Key findings
- Participation varies hugely. The median country has 69.6% of people aged 15 to 64 in the labour force. The range runs from 23.4% in Sao Tome and Principe to 89.3% in North Korea (World Bank, ILO modelled estimates, 181 of 186 countries at 2025).
- Women's participation is lower almost everywhere. The median for women is 61.3%, against 69.6% overall. In 23 of 186 countries women's rate is at least 20 points below the overall rate.
- The biggest gaps are in a few regions. Oman, Afghanistan, Iraq, Saudi Arabia and Iran have the largest gaps, with female rates between 5.3% and 34.9%.
- India's gap is large: 59.1% overall, 34.9% for women, a gap of 24.2 points. Nigeria has 83.3% overall and 81.9% for women.
- More people working does not mean more AI exposure. The link between participation and our AI exposure score is only 0.31. What matters is the type of work, not how many people do it.
What labour force participation means
Labour force participation is the share of people of working age who have a job or are actively looking for one. We use ages 15 to 64 throughout this article. People who are studying, caring for family full time, or who have stopped looking for work are not counted. A low rate does not always mean unemployment: it can mean that many people are outside the paid labour market altogether.
The figures come from the World Bank's World Development Indicators, which publish the ILO's modelled estimates. The ILO fills gaps in national surveys with a model so that countries can be compared. That means they are good for broad comparison, but they can differ from a country's own survey. We flag a few surprising cases below.
Highest and lowest participation
| Country | Overall | Female | Gap (points) |
|---|---|---|---|
| North Korea | 89.3% | 87.3% | 1.9 |
| Qatar | 88.1% | 63.6% | 24.5 |
| Iceland | 87.5% | 84.0% | 3.6 |
| Madagascar | 86.9% | 84.6% | 2.3 |
| Cambodia | 86.1% | 82.1% | 4.1 |
| Solomon Islands | 85.8% | 84.3% | 1.6 |
| Netherlands | 85.6% | 82.2% | 3.4 |
| Tanzania | 85.0% | 82.0% | 3.0 |
| Switzerland | 84.0% | 80.7% | 3.3 |
| Sweden | 83.9% | 82.3% | 1.6 |
The top of the list is a mix of very different places. Madagascar, Cambodia and Tanzania are farming economies where, in our occupation data, 53%, 29% and 52% of workers farm, and most adults, women included, work. Iceland, the Netherlands, Switzerland and Sweden reach 84% to 88% with women at 81% to 84%. Qatar is high overall (88.1%) but only 63.6% for women. North Korea shows the highest figure (89.3%), but this is an ILO model estimate for a country with very limited published labour data, so treat it with caution.
| Country | Overall | Female | Gap (points) |
|---|---|---|---|
| Sao Tome and Principe | 23.4% | 22.8% | 0.7 |
| Djibouti | 33.3% | 20.4% | 12.9 |
| Yemen | 34.0% | 4.8% | 29.1 |
| Somalia | 34.8% | 21.5% | 13.3 |
| Sudan | 37.9% | 14.6% | 23.3 |
| Afghanistan | 38.4% | 5.3% | 33.1 |
| Syria | 38.9% | 13.6% | 25.2 |
| Tajikistan | 40.4% | 23.2% | 17.2 |
| Mauritania | 41.9% | 27.1% | 14.7 |
| Nepal | 41.9% | 29.6% | 12.3 |
The bottom of the list includes several conflict-affected countries. Yemen (34.0%), Afghanistan (38.4%) and Syria (38.9%) all have female rates below 15% in these estimates.
The gender gap in participation
Most dots sit below the dashed line, which means women take part in paid work less than the national average. The distance below the line is the gap. The red dots are countries where the gap is 20 points or more. Most of the 23 are in the Middle East and North Africa, with South Asia next (Afghanistan, Pakistan, India and the Maldives).
A note on the measure: the gap here is the overall rate minus the female rate. Because the overall rate includes women, the true male-female gap is larger than the number shown. In Saudi Arabia, for example, the overall rate is 66.5% and the female rate is 34.9%.
The large economies
| Country | Overall | Female | Gap (points) | AI exposure |
|---|---|---|---|---|
| Japan | 82.3% | 77.0% | 5.3 | 4.92 |
| Germany | 80.0% | 76.0% | 4.0 | 5.30 |
| United Kingdom | 76.7% | 73.5% | 3.2 | 5.08 |
| Russia | 76.1% | 71.9% | 4.2 | 4.62 |
| China | 74.0% | 69.6% | 4.4 | 4.48 |
| France | 74.0% | 71.1% | 2.9 | 5.06 |
| United States | 73.7% | 69.0% | 4.7 | 5.06 |
| South Korea | 71.2% | 64.4% | 6.8 | 4.85 |
| Brazil | 71.0% | 61.5% | 9.5 | 4.13 |
| Indonesia | 70.1% | 55.9% | 14.3 | 3.44 |
| Saudi Arabia | 66.5% | 34.9% | 31.6 | 4.82 |
| Mexico | 66.3% | 51.9% | 14.4 | 3.83 |
| Bangladesh | 61.3% | 41.5% | 19.8 | 3.21 |
| Turkiye | 60.8% | 42.5% | 18.3 | 4.11 |
| India | 59.1% | 34.9% | 24.2 | 3.26 |
| Pakistan | 54.7% | 25.6% | 29.2 | 3.31 |
| Egypt | 47.4% | 19.9% | 27.5 | 3.87 |
India stands out: overall participation is 59.1% but only 34.9% of women are in the labour force, a gap of 24.2 points. Among the large economies in the table only Saudi Arabia (31.6), Pakistan (29.2) and Egypt (27.5) have wider gaps. Japan (82.3%) and Germany (80.0%) have the highest participation of the large economies.
Does participation connect to AI exposure?
| Overall participation | Countries | Average AI exposure |
|---|---|---|
| Under 55% | 32 | 3.71 |
| 55% to 64% | 32 | 3.79 |
| 65% to 74% | 64 | 4.13 |
| 75% and above | 58 | 4.35 |
Only loosely. Countries where at least 75% of people are in the labour force average 4.35, compared with 3.71 where participation is under 55%. The overall correlation is 0.31, which is weak. High participation is found in both rich countries with many office workers and poor countries with many farmers, so the number says little about AI exposure on its own. For the link between gender and AI exposure by occupation, see our gender gap analysis.
What this means
Participation tells you how many people are available to work and how many are shut out of paid work. For countries with a large gap between women and everyone else, AI is a double-edged question. If women are concentrated in clerical and office jobs, as in many countries, AI affects them first. In countries where few women are in paid work at all, the more pressing issue is access to jobs, not automation.
Rich countries with high participation and strong childcare, such as Iceland and Sweden, show that a high rate is possible alongside high AI exposure (4.95 and 5.32). The lesson is that the two numbers are about different things.
Limits to keep in mind
- Modelled estimates. These are ILO model-based numbers via the World Bank, which can differ from national surveys.
- Years. 181 of 186 countries are at 2025. The rest are 2021 to 2023.
- Definition. We standardised on ages 15 to 64. Some other sources use ages 15 and over, which gives different numbers.
- Participation is not employment. It includes people looking for work.
Look up any country's participation rate
Open a country page to see participation, unemployment, GDP and human development side by side.
Open the explorer →wb_economy.labor_force_participation_pct and _female_pct). We cover 186 countries with both values. The gap is the overall rate minus the female rate, so the true male-female gap is larger. Median figures are simple medians across countries and do not weight by population. AI exposure bands use each country's weighted average exposure (stats.weighted_avg_ai_exposure) and simple averages. Correlation is Pearson's r. Calculations are ours.
AI exposure scores are model-estimated: an AI language model (Claude Opus 4.7) scored each ISCO-08 occupation group on 28 May 2026, guided by published research. They are informed judgements, not measurements, and not forecasts of job losses. Read the full methodology and how the scores were checked.
Frequently asked questions
What does labour force participation measure?
Which countries have the lowest participation?
How large is the gender gap in participation?
Does higher participation mean more AI exposure?
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Sources
- World Bank (2026) - World Development Indicators, labour force participation rate, ages 15-64 (modeled ILO estimate), 2021 to 2025 (CC BY 4.0)
- ILO (2026) - ILOSTAT modelled estimates
- WorldJobsData (2026) - Occupation-level AI exposure scores, methodology
Spotted an error or know a better source? Report it and we will check it against the source data.