GCRI · Economic Development
Digital economy and inclusion
Digital technologies change how firms sell, how people pay, learn and find work, and how governments deliver services. Gains depend on connectivity, affordability, skills and trust, and many people and small firms remain excluded. This page sets out how digital adoption drives development and where it excludes, how inclusion is measured, and what evidence serves the ministries, regulators and agencies working to widen access.
Digital economy and inclusion on the record.
Digital economy and inclusion: how the risk develops.
Technology diffusion and inequality. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Coverage is not use
Many people live under mobile broadband coverage but do not use the internet, because of device costs, data prices, skills or relevance.
Digital payments widen access
Mobile money and digital payments bring people into formal finance, lowering transaction costs and enabling transfers.
Gaps follow existing divides
Women, rural residents, older people and people with disabilities are less likely to be online, reinforcing other inequalities.
Platforms concentrate gains
Digital platforms connect buyers and sellers widely, but market power can let a few platforms capture much of the value.
Connected activity in Africa.
Night lights, population density and true-colour imagery over Africa, 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
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 Africa.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 HLS S30Sources 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 HLS S30 (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
Digital economies change access to services across domains.
Findings are designed for the Exponential Technology series. UNCTAD's Digital Economy Report 2024 sets out environmental and development dimensions.
- Inclusion surveys.
- Evaluations of digital programmes.
Regional development dashboard.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
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 |
|---|---|---|---|---|
| Individuals using the internet | percent of population | annual | ITU estimates and household surveys | ESTIMATED |
| Mobile broadband usage gap | percent of population covered but not using | annual | GSMA and ITU data | ESTIMATED |
| Account ownership including mobile money | percent of adults | per survey | Global Findex | REPORTED |
| Gender gap in mobile internet use | percent difference | annual | Mobile gender gap surveys | REPORTED |
| Cost of entry-level mobile broadband | percent of monthly income | annual | Affordability data | Published statistics |
| Firms selling online | percent of firms | per survey | Business surveys | REPORTED |
Model digital economy and inclusion as a system. Run the cascade.
Assembled from the Nexus Standards area profile Financial and economic 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
- Sea-level rise
- Dam failure
- Coastal flood (set off downstream)
- Storm surge (set off downstream)
- High-tide flooding (set off downstream)
- Coastal erosion (set off downstream)
- Saltwater intrusion (set off downstream)
- Estuary flood (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Mass displacement (set off downstream)
- Coastal habitat squeeze (set off downstream)
- Wetland loss (set off downstream)
- Mangrove loss (set off downstream)
- Groundwater flood (set off downstream)
- Widespread power outage (set off downstream)
- Water supply shortfall (set off downstream)
- Climate-driven migration and relocation (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-14Conformance and crosswalk to external standardsNRS-H59Hazard family: Economic shocks and governance failure (NHS-1 soc.economic)NRS-H113Hazard family: Financial-system fragility (NHS-1 soc.financial-system)NRS-H118Hazard family: Organised crime and illicit economies (NHS-1 soc.crime)NRS-H114Hazard family: Macroeconomic and fiscal shocks (NHS-1 soc.macro-fiscal)NRS-H61Hazard family: Cascading failure (NHS-1 sys.cascade)NRS-U27Universe platform profile: Sovereign Capital Nexus
Observation
- IMF data services (WEO, IFS, FSI via SDMX) (listed for reference)
- BIS Data Portal (credit, debt service, property prices, banking statistics) (listed for reference)
- World Bank Open Data and World Development Indicators API (listed for reference)
- OECD Data Explorer (SDMX API) (listed for reference)
- Macroeconomic Dashboard
- Debt service as a proportion of exports of goods and services
- Financial Soundness Indicators
Example geography
- Western Europe: 152 hexagonal cells
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Rain rate, last half hour (IMERG), NASA GIBS
- Vegetation disturbance alerts, 30 m (OPERA DIST-ALERT), NASA GIBS
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 |
|---|---|---|
| ITU Facts and Figures and ICT indicators | Internet use and connectivity by country | Public, ITU terms (to check) |
| World Bank Global Findex Database | Account ownership and digital payments | Open, CC BY 4.0 |
| GSMA State of Mobile Internet Connectivity and Mobile Gender Gap reports | Coverage, usage gaps and gender gaps | Public reports, GSMA terms |
| Alliance for Affordable Internet affordability data | Broadband prices relative to income | Public, terms to check |
| UNCTAD digital economy statistics | E-commerce and ICT trade | Public, UNCTAD terms |
How digital economy and inclusion 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.
Sensitivity analysis
Which inputs move the result for digital economy and inclusion most, one at a time across their stated ranges.
Who decides, and what the evidence serves.
Economic Development 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 |
|---|---|---|
| Ministries of digital economy and ICT | Which inclusion barriers to address first | Inclusion profiles; usage gap analyses |
| Telecom and financial regulators | How to improve affordability and competition | Affordability evidence |
| Development partners | Which digital inclusion programmes to support | Evaluation evidence |
Outputs this topic produces
- Digital inclusion profiles by group and region
- Affordability and usage gap analyses
- Evaluations of digital payment and connectivity programmes
- Evidence notes for digital strategies
What experts ask about the digital economy.
- Who has access to digital infrastructure and devices, and who does not?
- How do digital payments and platforms change the costs of doing business?
- Which skills determine whether people benefit from digital tools?
- How do digital platforms affect competition, wages and working conditions?
- What new risks, such as outages, fraud and data misuse, come with digitalisation?
How diffusion shapes inclusion.
Technologies spread unevenly. Adoption depends on infrastructure, affordability, skills, relevance and trust. Divides appear between urban and rural areas, men and women, young and old, and large and small firms. Mobile connectivity has extended access widely, but quality and affordability vary.
Digital tools can reduce transaction costs and information gaps. Mobile money has expanded access to payments and savings in many economies; online platforms connect small producers to markets. Benefits depend on complementary factors: reliable power, literacy, legal protections and competitive markets.
Digitalisation also concentrates. Network effects favour large platforms, which can capture value and set terms for users. Dependence on digital systems creates exposure to outages, cyber incidents and fraud.
Methods and their limits.
- 01
Access and use surveys
Household and firm surveys of digital adoption. Rapid change makes data quickly outdated.
- 02
Transaction data
Payment and platform records. Access is restricted and raises privacy concerns.
- 03
Impact evaluation
Studies of digital programmes using causal inference. Effects vary with context.
- 04
Market structure analysis
Analysis of platform competition using economics. Digital markets change fast.
Effects of digital technologies are often measured over short periods; long-run effects on productivity and distribution remain uncertain.
How it is studied, and what the methods cannot say.
- 01Economics and development economicsGrowth diagnostics · Structural transformation analysis · Distributional incidenceApplied here
- 02Causal inference and impact evaluationRandomised evaluations · Evaluating place-based policies · Regression discontinuityApplied here
- 03EconometricsShock transmission models · Panel analysis across countries · Nowcasting with alternative dataApplied 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 the usage gap?
What is mobile money?
Why is there a gender gap in internet use?
Does digitalisation reduce inequality?
Are digital payments covered?
What data does the topic use?
What risks come with digitalisation?
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