placestats

Anaemia in women vs. Slum share of urban population

Fades after adjusting for income

Across 177 countries, Anaemia in women and Slum share of urban population rise and fall together at r = +0.65 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.32, which could easily be chance (q = 0.13, 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.

Comparing countries across the middle half of anaemia in women, 20.2% to 34.2% of women aged 15-49, slum share of urban population is worse by 21% of urban population at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 9.32% of urban population.

Link before adjusting (r)+0.647p = 2.2e-22 · 177 countries
After adjusting for national income+0.323p = 1.2e-5 · likely range [+0.18, +0.45]
p, allowing for regions0.027standard error 0.112 · 7 regions
Check for luck (q)0.129across 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.

Anaemia in women · % of women aged 15-49link with income -0.67 · high
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

Anaemia in women · % of women aged 15-49 →Slum share of urban population · % of urban population →10.5%59.9%0%92.5%Afghanistan: 45.4% of women aged 15-49, 71.6% of urban populationAngola: 43.8% of women aged 15-49, 62.7% of urban populationAlbania: 24.7% of women aged 15-49, 2.7% of urban populationAndorra: 16.4% of women aged 15-49, 0% of urban populationUnited Arab Emirates: 28.2% of women aged 15-49, 0.113% of urban populationArgentina: 23.5% of women aged 15-49, 14.5% of urban populationArmenia: 18.7% of women aged 15-49, 8.39% of urban populationAntigua and Barbuda: 20.3% of women aged 15-49, 2.65% of urban populationAustralia: 11.1% of women aged 15-49, 0.035% of urban populationAustria: 16.7% of women aged 15-49, 0.0466% of urban populationAzerbaijan: 35.6% of women aged 15-49, 26.9% of urban populationBurundi: 39% of women aged 15-49, 36.8% of urban populationBelgium: 15% of women aged 15-49, 0% of urban populationBenin: 50% of women aged 15-49, 64% of urban populationBurkina Faso: 44.3% of women aged 15-49, 87.9% of urban populationBangladesh: 37.6% of women aged 15-49, 51.5% of urban populationBulgaria: 25.8% of women aged 15-49, 0.197% of urban populationBahrain: 38.4% of women aged 15-49, 0% of urban populationBosnia and Herzegovina: 26% of women aged 15-49, 0.313% of urban populationBelarus: 22.1% of women aged 15-49, 0.004% of urban populationBelize: 21.3% of women aged 15-49, 15.7% of urban populationBolivia: 24.9% of women aged 15-49, 46.6% of urban populationBrazil: 21.3% of women aged 15-49, 14.9% of urban populationBrunei Darussalam: 18.3% of women aged 15-49, 21.6% of urban populationBhutan: 33.8% of women aged 15-49, 44.7% of urban populationBotswana: 30.9% of women aged 15-49, 39.6% of urban populationCentral African Republic: 43.4% of women aged 15-49, 68.9% of urban populationCanada: 14% of women aged 15-49, 1.1% of urban populationSwitzerland: 16.6% of women aged 15-49, 0% of urban populationChile: 15.7% of women aged 15-49, 7.32% of urban populationChina: 15.6% of women aged 15-49, 26.3% of urban populationCote d'Ivoire: 51.4% of women aged 15-49, 48.3% of urban populationCameroon: 38.9% of women aged 15-49, 32.7% of urban populationCongo, Dem. Rep.: 40% of women aged 15-49, 78.4% of urban populationCongo, Rep.: 46.7% of women aged 15-49, 75.3% of urban populationColombia: 22.4% of women aged 15-49, 9.7% of urban populationComoros: 29.9% of women aged 15-49, 48.5% of urban populationCabo Verde: 40.6% of women aged 15-49, 46.4% of urban populationCosta Rica: 13.7% of women aged 15-49, 3.55% of urban populationCyprus: 16.4% of women aged 15-49, 0.272% of urban populationCzechia: 24.6% of women aged 15-49, 0.0273% of urban populationGermany: 14% of women aged 15-49, 0% of urban populationDjibouti: 31.8% of women aged 15-49, 48.7% of urban populationDenmark: 17% of women aged 15-49, 0% of urban populationDominican Republic: 22.4% of women aged 15-49, 11.2% of urban populationAlgeria: 31.6% of women aged 15-49, 13.2% of urban populationEcuador: 20.2% of women aged 15-49, 57.8% of urban populationEgypt, Arab Rep.: 32.7% of women aged 15-49, 3.84% of urban populationSpain: 15.6% of women aged 15-49, 0.046% of urban populationEstonia: 24.7% of women aged 15-49, 0.345% of urban populationEthiopia: 23.1% of women aged 15-49, 64.3% of urban populationFinland: 13.6% of women aged 15-49, 0% of urban populationFiji: 29.9% of women aged 15-49, 9.4% of urban populationFrance: 13.1% of women aged 15-49, 0% of urban populationGabon: 59.9% of women aged 15-49, 38.8% of urban populationUnited Kingdom: 13.6% of women aged 15-49, 0.163% of urban populationGeorgia: 30.2% of women aged 15-49, 7.08% of urban populationGhana: 36% of women aged 15-49, 33.5% of urban populationGuinea: 45.7% of women aged 15-49, 44% of urban populationGambia, The: 46.1% of women aged 15-49, 37.1% of urban populationGuinea-Bissau: 45.7% of women aged 15-49, 59% of urban populationEquatorial Guinea: 41.3% of women aged 15-49, 64.7% of urban populationGreece: 15.6% of women aged 15-49, 0.05% of urban populationGuatemala: 10.5% of women aged 15-49, 37.6% of urban populationGuyana: 37.5% of women aged 15-49, 11.3% of urban populationHonduras: 18.8% of women aged 15-49, 31.5% of urban populationCroatia: 23.3% of women aged 15-49, 0.527% of urban populationHaiti: 45.9% of women aged 15-49, 51.1% of urban populationHungary: 24.1% of women aged 15-49, 7.2% of urban populationIndonesia: 26.7% of women aged 15-49, 19.4% of urban populationIndia: 53.7% of women aged 15-49, 5.41% of urban populationIreland: 14% of women aged 15-49, 8.5% of urban populationIran, Islamic Rep.: 25.5% of women aged 15-49, 44.7% of urban populationIraq: 27.1% of women aged 15-49, 49.3% of urban populationIceland: 14.7% of women aged 15-49, 0% of urban populationItaly: 16.4% of women aged 15-49, 0.02% of urban populationJamaica: 21.5% of women aged 15-49, 0.883% of urban populationJordan: 33% of women aged 15-49, 23.4% of urban populationJapan: 18.7% of women aged 15-49, 2% of urban populationKazakhstan: 30.5% of women aged 15-49, 0.794% of urban populationKenya: 32.2% of women aged 15-49, 40.5% of urban populationKyrgyz Republic: 32.9% of women aged 15-49, 2.44% of urban populationCambodia: 38% of women aged 15-49, 42.3% of urban populationKiribati: 26.9% of women aged 15-49, 5.93% of urban populationKorea, Rep.: 15.7% of women aged 15-49, 4.5% of urban populationKuwait: 27.7% of women aged 15-49, 0% of urban populationLao PDR: 28.6% of women aged 15-49, 54.8% of urban populationLebanon: 34% of women aged 15-49, 4.53% of urban populationLiberia: 42.5% of women aged 15-49, 60.5% of urban populationLibya: 29.3% of women aged 15-49, 16.6% of urban populationSt. Lucia: 19.9% of women aged 15-49, 0.441% of urban populationSri Lanka: 20.9% of women aged 15-49, 44.7% of urban populationLesotho: 34.2% of women aged 15-49, 25.6% of urban populationLithuania: 23.7% of women aged 15-49, 0.509% of urban populationLuxembourg: 15.4% of women aged 15-49, 0.0173% of urban populationLatvia: 21.4% of women aged 15-49, 0.593% of urban populationMorocco: 29.5% of women aged 15-49, 10.9% of urban populationMoldova: 28.5% of women aged 15-49, 6.5% of urban populationMadagascar: 37.2% of women aged 15-49, 65.7% of urban populationMaldives: 43.4% of women aged 15-49, 34.8% of urban populationMexico: 13.2% of women aged 15-49, 17.6% of urban populationMarshall Islands: 23.5% of women aged 15-49, 2.38% of urban populationNorth Macedonia: 21.5% of women aged 15-49, 0.278% of urban populationMali: 56.5% of women aged 15-49, 92.5% of urban populationMalta: 15.7% of women aged 15-49, 0.0375% of urban populationMyanmar: 39.7% of women aged 15-49, 58.3% of urban populationMontenegro: 25.8% of women aged 15-49, 8.77% of urban populationMongolia: 19.1% of women aged 15-49, 17.9% of urban populationMozambique: 47% of women aged 15-49, 55% of urban populationMauritania: 54.5% of women aged 15-49, 58.6% of urban populationMauritius: 32.8% of women aged 15-49, 48.7% of urban populationMalawi: 32.4% of women aged 15-49, 38% of urban populationMalaysia: 31.3% of women aged 15-49, 0.2% of urban populationNamibia: 24.9% of women aged 15-49, 41.4% of urban populationNiger: 47.1% of women aged 15-49, 70.4% of urban populationNigeria: 37.9% of women aged 15-49, 48.5% of urban populationNetherlands: 16.5% of women aged 15-49, 0% of urban populationNorway: 16.3% of women aged 15-49, 0% of urban populationNepal: 33.2% of women aged 15-49, 40.1% of urban populationNaoero: 22.5% of women aged 15-49, 0.597% of urban populationNew Zealand: 12.4% of women aged 15-49, 0% of urban populationOman: 34.4% of women aged 15-49, 0% of urban populationPakistan: 46.5% of women aged 15-49, 56% of urban populationPanama: 25.5% of women aged 15-49, 16.3% of urban populationPeru: 21% of women aged 15-49, 45.1% of urban populationPhilippines: 12% of women aged 15-49, 35.9% of urban populationPalau: 25.9% of women aged 15-49, 0.618% of urban populationPapua New Guinea: 28.8% of women aged 15-49, 22.3% of urban populationPoland: 23.4% of women aged 15-49, 4.22% of urban populationPortugal: 15% of women aged 15-49, 0.059% of urban populationParaguay: 23.2% of women aged 15-49, 15.1% of urban populationWest Bank and Gaza: 24.8% of women aged 15-49, 19.5% of urban populationQatar: 23.6% of women aged 15-49, 0% of urban populationRomania: 24.3% of women aged 15-49, 2.52% of urban populationRussian Federation: 24.1% of women aged 15-49, 2.62% of urban populationRwanda: 16.1% of women aged 15-49, 38.3% of urban populationSaudi Arabia: 17.6% of women aged 15-49, 0.55% of urban populationSudan: 34% of women aged 15-49, 73.7% of urban populationSenegal: 43.1% of women aged 15-49, 46.4% of urban populationSingapore: 16.6% of women aged 15-49, 0% of urban populationSolomon Islands: 32.6% of women aged 15-49, 1.95% of urban populationSierra Leone: 44.6% of women aged 15-49, 49.3% of urban populationEl Salvador: 14.4% of women aged 15-49, 16.5% of urban populationSomalia, Fed. Rep.: 46.2% of women aged 15-49, 48.7% of urban populationSerbia: 24.9% of women aged 15-49, 1.42% of urban populationSao Tome and Principe: 39.7% of women aged 15-49, 82.4% of urban populationSuriname: 22.2% of women aged 15-49, 15.8% of urban populationSlovak Republic: 25.3% of women aged 15-49, 0.066% of urban populationSlovenia: 25.7% of women aged 15-49, 0.527% of urban populationSweden: 16.8% of women aged 15-49, 0.336% of urban populationEswatini: 28.3% of women aged 15-49, 17% of urban populationSeychelles: 25.8% of women aged 15-49, 48.7% of urban populationSyrian Arab Republic: 31% of women aged 15-49, 41.1% of urban populationChad: 46.5% of women aged 15-49, 82% of urban populationTogo: 41.5% of women aged 15-49, 38.5% of urban populationThailand: 20.7% of women aged 15-49, 2% of urban populationTajikistan: 37.3% of women aged 15-49, 0.05% of urban populationTurkmenistan: 34.8% of women aged 15-49, 8.38% of urban populationTimor-Leste: 29.7% of women aged 15-49, 33.9% of urban populationTonga: 20.7% of women aged 15-49, 0.308% of urban populationTrinidad and Tobago: 22.1% of women aged 15-49, 8.63% of urban populationTunisia: 27.7% of women aged 15-49, 7.64% of urban populationTurkiye: 29.3% of women aged 15-49, 14.1% of urban populationTuvalu: 23.2% of women aged 15-49, 50.9% of urban populationTanzania: 38.8% of women aged 15-49, 70.1% of urban populationUganda: 27.2% of women aged 15-49, 52.7% of urban populationUkraine: 19.4% of women aged 15-49, 1.1% of urban populationUruguay: 24.5% of women aged 15-49, 1.3% of urban populationUnited States: 15% of women aged 15-49, 0.0932% of urban populationUzbekistan: 30.4% of women aged 15-49, 7.1% of urban populationSt. Vincent and the Grenadines: 18.8% of women aged 15-49, 2.76% of urban populationViet Nam: 20.5% of women aged 15-49, 32.5% of urban populationVanuatu: 30.4% of women aged 15-49, 3.06% of urban populationSamoa: 21.9% of women aged 15-49, 34.6% of urban populationSouth Africa: 31.2% of women aged 15-49, 24.2% of urban populationZambia: 27.1% of women aged 15-49, 48.3% of urban populationZimbabwe: 27.5% of women aged 15-49, 54.9% of urban populationAUSBFABHRECUETHFRAGABGTMINDMLINZLTCD

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.6472.2e-22177
By rank (Spearman), not adjusted+0.6261.1e-20177
Standard (Pearson), adjusted for national income+0.3231.2e-5177
By rank (Spearman), adjusted for national income+0.2230.003177

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 177 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: +0.324 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.299 (without MLI) to +0.357 (without IND). 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: IND (+0.034).

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 & Pacific28+0.13
Europe & Central Asia49+0.47
Latin America & Caribbean25+0.17
Middle East, North Africa, Afghanistan & Pakistan21+0.16
Sub-Saharan Africa46+0.34

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 third59+0.51
Middle third59+0.15
Richest third59+0.15

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
Christians103+0.38
Muslims49+0.37

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
France25+0.48
Never a European colony21-0.21
Spain18+0.32
United Kingdom57+0.25

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 = +0.286 after dropping 2 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 23years. 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 = +0.138, 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 anaemia in women moved did not tend to see their slum share of urban population move with it.

Across countries (adjusted)+0.323for comparison
Within countries+0.308each country vs itself
…and vs the same year elsewhere+0.138q = 0.139 · 169 countries
Year-on-year changes—q = —
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.

1,932 country-years (one country in one year), 169 countries, 2000–2022, about 11 years each on average. Share of the variation that happens within countries over time: 3% for Anaemia in women, 8% for Slum share of urban population. 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 Slum share of urban population trails Anaemia in women →lag -10: r = 0.105 (n = 1,099)lag -9: r = 0.102 (n = 1,270)lag -8: r = 0.117 (n = 1,270)lag -7: r = 0.122 (n = 1,433)lag -6: r = 0.139 (n = 1,433)lag -5: r = 0.143 (n = 1,599)lag -4: r = 0.158 (n = 1,599)lag -3: r = 0.162 (n = 1,758)lag -2: r = 0.174 (n = 1,758)lag -1: r = 0.131 (n = 1,932)lag +0: r = 0.138 (n = 1,932)lag +1: r = 0.109 (n = 1,767)lag +2: r = 0.116 (n = 1,767)lag +3: r = 0.096 (n = 1,607)lag +4: r = 0.104 (n = 1,607)lag +5: r = 0.090 (n = 1,441)lag +6: r = 0.100 (n = 1,441)lag +7: r = 0.088 (n = 1,281)lag +8: r = 0.098 (n = 1,281)lag +9: r = 0.075 (n = 1,113)lag +10: r = 0.084 (n = 1,113)

Same year (lag 0): +0.138. 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 women aged 15-49
% of urban population		Years
Gabon GAB59.9%38.8%2023, 2022
Mali MLI56.5%92.5%2023, 2022
Mauritania MRT54.5%58.6%2023, 2022
India IND53.7%5.41%2023, 2019
Cote d'Ivoire CIV51.4%48.3%2023, 2022
Benin BEN50%64%2023, 2022
Niger NER47.1%70.4%2023, 2022
Mozambique MOZ47%55%2023, 2022
Congo, Rep. COG46.7%75.3%2023, 2022
Chad TCD46.5%82%2023, 2022
Pakistan PAK46.5%56%2023, 2022
Somalia, Fed. Rep. SOM46.2%48.7%2023, 2022
Gambia, The GMB46.1%37.1%2023, 2022
Haiti HTI45.9%51.1%2023, 2022
Guinea-Bissau GNB45.7%59%2023, 2022

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

Sources

Anaemia in women

Prevalence of anaemia among women of reproductive age (% of women ages 15-49)

World Health Organization · CC BY 4.0 · updated quarterly

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 Anaemia in women and Slum share of urban population

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

More links with Anaemia in women or Slum share of urban population