Do Gender Inequality Index and Labor share of income go together across countries?

Across 129 countries the link before adjusting for anything is r = -0.25, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.29.

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

Comparing each country only with itself across 1990-2023, and setting aside what happened worldwide in each year, the link is -0.011 (130 countries, 3,772 country-years). Using year-to-year changes, it is +0.014.

Does Gender Inequality Index cause Labor share of income?

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.

Gender Inequality Index vs. Labor share of income

Holds up after adjusting for income

Across 129 countries, Gender Inequality Index and Labor share of income rise and fall together at r = -0.25 (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 = -0.57, q = 9.0e-5, allowing for the many pairs tested).

Comparing countries across the middle half of labor share of income, 42.6% to 58.6% of GDP, gender Inequality Index is better by 0.067 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0757 points.

Link before adjusting (r)-0.255p = 0.004 · 129 countries

After adjusting for national income-0.569p = 2.3e-12 · likely range [-0.68, -0.44]

p, allowing for regions0.000standard error 0.070 · 7 regions

Check for luck (q)9.0e-5across 18,622 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.

Gender Inequality Index · index, 0 to 1link with income -0.89 · proxy

Labor share of income · % of GDPlink with income +0.08 · independent

“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	-0.255	0.004	129
By rank (Spearman), not adjusted	-0.287	0.001	129
Standard (Pearson), adjusted for national income	-0.569	2.3e-12	129
By rank (Spearman), adjusted for national income	-0.499	2.0e-9	129

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 129 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: -0.315 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.597 (without TJK) to -0.553 (without QAT). 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: TJK (-0.028).

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	13	-0.57
Europe & Central Asia	45	-0.56
Latin America & Caribbean	22	-0.37
Middle East, North Africa, Afghanistan & Pakistan	15	-0.72
Sub-Saharan Africa	30	-0.53

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	43	-0.30
Middle third	43	-0.63
Richest third	43	-0.68

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	81	-0.55
Muslims	33	-0.52

By last European coloniser

	n	r
France	15	-0.42
Never a European colony	16	-0.65
Spain	18	-0.47
United Kingdom	34	-0.54

By official ideology

	n	r
Nationalist	16	-0.16
No strong official ideology	91	-0.60

By party system

	n	r
A few parties banned	17	-0.54
No parties banned	99	-0.58

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 = -0.553 after dropping 1 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 34years. 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 gender Inequality Index moved did not tend to see their labor share of income move with it.

Across countries (adjusted)-0.569for comparison

Within countries+0.215each country vs itself

…and vs the same year elsewhere-0.011q = 0.916 · 130 countries

Year-on-year changes+0.014q = 0.699

Read with care.

Much of the within-country association disappears once shared global year effects are removed, which points to a common worldwide trend rather than a country-level relationship.

3,772 country-years (one country in one year), 130 countries, 1990–2023, about 29 years each on average. Share of the variation that happens within countries over time: 12% for Gender Inequality Index, 12% for Labor share of income. 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

	index, 0 to 1	% of GDP	Years
Nigeria NGA	0.677	31.2%	2023
Chad TCD	0.67	48.5%	2023
Guinea GIN	0.609	42.2%	2023
Mauritania MRT	0.603	40.4%	2023
Niger NER	0.591	39.1%	2023
Cote d'Ivoire CIV	0.589	33.2%	2023
Sudan SDN	0.588	63.5%	2023
Benin BEN	0.573	61.7%	2023
Sierra Leone SLE	0.566	59.6%	2023
Togo TGO	0.564	50.1%	2023
Iraq IRQ	0.558	29.6%	2023
Cameroon CMR	0.558	64.4%	2023
Burkina Faso BFA	0.555	42.6%	2023
Lesotho LSO	0.534	66.1%	2023
Kenya KEN	0.526	54%	2023

United Nations Development Programme, Human Development Report 2025
Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table", American Economic Review, 105(10), 3150-3182, www.ggdc.net/pwt. Data: Penn World Table version 11.0, Groningen Growth and Development Centre, doi:10.34894/FABVLR.

Sources

Gender Inequality Index

United Nations Development Programme, Human Development Report Office · via Our World in Data · CC BY 3.0 IGO · updated annual

Built from deaths in childbirth, births to teenagers, seats in parliament, secondary schooling, and the share of women and men in the workforce. Five of those inputs are on the site and flagged. It is kept out of the gender score, for the same reason the HDI is kept out of the economy score.

Labor share of income

Labor share of GDP (Penn World Table)

Groningen Growth and Development Centre, University of Groningen · via DataverseNL · CC BY 4.0 · updated irregular (new versions every few years)

The share of a country's income (GDP) that is paid to people for their work: wages, salaries and benefits, plus an estimate of what the self-employed, such as farmers and shopkeepers, earn for their work. The rest goes to the owners of land, buildings and machines as profit, rent and interest. Where there is little data on the self-employed, the Penn World Table fills in a value by a fixed rule, so figures for poorer countries are rougher, and a few countries show the same value every year. No "better" direction is set.

How this was computed

Data used: most recent value in 2010-2025; values come from 2023. 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 18,622 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore Gender Inequality Index and Labor share of income

This page shows that two measures go together. It does not show that Gender Inequality Index changes Labor share of income, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.