Regional Mapping of Labour Market Information for Skills and Employment Policies in ASEAN
The Regional Mapping project focuses on strengthening labour market information systems in ASEAN member states post-pandemic. Objectives include identifying challenges, good practices, and priority areas. The study framework encompasses governance, data infrastructure, analytical outputs, and LMI dissemination. Methodological approaches involve international and national expert teams conducting desk reviews, interviews, and discussions. Recommendations aim to enhance LMI use in skills and employment policy-making at regional and national levels.
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INITIAL FINDINGS FROM THE REGIONAL MAPPING OF LMI FOR SKILLS AND EMPLOYMENT POLICIES IN ASEAN ASEAN TVET Council Regional Policy Dialogue on Labour Market Information For Better TVET Policy Plenary 2 Souleima El Achkar Hilal, ILO Project Lead and Technical Expert 30 31 May 2023
Background and context Aftermath of the pandemic, double disruption: need for strong LMI systems and effective skills development and anticipation systems. Project Strengthening labour market information systems in ASEAN member states for better skills and employment policies , linked to SLOM WG Work Plan 2021-2025, under joint leadership of Indonesia, Philippines and Vietnam. Regional mapping study of LMI for skills and employment policies in ASEAN member states Growing awareness about the importance of skills (not only qualifications and degrees) at the global and ASEAN levels 10/6/2024 2
Regional mapping objectives Produce a region-wide mapping of LMI for skills and employment policies. Identify: Current challenges Good practice areas Initiatives underway Priority areas Derive recommendations for strengthening LMI and its use in skills and employment policy making at the regional national levels, which AMS can use on a voluntary basis to develop action plans/ strategies. 10/6/2024 3
Framework and scope of the mapping study Built around some components of labour market information systems, specifically: Governance, actors and institutional arrangements Data inputs and infrastructure Analytical capacity and analytical outputs Uses and dissemination of LMI Highlight data ecosystems and issues relevant to skills development and TVET within the broader LMI space Identify gaps and challenges, good practice areas, initiatives are underway, and recommendations on priorities 10/6/2024 4
Methodological approach and implementation Implementation by team of international and national experts, with technical guidance and inputs from ILO and GIZ experts, from ASEAN Secretariat and the project s Technical Working Group (TWG) Desk review, Key Informant Interviews (KIIs)/ Focus Group Discussions (FGDs) Methodological tools: Desk review checklist Key informant framework Semi-structured questionnaires/ guidance for KIIs 10/6/2024 5
Process and progress update FINALIZATION AND VALIDATION INCEPTION IMPLEMENTATION 2023 FEB APRIL MAY JUNE MAR JULY Desk review KIIs Report drafting, revision, finalization FEB 13 MID-JUNE Submission of Regional mapping report to SLOM-WG Inception report Endorsed by SLOM-WG Coordination meeting with ASEC and co- lead countries MAR 31 MAR 2, TWG MEETING Presentation of initial findings ATC RPD END-JULY Final regional mapping report 10/6/2024 6
PRELIMINARY FINDINGS 10/6/2024 7
General points Considerable efforts are underway across AMS in: Improving LMI availability, quality, including specifically pertaining to skills Modernizing LMI (e.g. digitization for admin data; exploring Big Data sources) Making skills provision/TVET systems more demand-driven Some efforts/ initiatives are holistic in nature, backed by legislation and involving multiple stakeholders (e.g. TVET Revitalization in Indonesia), and others are specific addressing different aspects relating to LMI, skills anticipation and matching, demand-driven skills provision Priority areas/ areas needing more attention vary across AMS Many initiatives are in early stages: Opportunities to make adjustments, improve certain areas Much benefits from increased cooperation/ collaboration within and across AMS 10/6/2024 8
Governance, actors, and institutional arrangements Many actors on the LMI (and LMI-SKILLS) stage within AMS Generally, clear legislation, roles and mandates (complementary, not overlapping functions). In some cases, multiple acts pertaining to LMI, sometimes explicit LMI legislation (e.g. Cambodia sub-decree on LMI). Various types of institutional coordination bodies and mechanisms exist Include Steering Committees, Boards, Councils, National Coordination Teams Often tripartite or bipartite structures/ representation Sometimes, only partial/ sectoral coverage (as opposed to overall coordination) In some cases, mechanisms themselves are not clear, do not provide process for coordination on regular basis, or there are challenges in implementation (e.g. in theory coordination, but in practice, organizations work in silos) 10/6/2024 9
Governance, actors, and institutional arrangements (2) Despite clear mandates and coordination mechanisms, much fragmentation exists Difficult for potential users/ user groups to find data corresponding to their needs (data sharing, data access) Some duplication of efforts, particularly for new initiatives (e.g Malaysia skills frameworks) Skills related initiatives often disconnected from broader LMI data infrastructure (e.g. Singapore) Institutional arrangements influence access to and use of LMI Skills development and training often under Ministry of Labour/ Manpower, more connected with LMI ecosystem than vocational education side, usually under Ministry of Education Different degrees of centralization/ decentralization of LMI, of engagement of local level actors (e.g. local public employment services offices) in collection, processing and use 10/6/2024 10
Inputs and statistical infrastructure Foundational data infrastructure Essential for measuring labour demand and supply Labor force and other household surveys, censuses, establishment surveys, administrative sources, also real-time sources of data LFS: Relatively robust in AMS, but to different degrees (e.g. LFS frequency ranges from ad- hoc or 1 every 5 years, to monthly); depending on context, can improve: frequency, adherence to international standards, enlarge sample size/improve representativeness, ease of access to microdata Limited demand side info in some contexts; LFS underused in analyses; scope to expand/ improve establishment and employer surveys and other complementary sources of demand side info Administrative data on supply side (education and training) must be strengthened in some countries New sources of LMI being explored (e.g. online job postings) 10/6/2024 11
Inputs and statistical infrastructure (2) Skills-related infrastructure Enabling skills-specific assessment Quite advanced in some countries (e.g. Singapore), initial stages in many others (most countries primary use of skills proxies: educational attainment and occupation) Generally disconnected from broader LMI data infrastructure Lack of/ limited taxonomies, crosswalks from occupations to skills Skills needs surveys and tracer surveys in some countries can be developed/ strengthened in others; existing surveys primarily use skills proxies and/ or direct training needs Multiple definitions of skills within countries Many bottlenecks in making skills information/ initiatives meet needs of end users/ user groups (TVET and skills development providers; Career guidance providers; Job-seekers, others) 10/6/2024 12
Analytical outputs and capacity Range of analytical outputs varies across countries, depending on: Available data infrastructure Resources (Financial and human, analytical capacity) Stakeholder engagement (e.g. private sector; research institutes, etc.) Skills anticipation methods and approaches Often ad-hoc (e.g. tied to project, in context of pandemic, etc.), not regular or institutionalized Often with support from external partners/ donors (sustainability implications) Existing data sources methods/approaches not adequate to keep up with rapidly changing demand and translate this to policy Often disconnect between availability of analytical outputs and user needs Analytical capacity is a major concern/ constraint for many countries 10/6/2024 13
Uses and dissemination Use of LMI/ LMI-SKILLS depends on many factors: Policymakers (employment and skills policies) Disconnect between available analytical outputs and policy needs Long-term strategic planning (national development planning, topic-based e.g. 4IR) Short-medium term needs (e.g. depends on engagement of stakeholders; broader cooperation; rapid pace of changes makes some outputs less relevant) *** more challenging, critical. But good practice examples (e.g. Brunei IBTE) Limited institutionalized processes for evidence-based policies, ad-hoc nature/ availability of LMI for policy purposes Limited institutional capacity: in-house analytical capacity, resources 10/6/2024 14
Uses and dissemination Use of LMI/ LMI-SKILLS determined by: Analysts, researchers (academia, research institutions), practitioners Ease of access to microdata; granularity/ adequacy of data Existence of partnerships between public sector and others Job-seekers, students, career guidance practitioners: Data fragmentation, multiple sources and lack of harmonization Multiple competing portals/ dashboards Data presented by data source rather than thematically; not user-friendly 10/6/2024 15
RECOMMENDATIONS* *Vary, of course, depending on country context; good practices with regards to these recommendations do exist from AMS 10/6/2024 16
Governance, actors, and institutional arrangements Ensure legislative frameworks are up-to-date (e.g. may need revisions as new technologies are explored and adopted) Strengthen coordination mechanisms Designate dedicated entity to ensure coherence and coordination of activities linked to LMI and skills Responsible for ensuring all stakeholders understand their roles, and roles of others (avoiding duplication, enabling effective implementation of holistic policies) Ensure better connections between initiatives on skills data/ frameworks and broader LMI space Enhance the use of LMI in education sector beyond skills development (also from the perspective of promoting life-long learning, career pathways and ensuring consistency with national qualification frameworks) 10/6/2024 17
Inputs and statistical infrastructure Address data fragmentation and lack of harmonization Align definitions and classification systems used (consistency within country, and with international standards, definitions, classification systems) Develop centralized data portal, linking and combining data from multiple sources Improve LFS as needed: frequency, sample size/ representativeness, adherence to international standards, data openness/ access Develop and publish cross-walks and concordance tables between national and international qualification systems (when none currently exist) Improve administrative data collection, particularly on education side Modernize data infrastructure Connect supply side data with broader LMI 10/6/2024 18
Inputs and statistical infrastructure (2) Skills-related infrastructure Align definitions and classification systems used (consistency within country) Generate linkages between skills ecosystem and broader LMI Link skills to occupations Moving beyond proxies for skills, crucial in context of future of work, but requires coordination between stakeholders Ensure definitions/ taxonomies/classifications/ frameworks developed are useful, designed with end users in mind. Link skills to training programs While some initiatives may not be comprehensive in scope, may not problematic as long as usability is emphasized Leverage big data sources and associated techniques and technologies (web scaring, machine learning) as a complement Important area where cooperation among AMS can be impactful 10/6/2024 19
Analytical outputs and capacity Expand and institutionalize skills anticipation methods and their use in skills development Explore different complementary methods Move from ad-hoc to regular, institutionalized, sustainable processes Leverage partnerships (incl. with industry) Make end users and their needs a starting point in developing (and prioritizing) new analytical outputs Enhance in-house analytical capacity of key stakeholders Adequate resource allocation Ensure knowledge transfer and sustainability in all donor-supported initiatives 10/6/2024 20
Uses and dissemination Improve data access/ relevance/ useability to different user groups Access to microdata to researchers and practitioners Simplify portals/ dashboards: avoid multiplicity, duplication, ensure complementarity Tailor content of analytical outputs and dissemination methods to target groups Strengthen connections, feedback mechanisms between end users and producers of LMI/ LMI-SKILLS 10/6/2024 21
THANK YOU! 10/6/2024 22
