placestats

Anaemia in women vs. Governance score

Fades after adjusting for income

Across 168 countries, Anaemia in women and Governance score rise and fall together at r = -0.52 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.29, which could easily be chance (q = 0.27, 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, 19.8% to 35% of women aged 15-49, governance score is worse by 0.636 typical gaps at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.407 typical gaps.

Link before adjusting (r)-0.525p = 2.8e-13 · 168 countries
After adjusting for national income-0.287p = 0.000 · likely range [-0.42, -0.14]
p, allowing for regions0.098standard error 0.147 · 7 regions
Check for luck (q)0.267across 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
Governance score · typical gaps (standard deviations)link with income +0.53 · moderate

“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 →Governance score · typical gaps (standard deviations) →10.5%59.9%-1.41.47Afghanistan: 45.4% of women aged 15-49, -1.4 typical gaps (standard deviations)Angola: 43.8% of women aged 15-49, -0.522 typical gaps (standard deviations)Albania: 24.7% of women aged 15-49, 0.323 typical gaps (standard deviations)Andorra: 16.4% of women aged 15-49, 0.558 typical gaps (standard deviations)United Arab Emirates: 28.2% of women aged 15-49, -0.981 typical gaps (standard deviations)Argentina: 23.5% of women aged 15-49, 0.815 typical gaps (standard deviations)Armenia: 18.7% of women aged 15-49, 0.22 typical gaps (standard deviations)Australia: 11.1% of women aged 15-49, 1.38 typical gaps (standard deviations)Austria: 16.7% of women aged 15-49, 1.33 typical gaps (standard deviations)Azerbaijan: 35.6% of women aged 15-49, -1.33 typical gaps (standard deviations)Burundi: 39% of women aged 15-49, -1.09 typical gaps (standard deviations)Belgium: 15% of women aged 15-49, 1.41 typical gaps (standard deviations)Benin: 50% of women aged 15-49, 0.285 typical gaps (standard deviations)Burkina Faso: 44.3% of women aged 15-49, -0.784 typical gaps (standard deviations)Bangladesh: 37.6% of women aged 15-49, -0.359 typical gaps (standard deviations)Bulgaria: 25.8% of women aged 15-49, 0.74 typical gaps (standard deviations)Bahrain: 38.4% of women aged 15-49, -1.05 typical gaps (standard deviations)Bosnia and Herzegovina: 26% of women aged 15-49, 0.0899 typical gaps (standard deviations)Belarus: 22.1% of women aged 15-49, -1.26 typical gaps (standard deviations)Belize: 21.3% of women aged 15-49, 0.0883 typical gaps (standard deviations)Bolivia: 24.9% of women aged 15-49, 0.552 typical gaps (standard deviations)Brazil: 21.3% of women aged 15-49, 0.85 typical gaps (standard deviations)Barbados: 20.1% of women aged 15-49, 1.24 typical gaps (standard deviations)Bhutan: 33.8% of women aged 15-49, 0.61 typical gaps (standard deviations)Botswana: 30.9% of women aged 15-49, 0.872 typical gaps (standard deviations)Central African Republic: 43.4% of women aged 15-49, -1.02 typical gaps (standard deviations)Canada: 14% of women aged 15-49, 1.36 typical gaps (standard deviations)Switzerland: 16.6% of women aged 15-49, 1.47 typical gaps (standard deviations)Chile: 15.7% of women aged 15-49, 1.32 typical gaps (standard deviations)China: 15.6% of women aged 15-49, -1.2 typical gaps (standard deviations)Cote d'Ivoire: 51.4% of women aged 15-49, -0.116 typical gaps (standard deviations)Cameroon: 38.9% of women aged 15-49, -0.834 typical gaps (standard deviations)Congo, Dem. Rep.: 40% of women aged 15-49, -0.824 typical gaps (standard deviations)Congo, Rep.: 46.7% of women aged 15-49, -0.925 typical gaps (standard deviations)Colombia: 22.4% of women aged 15-49, 0.733 typical gaps (standard deviations)Comoros: 29.9% of women aged 15-49, -0.464 typical gaps (standard deviations)Cabo Verde: 40.6% of women aged 15-49, 1.15 typical gaps (standard deviations)Costa Rica: 13.7% of women aged 15-49, 1.34 typical gaps (standard deviations)Cyprus: 16.4% of women aged 15-49, 1.1 typical gaps (standard deviations)Czechia: 24.6% of women aged 15-49, 1.31 typical gaps (standard deviations)Germany: 14% of women aged 15-49, 1.36 typical gaps (standard deviations)Djibouti: 31.8% of women aged 15-49, -0.784 typical gaps (standard deviations)Denmark: 17% of women aged 15-49, 1.47 typical gaps (standard deviations)Dominican Republic: 22.4% of women aged 15-49, 0.621 typical gaps (standard deviations)Algeria: 31.6% of women aged 15-49, -0.695 typical gaps (standard deviations)Ecuador: 20.2% of women aged 15-49, 0.447 typical gaps (standard deviations)Egypt, Arab Rep.: 32.7% of women aged 15-49, -0.987 typical gaps (standard deviations)Spain: 15.6% of women aged 15-49, 1.32 typical gaps (standard deviations)Estonia: 24.7% of women aged 15-49, 1.41 typical gaps (standard deviations)Ethiopia: 23.1% of women aged 15-49, -0.741 typical gaps (standard deviations)Finland: 13.6% of women aged 15-49, 1.38 typical gaps (standard deviations)Fiji: 29.9% of women aged 15-49, 0.57 typical gaps (standard deviations)France: 13.1% of women aged 15-49, 1.24 typical gaps (standard deviations)Gabon: 59.9% of women aged 15-49, -0.529 typical gaps (standard deviations)United Kingdom: 13.6% of women aged 15-49, 1.27 typical gaps (standard deviations)Georgia: 30.2% of women aged 15-49, 0.0179 typical gaps (standard deviations)Ghana: 36% of women aged 15-49, 0.845 typical gaps (standard deviations)Guinea: 45.7% of women aged 15-49, -0.676 typical gaps (standard deviations)Gambia, The: 46.1% of women aged 15-49, 0.362 typical gaps (standard deviations)Guinea-Bissau: 45.7% of women aged 15-49, -0.465 typical gaps (standard deviations)Equatorial Guinea: 41.3% of women aged 15-49, -1.32 typical gaps (standard deviations)Greece: 15.6% of women aged 15-49, 0.914 typical gaps (standard deviations)Guatemala: 10.5% of women aged 15-49, 0.291 typical gaps (standard deviations)Guyana: 37.5% of women aged 15-49, 0.538 typical gaps (standard deviations)Honduras: 18.8% of women aged 15-49, 0.146 typical gaps (standard deviations)Croatia: 23.3% of women aged 15-49, 0.865 typical gaps (standard deviations)Haiti: 45.9% of women aged 15-49, -0.66 typical gaps (standard deviations)Hungary: 24.1% of women aged 15-49, 0.225 typical gaps (standard deviations)Indonesia: 26.7% of women aged 15-49, 0.125 typical gaps (standard deviations)India: 53.7% of women aged 15-49, 0.146 typical gaps (standard deviations)Ireland: 14% of women aged 15-49, 1.41 typical gaps (standard deviations)Iran, Islamic Rep.: 25.5% of women aged 15-49, -1.26 typical gaps (standard deviations)Iraq: 27.1% of women aged 15-49, -0.488 typical gaps (standard deviations)Iceland: 14.7% of women aged 15-49, 1.32 typical gaps (standard deviations)Israel: 14.6% of women aged 15-49, 0.783 typical gaps (standard deviations)Italy: 16.4% of women aged 15-49, 1 typical gaps (standard deviations)Jamaica: 21.5% of women aged 15-49, 1.01 typical gaps (standard deviations)Jordan: 33% of women aged 15-49, -0.271 typical gaps (standard deviations)Japan: 18.7% of women aged 15-49, 1.28 typical gaps (standard deviations)Kazakhstan: 30.5% of women aged 15-49, -0.796 typical gaps (standard deviations)Kenya: 32.2% of women aged 15-49, 0.217 typical gaps (standard deviations)Kyrgyz Republic: 32.9% of women aged 15-49, -0.725 typical gaps (standard deviations)Cambodia: 38% of women aged 15-49, -1.01 typical gaps (standard deviations)Korea, Rep.: 15.7% of women aged 15-49, 1.05 typical gaps (standard deviations)Kuwait: 27.7% of women aged 15-49, -0.696 typical gaps (standard deviations)Lao PDR: 28.6% of women aged 15-49, -1.14 typical gaps (standard deviations)Lebanon: 34% of women aged 15-49, -0.112 typical gaps (standard deviations)Liberia: 42.5% of women aged 15-49, 0.416 typical gaps (standard deviations)Libya: 29.3% of women aged 15-49, -0.885 typical gaps (standard deviations)St. Lucia: 19.9% of women aged 15-49, 0.552 typical gaps (standard deviations)Sri Lanka: 20.9% of women aged 15-49, 0.602 typical gaps (standard deviations)Lesotho: 34.2% of women aged 15-49, 0.671 typical gaps (standard deviations)Lithuania: 23.7% of women aged 15-49, 1.14 typical gaps (standard deviations)Luxembourg: 15.4% of women aged 15-49, 1.38 typical gaps (standard deviations)Latvia: 21.4% of women aged 15-49, 1.22 typical gaps (standard deviations)Morocco: 29.5% of women aged 15-49, -0.351 typical gaps (standard deviations)Moldova: 28.5% of women aged 15-49, 0.5 typical gaps (standard deviations)Madagascar: 37.2% of women aged 15-49, -0.193 typical gaps (standard deviations)Maldives: 43.4% of women aged 15-49, 0.0802 typical gaps (standard deviations)Mexico: 13.2% of women aged 15-49, 0.134 typical gaps (standard deviations)North Macedonia: 21.5% of women aged 15-49, 0.352 typical gaps (standard deviations)Mali: 56.5% of women aged 15-49, -0.753 typical gaps (standard deviations)Malta: 15.7% of women aged 15-49, 1.12 typical gaps (standard deviations)Myanmar: 39.7% of women aged 15-49, -1.39 typical gaps (standard deviations)Montenegro: 25.8% of women aged 15-49, 0.55 typical gaps (standard deviations)Mongolia: 19.1% of women aged 15-49, 0.716 typical gaps (standard deviations)Mozambique: 47% of women aged 15-49, -0.298 typical gaps (standard deviations)Mauritania: 54.5% of women aged 15-49, -0.421 typical gaps (standard deviations)Mauritius: 32.8% of women aged 15-49, 1.07 typical gaps (standard deviations)Malawi: 32.4% of women aged 15-49, 0.576 typical gaps (standard deviations)Malaysia: 31.3% of women aged 15-49, 0.169 typical gaps (standard deviations)Namibia: 24.9% of women aged 15-49, 0.69 typical gaps (standard deviations)Niger: 47.1% of women aged 15-49, -0.548 typical gaps (standard deviations)Nigeria: 37.9% of women aged 15-49, -0.0132 typical gaps (standard deviations)Netherlands: 16.5% of women aged 15-49, 1.32 typical gaps (standard deviations)Norway: 16.3% of women aged 15-49, 1.43 typical gaps (standard deviations)Nepal: 33.2% of women aged 15-49, 0.441 typical gaps (standard deviations)New Zealand: 12.4% of women aged 15-49, 1.32 typical gaps (standard deviations)Oman: 34.4% of women aged 15-49, -0.665 typical gaps (standard deviations)Pakistan: 46.5% of women aged 15-49, -0.437 typical gaps (standard deviations)Panama: 25.5% of women aged 15-49, 0.84 typical gaps (standard deviations)Peru: 21% of women aged 15-49, 0.522 typical gaps (standard deviations)Philippines: 12% of women aged 15-49, 0.0585 typical gaps (standard deviations)Papua New Guinea: 28.8% of women aged 15-49, 0.227 typical gaps (standard deviations)Poland: 23.4% of women aged 15-49, 0.996 typical gaps (standard deviations)Portugal: 15% of women aged 15-49, 1.26 typical gaps (standard deviations)Paraguay: 23.2% of women aged 15-49, 0.349 typical gaps (standard deviations)Qatar: 23.6% of women aged 15-49, -0.908 typical gaps (standard deviations)Romania: 24.3% of women aged 15-49, 0.749 typical gaps (standard deviations)Russian Federation: 24.1% of women aged 15-49, -1.14 typical gaps (standard deviations)Rwanda: 16.1% of women aged 15-49, -0.868 typical gaps (standard deviations)Saudi Arabia: 17.6% of women aged 15-49, -1.14 typical gaps (standard deviations)Sudan: 34% of women aged 15-49, -1.22 typical gaps (standard deviations)Senegal: 43.1% of women aged 15-49, 0.683 typical gaps (standard deviations)Singapore: 16.6% of women aged 15-49, 0.0449 typical gaps (standard deviations)Solomon Islands: 32.6% of women aged 15-49, 0.697 typical gaps (standard deviations)Sierra Leone: 44.6% of women aged 15-49, 0.299 typical gaps (standard deviations)El Salvador: 14.4% of women aged 15-49, -0.352 typical gaps (standard deviations)Somalia, Fed. Rep.: 46.2% of women aged 15-49, -1.01 typical gaps (standard deviations)Serbia: 24.9% of women aged 15-49, -0.0738 typical gaps (standard deviations)Sao Tome and Principe: 39.7% of women aged 15-49, 0.8 typical gaps (standard deviations)Suriname: 22.2% of women aged 15-49, 0.996 typical gaps (standard deviations)Slovak Republic: 25.3% of women aged 15-49, 1.02 typical gaps (standard deviations)Slovenia: 25.7% of women aged 15-49, 1.19 typical gaps (standard deviations)Sweden: 16.8% of women aged 15-49, 1.42 typical gaps (standard deviations)Eswatini: 28.3% of women aged 15-49, -1.05 typical gaps (standard deviations)Seychelles: 25.8% of women aged 15-49, 0.975 typical gaps (standard deviations)Syrian Arab Republic: 31% of women aged 15-49, -1.05 typical gaps (standard deviations)Chad: 46.5% of women aged 15-49, -0.999 typical gaps (standard deviations)Togo: 41.5% of women aged 15-49, -0.371 typical gaps (standard deviations)Thailand: 20.7% of women aged 15-49, -0.383 typical gaps (standard deviations)Tajikistan: 37.3% of women aged 15-49, -1.34 typical gaps (standard deviations)Turkmenistan: 34.8% of women aged 15-49, -1.39 typical gaps (standard deviations)Timor-Leste: 29.7% of women aged 15-49, 0.779 typical gaps (standard deviations)Trinidad and Tobago: 22.1% of women aged 15-49, 1.03 typical gaps (standard deviations)Tunisia: 27.7% of women aged 15-49, -0.257 typical gaps (standard deviations)Turkiye: 29.3% of women aged 15-49, -0.691 typical gaps (standard deviations)Tanzania: 38.8% of women aged 15-49, -0.315 typical gaps (standard deviations)Uganda: 27.2% of women aged 15-49, -0.646 typical gaps (standard deviations)Ukraine: 19.4% of women aged 15-49, -0.0525 typical gaps (standard deviations)Uruguay: 24.5% of women aged 15-49, 1.39 typical gaps (standard deviations)United States: 15% of women aged 15-49, 0.998 typical gaps (standard deviations)Uzbekistan: 30.4% of women aged 15-49, -1.04 typical gaps (standard deviations)Viet Nam: 20.5% of women aged 15-49, -0.933 typical gaps (standard deviations)Vanuatu: 30.4% of women aged 15-49, 0.885 typical gaps (standard deviations)South Africa: 31.2% of women aged 15-49, 0.883 typical gaps (standard deviations)Zambia: 27.1% of women aged 15-49, 0.212 typical gaps (standard deviations)Zimbabwe: 27.5% of women aged 15-49, -0.609 typical gaps (standard deviations)AFGAUSBENCHNCPVFINGABINDMLINZLRWASAU

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5252.8e-13168
By rank (Spearman), not adjusted-0.5612.5e-15168
Standard (Pearson), adjusted for national income-0.2870.000168
By rank (Spearman), adjusted for national income-0.3095.0e-5168

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 168 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: +0.237 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.307 (without CHN) to -0.263 (without BHR). 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: BHR (+0.024).

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 & Pacific20-0.30
Europe & Central Asia49-0.48
Latin America & Caribbean24-0.22
Middle East, North Africa, Afghanistan & Pakistan21-0.44
Sub-Saharan Africa46-0.14

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 third56-0.08
Middle third56-0.24
Richest third56-0.63

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
Christians100-0.21
Muslims47+0.12

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+0.23
Never a European colony21-0.36
Spain18-0.52
United Kingdom51-0.38

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 = -0.277 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.

Loading…

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

typical gaps (standard deviations)
Years
Gabon GAB59.9%-0.5292023, 2025
Mali MLI56.5%-0.7532023, 2025
Mauritania MRT54.5%-0.4212023, 2025
India IND53.7%0.1462023, 2025
Cote d'Ivoire CIV51.4%-0.1162023, 2025
Benin BEN50%0.2852023, 2025
Niger NER47.1%-0.5482023, 2025
Mozambique MOZ47%-0.2982023, 2025
Congo, Rep. COG46.7%-0.9252023, 2025
Chad TCD46.5%-0.9992023, 2025
Pakistan PAK46.5%-0.4372023, 2025
Somalia, Fed. Rep. SOM46.2%-1.012023, 2025
Gambia, The GMB46.1%0.3622023, 2025
Haiti HTI45.9%-0.662023, 2025
Guinea-Bissau GNB45.7%-0.4652023, 2025

World Bank, World Development Indicators
fh; un_sdg; vdem; wdi; wgi

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

Governance score

Governance score

PlaceStats · via PlaceStats · Derived work; each member keeps its own licence · updated recomputed with the panel

The average of 19 measures from 5 publishers. Each measure is first put on a common scale: 0 is the average country in an average year, and 1 is a typical gap between countries (a standard deviation). Each is turned so that higher is better. Measures are averaged within each publisher first, so a publisher with many measures does not count for more. Members: Civil liberties (FH), Political rights (FH), Pre-trial detainee share, Civil society participation, Political corruption, Electoral democracy, Freedom of association, Freedom of expression, Free and fair elections, Liberal democracy, Participatory democracy, Political civil liberties, Birth registration, Control of corruption, Government effectiveness, Political stability, Rule of law, Regulatory quality, Voice and accountability.

How this was computed

Data used: most recent value in 2010-2025; values come from 2011–2025. 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 Governance score

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

More links with Anaemia in women or Governance score