Control of corruption vs. Free and fair elections
Holds up after adjusting for income
Across 168 countries, Control of corruption and Free and fair elections rise and fall together at r = +0.72 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to +0.54 (q = 0.032, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a moderate positive link remains.
Comparing countries across the middle half of free and fair elections, 0.233 to 0.872 points, control of corruption is better by 1.38 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.863 points.
Link before adjusting (r)+0.720p = 3.5e-28 · 168 countries
After adjusting for national income+0.539p = 5.6e-14 · likely range [+0.42, +0.64]
p, allowing for regions0.002standard error 0.099 · 7 regions
Check for luck (q)0.032across 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
Free and fair elections · index (0-1)link with income +0.61 · high
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 | +0.720 | 3.5e-28 | 168 |
| By rank (Spearman), not adjusted | +0.769 | 4.4e-34 | 168 |
| Standard (Pearson), adjusted for national income | +0.539 | 5.6e-14 | 168 |
| By rank (Spearman), adjusted for national income | +0.595 | 2.2e-17 | 168 |
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 168 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: +0.181 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from +0.527 (without RUS) to +0.558 (without HKG). 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: HKG (+0.019).
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 | +0.67 | |
| Europe & Central Asia | 49 | +0.61 | |
| Latin America & Caribbean | 23 | +0.48 | |
| Middle East, North Africa, Afghanistan & Pakistan | 21 | +0.41 | |
| Sub-Saharan Africa | 46 | +0.68 |
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 | 56 | +0.68 | |
| Middle third | 56 | +0.54 | |
| Richest third | 56 | +0.57 |
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 | 99 | +0.53 | |
| Muslims | 48 | +0.38 |
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 | +0.69 | |
| Never a European colony | 21 | +0.50 | |
| Spain | 19 | +0.32 | |
| United Kingdom | 49 | +0.67 |
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 = +0.539 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.
The link also shows up within countries over time (r = +0.244). It holds even when looking only at changes from one year to the next (r = +0.092). 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)+0.539for comparison
Within countries+0.223each country vs itself
…and vs the same year elsewhere+0.244q = 0.000 · 175 countries
Year-on-year changes+0.092q = 0.008
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.
4,533 country-years (one country in one year), 175 countries, 1996–2024, about 26 years each on average. Share of the variation that happens within countries over time: 11% for Control of corruption, 5% for Free and fair elections. 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): +0.244. 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) | index (0-1) | Years | |
|---|---|---|---|
| Denmark DNK | 2.29 | 0.961 | 2024, 2025 |
| Finland FIN | 2.19 | 0.961 | 2024, 2025 |
| Luxembourg LUX | 2.02 | 0.944 | 2024, 2025 |
| Switzerland CHE | 2.01 | 0.942 | 2024, 2025 |
| Singapore SGP | 1.97 | 0.782 | 2024, 2025 |
| Sweden SWE | 1.97 | 0.945 | 2024, 2025 |
| Norway NOR | 1.96 | 0.966 | 2024, 2025 |
| New Zealand NZL | 1.93 | 0.954 | 2024, 2025 |
| Netherlands NLD | 1.91 | 0.954 | 2024, 2025 |
| Australia AUS | 1.85 | 0.962 | 2024, 2025 |
| Iceland ISL | 1.83 | 0.929 | 2024, 2025 |
| Germany DEU | 1.77 | 0.957 | 2024, 2025 |
| Estonia EST | 1.67 | 0.952 | 2024, 2025 |
| Canada CAN | 1.63 | 0.944 | 2024, 2025 |
| Ireland IRL | 1.57 | 0.945 | 2024, 2025 |
Kaufmann, D. and A. Kraay, Worldwide Governance Indicators, World Bank
Coppedge, M. et al., V-Dem Dataset, Varieties of Democracy (V-Dem) Project
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.
Free and fair elections
Free and fair elections index
V-Dem Institute, University of Gothenburg · via Our World in Data · CC BY 4.0 · updated annual
Whether elections are run without voter-registration fraud, widespread irregularities, government threats against the opposition, vote buying or election violence. This asks a narrower question than whether a country is a democracy, and one that is easier to answer. A state can run clean elections while barring the opposition from standing. It can also run dirty elections and still change government. It is the clean-elections part of the electoral democracy index. So its pair with that index, and with liberal and participatory democracy, is built from the same numbers and flagged as such.
How this was computed
Data used: most recent value in 2010-2025; values come from 2024–2025. 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 Free and fair elections
This page shows that two measures go together. It does not show that Control of corruption changes Free and fair elections, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Control of corruption or Free and fair elections
- Free and fair elections vs Women's political empowerment before +0.78 → after adjusting +0.72
- Free and fair elections vs Rule of law before +0.78 → after adjusting +0.65
- Free and fair elections vs Political corruption before -0.78 → after adjusting -0.65
- Control of corruption vs Liberal democracy before +0.74 → after adjusting +0.61
- Civil liberties (FH) vs Control of corruption before -0.71 → after adjusting -0.60
- Control of corruption vs Political rights (FH) before -0.67 → after adjusting -0.56
- Control of corruption vs Interpersonal trust before +0.73 → after adjusting +0.55
- Corruption Perceptions Index vs Free and fair elections before +0.72 → after adjusting +0.54