placestats

Anaemia in women vs. Women in management

Holds up after adjusting for income

Across 147 countries, Anaemia in women and Women in management rise and fall together at r = -0.44 (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 = -0.34, q = 0.046, allowing for the many pairs tested).

Comparing countries across the middle half of anaemia in women, 19.3% to 33% of women aged 15-49, women in management is worse by 6.34% of managers at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 6.23% of managers.

Link before adjusting (r)-0.440p = 2.4e-8 · 147 countries
After adjusting for national income-0.343p = 2.3e-5 · likely range [-0.48, -0.19]
p, allowing for regions0.004standard error 0.077 · 7 regions
Check for luck (q)0.046across 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
Women in management · % of managerslink with income +0.30 · 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 →Women in management · % of managers →10.5%59.9%5.89%71%Afghanistan: 45.4% of women aged 15-49, 5.89% of managersAngola: 43.8% of women aged 15-49, 16.2% of managersAlbania: 24.7% of women aged 15-49, 33.4% of managersUnited Arab Emirates: 28.2% of women aged 15-49, 23.5% of managersArgentina: 23.5% of women aged 15-49, 34.1% of managersArmenia: 18.7% of women aged 15-49, 31.9% of managersAustralia: 11.1% of women aged 15-49, 39.4% of managersAustria: 16.7% of women aged 15-49, 36.3% of managersBurundi: 39% of women aged 15-49, 25.7% of managersBelgium: 15% of women aged 15-49, 33.1% of managersBenin: 50% of women aged 15-49, 23.5% of managersBurkina Faso: 44.3% of women aged 15-49, 25.9% of managersBangladesh: 37.6% of women aged 15-49, 7.23% of managersBulgaria: 25.8% of women aged 15-49, 39.1% of managersBahamas, The: 18.8% of women aged 15-49, 59.8% of managersBosnia and Herzegovina: 26% of women aged 15-49, 30.2% of managersBelarus: 22.1% of women aged 15-49, 42% of managersBelize: 21.3% of women aged 15-49, 41.7% of managersBolivia: 24.9% of women aged 15-49, 22.3% of managersBrazil: 21.3% of women aged 15-49, 38.7% of managersBarbados: 20.1% of women aged 15-49, 50.4% of managersBrunei Darussalam: 18.3% of women aged 15-49, 36.4% of managersBhutan: 33.8% of women aged 15-49, 15.5% of managersBotswana: 30.9% of women aged 15-49, 48.2% of managersSwitzerland: 16.6% of women aged 15-49, 33.3% of managersChile: 15.7% of women aged 15-49, 31.6% of managersCote d'Ivoire: 51.4% of women aged 15-49, 22.2% of managersCongo, Dem. Rep.: 40% of women aged 15-49, 29.6% of managersColombia: 22.4% of women aged 15-49, 41.9% of managersCosta Rica: 13.7% of women aged 15-49, 46.2% of managersCyprus: 16.4% of women aged 15-49, 27.5% of managersCzechia: 24.6% of women aged 15-49, 26.9% of managersGermany: 14% of women aged 15-49, 27.7% of managersDenmark: 17% of women aged 15-49, 26.9% of managersDominican Republic: 22.4% of women aged 15-49, 47.8% of managersEcuador: 20.2% of women aged 15-49, 45.3% of managersEgypt, Arab Rep.: 32.7% of women aged 15-49, 22.3% of managersSpain: 15.6% of women aged 15-49, 37% of managersEstonia: 24.7% of women aged 15-49, 36% of managersEthiopia: 23.1% of women aged 15-49, 27% of managersFinland: 13.6% of women aged 15-49, 39.2% of managersFiji: 29.9% of women aged 15-49, 34.9% of managersFrance: 13.1% of women aged 15-49, 38.7% of managersMicronesia, Fed. Sts.: 22.1% of women aged 15-49, 18.2% of managersGabon: 59.9% of women aged 15-49, 19.2% of managersUnited Kingdom: 13.6% of women aged 15-49, 40.5% of managersGeorgia: 30.2% of women aged 15-49, 37.5% of managersGhana: 36% of women aged 15-49, 26.6% of managersGambia, The: 46.1% of women aged 15-49, 28.1% of managersGuinea-Bissau: 45.7% of women aged 15-49, 25.6% of managersGreece: 15.6% of women aged 15-49, 35% of managersGrenada: 20.9% of women aged 15-49, 43.3% of managersGuatemala: 10.5% of women aged 15-49, 39.8% of managersGuyana: 37.5% of women aged 15-49, 42.1% of managersHonduras: 18.8% of women aged 15-49, 46% of managersCroatia: 23.3% of women aged 15-49, 25.5% of managersHungary: 24.1% of women aged 15-49, 42.1% of managersIndonesia: 26.7% of women aged 15-49, 24.8% of managersIndia: 53.7% of women aged 15-49, 11.9% of managersIreland: 14% of women aged 15-49, 37.8% of managersIran, Islamic Rep.: 25.5% of women aged 15-49, 21.4% of managersIraq: 27.1% of women aged 15-49, 17.5% of managersIceland: 14.7% of women aged 15-49, 37.3% of managersIsrael: 14.6% of women aged 15-49, 32.8% of managersItaly: 16.4% of women aged 15-49, 25.3% of managersJamaica: 21.5% of women aged 15-49, 63.9% of managersJordan: 33% of women aged 15-49, 52.3% of managersJapan: 18.7% of women aged 15-49, 14.5% of managersKenya: 32.2% of women aged 15-49, 43.6% of managersKyrgyz Republic: 32.9% of women aged 15-49, 32.8% of managersCambodia: 38% of women aged 15-49, 23.6% of managersKiribati: 26.9% of women aged 15-49, 37.4% of managersLao PDR: 28.6% of women aged 15-49, 25.7% of managersLebanon: 34% of women aged 15-49, 28.9% of managersLiberia: 42.5% of women aged 15-49, 26.1% of managersSri Lanka: 20.9% of women aged 15-49, 29.2% of managersLesotho: 34.2% of women aged 15-49, 32.9% of managersLithuania: 23.7% of women aged 15-49, 37.4% of managersLuxembourg: 15.4% of women aged 15-49, 26.9% of managersLatvia: 21.4% of women aged 15-49, 44% of managersMadagascar: 37.2% of women aged 15-49, 28.6% of managersMaldives: 43.4% of women aged 15-49, 22.6% of managersMexico: 13.2% of women aged 15-49, 39.3% of managersMarshall Islands: 23.5% of women aged 15-49, 32.7% of managersNorth Macedonia: 21.5% of women aged 15-49, 36.4% of managersMali: 56.5% of women aged 15-49, 9.01% of managersMalta: 15.7% of women aged 15-49, 22.7% of managersMyanmar: 39.7% of women aged 15-49, 41% of managersMontenegro: 25.8% of women aged 15-49, 48.1% of managersMongolia: 19.1% of women aged 15-49, 40.5% of managersMozambique: 47% of women aged 15-49, 29.1% of managersMauritius: 32.8% of women aged 15-49, 35.6% of managersMalawi: 32.4% of women aged 15-49, 47.7% of managersNamibia: 24.9% of women aged 15-49, 48.2% of managersNiger: 47.1% of women aged 15-49, 21.6% of managersNigeria: 37.9% of women aged 15-49, 37.9% of managersNetherlands: 16.5% of women aged 15-49, 28.5% of managersNorway: 16.3% of women aged 15-49, 30.8% of managersNepal: 33.2% of women aged 15-49, 13.9% of managersNaoero: 22.5% of women aged 15-49, 56.7% of managersPakistan: 46.5% of women aged 15-49, 9.57% of managersPanama: 25.5% of women aged 15-49, 50% of managersPeru: 21% of women aged 15-49, 32.9% of managersPhilippines: 12% of women aged 15-49, 43.2% of managersPalau: 25.9% of women aged 15-49, 45.5% of managersPapua New Guinea: 28.8% of women aged 15-49, 17.7% of managersPoland: 23.4% of women aged 15-49, 40.6% of managersPortugal: 15% of women aged 15-49, 39.4% of managersWest Bank and Gaza: 24.8% of women aged 15-49, 23.5% of managersRomania: 24.3% of women aged 15-49, 34.7% of managersRussian Federation: 24.1% of women aged 15-49, 41.7% of managersRwanda: 16.1% of women aged 15-49, 30.8% of managersSudan: 34% of women aged 15-49, 13.1% of managersSenegal: 43.1% of women aged 15-49, 23% of managersSingapore: 16.6% of women aged 15-49, 39.9% of managersSolomon Islands: 32.6% of women aged 15-49, 25.1% of managersSierra Leone: 44.6% of women aged 15-49, 22.6% of managersEl Salvador: 14.4% of women aged 15-49, 39% of managersSomalia, Fed. Rep.: 46.2% of women aged 15-49, 27.7% of managersSerbia: 24.9% of women aged 15-49, 31.9% of managersSao Tome and Principe: 39.7% of women aged 15-49, 71% of managersSuriname: 22.2% of women aged 15-49, 42.2% of managersSlovak Republic: 25.3% of women aged 15-49, 34.1% of managersSlovenia: 25.7% of women aged 15-49, 34.9% of managersSweden: 16.8% of women aged 15-49, 44.7% of managersEswatini: 28.3% of women aged 15-49, 25.2% of managersSeychelles: 25.8% of women aged 15-49, 50.2% of managersTogo: 41.5% of women aged 15-49, 29.5% of managersThailand: 20.7% of women aged 15-49, 34.5% of managersTajikistan: 37.3% of women aged 15-49, 24.5% of managersTimor-Leste: 29.7% of women aged 15-49, 22.4% of managersTonga: 20.7% of women aged 15-49, 36.9% of managersTrinidad and Tobago: 22.1% of women aged 15-49, 52.7% of managersTunisia: 27.7% of women aged 15-49, 20.8% of managersTurkiye: 29.3% of women aged 15-49, 21.5% of managersTuvalu: 23.2% of women aged 15-49, 36.1% of managersTanzania: 38.8% of women aged 15-49, 29.7% of managersUganda: 27.2% of women aged 15-49, 25.6% of managersUkraine: 19.4% of women aged 15-49, 44.1% of managersUruguay: 24.5% of women aged 15-49, 33.8% of managersUnited States: 15% of women aged 15-49, 44.1% of managersViet Nam: 20.5% of women aged 15-49, 26.1% of managersVanuatu: 30.4% of women aged 15-49, 37.9% of managersSamoa: 21.9% of women aged 15-49, 40.7% of managersSouth Africa: 31.2% of women aged 15-49, 39.3% of managersZambia: 27.1% of women aged 15-49, 29.6% of managersZimbabwe: 27.5% of women aged 15-49, 34.4% of managersBHSFSMGABINDJAMJORJPNMLIMLTNRUSTP

best-fit line line after adjusting

Four ways to measure the link

MethodrpCountries
Standard (Pearson), not adjusted-0.4402.4e-8147
By rank (Spearman), not adjusted-0.4402.4e-8147
Standard (Pearson), adjusted for national income-0.3432.3e-5147
By rank (Spearman), adjusted for national income-0.3265.8e-5147

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 147 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: +0.097 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.386 (without STP) to -0.317 (without IND). 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: STP (-0.044).

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 & Pacific25-0.16
Europe & Central Asia43-0.19
Latin America & Caribbean23-0.08
Middle East, North Africa, Afghanistan & Pakistan12-0.22
Sub-Saharan Africa37-0.42

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 third49-0.30
Middle third49-0.53
Richest third49+0.01

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
Christians96-0.16
Muslims30-0.20

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
France14-0.58
Never a European colony13-0.35
Spain16-0.32
United Kingdom51-0.44

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 5 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 = +0.017, 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 women in management move with it.

Across countries (adjusted)-0.343for comparison
Within countries+0.161each country vs itself
…and vs the same year elsewhere+0.017q = 0.881 · 94 countries
Year-on-year changes+0.002q = 0.976
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.

1,539 country-years (one country in one year), 94 countries, 2000–2023, about 16 years each on average. Share of the variation that happens within countries over time: 4% for Anaemia in women, 19% for Women in management. 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 Women in management trails Anaemia in women →lag -10: r = 0.040 (n = 711)lag -9: r = 0.036 (n = 790)lag -8: r = 0.030 (n = 862)lag -7: r = 0.049 (n = 935)lag -6: r = 0.077 (n = 1,006)lag -5: r = 0.098 (n = 1,073)lag -4: r = 0.091 (n = 1,149)lag -3: r = 0.063 (n = 1,237)lag -2: r = 0.066 (n = 1,336)lag -1: r = 0.037 (n = 1,428)lag +0: r = 0.017 (n = 1,539)lag +1: r = 0.020 (n = 1,581)lag +2: r = 0.014 (n = 1,581)lag +3: r = 0.020 (n = 1,539)lag +4: r = 0.023 (n = 1,492)lag +5: r = -0.006 (n = 1,447)lag +6: r = -0.016 (n = 1,401)lag +7: r = -0.017 (n = 1,339)lag +8: r = -0.045 (n = 1,275)lag +9: r = -0.044 (n = 1,220)lag +10: r = -0.044 (n = 1,156)

Same year (lag 0): +0.017. 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 largest value on this curve (+0.10 at lag -5) is higher than the same-year one by 0.08. That gap is not a finding. It is the largest of 21 closely related estimates, and picking the biggest of many tests always makes it look bigger. That is exactly why nothing on this site is ranked or chosen by lag.

The data


% of women aged 15-49
% of managers		Years
Gabon GAB59.9%19.2%2023, 2024
Mali MLI56.5%9.01%2023, 2022
India IND53.7%11.9%2023, 2025
Cote d'Ivoire CIV51.4%22.2%2023, 2017
Benin BEN50%23.5%2023, 2022
Niger NER47.1%21.6%2023, 2017
Mozambique MOZ47%29.1%2023, 2022
Pakistan PAK46.5%9.57%2023, 2025
Somalia, Fed. Rep. SOM46.2%27.7%2023, 2019
Gambia, The GMB46.1%28.1%2023, 2025
Guinea-Bissau GNB45.7%25.6%2023, 2022
Afghanistan AFG45.4%5.89%2023, 2020
Sierra Leone SLE44.6%22.6%2023, 2018
Burkina Faso BFA44.3%25.9%2023, 2025
Angola AGO43.8%16.2%2023, 2025

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

Women in management

Female share of employment in senior and middle management (%)

International Labour Organization · CC BY 4.0 · updated quarterly

Based on surveys and reported irregularly.

How this was computed

Data used: most recent value in 2010-2025; values come from 2010–2025. 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 Women in management

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

More links with Anaemia in women or Women in management