of financing affect participation and outcomes across the life course and across different population groups, particul arly those at risk of exclusion? With reliable evidence, governments can make more strategic choices. They can invest where impact is greatest, adjust programmes to meet evolving labour market demands, and ensure resources are used efficiently, effectively and equitably – goals that are complementary rather than competing in the context of public skills financing. Advances in digital tools, including artificial intelligence, further expand these possibilities by enabling more granular analysis, improved forecasting of skills needs and more responsive allocation of funding. However, their effectiveness depends critically on the availability of highquality, integrated data. 1.2. Purpose and scope While closing data gaps has significant potential to strengthen policy deci sion making, doing so requires overcoming a set of structural, institutional and technical barriers. These chall enges are diverse and operate at different levels of the system, shaping how data are collected, governed, shared and used in practice. To support a clearer and more action-oriented understanding, this paper proposes a framework that groups the barriers into four categories: institutional, governance and financing barriers; human-capital and analytical-capacity gaps; legal and regulatory constraints; and technical and interoperability challenges. Figure 1 illustrates these four categories, summarising what each looks like in practice and why it matters for decision making and the financing of skills systems. E ach category involves distinct implications for policy design and requires different levers for reform. By str ucturing the analysis in this way, the section aims to provide a practical basis for identifying priorities and s equencing actions that enable progress toward more integrated and effective skills data systems. Throughout this paper, “skills” refers broadly to the knowledge, competencies and qualifications acquired through education and training, consistent with its use in international policy discourse. This policy paper examines how stronger data on skills outcomes and inv estments can enable smarter

enable progress toward more integrated and effective skills data systems. Throughout this paper, “skills” refers broadly to the knowledge, competencies and qualifications acquired through education and training, consistent with its use in international policy discourse. This policy paper examines how stronger data on skills outcomes and inv estments can enable smarter public spending decisions, and what obstacles currently impede this goal. While many of the data issues discussed are relevant to skills policy more broadly, the paper ’s focus is on data insofar as they inform financing and resource-allocation decisions: assessing programme effectiveness, targeting investment and linking spending to outcomes. Section 2 identifies current data gaps in education and skills policy and the consequences of those gaps for decision making. Section 3 provides an over view of the main data sources available for education, employment and skills policy, outlining what each source can and cannot support in terms of evidence and decision making. Section 4 discusses what is at stake – the potential 9

BETTER SKILLS DATA FOR SMARTER FINANCING OF EDUCATION AND TRAINING © OECD 2026

gains in efficiency and socio-economic outcomes if these gaps are addressed. Section 5 analyses the main barriers to progress, including institutional, governance and financing constrai nts, legal and regulatory challenges, capacity limitations and technical interoperability issues. Finally, Section 6 presents the conclusions. Figure 1. A framework for understanding barriers to integrated skills data systems

Note: The four barrier categories are not mutually exclusive. Effective reform typically requires action across multiple dimensions simultaneously.10 

BETTER SKILLS DATA FOR SMARTER FINANCING OF EDUCATION AND TRAINING © OECD 2026

Recent decades have seen remarkable progress in data collection across OECD countries. Governments now routinely gather information on enrolment, graduation rates, and basic la bour market outcomes . Administrative systems have become more sophisticated, and statistical agenc ies have expanded their capacity to monitor education and training. Yet despite these advances, significant gaps remain in the evidence available to guide skills policy. Many countries still lack a unified and comprehensive view of their skills systems. Information on education,

2.1. Fragmentation of data systems In many countries, skills-related data are dispersed across siloed systems managed by different ministries, agencies and levels of government (OECD, 2019 [4]). This fragmentation typically reflects the division of responsibilities across government portfolios and across levels of administration . In some systems, national governments set policy while subnational authorities manage delivery. In others , particularly federal systems, key functions are allocated across levels. For example, education is largely a state responsibility in the United States and a provincial responsibility in Canada. Data systems are typically developed by different public bodies, each aligned with the ir specific mandate and regulatory requirements. As a result, information is collected and mana ged in separate systems reflecting sectoral responsibilities – for example, education authorities focus on enrolment and graduation, 2 Current data gaps and their consequences 11

BETTER SKILLS DATA FOR SMARTER FINANCING OF EDUCATION AND TRAINING © OECD 2026

labour ministries on employment and training programmes, and tax and social security insti tutions on earnings and employment histories. While these systems serve important administrative functions, they are rarely designed to operate together. Fragmentation is not only institutional or technical but also semantic: systems may be connected yet remain difficult to use jointly if they rely on different definitions, nomenclatures, metadata or exchange standards (see Section 2.4). This limits their comparability and integration, making it difficult to link information across education, training and labour market outcomes. As a result, policymakers lack a consolida ted view of the skills system and face constraints in assessing how individuals move through it and how public spending translates into results. The consequences for policy are severe (see Section 3). Fragmentation limits t he ability to trace how investments in education and training translate into labour market outcomes. Without linking school or training records to employment and earnings data, policymakers cannot answer bas ic questions. How do graduates fare in the job market? Which qualifications lead to sustainable employme nt? Which training programmes deliver the best returns? Similarly, budget data on skills programmes may not be connected

Administrative systems have become more sophisticated, and statistical agenc ies have expanded their capacity to monitor education and training. Yet despite these advances, significant gaps remain in the evidence available to guide skills policy. Many countries still lack a unified and comprehensive view of their skills systems. Information on education, training, and labour market outcomes is often fragmented across multiple agencies and databases. Where data do exist, they are frequently incomplete, inconsistent, or incomparable across time and pl ace. The problem goes beyond the availability of basic statistics. One critical gap concerns financial information. While governments track overall education budgets, they often lack detailed dat a on how much is spent on specific programmes, and how spending is distributed across different population groups and stages of the life course. Understanding the flow of public resources is essential, but it represents only one piece of the puzzle. This challenge is closely linked to the absence or weakness of quality assurance mechanisms. Evidence from OECD work on adult education and training shows that increased spending, in the absence of robust quality assurance and outcome tracking, can lead to the expansion of lo w-quality provision and weak returns on investment (OECD, 2024[3]). Policymakers need a broader set of indicators to understand whether investments are ach ieving their goals. They need data on participation and completion rates, on how graduates pe rform in the labour market, on the quality and relevance of training, and on equity across differen t groups and regions. With these complementary indicators, policymakers can better see the full picture. They can track how skills are developed and used across the life course, or judge whether public reso urces are being deployed effectively. This section identifies the main dimensions of these data gaps, highlights whi ch policy areas are most affected, and explains how they hinder effective decision making. 2.1. Fragmentation of data systems In many countries, skills-related data are dispersed across siloed systems managed by different ministries, agencies and levels of government (OECD, 2019 [4]). This fragmentation typically reflects the division of responsibilities across government portfolios and across levels of administration . In some systems,

training records to employment and earnings data, policymakers cannot answer bas ic questions. How do graduates fare in the job market? Which qualifications lead to sustainable employme nt? Which training programmes deliver the best returns? Similarly, budget data on skills programmes may not be connected to outcome indicators, making it difficult to assess value for money. A further complication is timescale: tracing the link between education invest ments and sustainable employment outcomes can take years or even decades, by which point the economic and occupational landscape may have shifted substantially. The result is that governments often wor k with partial and disconnected information. This hampers their capacity to design coherent strategies or to allocate resources where they are most effective. Policy decisions are inevitably made with i ncomplete evidence, but the degree of incompleteness matters. Better data can narrow the margin of uncertainty, even if it cannot eliminate it. Without integration, however, opportunities to learn from past investments are lost. 2.2. Lack of longitudinal linkages A second major gap concerns the limited availability of longitudinal data that tr ack individuals over time. Understanding the impact of skills investments requires following people through their education, training, and careers. Yet in many countries, this proves difficult. Education, employmen t and earnings data are collected separately, often with no unique identifiers or data-sharing arrangements that allow them to be matched. Individuals can be observed at discrete moments – when they enrol in a course, when they graduate, when they register as employed – but the connections between these moments are often difficult to establish. Their subsequent trajectories disappear from view. These linkage s matter not only between education and work, but also within education systems themselves: tracking trajectories such as dropout, grade repetition, transitions between levels and access to higher education is ess ential for understanding where and why skills development falters. Some countries, notably the Nordic nations, have made significant progress in this area by linking education, employment and earnings rec ords through unique personal identifiers – demonstrating that integrated longitudinal data systems are achievable. Australia’s Person-Level Integrated Data Asset (PLIDA) similarly enables longitudinal trac king of individuals before

significant progress in this area by linking education, employment and earnings rec ords through unique personal identifiers – demonstrating that integrated longitudinal data systems are achievable. Australia’s Person-Level Integrated Data Asset (PLIDA) similarly enables longitudinal trac king of individuals before and after training, combining multiple administrative sources to assess outcomes such as post-completion earnings. France’s InserJeunes platform links school, apprenticeship and employment records to monitor, within a single framework, both further study and the labour market insertion of young peo ple leaving vocational programmes. The consequences are profound. Without longitudinal linkages, policymakers cannot assess the long-term effects of different education and training pathways. Critical questions go unans wered. Do graduates of vocational programmes secure stable jobs? How do their earnings evolve over the course of their careers? Does mid-career training improve employment resilience during economic downturns? E valuating the effectiveness of a training programme requires knowing not only who participated, but also how their employment outcomes changed compared to similar individuals who did not take part. In the absence of12 

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linked data, such analysis is rarely possible. Notable exceptions exist, particularly among countries that have invested in integrated administrative data infrastructures. The absence of longitudinal evidence also prevents governments from identifying which groups benefit most from training and which may need additional support. Without longi tudinal data, such disparities remain hidden. Policies risk overlooking those who are less well served, and resources may flow to groups who would have succeeded regardless of intervention. Ultimately, without these linkages, skills policies are designe