PISA reading vs. Political corruption
Fades after adjusting for income
Across 84 countries, PISA reading and Political corruption rise and fall together at r = -0.74 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.39, which could easily be chance (q = 0.085, 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.
Comparing countries across the middle half of pisa reading, 385 to 479 PISA points, political corruption is better by 0.356 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.197 points.
Link before adjusting (r)-0.742p = 6.5e-16 · 84 countries
After adjusting for national income-0.389p = 0.000 · likely range [-0.56, -0.19]
p, allowing for regions0.013standard error 0.092 · 5 regions
Check for luck (q)0.085across 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.
PISA reading · PISA pointslink with income +0.77 · high
Political corruption · index (0-1)link with income -0.68 · 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 | -0.742 | 6.5e-16 | 84 |
| By rank (Spearman), not adjusted | -0.767 | 1.8e-17 | 84 |
| Standard (Pearson), adjusted for national income | -0.389 | 0.000 | 84 |
| By rank (Spearman), adjusted for national income | -0.410 | 0.000 | 84 |
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 84 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: +0.354 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from -0.443 (without RUS) to -0.350 (without QAT). 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: RUS (-0.054).
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 | 13 | -0.71 | |
| Europe & Central Asia | 44 | -0.23 | |
| Latin America & Caribbean | 15 | -0.75 |
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 | 28 | -0.37 | |
| Middle third | 28 | -0.32 | |
| Richest third | 28 | -0.59 |
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 | 55 | -0.36 | |
| Muslims | 16 | +0.43 |
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 | ||
|---|---|---|---|
| Spain | 14 | -0.62 | |
| United Kingdom | 13 | -0.80 |
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 = -0.389 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 23years. 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 = -0.045, 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 pisa reading moved did not tend to see their political corruption move with it.
Across countries (adjusted)-0.389for comparison
Within countries-0.024each country vs itself
…and vs the same year elsewhere-0.045q = 0.582 · 56 countries
Year-on-year changes—q = —
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.
404 country-years (one country in one year), 56 countries, 2000–2022, about 7 years each on average. Share of the variation that happens within countries over time: 8% for PISA reading, 4% for Political corruption. 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): -0.045. 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
PISA points | index (0-1) | Years | |
|---|---|---|---|
| Singapore SGP | 543 | 0.013 | 2022, 2025 |
| Ireland IRL | 516 | 0.032 | 2022, 2025 |
| Japan JPN | 516 | 0.05 | 2022, 2025 |
| Korea, Rep. KOR | 515 | 0.156 | 2022, 2025 |
| Estonia EST | 511 | 0.025 | 2022, 2025 |
| Canada CAN | 507 | 0.026 | 2022, 2025 |
| United States USA | 504 | 0.06 | 2022, 2025 |
| New Zealand NZL | 501 | 0.014 | 2022, 2025 |
| Hong Kong SAR, China HKG | 500 | 0.085 | 2022, 2025 |
| Australia AUS | 498 | 0.029 | 2022, 2025 |
| United Kingdom GBR | 494 | 0.042 | 2022, 2025 |
| Finland FIN | 490 | 0.031 | 2022, 2025 |
| Denmark DNK | 489 | 0.004 | 2022, 2025 |
| Poland POL | 489 | 0.081 | 2022, 2025 |
| Czechia CZE | 489 | 0.171 | 2022, 2025 |
OECD databases as named on each indicator: PISA, Social Expenditure Database (SOCX), Income Distribution Database, Development Assistance Committee statistics, Family Database, Government at a Glance, Corporate Tax Statistics, employment and environment statistics
Coppedge, M. et al., V-Dem Dataset, Varieties of Democracy (V-Dem) Project
Sources
PISA reading
PISA mean reading score
OECD · via Our World in Data · CC BY 4.0 (OECD terms and conditions) · updated annual, except the snapshots noted per indicator
Political corruption
Political corruption index
V-Dem Institute, University of Gothenburg · via Our World in Data · CC BY 4.0 · updated annual
Expert ratings of corruption across the government, parliament, courts and public sector. It runs the opposite way to the Corruption Perceptions Index, where higher means cleaner. So the two should move strongly in opposite directions. V-Dem is also one of the datasets Transparency International combines into that index.
How this was computed
Data used: most recent value in 2010-2025; values come from 2015–2025. 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 5 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 PISA reading and Political corruption
This page shows that two measures go together. It does not show that PISA reading changes Political corruption, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with PISA reading or Political corruption
- Electoral democracy vs Political corruption before -0.71 → after adjusting -0.60
- PISA reading vs Religiously unaffiliated before +0.65 → after adjusting +0.57
- PISA reading vs Population 65+ before +0.69 → after adjusting +0.57
- PISA reading vs Researchers before +0.79 → after adjusting +0.55
- One-person households vs PISA reading before +0.72 → after adjusting +0.52
- Muslim share vs PISA reading before -0.55 → after adjusting -0.51
- Political corruption vs Regime type before -0.64 → after adjusting -0.49
- Frequent prayer vs PISA reading before -0.71 → after adjusting -0.48