placestats

Food insecurity vs. Tertiary enrollment

Fades after adjusting for income

Across 145 countries, Food insecurity and Tertiary enrollment rise and fall together at r = -0.70 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.21, which could easily be chance (q = 0.17, 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 tertiary enrollment, 16.7% to 75% gross, food insecurity is better by 28.5% of population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 7.15% of population.

Link before adjusting (r)-0.701p = 1.0e-22 · 145 countries
After adjusting for national income-0.205p = 0.014 · likely range [-0.36, -0.04]
p, allowing for regions0.026standard error 0.070 · 7 regions
Check for luck (q)0.167across 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
Tertiary enrollment · % grosslink with income +0.75 · 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

Food insecurity · % of population →Tertiary enrollment · % gross →1.1%88.1%2.57%165%Afghanistan: 80.8% of population, 10.9% grossAngola: 78.5% of population, 9.95% grossAlbania: 33% of population, 84.1% grossUnited Arab Emirates: 3.6% of population, 63.7% grossArgentina: 33.8% of population, 108% grossArmenia: 7.8% of population, 52.8% grossAntigua and Barbuda: 13.5% of population, 25.1% grossAustralia: 13.8% of population, 108% grossAustria: 4.7% of population, 92.6% grossAzerbaijan: 14% of population, 41.3% grossBurundi: 70.8% of population, 6.38% grossBelgium: 8.1% of population, 82.1% grossBenin: 65.6% of population, 10.2% grossBurkina Faso: 41.4% of population, 10% grossBulgaria: 11.6% of population, 69.5% grossBosnia and Herzegovina: 9.9% of population, 45.5% grossBelarus: 1.1% of population, 70.1% grossBelize: 45.5% of population, 24.8% grossBrazil: 13.5% of population, 69.7% grossBarbados: 31.1% of population, 50.8% grossBotswana: 53.9% of population, 21.4% grossCentral African Republic: 88.1% of population, 2.78% grossCanada: 10.2% of population, 76.3% grossSwitzerland: 2% of population, 73.5% grossChile: 19.2% of population, 110% grossCote d'Ivoire: 38.4% of population, 12.3% grossCameroon: 61.2% of population, 16.7% grossCongo, Dem. Rep.: 81.5% of population, 6.38% grossColombia: 27.8% of population, 59.3% grossComoros: 79.7% of population, 18.8% grossCabo Verde: 32.1% of population, 24.3% grossCosta Rica: 15.4% of population, 55% grossCyprus: 1.4% of population, 121% grossCzechia: 10% of population, 66.7% grossGermany: 4.1% of population, 76.7% grossDjibouti: 49.2% of population, 5.16% grossDenmark: 6.9% of population, 79% grossDominican Republic: 43.8% of population, 57.7% grossAlgeria: 17.6% of population, 54.4% grossEcuador: 33.3% of population, 67.9% grossEgypt, Arab Rep.: 30.8% of population, 38% grossSpain: 6.5% of population, 93.8% grossEstonia: 10.8% of population, 65.7% grossEthiopia: 61.4% of population, 5.92% grossFinland: 13.4% of population, 110% grossFiji: 29.2% of population, 75% grossFrance: 8.4% of population, 71.5% grossUnited Kingdom: 6.9% of population, 80.4% grossGeorgia: 26.6% of population, 89.5% grossGhana: 44.5% of population, 21.3% grossGambia, The: 54.8% of population, 13.5% grossGreece: 6.6% of population, 165% grossGrenada: 17.3% of population, 100% grossGuatemala: 48.8% of population, 27.3% grossGuyana: 25.5% of population, 26.4% grossHonduras: 41.3% of population, 23.3% grossCroatia: 6% of population, 81.2% grossHungary: 15.6% of population, 60.5% grossIndonesia: 4.5% of population, 44.9% grossIreland: 4.8% of population, 77.6% grossIran, Islamic Rep.: 38.8% of population, 58.7% grossIceland: 7.8% of population, 76.5% grossIsrael: 9.2% of population, 56.3% grossItaly: 1.7% of population, 75.9% grossJamaica: 56.4% of population, 26.2% grossJapan: 5.8% of population, 64.5% grossKazakhstan: 2% of population, 52.9% grossKenya: 73.9% of population, 10.4% grossKyrgyz Republic: 7% of population, 53.9% grossCambodia: 40% of population, 21.4% grossKiribati: 42.9% of population, 4.96% grossSt. Kitts and Nevis: 21.1% of population, 93.9% grossKorea, Rep.: 5.4% of population, 112% grossKuwait: 8.4% of population, 51.9% grossLao PDR: 35.6% of population, 14.9% grossLebanon: 42.4% of population, 54.4% grossLiberia: 80.8% of population, 10.8% grossSt. Lucia: 22.2% of population, 15% grossSri Lanka: 11.2% of population, 20.7% grossLesotho: 59.9% of population, 9.94% grossLithuania: 6.5% of population, 75.6% grossLuxembourg: 2.6% of population, 22.4% grossLatvia: 11.2% of population, 82.9% grossMoldova: 25.1% of population, 74.7% grossMadagascar: 72.6% of population, 6.15% grossMaldives: 13.4% of population, 48.5% grossMexico: 19.3% of population, 48.2% grossNorth Macedonia: 15.2% of population, 55% grossMali: 20.6% of population, 5.13% grossMalta: 9.2% of population, 80.5% grossMyanmar: 32.7% of population, 20.4% grossMontenegro: 10.4% of population, 55.2% grossMongolia: 4.8% of population, 69% grossMauritania: 66.3% of population, 5.97% grossMauritius: 31.1% of population, 45.8% grossMalawi: 81.7% of population, 2.71% grossMalaysia: 16.7% of population, 38.5% grossNamibia: 55.9% of population, 26.1% grossNiger: 50.9% of population, 4.15% grossNigeria: 74.8% of population, 9.74% grossNetherlands: 7.5% of population, 86.6% grossNorway: 7.8% of population, 95.5% grossNepal: 36.6% of population, 21.3% grossNew Zealand: 17.3% of population, 77.2% grossPakistan: 42.1% of population, 10.9% grossPeru: 41% of population, 71.2% grossPhilippines: 32.9% of population, 47.4% grossPalau: 28.1% of population, 71.8% grossPoland: 3.4% of population, 80.9% grossPortugal: 11.9% of population, 77.7% grossParaguay: 26.2% of population, 37.6% grossWest Bank and Gaza: 27% of population, 44.5% grossRomania: 18.6% of population, 55.2% grossRussian Federation: 2.8% of population, 60.4% grossSenegal: 29.1% of population, 15.3% grossSingapore: 9.5% of population, 97.3% grossEl Salvador: 45.5% of population, 32.8% grossSerbia: 9.5% of population, 70.6% grossSao Tome and Principe: 54.6% of population, 12.8% grossSuriname: 35.8% of population, 2.57% grossSlovak Republic: 7.5% of population, 54.5% grossSlovenia: 8.2% of population, 80.1% grossSweden: 6.9% of population, 84% grossEswatini: 59% of population, 6.44% grossSeychelles: 14.3% of population, 14.5% grossChad: 66.3% of population, 4.4% grossTogo: 58.5% of population, 14.9% grossThailand: 5.4% of population, 49.1% grossTajikistan: 25.1% of population, 35.6% grossTimor-Leste: 53.7% of population, 31% grossTonga: 14.8% of population, 42.9% grossTrinidad and Tobago: 27.6% of population, 23.8% grossTunisia: 25.4% of population, 38.1% grossTanzania: 58% of population, 4.02% grossUganda: 54.6% of population, 4.62% grossUkraine: 32.5% of population, 87.2% grossUruguay: 16.2% of population, 80% grossUnited States: 10.3% of population, 79.4% grossUzbekistan: 24.4% of population, 59.8% grossSt. Vincent and the Grenadines: 33.3% of population, 41.5% grossViet Nam: 10.7% of population, 37.6% grossSamoa: 18.6% of population, 16% grossSouth Africa: 20.7% of population, 23.5% grossZambia: 45.3% of population, 3.96% grossZimbabwe: 68.8% of population, 7.75% grossARGCAFCHLCODCYPGRCKORLUXMLIMWISYC

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.7011.0e-22145
By rank (Spearman), not adjusted-0.7164.2e-24145
Standard (Pearson), adjusted for national income-0.2050.014145
By rank (Spearman), adjusted for national income-0.1660.046145

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: +0.495 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.227 (without ARG) to -0.177 (without GUY). 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 (+0.029).

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 Asia46+0.13
Latin America & Caribbean25-0.31
Middle East, North Africa, Afghanistan & Pakistan13-0.23
Sub-Saharan Africa36-0.10

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.

nr
Poorest third49-0.44
Middle third48-0.03
Richest third48-0.30

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
Christians94-0.30
Muslims31-0.27

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
France20-0.05
Never a European colony16+0.05
Spain14+0.08
United Kingdom45-0.26

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 = -0.201 after dropping 1 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 = +0.037, 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 tertiary enrollment move with it.

Across countries (adjusted)-0.205for comparison
Within countries+0.171each country vs itself
…and vs the same year elsewhere+0.037q = 0.748 · 99 countries
Year-on-year changes-0.033q = 0.657
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.

793 country-years (one country in one year), 99 countries, 2015–2023, about 8 years each on average. Share of the variation that happens within countries over time: 3% for Food insecurity, 3% for Tertiary enrollment. 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+5years Tertiary enrollment trails Food insecurity →lag -10: r = -0.132 (n = 699)lag -9: r = -0.123 (n = 711)lag -8: r = -0.128 (n = 732)lag -7: r = -0.141 (n = 743)lag -6: r = -0.160 (n = 782)lag -5: r = -0.137 (n = 777)lag -4: r = -0.056 (n = 783)lag -3: r = 0.009 (n = 793)lag -2: r = 0.053 (n = 793)lag -1: r = 0.066 (n = 791)lag +0: r = 0.037 (n = 793)lag +1: r = 0.043 (n = 739)lag +2: r = 0.097 (n = 614)lag +3: r = 0.154 (n = 511)lag +4: r = 0.121 (n = 391)lag +5: r = 0.150 (n = 232)

Same year (lag 0): +0.037. 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 largest value on this curve (-0.16 at lag -6) is higher than the same-year one by 0.12. That gap is not a finding. It is the largest of 16 closely related estimates, and picking the biggest of many tests always makes it look bigger. That is exactly why nothing on this site is ranked or chosen by lag.

The data


% of population
% gross		Years
Central African Republic CAF88.1%2.78%2022, 2012
Malawi MWI81.7%2.71%2023, 2022
Congo, Dem. Rep. COD81.5%6.38%2023, 2020
Liberia LBR80.8%10.8%2023, 2012
Afghanistan AFG80.8%10.9%2023, 2020
Comoros COM79.7%18.8%2023, 2017
Angola AGO78.5%9.95%2021, 2024
Nigeria NGA74.8%9.74%2023, 2011
Kenya KEN73.9%10.4%2023, 2024
Madagascar MDG72.6%6.15%2023
Burundi BDI70.8%6.38%2023, 2025
Zimbabwe ZWE68.8%7.75%2023, 2024
Chad TCD66.3%4.4%2023, 2020
Mauritania MRT66.3%5.97%2023, 2020
Benin BEN65.6%10.2%2023, 2022

World Bank, World Development Indicators
World Bank, World Development Indicators

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.

Tertiary enrollment

School enrollment, tertiary (% gross)

UNESCO Institute for Statistics · CC BY 4.0 · updated quarterly

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2025. 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 Food insecurity and Tertiary enrollment

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

More links with Food insecurity or Tertiary enrollment