Do Political corruption and Price level go together across countries?

Across 161 countries the link before adjusting for anything is r = -0.70, on a scale from -1 (opposite) to +1 (in step). A version that uses only each country's rank gives -0.64.

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

Comparing each country only with itself across 1990-2023, and setting aside what happened worldwide in each year, the link is +0.007 (165 countries, 5,496 country-years). Using year-to-year changes, it is -0.002.

Does Political corruption cause Price level?

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. Price level

Holds up after adjusting for income

Across 161 countries, Political corruption and Price level rise and fall together at r = -0.70 (on a scale from −1 to +1, where 0 means no link). Comparing only countries with similar income (GDP per person) shrinks it to -0.47 (q = 0.031, allowing for the many pairs tested). Much of the first figure is that both measures rise with national income, but a moderate negative link remains. Caution: direction is not consistent across regions.

Comparing countries across the middle half of price level, 0.336 to 0.662 ratio to the US, political corruption is better by 0.283 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.185 points.

Link before adjusting (r)-0.704p = 1.9e-25 · 161 countries

After adjusting for national income-0.470p = 3.8e-10 · likely range [-0.58, -0.34]

p, allowing for regions0.026standard error 0.160 · 7 regions

Check for luck (q)0.031across 18,622 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

Price level · ratio to the US (US = 1)link with income +0.72 · 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	-0.704	1.9e-25	161
By rank (Spearman), not adjusted	-0.638	8.2e-20	161
Standard (Pearson), adjusted for national income	-0.470	3.8e-10	161
By rank (Spearman), adjusted for national income	-0.284	0.000	161

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 161 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: +0.235 (a minus sign means it grew).

How solid is it?

Leaving out one country at a time, r ranges from -0.487 (without BTN) to -0.457 (without RUS). 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 (-0.017).

Fragile: this link may not hold up.

direction is not consistent across regions

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	17	-0.44
Europe & Central Asia	48	-0.50
Latin America & Caribbean	23	-0.56
Middle East, North Africa, Afghanistan & Pakistan	19	-0.45
Sub-Saharan Africa	46	+0.17

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	54	+0.07
Middle third	53	-0.32
Richest third	54	-0.71

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	95	-0.42
Muslims	45	-0.32

By last European coloniser

	n	r
France	25	+0.26
Never a European colony	20	-0.57
Spain	19	-0.62
United Kingdom	46	-0.40

By official ideology

	n	r
Nationalist	25	-0.19
No strong official ideology	110	-0.45

By party system

	n	r
A few parties banned	25	-0.00
No parties banned	116	-0.45

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 = -0.460 after dropping 3 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 34years. 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 = +0.007, 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 price level move with it.

Across countries (adjusted)-0.470for comparison

Within countries+0.024each country vs itself

…and vs the same year elsewhere+0.007q = 0.931 · 165 countries

Year-on-year changes-0.002q = 0.935

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.

5,496 country-years (one country in one year), 165 countries, 1990–2023, about 33 years each on average. Share of the variation that happens within countries over time: 23% for Political corruption, 6% for Price level. 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): +0.007. 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 (0-1)	ratio to the US (US = 1)	Years
Chad TCD	0.952	0.363	2025, 2023
Nicaragua NIC	0.942	0.377	2025, 2023
Cameroon CMR	0.939	0.336	2025, 2023
Cambodia KHM	0.904	0.42	2025, 2023
Turkmenistan TKM	0.899	0.561	2025, 2023
Tajikistan TJK	0.893	0.291	2025, 2023
Iraq IRQ	0.892	0.371	2025, 2023
Azerbaijan AZE	0.89	0.319	2025, 2023
Uganda UGA	0.889	0.343	2025, 2023
Congo, Dem. Rep. COD	0.886	0.499	2025, 2023
Madagascar MDG	0.882	0.269	2025, 2023
Liberia LBR	0.877	0.542	2025, 2023
Somalia, Fed. Rep. SOM	0.87	0.403	2025, 2023
Myanmar MMR	0.859	0.177	2025, 2023
Equatorial Guinea GNQ	0.858	0.449	2025, 2023

Coppedge, M. et al., V-Dem Dataset, Varieties of Democracy (V-Dem) Project
Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), "The Next Generation of the Penn World Table", American Economic Review, 105(10), 3150-3182, www.ggdc.net/pwt. Data: Penn World Table version 11.0, Groningen Growth and Development Centre, doi:10.34894/FABVLR.

Sources

Political corruption

Political corruption index

V-Dem Institute, University of Gothenburg · via Our World in Data · CC BY-SA 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.

Price level

Price level of household consumption, compared with the US (Penn World Table)

Groningen Growth and Development Centre, University of Groningen · via DataverseNL · CC BY 4.0 · updated irregular (new versions every few years)

How expensive the things households buy are in a country, compared with the US in the same year, when money is changed at the market exchange rate. A value of 0.5 means a typical basket of goods and services costs about half what it would in the US. Richer countries tend to be more expensive. It comes from the same price comparisons used to adjust GDP for what money buys, so it shares a building block with GDP per capita (PPP). Years in which the Penn World Table flags the price comparison as unreliable are left out. No "better" direction is set.

How this was computed

Data used: most recent value in 2010-2025; values come from 2023–2025. 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 18,622 pairs at once, using the Benjamini–Hochberg method. Underlying data last updated V-Dem v16; V-Party v2. How the country data works · explore Political corruption and Price level

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