placestats

Food insecurity vs. Women in a union

Holds up after adjusting for income

Across 153 countries, Food insecurity and Women in a union rise and fall together at r = +0.18 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) flips the link to -0.31 (q = 0.018, allowing for the many pairs tested). The first figure pointed the other way only as richer and poorer countries differ. Caution: direction is not consistent across regions.

Comparing countries across the middle half of women in a union, 51.6% to 62.4% of women aged 15-49, food insecurity is worse by 5.22% of population at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 5.55% of population the other way.

Link before adjusting (r)+0.178p = 0.028 · 153 countries
After adjusting for national income-0.313p = 8.6e-5 · likely range [-0.45, -0.16]
p, allowing for regions0.000standard error 0.035 · 7 regions
Check for luck (q)0.018across 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
Women in a union · % of women aged 15-49link with income -0.44 · moderate

“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 →Women in a union · % of women aged 15-49 →1.1%89.8%33.6%81.3%Afghanistan: 80.8% of population, 65.9% of women aged 15-49Angola: 78.5% of population, 57.2% of women aged 15-49Albania: 33% of population, 65.7% of women aged 15-49United Arab Emirates: 3.6% of population, 67.4% of women aged 15-49Argentina: 33.8% of population, 49.7% of women aged 15-49Armenia: 7.8% of population, 61.5% of women aged 15-49Antigua and Barbuda: 13.5% of population, 33.8% of women aged 15-49Australia: 13.8% of population, 54% of women aged 15-49Austria: 4.7% of population, 56.6% of women aged 15-49Azerbaijan: 14% of population, 59.7% of women aged 15-49Burundi: 70.8% of population, 58.2% of women aged 15-49Belgium: 8.1% of population, 52.4% of women aged 15-49Benin: 65.6% of population, 65.7% of women aged 15-49Burkina Faso: 41.4% of population, 74.3% of women aged 15-49Bulgaria: 11.6% of population, 56.6% of women aged 15-49Bahamas, The: 17.2% of population, 38.7% of women aged 15-49Bosnia and Herzegovina: 9.9% of population, 65.3% of women aged 15-49Belarus: 1.1% of population, 62.4% of women aged 15-49Belize: 45.5% of population, 58% of women aged 15-49Brazil: 13.5% of population, 56.9% of women aged 15-49Barbados: 31.1% of population, 57.2% of women aged 15-49Botswana: 53.9% of population, 41.5% of women aged 15-49Central African Republic: 88.1% of population, 65.4% of women aged 15-49Canada: 10.2% of population, 51.6% of women aged 15-49Switzerland: 2% of population, 57.5% of women aged 15-49Chile: 19.2% of population, 48.5% of women aged 15-49Cote d'Ivoire: 38.4% of population, 62.7% of women aged 15-49Cameroon: 61.2% of population, 58.1% of women aged 15-49Congo, Dem. Rep.: 81.5% of population, 58.2% of women aged 15-49Colombia: 27.8% of population, 50.9% of women aged 15-49Comoros: 79.7% of population, 63.1% of women aged 15-49Cabo Verde: 32.1% of population, 45% of women aged 15-49Costa Rica: 15.4% of population, 41.7% of women aged 15-49Cyprus: 1.4% of population, 52.3% of women aged 15-49Czechia: 10% of population, 53.7% of women aged 15-49Germany: 4.1% of population, 54.4% of women aged 15-49Djibouti: 49.2% of population, 46.5% of women aged 15-49Dominica: 34.4% of population, 48.4% of women aged 15-49Denmark: 6.9% of population, 54.3% of women aged 15-49Dominican Republic: 43.8% of population, 53.2% of women aged 15-49Algeria: 17.6% of population, 55.5% of women aged 15-49Ecuador: 33.3% of population, 45.3% of women aged 15-49Egypt, Arab Rep.: 30.8% of population, 65.6% of women aged 15-49Spain: 6.5% of population, 50.2% of women aged 15-49Estonia: 10.8% of population, 54.1% of women aged 15-49Ethiopia: 61.4% of population, 66.3% of women aged 15-49Finland: 13.4% of population, 57.1% of women aged 15-49Fiji: 29.2% of population, 59.2% of women aged 15-49France: 8.4% of population, 54.9% of women aged 15-49United Kingdom: 6.9% of population, 49.7% of women aged 15-49Georgia: 26.6% of population, 65.3% of women aged 15-49Ghana: 44.5% of population, 53.4% of women aged 15-49Gambia, The: 54.8% of population, 63% of women aged 15-49Guinea-Bissau: 62.5% of population, 59% of women aged 15-49Greece: 6.6% of population, 52.4% of women aged 15-49Guatemala: 48.8% of population, 56.2% of women aged 15-49Guyana: 25.5% of population, 59% of women aged 15-49Honduras: 41.3% of population, 54.4% of women aged 15-49Croatia: 6% of population, 50.1% of women aged 15-49Haiti: 83.2% of population, 53.6% of women aged 15-49Hungary: 15.6% of population, 52.3% of women aged 15-49Indonesia: 4.5% of population, 70.3% of women aged 15-49Ireland: 4.8% of population, 47.1% of women aged 15-49Iran, Islamic Rep.: 38.8% of population, 69.6% of women aged 15-49Iceland: 7.8% of population, 48.5% of women aged 15-49Israel: 9.2% of population, 50.4% of women aged 15-49Italy: 1.7% of population, 51.8% of women aged 15-49Jamaica: 56.4% of population, 35.2% of women aged 15-49Japan: 5.8% of population, 50% of women aged 15-49Kazakhstan: 2% of population, 63.8% of women aged 15-49Kenya: 73.9% of population, 53.2% of women aged 15-49Kyrgyz Republic: 7% of population, 57.4% of women aged 15-49Cambodia: 40% of population, 67.2% of women aged 15-49Kiribati: 42.9% of population, 67.4% of women aged 15-49Korea, Rep.: 5.4% of population, 49.8% of women aged 15-49Kuwait: 8.4% of population, 59.5% of women aged 15-49Lao PDR: 35.6% of population, 61.6% of women aged 15-49Lebanon: 42.4% of population, 52.4% of women aged 15-49Liberia: 80.8% of population, 51.4% of women aged 15-49Libya: 35.1% of population, 43.8% of women aged 15-49St. Lucia: 22.2% of population, 53.3% of women aged 15-49Sri Lanka: 11.2% of population, 66.3% of women aged 15-49Lesotho: 59.9% of population, 46.9% of women aged 15-49Lithuania: 6.5% of population, 51.8% of women aged 15-49Luxembourg: 2.6% of population, 51.1% of women aged 15-49Latvia: 11.2% of population, 49.6% of women aged 15-49Moldova: 25.1% of population, 68.7% of women aged 15-49Madagascar: 72.6% of population, 62.3% of women aged 15-49Maldives: 13.4% of population, 68.4% of women aged 15-49Mexico: 19.3% of population, 53% of women aged 15-49North Macedonia: 15.2% of population, 66.1% of women aged 15-49Mali: 20.6% of population, 79.2% of women aged 15-49Malta: 9.2% of population, 52% of women aged 15-49Myanmar: 32.7% of population, 58% of women aged 15-49Montenegro: 10.4% of population, 57.6% of women aged 15-49Mongolia: 4.8% of population, 60.2% of women aged 15-49Mauritania: 66.3% of population, 66.1% of women aged 15-49Mauritius: 31.1% of population, 51.6% of women aged 15-49Malawi: 81.7% of population, 62.1% of women aged 15-49Malaysia: 16.7% of population, 57.1% of women aged 15-49Namibia: 55.9% of population, 33.7% of women aged 15-49Niger: 50.9% of population, 81.3% of women aged 15-49Nigeria: 74.8% of population, 67.6% of women aged 15-49Netherlands: 7.5% of population, 53.5% of women aged 15-49Norway: 7.8% of population, 57.4% of women aged 15-49Nepal: 36.6% of population, 75.1% of women aged 15-49Naoero: 29.9% of population, 57.7% of women aged 15-49New Zealand: 17.3% of population, 57.7% of women aged 15-49Pakistan: 42.1% of population, 63.4% of women aged 15-49Peru: 41% of population, 52.7% of women aged 15-49Philippines: 32.9% of population, 55.6% of women aged 15-49Palau: 28.1% of population, 48.3% of women aged 15-49Papua New Guinea: 57.3% of population, 66.9% of women aged 15-49Poland: 3.4% of population, 58.6% of women aged 15-49Portugal: 11.9% of population, 50.6% of women aged 15-49Paraguay: 26.2% of population, 54.8% of women aged 15-49West Bank and Gaza: 27% of population, 62.4% of women aged 15-49Romania: 18.6% of population, 56% of women aged 15-49Russian Federation: 2.8% of population, 57.6% of women aged 15-49Senegal: 29.1% of population, 65.5% of women aged 15-49Singapore: 9.5% of population, 43.8% of women aged 15-49Sierra Leone: 89.8% of population, 61.5% of women aged 15-49El Salvador: 45.5% of population, 50.7% of women aged 15-49Somalia, Fed. Rep.: 79.5% of population, 62.4% of women aged 15-49Serbia: 9.5% of population, 54.3% of women aged 15-49Sao Tome and Principe: 54.6% of population, 53.1% of women aged 15-49Suriname: 35.8% of population, 52.2% of women aged 15-49Slovak Republic: 7.5% of population, 49.2% of women aged 15-49Slovenia: 8.2% of population, 47.2% of women aged 15-49Sweden: 6.9% of population, 53.6% of women aged 15-49Eswatini: 59% of population, 36.4% of women aged 15-49Seychelles: 14.3% of population, 44.6% of women aged 15-49Chad: 66.3% of population, 72.6% of women aged 15-49Togo: 58.5% of population, 63.7% of women aged 15-49Thailand: 5.4% of population, 52.4% of women aged 15-49Tajikistan: 25.1% of population, 68% of women aged 15-49Timor-Leste: 53.7% of population, 56.4% of women aged 15-49Tonga: 14.8% of population, 50.7% of women aged 15-49Trinidad and Tobago: 27.6% of population, 42.7% of women aged 15-49Tunisia: 25.4% of population, 52.5% of women aged 15-49Tanzania: 58% of population, 59.2% of women aged 15-49Uganda: 54.6% of population, 57.8% of women aged 15-49Ukraine: 32.5% of population, 61.9% of women aged 15-49Uruguay: 16.2% of population, 53.6% of women aged 15-49United States: 10.3% of population, 52.1% of women aged 15-49Uzbekistan: 24.4% of population, 74.7% of women aged 15-49St. Vincent and the Grenadines: 33.3% of population, 51.1% of women aged 15-49Viet Nam: 10.7% of population, 72.8% of women aged 15-49Vanuatu: 23.3% of population, 68.6% of women aged 15-49Samoa: 18.6% of population, 62% of women aged 15-49South Africa: 20.7% of population, 33.6% of women aged 15-49Zambia: 45.3% of population, 55.7% of women aged 15-49Zimbabwe: 68.8% of population, 62.2% of women aged 15-49ATGBHSCAFIDNJAMMLINAMNERSWZTCDVNMZAF

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.1780.028153
By rank (Spearman), not adjusted+0.2180.007153
Standard (Pearson), adjusted for national income-0.3138.6e-5153
By rank (Spearman), adjusted for national income-0.2840.000153

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 153 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: -0.135 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.341 (without NGA) to -0.283 (without MLI). 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 (+0.030).

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 & Pacific23-0.23
Europe & Central Asia46+0.29
Latin America & Caribbean26+0.11
Middle East, North Africa, Afghanistan & Pakistan14+0.02
Sub-Saharan Africa39-0.19

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 third51-0.32
Middle third51-0.45
Richest third51-0.21

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.

nr
Christians97-0.23
Muslims35-0.25

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
France21-0.63
Never a European colony16-0.06
Spain14+0.22
United Kingdom50-0.16

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 = -0.273 after dropping 6 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 = +0.229, 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 women in a union move with it.

Across countries (adjusted)-0.313for comparison
Within countries-0.018each country vs itself
…and vs the same year elsewhere+0.229q = 0.065 · 86 countries
Year-on-year changes+0.110q = 0.282
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.

612 country-years (one country in one year), 86 countries, 2015–2023, about 7 years each on average. Share of the variation that happens within countries over time: 1% for Food insecurity, 3% for Women in a union. 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

0.30−0.3-100+3years Women in a union trails Food insecurity →lag -10: r = 0.033 (n = 1,029)lag -9: r = 0.045 (n = 1,023)lag -8: r = 0.050 (n = 1,008)lag -7: r = 0.054 (n = 994)lag -6: r = 0.052 (n = 985)lag -5: r = 0.050 (n = 965)lag -4: r = 0.064 (n = 939)lag -3: r = 0.081 (n = 900)lag -2: r = 0.117 (n = 844)lag -1: r = 0.161 (n = 756)lag +0: r = 0.229 (n = 612)lag +1: r = 0.250 (n = 461)lag +2: r = 0.230 (n = 356)lag +3: r = 0.232 (n = 212)

Same year (lag 0): +0.229. 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
% of women aged 15-49		Years
Sierra Leone SLE89.8%61.5%2023, 2019
Central African Republic CAF88.1%65.4%2022, 2019
Haiti HTI83.2%53.6%2023, 2017
Malawi MWI81.7%62.1%2023, 2020
Congo, Dem. Rep. COD81.5%58.2%2023, 2018
Liberia LBR80.8%51.4%2023, 2020
Afghanistan AFG80.8%65.9%2023
Comoros COM79.7%63.1%2023, 2022
Somalia, Fed. Rep. SOM79.5%62.4%2021, 2019
Angola AGO78.5%57.2%2021, 2016
Nigeria NGA74.8%67.6%2023, 2018
Kenya KEN73.9%53.2%2023, 2022
Madagascar MDG72.6%62.3%2023, 2021
Burundi BDI70.8%58.2%2023, 2017
Zimbabwe ZWE68.8%62.2%2023, 2022

World Bank, World Development Indicators
United Nations, Department of Economic and Social Affairs, Population Division: World Population Prospects 2024 and Estimates and Projections of Women of Reproductive Age Who Are Married or in a Union

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.

Women in a union

Share of women aged 15 to 49 who are married or in a union

United Nations Department of Economic and Social Affairs, Population Division · via Our World in Data · CC BY 3.0 IGO · updated biennial

A snapshot of how many women are in a union at one time, not a count of new unions each year. It counts couples living together as a union, which the OECD marriage rate does not. So the two can disagree without either being wrong, and their pair is worth reading for exactly that. The UN estimates it with a statistical model from survey and census data, for over two hundred countries. No "better" direction is set.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2024. 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 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 Women in a union

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

More links with Food insecurity or Women in a union