placestats

Anaemia in women vs. Christian share

Holds up after adjusting for income

A country is not a person. Countries where more people are counted as belonging to this group also tend to differ on the other measure, and that is all. It does not follow that the people in the group are the people with the different score. Making that leap is called the ecological fallacy: reading a pattern across places as a fact about individual people. It is the most common misuse of this kind of data. Religion also varies strongly with region, history and national income, so much of what looks religious here is really those. That is what the adjusted figures are for. The religion shares are estimates for two years only, and the survey questions are asked in rounds that can be ten years apart.

Across 176 countries, Anaemia in women and Christian share rise and fall together at r = -0.28 (on a scale from −1 to +1, where 0 means no link). It holds up almost unchanged when only countries with similar income (GDP per person) are compared (r = -0.35, q = 0.031, allowing for the many pairs tested).

Comparing countries across the middle half of christian share, 8.27% to 87.5% of population, anaemia in women is better by 6.56% of women aged 15-49 at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 6.22% of women aged 15-49.

Link before adjusting (r)-0.284p = 0.000 · 176 countries
After adjusting for national income-0.355p = 1.4e-6 · likely range [-0.48, -0.22]
p, allowing for regions0.009standard error 0.093 · 7 regions
Check for luck (q)0.031across 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
Christian share · % of populationlink with income -0.01 · independent

“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 →Christian share · % of population →10.5%59.9%0.0194%99.5%Afghanistan: 45.4% of women aged 15-49, 0.0194% of populationAngola: 43.8% of women aged 15-49, 93% of populationAlbania: 24.7% of women aged 15-49, 17.8% of populationUnited Arab Emirates: 28.2% of women aged 15-49, 14.3% of populationArgentina: 23.5% of women aged 15-49, 88.5% of populationArmenia: 18.7% of women aged 15-49, 97.3% of populationAustralia: 11.1% of women aged 15-49, 46.8% of populationAustria: 16.7% of women aged 15-49, 68.2% of populationAzerbaijan: 35.6% of women aged 15-49, 0.42% of populationBurundi: 39% of women aged 15-49, 95% of populationBelgium: 15% of women aged 15-49, 51% of populationBenin: 50% of women aged 15-49, 53.2% of populationBurkina Faso: 44.3% of women aged 15-49, 28% of populationBangladesh: 37.6% of women aged 15-49, 0.3% of populationBulgaria: 25.8% of women aged 15-49, 79.5% of populationBahrain: 38.4% of women aged 15-49, 13.7% of populationBahamas, The: 18.8% of women aged 15-49, 97.6% of populationBosnia and Herzegovina: 26% of women aged 15-49, 44.7% of populationBelarus: 22.1% of women aged 15-49, 85.1% of populationBelize: 21.3% of women aged 15-49, 74.4% of populationBolivia: 24.9% of women aged 15-49, 88.9% of populationBrazil: 21.3% of women aged 15-49, 80.7% of populationBarbados: 20.1% of women aged 15-49, 77.3% of populationBrunei Darussalam: 18.3% of women aged 15-49, 6.69% of populationBhutan: 33.8% of women aged 15-49, 0.55% of populationBotswana: 30.9% of women aged 15-49, 82.3% of populationCentral African Republic: 43.4% of women aged 15-49, 90.7% of populationCanada: 14% of women aged 15-49, 53.3% of populationSwitzerland: 16.6% of women aged 15-49, 61.6% of populationChile: 15.7% of women aged 15-49, 68.3% of populationChina: 15.6% of women aged 15-49, 1.78% of populationCote d'Ivoire: 51.4% of women aged 15-49, 44.9% of populationCameroon: 38.9% of women aged 15-49, 69.8% of populationCongo, Dem. Rep.: 40% of women aged 15-49, 96.3% of populationCongo, Rep.: 46.7% of women aged 15-49, 92.2% of populationColombia: 22.4% of women aged 15-49, 86.2% of populationComoros: 29.9% of women aged 15-49, 0.51% of populationCabo Verde: 40.6% of women aged 15-49, 78.3% of populationCosta Rica: 13.7% of women aged 15-49, 87.9% of populationCyprus: 16.4% of women aged 15-49, 67.7% of populationCzechia: 24.6% of women aged 15-49, 26.4% of populationGermany: 14% of women aged 15-49, 56.2% of populationDjibouti: 31.8% of women aged 15-49, 1.1% of populationDenmark: 17% of women aged 15-49, 76.9% of populationDominican Republic: 22.4% of women aged 15-49, 77.6% of populationAlgeria: 31.6% of women aged 15-49, 0.295% of populationEcuador: 20.2% of women aged 15-49, 88.5% of populationEgypt, Arab Rep.: 32.7% of women aged 15-49, 4.82% of populationSpain: 15.6% of women aged 15-49, 69.5% of populationEstonia: 24.7% of women aged 15-49, 52.6% of populationEthiopia: 23.1% of women aged 15-49, 61.6% of populationFinland: 13.6% of women aged 15-49, 72.3% of populationFiji: 29.9% of women aged 15-49, 69.5% of populationFrance: 13.1% of women aged 15-49, 46.5% of populationMicronesia, Fed. Sts.: 22.1% of women aged 15-49, 98.8% of populationGabon: 59.9% of women aged 15-49, 83.7% of populationUnited Kingdom: 13.6% of women aged 15-49, 49.4% of populationGeorgia: 30.2% of women aged 15-49, 88.2% of populationGhana: 36% of women aged 15-49, 71.3% of populationGuinea: 45.7% of women aged 15-49, 11.7% of populationGambia, The: 46.1% of women aged 15-49, 2.89% of populationGuinea-Bissau: 45.7% of women aged 15-49, 21.7% of populationEquatorial Guinea: 41.3% of women aged 15-49, 88.7% of populationGreece: 15.6% of women aged 15-49, 89.5% of populationGrenada: 20.9% of women aged 15-49, 87.5% of populationGuatemala: 10.5% of women aged 15-49, 92% of populationGuyana: 37.5% of women aged 15-49, 65.9% of populationHonduras: 18.8% of women aged 15-49, 88.5% of populationCroatia: 23.3% of women aged 15-49, 90.9% of populationHaiti: 45.9% of women aged 15-49, 88.7% of populationHungary: 24.1% of women aged 15-49, 72.4% of populationIndonesia: 26.7% of women aged 15-49, 10.3% of populationIndia: 53.7% of women aged 15-49, 2.21% of populationIreland: 14% of women aged 15-49, 81.2% of populationIran, Islamic Rep.: 25.5% of women aged 15-49, 0.143% of populationIraq: 27.1% of women aged 15-49, 0.375% of populationIceland: 14.7% of women aged 15-49, 74.9% of populationIsrael: 14.6% of women aged 15-49, 1.88% of populationItaly: 16.4% of women aged 15-49, 80.5% of populationJamaica: 21.5% of women aged 15-49, 68.6% of populationJordan: 33% of women aged 15-49, 2.81% of populationJapan: 18.7% of women aged 15-49, 2.25% of populationKazakhstan: 30.5% of women aged 15-49, 19.3% of populationKenya: 32.2% of women aged 15-49, 85.7% of populationKyrgyz Republic: 32.9% of women aged 15-49, 5.75% of populationCambodia: 38% of women aged 15-49, 0.316% of populationKiribati: 26.9% of women aged 15-49, 97.6% of populationKorea, Rep.: 15.7% of women aged 15-49, 32% of populationKuwait: 27.7% of women aged 15-49, 10.5% of populationLao PDR: 28.6% of women aged 15-49, 1.54% of populationLebanon: 34% of women aged 15-49, 27.9% of populationLiberia: 42.5% of women aged 15-49, 85.2% of populationLibya: 29.3% of women aged 15-49, 0.525% of populationSt. Lucia: 19.9% of women aged 15-49, 91.7% of populationSri Lanka: 20.9% of women aged 15-49, 5.59% of populationLesotho: 34.2% of women aged 15-49, 97.3% of populationLithuania: 23.7% of women aged 15-49, 92.2% of populationLuxembourg: 15.4% of women aged 15-49, 65.9% of populationLatvia: 21.4% of women aged 15-49, 77.3% of populationMorocco: 29.5% of women aged 15-49, 0.0855% of populationMoldova: 28.5% of women aged 15-49, 99.5% of populationMadagascar: 37.2% of women aged 15-49, 74.2% of populationMaldives: 43.4% of women aged 15-49, 1.65% of populationMexico: 13.2% of women aged 15-49, 89.2% of populationNorth Macedonia: 21.5% of women aged 15-49, 64.7% of populationMali: 56.5% of women aged 15-49, 2.57% of populationMalta: 15.7% of women aged 15-49, 88.6% of populationMyanmar: 39.7% of women aged 15-49, 6.14% of populationMontenegro: 25.8% of women aged 15-49, 76% of populationMongolia: 19.1% of women aged 15-49, 1.34% of populationMozambique: 47% of women aged 15-49, 61.3% of populationMauritania: 54.5% of women aged 15-49, 0.234% of populationMauritius: 32.8% of women aged 15-49, 32.9% of populationMalawi: 32.4% of women aged 15-49, 87.4% of populationMalaysia: 31.3% of women aged 15-49, 9.15% of populationNamibia: 24.9% of women aged 15-49, 90.5% of populationNiger: 47.1% of women aged 15-49, 0.556% of populationNigeria: 37.9% of women aged 15-49, 43.4% of populationNetherlands: 16.5% of women aged 15-49, 35.1% of populationNorway: 16.3% of women aged 15-49, 71.4% of populationNepal: 33.2% of women aged 15-49, 1.76% of populationNew Zealand: 12.4% of women aged 15-49, 40.3% of populationOman: 34.4% of women aged 15-49, 8.07% of populationPakistan: 46.5% of women aged 15-49, 1.27% of populationPanama: 25.5% of women aged 15-49, 91.2% of populationPeru: 21% of women aged 15-49, 94.5% of populationPhilippines: 12% of women aged 15-49, 91.5% of populationPapua New Guinea: 28.8% of women aged 15-49, 99.1% of populationPoland: 23.4% of women aged 15-49, 91.2% of populationPortugal: 15% of women aged 15-49, 85.1% of populationParaguay: 23.2% of women aged 15-49, 93.5% of populationWest Bank and Gaza: 24.8% of women aged 15-49, 1% of populationQatar: 23.6% of women aged 15-49, 12.5% of populationRomania: 24.3% of women aged 15-49, 98.5% of populationRussian Federation: 24.1% of women aged 15-49, 69.9% of populationRwanda: 16.1% of women aged 15-49, 97% of populationSaudi Arabia: 17.6% of women aged 15-49, 4.38% of populationSudan: 34% of women aged 15-49, 0.488% of populationSenegal: 43.1% of women aged 15-49, 2.4% of populationSingapore: 16.6% of women aged 15-49, 18.8% of populationSolomon Islands: 32.6% of women aged 15-49, 95% of populationSierra Leone: 44.6% of women aged 15-49, 19.8% of populationEl Salvador: 14.4% of women aged 15-49, 87.1% of populationSomalia, Fed. Rep.: 46.2% of women aged 15-49, 0.0262% of populationSerbia: 24.9% of women aged 15-49, 91.5% of populationSao Tome and Principe: 39.7% of women aged 15-49, 84.4% of populationSuriname: 22.2% of women aged 15-49, 52.8% of populationSlovak Republic: 25.3% of women aged 15-49, 73.7% of populationSlovenia: 25.7% of women aged 15-49, 65.4% of populationSweden: 16.8% of women aged 15-49, 60.8% of populationEswatini: 28.3% of women aged 15-49, 93.2% of populationSeychelles: 25.8% of women aged 15-49, 94% of populationSyrian Arab Republic: 31% of women aged 15-49, 3.84% of populationChad: 46.5% of women aged 15-49, 38.9% of populationTogo: 41.5% of women aged 15-49, 56.9% of populationThailand: 20.7% of women aged 15-49, 1.03% of populationTajikistan: 37.3% of women aged 15-49, 1% of populationTurkmenistan: 34.8% of women aged 15-49, 5.62% of populationTimor-Leste: 29.7% of women aged 15-49, 99.5% of populationTonga: 20.7% of women aged 15-49, 97.8% of populationTrinidad and Tobago: 22.1% of women aged 15-49, 70% of populationTunisia: 27.7% of women aged 15-49, 0.247% of populationTurkiye: 29.3% of women aged 15-49, 0.136% of populationTanzania: 38.8% of women aged 15-49, 64.7% of populationUganda: 27.2% of women aged 15-49, 87.7% of populationUkraine: 19.4% of women aged 15-49, 83.4% of populationUruguay: 24.5% of women aged 15-49, 44.5% of populationUnited States: 15% of women aged 15-49, 64% of populationUzbekistan: 30.4% of women aged 15-49, 2.78% of populationSt. Vincent and the Grenadines: 18.8% of women aged 15-49, 86.3% of populationViet Nam: 20.5% of women aged 15-49, 8.33% of populationVanuatu: 30.4% of women aged 15-49, 83.5% of populationSamoa: 21.9% of women aged 15-49, 97.6% of populationSouth Africa: 31.2% of women aged 15-49, 85.3% of populationZambia: 27.1% of women aged 15-49, 98.3% of populationZimbabwe: 27.5% of women aged 15-49, 87.2% of populationCHNCOGGABGTMHTIINDISRMEXMLIMRTPHLSAU

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.2840.000176
By rank (Spearman), not adjusted-0.2530.001176
Standard (Pearson), adjusted for national income-0.3551.4e-6176
By rank (Spearman), adjusted for national income-0.3551.5e-6176

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 176 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: -0.071 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.394 (without GAB) to -0.342 (without MRT). 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: GAB (-0.039).

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 & Pacific25-0.55
Europe & Central Asia48-0.34
Latin America & Caribbean27-0.23
Middle East, North Africa, Afghanistan & Pakistan22-0.15
Sub-Saharan Africa46-0.44

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 third59-0.44
Middle third58-0.29
Richest third59-0.30

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
Christians107-0.13
Muslims49+0.20

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+0.34
Never a European colony21-0.21
Spain19-0.28
United Kingdom58-0.56

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 = -0.384 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 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 women aged 15-49
% of population		Years
Gabon GAB59.9%83.7%2023, 2020
Mali MLI56.5%2.57%2023, 2020
Mauritania MRT54.5%0.234%2023, 2020
India IND53.7%2.21%2023, 2020
Cote d'Ivoire CIV51.4%44.9%2023, 2020
Benin BEN50%53.2%2023, 2020
Niger NER47.1%0.556%2023, 2020
Mozambique MOZ47%61.3%2023, 2020
Congo, Rep. COG46.7%92.2%2023, 2020
Chad TCD46.5%38.9%2023, 2020
Pakistan PAK46.5%1.27%2023, 2020
Somalia, Fed. Rep. SOM46.2%0.0262%2023, 2020
Gambia, The GMB46.1%2.89%2023, 2020
Haiti HTI45.9%88.7%2023, 2020
Guinea-Bissau GNB45.7%21.7%2023, 2020

World Bank, World Development Indicators
Pew Research Center, Religious Composition by Country, 2010-2020. Pew Research Center bears no responsibility for the analyses or interpretations of the data presented here.
One of these measures is published under a licence that forbids changes. Values are shown exactly as published and must not be republished in a changed or rescaled form.

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

Christian share

Share of the population identifying as Christian

Pew Research Center · via Our World in Data · Pew Research Center terms of use, excerpts with attribution · updated decennial · comparable from 2010 only

All Christian traditions together. The world's largest religion, and the one whose spread most closely follows income. Adjusting for GDP is there to remove that. No "better" direction is set.

How this was computed

Data used: most recent value in 2010-2025; values come from 2020–2023. 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 Christian share

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

More links with Anaemia in women or Christian share