Do Gender Inequality Index and Informal employment go together across countries?

Across 133 countries the link before adjusting for anything is r = +0.91, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.90.

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

Comparing each country only with itself across 2000-2023, and setting aside what happened worldwide in each year, the link is -0.003 (81 countries, 1,093 country-years). Using year-to-year changes, it is -0.030.

Does Gender Inequality Index cause Informal employment?

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. Informal employment

Holds up after adjusting for income

Across 133 countries, Gender Inequality Index and Informal employment rise and fall together at r = +0.91 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.59 (q = 0.013, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a moderate positive link remains.

Comparing countries across the middle half of informal employment, 16.6% to 84.3% of employment, gender inequality index is worse by 0.347 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.236 points.

Link before adjusting (r)+0.910p = 4.6e-52 · 133 countries

After adjusting for national income+0.592p = 7.9e-14 · likely range [+0.47, +0.69]

p, allowing for regions1.0e-5standard error 0.033 · 6 regions

Check for luck (q)0.013across 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

Informal employment · % of employmentlink with income -0.88 · 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	+0.910	4.6e-52	133
By rank (Spearman), not adjusted	+0.903	7.4e-50	133
Standard (Pearson), adjusted for national income	+0.592	7.9e-14	133
By rank (Spearman), adjusted for national income	+0.474	9.2e-9	133

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 133 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: +0.319 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.556 (without GUY) to +0.624 (without ARM). 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: GUY (-0.036).

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	14	+0.33
Europe & Central Asia	40	+0.12
Latin America & Caribbean	26	+0.53
Sub-Saharan Africa	39	+0.50

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	45	+0.42
Middle third	44	+0.47
Richest third	44	+0.65

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	88	+0.60
Muslims	30	+0.69

By last european coloniser

	n	r
France	19	+0.62
Never a European colony	12	+0.06
Spain	16	+0.78
United Kingdom	39	+0.49

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 = +0.592 after dropping 0 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 24years. 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 = -0.003, 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 informal employment move with it.

Across countries (adjusted)+0.592for comparison

Within countries+0.317each country vs itself

…and vs the same year elsewhere-0.003q = 0.990 · 81 countries

Year-on-year changes-0.030q = 0.680

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.
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.

1,093 country-years (one country in one year), 81 countries, 2000–2023, about 14 years each on average. Share of the variation that happens within countries over time: 2% for Gender Inequality Index, 4% for Informal employment. 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): -0.003. 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 employment	Years
Nigeria NGA	0.677	93.1%	2023, 2024
Somalia, Fed. Rep. SOM	0.675	81.9%	2023, 2019
Chad TCD	0.67	96.9%	2023, 2018
Afghanistan AFG	0.661	86.1%	2023, 2021
Liberia LBR	0.646	89.3%	2023, 2017
Guinea-Bissau GNB	0.632	94.8%	2023, 2022
Haiti HTI	0.618	91.6%	2023, 2012
Mali MLI	0.612	94.4%	2023
Congo, Dem. Rep. COD	0.604	96.3%	2023, 2020
Mauritania MRT	0.603	89.3%	2023, 2019
Niger NER	0.591	98.5%	2023, 2022
Cote d'Ivoire CIV	0.589	92.1%	2023, 2022
Sudan SDN	0.588	94.2%	2023, 2022
Madagascar MDG	0.584	96.1%	2023, 2022
Malawi MWI	0.581	92.7%	2023, 2024

United Nations Development Programme, Human Development Report 2025
United Nations Global SDG Indicators Database; series compiled by the International Labour Organization (8.3.1) and the UN Office on Drugs and Crime (16.3.2)

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.

Informal employment

Share of employment that is informal

International Labour Organization · via Our World in Data · CC BY 3.0 IGO · updated annual

Workers with no contract, no social protection or no registered employer, as the ILO (the UN's labour agency) makes them comparable across national labour force surveys. The World Bank's copy of this series was retired, so it comes from the UN's database for the Sustainable Development Goals.

How this was computed

Data used: most recent value in 2010-2025; values come from 2012–2025. 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 6 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 Informal employment

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