GCRI · Future of Work and Skills
Digital work and platforms
Digital platforms arrange work for drivers, couriers, care workers and online freelancers, often through algorithms that assign tasks, set pay and rate performance. Platform work offers flexibility and income, and raises questions of employment status, social protection, data and fair treatment. This page sets out how platform work is organised, how it is measured, and what evidence serves the regulators, platforms and workers involved.
How the work runs.
Skills and exposure console
Digital work and platforms · Labour market A · Year 2
Occupations by exposure
| Clerical support | 88,203 | 0.7 | Substitute | Data operations |
| Machine operators | 65,805 | 0.4 | Mixed | Robot maintenance |
| Care workers | 28,077 | 0.2 | Complement | Upskill |
| Analysts | 26,627 | 0.6 | Complement | Upskill |
Vacancies by skill
Reskilling pathway
- Assessskills profile
- Matchtarget role
- Train19 weeks
- Placeemployer
- Follow up6 months
Fig.Skills and exposure console: example names and illustrative values, not a record or a live system.
Digital work and platforms: how the risk develops.
Remote work, platform labour and new organisations. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Algorithms manage work
Platforms use algorithms to allocate tasks, set prices, monitor performance and deactivate workers, often with limited transparency.
Status determines rights
Whether platform workers are employees or self-employed decides their access to minimum wages, social security and collective bargaining.
Online work crosses borders
Online freelance work connects workers in one country with clients in others, complicating regulation and taxation.
Earnings are volatile
Income varies with demand, customer ratings, fees and unpaid waiting time, making it hard for workers to predict.
Connected activity in Southeast Asia.
Night lights, population density 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
Connected activity, for this page
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
- 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 (clouds leave gaps)
- Source
- VIIRS Global Flood Product (NASA LANCE)
- Provider
- NASA LANCE
- Licence
- NASA open data
- Vintage
- daily; Satellite detection; clouds leave gaps
- Newest at build
- 2026-10-14
- Resolution
- about 250 m grid
- Uncertainty
- Satellite detection; clouds leave gaps. 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
- 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.
Connected activity, 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/NOAA VIIRS · 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
- 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
- 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 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.
- Observed (EO): NASA/NOAA VIIRS (Suomi NPP). 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).
- 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).
- Optional layers (5 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
- Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
- Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 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
Platform work reaches into several domains.
Findings are designed for the Exponential Technology series. The EU Platform Work Directive (2024) sets rules on employment status and algorithmic management.
- Worker surveys with consent.
- Studies of algorithmic management.
Model digital work and platforms as a system. Run the cascade.
Assembled from the Nexus Standards area profile Digital, telecom and platforms: 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
- Sea-ice loss
- Coastal erosion (set off downstream)
- Biodiversity loss (set off downstream)
- Arctic winter sea-ice collapse (set off downstream)
- Polar vortex disruption (set off downstream)
Systems and couplings
- Digital, telecom and platforms
- Energy utilities and grids Coupling
- Media and information Coupling
- Finance, insurance and capital markets Coupling
- Defence and civil protection Coupling
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-H86Hazard family: Telecom and data-centre failure (NHS-1 infra.digital)NRS-H54Hazard family: Signal and platform disruption (NHS-1 cyber.platform)NRS-H93Hazard family: Identity, trust and cryptographic failure (NHS-1 cyber.identity)NRS-H94Hazard family: Systemic cyber events (NHS-1 cyber.systemic-cyber)NRS-T8Software and supply-chain integrity riskNRS-X6Cyber and digital security risk
Observation
- ITU DataHub ICT indicators (listed for reference)
- World Development Indicators (listed for reference)
- WMO Alert Hub and Register of Alerting Authorities (listed for reference)
- Proportion of population covered by a mobile network, by technology
- Fixed broadband subscriptions per 100 inhabitants, by speed
- Individuals using the Internet (% of population)
- Number of people per 100,000 that are covered by early warning information through local governments or through national dissemination mechanisms
Example geography
- North America: 243 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), 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 |
|---|---|---|---|---|
| Workers earning through platforms | percent of working-age population | per survey | Platform work surveys | ESTIMATED |
| Online labour activity | index of projects posted | daily | Online Labour Index | OBSERVED |
| Platform workers with social protection | percent | per survey | Surveys | REPORTED |
| Hourly earnings including waiting time | currency units per hour | per survey | Worker surveys and platform data | ESTIMATED |
| Court and administrative rulings on status | count | annual | Legal databases | Published statistics |
| Algorithmic management transparency obligations | count of jurisdictions | per assessment | Legal review | Published statistics |
How digital work and platforms 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 digital work and platforms: 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 |
|---|---|---|
| ILO World Employment and Social Outlook on digital labour platforms | Global analysis of platform work | Public, ILO terms |
| Online Labour Index (Oxford Internet Institute) | Online freelance labour activity | Open, licence to check |
| Fairwork project ratings and reports | Assessments of platform working conditions against principles | Public, terms to check |
| Eurostat and national platform work surveys | Prevalence of platform work | Open, Eurostat reuse policy |
| EU Platform Work Directive and national legislation | Rules on status and algorithmic management | Public, EU and national terms |
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 |
|---|---|---|
| Labour ministries and regulators | How to classify platform workers and regulate algorithmic management | Condition profiles; legal reviews |
| Social security institutions | How to extend coverage to platform workers | Coverage analyses |
| Platforms and worker organisations | What standards to agree on pay and treatment | Earnings and condition evidence |
Outputs this topic produces
- Platform work prevalence and condition profiles
- Algorithmic management transparency reviews
- Social protection coverage analyses
- Evidence notes for regulation
What researchers ask about digital work.
- How does remote and hybrid work affect productivity, wellbeing and career progression?
- Who works on online platforms, under what terms and with what earnings?
- How do employment classification and social protection apply to platform workers?
- How does algorithmic control change the relationship between workers and organisations?
Flexibility and its costs.
Remote work expanded sharply during the pandemic and has partly persisted for jobs that allow it, mostly office-based and better-paid roles. It can reduce commuting and widen access for people with disabilities or care duties, but may weaken mentoring and informal learning, especially for early-career staff.
Labour platforms match workers to tasks such as delivery, ride-hailing, care or online micro-tasks. They offer flexibility and low barriers to entry, but often classify workers as independent contractors, which can leave them without minimum wages, sick pay or occupational safety coverage. Earnings can be volatile, and ratings and algorithms shape access to work.
Measuring digital work.
- 01
Surveys of remote work
Measuring prevalence and outcomes, using social and behavioural science. Self-reported productivity can be biased.
- 02
Platform data
Data from platforms on tasks and earnings. Access is controlled by platforms, and independent verification is limited.
- 03
Worker surveys
Surveys of platform workers on hours, earnings and conditions, analysed with econometrics. Platform workers are hard to sample.
- 04
Legal and institutional analysis
Comparing how jurisdictions classify and protect platform workers.
How it is studied, and what the methods cannot say.
- 01Social and behavioural scienceJob quality surveys · Social dialogue studies · Learner experience researchApplied here
- 02Artificial intelligence and machine learningSkills extraction from job postings · Job matching in employment servicesApplied here
- 03Economics and development economicsTask-based analysis of technology · Labour market institutions · Just transition economicsApplied here
- 04EconometricsDisplacement effects with linked data · Training programme evaluation · Workforce projectionsApplied 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 platform work?
What is algorithmic management?
Are platform workers employees?
Is remote work more productive?
How large is platform work?
What is the risk for early-career remote workers?
Does the platform assess individual labour platforms?
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