placestats

Anaemia in women vs. Religion very important

Fades 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 102 countries, Anaemia in women and Religion very important rise and fall together at r = +0.57 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.28, which could easily be chance (q = 0.21, 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 religion very important, 15.6% to 71.9% of respondents, anaemia in women is worse by 9.53% of women aged 15-49 at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 4.54% of women aged 15-49.

Link before adjusting (r)+0.571p = 3.9e-10 · 102 countries
After adjusting for national income+0.279p = 0.005 · likely range [+0.09, +0.45]
p, allowing for regions0.065standard error 0.124 · 7 regions
Check for luck (q)0.205across 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
Religion very important · % of respondentslink with income -0.66 · 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 →Religion very important · % of respondents →10.5%56.5%3.25%98.9%Albania: 24.7% of women aged 15-49, 24.4% of respondentsAndorra: 16.4% of women aged 15-49, 9.46% of respondentsArgentina: 23.5% of women aged 15-49, 24.3% of respondentsArmenia: 18.7% of women aged 15-49, 52.7% of respondentsAustralia: 11.1% of women aged 15-49, 13.8% of respondentsAustria: 16.7% of women aged 15-49, 13.9% of respondentsAzerbaijan: 35.6% of women aged 15-49, 15.9% of respondentsBelgium: 15% of women aged 15-49, 13.2% of respondentsBurkina Faso: 44.3% of women aged 15-49, 82.9% of respondentsBangladesh: 37.6% of women aged 15-49, 94.2% of respondentsBulgaria: 25.8% of women aged 15-49, 21.9% of respondentsBosnia and Herzegovina: 26% of women aged 15-49, 39.8% of respondentsBelarus: 22.1% of women aged 15-49, 19.7% of respondentsBolivia: 24.9% of women aged 15-49, 58.4% of respondentsBrazil: 21.3% of women aged 15-49, 45.1% of respondentsCanada: 14% of women aged 15-49, 15.5% of respondentsSwitzerland: 16.6% of women aged 15-49, 11.3% of respondentsChile: 15.7% of women aged 15-49, 20.7% of respondentsChina: 15.6% of women aged 15-49, 3.25% of respondentsColombia: 22.4% of women aged 15-49, 58.4% of respondentsCyprus: 16.4% of women aged 15-49, 50.5% of respondentsCzechia: 24.6% of women aged 15-49, 7.67% of respondentsGermany: 14% of women aged 15-49, 13.5% of respondentsDenmark: 17% of women aged 15-49, 5.73% of respondentsAlgeria: 31.6% of women aged 15-49, 90.7% of respondentsEcuador: 20.2% of women aged 15-49, 53.6% of respondentsEgypt, Arab Rep.: 32.7% of women aged 15-49, 97.3% of respondentsSpain: 15.6% of women aged 15-49, 17.2% of respondentsEstonia: 24.7% of women aged 15-49, 6.54% of respondentsEthiopia: 23.1% of women aged 15-49, 92.8% of respondentsFinland: 13.6% of women aged 15-49, 11% of respondentsFrance: 13.1% of women aged 15-49, 14.3% of respondentsUnited Kingdom: 13.6% of women aged 15-49, 15.6% of respondentsGeorgia: 30.2% of women aged 15-49, 69.6% of respondentsGhana: 36% of women aged 15-49, 91.1% of respondentsGreece: 15.6% of women aged 15-49, 54.8% of respondentsGuatemala: 10.5% of women aged 15-49, 51.5% of respondentsCroatia: 23.3% of women aged 15-49, 20.6% of respondentsHaiti: 45.9% of women aged 15-49, 28% of respondentsHungary: 24.1% of women aged 15-49, 16.1% of respondentsIndonesia: 26.7% of women aged 15-49, 98.1% of respondentsIndia: 53.7% of women aged 15-49, 64.2% of respondentsIreland: 14% of women aged 15-49, 27.7% of respondentsIran, Islamic Rep.: 25.5% of women aged 15-49, 70.5% of respondentsIraq: 27.1% of women aged 15-49, 87.6% of respondentsIceland: 14.7% of women aged 15-49, 13.2% of respondentsItaly: 16.4% of women aged 15-49, 24.9% of respondentsJordan: 33% of women aged 15-49, 95.4% of respondentsJapan: 18.7% of women aged 15-49, 4.58% of respondentsKazakhstan: 30.5% of women aged 15-49, 28.7% of respondentsKenya: 32.2% of women aged 15-49, 86.7% of respondentsKyrgyz Republic: 32.9% of women aged 15-49, 56% of respondentsKorea, Rep.: 15.7% of women aged 15-49, 10.3% of respondentsKuwait: 27.7% of women aged 15-49, 86.5% of respondentsLebanon: 34% of women aged 15-49, 64.1% of respondentsLibya: 29.3% of women aged 15-49, 97.9% of respondentsLithuania: 23.7% of women aged 15-49, 12.2% of respondentsLuxembourg: 15.4% of women aged 15-49, 16.8% of respondentsLatvia: 21.4% of women aged 15-49, 8.84% of respondentsMorocco: 29.5% of women aged 15-49, 89% of respondentsMoldova: 28.5% of women aged 15-49, 32.9% of respondentsMaldives: 43.4% of women aged 15-49, 95.7% of respondentsMexico: 13.2% of women aged 15-49, 50.5% of respondentsNorth Macedonia: 21.5% of women aged 15-49, 53.7% of respondentsMali: 56.5% of women aged 15-49, 88.2% of respondentsMalta: 15.7% of women aged 15-49, 58.7% of respondentsMyanmar: 39.7% of women aged 15-49, 81% of respondentsMontenegro: 25.8% of women aged 15-49, 63.1% of respondentsMongolia: 19.1% of women aged 15-49, 8.76% of respondentsMalaysia: 31.3% of women aged 15-49, 72.4% of respondentsNigeria: 37.9% of women aged 15-49, 93.4% of respondentsNetherlands: 16.5% of women aged 15-49, 11.9% of respondentsNorway: 16.3% of women aged 15-49, 11.2% of respondentsNew Zealand: 12.4% of women aged 15-49, 14.5% of respondentsPakistan: 46.5% of women aged 15-49, 89.6% of respondentsPeru: 21% of women aged 15-49, 44.6% of respondentsPhilippines: 12% of women aged 15-49, 88.9% of respondentsPoland: 23.4% of women aged 15-49, 37.3% of respondentsPortugal: 15% of women aged 15-49, 20.1% of respondentsWest Bank and Gaza: 24.8% of women aged 15-49, 87.5% of respondentsQatar: 23.6% of women aged 15-49, 98.9% of respondentsRomania: 24.3% of women aged 15-49, 48.2% of respondentsRussian Federation: 24.1% of women aged 15-49, 17.4% of respondentsRwanda: 16.1% of women aged 15-49, 40.3% of respondentsSingapore: 16.6% of women aged 15-49, 34% of respondentsSerbia: 24.9% of women aged 15-49, 25.3% of respondentsSlovak Republic: 25.3% of women aged 15-49, 22.2% of respondentsSlovenia: 25.7% of women aged 15-49, 11.5% of respondentsSweden: 16.8% of women aged 15-49, 9.48% of respondentsThailand: 20.7% of women aged 15-49, 41.8% of respondentsTajikistan: 37.3% of women aged 15-49, 63.1% of respondentsTrinidad and Tobago: 22.1% of women aged 15-49, 78.5% of respondentsTunisia: 27.7% of women aged 15-49, 90.6% of respondentsTurkiye: 29.3% of women aged 15-49, 60% of respondentsUkraine: 19.4% of women aged 15-49, 19.5% of respondentsUruguay: 24.5% of women aged 15-49, 19.5% of respondentsUnited States: 15% of women aged 15-49, 37.1% of respondentsUzbekistan: 30.4% of women aged 15-49, 45.1% of respondentsViet Nam: 20.5% of women aged 15-49, 12.3% of respondentsSouth Africa: 31.2% of women aged 15-49, 55.8% of respondentsZambia: 27.1% of women aged 15-49, 75.7% of respondentsZimbabwe: 27.5% of women aged 15-49, 87.7% of respondentsAUSAZECHNGTMHTIINDMLINZLPAKPHLQAT

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted+0.5713.9e-10102
By rank (Spearman), not adjusted+0.5954.2e-11102
Standard (Pearson), adjusted for national income+0.2790.005102
By rank (Spearman), adjusted for national income+0.3000.002102

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 102 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: +0.292 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.236 (without MDV) to +0.322 (without HTI). 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: MDV (-0.043).

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 & Pacific13+0.39
Europe & Central Asia48+0.05
Latin America & Caribbean12-0.32
Middle East, North Africa, Afghanistan & Pakistan14+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 third34+0.22
Middle third34+0.40
Richest third34+0.46

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
Christians60-0.07
Muslims28+0.08

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
Never a European colony15+0.24
United Kingdom25+0.49

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 = +0.309 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 respondents		Years
Mali MLI56.5%88.2%2023, 2010
India IND53.7%64.2%2023, 2022
Pakistan PAK46.5%89.6%2023, 2022
Haiti HTI45.9%28%2023, 2014
Burkina Faso BFA44.3%82.9%2023, 2010
Maldives MDV43.4%95.7%2023, 2022
Myanmar MMR39.7%81%2023, 2022
Nigeria NGA37.9%93.4%2023, 2022
Bangladesh BGD37.6%94.2%2023, 2022
Tajikistan TJK37.3%63.1%2023, 2022
Ghana GHA36%91.1%2023, 2014
Azerbaijan AZE35.6%15.9%2023, 2022
Lebanon LBN34%64.1%2023, 2022
Jordan JOR33%95.4%2023, 2022
Kyrgyz Republic KGZ32.9%56%2023, 2022

World Bank, World Development Indicators
Haerpfer, C. et al., World Values Survey; EVS, European Values Study

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

Religion very important

Share saying religion is very important in their life

World Values Survey Association and European Values Study · via Our World in Data · Free for non-commercial use with attribution · updated irregular (survey waves, roughly every 5 years)

The strongest of four answers. The others are "rather", "not very" and "not at all" important. Thinking religion is important is different from attending services, and the gap between them is the interesting part. The United States reports high importance while attendance falls. Several Catholic countries show the reverse. No "better" direction is set, for the same reason as attendance. The survey runs in waves, not every year.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2023. 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 Religion very important

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

More links with Anaemia in women or Religion very important