Do Anaemia in women and Fixed broadband go together across countries?

Across 185 countries the link before adjusting for anything is r = -0.67, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.72.

Does the link hold within countries over time, not just across countries?

Comparing each country only with itself across 2000-2023, and setting aside what happened worldwide in each year, the link is +0.389 (189 countries, 3,844 country-years). Using year-to-year changes, it is +0.179.

Does Anaemia in women cause Fixed broadband?

This analysis cannot answer that. It shows only that the two go together. A link between two country measures, adjusted or not, does not show that either one changes the other. Country figures also say nothing about individual people.

Anaemia in women vs. Fixed broadband

Fades after adjusting for income

Across 185 countries, Anaemia in women and Fixed broadband rise and fall together at r = -0.67 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.32, which could easily be chance (q = 0.073, 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, 19.4% to 34% of women aged 15-49, fixed broadband is worse by 14.5 per 100 people at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 5.15 per 100 people.

Link before adjusting (r)-0.674p = 6.7e-26 · 185 countries

After adjusting for national income-0.318p = 1.1e-5 · likely range [-0.44, -0.18]

p, allowing for regions0.010standard error 0.086 · 7 regions

Check for luck (q)0.073across 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

Fixed broadband · per 100 peoplelink with income +0.88 · proxy

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

best-fit line line after adjusting

Four ways to measure the link

Method	r	p	Countries
Standard (Pearson), not adjusted	-0.674	6.7e-26	185
By rank (Spearman), not adjusted	-0.723	3.0e-31	185
Standard (Pearson), adjusted for national income	-0.318	1.1e-5	185
By rank (Spearman), adjusted for national income	-0.384	7.1e-8	185

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 185 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: +0.356 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.338 (without GTM) to -0.293 (without GUY). 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: GUY (+0.026).

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.

	n	r
East Asia & Pacific	29	-0.42
Europe & Central Asia	50	-0.46
Latin America & Caribbean	30	-0.19
Middle East, North Africa, Afghanistan & Pakistan	22	-0.47
Sub-Saharan Africa	46	-0.19

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.

	n	r
Poorest third	62	-0.27
Middle third	61	-0.30
Richest third	62	-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.

	n	r
Christians	107	-0.27
Muslims	49	-0.27

By last european coloniser

	n	r
France	25	-0.60
Never a European colony	21	-0.43
Spain	19	-0.30
United Kingdom	63	-0.39

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 = -0.305 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 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.

The link also shows up within countries over time (r = +0.389). It holds even when looking only at changes from one year to the next (r = +0.179). This is the strongest kind of evidence available here: it holds when each country is compared only with itself, and it is not just a product of shared trends.

Across countries (adjusted)-0.318for comparison

Within countries+0.094each country vs itself

…and vs the same year elsewhere+0.389q = 0.000 · 189 countries

Year-on-year changes+0.179q = 0.000

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.
The association is much weaker in year-on-year changes than in levels, which points to shared country-specific trends.

3,844 country-years (one country in one year), 189 countries, 2000–2023, about 20 years each on average. Share of the variation that happens within countries over time: 2% for Anaemia in women, 35% for Fixed broadband. 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

Same year (lag 0): +0.389. 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	per 100 people	Years
Gabon GAB	59.9%	4.69	2023, 2024
Mali MLI	56.5%	0.774	2023, 2022
Mauritania MRT	54.5%	0.947	2023, 2024
India IND	53.7%	3.15	2023, 2024
Cote d'Ivoire CIV	51.4%	1.6	2023, 2024
Benin BEN	50%	0.277	2023, 2024
Niger NER	47.1%	0.111	2023, 2024
Mozambique MOZ	47%	0.142	2023, 2024
Congo, Rep. COG	46.7%	1.27	2023
Chad TCD	46.5%	0	2023, 2024
Pakistan PAK	46.5%	1.47	2023, 2024
Somalia, Fed. Rep. SOM	46.2%	0.723	2023
Gambia, The GMB	46.1%	0.519	2023, 2024
Haiti HTI	45.9%	0.307	2023, 2022
Guinea-Bissau GNB	45.7%	0.386	2023, 2024

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

Fixed broadband

Fixed broadband subscriptions (per 100 people)

International Telecommunication Union · CC BY 4.0 · updated quarterly

Subscriptions, not people. A household line counts once, however many people use it. A country that went straight to mobile data can be well connected with few of these lines. Internet users, already on the site, is the measure of people.

How this was computed

Data used: most recent value in 2010-2025; values come from 2021–2024. 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 Fixed broadband

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