Policymakers often lack comparable evidence on who participates, what learners acquire and how programmes perform, limiting their ability to target reform and investment – yet, well-performing technical and vocational education and training (TVET) can help countries respond to changing skill needs by preparing young people and adults for occupations central to the green and digital transitions.
The global TVET evidence base remains fragmented. The Global TVET Data (GTD) mapping of 31 international datasets finds strong coverage of access and participation but weaker coverage of work-based learning, labour-market outcomes, quality, financing, governance and the teaching and training workforce. Data are also often difficult to disaggregate by sex, age, region and programme type and have different study design and different operationalisation mainly due to other priority population groups.
Availability does not guarantee use. Policymakers and other stakeholders must navigate different definitions, reference populations and reporting periods, while data are often not sufficiently timely, dispersed across institutions and often lack accessible metadata or the identifiers needed to link participation, completion and employment outcomes.
Closing the gap requires co‑ordinated action. Agreeing a minimum set of policy-relevant indicators, strengthening national statistical and analytical capacity, and investing in transparent platforms and knowledge-brokering functions that translate comparable evidence into decisions. Together, these efforts can support evidence-informed policymaking and better-performing TVET systems.
Making comparable TVET data work for evidence‑informed policymaking
Technical and vocational education and training (TVET) is central to preparing young people and adults for rapidly changing labour markets. It supplies many of technical and middle-level skills and competences in construction, manufacturing, health, care, transport and other occupations. Its importance extends beyond the green and digital transitions: many economies already face persistent labour shortages and regional mismatches between supply of and demand for workers and skills, while raising income and structural transformation are increasing demand for skilled workers in many economies (Fiest, 2024[1]) (Arias Ortiz et al., 2020[2]). Better information on the distribution of skills and workers, supported by more harmonised indicators, can help policymakers identify shortages and mismatches, anticipate changing skills needs and learn from how other systems respond. In 2023, 75% of employers across OECD reported difficulties filling roles, with skilled trades among the profiles most difficult to find (United Nations, Dept of Economic and Social Affairs, 2019[3]; ManpowerGroup, 2024[4]). These shortages do not make TVET the only policy response, but they increase the need for systems that can identify changing occupational requirements and adjust programmes accordingly.
Sound, evidence-informed governance is therefore essential for TVET systems to fulfil this role. Because TVET spans schools, workplaces, and adult learning, steering it requires evidence from both education and labour-market systems. Reliable national data already provides key information for policy decisions; relevant, high-quality and timely data can support skills-needs analysis, agenda setting, funding decisions, monitoring, and evaluation (OECD/European Commission, 2025[5]). Internationally comparable data adds a further dimension by helping policymakers and other stakeholders identify areas requiring action, benchmark performance, learn from peers and interpret national outcomes in a wider context.
Yet the international TVET evidence base remains limited and is not consistently used in policymaking. The challenge has two dimensions. First, the availability of international harmonised TVET indicators is constrained by uneven thematic and country coverage, system complexity, and methodological inconsistencies – for example in defining TVET and reference populations (see Box 1) (OECD, forthcoming[6]). Second, where data are available, accessibility barriers, institutional fragmentation and limited analytical capacity can prevent their effective use (Steiner-Khamsi et al., 2024[7]; Selwaness et al., 2022[8]).
When comparable and timely TVET data are unavailable or insufficiently used, policymakers face difficulties in identifying priority areas for action, comparing performance, assessing whether investment produces results and learning from peers. While these challenges affect all systems because of the overall scarcity of comparable TVET data, their impact is particularly pronounced in countries with limited national statistical and analytical capacity, where opportunities to complement international comparisons with robust national analysis are often more restricted.
Without comparable TVET data, benchmarking and international policy learning are severely constrained. In general education, internationally harmonised data has proven a powerful policy lever. For example, the OECD Programme for International Student Assessment (PISA) provides a common reference point for identifying performance gaps and informing reform. TVET has no equivalent instrument with comparable global reach. This makes it harder to determine whether weak outcomes reflect limited access, programme quality, occupational mismatch or wider labour-market conditions. The OECD’s PISA‑VET initiative is being developed to address part of this by assessing professional and employability skills in selected occupations, but it is still at an early stage (OECD, 2024[9]).
Limited comparability also weakens accountability for TVET investment and outcomes. Without consistent measures of completion, employment and earnings, governments can struggle to compare programmes, assess returns and justify funding decisions. In Scotland (United Kingdom), for example, apprenticeship monitoring has relied heavily on participation targets and short-term self-reported outcomes, prompting the OECD to recommend linked administrative data and longer-term monitoring (OECD, 2022[10]). In Benin, limited programme-level labour-market data prevent robust estimates of the returns to technical secondary education. Strengthening national data systems while improving alignment with international reporting frameworks is therefore an important policy lever. Without comparable evidence, the basis for performance management, investment decisions and cross-country learning remains fragmented (World Bank, UNESCO and ILO, 2023[11]).
All countries bear the cost of fragmented evidence, but lower-capacity systems face a greater risk of exclusion from policy dialogue and are less able to benchmark their performance. Ministries may lack the technical infrastructure, skilled personnel and data management systems needed to translate complex international datasets into actionable policy recommendations (UNESCO-IESALC, 2025[12]). Strong national data systems are the foundation for evidence-informed TVET policy. Comparable international data can add further value by enabling benchmarking, peer learning and cross-country analysis, but only when countries have the capacity to produce, interpret and use the underlying evidence.
The limited availability of TVET-specific comparable data leaves a structural gap in the global education evidence landscape. Unlike general education, where decades of international co‑ordination have produced robust monitoring systems, TVET has not benefited from such investments in comparable data infrastructure (World Bank, UNESCO and ILO, 2023[11]). As a result, policy decisions with significant consequences for learners, labour markets and economies are made with less evidence than those consequences warrant.
The consequences outlined above stem from a dual challenge constraining the global TVET data landscape for policymaking. On one hand, the availability of internationally comparable TVET data remains limited, shaped by persistent gaps in coverage and by technical constraints rooted in the complexity of TVET systems (see Box 1). On the other, where comparable data exist, practical and institutional barriers prevent usage in policymaking. This section sets out the practical causes of these challenges.
What the Global TVET data mapping reveals so far
Research conducted under the forthcoming Global TVET Data Report maps 31 international datasets with stakeholder input from a technical workshop and semi-structured interviews (OECD, forthcoming[6]). The mapping documents geographic and thematic coverage, time coverage, data sources, methodological standards and the scope for disaggregation. It confirms that policymakers do not draw on a single harmonised source: they combine datasets that differ in coverage, comparability, granularity and timeliness, often supplementing them with national statistics, qualitative evidence and expert judgement.
Geographic and thematic coverage remain uneven. Ten of the 31 datasets have global coverage, while nine focus exclusively on Europe. European countries therefore benefit from a denser ecosystem of established regional sources, including several datasets maintained by Eurostat and Cedefop, whereas equivalent multi-country sources are less extensive in Asia-Pacific, the Middle East and North Africa, and Sub-Saharan Africa. Access and participation (e.g. entry into participation in TVET) appear in 84% of datasets and relevance and outcomes (e.g. alignment with labour-market needs and learner outcomes) in 58%, while the teaching and training workforce (e.g. teachers, trainers and their characteristics), financing (e.g. resources and expenditure), quality (e.g. standards, processes, learning conditions and assessments) and governance (e.g. institutional arrangements and system management) are covered in only 42% to 48%. Opportunities to disaggregate indicators by sex, age, region, programme type or other priority population groups are also inconsistent. These gaps closely match the information needs raised by stakeholders, particularly on work-based learning, labour-market outcomes and the quality of provision.
A complementary indicator-level analysis shows that similarly labelled indicators are not necessarily comparable, underscoring the need for greater alignment across international definitions and classifications. For example, indicators measuring enrolment or participation in vocational education differ in the age groups and education levels covered, whether they include formal, non-formal or work-based learning, and whether results are reported as percentages or raw headcounts. Methodological differences therefore constrain comparison even where indicators appear to measure the same concept. UNESCO Institute for Statistics, ILOSTAT and UNESCO-OECD-Eurostat frameworks are the standards most frequently referenced, yet seven datasets rely on national definitions alone (OECD, forthcoming[6]). National definitions preserve important context, but without transparent mappings to common classifications they limit benchmarking and longitudinal comparison.
A common starting point: What counts as TVET?
UNESCO defines TVET broadly as education, training and skills development relating to occupational fields, production, services and livelihoods, delivered as part of lifelong learning at secondary, post-secondary and tertiary levels and including work-based and continuing learning (UNESCO, 2016[13]). National systems operationalise this broad concept differently. TVET may be school-based, workplace-based or mixed; offered before entry to employment or to workers already employed; and delivered by public, private, employer or community providers (Kis, 2020[14]; UNESCO Institute for Statistics, 2025[15]). These differences make it difficult to define a single statistical population that captures all forms of TVET while still distinguishing them from general and pre-vocational education. Expanding on current conceptualisations and co-producing measurable and inclusive definitions offers a promising foundation for more comprehensive and comparable TVET data in the future.
Differences in definitions and methodologies: How is TVET classified?
Data reported under the label “TVET” may refer to different programme types and learner groups. Some datasets classify programmes through the International Standard Classification of Education (ISCED), while others use national definitions or occupational classifications. ISCED 2011 distinguishes between academic and professional education at levels 6‑8 but it does not provide fully operational definitions, leading to inconsistent application and measurement of higher TVET (UNESCO Institute for Statistics, 2018[16]). Comparisons also change depending on whether the reference population is all young people, all learners, adults, apprentices or participants in particular programme levels. More consistent metadata documentation, combined with sustained international collaboration, can gradually strengthen the comparability and reliability of TVET statistics across countries. The main Global TVET Data Report will examine how more practical guidance, methodological explanations and country examples could help map national programmes, fields of study, occupations and sectors to international classifications such as ISCED, ISCO and NACE. For example, the informal expert team collaborating on reviewing the ISCED classifications, aims to clarify the definitions of higher professional education and work-based learning.
Non-formal and informal TVET: What is still excluded from the data?
In many countries, technical and vocational skills are acquired through traditional apprenticeships, on‑the-job training and other non-formal or informal pathways. When these pathways do not lead to an officially recognised qualification, they generally fall outside ISCED-based collections. The UNESCO‑OECD-Eurostat international data collection also focuses on formal education, while labour-force surveys do not yet consistently identify the type, duration or quality of non-formal training. As a result, a substantial part of TVET provision cannot be included reliably in international benchmarking or policy learning (UNESCO Institute for Statistics, 2025[15]; UNESCO Institute for Statistics, 2018[16]; Kis, 2020[14]). However, experience from the measurement of lifelong and informal learning suggests that careful conceptualisation can progressively bring these pathways into the statistical picture (OECD, 2026[17]; OECD, 2026[18]).
Barriers to using comparable TVET data for policymaking
Stakeholder consultations and broader evidence on education policymaking identify five recurring barriers between the publication of comparable data and their use in decisions (Suazo-Galdames, Saracostti and Chaple-Gil, 2025[19]). Figure 1 summarises these barriers; the examples below show how they arise specifically in TVET.
Relevance. Comparable sources are strongest on access and participation, but less often cover the quality of work-based learning, informal apprenticeships (i.e. skills training based on informal agreements between apprentices and experienced craftspeople) (Hofmann et al., 2022[20]), completion, employment outcomes, financing, governance and the teaching and training workforce. This limits, for example, policymakers' ability to assess whether programmes lead to positive learner and labour-market outcomes. Stakeholders in OECD and non-OECD countries identified these areas as priorities, indicating a mismatch between what is easiest to collect and what is most useful for policy (Steiner-Khamsi et al., 2024[7]).
Accessibility. A published “TVET participation rate” may use all 15-24-year-olds, enrolled learners or participants in selected programme levels as its reference population. Reference periods may also differ, including a specific survey period or the current academic year. Unless such metadata are clearly presented alongside the data, users may compare rates constructed from different populations or timeframes (Jadeau and Fogarassy, 2025[21]; OECD, 2020[22]). Until metadata is systematically reported, including definitions, reference populations, programme coverage, reporting periods, calculation methods and known data-quality limitations, this will continue to be a barrier. Missing observations, differences in data-collection methods and other national methodological variations should also be clearly documented, as they can affect the validity and interpretation of cross-country comparisons.
Institutional fragmentation. TVET data are often held by, among others, education and labour ministries, training providers, qualifications authorities and social partners. If these actors use different programme codes or learner identifiers, enrolment records cannot be linked reliably to completion, employer or employment data. The consequence is not merely duplicated effort: policymakers cannot reconstruct learner pathways across schools, TVET programmes, further education and employment, nor can they produce coherent national inputs for international reporting (Pham, 2026[23]; ETF (European Training Foundation), 2013[24]; Selwaness et al., 2022[8]). As a result, barriers to progression and lifelong learning often remain difficult to identify.
Analytical capacity. TVET analysis often requires linking education and labour-market data across different classifications and time periods. Estimating completion and post-programme employment, for example, may require matching provider records to social-security or labour-force data while distinguishing school-based, apprenticeship and adult programmes. Many systems lack the staff, secure infrastructure or data-sharing arrangements needed for this work (UNESCO-IESALC, 2025[12]; ILO; African Development Bank, 2023[25]; Selwaness et al., 2022[8]).
Understandability. Identical labels do not always describe identical measures. “TVET enrolment” can differ by age, ISCED level, formal status and inclusion of work-based components; “Employment after TVET” can vary by follow-up period and definition of employment. Without clear and accessible metadata, users cannot assess whether a comparison is valid (Kis, 2020[14]).
Together, these barriers increase the time and expertise required to turn a published indicator into a defensible policy conclusion. Improving data supply without addressing these barriers will therefore have limited impact on policy use.
Current initiatives can be grouped along the three pathways shown in Figure 2: expanding comparable data, strengthening national statistical and analytical capacity, and translating evidence into policy use. Progress is visible in each area, but initiatives remain dispersed and their TVET-specific coverage is still limited.
Expanding the availability of comparable TVET data
International organisations already provide important components of the TVET evidence base. The OECD’s “Spotlight on Vocational Education and Training” documents the diversity and outcomes of upper-secondary VET systems (OECD, 2023[26]) The UNESCO Institute for Statistics monitors Sustainable Development Goal 4, the global commitment to ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, including indicators on participation in technical and vocational programmes. The International Labour Organization’s ILOSTAT database provides labour-market and work-based-learning indicators, while the European Training Foundation produces evidence on partner-country TVET systems. These sources are substantial, but they do not yet form a unified TVET framework and often cannot isolate non-formal provision, apprenticeship quality, financing, governance or comparable post-programme outcomes.
Regional initiatives add depth, but their scope and functions differ. The Inter-American Development Bank’s CIMA platform (Centro de Información para la Mejora de Aprendizajes) brings together harmonised education indicators for Latin America and the Caribbean, and ILO/Cinterfor support knowledge exchange and south-south co-operation among more than 65 TVET institutions. The Association of Southeast Asian Nations established an ASEAN TVET Council with regional partners to support co‑ordination. These initiatives respond to regional priorities, but they were developed for different purposes and do not use a common set of TVET definitions, indicators, classifications or reporting practices. They therefore support regional comparison and peer learning, but not yet consistent benchmarking across regions. A cross-regional framework can complement them by linking regional data to agreed definitions and a minimum set of comparable TVET indicators.
Several targeted initiatives are addressing specific gaps. TGTA, a collaboration established in 2025 between the OECD, the International Labour Organization, European Training Foundation (ETF), UNESCO-UNEVOC and the BIBB, funded by the German Federal Ministry for Education, Family Affairs, Senior Citizens, Women and Youth (BMBFSFJ), is developing the forthcoming Global TVET Data Report and a feasible minimum set of priority indicators (see Box 2). The OECD’s Programme for International Student Assessment for Vocational Education and Training (PISA-VET) is being developed as the first international assessment of professional and employability skills in selected occupations (OECD, 2024[9]). UNESCO-UNEVOC’s Global Skills Tracker adds cross-country information on changing skills demand across occupations and industries (UNESCO, 2025[27]).
Building statistical capacity for TVET data analysis (and production)
Expanding the availability of comparable TVET data is necessary but insufficient. The scale of overall education support is substantial: the UNESCO Institute for Statistics reported training more than 250 national statisticians from about 70 Member States in 2017 (UNESCO Institute for Statistics, 2017[28]). Yet the Global TVET Data mapping and stakeholder consultations identify few international initiatives dedicated primarily to TVET statistical capacity, particularly for linking education, apprenticeship and labour-market records.
The Partnership in Statistics for Development in the 21st Century (PARIS21), whose Secretariat is hosted by the OECD, provides guidance covering statistical planning, production, governance and evaluation. Its approach demonstrates how countries can combine locally defined priorities with common standards and peer learning, including through national strategies for the development of statistics (PARIS21, 2020[29]).
The UNESCO Institute for Statistics (UIS) supports national education data systems through training, technical assistance, methodological guidance and diagnostic tools. Its Education Data Quality Assessment Framework promotes National Strategies for the Development of Education Statistics (NSDES): countries establish technical and steering structures, map data sources, identify gaps, assess quality and develop an action plan. UIS has applied this approach in the Pacific and other country programmes, providing a documented model for building the foundations required for international comparability (UNESCO Institute for Statistics, 2017[28]; Paris21, 2025[30]).
Evidence from Africa highlights the size of the remaining gap. An ILO and African Development Bank report identifies weak TVET research and data as constraints on stronger links between training and productive sectors (ILO; African Development Bank, 2023[25]). UIS guidance similarly notes shortages of skills to analyse, co‑ordinate and communicate education data (UNESCO Instititute for Statistics, 2025[31]). These sources document the need more clearly than a mature TVET-specific capacity programme: reforms such as expanding quality apprenticeships require systems able to measure employer participation, workplace training and learner outcomes (World Bank, UNESCO and ILO, 2023[11]).
Bilateral co‑operation programmes have made targeted contributions, particularly in lower-income contexts. BIBB, a core TGTA partner, has supported TVET data capacity in Sub-Saharan Africa through technical support (Haßler et al., 2020[32]). Germany’s GIZ has invested in TVET capacity building across Southeast Asia, supporting regional co‑operation among ASEAN member states to better align national TVET systems, and their data infrastructure, with labour market integration objectives. These bilateral partnerships provide sustained, context-specific technical support that multilateral programmes cannot always reach.
Across these efforts, a consistent gap remains clear: investment has focused on education data broadly and TVET system strengthening, but rarely on the statistical capacity needed specifically to produce and use comparable TVET data for policymaking. Closing this gap requires targeted investment in methodological alignment, analytical skills and data management systems directed specifically at TVET statistical capacity at the national level.
Bridging the gap: Translating comparable data into policy use
Even well-produced data do not automatically reach policymakers in a form they can act on. Countries need knowledge-brokering functions, located, for example, in ministries, statistical offices, skills observatories, research institutes or social-partner bodies, that frame policy questions, synthesise evidence, explain uncertainty and connect findings to decision points across the policy cycle, from problem identification and policy design to monitoring and evaluation.
Knowledge brokers play a critical but often under-recognised role in the TVET data ecosystem. By distilling complex international datasets into accessible analyses, identifying policy implications of comparative findings, and tailoring evidence to specific decision-making contexts of national policymakers, they perform a translation function that neither data producers nor policymakers can easily perform themselves. Domestic knowledge brokers understand institutional constraints and the timing of policy and budget processes. International organisations can support them by maintaining standards, producing comparative analysis, convening peer review and providing technical assistance.
Existing models show how this translation can work. The European Training Foundation’s Torino Process combines country-led self-assessment, data and stakeholder dialogue to review TVET performance and priorities (ETF (European Training Foundation), 2013[24]). Cedefop’s Skills Intelligence platform converts multiple datasets into interactive indicators that can be filtered by country, gender, age, occupation, education and sector (CEDEFOP, 2024[33]). BIBB’s annual VET Data Report similarly combines statistics, consultation and thematic analysis for German policymakers (Federal Institute for Vocational Education and Training, n.d.[34]).
Towards TVET Global Agenda Initiative: Defining what is needed and how to get there
The TGTA initiative has four complementary pillars: improving TVET data to support evidence-informed policymaking; fostering partnerships to ensure skills provision is relevant to labour market needs; promoting TVET innovation through Centres of Vocational Excellence; and enhancing knowledge sharing and capacity building to improve TVET quality, attractiveness, and responsiveness.
Strengthening the evidence base on TVET is a core priority of TGTA: the Global TVET Data Report (OECD, forthcoming[6]) will systematically map and assess existing international TVET data sources, examine their geographic coverage, the policy dimensions they capture, and their capacity to support cross-country and longitudinal comparisons. By identifying strengths, gaps, and opportunities for greater harmonisation, the report will provide a roadmap towards a more coherent, comparable, and policy-relevant international framework for measuring TVET. Central to this work is the development of a feasible minimum set of priority indicators, co-developed with regional stakeholders.
UNESCO-UNEVOC: Capacity building and data convening
UNESCO-UNEVOC has played an active role in strengthening the TVET data community and building national capacity to produce and use TVET statistics. Through its global network of more than 220 UNEVOC Centres – spanning lower-capacity countries across Africa, Asia-Pacific and Latin America – UNESCO-UNEVOC supports member states through capacity building, advisory services, knowledge sharing and research, helping embed stronger data practices at the institutional level (UNESCO, 2022[35]). At the international level, UNESCO-UNEVOC co-organised the pre-conference session on TVET data at the inaugural UNESCO Conference on Education Data and Statistics in 2024, which convened policymakers, statisticians and researchers to examine the current state of TVET data and identify priorities for future development (UNESCO Institute for Statistics, 2024[36]; UNESCO-UNEVOC, 2024[37]). These efforts reflect the recognition that strengthening statistical foundations of TVET requires sustained engagement across both national systems and the international community.
Initiative: Bridging the gap between evidence and policy use
The OECD Centre for Skills convenes peer-learning events providing policy makers with opportunities to exchange experiences, discuss common challenges and identify effective policy responses. Organised around thematic priorities, these events bring together academic research, international organisations and other initiatives, distilling complex evidence into accessible, policy-relevant discussions. Examples include the annual OECD Skills Strategy Peer Learning Workshop and the biannual OECD Skills Summit (OECD, n.d.[38]). Initiatives such as these are key to facilitating the translation from knowledge makers to policymakers – enabling more evidence-informed design and trade-offs.
The BIBB, the German Federal Institute for Vocational Education and Training, plays a central role in collecting, analysing and synthesising evidence on TVET, by translating complex data into policy-relevant insights. For example, it publishes an annual VET Data Report Germany, which provides data, indicators and analyses on TVET, following stakeholder consultations on key data needs. The report also presents international benchmarks and indicators, and examines a topical theme of particular relevance to education policy each year (Federal Institute for Vocational Education and Training, n.d.[34]). In a collaborative project, the BIBB has also started to develop comparative indicators for monitoring developments in dual vocational education and training across Austria, Germany and Switzerland (Dornmayr et al., n.d.[39]).
Strengthening the use of comparable TVET data for policymaking requires action across the data value chain, from production to analysis to use. The following recommendations are directed at both national policymakers and international organisations.
Agree and co-produce a minimum set of policy-relevant, timely and comparable TVET indicators. Countries and international organisations should prioritise a manageable core covering participation, completion, work-based learning, outcomes, quality, financing, governance and the teaching workforce. The annual UNESCO-OECD-Eurostat data collection demonstrates that this is feasible: countries map their education systems to jointly agreed concepts, definitions and classifications, producing comparable information on enrolment, graduates, personnel and expenditure that underpins OECD, Eurostat and UIS reporting (OECD, 2018[40]).
Invest in interoperable TVET data infrastructure. National systems need common classifications, persistent learner and provider identifiers, secure linkage arrangements and regular reporting. This does not necessarily require a single centralise repository: interoperability frameworks can enable national registries and administrative, statistical and monitoring databases to interact securely across education, training and labour-market systems. England’s Longitudinal Education Outcomes dataset links education records to employment, earnings and benefits data, while BIBB’s Occupations and Skills Radar combines VET and labour-market data to track occupational change. Together, they show how connected data can support both learner-pathways and outcome analysis, as well as workforce planning.
Build statistical and analytical capacity as a long-term function. Support should go beyond one-off training to strengthen governance, infrastructure, methods and routine data use. Emerging tools, including AI, can complement statistical expertise by helping analyse large and heterogeneous datasets, identify patterns and emerging skills needs, and support scenario analysis, provided appropriate safeguards for data quality, transparency, confidentiality, security and accountability are in place. Sustained capacity development is equally important. UIS assistance illustrates this: in 2021, it added 6 390 data points, expanded expenditure data for 170 countries and introduced new data for 16 indicators across 162 countries. At country level, Fiji’s EMIS tracks learners and returns assessment results to schools within the same year, while Gambia combines school report cards, stakeholder reviews and mobile attendance reporting adapted to limited connectivity.
Invest in knowledge brokering to bridge data and policy use. Governments should assign responsibility for translating TVET evidence into usable policy advice. BIBB’s annual VET Data Report combines indicators, time series, surveys and thematic analysis and provides the evidence base for Germany’s federal VET report. In general education, the Education Endowment Foundation’s Toolkit shows how concise, decision-focused synthesis can achieve broad uptake among practitioners.
Make data, metadata and analytical tools accessible. Platforms should allow users to filter and disaggregate indicators, download data and locate definitions, reference populations, methodologies, disaggregation criteria, codebooks and harmonisation procedures in one place. OECD Education GPS and the UIS Data Browser illustrate how comparative data can be linked to country profiles, metadata and reusable files rather than presented only as static tables.
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