Do Gender Inequality Index and Government spending per capita go together across countries?

Across 43 countries the link before adjusting for anything is r = -0.69, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.82.

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

Comparing each country only with itself across 2007-2023, and setting aside what happened worldwide in each year, the link is +0.174 (43 countries, 718 country-years). Using year-to-year changes, it is +0.051.

Does Gender Inequality Index cause Government spending per capita?

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. Government spending per capita

Fades after adjusting for income

Across 43 countries, Gender Inequality Index and Government spending per capita rise and fall together at r = -0.69 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.06, which could easily be chance (q = 0.87, allowing for the many pairs tested, and allowing for countries in the same region being alike). So the first figure mostly shows richer countries differing from poorer ones, not a link between these two things. Caution: sign flips when a single country is removed.

Comparing countries across the middle half of government spending per capita, $21,202 to $34,936, gender inequality index is better by 0.0832 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 0.00862 points the other way.

Link before adjusting (r)-0.687p = 3.7e-7 · 43 countries

After adjusting for national income+0.063p = 0.692 · likely range [-0.25, +0.36]

p, allowing for regions0.765standard error 0.197 · 5 regions

Check for luck (q)0.874across 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.

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

Government spending per capita · current international dollarslink with income +0.92 · proxy

Both of these rise and fall closely with national income, so a strong link between them before adjusting was close to certain. Read the adjusted figure, not the first one.

“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	-0.687	3.7e-7	43
By rank (Spearman), not adjusted	-0.823	1.3e-11	43
Standard (Pearson), adjusted for national income	+0.063	0.692	43
By rank (Spearman), adjusted for national income	-0.259	0.098	43

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 43 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: +0.624 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.180 (without LUX) to +0.323 (without IRL). 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: IRL (+0.260).

Fragile: this link may not hold up.

sign flips when a single country is removed
leave-one-out range spans 0.50 in r
correlation moves sharply once outliers are trimmed
sign flips once outliers are trimmed

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
Europe & Central Asia	30	-0.24

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	15	+0.16
Middle third	14	-0.27
Richest third	14	-0.21

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	35	+0.03

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 = -0.385 after dropping 5 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 17years. 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 = +0.174, 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 government spending per capita move with it.

Across countries (adjusted)+0.063for comparison

Within countries-0.703each country vs itself

…and vs the same year elsewhere+0.174q = 0.359 · 43 countries

Year-on-year changes+0.051q = 0.190

The direction flips. Comparing each country only with itself gives -0.70. Also taking out what happened to the whole world each year gives +0.17. So the first figure was picking up a trend both measures share with worldwide progress, not a link between them.

Read with care.

Almost all of the second indicator's variation is between countries, so the within-country estimate rests on a small part of the data.
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.

718 country-years (one country in one year), 43 countries, 2007–2023, about 17 years each on average. Share of the variation that happens within countries over time: 21% for Gender Inequality Index, 7% for Government spending per capita. 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): +0.174. 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.

The data

	index, 0 to 1	current international dollars	Years
Indonesia IDN	0.423	$3,279	2023, 2022
Colombia COL	0.393	$11,382	2023, 2024
Brazil BRA	0.39	$10,810	2023, 2021
Mexico MEX	0.358	$7,585	2023, 2024
Turkiye TUR	0.227	$10,287	2023, 2020
Romania ROU	0.227	$21,349	2023, 2024
Costa Rica CRI	0.217	$11,794	2023, 2024
Hungary HUN	0.213	$22,871	2023, 2024
Bulgaria BGR	0.208	$16,433	2023, 2024
Slovak Republic SVK	0.176	$22,559	2023, 2024
United States USA	0.169	$34,073	2023, 2024
Latvia LVA	0.117	$19,891	2023, 2024
Greece GRC	0.103	$21,055	2023, 2024
Chile CHL	0.102	$9,208	2023
Czechia CZE	0.088	$24,719	2023, 2024

United Nations Development Programme, Human Development Report 2025
OECD databases as named on each indicator: PISA, Social Expenditure Database (SOCX), Income Distribution Database, Development Assistance Committee statistics, Family Database, Government at a Glance, Corporate Tax Statistics, employment and environment statistics

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.

Government spending per capita

General government expenditure per capita

OECD · via Our World in Data · CC BY 4.0 (OECD terms and conditions) · updated annual, except the snapshots noted per indicator

All levels of government, in current dollars adjusted for what money buys in each country (purchasing power). So it grows over time with inflation as well as differing across countries. Where it is used for adjusting, it is measured in percentage terms (on a log scale), like income. Compared with national income, this measure answers a question nobody is asking. It is money per person set against money per person. So it moves with GDP per capita at about +0.86. Its development loading, which shows how much a measure just reflects how developed a country is, is +0.92. Rich countries spend far more per person because they have far more per person, and almost all of that figure comes from dividing by people. To ask whether government is a larger part of what a country has, you need a share of GDP, not an amount per head. Three measures here do that: government consumption, government expense and tax revenue. Against income those run +0.12, +0.29 and +0.32. That is a much smaller answer, and the real one.

How this was computed

Data used: most recent value in 2010-2025; values come from 2020–2024. 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 5 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 Gender Inequality Index and Government spending per capita

This page shows that two measures go together. It does not show that Gender Inequality Index changes Government spending per capita, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.