Do Employment in agriculture and Gender Development Index go together across countries?

Across 170 countries the link before adjusting for anything is r = -0.53, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.57.

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

Comparing each country only with itself across 1991-2023, and setting aside what happened worldwide in each year, the link is -0.158 (172 countries, 4,674 country-years). Using year-to-year changes, it is +0.015.

Does Employment in agriculture cause Gender Development Index?

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.

Employment in agriculture vs. Gender Development Index

Fades after adjusting for income

Across 170 countries, Employment in agriculture and Gender Development Index rise and fall together at r = -0.53 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.02, which could easily be chance (q = 0.95, 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 gender development index, 0.929 to 0.994 ratio of women's HDI to men's, employment in agriculture is 11.5% of employment lower at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 0.268% of employment the other way.

Link before adjusting (r)-0.531p = 1.0e-13 · 170 countries

After adjusting for national income+0.017p = 0.827 · likely range [-0.13, +0.17]

p, allowing for regions0.914standard error 0.150 · 7 regions

Check for luck (q)0.953across 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.

Employment in agriculture · % of employmentlink with income -0.86 · proxy

Gender Development Index · ratio of women's HDI to men'slink with income +0.65 · high

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	-0.531	1.0e-13	170
By rank (Spearman), not adjusted	-0.568	6.9e-16	170
Standard (Pearson), adjusted for national income	+0.017	0.827	170
By rank (Spearman), adjusted for national income	-0.014	0.853	170

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 170 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: +0.514 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.026 (without DJI) to +0.047 (without TCD). 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: DJI (-0.043).

Fragile: this link may not hold up.

sign flips when a single country is removed
correlation moves sharply once outliers are trimmed
sign flips once outliers are trimmed
direction is not consistent across regions

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	24	-0.29
Europe & Central Asia	47	+0.08
Latin America & Caribbean	26	-0.53
Middle East, North Africa, Afghanistan & Pakistan	22	-0.44
Sub-Saharan Africa	43	-0.10

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	57	+0.05
Middle third	56	+0.08
Richest third	57	-0.02

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	101	-0.22
Muslims	48	+0.01

By last european coloniser

	n	r
France	24	+0.14
Never a European colony	20	+0.28
Spain	18	-0.22
United Kingdom	54	+0.04

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 = -0.140 after dropping 11 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.

Loading…

Over time, within countries

Everything above compares countries with each other at one moment. This section compares each country only with itself over 33years. 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 = -0.158, 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 employment in agriculture moved did not tend to see their gender development index move with it.

Across countries (adjusted)+0.017for comparison

Within countries-0.503each country vs itself

…and vs the same year elsewhere-0.158q = 0.093 · 172 countries

Year-on-year changes+0.015q = 0.673

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.

4,674 country-years (one country in one year), 172 countries, 1991–2023, about 27 years each on average. Share of the variation that happens within countries over time: 11% for Employment in agriculture, 4% for Gender Development Index. 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): -0.158. 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 employment	ratio of women's HDI to men's	Years
Burundi BDI	85.3%	0.932	2025, 2023
Mozambique MOZ	73%	0.92	2025, 2023
Niger NER	73%	0.855	2025, 2023
Chad TCD	71.7%	0.787	2025, 2023
Madagascar MDG	69.2%	0.934	2025, 2023
Lao PDR LAO	68.6%	0.911	2025, 2023
Malawi MWI	65%	0.925	2025, 2023
Uganda UGA	64.9%	0.908	2025, 2023
Tanzania TZA	64.6%	0.951	2025, 2023
Mali MLI	63%	0.812	2025, 2023
Guinea-Bissau GNB	61.1%	0.878	2025, 2023
Ethiopia ETH	60.1%	0.886	2025, 2023
Congo, Dem. Rep. COD	58.5%	0.886	2025, 2023
Zambia ZMB	54.5%	0.949	2025, 2023
Zimbabwe ZWE	54.3%	0.944	2025, 2023

World Bank, World Development Indicators
United Nations Development Programme, Human Development Report 2025

Sources

Employment in agriculture

Employment in agriculture (% of total employment)

International Labour Organization · CC BY 4.0 · updated quarterly

Estimated with a model by the ILO. The classic measure of a country moving from farming to industry and services. It is one of the measures most closely linked to income on the site. No "better" direction is set, because a farming country is not doing badly by farming.

Gender Development Index

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

The HDI worked out for women divided by the HDI worked out for men. It is above 1 where women score higher, as they do in several countries on health. So no "better" direction is set: equality is the aim, and equality is the middle of the scale, not one end of it.

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 Employment in agriculture and Gender Development Index

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