Do Child stunting and Regulatory quality go together across countries?

Across 155 countries the link before adjusting for anything is r = -0.55, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.65.

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

Comparing each country only with itself across 2000-2024, and setting aside what happened worldwide in each year, the link is -0.087 (161 countries, 3,820 country-years). Using year-to-year changes, it is -0.009.

Does Child stunting cause Regulatory quality?

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. Regulatory quality

Fades after adjusting for income

Across 155 countries, Child stunting and Regulatory quality rise and fall together at r = -0.55 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.07, which could easily be chance (q = 0.47, 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. Caution: direction is not consistent across regions.

Comparing countries across the middle half of child stunting, 5.7% to 23.8% of children under 5, regulatory quality is worse by 0.66 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap reverses, to 0.0834 points the other way.

Link before adjusting (r)-0.551p = 1.1e-13 · 155 countries

After adjusting for national income+0.068p = 0.402 · likely range [-0.09, +0.22]

p, allowing for regions0.263standard error 0.055 · 7 regions

Check for luck (q)0.468across 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.

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

Regulatory quality · index (approx. -2.5 to 2.5)link with income +0.83 · 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	-0.551	1.1e-13	155
By rank (Spearman), not adjusted	-0.648	7.6e-20	155
Standard (Pearson), adjusted for national income	+0.068	0.402	155
By rank (Spearman), adjusted for national income	-0.084	0.299	155

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 155 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: +0.483 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.048 (without BDI) to +0.089 (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 (+0.021).

Fragile: this link may not hold up.

direction is not consistent across regions

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	26	-0.11
Europe & Central Asia	32	-0.02
Latin America & Caribbean	24	-0.20
Middle East, North Africa, Afghanistan & Pakistan	20	+0.13
Sub-Saharan Africa	46	+0.02

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	52	+0.05
Middle third	51	+0.01
Richest third	52	-0.22

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	86	-0.01
Muslims	48	+0.18

By last european coloniser

	n	r
France	25	+0.21
Never a European colony	21	+0.26
Spain	18	-0.07
United Kingdom	52	-0.04

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 = +0.031 after dropping 6 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 25years. 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 = -0.087, 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 regulatory quality move with it.

Across countries (adjusted)+0.068for comparison

Within countries-0.142each country vs itself

…and vs the same year elsewhere-0.087q = 0.264 · 161 countries

Year-on-year changes-0.009q = 0.833

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.

3,820 country-years (one country in one year), 161 countries, 2000–2024, about 24 years each on average. Share of the variation that happens within countries over time: 9% for Child stunting, 6% for Regulatory quality. 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): -0.087. 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 children under 5	index (approx. -2.5 to 2.5)	Years
Burundi BDI	55.3%	-0.814	2024
Niger NER	48.3%	-0.762	2024
Angola AGO	47.7%	-0.773	2024
Papua New Guinea PNG	47.6%	-0.6	2024
Timor-Leste TLS	45.4%	-0.734	2024
Guatemala GTM	44.6%	-0.309	2024
Congo, Dem. Rep. COD	44.1%	-1.28	2024
Afghanistan AFG	42%	-1.54	2024
Central African Republic CAF	38.9%	-1.27	2024
Madagascar MDG	38.4%	-0.749	2024
Mozambique MOZ	37%	-0.764	2024
Ethiopia ETH	35.5%	-0.833	2024
Sudan SDN	35.4%	-1.66	2024
Lesotho LSO	35%	-0.485	2024
Nigeria NGA	33.8%	-0.633	2024

World Bank, World Development Indicators
Kaufmann, D. and A. Kraay, Worldwide Governance Indicators, World Bank

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.

Regulatory quality

Regulatory Quality (estimate)

World Bank · CC BY 4.0 · updated annual

How this was computed

Data used: most recent value in 2010-2025; values come from 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 Child stunting and Regulatory quality

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