GCRI · Future of Work and Skills
Lifelong learning
As work changes, people need to learn throughout their working lives, not only at the start. Yet participation in adult learning is lowest among those who need it most: lower-skilled workers, older workers and those in small firms. This page sets out how lifelong learning systems work, who they reach, how participation and outcomes are measured, and what evidence serves the governments, employers and training providers involved.
How the work runs.
Skills and exposure console
Lifelong learning · Labour market A · Year 2
Occupations by exposure
| Clerical support | 86,766 | 0.7 | Substitute | Data operations |
| Machine operators | 47,032 | 0.4 | Mixed | Robot maintenance |
| Care workers | 19,455 | 0.1 | Complement | Upskill |
| Analysts | 27,329 | 0.4 | Complement | Upskill |
Vacancies by skill
Reskilling pathway
- Assessskills profile
- Matchtarget role
- Train20 weeks
- Placeemployer
- Follow up6 months
Fig.Skills and exposure console: example names and illustrative values, not a record or a live system.
Lifelong learning: how the risk develops.
Mid-career transitions and skill maintenance. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Those who need it least get most
Highly educated workers in large firms receive most training, while lower-skilled and older workers, and those in small firms, receive least.
Time and money are barriers
Adults face costs, lost earnings, caring duties and lack of time, which keep them from training.
Recognition makes learning count
Learning is more valuable when it leads to recognised qualifications or credentials that employers trust.
Employers and unions shape access
Training funds, paid learning leave and collective agreements expand access to learning where they exist and are used.
People and exposure in Southeast Asia.
Population density, night lights and land cover over Southeast Asia, from NASA GIBS. Each layer carries its own date.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
People and exposure, for this page
About this layer
- Evidence
- Estimated
- Source
- Gridded Population of the World v4.11 (NASA SEDAC (CIESIN, Columbia University))
- Provider
- NASA SEDAC (CIESIN, Columbia University)
- Licence
- CC BY 4.0
- Vintage
- null; Census-based estimate, static (2020)
- Resolution
- about 1 km grid
- Uncertainty
- Census-based estimate of people per square kilometre in 2020, shown as exposure context; not a count of people affected by any hazard.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS Black Marble (VNP46A2) (NASA LAADS DAAC)
- Provider
- NASA LAADS DAAC
- Licence
- NASA open data
- Vintage
- daily; Satellite radiance, corrected for moonlight and atmosphere
- Newest at build
- 2026-10-12
- Resolution
- about 500 m grid
- Uncertainty
- Satellite night-time radiance for the date shown; lights are a proxy for electricity use and outages, not a measurement of supply or access.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (persistent cloud still leaves gaps)
- Source
- VIIRS NOAA-20 Level 3, 8-day (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- 8-day; Satellite 8-day composite; persistent cloud still leaves gaps
- Newest at build
- 2026-09-14
- Resolution
- about 1 km grid
- Uncertainty
- Satellite skin temperature of the ground over the 8 days ending on the date shown, a heat exposure indicator; not air temperature, heat index or a heat warning.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed
- Source
- VIIRS NOAA-20 corrected reflectance (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite image
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite image. Satellite or model raster as the provider publishes it. Clouds, swath gaps and retrieval error leave gaps and noise; values are indicators, not measurements at a place.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Modelled (latest year GIBS publishes)
- Source
- MODIS Terra and Aqua land cover (MCD12Q1) (NASA LP DAAC)
- Provider
- NASA LP DAAC
- Licence
- NASA open data
- Vintage
- yearly; Satellite classification; latest year GIBS publishes
- Newest at build
- 2024-01-01
- Resolution
- about 500 m grid
- Uncertainty
- Satellite land cover classes for the year shown, including croplands; a classification with known errors, not a farm register.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
About this layer
- Evidence
- Observed (clouds leave gaps)
- Source
- MODIS Terra EVI (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite index; clouds leave gaps
- Newest at build
- 2026-10-13
- Resolution
- about 250 m grid
- Uncertainty
- Satellite vegetation index for the 8 days to the date shown, a habitat condition indicator; not a measure of species or biodiversity.
- Reached through
- gibs.earthdata.nasa.gov, only after you allow the live map
Keyboard, with the map focused
Hold Ctrl or ⌘ and scroll to zoom
Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth
Loading the latest NASA imagery.
Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.
People and exposure, framed on Southeast Asia.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.
Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA JPL OPERA DIST-ALERTSources and licences
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
- VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
- © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
- Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
- Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
- Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
- Live: NASA EONET Events are indicative, not authority warnings.
- Observed (EO): NASA JPL OPERA DIST-ALERT (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- NASA GIBS: We acknowledge the use of imagery provided by services from NASA’s Global Imagery Browse Services (GIBS), part of NASA’s Earth Science Data and Information System (ESDIS).
- Air quality forecast, 5 days, PM2.5 (CAMS via Open-Meteo): Contains modified Copernicus Atmosphere Monitoring Service information 2026.
- Weather data by Open-Meteo.com (https://open-meteo.com/), licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/).
- Optional layers (9 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.
Layers and dates
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
- True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
- Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024
- Enhanced vegetation index, rolling 8 days: MODIS Terra EVI, NASA LANCE · 13 Oct 2026
Lifelong learning keeps every domain’s workforce current.
Findings are designed for the Evidence Brief series. Qualifications follow ISCED 2011 and ESCO.
- Training evaluations.
- Skills gap studies.
Model lifelong learning as a system. Run the cascade.
Assembled from the Nexus Standards area profile Societal and information risk standards: the hazards it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Choose the components, pick an initiating hazard and step through the cascade. The Map view places the run on an example geography with open data. Every coupling weight is a stated assumption you can change. A teaching model, not a forecast, an assessment or a certification. To model a place of your own choosing, open the full System Builder.
Hazards
- Civil unrest
- Supply-chain disruption (set off downstream)
- Currency crisis (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-9Contextual UNIs (deployments, instances, programme instances)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-12Software, model and AI/ML artefacts (supply chain, model cards, dataset sheets)NRS-H117Hazard family: Social cohesion and atrocity risk (NHS-1 soc.cohesion)NRS-X7Information integrity and public trustNRS-D2Society, inclusion and human development riskNRS-H52Hazard family: Information-integrity attacks (NHS-1 cyber.integrity)NRS-H79Hazard family: Mental health crises (NHS-1 health.mental)NRS-H59Hazard family: Economic shocks and governance failure (NHS-1 soc.economic)
Observation
- WorldPop gridded population (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- Global SDG Indicators Database (listed for reference)
- Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 population
- Proportion of people living below 50 per cent of median income
- Number of countries that adopt and implement guarantees for public access to information
Example geography
- The Horn of Africa: 253 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
What is measured, and how well.
Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.
| Indicator | Unit | Cadence | Source type | Label |
|---|---|---|---|---|
| Adult participation in learning | percent of adults in the last twelve months | per survey | Adult education surveys | REPORTED |
| Participation gap by skill level | percentage point difference | per survey | Adult education surveys | REPORTED |
| Adult literacy and numeracy proficiency | score | per assessment | Adult skills surveys | OBSERVED |
| Public spending on adult learning | percent of GDP | annual | Budget data | Published statistics |
| Workers with paid training leave rights | percent of employees | per assessment | Labour law and agreements | DECLARED |
| Micro-credentials recognised in qualification frameworks | count | per assessment | Qualification authority records | Published statistics |
How lifelong learning is modelled, measured and tested.
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
System model
The couplings between the systems in lifelong learning: signs of influence, a reinforcing and a balancing loop, and cross-links where stress jumps.
Where the evidence comes from.
Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.
| Source | What it gives | Licence class |
|---|---|---|
| OECD Survey of Adult Skills (PIAAC) | Adult literacy, numeracy and problem-solving | Public results, OECD terms |
| Eurostat Adult Education Survey | Participation in adult learning | Open, Eurostat reuse policy |
| UNESCO Institute for Lifelong Learning Global Report on Adult Learning and Education | Global adult learning policy and participation | Public, UNESCO terms |
| Cedefop skills and training data | Vocational training and skills in Europe | Public, EU terms |
| National qualification framework registers | Recognised qualifications | Public records, licence per authority |
Who decides, and what the evidence serves.
Future of Work and Skills prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.
| Who decides | The question | Evidence this page serves |
|---|---|---|
| Education and labour ministries | How to fund and target adult learning | Gap analyses; evaluations |
| Employers and sector bodies | Which training to provide and co-fund | Skills and participation evidence |
| Qualification authorities | How to recognise short courses and prior learning | Credential reviews |
Outputs this topic produces
- Participation and access gap analyses
- Evaluation designs for adult learning programmes
- Recognition and credential reviews
- Evidence notes for skills strategies
What researchers ask about adult learning.
- Who takes part in adult learning, and who does not?
- Which forms of training lead to better jobs and earnings?
- How transferable are skills between occupations?
- How can learning be fitted around work, care and other obligations?
Why adult learning is hard.
Skills depreciate when unused and become obsolete when technology changes. Adults face barriers that young students do not: lost earnings while training, care responsibilities, uncertainty about which skills will pay off and, often, past negative experiences of education. Employers under-invest in general skills that workers could take elsewhere.
As a result, participation in adult learning tends to be highest among people who are already well educated and in good jobs, and lowest among those most exposed to change. Training that is short, close to real work and linked to actual vacancies tends to show better results than generic courses.
Evidence on learning and transitions.
- 01
Programme evaluation
Comparing outcomes of participants and similar non-participants, using econometrics. Selection into training is hard to control for.
- 02
Skills taxonomies
Mapping skills between occupations to identify feasible transitions, using economics and development economics. Taxonomies simplify real skills.
- 03
Longitudinal surveys
Following adults over time to see how learning relates to careers, with social and behavioural science. Such surveys are costly and rare.
- 04
Recognition of prior learning
Methods for assessing skills gained outside formal education. Recognition must be trusted by employers to matter.
How it is studied, and what the methods cannot say.
- 01Social and behavioural scienceJob quality surveys · Social dialogue studies · Learner experience researchApplied here
- 02EconometricsDisplacement effects with linked data · Training programme evaluation · Workforce projectionsApplied here
- 03Economics and development economicsTask-based analysis of technology · Labour market institutions · Just transition economicsApplied here
Where this page’s references lead.
The bodies and documents this page is read against. Status and editions change; each link goes to the issuing body.
Questions and answers.
What is lifelong learning?
Why do lower-skilled adults train less?
What are micro-credentials?
Does training help displaced workers?
Why do those most at risk train least?
Are learning pathways from the platform qualifications?
What are individual learning accounts?
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