Do Control of corruption and Tax revenue go together across countries?

Across 151 countries the link before adjusting for anything is r = +0.47, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives +0.50.

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

Comparing each country only with itself across 1996-2024, and setting aside what happened worldwide in each year, the link is +0.015 (155 countries, 3,081 country-years). Using year-to-year changes, it is +0.017.

Does Control of corruption cause Tax revenue?

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.

Control of corruption vs. Tax revenue

Holds up after adjusting for income

Across 151 countries, Control of corruption and Tax revenue rise and fall together at r = +0.47 (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 = +0.36, q = 0.012, allowing for the many pairs tested).

Comparing countries across the middle half of tax revenue, 12.2% to 21.8% of GDP, control of corruption is better by 0.672 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.386 points.

Link before adjusting (r)+0.468p = 1.4e-9 · 151 countries

After adjusting for national income+0.364p = 4.6e-6 · likely range [+0.22, +0.50]

p, allowing for regions0.002standard error 0.071 · 7 regions

Check for luck (q)0.012across 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.

Control of corruption · index (approx. -2.5 to 2.5)link with income +0.73 · high

Tax revenue · % of GDPlink with income +0.33 · moderate

“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	+0.468	1.4e-9	151
By rank (Spearman), not adjusted	+0.496	9.7e-11	151
Standard (Pearson), adjusted for national income	+0.364	4.6e-6	151
By rank (Spearman), adjusted for national income	+0.390	7.9e-7	151

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 151 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: +0.103 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from +0.346 (without DNK) to +0.389 (without FSM). 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: FSM (+0.025).

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	25	+0.40
Europe & Central Asia	48	+0.32
Latin America & Caribbean	24	+0.77
Middle East, North Africa, Afghanistan & Pakistan	12	+0.22
Sub-Saharan Africa	34	+0.52

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	51	+0.40
Middle third	50	+0.44
Richest third	50	+0.36

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	98	+0.36
Muslims	28	+0.11

By last european coloniser

	n	r
France	14	+0.88
Never a European colony	16	-0.27
Spain	19	+0.13
United Kingdom	49	+0.47

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 = +0.364 after dropping 0 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 29years. 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 = +0.015, 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 control of corruption moved did not tend to see their tax revenue move with it.

Across countries (adjusted)+0.364for comparison

Within countries+0.019each country vs itself

…and vs the same year elsewhere+0.015q = 0.669 · 155 countries

Year-on-year changes+0.017q = 0.802

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.

3,081 country-years (one country in one year), 155 countries, 1996–2024, about 20 years each on average. Share of the variation that happens within countries over time: 70% for Control of corruption, 4% for Tax revenue. 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): +0.015. 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

	index (approx. -2.5 to 2.5)	% of GDP	Years
Denmark DNK	2.29	33.4%	2024
Finland FIN	2.19	25.3%	2024
Luxembourg LUX	2.02	28.6%	2024
Switzerland CHE	2.01	9.45%	2024
Singapore SGP	1.97	13.6%	2024
Sweden SWE	1.97	27.2%	2024
Norway NOR	1.96	24.5%	2024
New Zealand NZL	1.93	29.5%	2024
Netherlands NLD	1.91	25.3%	2024
Australia AUS	1.85	23.6%	2024, 2022
Iceland ISL	1.83	23.2%	2024, 2023
Germany DEU	1.77	10.9%	2024
Estonia EST	1.67	22.7%	2024
Canada CAN	1.63	13.7%	2024
Ireland IRL	1.57	17.9%	2024

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

Sources

Control of corruption

Control of Corruption (estimate)

World Bank · CC BY 4.0 · updated annual

Measures how much people believe public power is used for private gain. It is based on perceptions: it does not count corrupt acts. And perceptions themselves shift with press freedom and with prosecutions.

Tax revenue

Tax revenue (% of GDP)

World Bank · CC BY 4.0 · updated quarterly

How this was computed

Data used: most recent value in 2010-2025; values come from 2012–2024. 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 Control of corruption and Tax revenue

This page shows that two measures go together. It does not show that Control of corruption changes Tax revenue, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.