Do Political corruption and Public social spending go together across countries?

Across 42 countries the link before adjusting for anything is r = -0.53, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.41.

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

Comparing each country only with itself across 1990-2024, and setting aside what happened worldwide in each year, the link is -0.156 (42 countries, 1,288 country-years). Using year-to-year changes, it is -0.020.

Does Political corruption cause Public social spending?

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.

Political corruption vs. Public social spending

Fades after adjusting for income

Across 42 countries, Political corruption and Public social spending rise and fall together at r = -0.53 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.34, which could easily be chance (q = 0.11, 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: leave-one-out range spans 0.16 in r.

Comparing countries across the middle half of public social spending, 17% to 24.5% of GDP, political corruption is better by 0.138 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.0729 points.

Link before adjusting (r)-0.534p = 0.000 · 42 countries

After adjusting for national income-0.335p = 0.032 · likely range [-0.58, -0.03]

p, allowing for regions0.022standard error 0.092 · 5 regions

Check for luck (q)0.106across 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.

Political corruption · index (0-1)link with income -0.68 · high

Public social spending · % of GDPlink with income +0.44 · 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	-0.534	0.000	42
By rank (Spearman), not adjusted	-0.412	0.007	42
Standard (Pearson), adjusted for national income	-0.335	0.032	42
By rank (Spearman), adjusted for national income	-0.130	0.418	42

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 42 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: +0.199 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.392 (without BGR) to -0.236 (without TUR). 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: TUR (+0.099).

Fragile: this link may not hold up.

leave-one-out range spans 0.16 in r
correlation moves sharply once outliers are trimmed

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
Europe & Central Asia	30	-0.50

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	14	-0.29
Middle third	14	-0.37
Richest third	14	+0.22

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	35	-0.22

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 = -0.165 after dropping 5 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 35years. 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 = -0.156, 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 political corruption moved did not tend to see their public social spending move with it.

Across countries (adjusted)-0.335for comparison

Within countries-0.184each country vs itself

…and vs the same year elsewhere-0.156q = 0.259 · 42 countries

Year-on-year changes-0.020q = 0.596

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.

1,288 country-years (one country in one year), 42 countries, 1990–2024, about 31 years each on average. Share of the variation that happens within countries over time: 17% for Political corruption, 7% for Public social spending. 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): -0.156. 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 (0-1)	% of GDP	Years
Turkiye TUR	0.804	10.4%	2025, 2024
Bulgaria BGR	0.643	20.5%	2025, 2021
Peru PER	0.63	7.55%	2025, 2023
Mexico MEX	0.574	10%	2025, 2023
Hungary HUN	0.488	18%	2025, 2024
Romania ROU	0.419	17.6%	2025, 2021
Colombia COL	0.388	14.1%	2025, 2023
Slovak Republic SVK	0.29	19.3%	2025, 2023
Greece GRC	0.276	23.7%	2025, 2024
Croatia HRV	0.254	22.5%	2025, 2021
Costa Rica CRI	0.206	12.6%	2025, 2022
Czechia CZE	0.171	22.1%	2025, 2024
Portugal PRT	0.166	24.1%	2025, 2024
Lithuania LTU	0.159	16.9%	2025, 2023
Italy ITA	0.157	27.6%	2025, 2024

Coppedge, M. et al., V-Dem Dataset, Varieties of Democracy (V-Dem) Project
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

Sources

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.

Public social spending

Public social expenditure

OECD · via Our World in Data · CC BY 4.0 (OECD terms and conditions) · updated annual, except the snapshots noted per indicator · comparable from 1980 only

All public social spending, as a share of GDP: pensions, health, family, unemployment, housing and the rest. The figures before 1980 are stitched together from older OECD tables and from Lindert's historical estimates, so they are left out. The OECD's social spending database (SOCX) itself starts in 1980. The four spending areas below are parts of this total and are flagged as such.

How this was computed

Data used: most recent value in 2010-2025; values come from 2021–2025. 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 Political corruption and Public social spending

This page shows that two measures go together. It does not show that Political corruption changes Public social spending, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.