Do Female labor participation and Government effectiveness go together across countries?

Across 177 countries the link before adjusting for anything is r = +0.26, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.22.

Does the link hold within countries over time, not just across countries?

Comparing each country only with itself across 1996-2024, and setting aside what happened worldwide in each year, the link is +0.015 (184 countries, 4,715 country-years). Using year-to-year changes, it is +0.026.

Does Female labor participation cause Government effectiveness?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

Female labor participation vs. Government effectiveness

Holds up after adjusting for income

Across 177 countries, Female labor participation and Government effectiveness 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 = +0.37, q = 0.0099, allowing for the many pairs tested).

Comparing countries across the middle half of female labor participation, 44.1% to 60.8% of women 15+, government effectiveness is better by 0.267 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.213 points.

Link before adjusting (r)+0.258p = 0.001 · 177 countries

After adjusting for national income+0.368p = 5.2e-7 · likely range [+0.23, +0.49]

p, allowing for regions0.000standard error 0.048 · 7 regions

Check for luck (q)0.010across 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

Government effectiveness · index (approx. -2.5 to 2.5)link with income +0.85 · proxy

“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	+0.258	0.001	177
By rank (Spearman), not adjusted	+0.215	0.004	177
Standard (Pearson), adjusted for national income	+0.368	5.2e-7	177
By rank (Spearman), adjusted for national income	+0.288	0.000	177

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 177 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: -0.109 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.350 (without IRQ) to +0.386 (without NGA). 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 (+0.019).

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	25	-0.02
Europe & Central Asia	48	+0.27
Latin America & Caribbean	29	+0.42
Middle East, North Africa, Afghanistan & Pakistan	22	+0.17
Sub-Saharan Africa	45	+0.28

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	59	+0.30
Middle third	59	+0.33
Richest third	59	+0.49

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	106	+0.34
Muslims	49	+0.30

By last european coloniser

	n	r
France	25	+0.35
Never a European colony	21	+0.43
Spain	19	-0.30
United Kingdom	55	+0.38

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 = +0.317 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 29years. 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 = +0.015, 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 government effectiveness move with it.

Across countries (adjusted)+0.368for comparison

Within countries+0.063each country vs itself

…and vs the same year elsewhere+0.015q = 0.868 · 184 countries

Year-on-year changes+0.026q = 0.296

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.

4,715 country-years (one country in one year), 184 countries, 1996–2024, about 26 years each on average. Share of the variation that happens within countries over time: 4% for Female labor participation, 7% for Government effectiveness. 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): +0.015. 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+	index (approx. -2.5 to 2.5)	Years
Madagascar MDG	82.9%	-0.863	2025, 2024
Solomon Islands SLB	82.3%	-0.869	2025, 2024
Nigeria NGA	80.7%	-1.01	2025, 2024
Tanzania TZA	80.3%	-0.292	2025, 2024
Burundi BDI	79.5%	-1.01	2025, 2024
Cambodia KHM	77.4%	-0.294	2025, 2024
Mozambique MOZ	77.1%	-0.716	2025, 2024
Uganda UGA	75.6%	-0.35	2025, 2024
Benin BEN	75.2%	-0.185	2025, 2024
Bolivia BOL	72.9%	-0.61	2025, 2024
Niger NER	72.8%	-0.77	2025, 2024
Moldova MDA	72.3%	0.258	2025, 2024
Angola AGO	72.2%	-0.785	2025, 2024
Liberia LBR	72%	-1.25	2025, 2024
Bahamas, The BHS	70.7%	0.709	2025, 2024

World Bank, World Development Indicators
Kaufmann, D. and A. Kraay, Worldwide Governance Indicators, World Bank

Sources

Female labor participation

Labor force participation rate, female (% of female population 15+)

International Labour Organization · CC BY 4.0 · updated quarterly

Government effectiveness

Government Effectiveness (estimate)

World Bank · CC BY 4.0 · updated annual

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 Government effectiveness

This page shows that two measures go together. It does not show that Female labor participation changes Government effectiveness, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.