placestats

Anaemia in women vs. Child stunting

Fades after adjusting for income

Across 155 countries, Anaemia in women and Child stunting rise and fall together at r = +0.51 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.10, which could easily be chance (q = 0.50, 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 child stunting, 5.7% to 23.8% of children under 5, anaemia in women is worse by 8% of women aged 15-49 at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 1.88% of women aged 15-49.

Link before adjusting (r)+0.514p = 7.7e-12 · 155 countries
After adjusting for national income+0.097p = 0.230 · likely range [-0.06, +0.25]
p, allowing for regions0.318standard error 0.089 · 7 regions
Check for luck (q)0.495across 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
Child stunting · % of children under 5link with income -0.80 · 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

Anaemia in women · % of women aged 15-49 →Child stunting · % of children under 5 →10.5%59.9%1%55.3%Afghanistan: 45.4% of women aged 15-49, 42% of children under 5Angola: 43.8% of women aged 15-49, 47.7% of children under 5Albania: 24.7% of women aged 15-49, 7.4% of children under 5Argentina: 23.5% of women aged 15-49, 10.7% of children under 5Armenia: 18.7% of women aged 15-49, 6.2% of children under 5Australia: 11.1% of women aged 15-49, 3.1% of children under 5Azerbaijan: 35.6% of women aged 15-49, 6.8% of children under 5Burundi: 39% of women aged 15-49, 55.3% of children under 5Belgium: 15% of women aged 15-49, 2.6% of children under 5Benin: 50% of women aged 15-49, 33.2% of children under 5Burkina Faso: 44.3% of women aged 15-49, 19.5% of children under 5Bangladesh: 37.6% of women aged 15-49, 25.1% of children under 5Bulgaria: 25.8% of women aged 15-49, 5.5% of children under 5Bahrain: 38.4% of women aged 15-49, 4.6% of children under 5Bosnia and Herzegovina: 26% of women aged 15-49, 7.9% of children under 5Belarus: 22.1% of women aged 15-49, 1.1% of children under 5Belize: 21.3% of women aged 15-49, 11.7% of children under 5Bolivia: 24.9% of women aged 15-49, 10.7% of children under 5Brazil: 21.3% of women aged 15-49, 8.9% of children under 5Barbados: 20.1% of women aged 15-49, 5.8% of children under 5Brunei Darussalam: 18.3% of women aged 15-49, 9.9% of children under 5Bhutan: 33.8% of women aged 15-49, 17.9% of children under 5Botswana: 30.9% of women aged 15-49, 21% of children under 5Central African Republic: 43.4% of women aged 15-49, 38.9% of children under 5Chile: 15.7% of women aged 15-49, 1.7% of children under 5China: 15.6% of women aged 15-49, 4.5% of children under 5Cote d'Ivoire: 51.4% of women aged 15-49, 20.3% of children under 5Cameroon: 38.9% of women aged 15-49, 27.2% of children under 5Congo, Dem. Rep.: 40% of women aged 15-49, 44.1% of children under 5Congo, Rep.: 46.7% of women aged 15-49, 16.3% of children under 5Colombia: 22.4% of women aged 15-49, 11.6% of children under 5Comoros: 29.9% of women aged 15-49, 17.4% of children under 5Cabo Verde: 40.6% of women aged 15-49, 5.3% of children under 5Costa Rica: 13.7% of women aged 15-49, 10.6% of children under 5Czechia: 24.6% of women aged 15-49, 2.4% of children under 5Germany: 14% of women aged 15-49, 2.2% of children under 5Djibouti: 31.8% of women aged 15-49, 20.9% of children under 5Dominican Republic: 22.4% of women aged 15-49, 5.6% of children under 5Algeria: 31.6% of women aged 15-49, 8.9% of children under 5Ecuador: 20.2% of women aged 15-49, 17.7% of children under 5Egypt, Arab Rep.: 32.7% of women aged 15-49, 12.9% of children under 5Estonia: 24.7% of women aged 15-49, 1.3% of children under 5Ethiopia: 23.1% of women aged 15-49, 35.5% of children under 5Finland: 13.6% of women aged 15-49, 1.7% of children under 5Fiji: 29.9% of women aged 15-49, 7.2% of children under 5Gabon: 59.9% of women aged 15-49, 13.7% of children under 5United Kingdom: 13.6% of women aged 15-49, 3.5% of children under 5Georgia: 30.2% of women aged 15-49, 4.7% of children under 5Ghana: 36% of women aged 15-49, 15.6% of children under 5Guinea: 45.7% of women aged 15-49, 26.6% of children under 5Gambia, The: 46.1% of women aged 15-49, 14% of children under 5Guinea-Bissau: 45.7% of women aged 15-49, 28.3% of children under 5Equatorial Guinea: 41.3% of women aged 15-49, 17.1% of children under 5Greece: 15.6% of women aged 15-49, 2% of children under 5Guatemala: 10.5% of women aged 15-49, 44.6% of children under 5Guyana: 37.5% of women aged 15-49, 7.1% of children under 5Honduras: 18.8% of women aged 15-49, 17.9% of children under 5Haiti: 45.9% of women aged 15-49, 21.6% of children under 5Indonesia: 26.7% of women aged 15-49, 22.6% of children under 5India: 53.7% of women aged 15-49, 32.9% of children under 5Ireland: 14% of women aged 15-49, 2.2% of children under 5Iran, Islamic Rep.: 25.5% of women aged 15-49, 4.8% of children under 5Iraq: 27.1% of women aged 15-49, 9.4% of children under 5Jamaica: 21.5% of women aged 15-49, 6.9% of children under 5Jordan: 33% of women aged 15-49, 7.7% of children under 5Japan: 18.7% of women aged 15-49, 5.2% of children under 5Kazakhstan: 30.5% of women aged 15-49, 4.4% of children under 5Kenya: 32.2% of women aged 15-49, 17.9% of children under 5Kyrgyz Republic: 32.9% of women aged 15-49, 11% of children under 5Cambodia: 38% of women aged 15-49, 22% of children under 5Kiribati: 26.9% of women aged 15-49, 14.7% of children under 5Korea, Rep.: 15.7% of women aged 15-49, 1.8% of children under 5Kuwait: 27.7% of women aged 15-49, 4.5% of children under 5Lao PDR: 28.6% of women aged 15-49, 29.9% of children under 5Lebanon: 34% of women aged 15-49, 10.1% of children under 5Liberia: 42.5% of women aged 15-49, 26.9% of children under 5Libya: 29.3% of women aged 15-49, 9.2% of children under 5St. Lucia: 19.9% of women aged 15-49, 2.8% of children under 5Sri Lanka: 20.9% of women aged 15-49, 10.1% of children under 5Lesotho: 34.2% of women aged 15-49, 35% of children under 5Lithuania: 23.7% of women aged 15-49, 1.6% of children under 5Latvia: 21.4% of women aged 15-49, 1.7% of children under 5Morocco: 29.5% of women aged 15-49, 13.7% of children under 5Moldova: 28.5% of women aged 15-49, 4.2% of children under 5Madagascar: 37.2% of women aged 15-49, 38.4% of children under 5Maldives: 43.4% of women aged 15-49, 14.2% of children under 5Mexico: 13.2% of women aged 15-49, 13.1% of children under 5Marshall Islands: 23.5% of women aged 15-49, 30% of children under 5North Macedonia: 21.5% of women aged 15-49, 3.8% of children under 5Mali: 56.5% of women aged 15-49, 23.2% of children under 5Malta: 15.7% of women aged 15-49, 3.5% of children under 5Myanmar: 39.7% of women aged 15-49, 24.5% of children under 5Montenegro: 25.8% of women aged 15-49, 8% of children under 5Mongolia: 19.1% of women aged 15-49, 7.1% of children under 5Mozambique: 47% of women aged 15-49, 37% of children under 5Mauritania: 54.5% of women aged 15-49, 21.5% of children under 5Mauritius: 32.8% of women aged 15-49, 7.8% of children under 5Malawi: 32.4% of women aged 15-49, 33.2% of children under 5Malaysia: 31.3% of women aged 15-49, 24.3% of children under 5Namibia: 24.9% of women aged 15-49, 16.4% of children under 5Niger: 47.1% of women aged 15-49, 48.3% of children under 5Nigeria: 37.9% of women aged 15-49, 33.8% of children under 5Netherlands: 16.5% of women aged 15-49, 1.6% of children under 5Nepal: 33.2% of women aged 15-49, 26% of children under 5Naoero: 22.5% of women aged 15-49, 16% of children under 5Oman: 34.4% of women aged 15-49, 12.9% of children under 5Pakistan: 46.5% of women aged 15-49, 33.6% of children under 5Panama: 25.5% of women aged 15-49, 14% of children under 5Peru: 21% of women aged 15-49, 10.6% of children under 5Philippines: 12% of women aged 15-49, 27.7% of children under 5Papua New Guinea: 28.8% of women aged 15-49, 47.6% of children under 5Poland: 23.4% of women aged 15-49, 1% of children under 5Portugal: 15% of women aged 15-49, 3.2% of children under 5Paraguay: 23.2% of women aged 15-49, 3.2% of children under 5West Bank and Gaza: 24.8% of women aged 15-49, 8% of children under 5Qatar: 23.6% of women aged 15-49, 5.4% of children under 5Romania: 24.3% of women aged 15-49, 7% of children under 5Rwanda: 16.1% of women aged 15-49, 29.8% of children under 5Saudi Arabia: 17.6% of women aged 15-49, 11.1% of children under 5Sudan: 34% of women aged 15-49, 35.4% of children under 5Senegal: 43.1% of women aged 15-49, 17.2% of children under 5Singapore: 16.6% of women aged 15-49, 2.8% of children under 5Solomon Islands: 32.6% of women aged 15-49, 29.8% of children under 5Sierra Leone: 44.6% of women aged 15-49, 25.2% of children under 5El Salvador: 14.4% of women aged 15-49, 9.4% of children under 5Somalia, Fed. Rep.: 46.2% of women aged 15-49, 23.9% of children under 5Serbia: 24.9% of women aged 15-49, 4.6% of children under 5Sao Tome and Principe: 39.7% of women aged 15-49, 10% of children under 5Suriname: 22.2% of women aged 15-49, 8% of children under 5Eswatini: 28.3% of women aged 15-49, 18.9% of children under 5Seychelles: 25.8% of women aged 15-49, 6.2% of children under 5Syrian Arab Republic: 31% of women aged 15-49, 23.5% of children under 5Chad: 46.5% of women aged 15-49, 31.5% of children under 5Togo: 41.5% of women aged 15-49, 23% of children under 5Thailand: 20.7% of women aged 15-49, 12.3% of children under 5Tajikistan: 37.3% of women aged 15-49, 13.1% of children under 5Turkmenistan: 34.8% of women aged 15-49, 6.5% of children under 5Timor-Leste: 29.7% of women aged 15-49, 45.4% of children under 5Tonga: 20.7% of women aged 15-49, 1.6% of children under 5Trinidad and Tobago: 22.1% of women aged 15-49, 7.8% of children under 5Tunisia: 27.7% of women aged 15-49, 8.9% of children under 5Turkiye: 29.3% of women aged 15-49, 5.5% of children under 5Tuvalu: 23.2% of women aged 15-49, 5.3% of children under 5Tanzania: 38.8% of women aged 15-49, 29.9% of children under 5Uganda: 27.2% of women aged 15-49, 23.5% of children under 5Ukraine: 19.4% of women aged 15-49, 11.7% of children under 5Uruguay: 24.5% of women aged 15-49, 6.5% of children under 5United States: 15% of women aged 15-49, 4.2% of children under 5Uzbekistan: 30.4% of women aged 15-49, 6.7% of children under 5Viet Nam: 20.5% of women aged 15-49, 19.2% of children under 5Vanuatu: 30.4% of women aged 15-49, 31% of children under 5Samoa: 21.9% of women aged 15-49, 8.4% of children under 5South Africa: 31.2% of women aged 15-49, 24.4% of children under 5Zambia: 27.1% of women aged 15-49, 32.3% of children under 5Zimbabwe: 27.5% of women aged 15-49, 23.7% of children under 5AGOAUSBDICPVDEUFINGABGTMMLINERPHLPNG

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5147.7e-12155
By rank (Spearman), not adjusted+0.5783.4e-15155
Standard (Pearson), adjusted for national income+0.0970.230155
By rank (Spearman), adjusted for national income+0.1720.033155

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 155 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: +0.417 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.066 (without IND) to +0.159 (without GTM). 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: GTM (+0.061).

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 & Pacific26+0.30
Europe & Central Asia32-0.01
Latin America & Caribbean24-0.19
Middle East, North Africa, Afghanistan & Pakistan20+0.53
Sub-Saharan Africa46-0.09

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 third52-0.03
Middle third51+0.13
Richest third52+0.38

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
Christians86+0.02
Muslims48+0.26

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
France25-0.01
Never a European colony21-0.05
Spain18-0.33
United Kingdom52+0.38

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 = +0.108 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 24years. So a link here can’t come from anything about a country that stays the same, such as its history, geography, institutions or culture.

The link also shows up within countries over time (r = +0.244). It holds even when looking only at changes from one year to the next (r = +0.189). This is the strongest kind of evidence available here: it holds when each country is compared only with itself, and it is not just a product of shared trends.

Across countries (adjusted)+0.097for comparison
Within countries+0.414each country vs itself
…and vs the same year elsewhere+0.244q = 0.002 · 161 countries
Year-on-year changes+0.189q = 0.008
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.

3,864 country-years (one country in one year), 161 countries, 2000–2023, about 24 years each on average. Share of the variation that happens within countries over time: 9% for Anaemia in women, 4% for Child stunting. 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+10years Child stunting trails Anaemia in women →lag -10: r = 0.125 (n = 2,254)lag -9: r = 0.138 (n = 2,415)lag -8: r = 0.153 (n = 2,576)lag -7: r = 0.168 (n = 2,737)lag -6: r = 0.183 (n = 2,898)lag -5: r = 0.198 (n = 3,059)lag -4: r = 0.211 (n = 3,220)lag -3: r = 0.223 (n = 3,381)lag -2: r = 0.232 (n = 3,542)lag -1: r = 0.239 (n = 3,703)lag +0: r = 0.244 (n = 3,864)lag +1: r = 0.243 (n = 3,864)lag +2: r = 0.245 (n = 3,703)lag +3: r = 0.245 (n = 3,542)lag +4: r = 0.242 (n = 3,381)lag +5: r = 0.237 (n = 3,220)lag +6: r = 0.230 (n = 3,059)lag +7: r = 0.220 (n = 2,898)lag +8: r = 0.210 (n = 2,737)lag +9: r = 0.197 (n = 2,576)lag +10: r = 0.184 (n = 2,415)

Same year (lag 0): +0.244. 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 children under 5		Years
Gabon GAB59.9%13.7%2023, 2024
Mali MLI56.5%23.2%2023, 2024
Mauritania MRT54.5%21.5%2023, 2024
India IND53.7%32.9%2023, 2024
Cote d'Ivoire CIV51.4%20.3%2023, 2024
Benin BEN50%33.2%2023, 2024
Niger NER47.1%48.3%2023, 2024
Mozambique MOZ47%37%2023, 2024
Congo, Rep. COG46.7%16.3%2023, 2024
Chad TCD46.5%31.5%2023, 2024
Pakistan PAK46.5%33.6%2023, 2024
Somalia, Fed. Rep. SOM46.2%23.9%2023, 2024
Gambia, The GMB46.1%14%2023, 2024
Haiti HTI45.9%21.6%2023, 2024
Guinea-Bissau GNB45.7%28.3%2023, 2024

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

Child stunting

Prevalence of stunting, height for age, modelled estimate (% of children under 5)

UNICEF, WHO and World Bank Joint Child Malnutrition Estimates · CC BY 4.0 · updated quarterly

An estimate from a statistical model. The agencies fit a model to household surveys and predict every country and year. So this is smoother than the surveys behind it, and it has values for years nobody measured. The survey-only series never reaches sixty countries in any year.

How this was computed

Data used: most recent value in 2010-2025; values come from 2023–2024. 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 Anaemia in women and Child stunting

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

More links with Anaemia in women or Child stunting