Female labor participation vs. Out-of-pocket health spending
Holds up after adjusting for income
Across 173 countries, Female labor participation and Out-of-pocket health spending rise and fall together at r = -0.26 (on a scale from −1 to +1, where 0 means no link). It holds up almost unchanged when only countries with similar income (GDP per person) are compared (r = -0.26, q = 0.033, allowing for the many pairs tested).
Comparing countries across the middle half of female labor participation, 44.1% to 60.8% of women 15+, out-of-pocket health spending is better by 5.25% of health spending at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 4.79% of health spending.
Link before adjusting (r)-0.262p = 0.000 · 173 countries
After adjusting for national income-0.259p = 0.001 · likely range [-0.39, -0.11]
p, allowing for regions0.002standard error 0.051 · 7 regions
Check for luck (q)0.033across 12,497 pairs
How to read these.r runs from −1 (one goes up as the other goes down) to +1 (they rise together), and 0 means no link. Adjusting for national income means comparing countries with the same GDP per person, so differences in wealth can’t explain the link. Income is used in percentage terms, since an extra $1,000 means more to a poor country than to a rich one. p is how likely a link this strong would be if there were really none. Below 0.05 means it is unlikely to be chance. Very small values are written in short form: 1.1e-32 means a decimal point followed by 31 zeros and then 11, so very close to zero. The likely range is where the true figure probably lies, and a range that crosses zero means the link could easily be chance. Countries in the same region tend to resemble each other, so the third figure allows for that, which gives a fairer p. The standard error is the typical size of the error in r. Finally, testing this many pairs means some will look strong by luck alone. A q below 0.05 means this link is unlikely to be one of those.
Female labor participation · % of women 15+link with income +0.07 · independent
Out-of-pocket health spending · % of health spendinglink with income -0.34 · moderate
“Link with income” is how closely each measure tracks GDP per person across countries, from −1 to +1.
Every country as a dot
best-fit line line after adjusting
Four ways to measure the link
| Method | r | p | Countries |
|---|---|---|---|
| Standard (Pearson), not adjusted | -0.262 | 0.000 | 173 |
| By rank (Spearman), not adjusted | -0.221 | 0.004 | 173 |
| Standard (Pearson), adjusted for national income | -0.259 | 0.001 | 173 |
| By rank (Spearman), adjusted for national income | -0.212 | 0.005 | 173 |
The rank version uses only each country’s place in line, from lowest to highest, so a few extreme countries count for less. The adjusted and unadjusted versions use the same 173 countries: those that report both measures and GDP per person. So the gap between them comes from the adjustment, not from a change in which countries are counted. Drop in the size of r after adjusting: +0.003 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.290 (without NGA) to -0.234 (without AFG). The bar above shows that range on a scale from −1 to +1, with the solid line at the full result. The single country that moves r most: NGA (-0.032).
Within each region
Countries are not separate, unrelated cases, and neighbors share a lot. A link that holds inside each region is far more believable than one that shows up only when comparing regions.
| n | r | ||
|---|---|---|---|
| East Asia & Pacific | 23 | +0.04 | |
| Europe & Central Asia | 48 | +0.04 | |
| Latin America & Caribbean | 27 | -0.28 | |
| Middle East, North Africa, Afghanistan & Pakistan | 22 | -0.23 | |
| Sub-Saharan Africa | 45 | -0.06 |
By income third
If the link shows up only in the richest third of countries, it is a feature of rich countries, not a general pattern.
| n | r | ||
|---|---|---|---|
| Poorest third | 58 | -0.26 | |
| Middle third | 57 | -0.26 | |
| Richest third | 58 | +0.10 |
By largest religious group
Countries grouped by a label they share, such as main religion, rather than by a number. Groups of fewer than twelve countries are not shown. These groups differ in income, region and history all at once. So a gap between them is a place to start asking questions, not an answer.
| n | r | ||
|---|---|---|---|
| Christians | 104 | -0.06 | |
| Muslims | 49 | -0.02 |
By last european coloniser
Countries grouped by a label they share, such as main religion, rather than by a number. Groups of fewer than twelve countries are not shown. These groups differ in income, region and history all at once. So a gap between them is a place to start asking questions, not an answer.
| n | r | ||
|---|---|---|---|
| France | 25 | -0.19 | |
| Never a European colony | 21 | -0.04 | |
| Spain | 19 | -0.25 | |
| United Kingdom | 55 | -0.33 |
Without outliers (countries far outside the middle half of countries on either measure, by more than one and a half times the width of that middle half): r = -0.150 after dropping 9 countries.
Two series, one country
Both measures over the years for one country you pick. The two charts are stacked so they share only the years along the bottom.
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Over time, within countries
Everything above compares countries with each other at one moment. This section compares each country only with itself over 25years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.
Within countries over time, there is no link that is clearly more than chance (r = -0.011, comparing each country only with itself over the years, and each year only with the same year elsewhere). Whatever the cross-country picture shows, countries whose female labor participation moved did not tend to see their out-of-pocket health spending move with it.
Across countries (adjusted)-0.259for comparison
Within countries-0.051each country vs itself
…and vs the same year elsewhere-0.011q = 0.903 · 178 countries
Year-on-year changes+0.011q = 0.736
Read with care.
- Almost all of this indicator's variation is between countries, not within them, so the within-country estimate rests on a small part of the data.
4,213 country-years (one country in one year), 178 countries, 2000–2024, about 24 years each on average. Share of the variation that happens within countries over time: 4% for Female labor participation, 11% for Out-of-pocket health spending. The margins of error allow for each country appearing many times, and for each year’s value being close to the year before.
Year-to-year changes are a cautious test, not a final answer. Looking only at changes from one year to the next removes each country’s own trends. But it also makes measurement mistakes louder, which pulls the results toward zero. A near-zero result here is weak evidence of no link, not proof.
With one measure years behind the other
Same year (lag 0): -0.011. A peak away from zero does not show which comes first. These series change slowly — this year’s value is close to last year’s. That makes the curve smooth and nearly symmetric, whatever produced the data. Even a sharp peak at one lag (a gap of some years) would not prove that one measure changes the other.
This curve peaks at the edge of the window (lag +10), so it is still drifting where the search stops. That pattern usually reflects a slow trend left over in the data, not a link with real timing. The peak value should not be read as the “true” link at the right lag.
The data
% of women 15+ | % of health spending | Years | |
|---|---|---|---|
| Madagascar MDG | 82.9% | 27.4% | 2025, 2023 |
| Solomon Islands SLB | 82.3% | 3.16% | 2025, 2023 |
| Nigeria NGA | 80.7% | 71.9% | 2025, 2023 |
| Tanzania TZA | 80.3% | 27.9% | 2025, 2023 |
| Burundi BDI | 79.5% | 24.5% | 2025, 2023 |
| Cambodia KHM | 77.4% | 60.6% | 2025, 2023 |
| Mozambique MOZ | 77.1% | 10.4% | 2025, 2023 |
| Uganda UGA | 75.6% | 32.2% | 2025, 2023 |
| Benin BEN | 75.2% | 42.4% | 2025, 2023 |
| Bolivia BOL | 72.9% | 27.3% | 2025, 2023 |
| Niger NER | 72.8% | 51.5% | 2025, 2023 |
| Moldova MDA | 72.3% | 27.5% | 2025, 2023 |
| Angola AGO | 72.2% | 32.5% | 2025, 2023 |
| Liberia LBR | 72% | 63.2% | 2025, 2023 |
| Bahamas, The BHS | 70.7% | 27.5% | 2025, 2023 |
World Bank, World Development Indicators
World Bank, World Development Indicators
Sources
Female labor participation
Labor force participation rate, female (% of female population 15+)
International Labour Organization · CC BY 4.0 · updated quarterly
Out-of-pocket health spending
Out-of-pocket expenditure (% of current health expenditure)
World Health Organization Global Health Expenditure Database · CC BY 4.0 · updated quarterly
The share of a country's health spending paid by patients themselves when they get care. Health spending as a share of GDP is already on the site and shows how much is spent. This shows who pays.
How this was computed
Data used: most recent value in 2010-2025; values come from 2021–2025. To adjust for income, each measure is first predicted from GDP per person (in percentage terms, on a log scale) with a straight-line fit. Only what is left over is then compared. This is called a partial correlation. The line is drawn the standard way (ordinary least squares), which picks the line that comes closest to all the countries at once. The error margins allow for countries in the same region resembling each other, using 7 World Bank regions. The q-value corrects for testing all 12,497 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Female labor participation and Out-of-pocket health spending
This page shows that two measures go together. It does not show that Female labor participation changes Out-of-pocket health spending, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Female labor participation or Out-of-pocket health spending
- Extreme poverty rate vs Female labor participation before +0.37 → after adjusting +0.59
- Female labor participation vs Youth not in work or study before -0.47 → after adjusting -0.53
- Employment in agriculture vs Female labor participation before +0.21 → after adjusting +0.48
- Control of corruption vs Out-of-pocket health spending before -0.55 → after adjusting -0.47
- Female labor participation vs Old-age spending before -0.36 → after adjusting -0.46
- Female labor participation vs Vulnerable employment before +0.19 → after adjusting +0.46
- Female labor participation vs Women's political empowerment before +0.43 → after adjusting +0.46
- Alcohol consumption vs Female labor participation before +0.42 → after adjusting +0.45