placestats

Food insecurity vs. Gender Inequality Index

Holds up after adjusting for income

Across 145 countries, Food insecurity and Gender Inequality Index rise and fall together at r = +0.84 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.37 (q = 0.042, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak positive link remains. Caution: direction is not consistent across regions.

Comparing countries across the middle half of gender inequality index, 0.112 to 0.49 points, food insecurity is worse by 37.8% of population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 20.6% of population.

Link before adjusting (r)+0.836p = 4.2e-39 · 145 countries
After adjusting for national income+0.367p = 6.0e-6 · likely range [+0.22, +0.50]
p, allowing for regions0.026standard error 0.125 · 7 regions
Check for luck (q)0.042across 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 Inequality Index · index, 0 to 1link with income -0.89 · 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

Food insecurity · % of population →Gender Inequality Index · index, 0 to 1 →1.1%89.8%0.0030.677Afghanistan: 80.8% of population, 0.661 index, 0 to 1Angola: 78.5% of population, 0.515 index, 0 to 1Albania: 33% of population, 0.107 index, 0 to 1United Arab Emirates: 3.6% of population, 0.04 index, 0 to 1Argentina: 33.8% of population, 0.264 index, 0 to 1Armenia: 7.8% of population, 0.18 index, 0 to 1Antigua and Barbuda: 13.5% of population, 0.24 index, 0 to 1Australia: 13.8% of population, 0.056 index, 0 to 1Austria: 4.7% of population, 0.033 index, 0 to 1Azerbaijan: 14% of population, 0.315 index, 0 to 1Burundi: 70.8% of population, 0.501 index, 0 to 1Belgium: 8.1% of population, 0.031 index, 0 to 1Benin: 65.6% of population, 0.573 index, 0 to 1Burkina Faso: 41.4% of population, 0.555 index, 0 to 1Bulgaria: 11.6% of population, 0.208 index, 0 to 1Bahamas, The: 17.2% of population, 0.325 index, 0 to 1Bosnia and Herzegovina: 9.9% of population, 0.157 index, 0 to 1Belarus: 1.1% of population, 0.08 index, 0 to 1Belize: 45.5% of population, 0.428 index, 0 to 1Brazil: 13.5% of population, 0.39 index, 0 to 1Barbados: 31.1% of population, 0.297 index, 0 to 1Botswana: 53.9% of population, 0.49 index, 0 to 1Canada: 10.2% of population, 0.052 index, 0 to 1Switzerland: 2% of population, 0.01 index, 0 to 1Chile: 19.2% of population, 0.102 index, 0 to 1Cote d'Ivoire: 38.4% of population, 0.589 index, 0 to 1Cameroon: 61.2% of population, 0.558 index, 0 to 1Congo, Dem. Rep.: 81.5% of population, 0.604 index, 0 to 1Colombia: 27.8% of population, 0.393 index, 0 to 1Comoros: 79.7% of population, 0.501 index, 0 to 1Cabo Verde: 32.1% of population, 0.298 index, 0 to 1Costa Rica: 15.4% of population, 0.217 index, 0 to 1Cyprus: 1.4% of population, 0.252 index, 0 to 1Czechia: 10% of population, 0.088 index, 0 to 1Germany: 4.1% of population, 0.057 index, 0 to 1Djibouti: 49.2% of population, 0.481 index, 0 to 1Denmark: 6.9% of population, 0.003 index, 0 to 1Dominican Republic: 43.8% of population, 0.417 index, 0 to 1Algeria: 17.6% of population, 0.443 index, 0 to 1Ecuador: 33.3% of population, 0.358 index, 0 to 1Egypt, Arab Rep.: 30.8% of population, 0.398 index, 0 to 1Spain: 6.5% of population, 0.043 index, 0 to 1Estonia: 10.8% of population, 0.061 index, 0 to 1Ethiopia: 61.4% of population, 0.497 index, 0 to 1Finland: 13.4% of population, 0.021 index, 0 to 1Fiji: 29.2% of population, 0.35 index, 0 to 1France: 8.4% of population, 0.034 index, 0 to 1United Kingdom: 6.9% of population, 0.083 index, 0 to 1Georgia: 26.6% of population, 0.257 index, 0 to 1Ghana: 44.5% of population, 0.514 index, 0 to 1Gambia, The: 54.8% of population, 0.578 index, 0 to 1Guinea-Bissau: 62.5% of population, 0.632 index, 0 to 1Greece: 6.6% of population, 0.103 index, 0 to 1Grenada: 17.3% of population, 0.226 index, 0 to 1Guatemala: 48.8% of population, 0.48 index, 0 to 1Guyana: 25.5% of population, 0.427 index, 0 to 1Honduras: 41.3% of population, 0.437 index, 0 to 1Croatia: 6% of population, 0.074 index, 0 to 1Haiti: 83.2% of population, 0.618 index, 0 to 1Hungary: 15.6% of population, 0.213 index, 0 to 1Indonesia: 4.5% of population, 0.423 index, 0 to 1Ireland: 4.8% of population, 0.054 index, 0 to 1Iran, Islamic Rep.: 38.8% of population, 0.482 index, 0 to 1Iceland: 7.8% of population, 0.024 index, 0 to 1Israel: 9.2% of population, 0.08 index, 0 to 1Italy: 1.7% of population, 0.043 index, 0 to 1Jamaica: 56.4% of population, 0.358 index, 0 to 1Japan: 5.8% of population, 0.059 index, 0 to 1Kazakhstan: 2% of population, 0.182 index, 0 to 1Kenya: 73.9% of population, 0.526 index, 0 to 1Kyrgyz Republic: 7% of population, 0.34 index, 0 to 1Cambodia: 40% of population, 0.506 index, 0 to 1Korea, Rep.: 5.4% of population, 0.038 index, 0 to 1Kuwait: 8.4% of population, 0.188 index, 0 to 1Lao PDR: 35.6% of population, 0.475 index, 0 to 1Lebanon: 42.4% of population, 0.36 index, 0 to 1Liberia: 80.8% of population, 0.646 index, 0 to 1Libya: 35.1% of population, 0.253 index, 0 to 1St. Lucia: 22.2% of population, 0.327 index, 0 to 1Sri Lanka: 11.2% of population, 0.367 index, 0 to 1Lesotho: 59.9% of population, 0.534 index, 0 to 1Lithuania: 6.5% of population, 0.07 index, 0 to 1Luxembourg: 2.6% of population, 0.044 index, 0 to 1Latvia: 11.2% of population, 0.117 index, 0 to 1Moldova: 25.1% of population, 0.146 index, 0 to 1Madagascar: 72.6% of population, 0.584 index, 0 to 1Maldives: 13.4% of population, 0.309 index, 0 to 1Mexico: 19.3% of population, 0.358 index, 0 to 1North Macedonia: 15.2% of population, 0.112 index, 0 to 1Mali: 20.6% of population, 0.612 index, 0 to 1Malta: 9.2% of population, 0.111 index, 0 to 1Myanmar: 32.7% of population, 0.478 index, 0 to 1Montenegro: 10.4% of population, 0.121 index, 0 to 1Mongolia: 4.8% of population, 0.284 index, 0 to 1Mauritania: 66.3% of population, 0.603 index, 0 to 1Mauritius: 31.1% of population, 0.352 index, 0 to 1Malawi: 81.7% of population, 0.581 index, 0 to 1Malaysia: 16.7% of population, 0.172 index, 0 to 1Namibia: 55.9% of population, 0.448 index, 0 to 1Niger: 50.9% of population, 0.591 index, 0 to 1Nigeria: 74.8% of population, 0.677 index, 0 to 1Netherlands: 7.5% of population, 0.013 index, 0 to 1Norway: 7.8% of population, 0.004 index, 0 to 1Nepal: 36.6% of population, 0.487 index, 0 to 1New Zealand: 17.3% of population, 0.082 index, 0 to 1Pakistan: 42.1% of population, 0.536 index, 0 to 1Peru: 41% of population, 0.34 index, 0 to 1Philippines: 32.9% of population, 0.351 index, 0 to 1Papua New Guinea: 57.3% of population, 0.584 index, 0 to 1Poland: 3.4% of population, 0.081 index, 0 to 1Portugal: 11.9% of population, 0.076 index, 0 to 1Paraguay: 26.2% of population, 0.412 index, 0 to 1Romania: 18.6% of population, 0.227 index, 0 to 1Russian Federation: 2.8% of population, 0.169 index, 0 to 1Senegal: 29.1% of population, 0.49 index, 0 to 1Singapore: 9.5% of population, 0.031 index, 0 to 1Sierra Leone: 89.8% of population, 0.566 index, 0 to 1El Salvador: 45.5% of population, 0.362 index, 0 to 1Somalia, Fed. Rep.: 79.5% of population, 0.675 index, 0 to 1Serbia: 9.5% of population, 0.117 index, 0 to 1Sao Tome and Principe: 54.6% of population, 0.492 index, 0 to 1Suriname: 35.8% of population, 0.391 index, 0 to 1Slovak Republic: 7.5% of population, 0.176 index, 0 to 1Slovenia: 8.2% of population, 0.042 index, 0 to 1Sweden: 6.9% of population, 0.007 index, 0 to 1Eswatini: 59% of population, 0.484 index, 0 to 1Chad: 66.3% of population, 0.67 index, 0 to 1Togo: 58.5% of population, 0.564 index, 0 to 1Thailand: 5.4% of population, 0.288 index, 0 to 1Tajikistan: 25.1% of population, 0.258 index, 0 to 1Timor-Leste: 53.7% of population, 0.394 index, 0 to 1Tonga: 14.8% of population, 0.444 index, 0 to 1Trinidad and Tobago: 27.6% of population, 0.262 index, 0 to 1Tunisia: 25.4% of population, 0.238 index, 0 to 1Tanzania: 58% of population, 0.504 index, 0 to 1Uganda: 54.6% of population, 0.524 index, 0 to 1Uruguay: 16.2% of population, 0.218 index, 0 to 1United States: 10.3% of population, 0.169 index, 0 to 1Uzbekistan: 24.4% of population, 0.291 index, 0 to 1Viet Nam: 10.7% of population, 0.299 index, 0 to 1Vanuatu: 23.3% of population, 0.556 index, 0 to 1Samoa: 18.6% of population, 0.416 index, 0 to 1South Africa: 20.7% of population, 0.388 index, 0 to 1Zambia: 45.3% of population, 0.524 index, 0 to 1Zimbabwe: 68.8% of population, 0.519 index, 0 to 1AFGALBCHECOMDNKIDNITALBRMLISLESOMVUT

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.8364.2e-39145
By rank (Spearman), not adjusted+0.8542.5e-42145
Standard (Pearson), adjusted for national income+0.3676.0e-6145
By rank (Spearman), adjusted for national income+0.4122.9e-7145

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 145 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: +0.469 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.333 (without NGA) to +0.390 (without IDN). 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: NGA (-0.034).

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.

nr
East Asia & Pacific20-0.10
Europe & Central Asia45-0.03
Latin America & Caribbean25+0.49
Middle East, North Africa, Afghanistan & Pakistan13+0.07
Sub-Saharan Africa37+0.32

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.

nr
Poorest third49+0.45
Middle third48+0.37
Richest third48+0.53

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.

nr
Christians93+0.46
Muslims34+0.34

By last european coloniser

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.

nr
France20+0.21
Never a European colony16+0.53
Spain14+0.45
United Kingdom46+0.41

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 = +0.367 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 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 = +0.127, 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 inequality index move with it.

Across countries (adjusted)+0.367for comparison
Within countries-0.151each country vs itself
…and vs the same year elsewhere+0.127q = 0.197 · 121 countries
Year-on-year changes+0.011q = 0.866
The direction flips. Comparing each country only with itself gives -0.15. Also taking out what happened to the whole world each year gives +0.13. 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 this indicator's variation is between countries, not within them, so the within-country estimate rests on a small part of the data.

1,008 country-years (one country in one year), 121 countries, 2015–2023, about 8 years each on average. Share of the variation that happens within countries over time: 1% for Food insecurity, 3% for Gender Inequality 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

0.20−0.2-100+4years Gender Inequality Index trails Food insecurity →lag -10: r = 0.114 (n = 926)lag -9: r = 0.135 (n = 944)lag -8: r = 0.132 (n = 948)lag -7: r = 0.125 (n = 952)lag -6: r = 0.102 (n = 955)lag -5: r = 0.103 (n = 962)lag -4: r = 0.102 (n = 973)lag -3: r = 0.119 (n = 980)lag -2: r = 0.146 (n = 983)lag -1: r = 0.153 (n = 995)lag +0: r = 0.127 (n = 1,008)lag +1: r = 0.120 (n = 867)lag +2: r = 0.094 (n = 731)lag +3: r = 0.028 (n = 580)lag +4: r = -0.038 (n = 475)

Same year (lag 0): +0.127. 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


% of population
index, 0 to 1		Years
Sierra Leone SLE89.8%0.5662023
Haiti HTI83.2%0.6182023
Malawi MWI81.7%0.5812023
Congo, Dem. Rep. COD81.5%0.6042023
Liberia LBR80.8%0.6462023
Afghanistan AFG80.8%0.6612023
Comoros COM79.7%0.5012023
Somalia, Fed. Rep. SOM79.5%0.6752021, 2023
Angola AGO78.5%0.5152021, 2023
Nigeria NGA74.8%0.6772023
Kenya KEN73.9%0.5262023
Madagascar MDG72.6%0.5842023
Burundi BDI70.8%0.5012023
Zimbabwe ZWE68.8%0.5192023
Chad TCD66.3%0.672023

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 Inequality Index

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.

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 Inequality Index

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

More links with Food insecurity or Gender Inequality Index