Do Child stunting and Human capital (PWT) go together across countries?

Across 119 countries the link before adjusting for anything is r = -0.78, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.78.

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 -0.002 (121 countries, 2,904 country-years). Using year-to-year changes, it is +0.021.

Does Child stunting cause Human capital (PWT)?

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.

Child stunting vs. Human capital (PWT)

Fades after adjusting for income

Across 119 countries, Child stunting and Human capital (PWT) rise and fall together at r = -0.78 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.36, which could easily be chance (q = 0.059, 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 human capital (PWT), 2.05 to 3.15 points, child stunting is better by 16.2% of children under 5 at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 7.47% of children under 5.

Link before adjusting (r)-0.777p = 2.8e-25 · 119 countries

After adjusting for national income-0.357p = 7.1e-5 · likely range [-0.51, -0.19]

p, allowing for regions0.037standard error 0.134 · 7 regions

Check for luck (q)0.059across 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.

Child stunting · % of children under 5link with income -0.80 · proxy

Human capital (PWT) · index (1 = no schooling)link with income +0.82 · 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	-0.777	2.8e-25	119
By rank (Spearman), not adjusted	-0.777	3.1e-25	119
Standard (Pearson), adjusted for national income	-0.357	7.1e-5	119
By rank (Spearman), adjusted for national income	-0.306	0.001	119

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 119 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: +0.420 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.388 (without GMB) to -0.308 (without AGO). 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: AGO (+0.049).

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	16	-0.25
Europe & Central Asia	24	-0.13
Latin America & Caribbean	22	-0.54
Middle East, North Africa, Afghanistan & Pakistan	14	-0.33
Sub-Saharan Africa	37	-0.13

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	40	-0.24
Middle third	39	-0.45
Richest third	40	-0.31

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	69	-0.43
Muslims	33	-0.30

By last European coloniser

	n	r
France	20	-0.04
Never a European colony	15	-0.63
Spain	17	-0.37
United Kingdom	40	-0.13

By official ideology

	n	r
Nationalist	17	-0.44
No strong official ideology	81	-0.31

By party system

	n	r
A few parties banned	19	-0.26
No parties banned	85	-0.37

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 = -0.367 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 = -0.002, 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 child stunting moved did not tend to see their human capital (PWT) move with it.

Across countries (adjusted)-0.357for comparison

Within countries-0.627each country vs itself

…and vs the same year elsewhere-0.002q = 0.991 · 121 countries

Year-on-year changes+0.021q = 0.821

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.
Much of the within-country association disappears once shared global year effects are removed, which points to a common worldwide trend rather than a country-level relationship.

2,904 country-years (one country in one year), 121 countries, 2000–2023, about 24 years each on average. Share of the variation that happens within countries over time: 6% for Child stunting, 9% for Human capital (PWT). 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): -0.002. 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 children under 5	index (1 = no schooling)	Years
Burundi BDI	55.3%	1.48	2024, 2023
Niger NER	48.3%	1.26	2024, 2023
Angola AGO	47.7%	1.54	2024, 2023
Guatemala GTM	44.6%	2.04	2024, 2023
Congo, Dem. Rep. COD	44.1%	1.83	2024, 2023
Central African Republic CAF	38.9%	1.6	2024, 2023
Madagascar MDG	38.4%	1.72	2024, 2023
Mozambique MOZ	37%	1.29	2024, 2023
Ethiopia ETH	35.5%	1.54	2024, 2023
Sudan SDN	35.4%	1.65	2024, 2023
Lesotho LSO	35%	2.02	2024, 2023
Nigeria NGA	33.8%	2.13	2024, 2023
Pakistan PAK	33.6%	1.76	2024, 2023
Malawi MWI	33.2%	2.14	2024, 2023
Benin BEN	33.2%	2.04	2024, 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

Child stunting

Prevalence of stunting, height for age, modelled estimate (% of children under 5)

UNICEF, WHO and World Bank Joint Child Malnutrition Estimates · CC BY 4.0 · updated quarterly

An estimate from a statistical model. The agencies fit a model to household surveys and predict every country and year. So this is smoother than the surveys behind it, and it has values for years nobody measured. The survey-only series never reaches sixty countries in any year.

Human capital (PWT)

Human capital index, from years of schooling (Penn World Table)

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

"Human capital" means the skills and knowledge workers bring to their jobs. This index measures it from schooling alone. It starts from how many years adults spent in school, then counts the early years for more than the later ones, following studies of how much higher pay tends to be with each extra year. A score of 1 means no schooling; the US is about 3.8. It uses the same schooling figures as mean years of schooling, so the two are close by construction. For that reason it is left out of the education score, which already counts years of schooling.

How this was computed

Data used: most recent value in 2010-2025; values come from 2023–2024. 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 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 Child stunting and Human capital (PWT)

This page shows that two measures go together. It does not show that Child stunting changes Human capital (PWT), or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.