Regulatory quality vs. Tax revenue
Holds up after adjusting for income
Across 151 countries, Regulatory quality and Tax revenue rise and fall together at r = +0.48 (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 = +0.40, q = 0.013, allowing for the many pairs tested).
Comparing countries across the middle half of tax revenue, 12.2% to 21.8% of GDP, regulatory quality is better by 0.602 points at the top of that range than the bottom. Comparing only countries with similar national income, the gap is 0.304 points.
Link before adjusting (r)+0.477p = 6.2e-10 · 151 countries
After adjusting for national income+0.401p = 3.6e-7 · likely range [+0.26, +0.53]
p, allowing for regions0.001standard error 0.064 · 7 regions
Check for luck (q)0.013across 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.
Regulatory quality · index (approx. -2.5 to 2.5)link with income +0.83 · proxy
Tax revenue · % of GDPlink with income +0.33 · 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 | +0.477 | 6.2e-10 | 151 |
| By rank (Spearman), not adjusted | +0.499 | 7.3e-11 | 151 |
| Standard (Pearson), adjusted for national income | +0.401 | 3.6e-7 | 151 |
| By rank (Spearman), adjusted for national income | +0.433 | 3.2e-8 | 151 |
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 151 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: +0.075 (a minus sign means it grew).
How solid is it?
Leaving out one country at a time, r ranges from +0.384 (without IRQ) to +0.422 (without CHE). 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: CHE (+0.021).
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 | 25 | +0.51 | |
| Europe & Central Asia | 48 | +0.40 | |
| Latin America & Caribbean | 24 | +0.44 | |
| Middle East, North Africa, Afghanistan & Pakistan | 12 | +0.28 | |
| Sub-Saharan Africa | 34 | +0.59 |
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 | 51 | +0.51 | |
| Middle third | 50 | +0.52 | |
| Richest third | 50 | +0.37 |
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 | 98 | +0.39 | |
| Muslims | 28 | +0.20 |
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 | 14 | +0.77 | |
| Never a European colony | 16 | +0.23 | |
| Spain | 19 | +0.52 | |
| United Kingdom | 49 | +0.39 |
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 = +0.401 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.
Within countries over time, there is no link that is clearly more than chance (r = +0.017, 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 regulatory quality moved did not tend to see their tax revenue move with it.
Across countries (adjusted)+0.401for comparison
Within countries+0.009each country vs itself
…and vs the same year elsewhere+0.017q = 0.541 · 155 countries
Year-on-year changes-0.000q = 0.999
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.
3,079 country-years (one country in one year), 155 countries, 1996–2024, about 20 years each on average. Share of the variation that happens within countries over time: 70% for Regulatory quality, 6% for Tax revenue. 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): +0.017. 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 largest value on this curve (-0.11 at lag +5) is higher than the same-year one by 0.09. That gap is not a finding. It is the largest of 21 closely related estimates, and picking the biggest of many tests always makes it look bigger. That is exactly why nothing on this site is ranked or chosen by lag.
The data
index (approx. -2.5 to 2.5) | % of GDP | Years | |
|---|---|---|---|
| Singapore SGP | 2.13 | 13.6% | 2024 |
| Finland FIN | 1.96 | 25.3% | 2024 |
| Denmark DNK | 1.93 | 33.4% | 2024 |
| New Zealand NZL | 1.84 | 29.5% | 2024 |
| Luxembourg LUX | 1.84 | 28.6% | 2024 |
| Ireland IRL | 1.79 | 17.9% | 2024 |
| Switzerland CHE | 1.77 | 9.45% | 2024 |
| Australia AUS | 1.72 | 23.6% | 2024, 2022 |
| Netherlands NLD | 1.72 | 25.3% | 2024 |
| Sweden SWE | 1.67 | 27.2% | 2024 |
| Norway NOR | 1.67 | 24.5% | 2024 |
| United Kingdom GBR | 1.51 | 26.9% | 2024 |
| Germany DEU | 1.5 | 10.9% | 2024 |
| Estonia EST | 1.4 | 22.7% | 2024 |
| Macao SAR, China MAC | 1.37 | 27.4% | 2024 |
Kaufmann, D. and A. Kraay, Worldwide Governance Indicators, World Bank
World Bank, World Development Indicators
Sources
How this was computed
Data used: most recent value in 2010-2025; values come from 2012–2024. 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 Regulatory quality and Tax revenue
This page shows that two measures go together. It does not show that Regulatory quality changes Tax revenue, or the reverse. These figures also describe countries, not individual people. See the ecological fallacy.
More links with Regulatory quality or Tax revenue
- Corruption Perceptions Index vs Regulatory quality before +0.91 → after adjusting +0.81
- Political corruption vs Regulatory quality before -0.80 → after adjusting -0.62
- Regulatory quality vs Researchers before +0.77 → after adjusting +0.52
- Civil liberties (FH) vs Tax revenue before -0.58 → after adjusting -0.51
- Governance score vs Tax revenue before +0.58 → after adjusting +0.51
- Tax revenue vs Voice and accountability before +0.58 → after adjusting +0.51
- Government expense vs Tax revenue before +0.55 → after adjusting +0.50
- Regulatory quality vs Women's political empowerment before +0.62 → after adjusting +0.49