placestats

Slum share of urban population vs. Undernourishment

Fades after adjusting for income

Across 158 countries, Slum share of urban population and Undernourishment rise and fall together at r = +0.66 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.23, which could easily be chance (q = 0.24, 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 undernourishment, 2.5% to 14.3% of population, slum share of urban population is worse by 18.7% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 6.02% of urban population.

Link before adjusting (r)+0.661p = 3.6e-21 · 158 countries
After adjusting for national income+0.228p = 0.004 · likely range [+0.07, +0.37]
p, allowing for regions0.023standard error 0.075 · 7 regions
Check for luck (q)0.242across 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.

Slum share of urban population · % of urban populationlink with income -0.78 · high
Undernourishment · % of populationlink with income -0.83 · 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

Slum share of urban population · % of urban population →Undernourishment · % of population →0%92.5%2.5%54.2%Afghanistan: 71.6% of urban population, 28.1% of populationAngola: 62.7% of urban population, 22.5% of populationAlbania: 2.7% of urban population, 5.4% of populationUnited Arab Emirates: 0.113% of urban population, 2.5% of populationArgentina: 14.5% of urban population, 3.4% of populationArmenia: 8.39% of urban population, 2.5% of populationAustralia: 0.035% of urban population, 2.5% of populationAustria: 0.0466% of urban population, 2.5% of populationAzerbaijan: 26.9% of urban population, 2.5% of populationBelgium: 0% of urban population, 2.5% of populationBenin: 64% of urban population, 14.3% of populationBurkina Faso: 87.9% of urban population, 13.1% of populationBangladesh: 51.5% of urban population, 10.4% of populationBulgaria: 0.197% of urban population, 2.5% of populationBosnia and Herzegovina: 0.313% of urban population, 2.5% of populationBelarus: 0.004% of urban population, 2.5% of populationBelize: 15.7% of urban population, 7% of populationBolivia: 46.6% of urban population, 21.8% of populationBrazil: 14.9% of urban population, 2.5% of populationBotswana: 39.6% of urban population, 24% of populationCentral African Republic: 68.9% of urban population, 29.8% of populationCanada: 1.1% of urban population, 2.5% of populationSwitzerland: 0% of urban population, 2.5% of populationChile: 7.32% of urban population, 2.5% of populationChina: 26.3% of urban population, 2.5% of populationCote d'Ivoire: 48.3% of urban population, 11.1% of populationCameroon: 32.7% of urban population, 4.8% of populationCongo, Dem. Rep.: 78.4% of urban population, 38.5% of populationCongo, Rep.: 75.3% of urban population, 26.4% of populationColombia: 9.7% of urban population, 3.9% of populationComoros: 48.5% of urban population, 15.4% of populationCabo Verde: 46.4% of urban population, 13.5% of populationCosta Rica: 3.55% of urban population, 2.5% of populationCyprus: 0.272% of urban population, 2.5% of populationCzechia: 0.0273% of urban population, 2.5% of populationGermany: 0% of urban population, 2.5% of populationDjibouti: 48.7% of urban population, 12.9% of populationDenmark: 0% of urban population, 2.5% of populationDominican Republic: 11.2% of urban population, 3.6% of populationAlgeria: 13.2% of urban population, 2.5% of populationEcuador: 57.8% of urban population, 12.1% of populationEgypt, Arab Rep.: 3.84% of urban population, 9.4% of populationSpain: 0.046% of urban population, 2.5% of populationEstonia: 0.345% of urban population, 2.5% of populationEthiopia: 64.3% of urban population, 19.7% of populationFinland: 0% of urban population, 2.5% of populationFiji: 9.4% of urban population, 6.8% of populationFrance: 0% of urban population, 2.5% of populationGabon: 38.8% of urban population, 25.3% of populationUnited Kingdom: 0.163% of urban population, 2.5% of populationGeorgia: 7.08% of urban population, 2.5% of populationGhana: 33.5% of urban population, 6.3% of populationGuinea: 44% of urban population, 11.4% of populationGambia, The: 37.1% of urban population, 16.8% of populationGuinea-Bissau: 59% of urban population, 22.1% of populationGreece: 0.05% of urban population, 2.5% of populationGuatemala: 37.6% of urban population, 11.8% of populationGuyana: 11.3% of urban population, 2.5% of populationHonduras: 31.5% of urban population, 14.8% of populationCroatia: 0.527% of urban population, 2.5% of populationHaiti: 51.1% of urban population, 54.2% of populationHungary: 7.2% of urban population, 2.5% of populationIndonesia: 19.4% of urban population, 6.3% of populationIndia: 5.41% of urban population, 12% of populationIreland: 8.5% of urban population, 2.5% of populationIran, Islamic Rep.: 44.7% of urban population, 6.8% of populationIraq: 49.3% of urban population, 14.9% of populationIceland: 0% of urban population, 2.5% of populationItaly: 0.02% of urban population, 2.5% of populationJamaica: 0.883% of urban population, 7.7% of populationJordan: 23.4% of urban population, 14.3% of populationKazakhstan: 0.794% of urban population, 2.5% of populationKenya: 40.5% of urban population, 36.8% of populationKyrgyz Republic: 2.44% of urban population, 5.1% of populationCambodia: 42.3% of urban population, 5.2% of populationKiribati: 5.93% of urban population, 4.1% of populationKorea, Rep.: 4.5% of urban population, 2.5% of populationKuwait: 0% of urban population, 2.5% of populationLebanon: 4.53% of urban population, 8.7% of populationLiberia: 60.5% of urban population, 35.5% of populationLibya: 16.6% of urban population, 16.5% of populationSri Lanka: 44.7% of urban population, 7.4% of populationLithuania: 0.509% of urban population, 2.5% of populationLuxembourg: 0.0173% of urban population, 2.5% of populationLatvia: 0.593% of urban population, 2.5% of populationMorocco: 10.9% of urban population, 7% of populationMoldova: 6.5% of urban population, 2.5% of populationMadagascar: 65.7% of urban population, 39.5% of populationMexico: 17.6% of urban population, 2.7% of populationNorth Macedonia: 0.278% of urban population, 2.5% of populationMali: 92.5% of urban population, 12.3% of populationMalta: 0.0375% of urban population, 2.5% of populationMyanmar: 58.3% of urban population, 5.4% of populationMontenegro: 8.77% of urban population, 2.5% of populationMongolia: 17.9% of urban population, 2.5% of populationMozambique: 55% of urban population, 21.8% of populationMauritania: 58.6% of urban population, 8.7% of populationMauritius: 48.7% of urban population, 8.7% of populationMalawi: 38% of urban population, 21.4% of populationMalaysia: 0.2% of urban population, 2.5% of populationNamibia: 41.4% of urban population, 18.1% of populationNiger: 70.4% of urban population, 12.9% of populationNigeria: 48.5% of urban population, 19.9% of populationNicaragua: 5.52% of urban population, 17.3% of populationNetherlands: 0% of urban population, 2.5% of populationNorway: 0% of urban population, 2.5% of populationNepal: 40.1% of urban population, 5.3% of populationNew Zealand: 0% of urban population, 2.5% of populationOman: 0% of urban population, 5.9% of populationPakistan: 56% of urban population, 16.5% of populationPanama: 16.3% of urban population, 5.7% of populationPeru: 45.1% of urban population, 6.9% of populationPhilippines: 35.9% of urban population, 3% of populationPapua New Guinea: 22.3% of urban population, 28.7% of populationPoland: 4.22% of urban population, 2.5% of populationPortugal: 0.059% of urban population, 2.5% of populationParaguay: 15.1% of urban population, 5.2% of populationRomania: 2.52% of urban population, 2.5% of populationRussian Federation: 2.62% of urban population, 2.5% of populationRwanda: 38.3% of urban population, 24.4% of populationSaudi Arabia: 0.55% of urban population, 2.5% of populationSudan: 73.7% of urban population, 11% of populationSenegal: 46.4% of urban population, 5.1% of populationSolomon Islands: 1.95% of urban population, 20% of populationSierra Leone: 49.3% of urban population, 24.1% of populationEl Salvador: 16.5% of urban population, 6.7% of populationSomalia, Fed. Rep.: 48.7% of urban population, 53.2% of populationSerbia: 1.42% of urban population, 2.5% of populationSao Tome and Principe: 82.4% of urban population, 17.4% of populationSuriname: 15.8% of urban population, 9.7% of populationSlovak Republic: 0.066% of urban population, 3.1% of populationSlovenia: 0.527% of urban population, 2.5% of populationSweden: 0.336% of urban population, 2.5% of populationEswatini: 17% of urban population, 14.7% of populationSeychelles: 48.7% of urban population, 2.5% of populationSyrian Arab Republic: 41.1% of urban population, 39% of populationChad: 82% of urban population, 32% of populationTogo: 38.5% of urban population, 9.1% of populationThailand: 2% of urban population, 4.6% of populationTajikistan: 0.05% of urban population, 8.4% of populationTurkmenistan: 8.38% of urban population, 4.3% of populationTimor-Leste: 33.9% of urban population, 18.7% of populationTrinidad and Tobago: 8.63% of urban population, 11.2% of populationTunisia: 7.64% of urban population, 3% of populationTurkiye: 14.1% of urban population, 2.5% of populationTanzania: 70.1% of urban population, 20.2% of populationUganda: 52.7% of urban population, 22% of populationUkraine: 1.1% of urban population, 6.9% of populationUruguay: 1.3% of urban population, 2.5% of populationUnited States: 0.0932% of urban population, 2.5% of populationUzbekistan: 7.1% of urban population, 2.5% of populationSt. Vincent and the Grenadines: 2.76% of urban population, 6.1% of populationViet Nam: 32.5% of urban population, 5.3% of populationVanuatu: 3.06% of urban population, 7.2% of populationSamoa: 34.6% of urban population, 3.6% of populationSouth Africa: 24.2% of urban population, 10% of populationZambia: 48.3% of urban population, 37.2% of populationZimbabwe: 54.9% of urban population, 19.7% of populationBELBFACHECODDEUHTIMLISDNSOMSYR

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.6613.6e-21158
By rank (Spearman), not adjusted+0.7712.0e-32158
Standard (Pearson), adjusted for national income+0.2280.004158
By rank (Spearman), adjusted for national income+0.2850.000158

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 158 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: +0.432 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.198 (without KIR) to +0.265 (without SOM). 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: SOM (+0.037).

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 & Pacific19-0.21
Europe & Central Asia48-0.29
Latin America & Caribbean24+0.35
Middle East, North Africa, Afghanistan & Pakistan18+0.23
Sub-Saharan Africa43-0.01

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 third53+0.11
Middle third52+0.51
Richest third53+0.36

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
Christians99+0.41
Muslims44+0.01

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
France24+0.06
Never a European colony20+0.47
Spain18+0.34
United Kingdom45+0.12

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 = +0.341 after dropping 8 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.

Loading…

Over time, within countries

Everything above compares countries with each other at one moment. This section compares each country only with itself over 21years. 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 = +0.039, 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 slum share of urban population moved did not tend to see their undernourishment move with it.

Across countries (adjusted)+0.228for comparison
Within countries+0.221each country vs itself
…and vs the same year elsewhere+0.039q = 0.814 · 143 countries
Year-on-year changes—q = —
Read with care.
  • Almost all of the second indicator's variation is between countries, so the within-country estimate rests on a small part of the data.

1,524 country-years (one country in one year), 143 countries, 2002–2022, about 11 years each on average. Share of the variation that happens within countries over time: 12% for Slum share of urban population, 8% for Undernourishment. 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+10years Undernourishment trails Slum share of urban population →lag -10: r = -0.000 (n = 795)lag -9: r = -0.021 (n = 953)lag -8: r = -0.027 (n = 953)lag -7: r = -0.025 (n = 1,100)lag -6: r = -0.021 (n = 1,100)lag -5: r = -0.016 (n = 1,239)lag -4: r = -0.002 (n = 1,239)lag -3: r = 0.008 (n = 1,384)lag -2: r = 0.017 (n = 1,384)lag -1: r = 0.028 (n = 1,529)lag +0: r = 0.039 (n = 1,524)lag +1: r = 0.066 (n = 1,677)lag +2: r = 0.077 (n = 1,521)lag +3: r = 0.093 (n = 1,525)lag +4: r = 0.095 (n = 1,389)lag +5: r = 0.105 (n = 1,393)lag +6: r = 0.098 (n = 1,249)lag +7: r = 0.095 (n = 1,253)lag +8: r = 0.083 (n = 1,107)lag +9: r = 0.076 (n = 1,111)lag +10: r = 0.057 (n = 957)

Same year (lag 0): +0.039. 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 (+0.11 at lag +5) is higher than the same-year one by 0.07. That gap is not a finding. It is the largest of 21 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 urban population
% of population		Years
Mali MLI92.5%12.3%2022, 2023
Burkina Faso BFA87.9%13.1%2022, 2023
Sao Tome and Principe STP82.4%17.4%2022, 2023
Chad TCD82%32%2022, 2023
Congo, Dem. Rep. COD78.4%38.5%2022, 2023
Congo, Rep. COG75.3%26.4%2022, 2023
Sudan SDN73.7%11%2022, 2021
Afghanistan AFG71.6%28.1%2022, 2023
Niger NER70.4%12.9%2022, 2023
Tanzania TZA70.1%20.2%2022, 2023
Central African Republic CAF68.9%29.8%2022, 2023
Madagascar MDG65.7%39.5%2022, 2023
Ethiopia ETH64.3%19.7%2022, 2023
Benin BEN64%14.3%2022, 2023
Angola AGO62.7%22.5%2022, 2023

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

Sources

Slum share of urban population

Population living in slums (% of urban population)

UN-Habitat · CC BY 4.0 · updated quarterly

Undernourishment

Prevalence of undernourishment (% of population)

Food and Agriculture Organization · CC BY 4.0 · updated quarterly

Estimated from the food supply and how it is shared out, not by asking anyone. It is the FAO's model of how many people get less food energy than they need. The survey-based food insecurity measure below is treated as measuring the same thing. So their pair reads as two tools agreeing, not as a finding.

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 Slum share of urban population and Undernourishment

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

More links with Slum share of urban population or Undernourishment