placestats

Anaemia in women vs. Free and fair elections

Holds up after adjusting for income

Across 165 countries, Anaemia in women and Free and fair elections rise and fall together at r = -0.59 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.33 (q = 0.021, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a weak negative link remains.

Comparing countries across the middle half of free and fair elections, 0.235 to 0.873 points, anaemia in women is better by 12.3% 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)-0.590p = 7.9e-17 · 165 countries
After adjusting for national income-0.328p = 1.8e-5 · likely range [-0.46, -0.18]
p, allowing for regions0.028standard error 0.114 · 7 regions
Check for luck (q)0.021across 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
Free and fair elections · index (0-1)link with income +0.61 · 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 →Free and fair elections · index (0-1) →10.5%59.9%00.968Afghanistan: 45.4% of women aged 15-49, 0 index (0-1)Angola: 43.8% of women aged 15-49, 0.293 index (0-1)Albania: 24.7% of women aged 15-49, 0.467 index (0-1)United Arab Emirates: 28.2% of women aged 15-49, 0.543 index (0-1)Argentina: 23.5% of women aged 15-49, 0.878 index (0-1)Armenia: 18.7% of women aged 15-49, 0.673 index (0-1)Australia: 11.1% of women aged 15-49, 0.962 index (0-1)Austria: 16.7% of women aged 15-49, 0.938 index (0-1)Azerbaijan: 35.6% of women aged 15-49, 0.111 index (0-1)Burundi: 39% of women aged 15-49, 0.107 index (0-1)Belgium: 15% of women aged 15-49, 0.968 index (0-1)Benin: 50% of women aged 15-49, 0.638 index (0-1)Burkina Faso: 44.3% of women aged 15-49, 0 index (0-1)Bangladesh: 37.6% of women aged 15-49, 0 index (0-1)Bulgaria: 25.8% of women aged 15-49, 0.64 index (0-1)Bahrain: 38.4% of women aged 15-49, 0.274 index (0-1)Bosnia and Herzegovina: 26% of women aged 15-49, 0.511 index (0-1)Belarus: 22.1% of women aged 15-49, 0.194 index (0-1)Bolivia: 24.9% of women aged 15-49, 0.765 index (0-1)Brazil: 21.3% of women aged 15-49, 0.838 index (0-1)Barbados: 20.1% of women aged 15-49, 0.875 index (0-1)Bhutan: 33.8% of women aged 15-49, 0.865 index (0-1)Botswana: 30.9% of women aged 15-49, 0.803 index (0-1)Central African Republic: 43.4% of women aged 15-49, 0.123 index (0-1)Canada: 14% of women aged 15-49, 0.944 index (0-1)Switzerland: 16.6% of women aged 15-49, 0.942 index (0-1)Chile: 15.7% of women aged 15-49, 0.956 index (0-1)China: 15.6% of women aged 15-49, 0 index (0-1)Cote d'Ivoire: 51.4% of women aged 15-49, 0.333 index (0-1)Cameroon: 38.9% of women aged 15-49, 0.12 index (0-1)Congo, Dem. Rep.: 40% of women aged 15-49, 0.18 index (0-1)Congo, Rep.: 46.7% of women aged 15-49, 0.076 index (0-1)Colombia: 22.4% of women aged 15-49, 0.756 index (0-1)Comoros: 29.9% of women aged 15-49, 0.183 index (0-1)Cabo Verde: 40.6% of women aged 15-49, 0.836 index (0-1)Costa Rica: 13.7% of women aged 15-49, 0.944 index (0-1)Cyprus: 16.4% of women aged 15-49, 0.909 index (0-1)Czechia: 24.6% of women aged 15-49, 0.94 index (0-1)Germany: 14% of women aged 15-49, 0.957 index (0-1)Djibouti: 31.8% of women aged 15-49, 0.167 index (0-1)Denmark: 17% of women aged 15-49, 0.961 index (0-1)Dominican Republic: 22.4% of women aged 15-49, 0.768 index (0-1)Algeria: 31.6% of women aged 15-49, 0.307 index (0-1)Ecuador: 20.2% of women aged 15-49, 0.827 index (0-1)Egypt, Arab Rep.: 32.7% of women aged 15-49, 0.137 index (0-1)Spain: 15.6% of women aged 15-49, 0.956 index (0-1)Estonia: 24.7% of women aged 15-49, 0.952 index (0-1)Ethiopia: 23.1% of women aged 15-49, 0.324 index (0-1)Finland: 13.6% of women aged 15-49, 0.961 index (0-1)Fiji: 29.9% of women aged 15-49, 0.54 index (0-1)France: 13.1% of women aged 15-49, 0.941 index (0-1)Gabon: 59.9% of women aged 15-49, 0.186 index (0-1)United Kingdom: 13.6% of women aged 15-49, 0.924 index (0-1)Georgia: 30.2% of women aged 15-49, 0.32 index (0-1)Ghana: 36% of women aged 15-49, 0.709 index (0-1)Guinea: 45.7% of women aged 15-49, 0.002 index (0-1)Gambia, The: 46.1% of women aged 15-49, 0.777 index (0-1)Guinea-Bissau: 45.7% of women aged 15-49, 0 index (0-1)Equatorial Guinea: 41.3% of women aged 15-49, 0.078 index (0-1)Greece: 15.6% of women aged 15-49, 0.944 index (0-1)Guatemala: 10.5% of women aged 15-49, 0.666 index (0-1)Guyana: 37.5% of women aged 15-49, 0.489 index (0-1)Honduras: 18.8% of women aged 15-49, 0.351 index (0-1)Croatia: 23.3% of women aged 15-49, 0.875 index (0-1)Haiti: 45.9% of women aged 15-49, 0 index (0-1)Hungary: 24.1% of women aged 15-49, 0.597 index (0-1)Indonesia: 26.7% of women aged 15-49, 0.518 index (0-1)India: 53.7% of women aged 15-49, 0.495 index (0-1)Ireland: 14% of women aged 15-49, 0.945 index (0-1)Iran, Islamic Rep.: 25.5% of women aged 15-49, 0.235 index (0-1)Iraq: 27.1% of women aged 15-49, 0.372 index (0-1)Iceland: 14.7% of women aged 15-49, 0.929 index (0-1)Israel: 14.6% of women aged 15-49, 0.873 index (0-1)Italy: 16.4% of women aged 15-49, 0.945 index (0-1)Jamaica: 21.5% of women aged 15-49, 0.884 index (0-1)Jordan: 33% of women aged 15-49, 0.592 index (0-1)Japan: 18.7% of women aged 15-49, 0.937 index (0-1)Kazakhstan: 30.5% of women aged 15-49, 0.339 index (0-1)Kenya: 32.2% of women aged 15-49, 0.493 index (0-1)Kyrgyz Republic: 32.9% of women aged 15-49, 0.426 index (0-1)Cambodia: 38% of women aged 15-49, 0.092 index (0-1)Korea, Rep.: 15.7% of women aged 15-49, 0.935 index (0-1)Kuwait: 27.7% of women aged 15-49, 0 index (0-1)Lao PDR: 28.6% of women aged 15-49, 0.236 index (0-1)Lebanon: 34% of women aged 15-49, 0.307 index (0-1)Liberia: 42.5% of women aged 15-49, 0.592 index (0-1)Libya: 29.3% of women aged 15-49, 0 index (0-1)Sri Lanka: 20.9% of women aged 15-49, 0.914 index (0-1)Lesotho: 34.2% of women aged 15-49, 0.785 index (0-1)Lithuania: 23.7% of women aged 15-49, 0.882 index (0-1)Luxembourg: 15.4% of women aged 15-49, 0.944 index (0-1)Latvia: 21.4% of women aged 15-49, 0.92 index (0-1)Morocco: 29.5% of women aged 15-49, 0.455 index (0-1)Moldova: 28.5% of women aged 15-49, 0.662 index (0-1)Madagascar: 37.2% of women aged 15-49, 0.183 index (0-1)Maldives: 43.4% of women aged 15-49, 0.643 index (0-1)Mexico: 13.2% of women aged 15-49, 0.545 index (0-1)North Macedonia: 21.5% of women aged 15-49, 0.701 index (0-1)Mali: 56.5% of women aged 15-49, 0 index (0-1)Malta: 15.7% of women aged 15-49, 0.903 index (0-1)Myanmar: 39.7% of women aged 15-49, 0 index (0-1)Montenegro: 25.8% of women aged 15-49, 0.608 index (0-1)Mongolia: 19.1% of women aged 15-49, 0.586 index (0-1)Mozambique: 47% of women aged 15-49, 0.089 index (0-1)Mauritania: 54.5% of women aged 15-49, 0.288 index (0-1)Mauritius: 32.8% of women aged 15-49, 0.865 index (0-1)Malawi: 32.4% of women aged 15-49, 0.596 index (0-1)Malaysia: 31.3% of women aged 15-49, 0.598 index (0-1)Namibia: 24.9% of women aged 15-49, 0.604 index (0-1)Niger: 47.1% of women aged 15-49, 0 index (0-1)Nigeria: 37.9% of women aged 15-49, 0.355 index (0-1)Netherlands: 16.5% of women aged 15-49, 0.954 index (0-1)Norway: 16.3% of women aged 15-49, 0.966 index (0-1)Nepal: 33.2% of women aged 15-49, 0.693 index (0-1)New Zealand: 12.4% of women aged 15-49, 0.954 index (0-1)Oman: 34.4% of women aged 15-49, 0.689 index (0-1)Pakistan: 46.5% of women aged 15-49, 0.174 index (0-1)Panama: 25.5% of women aged 15-49, 0.84 index (0-1)Peru: 21% of women aged 15-49, 0.861 index (0-1)Philippines: 12% of women aged 15-49, 0.271 index (0-1)Papua New Guinea: 28.8% of women aged 15-49, 0.27 index (0-1)Poland: 23.4% of women aged 15-49, 0.825 index (0-1)Portugal: 15% of women aged 15-49, 0.94 index (0-1)Paraguay: 23.2% of women aged 15-49, 0.633 index (0-1)Qatar: 23.6% of women aged 15-49, 0 index (0-1)Romania: 24.3% of women aged 15-49, 0.658 index (0-1)Russian Federation: 24.1% of women aged 15-49, 0.135 index (0-1)Rwanda: 16.1% of women aged 15-49, 0.227 index (0-1)Saudi Arabia: 17.6% of women aged 15-49, 0 index (0-1)Sudan: 34% of women aged 15-49, 0 index (0-1)Senegal: 43.1% of women aged 15-49, 0.701 index (0-1)Singapore: 16.6% of women aged 15-49, 0.782 index (0-1)Solomon Islands: 32.6% of women aged 15-49, 0.748 index (0-1)Sierra Leone: 44.6% of women aged 15-49, 0.319 index (0-1)El Salvador: 14.4% of women aged 15-49, 0.307 index (0-1)Somalia, Fed. Rep.: 46.2% of women aged 15-49, 0 index (0-1)Serbia: 24.9% of women aged 15-49, 0.212 index (0-1)Sao Tome and Principe: 39.7% of women aged 15-49, 0.753 index (0-1)Suriname: 22.2% of women aged 15-49, 0.864 index (0-1)Slovak Republic: 25.3% of women aged 15-49, 0.938 index (0-1)Slovenia: 25.7% of women aged 15-49, 0.916 index (0-1)Sweden: 16.8% of women aged 15-49, 0.945 index (0-1)Eswatini: 28.3% of women aged 15-49, 0.376 index (0-1)Seychelles: 25.8% of women aged 15-49, 0.884 index (0-1)Syrian Arab Republic: 31% of women aged 15-49, 0 index (0-1)Chad: 46.5% of women aged 15-49, 0.079 index (0-1)Togo: 41.5% of women aged 15-49, 0.309 index (0-1)Thailand: 20.7% of women aged 15-49, 0.457 index (0-1)Tajikistan: 37.3% of women aged 15-49, 0.094 index (0-1)Turkmenistan: 34.8% of women aged 15-49, 0.071 index (0-1)Timor-Leste: 29.7% of women aged 15-49, 0.857 index (0-1)Trinidad and Tobago: 22.1% of women aged 15-49, 0.872 index (0-1)Tunisia: 27.7% of women aged 15-49, 0.445 index (0-1)Turkiye: 29.3% of women aged 15-49, 0.417 index (0-1)Tanzania: 38.8% of women aged 15-49, 0.364 index (0-1)Uganda: 27.2% of women aged 15-49, 0.129 index (0-1)Ukraine: 19.4% of women aged 15-49, 0.604 index (0-1)Uruguay: 24.5% of women aged 15-49, 0.947 index (0-1)United States: 15% of women aged 15-49, 0.902 index (0-1)Uzbekistan: 30.4% of women aged 15-49, 0.304 index (0-1)Viet Nam: 20.5% of women aged 15-49, 0.527 index (0-1)Vanuatu: 30.4% of women aged 15-49, 0.853 index (0-1)South Africa: 31.2% of women aged 15-49, 0.849 index (0-1)Zambia: 27.1% of women aged 15-49, 0.461 index (0-1)Zimbabwe: 27.5% of women aged 15-49, 0.171 index (0-1)AUSBENCHNFINGABGMBINDMLINERNZLPHLSAU

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.5907.9e-17165
By rank (Spearman), not adjusted-0.6345.8e-20165
Standard (Pearson), adjusted for national income-0.3281.8e-5165
By rank (Spearman), adjusted for national income-0.3602.2e-6165

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 165 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: +0.261 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.352 (without CHN) to -0.311 (without GAB). 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: CHN (-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 & Pacific20-0.19
Europe & Central Asia48-0.55
Latin America & Caribbean22-0.43
Middle East, North Africa, Afghanistan & Pakistan21-0.24
Sub-Saharan Africa46-0.28

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 third55-0.22
Middle third55-0.24
Richest third55-0.60

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
Christians98-0.29
Muslims47+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
France25-0.11
Never a European colony21-0.21
Spain18-0.28
United Kingdom49-0.36

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 = -0.311 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.

Within countries over time, there is no link that is clearly more than chance (r = -0.062, 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 free and fair elections move with it.

Across countries (adjusted)-0.328for comparison
Within countries-0.142each country vs itself
…and vs the same year elsewhere-0.062q = 0.409 · 171 countries
Year-on-year changes-0.024q = 0.400
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.

4,104 country-years (one country in one year), 171 countries, 2000–2023, about 24 years each on average. Share of the variation that happens within countries over time: 10% for Anaemia in women, 3% for Free and fair elections. 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

0.10−0.1-100+10years Free and fair elections trails Anaemia in women →lag -10: r = -0.074 (n = 4,094)lag -9: r = -0.064 (n = 4,097)lag -8: r = -0.059 (n = 4,098)lag -7: r = -0.061 (n = 4,099)lag -6: r = -0.069 (n = 4,100)lag -5: r = -0.075 (n = 4,101)lag -4: r = -0.074 (n = 4,102)lag -3: r = -0.075 (n = 4,103)lag -2: r = -0.073 (n = 4,104)lag -1: r = -0.070 (n = 4,104)lag +0: r = -0.062 (n = 4,104)lag +1: r = -0.056 (n = 4,104)lag +2: r = -0.045 (n = 4,104)lag +3: r = -0.037 (n = 3,933)lag +4: r = -0.027 (n = 3,762)lag +5: r = -0.010 (n = 3,591)lag +6: r = 0.005 (n = 3,420)lag +7: r = 0.009 (n = 3,249)lag +8: r = 0.008 (n = 3,078)lag +9: r = 0.015 (n = 2,907)lag +10: r = 0.022 (n = 2,736)

Same year (lag 0): -0.062. 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

index (0-1)
Years
Gabon GAB59.9%0.1862023, 2025
Mali MLI56.5%02023, 2025
Mauritania MRT54.5%0.2882023, 2025
India IND53.7%0.4952023, 2025
Cote d'Ivoire CIV51.4%0.3332023, 2025
Benin BEN50%0.6382023, 2025
Niger NER47.1%02023, 2025
Mozambique MOZ47%0.0892023, 2025
Congo, Rep. COG46.7%0.0762023, 2025
Chad TCD46.5%0.0792023, 2025
Pakistan PAK46.5%0.1742023, 2025
Somalia, Fed. Rep. SOM46.2%02023, 2025
Gambia, The GMB46.1%0.7772023, 2025
Haiti HTI45.9%02023, 2025
Guinea-Bissau GNB45.7%02023, 2025

World Bank, World Development Indicators
Coppedge, M. et al., V-Dem Dataset, Varieties of Democracy (V-Dem) Project

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

Free and fair elections

Free and fair elections index

V-Dem Institute, University of Gothenburg · via Our World in Data · CC BY 4.0 · updated annual

Whether elections are run without voter-registration fraud, widespread irregularities, government threats against the opposition, vote buying or election violence. This asks a narrower question than whether a country is a democracy, and one that is easier to answer. A state can run clean elections while barring the opposition from standing. It can also run dirty elections and still change government. It is the clean-elections part of the electoral democracy index. So its pair with that index, and with liberal and participatory democracy, is built from the same numbers and flagged as such.

How this was computed

Data used: most recent value in 2010-2025; values come from 2023–2025. 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 Free and fair elections

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

More links with Anaemia in women or Free and fair elections