The Global Centre for Risk and Innovation (GCRI)
Technology changes what work exists, where and for whom. In developing economies automation, digital platforms and AI meet large informal sectors, young populations and limited social protection, so effects differ from those in high-income countries. This page sets out how technological change affects employment and wages in developing economies, how it is measured, and what evidence serves the labour and economic ministries involved.
GCRI · In development · service level R0
Jobs and technology in developing economies is the study of how automation, digital platforms and AI change employment where informal work is common, populations are young and social protection is limited. Automation may slow industrialisation, informality cushions and traps, and skills decide who benefits.
For employment and technology, in GCRI's spatial-intelligence reading, the map opens on population density, night lights and true-colour imagery with true colour from NASA GIBS, framed on Africa; every other NASA layer, live feed and forecast waits in the layer list.
The interactive map needs JavaScript; the layer list and sources are below.
Layers 3
Activity and hazards, 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.
For employment and technology, in GCRI's spatial-intelligence reading, the map opens on population density, night lights and true-colour imagery with true colour from NASA GIBS, framed on Africa; every other NASA layer, live feed and forecast waits in the layer list.Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.
Layers and dates
The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.
Employment and technology · National economy · Scenario: import price shock
| Food processing | ▼ 5 | 7568 | Backward |
| Transport | ▼ 4 | 3998 | Forward |
| Construction | ▼ 2 | 1818 | Backward |
| Retail | ▼ 3 | 8478 | Demand |
local units, billions, illustrative
Jobs, transitions and education. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.
If manufacturing automates, factory jobs that once absorbed rural workers may be fewer, changing the traditional path to development.
Informal work absorbs people displaced from formal jobs, but with low pay, little protection and limited productivity growth.
Digital platforms offer income opportunities for drivers, couriers and online workers, often without social protection or bargaining.
Workers with education and digital skills gain from technology; those without are more likely to be displaced or left behind.
Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.
| Term | Definition |
|---|---|
| Premature deindustrialisation | A decline in manufacturing's share of jobs at lower income levels than in earlier industrialisers. |
| NEET | The share of young people who are not in employment, education or training. |
| Social protection | Policies that protect people against poverty and risks across the life cycle. |
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 |
|---|---|---|---|---|
| Employment by sector | percent of employment | annual | Labour force surveys | OFFICIAL |
| Informal employment rate | percent of employment | annual | ILOSTAT | OFFICIAL |
| Youth not in employment, education or training | percent of youth | annual | Labour force surveys | OFFICIAL |
| Platform workers | estimated count | per survey | Surveys and platform data | ESTIMATED |
| Occupational exposure to automation | percent of employment | per assessment | Task-based studies | ESTIMATED |
| Workers with social protection coverage | percent | per assessment | ILO World Social Protection data | OFFICIAL |
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.
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 |
|---|---|---|
| ILOSTAT | Employment, informality and youth indicators | Open, CC BY 4.0 |
| ILO World Employment and Social Outlook and World Social Protection Report | Labour market and protection trends | Public, ILO terms |
| World Bank studies on technology and jobs in developing countries | Evidence on automation and digital work | Public, World Bank terms |
| Online Labour Index (Oxford Internet Institute) | Online freelance labour activity | Open, licence to check |
| National labour force surveys | Employment structure | Licence per survey |
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 labour and employment | How to support workers through technological change | Exposure and youth evidence |
| Ministries of education and training | Which skills to develop | Skills evidence |
| Social protection agencies | How to extend coverage to informal and platform workers | Coverage evidence |
Historically, many economies developed by moving workers from agriculture into manufacturing, where labour-intensive production offered jobs and learning. Automation could reduce the number of such jobs and make it harder for later developers to follow that path, though evidence on the scale of this effect is still developing.
Technology affects tasks rather than whole jobs. Routine tasks are most exposed to automation, while tasks requiring social interaction, physical dexterity in unstructured settings or complex judgement are less so. AI may reach into tasks once considered secure.
In economies with large informal sectors, technology adoption is uneven. Digital tools can raise productivity for some informal workers, while others are excluded by lack of skills, connectivity or capital.
Mapping occupations to tasks and their exposure to technology. Task data are mostly from high-income economies.
Tracking employment by sector and occupation. Informal work is under-recorded.
Assessing training and employment programmes using causal inference.
Exploring employment under different adoption paths using complex systems methods. Highly sensitive to assumptions.
Estimates of jobs at risk vary greatly between studies and are not forecasts.
The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.