Better skills data for smarter financing of education and training

4. What is at stake: Potential gains from closing the gaps

Strengthening the availability, quality and use of data in skills policy is not an abstract administrative exercise. The potential gains are concrete and considerable. Closing current data gaps and improving the effective use of diverse evidence sources could transform the way governments allocate resources, design interventions and evaluate results. Better data do more than refine analysis: they enable more rigorous financial decisions, improve system efficiency, enhance equity and support more responsive and accountable policymaking. This section outlines what is at stake – both the immediate benefits and the long-term gains that can be realised when decisions are grounded in robust evidence.

4.1. Sharper financial decision making: Reallocating to high-return programmes, avoiding low-yield spend, managing fiscal risk

Closing the data gaps outlined in Section 2 would enable more strategic and evidence‑based financial decisions in skills policy. Improved data would allow governments to conduct systematic cost-benefit analyses and identify which programmes deliver the highest returns on investment. With such evidence, funding can be redirected toward more effective initiatives and away from those with limited impact. This helps overcome the inertia that often keeps budgets locked in historical patterns rather than aligned with demonstrated results. Reliable data provide the analytical basis and accountability needed to implement such reallocations transparently.

Better data would also strengthen governments’ capacity to identify and address fiscal inefficiencies. In tight budget environments, investments that fail to deliver represent not only wasted resources but also missed opportunities. Timely monitoring data can act as an early warning mechanism, flagging underperforming programmes that require redesign or reallocation. For example, persistently low uptake of subsidised training vouchers in certain regions may signal flaws in programme design or communication. Moreover, robust longitudinal data allow for more accurate forecasting of future costs and benefits, reducing the risk of over-committing funds to initiatives that do not yield sustainable outcomes.

Enhanced data granularity further improves targeting and prioritisation. By identifying where marginal returns are highest, policymakers can allocate resources to groups, sectors, or regions where investment is likely to have the greatest impact. For instance, evidence may reveal that mid-career digital skills training in lagging regions produces stronger employability gains than marginal increases in funding for programmes that already serve well-connected populations – differences that are not always apparent without granular data. Data-driven targeting thus contributes to a more efficient and equitable allocation of public funds.

Finally, real-time and outcome‑based data systems can enhance both agility and accountability in public spending. Governments can move toward funding approaches that are more responsive to evolving labour market signals – for example, adjusting training budgets as demand shifts across sectors – while recognising that labour markets are inherently complex and cannot be fully steered through data alone. Performance monitoring frameworks linked to measurable outcomes can also strengthen accountability, ensuring that programme managers are incentivised to use public funds effectively.

In sum, closing data gaps has the potential to contribute to a virtuous cycle in which more evidence‑based investments lead to stronger outcomes, though the empirical evidence linking data investments directly to aggregate employment, productivity and growth outcomes remains limited and context dependent.

4.2. Economic and social returns over the short and long term

Comprehensive and reliable data can strengthen the already well-established economic and social returns to investment in skills. Across OECD countries, tertiary graduates contribute more in taxes, depend less on social transfers, and report better health and well-being than those with lower attainment (OECD, 2025[8]; OECD, 2021[9]). These associations, however, partly reflect selection effects: individuals who attain higher education often benefit from pre‑existing advantages that independently contribute to better outcomes. The returns to expanding tertiary access are therefore context-dependent. Even modest improvements in adult basic skills can be linked to higher employability, earnings, and productivity, while broader gains in literacy and digital proficiency support long-term growth (OECD, 2025[10]). Returns also extend beyond employment and earnings: education and training generate benefits in terms of learning outcomes, civic participation, socio‑emotional development and well-being, which data systems should aim to capture alongside labour market indicators.

Closing data gaps enables governments to allocate resources more effectively, directing investment toward programmes and populations that deliver the strongest outcomes. Integrated data systems allow for policies that build a more adaptable workforce, align training provision with labour-market demand, and expand opportunities for under-represented groups. Evidence‑based allocation thus improves both the efficiency and the equity of skills spending.

It is useful to distinguish between the immediate and longer-term benefits of improving data use. In the short term, closing selected data gaps can deliver tangible efficiency gains by revealing obvious overlaps or inefficiencies. For example, an integrated data dashboard may quickly uncover duplicate training programmes funded by different agencies, allowing consolidation and immediate cost savings. Such early actions are typically low-cost, achievable within a single budget cycle, and demonstrate the practical value of better data, helping to build institutional momentum for broader reform.

The larger benefits, however, emerge over the longer term. Developing comprehensive data infrastructure and analytical capacity takes time – new graduate tracking systems, longitudinal studies, or inter-agency data links may require several years to become fully operational – but once established, they enable continuous optimisation of spending and policy design. While rigorous evidence directly linking investments in data infrastructure to improved policy outcomes remains scarce, the experience of countries with more integrated systems – such as the Nordic nations and Estonia, where linked education, tax and employment records and unified digital identities enable routine tracking of outcomes across policy domains – points to meaningful gains in policy responsiveness and analytical capacity. Over time, this fosters a culture of evidence‑based improvement, in which the skills system evolves and adapts in response to changing needs. Each cohort benefiting from more effective investment contributes to sustained economic and social returns.