Corruption Perceptions Index vs. Regulatory quality
Holds up after adjusting for income
Across 175 countries, Corruption Perceptions Index and Regulatory quality rise and fall together at r = +0.91 (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 = +0.81, q = 0.0071, allowing for the many pairs tested).
Comparing countries across the middle half of corruption perceptions index, 30 to 57 points, regulatory quality is better by 1.19 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.917 points.
Link before adjusting (r)+0.909p = 1.0e-67 · 175 countries
After adjusting for national income+0.811p = 6.9e-42 · likely range [+0.75, +0.86]
p, allowing for regions0.000standard error 0.096 · 7 regions
Check for luck (q)0.007across 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.
Corruption Perceptions Index · index (0-100, higher = cleaner)link with income +0.72 · high
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
+0.909
1.0e-67
175
By rank (Spearman), not adjusted
+0.886
1.1e-59
175
Standard (Pearson), adjusted for national income
+0.811
6.9e-42
175
By rank (Spearman), adjusted for national income
+0.743
8.5e-32
175
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 175 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: +0.098 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from +0.803 (without RUS) to +0.824 (without BTN). 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: BTN (+0.013).
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
22
+0.84
Europe & Central Asia
49
+0.87
Latin America & Caribbean
29
+0.62
Middle East, North Africa, Afghanistan & Pakistan
21
+0.93
Sub-Saharan Africa
46
+0.81
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
59
+0.78
Middle third
58
+0.64
Richest third
58
+0.88
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
104
+0.81
Muslims
49
+0.84
By last european coloniser
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
France
25
+0.88
Never a European colony
21
+0.79
Spain
19
+0.69
United Kingdom
55
+0.76
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 = +0.811 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.
Loading…
Over time, within countries
Everything above compares countries with each other at one moment. This section compares each country only with itself over 13years. 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 also shows up within countries over time (r = +0.385). It holds even when looking only at changes from one year to the next (r = +0.131). This is the strongest kind of evidence available here: it holds when each country is compared only with itself, and it is not just a product of shared trends.
Across countries (adjusted)+0.811for comparison
Within countries+0.379each country vs itself
…and vs the same year elsewhere+0.385q = 0.000 · 180 countries
Year-on-year changes+0.131q = 0.000
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.
The association is much weaker in year-on-year changes than in levels, which points to shared country-specific trends.
2,305 country-years (one country in one year), 180 countries, 2012–2024, about 13 years each on average. Share of the variation that happens within countries over time: 2% for Corruption Perceptions Index, 2% 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): +0.385. 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 (0-100, higher = cleaner)
index (approx. -2.5 to 2.5)
Years
Denmark DNK
90
1.93
2024
Finland FIN
88
1.96
2024
Singapore SGP
84
2.13
2024
New Zealand NZL
83
1.84
2024
Norway NOR
81
1.67
2024
Luxembourg LUX
81
1.84
2024
Switzerland CHE
81
1.77
2024
Sweden SWE
80
1.67
2024
Netherlands NLD
78
1.72
2024
Iceland ISL
77
1.33
2024
Ireland IRL
77
1.79
2024
Australia AUS
77
1.72
2024
Uruguay URY
76
0.647
2024
Estonia EST
76
1.4
2024
Germany DEU
75
1.5
2024
Transparency International, Corruption Perceptions Index Kaufmann, D. and A. Kraay, Worldwide Governance Indicators, World Bank One of these measures is published under a licence that forbids changes. Values are shown exactly as published and must not be republished in a changed or rescaled form.
Transparency International · via Our World in Data · CC BY-ND 4.0 · updated annual · comparable from 2012 only
Measures how corrupt the public sector is believed to be, based on 13 surveys of experts and business people. Some of the same surveys feed the Worldwide Governance Indicators' Control of Corruption measure, so the two share ingredients.
Data used: most recent value in 2010-2025; values come from 2014–2024. 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 Corruption Perceptions Index and Regulatory quality
This page shows that two measures go together. It does not show that Corruption Perceptions Index changes Regulatory quality, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Corruption Perceptions Index or Regulatory quality