placestats

Food insecurity vs. Slum share of urban population

Fades after adjusting for income

Across 149 countries, Food insecurity and Slum share of urban population rise and fall together at r = +0.73 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.27, which could easily be chance (q = 0.23, 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 food insecurity, 9.9% to 45.5% of population, slum share of urban population is worse by 27.1% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 11.5% of urban population.

Link before adjusting (r)+0.734p = 1.8e-26 · 149 countries
After adjusting for national income+0.269p = 0.001 · likely range [+0.11, +0.41]
p, allowing for regions0.074standard error 0.124 · 7 regions
Check for luck (q)0.226across 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
Slum share of urban population · % of urban populationlink with income -0.78 · 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 →Slum share of urban population · % of urban population →1.1%89.8%0%92.5%Afghanistan: 80.8% of population, 71.6% of urban populationAngola: 78.5% of population, 62.7% of urban populationAlbania: 33% of population, 2.7% of urban populationUnited Arab Emirates: 3.6% of population, 0.113% of urban populationArgentina: 33.8% of population, 14.5% of urban populationArmenia: 7.8% of population, 8.39% of urban populationAntigua and Barbuda: 13.5% of population, 2.65% of urban populationAustralia: 13.8% of population, 0.035% of urban populationAustria: 4.7% of population, 0.0466% of urban populationAzerbaijan: 14% of population, 26.9% of urban populationBurundi: 70.8% of population, 36.8% of urban populationBelgium: 8.1% of population, 0% of urban populationBenin: 65.6% of population, 64% of urban populationBurkina Faso: 41.4% of population, 87.9% of urban populationBulgaria: 11.6% of population, 0.197% of urban populationBosnia and Herzegovina: 9.9% of population, 0.313% of urban populationBelarus: 1.1% of population, 0.004% of urban populationBelize: 45.5% of population, 15.7% of urban populationBrazil: 13.5% of population, 14.9% of urban populationBotswana: 53.9% of population, 39.6% of urban populationCentral African Republic: 88.1% of population, 68.9% of urban populationCanada: 10.2% of population, 1.1% of urban populationSwitzerland: 2% of population, 0% of urban populationChile: 19.2% of population, 7.32% of urban populationCote d'Ivoire: 38.4% of population, 48.3% of urban populationCameroon: 61.2% of population, 32.7% of urban populationCongo, Dem. Rep.: 81.5% of population, 78.4% of urban populationColombia: 27.8% of population, 9.7% of urban populationComoros: 79.7% of population, 48.5% of urban populationCabo Verde: 32.1% of population, 46.4% of urban populationCosta Rica: 15.4% of population, 3.55% of urban populationCyprus: 1.4% of population, 0.272% of urban populationCzechia: 10% of population, 0.0273% of urban populationGermany: 4.1% of population, 0% of urban populationDjibouti: 49.2% of population, 48.7% of urban populationDenmark: 6.9% of population, 0% of urban populationDominican Republic: 43.8% of population, 11.2% of urban populationAlgeria: 17.6% of population, 13.2% of urban populationEcuador: 33.3% of population, 57.8% of urban populationEgypt, Arab Rep.: 30.8% of population, 3.84% of urban populationSpain: 6.5% of population, 0.046% of urban populationEstonia: 10.8% of population, 0.345% of urban populationEthiopia: 61.4% of population, 64.3% of urban populationFinland: 13.4% of population, 0% of urban populationFiji: 29.2% of population, 9.4% of urban populationFrance: 8.4% of population, 0% of urban populationUnited Kingdom: 6.9% of population, 0.163% of urban populationGeorgia: 26.6% of population, 7.08% of urban populationGhana: 44.5% of population, 33.5% of urban populationGambia, The: 54.8% of population, 37.1% of urban populationGuinea-Bissau: 62.5% of population, 59% of urban populationGreece: 6.6% of population, 0.05% of urban populationGuatemala: 48.8% of population, 37.6% of urban populationGuyana: 25.5% of population, 11.3% of urban populationHonduras: 41.3% of population, 31.5% of urban populationCroatia: 6% of population, 0.527% of urban populationHaiti: 83.2% of population, 51.1% of urban populationHungary: 15.6% of population, 7.2% of urban populationIndonesia: 4.5% of population, 19.4% of urban populationIreland: 4.8% of population, 8.5% of urban populationIran, Islamic Rep.: 38.8% of population, 44.7% of urban populationIceland: 7.8% of population, 0% of urban populationItaly: 1.7% of population, 0.02% of urban populationJamaica: 56.4% of population, 0.883% of urban populationJapan: 5.8% of population, 2% of urban populationKazakhstan: 2% of population, 0.794% of urban populationKenya: 73.9% of population, 40.5% of urban populationKyrgyz Republic: 7% of population, 2.44% of urban populationCambodia: 40% of population, 42.3% of urban populationKiribati: 42.9% of population, 5.93% of urban populationKorea, Rep.: 5.4% of population, 4.5% of urban populationKuwait: 8.4% of population, 0% of urban populationLao PDR: 35.6% of population, 54.8% of urban populationLebanon: 42.4% of population, 4.53% of urban populationLiberia: 80.8% of population, 60.5% of urban populationLibya: 35.1% of population, 16.6% of urban populationSt. Lucia: 22.2% of population, 0.441% of urban populationSri Lanka: 11.2% of population, 44.7% of urban populationLesotho: 59.9% of population, 25.6% of urban populationLithuania: 6.5% of population, 0.509% of urban populationLuxembourg: 2.6% of population, 0.0173% of urban populationLatvia: 11.2% of population, 0.593% of urban populationMoldova: 25.1% of population, 6.5% of urban populationMadagascar: 72.6% of population, 65.7% of urban populationMaldives: 13.4% of population, 34.8% of urban populationMexico: 19.3% of population, 17.6% of urban populationNorth Macedonia: 15.2% of population, 0.278% of urban populationMali: 20.6% of population, 92.5% of urban populationMalta: 9.2% of population, 0.0375% of urban populationMyanmar: 32.7% of population, 58.3% of urban populationMontenegro: 10.4% of population, 8.77% of urban populationMongolia: 4.8% of population, 17.9% of urban populationMauritania: 66.3% of population, 58.6% of urban populationMauritius: 31.1% of population, 48.7% of urban populationMalawi: 81.7% of population, 38% of urban populationMalaysia: 16.7% of population, 0.2% of urban populationNamibia: 55.9% of population, 41.4% of urban populationNiger: 50.9% of population, 70.4% of urban populationNigeria: 74.8% of population, 48.5% of urban populationNetherlands: 7.5% of population, 0% of urban populationNorway: 7.8% of population, 0% of urban populationNepal: 36.6% of population, 40.1% of urban populationNaoero: 29.9% of population, 0.597% of urban populationNew Zealand: 17.3% of population, 0% of urban populationPakistan: 42.1% of population, 56% of urban populationPeru: 41% of population, 45.1% of urban populationPhilippines: 32.9% of population, 35.9% of urban populationPalau: 28.1% of population, 0.618% of urban populationPapua New Guinea: 57.3% of population, 22.3% of urban populationPoland: 3.4% of population, 4.22% of urban populationPortugal: 11.9% of population, 0.059% of urban populationParaguay: 26.2% of population, 15.1% of urban populationWest Bank and Gaza: 27% of population, 19.5% of urban populationRomania: 18.6% of population, 2.52% of urban populationRussian Federation: 2.8% of population, 2.62% of urban populationSenegal: 29.1% of population, 46.4% of urban populationSingapore: 9.5% of population, 0% of urban populationSierra Leone: 89.8% of population, 49.3% of urban populationEl Salvador: 45.5% of population, 16.5% of urban populationSomalia, Fed. Rep.: 79.5% of population, 48.7% of urban populationSerbia: 9.5% of population, 1.42% of urban populationSao Tome and Principe: 54.6% of population, 82.4% of urban populationSuriname: 35.8% of population, 15.8% of urban populationSlovak Republic: 7.5% of population, 0.066% of urban populationSlovenia: 8.2% of population, 0.527% of urban populationSweden: 6.9% of population, 0.336% of urban populationEswatini: 59% of population, 17% of urban populationSeychelles: 14.3% of population, 48.7% of urban populationChad: 66.3% of population, 82% of urban populationTogo: 58.5% of population, 38.5% of urban populationThailand: 5.4% of population, 2% of urban populationTajikistan: 25.1% of population, 0.05% of urban populationTimor-Leste: 53.7% of population, 33.9% of urban populationTonga: 14.8% of population, 0.308% of urban populationTrinidad and Tobago: 27.6% of population, 8.63% of urban populationTunisia: 25.4% of population, 7.64% of urban populationTanzania: 58% of population, 70.1% of urban populationUganda: 54.6% of population, 52.7% of urban populationUkraine: 32.5% of population, 1.1% of urban populationUruguay: 16.2% of population, 1.3% of urban populationUnited States: 10.3% of population, 0.0932% of urban populationUzbekistan: 24.4% of population, 7.1% of urban populationSt. Vincent and the Grenadines: 33.3% of population, 2.76% of urban populationViet Nam: 10.7% of population, 32.5% of urban populationVanuatu: 23.3% of population, 3.06% of urban populationSamoa: 18.6% of population, 34.6% of urban populationSouth Africa: 20.7% of population, 24.2% of urban populationZambia: 45.3% of population, 48.3% of urban populationZimbabwe: 68.8% of population, 54.9% of urban populationAFGBFABLRCAFCODCYPITAJAMMLIMWISWZSYC

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.7341.8e-26149
By rank (Spearman), not adjusted+0.7711.3e-30149
Standard (Pearson), adjusted for national income+0.2690.001149
By rank (Spearman), adjusted for national income+0.2880.000149

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 149 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: +0.465 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.237 (without VUT) to +0.340 (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 (+0.071).

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+0.13
Europe & Central Asia46+0.12
Latin America & Caribbean23+0.14
Middle East, North Africa, Afghanistan & Pakistan13+0.55
Sub-Saharan Africa39-0.18

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 third50+0.33
Middle third49+0.11
Richest third50+0.26

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
Christians95+0.44
Muslims35+0.16

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
France21-0.42
Never a European colony16+0.77
Spain14+0.22
United Kingdom46+0.17

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 = +0.269 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 0years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

Not enough history inside each country to look at this over time. That needs countries measured for several years in a row, and at least one of these measures is measured too rarely.

The data


% of population
% of urban population		Years
Sierra Leone SLE89.8%49.3%2023, 2022
Central African Republic CAF88.1%68.9%2022
Haiti HTI83.2%51.1%2023, 2022
Malawi MWI81.7%38%2023, 2022
Congo, Dem. Rep. COD81.5%78.4%2023, 2022
Liberia LBR80.8%60.5%2023, 2022
Afghanistan AFG80.8%71.6%2023, 2022
Comoros COM79.7%48.5%2023, 2022
Somalia, Fed. Rep. SOM79.5%48.7%2021, 2022
Angola AGO78.5%62.7%2021, 2022
Nigeria NGA74.8%48.5%2023, 2022
Kenya KEN73.9%40.5%2023, 2022
Madagascar MDG72.6%65.7%2023, 2022
Burundi BDI70.8%36.8%2023, 2022
Zimbabwe ZWE68.8%54.9%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.

Slum share of urban population

Population living in slums (% of urban population)

UN-Habitat · CC BY 4.0 · updated quarterly

How this was computed

Data used: most recent value in 2010-2025; values come from 2012–2023. 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 Slum share of urban population

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

More links with Food insecurity or Slum share of urban population