Do Food insecurity and Gender Development Index go together across countries?

Across 151 countries the link before adjusting for anything is r = -0.61, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.56.

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

Comparing each country only with itself across 2015-2023, and setting aside what happened worldwide in each year, the link is +0.028 (125 countries, 1,040 country-years). Using year-to-year changes, it is +0.030.

Does Food insecurity 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.

Food insecurity vs. Gender Development Index

Fades after adjusting for income

Across 151 countries, Food insecurity and Gender Development Index rise and fall together at r = -0.61 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.16, which could easily be chance (q = 0.40, 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: direction is not consistent across regions.

Comparing countries across the middle half of gender development index, 0.939 to 0.996 ratio of women's HDI to men's, food insecurity is better by 14.4% of population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 2.79% of population.

Link before adjusting (r)-0.611p = 8.5e-17 · 151 countries

After adjusting for national income-0.158p = 0.053 · likely range [-0.31, +0.00]

p, allowing for regions0.235standard error 0.120 · 7 regions

Check for luck (q)0.404across 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.

Food insecurity · % of populationlink with income -0.83 · 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	-0.611	8.5e-17	151
By rank (Spearman), not adjusted	-0.564	4.7e-14	151
Standard (Pearson), adjusted for national income	-0.158	0.053	151
By rank (Spearman), adjusted for national income	-0.077	0.348	151

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 151 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: +0.452 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.210 (without MLI) to -0.129 (without AFG). 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: MLI (-0.051).

Fragile: this link may not hold up.

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	23	-0.47
Europe & Central Asia	46	+0.10
Latin America & Caribbean	25	-0.12
Middle East, North Africa, Afghanistan & Pakistan	14	-0.58
Sub-Saharan Africa	38	-0.06

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	51	-0.19
Middle third	50	+0.05
Richest third	50	-0.03

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	96	-0.22
Muslims	35	-0.31

By last european coloniser

	n	r
France	20	+0.17
Never a European colony	16	-0.60
Spain	14	-0.00
United Kingdom	49	-0.22

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 = -0.147 after dropping 7 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 9years. 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 = +0.028, 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 food insecurity moved did not tend to see their gender development index move with it.

Across countries (adjusted)-0.158for comparison

Within countries+0.137each country vs itself

…and vs the same year elsewhere+0.028q = 0.907 · 125 countries

Year-on-year changes+0.030q = 0.759

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.

1,040 country-years (one country in one year), 125 countries, 2015–2023, about 8 years each on average. Share of the variation that happens within countries over time: 2% for Food insecurity, 3% 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): +0.028. 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 +4), 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 population	ratio of women's HDI to men's	Years
Sierra Leone SLE	89.8%	0.83	2023
Haiti HTI	83.2%	0.932	2023
Malawi MWI	81.7%	0.925	2023
Congo, Dem. Rep. COD	81.5%	0.886	2023
Liberia LBR	80.8%	0.865	2023
Afghanistan AFG	80.8%	0.66	2023
Comoros COM	79.7%	0.929	2023
Somalia, Fed. Rep. SOM	79.5%	0.793	2021, 2023
Angola AGO	78.5%	0.906	2021, 2023
Nigeria NGA	74.8%	0.892	2023
Kenya KEN	73.9%	0.944	2023
Madagascar MDG	72.6%	0.934	2023
Burundi BDI	70.8%	0.932	2023
Zimbabwe ZWE	68.8%	0.944	2023
Chad TCD	66.3%	0.787	2023

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

Sources

Food insecurity

Prevalence of moderate or severe food insecurity in the population (%)

Food and Agriculture Organization, Food Insecurity Experience Scale · CC BY 4.0 · updated quarterly

Asked of people, in a survey about running out of food, and reported as a three-year average from 2015. It is the one measure of hunger here that comes from what people say they went through, rather than from a model.

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 2017–2023. 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 12,497 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated DYB 2024. How the country data works · explore Food insecurity and Gender Development Index

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