Innovation and society
Innovation raises productivity and opens new possibilities, but its gains and costs are not shared evenly. Automation reshapes work, digital platforms reorder markets, and access to new technologies follows income, place, gender and age. This page sets out how the social effects of innovation develop and accumulate, how inequality in adoption and benefit is measured, and what the labour, education, competition and social institutions that hold the decisions need from the evidence.
- 01In briefDefinition and scope
- 02The question4 mechanisms
- 03Key terms3
- 04FieldH3 field plate
- 05Indicators7
- 06PipelineRun graph
- 07Sources6
- 08Decision holders4
- 09Methods3
GRF · Established · service level R0
The short answer.
The social impact of innovation is how its gains and costs fall across people and places. Automation reshapes tasks before jobs, digital platforms reorder markets, leading firms pull ahead of the rest, and access to new technologies follows income, place, gender and age, while public trust shapes uptake.
People and exposure in the Andes.
Population density, night-time lights, air quality, heat and floods, framed on the Andes as an example geography for this subject; each layer is a dated NASA product.
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People and exposure
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Latest available
NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Natural Earth
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Population density, night-time lights, air quality, heat and floods, framed on the Andes.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
Data: NASA GIBS · ASTER GDEM · Natural EarthSources 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 H3 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 H3 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 H3 cells where shown (modified). 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 H3 cells where shown (modified). NASA open data
- OMI nitrogen dioxide (Aura) and OMPS aerosol index (Suomi NPP), NASA; distributed by the NASA GES DISC; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 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 H3 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 H3 cells where shown (modified). NASA open data
- Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
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
- Nitrogen dioxide, lower atmosphere column: OMI Aura, NASA GES DISC · 10 Oct 2026
- Land surface temperature, day, 8-day composite: VIIRS NOAA-20 Level 3, 8-day, NASA LANCE · 14 Sep 2026
- Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
Innovation and society: how the risk develops.
Inequality and social impacts. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
Tasks change before jobs
New technologies automate some tasks and create others. Workers whose tasks are routine lose bargaining power before jobs disappear, and transition costs fall on them unless institutions share them.
Leading firms pull ahead
Digital markets with network effects and low marginal cost reward leading firms. Gaps in productivity and pay between frontier firms and the rest can widen even as the average rises.
Access follows advantage
Connectivity, skills and money decide who uses new services first. Those left out can lose access to public services as these move online, turning a digital divide into a service divide.
Trust shapes uptake
People adopt what they trust. Harms that are visible and unaddressed, from biased decisions to data misuse, slow beneficial uses and harden public opposition to whole technologies.
Terms used on this page.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Digital divide | Gaps in access to and use of digital technologies between groups or places. |
| Task exposure | The share of an occupation's tasks a technology could perform, which differs from actual job loss. |
| Frontier firm | One of the most productive firms in its industry, against which the productivity gap is measured. |
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 with basic or above basic digital skills | percent of individuals by age, sex and income | biennial | Household ICT usage surveys | OFFICIAL |
| Households with broadband access | percent of households | annual | Household surveys and regulator data | OFFICIAL |
| Employment in occupations with high task exposure to automation | percent of employment | periodic | Labour force surveys with task models | MODELLED |
| Productivity gap between frontier and other firms | ratio of labour productivity | annual | Firm-level microdata | ESTIMATED |
| Women among inventors on patent applications | percent of inventors | annual | Patent databases with name-gender matching | ESTIMATED |
| Public trust in science and new technology | percent of respondents | periodic | Attitude surveys | REPORTED |
| Adult participation in learning | percent of adults in the reference period | periodic | Adult education and labour force surveys | OFFICIAL |
From sources to decisions.
The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.
Fig. 02Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.
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 |
|---|---|---|
| Eurostat ICT usage in households and by individuals | Digital skills, internet use and use of online public services by group | Open, attribution (Eurostat reuse policy) |
| ITU World Telecommunication/ICT Indicators Database | Connectivity and access indicators by country | Licensed, ITU terms; key indicators free |
| ILOSTAT (International Labour Organization) | Employment, occupations and earnings by country | Open, attribution |
| OECD Survey of Adult Skills (PIAAC) | Literacy, numeracy and problem-solving skills of adults | Open, attribution (OECD terms); microdata by access |
| WIPO data on gender and innovation | Women's participation as inventors in international patent applications | Free, WIPO terms |
| Special Eurobarometer surveys on science and technology | Public attitudes to science, AI and emerging technologies | Free reuse, attribution |
Who decides, and what the evidence serves.
Innovation Nexus 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 labour and social affairs | How to support workers through task change: retraining, income support and transition services | Task exposure estimates with method stated; comparative reviews of transition policies |
| Education and skills authorities | Where to invest in digital and adult learning, and for whom | Skills and participation indicators by group and region |
| Competition authorities | Whether market power in digital markets limits entry and the spread of gains | Productivity gaps; concentration evidence in relevant markets |
| Telecommunications regulators and digital government agencies | How to keep public services reachable for people offline or with low digital skills | Access and skills gaps by group and place; service channel records |
Outputs this topic produces
- Distributional maps of adoption, skills and access by group and place
- Transition scenario sets for occupations with high task exposure, with assumptions stated
- Comparative reviews of labour, skills and social protection responses to automation
- Dialogue designs that bring affected groups into technology governance
What experts ask.
The core question is who gains, who loses and who decides.
- How does a technology change the demand for different skills and kinds of work?
- Who gains access to new products and services, and who is excluded by cost, connectivity or design?
- How are the gains from innovation shared between workers, firms, consumers and regions?
- Does automation of decisions affect fairness, privacy or due process?
- How do affected people take part in decisions about technologies that change their lives?
Mechanisms and drivers.
Technologies tend to complement some skills and substitute for others. Historically, gains in productivity have come with periods of disruption for particular occupations and places. Whether gains are broadly shared depends on institutions: education and training systems, labour market policies, competition policy, social protection and taxation.
Access gaps arise where innovations require infrastructure, money or skills that not everyone has. Design choices matter too: systems trained or tested on narrow populations may work less well for others. Trust is a social effect in its own right; when people feel innovation is done to them rather than with them, resistance and backlash can follow.
How it is studied, and where the methods stop.
| Method or data | What it shows | Limits |
|---|---|---|
| Labour market and task analysis | Exposure of occupations to technological change | Exposure does not equal job loss |
| Distributional analysis | How gains and losses fall across groups | Long-run effects are hard to isolate |
| Algorithmic audits | Fairness of automated systems | Access to systems and data is often restricted |
| Participatory and deliberative methods | Public values and priorities | Small groups may not represent everyone |
How it is studied, and what the methods cannot say.
- Policy, institutional and decision scienceMandate and gap mapping · Programme evaluation design · Comparative regulatory analysisApplied here
- Artificial intelligence and machine learningCoding policy and legal texts · Mapping patents and papers to technologiesApplied here
- Systems engineeringRequirements traceability for AI governance · Readiness gates for transfer programmesApplied here
Questions and answers.
Will new technologies reduce total employment?
What is task exposure?
Does Innovation Nexus take positions on labour policy?
Does innovation always increase inequality?
Why involve affected people?
Does the platform take positions on particular technologies?
Around Innovation Nexus.
Bring your expertise to this field.
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