GCRI · Economic Development
Employment and technology
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.
Employment and technology in seven parts.
- 01The questionEmployment and technology: how the risk develops
- 02Across the domainsTechnology reshapes jobs in every domain
- 03IndicatorsWhat is measured, and how well
- 04SourcesWhere the evidence comes from
- 05DecisionsWho decides, and what the evidence serves
- 06Core questionsWhat experts ask about employment and technology
- 07Mechanisms and driversHow technology reshapes work in development
Employment and technology: how the risk develops.
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.
Automation may slow industrialisation
If manufacturing automates, factory jobs that once absorbed rural workers may be fewer, changing the traditional path to development.
Informality cushions and traps
Informal work absorbs people displaced from formal jobs, but with low pay, little protection and limited productivity growth.
Platforms create flexible work
Digital platforms offer income opportunities for drivers, couriers and online workers, often without social protection or bargaining.
Skills set who benefits
Workers with education and digital skills gain from technology; those without are more likely to be displaced or left behind.
Shock propagation model.
The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.
Shock propagation model
Employment and technology · National economy · Scenario: import price shock
Output impact by channel
Most affected sectors
| Food processing | ▼ 5 | 7568 | Backward |
| Transport | ▼ 4 | 3998 | Forward |
| Construction | ▼ 2 | 1818 | Backward |
| Retail | ▼ 3 | 8478 | Demand |
Fiscal room
local units, billions, illustrative
Technology reshapes jobs in every domain.
Findings are designed for the Exponential Technology series. The ILO's Trends 2025 report and its 2023 working paper on generative AI set the evidence base.
- Labour force survey data.
- Studies of technology adoption effects.
How employment and technology 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 employment and technology most, one at a time across their stated ranges.
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 |
|---|---|---|---|---|
| Employment by sector | percent of employment | annual | Labour force surveys | Published statistics |
| Informal employment rate | percent of employment | annual | ILOSTAT | Published statistics |
| Youth not in employment, education or training | percent of youth | annual | Labour force surveys | Published statistics |
| 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 | Published statistics |
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 |
|---|---|---|
| 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 |
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 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 |
Outputs this topic produces
- Employment structure and exposure profiles
- Platform work evidence summaries
- Skills and youth employment analyses
- Evidence notes for employment strategies
What experts ask about employment and technology.
- How are automation and AI changing tasks in developing and emerging economies?
- Will technology reduce the labour-cost advantage that supported manufacturing-led development?
- How can education systems prepare young people for changing labour markets?
- What happens to workers displaced by technological change?
- How does informality interact with technology adoption?
How technology reshapes work in development.
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.
Methods and their limits.
- 01
Task-based analysis
Mapping occupations to tasks and their exposure to technology. Task data are mostly from high-income economies.
- 02
Labour force surveys
Tracking employment by sector and occupation. Informal work is under-recorded.
- 03
Impact evaluation
Assessing training and employment programmes using causal inference.
- 04
Scenario modelling
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.
How it is studied, and what the methods cannot say.
- 01Economics and development economicsGrowth diagnostics · Structural transformation analysis · Distributional incidenceApplied here
- 02EconometricsShock transmission models · Panel analysis across countries · Nowcasting with alternative dataApplied here
- 03Causal inference and impact evaluationRandomised evaluations · Evaluating place-based policies · Regression discontinuityApplied 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.
Will automation stop developing countries from industrialising?
What is informal employment?
What is platform work?
Will automation end manufacturing-led development?
Is informal work included?
Does the platform forecast job losses?
Which workers are most exposed to automation?
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