Do Government effectiveness and Undernourishment go together across countries?

Across 163 countries the link before adjusting for anything is r = -0.64, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.73.

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

Comparing each country only with itself across 2002-2023, and setting aside what happened worldwide in each year, the link is -0.211 (166 countries, 3,604 country-years). Using year-to-year changes, it is -0.046.

Does Government effectiveness cause Undernourishment?

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.

Government effectiveness vs. Undernourishment

Fades after adjusting for income

Across 163 countries, Government effectiveness and Undernourishment rise and fall together at r = -0.64 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.09, which could easily be chance (q = 0.65, 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 undernourishment, 2.5% to 13.9% of population, government effectiveness is worse by 0.657 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0756 points.

Link before adjusting (r)-0.637p = 5.8e-20 · 163 countries

After adjusting for national income-0.092p = 0.243 · likely range [-0.24, +0.06]

p, allowing for regions0.526standard error 0.137 · 7 regions

Check for luck (q)0.648across 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.

Government effectiveness · index (approx. -2.5 to 2.5)link with income +0.85 · proxy

Undernourishment · % of populationlink 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	-0.637	5.8e-20	163
By rank (Spearman), not adjusted	-0.733	9.1e-29	163
Standard (Pearson), adjusted for national income	-0.092	0.243	163
By rank (Spearman), adjusted for national income	-0.109	0.168	163

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 163 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: +0.545 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.111 (without MDG) to -0.048 (without HTI). 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: HTI (+0.044).

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	21	-0.19
Europe & Central Asia	48	+0.21
Latin America & Caribbean	26	-0.55
Middle East, North Africa, Afghanistan & Pakistan	19	-0.35
Sub-Saharan Africa	43	-0.20

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	55	-0.35
Middle third	54	-0.12
Richest third	54	-0.23

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	100	-0.11
Muslims	44	-0.34

By last european coloniser

	n	r
France	24	-0.64
Never a European colony	20	+0.14
Spain	18	-0.52
United Kingdom	48	-0.15

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 = -0.052 after dropping 9 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 22years. 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 shows up within countries over time (r = -0.211) but mostly disappears in year-to-year changes (r = -0.046). That points to slow trends particular to each country, not the two measures moving together from year to year.

Across countries (adjusted)-0.092for comparison

Within countries-0.318each country vs itself

…and vs the same year elsewhere-0.211q = 1.0e-5 · 166 countries

Year-on-year changes-0.046q = 0.109

Read with care.

Almost all of the second indicator's variation is between countries, so the within-country estimate rests on a small part of the data.

3,604 country-years (one country in one year), 166 countries, 2002–2023, about 22 years each on average. Share of the variation that happens within countries over time: 12% for Government effectiveness, 5% for Undernourishment. 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): -0.211. 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

	index (approx. -2.5 to 2.5)	% of population	Years
Luxembourg LUX	2.04	2.5%	2024, 2023
Denmark DNK	1.91	2.5%	2024, 2023
New Zealand NZL	1.85	2.5%	2024, 2023
Norway NOR	1.84	2.5%	2024, 2023
Switzerland CHE	1.84	2.5%	2024, 2023
Finland FIN	1.82	2.5%	2024, 2023
Australia AUS	1.81	2.5%	2024, 2023
Sweden SWE	1.77	2.5%	2024, 2023
Canada CAN	1.76	2.5%	2024, 2023
Netherlands NLD	1.75	2.5%	2024, 2023
Iceland ISL	1.65	2.5%	2024, 2023
Hong Kong SAR, China HKG	1.64	3.1%	2024, 2023
Ireland IRL	1.61	2.5%	2024, 2023
Germany DEU	1.55	2.5%	2024, 2023
Austria AUT	1.51	2.5%	2024, 2023

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

Sources

Government effectiveness

Government Effectiveness (estimate)

World Bank · CC BY 4.0 · updated annual

Undernourishment

Prevalence of undernourishment (% of population)

Food and Agriculture Organization · CC BY 4.0 · updated quarterly

Estimated from the food supply and how it is shared out, not by asking anyone. It is the FAO's model of how many people get less food energy than they need. The survey-based food insecurity measure below is treated as measuring the same thing. So their pair reads as two tools agreeing, not as a finding.

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 Government effectiveness and Undernourishment

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