Do Anaemia in women and Labor share of income go together across countries?

Across 130 countries the link before adjusting for anything is r = -0.25, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.24.

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 -0.036 (131 countries, 3,144 country-years). Using year-to-year changes, it is -0.008.

Does Anaemia in women cause Labor share of income?

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. Labor share of income

Holds up after adjusting for income

Across 130 countries, Anaemia in women and Labor share of income rise and fall together at r = -0.25 (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 = -0.32, q = 0.042, allowing for the many pairs tested).

Comparing countries across the middle half of labor share of income, 42.3% to 58.5% of GDP, anaemia in women is better by 3.7% of women aged 15-49 at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 3.47% of women aged 15-49.

Link before adjusting (r)-0.251p = 0.004 · 130 countries

After adjusting for national income-0.321p = 0.000 · likely range [-0.47, -0.16]

p, allowing for regions0.024standard error 0.108 · 7 regions

Check for luck (q)0.042across 18,622 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

Labor share of income · % of GDPlink with income +0.08 · independent

“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	-0.251	0.004	130
By rank (Spearman), not adjusted	-0.242	0.006	130
Standard (Pearson), adjusted for national income	-0.321	0.000	130
By rank (Spearman), adjusted for national income	-0.245	0.005	130

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 130 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: -0.070 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.344 (without ZMB) to -0.284 (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: GAB (+0.038).

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	13	-0.40
Europe & Central Asia	46	-0.40
Latin America & Caribbean	21	+0.02
Middle East, North Africa, Afghanistan & Pakistan	15	-0.45
Sub-Saharan Africa	31	-0.39

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	44	-0.21
Middle third	43	-0.38
Richest third	43	-0.54

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	82	-0.18
Muslims	33	-0.43

By last European coloniser

	n	r
France	16	-0.40
Never a European colony	16	-0.52
Spain	17	-0.03
United Kingdom	34	-0.36

By official ideology

	n	r
Nationalist	17	-0.16
No strong official ideology	92	-0.29

By party system

	n	r
A few parties banned	18	-0.66
No parties banned	100	-0.13

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 = -0.281 after dropping 3 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 = -0.036, 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 labor share of income move with it.

Across countries (adjusted)-0.321for comparison

Within countries+0.026each country vs itself

…and vs the same year elsewhere-0.036q = 0.640 · 131 countries

Year-on-year changes-0.008q = 0.813

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.

3,144 country-years (one country in one year), 131 countries, 2000–2023, about 24 years each on average. Share of the variation that happens within countries over time: 8% for Anaemia in women, 3% for Labor share of income. 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): -0.036. 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.

This curve peaks at the edge of the window (lag -10), so it is still drifting where the search stops. That pattern usually reflects a slow trend left over in the data, not a link with real timing. The peak value should not be read as the “true” link at the right lag.

The data

	% of women aged 15-49	% of GDP	Years
Gabon GAB	59.9%	27.5%	2023
Mauritania MRT	54.5%	40.4%	2023
India IND	53.7%	53.3%	2023
Cote d'Ivoire CIV	51.4%	33.2%	2023
Benin BEN	50%	61.7%	2023
Niger NER	47.1%	39.1%	2023
Mozambique MOZ	47%	54%	2023
Chad TCD	46.5%	48.5%	2023
Guinea GIN	45.7%	42.2%	2023
Sierra Leone SLE	44.6%	59.6%	2023
Burkina Faso BFA	44.3%	42.6%	2023
Angola AGO	43.8%	22.3%	2023
Central African Republic CAF	43.4%	16.4%	2023
Senegal SEN	43.1%	52.3%	2023
Togo TGO	41.5%	50.1%	2023

World Bank, World Development Indicators
Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table", American Economic Review, 105(10), 3150-3182, www.ggdc.net/pwt. Data: Penn World Table version 11.0, Groningen Growth and Development Centre, doi:10.34894/FABVLR.

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

Labor share of income

Labor share of GDP (Penn World Table)

Groningen Growth and Development Centre, University of Groningen · via DataverseNL · CC BY 4.0 · updated irregular (new versions every few years)

The share of a country's income (GDP) that is paid to people for their work: wages, salaries and benefits, plus an estimate of what the self-employed, such as farmers and shopkeepers, earn for their work. The rest goes to the owners of land, buildings and machines as profit, rent and interest. Where there is little data on the self-employed, the Penn World Table fills in a value by a fixed rule, so figures for poorer countries are rougher, and a few countries show the same value every year. No "better" direction is set.

How this was computed

Data used: most recent value in 2010-2025; values come from 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 18,622 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore Anaemia in women and Labor share of income

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