[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-210058-en":3,"doc-seo-210058-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},210058,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Better skills data for smarter financing of education and training - Policy paper","Robust and integrated skills data underpin effective education and training policymaking by enabling better allocation of public resources. Many governments still confront significant information gaps: fragmented or underdeveloped systems limit assessment of programme effectiveness, impede spending prioritisation, and weaken targeting of high-impact investments. This paper analyses how stronger skills data support evidence-based financing choices through a framework of four barrier categories and highlights promising country practices to guide reforms.","Policy Paper  \nBetter skills data for smarter  \nfinancing of education and training  \n2 􀁟  \nDisclaimers  \nThis work is issued under the responsibility of the Secretary-General of the OECD and does not necessarily reflect the official views of OECD Member countries.  \nThis document, as well as any data and map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area.  \nPhoto credits: © [Andrey_Popov/Shutterstock.com](Andrey_Popov/Shutterstock.com).  \n© OECD 2026  \n Attribution 4.0 International (CC BY 4 .0)  \nThis work is made available under the Creative Commons Attribution 4.0 International licence. By using this work, you accept to be bound by the terms of this licence ([https://creativecommons.org/l](https://creativecommons.org/l)icenses/by/4 .0/) .  \nAttribution – you must cite the work.  \nTranslations – you must cite the original work, identify changes to the original and add the following text: In the event of any discrepancy between the original work and the translation, only the text of the original work should be considered valid.  \nAdaptations – you must cite the original work and add the following text: This is an adaptation of an original work by the OECD. The opinions expressed and arguments employed in this adaptation should not be reported as representing the official views of the OECD or ofits Member countries.  \nThird-party material – the licence does not apply to third-party material in the work. If using such material, you are responsible for obtaining permission from the third party and for any claims of infringement.  \nYou must not use the OECD logo, visual identity or cover image without express permission or suggest the OECD endorses your use of the work.  \nAny dispute arising under this licence shall be settled by arbitration in accordance with the Permanent Court of Arbitration (PCA) Arbitration Rules 2012. The seat of arbitration shall be Paris (France) . The number of arbitrators shall be one.  \nBETTER SKILLS DATA FOR SMARTER FINANCING OF EDUCATION AND TRAINING © OECD 2026  \n􀁟 3  \nAbstract  \nRobust 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.  \nThis 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 financing barriers; human-capital and analytical-capacity gaps; legal and regulatory constraints; and technical and interoperability challenges.  \nBy 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.  \nContact  \nAndrew BELL (􀀍 [Andrew.BELL@oecd.org](Andrew.BELL@oecd.org))  \nRicardo ESPINOZA (􀀍 [Ricardo.ESPINOZA@oecd.org](Ricardo.ESPINOZA@oecd.org) )  \nBETTER SKILLS DATA FOR SMARTER FINANCING OF EDUCATION AND TRAINING © OECD 2026  \n4 􀁟  \nTable of contents  \nDisclaimers 2  \nAbstract 3  \nExecutive summary 6  \n1 Introduction 7  \n1.1. Why better data are essential 7  \n1.2. Purpose and scope 8  \n2 Current data gaps and their consequences 10  \n2.1. Fragmentation of data systems 10  \n2.2. Lack of longitudinal linkages 11  \n2.3. Coverage gaps across the skills system 12  \n2.4. Inconsistent definitions and quality issues 13  \n2.5. Timeliness and accessibility 13  \n2.6. Granularity: Equity and regional blind spots 14  \n3 Types of data sources for decision making, and what they can and cannot tell  \npolicymakers 15  \n4 What is a","cbCailFv05a5RCaH","https://ap.wps.com/l/cbCailFv05a5RCaH","pdf",1494141,1,42,"English","en",105,"# Disclaimers\n# Abstract\n# Executive summary\n# 1 Introduction\n## 1.1 Why better data are essential\n## 1.2 Purpose and scope\n# 2 Current data gaps and their consequences\n## 2.1 Fragmentation of data systems\n## 2.2 Lack of longitudinal linkages\n## 2.3 Coverage gaps across the skills system\n## 2.4 Inconsistent definitions and quality issues\n## 2.5 Timeliness and accessibility\n## 2.6 Granularity: Equity and regional blind spots\n# 3 Types of data sources for decision making, and what they can and cannot tell policymakers\n# 4 What is at stake: Potential gains from closing the gaps\n## 4.1 Sharper financial decision making: Reallocating to high-return programmes, avoiding low yield spend, managing fiscal risk\n## 4.2 Economic and social returns over the short and long term\n# 5 Barriers to building integrated skills data systems\n## 5.1 Institutional and governance barriers\n## 5.2 Human-capital and analytical-capacity gaps\n## 5.3 Legal and regulatory constraints\n## 5.4 Technical and interoperability challenges\n# 6 Conclusion\n# Annex A. Types of data sources for decision making\n# References","[{\"question\":\"Why are robust and integrated skills data essential for education and training policymaking?\",\"answer\":\"They support effective allocation of public resources and improve decisions on programme funding. Better data help assess effectiveness, prioritise spending, and target investments toward high-impact interventions.\"},{\"question\":\"What consequences arise from current skills data gaps?\",\"answer\":\"Fragmented or underdeveloped systems restrict the ability to evaluate programme effectiveness, guide spending priorities, and target investments. Gaps also affect timeliness, accessibility, definitions, and data granularity for equity and regional coverage.\"},{\"question\":\"How does the paper structure the main barriers to building integrated skills data systems?\",\"answer\":\"It groups obstacles into four categories: institutional, governance and financing barriers; human-capital and analytical-capacity gaps; legal and regulatory constraints; and technical and interoperability challenges.\"}]","Better skills data for smarter financing of education and training - Policy paper | PDF",1788642592,106,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"better-skills-data-for-smarter-financing-of-education-and-training-policy-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/better-skills-data-for-smarter-financing-of-education-and-training-policy-paper/210058/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-09-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are robust and integrated skills data essential for education and training policymaking?","Question",{"text":75,"@type":76},"They support effective allocation of public resources and improve decisions on programme funding. Better data help assess effectiveness, prioritise spending, and target investments toward high-impact interventions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What consequences arise from current skills data gaps?",{"text":80,"@type":76},"Fragmented or underdeveloped systems restrict the ability to evaluate programme effectiveness, guide spending priorities, and target investments. Gaps also affect timeliness, accessibility, definitions, and data granularity for equity and regional coverage.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper structure the main barriers to building integrated skills data systems?",{"text":84,"@type":76},"It groups obstacles into four categories: institutional, governance and financing barriers; human-capital and analytical-capacity gaps; legal and regulatory constraints; and technical and interoperability challenges.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]